diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2022-03-07 12:03:40 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2022-03-07 14:55:21 +0100 |
| commit | 7035cde3a53781721fe15a8893fdf328c789bdd2 (patch) | |
| tree | a1e4929faf9d645caedc0ed4dcc5036252497c63 | |
| parent | 77c248ca40b82ec00a756cd82f12968131f78959 (diff) | |
Remove nlmixr interface for release of mkin 1.1.0
I am postponing my attempts to get the nlmixr interface to CRAN, given
some problems with nlmixr using R-devel under Windows, see
https://github.com/nlmixrdevelopment/nlmixr/issues/596
and
https://github.com/r-hub/rhub/issues/512,
which is fixed by the removal of nlmixr from the testsuite.
For the tests to be more platform independent, the biphasic mixed
effects models test dataset was defined in a way that fitting
should be more robust (less ill-defined).
124 files changed, 3230 insertions, 4983 deletions
diff --git a/DESCRIPTION b/DESCRIPTION index c2aefe50..2492a7fc 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data Version: 1.1.0 -Date: 2022-03-03 +Date: 2022-03-07 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", @@ -24,7 +24,7 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, purpose. Depends: R (>= 2.15.1), parallel Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.19), numDeriv, - dplyr, lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix (>= 3.0), nlmixr + lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix (>= 3.0) Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4 License: GPL @@ -8,7 +8,6 @@ S3method(aw,mmkin) S3method(confint,mkinfit) S3method(f_time_norm_focus,mkindsg) S3method(f_time_norm_focus,numeric) -S3method(intervals,nlmixr.mmkin) S3method(intervals,saem.mmkin) S3method(loftest,mkinfit) S3method(logLik,mkinfit) @@ -18,7 +17,6 @@ S3method(mixed,mmkin) S3method(mkinpredict,mkinfit) S3method(mkinpredict,mkinmod) S3method(nlme,mmkin) -S3method(nlmixr,mmkin) S3method(nobs,mkinfit) S3method(parms,mkinfit) S3method(parms,mmkin) @@ -33,17 +31,14 @@ S3method(print,mkinmod) S3method(print,mmkin) S3method(print,nafta) S3method(print,nlme.mmkin) -S3method(print,nlmixr.mmkin) S3method(print,saem.mmkin) S3method(print,summary.mkinfit) S3method(print,summary.nlme.mmkin) -S3method(print,summary.nlmixr.mmkin) S3method(print,summary.saem.mmkin) S3method(residuals,mkinfit) S3method(saem,mmkin) S3method(summary,mkinfit) S3method(summary,nlme.mmkin) -S3method(summary,nlmixr.mmkin) S3method(summary,saem.mmkin) S3method(update,mkinfit) S3method(update,mmkin) @@ -65,7 +60,6 @@ export(get_deg_func) export(ilr) export(intervals) export(invilr) -export(invtffm0) export(loftest) export(logistic.solution) export(lrtest) @@ -94,9 +88,6 @@ export(nafta) export(nlme) export(nlme_data) export(nlme_function) -export(nlmixr) -export(nlmixr_data) -export(nlmixr_model) export(parms) export(plot_err) export(plot_res) @@ -105,19 +96,15 @@ export(saem) export(saemix_data) export(saemix_model) export(sigma_twocomp) -export(tffm0) export(transform_odeparms) import(deSolve) import(graphics) import(nlme) importFrom(R6,R6Class) -importFrom(dplyr,"%>%") importFrom(grDevices,dev.cur) importFrom(lmtest,lrtest) importFrom(methods,signature) importFrom(nlme,intervals) -importFrom(nlmixr,nlmixr) -importFrom(nlmixr,tableControl) importFrom(parallel,detectCores) importFrom(parallel,mclapply) importFrom(parallel,parLapply) @@ -128,7 +115,6 @@ importFrom(stats,aggregate) importFrom(stats,as.formula) importFrom(stats,coef) importFrom(stats,coefficients) -importFrom(stats,confint) importFrom(stats,cov2cor) importFrom(stats,dist) importFrom(stats,dnorm) @@ -148,7 +134,6 @@ importFrom(stats,qnorm) importFrom(stats,qt) importFrom(stats,residuals) importFrom(stats,rnorm) -importFrom(stats,sd) importFrom(stats,shapiro.test) importFrom(stats,update) importFrom(stats,vcov) @@ -2,8 +2,6 @@ ## Mixed-effects models -- Introduce an interface to nlmixr, supporting estimation methods 'saem' and 'focei': S3 method 'nlmixr.mmkin' using the helper functions 'nlmixr_model' and 'nlmixr_data' to set up nlmixr models for mmkin row objects, with summary and plot methods. - - Reintroduce the interface to saemix (now on CRAN), in particular the generic function 'saem' with a generator 'saem.mmkin', currently using 'saemix_model' and 'saemix_data', summary and plot methods - 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends @@ -12,7 +10,7 @@ - 'vignettes/web_only/dimethenamid_2018.rmd': Example evaluations of the dimethenamid data. -- 'intervals': Provide methods of this nlme function for 'nlmixr.mmkin' and 'saem.mmkin' objects. +- 'intervals': Provide a method of this nlme function for 'saem.mmkin' objects. # mkin 1.0.5 (2021-09-15) diff --git a/R/endpoints.R b/R/endpoints.R index 6bf52f07..e81e7a0a 100644 --- a/R/endpoints.R +++ b/R/endpoints.R @@ -10,8 +10,8 @@ #' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from #' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models #' -#' @param fit An object of class [mkinfit], [nlme.mmkin], [saem.mmkin] or -#' [nlmixr.mmkin]. Or another object that has list components +#' @param fit An object of class [mkinfit], [nlme.mmkin] or [saem.mmkin], +#' or another object that has list components #' mkinmod containing an [mkinmod] degradation model, and two numeric vectors, #' bparms.optim and bparms.fixed, that contain parameter values #' for that model. diff --git a/R/intervals.R b/R/intervals.R index 8ab2b7ec..258eb4ad 100644 --- a/R/intervals.R +++ b/R/intervals.R @@ -95,87 +95,3 @@ intervals.saem.mmkin <- function(object, level = 0.95, backtransform = TRUE, ... attr(res, "level") <- level return(res) } - -#' Confidence intervals for parameters in nlmixr.mmkin objects -#' -#' @param object The fitted saem.mmkin object -#' @param level The confidence level. -#' @param backtransform Should we backtransform the parameters where a one to -#' one correlation between transformed and backtransformed parameters exists? -#' @param \dots For compatibility with the generic method -#' @importFrom nlme intervals -#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the -#' class attribute -#' @export -intervals.nlmixr.mmkin <- function(object, level = 0.95, backtransform = TRUE, ...) -{ - - # Fixed effects - mod_vars <- names(object$mkinmod$diffs) - - conf.int <- confint(object$nm) - dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start)) - ndp <- length(dpnames) - - confint_trans <- as.matrix(conf.int[dpnames, c(3, 1, 4)]) - colnames(confint_trans) <- c("lower", "est.", "upper") - - if (backtransform) { - bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, - object$transform_rates, object$transform_fractions) - bpnames <- names(bp) - - # Transform boundaries of CI for one parameter at a time, - # with the exception of sets of formation fractions (single fractions are OK). - f_names_skip <- character(0) - for (box in mod_vars) { # Figure out sets of fractions to skip - f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE) - n_paths <- length(f_names) - if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) - } - - confint_back <- matrix(NA, nrow = length(bp), ncol = 3, - dimnames = list(bpnames, colnames(confint_trans))) - confint_back[, "est."] <- bp - - for (pname in dpnames) { - if (!pname %in% f_names_skip) { - par.lower <- confint_trans[pname, "lower"] - par.upper <- confint_trans[pname, "upper"] - names(par.lower) <- names(par.upper) <- pname - bpl <- backtransform_odeparms(par.lower, object$mkinmod, - object$transform_rates, - object$transform_fractions) - bpu <- backtransform_odeparms(par.upper, object$mkinmod, - object$transform_rates, - object$transform_fractions) - confint_back[names(bpl), "lower"] <- bpl - confint_back[names(bpu), "upper"] <- bpu - } - } - confint_ret <- confint_back - } else { - confint_ret <- confint_trans - } - attr(confint_ret, "label") <- "Fixed effects:" - - # Random effects - ranef_ret <- as.matrix(data.frame(lower = NA, - "est." = sqrt(diag(object$nm$omega)), upper = NA)) - rownames(ranef_ret) <- paste0(gsub("eta\\.", "sd(", rownames(ranef_ret)), ")") - attr(ranef_ret, "label") <- "Random effects:" - - # Error model - enames <- names(object$nm$sigma) - err_ret <- as.matrix(conf.int[enames, c(3, 1, 4)]) - colnames(err_ret) <- c("lower", "est.", "upper") - - res <- list( - fixed = confint_ret, - random = ranef_ret, - errmod = err_ret - ) - class(res) <- c("intervals.nlmixr.mmkin", "intervals.lme") - attr(res, "level") <- level - return(res) -} diff --git a/R/nlmixr.R b/R/nlmixr.R deleted file mode 100644 index 5d05f814..00000000 --- a/R/nlmixr.R +++ /dev/null @@ -1,584 +0,0 @@ -utils::globalVariables(c("predicted", "std", "ID", "TIME", "CMT", "DV", "IPRED", "IRES", "IWRES")) - -#' @export -nlmixr::nlmixr - -#' Fit nonlinear mixed models using nlmixr -#' -#' This function uses [nlmixr::nlmixr()] as a backend for fitting nonlinear mixed -#' effects models created from [mmkin] row objects using the Stochastic Approximation -#' Expectation Maximisation algorithm (SAEM) or First Order Conditional -#' Estimation with Interaction (FOCEI). -#' -#' An mmkin row object is essentially a list of mkinfit objects that have been -#' obtained by fitting the same model to a list of datasets using [mkinfit]. -#' -#' @importFrom nlmixr nlmixr tableControl -#' @importFrom dplyr %>% -#' @param object An [mmkin] row object containing several fits of the same -#' [mkinmod] model to different datasets -#' @param data Not used, the data are extracted from the mmkin row object -#' @param est Estimation method passed to [nlmixr::nlmixr] -#' @param degparms_start Parameter values given as a named numeric vector will -#' be used to override the starting values obtained from the 'mmkin' object. -#' @param eta_start Standard deviations on the transformed scale given as a -#' named numeric vector will be used to override the starting values obtained -#' from the 'mmkin' object. -#' @param test_log_parms If TRUE, an attempt is made to use more robust starting -#' values for population parameters fitted as log parameters in mkin (like -#' rate constants) by only considering rate constants that pass the t-test -#' when calculating mean degradation parameters using [mean_degparms]. -#' @param conf.level Possibility to adjust the required confidence level -#' for parameter that are tested if requested by 'test_log_parms'. -#' @param data Not used, as the data are extracted from the mmkin row object -#' @param table Passed to [nlmixr::nlmixr] -#' @param error_model Optional argument to override the error model which is -#' being set based on the error model used in the mmkin row object. -#' @param control Passed to [nlmixr::nlmixr] -#' @param \dots Passed to [nlmixr_model] -#' @param save Passed to [nlmixr::nlmixr] -#' @param envir Passed to [nlmixr::nlmixr] -#' @return An S3 object of class 'nlmixr.mmkin', containing the fitted -#' [nlmixr::nlmixr] object as a list component named 'nm'. The -#' object also inherits from 'mixed.mmkin'. -#' @seealso [summary.nlmixr.mmkin] [plot.mixed.mmkin] -#' @examples -#' \dontrun{ -#' ds <- lapply(experimental_data_for_UBA_2019[6:10], -#' function(x) subset(x$data[c("name", "time", "value")])) -#' names(ds) <- paste("Dataset", 6:10) -#' -#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) -#' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", -#' cores = 1, quiet = TRUE) -#' -#' library(nlmixr) -#' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem", -#' control = saemControl(print = 0)) -#' f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei", -#' control = foceiControl(print = 0)) -#' -#' AIC( -#' f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm, -#' f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm, -#' f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm, -#' f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm, -#' f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm) -#' -#' AIC(nlme(f_mmkin_parent["FOMC", ])) -#' AIC(nlme(f_mmkin_parent["HS", ])) -#' -#' # The FOCEI fit of FOMC with constant variance or the tc error model is best -#' plot(f_nlmixr_fomc_saem_tc) -#' -#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), -#' A1 = mkinsub("SFO"), quiet = TRUE) -#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), -#' A1 = mkinsub("SFO"), quiet = TRUE) -#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), -#' A1 = mkinsub("SFO"), quiet = TRUE) -#' -#' f_mmkin_const <- mmkin(list( -#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), -#' ds, quiet = TRUE, error_model = "const") -#' f_mmkin_obs <- mmkin(list( -#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), -#' ds, quiet = TRUE, error_model = "obs") -#' f_mmkin_tc <- mmkin(list( -#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), -#' ds, quiet = TRUE, error_model = "tc") -#' -#' nlmixr_model(f_mmkin_const["SFO-SFO", ]) -#' -#' # A single constant variance is currently only possible with est = 'focei' in nlmixr -#' f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") -#' f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") -#' f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") -#' -#' # Variance by variable is supported by 'saem' and 'focei' -#' f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") -#' f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") -#' f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") -#' f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") -#' -#' # Identical two-component error for all variables is only possible with -#' # est = 'focei' in nlmixr -#' f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") -#' f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") -#' -#' # Two-component error by variable is possible with both estimation methods -#' # Variance by variable is supported by 'saem' and 'focei' -#' f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", -#' error_model = "obs_tc") -#' f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", -#' error_model = "obs_tc") -#' f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", -#' error_model = "obs_tc") -#' f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", -#' error_model = "obs_tc") -#' -#' AIC( -#' f_nlmixr_sfo_sfo_focei_const$nm, -#' f_nlmixr_fomc_sfo_focei_const$nm, -#' f_nlmixr_dfop_sfo_focei_const$nm, -#' f_nlmixr_fomc_sfo_saem_obs$nm, -#' f_nlmixr_fomc_sfo_focei_obs$nm, -#' f_nlmixr_dfop_sfo_saem_obs$nm, -#' f_nlmixr_dfop_sfo_focei_obs$nm, -#' f_nlmixr_fomc_sfo_focei_tc$nm, -#' f_nlmixr_dfop_sfo_focei_tc$nm, -#' f_nlmixr_fomc_sfo_saem_obs_tc$nm, -#' f_nlmixr_fomc_sfo_focei_obs_tc$nm, -#' f_nlmixr_dfop_sfo_saem_obs_tc$nm, -#' f_nlmixr_dfop_sfo_focei_obs_tc$nm -#' ) -#' # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the -#' # lowest AIC -#' plot(f_nlmixr_fomc_sfo_focei_obs_tc) -#' summary(f_nlmixr_fomc_sfo_focei_obs_tc) -#' -#' # Two parallel metabolites -#' dmta_ds <- lapply(1:7, function(i) { -#' ds_i <- dimethenamid_2018$ds[[i]]$data -#' ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" -#' ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] -#' ds_i -#' }) -#' names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) -#' dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) -#' dmta_ds[["Elliot 1"]] <- NULL -#' dmta_ds[["Elliot 2"]] <- NULL -#' sfo_sfo2 <- mkinmod( -#' DMTA = mkinsub("SFO", c("M23", "M27")), -#' M23 = mkinsub("SFO"), -#' M27 = mkinsub("SFO"), -#' quiet = TRUE -#' ) -#' f_dmta_sfo_sfo2 <- mmkin( -#' list("SFO-SFO2" = sfo_sfo2), -#' dmta_ds, quiet = TRUE, error_model = "obs") -#' nlmixr_model(f_dmta_sfo_sfo2) -#' nlmixr_focei_dmta_sfo_sfo2 <- nlmixr(f_dmta_sfo_sfo2, est = "focei") -#' } -#' @export -nlmixr.mmkin <- function(object, data = NULL, - est = NULL, control = list(), - table = tableControl(), - error_model = object[[1]]$err_mod, - test_log_parms = TRUE, - conf.level = 0.6, - degparms_start = "auto", - eta_start = "auto", - ..., - save = NULL, - envir = parent.frame() -) -{ - m_nlmixr <- nlmixr_model(object, est = est, - error_model = error_model, add_attributes = TRUE, - test_log_parms = test_log_parms, conf.level = conf.level, - degparms_start = degparms_start, eta_start = eta_start - ) - d_nlmixr <- nlmixr_data(object) - - mean_dp_start <- attr(m_nlmixr, "mean_dp_start") - mean_ep_start <- attr(m_nlmixr, "mean_ep_start") - - attributes(m_nlmixr) <- NULL - - fit_time <- system.time({ - f_nlmixr <- nlmixr(m_nlmixr, d_nlmixr, est = est, control = control) - }) - - if (is.null(f_nlmixr$CMT)) { - nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "IPRED", "IRES", "IWRES")]) - nlmixr_df$CMT <- as.character(object[[1]]$data$variable[1]) - } else { - nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "CMT", "IPRED", "IRES", "IWRES")]) - } - - return_data <- nlmixr_df %>% - dplyr::transmute(ds = ID, name = CMT, time = TIME, value = DV, - predicted = IPRED, residual = IRES, - std = IRES/IWRES, standardized = IWRES) %>% - dplyr::arrange(ds, name, time) - - bparms_optim <- backtransform_odeparms(f_nlmixr$theta, - object[[1]]$mkinmod, - object[[1]]$transform_rates, - object[[1]]$transform_fractions) - - result <- list( - mkinmod = object[[1]]$mkinmod, - mmkin = object, - transform_rates = object[[1]]$transform_rates, - transform_fractions = object[[1]]$transform_fractions, - nm = f_nlmixr, - est = est, - time = fit_time, - mean_dp_start = mean_dp_start, - mean_ep_start = mean_ep_start, - bparms.optim = bparms_optim, - bparms.fixed = object[[1]]$bparms.fixed, - data = return_data, - err_mod = error_model, - date.fit = date(), - nlmixrversion = as.character(utils::packageVersion("nlmixr")), - mkinversion = as.character(utils::packageVersion("mkin")), - Rversion = paste(R.version$major, R.version$minor, sep=".") - ) - - class(result) <- c("nlmixr.mmkin", "mixed.mmkin") - return(result) -} - -#' @export -#' @rdname nlmixr.mmkin -#' @param x An nlmixr.mmkin object to print -#' @param digits Number of digits to use for printing -print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { - cat("Kinetic nonlinear mixed-effects model fit by", x$est, "using nlmixr") - cat("\nStructural model:\n") - diffs <- x$mmkin[[1]]$mkinmod$diffs - nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs) - writeLines(strwrap(nice_diffs, exdent = 11)) - cat("\nData:\n") - cat(nrow(x$data), "observations of", - length(unique(x$data$name)), "variable(s) grouped in", - length(unique(x$data$ds)), "datasets\n") - - cat("\nLikelihood:\n") - print(data.frame( - AIC = AIC(x$nm), - BIC = BIC(x$nm), - logLik = logLik(x$nm), - row.names = " "), digits = digits) - - cat("\nFitted parameters:\n") - print(x$nm$parFixed, digits = digits) - - invisible(x) -} - -#' @rdname nlmixr.mmkin -#' @param add_attributes Should the starting values used for degradation model -#' parameters and their distribution and for the error model parameters -#' be returned as attributes? -#' @return An function defining a model suitable for fitting with [nlmixr::nlmixr]. -#' @export -nlmixr_model <- function(object, - est = c("saem", "focei"), - degparms_start = "auto", - eta_start = "auto", - test_log_parms = TRUE, conf.level = 0.6, - error_model = object[[1]]$err_mod, add_attributes = FALSE) -{ - if (nrow(object) > 1) stop("Only row objects allowed") - est = match.arg(est) - - mkin_model <- object[[1]]$mkinmod - obs_vars <- names(mkin_model$spec) - - if (error_model == object[[1]]$err_mod) { - - if (length(object[[1]]$mkinmod$spec) > 1 & est == "saem") { - if (error_model == "const") { - message( - "Constant variance for more than one variable is not supported for est = 'saem'\n", - "Changing the error model to 'obs' (variance by observed variable)") - error_model <- "obs" - } - if (error_model =="tc") { - message( - "With est = 'saem', a different error model is required for each observed variable", - "Changing the error model to 'obs_tc' (Two-component error for each observed variable)") - error_model <- "obs_tc" - } - } - } - - degparms_mmkin <- mean_degparms(object, - test_log_parms = test_log_parms, - conf.level = conf.level, random = TRUE) - - degparms_optim <- degparms_mmkin$fixed - - degparms_optim_ilr_names <- grep("^f_.*_ilr", names(degparms_optim), value = TRUE) - obs_vars_ilr <- unique(gsub("f_(.*)_ilr.*$", "\\1", degparms_optim_ilr_names)) - degparms_optim_noilr <- degparms_optim[setdiff(names(degparms_optim), - degparms_optim_ilr_names)] - - degparms_optim_back <- backtransform_odeparms(degparms_optim, - object[[1]]$mkinmod, - object[[1]]$transform_rates, - object[[1]]$transform_fractions) - - if (degparms_start[1] == "auto") { - degparms_start <- degparms_optim_noilr - for (obs_var_ilr in obs_vars_ilr) { - ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), - names(degparms_optim_back), value = TRUE) - f_tffm0 <- tffm0(degparms_optim_back[ff_names]) - f_tffm0_qlogis <- qlogis(f_tffm0) - names(f_tffm0_qlogis) <- paste0("f_", obs_var_ilr, - "_tffm0_", 1:length(f_tffm0), "_qlogis") - degparms_start <- c(degparms_start, f_tffm0_qlogis) - } - } - - if (eta_start[1] == "auto") { - eta_start <- degparms_mmkin$eta[setdiff(names(degparms_optim), - degparms_optim_ilr_names)] - for (obs_var_ilr in obs_vars_ilr) { - ff_n <- length(grep(paste0("^f_", obs_var_ilr, "_"), - names(degparms_optim_back), value = TRUE)) - eta_start_ff <- rep(0.3, ff_n) - names(eta_start_ff) <- paste0("f_", obs_var_ilr, - "_tffm0_", 1:ff_n, "_qlogis") - eta_start <- c(eta_start, eta_start_ff) - } - } - - - degparms_fixed <- object[[1]]$bparms.fixed - - odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) - odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) - - odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names) - odeparms_fixed <- degparms_fixed[odeparms_fixed_names] - - odeini_fixed <- degparms_fixed[odeini_fixed_parm_names] - names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) - - # Definition of the model function - f <- function(){} - - ini_block <- "ini({" - - # Initial values for all degradation parameters - for (parm_name in names(degparms_start)) { - # As initials for state variables are not transformed, - # we need to modify the name here as we want to - # use the original name in the model block - ini_block <- paste0( - ini_block, - parm_name, " = ", - as.character(signif(degparms_start[parm_name], 2)), - "\n", - "eta.", parm_name, " ~ ", - as.character(signif(eta_start[parm_name], 2)), - "\n" - ) - } - - # Error model parameters - error_model_mkin <- object[[1]]$err_mod - - errparm_names_mkin <- names(object[[1]]$errparms) - errparms_mkin <- sapply(errparm_names_mkin, function(parm_name) { - mean(sapply(object, function(x) x$errparms[parm_name])) - }) - - sigma_tc_mkin <- errparms_ini <- errparms_mkin[1] + - mean(unlist(sapply(object, function(x) x$data$observed)), na.rm = TRUE) * - errparms_mkin[2] - - if (error_model == "const") { - if (error_model_mkin == "tc") { - errparms_ini <- sigma_tc_mkin - } else { - errparms_ini <- mean(errparms_mkin) - } - names(errparms_ini) <- "sigma" - } - - if (error_model == "obs") { - errparms_ini <- switch(error_model_mkin, - const = rep(errparms_mkin["sigma"], length(obs_vars)), - obs = errparms_mkin, - tc = sigma_tc_mkin) - names(errparms_ini) <- paste0("sigma_", obs_vars) - } - - if (error_model == "tc") { - if (error_model_mkin != "tc") { - stop("Not supported") - } else { - errparms_ini <- errparms_mkin - } - } - - if (error_model == "obs_tc") { - if (error_model_mkin != "tc") { - stop("Not supported") - } else { - errparms_ini <- rep(errparms_mkin, length(obs_vars)) - names(errparms_ini) <- paste0( - rep(names(errparms_mkin), length(obs_vars)), - "_", - rep(obs_vars, each = 2)) - } - } - - for (parm_name in names(errparms_ini)) { - ini_block <- paste0( - ini_block, - parm_name, " = ", - as.character(signif(errparms_ini[parm_name], 2)), - "\n" - ) - } - - ini_block <- paste0(ini_block, "})") - - body(f)[2] <- parse(text = ini_block) - - model_block <- "model({" - - # Population initial values for the ODE state variables - for (parm_name in odeini_optim_parm_names) { - model_block <- paste0( - model_block, - parm_name, "_model = ", - parm_name, " + eta.", parm_name, "\n", - gsub("(.*)_0", "\\1(0)", parm_name), " = ", parm_name, "_model\n") - } - - # Population initial values for log rate constants - for (parm_name in grep("^log_", names(degparms_start), value = TRUE)) { - model_block <- paste0( - model_block, - gsub("^log_", "", parm_name), " = ", - "exp(", parm_name, " + eta.", parm_name, ")\n") - } - - # Population initial values for logit transformed parameters - for (parm_name in grep("_qlogis$", names(degparms_start), value = TRUE)) { - if (grepl("_tffm0_", parm_name)) { - parm_name_new <- gsub("_qlogis$", "", parm_name) - } else { - parm_name_new <- names( - backtransform_odeparms(degparms_start[parm_name], - object[[1]]$mkinmod, - object[[1]]$transform_rates, - object[[1]]$transform_fractions)) - } - model_block <- paste0( - model_block, - parm_name_new, " = ", - "expit(", parm_name, " + eta.", parm_name, ")\n") - } - - # Calculate formation fractions from tffm0 transformed values - for (obs_var_ilr in obs_vars_ilr) { - ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), - names(degparms_optim_back), value = TRUE) - pattern <- paste0("^f_", obs_var_ilr, "_to_(.*)$") - target_vars <- gsub(pattern, "\\1", - grep(paste0("^f_", obs_var_ilr, "_to_"), names(degparms_optim_back), value = TRUE)) - for (i in 1:length(target_vars)) { - ff_name <- ff_names[i] - ff_line <- paste0(ff_name, " = f_", obs_var_ilr, "_tffm0_", i) - if (i > 1) { - for (j in (i - 1):1) { - ff_line <- paste0(ff_line, " * (1 - f_", obs_var_ilr, "_tffm0_", j , ")") - } - } - model_block <- paste0( - model_block, - ff_line, - "\n" - ) - } - } - - # Differential equations - model_block <- paste0( - model_block, - paste( - gsub("d_(.*) =", "d/dt(\\1) =", mkin_model$diffs), - collapse = "\n"), - "\n" - ) - - # Error model - if (error_model == "const") { - model_block <- paste0(model_block, - paste(paste0(obs_vars, " ~ add(sigma)"), collapse = "\n")) - } - if (error_model == "obs") { - model_block <- paste0(model_block, - paste(paste0(obs_vars, " ~ add(sigma_", obs_vars, ")"), collapse = "\n"), - "\n") - } - if (error_model == "tc") { - model_block <- paste0(model_block, - paste(paste0(obs_vars, " ~ add(sigma_low) + prop(rsd_high)"), collapse = "\n"), - "\n") - } - if (error_model == "obs_tc") { - model_block <- paste0(model_block, - paste( - paste0(obs_vars, " ~ add(sigma_low_", obs_vars, ") + ", - "prop(rsd_high_", obs_vars, ")"), collapse = "\n"), - "\n") - } - - model_block <- paste0(model_block, "})") - - body(f)[3] <- parse(text = model_block) - - if (add_attributes) { - attr(f, "mean_dp_start") <- degparms_optim - attr(f, "eta_start") <- degparms_mmkin$eta - attr(f, "mean_ep_start") <- errparms_ini - } - - return(f) -} - -#' @rdname nlmixr.mmkin -#' @return An dataframe suitable for use with [nlmixr::nlmixr] -#' @export -nlmixr_data <- function(object, ...) { - if (nrow(object) > 1) stop("Only row objects allowed") - d <- lapply(object, function(x) x$data) - compartment_map <- 1:length(object[[1]]$mkinmod$spec) - names(compartment_map) <- names(object[[1]]$mkinmod$spec) - ds_names <- colnames(object) - - ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")]) - names(ds_list) <- ds_names - ds_nlmixr <- purrr::map_dfr(ds_list, function(x) x, .id = "ds") - ds_nlmixr$variable <- as.character(ds_nlmixr$variable) - ds_nlmixr_renamed <- data.frame( - ID = ds_nlmixr$ds, - TIME = ds_nlmixr$time, - AMT = 0, EVID = 0, - CMT = ds_nlmixr$variable, - DV = ds_nlmixr$observed, - stringsAsFactors = FALSE) - - return(ds_nlmixr_renamed) -} @@ -227,7 +227,8 @@ print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nFitted parameters:\n") conf.int <- x$so@results@conf.int[c("estimate", "lower", "upper")] rownames(conf.int) <- x$so@results@conf.int[["name"]] - print(conf.int, digits = digits) + conf.int.var <- grepl("^Var\\.", rownames(conf.int)) + print(conf.int[!conf.int.var, ], digits = digits) invisible(x) } diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R deleted file mode 100644 index 94d4ed93..00000000 --- a/R/summary.nlmixr.mmkin.R +++ /dev/null @@ -1,250 +0,0 @@ -#' Summary method for class "nlmixr.mmkin" -#' -#' Lists model equations, initial parameter values, optimised parameters -#' for fixed effects (population), random effects (deviations from the -#' population mean) and residual error model, as well as the resulting -#' endpoints such as formation fractions and DT50 values. Optionally -#' (default is FALSE), the data are listed in full. -#' -#' @importFrom stats confint sd -#' @param object an object of class [nlmixr.mmkin] -#' @param x an object of class [summary.nlmixr.mmkin] -#' @param data logical, indicating whether the full data should be included in -#' the summary. -#' @param verbose Should the summary be verbose? -#' @param distimes logical, indicating whether DT50 and DT90 values should be -#' included. -#' @param digits Number of digits to use for printing -#' @param \dots optional arguments passed to methods like \code{print}. -#' @return The summary function returns a list obtained in the fit, with at -#' least the following additional components -#' \item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used} -#' \item{date.fit, date.summary}{The dates where the fit and the summary were -#' produced} -#' \item{diffs}{The differential equations used in the degradation model} -#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions} -#' \item{data}{The data} -#' \item{confint_back}{Backtransformed parameters, with confidence intervals if available} -#' \item{ff}{The estimated formation fractions derived from the fitted -#' model.} -#' \item{distimes}{The DT50 and DT90 values for each observed variable.} -#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} -#' The print method is called for its side effect, i.e. printing the summary. -#' @importFrom stats predict vcov -#' @author Johannes Ranke for the mkin specific parts -#' nlmixr authors for the parts inherited from nlmixr. -#' @examples -#' # Generate five datasets following DFOP-SFO kinetics -#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), -#' m1 = mkinsub("SFO"), quiet = TRUE) -#' set.seed(1234) -#' k1_in <- rlnorm(5, log(0.1), 0.3) -#' k2_in <- rlnorm(5, log(0.02), 0.3) -#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) -#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) -#' k_m1_in <- rlnorm(5, log(0.02), 0.3) -#' -#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { -#' mkinpredict(dfop_sfo, -#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), -#' c(parent = 100, m1 = 0), -#' sampling_times) -#' } -#' -#' ds_mean_dfop_sfo <- lapply(1:5, function(i) { -#' mkinpredict(dfop_sfo, -#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], -#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), -#' c(parent = 100, m1 = 0), -#' sampling_times) -#' }) -#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5) -#' -#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { -#' add_err(ds, -#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), -#' n = 1)[[1]] -#' }) -#' -#' \dontrun{ -#' # Evaluate using mmkin and nlmixr -#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, -#' quiet = TRUE, error_model = "tc", cores = 5) -#' f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) -#' f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) -#' f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") -#' # The following takes a very long time but gives -#' f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") -#' AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) -#' summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) -#' } -#' -#' @export -summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) { - - mod_vars <- names(object$mkinmod$diffs) - - conf.int <- confint(object$nm) - dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start)) - ndp <- length(dpnames) - - confint_trans <- as.matrix(conf.int[dpnames, c(1, 3, 4)]) - colnames(confint_trans) <- c("est.", "lower", "upper") - - bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, - object$transform_rates, object$transform_fractions) - bpnames <- names(bp) - - # Transform boundaries of CI for one parameter at a time, - # with the exception of sets of formation fractions (single fractions are OK). - f_names_skip <- character(0) - for (box in mod_vars) { # Figure out sets of fractions to skip - f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE) - n_paths <- length(f_names) - if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) - } - - confint_back <- matrix(NA, nrow = length(bp), ncol = 3, - dimnames = list(bpnames, colnames(confint_trans))) - confint_back[, "est."] <- bp - - for (pname in dpnames) { - if (!pname %in% f_names_skip) { - par.lower <- confint_trans[pname, "lower"] - par.upper <- confint_trans[pname, "upper"] - names(par.lower) <- names(par.upper) <- pname - bpl <- backtransform_odeparms(par.lower, object$mkinmod, - object$transform_rates, - object$transform_fractions) - bpu <- backtransform_odeparms(par.upper, object$mkinmod, - object$transform_rates, - object$transform_fractions) - confint_back[names(bpl), "lower"] <- bpl - confint_back[names(bpu), "upper"] <- bpu - } - } - - # Correlation of fixed effects (inspired by summary.nlme) - varFix <- vcov(object$nm) - stdFix <- sqrt(diag(varFix)) - object$corFixed <- array( - t(varFix/stdFix)/stdFix, - dim(varFix), - list(dpnames, dpnames)) - - object$confint_trans <- confint_trans - object$confint_back <- confint_back - - object$date.summary = date() - object$use_of_ff = object$mkinmod$use_of_ff - - object$diffs <- object$mkinmod$diffs - object$print_data <- data # boolean: Should we print the data? - - names(object$data)[4] <- "observed" # rename value to observed - - object$verbose <- verbose - - object$fixed <- object$mmkin_orig[[1]]$fixed - object$AIC = AIC(object$nm) - object$BIC = BIC(object$nm) - object$logLik = logLik(object$nm) - - ep <- endpoints(object) - if (length(ep$ff) != 0) - object$ff <- ep$ff - if (distimes) object$distimes <- ep$distimes - if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB - class(object) <- c("summary.nlmixr.mmkin") - return(object) -} - -#' @rdname summary.nlmixr.mmkin -#' @export -print.summary.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { - cat("nlmixr version used for fitting: ", x$nlmixrversion, "\n") - cat("mkin version used for pre-fitting: ", x$mkinversion, "\n") - cat("R version used for fitting: ", x$Rversion, "\n") - - cat("Date of fit: ", x$date.fit, "\n") - cat("Date of summary:", x$date.summary, "\n") - - cat("\nEquations:\n") - nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]]) - writeLines(strwrap(nice_diffs, exdent = 11)) - - cat("\nData:\n") - cat(nrow(x$data), "observations of", - length(unique(x$data$name)), "variable(s) grouped in", - length(unique(x$data$ds)), "datasets\n") - - cat("\nDegradation model predictions using RxODE\n") - - cat("\nFitted in", x$time[["elapsed"]], "s\n") - - cat("\nVariance model: ") - cat(switch(x$err_mod, - const = "Constant variance", - obs = "Variance unique to each observed variable", - tc = "Two-component variance function", - obs_tc = "Two-component variance unique to each observed variable"), "\n") - - cat("\nMean of starting values for individual parameters:\n") - print(x$mean_dp_start, digits = digits) - - cat("\nMean of starting values for error model parameters:\n") - print(x$mean_ep_start, digits = digits) - - cat("\nFixed degradation parameter values:\n") - if(length(x$fixed$value) == 0) cat("None\n") - else print(x$fixed, digits = digits) - - cat("\nResults:\n\n") - cat("Likelihood calculated by", nlmixr::getOfvType(x$nm), " \n") - print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, - row.names = " "), digits = digits) - - cat("\nOptimised parameters:\n") - print(x$confint_trans, digits = digits) - - if (nrow(x$confint_trans) > 1) { - corr <- x$corFixed - class(corr) <- "correlation" - print(corr, title = "\nCorrelation:", rdig = digits, ...) - } - - cat("\nRandom effects (omega):\n") - print(x$nm$omega, digits = digits) - - cat("\nVariance model:\n") - print(x$nm$sigma, digits = digits) - - cat("\nBacktransformed parameters:\n") - print(x$confint_back, digits = digits) - - printSFORB <- !is.null(x$SFORB) - if(printSFORB){ - cat("\nEstimated Eigenvalues of SFORB model(s):\n") - print(x$SFORB, digits = digits,...) - } - - printff <- !is.null(x$ff) - if(printff){ - cat("\nResulting formation fractions:\n") - print(data.frame(ff = x$ff), digits = digits,...) - } - - printdistimes <- !is.null(x$distimes) - if(printdistimes){ - cat("\nEstimated disappearance times:\n") - print(x$distimes, digits = digits,...) - } - - if (x$print_data){ - cat("\nData:\n") - print(format(x$data, digits = digits, ...), row.names = FALSE) - } - - invisible(x) -} diff --git a/R/tffm0.R b/R/tffm0.R deleted file mode 100644 index 56283a5d..00000000 --- a/R/tffm0.R +++ /dev/null @@ -1,51 +0,0 @@ -#' Transform formation fractions as in the first published mkin version -#' -#' This transformation was used originally in mkin, in order to implement a -#' constraint for the sum of formation fractions to be smaller than 1. It was -#' later replaced by the [ilr] transformation because the ilr transformed -#' fractions can assumed to follow normal distribution. As the ilr -#' transformation is not available in [RxODE] and can therefore not be used in -#' the nlmixr modelling language, the original transformation is currently used -#' for translating mkin models with formation fractions to more than one target -#' compartment for fitting with nlmixr in [nlmixr_model]. However, this -#' implementation cannot be used there, as it is not accessible from RxODE. -#' -#' If the transformed formation fractions are restricted to the interval -#' between 0 and 1, the sum of backtransformed values is restricted -#' to this interval. -#' -#' @param ff Vector of untransformed formation fractions. The sum -#' must be smaller or equal to one -#' @param ff_trans Vector of transformed formation fractions that can be -#' restricted to the interval from 0 to 1 -#' @return A vector of the transformed formation fractions -#' @export -#' @examples -#' ff_example <- c( -#' 0.10983681, 0.09035905, 0.08399383 -#' ) -#' ff_example_trans <- tffm0(ff_example) -#' invtffm0(ff_example_trans) -tffm0 <- function(ff) { - n <- length(ff) - res <- numeric(n) - f_remaining <- 1 - for (i in 1:n) { - res[i] <- ff[i]/f_remaining - f_remaining <- f_remaining - ff[i] - } - return(res) -} -#' @rdname tffm0 -#' @export -#' @return A vector of backtransformed formation fractions for natural use in degradation models -invtffm0 <- function(ff_trans) { - n <- length(ff_trans) - res <- numeric(n) - f_remaining <- 1 - for (i in 1:n) { - res[i] <- ff_trans[i] * f_remaining - f_remaining <- f_remaining - res[i] - } - return(res) -} diff --git a/R/transform_odeparms.R b/R/transform_odeparms.R index 174e7c2d..bf988331 100644 --- a/R/transform_odeparms.R +++ b/R/transform_odeparms.R @@ -230,11 +230,7 @@ backtransform_odeparms <- function(transparms, mkinmod, if(transform_fractions) { if (any(grepl("qlogis", names(trans_f)))) { f_tmp <- plogis(trans_f) - if (any(grepl("_tffm0_.*_qlogis$", names(f_tmp)))) { - parms[f_names] <- invtffm0(f_tmp) - } else { - parms[f_names] <- f_tmp - } + parms[f_names] <- f_tmp } else { f_tmp <- invilr(trans_f) if (spec[[box]]$sink) { @@ -96,9 +96,16 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). interpretation of the model parameters. * Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the - [nlme.mmkin](https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html) method. - Note that the convergence of the nlme fits depends on the quality of the data. - Convergence is better for simple models and data for many groups (e.g. soils). + [nlme.mmkin](https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html) and + [saem.mmkin](https://pkgdown.jrwb.de/mkin/reference/saem.mmkin.html) and + methods. Note that the convergence of the nlme fits depends on the quality of + the data. Convergence is better for simple models and data for many groups + (e.g. soils). The saem method uses the `saemix` package as a backend. Analytical + solutions suitable for use with this package have been implemented for parent + only models and the most important models including one metabolite (SFO-SFO + and DFOP-SFO). Fitting other models with `saem.mmkin`, while it makes use + of the compiled ODE models that mkin provides, has longer run times (at least + six minutes on my system). ### Performance diff --git a/_pkgdown.yml b/_pkgdown.yml index a214c209..77cb0323 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -43,10 +43,8 @@ reference: contents: - nlme.mmkin - saem.mmkin - - nlmixr.mmkin - plot.mixed.mmkin - summary.nlme.mmkin - - summary.nlmixr.mmkin - summary.saem.mmkin - nlme_function - get_deg_func @@ -54,7 +52,6 @@ reference: - mixed - intervals - intervals.saem.mmkin - - intervals.nlmixr.mmkin - title: Datasets and known results contents: - focus_soil_moisture @@ -90,7 +87,6 @@ reference: - mkinpredict - transform_odeparms - ilr - - tffm0 - logLik.mkinfit - residuals.mkinfit - nobs.mkinfit diff --git a/cran-comments.md b/cran-comments.md deleted file mode 100644 index e69de29b..00000000 --- a/cran-comments.md +++ /dev/null diff --git a/docs/404.html b/docs/404.html index 3215181e..ea7f1350 100644 --- a/docs/404.html +++ b/docs/404.html @@ -42,7 +42,7 @@ <a href="https://pkgdown.jrwb.de/mkin/reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="https://pkgdown.jrwb.de/mkin/#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="https://pkgdown.jrwb.de/mkin/#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 7ef16ef8..1363ef11 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -43,7 +43,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -105,7 +105,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 31 January 2019 (rebuilt 2022-03-02)</h4> + <h4 data-toc-skip class="date">Last change 31 January 2019 (rebuilt 2022-03-07)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -192,8 +192,8 @@ <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:18 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:18 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:15:58 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:15:59 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - k_parent * parent</span> @@ -201,7 +201,7 @@ <span class="co">## </span> <span class="co">## Model predictions using solution type analytical </span> <span class="co">## </span> -<span class="co">## Fitted using 401 model solutions performed in 0.145 s</span> +<span class="co">## Fitted using 401 model solutions performed in 0.165 s</span> <span class="co">## </span> <span class="co">## Error model: Constant variance </span> <span class="co">## </span> diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index 9efea93a..d7412a56 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -43,7 +43,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -105,7 +105,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 17 November 2016 (rebuilt 2022-03-02)</h4> + <h4 data-toc-skip class="date">Last change 17 November 2016 (rebuilt 2022-03-07)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -133,8 +133,8 @@ <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:22 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:22 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:01 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:01 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - k_parent * parent</span> @@ -239,15 +239,15 @@ <span class="co">## doubtful</span></code></pre> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:22 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:22 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:02 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:02 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> <span class="co">## </span> <span class="co">## Model predictions using solution type analytical </span> <span class="co">## </span> -<span class="co">## Fitted using 369 model solutions performed in 0.082 s</span> +<span class="co">## Fitted using 369 model solutions performed in 0.081 s</span> <span class="co">## </span> <span class="co">## Error model: Constant variance </span> <span class="co">## </span> @@ -351,8 +351,8 @@ <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:23 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:23 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:03 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:03 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> @@ -432,8 +432,8 @@ <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:23 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:23 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:03 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:03 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -442,7 +442,7 @@ <span class="co">## </span> <span class="co">## Model predictions using solution type analytical </span> <span class="co">## </span> -<span class="co">## Fitted using 581 model solutions performed in 0.131 s</span> +<span class="co">## Fitted using 581 model solutions performed in 0.132 s</span> <span class="co">## </span> <span class="co">## Error model: Constant variance </span> <span class="co">## </span> @@ -538,8 +538,8 @@ <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:24 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:24 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:04 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:04 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -548,7 +548,7 @@ <span class="co">## </span> <span class="co">## Model predictions using solution type analytical </span> <span class="co">## </span> -<span class="co">## Fitted using 376 model solutions performed in 0.079 s</span> +<span class="co">## Fitted using 376 model solutions performed in 0.08 s</span> <span class="co">## </span> <span class="co">## Error model: Constant variance </span> <span class="co">## </span> @@ -651,8 +651,8 @@ <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:25 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:25 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:04 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:05 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - k_parent * parent</span> @@ -716,8 +716,8 @@ <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> <pre><code><span class="co">## mkin version used for fitting: 1.1.0 </span> <span class="co">## R version used for fitting: 4.1.2 </span> -<span class="co">## Date of fit: Wed Mar 2 13:44:25 2022 </span> -<span class="co">## Date of summary: Wed Mar 2 13:44:25 2022 </span> +<span class="co">## Date of fit: Mon Mar 7 13:16:04 2022 </span> +<span class="co">## Date of summary: Mon Mar 7 13:16:05 2022 </span> <span class="co">## </span> <span class="co">## Equations:</span> <span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> diff --git a/docs/articles/index.html b/docs/articles/index.html index 9868dc6d..f340896b 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index e51356ee..60d2ef1c 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -43,7 +43,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -105,7 +105,7 @@ <h1 data-toc-skip>Introduction to mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-03-02)</h4> + <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-03-07)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -226,10 +226,10 @@ <p>Ranke, J. 2021. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="external-link">https://CRAN.R-project.org/package=mkin</a>.</p> </div> <div id="ref-ranke2012"> -<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin.</p> +<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf" class="external-link">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.</p> </div> <div id="ref-ranke2015"> -<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf" class="external-link">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p> +<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf" class="external-link">https://jrwb.de/posters/piacenza_2015.pdf</a>.</p> </div> <div id="ref-ranke2019"> <p>Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” <em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124" class="external-link">https://doi.org/10.3390/environments6120124</a>.</p> diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 5b1c333d..d45b0ff4 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -43,7 +43,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -105,7 +105,7 @@ <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 18 September 2019 (rebuilt 2022-03-02)</h4> + <h4 data-toc-skip class="date">Last change 18 September 2019 (rebuilt 2022-03-07)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 326fc121..43508280 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -43,7 +43,7 @@ <a href="../../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -105,7 +105,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 16 January 2018 (rebuilt 2022-03-02)</h4> + <h4 data-toc-skip class="date">Last change 16 January 2018 (rebuilt 2022-03-07)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html index 40e8a913..0ba9d5a8 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -43,7 +43,7 @@ <a href="../../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -105,7 +105,7 @@ <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change 10 February 2022, built on 02 Mär 2022</h4> + <h4 data-toc-skip class="date">Last change 7 March 2022, built on 07 Mar 2022</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small> <div class="hidden name"><code>dimethenamid_2018.rmd</code></div> @@ -118,8 +118,8 @@ <div class="section level2"> <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> </h2> -<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span> and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified, as many model variants do not converge when fitted with nlme, and not all relevant error models can be fitted with saemix.</p> -<p>This vignette is an attempt to satisfy this need.</p> +<p>A first analysis of the data analysed here was presented in a recent journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span>. That analysis was based on the <code>nlme</code> package and a development version of the <code>saemix</code> package that was unpublished at the time. Meanwhile, version 3.0 of the <code>saemix</code> package is available from the CRAN repository. Also, it turned out that there was an error in the handling of the Borstel data in the mkin package at the time, leading to the duplication of a few data points from that soil. The dataset in the mkin package has been corrected, and the interface to <code>saemix</code> in the mkin package has been updated to use the released version.</p> +<p>This vignette is intended to present an up to date analysis of the data, using the corrected dataset and released versions of <code>mkin</code> and <code>saemix</code>.</p> </div> <div class="section level2"> <h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a> @@ -155,20 +155,20 @@ error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png" width="700"></p> <p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png" width="700"></p> <p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png" width="700"></p> <p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> <p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png" width="700"></p> <p>However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by the fact that they did not converge:</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> @@ -222,14 +222,14 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> <p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png" width="700"></p> </div> <div class="section level4"> <h4 id="saemix">saemix<a class="anchor" aria-label="anchor" href="#saemix"></a> </h4> <p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> -<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. As we will compare the SAEM implementation of saemix to the results obtained using the nlmixr package later, we define control settings that work well for all the parent data fits shown in this vignette.</p> +<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.</p> <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span> <span class="va">saemix_control</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">800</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>, @@ -244,7 +244,7 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png" width="700"></p> -<p>Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> +<p>Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_sfo_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> @@ -256,18 +256,72 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png" width="700"></p> -<p>This is improved when the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced, it remains more or less stable already after 200 iterations of the first phase.</p> <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">)</span></code></pre></div> +<pre><code>Kinetic nonlinear mixed-effects model fit by SAEM +Structural model: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Likelihood computed by importance sampling + AIC BIC logLik + 706 704 -344 + +Fitted parameters: + estimate lower upper +DMTA_0 97.99583 96.50079 99.4909 +k1 0.06377 0.03432 0.0932 +k2 0.00848 0.00444 0.0125 +g 0.95701 0.91313 1.0009 +a.1 1.82141 1.65974 1.9831 +SD.DMTA_0 1.64787 0.45779 2.8379 +SD.k1 0.57439 0.24731 0.9015 +SD.k2 0.03296 -2.50143 2.5673 +SD.g 1.10266 0.32371 1.8816</code></pre> +<p>While the other parameters converge to credible values, the variance of k2 (<code>omega2.k2</code>) converges to a very small value. The printout of the <code>saem.mmkin</code> model shows that the estimated standard deviation of k2 across the population of soils (<code>SD.k2</code>) is ill-defined, indicating overparameterisation of this model.</p> +<p>When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of <code>SD.k2</code>.</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="va">f_parent_saemix_dfop_tc_moreiter</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, control <span class="op">=</span> <span class="va">saemix_control_moreiter</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"></p> -<p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero. This has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">)</span></code></pre></div> +<pre><code>Kinetic nonlinear mixed-effects model fit by SAEM +Structural model: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Likelihood computed by importance sampling + AIC BIC logLik + 666 664 -323 + +Fitted parameters: + estimate lower upper +DMTA_0 98.27617 96.3088 100.2436 +k1 0.06437 0.0337 0.0950 +k2 0.00880 0.0063 0.0113 +g 0.95249 0.9100 0.9949 +a.1 1.06161 0.8625 1.2607 +b.1 0.02967 0.0226 0.0367 +SD.DMTA_0 2.06075 0.4187 3.7028 +SD.k1 0.59357 0.2561 0.9310 +SD.k2 0.00292 -10.2960 10.3019 +SD.g 1.05725 0.3808 1.7337</code></pre> +<p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> <p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.</p> <p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:</p> -<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">AIC_parent_saemix</span> <span class="op"><-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span> <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, @@ -275,7 +329,7 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> <pre><code>Likelihoods calculated by importance sampling</code></pre> -<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span> <span class="st">"SFO const"</span>, <span class="st">"SFO tc"</span>, <span class="st">"DFOP const"</span>, <span class="st">"DFOP tc"</span>, <span class="st">"DFOP tc more iterations"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span></code></pre></div> @@ -286,7 +340,7 @@ DFOP const 705.75 703.88 DFOP tc 665.65 663.57 DFOP tc more iterations 665.88 663.80</code></pre> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> -<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span> <span class="va">AIC_parent_saemix_methods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span> @@ -298,7 +352,7 @@ DFOP tc more iterations 665.88 663.80</code></pre> <pre><code> is gq lin 665.65 665.68 665.11 </code></pre> <p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value. In order to illustrate that the comparison of the three method depends on the degree of convergence obtained in the fit, the same comparison is shown below for the fit using the defaults for the number of iterations and the number of MCMC chains.</p> -<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc_defaults</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span> <span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span><span class="op">)</span> @@ -311,114 +365,21 @@ DFOP tc more iterations 665.88 663.80</code></pre> <pre><code> is gq lin 668.27 718.36 666.49 </code></pre> </div> -<div class="section level4"> -<h4 id="nlmixr">nlmixr<a class="anchor" aria-label="anchor" href="#nlmixr"></a> -</h4> -<p>In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely the First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.</p> -<p>First, the focei algorithm is used for the four model combinations.</p> -<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/nlmixrdevelopment/nlmixr" class="external-link">nlmixr</a></span><span class="op">)</span> -<span class="va">f_parent_nlmixr_focei_sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span> -<span class="va">f_parent_nlmixr_focei_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span> -<span class="va">f_parent_nlmixr_focei_dfop_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span> -<span class="va">f_parent_nlmixr_focei_dfop_tc</span><span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></code></pre></div> -<p>For the SFO model with constant variance, the AIC values are the same, for the DFOP model, there are significant differences between the AIC values. These may be caused by different solutions that are found, but also by the fact that the AIC values for the nlmixr fits are calculated based on Gaussian quadrature, not on linearisation.</p> -<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">aic_nlmixr_focei</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span> - <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_focei_sfo_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_sfo_tc</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_focei_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span>, - <span class="va">AIC</span><span class="op">)</span> -<span class="va">aic_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span> - <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_const</span>, <span class="cn">NA</span>, <span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span>, - <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span><span class="op">)</span> <span class="cn">NA</span> <span class="kw">else</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span> -<span class="va">aic_nlme_nlmixr_focei</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span> - <span class="st">"Degradation model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, - <span class="st">"Error model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"constant variance"</span>, <span class="st">"two-component"</span><span class="op">)</span>, <span class="fl">2</span><span class="op">)</span>, - <span class="st">"AIC (nlme)"</span> <span class="op">=</span> <span class="va">aic_nlme</span>, - <span class="st">"AIC (nlmixr with FOCEI)"</span> <span class="op">=</span> <span class="va">aic_nlmixr_focei</span>, - check.names <span class="op">=</span> <span class="cn">FALSE</span> -<span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">aic_nlme_nlmixr_focei</span><span class="op">)</span></code></pre></div> -<pre><code> Degradation model Error model AIC (nlme) AIC (nlmixr with FOCEI) -1 SFO constant variance 796.60 796.60 -2 SFO two-component NA 798.64 -3 DFOP constant variance 798.60 745.87 -4 DFOP two-component 671.91 740.42</code></pre> -<p>Secondly, we use the SAEM estimation routine and check the convergence plots. The control parameters, which were also used for the saemix fits, are defined beforehand.</p> -<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">nlmixr_saem_control_800</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, - nBurn <span class="op">=</span> <span class="fl">800</span>, nEm <span class="op">=</span> <span class="fl">300</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span> -<span class="va">nlmixr_saem_control_moreiter</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, - nBurn <span class="op">=</span> <span class="fl">1600</span>, nEm <span class="op">=</span> <span class="fl">300</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span> -<span class="va">nlmixr_saem_control_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, - nBurn <span class="op">=</span> <span class="fl">10000</span>, nEm <span class="op">=</span> <span class="fl">1000</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></code></pre></div> -<p>Then we fit SFO with constant variance</p> -<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html" class="external-link">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png" width="700"></p> -<p>and SFO with two-component error.</p> -<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html" class="external-link">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png" width="700"></p> -<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed above for this model combination. Also note that the variance of k2 approximates zero, which was already observed in the saemix fits of the DFOP model.</p> -<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html" class="external-link">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png" width="700"></p> -<p>For DFOP with two-component error, a less erratic convergence is seen, but the variance of k2 again approximates zero.</p> -<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html" class="external-link">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png" width="700"></p> -<p>To check if an increase in the number of iterations improves the fit, we repeat the fit with 1000 iterations for the burn in phase and 300 iterations for the second phase.</p> -<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_tc_moreiter</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control_moreiter</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html" class="external-link">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc_moreiter</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png" width="700"></p> -<p>Here the fit looks very similar, but we will see below that it shows a higher AIC than the fit with 800 iterations in the burn in phase. Next we choose 10 000 iterations for the burn in phase and 1000 iterations for the second phase for comparison with saemix.</p> -<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control_10k</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html" class="external-link">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png" width="700"></p> -<p>The AIC values are internally calculated using Gaussian quadrature.</p> -<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_sfo_tc</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_saem_dfop_tc_moreiter</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<pre><code> df AIC -f_parent_nlmixr_saem_sfo_const$nm 5 798.71 -f_parent_nlmixr_saem_sfo_tc$nm 6 808.64 -f_parent_nlmixr_saem_dfop_const$nm 9 1995.96 -f_parent_nlmixr_saem_dfop_tc$nm 10 664.96 -f_parent_nlmixr_saem_dfop_tc_moreiter$nm 10 4464.93 -f_parent_nlmixr_saem_dfop_tc_10k$nm 10 Inf</code></pre> -<p>We can see that again, the DFOP/tc model shows the best goodness of fit. However, increasing the number of burn-in iterations from 800 to 1600 results in a higher AIC. If we further increase the number of iterations to 10 000 (burn-in) and 1000 (second phase), the AIC cannot be calculated for the nlmixr/saem fit, confirming that this fit does not converge properly with the SAEM algorithm.</p> </div> -<div class="section level4"> -<h4 id="comparison">Comparison<a class="anchor" aria-label="anchor" href="#comparison"></a> -</h4> -<p>The following table gives the AIC values obtained with the three packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> -<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r"> +<div class="section level3"> +<h3 id="comparison">Comparison<a class="anchor" aria-label="anchor" href="#comparison"></a> +</h3> +<p>The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains). Note that</p> +<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">AIC_all</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span> check.names <span class="op">=</span> <span class="cn">FALSE</span>, <span class="st">"Degradation model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="st">"Error model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"const"</span>, <span class="st">"tc"</span>, <span class="st">"const"</span>, <span class="st">"tc"</span><span class="op">)</span>, nlme <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_const</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_tc</span><span class="op">)</span>, <span class="cn">NA</span>, <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span><span class="op">)</span>, - nlmixr_focei <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_focei_sfo_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_sfo_tc</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_focei_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span>, - saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, - <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span>, - nlmixr_saem <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_sfo_tc</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span> + saemix_lin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, + <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span>, method <span class="op">=</span> <span class="st">"lin"</span><span class="op">)</span>, + saemix_is <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, + <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span>, method <span class="op">=</span> <span class="st">"is"</span><span class="op">)</span> <span class="op">)</span> <span class="fu">kable</span><span class="op">(</span><span class="va">AIC_all</span><span class="op">)</span></code></pre></div> <table class="table"> @@ -426,9 +387,8 @@ f_parent_nlmixr_saem_dfop_tc_10k$nm 10 Inf</code></pre> <th align="left">Degradation model</th> <th align="left">Error model</th> <th align="right">nlme</th> -<th align="right">nlmixr_focei</th> -<th align="right">saemix</th> -<th align="right">nlmixr_saem</th> +<th align="right">saemix_lin</th> +<th align="right">saemix_is</th> </tr></thead> <tbody> <tr class="odd"> @@ -437,36 +397,37 @@ f_parent_nlmixr_saem_dfop_tc_10k$nm 10 Inf</code></pre> <td align="right">796.60</td> <td align="right">796.60</td> <td align="right">796.38</td> -<td align="right">798.71</td> </tr> <tr class="even"> <td align="left">SFO</td> <td align="left">tc</td> <td align="right">798.60</td> -<td align="right">798.64</td> +<td align="right">798.60</td> <td align="right">798.38</td> -<td align="right">808.64</td> </tr> <tr class="odd"> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">745.87</td> +<td align="right">671.98</td> <td align="right">705.75</td> -<td align="right">1995.96</td> </tr> <tr class="even"> <td align="left">DFOP</td> <td align="left">tc</td> <td align="right">671.91</td> -<td align="right">740.42</td> +<td align="right">665.11</td> <td align="right">665.65</td> -<td align="right">664.96</td> </tr> </tbody> </table> </div> </div> +<div class="section level2"> +<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a> +</h2> +<p>A more detailed analysis of the dimethenamid dataset confirmed that the DFOP model provides the most appropriate description of the decline of the parent compound in these data. On the other hand, closer inspection of the results revealed that the variability of the k2 parameter across the population of soils is ill-defined. This coincides with the observation that this parameter cannot robustly be quantified in some for some of the soils.</p> +<p>Regarding the regulatory use of these data, it is claimed that an improved characterisation of the mean parameter values across the population is obtained using the nonlinear mixed-effects models presented here. However, attempts to quantify the variability of the slower rate constant of the biphasic decline of dimethenamid indicate that the data are not sufficient to characterise this variability to a satisfactory precision.</p> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> diff --git a/docs/authors.html b/docs/authors.html index 6a360df5..2f901309 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -26,7 +26,7 @@ <a href="reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/index.html b/docs/index.html index df842cce..02b4011c 100644 --- a/docs/index.html +++ b/docs/index.html @@ -54,7 +54,7 @@ <a href="reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -163,7 +163,7 @@ <li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li> <li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li> <li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> -<li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> method. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils).</li> +<li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.mmkin.html">saem.mmkin</a> and methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (at least six minutes on my system).</li> </ul> </div> <div class="section level3"> diff --git a/docs/news/index.html b/docs/news/index.html index 0a73f051..075b4e22 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -85,12 +85,11 @@ <h2 class="page-header" data-toc-text="1.1.0" id="mkin-110-unreleased">mkin 1.1.0 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-110-unreleased"></a></h2> <div class="section level3"> <h3 id="mixed-effects-models-1-1-0">Mixed-effects models<a class="anchor" aria-label="anchor" href="#mixed-effects-models-1-1-0"></a></h3> -<ul><li><p>Introduce an interface to nlmixr, supporting estimation methods ‘saem’ and ‘focei’: S3 method ‘nlmixr.mmkin’ using the helper functions ‘nlmixr_model’ and ‘nlmixr_data’ to set up nlmixr models for mmkin row objects, with summary and plot methods.</p></li> -<li><p>Reintroduce the interface to saemix (now on CRAN), in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods</p></li> +<ul><li><p>Reintroduce the interface to saemix (now on CRAN), in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods</p></li> <li><p>‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends</p></li> <li><p>‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ and ‘conf.level’</p></li> <li><p>‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations of the dimethenamid data.</p></li> -<li><p>‘intervals’: Provide methods of this nlme function for ‘nlmixr.mmkin’ and ‘saem.mmkin’ objects.</p></li> +<li><p>‘intervals’: Provide a method of this nlme function for ‘saem.mmkin’ objects.</p></li> </ul></div> </div> <div class="section level2"> diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 5bd267be..f1010950 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -11,7 +11,7 @@ articles: benchmarks: web_only/benchmarks.html compiled_models: web_only/compiled_models.html dimethenamid_2018: web_only/dimethenamid_2018.html -last_built: 2022-03-03T10:27Z +last_built: 2022-03-07T13:50Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index 35becede..a049850d 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -27,7 +27,7 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/CAKE_export.html b/docs/reference/CAKE_export.html index d36f5ed9..44297d1b 100644 --- a/docs/reference/CAKE_export.html +++ b/docs/reference/CAKE_export.html @@ -27,7 +27,7 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html index 85f068ee..1c47c7f7 100644 --- a/docs/reference/D24_2014.html +++ b/docs/reference/D24_2014.html @@ -31,7 +31,7 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index 335590f7..86df2efc 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -27,7 +27,7 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 49578dc7..7193e0f9 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html index c8bfb9dd..93602958 100644 --- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -30,7 +30,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html index c332446f..31c14505 100644 --- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -30,7 +30,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html index 151925d9..83ab4e56 100644 --- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -30,7 +30,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html index f8e368cf..f47cba8d 100644 --- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -30,7 +30,7 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html index a1004d39..1e0e270e 100644 --- a/docs/reference/FOCUS_2006_datasets.html +++ b/docs/reference/FOCUS_2006_datasets.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html index 9e3851fd..d507b9c8 100644 --- a/docs/reference/FOMC.solution.html +++ b/docs/reference/FOMC.solution.html @@ -27,7 +27,7 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html index 7f0ab8ea..d30c4e8a 100644 --- a/docs/reference/HS.solution.html +++ b/docs/reference/HS.solution.html @@ -27,7 +27,7 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html index bfb69a40..fec06e0a 100644 --- a/docs/reference/IORE.solution.html +++ b/docs/reference/IORE.solution.html @@ -27,7 +27,7 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/NAFTA_SOP_2015.html b/docs/reference/NAFTA_SOP_2015.html index 1862580b..ab3ae7b8 100644 --- a/docs/reference/NAFTA_SOP_2015.html +++ b/docs/reference/NAFTA_SOP_2015.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/NAFTA_SOP_Attachment.html b/docs/reference/NAFTA_SOP_Attachment.html index e602a2b0..a9f4e36f 100644 --- a/docs/reference/NAFTA_SOP_Attachment.html +++ b/docs/reference/NAFTA_SOP_Attachment.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png Binary files differindex 17a35806..ca982688 100644 --- a/docs/reference/Rplot001.png +++ b/docs/reference/Rplot001.png diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png Binary files differindex 86d1775b..de2d61aa 100644 --- a/docs/reference/Rplot002.png +++ b/docs/reference/Rplot002.png diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png Binary files differindex b1cdfcc9..f8bf10bb 100644 --- a/docs/reference/Rplot003.png +++ b/docs/reference/Rplot003.png diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png Binary files differindex b91e73fb..69fdb09d 100644 --- a/docs/reference/Rplot004.png +++ b/docs/reference/Rplot004.png diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html index 8caa8958..dfae0f69 100644 --- a/docs/reference/SFO.solution.html +++ b/docs/reference/SFO.solution.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html index 45933071..40ed1338 100644 --- a/docs/reference/SFORB.solution.html +++ b/docs/reference/SFORB.solution.html @@ -30,7 +30,7 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index 50b55748..de773982 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -28,7 +28,7 @@ may depend on the predicted value and is specified as a standard deviation."><!- <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/aw.html b/docs/reference/aw.html index 534cdf80..1182ab8c 100644 --- a/docs/reference/aw.html +++ b/docs/reference/aw.html @@ -28,7 +28,7 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index a57164cc..806e94ca 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -33,7 +33,7 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -206,7 +206,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class="r-in"><span class="va">f_d_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 3.933 0.000 3.934 </span> +<span class="r-out co"><span class="r-pr">#></span> 4.361 0.964 3.998 </span> <span class="r-in"><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span> <span class="r-in"><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span> <span class="r-in"><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span> @@ -214,7 +214,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span> <span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 1.496 0.172 0.968 </span> +<span class="r-out co"><span class="r-pr">#></span> 1.473 0.118 0.917 </span> <span class="r-in"><span class="va">ci_profile</span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 96.456003640 1.027703e+02</span> diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html index ca57addf..57516fba 100644 --- a/docs/reference/create_deg_func.html +++ b/docs/reference/create_deg_func.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -120,8 +120,8 @@ <span class="r-in"> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> <span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.446 1.000 0.446 0 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.756 1.695 0.756 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.406 1.000 0.407 0.000 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.698 1.719 0.695 0.003 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> @@ -134,8 +134,8 @@ <span class="r-in"> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> <span class="r-in"> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.896 1.000 0.896 0 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.685 1.881 1.685 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.855 1.000 0.856 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.588 1.857 1.587 0 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html index 374d2287..f0bc23ee 100644 --- a/docs/reference/dimethenamid_2018.html +++ b/docs/reference/dimethenamid_2018.html @@ -31,7 +31,7 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -166,321 +166,19 @@ specific pieces of information in the comments.</p> <span class="r-in"><span class="va">f_dmta_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span> <span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO3+"</span> <span class="op">=</span> <span class="va">dfop_sfo3_plus</span><span class="op">)</span>,</span> <span class="r-in"> <span class="va">dmta_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="nlmixr.mmkin.html">nlmixr_model</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span> -<span class="r-out co"><span class="r-pr">#></span> function () </span> -<span class="r-out co"><span class="r-pr">#></span> {</span> -<span class="r-out co"><span class="r-pr">#></span> ini({</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 = 99</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 ~ 2.3</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 = -3.9</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 ~ 0.55</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 = -4.3</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 ~ 0.86</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 = -4.2</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 ~ 0.75</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 = -2.2</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 ~ 0.9</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 = -3.8</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 ~ 1.6</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis = 0.44</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis ~ 3.1</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis = -2.1</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis ~ 0.3</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis = -2.2</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis ~ 0.3</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis = -2.1</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis ~ 0.3</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_DMTA = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_M23 = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M23 = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_M27 = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_M31 = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M31 = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> })</span> -<span class="r-out co"><span class="r-pr">#></span> model({</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0_model = DMTA_0 + eta.DMTA_0</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA(0) = DMTA_0_model</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 = exp(log_k_M23 + eta.log_k_M23)</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 = exp(log_k_M27 + eta.log_k_M27)</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 = exp(log_k_M31 + eta.log_k_M31)</span> -<span class="r-out co"><span class="r-pr">#></span> k1 = exp(log_k1 + eta.log_k1)</span> -<span class="r-out co"><span class="r-pr">#></span> k2 = exp(log_k2 + eta.log_k2)</span> -<span class="r-out co"><span class="r-pr">#></span> g = expit(g_qlogis + eta.g_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 = f_DMTA_tffm0_1</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - f_DMTA_tffm0_1)</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - </span> -<span class="r-out co"><span class="r-pr">#></span> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - </span> -<span class="r-out co"><span class="r-pr">#></span> g) * exp(-k2 * time))) * DMTA</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + </span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA)</span> -<span class="r-out co"><span class="r-pr">#></span> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23)</span> -<span class="r-out co"><span class="r-pr">#></span> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27)</span> -<span class="r-out co"><span class="r-pr">#></span> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)</span> -<span class="r-out co"><span class="r-pr">#></span> })</span> -<span class="r-out co"><span class="r-pr">#></span> }</span> -<span class="r-out co"><span class="r-pr">#></span> <environment: 0x555560091f40></span> +<span class="r-in"><span class="fu">nlmixr_model</span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span><span class="op">)</span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmixr_model(f_dmta_mkin_tc):</span> could not find function "nlmixr_model"</span> <span class="r-in"><span class="co"># The focei fit takes about four minutes on my system</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span> -<span class="r-in"> <span class="va">f_dmta_nlmixr_focei</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span> +<span class="r-in"> <span class="va">f_dmta_nlmixr_focei</span> <span class="op"><-</span> <span class="fu">nlmixr</span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span> <span class="r-in"> control <span class="op">=</span> <span class="fu">nlmixr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">500</span><span class="op">)</span><span class="op">)</span></span> <span class="r-in"><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span> -<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span> -<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span> -<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate jacobian</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:02 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate sensitivities</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:04 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate ∂(f)/∂(η)</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:01 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate ∂(R²)/∂(η)</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:08 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in inner model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:07 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in inner model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:07 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in EBE model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:00 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in EBE model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:00 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling inner model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in FD model...</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in FD model...</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling EBE model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling events FD model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> Needed Covariates:</span> -<span class="r-out co"><span class="r-pr">#></span> [1] "CMT"</span> -<span class="r-msg co"><span class="r-pr">#></span> RxODE 1.1.4 using 8 threads (see ?getRxThreads)</span> -<span class="r-msg co"><span class="r-pr">#></span> no cache: create with `rxCreateCache()`</span> -<span class="r-out co"><span class="r-pr">#></span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> F: Forward difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> C: Central difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> M: Mixed forward and central difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> Unscaled parameters for Omegas=chol(solve(omega));</span> -<span class="r-out co"><span class="r-pr">#></span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span> -<span class="r-out co"><span class="r-pr">#></span> |-----+---------------+-----------+-----------+-----------+-----------|</span> -<span class="r-out co"><span class="r-pr">#></span> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................| o1 | o2 | o3 | o4 |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................| o5 | o6 | o7 | o8 |</span> -<span class="r-out co"><span class="r-pr">#></span> <span style="text-decoration: underline;">|.....................| o9 | o10 |...........|...........|</span></span> -<span class="r-out co"><span class="r-pr">#></span> calculating covariance matrix</span> -<span class="r-out co"><span class="r-pr">#></span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating residuals/tables</span> -<span class="r-msg co"><span class="r-pr">#></span> done</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>S matrix non-positive definite</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>using R matrix to calculate covariance</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span> -<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 553.721 10.570 564.258 </span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmixr(f_dmta_mkin_tc, est = "focei", control = nlmixr::foceiControl(print = 500)):</span> could not find function "nlmixr"</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_focei</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> nlmixr version used for fitting: 2.0.6 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:27:22 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:27:22 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Equations:</span> -<span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * DMTA</span> -<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M23 * M23</span> -<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 563 observations of 4 variable(s) grouped in 6 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Degradation model predictions using RxODE</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 564.08 s</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 </span> -<span class="r-out co"><span class="r-pr">#></span> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> -1.7554 -2.2352 -3.7758 0.4363 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for error model parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low rsd_high </span> -<span class="r-out co"><span class="r-pr">#></span> 0.70012 0.02577 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> -<span class="r-out co"><span class="r-pr">#></span> None</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Results:</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood calculated by focei </span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 1857 1952 -906.5</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 98.0116 95.243 100.780</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0184 -5.213 -2.824</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -4.2033 -5.013 -3.394</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -4.1728 -4.999 -3.347</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.4831 -3.398 -1.568</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.8423 -5.450 -2.235</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.4682 -2.188 3.124</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -2.0823 -2.591 -1.574</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -2.1265 -2.686 -1.567</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -2.0795 -2.735 -1.424</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 lg__M23 lg__M27 lg__M31 log_k1 log_k2 g_qlogs</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0154 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0164 0.0031 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0131 0.0018 0.0541 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -0.0306 0.0045 0.0019 0.0011 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0527 -0.0043 -0.0037 -0.0003 0.0375 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1005 0.0076 0.0074 0.0013 0.0910 0.1151 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -0.0308 0.0362 0.0024 0.0021 0.0058 -0.0070 0.0145</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -0.0309 0.0062 0.0353 -0.0229 0.0047 -0.0082 0.0146</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -0.0308 0.0061 0.0419 0.0547 0.0033 -0.0055 0.0104</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_0_1 f_DMTA_0_2</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis 0.0118 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis 0.0086 -0.0057 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Random effects (omega):</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 4.224 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 1.041 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.4609 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.0000 0.4728</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 eta.log_k2 eta.g_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.635 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 1.662 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 4.36</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.1909 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.2232</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.3149</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low rsd_high </span> -<span class="r-out co"><span class="r-pr">#></span> 0.82408 0.03045 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 98.01163 95.243379 100.77988</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01798 0.005443 0.05940</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 0.01495 0.006652 0.03358</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01541 0.006746 0.03520</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.11083 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.09474 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.08827 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.08348 0.033429 0.20848</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.02144 0.004296 0.10704</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.61496 0.100857 0.95788</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> -<span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.11083</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.09474</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.08827</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.70616</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> -<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA 12.96 64.24 19.34 8.303 32.32</span> -<span class="r-out co"><span class="r-pr">#></span> M23 38.55 128.06 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M27 46.38 154.06 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M31 44.98 149.43 NA NA NA</span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in summary(f_dmta_nlmixr_focei):</span> object 'f_dmta_nlmixr_focei' not found</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_focei</span><span class="op">)</span></span> -<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_dmta_nlmixr_focei):</span> object 'f_dmta_nlmixr_focei' not found</span> <span class="r-in"><span class="co"># Using saemix takes about 18 minutes</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span> <span class="r-in"> <span class="va">f_dmta_saemix</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> @@ -496,8 +194,8 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 55.3899, R2 = nan</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in out[available, var]:</span> (subscript) logical subscript too long</span> -<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.58 0 12.58</span> -<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.99 0.008 13</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.76 3.069 11.79</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 13.77 4.719 12.37</span> <span class="r-in"></span> <span class="r-in"><span class="co"># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span></span> <span class="r-in"><span class="co"># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span></span> @@ -506,203 +204,17 @@ specific pieces of information in the comments.</p> <span class="r-in"><span class="co"># convincing for the parent. It seems we are fitting an overparameterised</span></span> <span class="r-in"><span class="co"># model, so the result we get strongly depends on starting parameters and control settings.</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span> -<span class="r-in"> <span class="va">f_dmta_nlmixr_saem</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span> +<span class="r-in"> <span class="va">f_dmta_nlmixr_saem</span> <span class="op"><-</span> <span class="fu">nlmixr</span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span> <span class="r-in"> control <span class="op">=</span> <span class="fu">nlmixr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">500</span>, logLik <span class="op">=</span> <span class="cn">TRUE</span>, nmc <span class="op">=</span> <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span> <span class="r-in"><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → generate SAEM model</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-out co"><span class="r-pr">#></span> 1: 98.7179 -3.4492 -3.2592 -3.6952 -2.1629 -2.7824 0.8990 -2.8080 -2.7380 -2.8041 2.7789 0.6848 0.8170 0.7125 0.8550 1.5200 2.9882 0.3073 0.2850 0.2877 4.0480 0.4153 4.5214 0.3775 4.4419 0.4181 3.7069 0.5935</span> -<span class="r-out co"><span class="r-pr">#></span> 500: 97.8519 -4.3891 -4.0888 -4.1247 -2.9246 -4.2755 2.6294 -2.1212 -2.1380 -2.0739 3.1293 1.2665 0.2763 0.3429 0.5743 1.5561 4.4991 0.1499 0.1551 0.3103 0.9514 0.0341 0.4846 0.1068 0.6597 0.0767 0.7836 0.0360</span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating covariance matrix</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span> -<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling EBE model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> Needed Covariates:</span> -<span class="r-out co"><span class="r-pr">#></span> [1] "CMT"</span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating residuals/tables</span> -<span class="r-msg co"><span class="r-pr">#></span> done</span> -<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 785.825 3.841 153.598 </span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmixr(f_dmta_mkin_tc, est = "saem", control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)):</span> could not find function "nlmixr"</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0.001</span> <span class="r-in"><span class="fu">traceplot</span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></span> <span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in traceplot(f_dmta_nlmixr_saem$nm):</span> could not find function "traceplot"</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> nlmixr version used for fitting: 2.0.6 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:30:09 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:30:09 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Equations:</span> -<span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * DMTA</span> -<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M23 * M23</span> -<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 563 observations of 4 variable(s) grouped in 6 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Degradation model predictions using RxODE</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 153.313 s</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 </span> -<span class="r-out co"><span class="r-pr">#></span> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> -1.7554 -2.2352 -3.7758 0.4363 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for error model parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.70012 0.02577 0.70012 0.02577 0.70012 </span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 sigma_low_M31 rsd_high_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.02577 0.70012 0.02577 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> -<span class="r-out co"><span class="r-pr">#></span> None</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Results:</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood calculated by focei </span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 1966 2088 -955.2</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 97.852 95.86386 99.840</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.389 -5.35084 -3.427</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -4.089 -4.54432 -3.633</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -4.125 -4.63280 -3.617</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.925 -3.54158 -2.308</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.275 -5.81760 -2.733</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 2.629 -0.01785 5.277</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -2.121 -2.44462 -1.798</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -2.138 -2.47804 -1.798</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -2.074 -2.53581 -1.612</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 lg__M23 lg__M27 lg__M31 log_k1 log_k2 g_qlogs</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0164 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0267 0.0028 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0179 0.0023 0.0755 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0385 -0.0034 -0.0054 -0.0029 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0381 0.0115 0.0087 0.0093 0.0786 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0656 0.0021 0.0051 0.0001 -0.1177 -0.4389 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -0.0604 0.0554 0.0054 0.0039 -0.0082 -0.0022 0.0119</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -0.0601 0.0091 0.0577 -0.0350 -0.0081 -0.0057 0.0137</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -0.0515 0.0083 0.0569 0.0729 -0.0059 0.0005 0.0073</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_0_1 f_DMTA_0_2</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis 0.0167 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis 0.0145 -0.0060 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Random effects (omega):</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 3.129 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 1.266 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.2763 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.0000 0.3429</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 eta.log_k2 eta.g_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.5743 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 1.556 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.000 4.499</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.1499 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1551</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.3103</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.95135 0.03412 0.48455 0.10682 0.65969 </span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 sigma_low_M31 rsd_high_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.07670 0.78365 0.03598 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 97.85189 95.863863 99.83992</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01241 0.004744 0.03247</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 0.01676 0.010627 0.02643</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01617 0.009727 0.02687</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.10705 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.09417 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.08919 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.05369 0.028968 0.09950</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.01391 0.002975 0.06500</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.93273 0.495538 0.99492</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> -<span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.10705</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.09417</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.08919</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.70959</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> -<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA 13.81 49.3 14.84 12.91 49.85</span> -<span class="r-out co"><span class="r-pr">#></span> M23 55.85 185.5 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M27 41.36 137.4 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M31 42.87 142.4 NA NA NA</span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in summary(f_dmta_nlmixr_saem):</span> object 'f_dmta_nlmixr_saem' not found</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">)</span></span> -<span class="r-plt img"><img src="dimethenamid_2018-2.png" alt="" width="700" height="433"></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_dmta_nlmixr_saem):</span> object 'f_dmta_nlmixr_saem' not found</span> <span class="r-in"><span class="co"># }</span></span> </code></pre></div> </div> diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index 1476eed8..f764b742 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -32,7 +32,7 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -104,8 +104,8 @@ advantage that the SFORB model can also be used for metabolites.</p> <div id="arguments"> <h2>Arguments</h2> <dl><dt>fit</dt> -<dd><p>An object of class <a href="mkinfit.html">mkinfit</a>, <a href="nlme.mmkin.html">nlme.mmkin</a>, <a href="saem.html">saem.mmkin</a> or -<a href="nlmixr.mmkin.html">nlmixr.mmkin</a>. Or another object that has list components +<dd><p>An object of class <a href="mkinfit.html">mkinfit</a>, <a href="nlme.mmkin.html">nlme.mmkin</a> or <a href="saem.html">saem.mmkin</a>, +or another object that has list components mkinmod containing an <a href="mkinmod.html">mkinmod</a> degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.</p></dd> diff --git a/docs/reference/experimental_data_for_UBA.html b/docs/reference/experimental_data_for_UBA.html index 862c63e4..e87105b6 100644 --- a/docs/reference/experimental_data_for_UBA.html +++ b/docs/reference/experimental_data_for_UBA.html @@ -54,7 +54,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/f_time_norm_focus.html b/docs/reference/f_time_norm_focus.html index 4bec2bd5..eb0e6f73 100644 --- a/docs/reference/f_time_norm_focus.html +++ b/docs/reference/f_time_norm_focus.html @@ -27,7 +27,7 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/focus_soil_moisture.html b/docs/reference/focus_soil_moisture.html index 726f87c4..9189ef76 100644 --- a/docs/reference/focus_soil_moisture.html +++ b/docs/reference/focus_soil_moisture.html @@ -27,7 +27,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/get_deg_func.html b/docs/reference/get_deg_func.html index 55d325f5..130e786a 100644 --- a/docs/reference/get_deg_func.html +++ b/docs/reference/get_deg_func.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html index 5322c41e..d0cb6372 100644 --- a/docs/reference/ilr.html +++ b/docs/reference/ilr.html @@ -27,7 +27,7 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/index.html b/docs/reference/index.html index 552829bf..90387a56 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -180,10 +180,6 @@ of an mmkin object</p></td> </td> <td><p>Fit nonlinear mixed models with SAEM</p></td> </tr><tr><td> - <p><code><a href="nlmixr.mmkin.html">nlmixr(<i><mmkin></i>)</a></code> <code><a href="nlmixr.mmkin.html">print(<i><nlmixr.mmkin></i>)</a></code> <code><a href="nlmixr.mmkin.html">nlmixr_model()</a></code> <code><a href="nlmixr.mmkin.html">nlmixr_data()</a></code> </p> - </td> - <td><p>Fit nonlinear mixed models using nlmixr</p></td> - </tr><tr><td> <p><code><a href="plot.mixed.mmkin.html">plot(<i><mixed.mmkin></i>)</a></code> </p> </td> <td><p>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</p></td> @@ -192,10 +188,6 @@ of an mmkin object</p></td> </td> <td><p>Summary method for class "nlme.mmkin"</p></td> </tr><tr><td> - <p><code><a href="summary.nlmixr.mmkin.html">summary(<i><nlmixr.mmkin></i>)</a></code> <code><a href="summary.nlmixr.mmkin.html">print(<i><summary.nlmixr.mmkin></i>)</a></code> </p> - </td> - <td><p>Summary method for class "nlmixr.mmkin"</p></td> - </tr><tr><td> <p><code><a href="summary.saem.mmkin.html">summary(<i><saem.mmkin></i>)</a></code> <code><a href="summary.saem.mmkin.html">print(<i><summary.saem.mmkin></i>)</a></code> </p> </td> <td><p>Summary method for class "saem.mmkin"</p></td> @@ -219,10 +211,6 @@ of an mmkin object</p></td> <p><code><a href="intervals.saem.mmkin.html">intervals(<i><saem.mmkin></i>)</a></code> </p> </td> <td><p>Confidence intervals for parameters in saem.mmkin objects</p></td> - </tr><tr><td> - <p><code><a href="intervals.nlmixr.mmkin.html">intervals(<i><nlmixr.mmkin></i>)</a></code> </p> - </td> - <td><p>Confidence intervals for parameters in nlmixr.mmkin objects</p></td> </tr></tbody><tbody><tr><th colspan="2"> <h2 id="datasets-and-known-results">Datasets and known results <a href="#datasets-and-known-results" class="anchor" aria-hidden="true"></a></h2> <p class="section-desc"></p> @@ -345,10 +333,6 @@ kinetic models fitted with mkinfit</p></td> </td> <td><p>Function to perform isometric log-ratio transformation</p></td> </tr><tr><td> - <p><code><a href="tffm0.html">tffm0()</a></code> <code><a href="tffm0.html">invtffm0()</a></code> </p> - </td> - <td><p>Transform formation fractions as in the first published mkin version</p></td> - </tr><tr><td> <p><code><a href="logLik.mkinfit.html">logLik(<i><mkinfit></i>)</a></code> </p> </td> <td><p>Calculated the log-likelihood of a fitted mkinfit object</p></td> diff --git a/docs/reference/intervals.saem.mmkin.html b/docs/reference/intervals.saem.mmkin.html index d2eb31de..ddc565b3 100644 --- a/docs/reference/intervals.saem.mmkin.html +++ b/docs/reference/intervals.saem.mmkin.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/loftest.html b/docs/reference/loftest.html index 75578cbe..a96e6932 100644 --- a/docs/reference/loftest.html +++ b/docs/reference/loftest.html @@ -29,7 +29,7 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index dc1bfbba..c41950a3 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -30,7 +30,7 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/logistic.solution.html b/docs/reference/logistic.solution.html index 5c3c2b42..ed6e29dc 100644 --- a/docs/reference/logistic.solution.html +++ b/docs/reference/logistic.solution.html @@ -27,7 +27,7 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/lrtest.mkinfit.html b/docs/reference/lrtest.mkinfit.html index d4355c43..6adc1245 100644 --- a/docs/reference/lrtest.mkinfit.html +++ b/docs/reference/lrtest.mkinfit.html @@ -30,7 +30,7 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html index 03b0263d..29b17d1a 100644 --- a/docs/reference/max_twa_parent.html +++ b/docs/reference/max_twa_parent.html @@ -32,7 +32,7 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html index 94cd1450..9b698e64 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -28,7 +28,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mean_degparms.html b/docs/reference/mean_degparms.html index c31e310c..aaeb44ea 100644 --- a/docs/reference/mean_degparms.html +++ b/docs/reference/mean_degparms.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mixed.html b/docs/reference/mixed.html index dc0d1681..af73f22a 100644 --- a/docs/reference/mixed.html +++ b/docs/reference/mixed.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html index be37f095..ed0abedd 100644 --- a/docs/reference/mkin_long_to_wide.html +++ b/docs/reference/mkin_long_to_wide.html @@ -28,7 +28,7 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html index 06bea7e7..37c84761 100644 --- a/docs/reference/mkin_wide_to_long.html +++ b/docs/reference/mkin_wide_to_long.html @@ -28,7 +28,7 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index 25edbf38..73a14840 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -29,7 +29,7 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html index bae7488f..7f4ff290 100644 --- a/docs/reference/mkindsg.html +++ b/docs/reference/mkindsg.html @@ -29,7 +29,7 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html index b72017fe..dc2cdab8 100644 --- a/docs/reference/mkinerrmin.html +++ b/docs/reference/mkinerrmin.html @@ -27,7 +27,7 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html index 5dc188fe..95206b61 100644 --- a/docs/reference/mkinerrplot.html +++ b/docs/reference/mkinerrplot.html @@ -30,7 +30,7 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index a0644947..c006ddab 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -34,7 +34,7 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -331,15 +331,15 @@ doi: <a href="https://doi.org/10.3390/environments6120124" class="external-link" <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.0 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:30:39 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:30:39 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:11:56 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:11:56 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.046 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.045 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -480,9 +480,9 @@ doi: <a href="https://doi.org/10.3390/environments6120124" class="external-link" <span class="r-in"> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">)</span></span> <span class="r-in"><span class="op">}</span></span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> -<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.699</span> -<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.595 1.115</span> -<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.252 1.574</span> +<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.573</span> +<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.642 0.941</span> +<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.517 1.442</span> <span class="r-in"><span class="co"># }</span></span> <span class="r-in"></span> <span class="r-in"><span class="co"># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span></span> @@ -508,8 +508,8 @@ doi: <a href="https://doi.org/10.3390/environments6120124" class="external-link" <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit.FOMC_SFO.tc</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.0 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:30:53 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:30:53 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:12:07 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:12:07 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> @@ -518,7 +518,7 @@ doi: <a href="https://doi.org/10.3390/environments6120124" class="external-link" <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.916 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.67 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index a5cc3d7a..72a9ea52 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -30,7 +30,7 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -267,7 +267,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-in"> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span> <span class="r-in"> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span> <span class="r-in"> name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/RtmpNObdrK/filecabc85d9446.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> +<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/Rtmp1TiIZY/file1180516cafe4a2.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> <span class="r-in"><span class="co"># Now we can save the model and restore it in a new session</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span> <span class="r-in"><span class="co"># Terminate the R session here if you would like to check, and then do</span></span> @@ -319,7 +319,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> })</span> <span class="r-out co"><span class="r-pr">#></span> return(predicted)</span> <span class="r-out co"><span class="r-pr">#></span> }</span> -<span class="r-out co"><span class="r-pr">#></span> <environment: 0x5555673b8fd0></span> +<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55555f6c4cd8></span> <span class="r-in"></span> <span class="r-in"><span class="co"># If we have several parallel metabolites</span></span> <span class="r-in"><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span> diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html index 2d7f6228..2e787240 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -27,7 +27,7 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html index 896c30a0..79599787 100644 --- a/docs/reference/mkinplot.html +++ b/docs/reference/mkinplot.html @@ -27,7 +27,7 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index b52d9ece..c197e6dc 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -28,7 +28,7 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -337,10 +337,10 @@ as these always return mapped output.</p></dd> <span class="r-in"> solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span> <span class="r-in"><span class="op">}</span></span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> -<span class="r-out co"><span class="r-pr">#></span> 4 analytical 1.0 0.005</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve_compiled 1.2 0.006</span> -<span class="r-out co"><span class="r-pr">#></span> 1 eigen 4.2 0.021</span> -<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 40.8 0.204</span> +<span class="r-out co"><span class="r-pr">#></span> 4 analytical 1.00 0.004</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve_compiled 1.50 0.006</span> +<span class="r-out co"><span class="r-pr">#></span> 1 eigen 5.25 0.021</span> +<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 52.00 0.208</span> <span class="r-in"></span> <span class="r-in"><span class="co"># \dontrun{</span></span> <span class="r-in"> <span class="co"># Predict from a fitted model</span></span> diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html index 54d612ef..cd3b4009 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -29,7 +29,7 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index 4012df67..1ca0190a 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -29,7 +29,7 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -167,10 +167,10 @@ plotting.</p></div> <span class="r-in"></span> <span class="r-in"><span class="va">time_default</span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 6.055 1.405 2.191 </span> +<span class="r-out co"><span class="r-pr">#></span> 4.710 0.665 1.757 </span> <span class="r-in"><span class="va">time_1</span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 6.746 0.004 6.750 </span> +<span class="r-out co"><span class="r-pr">#></span> 5.644 0.001 5.645 </span> <span class="r-in"></span> <span class="r-in"><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html index 28e44864..0711886a 100644 --- a/docs/reference/nafta.html +++ b/docs/reference/nafta.html @@ -30,7 +30,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index 5eebecaa..746cc6aa 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -29,7 +29,7 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html index 56770856..4b59afed 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -28,7 +28,7 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/nobs.mkinfit.html b/docs/reference/nobs.mkinfit.html index 0fc1ca7c..34da24d9 100644 --- a/docs/reference/nobs.mkinfit.html +++ b/docs/reference/nobs.mkinfit.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/parms.html b/docs/reference/parms.html index 00726a1d..1ddd3eb9 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -28,7 +28,7 @@ considering the error structure that was assumed for the fit."><!-- mathjax -->< <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/plot.mixed.mmkin-3.png b/docs/reference/plot.mixed.mmkin-3.png Binary files differnew file mode 100644 index 00000000..0840c7da --- /dev/null +++ b/docs/reference/plot.mixed.mmkin-3.png diff --git a/docs/reference/plot.mixed.mmkin-4.png b/docs/reference/plot.mixed.mmkin-4.png Binary files differnew file mode 100644 index 00000000..a78087bb --- /dev/null +++ b/docs/reference/plot.mixed.mmkin-4.png diff --git a/docs/reference/plot.mixed.mmkin.html b/docs/reference/plot.mixed.mmkin.html index da382eb9..b405b84a 100644 --- a/docs/reference/plot.mixed.mmkin.html +++ b/docs/reference/plot.mixed.mmkin.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -197,9 +197,8 @@ corresponding model prediction lines for the different datasets.</p></dd> <span class="r-plt img"><img src="plot.mixed.mmkin-2.png" alt="" width="700" height="433"></span> <span class="r-in"></span> <span class="r-in"><span class="va">f_saem</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in saem(f, transformations = "saemix"):</span> unused argument (transformations = "saemix")</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_saem):</span> object 'f_saem' not found</span> +<span class="r-plt img"><img src="plot.mixed.mmkin-3.png" alt="" width="700" height="433"></span> <span class="r-in"></span> <span class="r-in"><span class="va">f_obs</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span> <span class="r-in"><span class="va">f_nlmix</span> <span class="op"><-</span> <span class="fu">nlmix</span><span class="op">(</span><span class="va">f_obs</span><span class="op">)</span></span> @@ -213,7 +212,7 @@ corresponding model prediction lines for the different datasets.</p></dd> <span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="va">f_nlme</span><span class="op">$</span><span class="va">bparms.optim</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span>, A1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>,</span> <span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">180</span>, by <span class="op">=</span> <span class="fl">0.2</span><span class="op">)</span><span class="op">)</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem</span>, pred_over <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>nlme <span class="op">=</span> <span class="va">pred_nlme</span><span class="op">)</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_saem, pred_over = list(nlme = pred_nlme)):</span> object 'f_saem' not found</span> +<span class="r-plt img"><img src="plot.mixed.mmkin-4.png" alt="" width="700" height="433"></span> <span class="r-in"><span class="co"># }</span></span> </code></pre></div> </div> diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html index 0ad6ffc0..4c2ff570 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -28,7 +28,7 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html index 0994c4b6..d7ad89dc 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -30,7 +30,7 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/plot.nafta.html b/docs/reference/plot.nafta.html index d3407a24..a14ed303 100644 --- a/docs/reference/plot.nafta.html +++ b/docs/reference/plot.nafta.html @@ -27,7 +27,7 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 22180913..1367ccf5 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -10,10 +10,6 @@ lrtest intervals, nlme - nlmixr -nlmixr - - "><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> @@ -41,7 +37,7 @@ nlmixr <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -93,7 +89,7 @@ nlmixr <div class="col-md-9 contents"> <div class="page-header"> <h1>Objects exported from other packages</h1> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a>, and 1 more</small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/intervals.R" class="external-link"><code>R/intervals.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/lrtest.mkinfit.R" class="external-link"><code>R/lrtest.mkinfit.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small> <div class="hidden name"><code>reexports.Rd</code></div> </div> @@ -108,10 +104,6 @@ below to see their documentation.</p> <dd><p><code><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></code>, <code><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></code></p></dd> - <dt>nlmixr</dt> -<dd><p><code><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></code></p></dd> - - </dl></div> diff --git a/docs/reference/residuals.mkinfit.html b/docs/reference/residuals.mkinfit.html index 19e077c5..c27cea76 100644 --- a/docs/reference/residuals.mkinfit.html +++ b/docs/reference/residuals.mkinfit.html @@ -26,7 +26,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/saem-1.png b/docs/reference/saem-1.png Binary files differnew file mode 100644 index 00000000..08939d4f --- /dev/null +++ b/docs/reference/saem-1.png diff --git a/docs/reference/saem-2.png b/docs/reference/saem-2.png Binary files differnew file mode 100644 index 00000000..b737db03 --- /dev/null +++ b/docs/reference/saem-2.png diff --git a/docs/reference/saem-3.png b/docs/reference/saem-3.png Binary files differnew file mode 100644 index 00000000..99f2a2d6 --- /dev/null +++ b/docs/reference/saem-3.png diff --git a/docs/reference/saem-4.png b/docs/reference/saem-4.png Binary files differnew file mode 100644 index 00000000..5f65ba2e --- /dev/null +++ b/docs/reference/saem-4.png diff --git a/docs/reference/saem.html b/docs/reference/saem.html index 8b86b5a2..cc597665 100644 --- a/docs/reference/saem.html +++ b/docs/reference/saem.html @@ -28,7 +28,7 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -203,29 +203,16 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-in"><span class="va">f_mmkin_parent_p0_fixed</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">ds</span>,</span> <span class="r-in"> state.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, fixed_initials <span class="op">=</span> <span class="st">"parent"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span class="r-in"><span class="va">f_saem_p0_fixed</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_p0_fixed</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"></span> <span class="r-in"><span class="va">f_mmkin_parent</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span class="r-in"><span class="va">f_saem_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"><span class="va">f_saem_fomc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"><span class="va">f_saem_dfop</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"></span> <span class="r-in"><span class="co"># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span></span> <span class="r-in"><span class="co"># functions from saemix</span></span> <span class="r-in"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> <span class="r-msg co"><span class="r-pr">#></span> Loading required package: npde</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> Attaching package: ‘npde’</span> -<span class="r-msg co"><span class="r-pr">#></span> The following object is masked from ‘package:nlmixr’:</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> warfarin</span> <span class="r-msg co"><span class="r-pr">#></span> Package saemix, version 3.0</span> <span class="r-msg co"><span class="r-pr">#></span> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span> <span class="r-msg co"><span class="r-pr">#></span> </span> @@ -233,26 +220,30 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-msg co"><span class="r-pr">#></span> The following objects are masked from ‘package:npde’:</span> <span class="r-msg co"><span class="r-pr">#></span> </span> <span class="r-msg co"><span class="r-pr">#></span> kurtosis, skewness</span> -<span class="r-msg co"><span class="r-pr">#></span> The following object is masked from ‘package:RxODE’:</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> phi</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_sfo</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_dfop</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so):</span> object 'f_saem_sfo' not found</span> +<span class="r-msg co"><span class="r-pr">#></span> Likelihoods calculated by importance sampling</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC</span> +<span class="r-out co"><span class="r-pr">#></span> 1 624.2598 622.3070</span> +<span class="r-out co"><span class="r-pr">#></span> 2 467.8664 465.1324</span> +<span class="r-out co"><span class="r-pr">#></span> 3 493.9811 490.4660</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span> +<span class="r-plt img"><img src="saem-1.png" alt="" width="700" height="433"></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"individual.fit"</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span> +<span class="r-plt img"><img src="saem-2.png" alt="" width="700" height="433"></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"npde"</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span> +<span class="r-out co"><span class="r-pr">#></span> Simulating data using nsim = 1000 simulated datasets</span> +<span class="r-out co"><span class="r-pr">#></span> Computing WRES and npde .</span> +<span class="r-msg co"><span class="r-pr">#></span> Please use npdeSaemix to obtain VPC and npde</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"vpc"</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found</span> +<span class="r-plt img"><img src="saem-3.png" alt="" width="700" height="433"></span> <span class="r-in"></span> <span class="r-in"><span class="va">f_mmkin_parent_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> <span class="r-in"><span class="va">f_saem_fomc_tc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so):</span> object 'f_saem_fomc' not found</span> +<span class="r-msg co"><span class="r-pr">#></span> Likelihoods calculated by importance sampling</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC</span> +<span class="r-out co"><span class="r-pr">#></span> 1 467.8664 465.1324</span> +<span class="r-out co"><span class="r-pr">#></span> 2 469.9096 466.7851</span> <span class="r-in"></span> <span class="r-in"><span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span> <span class="r-in"> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span> @@ -273,18 +264,303 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-in"><span class="co"># When using the analytical solutions written for mkin this took around</span></span> <span class="r-in"><span class="co"># four minutes</span></span> <span class="r-in"><span class="va">f_saem_sfo_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"><span class="va">f_saem_dfop_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"><span class="co"># We can use print, plot and summary methods to check the results</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'x' in selecting a method for function 'print': object 'f_saem_dfop_sfo' not found</span> +<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by SAEM</span> +<span class="r-out co"><span class="r-pr">#></span> Structural model:</span> +<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> +<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> +<span class="r-out co"><span class="r-pr">#></span> * parent</span> +<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> +<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> +<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_A1 * A1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Data:</span> +<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 842 836.9 -408</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 93.7701 91.1458 96.3945</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.8116 -7.5998 -4.0234</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.9608 -1.3654 -0.5562</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.5841 -3.6876 -1.4805</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.5228 -5.3254 -1.7203</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1027 -0.8719 0.6665</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 1.8856 1.6676 2.1037</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.7682 0.7668 4.7695</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7447 0.4047 3.0848</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4525 0.1620 0.7431</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.2423 0.4560 2.0285</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0390 0.7601 3.3180</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4439 -0.3069 1.1947</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-ANY-method.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_dfop_sfo' not found</span> +<span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_saem_dfop_sfo' not found</span> +<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:15:26 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:15:26 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Equations:</span> +<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> +<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> +<span class="r-out co"><span class="r-pr">#></span> * parent</span> +<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> +<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> +<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_A1 * A1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Data:</span> +<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 8.741 s</span> +<span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Variance model: Constant variance </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 </span> +<span class="r-out co"><span class="r-pr">#></span> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> +<span class="r-out co"><span class="r-pr">#></span> 0.1356 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> +<span class="r-out co"><span class="r-pr">#></span> None</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Results:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 842 836.9 -408</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 93.7701 91.1458 96.3945</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.8116 -7.5998 -4.0234</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.9608 -1.3654 -0.5562</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.5841 -3.6876 -1.4805</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.5228 -5.3254 -1.7203</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1027 -0.8719 0.6665</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> +<span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_A1 f_prnt_ log_k1 log_k2 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -0.0160 </span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.0263 0.0612 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0100 -0.0014 -0.0033 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0131 0.0050 -0.0011 0.0071 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0419 -0.0199 0.0026 -0.0765 -0.0707</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Random effects:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.7682 0.7668 4.7695</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7447 0.4047 3.0848</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4525 0.1620 0.7431</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.2423 0.4560 2.0285</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0390 0.7601 3.3180</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4439 -0.3069 1.1947</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 1.886 1.668 2.104</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 93.770115 9.115e+01 96.39447</span> +<span class="r-out co"><span class="r-pr">#></span> k_A1 0.002993 5.005e-04 0.01789</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_A1 0.276720 2.034e-01 0.36443</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.075467 2.503e-02 0.22753</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.029516 4.867e-03 0.17902</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.474353 2.949e-01 0.66073</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> +<span class="r-out co"><span class="r-pr">#></span> ff</span> +<span class="r-out co"><span class="r-pr">#></span> parent_A1 0.2767</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7233</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> +<span class="r-out co"><span class="r-pr">#></span> parent 14.56 58.26 17.54 9.185 23.48</span> +<span class="r-out co"><span class="r-pr">#></span> A1 231.62 769.41 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Data:</span> +<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 97.2 95.78623 1.41377 1.886 0.749758</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 96.4 95.78623 0.61377 1.886 0.325498</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 71.1 71.34666 -0.24666 1.886 -0.130812</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 69.2 71.34666 -2.14666 1.886 -1.138429</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 58.1 56.49768 1.60232 1.886 0.849749</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 56.6 56.49768 0.10232 1.886 0.054262</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 44.4 44.53511 -0.13511 1.886 -0.071650</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 43.4 44.53511 -1.13511 1.886 -0.601974</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 33.3 29.77451 3.52549 1.886 1.869656</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 29.2 29.77451 -0.57451 1.886 -0.304675</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 17.6 19.32540 -1.72540 1.886 -0.915023</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 18.0 19.32540 -1.32540 1.886 -0.702894</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 10.5 10.42781 0.07219 1.886 0.038282</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 9.3 10.42781 -1.12781 1.886 -0.598107</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.5 3.74190 0.75810 1.886 0.402037</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.7 3.74190 0.95810 1.886 0.508102</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.0 1.96485 1.03515 1.886 0.548966</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.4 1.96485 1.43515 1.886 0.761096</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.3 1.09395 1.20605 1.886 0.639596</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.7 1.09395 1.60605 1.886 0.851726</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.3 4.72702 -0.42702 1.886 -0.226458</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.6 4.72702 -0.12702 1.886 -0.067361</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.0 7.51314 -0.51314 1.886 -0.272128</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.2 7.51314 -0.31314 1.886 -0.166063</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.2 9.63719 -1.43719 1.886 -0.762179</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.0 9.63719 -1.63719 1.886 -0.868244</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 11.0 11.84931 -0.84931 1.886 -0.450409</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 13.7 11.84931 1.85069 1.886 0.981468</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 11.5 12.82336 -1.32336 1.886 -0.701808</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 12.7 12.82336 -0.12336 1.886 -0.065418</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.9 12.89456 2.00544 1.886 1.063533</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.5 12.89456 1.60544 1.886 0.851403</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.1 11.55919 0.54081 1.886 0.286806</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.3 11.55919 0.74081 1.886 0.392871</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 9.9 10.42334 -0.52334 1.886 -0.277539</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 10.2 10.42334 -0.22334 1.886 -0.118442</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 8.8 9.37987 -0.57987 1.886 -0.307519</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 7.8 9.37987 -1.57987 1.886 -0.837844</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 93.6 90.95702 2.64298 1.886 1.401639</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 92.3 90.95702 1.34298 1.886 0.712217</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 87.0 84.77506 2.22494 1.886 1.179942</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 82.2 84.77506 -2.57506 1.886 -1.365616</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 74.0 77.60962 -3.60962 1.886 -1.914268</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 73.9 77.60962 -3.70962 1.886 -1.967301</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 64.2 67.50646 -3.30646 1.886 -1.753499</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 69.5 67.50646 1.99354 1.886 1.057221</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.0 52.48909 1.51091 1.886 0.801271</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.6 52.48909 2.11091 1.886 1.119465</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 41.1 39.54372 1.55628 1.886 0.825335</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 38.4 39.54372 -1.14372 1.886 -0.606542</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 32.5 33.87968 -1.37968 1.886 -0.731676</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 35.5 33.87968 1.62032 1.886 0.859298</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 28.1 30.41071 -2.31071 1.886 -1.225427</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 29.0 30.41071 -1.41071 1.886 -0.748135</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 26.5 25.36386 1.13614 1.886 0.602524</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 27.6 25.36386 2.23614 1.886 1.185881</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.9 2.74863 1.15137 1.886 0.610600</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.1 2.74863 0.35137 1.886 0.186341</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.9 5.92686 0.97314 1.886 0.516081</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.6 5.92686 0.67314 1.886 0.356983</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 10.4 10.38800 0.01200 1.886 0.006362</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 8.3 10.38800 -2.08800 1.886 -1.107320</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 14.4 16.93529 -2.53529 1.886 -1.344524</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 13.7 16.93529 -3.23529 1.886 -1.715751</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.1 22.33044 -0.23044 1.886 -0.122209</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.3 22.33044 -0.03044 1.886 -0.016144</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 27.5 24.42300 3.07700 1.886 1.631809</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 25.4 24.42300 0.97700 1.886 0.518127</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 28.0 25.51140 2.48860 1.886 1.319768</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 26.6 25.51140 1.08860 1.886 0.577313</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.8 26.80282 -1.00282 1.886 -0.531818</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.3 26.80282 -1.50282 1.886 -0.796981</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 91.9 91.08733 0.81267 1.886 0.430980</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 90.8 91.08733 -0.28733 1.886 -0.152377</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 64.9 67.55332 -2.65332 1.886 -1.407123</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 66.2 67.55332 -1.35332 1.886 -0.717701</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 43.5 41.65811 1.84189 1.886 0.976800</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 44.1 41.65811 2.44189 1.886 1.294994</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.3 19.65773 -1.35773 1.886 -0.720038</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.1 19.65773 -1.55773 1.886 -0.826103</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.2 10.65118 -0.45118 1.886 -0.239269</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.8 10.65118 0.14882 1.886 0.078925</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 4.9 3.11694 1.78306 1.886 0.945601</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 3.3 3.11694 0.18306 1.886 0.097082</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.6 0.43165 1.16835 1.886 0.619603</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.5 0.43165 1.06835 1.886 0.566570</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 1.1 0.05441 1.04559 1.886 0.554503</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 0.9 0.05441 0.84559 1.886 0.448438</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 9.6 7.66431 1.93569 1.886 1.026546</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 7.7 7.66431 0.03569 1.886 0.018930</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.0 15.57948 -0.57948 1.886 -0.307311</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.1 15.57948 -0.47948 1.886 -0.254279</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.2 20.38988 0.81012 1.886 0.429625</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.1 20.38988 0.71012 1.886 0.376593</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 19.7 20.16439 -0.46439 1.886 -0.246276</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 18.9 20.16439 -1.26439 1.886 -0.670535</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 17.5 16.40918 1.09082 1.886 0.578489</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 15.9 16.40918 -0.50918 1.886 -0.270030</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.5 10.12011 -0.62011 1.886 -0.328861</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.8 10.12011 -0.32011 1.886 -0.169764</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.2 5.79080 0.40920 1.886 0.217011</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.1 5.79080 0.30920 1.886 0.163979</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 99.8 97.38786 2.41214 1.886 1.279218</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 98.3 97.38786 0.91214 1.886 0.483731</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.1 79.25431 -2.15431 1.886 -1.142481</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.2 79.25431 -2.05431 1.886 -1.089449</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 59.0 55.69866 3.30134 1.886 1.750781</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 58.1 55.69866 2.40134 1.886 1.273489</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 27.4 31.64893 -4.24893 1.886 -2.253314</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 29.2 31.64893 -2.44893 1.886 -1.298729</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 19.1 22.57316 -3.47316 1.886 -1.841901</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 29.6 22.57316 7.02684 1.886 3.726507</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 10.1 14.11345 -4.01345 1.886 -2.128430</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 18.2 14.11345 4.08655 1.886 2.167199</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 4.5 6.95586 -2.45586 1.886 -1.302400</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 9.1 6.95586 2.14414 1.886 1.137093</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.3 3.31753 -1.01753 1.886 -0.539619</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.9 3.31753 -0.41753 1.886 -0.221424</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 2.0 1.63642 0.36358 1.886 0.192816</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 1.8 1.63642 0.16358 1.886 0.086751</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.0 0.61667 1.38333 1.886 0.733614</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.2 0.61667 1.58333 1.886 0.839679</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 4.2 3.67247 0.52753 1.886 0.279763</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 3.9 3.67247 0.22753 1.886 0.120666</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.4 8.36240 -0.96240 1.886 -0.510385</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.9 8.36240 -0.46240 1.886 -0.245223</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 14.5 12.80590 1.69410 1.886 0.898422</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 13.7 12.80590 0.89410 1.886 0.474162</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 14.2 13.99625 0.20375 1.886 0.108053</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 12.2 13.99625 -1.79625 1.886 -0.952596</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.7 14.22730 -0.52730 1.886 -0.279641</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.2 14.22730 -1.02730 1.886 -0.544803</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 13.6 13.33713 0.26287 1.886 0.139406</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 15.4 13.33713 2.06287 1.886 1.093991</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 10.4 11.84008 -1.44008 1.886 -0.763708</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 11.6 11.84008 -0.24008 1.886 -0.127318</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 10.0 10.30732 -0.30732 1.886 -0.162980</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 9.5 10.30732 -0.80732 1.886 -0.428142</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.1 8.33981 0.76019 1.886 0.403149</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.0 8.33981 0.66019 1.886 0.350117</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 96.1 93.70349 2.39651 1.886 1.270926</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 94.3 93.70349 0.59651 1.886 0.316342</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.86253 -3.96253 1.886 -2.101429</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.86253 -3.96253 1.886 -2.101429</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 69.4 70.18665 -0.78665 1.886 -0.417182</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 73.1 70.18665 2.91335 1.886 1.545019</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.6 64.03245 1.56755 1.886 0.831308</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.3 64.03245 1.26755 1.886 0.672210</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 55.9 54.71491 1.18509 1.886 0.628480</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 54.4 54.71491 -0.31491 1.886 -0.167007</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 47.0 49.63436 -2.63436 1.886 -1.397065</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 49.3 49.63436 -0.33436 1.886 -0.177319</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 44.7 45.08853 -0.38853 1.886 -0.206049</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 46.7 45.08853 1.61147 1.886 0.854600</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 42.1 41.07653 1.02347 1.886 0.542772</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 41.3 41.07653 0.22347 1.886 0.118513</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.3 4.08295 -0.78295 1.886 -0.415218</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.4 4.08295 -0.68295 1.886 -0.362186</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 3.9 6.04367 -2.14367 1.886 -1.136841</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 2.9 6.04367 -3.14367 1.886 -1.667165</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 6.4 7.59693 -1.19693 1.886 -0.634761</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 7.2 7.59693 -0.39693 1.886 -0.210502</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 9.1 9.86436 -0.76436 1.886 -0.405361</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 8.5 9.86436 -1.36436 1.886 -0.723555</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 11.7 10.99397 0.70603 1.886 0.374425</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 12.0 10.99397 1.00603 1.886 0.533522</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.3 11.91274 1.38726 1.886 0.735696</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.2 11.91274 1.28726 1.886 0.682663</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 14.3 12.66519 1.63481 1.886 0.866981</span> +<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 12.1 12.66519 -0.56519 1.886 -0.299733</span> <span class="r-in"></span> <span class="r-in"><span class="co"># The following takes about 6 minutes</span></span> <span class="r-in"><span class="co">#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</span></span> diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html index 8c39ca1b..e1032453 100644 --- a/docs/reference/schaefer07_complex_case.html +++ b/docs/reference/schaefer07_complex_case.html @@ -28,7 +28,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html index 63e23326..7fef34e4 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -27,7 +27,7 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html index b4d25f4f..f01d1048 100644 --- a/docs/reference/summary.mkinfit.html +++ b/docs/reference/summary.mkinfit.html @@ -30,7 +30,7 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -180,15 +180,15 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <span class="r-in"> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.0 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:42:12 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:42:12 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:15:30 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:15:30 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 131 model solutions performed in 0.029 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 131 model solutions performed in 0.028 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html index 2bc50dac..d2ed2a86 100644 --- a/docs/reference/summary.nlme.mmkin.html +++ b/docs/reference/summary.nlme.mmkin.html @@ -30,7 +30,7 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -201,8 +201,8 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.155 </span> <span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:42:16 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:42:16 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:15:33 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:15:33 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -212,7 +212,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.545 s using 4 iterations</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.528 s using 4 iterations</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/reference/summary.saem.mmkin.html b/docs/reference/summary.saem.mmkin.html index 2fe81023..26ece42d 100644 --- a/docs/reference/summary.saem.mmkin.html +++ b/docs/reference/summary.saem.mmkin.html @@ -30,7 +30,7 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -196,10 +196,269 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-in"><span class="va">f_mmkin_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">dfop_sfo</span><span class="op">)</span>, <span class="va">ds_syn_dfop_sfo</span>,</span> <span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span> <span class="r-in"><span class="va">f_saem_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin_dfop_sfo</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in rxModelVars_(obj):</span> Not compatible with STRSXP: [type=NULL].</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in h(simpleError(msg, call)):</span> error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_saem_dfop_sfo' not found</span> +<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.0 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:15:47 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:15:47 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Equations:</span> +<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> +<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> +<span class="r-out co"><span class="r-pr">#></span> * parent</span> +<span class="r-out co"><span class="r-pr">#></span> d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> +<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> +<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_m1 * m1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Data:</span> +<span class="r-out co"><span class="r-pr">#></span> 171 observations of 2 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 12.164 s</span> +<span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 </span> +<span class="r-out co"><span class="r-pr">#></span> 101.65645 -4.05368 -0.94311 -2.35943 -4.07006 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> +<span class="r-out co"><span class="r-pr">#></span> -0.01132 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> +<span class="r-out co"><span class="r-pr">#></span> None</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Results:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 828.1 822.7 -400.1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 100.74378 97.813 103.6747</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06168 -4.171 -3.9523</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.92584 -1.313 -0.5389</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.81914 -3.602 -2.0362</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.63916 -4.327 -2.9516</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02927 -1.152 1.0939</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> +<span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_m1 f_prnt_ log_k1 log_k2 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.3571 </span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2190 0.2189 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1248 -0.1042 -0.0514 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0130 0.0043 -0.0049 0.0883 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0643 0.0437 0.0354 -0.3477 -0.2589</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Random effects:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 0.73313 -7.46512 8.9314</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k_m1 0.06488 -0.06041 0.1902</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.41955 0.15206 0.6870</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.81750 0.29140 1.3436</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.75265 0.27939 1.2259</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.34411 -1.70964 2.3979</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.86164 0.67928 1.04400</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07973 0.06437 0.09509</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 100.74378 97.81291 103.67465</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01722 0.01544 0.01921</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.28377 0.21203 0.36843</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.05966 0.02727 0.13052</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.02627 0.01321 0.05226</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.49268 0.24004 0.74912</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> +<span class="r-out co"><span class="r-pr">#></span> ff</span> +<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2838</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7162</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> +<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> +<span class="r-out co"><span class="r-pr">#></span> parent 17.17 65.72 19.78 11.62 26.38</span> +<span class="r-out co"><span class="r-pr">#></span> m1 40.25 133.71 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Data:</span> +<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.005e+02 -1.069e+01 8.0584 -1.327e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.005e+02 3.607e+00 8.0584 4.476e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.580e+01 -7.103e+00 7.6867 -9.241e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.580e+01 -3.030e-01 7.6867 -3.942e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.722e+01 -5.418e+00 7.0070 -7.733e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.722e+01 7.282e+00 7.0070 1.039e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.278e+01 -1.282e+00 5.8665 -2.186e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.278e+01 -2.482e+00 5.8665 -4.231e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.418e+01 2.128e-02 4.4047 4.831e-03</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.418e+01 -4.579e+00 4.4047 -1.040e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.235e+01 -8.510e-01 2.7194 -3.129e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.235e+01 -3.551e+00 2.7194 -1.306e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.282e+01 -7.213e-01 1.3369 -5.395e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.282e+01 7.787e-01 1.3369 5.825e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 6.2 6.184e+00 1.593e-02 0.9927 1.605e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 8.3 6.184e+00 2.116e+00 0.9927 2.131e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.2 3.092e+00 -8.915e-01 0.8962 -9.948e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.4 3.092e+00 -6.915e-01 0.8962 -7.716e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.3 1.147e+00 -8.468e-01 0.8665 -9.773e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.2 1.147e+00 -9.468e-01 0.8665 -1.093e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 2.2 3.190e+00 -9.901e-01 0.8984 -1.102e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 3.0 3.190e+00 -1.901e-01 0.8984 -2.116e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 6.5 6.422e+00 7.828e-02 1.0023 7.811e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 5.0 6.422e+00 -1.422e+00 1.0023 -1.418e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 10.2 1.002e+01 1.782e-01 1.1751 1.517e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 9.5 1.002e+01 -5.218e-01 1.1751 -4.440e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 12.2 1.265e+01 -4.548e-01 1.3268 -3.428e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 13.4 1.265e+01 7.452e-01 1.3268 5.617e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 11.8 1.093e+01 8.735e-01 1.2253 7.129e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 13.2 1.093e+01 2.273e+00 1.2253 1.855e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 6.6 7.845e+00 -1.245e+00 1.0647 -1.170e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 9.3 7.845e+00 1.455e+00 1.0647 1.366e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.322e+00 -1.822e+00 0.9605 -1.897e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.322e+00 7.779e-02 0.9605 8.099e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.008e+02 1.724e+01 8.0794 2.134e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.008e+02 -9.578e-01 8.0794 -1.185e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.538e+01 -5.183e+00 7.6535 -6.773e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.538e+01 -7.834e-01 7.6535 -1.024e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.604e+01 1.006e+01 6.9134 1.456e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.604e+01 -7.635e+00 6.9134 -1.104e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.170e+01 6.196e+00 5.7814 1.072e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.170e+01 5.996e+00 5.7814 1.037e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.556e+01 4.370e-01 4.5130 9.683e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.556e+01 -8.630e-01 4.5130 -1.912e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.869e+01 -2.085e+00 3.2024 -6.512e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.869e+01 -1.885e+00 3.2024 -5.888e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.103e+01 1.072e+00 1.8850 5.688e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.103e+01 3.672e+00 1.8850 1.948e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.231e+01 8.760e-02 1.3062 6.707e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.231e+01 -1.512e+00 1.3062 -1.158e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.222e+00 -4.220e-01 1.0363 -4.072e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.222e+00 6.780e-01 1.0363 6.542e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.431e+00 -1.313e-01 0.8692 -1.510e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.849e+00 -1.486e-01 0.9146 -1.624e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.849e+00 8.514e-01 0.9146 9.309e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.298e+00 8.021e-01 1.0397 7.715e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.298e+00 6.021e-01 1.0397 5.791e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.051e+01 -4.109e-01 1.2020 -3.418e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.051e+01 -2.109e-01 1.2020 -1.755e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.218e+01 -1.476e+00 1.2980 -1.137e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.218e+01 2.421e-02 1.2980 1.865e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.043e+01 2.682e-01 1.1976 2.240e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.043e+01 2.068e+00 1.1976 1.727e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 7.931e+00 1.169e+00 1.0688 1.094e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 7.931e+00 -5.310e-01 1.0688 -4.969e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.717e+00 3.829e-01 0.9748 3.928e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.717e+00 -1.217e+00 0.9748 -1.249e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.010e+02 5.220e+00 8.0970 6.447e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.010e+02 5.920e+00 8.0970 7.312e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.352e+01 1.388e+01 7.5058 1.849e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.352e+01 2.581e+00 7.5058 3.439e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.127e+01 -1.866e+00 6.5363 -2.855e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.127e+01 1.334e+00 6.5363 2.041e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.441e+01 -5.112e-01 5.2072 -9.818e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.441e+01 -2.011e+00 5.2072 -3.862e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.843e+01 2.573e+00 3.9560 6.505e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.843e+01 -1.327e+00 3.9560 -3.353e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.447e+01 1.631e+00 2.8801 5.664e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.447e+01 2.131e+00 2.8801 7.400e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.974e+01 3.570e-01 1.7945 1.989e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.974e+01 5.700e-02 1.7945 3.176e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.193e+01 -6.276e-01 1.2833 -4.891e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.193e+01 -1.228e+00 1.2833 -9.567e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.208e+00 9.920e-01 1.0357 9.578e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.208e+00 9.200e-02 1.0357 8.883e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 -6.821e-13 8.000e-01 0.8616 9.285e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.796e+00 4.189e-03 0.8735 4.795e-03</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.796e+00 5.042e-01 0.8735 5.772e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.656e+00 -4.556e-01 0.9382 -4.856e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.656e+00 -5.556e-01 0.9382 -5.922e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.280e+00 -1.480e+00 1.0854 -1.363e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.280e+00 1.820e+00 1.0854 1.677e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.094e+01 4.571e-01 1.2262 3.728e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.094e+01 1.857e+00 1.2262 1.515e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.150e+01 1.650e-05 1.2582 1.311e-05</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.150e+01 -9.000e-01 1.2582 -7.153e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.187e+00 -1.687e+00 1.1309 -1.492e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.187e+00 -5.875e-01 1.1309 -5.194e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 6.854e+00 4.461e-01 1.0203 4.372e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 6.854e+00 1.246e+00 1.0203 1.221e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 4.908e+00 3.917e-01 0.9463 4.139e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 4.908e+00 -1.108e+00 0.9463 -1.171e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.006e+02 4.116e+00 8.0656 5.104e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.006e+02 -1.228e+01 8.0656 -1.523e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.733e+01 -3.133e+00 7.8079 -4.012e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.733e+01 -2.733e+00 7.8079 -3.500e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.122e+01 -1.312e+01 7.3237 -1.791e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 96.5 9.122e+01 5.280e+00 7.3237 7.209e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 76.2 8.039e+01 -4.193e+00 6.4673 -6.484e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 77.8 8.039e+01 -2.593e+00 6.4673 -4.010e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 70.8 6.513e+01 5.670e+00 5.2637 1.077e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 67.3 6.513e+01 2.170e+00 5.2637 4.123e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 43.1 4.437e+01 -1.272e+00 3.6411 -3.493e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 45.1 4.437e+01 7.281e-01 3.6411 2.000e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 21.3 2.129e+01 7.295e-03 1.9038 3.832e-03</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 23.5 2.129e+01 2.207e+00 1.9038 1.159e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.187e+01 -6.922e-02 1.2798 -5.409e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.187e+01 2.308e-01 1.2798 1.803e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 6.901e+00 9.863e-02 1.0223 9.648e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 6.901e+00 -7.014e-01 1.0223 -6.860e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 7.958e-13 1.600e+00 0.8616 1.857e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 6.965e-01 2.035e-01 0.8634 2.357e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 1.970e+00 1.730e+00 0.8758 1.976e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 1.970e+00 3.043e-02 0.8758 3.474e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.090e+00 -4.896e-01 0.9213 -5.315e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.090e+00 -2.896e-01 0.9213 -3.144e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.699e+00 4.006e-01 1.0138 3.952e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.699e+00 -9.938e-02 1.0138 -9.803e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.139e+00 3.609e-01 1.1284 3.198e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.139e+00 1.609e-01 1.1284 1.426e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.777e+00 -4.772e-01 1.1100 -4.299e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.777e+00 2.228e-01 1.1100 2.007e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.654e+00 -5.373e-02 1.0119 -5.310e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.654e+00 1.046e+00 1.0119 1.034e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.695e+00 -9.950e-01 0.9394 -1.059e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.695e+00 -1.195e+00 0.9394 -1.272e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.012e+02 9.182e+00 8.1159 1.131e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.012e+02 1.088e+01 8.1159 1.341e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.450e+01 -9.992e-01 7.5834 -1.318e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.450e+01 -3.499e+00 7.5834 -4.614e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.289e+01 -1.189e+01 6.6644 -1.784e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.289e+01 6.814e+00 6.6644 1.022e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 60.4 6.541e+01 -5.014e+00 5.2861 -9.486e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 59.1 6.541e+01 -6.314e+00 5.2861 -1.195e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 56.5 4.682e+01 9.684e+00 3.8308 2.528e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 47.0 4.682e+01 1.835e-01 3.8308 4.791e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 30.2 3.039e+01 -1.888e-01 2.5715 -7.343e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 23.9 3.039e+01 -6.489e+00 2.5715 -2.523e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 17.0 1.783e+01 -8.306e-01 1.6623 -4.996e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 18.7 1.783e+01 8.694e-01 1.6623 5.230e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.3 1.186e+01 -5.608e-01 1.2793 -4.383e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.186e+01 3.924e-02 1.2793 3.067e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.932e+00 1.068e+00 1.0688 9.997e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.932e+00 1.684e-01 1.0688 1.576e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 4.974e-14 7.000e-01 0.8616 8.124e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.106e+00 -1.063e-01 0.8965 -1.186e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.106e+00 -5.063e-01 0.8965 -5.648e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.331e+00 -3.231e+00 1.0879 -2.970e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.331e+00 -8.307e-01 1.0879 -7.636e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.568e+01 8.233e-01 1.5181 5.424e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.568e+01 3.323e+00 1.5181 2.189e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.214e+01 7.593e-01 1.9643 3.865e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.214e+01 1.059e+00 1.9643 5.393e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.436e+01 -2.163e+00 2.1250 -1.018e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.436e+01 3.673e-02 2.1250 1.728e-02</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.887e+01 -3.367e+00 1.7335 -1.942e+00</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.887e+01 9.335e-01 1.7335 5.385e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.376e+01 1.139e+00 1.3951 8.162e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.376e+01 4.387e-01 1.3951 3.144e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.886e+00 1.014e+00 1.1677 8.687e-01</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.886e+00 5.144e-01 1.1677 4.405e-01</span> <span class="r-in"><span class="co"># }</span></span> <span class="r-in"></span> </code></pre></div> diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html index 7d79341c..39a92dd1 100644 --- a/docs/reference/synthetic_data_for_UBA_2014.html +++ b/docs/reference/synthetic_data_for_UBA_2014.html @@ -41,7 +41,7 @@ Compare also the code in the example section to see the degradation models."><!- <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -248,8 +248,8 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-in"> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span> <span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.1.0 </span> <span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:44:06 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:44:06 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Mon Mar 7 13:15:49 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Mon Mar 7 13:15:49 2022 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -258,7 +258,7 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 833 model solutions performed in 0.756 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 833 model solutions performed in 0.624 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html index 39b9becb..327e8ae9 100644 --- a/docs/reference/test_data_from_UBA_2014.html +++ b/docs/reference/test_data_from_UBA_2014.html @@ -27,7 +27,7 @@ <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html index 25d0e76b..95e8c6b6 100644 --- a/docs/reference/transform_odeparms.html +++ b/docs/reference/transform_odeparms.html @@ -31,7 +31,7 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/docs/reference/update.mkinfit.html b/docs/reference/update.mkinfit.html index 036283d5..ff175937 100644 --- a/docs/reference/update.mkinfit.html +++ b/docs/reference/update.mkinfit.html @@ -29,7 +29,7 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> diff --git a/man/endpoints.Rd b/man/endpoints.Rd index a37ff98d..75fa9503 100644 --- a/man/endpoints.Rd +++ b/man/endpoints.Rd @@ -8,8 +8,8 @@ with mkinfit} endpoints(fit) } \arguments{ -\item{fit}{An object of class \link{mkinfit}, \link{nlme.mmkin}, \link{saem.mmkin} or -\link{nlmixr.mmkin}. Or another object that has list components +\item{fit}{An object of class \link{mkinfit}, \link{nlme.mmkin} or \link{saem.mmkin}, +or another object that has list components mkinmod containing an \link{mkinmod} degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.} diff --git a/man/intervals.nlmixr.mmkin.Rd b/man/intervals.nlmixr.mmkin.Rd deleted file mode 100644 index e6da8901..00000000 --- a/man/intervals.nlmixr.mmkin.Rd +++ /dev/null @@ -1,25 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/intervals.R -\name{intervals.nlmixr.mmkin} -\alias{intervals.nlmixr.mmkin} -\title{Confidence intervals for parameters in nlmixr.mmkin objects} -\usage{ -\method{intervals}{nlmixr.mmkin}(object, level = 0.95, backtransform = TRUE, ...) -} -\arguments{ -\item{object}{The fitted saem.mmkin object} - -\item{level}{The confidence level.} - -\item{backtransform}{Should we backtransform the parameters where a one to -one correlation between transformed and backtransformed parameters exists?} - -\item{\dots}{For compatibility with the generic method} -} -\value{ -An object with 'intervals.saem.mmkin' and 'intervals.lme' in the -class attribute -} -\description{ -Confidence intervals for parameters in nlmixr.mmkin objects -} diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd deleted file mode 100644 index c1a203eb..00000000 --- a/man/nlmixr.mmkin.Rd +++ /dev/null @@ -1,245 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/nlmixr.R -\name{nlmixr.mmkin} -\alias{nlmixr.mmkin} -\alias{print.nlmixr.mmkin} -\alias{nlmixr_model} -\alias{nlmixr_data} -\title{Fit nonlinear mixed models using nlmixr} -\usage{ -\method{nlmixr}{mmkin}( - object, - data = NULL, - est = NULL, - control = list(), - table = tableControl(), - error_model = object[[1]]$err_mod, - test_log_parms = TRUE, - conf.level = 0.6, - degparms_start = "auto", - eta_start = "auto", - ..., - save = NULL, - envir = parent.frame() -) - -\method{print}{nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), ...) - -nlmixr_model( - object, - est = c("saem", "focei"), - degparms_start = "auto", - eta_start = "auto", - test_log_parms = TRUE, - conf.level = 0.6, - error_model = object[[1]]$err_mod, - add_attributes = FALSE -) - -nlmixr_data(object, ...) -} -\arguments{ -\item{object}{An \link{mmkin} row object containing several fits of the same -\link{mkinmod} model to different datasets} - -\item{data}{Not used, as the data are extracted from the mmkin row object} - -\item{est}{Estimation method passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} - -\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} - -\item{table}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} - -\item{error_model}{Optional argument to override the error model which is -being set based on the error model used in the mmkin row object.} - -\item{test_log_parms}{If TRUE, an attempt is made to use more robust starting -values for population parameters fitted as log parameters in mkin (like -rate constants) by only considering rate constants that pass the t-test -when calculating mean degradation parameters using \link{mean_degparms}.} - -\item{conf.level}{Possibility to adjust the required confidence level -for parameter that are tested if requested by 'test_log_parms'.} - -\item{degparms_start}{Parameter values given as a named numeric vector will -be used to override the starting values obtained from the 'mmkin' object.} - -\item{eta_start}{Standard deviations on the transformed scale given as a -named numeric vector will be used to override the starting values obtained -from the 'mmkin' object.} - -\item{\dots}{Passed to \link{nlmixr_model}} - -\item{save}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} - -\item{envir}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} - -\item{x}{An nlmixr.mmkin object to print} - -\item{digits}{Number of digits to use for printing} - -\item{add_attributes}{Should the starting values used for degradation model -parameters and their distribution and for the error model parameters -be returned as attributes?} -} -\value{ -An S3 object of class 'nlmixr.mmkin', containing the fitted -\link[nlmixr:nlmixr]{nlmixr::nlmixr} object as a list component named 'nm'. The -object also inherits from 'mixed.mmkin'. - -An function defining a model suitable for fitting with \link[nlmixr:nlmixr]{nlmixr::nlmixr}. - -An dataframe suitable for use with \link[nlmixr:nlmixr]{nlmixr::nlmixr} -} -\description{ -This function uses \code{\link[nlmixr:nlmixr]{nlmixr::nlmixr()}} as a backend for fitting nonlinear mixed -effects models created from \link{mmkin} row objects using the Stochastic Approximation -Expectation Maximisation algorithm (SAEM) or First Order Conditional -Estimation with Interaction (FOCEI). -} -\details{ -An mmkin row object is essentially a list of mkinfit objects that have been -obtained by fitting the same model to a list of datasets using \link{mkinfit}. -} -\examples{ -\dontrun{ -ds <- lapply(experimental_data_for_UBA_2019[6:10], - function(x) subset(x$data[c("name", "time", "value")])) -names(ds) <- paste("Dataset", 6:10) - -f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) -f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", - cores = 1, quiet = TRUE) - -library(nlmixr) -f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem", - control = saemControl(print = 0)) -f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei", - control = foceiControl(print = 0)) - -f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem", - control = saemControl(print = 0)) -f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei", - control = foceiControl(print = 0)) - -f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem", - control = saemControl(print = 0)) -f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei", - control = foceiControl(print = 0)) - -f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem", - control = saemControl(print = 0)) -f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei", - control = foceiControl(print = 0)) - -f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem", - control = saemControl(print = 0)) -f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei", - control = foceiControl(print = 0)) - -AIC( - f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm, - f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm, - f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm, - f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm, - f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm) - -AIC(nlme(f_mmkin_parent["FOMC", ])) -AIC(nlme(f_mmkin_parent["HS", ])) - -# The FOCEI fit of FOMC with constant variance or the tc error model is best -plot(f_nlmixr_fomc_saem_tc) - -sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), - A1 = mkinsub("SFO"), quiet = TRUE) -fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), - A1 = mkinsub("SFO"), quiet = TRUE) -dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), - A1 = mkinsub("SFO"), quiet = TRUE) - -f_mmkin_const <- mmkin(list( - "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), - ds, quiet = TRUE, error_model = "const") -f_mmkin_obs <- mmkin(list( - "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), - ds, quiet = TRUE, error_model = "obs") -f_mmkin_tc <- mmkin(list( - "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), - ds, quiet = TRUE, error_model = "tc") - -nlmixr_model(f_mmkin_const["SFO-SFO", ]) - -# A single constant variance is currently only possible with est = 'focei' in nlmixr -f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") -f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") -f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") - -# Variance by variable is supported by 'saem' and 'focei' -f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") -f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") -f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") -f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") - -# Identical two-component error for all variables is only possible with -# est = 'focei' in nlmixr -f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") -f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") - -# Two-component error by variable is possible with both estimation methods -# Variance by variable is supported by 'saem' and 'focei' -f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", - error_model = "obs_tc") -f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", - error_model = "obs_tc") -f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", - error_model = "obs_tc") -f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", - error_model = "obs_tc") - -AIC( - f_nlmixr_sfo_sfo_focei_const$nm, - f_nlmixr_fomc_sfo_focei_const$nm, - f_nlmixr_dfop_sfo_focei_const$nm, - f_nlmixr_fomc_sfo_saem_obs$nm, - f_nlmixr_fomc_sfo_focei_obs$nm, - f_nlmixr_dfop_sfo_saem_obs$nm, - f_nlmixr_dfop_sfo_focei_obs$nm, - f_nlmixr_fomc_sfo_focei_tc$nm, - f_nlmixr_dfop_sfo_focei_tc$nm, - f_nlmixr_fomc_sfo_saem_obs_tc$nm, - f_nlmixr_fomc_sfo_focei_obs_tc$nm, - f_nlmixr_dfop_sfo_saem_obs_tc$nm, - f_nlmixr_dfop_sfo_focei_obs_tc$nm -) -# Currently, FOMC-SFO with two-component error by variable fitted by focei gives the -# lowest AIC -plot(f_nlmixr_fomc_sfo_focei_obs_tc) -summary(f_nlmixr_fomc_sfo_focei_obs_tc) - -# Two parallel metabolites -dmta_ds <- lapply(1:7, function(i) { - ds_i <- dimethenamid_2018$ds[[i]]$data - ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" - ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] - ds_i -}) -names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) -dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) -dmta_ds[["Elliot 1"]] <- NULL -dmta_ds[["Elliot 2"]] <- NULL -sfo_sfo2 <- mkinmod( - DMTA = mkinsub("SFO", c("M23", "M27")), - M23 = mkinsub("SFO"), - M27 = mkinsub("SFO"), - quiet = TRUE -) -f_dmta_sfo_sfo2 <- mmkin( - list("SFO-SFO2" = sfo_sfo2), - dmta_ds, quiet = TRUE, error_model = "obs") -nlmixr_model(f_dmta_sfo_sfo2) -nlmixr_focei_dmta_sfo_sfo2 <- nlmixr(f_dmta_sfo_sfo2, est = "focei") -} -} -\seealso{ -\link{summary.nlmixr.mmkin} \link{plot.mixed.mmkin} -} diff --git a/man/reexports.Rd b/man/reexports.Rd index dfbb76a7..43d27ac1 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -1,13 +1,11 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/intervals.R, R/lrtest.mkinfit.R, -% R/nlme.mmkin.R, R/nlmixr.R +% Please edit documentation in R/intervals.R, R/lrtest.mkinfit.R, R/nlme.mmkin.R \docType{import} \name{reexports} \alias{reexports} \alias{intervals} \alias{lrtest} \alias{nlme} -\alias{nlmixr} \title{Objects exported from other packages} \keyword{internal} \description{ @@ -18,7 +16,5 @@ below to see their documentation. \item{lmtest}{\code{\link[lmtest]{lrtest}}} \item{nlme}{\code{\link[nlme]{intervals}}, \code{\link[nlme]{nlme}}} - - \item{nlmixr}{\code{\link[nlmixr]{nlmixr}}} }} diff --git a/man/summary.nlmixr.mmkin.Rd b/man/summary.nlmixr.mmkin.Rd deleted file mode 100644 index ab8abd5d..00000000 --- a/man/summary.nlmixr.mmkin.Rd +++ /dev/null @@ -1,103 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/summary.nlmixr.mmkin.R -\name{summary.nlmixr.mmkin} -\alias{summary.nlmixr.mmkin} -\alias{print.summary.nlmixr.mmkin} -\title{Summary method for class "nlmixr.mmkin"} -\usage{ -\method{summary}{nlmixr.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) - -\method{print}{summary.nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) -} -\arguments{ -\item{object}{an object of class \link{nlmixr.mmkin}} - -\item{data}{logical, indicating whether the full data should be included in -the summary.} - -\item{verbose}{Should the summary be verbose?} - -\item{distimes}{logical, indicating whether DT50 and DT90 values should be -included.} - -\item{\dots}{optional arguments passed to methods like \code{print}.} - -\item{x}{an object of class \link{summary.nlmixr.mmkin}} - -\item{digits}{Number of digits to use for printing} -} -\value{ -The summary function returns a list obtained in the fit, with at -least the following additional components -\item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used} -\item{date.fit, date.summary}{The dates where the fit and the summary were -produced} -\item{diffs}{The differential equations used in the degradation model} -\item{use_of_ff}{Was maximum or minimum use made of formation fractions} -\item{data}{The data} -\item{confint_back}{Backtransformed parameters, with confidence intervals if available} -\item{ff}{The estimated formation fractions derived from the fitted -model.} -\item{distimes}{The DT50 and DT90 values for each observed variable.} -\item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} -The print method is called for its side effect, i.e. printing the summary. -} -\description{ -Lists model equations, initial parameter values, optimised parameters -for fixed effects (population), random effects (deviations from the -population mean) and residual error model, as well as the resulting -endpoints such as formation fractions and DT50 values. Optionally -(default is FALSE), the data are listed in full. -} -\examples{ -# Generate five datasets following DFOP-SFO kinetics -sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), - m1 = mkinsub("SFO"), quiet = TRUE) -set.seed(1234) -k1_in <- rlnorm(5, log(0.1), 0.3) -k2_in <- rlnorm(5, log(0.02), 0.3) -g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) -f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) -k_m1_in <- rlnorm(5, log(0.02), 0.3) - -pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { - mkinpredict(dfop_sfo, - c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), - c(parent = 100, m1 = 0), - sampling_times) -} - -ds_mean_dfop_sfo <- lapply(1:5, function(i) { - mkinpredict(dfop_sfo, - c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], - f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), - c(parent = 100, m1 = 0), - sampling_times) -}) -names(ds_mean_dfop_sfo) <- paste("ds", 1:5) - -ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { - add_err(ds, - sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), - n = 1)[[1]] -}) - -\dontrun{ -# Evaluate using mmkin and nlmixr -f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, - quiet = TRUE, error_model = "tc", cores = 5) -f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) -f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) -f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") -# The following takes a very long time but gives -f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") -AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) -summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) -} - -} -\author{ -Johannes Ranke for the mkin specific parts -nlmixr authors for the parts inherited from nlmixr. -} diff --git a/man/tffm0.Rd b/man/tffm0.Rd deleted file mode 100644 index 89bee7d9..00000000 --- a/man/tffm0.Rd +++ /dev/null @@ -1,46 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/tffm0.R -\name{tffm0} -\alias{tffm0} -\alias{invtffm0} -\title{Transform formation fractions as in the first published mkin version} -\usage{ -tffm0(ff) - -invtffm0(ff_trans) -} -\arguments{ -\item{ff}{Vector of untransformed formation fractions. The sum -must be smaller or equal to one} - -\item{ff_trans}{Vector of transformed formation fractions that can be -restricted to the interval from 0 to 1} -} -\value{ -A vector of the transformed formation fractions - -A vector of backtransformed formation fractions for natural use in degradation models -} -\description{ -This transformation was used originally in mkin, in order to implement a -constraint for the sum of formation fractions to be smaller than 1. It was -later replaced by the \link{ilr} transformation because the ilr transformed -fractions can assumed to follow normal distribution. As the ilr -transformation is not available in \link{RxODE} and can therefore not be used in -the nlmixr modelling language, the original transformation is currently used -for translating mkin models with formation fractions to more than one target -compartment for fitting with nlmixr in \link{nlmixr_model}. However, this -implementation cannot be used there, as it is not accessible from RxODE. -} -\details{ -If the transformed formation fractions are restricted to the interval -between 0 and 1, the sum of backtransformed values is restricted -to this interval. -} -\examples{ -ff_example <- c( - 0.10983681, 0.09035905, 0.08399383 -) -ff_example_trans <- tffm0(ff_example) -invtffm0(ff_example_trans) -} @@ -3,52 +3,49 @@ Loading required package: parallel ℹ Testing mkin ✔ | F W S OK | Context ✔ | 5 | AIC calculation -✔ | 5 | Analytical solutions for coupled models [4.2s] +✔ | 5 | Analytical solutions for coupled models [3.3s] ✔ | 5 | Calculation of Akaike weights ✔ | 2 | Export dataset for reading into CAKE ✔ | 12 | Confidence intervals and p-values [1.0s] -⠋ | 1 | Dimethenamid data from 2018 -✔ | 1 27 | Dimethenamid data from 2018 [63.8s] +✔ | 1 12 | Dimethenamid data from 2018 [31.2s] ──────────────────────────────────────────────────────────────────────────────── -Skip (test_dmta.R:164:3): Different backends get consistent results for SFO-SFO3+, dimethenamid data +Skip (test_dmta.R:98:3): Different backends get consistent results for SFO-SFO3+, dimethenamid data Reason: Fitting this ODE model with saemix takes about 15 minutes on my system ──────────────────────────────────────────────────────────────────────────────── -✔ | 14 | Error model fitting [7.0s] +✔ | 14 | Error model fitting [4.7s] ✔ | 5 | Time step normalisation -✔ | 4 | Calculation of FOCUS chi2 error levels [0.8s] -✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.1s] -✔ | 4 | Test fitting the decline of metabolites from their maximum [0.5s] -✔ | 1 | Fitting the logistic model [0.3s] -⠇ | 9 | Nonlinear mixed-effects models -✔ | 1 14 | Nonlinear mixed-effects models [1.6s] +✔ | 4 | Calculation of FOCUS chi2 error levels [0.5s] +✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8s] +✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3s] +✔ | 1 | Fitting the logistic model [0.2s] +✔ | 1 12 | Nonlinear mixed-effects models [0.2s] ──────────────────────────────────────────────────────────────────────────────── Skip (test_mixed.R:68:3): saemix results are reproducible for biphasic fits Reason: Fitting with saemix takes around 10 minutes when using deSolve ──────────────────────────────────────────────────────────────────────────────── ✔ | 3 | Test dataset classes mkinds and mkindsg -✔ | 10 | Special cases of mkinfit calls [0.6s] -✔ | 3 | mkinfit features [1.1s] +✔ | 10 | Special cases of mkinfit calls [0.4s] +✔ | 3 | mkinfit features [0.6s] ✔ | 8 | mkinmod model generation and printing [0.2s] ✔ | 3 | Model predictions with mkinpredict [0.3s] -✔ | 16 | Evaluations according to 2015 NAFTA guidance [2.0s] -✔ | 9 | Nonlinear mixed-effects models with nlme [9.1s] -✔ | 16 | Plotting [1.5s] +✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.4s] +✔ | 9 | Nonlinear mixed-effects models with nlme [8.0s] +✔ | 16 | Plotting [1.3s] ✔ | 4 | Residuals extracted from mkinfit models -✔ | 23 | saemix parent models [29.3s] -✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.6s] -✔ | 7 | Fitting the SFORB model [4.6s] +✔ | 23 | saemix parent models [26.7s] +✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4s] +✔ | 7 | Fitting the SFORB model [3.6s] ✔ | 1 | Summaries of old mkinfit objects ✔ | 4 | Summary [0.1s] -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.5s] -✔ | 9 | Hypothesis tests [9.3s] -✔ | 2 | tffm0 +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2s] +✔ | 9 | Hypothesis tests [8.1s] ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 145.2 s +Duration: 99.2 s ── Skipped tests ────────────────────────────────────────────────────────────── • Fitting this ODE model with saemix takes about 15 minutes on my system (1) • Fitting with saemix takes around 10 minutes when using deSolve (1) -[ FAIL 0 | WARN 0 | SKIP 2 | PASS 240 ] +[ FAIL 0 | WARN 0 | SKIP 2 | PASS 221 ] diff --git 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computed by importance sampling 1311 1315 -649 Fitted parameters: - estimate lower upper -parent_0 1e+02 99.13 1e+02 -k_parent 4e-02 0.03 4e-02 -Var.parent_0 5e-01 -2.04 3e+00 -Var.k_parent 1e-01 0.03 2e-01 -a.1 9e-01 0.75 1e+00 -b.1 5e-02 0.04 5e-02 -SD.parent_0 7e-01 -1.09 3e+00 -SD.k_parent 3e-01 0.20 4e-01 + estimate lower upper +parent_0 1e+02 99.13 1e+02 +k_parent 4e-02 0.03 4e-02 +a.1 9e-01 0.75 1e+00 +b.1 5e-02 0.04 5e-02 +SD.parent_0 7e-01 -1.09 3e+00 +SD.k_parent 3e-01 0.20 4e-01 diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index ec96fbc2..10696082 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -160,8 +160,8 @@ DFOP_SFO <- mkinmod( m1 = mkinsub("SFO"), quiet = TRUE) dfop_sfo_pop <- list(parent_0 = 100, - k_m1 = 0.005, f_parent_to_m1 = 0.5, - k1 = 0.05, k2 = 0.01, g = 0.5) + k_m1 = 0.007, f_parent_to_m1 = 0.5, + k1 = 0.1, k2 = 0.02, g = 0.5) syn_biphasic_parms <- as.matrix(data.frame( k1 = rlnorm(n_biphasic, log(dfop_sfo_pop$k1), log_sd), k2 = rlnorm(n_biphasic, log(dfop_sfo_pop$k2), log_sd), @@ -186,11 +186,12 @@ ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { mmkin_sfo_1 <- mmkin("SFO", ds_sfo, quiet = TRUE, error_model = "tc", cores = n_cores) mmkin_dfop_1 <- mmkin("DFOP", ds_dfop, quiet = TRUE, cores = n_cores) mmkin_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores, + control = list(eval.max = 500, iter.max = 400), error_model = "tc") # nlme dfop_nlme_1 <- nlme(mmkin_dfop_1) -nlme_biphasic <- nlme(mmkin_biphasic) +nlme_biphasic <- suppressWarnings(nlme(mmkin_biphasic)) # saemix sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") @@ -201,13 +202,6 @@ dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix") saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) -# nlmixr saem -tmp <- suppressMessages(capture.output(nlmixr_saem_biphasic <- nlmixr(mmkin_biphasic, est = "saem", - control = nlmixr::saemControl(nBurn = 300, nEm = 100, nmc = 9, print = 0)))) -# The FOCEI fit takes too long... -#tmp <- capture_output(nlmixr_focei_biphasic <- nlmixr(mmkin_biphasic, est = "focei", -# control = nlmixr::foceiControl(print = 0))) - # UBA datasets ds_uba <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) diff --git a/tests/testthat/summary_nlmixr_saem_biphasic.txt b/tests/testthat/summary_nlmixr_saem_biphasic.txt deleted file mode 100644 index 144cbac7..00000000 --- a/tests/testthat/summary_nlmixr_saem_biphasic.txt +++ /dev/null @@ -1,97 +0,0 @@ -nlmixr version used for fitting: 2.0.6 -mkin version used for pre-fitting: Dummy 0.0 for testing -R version used for fitting: Dummy R version for testing -Date of fit: Dummy date for testing -Date of summary: Dummy date for testing - -Equations: -d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * - time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) - * parent -d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) - * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * - exp(-k2 * time))) * parent - k_m1 * m1 - -Data: -507 observations of 2 variable(s) grouped in 15 datasets - -Degradation model predictions using RxODE - -Fitted in test time 0 s - -Variance model: Two-component variance function - -Mean of starting values for individual parameters: - parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.65 -5.38 -0.09 -2.74 -4.53 - g_qlogis - -0.14 - -Mean of starting values for error model parameters: -sigma_low_parent rsd_high_parent sigma_low_m1 rsd_high_m1 - 0.79 0.05 0.79 0.05 - -Fixed degradation parameter values: -None - -Results: - -Likelihood calculated by gauss3_1.6 - AIC BIC logLik - 2400 2468 -1184 - -Optimised parameters: - est. lower upper -parent_0 100.35 99.0 101.7 -log_k_m1 -5.35 -5.5 -5.2 -f_parent_qlogis -0.09 -0.3 0.1 -log_k1 -2.76 -3.0 -2.6 -log_k2 -4.49 -4.6 -4.4 -g_qlogis -0.17 -0.4 0.1 - -Correlation: - pr_0 l__1 f_p_ lg_1 lg_2 -log_k_m1 -0.4 -f_parent_qlogis -0.4 0.4 -log_k1 0.2 -0.2 -0.1 -log_k2 0.0 0.1 0.0 0.3 -g_qlogis 0.1 -0.1 0.0 -0.4 -0.5 - -Random effects (omega): - eta.parent_0 eta.log_k_m1 eta.f_parent_qlogis eta.log_k1 -eta.parent_0 0.08 0.00 0.0 0.00 -eta.log_k_m1 0.00 0.02 0.0 0.00 -eta.f_parent_qlogis 0.00 0.00 0.1 0.00 -eta.log_k1 0.00 0.00 0.0 0.09 -eta.log_k2 0.00 0.00 0.0 0.00 -eta.g_qlogis 0.00 0.00 0.0 0.00 - eta.log_k2 eta.g_qlogis -eta.parent_0 0.00 0.0 -eta.log_k_m1 0.00 0.0 -eta.f_parent_qlogis 0.00 0.0 -eta.log_k1 0.00 0.0 -eta.log_k2 0.03 0.0 -eta.g_qlogis 0.00 0.1 - -Variance model: -sigma_low_parent rsd_high_parent sigma_low_m1 rsd_high_m1 - 1.04 0.05 0.82 0.06 - -Backtransformed parameters: - est. lower upper -parent_0 1e+02 1e+02 1e+02 -k_m1 5e-03 4e-03 6e-03 -f_parent_to_m1 5e-01 4e-01 5e-01 -k1 6e-02 5e-02 8e-02 -k2 1e-02 1e-02 1e-02 -g 5e-01 4e-01 5e-01 - -Resulting formation fractions: - ff -parent_m1 0.5 -parent_sink 0.5 - -Estimated disappearance times: - DT50 DT90 DT50back DT50_k1 DT50_k2 -parent 25 150 45 11 61 -m1 145 483 NA NA NA diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_biphasic_s.txt index a353d821..6b203991 100644 --- a/tests/testthat/summary_saem_biphasic_s.txt +++ b/tests/testthat/summary_saem_biphasic_s.txt @@ -13,7 +13,7 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -507 observations of 2 variable(s) grouped in 15 datasets +510 observations of 2 variable(s) grouped in 15 datasets Model predictions using solution type analytical @@ -24,7 +24,7 @@ Variance model: Two-component variance function Mean of starting values for individual parameters: parent_0 k_m1 f_parent_to_m1 k1 k2 - 1e+02 5e-03 5e-01 6e-02 1e-02 + 1e+02 7e-03 5e-01 1e-01 2e-02 g 5e-01 @@ -35,37 +35,37 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2369 2379 -1170 + 2334 2344 -1153 Optimised parameters: est. lower upper parent_0 1e+02 1e+02 1e+02 -k_m1 5e-03 4e-03 6e-03 +k_m1 7e-03 6e-03 7e-03 f_parent_to_m1 5e-01 4e-01 5e-01 -k1 6e-02 5e-02 7e-02 -k2 1e-02 9e-03 1e-02 -g 5e-01 4e-01 5e-01 +k1 1e-01 9e-02 1e-01 +k2 2e-02 2e-02 3e-02 +g 5e-01 5e-01 5e-01 Correlation: pr_0 k_m1 f___ k1 k2 -k_m1 -0.3 -f_parent_to_m1 -0.3 0.3 -k1 0.1 -0.1 -0.1 +k_m1 -0.2 +f_parent_to_m1 -0.3 0.1 +k1 0.1 0.0 0.0 k2 0.0 0.0 0.0 0.1 -g 0.1 -0.1 0.0 -0.3 -0.3 +g 0.1 -0.1 0.0 -0.2 -0.2 Random effects: est. lower upper -SD.parent_0 0.02 -89.53 89.6 -SD.k_m1 0.20 0.07 0.3 -SD.f_parent_to_m1 0.32 0.20 0.4 -SD.k1 0.38 0.23 0.5 -SD.k2 0.33 0.20 0.5 -SD.g 0.26 0.06 0.5 +SD.parent_0 0.03 -49.24 49.3 +SD.k_m1 0.23 0.13 0.3 +SD.f_parent_to_m1 0.30 0.19 0.4 +SD.k1 0.40 0.25 0.5 +SD.k2 0.34 0.21 0.5 +SD.g 0.21 0.06 0.4 Variance model: est. lower upper -a.1 0.90 0.76 1.03 +a.1 0.93 0.79 1.06 b.1 0.05 0.05 0.06 Resulting formation fractions: @@ -75,5 +75,5 @@ parent_sink 0.5 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -parent 26 146 44 12 60 -m1 144 478 NA NA NA +parent 13 73 22 6 32 +m1 105 348 NA NA NA diff --git a/tests/testthat/test_dmta.R b/tests/testthat/test_dmta.R index 2927b711..7f0a3a67 100644 --- a/tests/testthat/test_dmta.R +++ b/tests/testthat/test_dmta.R @@ -32,20 +32,6 @@ test_that("Different backends get consistent results for DFOP tc, dimethenamid d saem_saemix_dfop_tc_mkin <- saem(dmta_dfop_tc, transformations = "mkin") ints_saemix_mkin <- intervals(saem_saemix_dfop_tc_mkin) - # nlmixr saem - saem_nlmixr_dfop_tc <- nlmixr(dmta_dfop_tc, est = "saem", - control = nlmixr::saemControl(nBurn = 300, nEm = 100, nmc = 9, print = 0)) - ints_nlmixr_saem <- intervals(saem_nlmixr_dfop_tc) - - # nlmixr focei - # We get three warnings about nudged etas, the initial optimization and - # gradient problems with initial estimate and covariance - # We need to capture output, otherwise it pops up in testthat output - expect_warning(tmp <- capture_output(focei_nlmixr_dfop_tc <- nlmixr( - dmta_dfop_tc, est = "focei", - control = nlmixr::foceiControl(print = 0), all = TRUE))) - ints_nlmixr_focei <- intervals(focei_nlmixr_dfop_tc) - # Fixed effects ## saemix vs. nlme expect_true(all(ints_saemix$fixed[, "est."] > @@ -59,18 +45,6 @@ test_that("Different backends get consistent results for DFOP tc, dimethenamid d expect_true(all(ints_saemix_mkin$fixed[, "est."] < backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod))) - ## nlmixr saem vs. nlme - expect_true(all(ints_nlmixr_saem$fixed[, "est."] > - backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod))) - expect_true(all(ints_nlmixr_saem$fixed[, "est."] < - backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod))) - - ## nlmixr focei vs. nlme - expect_true(all(ints_nlmixr_focei$fixed[, "est."] > - backtransform_odeparms(ints_nlme$fixed[, "lower"], dmta_dfop$mkinmod))) - expect_true(all(ints_nlmixr_focei$fixed[, "est."] < - backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod))) - # Random effects ## for saemix with saemix transformations, the comparison would be complicated... ## saemix mkin vs. nlme @@ -79,18 +53,6 @@ test_that("Different backends get consistent results for DFOP tc, dimethenamid d expect_true(all(ints_saemix$fixed[, "est."] < backtransform_odeparms(ints_nlme$fixed[, "upper"], dmta_dfop$mkinmod))) - ## nlmixr saem vs. nlme - expect_true(all(ints_nlmixr_saem$random[, "est."] > - backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod))) - expect_true(all(ints_nlmixr_saem$random[, "est."] < - backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod))) - - ## nlmixr focei vs. nlme - expect_true(all(ints_nlmixr_focei$random[, "est."] > - backtransform_odeparms(ints_nlme$reStruct$ds[, "lower"], dmta_dfop$mkinmod))) - expect_true(all(ints_nlmixr_focei$random[, "est."] < - backtransform_odeparms(ints_nlme$reStruct$ds[, "upper"], dmta_dfop$mkinmod))) - # Variance function # Some of these tests on error model parameters fail on Travis and Winbuilder skip_on_travis() @@ -106,21 +68,6 @@ test_that("Different backends get consistent results for DFOP tc, dimethenamid d ints_nlme$varStruct[, "lower"])) expect_true(all(ints_saemix_mkin[[3]][, "est."] < ints_nlme$varStruct[, "upper"])) - - # nlmixr saem vs. nlme - expect_true(all(ints_nlmixr_saem[[3]][, "est."] > - ints_nlme$varStruct[, "lower"])) - expect_true(all(ints_nlmixr_saem[[3]][, "est."] < - ints_nlme$varStruct[, "upper"])) - - # nlmixr focei vs. nlme - # We only test for the proportional part (rsd_high), as the - # constant part (sigma_low) obtained with nlmixr/FOCEI is below the lower - # bound of the confidence interval obtained with nlme - expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] > - ints_nlme$varStruct["prop", "lower"]) - expect_true(ints_nlmixr_focei[[3]]["rsd_high", "est."] < - ints_nlme$varStruct["prop", "upper"]) }) # Compared to the 2020 paper https://doi.org/10.3390/environments8080071 @@ -148,19 +95,6 @@ test_that("Different backends get consistent results for SFO-SFO3+, dimethenamid "Iteration 5, LME step.*not converge") ints_nlme_mets <- intervals(nlme_sfo_sfo3p_tc, which = "fixed") - # The saem fit with nlmixr takes only about 15 seconds - tmp <- capture.output( - saem_nlmixr_sfo_sfo3p_tc <- nlmixr(dmta_sfo_sfo3p_tc, est = "saem", - control = nlmixr::saemControl(print = 0))) - ints_nlmixr_saem_mets <- intervals(saem_nlmixr_sfo_sfo3p_tc) - - # We need to exclude the ilr transformed formation fractions in these - # tests, as they do not have a one to one relation in the transformations - expect_true(all(ints_nlmixr_saem_mets$fixed[, "est."][-c(6, 7, 8)] > - backtransform_odeparms(ints_nlme_mets$fixed[, "lower"][-c(6, 7, 8)], sfo_sfo3p))) - expect_true(all(ints_nlmixr_saem_mets$fixed[, "est."][-c(6, 7, 8)] < - backtransform_odeparms(ints_nlme_mets$fixed[, "upper"], sfo_sfo3p)[-c(6, 7, 8)])) - skip("Fitting this ODE model with saemix takes about 15 minutes on my system") # As DFOP is overparameterised and leads to instabilities and errors, we # need to use SFO. diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index ae8743af..6fb06656 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -57,7 +57,7 @@ test_that("saemix results are reproducible for biphasic fits", { # k2 is not fitted well ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) - expect_true(all(ci_dfop_sfo_s_m[, "upper"] > dfop_sfo_pop)) + expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) # I tried to only do few iterations in routine tests as this is so slow # but then deSolve fails at some point (presumably at the switch between @@ -73,18 +73,3 @@ test_that("saemix results are reproducible for biphasic fits", { expect_true(all(ci_dfop_sfo_s_d[no_k2, "lower"] < dfop_sfo_pop[no_k2])) expect_true(all(ci_dfop_sfo_s_d[no_k1, "upper"] > dfop_sfo_pop[no_k1])) }) - -test_that("nlmixr results are reproducible for biphasic fits", { - - test_summary <- summary(nlmixr_saem_biphasic) - test_summary$saemixversion <- "Dummy 0.0 for testing" - test_summary$mkinversion <- "Dummy 0.0 for testing" - test_summary$Rversion <- "Dummy R version for testing" - test_summary$date.fit <- "Dummy date for testing" - test_summary$date.summary <- "Dummy date for testing" - test_summary$time <- c(elapsed = "test time 0") - - expect_known_output(print(nlmixr_saem_biphasic, digits = 1), "print_nlmixr_saem_biphasic.txt") - expect_known_output(print(test_summary, digits = 1), "summary_nlmixr_saem_biphasic.txt") -}) - diff --git a/tests/testthat/test_tffm0.R b/tests/testthat/test_tffm0.R deleted file mode 100644 index 82e1ee94..00000000 --- a/tests/testthat/test_tffm0.R +++ /dev/null @@ -1,10 +0,0 @@ -test_that("The formation fraction transformation tffm0 is reversible", { - ff_example <- c( - 0.10983681, 0.09035905, 0.08399383 - ) - ff_example_trans <- tffm0(ff_example) - expect_equal(invtffm0(ff_example_trans), ff_example) - - ff_ex_2_trans <- c(0.5, 0.9, 0.99) - expect_true(sum(invtffm0(ff_ex_2_trans)) < 1) -}) diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html index 2a561b97..aabb37ee 100644 --- a/vignettes/web_only/dimethenamid_2018.html +++ b/vignettes/web_only/dimethenamid_2018.html @@ -1318,7 +1318,9 @@ window.initializeCodeFolding = function(show) { showCodeButton.append(showCodeText); showCodeButton .attr('data-toggle', 'collapse') + .attr('data-bs-toggle', 'collapse') // BS5 .attr('data-target', '#' + id) + .attr('data-bs-target', '#' + id) // BS5 .attr('aria-expanded', showThis) .attr('aria-controls', id); @@ -1402,6 +1404,7 @@ if (window.hljs) { + <style type="text/css"> .main-container { max-width: 940px; @@ -1423,6 +1426,9 @@ button.code-folding-btn:focus { summary { display: list-item; } +details > summary > p:only-child { + display: inline; +} pre code { padding: 0; } @@ -1580,7 +1586,7 @@ div.tocify { <div id="header"> <div class="btn-group pull-right float-right"> -<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> +<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm dropdown-toggle" data-toggle="dropdown" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> <ul class="dropdown-menu dropdown-menu-right" style="min-width: 50px;"> <li><a id="rmd-show-all-code" href="#">Show All Code</a></li> <li><a id="rmd-hide-all-code" href="#">Hide All Code</a></li> @@ -1591,7 +1597,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluations of the dimethenamid data from 2018</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 10 February 2022, built on 28 Feb 2022</h4> +<h4 class="date">Last change 7 March 2022, built on 07 Mar 2022</h4> </div> @@ -1599,8 +1605,8 @@ div.tocify { <p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> <div id="introduction" class="section level1"> <h1>Introduction</h1> -<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span> and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified, as many model variants do not converge when fitted with nlme, and not all relevant error models can be fitted with saemix.</p> -<p>This vignette is an attempt to satisfy this need.</p> +<p>A first analysis of the data analysed here was presented in a recent journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span>. That analysis was based on the <code>nlme</code> package and a development version of the <code>saemix</code> package that was unpublished at the time. Meanwhile, version 3.0 of the <code>saemix</code> package is available from the CRAN repository. Also, it turned out that there was an error in the handling of the Borstel data in the mkin package at the time, leading to the duplication of a few data points from that soil. The dataset in the mkin package has been corrected, and the interface to <code>saemix</code> in the mkin package has been updated to use the released version.</p> +<p>This vignette is intended to present an up to date analysis of the data, using the corrected dataset and released versions of <code>mkin</code> and <code>saemix</code>.</p> </div> <div id="data" class="section level1"> <h1>Data</h1> @@ -1693,7 +1699,7 @@ anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol)</code></pre> <div id="saemix" class="section level3"> <h3>saemix</h3> <p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> -<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. As we will compare the SAEM implementation of saemix to the results obtained using the nlmixr package later, we define control settings that work well for all the parent data fits shown in this vignette.</p> +<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.</p> <pre class="r"><code>library(saemix) saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) @@ -1706,7 +1712,7 @@ saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 300), nb.chains control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")</code></pre> <p><img 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" /><!-- --></p> -<p>Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> +<p>Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> <pre class="r"><code>f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")</code></pre> @@ -1716,14 +1722,66 @@ plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")</code></pre control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")</code></pre> <p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdZ1wTWfs4/JMChF4jvQgqTdC1gCgqWBAriKAoYkFsqKuLvWDvBfanK7IqKKKoCK7YEBsoVoqIIoJY6DX0Eggk87yY584/G8pSEhLC9X2xn+QwmbkSz+bKnErAMAwBAAAAQLQQBR0AAAAAAHgPEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEjwAAAAggiDBAwAAACIIEnxXpKSkEP5HQ0PD09OzpqYGIRQZGYkXZmRkcD5NSUkZOHAg4d8iIyMRQqmpqQQCQV1dncVitXPF/fv3q6mpGRsb3717t2feI+g7AgMDCQRCcXExV3lJScnhw4cFEhIAbC3rZ3Nzs6enp6KiopaWlq+vrwBjE3KQ4Ltu1apVYWFh7u7uly5d2rNnD7ucQCC8e/cOIZSQkEAgEPDCM2fOhIeHOzo6IoSCg4PDw8NHjhyJELp58yaBQCgqKoqLi2vrQtHR0Xv27Dlw4ICtra2Li0t5eTl/3xjo82pqaiIjI52dnTkrNgBCIiQkJDAwcP/+/Y6Ojhs3bnz//r2gIxJSkOC7bsSIES4uLseOHXNxcfn777/Zt+BGRkZv375FCMXHxxsbG+OF9vb2c+bMMTIyQgg5ODjMmTNHQ0MDIXTz5k1nZ2d5eflbt261daEHDx7o6ektX7588+bNDAYjJiaG7+8N9EmnT5+WkJCIi4tLSkratm3b169fBR0RAP8Pu37m5uZaWVmtW7du8+bNCKG0tDRBhyakIMHzwPDhw+vr6/Pz8/GnVlZW7969wzAsISFh1KhR7bzww4cPmZmZrq6uM2fODA8Pb6uVvri4WFlZGSGE/7e0tJTX7wAAFBsbu2nTpsDAwLFjx9rY2Hz9+nXt2rWCDgqA/x9n/dy9e/ebN28QQlFRUQih9r9m+zJI8DzAbofHWVlZpaamfvjwob6+ftiwYe288MaNG5KSkvb29k5OTsXFxW210rMTP5FIRAhhGMajwAH4fzw9PTU1NRcsWCDoQABoRcv6GRQUtH79+n379rEbSgEXSPA88OHDB0lJSU1NTfypnp4elUo9e/bsb7/9RqFQ2noVhmFhYWF0Ol1aWtrJyQkhFBYW1uqRVCq1pKQEIUSj0RBCampqvH8PoM+ztrbOysq6dOmSoAMBoBVc9dPb29vLy+vChQu7d+8WbGDCDBJ81yUnJ9+5c8fHxycsLGzlypX47TVCiEAgWFlZhYaGWllZtfPyd+/eZWdnb9u2LTw8PDw8fMKECREREa220k+ZMiU3N9fPz2/fvn0UCmX8+PF8eT+gb7t06ZKdnZ2Pj09dXZ2gYwGAG2f9vH//vp+f39y5c+Xl5SMjI7OysgQdnZCCBN91Z8+enT17dmBg4JIlS/bv38/5p9GjRzc2NrbfM3Tz5k1xcfHt27fPmTNnzpw5Hh4excXFL1++bHmkg4PD7t27jx079uLFi5CQECUlJR6/EwAQIhAIBw4cKCwsPHHihKBjAYAbZ/188uQJQigkJMTR0dHR0TE6OlrQ0QkpAnToAgAAAKKHLOgAwL98+fJl06ZNXIWhoaGKiooCiQcAAEAvBXfwAAAAgAiCPngAAABABEGCBwAAAEQQJHgAAABABEGCBwAAAEQQJHgAAABABEGCBwAAAEQQJHgAAABABEGCBwAA4cJkMtmPk5OTr127lpGRIcB4QC8FCR4AAIQLe8dIPz+/GTNmPHr0aMqUKVeuXBFsVKDXgZXsAABAuFCp1NLSUoSQlpZWQkKCurp6aWnpmDFjvn37JujQQG8Cd/AAACCkVFRU1NXV8QckEknQ4YBeBhI8AAAIFzExMVNT06lTp1ZXVwcHB2MY5unpaW1tLei4QC8Du8kBAIBwKSgooNPp2dnZWVlZampqGIbp6+t7e3sLOi7Qy0AfPAAACJ2mpiYmk0mhUKqqqhISEvT19fX19QUdFOhloIkeAACEy9WrV5WUlAwMDE6dOmVhYeHn5zdmzJjAwEBBxwV6GbiDBwAA4dK/f/9nz57p6upaWlquXLly+fLlNBrNysoqMzNT0KGB3oSPffAsFislJaWoqKi5uVlTU3Po0KFEIjQYAADAfyCTyXiD/JIlSyZNmoQQUlBQEHRQoPfh1x18TEzM6tWr1dXVNTU1EUIFBQW5ubmBgYHjxo3jx+UAAEBkLFu2jEajbdmyZcyYMQihtLS0I0eOEInE4OBgQYcGehN+JXgzM7M7d+4YGBiwS3Jzc52dnd+/f9/BM7x+/frevXv8iE2EERobEYYhCQmMQBBIADo6Ol5eXgK5dA+D+tkFUD87iMVi/fPPP/369Rs7dixC6P79+z9+/Fi1apWEhEQHzwD1swtEr37yK8GbmJgkJiZKSUmxSxgMhrW1dXx8fAfPsGXLli9fvsAdf6eQTp1CpaXMjRsRldrzV6+qqgoJCcnNze35S/c8qJ9dAPWz4zrVxVlfX9/Y2MhZ4uPj8+PHDxsbG74HKkJEr37yK8H7+/ufOXPGwcFBS0uLQCAUFBTcvXt37dq1K1eubPX4EydOfP/+nbPk+fPnI0aMuH79OrskLCzsx48f27dv50fAoiHIyKg8I8MjPV3J0LDnr56Xl2dlZdVbvkC7acuWLVQqdfPmzfjT8vJyExOTkydPLly4ULCBCTOonx3U2S7OadOmvXv3jrOkpqZm+PDhnIW1tbWjR49+//69pKQkX4PvvUSvfvJrkJ2Xl9fMmTOjoqIKCgoQQjo6Og8fPtTW1m7reBMTE3l5ec6Sx48fFxcXc5YUFhbm5+fzKWAAukNJSWn+/PmFhYWCDgSIgt9///3Bgwcd7+J8+PAhV4mlpaWKigpnyc2bNz9//kyj0dr5HgYiho+j6LW1tVesWMFZwmQy21pOefr06VwlgYGBnC38CCEWi8VisXgbJAC8IiYmJugQgIhgMpn4EvRsqqqq3WxtraqqQgi9evWqurq6rZZUIGJ6bqna4uJifM3FLp+BxWJxbpMMQI+xsbF58eIFZwmBQBg1ahS7iR4hRCQS4Qco4Im1a9cOHz68ZRdnd87Z1NSEEFqwYAGZTF66dKm4uDiPggXCq+cSPJVKLSoq6s4Z4A4eCEpsbCxXiaWlpZKSEmcJgQDLRgHe6GwXZ0fgCV5CQoJEIlVXV3M14AORxMcE39jYGB8fX1xcLCkpqa2tbW5urqqq2p0TQoIHwgwSPOChTnVxdkRzc7Odnd2oUaOuXbsWEBCwa9eubscIhB2/EvyTJ0+WLVtmaGj47t07fIJHeXl5WFiYsbFxq8c7OzsnJydzluTn5w8ePJizBJrogTCDBA/4p/tdnAwGY/z48Tt27KBQKBcvXnRycjIxMeFhhEAI8SvBr1u37tmzZwMHDszJyXF1dY2Li/v06dOKFSvi4uJaPT4gIKC6upqzZPbs2f369eMsgTt4IMwgwQP+ab+Lc+7cuUlJSZwlBQUFnDdI5eXlV65c2bRpE0Jo4cKFe/bs8fHxiYiI4F/AQBjwK8E3NjbiMzjV1NTwuUOmpqbtTCJSUVHh6hOiUChcCztAggfCDBI84KGWC92008V59uzZmpoazpLZs2dTOVZrUVJSevz4Mb6+vba29pQpU1JSUvgXPBAS/ErwTk5Os2bNcnBwePz48fjx4zEMmzJlir29fXfOCU30QJjBKHrAK51d6IZKpVL/vfhayxskzgZ5Dw+PhQsXZmVl6enp8T56IDT4leBPnjwZGhqakJAwffr0ZcuWIYQOHz5sYWHRnXNiGAZfoEAgWk6TQwgNGzaM8yncwQNe6exCN501e/ZsW1vb1NRUSPCijV8JnkAgODs76+joFBcXP378WFtbu/3sXlpaytXE1NDQwJXOIcEDQWl1mhzXPRMkeMAr/FjohoumpuatW7fy8/Pd3NxkZGR4eGYgPIRlFP2aNWtaDhLhWooBmuiBMCMQCPADFPAEPxa64TJ27Fh3d/crV648ePDg7t27PDwzEB7CMoo+LCyMq6TlHRIMsgPCDO7gAa/wY6EbLvb29niNvX//fn19Pde64EA0CMso+o6AO3ggzCDBAx5qudBNO44dO/bz50/OkqysrPY3R1BRUUlNTT1+/HhwcHB5eTkkeJHUy0bRNzc38ypCAHgLRtEDQTEzM1NUVOQsefz48X+uNm9iYnLhwoX4+PiYmBh3d3d+BggEo9eMoq+qqrp///6vX7/evn1rZWXFu0gB+G+NjY319fWcJS1/a8IdPBCUadOmcZW03I2zVWJiYo6OjllZWXwJCwgaH0fRu7m5ubm5sUvaz+4RERHfv3/nLCksLJSWlmY/lZGRcXd33759+5cvXyDBgx7m4OAQHx/PWcI16QNBgge9k6am5q5du6ZOnTpixAhBxwJ4rOd2k2tfcXFxRUUFZ0lzczO+/RGORCJZW1uj1r5YAeC3R48ecZXANDkgGszNzSsrKz9+/AgJXvQIS4L38vLiKomJiZGXl+csIZPJ6H+bHgIgbCDBg97I2trayMjo+fPnQ4cOHT58OIFAEHREgGeI/32I0MATPIPBEHQgALQCEjzojQgEwqJFi65fvz5y5MjPnz8LOhzAS8JyB98R+KwPuIMHwglG0QNBefnyZXFxMWdJeXm5rKxsB18+aNAgCoXS0NDw6dMnc3NzPgQIBKM3JXhlZWUECR4IK7iDB4Ly/PnztLQ0zpLy8nKu7bbbMWfOnFmzZu3YsePkyZMODg5cvwx+/vxJp9MNDQ3xNlTQiwjLP9j69eu5KmhGRgbXbkhqamokEqmsrKxnQwMA0en0hoYGzpLm5maudA4JHvBQy+1iub4POe3du5erxNLSkmtmfPvExMS8vLwuXLjw999/49vGsy1atOj169ezZs1auXKljo6Ouro6fq8FhJ+wJHgPD4/S0lLOkrVr13L9kCSTyYsXL8bXbqTRaDQazcjI6D/PvHPnzunTp48ePZq3AYM+xcnJiWsjL5gmB/ins9vF8kT//v03bNiQl5fHWZiVlfX161eE0Pv37799+8ZgMCwtLUNDQ/kXBuAhYUnwQ4YM4SqRl5dvuRLT/Pnzjx07hhC6ffv2wYMH//rrr1mzZrV/5i9fvhgYGAwfPlxCQoKz/NOnT01NTcOHD2/5kh8/foiLi/N25WcuLBarvr4e38Tp06dPenp6cnJy/Lsc4MJkMkkkEv44OTk5LS1txIgRhoaGbR0fFRXFVdJymhyRSOTqBwWga/i9XWxb9PT0fHx8NmzYwN5GdsWKFWJiYkuWLLly5Up1dTWdTofb916kN42iRwhRKJSUlBQWi1VdXZ2bm5uQkICXV1ZWcrWgsjU1NQUFBS1dupSr0NfXt+UON7hDhw5dvXqVt5HHxcW9fPmS/fTWrVvr1q3DH/v4+Dx9+pS3lwPtU1NTwx/4+fnNmDHj0aNHU6ZMuXLlSnfOSSKRuBbDAaBremC72FYtWrSoqKho9uzZCCE6nX758uWXL19qaGhs3rzZzMysqanJxMQkMTGx1fsiIIR6WYKXk5MrLS319/evqqpCCFVXV+Plo0ePplKp+HLKLBYL786fOXPmqlWraDRaWlpaSkoK3nnv6upaXV19586d4ODgtsbrVVVVNTY28jbyoKCgJ0+esJ/W1tZWVlYihFJTU4uKivBlUOvr6wMCAlp9OT45kEajwT0ib506dSoxMTEkJCQhIeHgwYPdOZWjoyMMEAE8gW8Xu23btr/++uvs2bM7d+4cPnw4vuY3XxGJxKVLl+bm5iKEMjIyVq9eraCg8PTpUxMTk8WLF8vLyycmJk6YMCE5ORkGO/cKvSzBm5ubDx48+PTp0/jNN76FfHl5eVFRUW1t7ZcvXxBCycnJgwcPPnz48IsXL/7++++EhISqqqq0tLTbt28XFxc/ffq0tLSURqMhjuXEo6OjOdu+Ghsb6+rq8J3rcnNzy8rKfH196+rqWjYSTJgwYe/evUlJSQcPHiwuLmb/LFi6dCn753ZDQ8P58+e5fk80NjYmJibm5OTs27cvPj6eTqcjhLKyso4ePco+4NSpU+zjDQwMamtrAwIC8IaHjIwM9p9yc3PhB3WXqaio4LdKKioq7Eb7rlFXV6+pqYFueNB9Xl5ejx8/1tfXp9FopaWl+HaxK1eubOv4srKyn//W2NjYtap4/vx5AoFw6dKlly9fNjQ0WFtbKykpIYR0dXVVVVUlJSVdXV0xDMO/e4GQE5Y++PHjx3O2YOOGDRvW8kgFBYVXr17hj/FlGeLi4vBlbj9+/Lhr1y5fX18Mw3bu3Ikfg2EYXtGDgoLi4uLKyspoNFpNTQ2BQCgoKBg7dmxcXNz9+/fV1dUtLS0RQiwW68uXLw8fPkxMTDx69Oi4cePIZLKSklJBQYGWltaGDRt+/Pjx8+fPyZMnT548+dWrV+bm5nfv3r1+/frHjx+rqqqePHnCYrEuX77s7+8vKSmJEAoICPjjjz8QQvhi+9nZ2Zs2bdLV1c3Ly/vtt9/wDq3g4GACgTBkyBAGg1FVVWVtbX3r1q0jR45s3LgRfxclJSWPHj2qr68vLi7evHnz77//zu4wLioqyszMDAoK0tLSKioq+o8NpABCCCExMTFTU1MdHZ3q6urg4OBFixZ5enriayF3GZFIlJGR+fLly+DBg3kVJ+izOrVd7IoVKz5+/MhZkp+f3+VZbRYWFitXrhw1ahSRSGR/z0ycOBH/Qlu6dOmRI0dcXFw+ffrUqYH6oOfxMcHX1dUhhKSlpQsKCuLi4oyMjFqOpGN78eIFV0nLQUw4FRUV9uPq6uqUlBRHR0f8KYZhp0+fxu+GW/r16xfe85qZmdnY2Kiurh4VFdXQ0KCpqVlaWsqeGVJbW4vvrRQbG2tra8tgMBgMBplMrq2traurCwkJOX78uLm5uZSUVFxcXFNTEz4Dlclk1tTUSEhIpKen6+rqIoS+f/9uZmb2/v17vCkeIYSPGHBwcEhJSXFwcEAIlZeX4399/fr1hw8f7ty509jY+OrVq9TUVBqNhn+ACKGmpiYGg+Hi4rJs2bK6urq6ujp8XOuePXtMTU0xDKupqbl27drcuXPxVgfwnwoKCuh0enZ2dlZWlpqaGoZh+vr63t7ebR1fX1/P1WvTcpocQsjd3d3f39/f358vQQPQhoiICK6Str4/O2LatGlv376Ni4tbt24de49veXn5qVOnIoRIJJKzs/Px48ejo6NdXV27EzbgN34l+JCQkHXr1pHJ5L179/r5+VlZWW3ZsmXz5s1r167t5pm9vLzu3LnDfjp37lz8gZaWlrS0NGfbNZeysrLXr18jhNavX08mkxUUFPAZd/h/3759S6fTJSUlORvG2b8VampqMjIyKBQKnU5PTU1VVVUNCgrCv/HZizsWFRVRKBQLC4vly5cjhI4ePerv7z9u3Dj2chN4Pzo+vYrdH89e+4xOpxcVFZWXl8+cORMh9P3794aGBiaTSSQS2R3zVVVVjx49wn9qpKSkpKenv3z5Em/nqK+vj4mJGdTUJNnlT7aPYTAYhoaGRkZGSUlJoaGhTk5O+A1Kq5ydnd+9e8dZgjcCcR02Y8YMdicLAF0WEBDQ8p4HIXT9+vUeuPqaNWvevn0rKSl5+vTpVg/AV7sLDAwcO3YsPpEPCCmMP/T19QsLCysqKmRkZF69eoVhWGlpaf/+/Tt+BgsLi+nTp7csz8zMRAi5uLgghDinkZiamp49exb9b8l6hJCGhgb+oNUu6pEjR3KVjBs3bubMmfiCuC2JiYlpa2v379//Pz9SfD0KHR0droWdlZSUMAxTVVVt64V4Xxdu8+bNCKHKyko/Pz92IdfAWjxULS0thJCUlJSSktIWhE4gVJaezqt/x07Jzc3V0tISyKU76++//zY0NGQwGIcOHerfv/+iRYt0dHTOnz/f8TO0Wj/r6+slJSWPHDlSXV3NYrFevHhRV1fH08B7t0BDQ6ifHVFbW7tkyRInJ6en/9bxM7T1/dlB6enpJSUlbf01Jyfnzz//RAhpa2t3+RJCSPTqJ78SvJ6eXmNjI5PJVFVVTU9PxzCMTqfr6+t3/AxtVVAGgzFo0KDr169PnDiRc2P4IUOG4HPbxo0bh3ddHzhwAP8TezYdp/Dw8ClTpnDlV/ynAD6cpN0kzo2d+Nmz58lk8qBBgziPoVAoJ0+eZE/uZ/8QaXX7Jvx38alTp/5zfjxnTxue4JdMn46P9uphvegLVEdHp6qqCsMwLS2tsrIyDMNKSkoGDBjQ8TO0VT/xH52bN29ev349QmjRokWpqansvz558iQvL4/z+IiIiOzs7C6+jd5G9L5A+SclJWX79u1dfnk3E/x/YjKZUlJSRCIxMzOTf1fpjsjIyE2bNu3fv7/jLxG9+smvJvq5c+daWlqKi4tPnjx5yZIlbm5uUVFR7O6cllJSUrhWsquqquLaLhYnJiZ25syZkSNHvnz5sqSkBP1vkw8ymYznQgqFgr9QWloaIeTh4WFiYmJgYPDjxw/O81haWsbExERHR7NLmpqa8MF6ioqKBw8enDFjBuLYQURBQYHdm46zsbGJjY3FHw8ePPjXr18IIWtra7wZrbm5+du3bwghSUlJvKm/oaGB3dNPIBCOHDly6dIldXX16urqmpqa9PR0zpPn5+cjhNiD7Lj069cPf++IYy4A25MnT+Tl5d3d3efMmTNy5MiHDx82NTURicTc3FwMw5qamjAMk5GR6d+/v7i4OJFIVFNTa2pqqqmpKS0tZTKZZDJZWlqaSCSSSCQxMTG84ZpCoUhISJDJZAqFghBisVji4uLV1dXGxsZSUlKtBinMqFQq3mOioKCAt7h0cwg927Jly/bs2XPy5EmE0JgxY65cuRITExMeHk4gEMLDw69duzZmzJibN29WVlZmZmbW1ta6u7vv3r1769atHb/E2bNn3717JycnhzdZAZFkbm4uzPu+EInExYsXnzt37sKFC1u2bOFa/aa2tvbbt2+tjpL+TxiG4YtCNjc3i4mJlZaWjhgx4vbt2/Hx8atXr27rVUePHnVycsLvqWg0WnBwcGho6IcPH/T09DZt2pSTk4N/3TU3N3/58qWd0WAihl8J/tixY9OnTyeRSGPGjHn27FlUVJSjo6OHh0dbx1+6dAmf5MZWVFTU1hBNOzs7hJC/v/+1a9ciIiKoVGpjY6OYmNjEiROnTp2qoKAgLi5uY2Mzfvx4AwOD//u//5OSknr48CHXOmWSkpJcfa719fX48Do5OTm8MXz8+PEaGhoYhk2YMCEiIgL/NaCsrIxhWHl5eWBgoJ+fn7+/P4vFmj59+vz582/fvu3l5cXVT6ahoYH/tiCTyexkLCUlZWpqKicnd/HixZcvX9rb28+ePfvNmzecy51y7k4mISHBHuQlJyc3ZMgQOp3Onk3AJSYmhqKtffXq1b1796alpc2cOVNaWppAIGhpaYmJiSkoKDQ3N5eVlT158gQ/Z15eHplMlpWVVVVVJZFITCaztraWxWKxWKzGxkZ8ciCDwaDT6QwGg8Vi4aMOmUymtLT01q1b8e6S3uXQoUOjR4+eNWvWoEGDbGxs7OzsHjx4wF56qDu2b99ubm6+Z8+eTZs2jR8/3t/f38/Pz9LSEt+ty8rK6v3793p6enV1dfhcTU1NzaCgIHaCZzAYLRdwfP/+fVlZ2bRp0xBC9fX1W7ZswTBMTk7Oy8vr/v37W7ZsaX8P77y8vDdv3rBHq4BeoVNr0QvE9OnTz58/f/z4cV9f38jIyMLCQmdnZwzDFBQUzp8/v2nTpjlz5sTGxvr5+bm4uPj7++Ojstgv//z58+PHjzdu3Miu89XV1UZGRvLy8qNGjXrz5o2ZmRk+ZDUnJ8fe3r6ystLAwEBZWRlvZ718+XJjYyM+dTA4OPj06dPKysr6+vpkMtnf33/Pnj0IIXl5+fz8fAcHhydPnri6us6YMaOgoGDXrl2/fv1id+CKON42CLQPH3jcQR1pYrp16xZCaPjw4UOGDLG2tmaX02i0oqIiDMPodDpewrWP5/Hjx1kslo+PD/503759FhYW7L8eOHCAyWS+f/++qKgoJCQkPj4ewzD2LHZzc/O4uDiEEN4MfuLEiSFDhhQWFuIX4hrHPmTIkGPHjikoKJw4ceK3335DCJFIJFVVVT09vefPn0+YMIEdM77W3qtXrxQUFPDXBgQE6OnpycrK6uvrl5SU4N/vCKGkpKQZM2Zs2rTJzs4O77YnEolbt27V0dHZTaGIWBMT/1RUVFy+fPnAgQM7duzw8/P7+vVrp17e8SbQO3fuLFy40N3dff/+/RkZGV+/fj18+PCIESMOHTq0Y8eOO3fu6OjovHnzBsOw3NxcY2PjNWvW1NTU0Ol0R0dHb2/v+vr6NWvW9OvXD8OwrKwsvKrTaDR1dXW8OyA8PDwyMpLdEYCv3NDQ0JCRkXHnzh1HR0dxcXEqlcpisTAMCwsLe/36dec+Kd4RvSZQPnn+/LmhoaGNjY2bm5ubm5utre2AAQNevHjR1vFOTk76/yYhIWFhYcHvOLOzsznH6tva2uK/PjlnnPbv3x+/uUrn+Hf/8OGDt7e3rKzssWPHdHR0NDU116xZ8+nTJ/wl+HQnEomEfy3jg43Ya41/+/YtJydHUlISv+0pKSkhk8ni4uIEAsHBwcHIyEhSUnLGjBk6OjoFBQX37t1DCHH9aD58+HCrb0f06mfPJfiioqJO/Z7oyBfoo0eP9PT0UlJSXr161U7tx5FIJF9fX/wf+Pv37xiGRUVFmZmZIYRev37N2boQFBTU6hmIROIff/xhYWGBr6CH/14JDQ11d3fnOow9+u/69esYhpWVlbFYLPwG0czM7Ny5c7Nnz25qasLbzHG5uV8nV2gAACAASURBVLmenp4sFsvNzW3UqFH9+vX7/PnzsGHDgoKC8P8xli1bhjc5fP/+fc6cOQcOHMAw7MSJE2QyGX87GIYdkpcXsQrKP0wm88OHDw8fPrx7925SUhKTyWzn4LKysh//NmTIkGnTpvEkknnz5llZWUVHR+PdTAQCYcOGDUOHDpWVlSUSiTY2NqqqqgQCgcFgnDx50srKqrKyEsOwJ0+ekMnkfv36USgUvD/l4sWLdnZ2BAKBSCROnDiR/fV66tQpeXn5Q4cOJSYmGhoabty4Eb9uUlJSUlLS1atX169fb2hoGBwc3DK29PT0qVOnenh40Ol0/Lo5OTkmJiZ//fUX15GfPn3Cf09XVVW9fv0a/z3BRfS+QPlk8ODB7P+pcTk5Oe0kbBqNxlU/ly9ffuTIEf5HinEOhEIIDRo0CB+qvGbNGm1t7efPn1OpVHFxcU1NzdGjR4eGhn78+NHd3Z3dXctOvUQiUVZWdvLkya6urjExMc+ePZszZw5CSEND4/Dhw8nJyRcvXsTbMObNmzdo0KApU6aYmZktXrx48uTJJiYmX758YY95mjZtWmFhIT62pqGhQUJC4tixY3PmzHnw4MGAAQPWrVtHIBCcnJwOHjx448aNiooK9nsRvfrZcwvdUKlUPMfzkImJybhx4zrYU6WoqGhnZzd//vzr16/j3cb29vYjR44cMWLEiBEjBg8efOXKFfxbvtW+f4SQuLj43LlzTUxMKBSKuro63ms7d+5cvLeejUKhnD59+tmzZxcuXMB7GfD77FOnTlVWVpqamjo6OpqYmJDJZHwAPE5LS+vChQsIoaFDh06ePHnUqFEDBw6UkZHR0NDAf/8OGzbs/fv3q1at0tPTU1RUxH/Vzpgx4+TJk+zfExISErCOWkd0dreu5cuXcy0kUlhY2FY96awTJ04YGBjMnTt35MiRHh4ezc3Nq1evlpGROX36dE1NzYYNG/r3729qaurt7X3r1q2IiAj8upMmTSopKSESiba2tsbGxk1NTZ6engghd3f37OzsZ8+e6enpbd26dfny5SQSSVpaet26dfv27SORSFFRUStXrtTW1v7rr79CQ0MbGxsVFBSUlZU3bNjw5MmTp0+furq6btmyZd68eWQy+cOHD83NzXQ6Hd9AbNOmTXl5eZWVlTt27Pj58yeRSJw0aRKZTH779m1QUNDEiRP37duH77ynpKQ0Z84cFxeX/Px8CoXi6OiIj94AHdHZteiVlZW5esEVFBR4NaykfeHh4VevXt26devSpUt//foVGxtbUlKyb9++9evXOzo62tra/v7772QyWUJCwtvbu6amxs7OLiQkBH+tu7s7PtPKzs5u27ZtDQ0N06ZN27BhA/7XAQMGKCsrOzs7Dx06lEqlDh06lN3nhV83Li5uzZo1CKEDBw6YmJiEhYV9/fpVTExs9uzZ7O4MCQmJd+/eDR06FH86adIkMTExMTGx0NDQ0tLSyMhILy8vXV1dFotFJBId8/KkENqwYUPDvzcy5Z/ly5dPnjyZf+fn1waXR48eXbx4MVcd7RR8oYb79+/zKqQLFy64ublduXIlICDgw4cPLTu0ZGRkJCUlTU1N2UPnuDg5OV2/fp1rV7qWfv36hQ+q9/T03L59O+dcvi9fvkhKSurr63cw5szMTD09PTyXFxcXh4eH4xX66tWrysrKU6dOra2tPXLkyKFDh/DjAw0NK75980hPV2p7YzT+ycvLs7KywheyFnJmZmZ37tzpzm5dW7ZsoVKp+GzG7rt69erff/8dEhKip6dHo9GuXLmycOFCfAWFoqKiqqqq8vJyOzs7ExOTtiKsra1NTU1VUFAwNDT88OHDvXv3tm7dyjnK5J9//rl+/fqqVascHBwIBEJtbS2GYUuXLrW1tbWysmIymf7+/gEBAfjYQ1NT0/z8fDk5OXNz87CwsIqKimvXrsXGxlZWVsrKyh46dCgzM/PMmTMfPnxgr99sYmJSUlJSUVHBZDJdXFwqKioSExNrampYLJaYmJiUlNSsWbNMHzwglpWRd+wg9OsnIyOjpqbGYrEKCwvxdYTq6+s5B2zKyMjgkwzZJXV1dXh4nKSlpTEM4/q/kkAg4F1dEyZMGDBgAF7Yi+qnv7//mTNnHBwctLS08DU37969u3bt2nZWq+XC2/rZvtzc3MTExNmzZ+fn5/v5+R08eLDlj7m0tDRTU1Ntbe3c3FwKhWJvb79+/fqBAweyp9EzGAx8hG9bE5VxOTk5NjY2/fv3f/bsWVVVlY+PT0lJycGDB9n/yl0IPjc3l0Qi0en0TwsXMvLzNf78U6yneuhHjx7N/gT4UT/5leClpKRGjBjh6uq6fPny9v/BcL6+vlxr1Ny5c8fQ0LDl+rXdVF1dXVpayvnNzhlDYmKiv78/uxe81wkyMirPyIAE/5/wTbE40wmDwbC2tu74dnA9+QWKKy4uFhcX58nioC4uLg4ODsOHDzc2NuYsx2da02i06OjoxYsX441M7az94Ovrq6mpqaGhoamp2b9//7KyMicnp+3btw8aNMjAwKCxsTE2NpbFYpWUlEhJST1+/Ljf5cuKzc35bm4sZeWGhgb8i1VdXV1aWlpCQkJKSgrfdQlXW1uLDw5FCFEoFCaTKSEh0fLLpLa2lkAgMJlMzuEvGIbhc14WLlw4duxYvLAX1U+EUG5ublRUFL4Ml7q6+rRp0zq1gXXP18//5Onpefr06ZSUlFu3bklLS7OnMQsP0fv+5FcTvbS09PPnz8+cOTNq1Cg3N7cZM2ZwTQrngjdHc5akpqZqa2v//PkTf8pisTIzM3kyI6uuri4vL69luampqZaWVkpKSgfPg2EYnU7nVUj4pL5uwr8fExISKN3uDWlsbOzsj2Ked8HwD75bV8s7pE6dpLy8nE/1kyeVoZ36ib/TkpIS9mRLHJ4+NTQ08CGfLBYrJycnJyenrZDw8cwsFgu/DUL/W3wiLy8P/18Mv5PDNxdfuHDhx4cPmwoK7O3tKZ3JVa3qeP1k/xv1ovqJWluLnslkdqrVXdjqp7u7O74kyfjx44lEYmxsLHx/cuJH/eTXHTyVSsXntdfU1Jw/fz48PJxGo9nb2585c6aDZzh37hw+mRhXU1NDo9G6vH0Cp+bmZhKJ1P7Moo7AJ5IJVUiDmUwKhn0hk1tfjr+TIeHT6jr1KiMjowcPHnT74j2hm3dIUD+7AOpnlxUXF+ObJnTweKifXSCC9ZO3Y/bYVFRUuEoKCws7tRQoF3ydnO4F9f8bMWJEQkJC98/zzz//ODo6dv88GIYNHjz48+fP3T9PaGjo/Pnzu38eDMMMDAy4xvGKvE5N4+QC9bMjoH52GZPJxKf+dg3Uz44QvfrJryb6lhtzqamp4buwACCEOnuHBABftVzoprPrZwPArwS/ffv2xsbG+Pj44uJiSUlJbW1tYV52EQB+TOMEoGs6O40TgFbxK8E/efJk2bJlhoaG+BxEIpFYXl4eFhbGNWoXAEGBOyQgtH7//fcHDx50ZxonAIh/CX7dunXPnj0bOHBgTk6Oq6trXFzcp0+fVqxYgS/yCoBgwR0SEGadXegGgFbxK8E3NjbiX51qamqFhYUIIVNTU/xB15DJZF4tzEQikXgydBNC6r14fockhJ88hNR78WQaJych/OQhpB7Ar2lyGzduTElJcXBwePz4sbKy8qVLlyZPnmxkZPTXX3917YRMJrO0tBTfhKCbCgoK1NXVuz+nAt+TjSftuvn5+RoaGt0PicFgVFZW4iugdT8k9hJLoqf7C91wgfrZEVA/O66b0zi5QP3sCNGrn/xK8BiGhYaGJiQkGBkZLVu2jEwmJyQkcO7YBoAAdX8pUAAAEHL8SvAACDne3iEBAICwgQQPAAAAiCDuHdUAAAAAIAIgwQMAAAAiCBI8AAAAIIIgwQMAAAAiqHckeH9/fxMTE3Nz85iYmE698MCBAyYmJtra2keOHGnrVF0+eddER0ePHDlSV1f3/PnzAg+prq4uICCA/bQjkfTwx9UrQP3kE6ifPAH1k096Qf0U4E52HZSTk2NkZFRXV5eZmWloaMhkMjv4wqdPnw4bNoxOp5eWluro6CQlJbU8VZdP3jUVFRUDBw4sLS2tqqoyNDSsqqoSYEifPn3y8PBwdXXFn3Ykkh7+uHoFqJ98CgbqJ09A/eRTML2ifvaCO/ioqCgHBwcpKakBAwaoqamlpKR08IVlZWUrV66kUCgqKirW1tb4vGeuU3X55F0TGRk5a9YsFRUVOTm5z58/y8rKCjCk69evV1ZWsp92JJIe/rh6BaiffAoG6idPQP3kUzC9on72ggRfWFiopaWFP9bS0ur4np5z585dsWIFQujz589v3ryxsbFpeaoun7xrcnNzs7KyzMzMdHR0Tp48SSAQBBjS4cOHvby82E87EkkPf1y9AtRPPgUD9ZMnoH7yKZheUT8Fvxr+f2KxWJyLDDc3N3f8tRiG/fnnn+fOnbt9+7a8vHzLU3Xn5F1Ap9MzMzNfvnyJYdiIESPs7OwEHhJbRyIRVGzCDOonX0Nig/rZNVA/+RoSm3DWz16Q4DU0NHJycvDHBQUFGhoaHXwhk8l0cXFRUFBITEyUk5Nr9VRdPnnXUKnUKVOmKCoqIoTGjh2bnp4u8JDYOhKJoGITZlA/+RoSG9TProH6ydeQ2IS0fvK7k7/7srOzBw8e3NjYmJubO2DAgI4PTAgNDZ03b177p+ryybvm69evpqamZWVlJSUlOjo6aWlpgg3p6dOn7EEiHYmkhz+uXgHqJ//igfrZfVA/+ReP8NfPXnAHr6Ojs2bNGmtra4TQxYsXicSOjhuIi4uLiopSV1fHn164cGHGjBlcp+ryybvGyMhoxYoVlpaWGIbt2LHD2NgYISTYkNhaXrcjJT0TmzCD+snXkNigfnYN1E++hsQmnPUTNpsBAAAARBD8wgUAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECR4AAAAQARBggcAAABEECT47pKVla2rq6uurr5w4UJnX8t+VWRk5Lp16/gQHejroH4CYQb1k68gwfNGTU3NlStX2j+mqamprVdNmTLlwIED/AoO9HlQP4Ewg/rJJ5DgeWPLli2pqamHDh1CCO3du3fAgAFDhw69fPkyQig6OnrVqlWTJ0++d++ep6fnwIEDdXV1T5w4wfmqZ8+e+fj4YBi2adMmExMTU1PT0NBQhNCzZ8+cnJzGjBljYmKyY8cOgb5F0ItB/QTCDOonv2Cge2RkZGpra/Py8qytrTEMe/DgwcSJE+vq6srLywcNGpScnPzo0SMNDY2SkpL09PQlS5awWKzKykp1dXUMw9ivun///tq1a2/fvm1jY9Pc3FxSUqKurl5YWPj06VMZGZny8nIGg6Grq1tSUiLgdwt6G6ifQJhB/eQrsqB/YIiauLi4oqKiefPmIYTodHpycrKGhsbYsWOpVCqVSt2xY0dQUFBycnJFRUWrr3VyciKRSFQq1dLSMiEhQUpKytbWVlFRESE0cODA6upqKpXa028JiBCon0CYQf3kLWii5zFJScmNGzfeu3fv3r17aWlpCxcuRAjJysoihJ4/f+7s7EwgEFatWqWqqtrytRiGEQgE/DGJRGKxWAghZWXlHgwfiDion0CYQf3kLUjwPMNkMhFCtra2ly5dYjAYVVVVgwcPLioqYh+QmJg4b948Dw+PmpqakpISvP7hr8JZW1v/888/LBarrKzs9evXI0eO7Pl3AUQV1E8gzKB+8gMkeN5QUVGpra3dvXv32LFjJ0+ebG5uPmzYsJ07d2pra7OPcXNzi4mJsbS0DAoKsrW13bVrF/tV+AFOTk5DhgwxNze3sbE5deqUhoaGgN4NEDVQP4Ewg/rJJwQMwwQdAwAAAAB4DO7gAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR4AAAAQQZDgAQAAABEECR6Avqi5udnT01NRUVFLS8vX15fzTykpKYT/0dDQ8PT0rKmpQQhFRkbihRkZGZxPU1JSBg4cSPi3yMhIhFBqaiqBQFBXV2exWO0Es3//fjU1NWNj47t37/LzTQOR1U59DgwMJBAIxcXFXC8pKSk5fPhwD8YoAJDgAeiLQkJCAgMD9+/f7+jouHHjxvfv33MdsGrVqrCwMHd390uXLu3Zs4ddTiAQ3r17hxBKSEggEAh44ZkzZ8LDwx0dHRFCwcHB4eHhI0eORAjdvHmTQCAUFRXFxcW1FUl0dPSePXsOHDhga2vr4uJSXl7O8zcLRN5/1mdONTU1kZGRzs7OnBVbJEGC72l+fn4qKirTpk0bPHjw0qVLBR0OEFK+vr56enqKiorz5s2j0WjBwcEEAsHR0VFaWtrOzs7Pz09VVXX06NFpaWkNDQ0rV65UVlYeMmRIUFAQ/nKualZdXT1z5kwZGRkVFZWtW7cihHJzc62srNatW7d582aEUFpaGlcAI0aMcHFxOXbsmIuLy99//82+BTcyMnr79i1CKD4+3tjYGC+0t7efM2eOkZERQsjBwWHOnDkaGhoIoZs3bzo7O8vLy9+6dautd/rgwQM9Pb3ly5dv3ryZwWDExMTw9pMEwkDg9RkhdPr0aQkJibi4uKSkpG3btn39+rUnPwHBwEAPio+PJxAIy5cvP3DgAEJoyZIlgo4ICKOoqCiE0KpVqwIDAxUUFNzc3C5fvowQWrx48b59+xBCQ4cOvXLliqys7Lp16/bv36+oqBgbG/t///d/RCLx1atXLavZqVOn5OTk7t69u3HjRoTQz58/2dc6d+4cQigtLY1d8vHjR4TQxYsX8afHjx9HCOXk5Ny5cwch5OHhMWTIEBaLpaCg4OHhgRD6+PEjfuS2bdsQQpWVlfjTpKQkhFBERMTChQtVVVWZTGarb3bu3LnDhw/HMKyqqgohdO7cOX58pECABFufL168iBC6ceOGmJhYSEgIu3zv3r1kMrlHP4geR+7B3xIARUdHE4lEX19faWnpM2fOCDocIKQePXokJSX1559/SkhIvHjxIioqavLkyQih3bt3y8nJ7dmzZ8mSJe7u7mfPns3NzU1LS6uqqrK3t8cwjMVixcbGYhjGVc28vb2HDRt2584d/Ku2uroav1BQUND69ev37dvHvhdvid0Oj7OysgoODv7w4UN9ff2wYcPY91gt3bhxQ1JS0t7enkAgXL16NS4ubvz48S0PY7cNEIlEhBCGYZ39uICQE4b67OnpqampuWDBgp596wIGTfQ9ik6nE4lEEolEIBDExcUFHQ4QUhiG4UPVEEIkEomdAiUkJPBCCoWC/pd65eTkxo8fT6fTGxoampubd+zY0bKanTlzZsaMGUZGRitWrGBfxdvb28vL68KFC7t3724nmA8fPkhKSmpqauJP9fT0qFTq2bNnf/vtNzyMtt5CWFgYnU6XlpZ2cnJCCIWFhbV6JJVKLSkpQQjRaDSEkJqaWoc/J9A7CEN9tra2zsrKunTpEv/frhCBBN+jxo0b19TUtG3btl27duXl5Qk6HCCk7Ozs6urqNm7cGBIS8s8//9jZ2bVz8KRJk96/f//48ePTp09LSUmlp6e3rGYpKSkyMjL6+vrR0dEIIQzD7t+/7+fnN3fuXHl5+cjIyKysrNTU1O3bt//69Qs/bXJy8p07d3x8fMLCwlauXInfXiOECASClZVVaGiolZVVO1G9e/cuOzt727Zt4eHh4eHhEyZMiIiIaHUs/ZQpU3Jzc/38/Pbt20ehUFq9ywe9mjDU50uXLtnZ2fn4+NTV1fXAWxYWguob6LO2bt2qqKhoY2NjaGgIffCgLceOHdPR0ZGXl3d2di4pKcH7LPPy8vDb3ICAAAzDRo0a5ejo2NjYuGrVKkVFRU1NzaCgIPzlXNUsKSlp6NChmpqav//+O0Lo1KlT+AO2gICA8PBwhFBsbCzeB49TV1dftmxZdXU1hmF4H/zTp09PnDiBELpx4wbeu9lqH/z69evFxcWrqqrwP129ehUhFBMT0+qb3b17t6qqav/+/W/dusXnzxUIhgDrM15Li4qK8KH1e/bswc/ZF/rgCRj0ePWgt2/fhoaGrl69WkpKyszMbPPmze23jgLQBVDNgCiB+txlMMiuR5mamtJotDFjxhCJxBkzZnh7ews6IiCChLaaffnyZdOmTVyFoaGhioqKAokH9ApCW5+FH9zBAwAAACIIBtkBAAAAIggSPAAAACCCIMEDAAAAIggSPAAAACCCIMEDAAAAIggSPAAAACCCIMEDAAAAIggSPAAAACCCIMEDAAAAIggSPAAAACCCIMEDAAAAIggSPAAAACCC+JXgmUwm+3FycvK1a9cyMjL4dC0AAAAAcOFXgldTU8Mf+Pn5zZgx49GjR1OmTLly5QqfLgcAAAAATvzaLpZKpZaWliKEtLS0EhIS1NXVS0tLx4wZ8+3bN35cDgAAAACc+N4Hr6Kioq6ujj8gkUj8vhwAAAAAEP8SvJiYmKmp6dSpU6urq4ODgzEM8/T0tLa25tPlAAAAAMCJzKfzFhQU0On07OzsrKwsNTU1DMP09fW9vb35dDkAAAAAcOJXHzxCqLGxMT4+vri4WFJSUltb29zcvFMvf/369b179/gUm6giNDYiDEMSEhiBIJAAdHR0vLy8BHJpvmpoaKDT6ZwlCQkJz58/F1Q8vRTUzx4D359dIHr1k18J/smTJ8uWLTM0NHz37t3QoUOJRGJ5eXlYWJixsXEHz7Bly5YvX76MGzeOH+GJKtKpU6i0lLlxI6JSe/7qVVVVISEhubm5PX9pfps6der79+85S2pqatTV1desWSOokHojqJ89Br4/u0D06ie/EryRkdG9e/cGDhyYk5Pj6ur65s2bT58+rVmzJi4uroNn2LJlC5VK3bx5Mz/Ca1V5eXlISAiZTF6+fLm4uHg7R2IYlpOTo6ur27ULYRj2/v37UaNGde3lnJqamn79+iUtLf3q1at+/frFTpsm29DQ7O1tMGqUuLg4g8FQUVGRk5NTUFCQlJSUlJRkMBh1dXWysrJEIrGqqorzVDIyMmJiYh2/dF1dHYPB0NLSYn9WeXl5VlZWfeQL1NLSkkql3r9/H39aXl5uYmJy7NixxYsXCzYwYRZkZFSekeGRnq5kaNjzVxfh+tnqD1BLS8tXr17hT6F+doTo1U9+9cE3NjZqamoihNTU1AoLCxFCpqam+ANhU1dXV1tbe/PmzdOnT2dlZTGZzNu3b1+9elVdXf3q1avV1dWysrIuLi5kMtnT01NGRkZWVlZZWfnYsWOvX78uLi6WlZVFCMnJybFYrAEDBiCE8vLyPn78aGpq2r9///v3748cOVJVVRW/1q1btyQkJH7+/Ll9+/bv379raGjQ6fS//vpr6tSpZmZmt27devXq1cCBA9euXYsfn5WVJS8vr6ioiD/9+PGjoaHhs2fPbty4QaFQsrKyioqKMjMz+/XrV1FRUV9fv09aGiFUVFSUePMmhmFEIpFGo9XU1FRWVtLp9ObmZjExMQkJibq6OhKJRKFQOD+H2trapqYmEonEYrHa+tknLS3NYDCampooFIqYmBiZTD5w4MCCBQv48y/TmygpKa1fv/7z58+CDgT0Rbdv325oaOAsmTRpkry8PPupkpLSqlWrYJZyX8OvBO/k5DRr1iwHB4fHjx+PHz8ew7ApU6bY29u3dbytrW1sbCxX4ejRo/l6B19XV3fu3Lng4ODs7GwSiTRgwIDr16//888/R44cWbBgQXR09NGjR798+UIgEG7fvu3k5BQcHEwmk/v375+VlYVh2Pz585OTkzEMIxAICCEMw3R0dI4fP7527Voajebs7Dxs2DAfH59x48ZNmjRp5cqV3759mz9/Pr7Gn62trZOTk4GBwaNHjyoqKrZv325ubv7t2zcDA4OgoKDr168fOXIEw7CdO3fW1taOHz/+27dv586dW7p0KYvFamhoyM7OHjx4cP/+/ZlMpouLS2ZmZlBQkJiY2CVj4/KMjN27dwvkF2gfp6en9+nTJ0FHAfoivH2Os4RMJhP+3ZGsqqqakpLSs3EBAeNXgj958mRoaGhCQsL06dOXLVuGEDp8+LCFhUVbx8fExHCVWFpasu9c+eTcuXO7d+9WV1efNGkSjUZ7+fIlQui3335btGiRra1tUFBQdXX16dOnJ02a5OLi4uHhsWLFig0bNiQlJcXExOjp6R05csTCwsLDwyMjI6O+vn7SpEknTpxwdXUdNGiQnZ1daGhoeHh4QEDA0aNHd+7cuXPnTkVFxWXLlo0ZM6ahocHZ2VlLSys+Pn7p0qVOTk6fP38+dOiQo6PjlStX9u7di/8qIhKJAwYMqKqqevLkSV1dnZmZmZKSkrq6ury8/MuXL9mtAkBIaGpq5ufnCzoKAFqnrKxcVlYm6ChAj+JXgicQCG5ubm5ubuwSCwsLJpMpJGvd/Pr16+3bt5GRkX/99ZeHhweDwaDRaPifyGSykZHR2LFj169fv2nTpnXr1iGEkpKSdu3atWbNGj09PWNj44ULF1ZWVi5dulRLS4vztE5OTn/88Ye48hxA9wAAIABJREFUuPixY8e2bduWn59vZ2e3ePHi4uLiPXv2NDQ0+Pn5SUlJ4QdHR0cPGjSoX79+BAJhxowZRkZGkyZNIpFIBw4c2LFjx/Xr14cMGaKqqkqlUiUkJFgs1v79+93d3Q0MDHr4sxJVLBYrJSWlqKioublZU1MTHwranRNqaWnl5eXxKjzQx9XV1SGEpKWlCwoK4uLijIyMhgwZ0p0TqqioQILva/iV4FsqLi7GJ8T3zOX+/vvvfv36zZ49G3/KYDAcHBxOnz49cODA6urqzZs3R0ZGksnkixcvIoTExcU1NDQ4X+7s7KysrLx+/Xr8qYSExIkTJzgPUFBQUFBQ4LoogUA4deoU3mhvZmZmZmaGEKJQKLq6upcvX+Y6eOzYsZxP2aEihCQlJT08PDj/SiQS9+7d26lPALQjJiZm9erV6urq+EiRgoKC3NzcwMDA7ow61tTULCwsZLFY3fyhAEBISMi6devIZPLevXv9/PysrKy2bNmyefNm9ugcLmlpaQUFBZwlVVVVXC2gcAffB/VcgqdSqUVFRT1woU+fPr148eLGjRsfP3588+YN/rM3NDQ0Ojrax8fnxo0b//zzT2RkJEIoJCTEsI2+6rlz586dO7cLVxeSJgrQvt9///3BgweczSG5ubnOzs5cQ5E7RUJCQkFBobi4GF+bGYAu27t3b3p6OoVC0dbWfvTo0ZgxY2g0moWFRVsJ/uzZs1wD6AoLCzkH2SGEVFRU8P1BQN/BxwTfsgm0Z7qN169fHxsbSyAQTE1Nx40bt2HDho0bN0ZERGzcuPHChQuFhYURERF79+6dPn360KFDeyAeIISYTCZXGlZVVW2necnDw4NrAF16erqpqSnXYTo6OtnZ2ZDgQTexWCwlJSUymSwtLa2iooIQkpGRIbS9+srZs2e5SvBpnJwleBM9e1Aw6Av4leD50QT6n5qamhBCCQkJMTEx4eHhGzZs2LBhw/79+/ER6X5+ftnZ2RoaGkpKSoGBgVRBLGUAhMTatWuHDx/u4OCgpaVFIBAKCgru3r3b1u0RQmjXrl0VFRWcJYsXL245CFRXVzc7O5snKxyAvmzu3LmWlpbi4uKTJ09esmSJm5tbVFRUO7OQOkJCQoJCoVRXV3Pd2QMRxq8Ez48m0PZVVFRMnz69oKBARkbGxsbGxsYGIXT//v2JEyfiI9QGDBgQGBgYERHh4+MD2b2P8/LymjlzZlRUFN5zqaOj8/DhQ21t7baO19fX5yqRlpYmk7n/99HT0/v16xfPowV9zbFjx6ZPn04ikcaMGfPs2bOoqChHR0eucTldgLfSQ4LvO/iV4DvbBLpo0aK0tDTOkvT09I6va9vc3Lx169a3b98qKSm5u7tz/unZs2fsx7Kysi9evIBN7QBCSFNTc+TIkewuJLypqZv09fWTk5O7fx4A2I2dEydOnDhxIkIIX0KjO/r161dSUoKvxwX6An4l+M42gR4+fLi4uJizZPHixR2/z37z5s3FixfnzZu3devW3377rZ0jIbsDxLcuJH19/fDwcB7FCMD/0/4spICAgKysLM6S3NxcCQkJrsPwBM+nCIEQ4leC72wTqJaWFteccmlp6Y5PNyoqKnJycrpx40Z3YgZ9B5+6kAwMDH78+NHt6ADg1v4sJM4FrXEkEqnl96eqqirXfRQQbXwcRa+trb1ixQr+nZ9TamqqiYlJz1wLiIDOdiF1kK6ublFRUUNDA9c6/wB0VqdmIc2fP5+r5Pbt2zIyMlyFampqcAffp/TcPHi+yszMnDRpkqCjAL1GZ7uQOohMJuvp6X3//n3w4ME8iRP0TXzqQlJVVf369SuPYgS9gIgk+ISEBFjoDXRcZ7uQzp8///PnT86SvLy8Vm/TjYyMMjIyIMGD7uBTF5Kamtrz58+7HR3oNUQhwWMYVlRUxLXWLADt69QoehkZGa4+TiKR2OqCISYmJmlpaXPmzOFxuKAv4VMXkrq6es8sJwqEhCgk+C1btigpKeH7sgPQEZ1tAm25532rfZwIIRMTk3v37vE8YNCndLYLqYPTjNXV1QsLC/kSMRBKwpLgd+zY8f37d86S79+/d3AUfWho6PDhw/kTFxBN/FuIyczM7NChQ908CejjOtuF1MFpxniCh9Vq+w5hSfAzZ87k2mozOTm5Izfl48ePr6mp+fPPP/kWGhBBfGoCRQgZGxtnZ2fT6XRJScnunw30WZ2ahdTBacYUCkVGRqasrAxf3x6IPGFJ8FZWVlwlJ0+eFBcXb+clDAbDyckpLi7ujz/+aLmSKADt4NMoeoSQmJiYkZFRSkoKrEgPhJCGhkZ+fj4k+D6iF29c/fr16wcPHigoKEybNk3QsYBexsvL6/Hjx/r6+jQarbS0FG8CXblyJU9OPmLEiKSkJJ6cCgDe0tLS4morBSJMWO7gu+Do0aMIoQULFuALNQPQKXJycsuXLycQCElJSV+/fq2treXVmS0sLF68eLFmzRpenRCA9tFotOrqas6ShoYGFovV8khtbe2cnJyeigsIWG9N8NnZ2Y8fP7569SrMRwJdcP78eV9f38+fP584ceLixYtjx47duXPnrl27li9f3urxnz9/5hrEVFVVpaCg0OrBVlZWhw8f5n3QALRh1apVXLsc5efni4mJtTxSW1s7Nze3p+ICAtYrE3xFRcWyZctUVFScnZ1bbqgAwH86dOjQ58+fxcTEzp07l5KSoqSkVFpaOnr06LYS/Pnz59PT0zlLCgsL20rwxsbGNTU1+fn5PNmhDoD/1HKLI0tLy1Y369LV1Y2KiuqRoIDg8XG7WBKJhD9OTk5OS0sbMWKEoaEhT07+7t27xMTEgIAAyO6ga6hUKoPBQAgpKCjgg43Z1bVVZ86c4SqxtLRsa6QSgUAYP3788+fPuXYuBkDg9PT0uPadAyKMXwleTU2ttLQUIeTn53fy5MkJEybs3Llz//79ixYtavX4c+fOZWdnc5a0ut0h7sePH66urq6urjwPG/QRhw4dGj169KxZswYNGmRjY2NnZ/fgwYN169bx6vyTJ09+/PgxJHggbCDB9yl8b6I/depUYmKiurp6aWnpmDFj2krwSkpKXINEyGRyWzdVKSkpsLIN6I4pU6bEx8dHRkYqKCgYGRlRqdSIiAgjIyNenX/q1Km7du3ibMcCQBhoaGiUl5fDOg19BN8TvIqKCr6iiIqKSjtfdvPmzeMquX37trS0dKsHJyUlLVu2jIdBgj5IQUFh8eLFfDq5tra2rq5uXFycjY0Nny4BAFtERATXSqCFhYWtfn8SiUQ9Pb2fP3+ampr2VHRAYPg1D15MTMzU1HTq1KnV1dXBwcEYhnl6elpbW3f/zEwmMy0tbdiwYd0/FQD84+LicuPGDUFHAfqE4uLiin9rbm5uampq9WADA4MfP370cIRAIPh1B19QUECn07Ozs7OystTU1DAM09fX9/b27v6ZCwsLlZWV21/kDgCBW7BgwdChQ319faWkpAQdCxBxXl5eXCUxMTHy8vKtHjxw4MBv377xPyggePy6gz969GhlZaWRkZG9vf3QoUOJROLOnTu70+vT3Nx8+/btgoKCtLQ0Xo3GB4B/tLS0rK2tr127JuhAAPgXQ0NDSPB9BL8S/P79++fNm+fv799WM1Fn1dfX79+/PzQ09M6dOxYWFjw5JwAdZGNjQ/i3+Pj4/9x509vb+8SJE83NzT0TJAAdYWRkxLWoAxBV/Erw0tLSz58/b2xsHDVqlK+vb/d/MMrJyU2bNq2pqenKlSseHh48CRKADoqNjcX+zcLCgms/upbGjRunra0dGBjYM0ECUdLU1NTQ0IAQqqqqevr06c+fP3l1ZhMTE67N44Go4uMoejKZ/Mcff3h6ep4/f37x4sU0Gs3e3r7lgiG4hw8fcm2BUFJSwjUKlEQi1dbWMpnMQYMG8S9sAHjI19d3ypQps2bN+s9fAwCwXb16dfXq1XJyct7e3ufPnx8wYMCHDx8OHjzY1uyh33///evXr5wl6enpbW363q9fPyKRWFhY2OU6yWAwYBRUr8D3aXKysrIbN27cuHFjUVHRvXv32josNTWVa2AnkUjkWmqRTCZ/+/YNvihBLzJkyJDVq1e7ublFR0e3ujY4AC35+PikpKTo6upaWlpu2rRp+fLlNBrNysqqrQTv6elZUlLCWbJ27Vo5Obm2zm9mZvbp06eOf5e+f/9eVVVVT08PIYRhmJ2d3YoVKxYsWND4/7V35wFNXOvfwE8W9l1BdnABQTbBDRRUcLd1K8XtWq21uC+tWm2rdWm1tnXDqletCm5VrFdFUcQdl2qLoogoIqggwcgiEJaYBJLM+8e8Nz8uCI0kIWH4fv5KJjNnnoSHPJmZM+dIJBhOVJdpqsDX7zBvZ2fX0EDfhJClS5fWX1K/wD9+/NjJyUldQQI0gxUrVty/f3/KlCkHDx5EjQdlcLncjh07EkKmTp06aNAgQkhDEx/Q/Pz86iyxsLBo5CC7a9euaWlpQ4cOJYRUVFSwWCwzM7OGVt66detPP/1kbW3t6uq6bt26vXv3pqSkPH/+PD8/f/369Tt37oyIiGjobIFCZWVlfn5+ly5dGl9NQS6Xy2SyRv5fysrKLC0t/3G/rZymrsF/++239RfKZDJV2uRwOPS8IKo0AtDM2Gz2H3/8IRaLBw0ahFFCQRn9+vUbPXr0rVu35s2b16FDh4yMjM8++6xPnz7qar9bt27btm1bsmRJ3759BwwYMGjQIIqi6q8mEAj++OOP5cuXDxo0yM/P76+//urateu2bds2bNgwePDgPXv2SCSScePGBQUFxcTELFq0qKSkpPbmpaWlZWVl2dnZL1682LFjx9ChQ2fNmtVIVA8ePCgvL79//354eHjbtm1DQ0MfPHigePWPP/5Ys2ZNQEDADz/8EBwc7OLi4u/vf/ny5fd641KptFVNptd8s8kVFhbSN8Q3uQUul0sIGTZsmPqCgtZLLpenpaUVFBRIpVJHR0f6Zk4N7cvQ0PDEiRObNm3q2bPnlClTZs2a5e7urqF9AQPs2bMnLi5OMaH7ixcvevTo0Xh1fC8jR448derUxo0b9fT0bGxsDAwMfv3113nz5tHfsVlZWQ8fPszNzU1ISLh58+b8+fOjoqIIIampqWKx+MKFCx999NHs2bOlUmlhYeHZs2c3b9781Vdf6enpicXiHTt20LsoLS0dOnRoTk6OXC7v0qXL27dvPTw8YmNj7969KxQKKysrBw4cKJFIPv30U7r39MWLF7/++msLC4vbt2+7uLhs2LBh9erVAQEBYWFh48aNk8lkX375JZvN7tWr148//hgaGjp48ODHjx9PnjyZoqhjx45dunQpICCgXbt23t7eVlZWqamp48aNk0qlQqEwIiLC3Nx8woQJx48f37VrV0lJybFjxwICAgghf/7558CBA7OyshISErKysgJyciwICQwMNGnfnhBSWFjYpk0bBwcHMzMz+pOpz9DQsPbt3xRFCQSC2isYGRkZGhoSQsRisUgkqrPaF198MWLECHX9WetrvgJvY2NTUFCgSgtcLldPT2/SpEnqCglaraSkpNmzZ9vb29MzuvL5fB6PFx0d3a9fPw3tkc1mL1myZNKkSVu2bOnXr5+1tXX//v39/f07d+7s7Oxsa2uL8XBAgc1mf/zxx4qndA1Q8QxobRYWFkePHk1OTjYyMrp+/bq1tfWUKVMePHgwbdq0tWvX3r17VyAQ+Pn5vX79+vjx42PGjKG3ooti79696adcLtfR0XHmzJkDBw58/Phx9+7d/fz8WCxWUlJSfHw8fdV148aNpaWlFy9ezMzM/Pvvv1++fDlmzBhvb+/Bgwf/+OOPcrn8+PHjkyZN0tfXj46O9vPzu3PnzqJFixYtWuTo6CiXyx8+fHjz5s1vvvmGELJixYrRo0d37dpV8S5EItGmTZtiYmL69+9vaGhI33TA4XAOHDiwfv16Ly+voqIimUyWlZWVk5Pzyy+/2NnZLVu27OnTp9OnT6+oqKAoytnZedasWfr6+t27d+/UqZOBgQGprj5//ny5vj6bzba1tS0pKSkoKKioqGjow1eUbTabzWazWSyWqalp7RXevn0rkUj09PQMDAz09PRYLBaHw+FyufTPgtpvRxNYqhxSN07FIyT6GvySJUsUSw4ePLh69Wo13i7CPDGenqVPn07LzGyjjbGA8vPze/fu3SLOgPn6+p46dapTp06KJTweLyIiIjk5+Z3r1++lfPfu3S5duvz1119N2LtcLk9JSbl161Z6enp2dnZ+fn5hYaFcLjc1NbWwsOBwOIruUXp6enW+L1q0kNu3Td6+/bNPH2Fz/ZpZsGDByJEj6cctKD/ra/wM6IABA5KSkuos7NOnz61bt5Rsf+vWrStWrJDL5Z07d547d263bt38/f3fN8gLFy5ERERYW1vn5uZ6eHjcv39f8bO1/vQ2FRUVpqam+/btW7RokVAozMrKcnJyoutfnUpRXV39/PnzRq7fnzlzJiAgoLq6WiqVXrx4ccGCBb1797527VrtS/hJSUn+/v5WVlZ///33jh07evbsGRAQEBISsnfv3i5dugQHBxMmfn9qqsCrfoS0dOlSDoej6Jcnl8ufPn1aVlbm7OysYmxCobChaWzeC0VRIpFILQde6gopffJkMY/ne+iQocqfkkQicXNze69NCgoKxo8f3yK+QL28vFJSUmr/7aqrq0NCQu7cufPO9dPS0ujpjxW2bdvm4OCg+AEql8uzs7NVSQaJRCIWi4VCYVVVleK/UiqVKk7rNa1NtXRyVlc71C+/kOJi1tdfk//tP9sEUqnUwcHhH1fz8vKysrKiH7eg/KxPLpcXFxfb2toquX79789/zE+JRFJaWtqmTZvG/9bKfFkVFRXZ2Ng03gNO8f0pEomKiopcXV0bb1P5kCorKw0NDZvQoZV535+aKvDve4RU386dOzdu3Kh4WllZ+ebNm4YuhLwXqVTK4XBU734pl8vlcrlOheQjkxlS1GMut+k1oVZITk5O7/tP4unpmZCQoPLONW7Hjh3btm0bPXo0fdDA5/Pj4+PnzZs3c+ZMJVtAfjYB8lN5Kp4BRX42AQPzk9KMLl26CIXC2kskEknPnj2b3GBiYuKwYcNUjouiKKpHjx53795VvZ24uLgxY8ao3g5FUT4+Punp6aq3c+TIkYkTJ6reDkVRnTp1evbsmVqa0k15eXm//fbbqlWrVq1atWvXrry8PFVaQ34qA/mppKtXr3p4eISGhk6aNGnSpElhYWFubm7Xr19vcoPIT2UwLz811clu3rx53bt3r3+EpKHdAbwvZ2fnGTNmaDsKgHdYsGBBQkKCKmdAAYjmetHPmTNn5MiRiYmJfD6fEOLi4nLu3DnVL58DADCeTCarM8ycra0tpbEO0cBUGrxNDkdIAABNgDOgoBbNdx88AAAoA2dAQS1aTIHncrkcDkctTdHjDKjeDkICBR385BFSi6beM6A6+MkjpGagwYFu1EsmkxUXF9vZ2aneFJ/Pt7e3V/2eCqlUWlJSovydqY149eqVg4OD6iFVV1cLBIJ27dqpJSR6DANQBvJTGchPbUF+KoN5+dliCjwAAAAoT1OzawAAAIAWocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADBQyyjwO3bs8PLy8vPzS0pKeq8N16xZ4+Xl5ezs/NNPPzXUVJMbb5oLFy707NnT1dV19+7dWg9JKBTu2rVL8VSZSJr542oRkJ8agvxUC+SnhrSA/NTybHZKyMvL8/T0FAqF2dnZHh4eMplMyQ0vX77crVs3kUhUXFzs4uJy7969+k01ufGmKSsrc3d3Ly4uLi8v9/DwKC8v12JIDx8+nDZt2oQJE+inykTSzB9Xi4D81FAwyE+1QH5qKJgWkZ8t4Ag+MTFx9OjRxsbGbm5udnZ2aWlpSm5YUlIyc+ZMQ0NDa2vrkJAQHo9Xv6kmN940p0+fHjVqlLW1tbm5eXp6upmZmRZDio2NFQgEiqfKRNLMH1eLgPzUUDDIT7VAfmoomBaRny2gwL9+/drJyYl+7OTkVFBQoOSG48aNowdzTk9Pv337dmhoaP2mmtx40/B4vNzcXF9fXxcXl40bN7JYLC2GtG7dujlz5iieKhNJM39cLQLyU0PBID/VAvmpoWBaRH5qfzT8fySXy2sPMiyVSpXflqKoLVu27Ny58+TJkxYWFvWbUqXxJhCJRNnZ2Tdu3KAoqkePHkOGDNF6SArKRKKt2HQZ8lOjISkgP5sG+anRkBR0Mz9bQIF3cHDIy8ujH/P5fAcHByU3lMlkY8eOtbS0TElJMTc3f2dTTW68aWxsbIYOHWplZUUI6du3b2ZmptZDUlAmEm3FpsuQnxoNSQH52TTIT42GpKCj+anpi/yqe/nypY+Pj0Qi4fF4bm5uyndMOHLkyPjx4xtvqsmNN82TJ0+8vb1LSkqKiopcXFwyMjK0G9Lly5cVnUSUiaSZP64WAfmpuXiQn6pDfmouHt3PzxZwBO/i4jJ37tyQkBBCyN69e9lsZfsN3Lx5MzEx0d7enn66Z8+eESNG1GmqyY03jaen54wZMwIDAymKWrZsWZcuXQgh2g1Jof5+lVnSPLHpMuSnRkNSQH42DfJToyEp6GZ+YrpYAAAABsIvXAAAAAZCgQcAAGAgFHgAAAAGQoEHAABgIBR4AAAABkKBBwAAYCAUeAAAAAZCgQcAAGAgFHgAAAAGQoEHAABgIBR4AAAABkKBBwAAYCAUeAAAAAZCgQcAAGAgFHgAAAAGQoEHAABgIBR4AAAABkKBV5WZmZlQKKyoqNizZ8/7bqvY6vTp0/Pnz9dAdNDaIT9BlyE/NQoFXj0qKysPHjzY+Do1NTUNbTV06NA1a9ZoKjho9ZCfoMuQnxqCAq8eS5cuffTo0Y8//kgIWb16tZubm7+///79+wkhFy5cmDVr1uDBg8+cORMZGenu7u7q6rphw4baW125cmXFihUURX311VdeXl7e3t5HjhwhhFy5ciU8PDw4ONjLy2vZsmVafYvQgiE/QZchPzWFAtWYmppWVVXl5+eHhIRQFJWQkDBw4EChUFhaWtq5c+fU1NTz5887ODgUFRVlZmZOnTpVLpcLBAJ7e3uKohRbnT17dt68eSdPngwNDZVKpUVFRfb29q9fv758+bKpqWlpaWl1dbWrq2tRUZGW3y20NMhP0GXIT43iavsHBtPcvHmzoKBg/PjxhBCRSJSamurg4NC3b18bGxsbG5tly5bFxMSkpqaWlZW9c9vw8HAOh2NjYxMYGHj37l1jY+OwsDArKytCiLu7e0VFhY2NTXO/JWAQ5CfoMuSneuEUvZoZGRktXrz4zJkzZ86cycjI+OSTTwghZmZmhJCrV69GRESwWKxZs2bZ2trW35aiKBaLRT/mcDhyuZwQ0rZt22YMHxgO+Qm6DPmpXijwaiOTyQghYWFh+/btq66uLi8v9/HxKSgoUKyQkpIyfvz4adOmVVZWFhUV0flHb0ULCQmJi4uTy+UlJSW3bt3q2bNn878LYCrkJ+gy5KcmoMCrh7W1dVVV1cqVK/v27Tt48GA/P79u3botX77c2dlZsc6kSZOSkpICAwNjYmLCwsK+++47xVb0CuHh4V27dvXz8wsNDd20aZODg4OW3g0wDfITdBnyU0NYFEVpOwYAAABQMxzBAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKvK6QSqWRkZFWVlZOTk6bN2+u/VJ0dDSLxSosLFRmZQCNQu6B7igqKlq3bp22o9BdKPC64tChQ9HR0T/88MOYMWMWL16cnJysrpUB1Ai5B7qgsrLy9OnTERERq1at0nYsugsFXlmbN29u3769lZXV+PHj37x5c+DAARaLNWbMGBMTkyFDhkRFRdna2vbp0ycjI0MsFs+cObNt27Zdu3aNiYmhN4+KirK2tv7ggw98fHw+++yzioqKkSNHmpqaWltbf/3114QQHo/Xu3fv+fPnL1myhBCSkZFRP4atW7caGBjcvHlTmZWhddJuotbZvPnfPjBJ/fRTuHfv3jfffPPkyRNtxdYyUKCExMREQsisWbOio6MtLS0nTZq0f/9+Qsinn376/fffE0L8/f0PHjxoZmY2f/78H374wcrK6tq1a7/++iubzf7zzz/v3LnDYrGmT5++Zs0aQsjUqVM3bdpkbm4eHx+/ePFiQsiLFy8U+9q5cychJCMjQ7Fk7969hJCjR4/q6ekdOnSodmD1V4bWTLuJWn9zbXwGwByNpB9t9erVXC5XK7G1CCjwSvniiy+MjY3FYjFFUVOmTLGxsaG/N58/f15cXEwI2bJlC0VRgYGBY8aMGThwIJvNNjQ0NDAwIISsXbt2zZo1HA6nsrJSLpe3a9eO/uJLSkr64osvOnfuTAh58OABvaPo6Gh9ff3vv/++9t7pAm9qatq+fXuZTKZY/s6VoTXTbqK+c3MAVbwz/RRQ4BuHU/RKoSiKxWKxWCxCCIfDkcvl9HIDAwN6oaGhISGEfmxubt6/f3+RSCQWi6VS6bJly0QiEZvN5nA4LBZLX1+fELJt27YRI0Z4enrOmDFDsZdFixbNmTNnz549K1eurB9DSEhIbm7uvn37lFkZWiftJmr9zQFU8c70A+WhwCtlyJAhQqFw8eLFhw4diouLGzJkSCNCMSSzAAAgAElEQVQrDxo0KDk5+eLFi1u3bjU2Ns7MzOzXr19NTc0333zz3Xff5efnE0LS0tJMTU07dux44cIFQghFUWfPno2Kiho3bpyFhcXp06dzc3MfPXr07bff5uTk0M3u27dvyJAhK1asEAqF9VfW/GcALYB2E7X+5gCqqJ9+db4V4R9o9fxBS/LLL7+4uLhYWFhEREQUFRXRZz7z8/PfvHlDCNm1axdFUUFBQWPGjJFIJLNmzbKysnJ0dIyJiaE3//rrr62srEJDQz08PKZOnXrv3j1/f39HR8cFCxYQQjZt2kQ/UNi1a9fx48cJIdeuXaNP0RcUFNA9lletWlV/Za1+NqBDtJio9TfX5gcBLV/99KudbBRO0f8TFkVRmvnlAP/nr7/+OnLkyOzZs42NjX19fZcsWYLz6qCDVExU5DmATuFqO4BWwdvb+82bN8HBwWw2e8SIEYsWLdJ2RADvoGKiIs8BdAqO4AEAABgInewAAAAYCAUeAACAgVDgAQAAGAgFHgAAgIFQ4AEAABgIBR4AAICBUOABAAAYCAUeAACAgVDgAQAAGAgFHgAAgIFQ4AEAABgIBR4AAICBUOABAAAYCAUeAACAgVDgAQAAGAgFHgAAgIFQ4AEAABgIBR4AAICBuNoOoEG3bt06c+aMtqNoYVgSCaEoYmBAsVhaCcDFxWXOnDla2XUzQ342AfKz2SA/m4B5+cmiKEqNzanR0qVLHz9+3K9fP20H0pJwNm0ixcWyxYuJjU3z7728vPzQoUM8Hq/5d938kJ9NgPxsNsjPJmBgflK6oX///nUCY7FYQUFBaml83bp1hw8fVktTOi7aw2MDISWZmVrZO4/Hc3Jy0squNa1+fhJC1JWf7+XUqVNdu3atqKho/l2rDvmpJKlUqnh8//7933//PfM9P7QlS5asX7++9pI//vhj7dq16omPoZiXn7pyDf7atWt1IuvZs2fbtm3V0jiPx3v06JFamoLWqX5+9urVS135+V5ycnLS0tJayWFoq2VnZ0c/iIqKGjFixPnz54cOHXrw4EFV2iwoKMjMzFRHdNBi6O41eDWqqanh8/najgJAJUKhcOHChfR39Js3b7QdDjSHTZs2paSk2NvbFxcXBwcHT5kyRZXWysrK1BUYtAitpcC/evVK21EAqKSkpGTPnj304+LiYu0GA83D2tra3t6efsDhcFRpiqIoFPjWRldO0WtUTU1NnVOa1dXV9+/f11Y8oHUymUzxODU19fDhw0+fPtViPMqQSqWKxyjwzKanp+ft7T18+PCKiooDBw5QFBUZGRkSEqJKmxRFlZaWqitCaBE0eAQvl8vT0tIKCgqkUqmjo6O/vz+b3eDvifT09MLCwtpLKioqLC0t1RJJTU1Nfn5+7SWpqanTpk17/PixWtqHFsfOzo6ukVFRURs3bhwwYMDy5ct/+OGHhk6BajQ/lVRTU0M/GDBgQFFRUXPuGpoZn88XiUQvX77Mzc21s7OjKKpjx46LFi1SsVkU+NZGUwU+KSlp9uzZ9vb2jo6OhBA+n8/j8aKjoxu6bWP37t11OoDw+XwLCwu1BCOVSoVCYUlJiaJXlFAozMvLU0vj0KIpeY1To/mpJPoI3svLa/To0bp/vgFUZGRk5Onp6enpST9dvnx57dNOTSCXy0tLSymKYmnpJm9ofpoq8AsWLEhISOjUqZNiCY/Hi4iISE5Ofuf627Ztq7MkMDDQ2tpaLcFUV1cTQvLy8hQFns/nV1VVnT9/PiwszMDA4H0bFIvFLBarCRuCrlHyGqdG81MZ27dvFwgEhBBLS0s7O7ubN282265BFxQWFtKH8u98defOnS9fvqy9JCEhISAgoM5qUqm0srLS3NxcU1GCjtHUNXiZTEZ/byrY2to2lJ1KunLlir29fVRU1DtfvXTp0scffyyXy+u/VF1dbWxsXPsfgD5jP3z48IsXLyrOfCpv7dq169evf9+tQHdo4hqn5ojF4jVr1mzfvt3U1LRDhw52dnaKU/R79+797bff6m/y+++/X7p06dChQ80bKWiKjY1NQUFBQ6+2adPG6n9VVlbW6VlMf/2WlJRoPFbQGZo6gp83b1737t1Hjx7t5OTEYrH4fH58fPy8efNUaZPP5xcUFNy6dWvhwoX1X71169bJkye3b9/++vVrV1fXWbNmKV6qrq52d3fPyclRLJFIJFwuVyqVxsTE7NixIzEx8b0iuXbtWu2TE9DiaOgapyZkZWUdPHiQruhBQUG///57VlaW4rv+2bNnJSUlM2fOrL1JWVnZlClT1q1bl5GRMXnyZC0EDSq7d+9efn5+aGgofSWIzWbfvXt3xIgR71x5/PjxdZacPHnSxMSk9hJFge/QoYNmQgado6kj+Dlz5ly8eLFjx45v3rwpLi52cXE5d+5cna+h90WfoqxdpxVOnDjx/fffE0KuXbtWVlb25MmT2q9WV1d7enrW3rC6unr27NmEkIsXLz558uR9Ty2Ul5e/MwxoQehrnMOGDaO7fy5fvlxfX19bwWRlZR0/fvydL6Wnp+/fv59+TF8VsrOze/36Nb3k2rVriscKdErfu3ev/kvQIvz000/h4eHHjh3z9/dPTU2lF06fPl31ljGCQquiwV70zs7OM2bMqL1EJpM1dJnz8OHDdTq68/l8IyMjxVORSHTu3LlOnTo9f/6cEPLs2bMOHTooWqPPO/n6+j5//tzS0rJOU3SBv3v3rmKJRCJxdXUNCwtLSkp6+fJldHR0ZGSkku+rqqqqtLQU/VEZpvFrnNHR0dnZ2bWX8Hg8Q0NDde396tWrZ86ciYiIqP9SRUXF69ev9fT0ZDKZmZkZIcTc3FwulwuFQrlcnpWVVf8a06lTpwghp0+f9vHxyc/Pr6mpwUFby/Lvf/87LS2tbdu26enpH3/88b179+g/vSroy5co8K1K890HX1hYyOU2+HtCLBaX/S+ZTFbn2zYoKCg+Pp7D4RQXF0+YMGHnzp21NyeEXLp06cWLFxKJ5NmzZ7U3lEgk3t7eioVyubyqqsrAwGDr1q30ks2bNyvfQ3XTpk18Pr+oqOjt27dKbgK6r/FrnIaGhnWucXI4HDX2Ri4tLb169eo7A3j79q1cLu/cuXNsbKyiX4u9vT2fz9++fXtZWVmdw3SxWEyf66qpqZFIJPv37/fz84uPj1dXqNAMzMzM6BPsvr6+s2fP/vLLL9XVMgp8q9J8I9k1/gX6+eef11mSlJRU+0erkZHRqlWrCCFubm5Hjhy5d++et7e34lWRSBQaGmpra2tiYpKbm5uTkyOVSrlc7smTJ/38/Kqqqnx9ffPy8uiF0dHRu3fv3rt3b6dOndhsdt++fa9fv56VldWlSxdl3khpaSmXy+3cufORI0ccHR2HDx/+fh8E6IaamhqZTGZoaFheXn737t2OHTt27NixoZUnTZpUZ8nJkydNTU3VFUx1dbVYLI6JiZk8ebKzs7NieWJiIv0z1NjYeNy4cePGjaOXOzg48Pn8srIyAwODkpISOrEJIcnJyTNmzHBzc4uIiDh+/DjdbFVVVZ0u1ip68uRJRUVFYGCgGtuE2iZOnBgcHDxz5swZM2Z8+eWX4eHhEyZMUPGIgj5eQoFvVZrvCJ7NZtva2qrejr+//969ex0cHI4dO3b27Nm1a9du2bLll19+oTsTde7c+c6dO9XV1bm5ubdu3fr444/Pnj1bVVVF3w2Vm5tLCCkrK6MoytjY2MjIyMnJacKECQMGDMjKylIyAIFAYGtr6+HhsW3bNsXFUWhZfv/99zZt2nTq1GnTpk29evWKiooKDg6Ojo7WVjwSiYQQsmLFii1btihGrKupqfnwww/pzGzTpk3t9R0dHV+9elVWVmZubt6uXTvFT+eioqKHDx8+fvyYnv6upqamsrKSEFJVVaXGaM+ePXvgwAE1Ngh1rFy5cv369fRISiwW6/jx46NHj/7Xv/6lSpsURVlaWmIMxFal5Q1V6+Pj8+jRowEDBvTs2TMyMnLlypVfffVVWVkZfUDv6ekplUptbGwyMzPpgZczMzMrKyvNzMy6dOny3XffxcbG0ufz6TNgnp6eBgYGAQEB/zjP0vbt2+nznElJSStXruzevfvDhw/pDgHQ4qxYsSItLS0vLy82Nvarr75KSEhIT0//+eefNbfHFy9ebNq0iRASFxcXFxf34sWLCxcuKF6l7+SUy+U7duzo1q0bvfCXX36hhyUJDAykT18pODo6ZmRkHDp06LPPPnN0dFR0OqGPz7Kzsz/88ENCiEgkunPnDiHk2rVranwvYrG4oqJCjQ1CfQMHDlScsOFwOBMnTqx9UbJpbG1tMQZiq6KpU/S7du26fv16/eWxsbEqtuzj40MI8ff3HzlyJH1zCH35nB69ISAgYMCAAd7e3n///XdAQICent6DBw8oijIwMOjSpcvWrVsvXLhAHwzRv46/+eabDh06UBR148aNxvebmJjYrl27YcOGFRQUfPTRR/T8s+Xl5Sq+HdAKLpdLn5CfOnXqoEGDyH/zQUPOnj07evRoHx+fxYsX7969+/r167/++uvWrVuHDh1Kr1BdXR0ZGbl9+3axWPz48ePi4mJra+tVq1bZ2Nj06NFj6NChvXv3rt2gk5PTuXPnJBJJnz59nj9/zuPxgoKCCCH0jKK2trYdOnSYOHFibGws/YWu6Imtoj///FNfX18sFiPzWxyKotq1a4cC36poqsBPnjw5OTm5oqJizpw5yqw/f/78OsfQmZmZ7+zE1Ldv38TExGHDhlVUVOjr63t4eGRmZhoaGjo5ORFCZs2aNX369CtXrqxZs6Z9+/b9+/e/ceMGXfu9vb1ramoEAoFAIHB3d6dvZA8LC6NfWrt2bWFhYSMXESorK3Nzc1+8eGFqampjY9O7d+9t27atXbv2fT4V0BX9+vUbPXr00qVL6bEZMjIyfvrppz59+jS0vvL5+U65ublyuZzH45WWlhYVFYlEol9//TU7O3v37t30nSZisbhXr17Tp0/fs2ePXC6fOHHi9OnT5XJ5aGjoypUrHRwc6jTo7Ox85cqVnj17BgUFXbt2LSUlZdiwYWZmZs+fP3d2dqbvnP72228Vv6dLS0sTEhLow3qFJ0+eJCQkLF68+O+//67zA6Ih8fHxhoaGFRUV9Omxe/fuOTg41BnSCnQTRVF2dnaYZKtV0VSBNzExWbhw4dGjRwcOHKjM+tOnT6/z03LevHnvHOuby+UOGzaMEGJubl5ZWXn//v3hw4e/fPmS7pHHYrG4XG7Pnj0fPXqUlZXVqVOn9PR0Y2Nj8t9Df3p8m40bN9b+0vT29n758uXp06fr3NdXW1VVVUZGxu3bt+lfEoaGhlOmTFm+fLky7w50zZ49e+Li4hTjHr548aJHjx61B0eqY+bMmXW6iM6fP1/5sejpE9oCgeDrr7++f/++np4ePdHRkiVLFAXeyMho165dV69e9fPzO3HihEgk0tfXX79+ffv27es36ObmJpPJJk6caGtr6+vr+/nnnwsEgg4dOpSUlMyePfvvv/8mhPj6+pqYmFhYWHz55ZdLly5NTk7+8MMPc3NzT548SQ/p8+DBg7Nnz44YMWLMmDH0VDoXLlwYPHhwI5NCicXihISEjIwMNzc3QsiWLVuCgoLmzp2r5OcAGvLLL7+8ePGi9pLc3Nz6dy05ODicOnUqMzNTMcQ9MJsGe9H7+fn5+fkpv3KdJRYWFnp6eo1vpa+v36tXrx07dtQZXblNmzbm5uZ///33xIkTc3Jy6J8OPj4+HA7n8OHDO3furHOsZmpq6u7unpaW1si+qqqqOnbsePPmTfqrjRBiYGBA942CFofNZn/88ceKpw0NEKbg4+ND/0BUMDc3/8f8VBCJRMuXL4+Li6MPqadNm7Z7924rKyt6QIU2bdqIxWL6rnp6lLoTJ07cvn3b1tb2ndWdEOLn57d79+4JEyYQQughxy9evEj3IQ0NDVV07w8KCqqsrFyyZMkff/xB98G+ffv24sWLp0+fbmZm9urVq+vXr48bN07RPftf//rX/fv3XV1d6+yuurqaHgVILBbTe3n9+jV9TqL2YBWgLX5+flZWVrWXXLp0qc6fhqIoU1PTmTNnRkREPHjwoJGbloExWl4nuzrYbPbEiRPrLx84cODNmzfbtWsXEBBAX141MjIKDg7u2LFjUlJS/WlCtm7d2vjJK6FQGBIScvXqVcU/kr6+fk1NjYoD7ENr8PLlSwsLiyFDhgiFQhaLFRUVZW9v/8UXX1hZWW3evJkQIhKJFF/HdnZ2vr6+tra29JmqhtBFmhASEBAQHh5O110ulztixIjVq1fT6/z222/nzp0jhIwdO5au4nSf/PDw8Bs3bpw9e5YQ8uzZM7rbqVAorKysrH8va0VFhaura2Rk5MOHD8ViMd3O27dvjx07dv369dTUVD6fr7ZPCppk+PDhM/6XjY1NnYGY6A6b27ZtMzExGTx48I0bN+7du6etgKF5tPgC35Dhw4cbGxsPGzZs7NixH3zwAb3w+vXrPXr0eOf6vXv3fvToUUMTzxQWFpaXl4eGhubl5SkGBWOxWPr6+hgNFBonlUoPHz5sYGAwatQoDoczY8YMIyOjuLi47777rn///nFxcfQcX7Xvqr9z586DBw9++eUXJXcRGxvLZrO5XG6d4cc7depEz6Bob29/+vTpp0+fJiQkEEIuX77cv3//69evf/TRRyKRSCqV1tTUHDt2rKampv48dQkJCQUFBdHR0cHBwYqL+gEBAXR4KSkp+/btoxfu27ePx+M15TMCzVNMFHv8+HFnZ+fQ0NBevXqZmpquXr06ODh48uTJP/zww6VLl7Zt2/bs2bPy8nK648jJkycfPnx4/fp1kUhEt1NdXS0QCBQnL4VCIT0Zt2JHUqm08XHD6Ok9i4uLpVKpRCJ59OhRRkYG/d2bn5/P5/Prd+EUCAS3bt2Sy+WKMBT3cdCt1W6coig6vDojB5w7d+7+/ft3796l7x2tTSKRMPJ0LGPP0gQHB3fv3t3ExKR79+7du3f/x/XNzMzat2+fnp6uuEmJEFJSUkLfLr9t2zahUDho0CAOh1N7ZBtHR0cej1e/DxQAIaSkpGTXrl137tyRy+UGBgZhYWGrVq36+uuvCSG9evUihHTu3PnUqVNTpky5c+dO7Sv6hoaGdnZ2yu9IX1/fycmpb9++Hh4e71zBzMyMz+d37dpVIpGwWCzFaad58+bFx8fLZLKysjL6W5W+GK9AUdSff/5JP1bcTK+vr+/v768YBELxNbpz504ul4vpbXSTosA7OzsfPHjw+++/t7a2Pn369LFjx549e9axY8cjR47Q534WLFjA5XJNTU3Nzc2LioroEzxmZmYeHh5mZmYZGRlVVVVSqdTe3t7d3Z2+6UMul4eFhbFYrJqamrKysuzsbHd3d0LIq1evLCwsuFwui8USiUTGxsbW1tapqakGBgbV1dVsNpvFYlVUVMjlcrqbZ0pKCovF0tPTMzU1dXBwsLOzo68KlZWVCQQCT0/PzMzM4OBgeomFhUWbNm1evnzp7u5eU1NTXl5eVFSkp6dnZGRUUlLi6uqan5/v7Oxsa2ubk5NjYWHB4/HoXKXvmnZ2dn7y5Im7u3t5efmDBw/EYvFimcySkFGjRhEbGw6Ho6+vLxQKpVKpkZGRYqIKiqKEQmFNTQ2LxTIzM6PPyf3jQMJsNpuiKMX/HZvNpjsALV68uPETdSrSlQIfFhZW/1bd2rX2fTk6Ov7jnW919OnT5/bt27V3umzZsvbt23/22Wfx8fHGxsbOzs6bN2/u3LmzYgVXV1f1DiECuik0NLT+bZ//mJ8vXrzYsmWLXC739PQcPHgwIWTFihW1V1i9evWDBw/i4uKqq6u9vLxUibBTp04DBgyYNm3aO191cXEh/x1OZ9euXTNnzjQ0NFywYEFgYKCFhUVpaekff/xBH9bQZX7v3r1Dhw51dna+cOHCjh076rT2r3/9q3v37jExMd27d793755YLL59+3afPn1KSkpwf3xLQZ+J/OSTTz755BOJRGJgYEAfecvl8ry8vIyMDGtr6/Ly8gEDBrx58yYnJ6e4uNjIyOjZs2fz588XCoUGBgbXrl3z9vZeuHChSCR6/vx5cXExXZhLS0tHjhzp5eVlZWVVXl7+6tUrc3Pz58+fh4aGPn/+fPPmzdu2bWvfvn1paSmbzT5y5MgHH3zQtm3bzMzMioqKlStXnjt3buLEicnJyRKJJDs7u2fPngYGBvSgkywWy8PD49y5c2/fvv3oo4+SkpIsLS0NDQ1LSkronwIdO3Z8+fIlPf+Cu7v7ixcv6JtWJkyYYGlp+eDBg/DwcDMzs8LCwvj4eCsrKycnJ/ruQS8vL2NjY8sDB0hx8YYNG2Rt2sjlcvqsGJvNFolEcrmc/nlEn7jV19enKKqystLExIQe+5wQwuFwGjp1QW+uuO9GMS2Lr6+vZv/MlK7q1avXhx9+2Jx7PHjw4NixY2svGTt2rJeXF/2fsGPHjvqbjBo16vTp080V4D+L9vDYQEhJZqZW9s7j8eh/mNZAmfykf7MaGBjQU86/06+//koIsbW1VTGe27dvFxUVNfSqYoDS8ePHC4XCKVOmTJgwgX7J0dGRfon+hTFu3LgzZ86Ym5ufOnWKoih6RnnFrE50n/+DBw9evXqVw+H8+uuvbDa7R48eenp6hYWF9vb2ERER+/fvbygM5GezqZ+fq1atWrVqlZbCaRmYl5+6cgSvC0JDQxcvXkxRlFAojI6OtrGxSU5OzsvLo199508tQ0ND+vwVQH30+UA/P78pU6Y0tA59JnPv3r0q7qvxG9mtrKyCg4PFYvHWrVuNjY1jYmIULyk692VkZERERMTFxSUlJVVUVNCH8vRh/Zw5czIyMnJzczdu3Hjq1CkzM7OwsLDt27d369bNxMQkJSWFEPLpp5++fv367NmzUqn0008/VfHtgCaocXokaBFQ4P+Ps7Nz27ZtExMTZTIZPX1Tx44d+/btm5OT8+23376zdx4KPDSkrKxs9uzZhoaGjZ/JDwwMHDt2bCNj7KgFm82Oj4/ncrn0DaW1Z22mZ8ajTy2uXLny/Pnz9HDl9Ml2+tzjzJkzvb296ZvlIiIi6Jta6WEDLCws6B8B58+fJ4SIxWL6LnzQNRRFNTLCATAS/t7/Y+TIkSNHjlRMLPvBBx9ER0fPmDFjzpw575z829DQUPlZaqD1kMvlixYtevny5fr16xU3rb1TmzZtjh07VmcuGU2gB4eovzwqKmrQoEFGRkbJycm+vr50r/sPPviAHqvu7du333zzDT3RA93P6N///nftaffS0tIUo/zSfamKi4vpbUGnUP/tZAeth64U+BcvXtz7X3T3xWYOIzw8XC6XL1myZOTIkYcOHZo6daq7u3udjlG1GRoa7tixo6SkRC6Xb9++/a+//qp/19zixYt/++03DQcOmvX48ePL/6uioqKhmyoJIXfu3Nm/f7+Pj8/06dPfqz988wsODnZ2dra0tKQ79tPd8eixIO/fv//zzz/b2Ng0snmbNm0SExM///xzFou1YcMGY2NjDw+PBw8eNFP0ANAwXTlF/+OPP9YZSC43N7fOTb3NICgoaNeuXbNmzfL39//kk0/+cX0jI6Py8nJHR8ctW7b89ttv9Hgmjx49qn3L07Nnz3g83syZMw8cODBlyhQej+fo6Fj7HCnovp07dz59+rT2Ej6f39BQtW/fvl2/fj2Xy23fvv07T/zoms8++0wxUPT+/fu7dOni6+t7+/Zt+t4heubZRgQFBQUFBZmZmQ0YMIAeFPLEiRP0LA/QPJYvX56dnV17ybNnz+p8yeAIvhXSlQJffyruwMDAxg8dNGTatGk8Hm/lypXKrGxkZCSXyyUSycGDB0tKSiorKysrKyMjI//zn//k5OScPXt2/vz5VVVVQqGwqqpq6tSpjx492rhx47Rp0+j3KxKJ5s2b99lnn4WEhNANXrlyxdDQMDg4WIPvEN7f9u3b6ywJDAysPx4iLSEhgR6VVjHCko7r06fP6dOn6ccdO3Z89epVTk7O69evZ86c2bt3byUHnI6KipJIJGvWrCkqKjpx4sSGDRvat28/duxYTQYO/9/w4cP9/f1rL0lNTa1zgIQC3wrpSoHXHXp6esrPEacYfeyvv/5SjO3M5/OFQmFcXNzBgwfnz58vEolqamrojksnTpwg/x0ulBDy9OnTmJiYAwcOpKend+nShRCyaNGiUaNGNVLgy8vLHz16hF8Auow+GnZycnrnNW/dZ21tXVVVlZmZKZVK58+fr/yQ+wYGBtOnTxeJRN9//31GRkZZWRkKfPNQHCEobNy40cDAoPYSFPhWSFeuwbdQs2bNGjlypIWFhbW1tZubm6Ojo6+vb05OzqhRo5YuXZqenr5ly5YnT56UlJRERkYSQnJycrhcrqLAP3nyhMvlymSy+Ph4qVR68+bNx48fp6SkzJgxg55vnlZYWKjotfTFF19MmDABo3/rJoFAkJKSIhAIxo0bp+QErLqJvrgQGBj4zokeGmdkZERRVFlZGd2vHnQECnwrpMEjeLlcnpaWVlBQIJVKHR0d/f39mXeThomJyYgRI9LS0v7zn/+UlZWZm5u/fft28uTJ169fd3d3z8/PX7JkCZfLzcnJycnJoYcnPHTo0OTJkxMTE83NzU+cODFhwgRnZ+dDhw4VFhZGRUURQs6fP89isfh8flxcXFRU1OzZs9etW2dnZxcUFHT79u3Lly/z+fzt27evW7dO2+++ZdNEfp47d27q1KlhYWETJ05s6d0szMzMBg0a1LRtjYyMCgoKFMOG071lMX3Ze2kN35+gaZr6l0tKSpo9e7a9vT09Thafz+fxeNHR0f369Xvn+kePHn358mXtJXw+v0XMREnfUET3QKZt3bp1zZo1aWlpK1asOHLkyNChQ+Pj40Ui0dSpU7lc7ujRo11dXcPDw0NCQsphJ50AAA1zSURBVF69erVp0yZLS8uffvrp8ePHAwYM6NChQ3R0NEVRCQkJtra2AoHg+PHjKSkp9JdjmzZtjIyMoqKi6KFFoMneNz8PHz6cn59fe0n9/Hz8+PHJkydramouXbr0888/ay745rFy5cpx48Y1bdt27drdv39fX1//7du3N27c2LVrV2JiYmpqak5Ozrv7JcL/et/8VAaO4FshTRX4BQsWJCQkdOrUSbGEx+NFREQkJye/c32BQFDn3ll6VGQNhadGTk5O9PQhCoMGDaKvfq1Zs2bu3LlGRkYPHz4cNGiQ4n7ouXPnLl++/PLlyxwOJyQkxMjIyN/f/8GDBxMnToyMjKyoqBAIBElJSVVVVWZmZnfv3rWysqI/nNjY2ODg4KtXr9JzgEKTvW9+ikSiOvkpl8up/50p+PLlyydOnPDx8cnPz6fnaG/R6HFsmqZLly7FxcUVFRXR0dELFiygF2ZmZtaZ+Asa8r75qQwU+FZIUwVeJpPZ29vXXkKPtt3Q+vW/TZKSklpoHyVLS8uRI0fSj+l7oA8cOODs7KxY4fPPPx8yZMjhw4fPnDlDT0O0aNGixMTE0aNHE0KOHTtGCCkrK3v69KmJiUlqauqUKVOysrIKCwuDgoL09PRsbW0xkbOK3jc/6S4UtSUlJdWZQqq6utrIyOjIkSOK4Y1bLVtb20GDBt26dUtR3fX09D799NM5hBBCvvnmm5+jo5thbJ+W633zUxko8K2Qpgr8vHnzunfvPnr0aCcnJ/qKcnx8/Lx58zS0Ox1X+5c4IcTc3Nzb23vdunWK6+iTJ0+uM8mmlZVVUFAQ+e8Y+J07d1bMYufr60tR1J49e6ZPn94c0TORJvKzpqZm4cKFvr6+Gp8hSud9+OGHFhYWbDb7+PHjZmZmlZWVQ4cOPXv2LIfDITJZcnKyi4uLl5fXpEmTOnTo4OTkZGFhIRAI9PX1TUxMOByOubm5RCLJzc0tLS3Nz883MTGhJ++ysLDgcDh6enrGxsYymYzu3k/PTU7P90UIoTenwzA2Nvb29m5ouAJdhu9PUAtNFfg5c+aMHDkyMTGR7u/t4uJy7ty52kex0GRcLjc8PPy7776LjIzET/Km0UR+1tTUKH9HGbN99NFHhBB6tBwPD49r165t2LChqKjILCuLCAQXL14079Tpzz//PHr06OXLl/Py8kQiEV3U3759K5VK6fkd2rdv37ZtWycnJ6FQSFGUVCqlB70XiUQSiYTNZkskEj09PRMTE0NDQwMDA7pXo1QqFYlEdLe+6urqRYsW0SfGWhZN5CeO4FshDfZrdXZ2pieXVFBMggsq2rlz55UrV44ePdqEu5iApvb8rK6ubv6xF3WZjY1Nx44dR44caWtr6+npmZSUFBsQUCYQ6OnpGRkZDR48ePDgwdqOUXepPT9R4Fuh5rtxpbCw0M7OrqHLSIsWLUpPT6+95OnTp/g10BAulxsbGztx4sSFCxe6urqam5uLxWKJRPJBXp4pIdOmTRMaGzdPJF988cWIESOaZ18a1Xh+Lliw4MmTJ7WXPH36tPZtSwKB4NixY6p0TGOk2NhYOzs7+h4QY2NjFJgmUz0/4+Li5s6dq9koQcc0X4G3sbEpKCho6NUpU6a8efOm9pKtW7fa29u/ePGCfiqXy7Ozs43VUbeEQqFajrQoihKJRFoMKTo6urS0tLCwkD45zOFwhKtWyUWivn37EpVH+ZVKpQ4ODv+4mp2dneJv1MjfV/c1np+RkZGK0dpp27Ztc3BwULx3mUwWGRnp4eFx/fp1FSNhTH7Snj9/rnj89u1bQsjdu3cNVU4ViUTi5ub2Xpu08vycPn26u7s78rMRzMtPloo9Mxuh4kANO3fu3Lhxo+JpZWXlmzdv1DJWhlQq5XA4qh9MyOVyuVyuUyH5yGSGFPWYyxWpIyQnJ6f3vajs6emZkJCg8s5bAORnEyA/mw3yswkYmJ+UZly9etXDwyM0NHTSpEmTJk0KCwtzc3O7fv16kxtMTEwcNmyYWmLr0aPH3bt3VW8nLi5uzJgxqrdDUZSPj096errq7Rw5cmTixImqt0NRVKdOnZ49e6aWploD5KcykJ/agvxUBvPyU1cGugFoTrt27XrnucrY2NjmDwagDuQnqIWuDHQD0JwmT56cnJxcUVExZ84cbccCUBfyE9QCA91Aa2RiYrJw4cKjR48OHDhQ27EA1IX8BLXAQDfQSvn5+fn5+Wk7CoB3Q36C6pp1oBtVcLlcdd0Wz+Fw1NJ1EyGBgg5+8ggJFHTwk0dIzUCDt8mpl0wmKy4upuduURGfz7e3t1f9ngqpVFpSUmJra6t6SK9evXJwcFA9pOrqaoFA0K5dO7WERE9VCcpAfioD+aktyE9lMC8/W0yBBwAAAOW9x8gzAAAA0FKgwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMFDLKPA7duzw8vLy8/NLSkp6rw3XrFnj5eXl7Oz8008/NdRUkxtvmgsXLvTs2dPV1XX37t1aD0koFO7atUvxVJlImvnjahGQnxqC/FQL5KeGtID81PJsdkrIy8vz9PQUCoXZ2dkeHh4ymUzJDS9fvtytWzeRSFRcXOzi4nLv3r36TTW58aYpKytzd3cvLi4uLy/38PAoLy/XYkgPHz6cNm3ahAkT6KfKRNLMH1eLgPzUUDDIT7VAfmoomBaRny3gCD4xMXH06NHGxsZubm52dnZpaWlKblhSUjJz5kxDQ0Nra+uQkBAej1e/qSY33jSnT58eNWqUtbW1ubl5enq6mZmZFkOKjY0VCASKp8pE0swfV4uA/NRQMMhPtUB+aiiYFpGfLaDAv3792snJiX7s5ORUUFCg5Ibjxo2jB8NPT0+/fft2aGho/aaa3HjT8Hi83NxcX19fFxeXjRs3slgsLYa0bt262pNRKhNJM39cLQLyU0PBID/VAvmpoWBaRH5qfzT8fySXy2sPMiyVSpXflqKoLVu27Ny58+TJkxYWFvWbUqXxJhCJRNnZ2Tdu3KAoqkePHkOGDNF6SArKRKKt2HQZ8lOjISkgP5sG+anRkBR0Mz9bQIF3cHDIy8ujH/P5fAcHByU3lMlkY8eOtbS0TElJMTc3f2dTTW68aWxsbIYOHWplZUUI6du3b2ZmptZDUlAmEm3FpsuQnxoNSQH52TTIT42GpKCj+anpi/yqe/nypY+Pj0Qi4fF4bm5uyndMOHLkyPjx4xtvqsmNN82TJ0+8vb1LSkqKiopcXFwyMjK0G9Lly5cVnUSUiaSZP64WAfmpuXiQn6pDfmouHt3PzxZwBO/i4jJ37tyQkBBCyN69e9lsZfsN3Lx5MzEx0d7enn66Z8+eESNG1GmqyY03jaen54wZMwIDAymKWrZsWZcuXQgh2g1Jof5+lVnSPLHpMuSnRkNSQH42DfJToyEp6GZ+YrpYAAAABsIvXAAAAAZCgQcAAGAgFHgAAAAGQoEHAABgIBR4AAAABkKBBwAAYCAUeAAAAAZCgQcAAGAgFHgAAAAGQoEHAABgIBR4AAAABkKBBwAAYCAUeAAAAAZCgQcAAGAgFHgAAAAGQoEHAABgIBR4AAAABkKBV5WZmZlQKKyoqNizZ8/7bqvY6vTp0/Pnz9dAdNDaIT9BlyE/NQoFXj0qKysPHjzY+Do1NTUNbTV06NA1a9ZoKjho9ZCfoMuQnxqCAq8eS5cuffTo0Y8//kgIWb16tZubm7+///79+wkhFy5cmDVr1uDBg8+cORMZGenu7u7q6rphw4baW125cmXFihUURX311VdeXl7e3t5HjhwhhFy5ciU8PDw4ONjLy2vZsmVafYvQgiE/QZchPzWFAtWYmppWVVXl5+eHhIRQFJWQkDBw4EChUFhaWtq5c+fU1NTz5887ODgUFRVlZmZOnTpVLpcLBAJ7e3uKohRbnT17dt68eSdPngwNDZVKpUVFRfb29q9fv758+bKpqWlpaWl1dbWrq2tRUZGW3y20NMhP0GXIT43iavsHBtPcvHmzoKBg/PjxhBCRSJSamurg4NC3b18bGxsbG5tly5bFxMSkpqaWlZW9c9vw8HAOh2NjYxMYGHj37l1jY+OwsDArKytCiLu7e0VFhY2NTXO/JWAQ5CfoMuSneuEUvZoZGRktXrz4zJkzZ86cycjI+OSTTwghZmZmhJCrV69GRESwWKxZs2bZ2trW35aiKBaLRT/mcDhyuZwQ0rZt22YMHxgO+Qm6DPmpXijwaiOTyQghYWFh+/btq66uLi8v9/HxKSgoUKyQkpIyfvz4adOmVVZWFhUV0flHb0ULCQmJi4uTy+UlJSW3bt3q2bNn878LYCrkJ+gy5KcmoMCrh7W1dVVV1cqVK/v27Tt48GA/P79u3botX77c2dlZsc6kSZOSkpICAwNjYmLCwsK+++47xVb0CuHh4V27dvXz8wsNDd20aZODg4OW3g0wDfITdBnyU0NYFEVpOwYAAABQMxzBAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADAQCjwAAAADocADAAAwEAo8AAAAA6HAAwAAMBAKPAAAAAOhwAMAADDQ/wOkom7I5jjRdQAAAABJRU5ErkJggg==" /><!-- --></p> -<p>This is improved when the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced, it remains more or less stable already after 200 iterations of the first phase.</p> +<pre class="r"><code>print(f_parent_saemix_dfop_const)</code></pre> +<pre><code>Kinetic nonlinear mixed-effects model fit by SAEM +Structural model: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Likelihood computed by importance sampling + AIC BIC logLik + 706 704 -344 + +Fitted parameters: + estimate lower upper +DMTA_0 97.99583 96.50079 99.4909 +k1 0.06377 0.03432 0.0932 +k2 0.00848 0.00444 0.0125 +g 0.95701 0.91313 1.0009 +a.1 1.82141 1.65974 1.9831 +SD.DMTA_0 1.64787 0.45779 2.8379 +SD.k1 0.57439 0.24731 0.9015 +SD.k2 0.03296 -2.50143 2.5673 +SD.g 1.10266 0.32371 1.8816</code></pre> +<p>While the other parameters converge to credible values, the variance of k2 (<code>omega2.k2</code>) converges to a very small value. The printout of the <code>saem.mmkin</code> model shows that the estimated standard deviation of k2 across the population of soils (<code>SD.k2</code>) is ill-defined, indicating overparameterisation of this model.</p> +<p>When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of <code>SD.k2</code>.</p> <pre class="r"><code>f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control_moreiter, transformations = "saemix") plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")</code></pre> <p><img 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" /><!-- --></p> -<p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero. This has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> +<pre class="r"><code>print(f_parent_saemix_dfop_tc)</code></pre> +<pre><code>Kinetic nonlinear mixed-effects model fit by SAEM +Structural model: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Likelihood computed by importance sampling + AIC BIC logLik + 666 664 -323 + +Fitted parameters: + estimate lower upper +DMTA_0 98.27617 96.3088 100.2436 +k1 0.06437 0.0337 0.0950 +k2 0.00880 0.0063 0.0113 +g 0.95249 0.9100 0.9949 +a.1 1.06161 0.8625 1.2607 +b.1 0.02967 0.0226 0.0367 +SD.DMTA_0 2.06075 0.4187 3.7028 +SD.k1 0.59357 0.2561 0.9310 +SD.k2 0.00292 -10.2960 10.3019 +SD.g 1.05725 0.3808 1.7337</code></pre> +<p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is related to the variance of k2 approximating zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> <p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.</p> <p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:</p> <pre class="r"><code>AIC_parent_saemix <- saemix::compare.saemix( @@ -1766,101 +1824,19 @@ print(AIC_parent_saemix_methods_defaults)</code></pre> <pre><code> is gq lin 668.27 718.36 666.49 </code></pre> </div> -<div id="nlmixr" class="section level3"> -<h3>nlmixr</h3> -<p>In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely the First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.</p> -<p>First, the focei algorithm is used for the four model combinations.</p> -<pre class="r"><code>library(nlmixr) -f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") -f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") -f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") -f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")</code></pre> -<p>For the SFO model with constant variance, the AIC values are the same, for the DFOP model, there are significant differences between the AIC values. These may be caused by different solutions that are found, but also by the fact that the AIC values for the nlmixr fits are calculated based on Gaussian quadrature, not on linearisation.</p> -<pre class="r"><code>aic_nlmixr_focei <- sapply( - list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), - AIC) -aic_nlme <- sapply( - list(f_parent_nlme_sfo_const, NA, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc), - function(x) if (is.na(x[1])) NA else AIC(x)) -aic_nlme_nlmixr_focei <- data.frame( - "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), - "Error model" = rep(c("constant variance", "two-component"), 2), - "AIC (nlme)" = aic_nlme, - "AIC (nlmixr with FOCEI)" = aic_nlmixr_focei, - check.names = FALSE -) -print(aic_nlme_nlmixr_focei)</code></pre> -<pre><code> Degradation model Error model AIC (nlme) AIC (nlmixr with FOCEI) -1 SFO constant variance 796.60 796.60 -2 SFO two-component NA 798.64 -3 DFOP constant variance 798.60 745.87 -4 DFOP two-component 671.91 740.42</code></pre> -<p>Secondly, we use the SAEM estimation routine and check the convergence plots. The control parameters, which were also used for the saemix fits, are defined beforehand.</p> -<pre class="r"><code>nlmixr_saem_control_800 <- saemControl(logLik = TRUE, - nBurn = 800, nEm = 300, nmc = 15) -nlmixr_saem_control_moreiter <- saemControl(logLik = TRUE, - nBurn = 1600, nEm = 300, nmc = 15) -nlmixr_saem_control_10k <- saemControl(logLik = TRUE, - nBurn = 10000, nEm = 1000, nmc = 15)</code></pre> -<p>Then we fit SFO with constant variance</p> -<pre class="r"><code>f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_sfo_const$nm)</code></pre> -<p><img 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nzLbI7T1kM47HO7mzMyJut0GvNntLU1nTz5zbXXLpQidQ+UYNDr9baOG1cgiQ2fD5Yu7b779tvQb1yK5ub606ePX3fdYs1/b4GAu6urPTNzirbmQ45yvrndzmPHDs6ceZ3VqlGuoPnmcORpfp9zOtuOH//q6qtvQEe5GuiZb5N0Oo3LQnt7c1XV0Wuuuclo1PgpQqEuj6cFn2+hUO98e/31/udbS0tDdXXF/Pk3a/6rCQY9Xm/b4OdbMBiUlniTyRwK9VEoQ6HQyZNVK1fe9+1vL/v00y2ffrrlzjvvZlkmN3fi4sW3Wa3WzZs//PTTLXfd9V1UXxTFcDjU0zYwZUqh7HEcx/p8XpstWTXibCwIAs+ytMkUp/kdkWUZv79rKMYQr3m5YFna7/fZ7Sma37YFgWNZBh8Dy1J33939Wv+LX9TOmtVPCHqGoQMBX1JSquZPwfMcxzFmc6x7OTabPPcdYjQIeAKBQLjUiI+P93i6Q7EyDC178YqLi8vJyS0oKAKA2bPnvv32awCQnZ27YkW3x93s2XPfeec1rH78vfc+CJHxeJzffHNwypRSqzVR24BpOuhyNQzuLbP9+PHDxcVXaD5lCIf9bndTevqk2JVXGR0dLSdOfF1ScqXJpFGBRm+ZuGaFh1fIycm7/PK86D20tjaeOnWstHSG5k/Ro1kVan5FQIx+Iw4CgUAYfpKSUiQ7bafTmZTUR8ey23v/q9PpkLrZ1NQgmdDr9XrNG28EAoIIeAKBQBh68vMnuVzO1tZmURSOHCkvLb0clTc1NTAMPWnS5M7OjsbGelEUDx0qQ9Z2Xq/nH/94z+Nxi6Jw6FBZUZH8oJ1AGBBki54w5mAY5ptvjsyaNUdWHt1rmUAYEDqdfvnylZs3f8iy7JQpRZdfPh2Vv/76K/ff/3BOzoQVK+755JOPAoHAhAl5S5bcDgBTp5Z6PO633lrH83x+/uRFi7RkXiEQJIiAJ4w5tm375Pz5WpmAj+61TCBoICdnwqOPPi4rfPrpZ9DFhAl5q1f/SHZ37tz5c+fOH47BEcYAREchjC1OnapqaWlSlktey0ajafbsuceOHR3+sREIBMIQQjT4EQbLsk899dSqVauKi4sv9lhGHn6/b/fuHbfc8u0tWzbKbkXxWmYY+vDhg+g6OTk5IUHue4k8oFpaGlAAMiXPPff8smW3FxRMjjQwjmNCIS9NU4Nw7vIDQHPzec2WWRxHh0JdDFOr+WzC7+8CgMbGOs1eUixLh8NdDFMjGQ+HwxRAPrp2Otvr633Re/D5vADQ0FAzCF+vUDjsZ9lzMda3WKxpaUOfB4xAGDxEwI8wduzY8fvf/z4vL48I+IEjfvzxhwsX3mKxqPjwRPFaZhhm37496Lq4uDglRR7cB6VkbGlpUHVpEUXxr399xWjU3XnnUpXbPbUEQXC7uwbycWRPEQCgqales1uNKIqiOCRjqNUcKkc5BprWSQLe5eqor1fZfcFB2TwbGmqGcAzRSU1NvxQEfEdH52233b1+/XqUuJ1AACLgRxwo47WUTZkQO4cPH0xNdeTnF3R0tCnvRvFaTkhI/NnPfhGlZxTo5sor56oGuuF5HgAmTiy89tqFkXoYqkA3M2fOu+iBbmbPvn5oA91IFBSUXHttSfQeUKCbuXNvvOiBboYZURR9Pj/D0Bd7IIRLCCLgRxhIWSQCXgNNTY3V1VXHjh0RBJFlmWef/dUPf/hTSSQnJaVUVlaga6XX8mBAPxYS8wTCBQJ50pOVgYBDBPwIAwl49C9hQNx++3J00dHRtmHDO489tgb9t6mpweFIz8+ftGXLxtbW5oyMcUeOlA+hCT36sYiAJ1xQUPh3QSArA6GXES/gGSZI0wFZYSjkBYBAwMXzGhMK8TyLetBsqhMKdQGA3+9kGI3blRzHoB7waIXBoBd17vN19NtDOOwDAL+/U3PERJalUQ/SiabfTwGkSf37fOFYxuDzdQwiuHQY9RBjfYPBHB9vi71/yS9Z1Wt58CCliqhWhAsK0eAJSka8gOc4JhSSG9bSdBAAwuGAKLLaukVaVzjs12yqwzAhNAae13gqhkyWwmG/sluaDik/daQxhEL+QeR+EAAgFPJJ30MopJMEPMOEQiF/xMYA0POKEAr5B5Giiu8ZQ0yYzUJ0Ae9wZEjqO2B+yapey4OHbNEThgEi4AlKRryAt1iSLBZ5CkmDoaO+viE1NWeQ2eRSU3M1Z5PT6VobGhrT0nIHmU0uLW0Cbi5ks6UDgMWSnJ6e328PotjU1NTscEwcZDY5hyNP2smIx0y4bLb09PT06D1wXH1LS0t6et4gs8nF8nkvTYiAJwwtwaDX73fKCru62gHA5+tsb6/R1i3SapzOBs1/qj6fH/VgMmlccNAYOju1O4N0dXUBQGfneYNBo3RDmlVnZ52k1bAsBdCdYMbn62hv70fZ6OryAkBHR91g8kcDQEdHbYz1zWaL3a7iyjHiBfxYg5zBjziIgCcMLXq90WyWqy6oRKczKG/FCM9zNO03mSyaxVI4zAGAyRSvORU6z7M0zZnNFs0vGUYjAwAmk8Vo1BwQgmGYoMlklcaAD8ZgMJvN/WyTGI1hADCbLZpdObAxxFTfaFT/womAH2EQK/oRxxg0suvo6EhMTIyL07h3RYiO2WxRZgoPhTgAMBotdnuGtm5pOkjT/sTEVM2+mhxHAUBiYtpg0sXSdCAxMU2z2RDDCABgszkGky6WYYI2m0M1XWx8vM1u78fEJxRiAcBmSx9MuliGCdrt6SRd7NiCCPgRxxg0slu0aNGvf/3riz2KsQU6RBtT04zQL0TAjzDIFv2IQ1WDD4fDzz//vNvtvkiDurD4/X6/vx/rS8LQ0uMmRwQ8oRci4EcYRIMfcaiewR88ePCJJ5748ssvL9KgLiyCIJB30GEGHZwj4ywCAUEE/IiECPgRhOoWPcdxMHoP5kVRJAJ+mCFucgQlRMBf6nz66adXXXWV9F+iwY84VDX40W15J4oimaLDDEVRFEWRSHYEHCLgL3XOnTt39GhvbnIi4EccY1DAk/l5UaAoinzzBBwi4C91ZH+xZOdzxBFFlo9WAQ8AHo/nYg9hzKHT6YiAJ+AQP/hLHdlxJtHgLy40HZAFDwaAQADF23dynDwtAgB4ve0AEAr5vd7eNLV+vwv9iwpR7gOfr3MwSU5RDzSt0YmZ5xkA6Orq0Jx/ASVK6Orq4Dj2gw8++M537pw//1pNY2iXgoiFwxRAd7TEUKjL6w1FbNxTB/Wg+VNwHA0A+I8VHaPRrAymeVEgGjxBBhHwFxOPx5OUFNPSIIoiHvFgFGt+lzg8zymTG6F0OCwb0ulUfhf0QsAwNN6QYcIAQNNhVIje2xgmNMiUPAwTHET+BQH1oDn/AhKNDBNE6QM8Hqfyu+pvDCL05JJA0HTvYFiW7rdDNAaaDmgW8Oh7GOjIVamvr9u6dXMwGJw27bKFCxfLhuTxuLds2dja2pKZOX758pVxcfH9NomOTkcEPKEPRMBfNGprawsKCr766qsrroiWmVS2J0/84C8uFovdYrHLCt1u5/nz51NSsqXs8jiBAAUAJpMFD6dvt1cDgNWaggp7ch/kaA4ipte3ATSmpuYiOaEBLPeB5mWhGaApLW0CRekBIDExfaAZBEKhLo+nxeGYKAm2EKax22yO9HRH9B54vgGgxeHIG8xeiNfbNvjcB4LAb9y44fbbl2dn57zzzuuVlRV4DmJRFN99942FCxdPmlSwffu/ysr2XX/9TdGb9AtFkS16Qh/IGfxFw+Px8Dz/1ltvRa8mk+hki37EESWS3WjdiSGzFABqas7Z7Ul5eZOMRtPs2XOPHTuK362tPZuQkFBYWKTX6xctunXmzKv7bdIvRIMnyBjTAn7nzp2TJk1CHsmIzz///Mc//nGUJm+//fZLL700JE9Hi3ttbT/5gtBaOXPmzMrKSiBL5whkDFrRk1kKAF6vOy2te7/B4Uj3evtYHbpcTovF8uGHH/zlL3/YsuUjlNA5epN+IRo8QcaY3qKvq6urqakJh8MJCd07q19++eUHH3xgNptXrbrXZlP5cj7++GOXy/XDH/5w8E9Hi3u/f5Borfz666/Pnj07bdo0snSOONBPhr9HwkUS8BzHac6hOSDILAWAYDAoJVUzmcyhUBC/GwqFTp6sWrnyvm9/e9mnn2759NMtd955d5QmgYD/+eefQdfTppWmpsrPiQBAp6MaGmp37946mGFXV58dTHMAKC/fNcgeBj+GAwd2DOEYOE4HcEtPecXu3Q2x9LB37/YhHEN00tIypk2boSwf0wIeLUD4ebYgCF6v99lnny0oyF+8+DrVJkO1bA1IwEPfvXqW1WhIRRh+LhE3OUEQxo0bt27dumXLll3oZxEBDwDx8fEeT3euAYahZbYRcXFxOTm5BQVFADB79ty3334tehOz2bxkSfcPZ7PZrFZ5urZg0E9RlN2eXFhYqm3AHMcEAu7ExFTNRhiBgK+pqW7ixCkmk0ZTEpalg0FPYqJDc8pav9/b3Fyfn1+s+V2WZcPBoNdmc0iGICzba+mZkZFdWNiPZXRXl7u1tXHy5KmaDUEYJhQKddlssWaTi2R5QwS8XMAjTSvS2iQ5rf3617+eN2/e9ddfr/np0QX8fffdd/fd/98VVxRu2rRJOVqapjU/lzDMqEq74ReBLMs6nc7W1tZheJbyL2sMkpSUUllZga6dTmdSUjJ+127v/a9Op0PyLEoTg8F41VWzojzO43HqdDqLJWH8+FxtA6bpoMvFOxzZmi09nc72pqa6jIzxg0kX63YL6enZmhOtdnS0NDfXjxuXNZh0sR6PmJGRo5ouNikpZfz4lOg96HS61tbGzMycwaSL9Xph3LjcQaaLvSACvqLi6J49OzmOKy6etmjRYorS7d+/Z8eObXidNWuewk2O169f29TUve8xa9acW265DQbnMRILSMTKvMz7FfDo1quvvur1ei+cgN+0adOkSRNLSyeXlx9CJXjNcDis+bmEYSbKFr2scBiGMcx7BsP2rEuQ/PxJW7ZsbG1tzsgYd+RIuWQP39TU4HCkT5o0efPmfzY21mdl5Rw6VFZYWBSlSYwQP3iCjKEX8G1tLZ999snDD/8gMdH2/vtvffVV+cyZc+bOnXf11degCrW1NYcPl8kcitxu15NPPo02dnoSGw/KYyQWVDV4dBFJ+ZDSZAmCEAgMylM2uoDneZ7neWWIG/TvUAn4pUuXPvLII4sXLx6S3giqRNHgJXH77LO/Z9nAL3/5zIUexvAIeLJFDwA6nX758pWbN3/IsuyUKUWXXz4dlb/++iv33/9wTs6EFSvu+eSTjwKBwIQJeUuW3B6lScxP1I1Ws02CNoZewJ85U11QMCU5ORUAZs68urz8wMyZcyhKp9d3i+1du764667v4k0YhqEoSrarI3mMAMDs2XOPHj0y5AJeVYNHF/1q8BdawAuCwHE8gPzlY2i36Hfs2DF79mwi4C8osZzB7927TxQvrDavasx/QZ81xgU8AOTkTHj00cdlhU8/3f0aN2FC3urVP4qlSYwQDZ4gY+gFPK53UpROshlBHDlyKC8v327vY6TgdrtEUXzllT91dXXl5k5YsmRZQkJiFI8RURRpuluFlY6v+o4B5eLkOC6aMRq6y7KMVE3aMmVZThAEjmNlmjzP86ic53m/38dx7IkTJ5KSkrOyxiu/B9ShXq8+BoahUQXVQQqCwLIMvhyjmuij0TS9ceNGr9dz3333RfmAPWOI+CXwPM8wdNQK3d+DdD7CcQBglJpzXD8LCgpqxnGs5sMkaQwx1qcoHXI6ukSI4iYnLceCIKAvapiHcUGfNcbP4IcfEoueIGPoBfzkyYUHD+5va2tNSEg4eHBfONwbiYrn+YMHDzzwwCOyJizL5OZOXLz4NqvVunnzh59+uuWuu74bxWOkq8v74ovPouvS0stTUhJVR3LkyL7oQz137hQAHDy4y2br7qGpqQ5d1NWdPXOm5syZGlkTl6ujq6vrX//6p9frbWg4v2/f54888qOkJPutty6cP/8a5RJV6jIAACAASURBVCMOH94T6emHD+8HALfbuW/f58q7PM83NtafPdvrJX/q1LF9+5LPnDkBAJ2dbS+99KLL5c7PHxf9M6IPGOkWx3G1tadVB4CDfw/BoAFgEbo+ffr4vn1N/Q4AAPbv/yKWajGOIToOR2ZJyZWDfNwQoqrBywrRi+MFHcZwCniyRX9RIAKeIGPoBXxWVs6iRbd+9NEGiqKmTi3FNe+qquMOh0MZzjM7O3fFinvQ9ezZc995px+PEYvFunz5SnSdkGCNj5d7CPj9XefPn508uUR6RZBgGPbNN9+677574uLiHI6DADBlSmlKSrdVZHJyGrpwODLGjx9nt2fI/BwslgSG4ez2dJZlRVFXUnIlRekPHTpy9mzt6tWP4TV9Pk99fU1hYanRaFR+S42NTRxHAUB8vEVVGomiaLMljRuXIZWMH5+bnT15+/ZdAGA2x1uttkAgHF2Seb2uxsa6oqLLI2m0oigmJzuidELTwWDQk5SUKenffn+vIp6dPbGkJCNC025crs6WlvqpU6dr1uBpOhAMepOT5XskkTCb47Q96AIRi4AfBg2enMGPesgWPUHG0At4juOKi0uuuOIqAKiuPpmc3OtRUFFxtLCwWNkE2c9nZeUAgF6vNxiMENVjxGg0Tp0azdcTOSekpDgsFqvs1rFjx37+818sWHDDNddcEx9vBYCUlHSHo/sswGzufleIj7cmJiakpWXo9X3Es9Fo0un0dnsKADAM43BkSrfwawBA8iw1NV0pb7q6umbNmjNz5kw0VFlD6DHlMxqNCQm9dgkJCXa/P1RZeQJ1bzAYdTq9sq2sHwBIS8tAX6kMZE9gMsVF6SQY9Or1nMMxTtqi1+l6XUYSE5Mcjn5cQlmWbWkBhyNTs4APBNx6PR/9k8bIwYP7y8r2chyXlzfpttvulHnrqrpyDJ4oAl46EhIEgeeJBk8YFCRULUHG0IeqDQYDa9e+2NXlZRh6//49V145E5WzLFtTc27y5AK8clNTA8PQXq/nH/94z+Nxi6Jw6FBZUdFUAMjPn+RyOVtbm0VROHKkvLT08iEZHlpS0TIXxcgu0vFhe3u7ZGcnnbJHqa9KMBjkOK6rqwsiLII9/Qt4r5IBPwCwLIvM7GN/qBJ8/LGzZcuWwTz0ItLW1lJWtvfBB1c/9thPAwF/WdleWQXkyvHzn//vz3/+v4sWLRmq50bxiGtr685JKgiC6q/pdDq/+uqrCzqMqqqqlJSUlpaWIXkK/iwibIYZEqqWIGPoNXibzX7NNde9+upLZnPcjBmzpk3rFswNDeeTkpKQdb0E8hiZOrXU43G/9dY6nufz8yejtXWQHiORwO17o7jJRfo7OXXqVFFREd5cJuBbWlo2b968evXqfscQxYoe71kCTwzPcVwkkRA7qHllZeXp06cLCwtjbDVyXfDdbtdll0232ewAMGVKcVtbn5Avqq4cQ0IUDX779u5gloIgqm7Rv/TSS+vXr29oiCk0ZnQiafCtra1ut7uzszM5WSUVnjaIgL8oEA2eIOOCBLqZNWvOrFlzZIX5+ZMfe2yNrFDyGJk7d/7cufNldwfjMRIJXKxqc5OLrsFv3br1P/7jPx544IF+xxCLgJe9fEiVkQYfaZB79uyZN29ev7Ee0QC++OKLZ555pt+kdrKxjUSKikqKikqCwUBra8vx48euu+5b+F1VVw50KxQK/utfm9F1VtZ4u90m65lhGAA4e7ZKNTrm2bNVABAI+E+c+FoqrK8/BwDBYAAVBgJ+QeBPnaqQRXNqaqqXNYwESjB/+nRlJHsLp9MFAO3tzbLeamtPo4aiGGLZkNPpH4S1RAgATp2qQL4ezc31sYwch+dZlg27XH4pJz1N6wCkEDHnT5xwRu8hHA4CwMmT32jOat8zhlidYG22pJycweaWHRIoSicIxHOB0MuYC1Wr1OBxiSULdLN27cvFxVNvvPFGvIIkaFUFPPJQjy4FcQEfDofffffdO+64w2Lp1h03btyIPOxlgW5i3KI/ffr0ggULdu/efd11KrH0lcMAgK1btz722GN/+ctfoteXtRqhNDbW79ixTRRFhyMdL1d15UC3RFGUnEEYhlU67ElumXjcAtldmT8k+hpZtrs3QeB5nsfdEXseLfA8/8EHfy8tnVZUFG2XpWdScaKoPvdYlsGfKBsew9A8z/G8MDiHRjSGXqfT2J0bEYLQPQZJPHOcDrvL99sh5hqq3S2zZwwx1r9U/iJ0OupCm2oSRhZjWsArNXhJML/++hv33XfHK6/87dprr8UFfL8aPEoDg/e5cuXKhx9+eMGCBVIJrrtXVlbee++9KSkpUrSZ9evX+3w+6CvRAYuxg54SaYseaZMeT7REk//5n/+5evXqzMxuszWn07l3b/eBtCzh2MMPr77jjsXLlvVaToxcDR5RWFhcWFhcVrZv69aP77lnlVSu6sqBsFis9977YJQ+3W7nsWMHp0y5TOkhAgA0TQGAyWS+/PLZUuHp0/UAwHE8KjSb42g6PG3aVbIw4A7HxwDU66+/s2rVqhUr7o0yBqez7fjxr4qLr4iUdgKdsqekpOPDAICODh8A5OcXFxdPdbubMzImac410t7eXFV1tKTkKiRcMzNzZM/ql1Coy+NpGTeuQHrRCfW62UJOTv7ll/ejK7e0NFRXV5SWztSc5yMY9Hi9bZmZU7Q1HwYEgZfeoiQ4jtHpdBzHsqzGQzSOYwCA4+hI74ix98CyGq27eL67B0HQGPcJfTMsS1OUxs0MqQdpE5RlASBOusuy/bxF9fQQFgSNf0pSDzG+bet0epk9OGLMCXhcuCJZqLpFf/58PfToVXhzpQYvk+iy/zIMs2HDhunTpysFPN4zfrBN0zTqJIoGz3FcJA0e1UGvCKqEQqE///nPpaWld9xxB945ALzwwgvvvvvu11/3bqtu3LgpLy9r2bKVspojkQMH9losFuTfkZOTe/hwGX5X1ZVjSIhyBs8wjCiKFEXxPK+qe4miiKbB4KPWRzqDvxDuc4OJZOfxeFm2ITd3whCOZ5QRCnV1dbXLCoPBkE6nC4X8nZ3nB9O5292sua3fHwAAl6vJZBrUn4/L1ai5rc/nRz0YDIOKduVy9Rq+sCwF0L1/5vc7Ozu9/Y3BBwBOZ4PmnHgIp7M+xppxcQnJyVnK8jEn4PGlB4lVVQ2eYZj9+8tra+vmzJmLN+9Xg5e9NDidTlzzxp+CL6l46FmGYVQT3uBGdizLtra2qi6gqE4wGFTewp+OJIesVXt7e3t7n4VDedI/cgW83W7fv39Pbu5Ei8V6+PDB3NyJqBwl//B6Pdu3b1216hG73S65cgwJUQS8KIp/+ctfxo8fH8VNDgn4KAKYYZgjR44UFvaj2g6ngB+Mkd0zz/yhvr7lyy+/HMLxjDLi420mk3yfxut1UxRlNMalpWl8N2KYcFdXW3LyeFVdMBZ0us6mppaUlKxI20gxjCHY1dWRkpKteQOGotqbm1tTUrI1p6yl6YDP15mSkiOJZzybXEJCalpaP77BgtDS2tqWmqo9m1w47Pf7nampsWaTi/R1jWkBHwqFILKb3MmT1ZIyLWuLdyKT6EoNHhSrp1KDZ7AZhGnwguzlA9+ib2hoyMhQCTLT79oqDV4p4FXfRWRmOyNXwJeUXOZ0dr7zzmsMw+TlTbr11qWoPIorx5AQXYI+++yz8+bNi3TgIunuUQTw5s2b77777pqa09qGcakJ+HA4PHI9NYYHnU6vXNANBpNORwFQRqPGQE/o9zIYzJrTxaKGBoNZ8xiQRYjBYNYsGtHbidFoNho1potFBw1Go1n6knF3Zb3eqBq7TG0McZo/BcvSqIdLMV3spQy+RR9Fg4cerXr//v1SCap59uxZ9GbQE5de5Qze4/GYTDoAeOaZ34BipausrITIAp5hGNSJIAiyZDOxuMnhSedUkTR4pXUhvkkglSsPKSL1fOkzf/4N8+ffICuM7soxeKJo8ADQ0tLC96Datt8t+kAgIIoiPoVUia7BD23i2sEIeOUkJMQI8YMnyBj6QDeXONG36PFrmmagr+iV2iIBz3GcdBcXvQAwffr01157HXosm9Cq+u9//3vdunUAsGHDBui7/KkKeJ7n8YUuRiM7bQJeaiX7NpQ6/aVjMzxSiC7gkQiP8muKotjZ2Rldg4cYJHQkQX6pafBEumtGp6PInycBZ0wLeGUQOqUGryr+kTz2er11dXWyW+jC5XJ5PF7oUehRt++///4LL7zQ0dGBgsHhS62qgJfZssrc5PAde5wYBbzMRF9V6qPF4tSp08rmhNiJLuChZysoSqjaSOIf0dTUBAD9RrqNrsEfPnw4evMBMRgBLwhEg9eITkf84Al9GHMCHt+iRyL2Jz/5yc6dO9HdvgKekZXgJnjowu/3owuZgJckJS7gWZb1+/1+v19pY/zZZ59JdnYcx6la0ePqNTLEky2gmzZtWr9+fXQBLwjCZ599BpE1+I6Ojj179uCft7PTJetBtWdCJFRVZ/wH4jguUiQ7qVokAR/LIT3eVSQBX1NTE7157EhjJlv0wwzZoifIGHMCXqnBb968effu3eguvrIoPdplB+2AWb/LBLx0IYlqAGAYxu/3Sy8H+FL7xRdfnDlzRuoc1VEq2dF3y//+97+/+eab0QX8sWPH7r33XlDbjZf+xQOkg0IyNTdr96IZm0iqc2VlJQphJKPHmEOLgF+9evWxY8cAoN8IJ8NmRb99++f4EwcOEfAaIaFqCTLGqICX5VnBk3pJNZEGH2WLHrBlUbkkob2ynjhl3fb2oVBIejmIZLwWSYMXBKG1tVW1ifRfSWxHWiJDPXFDZAJe2qLHu1UVCTI/OkK/SBJ0wYIFr7zyCl6IiOIIJ7PtUFJRUSF1cu5c7U9/+tNIwxgeAc9x3O233473PFCIdNcM0eAJMsacgFdu0UMEOY3vriOka2QGr9oQ6wFp8L3bpyi+rKoGj3fOsqzy0QBA0/TPfvYz1SbSo6ML+JaWlrNnz0ptVbfo8W6V7nzK/xL6RdqiD4fDkgavFPDRl+ZIX7uUh4bj+IqKE2vXvhypn+hb9EP1s+LzSusZvEBkvDZ0OkpzEDrCqGTMCXjlFj30rG779u2rr++NHFRbex4iaPCS2iQtYUoBrzyDZxiG53lJg1fq3+hCMs7neaG+vjeik9I/OIoGr7q2PvHEEw8++KA0zkgavLTW93xLRMAPCulblZwqQU3Ao1svvPDCP//5T1lbiPy1O51OrBN1HR0xPG5yuGgmZ/DDDAqJeLFHQbiEGKN+8LJFDV2sXr1aUs0BoLGxGSJo8NLRe3l5ObpQLkm4sEQb4yzLiqKoPLaX/Rcp+uhxBw4ckiqEw+FI7wRSD9E1+HA4jAuYfjV4VZFAVpCBgs8EPG6SRCgU4nkemcF/8MEHRUVFy5cvx9tC5K9dKm9tba2ra0AlqoE4IgnyodXgcQ1Sg4Dneb66+qzVmjgkg7no1NfXbd26ORgMTpt22cKFi2WZhNavX4sCJAPArFlzbrnltkiFMaLX68kWPQFnjAp4SaXGC2WLL9KNVDX4KAJepsGjf19++eU777yT70kfpzowfIseK8H98mmZ2B7oGTxeWF1d/fLLL8u6Ut2il4kEIuAHiiRBKYqSflz8t0AuakajAVXDv3CpWiTTB+nneO655w8f/goi6+LDcwaPu2lpEDYej+fcudrLLrtsSAZzcREEfuPGDbffvjw7O+edd16vrKwoLb0Cr+B2u5588mkUUVWS/aqFMUJRxMiO0IcxukUfCoUOHDhw/PhxVIhLfQl0fB5dg1eN9qr6Xyl0fKSIY0pPdJmRXTAYjG5Fj44/YxTw586dk87jZaOVbdFH3zYg9Au+Ra8q4BFIg48U0m7v3r0oaBJCec4tZR+I4lCneldW/uabb73xxhuxfTL15ygHGTv4i+ZIp6bmnN2elJc3yWg0zZ4999ixo/hdhmEoioqPt+j1Br3egMKeqxbGDrGiJ8gYoxq82+0+duyYbIs+uhkzfq1qKCeKIh43WCYdJf1bdZMW1JY22WGk2+2OfYv+5ZdfXrNmTZTPohp3RXWLXqYRjvEVJBz2BYPyVLwoiZbX20rTKuGvUeIvZCcfCHhQliq/3ymrJgiC293EMOFQyC9lsgqFuqQK7e3nzWYOABobm66++oZduz6dNClP+k39/u78gZ2d9TyvkgzD7W4BgFAogKfJAgCfrwMAQiG/398JAO+//y4ALF16o7KHfvH7PbgGHwx6Zc+KoQcXAHAcs3v3ttLSqRRFhcMUQDa6Gwi4XC4VP0OcQMAFAG5340DVXwkUDj32kZtMloSEVGW51+tOS3Oga5TNCL/rdrtEUXzllT91dXXl5k5YsmRZQkKiaiGqz3Gs9Ipgs9msVovsccGgn6J04XC4uTnWFGQyOI4JBLp4vlFzvuBAwAcAbW3NmhO9sCwdDKIxaPz5/H4vALS2NuFprwc4hnAw2CUIDdIUYlkKIAddezyu5mZ/9B66utwA0NLSoDllDsOEQqEuQaiPMRZ9XFx8SopDWT7iBTxNB8Jh+dcdCHgBYOPGDQsX3hgX1yftgd/vBoDOzlYUKx4RCvm93jbk0iZDEASvt9sv3ONx9jw03HPRm63a42n9r/96+vPPu2PmMEwYehYLAHC7O1D+AI+nQ/WD+HxOr7cNl6Ycx+KajNPZoVTZpbEBAMOEOI5BkqOurs7rbQsGvaIonj59PCsrSxqScuRonF5vG00HASAQ8KBu0b8cx3d1taMM37KGwaDX6+0nLwgSUV5vm+asCeh7wz9pdIxGs8XST7qnQUBRlPKPVgcAFKVTu9XH7oxl+Z46KusXz1MoDzDWD/7KKKJyt7srFAp1dDjz83szyKEsmQDA86LqMFBXLMv+9rd/eOihBxyONFS6ZctnAMDzAlrOUIqjCD30A0Xp+lpuDrgfUaQAoKam7vrrb9m27eNZs2b0nTbq37BsDABAUXrNAp6iBNRDzPXVJ3YwGDSbu1/4TCZzKNQnwSPLMrm5Excvvs1qtW7e/OGnn265667vqhai+jRN/+tfm9D1tGmlqal25RN1OioQ8J8+fTzGkavS1jZYP9i6uupB9jD4MdTUnBzCMXCcThLwbW2Np0/H9PJ39mzVEI4hOmlpGaNTwPM8S9Pyl3qWDXs8nvvu+/6bb75yyy03ocJ1694KBPwpKSkAwDBhXFAxDE3TAUl8FhdPOXmye44KgiD1L11IGjzD9KZ5DYcDX3yxs67ufM/A+uzwB4M+JLyDQfVM7TQdpOlA3+R1fbboGUblDB7/7DzPCQKPPpcoijQd4Di6rOzwL3/5zMmTR2y2ROltAwBcLnl8OpoOoDRKDBNG3QaDfjQMmg5IkgZ/DeI4Wvnly0B90nRQc1YkFMKl3wcND3FxCXFxCYpiJ8AZmy3dalXeAqs1BfufPjl5PABYLCoLdEJCCgCl0xlRHQAwmXoVNas1FZXbbG0AYLEk22y96QQlAZ+Y6EhOzlTrvBkAmptbn3/+T9dcs+D227vPuU+cOAUAOp3Bak1hmGYAnU5HSQMYECzbZ5fIZLIMtB+Xqwt6jsCMxoTk5PEh7EXUak1KTu7n1S0c5gHqk5IyNStPwaDH6w1r+wZw4uPjPR43umYYWpZBNTs7d8WKe9D17Nlz33nntUiFCIvF+rOf/QJdU5ROr5e/vni9LorSWSyJ1167UNuAaTrk8TSlpuZqzibncnVUVR2dMWOe5nSxNB3weFrS0iZqzsPW2dl26tSxWbOuM5k0ZpMLh31eb5vDkSdNIfxYtaCg5Npri6P30N7efPp05Zw5N2j+FKFQV1dXe3r6pBj1okjVRryAt1iSlOqawdDB88cAwGJJSU/v1nK++qrC4/Eg+2STKQFvZTTGp6fnSzJsypRiScADgNSDIHSvtpLnmF7f+5fQ2upDhvc9fcYBZiZjNCagHIJxceorlN0+Lj09H08Mr9MZ8OVSrzcpDe8djjzppzUY4vR6U1JSJrqVnp4vik0tLa0cxyckZKSnj8NzSFdXn+n7fCo9PT8uLhEALJZk9JFDIT36sA5HnvRBdLpeC22bLT09PV3140hwXH1LS0t6ep5mDT4QcHd1tUu/wohDZkihLJTgkP7en8E8fqgv3ZVeDSMZ2eEnRMokyOXl5WvWPPnUUz8SBB7fNhgoSt/LAYHv8I/0k/ikpJTKym5/WqfTmZSUjN9FpvJZWTkAoNfrDQZjpEIERVHRpSY6s+d5AW81IHie1el0BoNRcw9Inun1Bs09cJwBjWEQ6WL1ADDIT4HGIAl4fCLrdHqDoZ93R9RwcJ+iewyDTBc7yo3s8DWC47jOzk5ksSxbRmVGdriXkdKXDCKcwdfW1uLrpvI8G11Lln0ylBZtsogfsgVddXj4sT26QBIlinM8XpmcwV8I8B9RNdCNBJqWqlb0gP0QqLCurk71VQAVOp3O2bNnS8mQoO+viT8ClQeDwbKyclCY8Q+UQbrJqf65jVDy8ye5XM7W1mZRFI4cKS8tvRyVNzU1MAzt9Xr+8Y/3PB63KAqHDpUVFU0FANXC2CFGdgQZI16Dj4RSpKFg4FIudqUBvFQZvQPitxBKTza8E9mf1vbtn8+dexXuNYeu8QTzygFHMbJTjfDF87w0WtxNTvUbiLJi4k9/9dVXf/CDHxgMBuImN1A++eQTu90+f/58vFBVwKvC96DaVqbBP/LIIwsWLFDtBACam5sPHTpUU1MzceJEvBWaukoBD1jehMEI1kG6yak6rYxQdDr98uUrN2/+kGXZKVOKLr98Oip//fVX7r//4alTSz0e91tvreN5Pj9/8qJFSwBAtTB2KErXb0oCwphiDAl4fFHzer1JSUmRKuPml6pW9NJSG0XANzY2VVeflQr/8Ic/oMr4JjzO4DV4lENWJuCR81UU3zm8H1ShqqrK5/MlJydLkex8Pp/NZld+ZIKM5557LisrK4qAj+ImBwDf//5DsWjw0i0pmSEOngRZOT+jCHi0L8VxXG1tndfrtdtVrAT6ZdBb9KNHgweAnJwJjz76uKzw6aefQRdz586fO3e+7K5qYYwQDZ4gY9QKeIRMg5eu3333XaVLm1QhOTlZdguhdGGPIuChryO7x9PtJHPq1KkoQ+37PiFfLlGd5ORkvV7f2dkpe+g333yTnZ2t0OB7I/fFqMFDX/HAsmxqatrx48eLioqACPio4MFoJaJo4TK++eYbjuPOnDlTU1ODW8hHaqv6sqgaqxH6avDKM3jANHin09nc3Dx4Aa9hqowyAT/M6HQk2QyhD6P2DB4tDtEVINm19LcxdepU1WohzKI3LS0N+hPwn332hWRUJRHJD76pqQnkC5w8rA0azMSJEyUfd9kWhXKLXoMGD323E0RRROYL6FakHQgCAAiCECmSDEI1Fr0E2p9vbW3ds2ePsppSwKsGTcINSpTzU3kGL9VhmN7Uxppf4wYp4FU3zAgxotORWPSEPoxaAY8iakXS4GXItsfj4+NltxC4gEee5UpLPZyqqlN+v181YoPNZpOVoDhl+OPOnDn71lsfSP+VFmWdTidtPyi39FW1cG0avOruRaRQuwToeRmSFcY4CQGA4zhUWfqSo78coGjKpaWl+BzDt+iVGjz6F887rNyihwim+E6n86abbmpublbekj0llg+rymg6gx9+dDo9bgNBIIxiAQ8Q89oqO7DEBTxgaxYu4FEIi+gaPGLGjBnKQikChgS+LiPC4XBnZ2+8M0n1pyhKEvDSANavX4+C5eFCuq2tvaamDl2/+uqrkU4HIIKAxz/d448/jhwQIoXaJUBPuDploXQtCU7V2cLzQhQBr9Tgf/vb3wLAr371KzyaE/5KF2l+NjY2KsvRBOvo6IQIfyz19fU7duzAIxwrif1tBgAqKyvffPPNSM2JBj9QSCx6goxRK+CjG9lFr2y1WvG7ynDf0COhpWzcEFnAK1N7Wa1W2TsERIiHjyONX1XAf/zxx0oN/t133zty5BsAqKmp+eMf/4gnw1V9umyLHt+NP3HixLFjx4Bo8FGJ/Qwe5Z7B/TUgZg1e4ty5cwCg0+lws1BUTblFj3eF/7LSlON5ThCEjo4O1WdJNfGXAyW4UO5XwG/YsOG///u/+zYnAl47ZIueIGPUCnhEjBuGsi16h6NPzD+pE9zHCQl43Iw5kmxWhkS+7LLLlPv2Sg1ehtvdHRVLdYseiQSZgJc+8g033NDWFi3Uq+zpoii2t7dfd911eB0harIcAsSgweMCXiaYATOzUH2LUmrwKByh7EUhkpEdPj8jCHhe2uBV/WNBNSsqKpS3sA87gD12yW4UKyFn8NrR6Ui6WEIfRq0VvVKD73eLHlXOyMhA5+sSH330UVtb22OPPYZr8BaLPNlD7Bp8cXGxUtz2q8FLdvgURUnvB9KHkgT8+fPnpQ+FC+x+PbChrwbf2dkps6frd4SXPgcP7i8r28txXF7epNtuu1OWEiN69u5YUD2DV9XCUQADpQaPLqTDoFgM9CiKwueYZg2e43qdPqJo8NHD4AxIg1eGdiBGdoOBuMkRZIxaDR4tDtEFvOQIhAv4BQsWyA7IN27c+Prrr0PfLXqZlg8D0eCllT3SuhwdfIteqo+WbEEQJPspXMAD9vEpilJ6QMl0KVEUlfv5Spkxsmhraykr2/vgg6sfe+yngYC/rGwvfhdl77755m8//viapqZGKcjogOjXih7d/eSTT06dOqXX65Ua/IDO4BEURd1+++2yatE1eK/XK/0X1+Al/TuKBt+fgB/AGbxsisoGSWTVQKEoskVP6MOo1eAR0QV8YmKi1+uVqqG1RqfTyfbPGYZBCy7ewxVXXCHrzedTzyKTm5trMpnwnW2DoTvTs9VqlfRymQ4dBXyLXqnBS7Lh7bfffu21N6RW0leh0+kuu+yyvXv3AsC3vvWtnTt3Sl3hGr/SlmqkC3i323XZZdNRxJ4pU4rb2lrxu1L2J3QH+AAAIABJREFUbgCYPXvu0aNHSkvlP3G/xGJkJ4rifffd5/V6rVbrxIkT8R1vjuPQm9+ANHidTpeXlyerFt3Ibu/evWvXrv3hD3+I15EsACCCFFf2qcTr7c1vG4uAV2zRkzP4/mGYEE3LYxwFg106nY7nOZT/VwPoRw8EXJrz9IRCXtQDx2lMDYVyWfn9Ts3pYlH6Sr/fqdw6jXkMNAD4/Z3SHh7DAEC3RhcO+3y+fuyQwmEf6kHzN4lSaPp8nTGGojcYTPHxKoErRrGAlzuGKRWChITu3F+4DTm+AY5gWRYtuHgPStv4r776SnUckydPzsjIwM3xjEajUsCjv661a9f2+8FUjeyk9wNpm6GyshJ/55C+Cp1ON3PmTCTgr7rqKknAI9mDrlU1UaTlj9xlt6iopKioJBgMtLa2HD9+7LrrvoXfjZ69O0ZUjexkb5ksy6IfXa/Xyw56pPeD6GfwMiiKQlEZ8Gp43Dqpc7yVlE6wr2bvi/KsfjV4mqZ//ONeozkNW/TIxE91wAQJjmNCIbk6QdNBnU7H87zyVoz02H8ENCc4Qaksw+GAIGi01MHGoK0DYBg0Bj/HaZRuaBcqHPZLWZdYlpIEPMuG+/2GUWLuUEjdR3ogY4j1pzSbLWNLwMdyBi+94uH257j4RDAMoxTwyo33SAsfrnAjEhMTkaJms9lQfBtpeOXl5aqdJCQkSAZ9qlv00iaEJBuiyAPUfMKECfgZMMdxtbW16FpVE5WFSR+hNDbW79ixTRRFh6NPHrwo2bsDAf9rr72MrgsKClNS5DEMUDShiopDwaCfpkPl5bvwu2fOnJCuw+HQrl2fomtRFEIhuaKDJmFLSwPqpL291+n81KmK8nIHAJw40Sdf0enTFaFQ7wtBVdXRlJS4srL9AHD2bJU0mJMnv8Fb1defQ7fwefv++/9EFydOfJ2cLPfkRMcWzc0Nsg8o4Xa78VcKl6sjUk1Ec3M9y7J4nY8/3oR9ruPl5Sk0rQPoNvasqTlVXt4SpUPp4xw+vEdzTjxRFASBr6+P5u6Pk5zsKCycpu1Z2rBY7Mp0wx6Pk6IolBZSW7c0HXS5GlJTczSni9Xr2xsaGlJTc+Lj5SZKMRIO+93uprS0XM152CiqpbGxKS1tguZ0saFQF0pZq5ouNjHRkZ6uknkdRxAam5ubHQ7tSW+DQa/X24onC9XGqBXwiOhHepKAx88C8S16iqJEUaRpur293el04j3IzKMgsoBXvjEkJiaiRyQmJsqaq547TpkyZf78+evWrVOOUJK4eNS5SJ9X1nzSpEn4CybP811dXVJbpSz//PPPpcwlI5fCwuLCwuKysn1bt358zz2rpPIo2bsNBuPUqaXoOi0tzWaTJ30Ph0Pt7c0pKQ6dTh8MhlJTM/AvNjGxdy0WBHHv3nKpW9x/HcdgMDkcmQBgNvcOw2q1oUK7vQmvbLenxsf3Gs0lJNgdjsxNm7YCgNlsQU2++aaio8OFtzKb49GtvqcAfM/d7oZ9H9QAACaTWXkLQdN9ojTq9YZINRFIDOB1TKbeLwR9kHC4928nMdGuMH2R4/f7XK72tLRxGmwkESxL03QgISElxvpWa2L/lYYFvZ6EqiX0YdQKeFmo2o0bNyojcElauNfrxW3QpNXZYDCwLIuOz2maFgQBiXwYiIBXavBXXXXVhg0bAMBms5nNZqvV6nK5opzB/+Uvf7Hb7ZKAV9XgpVMG1UQ4OFJzmbUBvkWvqsFv3759RDvBHziw12KxXHHFVQCQk5N7+HAZfjdK9m6z2XzjjTdH6dntdra3N2dn5xkMxsbGs1ZrcmZmr9ByOHrPbkRRTEzsznJkMpkSEuSbAT1PjM/PLwIAvEJ8fAIqbGjo44Ixfnwurjenpmbk5xch8WazJaMm9933fRTGQCIhwY5u4T+0Xt89H2y2FHQXp66uGQDi463KWwifr09gZqPRHKkmIjExiaIovA4uLNPTx+fnF2HBpcDhyMzPj/bGAAAtLQ0uV3te3hTNx5/BoMfrbcvMnKKt+UWEooiAJ/RhFFvR99mi//LLL5VB1CUBf/jw4QMHDqBrXMAjKY4EPLJBM5lMSDrGvkWv1OCvuuoq9Aij0Xjs2LHm5ubp06dHcUKT9aA8g9+2bRvawB+ogJcFSIku4CFyIP0Rgd1uP3TogMvlDIfDhw8fzM2diMpRfu5I2bsHhNJ2HeQqMofvFSlnkVTtm2++kb3t/e1vf1N2CAAURSkD3UiJgqRCmZ8k35MLWBRFKbuStNWPB22U6NfKEllISfQbMiG6FT05gx8o6Az+Yo+CcAlxQTT4ioqje/bs5DiuuHjaokWLKUq3f/+eHTu24XXWrHnKau3d6jx1qmrXri98vq7MzKzbbrvDbk8CgPXr1zY1ddumzZo155ZbbhvoSI4cOYIuVCUTroVLxm5o2UV5mWQCXhAEnU5nNBo5josUqUaJUoPX6XSoZ4qipkyZAgBGoxG35FeC9yCzohdFccmSJdL7gdL9STkeNHiZ/zRyKJDaqq4Ug4lyw/P89u3bb7zxRpn3+bBRUnKZ09n5zjuvMQyTlzfp1luXonKUnzsnZ4Jq9u4BEYuAl34X3E0uJSXZ5XJL1c6ePTt9+vTdu3fjbZHQbWlp2b17N96/LNCNqoBXmrOhaqhCfn4++kuRdmiiCPgoRnYs2+dWLAJeNksjOe4TYoGEqiXIGHoB39bW8tlnnzz88A8SE23vv//WV1+Vz5w5Z+7ceVdffQ2qUFtbc/hwGS7dPR735s3/uPfeBx2OjO3b/7V16+aVK+8HALfb9eSTTyN5MPATNREAduzYgf6juirheo8UMhOJvc2bN95113eUAh7dFQQhNzd3ypQp1dXVUg+RtFtl3DpJdZPKpVdv1UVN9n4g26LHhTGueUf6U5eaGwwG/Btoamq6oBr8k08++cILL9jtdrvdPnny5Jdeeqm4uFhzb9qYP/+G+fNvkBVK+blVs3cPCFUdV+bLLoV6xQPd/O53v3n44dVSNeT+EAwGZS8Hra2tN9xwgyyngOyohef5I0eOVFVVQdT082iQaG5LpgCSSFb9ofsV8DINvt/Eg4IgMAzj8XiSkpLwRyivlTQ1Nf3P//zPz372s8mTJ0d/ytiBaPAEGUO/RX/mTHVBwZTk5FSDwThz5tXI4peidHq9Qa83UBS1a9cXixcvxZs0NJzPy5uUlZVjMpnmzJnX2NgAAAzDUBQVH29BDbX5G0hrhGp8TVzvQQsiACxYsAAAFi68yW63oQpo9xvpXpJ+b7FYZK7wkRa+qVOnyiS0Xq9HqrNULiVyVl3UZHsAFEUlJyejEplmhqtEkf7Upd5uvPFGXINnWTa6mxwMQsDv27fvr3/967Jly1atWmWz2aqrq6dOnbpy5coo+W/q6ur+7//+74477li2bPnWrZ+Xl5dLHoBnz579yU9+8tRTT2kbzIVDVYOXbTvv2tVtNI6/YMn26qXEbpLZo8Vi4Tju9OnTym9MuUV/8OBB9F1FEfC4Bi9lRkAZYyGqm1xDQ0N7e7vqx5ds9PBPEQVRFFmWPX36tFQSuwb/4osvrl+//oEHHoj+iDEFiWRHkDH0GjweL4WidJJlMuLIkUN5efloB16iqGhqQUG3SUtLS1NmZhYAuN0uURRfeeVPXV1dubkTlixZlpDQbYAjCILkpmww6A0GeUADlFcNDSYUCoZCoUjuZ0omT54UCgUZJkRR3Ro2SuQaCARomtbpKHRIHwoFZf4LkZYzme09z/M0HUbWTKIoIncsiqJYlg2FgsqFNS0tberUqTU156QSURSXLLl18+ZNS5fe/vLLLz///O/xZ0nDiLK88jwHAPHx8cFg77lsIOAXhO6nh0Ihmlaxp5P1yTBMKNQtzLZv3+50uoLBQHZ29s0395qkNTU1/ehH/+/MmZpp06atW/eqxWL5zW+ecTqdv/71/65bt27btm2vv/7axIl5r7zyytVXX52UZN+7d+/u3XvC4fC5c+cYhpk9e7bf73voocfRgHmeX7p06a5duziOe/zxx3FnNvxL1uwhMxikeMC4gN+7d+/Ro0fxatJ3aDKZMAHfZw4j3ZfneSnwsNlsxv+ycGQCnud5lEYWH4ly3VcV8Pv2lcka4qBODh8+/Pzzzz/33HOyu01NTc8997zyU0QBfZyDBw/OmjVLOc7oAr68vNxoNJaVlXV2duJhAMYyRIMnyBh6AT95cuHBg/vb2loTEhIOHtwXDvce5vE8f/DggQceeETWxGg0IU2ysvLYF198dtdd3wUAlmVycycuXnyb1WrdvPnDTz/dgsoBwOfr+vOfu6VaaenlKSkqbipocWBZprx8l9PpUlYAAL/fqyysqvqapt3Qnbuzd9v8m2/Kzp8/I52Ul5fvcrs78YZdXeqhUY4dOyB9CQaDnuf5I0f2BoN+APB6ncgJ2O/3dnRAefkuZSdxceaTJ786c+ZcTw+Gri73oUO7m5rOAcCGDR/cddetUmWfr6ujoztAW2enenYZQeCbm+sBoLb2lKSxAcCJE18Hg0GDwcBxXEVFeWNjnbJtINAn8MK5c1Xl5U0A4HK5V6xYhaSCxWL5wx+eKSoqQHV++cvf1taenzt31o9+tPr48d7XrJUrl86bd9V///f/Llt2BwDEx8chIzKKoubMmWW3W2fNumLJkptTU1NEUaytPe9yuY8erWBZ9uDBsqKiyT/96Q+TkpJU3awdjsySkitVP/sFpa7uPHoXlKRjU1PTPffcIwv6K2nVRqNREsyyqFuSBi8JPGT5EckGU3YGr9zFibRFLxPwTU0tUifKB0mdKE/o3W73hAkTZNIlelBb6BHnkvkL9NXgpdcUJTRNHzly5Lvf/e6bb75ZVlb27W9/O/qDxgg6nQ4PFUwgDL2Az8rKWbTo1o8+2kBR1NSppXhEsKqq4w6HAz99lwiFgh9//KHX673nnlXp6eMAIDs7d8WKe9Dd2bPnvvPOa1Jli8W6fPlKdJ2QoJJ61e/vqq9vBACK0pWUXFlTU6s61KQkFVfXgoKpxcXFLEsbDH1ygSQkpLz33j+sVqtOpxcEsaTkytTUPj65kaJDXHHFbOmMMy4ujqaZkpIrk5NTAMBmS0aiyGZLSkiwlZRcGRcnDxARFxdXUnKlKHarpKmpqYmJ9pKSKxkG7QFAUVGvvXcwGJJEQCT33MTExIyM8QCQnT0R31MdNy7XbDYXFhZUVZ3Mz5+SnPy1sq3M9Sg7e+KRI19s3frpokU3iaL4+98/e+bMmV27dv/5z69u3rwxOTmptrZu//6Djz76wC9+8T/KiA0lJVdeffV1x48f//rro9/5zoqamlqe5wsLC2Rx/mk6kJWVmZw8/nvfU/1AcsxmdefyC41Sb/7yyy+VIf0lAW8ymaTExEaj+ha9JFMNBkM4HMblNNoNAoUGv2HDBmkbv18NHv2rdMePosGD2kmN3+9XDX2o7AQHjyCJQCGDEKrZjRsaGpxO59KlS4PB4J133rlp06Zt27ZdsgI+evoiVSPiwWQ80ul0+BdIIAy9gOc4rri4BHkbV1efRJIMUVFxtLBQxa6K57l33nlt4sRJK1bcK4kBNPWzsnKg2964V8UxGnsDj6ii1xskN7nf/Oa5G2+8UbWaxWJVFqaljXM4Mmk6oNPpcHlWVnaIYVibzaDX6zmOczgyIzkx4xiNxvT08dLgzeY4gK6MjCzUNj7e0hPPJM5gMDocmUq/KVSektJ96hkfHx8XF+9wZKakNAEAz/OpqRl9W1A9j1Z/4fh//++/UJhSmy0ZF7rx8VadTo+kY0tLBx5yRALP5gkAiYlJH3+8Y9eu3R0dnVdeeeWaNU8CwPbt25cuXbpp05Yf//jHv//94waD4dZbFzkcmaohmRyOzIKCojvuWA4AJSXqnmmBgFuv56OHTLkUkOTZgw8+uGbNmnvvvVdVwuEavBQsWTVuNq6LGwwGKRUN4ne/+90TTzwBABRFpaamIgdOmqb37dsnicxI4eulW6hD5dOjnMGDmoBX1k9PT49RwPfdlu+9VjX4ePDBB5HLa2Zm5vXXX//YY4/97ne/+/73vz99uhavhwsKSl90++3Ls7Nz3nnn9crKCll2A6URcb9NokOs6Akyht7ILhgMrF37YleXl2Ho/fv3XHnlTFTOsmxNzbnJkwvwysgF+eTJEyaTeeHCxbgM8Ho9//jHex6PWxSFQ4fKioqmDnAgIgAEAoEXX3xR5lYkoQxWgxfKdj5ffPFF6PGSR2fzMmEcxVBf+lzojxn52uHler0eN7LDn4vqSDXNZjNeoty2lZbFSKdxVqtV8oO/9dZbpfPyt99+2+nszvFQUVGhulIo+zxx4gQAVFZWSv0sWrRo0aJFL7300o9//OONGzf+4Aer4+Mvjko9zEgWDBUVFbt27dq1a5fqKTKuwUsCXtUhHtfg0RY93mFubi66oCgqNzf3/PlzubnZECE/TSQNHlVWOi4OVINX1k9LS+tX2ChdQ/EJphrV/8CBAzRN22y2mpoaq9W6Zs2anJycxYsXo3l4SSGlLzIaTbNnzz12TG6KoTQijt6kX/R6cgZP6MPQa/A2m/2aa6579dWXzOa4GTNmTZvWrZY1NJxPSkpKTk7FKyMX5ObmpvPna3/1q/9ChRaL9Yknfj51aqnH437rrXU8z+fnT160aInmIUUyN1NdVfE4tco3AF0PACCFB0HI1iO0g4oEudTnlClTUCulmxy+2JlMJumYUybgpUg7qC2u1U2cOLGurq7fULVSJB+KolJSUubMmbNt2zYA+OKLLwAAOR2Fw2HV5n6/H6D3O6FpGp06A0BWVpZUvmbNmvnz569du/Y73/nOT3/649Onj8s7Go3g39gH/z973x0fVZW+/05vmT6TXkiBBEKo0oJgW4OAIqCAoiKWde3tJ+uqq65tV1TWXcXVL65YWNFVUMSG4q6ygoAQ6ZAAAiGkl+l97p3fH29yOLl3ZjKZAJJ4nz/yubn33HPPLXOe8/b33nv77beXLVvGb0bYUaFQENGZlqG1Wi2GydEvF30j6EuQU/B7kMlkcnlMQTyWBI/7+RJ8TwmezytisbjbQHa+ip6+Qf5VDh8+jG6MI0aMQLOCXq//6quvLrroot///veff/55/MudYcQvXxTVibiXFY+ETHYCODgtiW7Gjp0wduwEzs6CgqK77nqAsxNDkHNy8ioqpvH7KS+fXF4+Obkx0HNLLG/eHknwCMxRg/T82GOPhcPhxYsX4yGOyTA9Pa2urp4jwc+YMePuu++GTnomIbzE/RV/nwqFgiL4LgNQKBSEngEgEomQAnQ6nQ4SkOBJHDxn6UBGAgA+ny/q7Oz1egFOmvbfe+89lmWLiooOHz5MB7WPHz9+/vz5CxYsmDJlCjr0/RpAP3DMGBP1wyMvaPTo0WSJqVarySIvPz8fozo5NniOFz05lywT4wjisST4HqnoydUTIXiilIqD+BWh+IuMl19+GQDUavVdd91Fdg4cOHDRokX33XdfZWXlH/7wh2uvvTovj2O0+mUQp3wRxHAijnOK3+/78MOVuJ2Tk2M0covNhMMh/BJ27NicbFAxEwoFmprae5HGPwgABw7sjDq19mQMtqSLrGCh1X37fkq6khvLhkOhID2GUEgE0BHoUVt7dNeu6GGiBMFgAAD27Nme9F0wTDgcDjY3J7rC0+mM+fmD+Pv7cS76k1Nhjwielqr5Ij6K7ziTyuVyk+mkhwFnjkNjNofgOUlwSbVQiUSCkyYOm/zIoTsVPQA89FCH5gOvRScvi3rXdKpaAODUj8Gdfr8/VhZVGrimWbx4scfjmTRpEtkvk8nefffdbk/vZ+A/cFp1RHziCG+pVCp8yG+99dY554xWqZQejxcASNYXvhc9/Y1xJHgA0Gi4HppxbPB0YqUEJfhTTvB8FX18Cd7j8YhEovr6er2+C71dfvnlDz300JgxYyKRSFXVgcmTy//5z/euvPLKmTNnxh/AaUWc8kUQw4k4zikikYj8K5XK+LHBkQgrFuPvWpLIj5cPlhWzbEgqlSVN8OjiJ5XKkiZ4hhF1jiFpamRwDEkTPMMAy4bpMUQiJwfDcQiLCvz5SKXSpJ+kSASRSLjbC9Gjirq/3xI8jVgqemRrzjREPyl+vQpaRQ8UYfMxffrUl19+hW9rp0/kZ7Ij2XA54yGkrlariSGfc0X8VZM63/zcPnK5HC1/tA6Ak04O/boPHDhQWloa69YIjh07BgAjRowoKEiyQmV/AnnyBDRFyWQyDgsSVdDQoUMBQKNRI8GTqBCODR66phMm9nvydf2//3fn/v0H6WEwDPPjjz/++OOP9Eeu0+mcTictwccS/QOBwOLFi++9915UDpFO+D+oqCr6JLzo6VOiLjLuuOMODrsDQG5u7k8//TRt2rTJkydv2LDhvfdWqdXqd999l6iR4g/jNCFO+SKI4UQct+KRkoQORYXd3oZ0UlIynJYQEgeWi7Va85MuF9vW1rxnT/vAgaW9LBebmlqYdKHVlpaGfftsxcVlvSwXm5ZWFLVcbGZmbmlpbvweGhtPVFXZBw8e0ctysenpg5Je6CD6ebEZBErw48aN47Th5PgkO3FDKpXyJz48hRBwHIK//vrroHNejiXB0/8Saea8886jux00qJj0IJFIXn755WeffRZ4qnXoJPjW1o7ofH66sWuuuQZ4Knq67hkADBgwQCqVejweeqrlT6kInILNZnPUo782PPDAIs4emgj5UjIhePzMiDqHzM4cL3oAuO6668jp5LGTL8FgMJBOSA+rVq169tln6Z9DeXk5JGaD37Nnz+OPP759e0c1vE2bNvHvi25PI3GCj5WeNqoX/eTJ0W12gwYNOnz48PLlyzdu/G7Zsr83Nzc988wz77333tSpUz/++OMvvvhi5cqVS5cutdlsnBNDoVBra2tTU/SkEb1BrPJF6Fkc1Ym4lxWPMH2W4GcngOBXIcEjwRcWFnLy2UUleCJkL1nytNvNXnPNtfRRFH8TIXiMUouloudI8BKJBCfNSCRSXFx85MgR0g9O90SpjsVpIBrBd6sWQ90vR0WflZX18ccf33jjjTj36fX6m266ieMBrlaradmRhkKhQPHuVwKPx+Z2cxOwoOSNKY1p2GwdSYeifmlery0S8QOAy9Vosyn1evIYO8jSbm8KhzuoNBI5KXAj3O6mQYOKDh48bLOdaGoy4dU5H4XH43Q6W4PBAMuGAUCrTXG53KNGle7Zs9vrdTU1HW5trQGAQMBJThGJRCqV0u22NTUdPnhwJwC0tBxvajoMACtXvtt5aQfuIWhpqeHcHcuGGCbMacaBz+cEALe7nTQLBk/mT/R4HE1Nh/1+EUAh7klPTxs4MCN+n06no6iowOmsu+mmuddcM+Oyy+bNnj2bHH3qqSdHjRpeVXXw/PPPHTZs6CeffP7DD1tDobBYLL7qqiuKigoyMtINBp3T6Roxoj4rK9Pr9bIs+/33P/z0066SkkHz5s3mX1Gh0BgMUWI4xWJJ1PJF6Fkc1Yk41ikJgqSv7tFZAvoxfkUEj/yn1+udTieyF1LdgAEDFAoFKRtDJuIxY0bV1nLX+4lL8EqlArqT4Mn+vLw8FI+wng3dLa1O72o+iK6ijwNyUY4Vf+bMmXfffTcSvEgk0mq1gUCABOyRWICoSE9P76USqW9BLldpNNz8SAwjhWgT6/r1G3CDFO4TiURk5aRSadFqrtdblEqtydShjyWZoMRiOb6uSCTCseACgEZjsFpTDx48rFIZNBoTitC8b0AkEknb2tpxbFqt1uVyDxs2srz82PHjtRqNSaHQAoBGc1JDY7Va8vPzASQajQkAF6kdt0ySqITDDOchyOUdKSUWLbr788/X799/QCqVBwJBhyOQmRkzgYFYLAMAqVRBeqONYiKRVKMxoV0ZUV5eXlTUTbgs5k5Wq01isVijga+++mLhwpsnT550+eWX1tTUvv76G+vX/2fy5EkfffTZW2+tLCkpfvjhP6SmWmtrT7zwwovQVRWhUikjkYjfHwCArKxMsVjGf/UQO8MVxChfRIobRXUi7k3FI/yBCxK8AIJ+S/D8Opv49S9ZsqS+vv6xxx6DTrYuLCycNWvWnXfeiY1pEuWbshIkeOKgR+tggUoZxpHglUolUZlySsvEIvhYKvo4IARP98m5a6SiYDCIlFBaOmTYsMFNTe0Oh8Pj8fA1sYMGRXHd7MeQyZQyGTesPxTi2pIRdXX1uEEIXqFQkJKsRqMFdxoMqSqVduHC+d9++z0AaDQdorxEIgfoeB38FIcajREdOTUafUqKKRAIAQDH7hiJiABOOruhZVSj0SuV6kgEUlJMKpUOAFJSThK8RCJVKJQA4pQUk0ymAgCZTJWSggTfwRzhMIt7CAjB5+fnoeVYLlcwDOP1hjgtaUgkHQRP2ohEXTxgUlJMtFpKpVLH6Q3hcnkAICXFiGuFlBTTV1+tx0NDh44iNYIZhmlubk5NTSVf/j333K9SqXbv3q1QiBnGe/Roy88//4xHy8vLsQbVWQ5BghfAQb8leBo0wWu1WkLbYgqkMb3NTwZHe9EDj+DVajVW8SLR6lKpBADeeOONpUuXvvrqq5mZmdiSY4NHKc3n8zmdzqgSPH0WOYUztm4JnvA6Z3nBGYlCoSASvNlsfu65J++55485OTn33nvvzTffTHeYm5tbXi6413WALzmR5LUkm6xSqSQEf/XVV69ZswY613/DhnV4NZKkbGiDR4KP6jbFyaYAnd8bQTgcptdk2AmuPmOFyfGPhsPh6urqQCBAuuKHpZBDYrEYw/HxW42fjj6+kx3/efJ9YpKGRCLheJ+kp6cDwKRJk7xeu8PRNGHCxafqWmcMJDfGLz0QAWcL+i3B07MGki7OhiNHjqyt7cj/jGwtkUhikWgsCZ4viOPEpFKpOASPvQ0ZMuT+++8vLi6eOHEifRX6upFI5Ouvv66rqzsDKnqkEL6eADqpiPjsROVhAAAgAElEQVRv4yn4iEjidIInnnhi4cIk42H6H+JMrESjQ39RCoWipKRk3LhxBoOBZYPk5RI3C3wLcrnc4/HwHz5ZNHTVxHCzK9IUa7FYlEplamoqoXB+JjvyAZA7CofDjz76aHt7O/F6ixMmJ5FICguLvvtuQyJkc+DAAehK6i0tLWSbL4nGMRUJgM5fsSDBCyD4VXjR0xK8yWQi05lKpUI2jUp1EE1iiErwhFlJe4rgOw4VFRXdc889HNGfI8Gjn93pU9GTrtAtLtZd43qFTporkUiipv1JOtq1/yESicSZWGkJnt45ZsyYLVu2IG+R18fxosdvRqvllg4ib4R+jxwK5BB8RkaGw+E455xzOATfrQQfCoV8Ph8/kx3LslixnhC5TCYdNWokJCZNHjp0CLr+VHfu3EW2T6sE3y+B/gqCBC+AoN8SPA1UiloslpSUFJ1OR6YJtVotkUjiSPCUY3MHOAR/4YUXPvLIIyRwGXvOzs6+9957O1X00Uk3qooeZ1WOBE9P4hxJi9Nnt/KNyWSaO3fuiBEj4qvoxWJxMBjctm0bdAbe4COK41EoIL7YRCR4+g1ynif58GiCj0Qi+FrplEoYAR9Vgud8b6FQiCZ4Yl2KqqLv/CajSPAMw9AlYkmY3P/93/9hvgRyFZJgJHEVPXl04XCYVv4LEnxPgW4HggQvgOBXMWXjPHXFFVfU1dXJZDIyTajVaplMRlOXXq+np2CNRsOppIkqfcLoRUVFTz/9NIfgBw4c+Mgjj8Qn+KgSPM6GcSR4jqTF6bNbCV4ul//73/8uLi7mJ6nlEzyG6uE0PWDAgLy8PEFej4P4edejEjxniUZeH/nkaAmeNhhjA9ozn98JdCa/oymWvMGoKvrOsmYiPBE6qaKpqWnv3r3EdQAoCd5ms6GfAS3B0992fGmSk8kOzVsE/HP5ZW0F0BAkeAEc9FuC59fUEovFqJqmVfQymYyW4H/88UcOjeGcQhqIxeKHH374tttuo9u8+OKLdAkZmj57JMGT8t6xbPC9lOA50XqxCJ7vXf+nP/3p448/FiT4OCAhZFGfEiH4OEs04h/HkeDxCzzvvPNSU1NxP+kkjgRvNptHjhzJIXhyxagSPO2CFwqFPv30U6x5s3379qNHj9ISPK2ip+vKQ6eyBxIL2eI42SHBk9vhS6ICwceHYIMXwMGvYsrGWYbMboQmUZpHAzPu4Vs6cZ4qK+soPy8Wi2fOnDlhQpdSOldddRVOvjhRokDfGxU9X5GbCMFnZ2fHfw70MgW6EgxHgif76fELBB8HOKtKpdKoJETU6bHiL4ASr0mbNWvWOJ3OadOmrV69etiwYePHj6cbxFfRz5w5c/z48ZwCdLEkeKwgrNNpcWAymaylpWXGjBlr166FToU8rTznEzwlwctof5H4Knq6RDLwfqf8xQEnT58ADvDJCxK8AIJ+O2XzJXgyD9IEL5fLaRV9rGk3TgO6GU6v8+fPh+4InlNGVtRZ3B130r7WiRO8xWKJei3+KXFU9LzteH77AgiwGDwaffhHxZ25Ewg38/P7ku+QvOWGhgasfY652Di+nEjwcrk8Le1k8TSiBiC+cjTB0xI8XdzIaDTu3Llz5szLyVBRXkeRGlvS6WmDwSAxn+PCFElFJpMplYrEVfQcCR6HSgbJl0QFJ7v4wFlIkOAFEPTbMDkaHMmAVOsiEjyHaGngrMopEsMHrYDtlDNEcdoTdSi5Li3B0wRPmwxo6ZDf8+lT0dNjFhAVLBsBgKlTp37zzTf8oxwbfFlZ2ebNm/nNpFJpMBjkvEfyOgjBE9O7VCo1GAz0wo60wUT3sQg+PT29vb2dpDMSiUTDhg0jmieZTIbyOjpaIrXTBM+yrMfjSUlJIW72+Ol+8806lvXhVfBbTYLgSWVFgeBjweu1u1ytvJ1e/FSam4/qdMkkl8T30NZ2POmBYb7k1tYazIWQ1BgiANDaeizpMaBdqaXlaHIl9cgYWlqOkj2h0Ml8yU5nc1OTM/qZncDQkubmI0kLRTiG5uafE2wfK19yvyV42ueJSMb4L8k2gwRPO7WdEgm+02wvIn/54FSE4xM8UaLSZHzJJZeQHvhrkW6nP3KKxWLJy8ujVfqxVPToRY8477zzRowYsXNnot/crwrIRrNmzfruu+9wT3l5+Q8//IDbHBW9WCyOqm2OSvC02I0b5EVLpVLOLFZcXPL5519ApyBO16MDarlmMpkYhiE5mzs1BzJyIpakwy+QT/AAYLfbaYJHIs/JyampOYi9XXjhhStXrsQe7Hb7jBkzli9fXlRUxH9ohMg7Cb6Lit7r9QJ0PCvBix4hlSrUam79J4YRdb5HNf9oIgiHw36/U6lM4VfRTBCY0lGl0iYtDITDIb/fpVRqk6bGQIABAJVKl/TXEg4H/H6PSqUlxV7pb18uV6nV3ayffL4QAKjVul48yUAg4FGp9AnmAY+VL7nfEjzAyXmNOK/hv4MGDVq9evUVV1whk8ksFovZbD5VKno6okkk6l6CHz16NP7LcbKLRfC0rJZ0HDz2g5Ve+YeiBumR/s1mM0AfJviqqv3ffrve5XJmZGTNmDFbrzfQR//5z39gEU8AGDt2wtSpMxLvORLpUC+TtzBkyBBC8BwJPmbxZp6nPVAvmkxYxIteKpVyusrKyqKvGEdFDwA+n++tt94il8DrjhkzhijwEVEJftWqVU8++STWGiYSPP1jwVg+VPVv27bt+++/r66upgmerDz4Ejz9r9/vJwQvGIkQcrlKLufWJmAYcafuRK/VWpPoNhDw+v1OjcaUdLnYYBBdOky9KRfr97tSUsxJF1r1+8MAkJJi7k25WL/fk5JiiVouVqnU8l21OPB4AgCQkmLpTbnYQMCj1Vr483yP0OcJPhDw+P3cKl4eT5TSZx5Pu8PRURRywIAMAAiFPO+8s0wmk3322Ze43+1ulUhCAMAwYQBwuVpRBI9EOjSNLMuQTrqCBYC0NIvRaDAa1Q5Hk9/vBIBwOBi1PcP4AUAmY/FoIOBhWdbttgNAMOidPfuytDTz66+/CQDhsN/haMIKZoGAm/TGr2kWDvsgGkSdBU78fleMwZ+8wUDAEwjQz5MFAKezGS0OpBnC63U4HH6IC6wY5nA0Jf2lhkIB7CHB9jKZQq028Pfb7bY1az647rqbrNa0r7767PPP18yfv5BuYLO1//73j3ZauHvGJaiiJ5I6UPXagUfw8T0z0E+N0B7tGQcAt9566+HDhzFWU6VScXz66CoJfILndFVdXf3mm29CJ3HiucXFxXZ7l58Pba0nWL58uc1mw6SQ5Co0wZvNZpFIhEUI0ZbPcbjjCO7As8HjelewKCcOwYteAAd9nuBZNhwKcTkmHA7x45IZJki1ZABAIhHJZGIAhmVDnW1C2AYFsnA4wNGxi0QR/uWgUwYaPHjQ0qUvAEAo5GeYMJqzo7ZPS7MYDHqVSoZHGSYUiUSwVibLMtOnXzxlygXFxUUPPPAI9hAOow8zS3pjGG5NbgBWHK0I929/u7ClpfXjjz9lmCjPitsFG0Z/MQTefijkR4LncDR5XHGAS6VQKJD0SrSzh24uRBBLzqutrcnPL8zKygGACRMmLV/+Gn00GAyKRKKkJQ+i6yYUS4R1hmE4KvpYBI8uctiYyNCkMW7cfvvtDz74YF5enk6nW7Ro0bXXdilnnJpqValUPp+PRLvFkeCJUE5nXVSr1dnZOZwnwx8qLdaTq9DOmyqVSq1Wo0EUmy1btuzIkSP33Xcf/cSgOwk+foIBATSEOHgBHPR5glep9CoV1+AkFrfw5wWTKdtiycNtszn33XffveyyWehYpNd3+CFbrXkoeAUCnvb2E0ZjllyuBACFokMhplCoSSc0LJZUANDpLNTRxjjtZ83KmznzKiLUYo2sztqdBjzl6quveeCBR7Rao8WSFwhIAUCns5LeWJaro9PrUyUSCcuy999//1tvvYVmVADQ6cxebwgA9Hpr1MEAAN4mAGg0Rp2uwxCQkZFx6aWX4eNCiValSqHP0mot3bruB4MigDqLJTdpCd7jsTmdzbFGnjhKSoYMHNiR5r2hoS4jI4s+arO1RyKR1177u9PpzM3Nu/TSWSkpHYq4SCQSCHQsL+iYCwKGCROvco6UPHXqJZ999rlYLELTMmH9cLhLOncUgpFiWZahCV4sFmHjzutGpFIJ9qDX6/R6HR7FaX369OlHjvyckZHZeWKYM913diWCTv05XjEcDkkkHYp6jo2AX1oGOrNDolOV3+8PhYLQ1WlOJEKXgkA4HMILffHFF263+667Oso2khv88ssv3W6XUqnETmgJPhwOBYN0bjsmHO5GPMXlaTgcEouTFGQZJsyyLOcFxYFIJEpaE3tqkUjkgoBfFc6K7/LMgJ6XRSIRBrNxDvFJKEEb/Jw5c7788kvO0ahkQI+B3qZt8HQDOlUt3zpOTPXQmWUvFAoVFhaq1WpC8CTQPw7FRrXBl5eXX3LJFIejkXPRPgqZTI7S9d69u9av/3Lu3Gvoo6FQMDd3wLRpMzQazZo1q774Yi1p4Ha7/vrXv+B2Wdkwk4mbwBg6Wa26eheqYQCgoeE4ALhcduzcbm8DAJutBQBcLsfGjV/zO0Fy2rRpPf2mamoOYePm5joA2Llzs93eHgwGovawffv/UFxubKwNhTyhUMhmO+lu3dhYi2dVVe0CgD17tuP+HTt+aGmpqas7DgDHjh1qb2+g+3S5TvoMi8Xi66+/+s0333W7XQDg8XgAYMuWbw8d2g8Ahw7tBYDDh/cDwL59lSzLHD16aOPGr/ft24Gn2+1tZNiE4Juamr7++hOTyXjs2HHokvfGs3Hj1w0NDoCOn+rBg/s2bqzl3zUfP/zwn0SaxcGhQ0cSbGm1ppeWju7l5U4JBBW9AA76LcGHQtwMG3HoLY4XPR0mhy73UXvgRNORbhMUWzle9PSoYqWaMZvNL7300rp167744ouCgoIjR45gXn3gLSySJnj+zfbS4+MXh8/n/eSTVQ6H49prb0hNTacPZWfnzpvXoe4eN658xYo3yCGlUnnppbNwW6fT8gu7eb2e5uYWAMjKGqDRdCg5MjKyAcBksgCAXK4wGs0SiQQvqtPpBw0qo3sIhwMejx0N6sXFw+RyOcnbmpGRi40tljQAKCoaYjSaW1ttnB48HmddXU1+fgm+I7M5NTMzk2EYonzCnXhWW5sHACyWjria/Pzi/Pz8tLTdOGxejH6XcMqrrrrmzTffRZ8DRE5OweHDtWKxOCMjp6GhNjMzFwAKC0ukUpnJZB00qGzr1t3YUqFQkWHTioGsrIJdu3bl5ORAlzx9skGDyuTyk74X6elZgwZF8a6g4XDYmppODBxY2lMvCoJg0OvzuYhWr1skbdY55RAkeAEc9FuC37atkrMnEXrjU1pWVtaOHTtwv9VqjUXwYio9J33FBGcZWoKnLhFPggeAu+66q7KyMisra+LEiUeOHEEJHngEn5pqra+vh8SeAMRd7vRpN2aGCa9Y8caAAYXz5l3HfxToP48WeolEIpWejLGRyeSjR4+N07PN1oZyp8WSRqZ7qzUNAAwGEwBIpTKdziCVSvV6IwBoNCnIggSBgKe9nVUolGKxODMzl1aSWyyp2NhoNANAVlaeTqdXqVScHtramurqatLSMpVKlUgk0ukMZjO6Up+8U73eiGelp9cCQEpKhyoiPT07MzPXZDIDgNls4Sx9aMIQi8WZmTnQ1WPOZErVavVisdhotDQ01OJ1MzNzpVKpRqNNS8tSqzsWPW63x2xOwxASOvdte7vjd7+7/YUXXgAAmUxKXSvX7T45foPBnJnJTRDEgUgkamo6kZGRk3SEktdrdzhEGRm53Tc9yyAQvAAO+vB8HR/hMPcrj0NOhYWFmA6M3waz4uD+22677Xe/+13UHujsXdTOUyDBxyF43GOxWDBsIyrB33zz9XfccUf8mD34dUjwBw7sk8sVFRXTOHdRV1cbDAYcDvsHH7xrt9siEfbHHzeXlAzpUed8L3q6ggu6vJGw9The9CJeVSGOk51UKr344otnzIgXwieVSom7H2rsUZmEIjLpn3jP4YsmWijO8GgnO3KD9E50IOAsTFHdxbLsmDFjkLkB4MCBA59//nnnE2OhM+SP2PKB+uo4teYEdAtBRS+Ag34rwfOd7OKQU2lp6cMPP3zPPffEojTcP3v27CFDos/72ICu6YlnJyjyxrHB0/nG+b1dcsklRUVFDQ0N2LKsrOx///sfTfAKhZycHucJEN1DfILv0xJ8fX1dTc3RJ554CP9VqzWLFv0RAJYvf23hwluGDCmz221vv/06wzAFBUVTplzaw84boGscPJ/gSSXDOHHwfLsMIXtSNn7BggXxB4MEj520trbiFb/++uuxY8eSBkCRdKcFSgpdAwEQNJeTbuMQ/MiRIxYuXJiVlYUEX19f39zczO8Nf6GzZs167733kNp//vlnMpji4mLcKdBV4hAkeAEcCATf5Si/DS2ax3ExwwacRF09kuAZhnn77bchhgSvUChEIhG/kMncuXMB4J577gEAmUz22Wef6XQ6qVRKMkmhv7RggweAioppFRXT+PsfffQZ3Cgvn1xePjm5zisrd0JnnlfcQN0PXYOVSPB09ngaxMkjvgTf7WCwH2x54sQJHMAFF1xAN4BkJXg8l/59YZgc+VoyMzNJeD1Wo6Ebc4LisEoTcvmBAweg84M3Go0HDx7817/+NXJkebf3e9bi+PFjn3++xuv1Dh06rKJiWlSDXTAY3LmzcuzYjvpVvcm2hJEaAsELIBAIvgNms9lkMsWX4LsleE6GI05WuDgQiUQsy9psNoghwRsMhsrKSlLUjgNSyESr1b755pvTpk0bPXr0iBEjwuEwzilRLQg0aAm+v9rgTytIkhZ8X+vWrUtPTwcA9MgjKnp8U3TKYRq0BE9SGpBARCLBdzsYq9VqsVjilqbtIoV3EnyHdoEjwdNZ7WgbBAFPRX/yoizLcviGSOT4C8Xe0DiFFzIaDQqFIi0tbcuWLQsWLNi+fV+393t2gmWZ1avfnzlzTnZ2zooVy/fu3V1WNoLfbN26T2tqjhKC7022JWwvELwAgn47X9Nevoj45DR//vzjx4/z99NidJwesrKyrFYrqdjdeW68MDn+VTjbMplULBZjpD4AjBw5MtbkjvI6TgoLFy60WCylpaX0mBNX0QN1m/1Mgj+tIOYVZMecnBzMNm+1WgcPHiwWi5VKpVKp5Bd4pUFs8FKpdPz48eeffz4AFBQU4NHEJfjdu3ffcccddEvOFXskwdMgN0gjPsFzEthx8tvgtZDasWVubrbT6UBbWCQSIdEEfQ5Hjvys1xvy8wtlMvm4ceW7du3gt6mq2t/QUEf+JdmWJBKpRCLt6XpasMEL4KDfSvB0LnpEt+TED3+ChFX048ePpw2Nnef2QEXP31YoFOvWrTr33Cndno4Ez5l5Z8yY8eGHHyaooo9qg+ffLx4VJhA+OBK8WCzGjEl6vf7ZZ5/dtWvXzTffPGPGjC1btkDsFyGVSoiTnVQqxSUCeR2JS/B4YrcSPJVLB+0C0W3wNKJK8BwVPX3RWBL8oUOHduzYQQaJ1N7pZNolHhVD7fsiHA6bxdKRE95qTXU47JwGbrfru+++mTr1srVrV+OeBLMtiURiugoUgmHCuDMYDCaepYcGLtTC4RAdedEjYNJJhgknNwCgsgwlncEQv7dwOJS0upGMgeRKCocBoONH0aNsS724i44xJM4gUbMt9VuC76mKPhbwLI1GIxKJolYAi392byR4ABg6dEgidZloCZ5gyZIlH374IS3B996LXkxV3hNAg2E6crUiY4nF4rS0tIcffvjCCy/MyspCp/eMjIzt27dD7BdBiA2V+ZzkCgUFBUVFRYnXyBo2bBjJgxRfgqdrIfJV9DSIlYFGXV1d4hI8Evwrr7zy7rvvQtelBv5FPTMZ8Pbt2wGSdIz4ZeH1esmPVy5X+HwcVUTkk09WVVRMpWeVONmWvF7PCy90OIsMHVpmNkepF4evYO/eSoMh+bJ7iWf4iYXt27/vZQ+9H8OPP244hWMIh8UAUzv3J5ptafPm/57CMcSHxZI2dOg5/P39luD5KvreEHxFRcUf//hHq7VnNZoSl+D5V+wRcB7huODRpE7TfFTQEjzZjqqiF4vFgo2PD5LDgBC8SCR65plnOM2IC17UTogNHv3tOTWFp02bNm1aFCfBWMjNzU1LS6urq4MYEjxHRU88/OMoCZ566ik+wS9cuLCioiJBCR5X3qFQCOmcr6IvKRlIDxjL1fRFqFQqu92G28FgQKnsklt627YtZrO1oGBgS8vJTD5xsi0pFMo5czoy+mk0GrWam6na43G1tbUDQFZWXmnpqCQGHAoF3O42nS416cy7Lpfj+PGfBw4sTbqSWyjkd7vb9fq0pNMYOJ222tqjxcVldCqLHiEY9Hk8NoMhnSoXe/IHm5mZV1raDRHY7W11dTWDBw9P+i4CAa/XazcYMhKkA5JrnIN+S/B8FX1yGhtRZ53WQYMG9fzcZGzwSYwzPz9/wIAB+fn5/D57aoPvVoIX/OyiglRbISr6qM3i2+BpJzsSCtGb9MCEjBOxwZOlSRwJfsSIEVHpv729nT9OsVjMKWcHVA14JH5aRY97zj9/Ej1gdLDvizAYTHv3dqTwa2trMxiM9NG6uhPV1ft37apk2UgoFFy8+Ik77/x/uCCImm1JKpUOGRLdxxYhk8lRRZ+SordaM5IYcCDgFYkCFkta0uVixWIJwM8mU2pvysWKREGLJb136f2Pms1pvSkXKxaHLJb0qOVitVq91RpFfUKDYRiAmt7chdfrkEjCVmuiBB8L/XayPlUqepz70CO6pxCJEr1oLBV9grj88suPHj3KmXlpUu+Rk118CV5Q0UcFR4KPxZGERGMdxUN6vV6v1yPB92ZFRVeP5e/nOdlJgCfBc6rVoZMB/xsIBAJRl4O0Bz4CCZ5hGHxieK2XXnoJOmkevUZIb1Fr2fUJFBQUtre3NTbWRyJsZeXWsrLhuB8TK82cOefBBx9/8MHHb775NqPR9OCDj2s0Kb3MtoSExLGJCPg1o98S/KlV0Sc3ySYu7/aS4OP0iYt6jIFOxAZPj1mwwScOIsHL5fLi4uLs7OyozRL0on/nnXf+8Y9/kNJzSY8qlgQfVUVvNpsVCoXFYqVXJ5mZmfSJ2HLWrI7M/MR+HIvg+fRM8tMhD+FIMECUdhfoBwQvFkvmzJm/Zs2qpUtfTE1NGz58JO5fvvy1pqbGqKcMGVI2blz522+//uKLi1mWvfjiHlhkQPCiF8BDf1bRDxgwoLm5mYTZ9EZFn7QUdWYk+Dh9ohlpxIgRkFgcvEKh6NYGf0qG189ACP6ee+658sorYzVL0AaPSXJ6L8ETWTyqBE/KveAbz87OPnTop6ysQU6nG/efd955GRldlL04vKlTp3700UcA8Pbbb8+ZMwcAgsFgbyR4RKcE38VfpO8SPADk5OTdeuvdnJ0ksRLCak27664HyL+9ybYk1IMXwEG/naxZNqLT6ehJpzcq+qTPxZ9ctzgdBE/rivPy8pYsWVJeHjMpGCF1lUpFHhoJwScQJPhYICr6kpKSiy66KFaz+Cp6qbRLuUJ8/qdERc+X4EUiEUeCh04tPTnrhRde4HwDtLUeAIqKijC5UyAQ4H8YYrGYTzb19fVogEemp33I6VTN/UCCP/MQJHgBHPRbCT4SiXDEzTOvou9RJruo272B2Wz+4IP39PoOR637778/TmNC8EqlErdfffXV+fPnc3wVBQk+EmExyJUGy4YxTC4SYRgmXgSwRCKK2oxlUV8tEYlE5JDBgL48bPw+O3tgAIBhwnRjlI8VCoVCIed0IpPJiASP4+nsIUS9YZYXb80yTIjslEjEqalWl8sVDAYVCjnVQ4fPRyjEpeennnpq+PBhGDANAFptCjlEVPQME4pEOljK7z9ZdA4XBok9h5M99BSkhwTbi0QisfismEjFQi56AV1xWr7L3bt3bNjwn3A4PHjw0ClTpolE4k2bNnzzzTq6zQMPPEIqZ0OMpM2JZHKOjQiak6+44orvv/++ubn5zBM8KdCe4FX4273E5MmTDhzYmUhLWmrH7YEDB+p0Oq/XwWn2Kyd4r9fudLZwdno8PqQTu72huTle6Krb3QoAdntj1GYM449EWHIoPV1vMhnt9hMuV/dfESaEaWs7TsqtYpcAcPPNC2bOnMa5olQqcbs7Uq+0th4jr7W19ThxULXb630+G32W3V7f3Kz3eNrwX4ejHr/WQMCv0aicziYAaG2t6cyiGHE6u5yOqK8/4vE4cZtlT+ax6YyDF7W0HPP57J335SQNXK6W5uZuouZcLicAtLQcTboePCL+e6ShVGqNxszu251+CAQvgINTT/BNTQ1ffvnpLbfcodXqVq58e/v2rWPGTCgvnzR+/ERscPTokW3bNtPsHjVpc4KZnGOBZSPo8p2bm2uxWJqbm8+8Df7ee2+77LKY5lj+VfjbZwwo6i1YsODcc8+trq6ONQzBi16p1Eql3JBTicSOelG9Pt1kyolzusmUCQAGQxqnWSjkd7laUlL0EomUHLrqquvnzr0uYSVQK0CDwZChUJwcnkqlAYCSkqGTJlVw2stkskikI5mxxZIHAMGg1+1uMxozRSIJZsgxGDINBkfnPUoYhjEYMk2mHKMxvfN2sjC82+fzS6VyjcYM0GQ0ZmF8l8Fg8noD/KGq1UaZrGOQ2dkn6zMRFb3RmK3RdBRmZNmT35tGYzKZdPGfQzjcANBsNGb3IgTZ7fHY4r9HGklf6JQDRyIQvACCU0/whw5VDxxYbDSaAWDMmPFbt/4wZswEkluRZZlvv11P0jMhSNJmABg3rnzHjsqyshFRdyY+DFTRWyyWAQMGDBkyZP/+/b2xwSdH8GPHjiaJxOPjFyd4tKouWrTIarUeOnQIYtyyIFJs6xgAACAASURBVMFLJDKJhBsCJ5P5MGpDqVQrFPEigNXqFACQy1W8ZhEAUChUYrE4fg+xIJMpOns+aTVHsp8zZx6/T5lMjirxF198EY9GIgz2IBZLZTJZOBxWKtU4YAz/YxgGbxB3AoBKlYLRxgzDiMViMgaZTA4Aer2htfVn/lDFYikJYrVaTxbWI34MCkVHD9BZhxchlcqx/HEcYAy3QqFOmncZJog9JHf6LwjByU4AB6ee4BmGISo+kUhMcjkhKit/zM8v0OsN9M6oSZvjZHJmWZb8K5VK+BmLiHFx69YtCoXif//7HwD4/T5i+esWwaAvGAz5fD78tQQCfl6myW57COBFE/F5oZ2Nw+EQXisQ8AeDIZ/Pm/RUhQ5Kfr9PIunWoBgBAIZhfD4v5pEOBgM+n9fv7xgDKVRFrz+CwaDP180jRSusz+dNeuFCxpBge4lEknSOi6RBvOjjN4vvRZ+Tk8MJS+slsBJd1NQ0arXa5XIBQNTseDKZzOfzkbB+TMHr9/s5TnZ0Xlv+yk+lUkUtFROJRAgJYbwA2Q88nRmWuxWQIMSdRQgFCIDTQfBFRYO2bNnU1NSYkpKyZctG2keGYZgtW3648cbfcU6JmrQ5TiZnl8v50kvP43ZZ2XCTSQs8sCzr83l27doMADZbCwBs27ahpyHFR4/WNDbWAsDu3Vubm4/16FzEjh0/JHaharJ9/PjhrVu/pceQxHVpJJIaurm5HgD2799us52oqjoEAFVVO+XyMGcMzc31dBmJn3/ev3VrHa+zKPjxx+96OGouEn8OVmtGcqk6ewOWZS0WS6zwd4L4XvS33XbrnXfedQpHhckNo372Wq3W6XTGGgwZp8lkgq4peIFaMdBZcfirFqlUyg+TAwCWZQkJYUkeGnR5JOi69hXQLdCS8kuPQsDZglNP8FlZOVOmTP/oo/dFItGQIWW05L1//x6r1Upb3xFRkzbHyeSs0aRcd91N5FylkiuuuVz2SATUas3w4eMAwGxOBYARI8Ynrl5Gm6jBkJGa+iUAlJaO6nbu5sDhaD927NCQISOJsjEO9u496dGTnZ2Pw+60iWYl7S5ks7UeP/7z0KGju82YmJW1AQBGjBiXnp4OoAKAoqLS4cPHBQIej6fdaMzGCTc1dS0tHOfmFg4f3s1jaWtrPnHi6LBhY3shwbu93h7YRM+8+A4ALMtOmDCeUy+Yj0GDBt1yyy1YCzUqTq2BhlOuhkZKSgqmqY96RULnOFQOwScowUulUlQScPCvf/3r8OHDeGl+LSUcz4ABA/BfIS9bjyAQvAAap57gw+Hw4MGlI0aMBoDq6gNGo4kc2r17x6BBg/mnRE3aHCeTs1QqLSgo4vdDEIlEACIymcxotAAALg5MJmvis2cg4GEYj8FgVqs1AGA0WrCrxIHmAL3eRDs9xQIpCgkAGo0Wr+X3u1jWZzCYe6GiDwCAwWDutu5CSooOANLSMo1GU1ZWjkgkyszMNhotXq8sEvEbjWZcZKhUGpotNBqt0RhFfUIDVS9GoyVp6vJ4JACBnj7/MwyWZRN5TVqt9v/+7//OwHgQcRQGarUa9edxJHiJRILZ9EiVPFILB5tJJJLBgwd/9913EEOCb29v53e+YcMGchVRRwHik1pl/NJIYYWkC272YzBMiF+PNRTyA4BYLA4E/IFAz+yJnT0EACAY9CduyozRgy9pRx2qhyQnPdID+pQk1UMQAAIBH5WMAQA6fDLC4WAg0M3zCYexB2/SuehJDwlOm2KxBD1gODj1BO/1et5449WbbrpNqVRu2rRh7NgJuD8UCh058vO0aTPoxnV1tVZrakFB4dq1qxsb69PS0isrt6IzXdSdiYNlWfJoepOsppeZ7BJE7+P1ewmcsrEe3cCBA2tra7OysvjNhDj4WGDZSG9yyp4mxEl2q1Qq0UUjvgRPsunRawVSmFgikRAjelSCjz88sozoSvAi4NU+FkDD73c7nc2cnV6vDwDEYrHb3d7enlA906hwOBq6bxQDbrcHAOz2Brk8+Xq1AGC31/diDG4AsNnqMWtTL8Zw0vgYCokAOoqNeTzt7e3dxGq63S4AsNnqejlb2myJOqAolSlGY5QZ+9QTvE6nnzjxvGXLlioUynPOGTt0aEeJhdraGoPBgN71BMuXv7Zw4S05OXmYtDkUChUXl2DSZpLJmd6ZOCKRkzOORCLpZa7Z081qvawm13uQjCj4b1R2ByGTXWxELYj+i4MwKP8QKS6s00UJPKMry+Gqjq5dm7iKPsHhhUKh6667bsWKFdBpg+er7gUQqFQ6uZybZdLhsNXW1kkkEqVSh3GPPUUw6Hc6m4zGTH6cSIIQi1vr6hpI8GRSY/A6nS0mU/JRjiJRc319o8mUnfQaMRDwuFytJlNOVwm+AykpZovFEP3MTrBsQ2Njk9mck7QE7/e73e42szk3cQk+6v7Tkuhm7NgJRHAnKCgoolMuI0ha5qhJm6PuTBCRyMkJtze01Bvpv6dXkcvlTz75ZEUFN2T5DCA/P7+4uLhbGXTChAk2W/iNN+K3+jUiEmHPQglepVJJpdI4BC+RSPT6KLUvORZ3ThncqAQfX4IfOXKkWCw+cOAA7VdPX4XkrMV+BIKPA7FYwp/QMT5QKpVGIkDSDPQIqEeRShVJl4vtHIMiuQFAp2VTKlUkTY24OpHJFFFV1okA1eMymYI8ZNpMJJHI4tRT7joGZdJ3gYYGmUwplIuNDoy4wW2xWJz05HsmVfQSieTBBx/My0tm9d1LzJ49u6qqKpFmL7744hkYT58Dw5yNEvz8+fM3bNgQdWBI8HK5POoMgnRL6Dyqk9306dM1Gk2CEvwzzzyzfft2o7FLTXRawaDRaHCnQPC9gRAmJ4DGWTclnSpwJHiM9kkCZ1JFfxaKgAISRCQS4aVt/+WRkpISq8JQHO09AEyfPh26EjxfRX/DDTeIRCKzucPoFl+C59iA+GMgEjxeQiD45CB40QugcdZNSacKmMkOt81mc9JiMa2ZPH3oTb48AWcDzk4bfBzQhM0HR4InXvS4Jy0t7ZJLLikrKwOAG2+8cenSpRCN4GlNJioMOLSNpeqQ4ElAvCDB9wYCwQugcVYUQTodoJ3s/vSnPz300EPJ9YPuZt26C/USOG9i3jEBpw9VVfu//Xa9y+XMyMiaMWM2J6Nib4obMQzTtxQw8SV4jkI+EonQe5RK5ZdffoktxWJxeno6RFsrEIKfO3fuuHHjgPLsQ9AEj2VngfKi12q1UcPoBcSBRBKlRK+AXy36kszRI0QiEarKtZRY+HqKK664YsWKFWT2OU3ASW3UqDOdf+1XBbvdtmbNBzNmzL733gcNBsPnn6+hj2Jxo0suuezuux+oqztBcjAkAq/X63A4k3ao+UWQOMHL5XKODT5q4zgS/M0334wKg1mzZtFXRD3BxIkTgXLmJ/0cPny4sLAwydv7tUKQ4AXQ6M8Ef0pc341G47XXXtv7fuKDONmd7gv9mlFbW5OfX5iVlSOXyydMmHTiRJdYYVLcSCaTjxtXvmvXjsR7drvdbW3t3frWnlWIT/AcizuxwccPmo/aCX3o0UcfRakdgQR/xRVXACXBk8apqamnW3PW/yAWS4TcfwII+u3vh5bgz34ITnZnACUlQwYOLMbthoa6jIwusf5xixsxTU2NuC2Xy/nxtZ1pxSIuVzcZMGIhGPT5/QG325l0CDJmDPR4XJiHq1uwbEdtVjLmYNDr9wdcLqdYLMGjfr/X5eqIQEFm93rdLhd3hHj7LMviGNxuJ2ZOJAnR/H5f1CcjlUpcLgcO2Gw2aTQaj8cTCARdLgfaRzjrCb/f53J1c3dY/MLlcib98/f73X5/IPFXKZXKVKqzpfScVCoVJHgBBP2Z4H/pIfQAOIcOHz78lx5If4ZMJkcZe+/eXevXf8mpWRynuJHH41m2bClul5UN45ckxyRiDkdbZeXG3oywpib5BGSIPXu2JdiypaUeABgmzBkzjiE1VXv//XdUV+8EgHA4GA6H3G4HAOzZ8yPf2nX06AEA8HpdWDNp166tuL+hoaM+0KFDezWaDqqmf5g+n7uycmNNzSEAqK8/otOleDyeEyfqW1pasUEgcLJUFQAcO3aosjKhR5Rgkac4SPxdWK3ppaWje3m5UwVBRS+ARr8leOhTTumcAhsCThN8Pu8nn6xyOBzXXntDamo6fShucSPNLbfcidtRJfiWlmYASEvLGj363OQGFgz6nM7m3iQRczjaDx/eX1Y2JsFaO5s27QAApVJFxsxJInbRRdNxv05n8PsDVmsGAJxzziQSz0bAMHIA0GoN+fnFR49WDx8+DiX4qqrj2KCkZPjo0R3RerQhIz09e/Toc9PSBtTUNPzmN9NVqidlMmlBQV5aWj5K8BpNCsDJnKwDBgwcPbqbcJjW1qaamkMjR5b3QoJ3ud3tiSeDi1PoIRG3zWAwuHNnJckM1htPTxAIXkBX9FuCp3PRn/3AoQoWx9MKhgmvWPHGgAGF8+Zdx/824hQ3EoslHH0+B5jUXa3WaLVRssIlgkBAGgw6UlJ0SScRCwb9AKDRaBPMEqrV6gBAKpWSMfv94mDQqdXqxOIu36FKpRKLJSi4G41m/vrGYDBhV6ipTknRYQXF4uISbKDRpJCr0Lyr1Wq1Wv3gwfrly98EAJlMNnfunJQUjVarR2LjcKdSqdJqu7k7t9uJd5d0rlOJJBIOe5J+lQTotjlz5pzs7JwVK5bv3bs7akGNdes+rak5igSf4ClxBy940Qs4iT4j4/YUfcsGLzjZnQEcOLBPLldUVEzjsHtdXW0wGCgoKGxvb2tsrI9E2MrKrWVlPTCXiMVi4mfeVxDfyY7TkjjZRb3HWE52mZmZuEEfItsGg2Ho0KF0e4VCwSkP34d+wnwk4rZZVbW/oaGuR6fEhyDBC6DRb0XGU+VFf2YgEPwZQH19XU3N0See6MiIoFZrFi36I8SteJQ4tNoUvT5K1ZazFnEKzfFb4vJFKpVG/U3FcrAnndOqqZSUlNbWVgD497//zSm7sHz5cp2ui/6/TxN8HLdNhNvt+u67b6ZOvWzt2tXdnhIOhwjf63Q6jYZrKPF63QDAsqzb7ayvP57EgMPhoMfjZJgTHBVO4vB4XADQ1FSfdKGXUCjg9eIYknz16CzS2FiXtEI0FPJ7vU6WrSX2kVBIBJCD23Z7e329O34PTqcNABoaantR6dvn8zlZ9niCLKZUqkwmK3+/QPBnBQSCPwOoqJhWUTGNvz9+xaME8cYbSy+66NLkB3fGkbgEr9FosrKy5HJ5LBVFrKR4pHP6xOLi4mPHjl1zzTX8okqjR4/2+Zx2ewO/h76IOG6bAAAQ+eSTVRUVU2mfhjinBAKBzz77GLeHDi0zm6NbEEKhoN3efvDgnqSH3dTELUTbUxw7Vt3LHno/hiNHDpzCMYTDYkLwTU0nDh5MyAHz8OH9p3AM8WGxpP3aCP6XKayeHAQbfF+H2WzqWyr6OCp3DpYuXRqJRF566aVYjXNzczGdLWc/oXz6ROTsBJM29mkJPo7bJgBs27bFbLYWFAxsaWlK5BSNJuXxx/8S53J2e9vOnVv0eqPZnHb++dOTGHAg4G1vr7Va85N2BGlra96zZ9u4cRckHTfo97tttrrU1MKk00a1tDTs2/dTeflvEvQ25QNXmWlpRUT+psvFFhcPO//8YfF7aGw8UVW1a9KkKUnfhdfrcDga09MH9ZLF+jGjRLCwdJ+AIMELOMNAgh8zZky3LdGUHkeCl0qlCoUiQQm+M6o+wSrXfZjg47htAkBd3Ynq6v27dlWybCQUCi5e/MSdd/6/+KckAsEGL4BGvyX4vuVFLxC8gDMMJF1Ocvg4mDlzpsFgiHU0Kv1HJfgeVW/qQz9hPgoKCteuXd3YWJ+Wll5ZuZX4w9fV1VqtqTNnzsF/W1qa3n9/xV13PRDnlMQhELwAGv2W4PuWih7nO0FFL+CMIXEnO8To0aNHj46ZzkUmk/HXCnEk+AQJvk9L8GKxJKrbJnHqTPyUxCGEyQmg0ecZxe93+3xOzk632wkQYZiQzVafXLeYqtPhaOppoglqDHbsIREzp9vdBgBer40eMMuGAcBub0x6peLx2LCHpHUDmIXUbm8AEHUOVQSQQfq32XwxTwYAAK/XDgA2W0PSy61wOAgAib9KuVyl0fRYt/lrQ08JvtveekTwCX7SfV2nFdVtkzh1IqzWNBTf45ySOAQJXgCNPk/wkUgkEuF+0JEIy7IRAOAfSrhbtvNv0ilvsQcmEul+iYDTnVjcZcA4BgAmEkmSGzvvgulF3t6OHgjBdx1MlIfPG0OvXkTXMSSEzucmIB5OOcHzK7gbDIbi4uK2tjbapQ6vmOB1+7QE/4tAKpWGQkKxGQEd6PMEr1JpVSp+LdcWAFAoVCZTTnLdBgKe9vYTBkNG0qlDWVYGUGMwZCoU3Zs5Cwt9SqWyoKCUHrDf77LZ6g2GrKSDKUMhMUCt0ZgVJ5tmfKAzp9GYTTQZtD5CozGZTKb4Pfj9EYATJlN2b/QQTmdz0q9SQFQkHiaXCJRKJT+FrUQiGTVq1I4dO/g2+PPPPz+RbgWC7ykkEonP5/+lRyHgbEGfJ/hYiEQiROg8+zFo0CC3293XFZIC+hBQgk86IQkHS5cuzcqKks1XJBJxvmr8NzU1NZFuBYLvKcRiQUUv4CT6M8H3rdlBYHcBZxKoUT9VsfsokTc3c/0k+ASPV0xwYdGH/GTPEgg2eAE0+hIF9gh9K0xOgIAzDCT4UyXBxwKf4PGKCQaM9K01+tkAqZRL8Dab7ccff/ylxiPgl0W/leAB+lKqWgF9BeFwMBwOcHYGg14ACAQ8EkmS3oyhUAB74HeeIIJBH/YAkKCPVQgARCLW73d1jgF7cItESft8dPTAMEQxwIrFInIJAEDKZpgAvZPqwQ8Afr8bf7wiUYRz1O8PdTcG7MHVC8+Vjh4SbC8WS+XyhCr4nQFIJJJwuMsH8Oqrr77wwgvt7e2/1JBOOY4ePXr33Xe//vrrSqUyTm6GWNi+fXthYaHR+KsItOm3BN+34uAF9BUEAm6ns4Wz0+PxAYDT2RwI9EogdjqTT8Ht8XigIywzoR+1z2cDgFDIy4k/tNubYpyRyBhc0DUsc/78Wa2tbfQlGCYAAF5vu80WM5MoSUePEZIEXq/dZnPEH4PX68Qekg5wRSQelqlUas8egpfJZKFQlzWQ3W53OrmBxCzLDhkyZMmSJdOnJ5PU9ozhz3/+8759+95991165+7duz/77LORI0eOHj36s88+ow81Nze//vo/J04cFafP6dOn33333Y888shpGfFZhn5M8H3MBi+gT0CtNqpU3Dofdnv7iRN1ZnOuRpMS9axuEQx6bbZ6szk36TTg7e3NJ040WCx5CdaDV6tdAGA2Z6alFeGeQMBttzdarQOSLiYmFjfU1zdZrflYDx4Apkwp4rQxGtMAICOjKC0tg9+D3+9yOJpSUwuQnjkBMjpdalpalIoaNCKRE42NzamphUlL8D6fw+lsIY+lW5xVgoRMJg8GgwDw9NNPMwzz+OOPHzx4kGGYK6+88vbbb7/wwgsBoKam5rHHHquurq6qquIQ/IkT9TKZ0WJJyAWSg5deeuniiy/k7PR4PFdeeeUjjzxy7rnnJtHnvn37+PaFQCAAAI2NjSdOnOAc+uKLL/74x8c++eS9qF298847ixcv9vl8Pp8PALZu3VpYWIgxnHv37uUUL+4f6M8Ef1b98AT0D4hEIr4GG9lILJYkTSpnvgedTr9gwYLy8omk/ZkZAxaftVisUduQHnCDY78XibpftJ/Cu0ju9DOAQMDLtyCg+kQkYgIBf2XlD6+99mpBQf69995aXX0AAFavXm2xGEaPLgWA//73q3feeQcAWlsb//GPvxcWFgwfXnbjjbdOm1bxyiuvTZ486bnn/tzTIdls9nvuuef55/98zjnDNm36btSoEfjuqqqq161bV14+tqxsYCL9MEwIAFyuVpy9nU57a2uLw9FFq2Szdfzb1tZKH6quPvTXvy4BAL8/4HS2cBxIP/lk1XPPPXfXXb8NBgMej8Nub5w0adKNNy5YvPiZqqqD48dPXrdu7fjxYwHg8cef3LNnb1HRwGHDhl577dUAEAyKADoWPT6f0+HoJrsX5l5zOpuT/opQd+VwNCdIYjKZQq2OYq0QCF6AgF8jRCLR22+/feave+GFF7a1tfGz4kSFoISLCpYNo6MADaQEiUQUDAaeeOKZurp6jUb93//+t6rqIDbYv//AunXrLrzwvLVrP8U9x44dfe65v44bd45Wq/366/9YreYjR2qs1lTs/IMPPmLZyA8/bFm06N6cnJMxkMFgcObMq5999olhw4aSabalpREAPB53a2vbnDnX/+tf/5wy5TcA0N7eBgAffbTm1ltvoCfk+voGi8Wye/fejz/+9JlnHqNujT1woHro0GFYKszr9Tgczt27d7a0tE2ePBHbrFv3NW74/X76Ofztby/t2bMXAOrq6t1uR0pKCgBs3vzjzTffsXbtB/v3HwCA77//PhQKv/POyjvu+G0oFKqs/CkU8re3twDAoUMHR48eFgqFv/zy64MHD//3v/8bMqRk3rxZ0FEPvgMME+I/fA5wmRIK+ZMmeEylGg4nmtIgFtn1Z4IXZgcBAs42TJw4ceLEiQk2Fn7CUaFS6VQqHWenVNoGcFSrNfl8gY0bNwPAwYOHH3vspCz+/fc/VFbuvOGGG9as6bBbb968nWVZj8dfWbkTAI4frwsGg1VVBy2WPAB4++33d+3aFQgEMjNzg8FgWloa2q1PnDixbVvl0aONWm3qRRdd9NBDDz300EPHj7fhKKqqDgHA66+v+P77bZ988gk6BOzcuXvr1r2XXnrpnDlzJk2atGLFiqqqqqefftrj8fzzn2+98MLftNoOW8y+fbsvuujylSvfnT37CpvNxjAQiUSWLHl1z549P//8MwDU1taS8YfDjMWS19TUNGLEiHXr1n3wwUe4f9GiRxsb7Q8//LBer//ssz83N7ds3FhZX98MAOvWfceybHt7u8MRAoDt23eo1dYtW3YBwJtvrty9u3rjxo0HDx7GfpxOd32946WXXnrlldfJY0xJMVss5vgvKByWAJwwm3N7WS7WbM4VysVGhyDBCxDQ1yEkh+gpZDJZa2sr+bexsZE+6vV6X3nlFfJvTU0NADQ3N6PZvq6uHgBcLteHH37Y3NxMjN9LliwZOHCgzWYbO3bsxRdfvGrVKgA4fvz4kSNHXC7X4sWLzz33XNSHv//+v48dqwGADRs2bNiwgb70vHnzVCoVwzD79+/fv38/AGzZsqWuro5l2RtuuOGZZ57Jy8tTKpUulxMAqqsPPvfcc6+99hoay6urq+vq6ioqKu68887s7GzSp8vlevDBB7du3drY2Pib3/yGxAcyDLNkyZJvvvlm69ateBf79u1zu90AgLYJALj66qtx48ILL6yrqwOAn376aceOHREqrfeJEycee+yxTz75BEAO8I/k3sgvC4HgBQgQcJZCkOB7Cp2ui2Rvs9m6PYUsAlpbWwDA7/fff//9HP+1mpqaYDD46KOPrlu3Dun5+eefR/Z1OBznn3/+7bffDgBVVdWxruL1er1eLwC4XB3eA++//z5ubN68eeTIkVdfffVvf/vbv/zlOQB47rnnMjIy6us7AhmqqqoAYP369VlZWXSMAMMwzz33XOfgTy5r8FBDQ8MHH3ywc+dOfA5I8ASHDh3Cja1bt5KdEV7RDrzZd955JyrBh8PhSCRyqrJFnQ4IBC9AgICzFIIE31NkZnaJTSBCrdFo7JbsbTY7btDsrlAoAoEAivhbt27dunUr8hmyO4Jl2aVLl5J/hw0btnv37lhX4SfaQyJfvnz5m2++iRTr8/mOHDnCP3fDhg0OhwMAKioqvv76a34Di8VCmL65ufmJJ57AbZ/Ph8uLngKVHJzIQwDYtGnT+++/v2bNGqfTmZqaqtfrVSrVzp07FQqFyWRKS0tVKmWFhWsVCqVarc7OzhaJRC6XS6VSRSKRzMzMcDjc0NCA3Wq1Wsz7FAqF3G633+/3+XwsG3Y621lWarfbrVZrOBz2eDzBYPC888674447Eh9//yT4QCDg8/nRTUOAAAF9FALB9xQqFbfkDwD85S9/mTt37rPPPvv6668PHjy4paWltbVVo9Fg7oQ4kEgk06dP/+ijj+idNNuJxWKW5RZvvOaa+TTB5+bmZmVlbd68udvB8wVoDo4ePYob55xzTlSC//Of//z73y+y2x0AwDDMwYMHJRKJyWTavn07TfC4asnNzW1ra6MfQlpaWlNTEwDodDpMHoArGxosy7799oqbbrpJq9WOGzeutLTU7/e73W6fz3fBBRcAgM1mO3iwurb2+JEjNQCiYDCICya5XB4MBkmiAqVSKZVKpVKp3d6xrhKJRAaDQSQSabVaj8ejUMjS0zMBYPfu3XK5XKPRaDQaHF7i6J8E39ra2tTUfDZrTgQIENAtBBV9T6FWc7MgqFSqP/zhDwAwduzYN954Y+fOnS+88ML27dszMjL+8Y8OtXOsDPY5OTkPPfQQEnxULjebzS0t3LxPRuPJCpMDBgzYuXPn3r17SRw8EhjSZ1FR0eHDh5O4zVtuuUWn0+F90RgzZkxGRiYSPEKj0UydOpWY3gFgyJAhI0aMWLly5fz581evXn348OFIJDJgwIBjx479/ve/37x586pVq5566uFly95pamrmaP4B4LXXXquuXjRr1qyVK1fGopjGxhNVVbsmTZqCTnbBYJDkhEYxHQCQy3Gn0+mUSCQajYb0gE526emDeqmH7p+/H3zuSddIFSBAwNkAQYLvKfhpjgYMGIAb8+bNW79+vVwuf/jhhz/66KPbb79dIpHgVLlw4cKioiIAkEoluKjSaDQ6nW758uWk7t/1119P+iSsY7VGyTtkMp3MApuXl6fXpaYCUAAAIABJREFU68eMGVNYWIh7nn766Ztvvhm3acU+QiKR3HzzAryo0WicPXt21EVeamrqb37zGwBQq9V33303JqxVKBQ5OTmlpYO12hSFQoGD1Gg0CxYswAa33357bm7uXXfdVVJSAgAqleq+++575JFHcnNzly1bBgAlJSU33HADAGRnZ+3evXvq1Kn8S+/du1er1b766quJC5B0xQeZTGY0Go1GI83cOp2OZvdTiP5J8LhuSrCghQABAs5OiMViQYjvEVJTrVKpdNmyZffddx/uIbnqtFotZrJDlJaWvvzyyy+++CIATJgwYd++fQCQmZkxffo0ALjxxhtramouuOACg8Egk8n0ev1VV11Fzi0rK8MNk+mksA4AhYWF11131YQJEzDPwWWXXbZ27VoAkMvlFRUV2CYjI+Oaa66ZM2fOiy++WFxcjO83LS1Nr9dLpdL77rvnyScfzsvLA4Bly5atXr2aXw9JLperVKrRo0e//PLLY8aM+fvf/z5v3jyTyVRYWGg2m//2txf//e+3Dh06uGDBAgAYOXLkRRddlJOTAwATJ048dOjQrbfeiuoElUp12223PfXUUzU1NeXl5fPnzx81apRKpQIAhUIOAOPGjQOAwYMH00aKF1544eeff8b8d2c/+icFYjpugeAFCOjTQBEzkGT9nV8j8vPzbTZbSkrKf/7znxdffPHGG2+cP39+rMa33XZbY2PjnXfeqVQq5XJ5RkaGQqG4/fbbP/30M7lcjlKvTqc7cODA8ePHL7jggk2bNs2bN89gMCxZsuTiiy/Go2q1OhwOB4NBi8Xy5JN/ysw0Go1GtVpdVlb2/vvvq9UdPgFLly5VqVR//etfTSbTqFGjPvjgA9z//vvvz507t7CwcOPGjWvWrBkypBgAvv76q61bf8QaxDKZ7PLLL8/JyRGJRM8///w999xTWlqK595555133nknALz22mt/+9vf0P9OLpepVMq0tDQc/8CBAwFAoVBYrdbZs2fjcmHkyJEAQMYGABqNBjPeBwKB0tLS7OwsALjqqqsWL16cmZlJJ/RVq9WnR9g+LTgtFLh7944NG/4TDocHDx46Zco0TP1ot9vWrl3d2NiQkZE5Z858WpW0adOGb75ZR/fwwAOPaDQp//znP+rqanHP2LETpk6dkeAAOlX0gn5PQBQcOLB30KASfg6KpL83AacJfZ3gjx8/9vnna7xe79ChwyoqpnHq32zZsmnz5u/D4XB+fuGMGVcg9/T+I8QMbuedd966desqKiriG3HT0tL++Mc/IpXm5uZ6PK7i4mLoKh0VFhaigr28vFyhUCiVytGjRwNARUXF9ddf/8wzz8hksqFDh6anp0+ZUrFnzzYAMBgMGRkZNIOKxeLrrrvunXfewf4JkK0nT54sEolmzZrl97tttjqtVjtlyhRsIJfLU1NTn3/++f/973/PP//8JZdccskll/BvRKlUKpVKes/IkSOvuOIKjKNTKpVGo5E00Ov1s2fPHjt2LL+fvLy8bdt+wHJHZrP5u+++41Tn61s49QTf1NTw5Zef3nLLHVqtbuXKt7dv3zpmzIRIJPKvf71ZUTGtsHDgV199tnnzxgsuuJicUl4+afz4juRWR48e2bZtMxbtsNnaf//7R/G771FtKDTdCU52Avhoa2v95JPV9933IJ/gk/7eBJwmiMXivquHY1lm9er3Z86ck52ds2LF8r17d5eVjSBHm5oaNm/+/qabbpPL5f/+9782b/7+vPMuglP3EUqlUsKRcSASiZ566inclslkCoXMarVAbPWnXC6Xy+VGo/GJJ5644YYbUPUdiUQmT55Mu+ChmZlz7ogRI/geeSUlJe+9997MmTNjjVAul+NMXlJSMn369DFjxnR7U4jrr7+e+A2QNQpCJBKtXr06kU4KCgoAgOdK32dw6n88hw5VDxxYbDSaAWDMmPFbt/4wZsyEo0cPp6SkDBpUAgBTpkynYygBQCQSSyRiAGBZ5ttv18+dew0ABINBkUgUNeqjW3RK8H11ahBwmvDhh+8eOlTNj2qF3n1vAk4TiBdYX8SRIz/r9Yb8/EIAGDeufMeOSprgbbb2YcNG6nR6ACguHtzU1Ai/9Ecol8sYRq5UKi0WC5qi+bBYLCg+PfbYyQTyIpHo448/puPNXnnlFY55PhbEYjFt3edj/Pjxw4cPB4DU1FROcdjE8dZbbyV3Yl/HqadAhmFIOKNIJLbbbQDQ3t6mVqtXrXqvoaE+OzvnkksujXpuZeWP+fkFer0BAGy29kgk8tprf3c6nbm5eZdeOislRUsu0dzckX1JoVDwpwC/3yuVSlmWdbm6qR4dC8Ggz+8PuN3OpEtn+nxeAHC7XcFgkhrGYNDr9wdcLmfSfkZ+P47BmXRKZL/f7fcHXC4HESbcbhGAjvTvckUhy649+ADA5XIkHe/h83WMIcH2Uqks1hQ5Z841APDMM4/yD8X53kKh4O7dO3Fbp9Py/V29Xg8ANDc3JFhDhY9wOODxOBnmRNLfm8fjBICmpvqkGTEU8nu9TpY9kbTg6HLZAaCx8UTS31vnGGrxa/H7vbQjvd3eVl/fTei2w2EDgIaG2qTvIhj0+nyuSOR4gu1VKrXRGMXlyuGwWSwdTuZWa6rDYaePlpSUlpSUer2exsaGPXt2EfE91kd4BiCXyyMRJQCsX7+e+N5zMH36dL8/SgUUk8lkMpna2prx3/Hjx5+qUXGi8AX0CKee4IuKBm3ZsqmpqTElJWXLlo04v/t8vgMH9s+fv+Cyy2Z98cXaL75Ye8UV3FUbwzBbtvxw442/w39DoWBu7oBp02ZoNJo1a1Z98cValOwBwO12LVvWEV9RVjbcZIryG1i06O7i4rzKyo29uZeamtrenA4Ae/Zwixmf+THs3LnlFI7B65UCdKj+jh49WFlZl0gPP/206RSOIT6s1ozS0lE97T/O9+b3+z/77GPcLiv7/+ydeXwU9f3/37P3bnazu0k2ITdJyEUICUcIh4JXAQXxKmAVLFZF8aB+29p6tf1aa3/1q9baWttSVERUrGABi2KVgoLhFhNIuAnkvvZK9po9f398kmGYPTObY0nezz94zH7mM595zzCZ17zfn/fn85mYkMBd56PPwtP9PSmH9vaOKFuorz857DacO3dioGyYNm2iRpP029/2lre1NZ8+HdFjcOZM7UDZEBadbkxAgbfZbMwHn0QiJV/8HJqaGr78cofP59PpkiHkQ2i329599y2ynZOTk5Cg5jTl8bgBoK7uKG9/4PbbF3o8ru++O0BR1LlzxwPWueGGWQAQ7L3qdrsA4Pjxw7xt8Hq9Ho+zubmDtz9AbKipOcD7C8/r9Xg8rpaWDoBeG1wuCqC3E/nixbNHjrQFP5rUdwHA0aP7eF9Fnw3cHo1gqNUJ48aN9y8feIFPT8+cN2/Bxx9vpChq/PhS8t0qk8kyM7Py84sAoLJy5vr1b/ofWFd3TKfTkd53AMjIyFq6dBnZrqyc+e67lw5RKlUrVz5KtgN68N3dRgAoKZniPyo0QpxOe3d3R0JCOm+PymTSnzt3orR0mv8wj4htsHV3dyYkZPL+azEYOurrT5eXT4/Gg7dY9ImJmZd78L3k5BRMmZITuoXOzraGhrOTJ8+KwoPvtlqNZIWrSOA3/0GI502liv/1r/9fiGONRn119f6KijnM09tfaNpqMDTpdDkiEc+nRa9vP3bs8PTp1/F+5h2OHqOxJSUlj/cz39HRUld3dNas74nFPK/Cbu82mVrHjMlnnje7HRiBLyqaeM01E0O30NraeOpUzezZ83mv1Gmzmczm9tTUwvBVQyKXy0n8EgCcTjrg/0tBQXFBQfG+fXu3b9+6bNm9IR5CgUDATCAjlytkMm6MyuWibTarVCrj/cc+fXql02mTSuW8pZGmabvdJpXKeU9g4PG4XS6fVKrg/bqgaYfdbpNKFbxfmx6Py+UCtg1C4SVjxGKJ/833w+5w2GQy/neS2BDBiXqRSALHDgde4N1ud3FxSXn5FAA4deoEeSjV6ksJF8HGttbUHC0oKGZ+klTS9PRMABAKhey3tlAoTE1N92+BweVyAkBcnEqh4DmggaZFTqdZqYwXCnkGPGnaDgBKpUoqlYWtHBCHQ+B0dqtU8VG8qiwAoFTG857zRygEt9uiUqkDPqkymUIVLoJIQusqlZr3X6xA4PV4bCoV12UZWEI8bwjCA40m4fjx3hlb9Xq9RnNZ0llV1R6FQkHek5mZWYcO7YOQD6FUKlu8OOiANwAwmfTffbc/L684Lo5nVJ+mbQZDY3RfmR3Hjhny80t4pxGQLPrk5Dzenymdna21tcbCwtJgmhcW8pWZkjKOefGyk+zS0rJKSrJCt9DW1nTypKm4uDzK5WJjcSY7m836xht/7O42O530N998NXlyBQDk5Y3r6upsamrw+XwHD+4j2XYA0NzcSLqoXS7X+fPnxo3LZ9oxm03//Od7JpPR5/MePLivqChA/AFBooc8hPi8IQNLbm6ewaBva2vx+bxHjhwoLS0j5eR5U6vVBw9WGQx6h8Nx6ND+rKyxgC89ZKAZeA8+Pl49a9acNWtel0plU6dOmzChDACEQtHSpcs++eRjq9WanZ2zcGHvoIi33vrbihUrMzOzGxsvajQakntPGD++1GQyvvPOPzweT27uuHnzAuflIUiUkIcQnzdkYBEIhIsX37VlyyaXy1VYWFRWNomUk+etpGSiXt/17rtvOp3OnJy8BQtuAXzpIQPNoAwkmzZtxrRpMziF2dk5q1Y9zin85S9fIBu5ueMee+xnnL0zZ86eOXP2YFiIjGaeeeZ59k/mIcTnDRlYMjOzH3poNaeQed5mz75u9uzrOHvxIUQGEJzNA0EQBEFGICjwCIIgCDICGZlzvalU6rKySt7p6wAgFssSEjJ5jxcCgPj4hLKySt7jhQBAIpEnJGRGM12lVptUVlbJO5MTAKTSOI4NCgV88UXvdt+iD6FITEwuK6uMJhdUJlOJRDwTYocGpTK+rKyS9/g06Hveovmfio/XlpVV8s4cBgCJRJGQkElR/Fdw0GgSy8oqoxmAIJUSGyhWyaXnbXwEOWcJCbqysspo/mqkUmVCAv8/2+HCZrOfP99YWkrzzqIXi6VRPoQWi+X8+cbJk528s+jJS4/3uCEAMJt7zp9vnDbNzftvgTyE7KFeItGlh7C4OPBRbEwm8/nzjbNmeXkvd9z34o0qhR5GqsCLxZKAU09EjkAglEqjmjBSIpFIJFHaIJJKo/oPkkik0bzxAUAoFHH+4IVCuOGGfrQglcqi+dIKaEOsIRaLh/15i41nPtrnzf+ZFwjweYsIl8vV2HiRTHfDj+gfAKeT2OANX3UQbaAbGy8ys6nysiHah9DhcDQ2XozChAF7CDFEjyAIgiAjEBR4BEGQKx6RSKTVJvCeQm5AEIvFw26DRCLRahOiD25Hg1QqHXYbCFQ0oQwEQRAEQWKTUFH+EycCrzfAprh4wsAZgyAIgiDIwBBK4P/5z/fCHh96EQ4EQRAEQYaFUALPe6UWBEEQZMhoaLiwffsWm802YcLEuXNvimaUYCTs3//Nvn173G53Tk7eokV3kAUzA9oweIaZTMZt2za3tbWmpqYtXnwXGaQ6xDbU1Bz96qudbre7uHjCvHmhTjfE/0EMoQT+iSeeHRojouS5554abhOQGGL8+NLQ625FCT5vCJvi4pIlS5YNowFer2fz5o233ro4IyPz3XffOn68prS0fPBO197eum/fnvvuWyWRSD78cMO+fXvmzLk+oA2DZ5jP59uw4e25c2/Ky8v//PN/79u399prvzfENrS3t3722ScrVz6iUsW///47hw8fqKiYMcQ2hIX/d4TX6zly5OAAmoIgCIL0l/Pnz6nVmpycPLFYUlk5s7r66KCezmg0TJw4KT5eLZPJCwuLyZr3AW0YPMPq688qlcqCgiKhUDhv3oKKiulDb8OZM6fy8wu12kSRSFxRMb229tjQ2xCWiIbSO530p59uO3/+LFlnneByuT0e95Qp0wbNNgRBECQMZrMxKUlHtnW6ZLPZNKinKyoqKSoqsdmsbW2tx45Vz5lzfTAbBs8wg0GvUCg2bfqgtbUlIyNz/vyFQ2+Dx+NhxqBRlIB86AyxDWGJyIP/73//U139bU9Pt4NFNFMmIQiCIAOCzWaTSnsnEJRIpHa7bQhO2tTUsGPHJ04nrdMlB7Nh8Ayz2+0nTtSVlU1aufIRAPj0021Db8O4cQVnz55ub2+zWi379+91OOxDb0NYIhL4kyfrAKj58xcmJurGjs29+ebbx47NFYnEK1asHGz7EARBkBDI5XKnsze26nTS0ayJEDkFBcUPP/w/kydP2759azAbBs8wmUyWmZmVn18klcoqK2eeOXNq6G1IT8+cN2/Bxx9vfPfdN3Nzx5Hp94fYhrBEFKK3WCwajaaycpbH4/n228OTJ1eUl09+5ZX/9913R7KzcwbbxJhi1apVycnJNTU1//rXv4bgdM8884xIJNq9e/dXX33V32PvuOOOCRMuzVLg83ntdkdzc/POnTvb29uZ8kceeSQpqXcO8zVr1rS2tpLt6dMr582bT7bJ9d57771ZWVnBTvf111/v2rULAJKTU1ateggAfD7fH/7wB4vF0l/LkeFiiB9vZEDQaBKOH68h23q9XqPRDurpqqr2KBSK8vIpAJCZmXXo0L5gNgyeYWr1paYEAgFZGGaIbXC73cXFJeQ+nDp1QqtNGHobwhKRB69QKGw2m81mTU5O0eu79PouAMrr9ZLvJuSKgKIECoUiPz//gQceYAs/m8zMS/qdnp7B70SlpRP6zkiVRLLeHIIgUZCbm2cw6NvaWnw+75EjB0pLywb1dGq1+uDBKoNB73A4Dh3an5U1NpgNg2dYXt64rq7OpqYGn8938OC+goKiobfBZrO+8cYfu7vNTif9zTdfTZ5cMfQ2hCUiDz47O+f48eo///mV1at/RlHw1lt/k0plDoddpYofbPuQKPH5vP/4x1oAkEgkWVlZc+bMFgpF8+fPP3XqlMvl4lTOzMw4ePAA2c7I4Ar8tm2fSCS9K4HeddddSqWyvb1t69ZtpITx1NlfDyUlJQcOHBjoa0IGi02bNotEQrvdPtyGIP1AIBAuXnzXli2bXC5XYWFRWdmkQT1dSclEvb7r3XffdDqdOTl5CxbcEsyGwTNMKBQtXbrsk08+tlqt2dk5CxfeOvQ2xMerZ82as2bN61KpbOrUaRMmlA29DWGJSOCvu25uS0tzd7dJLldMnDipuvpbm80KAGRwwihHq9Ved911aWlpSmVcZ2fXqVOnvvmmyuv1MBV0Ot28eXPT0zNbW1u2b9/+0EMPiUSiLVu2VFdX9+tEAoFwxYofZmZmms3mtWvXRhj69vmAibpfvHjRaDTecccdcXFxFRVTq6r2MdWMRqNWq8nMzCQ/4+LiNBqNwWBISEhg6uj1Xcy22+0GAJp2Mo0TMjIyNBoNAJw4cbK4uCgzM0OtVpvN5n5dKTJ4CIWiysppZWVlWq2Gpp3t7W17935z4cIFsvf737+DE6IP/fQ+9dRTEonkm2++UalU+fn5Ho/n7Nmz27d/mpeXd/XVV+l0OqPR+N//7jp9ujfUJ5FIZ82aWVIyXq1WO52uzs7Oqqqq06dPD8edGFFkZmY/9NDqITvd7NnXzZ59XSQ2DJ5h2dk5q1Y9Prw2TJs2Y9q0GcNrQ2giEnitNuHhh3/c3NwEAIsW3TF2bK7BoM/IyCwoKB5k82KdjIyM5cuXk4mcACA9PT09Pb2goOCtt972+bwAoNFoHnjgfrFYAgA5OTkrVqwQCnnOPXDjjfMzMzOdTvr99z/g3bFdV1d34403KhSKzMwsgEsCb7fb3W63TqdTqVQ9PT3EfW9qamILfCSUlpYCgMVi2bHjs+LiQgCqpGQ8+0sCGV4WLLhp0qRJAOD1epVKiVI5Ljc377333jt37px/5Qif3hkzpgsEvQuIlZeXp6Wl6nTJZCmtlJSUJUsWv/HGGwaDAQBuv/3WwsIiUlMkEmdnZ2dlZb3zzjsXL14cnMtFkFFNRGKzd+/u7m4z6WsRCATl5VOuu24uqjtFUQsWLJRIJBaLZd26dS+++OLOnTsBICMjo6JiKqlz443zxWKJzWZ755131qxZ43A4+E1SOGnSpKlTp/p83o8+2tTR0R7+gCB4vV7yqk1I4CZ6NDY2Ql9knvxLSiKHogSk0/3Eibru7u6GhkYAKCnB5YhiBZFIXF5eBgBVVVW/+93vXnvtta6uToqipk+vDFg/wqfX4/G+9977a9eu7erqBIDk5JTa2tq//OUvJOlSKBTm5OQCQHx8PFH33bt3v/DCC2+88YbD4aAoqqioaPAuGUFGMxGJzc6dn//pTy+vWfPnvXt3G436wbbpSiExMWnMmBQA2Llz58WLFx0Ox969exsaGgCA6JxIJMrPLwCAvXv3XrhwobW1dfv27TxOlJ6evmDBTQDwxRdfnD17NkqzSfdKfLyaU07knOTZpaenA0BjY1O/Ws7NzYmLiwOA2to6AKitrQWAtLQ0kl+KDDsymZQo9NixY8ePH+90OjdseG/t2rVffvlf/8qRP71nz549e/ZMc3Pz8eO1pOTrr7/u6urau3cv6atSKOQA4HK5Nmx4b8OG96qqqrxen1wuF4lEAKBQKAbnchFktBNRiF6t1pjNptbWltbWlp07P09NTRs/vrSkpFSrTRxs+2KZpKRe3SKizmxnZWUlJiYCQGJiEglUNjU19e1t9Pl8pDBy8vPzyQbp3o4SMl6zu5vbL07kPDMzg6Ko9PQ0p9PZ0dHRr5ZJep3FYiE3pK6ubv78+RRFTZhQsmfPnugtR6LEYrGcPXtm3Lj8tLS022+/3efztbS0fPdd9ZEjh/0rR/70dnZ2kg2SmcGUeL1er9cn6HMi7HZ7Z2fHzJkz5s37XmJikkAwROttIMioJSKBf/zxXxgMXefPnz1//mx9/Tm20q9c+dhgmxj79M1XSLZ9ACAUCgFALBaxC8kGD4EHgJ6eHpVKNXXq1AMHDhkM/IMoFCUg3eoGg5GzS6/vstvtqampqampEom0vr6epBFEiEgkKi4uBgClUvmrX/2KvQsFPnb44IONRUWFJSWl48blSSQSkjWSl5f74YcfcmoO4NNLUCgUDz74oEKhsFqthw4dampqmjVr1pgxY3hfC4IgoYlI4AEgISEpISFp6tTpPp/36NEju3Z9YbH0tLa2DKpxMU5Xl4FsZGVlGo3Mdhb0eTBGY6+IpqamETcoLS2Vh+Ny7ty5rVu3rl79mEgk/t73vvfhhxt52zx+fDGJogfsX29sbCwoKKioqAhWIQT5+eOY6Rg5JCenJCXpSActMozIZDK5XNHS0lpXd0IoFOXk5MyZMzsjI6OgoEAqldI0za48UE8vQ0lJiUKhAPD9/e9/7+npAYAbbrghiqtBECQMkQo8TdPnz589c+bk2bOne3q6SeEoHwev13e1t7enpKRcf/31er2+o6OzomJKdnY2ANTV1QGA1WptbW1NTU29+uqrWlqaXS73woULeZyosbGxp6fn4MFDM2fOLCoqzM7OjjzrmKIgJWUMAEgk4szMzGuvvYYYduhQgKgsEXgyUw0TmI2Q0tKJAGCxWN5//32mUCaT3XPPcgBqwoSS3bt396tBZMAZN27cHXfcAQAbNmw4d+5cQ8PFjo6OjIwMj8fjPynCQD29DH0hASojI/3s2XNTp05Vq7mJIAiCDCARCfy6dWsaGy96vb0B27g45fjxE0pKJpK8+lGLz+fbvn378uXLVSrVfffdx5Q3NTUdPNi7kO6XX+68++67VCrV/fffDwA07eAd5Ny7d+/UqVMkEum8efPWrPkHgC/8MQAUJXjooQfZJR6PZ8eOHeyFARmI1y4UigB8/RJ4qVRKEgVOnTrFGRnf2tqWmpqKAh8LnD17xmDQJyQkLlu2zOVyicUiAAoADhw4wPx1sxnApxcAzp2rv+EGL0UJlixZCgBer9fhoGUyqUqliuKaEAQJSkQBt4sX671er0IRN2XKtHvuuf+nP336pptuyc7O4f2nPmJobGz861//dvz4caPR4HQ6W1padu3a9fbb65jX5fnz5zZs2NDQ0EDTdFNT01tvrWPPgdMv7Hb7/v0HACA1NXXixNL+Hu7xuDs7O0+cOPmPf/zj+PHjAeu0tLQQy/V6fb+mMysqKiIZ0adOcWcvJiWJiUkkkIAMIw4HvW7dO/v379fr9T6fj6bptra27ds//e9/dwWsP4BPLwC0t7dt3vyxXt/ldNIXLlx4++23z549AwA5OTkpKSm8m0UQJBiUzxfeEdy2bXNJycScnLzYTHx97rmnhtuEwFCUIDk5GQAslh6r1QoACoXiiSeeAID169fX19cPs30jlPHjSxcvvmvw2o/Z521gwac3QoqLS5YsWTbcViBIACIK0S9adMdg2zEi8fl8K1b8UCaTdXV1bdz4ocNhv/HGGwHA4XC0tLSGPRxBhhF8ehHkSifSJDuEF74tW7bcdtutSUlJjz76CClyOOiPP/6Yph2TJk1atGhRiINff/0v7Onf2URzLIJERqind3gtQxAkElDgB5dTp0699tqfxo8fr9VqfT6fwWA4caLO4aDJrrVr14Y41mw2hWiW97EIEiEhnl4EQWIfFPhBx263HzlyxL/cZrPZbDZ+bUZzLIJETrCnF0GQ2GckCHzAqc6jGc/DtEEGEfE/fvht8Pl8MOA2MEOmhUIIm3ZJsjiH8j7ExSmjO1cY8HkLacIwP2+DZEMI4uJwmB8So0SURX/FYTB01tQcnDbtGoUijl8LNG01GJqSk3OFQjG/Frq62o4fPzJjxvVSqYxfCw5Hj9HYkpIyjlmLs7+0tzefOPHdVVfNFYl4XoXNZjab28aMyWeWEevpgfi++Y3Wr4fly8O00NLScPr0sTlzbuL9zrW+g5CTAAAgAElEQVRajd3dHamphfwOHwKMRn119f6Kijm8vy3I86bT5YhEEn4t6PXtx44dnj79OplMzq+FvuctTyDg+d3f0dFSV3d01qzvkRVmeWC3d5tMreznzW4HZjGat96Ce+8N00Jra+OpUzWzZ8/n/Vdjs5nM5vZYft4QJEJicdgbgiAIgiBRggKPIAiCICMQFHgEQRAEGYGgwCMIgiDICGQkZNEjyJDhcFjs9m5OYU+PBQC6uzucTu6uCPF63QBgNnfwng3aYjEDgNncbrfzTHDzel2kBd6J9FarEQBMpjayMAEPPB43AJhMrYwNDgcFkNrXvsloDDM61GYzkRaYNL3+2+ACAKMx0rWwJRJZXFyAgRUIMuygwCNIP/D5vEQA2BB59njc/rsibxYAvF6Xz8dTllg28JRnn88DvfLGswWyFI3X6/J4eI7NIS0QmSewL8fn84S9w30tuHgLPNNChPU9Hp5DVBBksEGBH9VUVVUlJSUVFBQMtyFXDHJ5vFwezykUCvUA9VptWpTD5LTadN7D5CiqHaAhISE9ymFyCQkZvIfJeb0tAI0JCZlRDpNLTMxkD5NjUCoTk5ISQ7fgcgkAmhMTs6IcJpeUlM3vcASJHbAPflSzevXql19+ebituOLp6tL/7GfPksV8EQRBYgQU+FGN2+32ePiv8I0QaNpx5Mh3XV24ug+CIDEECvxoZ0ROZTjERD0xKoIgyMCDAj+q8fl8KPADBd5JBEFiChT40Q7KEoIgyIgEBX5Ug+o+IGCIHkGQGAQFflSDIfoBBO8kgiAxBQr8aAdlKXrQg0cQJAYZ1QL/yiuvPPvss8NtxTCDAj9w4J1EECSGGLqZ7E6erNu164uenu7U1PRFi25XqzXsvbW1NV9+uYOm6by8/EWLbiczYZlMxm3bNre1taampi1efBfvKbqCUVVVpdfrB7bNKwsM0SMIgoxUhsiDN5mMW7b8c9Gi2x9//BcajWb79i3svVarZcuWj269dfFjj/3UarVUVe0BAJ/Pt2HD29OnX/XTnz6dmJi0b9/eAbfK4/GMcnlDgR8QMESPIEgMMkQC39h4MScnLz09UyKRzJhxdVNTI3uvwaBXKlXZ2TlyuaKwcHxHRzsA1NefVSqVBQVFQqFw3rwFFRXTB9wqr9eL8oZ3YKDAO4kgSEwxRCH6oqLx+fmFZLu1tTk1NZ29Nzk5xel0njxZO2ZMWm1tTWlpOQAYDHqFQrFp0wetrS0ZGZnz5y9k6tts1u3bt5Lt9PR0tVrFOZ3TSQPAmTO1oZet/Prrr/Lycmtrv/Xf5fW6nU67Xt/De00qp9MBAKdPH+O96IXH43a57Hq9hbeDSNN2ADh5sjrYVdC0w2QyBLwDfTa4XC6HwWBhVhizWoUAZWS7uflCba0htA12uw0A6uqO8rCf4HY73W7aYLBGWD8+XpOZmRtJzRMnjhcUFAmForCFoUEPHkGQGGSIBF4slojFAADHj1d/8cVnS5bczd4rlcpmzpz94YcbxGJxfLymvHwKANjt9hMn6u66656bb77t00+3ffrptjvuuJPU9/l8DkfvIlMul9Pt5i7sSJab9HjcofOe7Ha71+v1PxwAvF6Px+N1u928393EBrfbLRB4+bXQZ4MrChs8AOB2B1060+v1+HyB70BfC25iAyPwbreXtdcT4ljmFMSG/hofyIaIIGcMi17ftXXr5v/5n1+wtTxgYYSgB48gSEwxiAL/7beH9u3bAwDz59+cl5dvt9u2bt1kNpuXLbs3OXkMu2Z9/bnDh/f/+Mc/VypVu3b9Z/PmD+688x6ZTJaZmZWfXwQAlZUz169/k6kfF6dcvvy+EKc2GDprag4WFZUpFHEhbaQUCmVZWaX/DrJ8Z3JyrlDIc7Hnrq6248ePjB8/SSqV8WuBLN+ZkjKOdwygvb35xInvJkyYKhIFvgqpVK5WJwS8AwSbzWw2t40Zk898IvT0XNqblZVXVpYX2oaWlobTp49NnDiN92eK1Wrs7u5ITS3kd3hAPvrovTNnTrlcrrCFCIIgVyiDKPCTJ1dMnlxBtj0e97vvvjl2bN7Spcv9X/T19efGjSvUaLQAMHnytDVr/gwAarWWqSAQCASCQUkX8Hp5utfDyPr167/++uu1a9cOSGuj0+9cvPhuAHjhhV+GLYwEDNEjCBKDDFGI/sSJWolEOnfuTZzy5uZGnS45IyNz+/atlZUz4uM1hw7tz8jIAoC8vHFbtnzU1NSQnp558OC+goKiAbfqCk2yO3To0FdffTUgTWEWfSRYLD1/+curZLu4uDghQc2poNcbAODcuZN79/6H70l8Xq/33LmLvI30+bwAcOjQHt4fGz6fz+cbABsOHNjFdOhEbwNNCwBuINtnztTu3dscugXyyV5V9eUA2hCaxMTk4uJyfudCkEFliAS+paX54sX65557ivxUKOKeeOJZAHjrrb+tWLGyoKBYr+/asOFtl8uVkZF1yy3fBwChULR06bJPPvnYarVmZ+csXHjrgFtFUdSVKG8+n8/tdg9gawPV1EhFIpFcddUcsq3VapVKbr+PQCAFAK02KSsrTIdFMNxup91ujotL4N0dY7NZ2tqa0tOzgnXHRGADbbd3K5WJvBNLLZbujo6W9PQcoZDnVbhctMPRrVQmMUERh+OSTick6LKywnR49fSYOztbMzNzeV+Fy2V3OCwqlS7C+uH6ARFk2BgigZ879yZ/9x0AfvnLF8jGjBlXz5hxNWdvdnbOqlWPD55VV6j/6vP5Ghoampub09PTw9cOjtPppGn6SrwDQ4xEIp01a06ICg6HGwA0mgTeAk9yPnS6sSKRhF8Len17W1tTWlo27/mg+nI+cgQCnq+Fjo6Wjo6WjIyxZKIqHtjt3SZT65gxl+TZbr+0NzExOSsrOXQLra2NROCj+FQymc3tqak8/ysRJHYY1VPVXrkC7/V6e9jZbrx48cUXL1y4cCXeAQRBECQsKPBXnrwRm3mnB545c2bx4sVOp7O7uxswRD8QYJIdgiAxyGgX+Csxi57YTMa486CmpmbTpk0dHR0o7c8887z/IMaAhZGA9xNBkJhitAv8cJvAhyg9ePJl4HK5SAtX6E2IKdCBRxAkBhm9Ah+lTA4qO3fuDLHMHbGcdyI9OZxJr0OBHyjwTiIIElOMdoGPzZfywoULN2zYEGwvsZl3iJ4c7nK5YvkOIAiCIFGCAh9z8ub1eh0OR4gJU0nU4fHHH7/77ruD1QndPgA4nc6YvQNXHJhkhyBIDDJE4+BjlhiUN5qmIaRhZFd9fT2/6UQYD/7gwYOhT4T0C7yTCILEFOjBD/9L2WQysXvcOQJvNBpzc3OPHTvGVCC7TCYTvwQCcpTL5TKZTNGYjTCgB48gSAyCAj/8Av/www/fe++9zE+HwwEsw/R6fX19fWNjI1OB7HI4HPwEnskuJL34sXAHRgZ4JxEEiSlQ4Af9pexyuYhmB8NqtVosFuan0+kEVnq//2A2Zld1dfW2bdv6aw85nBH41tbW7777rr+NIAiCIDHOaBd4thNssVgGcBEXhlWrVt11110hKni9XrYZHMfaX+CZbbvdXl1d3V97mC8bcqJvv/32kUce6W8jCBsM0SMIEoOMdoFnC+e8efN+9atfDfiJurq6urq6QlTgCDxH0UMIPPCKQDANMgPtqqurH3vssf62g3DAED2CIDEFCvyll7LRaByMvDOXyxU6MMCZMbe5uRkuE3iunezKPESFCV0w7Vit1j179vS3HYQBPXgEQWIQFPjLvOHBcMLcbndogWe6wwnz589nG+bflRCNkRaLpbOzEy734CFWZ/S7skAPHkGQmGL0joMfJIE3mUwvvPDCb37zG6aEpumwAs/oq8fjsdvtAPCHP/zhf/7nURjoEP1PfvKTtWvXgt9XRQiBr6o6+PDDsy9evCiT8VmCZYTh8bjcbu4cRC6XEwDcbhdN2/g163LRAOB02j0enlkgTAsUxfMZZrXAcyV1pgWvl+dVuN1OAKBpOxMUoWkAUDB7aTpMy30t2HivB8+0EGF9gUAoFkv5nQtBBpXRK/AE3mJZV1eXmpqq1Wo55d99993LL798zz33pKbqSMmRI0dycnJC28DJmQcAk8nkcNBwucDff//9t912WzQCb7FYOEl2YdtpbW3r6OiwWq0mk4miKIUipV9nHGE4HD3d3Z2cQoulCwDsdpPB0BjooEgxm9t4H2u1WgHAZGoVi6P6ozYaW6KwoQcAjMZmflMwsWxoYrYdDgqgoK99g8FgDmdDN2mBoqIKT0b+XymTqbTatGjOhSCDxOgV+Cg9+IULFy5btoztqROIarId4rB98Gxnmi267KaIYdu2bUtLS4smXM/O3YvQg/d4eofVzZs3b9y4cevWbe7XGUcYCoVGJlNxCh0OAQAoFAnJybn8mnU6bSZTW0JCpkgk5teCwdDZ1NSamJjFO9BC01azuT0pKUsg4PlaoKi2lpb2pKRssVjCrwWHw9Ld3aHTjWXk2W6/tFel0iUnJ4Zuwettbmvr0OlyeHvwdnt3T09X5P+VmIGBxCyjXeA5Yhm5XjqdzoCj2/1nj/H5fMEEvrW19ZprrrFYLDpdr7t/+Xg5N0X1lthsNgj0UdJf2F37bIE/d+6c0WjkBCSMRuPzzz+n06kBwOVy1dTUpKen8z71yICiBEIh1zUUCsUAIBAIyAYPiKYKhaIoWhAOUAti3gLPtBC9DYzAs2MBAoEwbGyAdRU8BZ5pgd/hCBI7jHaBZwTv+eef1+v1kWsnZ2wbg78HH0LgDx8+fPr0aQBITExkH85si0S9Td1zzz1ut9vXB+cqIocxjCPwLpfLarVyBP7o0aOvvvra/fffA32r02IuXggwyQ5BkJjiihd4n8/n83FVx+fz1tTU7tlz5Jlnngl2IMlm8vl8Xq/H6/WSEfBer9frJS44iUt7gvXkEYEkldmQDCy32820QATevyZTmVQjFdgJXG63SyQCj6e3pKmpkfTWcwbNB2yZuQ99jQuY+szlc9Ta4+k18tVXXz1z5uwbb/yF3CIi7U4nDQB9lyxk2iej+ELAupM8I5lMCxHWpygqyv5XBEGQEcAVL/BWq7Gnh5v0ZLXaqqoO7Nmz7/77lwY7sLu7GwDcbld7+1m3u1c8bDZze/tZpk5X18Vgh3s8LovFwK5M0OubAECvb9BqhQDQ1XXB5/PZbFb/mgCg1zeTDZp2kAom06UcIoOhNS0t1WBo7qvc6vG4rVaD3d7D1LFY9AFb7rvGHgDo7DzPhCsdjt5jjcYWjmR2dNRLJDQA7N+/98yZc+3tZ8mpicC3t58HAJq2dXScB8gnh5jN7e3t3cHO3mdDNwC0t5+LsqcyxGVyGPqkJ+yFRRAkBrniBV4mU/rnJVGU0ev1UhTFftE/+ODDK1bcM2PG9L46CgCgKIFWm8Ysvi6VKsghLhdtsejV6pTgPXkCiUTuLyRyuRoAlMpEpTIBoE2tTvH5fE6nk6nZ3Ny8atWj69ev02jUO3bs7jNGQCp4vZfG25Bkrri4BPLT5xMBUFKpkj0mJ7SYeTwdAO1qdapI1PsfLRT2HqtQaDjOd3x8MmlKKJQIBCKtNk2h0AKAy+UGAIUiAQCEQrFGk8ocEhen1WqVwc5OcDopgA6tNpW3CjocVrvdHLlm8+5CjhIM0SMIElNc8QIvEklEIm7KrkRCVlqj2AnPGzf+c+LE8muv/R752dl5AQA8Ho9MphIInKRQIBCRQ0iMVyqNC5FrQ1Ei/4RqihIDgFgsF4vlACCVKgHAbncwNS9caP7ii51dXSa1OnHz5o9Joc/XK+disZ3TFCPJbrcXevOPLn1zCIVifxsYxOJuuPwbiPleEYlkHEGSSBR9TQkEAqFMphKJpABw8WIjywxKJruk6GKxLGzOtlhsBACZTMVb4D0et90OIS4TQRAE8WfEdlV6vV4iYO3t7enp6SdPnuTMCNvS0gIABoOhqKiooaGBFA5ekp3b7d61a9dbb73lPxU8sHLr2IWcvDaXy0Uy7Jh4Q78M5tRn7k+IXeTf/fsP+RuDsMEQPYIgMcgV78EHw+vtzTbv7OxsaWlpaWnhSBpZldXlcp06dYpZbT1yveR8LjAEHCYHALNnzz5//nxycvL//d//QfCZYk+ePOnX1KU5cHw+3/r168mnSX8N5pzIP7GfPZue/xhCUh+j0CHAe4MgSEwxkj14k8m0ZMkS4u/6DzBj+8GM2g24B880uG/fvvb2dmZ6mWATyen1+mBNkTN2dHSwTxfMYJPJpNVqjx79rrOzq66ujhQuW7Zs586dTGucQ5imPB6PzWYjAQNmL3rwIUAPHkGQGGTECrzP57Xb7R999BHxif0HmNE0zWyzxT4SamtrTSZTJB48R4AZgQ82kRyZ0IZA8s/ZAh8sbOCP2Ww2mUzt7e3vvPPBAw+sBIDHH3/8X//6F9M+++wcU71e76lTpzZt2sSZjw9Q4EOC4Q0EQWKKESvwZIJV6JNM/yVbSIie0F8PniO9bEihvwfP7GW+AALOOGtnzcz5wgu/h3ACH8xgphPd7XaTT5nPP/+c/fUQQuDJLrvdjh48giDIlcuIFXiOxPrnvkUi8H/6058/+eQT/8aJUvprJPh9SYQQ+IAePHv6WyL2L774f0wdfzkPWDJ58uQvv/wSAHy+S/PocWr6G8/ug+eUAPbBhwRD9AiCxCAjNsmOHXCGQB48O8ssoMA7HI5XXnmltbXt7NmznOXg2FEBNjU1Ne3t7RCBB88J0TPV/DPkL1y4wPyMROCdTufRo0fJDLjEjAgFnlRoa2s7e/Ys+MUY0IMPC379IAgSU4xYD54J0RNN8k9u9x+Qxqlw6NC3zc0tXq+XBOQJhw8f9ng8PT09wPpo+NWvftXa2goAixcv/v3vfw+srvRgAn/33Xdv2rSJXc6xhDmWE6IPe+FsUSdfBf7XDkEE3uPx/PznPz937hz4fU+MSIE/ceI4e/31hoYLf/3rH1955Xeff/5v//mPQ4AePIIgMciIFXhmHtZgIfqAHjzhlVdeWbr0ri1bPmW3AADt7e0VFRX/+c9/SK47aa2rq+v555//+uuvAcBkMpG4em1tLefYPqt6Bf7MmTPfffcdU85U66/A+5eQ9pnrjTxE7/P5Dhw48O677zI/R7YHr9d3bd26mbnhXq9n8+aN8+ffvHr1z5qbm44fr+lvg+jBIwgSU4zeED1b4Zieb1Khrq5u69Zt/k2RbLXt27enpaVx2iTb3d3dHFUOJvBweRp/CA8+xNQ0/u3D5Z8yxAlnltVhVzt//jznQK/XyzaJ48EHDANcuXz00Xtnzpxid4icP39Ordbk5OQBQGXlzKNHj5SWlg+fgQiCINEyYgWeCdHv2bMHAJiwM1OBLaUvvvgi2TAajXD5WDX2UWTj+PHjY8aMgcsFnsgh86HACPaf//w6uymDwXD8+HGyzVFTf6v4efB+XRKBQ/RWq9W/KU4kg5n/By7/ThoBLF58NwC88MIvmRKz2ZiUpCPbOl2y2Wxidjkcji+//Ixsp6SMUau50++TRX30+rbTp4/xs8fjcdO0xWx2CAQ8g2oOhx0A6utPCoU8/6g9HhdNW7u7HbzX4rPbbQBw7twJ3lfhdjudTltPDw3Q2+vhcAgASsh2W1vT6dPG0C3YbBYAOHOmlne/SZ8NzvBVAQBAqYxPS8vmdy4EGVRGrMAzfaibN28GgLVr18Ll+sT23gwGA9k4ceLEyZMnN27cyG6K444zQsvWPIfDQaaoYw758MOPlErRO++8w27KarUyU80MuMB7PJ7Tp0/rdDq4FFH3+Xw+Zq08NsH64Nk/m5qaODdhxAi8PzabTSrtnfZfIpESrSJ4PO7z53vXshMKBV6vg3Os3e4gLRiNXfzO7vP5vF63w+HiLUukT8psNkazLK/X66FpFyOu/YXEikwmfdQ2XPoroOlL3wo2myXsHSZPvsnUxfsq/G0IB2ZgIDHKCBb43g3yB098a0YOOzs7f/vb3zKVGbFvaWlhTxbb19RlHrzH4/EP+1dXV//pT39iDvF6vc8//8INN8z211HGdWaPiGOE02y+tFxsf5PsPv744x/84AdnzpyBy0L0/Uiy43xesEcS+vdxjDDkcrnJ1OsdOp20TCZndsXFKVevfiLEsW1trQCQkZFTWXktv7PTtNVgaNLpcvxXTooQvb792LHD5eUz2Jb3C4ejx2hsSUnJ470cX0dHS13d0SlTrhKLeV6F3d5tMrWOGZPPRBFYE0NAbm5RZWVR6BZaWxtPnaqpqJgTfB3IMNhsJrO5PTW1kN/hCBI7jNgkO44UEXeZEUuTycR2oBlhc7lcnLlgAYAMGwOW4vpn5re1tbEPIX3tLpfbf8p3RsLZHQGMtZ9//jmnMHIP3m63ezwekuXH2ObzBR4m5++Le73eEAK/e/fugEeNGDSaBL2+1zvU6/Uajba/LYzgrx8EQa5ERqzAA1z2tiVaxcm8Y2ALG1vVCOyR6OC3FgvZtlgs7EOIGDscDk53PgAwHxDsXYw9/uPg/eefuewi/Va1IZ8jrKT3fmTRc0L07Fuxb98+GNECn5ubZzDo29pafD7vkSMHSkvLhtsiBEGQqBj5IXoC8deDCTxb2PwFPnSI/rXXXgO/gXZer9fn8x46dJS9eAwHdppbQMM4I9MiFPi3336bVdnndrvtdvuyZcsiEXjO5wg7yMGEBOx2OwDPIHAsIxAIFy++a8uWTS6Xq7CwqKxsUuTH4jB4BEFikBEr8BxI4Dq0WBLCCjw7RL9///4333wT/JarIXXYvez+sNXU6XSaTCapVMqOBBw/Xvv22+8xNpOh9gHx+XwURRHz2J0Rb775dnV1DQBs3LiRDO1jW+jfCDN8H/w8eCYM8MorrwA8G+K6riCeeeZ59s/MzOyHHlrNuzUM0SMIElOM2BB9wLdtMA+eTSQePKP0NTU1ZGQd29mFXoH3OZ2hFqljC7zL5dq9e/fp06fZs+YBgF5vCDhNPcP69esrKirmzp3LmEc+NcgXDNMd4PF4OBb6f+J0dXVxOgjYdRgP3n8APYIz2SEIEoOMOoFvbGx0OBxkxviA+O8iTR04cKC5uRlYHjw76410UTOQOqFXoeV02/trMPtcweju7j58+PDhw4dbWlr8BZ6t0Ox16iCQwD/77LOcJLuAE934fwAhBPTgEQSJKUZLiJ7g9XqnT5/+2GOPyeVBe5HJMDM2RqPR4/Hcfffd48ePB4Curi6yVhvjykOgHHWfzxda4DnKvXPnzqIi7hCgsAJPMJlMx44dIzYQkSb/MrP9gN/sPeRjRS6XM8LvcDg4SXYB5wUK0buBIAiCxA6jS+A//PDDtrY2s9nMTGniD1lIhs2rr76akZFhs9lIkLy9vZ1ZMi6Y0/bhhx+6XM5+uXR//etfU1NTOYU+vwXgg8F8bZCvCvZAPqYCu/7hw4cBoKKiguna95/JLqAHjwLvD4boEQSJQYZO4E+erNu164uenu7U1PRFi25XqzXsvbW1NV9+uYOm6by8/EWLbheLJd9889WXX+5g1/nZz56Ji+POEhqMgOJK1nx7++23/YPhDAcOHPAvNJvNNE37Z9IFk/CWlpYI7WTj/23h9UYq8Iyvzw7Rh10SjR3J8J/oJqDA+4/sRwgYokcQJKYYIoE3mYxbtvxz+fL7dLqUzz//9/btW+66awWz12q1bNny0bJlP0pOTvnoo/erqvbMmXP9zJlXT58+i1Sorz9/6NC+yNUdQr5tQ3TABzuQLMQSaCxcVO90mUzGTp3zHzTv80UUomcbQwT+yJEjcHmIPiAqlYp1rogEHj14f9CDRxAkBhmiJLvGxos5OXnp6ZkSiWTGjKubmhrZew0GvVKpys7OkcsVhYXjOzraAYCiBEKhSCgUURS1a9cXN910S7/OOLDulM/n8/fgzWbzt99+G02zMpmM/dNfy8MqNKvmZSH6zs5OiKC/XKm89M0UocCP4LluogY9eARBYogh8uCLisbn5/fO7dza2pyams7em5yc4nQ6T56sHTMmrba2hrNM55EjB3NyctkhfZqmDx/eT7a1Wq1SGcc5nc3GXSotIAKBIEK50us73W43p9m6urq6urpIDg+GUBj4A4sxzGIJfCFSqZTTy7Bly+a8vDzoW9GLEFbgfb5LFVwuZ3f3pSXUjEY9uyny3eB2u6zWS/0Ien1HQwO3W4EDabOxkf/gOqfTTtMWl+tchPUVCmVSUgrv0yEIgowMhkjgxWKJWAwAcPx49RdffLZkyd3svVKpbObM2R9+uEEsFsfHa8rLpzC7PB7P/v1VP/rRg+z6NO3Yu/crsj1+/HitNp5zughlWyiMVOC7ujog4u+G6FGr441GEwBYrdygPWHOnKv+85+d7JL169+bP/8GuHy4fFiBp2m2hDu7uy8tdWM0dtrtl66XLBTmcrnYw+30+o6GhjDZBuQONzREKs/++Hw+n89rNHaHrwoAAElJKUMs8BiiRxAkBhlEgf/220P79u0BgPnzb87Ly7fbbVu3bjKbzcuW3ZucPIZds77+3OHD+3/8458rlapdu/6zefMHd955D9lVV3dMp9Nxet/j49W/+MWvQpzaYOiMJEQvkUhdrohSxrRaHQBIpQM8RWtcnFKvN3AKMzMzZ82aRZasVSoVAQ9MT88SiUScnACrlQYA9iJaYT9fxo7NZ7alUrlWm8TaVXDixCVV9np9ACCRSBWKS/8XBQUTrrpqQuhTtLQ0nD59bNas7/FWQavV2N3dEfure2GSHYIgMcUgCvzkyRWTJ1eQbY/H/e67b44dm7d06XL/F319/blx4wrJ+l2TJ09bs+bPzK6amqMFBcU8zh7J21YkivTyidsaelw7DwIa8OSTTy5cuJAI/PnzFyBQQJ6iqOTkZE6u/p49e+Dyde3C3gQxiav0VQ4xDp7ZFU0WfWdnZ0tLS1ZWllbb77XaYhn04BEEiUGGKMnuxLhiJ2wAACAASURBVIlaiUQ6d+5NnFdhc3Oj00lnZGSePn2iq6vD6XQeOrQ/IyOL7HW5XOfPnxs3Lj9Qk2GIxJsadoFn6yuDQCBg7lJTUwtcPpiNQFHUyZMny8vLuQf3E84dCCHwTCHvLPq33357zJgx5eXlhYWFTz75ZGdnJ2eN3X5x6tSpTz75hPfhgwF68AiCxBRD1Aff0tJ88WL9c889RX4qFHFPPPEsALz11t9WrFhZUFCs13dt2PC2y+XKyMi65Zbvk2qNjRc1Go1Wm8jrnOHftgH1NSCffvopROG8MivBcIiP52YPAEBcXBwj8OTDQqFQmEwmdh2KolQqlUAQ7fdZCA+elOTm5ggE1Nmz55kSfjK2bt26xx9/fNq0aXfeeefGjRtfeumlF198EQCuvfba5557rqKigjOgAAAOHjz48ccff/LJJ3a7bdq0yZmZuUaj8fDhw4sWLdq/f//OnTsfeeSRm2++mYcx0WCzmS0W7gqBJBfSajV1dPDMJSR3Va9v5B0MsFh6AECvb4j8qfazwQsAXV0XAXjaQFZS6Oq6KBQKw1YOYoMPADo7LzAlDgcFkNPXfmdHR5ikzp4eEwB0dtZTFM+/DnIfIv+vlErj1GpM6kRikSES+Llzb5o79yb/8l/+8gWyMWPG1TNmXM3Zm5s77rHHfjZ4VvXXg2cv8NovBAJBQMeXs8IbYdGiRcyJ7HYHADz55JM7d+7cunUrU4fIQPSRYUYMZDIZR+A//fRTk8lUXl724ou/mjBhBrPerr/Av/7669dff31xcbHX66UoauXKlU6n88033yS3t6tL//LLf/700//k5uZu3bo1OTn5xz/+8e9+97vTp0+Xl5e/9NJLs2fPnj17dkJCwo9+9KNJkya5XK6nnnpq165dHR0dUql06dKlQiF8/fWeL77YnZCQkJCQ8Pvf/z4nJ+ell15auXJllJfPA5FIIperOIUuly/YrgjxeFx2e49MFsdOoegXTqcbAGQypUQi4deC2+10OCwymZK3NNrtTmJD5H9ZAW2Qy5Wsj4xLD7lEIgs+x3QvVqsDAGQy/p+/LhdN09bI/ytFoqDTYiLI8DJip6qNxNHU6XRGozFy2SYLx/EgmMAHnDFXJBIxym2z2Ymdubm5/jWjF3jyIo6Pj8/Ly9Pr9Wwjd+/eDQB5eTlxcQqRSMQIPCdx78KFC48//vh99933k5/8ZMqUKVdfffWOHTsoisrKynr++ecB4J57VtTW1j3yyCOvvfYa8859+umnycYDDzzw0UcfrVq1yuPxbNmyhRQmJSXdc88906dPv/HGG5VKJSfJzuFwSKXS4er2lkjkEglXZNxuAdmlUun4NUvTVru9Jy4uQSTiKc9OpxcA4uISZDKeqaAOR4/DYVEqEwUCnq8Fu90FAEploljM8yrs9m6Hw6JUJjEfGexPBZlMxZ6aKSAWiwMAVKok3p9KNpuJpq28/ysRJHYY1QIvkUjkcjlvvzxygjkTAR0dgUCgUChIkjxRU4FAwIl5DpS8EQ/+0Ucf3bdvX1dXl3/WPTkRc3a73c5ZtXb37t0ej2fTpk00TVut1h07dtxyyy1JSUnr1q37xS9+8e9//7u6uuY3v3n62Wd/G9DmuLi4FStWXHPNNQqF4tChQw0NDRKJZP78+enp6f6VCf7BfARBEMSfESzw4etQFBV9N3Yk9Ffg1Wo1cXmZkoACP1AheoqiCgsLz5w54x9mIKdg7O/u7u7pqWdX2Lx5MwAYDIZ33nmnpKTkf//3f2+99dajR49u2LBh5cqV27ZtKywsuOqqGaHNGDt2LAAsWLAgyssZRoKlWSAIggwXI3Y9+EiS7CiKGuxIL1HrgH3t0Kev+fmXDRMgJrGj90KhsLCw0L/OQIXoKYrSaDQBM+Q5Hjz4hUYOHz582223abXaF154YcuWLd///vdFIlFFRcXTTz/9wQcfuFyuDRvewVFkCIIgQ88IFvjwCAQC3h58hGlEUqnUZDItW7aMXcikQZFGVq9ezZZAYhKnpLj4sskA/D8CIsFfaBkPnjiggRIFKAg+3MBms3V0dMydO9dgMDz99NPjxo1jdj3xxBP33Xffhg0bgn3cIAiCIIPKiBX4SOKl0XjwEU7VQnxfzkD2uLjeyfMlEolWq9XpdGEFnvMhQvZOnTq1Xzb7f80wHjxFUU6n0z8dgZxoxYofBmxw/fr1Xq83oBlyuXzt2rWLFy/ul4VXNBiiRxAkphjBAh++TjQefIQCT9rnnIXtwTc3Ny9dupRdwV/ghUIh50PEP3IeCf712R58V1dXbW0tpwI5UWpqYC+8qqpKJBJFP98OgiAIMuCMYIEfAA9eIpEEq5CSEnRqC/aQNrJeHNMICaqzBZ4490wF/w0I7sH3V+D9R0izBT7gIaQ8RH/E2LFjeQ96Hklgkh2CILHGiBX4SAibRa/RaAIOQE9ISJg8eXKwo9iCR4bzMmch668zXdqMQjP6ytQcGoEnprInx+VAikOcKCsrq182IAiCIEPDCBb48O5UCGEjBHNt16xZQxZfD9Yss02kkSkhve+M0DJZckyFgAIvFAoH1YOvrKwM7cEHO5FEIqmoqOiXDQiCIMjQMGKDq2HDpUS8Q3vwwQReKBSGENcQHepk8nmNRkN+rlq1ijkR51j2eTMyMpxOZ0AzQhjvj38yPCnRaDT8QvTPPffcz36Gs3ADYIgeQZDYY8R68KHftkuXLs3IyODtwfvPPMPZy2xzPPjk5GQASE1NJY0zHfn+us4+b05OTkAPvr/LigTz4EOG6EN58MnJydgBjyAIEpuMUoEvKyuTy+VqtTqcBx94MhneHvzChQs/++wzMrMNuwX/ED1nL1OemZnJNNjfcfAcga+oqEhNTfUfp8cmtMDzXrhs5IEePIIgscYodb8kEslTTz1VWFi4YsWKENWiD9FzPPjs7Oz58+d/9dVXEOg7AFjBcLZnzO6Dnzx58sMPP3zNNddA/wWeUz8lJSUvL89gMEDwSfH4dfZfodTUHP3qq51ut7u4eMK8eTfxXlQNQRAkFhjBAh/KnRKLxUTa+fXBRx6i53jwOTk50Of4BvTgGSeb2Uvi5+wvgCeffJJs91fgy8rKBALBt99+S36S1d4IKPDt7a2fffbJypWPqFTx77//zuHDByoqwkyhjyAIEsuMWB8lWLiUKCWjo8GELT5eJRJxp5dhYDz4YPLPrgl+EfhHH330tttuY6sms+0/go4zVY5CoWCO6q/AV1ZWbtu2jfk5YcKEsIdwFpsZwZw5cyo/v1CrTRSJxBUV02trj/W3BQzRIwgSU4xYDz7Y21YikTgcDkbgg0nXsmV3GwydVVUHQ3vwkyZNYhxiBn/XnCPwycnJJSUl//3vfznVIJAHz/6S0Gg0L774InNUfwU+YKae/7Z/ndEg8B6Ph3lmKEpgMhmZXV6vp729jWxLJBL/XEWbzQoATifd02Pmd3an0+5w0BZLt1DIM63BbrcBgNXa43IFGHARmQ02h4Pu6enmvZI6scFi6RaJeF4FTVsdDrqnx8z0j9jtFEA82XY47D09Ya7O4bADQE9PN++H1uGwEBsirC8SieVyRfh6CDLkjDqBF4vFDoeDkcYQPjqJjYfug+dMMk9gv1kyMjLYZ2HLdsA+eH8Pnkl0BwCFQkEy8An+SsNBJBKxdYuTLc82gClXKBQCgYCmaZfLBaNJ4MeNK9i//5v29jalUrl//16iEwSr1bpmzetku7R0YkJCvP/hFEW1tTUdObI3GhsuXmyM5nAAOHbsUJQtRG9DdfWBAbSBpoUA88n2hQtnjhyJyLyjR6sG0IbQ6HRjSkqmRHk6BBkMRqbAt7W1NTQ0BdxFRFEmk5GfIVZq79P3UAIfUGLZbep0SeDnwQPAtddea7FY/A/x9+DJNwSpwBmTFkzg09LSWltbfT7f7bfftmXLVmYMPUfgA27/5Cc/Wb58+bXXXtvS0sKU816S5woiPT1z3rwFH3+8kaKo8eNLzWYTs0uhUCxffh/ZlstlzMPD0NPTDQA6XWpZWSW/s7tcjp6eTrU6VSjk+SfZ3W2srz9dXFwukfQvrsPgdNosFr1Wm0ZRPD14k0l/8eLZkpIpvAdP0rTNatVrtRnMI+dwXHr2MjNzy8rCLE5oMHQ2Np6fOLGCd44kTVusVmNCQmaE9cXiMN/ZCDJcjEyBd7ncNE0H3EUcYuYdHdqDD1aBCdGHEPi0tNSWllaOQDKyPWfOnDlz5rBPxzaPXUJMJW1ykt2CjVL7xz/+sXLlyubm5khcds7P+Pj4goICznD80eDBu93u4uKS8vIpAHDq1AmtNoHZJRSKcnPHBT8UyKK6Mplcq03id3aatno8Vo0mQSTiqRZerwcA1OoEmSxAVCkSHI4er9eu0SQKBDxfC6R3QKNJ4K15dnu3z2fXahNZIfpLe+PilFqtMnQLJPSi0STy7miw2UQ+H837vxJBYoeR/+LmEKEHHzZEX15e/oMf/CAtLU2hUEycOJG9l7S5fDlZA/4ygQx2Ov8KjA/Ew4PXaDRkyjyKoshqNwTmckQiUUCBl8vlZI79USjwNpv1jTf+2N1tdjrpb775avLk/k3Bi+PgEQSJNa54D95mM1ksBk5hT09HsPpCISUQCCQSd0fHeQDweJz+r+YxY1KmTSv7979bvF6Px+Pyb8Rkah07NumPf/ztk0/+Oj5edfvtC2pqapi9bjcNAAKBBwBo2trRcb6np6vvwOaOjgBfDD6fl2x4vS5imNXae1EpKUkdHecNhk6yn+wlWK1d/k1lZmbk5Oj++tdXli17wO2mBQLBzJmVAFBVdcBi0Xd1XQQAqVTicNCXN2UEgLy8nNLSnI6O84w9LpcjxP3s7u7o6LAE3MXQ02MCgI6Oet5hfuKbsq0NjVQap1b3ewLd+Hj1rFlz1qx5XSqVTZ06bcKEsn4dTlERrVCMIAgyZFzxAi8SSeRyFadQIgkQpRSLxS6XSyKRlpQUl5f3rgUnFIqFQqHb7WbXvOqqmdddd+2nn34uEHAntCFfAwqFipxULJYIBAKp9LLTkcg5KRSJxHK5SqGII7uYAzmwku/EpIJc3ntIQUGBXK6Kj3eTltmHazSJ/k3FxcUplZrJk6fGx6vEYjFFCWbOnDFmTEpV1QGZTEEOl8lkNO1kNyWRkI4AISlk7CkqKgQAmSzO/0QAIJHI5fIwum2x2AFALlfy7sh3uRw0bQt43wIiEvHshJ42bca0aTj2HUGQEcIVL/ASiUIi4Y5Rkcub/WvOnTtXJpPRNN3U1KRS6Ujhgw8+tGfPnvfee49dUyyWyeXqkpIiu91dV3dCJpM5HA6yq6ys7Jprrpk0qZKsCyeVxgkEQpnsMu0hciiVKkhTKpXuttuWrlv3/p49e+Ljk5lTX35GCbNBKkyYMKnvAuUqlU6l0hUVFYnFUvbhM2bMKS0tPXbsshHbIpGY1LnxxgXZ2ZlffrkrIUGnUKgBQKFQK5WJACCTybu7e9hNyWRK9tmZYU6VlTMAIC5O628zAMhkKpUqjO729NgBQKUKOhtuWKxWI03bAt63WAJD9AiCxBYjv2+VIT09fdOmTVqtlq00Dz744M0330y2OUu63XnnHevXv0NRFFnEnaDVal999VWi7tDXq82RLuLBk/5yskur1ZaXl0N/+uDVajXHKolEwumDFwgEHH0dO3Ys08LLL7+8ZMn3X3/9pdWrVzPD8UlrycnJWu1lms1JBvTrgx/5WfRRIhBQXq93uK1AEAS5xMgUeEaf2IrIaBhHj/3FNT09nX0UO5ct4LEc2WbPRBvsXBxCLDbDHiLvP2Usp/2EhATOKXS6JKlUyhhD6t93332nT5/2byfYqrWjYZhclGCSHYIgscbIFHgGlUqVlJTEONwQSOD9pbS4uJjZW1RUVFJSwj6cfSzbg+dMVkPS1zmucDCl9F8ulrGKKcnOzvafV4fToFwuD/gNwZkxVygUMhECdjv+3xkBv2CQQKDAIwgSW4zMFzeRq0WLFv3rX//629/+RuatI4VLliy5//772ZWZWe38VRYA3nvvvWeffZbTMvsnsxIMEy0gHn/AUXYhht1zbPAX+Ndff/2tt97iHLho0SL2TzIPnX/7nBC9vxmhPXgU+LCgB48gSKxxxSfZhWDMmBQymcyDDz4IfVp10003caoxY+L9NZUQcE5ZQmFh4YQJE5jB5WTOOOLBU9RlAh/ag9doNJz2/Y1hT1LL8Oijj/785z9nfhYVFXV0BBjSxvHg/c0gXfL+HjwKfIRgHzyCILHGyHxxB/Sbg4kr48HfdtttAQ9nq2Nm5mUTWK5cufLf//43qc/MK8fMHs+ZSC6EDcxUOSH64APCafD555/fuHGjfzXmEoLdCrKynL8Hzw7R855/dHSAHjyCILHFyBR4QoTeMyPwZLF2/5rsFWZfeeUV/xY4+tcn8Hw+MkJ48AERiUTsCWtD9/H7J8kz9EUdQoXof/jDH2K2XTAoCpeLRRAkthjJAs8QWlxTUlKIvN1xxx1vvvmmf02itSSS75/HztRnBD4+Pj4uLi4pKcnfgw8GkwbIQ+Crq6uvueYazuEcwnrw7DXrwO9TgJTfddddKPDBoCgKAAUeQZAYYmQKfLBR3QHJysoiYW2tVrtgwQL/+kTeEhMTIYiCcgQ+Li7OYDDMmTOboiKNIqxevXrJkiXsCkxrYTW1uLg47Oq3zFo1wcxITU1NTEwMPUyOSSdE/KEoAXrwCILEFKPifR12MPfEiRPnzZun0+kC1szPz1+5cuW8efMgiMBzQvSsofORZtEnJCQUFRWx209ISHjppReVyrhInOawHfbsIETAahqNZtKkSYGGyV3y4P3n1UEYKAowyQ5BkJhiZAp8QA9+2rRpweqPGzdux44dCoUioMCrVKq///3vOp0OQnrwTF84yw+ONIuegd3+o4+ukstlkTjNDz300L333qtUKoMtIEsEPi4uLoQZ7Jx/jgdPvgykUmlSEq6hGRgcJocgSKwxKvKiiUrdfffdYWsmJiY+9thjM2YEWHGE00vNxt+DZzYiF/iAA9Ii7MW/+eabFy5c+OqrrwZLdCfNKhQKjoWcOowPyhH4oqKigwcPTp061X+mndEGWfmGU2i391AU5XQ6/Bc2jBC32wkANpuZ9yrmDkcPAFitJrfbHrZyEBto0gKzFnv/bbAAgNVqZNYy6C8uF7HBCND7iNrtFEDvtMo0bbVY6NAt0LQVACwWI+/uJJfLDgCR/1eKRBKylAOCxBojU+ADevCRKKVAIPjTn/4Efa8JNkQ7I0myY5030mFyEETgH3nkgblzFwU5gns4Z3I6Nrm5uRMnTszIyCD2B7uKYOPgAaCiogKCr0A/enA67cxKvgwOh42iqIC7IoR4/zabiXcaIxFXu93kdvMUV8YGfocDAE1bSAsBH7DIbSCLFxMcjssE3mrtDmeDFQBsNgPvz5Q+GyL9r5RK41DgkdhkZAo8IcIkuwgRiURsj9z/RGq1WigUejwe1nn78ZERsML999+TkjIueuOLioqqq6vJ9rp162688caABrBD9H3XctlbEgU+Lk7rv7ae0ainKEomU/H+z6Jpq8HQlJSULRLxvMMiUXtjY3NS0liZjGeUxeHoMRpbdLqxAgHP1wJFtTQ3t+p0OczqiP3Fbu82mVqTk3OZB8/OikfExyenpCSHbsHrbWxtbUtOzuMdC7HZTGZz+4D83SHI8DIyBd7fb45e49VqdbAUM+Lvvvbaay6Xa8aMGQFD9GEJ6MEPBj/84Q+DGcB23EUiEftjhYACHwwcB48gSKwxMgWewE9og7FixYr58+eHOJFOpxszZkxAAyACD37mzJlisXgYx6FNmzaN6YMXiURpaWmtra1qdTyAm6mDAh8MHCaHIEisMVIFfuA9eJFIlJGREXCXTCYTCARqtVosFnMS6yLvg7/++uvj4+N5d15Gz69//Wtm+/nnn5fL5aWlpS6XzWxuY8pR4INBUZTXiwKPIEgMMVIFHoClpoOtmrfcckt1dTXJcVOpVKxkdQEVcR88AAgEghhJU581axbZcLkuKw82DA/BED2CILHGyBR4jo6WlZU1NDQM3umEQiFZrAUAjh49yjj67CS7gIZxoCgqRgQ+GCjwwcFx8AiCxBYjU+AJjJqKxeIhC33n5uayDeAoetil4RjvPzZBgQ+GQIACjyBIbDGSZ7Jj/xyW5DW2vkeSBJCWlpadnT24NkUHCnwwKArXg0cQJLYYOg/+5Mm6Xbu+6OnpTk1NX7TodrVaw95bW1vz5Zc7aJrOy8tftOh2Mo42YGF/uDSqe/gEvh9j8Q8fPhzjq7mgwAcHPXgEQWKLIZITk8m4Zcs/Fy26/fHHf6HRaLZv38Lea7Vatmz56NZbFz/22E+tVktV1Z5ghRFCxJQtrsOSnc6ei56xJGT9mFZ3ABjegXyxDIboEQSJNYboZd3YeDEnJy89PVMikcyYcXVTUyN7r8GgVypV2dk5crmisHB8R0d7sMJ+wRb4YZKl/vXBxz5isRgXlAsIhugRBIk1hihEX1Q0Pj+/kGy3tjanpqaz9yYnpzidzpMna8eMSautrSktLQ9WSHC73Q0NF8i2XC6XyaSc09lsPQDgdDqMxi4AcLtdFEWR7QhxuRw2m81k0vOettNi6RaLRV6vh5yXph0AYDR2RR5LcDptxAbes2pbrT0AYDLphUKeV0HTVpvNRqZiBQCpVJyYmGg2X2rfaAyz+IfNZgEAo7ErmlnWbTZb5P99Eok0Lm7ov0LQg0cQJLYYIoEXiyWk9/b48eovvvhsyZLLFnaTSmUzZ87+8MMNYrE4Pl5TXj4lWCHBarW8++6bZLu0tCwhgfs27+mxAIDB0FldfQAAjMYun89LtvtFY2NLfw9h89xzTyclJZLzdnS0AEBNzSGywvqQ2QAAx48fibIFxobrr59ZWjpl+fLe8oaGc9XVzZG0UFNzcKBsCItOl1pSMjnK0/UXigIAFHgEQWKIQRT4b789tG/fHgCYP//mvLx8u922desms9m8bNm9ycmXTelaX3/u8OH9P/7xz5VK1a5d/9m8+YM777wnYCGpr1LFr179RO8FiIT+a1NevFgPAMnJaZWV1wJAcvIHEomUbP//9u49vIkyfxT4d3JPmjRJaamlF4FyKYvcSguUFcHFAy5lRRRQ6k/h8fx2D7KsoIjiYZWfgoL+WHd5QNajRwQFe0AKgrDoChQLtIW29EYp0FB6J03bXNrck5k5fwyEGmibTsjF8ft5eHim05l3vkne5jvvO+/M6yOn02o0agcMSGLd9jUY2gBgwoSpIpEYAI4f/wkAMjJ+53sJDofFZGqNjn6Q9bQZ7e2t169fTkubxvpV2GxdXV1tMTGDPb0IXV13TlCSk38zefKI3kvQ6Vpu3Lg6adIM1i14q9VkNncMHDi0700BwI/nGvkzqLP7ZLsIIRQOApjgU1PTU1PTmWWSdH/11eeDByc/88zzd3/R37hxfdiwkSqVGgBSUyd9+um2nlYyeDyeWh3Vy6HFYjEACARCqVQGAEKhSCAQMMs+4vFokUgolUr5fJbjxi0WMQBIJFKxWMLEAAD9ioEgSJtNKJXKWCd45smyEomU9fzcNO1yOIRSqcyT4N13nkwPIpFIKu0jC3peOOsET1EOp1PYr7eOBWZQ53/8x4sDB8Z+883X+flnpk+f6fvuBIFd9Aih8BKkMV/V1VUikXjWrDle3/LNzY1OpyMhIfHater2dp3T6SwqKkxISAKAe65kJ3SD7NAvhp+DOnGQHUIo3ATpGnxLS3N9/Y133nmT+VEmi1iz5q8AsHPnJ0uX/mnEiFEdHe179nzhcrkSEpLmzVsAAPdc6SOvB78zs5vf55fUT/dlwhsUOL0M6vQFtuARQuEmSAl+1qw5s2bNuXv9W2+9xyxkZEzLyJjm9dt7rvRdGNwm9zOY4MNZ74M69+7dxSwPGTI0KirSa1+3m+TxeHp9e0nJWXZHp2nK7XY2N+tYVxK32wUAlZVFrKs6RZEk6Wpp0XlNxtjfGMrLz/txOYaJoc2zxuHgAUxlluvqakpK+uhZcbmcAFBams/6VdwdQ+9UqgHJyaPYHQuhgOLms+jvvvs85AkeW/BhrpdBnXy+YNCgWzd2qlRqhULuta/D4SAIgsfjKxRKdkcnSbfDYZZIIllXVLvdZrNZ5XIF6wGVJOlyOCxSaSTr2zJtNqvNZpXL2b8Kt9vpdFplskhPehYK7xQlkUj7fIetVrPdbpPLlX6cKjEx+PpRBnp0CEKscTPBM0L+JDsvmODDWS+DOiUSydy583vZ12Do4PF4Mpl8xIgx7I7ucFj0+qaYmCECQX+fx3xLR0erXt82ZEiKRMJyQkK7vctgaImNTWb97AedrsVgaE9OHtX/p0rfYrN1Go03H3hguOckw2a789sHHkgYMSKh9xJu3mw0GvXDh49mPTTVajWaTK1xcSPZ7Y5Q+Ah9x3UghMlkM14xYIIPZ34O6sRBdgihcMPlFrzHww8/LJFIQhsDJvgw58+gTgDg8TDBI4TCCzcTvNco+qysrKysrJBGhH4B/BnUiS14hFC44WYXPSPcWszhFg+6jzDBI4TCDTcTfHim0vCMCt0XPB4P74NHCIUVbiZ4RlglVLwGz20EQdA0tuARQmGEmwne6xp8OMAEz2042QxCKNxwM8GHJ0zwHIbX4BFC4YabCR5b8CjIsAWPEAo33Ezw4QkTPIcRBEFROMgOIRRGMMEHCWZ3bsMueoRQuOFmgr/dRR/qOH4OczyH4XSxCKFww80Ez3qmyMBRKBQKhSLUUaBAwUfVIoTCDVcTPECYtZiXL19eUlIS6ihQoBAEDrJDCIUXTPBBIhQKY2NjQx0FChSBgO9wOEIdBUII3cHVyWZCHQHiKJqm735iHU1TMpmsvb2Zoki2xVIAQFFkOJTA+grXfYyBIG4NaKAoAOB7ftvnrQrdXgVL/S2BIAjPWGKa2QAAGCxJREFU7PUIhRVuJnhGWLXgETdYrYbOzjavlRaLTSaTGo2G1laNP4V3dDSw3tdisQBAe3u9UOjXH3VbWx3rfbu6ugCgre0Gn8/3JwadrtazbLcTACOY5c5OXWurqfd9Ozs7AUCnu+5n0vX9o5RIFGr1IH+OhVCAcDPBY2pHASIWy9VqoddKHs8okUhsNgfrL3qXy2E2d0RGDuTzWf9JdgDcVCpjxWIx2xhsZrNBpYolCJbpmaJ0AK0q1QMCgfdb5COn02axGFSqOM+fsM1257cymUqtjui9BJeLB6BTqeJ4PJavwuGwWK0m3z9KHo+b36KIA7hcNTHNo/tOIBAJBCKvlTabUywWORwOiYTljRJMc1Msjri7cB+JRFamBIlEyq4EAAAwiMVy1hlLKOwCALFYLhSyfBXMrYYSidzT/u5+76FQKJFIJH3FYAQAiUTBOsFTFAlgYv1RIhQ+fvEJ3m4322ydXistFgMAOBwWg6GFXbHMFTiTqZV1R5/ZbGRKEApZtmYoyg0ARqOW9ZkK8z4YjVrWXaZutwsAjMabnuuyZjMBEOcp32Cw9bgzAABYrUYAMBhusj7dcrudAOD7RykSSSMi1CwPxpZIJLLb7UE+KEII9eIXn+BpmqZp7+Ewt4dB3eNXPhdL3f6f9dNLmBJImmZ5inD7VZA07degJ5om/XgEy60SPAn+58H0/Q4zbTLWH8TPY/BJSKZtFYvFLpfL7XYLBL/4vymEEDf84r+MpFKFVOrdmUaSQgCQSiOjohLZFetwWPT6JpUqjs9n3f4WAtSrVIPE4j46FXtit3cZDC0qVTzrzkaXiwfQqFbHs74marWaTCatWp3g6cno3h8REREVFRXVewl2Ow3QFBWV4E8/RGenjvVHGRwSiRgArFZrZGRkqGNBCCEAbt8Hj1DQSKVSuD2EGyGEwgE3E3wYTheLuE0ulwGAydTHTVwIIRQ03EzwCAWZVCoDALPZHOpAEELoFm4meGzBoyCTSiVw+0kvYaKtrW3t2rUul+s+lnnp0qX09HR/zmOcTieeBiEUHNxM8AgFGXMNnnmcnO/MZnPgJpktLCz84IMP6uvre9+MoqiPPvpIr9cDwLlz5+fPf9prg6tXr3oes19dXV1cXKzVar222bdvX1FRkS9Rvf7663/4wx+ampp8eg0IIT9wM8FjCx4FmVgsAgCbrbenArjd7u4/tre3p6SkvPLKK0aj0WAwPvLInKqqqu430z/77LNbt25lHRKTlXu5O9/hcLz55ptnz55dvXr1yZMnr1y5evZswXfffefZ4Pr16263e9y4cbt27WJ6ApgXyPxfXFxsNBqZLdevX//222/f8yhXrlxZsGBBTk4O82NVVVVeXt7gwYNbW1u7b5adnf3Xv/51+fKXWb9ehJAXbiZ4hIKMeQK80+nsaQOTyaRSqbKzs0+fPt3S0gIAmZmZzc3NW7duXblyZVlZmUZTW1BQEBUVdejQoalTp3788ccXL16sqKjwlHD48OHDhw/3GUlZWdm5c+c8wXgSPEVROTk5zMrc3NycnJyqqqrNmzczqbezs/P3v//Djh2f0zRtNBobGxuPHz8+bNiwSZMmORyOhoYGtVr9wQcfrFy5EgCsVisAzJ49e8eOHUzhdru9vLycWW5ubl6yZInnuFu2bMnJyTl48CDzY3FxMUVRJEkWFRVt376dWXnjxo1ly5a99957+/fv/8tfXm9sbLx+/Trzq507d/b97iOE7uUXfx98L7AFj4KGeb5NXV3dunXrli1bxufzq6qqJkyYoNPpVq5cuW3bthdffNFisWRlZREEsXbt2vfff9/TeX7x4sWqqksAUF19xWazPffcczabrby8nMfjdW9/b9myxWAwzJs3z+vQR44cGThw4JAhiSRJzp79uEajGTZs2L///e8DBw4AgN1uLy4uVqlUDodjwYIFR48ezczMXLt27eXLl5nCmf72zs7O9vZ2po9h/fr1+/btW7VqFQCUlpYCQElJicVi2bBhA3MNQqvV1tbWdnZ2ajSav/3tb83NzTqdzvPExr///e9ffvnl5MmTly9fTpLk6dOnAWDfvn07d+7cunWrZ5jC0qVLOzo6Hnnkkerq6nXr1jF3GFqttpycIzk5R+Lj41NSUg4dOlRVVRWYTwwh7uNsgicIAhM8ChrmUULvvvsuSZJ79+6NjY0tLy8fO3bsCy+8cOLEif379xcUFDBb0jR94cKFqVOntre3M2suXbrELDQ0NMDtDnCmlexJ8AcPHiwtLRWJRBcvXhSLxaNHj2bWZ2ZmnjlzhiAIlUoZFxd7/nwxAMTHx1dXVx86dAgA8vLyPv300wkTJqxZswYA6uvrz58/r9FoPCPdmAS/adMmzxWE3bt3m0ymH3/80fPqfvjhB+g2wmDZsmU2m83tdu/ZsyciIoLpqGf68GmaPnUqFwBWrFgxduzYHTt2MG1xkiTfeOON7lccOjo6AGDDhg1Hjhy5+/1sbm5ubm7+4YcfvHryEUK+42yCR6hfzp376cSJ77uvee21dRERch935/EIgUDA5Mj6+nqmdV5UVGQwGABg/fr1Pz/WuXteGi8rK/dac/78+WnTptnt9ps3b1osFovFMnHixISEhIqKCrVaTdP0999/T1EUAHR2dnpy4cWLF3/7298yyydPnmxsbGxsbLx69SoAFBcX79q1ixlSx2hsbASAtrY7c+Ayd/N3vzrgxTPIzuVyeS7Du93uV19dY7OZmfMVmqZ/97vfdR/Df8/xBIWFhb1c13jttddcrjvfUV9//fWuXf9n3rx5FEVlZGRIpVKCIKKjo1tbW5uamlQq1ZQpU3oqyul0MqHa7XapVCqTyfh8vs1mUygUer2eOd0xmUwURdntXWaz3mSiRSIRAIhEIoFAQJKkSCSKiYnpqXyEwhCXEzy24JHvpk6dNmXKraR440ZtUVGB79mdIRKJvIbRAYBG87NpxRUKRVdXV/fsPnz48JqaGoIgZDJpXV2d1+5MQ9ZrZVNT06JFixYsWHD8+HEmuzMcjluZkiRJpgMAAGpqaphtqqurAWDPnj18Pl+pVHqeycN0G9zN08HQ3ciRI2NjY/Py8u65y969X0dG3nludE936K1du/bgwYPXrl1jXkv3X6WkjExPH//NN4eZt6i+vh7gzuR4J0+eHDjw6po1a4RCoWdgf3dCoVAqlUZESBUKpdlsjo6Orquru1+PF1yyZMmuXbvuS1EIBQeXEzxCviMIHp/PAwCKInNzf1y06Ln+liCTyTxptSdpaWm5ubmeH//4xz/GxsZu3Lhx4MCBQ4cmFRT4dKcZAJw4ceLEiRO9b8Pn8yMiIpgGuofL5XK5XM8888y3335L0zRN01737m/Y8O7bb69nbt7j8XjdTyAAYMyYMa+//vrhw4ffe+89z0qZTCaRSJgb3Ds77/EkgN27dy9ZssTz48qVK0Ui0bvvvuu1mVQq/ec/t48cmdjaqv/pp7y7U/iqVavef//j9vZ2tVqdn5/PzJFoMBhiY2MHDRrU0dFRWFjY0tJUU3MlNjZBoVAYDIb58+er1Wqm8MjISD6fL5FIurq6zGYzn8+naZqiKJlMNmDAAGYMAUEQQiHYbCY+PxIASJL0nB8MHjy49zccoXDD2QRPEIRnAjSEfFdScmHIkKFKpcqzxuVyVlSUMcuRkYqIiAivXaxWCwDs3fvl3r3ZX3751aOPzrh+/XpDw88yq1QqffnlFW63myTdZ86cZTLoG2+s3r37KwCYPn1aW1sr3M6p06c/cuFCEXMxftKk9KKiYs/t8jwecxbS44x5TCcBn8//8cfvS0vLVq9eQxDE9OmPnD790/jx4xsaGvR6fULCoEcemXb58uX4+Pi8vDOefTdt+q/nn39eo9Hs2bOXJMlp0x4uKiqeNu1hjUZz40YdRVGJifHx8bHLl/9JqVRkZ+9jhuDNmvU/yssrJk5MPXDg1r1wTz31pFbbWlZWPm7c2IKCQqVSPm7c2IqKyszMOTKZzO22z5kz++TJEzU1Gp1O95vfjJo7N/OTTz6trCylabfRaNqy5QORSLR69Zpjx45PmDC+tPRWeImJg/R6HY8HJlPH6NEjvV54fHzs00/PM5kMra1Nw4ePZj3Rs9NptdkilcrYu3/V0nKP3g6pVKZWR7M7FkIBxdkEjxALJEkWFua/+OL/6r7SbrcfPXqIWR4zZmxU1L3nixOJqKys+Xv3fp2aOmb8+NE7dnzG5/OtVhtBEDRNZ2bOevzx6QAwd+7MU6cyTp8+W1JS1tioiY+PlkolERESu10OAHPnzh44MOaZZ5765JOdOTlHAOCppzLHjEn5/POvsrIWfv31Ny+99D/PnSssK6skCEIkEvF4hM1mnz9/7s2brYWFRQCwb98X77yzubz8EkE4UlNHqVRKgiDWrl2ZmBi3aNH8qqrqjz/+v+PHp2g0V9Vq5bBhD+blnUlKSmhoaFq5ctnEieNqaqqWLl1UU3O1tLScz4fs7M8VCnlrq660tOLDD7eSpOPatUoASE9/KC4uasmSZZMmTVy8eP5jjz3sdLoOHMjh8/kkST7++KNSqaS+vnHy5LRTp/IiIvj/+Mf7n322e8aMh4cPT9ZoqgBg48b//corb+p0urffXhMTE5ORMaG2trr7+/nSS0unTk0bPTr1ySdvrdFqm69d+9lpU09qavwde9/aqvNxy5iYBzDBozBFc1FHh47P52/evJl1CXa7uaXlitvtZF1CW9vN3NyjdruNdQk2W2dLyxWSdLMuQattys096nKxfxUWi7Gl5QpFkZ41nZ00wK1/X37ZdwnNzfW5uUcpimIdg9msb2m5wnr3fqmoKM3O3t3fvfT69tzco2ZzF03Tly5dcjqdFEXpdLr8/Py33npLo9FcuHChqamp+y7Hjh1bt24ds9zW1tbZqV+9egUAaDQaZqXL5Vq+fDkAaLXazs7OL774wuFwrFixorm5uampSS6Xq9Xqa9euWSyW3bt3G43G1tbmrKyFSmUkRVGLFy9OSkpiylmzZs1nn312d8wkSTqdTpIkz58/v3jxYpFIdLu+uZgNsrOzT5486dmeGTr3xRdfdC/ku+++y8vLY5bLysoAIC1tAgBcvnzZl/ctMzOTIIiuri7PGqvV5FXfrNY79W3nzr7LbGlpyM096s9fjcViCFp9QyigsAWP0B0VFaUjRozypwTPDWwxMTExMTEZGRkAkJyc7LXZnDlz5syZwyxHR0c7HBaVSgkAsbG3eoYFAsH48eMJgoiMjJRKpUuXLgWAbdu2Mb+dP39+fn7+8OHDAeCFF14AgI4O+3/+5wtbtvyDIIisrKwZM2YwW3744Yf3jJPH4zG9/ZMmTUpISGAuVHf37LPPdv9x1KhR69atmzlzZveVc+fO9SyPGzeuqKjg229ziotLZTJZ7+8SY9CgQWlpaXJ5/wYzIoR8xNkEP3fu7IyMHu+ZQehuLpertvb6nDlPhOTojz467bXXVnfPdgsXLoyLi2Oecu/lySefHDnS+yI0QRBMnu6ed32xYsWKxx57rPdteDzexo0be98mKSlp4sTxb765NiEhwZfjbt++nSRJX6NECPUTZxP8qlXL09LSQh0F+iVpbKxXqVRq9YCQHH3o0MGbNr3ffY1KpeopVT/11FP38dBJSUlJSUl2+32YCk+tVr3zzjvM+PY+MTeaI4QCBJ9Fj9AtQ4cO+8tfXgt1FAghdH9ggkcIIYQ4CBM8QgghxEGY4BFCCCEOwgSPEEIIcRAmeIQQQoiDuHmbnMNh1+u7eprMyhcCgUihiOHxfLrb555sNrte3+XPbb4CgVihiPFnTjyr1arXd/Xy3PI+iUQSrxjEYti8+dZyaqqvMdA0RRAs30yRSKpQhPU0neFT39xu/+sb+5N+m82m13dRFPsYhEKxQhHTfRYJofBOffPlvleLhalvtB8xhHt9Q8hH3GzBW632ysryu+fu9B2fL5TLo/z5srNYLJWV5f4leJGfMXR1mSsryymK/ZedQCCWy6O6f+GKRPDGG7f+3X5oW29MJlNlpfc05/0iFErk8ih/Sgg0m81eWVnuT4Jn6ps/Cd5qtVZWlpMk+zp/v+obSbI/oWTqW/cTSoHgTn0bM8aXGLoqK8v9S/DiMK9vCPmImwkeIYQQ+pXDBI8QQghxEOFPX1bYcrtdXV1dSqXSnz5PP7lcTrPZrFSqmCk9QsLpdFosZpVK7c+FfD85HA6r1RLaGAKNqW+RkUofH9EaCC6Xy2zuCo/6pvKnn9/vGBwWC8frG0I+4maCRwghhH7lsIseIYQQ4iAO3ibX0FB37Ni3Vqv1oYfGzpo1J9C9hYWF5woKzrjd7iFDkp944mlmgqx7xhC4wIxGw5EjOVrtzbi4QQsXZkkk0uDHUFFR+tNPJ91u96hRD82e3dvhgvwBBRrWN6xvCIUpmltI0v3RR5tqazVOp+Pzz/9ZUVEa0MNptS0ffbTJZDLabNZduz49ffpETzEELjCKorZt+9vVq9Vut/vYsW9Pnfp38GPQals2b35Hr293uZy7d3924UJ+8GMICaxvWN8QCltcO5mtrb2uVKqGDEkWCkWTJ08tLy8N6OEMBv3YsRMiI5USiXTkyFFGo6GnGAIX2I0bGrlcPmJECp/Pnz07Mz19SvBjqKm5Onz4SLV6gEAgTE+fUlVVGfwYQgLrG9Y3hMIW17roTSZDdPStp1DFxAw0mYwBPVxKyuiUlNFWq0WrvVlZWT59+syeYghcYHp9h0wmO3Ag++bNloSExMcfnxv8GEiSpG+P1iQIHpN4ghxDSGB9w/qGUNjiWgvearWKxWJmWSQS22zWIBy0qanh+++/czodMTEDe4ohcIHZbLbq6svjxk3405/+DAD/+teR4McwbNgIjeZaa6vWYjEXFp61223BjyEksL5hfUMobHEtwUulUqfTySw7nQ5m+E+gjRgxavnyV1JTJx07drinGAIXmEQiSUxMGj48RSyWTJ48tabmavBjiI9PnD078+DB//fVV58PHTpMKpUFP4aQwPqG9Q2hsMW1BK9SRXV0tDPLHR0dKpU6oIfLzz9TVlbCLCcmJun17T3FELjAlMo7RfF4POY5J0GOwe12jxo1+qWXVi1btjI2Nk6tjgp+DCGB9Q3rG0Jhi2sJfujQZL2+Q6ttoWmqpOT8mDHjAno4pVJ54UK+Xt9ht9uLigqTkgb3FEPgAktOHtbe3tbU1EDT9IULBSNGpAQ/BqvVsmPHPzo7TU6n49y5n1JT04MfQ0hgfcP6hlDY4uCT7Bob648dO+xyuUaOTJk1a073mdACIS/vVGlpsdPpHDIkOTNzHtNbeM8YAhdYff2Nf/3rsMViefDBIXPnPhmSGC5cKMjLOyUWS9LSJmVkTGNWBjmGkMD6hvUNofDEwQSPEEIIIa510SOEEEIIMMEjhBBCnIQJHiGEEOIgTPAIIYQQB2GCRwghhDgIEzxCCCHEQZjgEUIIIQ7CBI8QQghxENemi0UI3Xf//d8brVbLokXPjRr1UFNTQ1dXp1od9cADg0IdF0KoN9iCRwj1Q0HBmf3795aUXAh1IAihPmALHiHUh1dfXQsAPB4/1IEghPoBn0WPEOqDp4s+P/9MU1MDszI6OubPf36Vosi8vNzq6ksGgz4qakB6+pSJEyczG2zYsI6iqOeff/H69ZqqqspVq94I3StA6NcIW/AIIV+NH59qt9va29vi4gaNG5cKAPv27b12rZrP56vVA1pbtUePfms0GmfOnO3Z5dy5M7W1NQIBftUgFGz4V4cQ8tXEiZNrazXt7W3x8YmTJ/+2rq722rVqgUDw8strFIrIqqqKAwey8/Pz0tImK5UqZhettmX69JmJiQ+GNnKEfoUwwSOEWKqrqwUAuVxx8WIRAFAURRA8iqLq6mqZ9j0ApKdPmTHjsVBGidCvFSZ4hBBLJpMRAIxGw+nTJ7qvt1otnuWkJGy7IxQamOARQixFRioBYPTosQsWLO5pG4LAe3ERCg1M8AihfnM6nQCQkJAIAHV1tRaLOSJC3tamO3RoP01TCxc+FxU1INQxIvRrhwkeIdQPERFyAKiuvsTj8ebNWzB48NC6utrt2z8aMCBaq71Jku7x4ydidkcoHGDvGUKoH6ZMeTguLp6maaPRAADPPbd06tRpERFynU47YED073//xBNPPB3qGBFCAPigG4QQQoiTsAWPEEIIcRAmeIQQQoiDMMEjhBBCHIQJHiGEEOIgTPAIIYQQB2GCRwghhDgIEzxCCCHEQZjgEUIIIQ76/6strwfUUo+XAAAAAElFTkSuQmCC" /><!-- --></p> -<p>and SFO with two-component error.</p> -<pre class="r"><code>f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_sfo_tc$nm)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed above for this model combination. Also note that the variance of k2 approximates zero, which was already observed in the saemix fits of the DFOP model.</p> -<pre class="r"><code>f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_dfop_const$nm)</code></pre> -<p><img 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x+JcJemad7eq1SqysrrgopjWYaiSLlcGeW+i1A4jmVZRiIZMHjCoDAMTdOUQqGKWQLHsSzLomjbsUHTFMMwCoUyDh0YjuNw/KIOXi/W2dm3EaOggBk4vEQfFEUCcDJZpLOeIoOWs+J4tKHFSTIgl8deXPSUlY1NT88ILZ1lGaVyaMHehTAMDcDFU/f8fgLDsHi+d4ahMAwTfu9DhSB8Eokkni+Cpkkclwi/d5MJHA4AAKUSCgoGl+DzeeRyRYRjSKLTQRpziN9Ly6RJU0ITfT6vTCaLZ/oy9HMeKj6fRy5XDhQpNgo4mqYkEmn0580EwbKs3+9TKlUxvwXHsQxDSyQyock4cwYYBgAgNRVSUgaRwDB0IOBXqdTxvAXL0lJptF9lWlpw44MYBgNfVFRy4MA+s7lbq9UeOLDX7ycAQKFQ1tTMWLt2tUwmS0zUjx8/SfhIhLter2fVqhXouqpqXHJy4pV8lyHBcRBrH+Mq5dy51P/+776G5sMPdyUnX3UOltNpS00NX/UvLdOm3RSa2Nj4ndvtuP766TGLtdu7WJZJTs6JWcKxYwclEkll5fUxS7BYzkulCr1+aEFthRw8+FVysrGoKNqDX0Mxm8+p1fqEhIst6xNPwLvvAgBMmADffju4hN27vygsLM/OjqIvEA6OY7u7z+r16SpV8Ck7w8LcufeFJu7bty0rKz8/vzhmsSZTY2KiUaMJPiowShiG3rNna1nZuPT07Jgl9PQ0GQxZSqU2Ngler7u+/uvx42/Q65Njk0CShNXalpKSL3RFUlPBYgEAePllWLx4EAkWi/nEiUM33HCLUhmjU0cQLofDlJ5eHHMXATEMBj4rK2f27Ls+/ngNhmFjx1ahSZSWlqZDhw4888xzWm3Crl1fbtjwkXDRf4S7CQmJP/vZf/W9jFQS2kN3Om2nTx8bN+6GmD9rv9/jcvWkpOTF3CU0mztbW89MnXpTzKMIPp/D63WkpubH9jgAtLc3Wa0948dXxyzB7bbQtN9guPjTdTovfiDjx9+Qnz/Ilstz5xooiiwvnxCzDg5HN4ZhOt3g+7YRV8Z9FxEREbkKGQYDT9N0eXkF8sIbG0+hsyBbWpqKikr1egMATJw4ZdmyN4WPRLiL43jk0yQJwgsASqUKnYIVm8p+v0ylUsds4NGgmUqljtnAMwxBUbI4XgGkUhmGYfFIIEkFjrNCCcJTtpVKlWqwHpREImEYPB4dCEIe51uIiIiIjBKGYT7J5/O+9dbrLpeTJAP79u2eOHEyAGRn55w5c8pi6SFJsr7+QHZ2Lsrc2dlOkoGB7oqIiIiIiIiEZRg8+MRE3Y03zly27G8KhfL666dUVo4DgJKScqvVsnr1exRFZWfn3nPPf6DMK1e+s3jxEwPdjYZAgPz222PFxeOyskS3T0RkEJYuXdra2rpixYrhVkRkyHzwwUdWq2P9+o+HWxGRq4XhCXQzZUr1lCnBk8HV1dOrq4OXI7344isR7kaDyWR69tn/KSgou+++GNd9iIiMHk6dOtXU1DTcWojEQmvr+a4u83BrIXIVcVVs+bisSKUSAKBpergVERG5BmBZVvyxXKNcJUeUilw9jPxY9GhHpthmiVxW0Ib1oESOYzmOY5jY29xLIYHjODZ6CSzLkCQpzD9UCQNoEacECJLAcRLkn3AcxzBR/bpZlolZB45jhyohnugFscEwLEVRDMNIJLFvZBcZSYgGXkTkEmC3d1JUcKhqgnDTNNnT0xyn8HgkkKQPx/HoJRCEOxAggvLTdMDvd8esA01TPp8zzs/B67V7vXb+T4JIB9ABAE2TPT2tUQjgPB5rT88QziENxeXqdbl6o8yckVGCjga/YjAM09zcMmfOnK1bt17JckWuWkaUgScIN0EEhyZGKS6XxWaL8TRP5B/Y7Z0xb3JDDZPN1h7HNjmK49iYXwEA/H43yzLxSKBpMkgHt1sJkIquHQ6TzTZIL4okfTRNx6MDRQUAIHoJWm2KXB57AMHo0enS+POkeex2H00zSUmxh6nxeKwsyyYmpsYsobvbKpFIotdBKlWyLAjzu1xmHJdptZE2o0bm/PkupTIhns/Bbu9UKrXCIDMKhfyCwrLoJDep1fqYdeA41m7v1GqT5HJN1A9dLuvOsnRvb2toOnJjzp9vMZvPxSzc7bZ4PNZYFWMBwOUyY1hc57I4HKaYm8pAIAAAdntnIGAfNHNYUJxVq7VdqALLFgBIAMDjsZnNtsgS3G4PAFgsrbJBo3sOqAMHQ+nZKxQavT5MRK8RZeAxDMOw4LEp9BEzDBd6K2qxLABgmCTmOoeiEcUjAYAGCPN2Q9IC6RCHBByAFUoQRlnCMDwK4Vj8b4FhQ3iLKxY6UCYLEwsWRdxUKGLfvuHzOQAgHgko8mj0EjAMZxhGmB/DcIlEGo8OGIZdCgkyoQR+EDr6T1gqlcesAxqij0fCJQTDcI0mTH+LYVj0f9i70eB298rl6pj7xCiStFyujVkBlmW9XqtSmRB9lNYgcNwHAEplgkYTY8xBhqF8PodKlSgMDc63dXK5atC3oygMAFQqvUIRY6Atigr4/S6NxhBlN3GgGMwjysArldrQAIc+Hw0AMpnaYMiMTSyKGqjXp8cc6MbnowDaDYbMmA28x2P1eGwxvwIA2O0uHHfFI8HpNFMUIZSgFXzYOl2aYbAAlyZTN0AgHh1stg4Mw+KRIBIZlmXFhVpXORiGhx1QYRgGAGiaiXm4xe3uVSjU8YSqBQClUhOzAgxDe73WsC15lGCYDABUqsSYdSBJwudzqNU6YahavuWWy1Va7SAdIL+fAgCNRh9PqFpk4OMMVTvyV9Gj9SZRLsMRERnliKvor12QgRf7ZyI8I9/AoyF6sc0SEYkGjuNEC3GNggw8SZLDrYjI1cLIN/AYhkkkErHNEhGJBtGDv3a57babQfTgRQQMzxz88eNHdu/eQdN0eXnl7Nlz0DRDQ8Px7du3BAKBwsLiefPuDzrV2OGwb9q0obvblJGR+eCDC4c0t6FQyAkirlWdIiObAwf27d+/h6bpgoLCefMekMv71b3ly9/q7Oxbtz9lSvWdd84bDh2vBBzHuVwu0UJco9x007RXX/0b78GvXLlyxowZRUVFw6uVyDAyDAbebDZt3vzZE0/8NCEh8cMP3z906ODkydVer2fjxnWLFv3AaExbt+7Duro9M2feyj/Ccdzq1e/dfvucwsLirVs/379/78033xZ9iXK5Ap06LyISitls2r9/z+OPPymXy9euXb1/f7+6BwB2u+25515EVj/ONS9XOYcOHdq7dy8KHSFymYjcm2xra62t3ejz+Sorr7v99jlDqm9oKTVv4J955pn//u//fuGFFy6h8iLXFsPQWp0921hcXGowJEulssmTb2ho+A4AbDarVpuQl1egUqlLS8f29PSLqNzSck6r1ZaUlEkkktmz75o8+YYhlahUKnw+36V8B5ERhN1uu+66CYmJOqVSVVpa7nD02z5LkiQ6oFYikUokUhwfyQbe4/EAAE3TaBuuyCWH700+/fSzXq9n//49wrssy2zYsOaOO+7+2c9+2dnZceLE8SEJl8n6gnqh/egMw4TOxx8/fvz2228XB2lGCcPQVWcYhm8+MAxH7anRmEaS5OnTDenpmQ0Nx6uqxgsfsdmsarV6/fqPTKau7OycO+6YK5TW09ONrhUKRWhgAYLwyuUyj8ftdjtjUzgQ8Pr9AbfbFXPjjsYP3G5nzNvkfD6v3++P+RUAgCQDLMvGI8Hn89F0Px0IQgrQF/fD63W73cGRXoKgaYph6Hh0IAgCAKTSaCWoVOqBdojylJVVlJVV+Hze7m7Td98dC3XfOY575503XC5Xbm7e3Ln3abUJ6JbX63G5+jRRq9Wh1YOiSJZl4vzMOS6ub42m6egluFx9caLsdiv/UyIIQiJhJJLYdWBZliQD8byF3+8H8AJc/CopSgUgBwCGYVBckUEJBGL/BXEc6/cHvF5P9OsTEhLC7MPme5MAUFpabjZ3C+82NzfpdPqCgkIAmDq15siRw0EtYWT4qm42mysrK/1+f1gDv23btp6enqysrOgli1yjDIOBLyoqOXBgn9ncrdVqDxzYi4yfQqGsqZmxdu1qmUyWmKgfP36S8BGCIE6dOrlw4SN3333fF19s+uKLTQ88sADd8njcy5b9DV1XVY1LSkoILVEikfT0dB8+vDcetc+fjz3+GuLw4X1xxl1pbW2LW4e4PgQAALgYXOns2VSAKej6u+8OWSxRDZNcCh3ORJmvomJiamqYAE+hdHS0bd++heO41FSjMJ2iyNzc/Dlz5mk0mo0b13/xxaaHHvo+uvXdd8e2bv0cXU+fPm2g7t+leN+zcT4fpQ6nTh1DF/X1X8ccoyMsPT1ET0/XJRRosVQB5AIAQXijfLv29ub29jjDBg+hEZg5c05ohz5yb9LptKek9EUtTE01Op394nJu3VqLnBmlUlVSUhiuTA7DMI7jvvrqS5vNBgCdneePHTsozNHc3AgAq1atuPPO2aHPBwI+qbQ35ij6yHNrb28ymztjlkCSRHe3NeagI2grQVPTqUF79gPBsixF+c1mm3B+hKYnIXPZ3d1x7Nggb4f6VadOHYn5LViWpiiyp8ceZaCbxERDQUFJaPowGPisrJzZs+/6+OM1GIaNHVuFKnFLS9OhQweeeeY5rTZh164vN2z4aMGCR/hHlEplTk5ucXEZAEydWvPBBxcPq9ZqE5544il0HdaDd7nsUqk0MdEwadK02BQOBLxutyUpKSdmD76nx9Te3jRp0o1xePBOgnAmJ+fG9jgAdHa22my9VVWTY5bg8dho2q/XXwwyY7NdrD9VVdfn5Q3iwbe0nKYosqTkuph1cLl6ACAx0ThoToRKFW3csZKS8pKS8v3799bWfrpo0WN8enZ27vz5i9D11Kk1q1ZdrHtVVePy8vLRdVgP/vz5cz6fu7x8QpQ6hOJy9XIce/58z1NPPb116xaVKky8vMicOXNCIsELC8dGk9lk6nNwKysnJyYmomuHwySRyBMSkodaNM+JE4d1OkNOzpiBMqxbt+6770787ncvD5TBZmtXKhPUaj2fkpLSt8xWpdJE89P+9tt92dkFRmOMIZI4jrVa2xMSkhWKaAOwRPixD9Sb9Pl8fL9KLlcQxNAmFtGOIZqm0UAXhNsejAbnCSIwJMki1yjDYOBpmi4vr0A+emPjKYMhCQBaWpqKikr1egMATJw4ZdmyN4WP6HQXIyvhOC5sSSUSSUZGpLEmiiIlEhwGGDGLBqkUoyh3QkJizN0xNPKZkKCLI9gtzXGBmF8BAORyBY7j8UhgWT9FYUIJKsFWBo0mISHM6Ek/pFIZy7Lx6EBRbgzD4pEQSl3dHrVajSpkTk5uff1+4V20fj4rKwcAJBKJ0C3QaLQaTaTmXiaT47gkHm1p2suyTFeX6ejRoxRFG41DFiWVSiWSaHXg306hUPGPBAIOqVQRz1vgOC6XDyjh5ZdfXrp0aXl5+WuvvT6QBJ+vV63WCCXwPfno306hUMb8FhzHer09Gk2/ePgxM1BvUqVS8UtASDIQtFdo9uy7Iovdt2+bXC6naTotrS/kfkKCYdy4qcI8e/bUA0BaWlZQOsJkakxMNMYTyW7Pnq05OYXp6dkxS+jpaTIYsmKOZOf1uuvrvy4sLNfrY+ySkiRhtbalpOQLI9nxC0/T07PHjRvk7SwW84kTh8rLJ8QTyc7hMKWnF197kex8Pu9bb73ucjlJMrBv3+6JEycDQHZ2zpkzpyyWHpIk6+sPZGf3uaqdne0kGSgsLLJYejs62jiO++ab/SUlZUMqUSKRilt7RQZCp9N9802dzWb1+/319Qdyc/NROqp7Tqfj3//+l8Nh5zj2m2/2l5VF5QpfWtCoo9sd+3luUcKvvbpUi7A6OzvR4R8RaGxsFK7LGdnU1e05evQwus7JybXZLMK7en2S1dqXYrVakcMzJNAQpsvlQn+GzsGjb1YMhjNKGAYPPjFRd+ONM5ct+5tCobz++imVleMAoKSk3Gq1rF79HkVR2dm593XuyjMAACAASURBVNzzHyjzypXvLF78RE5O3vz5iz777GOv15uXVzB37r1DKlEikVCUaOBFwlNRcZ3Valm1agVJkgUFhXfddQ9KR3Vv7Ngqh8P+/vvvMgwzZkzR7NlzI0u7HKBF0a+88sqqVasua0ERDDxJkkEbuqJh+vTpjz/++KxZN0bIg8aTQ8/iG5HodLp9+3bn5uar1Zqg3mRqqnHMmMJNmzZ0d3elpaUfPnxwSCvsEOiQPdHAiyCGZ8PrlCnVU6ZUByVWV0+vrp4elPjii6+gi7y8gief/HlsxaF5qdieFRkNzJhxy4wZtwQl8nWvpmZGTc2MK67URZDxczpjX4UeJX5/XzyoIANPUZTRaPzggw/mzRtakB+v18sbm4FABn6UePCRe5M5OXkPPrhw48b1FEWVlpaNGzfk1Rs/+cmS3/52qWjgRRCjIqKFVCoaeJFrGGTgBx02Jwhiy5Yt9913X8wF8Qb+hRdeWLp0aWlpKZ/udDq7u7sHfjQYq9WqVqsZhhlUbYvFAqPGg4fBepM5OXlLlvwsZuGTJk0EwWxOqCFH3SnRwI8SRnLUDh4xFr3INU2Up4Rt2bLl/vvv7+qKfTcab+DXrVv39ddf8+k0PeRjyu67775f/epX0Rw+e/78eRg1HvzlBi3C93q96M/QBRDIuR90YYTIyGBUGHgcxzluQP/A7XZ/73vfG5J3IiJyJYnSg0dj+LyRjgHhsyaTib9GA2BDMvAOh8PhcETjwaO3Ew38JUEul8EFNx3CeerIuRc9+FHCiBqi9/s9BBE84efxuHAcI0m/3R7es2loOLVmzZr58++bOTP8blqGoQDA4eiOZxc7ANjtXTFLoOkAx3EDvUI0+P0elmXikUBRAY6jhRI8HgVA314Up9NstzORJZAkQdN0nDpgGEQvQas1yGQx7lS5eojSwKO2O57BKqFj9/vf//7mm2+ePn06ADDMkA08TdMURUXjwaPxidEzRH9ZQQsh+cjcoYYcfcWiBz9KGFEGnuMYZIyFsCyDYTjDhLmFoGkSAAjCO1AGlmUAgGGomM1z/BI4jgXgBtLwiknguH4SWFYquKaRGYgogeM4Nh4dAFiOw6KXwLJXyC+kKH+oiWIYmuPYQCD2cxBYlmFZNhBA86aBUFFffbU7Jye7sLAQANxuFwB4vW5hNpZlMAyi1IEkL3rwFEUdPlw/ZcokjmORZ08QvujfhSRJv59gWdbvJziOYxh6oGd5Ax9BOKp4wgwMI0fNF8exgUBUgxY0Tcb8XaAhwCFJUCiiDbIUgzJeryNcOsdxDAhCDhOEz+OxCfP4/T4A8HrdQek8gYAv5tEU1ND5/d6BhEchgQUAv9+DmuUYQNGBCMIllcbY2KLmxedzSiQX2zeOM6CgciRJeDyDHF3m93sAwOt10HSMh5xRVAAAvN5oI9lJpTKlMkwckhFl4FUqXWgYChzvxXEMw2QpKXlhn0pIsACAXJ44UAYUcyA5OSfmQDeBAAB0paTkxWzgPR6rx2MbSMNocLkIt9sTjwSn00xRhFDChVhnAAAGQ1ZKyiASenqsGBaIRwebrQPDMIPhqguj7XSaKSrYzAQCXpqmbLZ4gxy73VYAIAhfqKinnnpqxoya3//+1wCAWlWrtd1mu/hTpyg/w+BR6oCGmniczl70IEG4AcDlskT/LiTp93pdNE17vS6Gofx+90DPon4hw1APPfTgD37w/SlTJoXNRhAu4ficx5MEkAoAUX/CnM/nsMVod/hCbQDRisjIKImydR4qHMd6vWHU4DgWw1DIhL7v0e/3BeVEnUWfzx1WAgCQpI+iYjRLyDyTpMfrjbGpRPj97pibSjQ44fe7cXyQAcWBQN0bgnAJVeA4HYAEAEiSGOij40EGniAcNB1X0F+v1z5oToRCoRn5Bn4gMAxnmAH7pKhSimNWIvFgMGQBBNcxp9NP06zROGCI1kFxOs0sy2o0SQAAgIUThUkkSpSuVCYCQFNT9y23XNysbzbbJRJc+GBbW3tHR3tNTU1ocQpFQnZ2ltvtQdP5CkWi0TjGZuuUSpXobvTvwrIcjss5jpNI5FKpTK3WDfQsGmXBMPzzz7fU1EyfO/dBAGhpae3t7Zkype+kA4vlvEqVKIywJpP1eXhSqTwarc6cadJqk43GGDuXHMf29rYmJqaGbUYH4LJYdwDAcWlaWphT3s+da9HrjQDAMH1FMwwE5ZRIFACA44qwEkymxoSElHgi2TU2nk1MTEtLiyuSnV6fEU8ku+bmVoMhK85IdsnJOcJIdnz0VK02KS0tKbIEicTc0dGZkpIfZyQ7o3FMnJHsRoWBx3EswujxhUGh2JcmiYgIR/N4MAzHMCzmozsuSOgbMW1v7/jRj368cuVKYQaGYWmauVAEBgD/939vLlnypEAChmG4UIdXX31txYoVYQ9QZllOpVJjGI4MPMOwEokMwzCGQQPUTPTvQtMMOrfU7fYABOuA4DgOwzD0A2RZjmVZn49A2f7611cPHDjw7bffut1uFBU/SALHkfwLRqkVjkviOEaFjVPClUGhUGAYFmGbHJoQEf2ZUcKoWEWPYXiEWSVU48WN8iJXLcgEut3u9957L2imX3jmd5RtN0VRBEGE/UUwDCORSIzGvhNQ/H4/kkmSQ46OQlEURVEMwwi32wk5ceJEYmJiZ2cnKgL9z+/vIkkyEAj84Q9/0Ol0e/eGOSwO5RcJAsdxrVbb2tqK/gytDKihE1fRjxKGx4M/fvzI7t07aJouL6+cPXsOGoVoaDi+ffuWQCBQWFg8b979MlmYuJgkSR49ejg0Cl5kcByL0BxccCDERbwiVynCymmxWAKBgEajSUpKAgDhPrQLM6CDtN38mvzQ0LMsy0okEq22b3T0j3/8Y3Nz8xtv/AE59ENaRU9RFL9g2+8P0+fo7Oz0eDw7d+5E+iDD43D0rQ5jGIZhGBQDp7e3t7g4PexbiISCuk3oOrQyoM9Z9OBHCcPgwZvNps2bP1u06LGnn362p6f70KGDAOD1ejZuXHfvvQ8+/fSzXq+nrm5P2Ge3bPns4MF9Qy0Rx/EIzYG4S0fkKkdYOQ8dOvTAAw+88MIL/C3e7g4UD+f48e8eeuihIGlh+wHIg+fPXGYYBu2GdziGbOAZhuGnvXi/XAi629zcLFSej9LDMH0j/DDAb1P04AdCrb64en8gD1408KOEYfDgz55tLC4uNRiSAWDy5BsOHqybPLnaZrNqtQl5eQUAUFo6tq2tNfTB06dPmkydMZSI41gE+y168CJXOUJj5vV6Gxsbi4qK+FtBQ/ShZrih4fS6desoikKWGw3Oh7XWDMPgOC707FG2GMZ1WZblrUjYstBd3vaj2Cz8IwzDuN3uU6dOwQC/zWt3Tu306ZO7dm1zu10ZGVnz5t2v0+mFd5cvfwudUAwAU6ZU33nn0IL/A4BSqeSvxSH6Uc4wGHjh0ZAYhqPzj43GNJIkT59uSE/PbGg4HnqMksfj/uqr7XfeefemTRuE6SzLOp19w3pSab/juhEkGcAwnGFotD8yFLTaKBDwD5TB7/eTJEUQvpi3yVEU2mrvi2fvB9IhtscBgKYpjuPikRAIBGiaFEogSQlA30JTv58giEG2zzIMw7JsfDqQGIZFL0EuV0gkg39rBw7s279/D03TBQWF8+Y9EDR23dbWWlu70efzVVZed/vtc+Jc1xoDwmbaZDK5XC6hUecd5YFcc7Q7mSRJZOAjhM0J8uB5aWiJ2ZA8eJZlBzq6BoHu8reQgadp2u/3Hzx4kGGY3t7ebdu2wcgy8A6HfePGfz/88OOpqWlbt35eW7tx4cLFwgx2u+25515ENTC2mhaNgRc9+FHCMBj4oqKSAwf2mc3dWq32wIG9fj8BAAqFsqZmxtq1q2UyWWKifvz4oL2w3Kefrr/99juFo08It9v1f//3F3RdVTUuKSnMJhYcx7xez8GDu8Lqc+rUEQBoaTkzUAZES8v5KF9wIA4e/CpW+95Hc3NL3DpEesfoOMtfnT6dCtC3keno0QNmc1R291LocDLKfBUVE1NTMyLnMZtN+/fvefzxJ+Vy+dq1q/fv3zNz5q38XZZlNmxYc++9D2Zn56xatfLEiTC9z8uN3X5xOyxqmoUGXjhvDQAEQWzfvn3WrFlr1qzR6XSZmUloDfzx48ebmpruuOMONAxOUZTH4+Gn23kJQQYeGeAIo/oOh+O11177n//5n6BekdDAo1D2Qr777rsnn3wSQuw0TdOffvrpggULbr314leAuuDr13/8xz/+vzNnzvA5B/7Arl7a288XFBRmZeUAQHX19JUr3xHeJUkSwzCVKq4IOUIDz3Fc0Dm/6EsUNw2NEobBwGdl5cyefdfHH6/BMGzs2Crkf7e0NB06dOCZZ57TahN27fpyw4aPFix4hH+kvv5AcnLqmDHFvb3mIGlqtebBBxeia61Wo1IF7zv0eFwYhstk8oqKiWH16e52AIDRmDlQBpIkvF67Xp8es+tms/WaTO0VFRNi9uD9frff79HrB7FVETCbO10ue3FxZcwSfD4nw5AJCal8Snf3xX2iJSUVubmDTIt2dLTQNJWfXxKzDh6PFcOwC/vCBycxcfAdvXa77brrJiQm6gCgtLTcbO53KkFzc5NOpy8oKASAqVNrjhw5fAkNPE3TkydPfvPNN6dNCx8mGSE8KDbI8WVZ9sSJE8gwIzPMMMz69etnzZr12muvsSy7fPnfkP+9fPny2traurq6L7/8EgB6e3sLCws3b958880388LRIrvQIXo04hbWEa+vr//d7343f/78sWPHCtOFQwuhD3Z0dCCzHWSnGYZBPRibICTNmjVr5syZ3tHR2dbWJvzoInxiVy1lZWOLi/vO6DOZOjMy+oVsstttHMe9884bLpcrNzdv7tz7tNqLHktXVwf6SHEc1+kSIQQ0RCeV9mvVzeYuYTcOjZScOXPm7NnGlJTgneI+nw/ASZIxrm9AY0Ver9tut8Qswefz4bidIGLsgiCP0e12xhyPj6YDPp/P4bAJx4M5LgktWSMIn90+iCfj8bgAwOm0EYQics6BCAR8Pp/PbrdGaTJkMrlWG6ZKDIOBp2m6vLwC+eiNjacMhiQAaGlpKioq1esNADBx4pRly94UPtLZ2dHYePLYscMsy1EU+ec/v/zUU89qNFoAkMlkY8dWRShOIpHiOIZh+ECeXEKCHgDUau1AGQjCheNUSkp6zEP0JEkCtKemZsQRyU7u8WCDOqMRcLudHo8rHglOJ05RRErKRQk6QdjApCRjamqYp4T09powLK63kEgYDMMMhtglhFJWVlFWVuHzebu7Td99d0zovgOA02lPSel7sdRUIz8fBAA9Pd3t7X0mx2hMCd0K7/N5KIrs6mqDAbDb7UePHt237+sxY3LDZvD5HBzH8YHJAODIkcMA4Ha7kFiapgOBQGvrOZVKxQcoNZu7u7ravF4PRVGBgB+ZzPb2NoIgbDYrytPYeDIQCDQ2niwtLeSFezxumqbQjNIFBXwrV36wdOn/CgsVYjabAKCzs02vv2hFOI7jOI4/8iQQCHi9buGzFktfT53XGUEQPqu1BwCs1osWwuVy2u0Ot9vNMAwvhCD6Ju8piuzqiuqkKKfTHvNPmOM4l8tFkiCTOQfPDQAAGRk5ob93mUyOxkdOnDi2bdvmhx76vvAuRZG5uflz5szTaDQbN67/4otNwgxffvnF+fMtAKDVJkyaNC5soWZzB037ASApyWCz2QHg22/3C3sDyPYAwKFD+zIy0sLJiP2oCER7e3N7e3N8MuLVoanpVJwSgnSg6dsA5ABgNnccO3Z2gEf6cerU0Tg1aG+P9nNISUmrrLw+NH0YDLzP512x4u3HH39SqVTu27cb7XnLzs6prf106tTqxER9ff2B7Oy+9q6zsz011XjvvQ+iP3t7zWvWrHr66V8OqUQMi3SanLiKXgQAOjratm/fwnFcaqpRmO7z+dARnAAglyuE0//NzU1bt36OrqdPn4YPML5z5sx34YrrbGvr+OyzLQDQ2toUNg+Pw3HRnUVdCpfLgR5Bjuxbb71599132mx9RrG7u+vMme88Ho/T6ayr248MbVdXJ0mSDkefgW9qagSA9vYWYdFOpy0Q8Pt8bj6FILwNDSc9Hg8AOJ12lNlisSqVSq1WAwBtbU0AsHPnl//v//3lV7/6TxzHIGRI3+/32e0WoVfX3t432cTrjPB6PSZTO/QftPB63YsW/chmszMMw2vr8egvXLgjf3o8vb2m3l7T4Pki0RN91oyMnLDpBOH79NP1Tqdz0aLHjMZ+2/+ys3Pnz1+ErqdOrVm1aoXw7t1334c+UgzDNZoww/jHjh1MTc3Izs4D2G80piEDX1Y2ITPzYoeYH4YsKakqKQkeS7NYzms0BpUqjC8YDSzLHDmyPz+/JDnZOHjuASTYbB2JialyeYzzFAThO3ny25KSqoSE4LDlUUJRAaezW6/PkEovDmXx3nxGRu6kSWE7RhdxOGxNTSerqibL5TF78F6325KcnBPlsHHo4rO+9NiKj4fERN2NN85ctuxvCoXy+uunVFaOA4CSknKr1bJ69XsURWVn595zz3+gzCtXvrN48RM5ObFHLwcAHMfRNGRYxFC1IgBQUlJeUlK+f//e2tpPFy16jE9XqVRoHSgAkGRAGHty8uSp48f3TetIJJJQd+3s2ZMej3PChDBhG375y//65JOPnU4XAKSmZk2bdntYrRyObpZlDIaLUf5VKi0AKJUa4SOvvvr33/zmFaPxUz7PtGm3S6Uyh8P5t7+9O2fO7QAgkcgYhklN7bMoBQVlAFBQUIrk/OMf/7juuuv0+mQMk+XkFMCFzaU4LklK6hvIVasTUOaZM2+qqKh4662/A4DFQgCA2ez48sudq1Z9iHbnX1ia1zdGyjCQmZk3Zkwpr7DV2jcAm5TUb9hHoVAdP94I/U8J0mgST548iQ4yr6mZheM4AGg0fS4aQQQG+vSE7Nu3raCgJDMz9lC1PT3NOp0RxQOOhrDDdQxDr1q1Ij+/cP78h0MzoPXzaIZeIgleMpycPMgQGYZhCoUyLy8fAHJyck6fPg0AUqmMN3Ucx/l8RE5OTnt7u1QqDzWBHo9Co9FqNDGaRhQwVKlUxWxcGYb2+RQaTULMoWpR9VCrNTHrQJJEIGDXahOFoWr570qhUCYkKMM/eQF0+pFGkxBzqFqpFKMod0KC7poMVTtlSnVosJrq6unV1dODEl988RXhn6mpaUN132GwbXJogvDIkSNDFSsyMqir26NWq9GcUU5Obn39fuFdvT7pxInj6NpqtaJZJIREIg0boZYHx3EMw8J2rp1OZyBAIv/b7/cL87z22mspKSkPP/ww9PUb+i1SQ7aToij0CL+DjqYvbk4JBAJSqQxVbJvNhoxlIBCgKIo3nKhHyzAskvPqq6/NnTu3ra09NTUVrdKaOHHioUOHSJLiY+zTNI0ye71er9fr8Xj1ej0qFLn4LMuhDEGr6np7LTiOC9+Rt21mcz+fmGEYFLdOuAqsvb2dHzjBcQmaY+a77LxWg4LjYXbZRAnHsTiOSyTSmCUgTp1qkMsVt98+JygdDVU6nY6tW2sfe+zHOp3um2/2l5WNDSskMkuXLn3qqadWr16N9iAIXZfe3l6bzTZr1qz29vaurq5x48KP84uMGEZJqNpIkezQDyCeU7RFrml0Ot0339TZbFa/319ffyA3Nx+ld3a2k2RgzJhCm83a3d3Fcezhwwerqi5Nm+hyudxuN7+k+ezZs3/605/QrfXr12/atEmYub6+nreIyPK1t7d/8skn586d4ys2iu0qzIOEO52uZctW8ol8HjRuz4+isyxLkuTJkyelUilaRa/T6QBA2CfgM9M0XVtbm56ebrfbUR8Fudf19fVocVxQf3rt2vVfffX1nj17goLuAUBQGFq0pAD6/x47Ozspqm9JHf++gp7NtbTarqur8/z5lpdffgH9+8tffo/SV658x2zuHju2aurUmvfff/e11/7MsuxttwX3A6JBKpVmZWVpNBr0p9DAo29w/PjxAMCHsxUZwYyKw2akUmmEVgA1fOIc/KilouI6q9WyatUKkiQLCgrvuuselM5PDz344MKNG9dTFFVaWjZu3IRLUmggEOCPeyEI4tNPP33hhReee+45HMdpmg7akOb3+ydPnvzNN9/AhepqtVrnz5//k5/8hM9DkiRJkgqFIhAICFfasywrPE5pIAOPAuYwDCOVStEqeoPBgDLwPw3e6NI0jebIm5qa0C8LnW5y7733/vrXv3755ZeD+tPnzjVv3LiptvbJTz/9dN68eSAwz0FTYz6fL7LBPnfuXEVFBQxm4N1u944dOzIzMysqKnhTdzVw++1zQt13EAxV1tTMqKmZEX9BhYWFarXa5/OFGvhx48ZJpVJxUnI0MCoMvEQi4dsmv98v3CcKABs3bgQx8uXoZsaMW2bMuCUokW9zc3Lyliz52aUtUeih7tu3D4Vsu+WWW95++22apoV3z5w55/V60dw2CMauKYpCodoRXV1dzc3NWq02EAg0Nzd3d3cH9VmRk80/jroXQpeaJEmapmUXSEhIgP4Gnu+R8DbV4/EIPXiWZVtaWuBC70EIiljHx60bqD8tfKOwtLe3Bxn4oF8ux3Hvv//+m2+++e233wLAmDFjkpKSCgoKZs6ckpcX+/7Ma44777zz4MGDVVVVwskO9L3I5XKlUika+NHAiDLwJOkLBIKjXhOEUyqVUBTldvfu2vX1ggWLmptPaTSaJUuefvTRRdXVU9HOE5Ik3O7esGJpOgAXdmDHppjf7wEAt7s3ZgkkSXAcN5CG0UnwsSwbjwSK8rMsLZRAEHKAvpUsXq/N7R6kk0TTAYah4tGBpkkMw6KXoFIlSqUxLmS9TFAU1dHRIfTRkXUHgN27dzc0NPAefENDQ1KSau3a9X6/X6VSYRjGcZywXd69ezd//fe//72+vj4vL89qtfr9frQ5XlhuUCxYZK15m40iy3IcJ5VK09PTs7KykB9P0zQ/oR5q4FGfQHhr9+7dDMMIhxYQQfF5ggz8gw8+uG7dOohipowgiOeee27BggX821EUtXTpUr/fv2TJkpycnI8//vixxx5LTk5evnw5AKxevTo5OXnz5s3r1q17//01n3/+BX9W3ogHeTJCA3/48GEAkMvlCoVCjHUzGhhRBp6mSYJwByUGAgTy4AnCbTJ1EIS/t7cbx43//vf64uKC8ePHooM0jhw5RhBun89XX//tzJn9oo6gLXbISMcGRfkBgCDcMRt4jmMAuNC3ix6aJjmOjUcCywbrEAioeQPv93sIYpDZUIahWZaJWweIXoJcrr7aDPyKFSuee+656667LuzdDz/8EBl4iqKuv/76pUtfOnWqEQDQyDk//I7o6Ojgr9EgeUZGhslkIknS7XaH9ZKDPPilS5dmZmYuWbKEZVnUA5DJZD/+8Y8fffTR559/HuVEC+j4RxYsWNDe3hcsnTfwvMve1tbW1dUVtIYALhh4Yah54d2JEyciAz8ofr//jTfeMBgMDFPDi3rppZdQ0atWrfrzn/9cXV1dV1eH7j7++OMA0Nra+v77y15//e0pU6YsX758+vTp/NbHEQwy8MIeIRpoET340cOIMvBqtV6t1gclSqW9UqmUZVmjcYxWmwIAOl1mV5eNphm12mA0jkHrd1wut9E4ZvXq1Y88stjtdgvn7QjC5XCYUlLy4ohFf767u9toHBNHoBurx2MzGsfE9jgAeDykx+OLR4LTaaYoIiUln0/RCz7s5OTcQV0ji8WBYYF4dLDZOjAMMxiyBs96teL1et1u90DNa1tbG5oO//rrr/1+//btO9vbOwBApVKFGnghyPpmZ2fPmTPnpZde2rFjR4QjXkDgc3d2dp44cYIkSZSC1qgrlUo+LDQfCtfn81kslm3btvFdh8OHD9fW1kL/MXnhqL6gXHQcA/HJJ5/MmzcvKMP3vvc9/ny8yBAEwbKs1+sVTr2np6dzHLdhwwa5XH7o0KGVK1cGPZWfn3/TTdNmzZr9/e8/etttt2VnZ2/dujUo9B4AcBzX09MTCATkcnlPT09ycnJW1jVc05CB5+diXnrpJZqmMQwzGAyiBz9KGFEGfiD4OXjkN9A0jQ7B/Mtf/vLII4+glgKtRSIIguM4cUW9yOUDVULeLQ4CmS6Hw4FM5tat21F6VVUVMqUD9QxQOy6RSFCz/ve//z1sNr5Z58222+2eMGECTdPoEHF+BFtg4PtizgQCgcOHDwuDyG7evLm+vh76HwjrcrnCGfgAANTX1//85z9HZ8kI7yqVylmzZm3fvj2szkFyGIbxeDy8hMTExLq6OqVSOX78+JUrVy5evHjRokVhn83Lyz179uzhw4cXLlxYWVk5ZcqUxYsXT58+vaWlpba2dseOHefOnRPGN8Uw7I477rjvvvtMJhPLsikpmtTUzBkzbsnIuJSBFGOAYeienqbQdJZl3G6LycRe+JOVy+WnTh01mSa2trb96U9/qqgoT0oyJCaCVIrbbN0mU2OoEJerx+UaQjyf/gqwAOBwdHNcmAOCo8duj+XUUATqSlqt7QQRY7hchMXSKvyTZYsAJADgdltMJmvkZz0eLwD09DTLZHFZ2O7uqELmAYBSqQ3r9owWA89bcQCgKAo1NxaLpa2tjXcF+JM3xQV3IkOFIFwoyocQigqwLOPx2AKBAMtyKhWaE/UAgMcTfpbB7XZJJJLW1ta2tn7BPmk6IJVKQBA9JggkkOMYjos0UcJvKOePdTh9+iT6CTQ2NgJAUVG+x2MDAP4QPjSui9i06ZPQQqH/3PmePbtClUS/OFSozWYOmmQhCOf3vvdgNAbe43FyHOdyOWi6r8SCgvzUVCcA/O53L23a9Pmbb/7V73eFfTYQ8AUC7srKkr17d3700b+/+GLzT37yE6RqTk721KlTfvCDR1NSklUqldfrS042tLa2kHWEbAAAIABJREFUffTRmieeeEKtVkkkUjQJAgDZ2VkURVksVoPBwHFcQ8ORsAv1tdpoT0wYKjiO63Rhgqlh2HmlUiu8lZKS3NZm0unSVCoXALjdHrVardOlqdUalg0jxOk0K5UJCkWMUeRYlgFoVqsTdbqYI9mxbnevWq0XBpkZEgThA2jTaAzodIkYoGnK67VptcnCKBd8wBmlUqvTDWI3WdYGYEpISIl5Joii/D6fU6czAkQ16CuRXDWR7K486BwOhmG2bNkCADRNnzhxAt1avHgx337xq5cviYF/6qmnysrKnnrqqfhFXVVYLJbf//73f/zjH0PP9RnNeL12tNJCCEX5GYZ2u3v/8z9fcDhc7733dwBAgWD5EfIgCIKQy+UMw3zzzYH+onzIwIcik8koivJ6UagZSiKJVHv5wDJ8sPetW7cFFYTWME6cOHbWrJu2b//K7b442PDWW/8QZna5wgRmdzj6eU5obSD6ZaFFA7/+9UsJCf3ilPl8do7rW383adK4w4ePDaS/y2UDgM8//9xun4lS0CcMAPPmzZo3b9bACzC5QMCD7mIYLFx4z8KF97S2th0/3pCXlzN2bFmos1VTM2Hhwnu6u81JSQa5XG6xWH0+4ttvj+3deyApyZCcbHC53AaDnmV9bneYb1OrNUTZOg8VDMND5yIBAMMwmUwpvKXT6Tdt+vyf//xAKlUBgMPhTE9PV6v1Go2WptlQITt2bGloaHr++f+JTTHUx5XL1WHVi1KC292rUGhijmTHcRIAUCq1MetAkoTXa1MqE8JGspPJlGr1IJHsfL4AAKhUiTFHsiMIl8/nVKmuzUh2x48f2b17B03T5eWVs2f3HbDd0HB8+/YtgUCgsLB43rz7ZbJ+p0+ePn1y165tbrcrIyNr3rz7dbohfHmoZaQoauvWrQBw9uzZF198Ed3iFzADAMMwaGvNJTHwO3bsGGgY9prm66+/fuONN374wx9WVsZ+MN3IIyUlTAxUl4tkGC4jo9Ru93Z1mTMySgFArUb7y8NPA9E0gw4j4bh+XXK9Pl2jSQAIE0o9MTHRarWSJA0AWq3hpz/95fPPvzzQbnJ+Hft334U/cjc5ORvpmZFRWlV1Q1ZWlss14JJGjyeMYZPL+wVzRdO9KPAcOhXm8OF+h3BgGFZUNOH8edsFBSIF+lYoEgGgt9cK0PeCMpkCKRyZM2eaEhONGRkFwsSMjNLq6tsiP4iEo5W2en361Km3PvnkoKVdLaSnpzc3N8OFIRa3252fnw8ASqUydCtje3v766+/vXPn1z//+bNBe4lFrlGGIZKd2WzavPmzRYsee/rpZ3t6ug8dOggAXq9n48Z199774NNPP+v1eurq9ggfcTjsGzf+e968+3/+81/p9fra2o1DKhGtG6IoCrVu/FBbEDRNHzt2DC6RgXc4HFf/UH+E1V4D0dPTA+J50kPE7XbzZ7qjeaLQ5hVBkiS6FdQ7xHGcnxQPAh0Gajab4cIcfNDR7ENCeNgoinWDAuaHRTh6z4PqRuqFswWR2ui3wE/8C8nOzpbL5dOmTXvzzTcTEhIix6X57W9/G8VLiPQxZ86cQCDAsuyFY385ZLmVSmXQT5iiqNLS0l279oD46x5BDIOBP3u2sbi41GBIlkplkyff0NDwHQDYbFatNiEvr0ClUpeWju3p6Xfue3v7+YKCwqysHLlcXl09vaOjfUgl8h48MmZ8TNAgXC4X8nsYhon5LGEedLplnEIuN3PnzuV3Q0UJHz4dBp4PFgnC4/EIY8bBwJ1Iv9+PrKZw2RoASCSSgQx8ZmYmXJjklkgkABCPgRe6biqVSqFQRAjyGPYt0LK7hx56CP2JDHZY056cnAwACxcuBICEhISnnnoqOTk5yMAjjzNyiSIDgeIjCbdUoJm1UAPf3d2NlhjDwL1PkWuOYTDwQvOJYTg6qstoTCNJ8vTpBofD3tBwPD+/306qsrKx/PlyJlNnRsbQ9q6gtRJ+vx+1DidPBg9Oot1rp06d4g18dXX166+/HsPb8fDFXQH++te/Cucaosdms/X2Di3sDHopdHLJggULYih0VIFmPZubm/mREt5ezpw5c/bs2QM9GOrBI089iPT09FWrVvF/IgMfzyZvPmQeIoah2i+++AIujCvABQMftpcgl8v50PeIsWPHFhUVCXeT3nzzzUNV4Crn9OmTb7/9xv/+79JVq1Y6ncH9nra21rfffv2vf/3D1q2fRzjkOkrQt9DS0sKv+RjIwAvP5xU9+BHDMMzBFxWVHDiwz2zu1mq1Bw7s9fsJAFAolDU1M9auXS2TyRIT9ehoLx6ZTI4agRMnjm3btvmhh77P33I6Ha+//md0XVU1LikpIbRENOq4c+fnA6mUkZHe1WU6cmS/2+0CgLq6HU1N5/bv/3r8+GI+T2PjuejfkaZphmHM5s6vvqrlE7/66ovQbfCBAKlQROtvnT59Jmz6888/f/Zsw4IFDwwqQagPADidjvb21qDEyJw5cwIADh3a6/dbhZMdBw7sOn8+/MKxyDrExNHBswAAQEXFxNTU4dzUZLc70OA5byn5bl91dXVhYSFaFxLK8ePHhX/iOP7b3/42OTn5o48+EqYvW7YsL+/i9H+QB//b3/723/9ee/Jkv87fPffcc/To0fPnz4cWqtfrCwr6zVLzosrLy4fUieTXYEYYcsdxHNl4PgVtBXzllVd4jzM9PT0vLy+sttciaLbx4YcfT01N27r189rajQsXLubvsiyzYcOae+99MDs7Z9WqlSdOHK+qGh9PcbfccguO4z6f7/PP+1o/NA6kUqkIgjhy5AhN05s3b87PzxcuqREN/IhhGAx8VlbO7Nl3ffzxGgzDxo6tQn3YlpamQ4cOPPPMc1ptwq5dX27Y8NGCBY8InyII36efrnc6nYsWPWY0pvPpKpV67tz70LVOlxg6jOn1eqTSegBISkqHAZg0aVJX1+dpadnoLOHc3CIMw3S65JKSKriwYyExMTX6BY1PP/0MAKjVWiTB4bD29HSVlFQGBbr54ostzzzznw0Nx4LGVA8f/jYvLy8lJZlPCQS8gYA3MbFv88mOHTtraqpRG8qyLE3Ten2ftgNhsXS73Q50CjgPjuMymSLygzwE4WIYymBIBYCkpPT8/H4rmwoKSnNzB4lkZzK1MQydnR17oBuv145hWPTrY7XaGLfKXCqEp70FpUgkksrKyrKyspqamvfffz9ovCdopYhEIqmurj58+PBHH31066237tixA6XjOC6VStHZ7RBi4HNzc5OSgj8riUQykIs/a9astLR+a9z4nCg0ffTwHZqBZhaQ8kajMSUlJSgd7Qvg5VRUVIwYA8/PNgJAdfX0lSvfEd5tbm7S6fQFBYUAMHVqzZEjh+M08CkpKSqVas2aNXwvEH0dyIP/3e9+t3Xr1kAgMGbMmF/84hf8U+IQ/YhhGAw8TdPl5RXIR29sPGUwJAFAS0tTUVEpOmx74sQpy5a9KXyEYehVq1bk5xfOn/9wkI2Uy+WTJk2JUJzN1otchJaWAWfuFy9+7LPPPk9MNKCpg+RkI8eBSqXOzMyFvkh2XFpaTvSR7D77rBYAZDIFksBxXE9PV2ZmbpDydXUHfD6fXp+i1/drhSdPrn7iiSd+85vf8Ckokl16ei4AWCyWRx55bO3atWiaEw2+KZUaVBYAPP/881OmTLn//vuFMv1+n8/n4fMgWJbjOAhKHAgUyU6jSQAAhUIVtNo5LQ3NBUfC4bCQZCDK4sJis+GXI5Jd5D0ay5e/1dnZV3mmTKm+88550UvmV7OjfhgKqohSJBLJDTfcwLvFH3zwAZ9Zo9EEzcGjriey3OggV14ISkdeV5CBx3FcLg+25TiOD+RVC0fLEbyosBMEofBdjcTERIlEwjBMhAdxHD9x4kTofkuZTJaamoomjyQSidDFv9YpKxtbXNzXMw6dbXQ67SkpfYsT0fHw8ZeoVCoPHjx45kzf4F9ubi5c8OADgQCy5efOnfvXv/7FPyIa+BHDMPxyfD7vihVvP/74k0qlct++3VOmVANAdnZObe2nU6dWJybq6+sPZGf32YDOzvbUVOOZM6flckXYYxajISnJABfieIRl8uTJcGFcHQAIguCvI/PGG2/s2LEjNPI2P5cf+XE07xU0Pblly5aurq6gA0OFoFvvvvtuZmbmtGnTgo7xAIANGzbY7fYgAx8WiqKi2cvHMMzZs2czMgxw4aWOHDlyyy39jl+jafoaDasQedQUAOx223PPvYhM3VC3pQq3qxEEkZCQwNcKZLMRK1as+OKLL7q7u9GfNTU127b1256OPGn0v1wu531cZNFVKpXQwPNuN47jcrkMADQaDcMwpaWlx44dU6lUAxn4UFOK3jo5Oen+++/fuXOn8Bba4A4AWq2WYRiCICQSybhx49BeU51O95e//OUXv/hFhH6fXC4Pq4lKpTIYDBaLheM4ZOBRX2EgOdcQEWYbAcDn8/HfnVyu4KMSIT777GOTqQsAVCr12LFhTsajabqrq81q7bdCWaGQAQC/iUOjkR0+vNfh6PV43FbrxfU33d0Xd2AeP16vVMa2fpYD+P/sfXl4FFW6/lfVXb3vSTr7vifsJIQkrLJK2EcEGXRArw5eHRy9bnOZcWTU38zouI+MA4LDBRdkMaIoCrIIYROUVZaQQPaks3Q6vVdXVf/+OKFSVHc6TQcJxHofHp7qU6dOna5Un+982/vBlSsX6+quhHQ5eL1einLX1jZxfx3XBbScXrx4mktTc51zYCiKrKszcfUxiioAIACgvr76+PHqwCMgFqbTp7/vxbegadpTXx9sgJRWa0hL41MvQ5+syBqNtrh47KpV/5RKZXl5IwYMGAwAGRnZra0tGza87/F44uIS2JA6VJO7vr6uquryihWdbNUKhfKpp/4Y/B2RlfLSJf9OdIlEgpQMiqLQcrx9+3aaphmGqaur27Zt2+LFv/Z74R//+Mf33nsPwzCSJLk2djaKECXdBQCXX48FIgPpLnR51apV5eXlALBr1662traVK1cirYtLJUZRVJAp+B6Px294Mw9bt25dsGBBRcVPKpUUzdlisfAyrVtaWtLTu3WC3MoIbDUlSRLDMLk8RG4v7lOyWq1qtZqrwXN7cl+hu+66a+fOnXFxcWPHFn/wwcZ33nkHbddQH1R4hivgjUYjWsH9afCSpKTEOXPmbt68eezYsSdPnlyxYsUjjzwCAKh4PHcOvho8kjfLlj3ywAMP8Fib1Go1Cvh/8MEHf/vb3y5fvvyrr75i1XEcx6OiogBg+PDha9as4Q2LYVh6erpGowF/GDdu3Llz5xD3vkgkIghi2LDB8+ff/cc/Pt8P3MPdeRsBQC6Xo6BjACBJN48mJSwsAokciUSiVvvxPdntNqlUyjul1xsQ/QCCVqtTq7VqtcbtJrk8PFyLEYaJ/Y7fI7xexmrtkMkUCkWgXMfAIzidHVKpsjtqth5BUR673SqXK6XSEFP5GYZyuWwymZprsmWFve8T9oXL5XQ6HUqlOmTjE0WRJOlQKDRBciXJ5d1s2UO7fS8xYkQhUty5KCwcXVg4mteIanLHxyeGrL7D1VWvO+6wIUOGoD8DTdNoOa6rq0Ma/Lp165YvXz5//jWqMMMwaF/23XffofipL7/8cvbs2WwHdk3vcTFCa3RDQ0NhYeFXX3115cqVsWPHItncXQban/70J9bmaTKZ5s6dO2rUKLhWwCOy7sC3BoCDBw+2t7cHk1LV1tbGMEx1dU1OThrL589j7H/44YefeuquX//a/2boVkZgq6nZ3Ob1et99982Ojo6EhMTp0+eoVJ3e6OrqKxcvnkfHCQkJYjF/t261trvdXe/AuXOnXK4Os7mT6K29vbWy8jx7Fscx7oUAoNfrlEoFAMTHR9XWVsJVkjin086y2pGkvbLyfHZ2JjJQabWqysrzXi8NAFqtJiJCJxaLcBybNGlcXFzU+fMXAMDttmq1KgCIjY2prLzMnbDDYeNOCa4SvDgc9traSlZlB4DHH1929Oj3ZWWHEhIS5s+fSxDesWOLa2urWb7e5uaGgoIRzz77pNGo933msbExaWkpdruDd7urN6VwHAiCcLvdZnPLuHGjsrKSS0omr1//4cmTp3id3W5XZeUV30F8xvS2tppIMuQSal6bzWy1uoIvTpicnOlbXCqAtxEAdDrDmTOdX7C1tRW5LFkUFfGXRx6amxvDwiKTktK5jRMnTjp7titvKCUlMyNjYHx8st1uP3r0e7bdbu9aHg0GY5BxOXB1B4y2hjRNNTTURkXFRUXFBXk5D4hmX6+PDZnJzm63mkwN8fEpOl1Yz739gSSdra3V4eFJXCY7djceFhaZkRGIiwkAWlqa2tqaU1KyesNk197eEBWVflsy2d1kIHncnWNp0qRJSMCzGvzhw4c9Hg9N06tWrQIAl8vF/SVOmDChuLj4xRdfZLUfXgQQK/l6NCqinvX19ZcuXXrkkUe++eabV155BW0L/GrwqNoVq2YhXR/VCOGa6CmKcjqd1dXVU6ZM2bVrV3cVsX744QeapgNX1kGVxdF2AT1A9KU8Hg9Pgz958uTKlabbUcAHtpp6PGRCQtK0aTOVSmVp6eYvv9zGdmhra/3pp9PoWC4nfAU8SZJcFbm+vkalkrL87S6Xvbm5yy7KFfAk6QCAyMgIZPO02ztQT0Rz6/G4CYLIzc3Ozs5Uq2XNzQ0qlRwA/ud/fjd27Eh2zC1bPlCpFHPmTB81aqTBoC4uzj99+rRYLLZYWkUiDK7yQwAAjuMYhtE0TVEkd0pwlaGTJF2trU0EQbCv2aBB2ceOHQMAtVoJ4GlubhgxYsiIEUPeeOOdw4ePQCdpPDl79rTycj9lUeLiYh599EGGYXi3Q7j33vk2m23p0scBwOm0Dx2aS9MZZnOr37QxiqL8DuILm63D5Qoq0cMvaNrjcDiDX3CTk/3w6507d9avtxH5IlNSUrdt29LYWB8ZGXX8+JFeRtgh/O///u+7776LSuSRJIlMMr5xD+g3rlQq7HbHzp07WRqDHnHvvfcyDBNktV8BNxm/CAGPNPjuBLxIJEIdWAF/+vRpACBJEslOh8OpVHYtvt9//73Var3rrrvYxY5n5+TGVQWeGOqJROw333wDAPv370f5Kn6vRcKV554vKyvjNaKqOTU1NefPnz9+/Hh3Ap6tI8m2fPrpp3v37n3zzTfRx3/84x8nTpz4+OOPn376aXa26EbHjx/33Rn88MMPrHnj9kIAq2lcXML8+Z3VyQoKitav77I2DxkynJfPycOFC6fF4i6NMyNjUEFBAUW9hj4mJWUUFHQleet0BoAr6HjQoHwAyM8fqVAQADBy5DjE99LRQQFAbGyiwXApJ2fAJ598gvp/8cVuAJg4cVph4QQAQF+hoGCcSqWSyRQikWjAgDwA+O67YxER3xYUjP/8828BYPDgoRcvXpo8efLUqVPT09NnzJiRnJzOnRIAhIcbASA2Nr6gYLxKpWpra0MB2IMHF0RGfoumzb1k4ULbxx9vAYDMzEGoPSrKT9KEwRBx5509xIg8+eSfLBbLqFHjCwrGNzVdUih0BgM/3h4AlEoVb85+sW/fl4mJaXFxyT329Auvl2lsLNfpouTyXuVldOdtRL7I+PjEefMWlpZu9ng8mZlZgwcP7c29EIxGo0qlcrvd8fHxFRUVSMD7pjYg28z99y96++1V27ZtW716dZDjm0wmpGkIuAXxixDwSORwmRzkcjlN0xKJxGazabVangaPsGvXLvTR6XQiSykCSZLHjx//29/+1nsBj2Qk96bbtm27fPlyd9dyRSwP3O0Lsp+jzseOHZs5c6bZbH7ttTfnz5/DvQR53bhyetOmTQcOHGAF/MqVK9Fkampq2LufP38eADo6Ony9AC6Xq6amhpuWfVsgsNUUxc8jD71IJBKLr881yDBdrha323327Nl9+/ahjzwOGalUyobOIQWLIAjUh9W30Oosk8lUKhXXhY+S19kW1M3Xob5s2bL58+ezHcaPH//pp5+uXLkyNTUVbRN9133UEzkj5XL57Nmz586d++CDD4aFhaFTvEiCsWM7a8Cw+7zExMSSkinbt1+T7h/MLlAul0+cOPFXv+pid+gHBOmTJ0/z621EvkgAiI9PXLp02Y29KXp/8vLyWAHP0+B1Oh2KxXniiUfefff97ryZfoHshTd0vgJuGPqVgPd4XG43/9V0Oq1oDaqu7gp9TExM6OjoMBojTpw4lZqa6HC0azSa+voarlGdDWnu6DCHhyvsdjOyziE5d+jQodbWzqrAFksbqrB59WOnk5WmKdTudtsBwGZr48mPqqorAGC1mrmNSKa63Q7umCTp9Hq9FkszAHDduixstg62P0VRra0tO3ZsZ9u/+27PO++8W1yczx0TiS6KotjG1tZmj4dkP7L+1IMHywDA5XIyDI1Sd0wm06hRowCmsqPp9Xqz+fLzzz+3ePF9w4f71zwoimSfSWigaQ8ABD+CTKYSi3sIMghsNbVY2r/+evuSJb/VarVHjx7KyvITqhoAXKvyli1bdDodSnCPjo5esmQJt6dUKlWr1ajaOhK0BEFoNGocx9kcdBQwIZVKZ8+ezY1ORx1YWWswGBQKhW+yu1QqRVlS6NTAgQPb29tRHHt3llvUjoz5U6ZMmThx4j333LNgwQKCIPwKePam3FedG3KVmppqMpl4V/mFVqvldXv11VcvX7584cKFtLS0bkJmBfgB+rNOmzZt48aNKIInIyODJbMrKipKTExEBEoEQcydO3vz5q3BD+7xeK5rQyDgZqJfCXi32+FbLNLpdPDUBZFIFBER1tHRMXZs0YkTpyjKYbU2h4cbamurgJPIy8YTOZ0dALE2WysAUFRnhDx3u/Duu6uKi4dnZqbr9TqGYb77rjObiKYZNB9UAtxqbeYJ+ObmFgCw2a4R8Egzbm5uQtfW1NTZ7Y74+FiJhHA6LdBNITK73Wq1Nv/446n77lvqdDouXLj4t7+9AgB2u8VqbbZaWwGAJN2vvPJydLRx1qyS2tr6PXv2AIDH42EfmsNhI0mS/cgq9ygUy+Wy0zRltVrAXwzgqlWvP/rovP/85//27t377bfbUO1zHijKTdN09wU9g0XwI4jFRI8CPrDVNCdnYHu7ed261TRNp6SkTZky/bqmytXgjxw5MmRIp1c1PDyclyHmV8DPmjUjMzONVaxZDZ4th8htZ8VhcnJySkogNiG2PzsH1OKbs35VwIsBgA2GR7YBvwJeIpGgWDyuMWDQoNxDh44iQ+68efMCJ8ez0Ov1vD1KTk5OZGTkhQsX5s+f/9JLPQ4goBPob1FSUnLgwIGsrCwAGDRoUEFBwb59+4YMGbJgwQKU74MYk0aNKt64cZPb7Q6S8Njj8Qga/C2LfiXgVSqDSmXgNba1NdfW1nPzaHU6XXp6Vk1NfUpKNgBER6dGR2fK5UoMk6KzaJFlIZfrASAyMg3HRciSyYPd7liw4IEVK1Y8++yz33777f33d2YTeTwej0eekJDAMFUmU3N0ND+q9mrAmp+/wo4d35rNNIZhr7+++uOPPyYIYv78ucuXPw/dxt+JoqMzDx4809p6zeQJQhkdnanVXgQAhoEdO/akp6cvXfrE1q27UOyx1+s1GtPQMu31imiaYYtv8uKJMExSV9fodPpP0B8+fMzOnbuee+650tLS0aOn/fDDDzxONAAwm+0k6Q6muGd3aGurveFENz1aTYuKxhQVjQltcO5OyO12o92bRCLxpVhXKBQGgwEFbLImeomEGDAgl+3DCnjetTxZ+8wzzzz22GMBZoUsAVzZjEbgEdH7jtzjKQzDJBJJZGTkxIkT2cZJk+5ITk59+eXXTp06JRKJ/vrXvwaYG4t//OMfvpZ8dDtf74OAAGDZ64qLi9lGRCC4ZMmS3/3ud8uWLYOrpMJoa87NyA8MiqJQ3Y1grDICbjJuv3io0MBby7RaLWILgavLJUEQKOcNJe9ywbXbsxShPJAkiexd3M0swzANDd3G93q9XmTt//HHH33P2u32p5566plnnkEOfo/HYzI188hPeBMAgJaWFl67y+UqKChAVDwoNQDtD7iud/bY7XZzAwJ4O4k///mF/PxxFy9e7C65c+DAgZs3b16yZInH40lLS1u3bl13s/3lgBXwOI673W70jk2aNIkNdGDxl7/8Ze3atQMHDgSOBs/rg9ZcVITNt519ybmquV+gd577dzQajWlpabm5ubyeXBN94Jty2xUKBW8vW1CQjwhwghcDgwYN4hKkIwgCPgQg6kOewL733nvZRvS+GY1GAFAo5HA9ZHZogRKU+FsT/UqDD4CsrCy2eodMJnvyySfvueeeEydOAABi2yAIAgnv1NRUXrm5c+fOp6d3qowBaGF8I+Z8P/o95bciO8Mw+/fvLygoYFdqu93RI73dxo0bee0dHR1Hjx5FFTwpiqYoColt7q7F4/GgFd/lcnEFP0/A19R0xspqNBqekYOFSCRau3ZtRUXFww8/fP/99+fn5+fkXJ/Tup+BfYZqtdrtdh88eBCurqQ8IGE2dOjQ06dPKxSKKVOmsPZ8FgaDQSaTZWbyTSDsJjXIWfnKZp1OhwiUfHviOB4f7yetmWu957X73QKiWvW9TLIQBDwAeL0M8hj6trvddl8HVkHB8G+++cbpvMYViOM0AHi9pNXaLBJ5AWD8+NEAIBZjANDcXKtW9/yQSZJEJLgmU43XG84wNAC4XNbQ3HAej+dqxlOHxxPijgFtNRyOdpEoxFp8KPbIbjdzfyBebxjSh91uu9XaQ8wB8qXa7a0eT4h1HSmKBACbrSVIohuxWOI3v+OXIuDDwgwAkJ6enpKSUl1dHRMTExMTc/bsWbi6vWVzfH29gxs2fDhz5gQA+O6771Aym18g0Yi4vVjwUuEpivrv//7vqVOnzp07N7CABwC73Y50bvTR4XD4Zqaxcdfof19qHZTYPHh7AAAgAElEQVQ7gPRIrgb/3XffsX0eeeSR//u//4OeNHh2JgEEPEJqamppaWlERMSzzz5rtVqLioqmTp168eJFm60tNjYmNzfvl7NAsxq8Vqs1mUwoHGnGjBnd9UfbTblcvmPHDgAwm+u5Z/V6vcVi8eUmGjp06Ntvv420/2AQwPDu2zMiIjw/P6+7QfwG3ndn48nMzOxlkgWac0REBI7TPSWp9Ft4vYzTafXX7qUo0vfUiBFD77lnHq8dCXUMY5xOq0QiAoCwMB0ASCRiADCbW5xOP0mJPNjtDvSGm83NKpUUrRgk6fI7vcBgGCY7O/8f/3hx2rSJJOn0eEKkLETLqdvtQDuVEIC+kdtt51qhvN5O75XfJ8wDSTqhM2ipW5UsmDk4nUERkgKAVKq4hQT8qVM/7tv3LUVR2dkDpkyZhny9Z8+e2rVrh9vtTk1NnzlzLkFcs4pVV1/Zvr3U4XAMGDBo8uRp18vvg3iJNRpNcXExuz6OGjXqd7/7HfJFsSLH17ZptVrHjZu+YsVfDhwoQ3sCv/B4PFeuXLn//vu5jTwB//nnn69evXr16tWbN2+ePHkyavzqq6/YDnq9nmWNZhiGK+BJ0uMr4EtKSkpLSwGgurr6448/9lXxuWoZyp1jGMblcnH589evX//GG28YDAa3280wTGNjY0VFRXFxcXdEPdxiJ91BoVC8/PLLy5Yti4uLO3z48P/7f/+PPRUR8fiiRYseeuihqKgoHMfLysqSk5MZhmlubsZxfM+ePRKJpKGhobi4ODc3Vy6XV1VVsc+kouKc2dw+ZEh+U1NTbW2t2+12u92tra1/+ctfUPTQrQZWwCckJLCBmQHYA5HGHCAfzO+1UqmUxyMbGMEL+CFDhpw/7/+dR5P0/b0EEPDIZtYboODB7OzsxYvz1q7t5WC3K3BcbDT6CaIsL69QKvW+p6ZNS5k2jV9LOi7OBgAREfFGY4rRGA8AOl0kABgMkQAglfoZxxesRRPDVNXVbUOHDjl//oJGE2E0XjeTndvtbm9vb293AYBWGxkCk11NTc38+fM/+GA9AOj1Mb1ksjMY4rhMdqzhSanU+yVn5ALHm2pqasPCEnrJZBcRkXT7Mdk1NTV89dXnDz30iFqt+fDDdceOHcnPL7TbbaWlmxYtut9ojNy06cODB/ePHTuBvaT3ZZKR/JZIJNzw47S0tLfeegsds0tSdDS/fPilS5fsdseWLVt9Q5C48Hg8lZWVPKGIPn7//TGNRgIcutympibWC8t1X02cOJHLCUWSJDsgy6QLV+NdSZJMTk5WqVQ2m621tfXNN9/0FfAo6Q7BarUhnnxeKVJ2Dmjz+8ILL2zatMlkMnWXx98dhTgPjzzyyIQJExITEy9evNjW1jZ06NCysj0XL148ceKnd9555/XXX8dxHMdxnhcDFQhXqVTsn4YHVAgLw7DY2FipVCqVSvV6fXV1dd8KeJutDaXwcUGSTvTt1Gr1hAljDxw4gNo9HrvF0sQfohOUTCZjz3o8Lq/X233nnkFRJMPgvBGUSoIgCBz39Djy9OkTJ04sIEmnb0+GIQGAIDDeKYIQYdg1c2YY2u129OZbIOMzw9BKpRQAnE4LTZMAcgCgacpi8WOv9oXLZe3FHJBG1UGSwWqWWm0PhKZ9C2SqROGcaJeGFkl0HEzmW319PUuGsXbt2nfffbc35e/QQtebjLtDhw4dOnTonnsWvvji/4Y8SD9DHwj48vIL6emZen0YAOTnjzxy5GB+fmFbW6tKpU5MTAaAzMyc6uor3Et6XyYZyW9fpyYL9HIbDIaSkpIXXniBewqxNB8/fnzcuHEBbuHxeHj2eQCgafrKlStz5tz16qsvjR8/nRVmHo+HXe654NXGPnHiBOuz9Hi6NHiFQrFx48Y777xToVC88sorDz/8MABUVFQEcNIDwA8/nOzo6DCbzb52iKt2LTcA/Oc//0F6Zy8FPAAgoTt48GD0MTk5MTY26vHHn3n99df37t3b2NhI03ReXt7FixfDwsLi4uIuX748ZswYFER27ty5uro6l8sVExNjMBi8Xi+GYQoFYBhGURKdTuebtN2HoCiSoviuFoahUR58bGw0l8hWo1F2Z4GMjY1KTIxnzzIMA+AN2VwJAF4v4/XyRxg4MKu8/IRS2e00rh3BC0D79kQm0DFjininJBIJjuPcRq/XyzBU774F8oy6ULgfw3iQuxcAvF4myJFpujdz8AIATXt6ZK+6XZCcnLxx40a0pkVERMBV4xDyufCqFXMxYcKEKVOmPP300/fccw+74JhMpiBLYHSHqwK+2/v2COQ3PHLkqMXCX4d/segDAc8WWwMADMNR9SSjMZIkyfPnz0ZFxZw9y1fQe18mGYU1+YYfs0AvN4pgio6OxnG8rq7OaDSaTCbUob6+3lfxBYCkpKQrV64AAMoV4Z1FOSQA4HA4gRNYR5Lk3r17fUfjxfBztVsubzxBEGPHjpXL5bm5ufPmzTt69Oj777+P6mcHwBdffAUATU1NR48e5Z1CP1RuQKzX6+1uLWN5V0KGwWDgVrMtLOysPMTdgWVnZ2dnZ/MuRGlyERF8K0ufQ6fzU0mvtbUDWdg+/3w7mwHxySef3HFHSXfjPP74s48//iz70WyuZxg6LCw+5InV1TWKRKLwcL7nO7xnB2snWlqqxGKp7xcMD48FgIKCsbzBVSqNRCLhNlZUXJbLNb5zCB5NTZcUCq1aHR4eHg0AYWFxrAlXLJYEN/JZpVIf8hwQVa1KFdZLqtpbBxiGsYTzBQUF0KXBBxLwDMNUVFQgx5/JZEK8lnDVVh9gW9Aj0OLpd40NBjU1NWzKktN525ccvFHoAwGflpZx+HBZU1OjSqU6fPiAy+UEAKlUVlQ0ZuPGDQRBaDQ6HsV3gDLJHR2Wf/2r09adk5Oj1/OVy6vxZQ4AqKu7cuCA/yg5VL9LIhGfOXN00aK7PR7PG2+sjI7uEvBut3vLli2+F6JyywBw+vSJ1FT+Qnz69DGzuQEASJI8cGDn+fOdkfwXL551u90ymdTl6lL7DAZ9R0e3xkan01Vdzdbt8B4/vn/Tpv/TaNSHD+/2ev0r7hiGYdg1XCsIvr/Dgwd3m0xViCMPPbHdu79A2wscx7xekMlkrCvB4fCzQT52bH9dXQ+BrwxDeb3Q3Z8gGCCFGMO6jYTgIStrcHh4X1pK0SbJaDSyLzDSlvoBAkTR/3wp0Ui/FFKubyx0Ot2ECROQpQ2Z6LsTtP/5z3+qqqpsNltpaSmX7AsJeKTGhAYk4Hk0HsFj2rRpbPZTP6gpfKPQBwI+NjZ+ypSSrVs/xjAsJ2cgUscvX644duzwY489rVKp9+z5ZsuWjxYsuI+9JECZZIlEOnz4CHQcERHmq1y6XA6TqQEVXtTrw2JiEvzOSqPRAkBeXl5MTMLSpUvNZvMbb6xMTk4JD4/49ts9qI/fEq56fadjnmFAo9HxztbVNQ0cOBgASJKMiYlXqTq3/xgmAgCVSsUK+PvuW/Sb3yz68cduS8gzDCOVdnGSx8QksFylBoN/y4REIsFxLMB+Ni4utra2DgCef/6vBQUjuFsBgyESCaeSkmkzZpS89NLfqqqqAUCjURcUjNy27UveUEZjTExMoMJ0ANDa2kTTlNEYOk2Ny2UFAJksWBNCyHXcbxTQK4Pj+KBBgxA5aJD8Ibc+srKyhg4d6msV+1ldJ8gU1w8E/LlzZzIyslDwLxfvvbcScUgDwIgRhXfeOfMmTAbH8V27dgFAQ8MFqVQqkUjYsFYe6uvrAcBut3/44Ydcfznq3xsDOxLwvnnIdXV13ZXL4uLSpUusxfHs2XN33RXyRPoV+kDAUxSVnZ2LdPQLF84hAXn5ckVaWiYSw8OGjVi16m3uJQHKJMtksokTp0L3aGtrNpkakFF39uxfpaT4D8UKC4sAgKeeegZ1QO9rWFhEZmY2K+AR1Go12t4qFAqHw5GWlm4yNVdWVjocTgzryv4aN27c3r17//nPfzEMBgBuN5mSksUGdq5f/yF0ip9WAFCpVG+//Y5Go7FYHADALbzNwuFwVFV1ZqIrFEruF4mMvOYHIJVKkbFdKpViWCABbzRGIgFfW1uXlnbNTzoiIgbNITEx+cEHH3799c6/yIABA/3WpU5ISE3uqVKXy+UgSXd3f4Jg8HMw2f2sQCYHHMfz8vLS0tLOnDnTbwT8wIEDEXcND88995zfrfANQWFh4QMPPHDbFTTiobW15bPPtjz++DO+At5sbnv66T8hj2EvI6hDRmpq6uHDh2fOnPncc8/l5V2TIYmWPpvNxsYGEQRBURQSzDabLWSaA2Qv5BkXa2trExMTd+/ezRYx6g7cZOPq6roQJ9Hv0AcvkMNhX7nyjY4OC0m6y8r2DRuWDwBxcfEXL55raTGRJPn994fj4jr17Lq6GpJ0p6SktrW1NjbWe73M8eNHkE58XZBICADw9elyOlxD3snyafim+bImVnQgEokQ7Wh5eflTTz3FdnvjjTfQASo+i8gTWCc9MiKxuXmzZ89GkWvIAsHLkpLL5RiG2e0ONnmdlyvFSytnuWUIggiccc4aPLxeL4/nxOFwoB0xzxKr1WpTU1NHjhzZXSqUAC6QUQQl1CLVth+URAuMgoKCkSNH/kyDR0ZGvvfee76/ytsImzZ98O9/v+W3ZBRJkhiGyeUKkUgsEon7qvLy8OHD9+3b9/nnnyOCLC6Qy7K8vByp8gAwfvx4uVx+o3zwvDC91tZWhmG4vgC/8Hg83D3lvn1+4pd/meiDNVqj0RYXj1216p9SqSwvb8SAAYMBICMju7W1ZcOG9z0eT1xcwqxZnRaWG1UmGUnEANLOl+wTXRUfz3erR0ZGVlZWwlVfo0gkYsUtN0+dzQ9GDHoM46Vpmpt9zp0PO0JiYqJOpzMajYgfCmHBggVnz545evR73wt9Pw4fPnz06NGI/lYikUgkEl/+WhY6XZdPgVfU2W63IwHPZUlbtmzp7Nl3x8fHHzp0KCkpqaqqu4EFdILV4OHqk+z3Al5AYMyb92sAeOmlP/meMpvbvF7vu+++2dHRkZCQOH36HJWqyxt15UolsoGLRCKDwU8qNsMwdru1ublbeuweYbXaaFokEuHIhGkyNfBGQ5b82tpadrnwehlkywSAhoY6tTrBam0PwYeCbtTR0WG12rzeZoKwAgAqAHb48KF33/3XX//6YncaGut0F4vFFEW1tZnN5haPJ0SSGYoirVYbhjWJRF3rKsNEIn3Ybrc2N/eQLNDRYQGA1lZTAMaLwCBJp91uE4kafQtY+4VEItNq/bwSfaOEjRhROGJEIa+xsHB0YSHf9nujyiR3VyGb14En4JVKJfsXQq8OQRAZGRmHDh1CfSQSCVfAc6FWq5HPFXHJMQxz4sQJthY4Ajsf9iA9Pd1sNpeUlHAFvEwmmz//Lq6A592RTQFwuVwrVqxg/WcymayoqKitrc03ZAbDsG+//TY6Ovqzzz7z+0DYXTNXwN933z3Dh3eWXRk9enRVVVD5x7c+zp//ac+enVZrR3R07MyZc7Xaa2IpekOyhDR4QcALCAYeD5mQkDRt2kylUllauvnLL7fdffev2bN79+6qqroMACqVevhw/1bM5ma+SL5+NKLqlwBw+XLF2bPXeGGsVn6ArdNpI4jOZfPSpfMZGQl1dVV1dde996+vbwAAi6W9vr4RoLNU9+eflwLAqlWrKYrasWM7w/gP4mOjAebNm+12k1u3bjt//ozfgpbXM59G7keangQgAYDm5oazZ/2QOvuivPxMbybgO4cACA+P1Gr90E3+UqyskydPfvPNNwNY9tCyywp4ZH/WaDToID9/WFpa5kcffbRo0SIU8YFhGCvg/XpVRSJRcXHxgQMHkHOIYRhfDgdfDZ7XjjBv3rxLlzrTUbKzs10uF69/bGysXq/XaDRVVVUoCxm1S6XSDRs2vPjii7zqogAwYMAA5Fk4fPjw3Xff7WsE+9WvfsV9MhqNJicnOzGxK0Rx6NChW7aU9YMaE+3t5tLST+6994GIiMivv/5i+/bShQsXs2d7SbKELIesid5gMATI1RTwC0dcXML8+YvQcUFB0fr1a7hnFy78DRtH5tdBduTI3ujo+ISE1JAn0NR0Sa0Oz8v7aePGrQBgMESOGjWZ28HjoYYOHYoMhKiydnb2QLvd3dDQBACRkfEAkJ4+IDIyxt/wgYBchB6PJz09RaeLlkqVAPDww0/CVfd8W5sdTaaxsTEiIoJrJGht7dQ0Zs+e53I5tm7dduzY6T/8Yflf/vLChAl3cGvoBQOSdJnNtQZDPJfJjl2TExJSR43qIdqotbX53Lkf8/LGhLybR6RMRmNKkOpEd4r+L6WaXExMDCqJ2B14Kr5IJFIoFEaj8WrpawlyoLI0nDKZjBXwM2fO9C2pguP4119//Yc/dFYZb25u8a2u5qvB+/2o0WhUqk6Df0JCwqxZs3jVvktKSpqbm9EMuQLet2gYCzZMqaCggLvvYY/ZcAHEePXMM88sW/bf3BGeeOKJMWNCLKJ6S6Gmpio5OTU2Nl4ikRQWjq6treGeZUmWCEJSUFB08qSf0n8BwDPRJyYm9psgOwE3HHV1NWwIvUgkEouvWQckEqlMJkf/xGLC9x8A4LjI76kg/+E4LhKJc3M7i/hdvnyZO6BIJHY6nSxXR1JSEgAkJyezYUlWq42dOfef0+n68ccTgW+NYoFtNjuGYSKR2Gxuv+OOCVy/YWtrK5pDdnbOBx98yL2W9b8PHDgQTaaiotLjoV588cXx4++orLx8nc9BjOO477NFCOYJo82HWCwO+Q+BIjCuq7/fN+qXosH3CLTssj5pDMMuXLgQFRX15ZdfAgCG4UhplsvlSF4iBzxBEEhTLykpQVmYOI6npqaWl5cj9zzriT906Cja5D744IM6ne6tt95yu91BavASiYRNPSII4vXXX/edP5qM0WhMT09HNUmlUmkAAc+tqcO9e0JCAktegYB+zxMmTMjLG8Ar8dQ/4uyysnLS0zvrszU01EVHXxOiH4Bkqbz8wokTx9FxZma6r9/RarUgBqGffvoRwzCCwOVyKc/mGRgk6QTwNjaGUpsLwWbrwHH8um7Kg9vtwHGcIOp77toNSNLd2mryG1YWJFwum1jcLBZ32ZnM5gSAcABwuRxnz57v/tJOeL3exsZai8V/9lcQ8Lpctubmdq5fNjBycoYG6UAFgLq6GvR2ff319iVLfqvVao8ePZSV1TeVGFkjU2lp6datW++6mnNmtVq9Xu+4ceOcTueBAwdef/31WbNmicViltqyudnkd8D333//mWeeCVxStqamBgBomkaZwxcvXty/fz+3A4q/I0nSZrOhJY4FSwiGQoAlEonZ3H7+/PmrlB67MzIyru8R9Bf0hwWahV+6UFTYx+22Y5j/0ikIcrlEKpXK5WKUaQ0A4eFainIyjAcAcBzDMC8ATJgwDhXMkEqlcrlMIiEAGJfLCtBpOiMIIjs7q7y8nCQdLheu0SivTqMz/u7JJ3+fkBCvUEhXrHhRJMIBQKvVDhs2iL0vcAobAMDEiXckJETX1HR6fTAMuD25EInwgoIRBoOapt0AcM898zMzM1wuK5r51T4ipJqHhenYcbjVvqXSa7YaS5b8pri4APWkaY/Xy3Dvzh3Z7ba7XD2weNI0xTB0d/MPBgxDYRgW/AgEIe9ub8vpI0EbqjNnTu7c+RXX6wkBSZYoikI0TQDg8VBIWb92tgzDMBiGoRyKJ574HUVRFNUDWwAXNE0DeK/rEh4QI2FvRmAY2uv1Ylgfz4GmcQA/tYy93mCfD8PQvZiDl6YZHKd+pgRAFE2ckzOwvd28bt1qmqZTUtKmTJn+s9ysJ+Tl5b322msrVqywWCzPPfccK+CRJTw7O/vxxx+/dOkSii4iCAIl4xAEYTb75xi12+0ul8vpdAbgSGCFNArF/+STT3gdUCAR8njygu3Zj2q1WqtVDx8+xG63szmca9euXbp06aVLl9LS0q7nMQSCy+W6++67JRLJfffdN3PmzeAqCA39SsC7XDbfOsSISd5iaXI6A229S0rG5eZusVr5O1CXywKdctENADhOkqQDACQSsUIh+fOfn05OTjSb6ymqc6EvLi4AQJyLJorqIIjOZYgNsHc4Ws1mERoEwxgAGDOmcNSoYdzCoPn5gz/44KOrA+Y7HK2oJwB4vR5eCVEWGObFMNpsrrfb2wFg7tw78/KGms31aWmdKqlSqRgzpvirr3YCgFjcVYqUu59g42UQ5syZSlEdZnNXZA337mj3g2CxNJnNPayeHo+Touju5h88SDLYEfT6GJGoZ1Ycp9Px2WebLRbLokVLjMZrOFkDkCxlZ+dmZ+cGGPbChdPI7Dl4cEGQE+ah91S1J08eEYlEAwb4CcAJEt1R1QaPI0f2hoUZ09JC10ebmi4pFDq1uotfl41kkMuVwTzeffu+jIlJjIvriauhGyCqWp0u6kZR1S5ffk3BCzaauKhoTFFRH3u+cBx//PHHDxw4sHXr1rq6ura2NlRn64svvgAAvV5PEER2djYqaUEQBDJ8xsbGcoN5XS7XnDlzXnvttezsbGTHstlsrIB3OBzLli176aWXIiMjAeCFF15gi0vZbPbGxkbfWlNcAY+y9Twez9q1axcvXoxIapctW6ZWq+12q0wmtdsdrNR3OBxXrlzJyMjYt2/f6NF+ODx4sNvthw59P2NGkt+zn3/+eWnpn/V6/f79+7OysubOnVtWVoa4fm9B9CsBr1TqFQr+z89sbqmtrQ8PT+yR1Cw+3s8CFBFRAwA4jqvVBgCIiEjS62sAIC0tY9CgYffd91vUbcCAYejg1VfffO211wAgKipNJpNFRl5C7awGHx2dHhERodMZAUCp1AKAUqmLjLxma/nb3y57/PE/IPtSbGxKZGRaTg4WFRXZ2Njk25nF6NHj4uJiIyPTDIYzABAWloB6JiQ4AEAkEqnVar2+09ocFhbNjqNWdwWNazTX5FqMGDGOdbB1dDRTlNNg6IqzU6m6OoeHJ0b2xAnb1mbDcXd38w8GZnM9hmE6XbBc9MGEqNA0tX79mqSk1Pnz7/W1qQYgWQoGNE33A9o1Ab9AIL28o6OjrKxsxowZAIAKXbLVolnF/amnnho/fvx//vOfgwcPvvnmu//6178AoLKycseOHcOGDXvppZeQgLdarexiUllZuWbNmjlz5pSUlADAuXPn2GzeH388BdAVmxYbG1tXVwcAZrOZYRgk4NesWbN8+fLm5ualS5fm5OQg9+izz3YWcZDJpG1tFtYjYLPZLBaL1+vduXNnampqTEwPAYCbNm1euvS/zeYSrdZPuMyxY8fU6ktWq/XRRx997bXXhgwZ8vvf//6zzz5DHIu3GvqVgMcwDFHAXtuIAwCOi3A8lHUWlaXHMEwi6eR7QQEXmzdv5laPjY7ufGnkcvnVeD0JjovQVcAhWlIqVShMA646v3U6HW9uMpn86aef/vvf/56fn3/nndNwXBQWFjZx4rgNGzYSBNHdF2HrzyKjNNszOjpaIpEsW/ZISkr84cOdBbkVCiU7DjfIjuubBwC93sB2wzAMAOPeneuDx3FRj7QcviNcLzAMw7BejeCLc+fOSiTSyZOn8dqRWzQlJXXbti2NjfWRkVHHjx+53jKGbrf7lqp6J0BAkNDrO/eyqEimxWJB6eashx5JeolEEh0dPWPGjK1btzY2NiIDIQCg2leIMgQJ+KqqqpSUlGPHjp0/fx4NxWr8XPf82rXrS0q6TP2PPvooClWuqakpLS1FhPler7e5uRld3tTUhOp/shFUUqnM4XCyY1ZVVa1fvx4AXnjhhZMnT3aXGMwC7QbMZrPfzHKj0VhW9sNnn3320EMPEQTx1ltvTZs2LTY29u233166dKnX6z1z5ozH47JabcEXHnS5XC0tLTKZzGAwsCHSbW3murrjLpe7ubm5oqKipqamvLx83LhxTz/9dJDDQj8T8D8HkAzGcRwJM5FIxB5wu7HpEDKZDFU0R93Y+DWkjkdGRiIJis7q9fqBAwcuWbLE974DBw4EgCeeeCIuLg61LFo0f8uWbcHEtaFXhH1RoqKimpqaWlsbmpsbfvjhJ96E4Vry8Iceemj79u3IT4++S4Ab9Q/dtL6+rqrq8ooVnfkOCoXyqaf+CDeIZMnj8QiJ7wJuR7DR8vfdd9+2bds+//xzuVw+b948VgPWarVr1qxBKjhcVejdbjfypiOn5Jdfftne3o4E/IkTJ8aPH//qq69+/PHHyFR28uTJBQsWuN1ubjxdfX0TlxGPS0RvMpnYUxMmTHj00UcBYO/evU1NTXK5nF3HpFKJ2+3mpiXv2dNJN84L3POL48d/AAAUYQAAzz333MKFCwE6CbZLSkrS0pL+53/+h53GqVOn/vrXvz788MMbNmxwOp2s7z82NnbZsmXTp0+Pj4+XSCRsKE9lZeVHH310+fJlkiQrKiqsVuu5c+fQQyMIAsMw36rfUqk0Pj4+OTk5+LBNBEHA9wC0XRWJcBSJxgp4HoskqwQrlUq5XB4TE4P+Erx4eFbpR+l2kydPXrt2rd/7cvcBCIMHD1CpVCEIeADQ6XRtbY3c+XALz7M/jKysrFGjRkkkErT57VH1DMyDe7tg8uRpvuo73CCSJbebFAS8gBsNLwrb9AXD0DQdejAjd4T77lt05szpjz/eSFHUzp07nU6n0+mMjDRyx//Nb+4FANSiVnca/zo6LDTtIUkXAHR0dPz00xkUi9rebqZpDxKcSOG5dKmcpj3t7W1sIjsA2Gw2t9uVmJhAEMSlSxUGg04ul6MV6dNPP42O7tx2WK1WJErfeecdAAgLM6BpMAwlkUgaGhq42Uas0DWbzU6n3a/e0tTU1NZmTk5O+uSTTQCwf/93AwYMcLlcL7zwQnu7mWHeRFnlCQnxvCeclpby3nurhg0bumPH15GRxqVLf6tQyM+ePVFeXr18+b+X2TIAACAASURBVPJnnnkGAMRi8eDBgx0Oe1OTyWw2S6XS9PR0r9ebkZGemppy//1L4uPjSZKsqalhGEapVAIwGObJyRmq0WjCwsKjo6PYxdbv3xfDMBz3Ixr6RsCfOvXjvn3fUhSVnT1gypRpGIaXle3btWsHt8+TTy5XKrvMxWfPntq1a4fb7U5NTZ85cy6ynN8EIDsVq8GjLLj09HSe8Bs6dOjq1atTU1PDw8Mfe+yx2bNno3bem8RavRYuXDhgwIAADAwov44nzlnDQGD4CngWaD5z5syZOrWrQs/gwYORCau0tFSr1RIEgX5OPUqmkIkYfzkgSVJIfBdwY0HTtMlU6dvOMLTdbvZ7KnjYbK02W6e4ffnlP33zzdcmUwtLjknTzu7Gx/FOwdPQUK3Xy1paOiu+VFdfbG9vBoCjRw+ZTJVtbV1x0I2NdSZTZVXVNcwTdrvj1KmTUqlELpelpCSlpUWp1UqNRtXe3vHNN99ER3fxRNXXd114773z0cTcblIiIdig5qSkhLq6Bi6JeHX1T2xeH4vVq9etX7/RarXdd98CtPmorq40mSr/+c9VAPDJJ594PP8AkAKA3W42mfwweM6bN3XevKlXn6E9NtawdGnSiy8+c+zYj62tbWZz+/HjJ3S6tIiI8Pj42GnTJrP7oWsx3F+jx2yu8dfeBZlM5bcEVx8I+Kamhq+++vyhhx5RqzUffrju2LEj+fmFRUWjR47slHaXL1d+//0hrnS3222lpZsWLbrfaIzctOnDgwf3jx074ebM1tdEP378eC6PLNvtv/7rv9BxfHw8y2DP03EHDOhkkFCr1YH5lZBJgHc5az8IDCTa/drP0YAPPPAAGykDnPI56JJnn3325Zdfbm9v71GDF3TTHiFo8AJuOHBcpNf7iRTDsCq5XO33VJAwm+vlcq1MpmRbIiKMJlNXMYvc3MHdjc+WgaZpTK+Pkcs7heiaNR9YLMiLb9PrY9ra2rVaLdLjHQ63Xh8zcuQkdhBUwPq77w4OHjxo+PBhBoMhMTFLq9Xl5uaePXu2vPySydQmk8lIkmQYhpvdM3fuPDQxh8PObqmffvrJ5cufnTTpTi7Pt9nsDgvTisUi7g9zzZoN1dU1APDaa++gFqvVuXTpU19++RUANDe3ANiRgJfL1Xp9D1t2r7cVoEGrjTQapYmJmYE7+wVJOu12s04XHaRN3q/6Dn0i4MvLL6SnZ+r1YQCQnz/yyJGD+fmFGIajpHCGoffs2cnLRW5ra1Wp1ImJyQCQmZlTXX3lps2WrduIVOrrre/E1XGjo6P9ctT4BRLwvhp8MG5v9E4E0OB5g3D5+wDgD3/4w4YNG9rb23uUTEL4WI/weDyCBi/gxgLDMJnMT/InhmFisdTvqeBBENeM8MUX2ydOnFhRUYE+Tpo0tbvxw8M7s2icTlImU7O1s3ft2o0ObDa7TKZubGwaNmwYqsphtdpkMnVDQyfjukgkev75FUhTkssVa9a8j9qffvqZ1NRUFFzW1tYuk8mkUqnFYkFxfAgajQFNjKZBqVQAQGpq6t///goAKBRd+xUAGDdu4siRI1NTU1evXn369OmMjAy5XI44+IDD4Hnu3IV9+zqrdzIMwzKdiMVSmayHX7RE4gAAqVTJzaq9LiArgkym6mXJ4D4Q8DRNs6X9MAxnk4wRjh8/mpycwqv2YTRGkiR5/vzZqKiYs2evjwy8l0DCD8cx5LS+3rAy7uIul8uDL3MZFRWlVCrZOBeE3/zmN8EU4tRqtTiOs6Vgubj6da55aVhBzn471I2bJuAXgujqER6PR3BkCLh9kZSUlJOTU1FRgWpZBVjBWKMgirpniWtYoOh0u93Oxs2ZTCaLxcLaz8VicVJSJ4U290ZI5KPxW1paCIKQy+UWi4WTmqTkZqkh6zcbVM/qIWPHjt23b5/b7S4vL9+3b59YLF63bt0TTzwhEolQuVsuWlvb0MEjjzyC3Py3I/pAwKelZRw+XNbU1KhSqQ4fPsBygQEATdOHDx+8//7f8i6RSmVFRWM2btxAEIRGoxsypMtRYbNZP/ywk+M9JSVFr+c7V1A0ypkzx0IurozjuMvlMhgUixcvMpmqWltre77mKpqbu6xbFOU5fvw6ChXv2fMlRVnRJQxD0TQ9Y8ZEAOhxELEYduwoNZmqTKaumk5ut4uiPCZTHQBUVJw7frzr92M0qu+4Y+zu3fsuXDjR3FwNAChAxmDQcO9F0x6v11tVxWWHbmOPz5w51tbWAxepy+Xwepnregg8UBSJYZhIdDnI/ikpWXp9eM/9eo329kZfFkWXy2YymdRqdUtLiIV1KcoD4A35cgDweFw0jfVmBIoiadrTmxFomnI6O3ozAsMwDofF7e4Krna5wgBUaHotLUHVT7PbzS0tIetDXgCw2VoRi1QwCA9PDPVetxaQZB01apTVauUG5/LAOra5Ap4gutzh7e3tTqeTYZhhw4aVlpY6HA6LxfLxxx+zI0yaNImVyrx8XeAIeKVSmZSUxK2PlZ6ezq18nZ2dCZyAJ6TASKXSTz/9FCktSPW/cOGC2+3etGnTgAEDvD4MhWzcX15e6CRRfY4+EPCxsfFTppRs3foxhmE5OQO55N4//XQ6IiKC631HuHy54tixw4899rRKpd6z55stWz5asOA+dEokEsXEdO4HtVq9b+SC2+1yOOxKpTpk4nSCECuVioSEpD/84ToSEBFouuu9IQhCrQ6RBsvjcXk8bl8an+7geyOv18swNGL7UalU3A5qtXbw4MG7d+8zGCLQA5RKpePHj/3b317iWixI0skwFNdAp9F0sf8qlWq1ugeFnqI8DEOH/BAAAC3xqNJUMOCV6/j5IBKJfalqMQxvaGgMD48InsCcB5qmALCQL++cBYb3ZgSK8vRyBAwDHBf1bg5uHL9mDqzpEsOCfT69+xZej8eNYb36FrcpkOwsLi5+/vnnA3Tj5M1bAeDo0aMAEBYW1tjYaYG3Wq1lZWUAEBcXFxMTc+nSJeDEtwNAdnY2a7v1FfD5+fmffvppR0eHVqv9+uuvJ06cWFZWhmhwuBFFAKDTaYGz4UACnlv7G+05Tp48CQCXLl3yy1HT1tbG+163I/pAwFMUlZ2di7TwCxfO6fVdduBTp37MyMj2veTy5Yq0tExEIjZs2IhVq95mT8nliunT5wS4XVtbc2urKTk5s0cmu+4glUqjoqLS03ND4FfhcibL5YqMjIGhzcFma7XZ2qKi0kO7HAAqK8+bTPWRkbEAkJiYzpsJKu+YmzsU/RjuuGNiVFRUdvY1rhCLpcnjcYaHJ7EtGRldPrDk5MzknmhAf/rpB5J0h/wQAKCtrRbDML/xon0LLosqC5OplWG8UVExIcc9Iara3oRNEUSNSOQ/JitI9J6qFscvSqXK3syhqemSTKbmPmTWOyQSEcGNfEKh0IY8B0RVq1TqbhRV7blzZzIysnwLJVRXX9m+vdThcAwYMGjy5Gm9dMHeECDxGUB3R2B16A8++MhojEJm7fDwcCTgpVKp2+1esWIFACBGF9R51apVAJCSkvLHP/5x9OjRcXGxSqXCbncgQhsunn322ZUrV9bU1BAEIZPJioqKysrKZs+e/c477/AEvFgsVigUbKEvlOqsVCp5McusWR5tNXhg7f/jxo2TSCQUhQfNW3MLoQ/eHofDvnLlGx0dFpJ0l5XtGzYsH7V7PJ7Kyoq0tGtkWF1dDUm64+LiL14819JiIkny++8Px8Ul+Bv458LChfeMGzcqtGtlMhn7Vt0KtdeQRu7rrRCJRBiGsT71V1999amnnupxNDb8XkB3oGk6uceNj4BfGFpbWz77bIuvl5ph6C1bPp46dcayZU/W1dWyHMl9C6TC9ki3zjLcnT179p133mEYRiKRsFFESEtGGrxMJlu4cOHixYvRqaSkpKNHjy5ZsiQtLY0giLVr/wkA0dF+6KjRHgKtqC+//PJjjz02b948uVzuuxBpNBrWBjBz5kyRSKRSqbgCnq2XDVeZ7blGfjYsCcdxrVb72Wefcc/eRugDAa/RaIuLx65a9c9///uf2dm5AwZ07tRqaqp0Oh2Krmexdu27TU2NGRnZI0cWb9jw/ptvvmw2t82addfNnPBrr71SVDQitGvFYjGiW4Jbg/cNzcF3JomJienp6ddLkySgR1AUfSts7ATcOti06YN///stv8VzKysrtFpdcnIqQUgKCopOnvzx5k/PF1FRUQRB5OYGqqsEADKZjLV1o5KbERERrH07klOpQiqVPvbYYyx1fGRkJLs5AIABA3JwHPcrUBGJHqvcv/HGG2PHjh06dCji/eRi/vx5kydPRseTJk0qKyt7/vnnMQxjf4zDh/Mzzp9//vns7E778fTpU9AB2hNMnTo15BCuvkXfLD0jRhSOGFHIa0xJSfvd757kNbJsYoWFowsLey4EdAuCdQXdCgs9Eu2+M5k3b968efP6Ykb9HAwjFJsRcA3mzfs1ALz00p98T1ks5vDwTmUUlYe/qTPrBgsWLCgsLAwmA2j//v2zZ888d+4Cill75ZVXIiIiGhoaDhw4wE0IQpZCVvbzZLlerztx4nhu7iDf8efPn//111/zlHtkFeDh+ef/rNN1bRoKCgqQBSIuLu7KlSsAMHTo0K1bt2IYhqaalJQ0duzY2NhYtAympiYDgEgkYs0Mtyn6XuT0eyBpimHYrbDQdyfgBfwc6OiwChq8gODhcDhYN5lEInU6HdyzW7Z8XFdXAwAKhWLgQD+lLynKU1t7uanpOtJ8eKBpD47X+nX8NzcHkwThDQ/viqkKC1Oo1aLCwuEHDhwgiC7rYE1NOY67KIpC8pWi3EeO7GFHoGkKx0Xff7/Pd/TW1noA6Oho5fT3mYGXAYBz5370GzL18st/ufvu+8Ri8ciRg9au/VdHh+X3v38WAO69d77bba6qunh15gYA0Om0S5bMR/eiqFEABADU1l4+cuRK4KeAkul//PEQjodoE/V6GYahq6uDrYut04VnZvqJbRKWnp8dyLYjk0lvHQEvZGb7RXdxT++9txItrAAwYkThnXfODHLA0tJtLpfrVvi7C7gtIJfLWV4QknTzaFKSkpJRkDlBEBERflzUtbVXlEq13xpoQcJma5NKFQQRIvei18twWWBjYxPlcllsbDwAJCV1RqIMGTIoL6/T41lYWHDw4OHU1FT263i9jN3eLpOpxGI/a1RCQhIAGI1Rfr8+gsdDNjTUaLVhfklmKAoDgAceWJySkpGSknHxYjlqLygYGRERnZycdvVjHkEQarWavRG76VEq1QHujuBw2FtaGsPCjCFv7inK7XLZVSo9QFBbBIXCL/GtIOB/fqC/sVwuvxUWekGD7w4o7unxx5/xFfBmc9vTT/+J5TQMfkzEsCE8bQFBQqczsIF1ra2tKG+IxfDhPYS5NTTU6PXhSUmh59o0NFzQaIxKZYhbBJqmDIau7PPs7EE4jmdlVQFASkr6sGHDfvjhh0GDhqSkdFZmmzFjFklSK1f+m10baZoymSr0+liZzI/EEosVYrE4KyuXHcEXdru1oaEmJiaBa6JnYTBE4Th+553T0QgeT6f4HD36Do1Gg+OdO5vExLjs7GyRSMTeiF289frwlJQeEgpaWppaWhoTElJDZrJzOjva2xuiotJvPya7nw807aEofqUdj8cNACTpDDlIgh0htGft9TIYhslkMrVa5XY7er7AHxDhSciXw1WaGq+XBgCG8YQwFE1TXi/DvdDjESF+ZgAgSafbzSeL4IFhaIZhevMtGIbGMCz4EQhC0h1LMxebNn1QXn6BW5GCBUmSGIaFlmMpl8vg1giuFHCLo66uJiLCmJKSum3blsbG+sjIqOPHj9xMys4bhdGjC43G6B07vpHJZMh4ifLrwsPD8/PzGxoa1q1bx3Z+9tlnn3zyyeB/IAkJCefOnUtKSgp5ejqd7uLFi2xiC9q1R0VFId5PtCMPDw+TSCQvv/x3s/mWiIHoDfqVgHc6rVZrM6/RbncAQHt7g0TSK4YKs7kutAspyk4QhF6vjYmJaGvroShQYPTmcqfTyjCU09kOAHZ7S1tbiE+DOwerVQnQWa6+vb2hra2HOpUk6aAoupcPAa7nOej1McFQcweIezKb27xe77vvvtnR0ZGQkDh9+hyVKliub78lAQUI8MXate8uXvxQfHzivHkLS0s3ezyezMyswYOH9vW8rhvp6akzZsw9fvxHNux80KBBK1eunDVrFo7jvoRx1/vrSEtL6+UMU1NT2WODwSCTydjq3kjAo1vcccd4grjtqbj71dKjUGikUr6mJRK1AtQbDLEhW0vcbrvV2mIwxIeWKfHQQ4+kp2epVNIxYyaFTN7ucFiczo6wsPjQLgcAu91jtzt1ukgAMBqTwsOvm7fEZmujKLdO1+V/0mi6HojBEBse3oMG39ragWFkbyg8OzpMAJhGE2z+fe95xzweMiEhadq0mUqlsrR085dfbmMrIZ04cfy77zoLaQwfPkws5r8eFOUCgIaGKwFiggKDYWiv1ysS+SHiCBJutwvDsJAnAAA0TWEYhuPnQh7B5XI1Nta0tjb1Yg4eDMO5YVMmUyZADADY7bYjR77v/tJOeL3eqqpLdXVXejOH7gLQ/GLEiHEB8k6XL3+B+5FNF4qPT1y6dFnIk7xF8NBDD7HHBEE8/PDDAHD33XfffffdfTcpP9BqtaNGjWK3HUjA+03Bv03RrwQ8jot97bEoWEMsloYcOUJRJAAQhDQEJjsAiIiIKi4eVV5+RqnUhJxoLhLZASDkrwAAOC7GMEwuVwKAWq0NYSgcF2EYxr2Qu/kWi6VET8IUx0UYhvfmW2AYzpvDz424uIT58xeh44KCovXr17CnDIawnJzOyNWIiGhfAY/SgtPTs3qMyukOLpfN62XYypshwGSqxzA8IiJ0HjqnswPHRcHTA/uivr5aLlf0piiA3W4mCJlE0rVHZ50mYrE4mMdbXV2hUml6QZPstdnMMplSLL7ttbqbgHvuuaevpxAs1qxZQ5IkOiYIwmg0clP2b3f0KwEvoEeUlJRs3779tmZXvslA8fMoElgkEnHJ7RMSklBYb3cgSfLVV/+6YMEiose9TzdAVLW9sdxYrRZurFAI6D1VbXNzo1Zr6M0cmpouKRQ6LlUtWytRKpUFM3JNTWVYmDEuLkRWQURVq9NF3SiqWgG3CBISruFF3bZtW0SEgS0Oe7vjtmTnERAyFArFtGnT+noWtwcQTbLF0v7JJx+0t5u9Xubo0UNZWX7yj7sDhmFjxhSHLN0FCBBwk1FQUBAXF9fXs7hhEDR4AQL8A8U95eQMbG83r1u3mqbplJS0KVOm9/W8BAgQICAoCAJegIBOdBf3VFQ0pqhoTF/MSIAAP2AY2mLxE7Ho9TJOp9VsDpb+zC8cDgtJOkOeGADY7e1mc4i2YRTvZrO1OZ0doY3gcrkAwGpt8XrdoY2AvkVHRzM3qtrrjUIGb6fTajZbA49gs1kAwGJpcjpDpBSjaQoA2tsbgiS6IQiZSmXwbe//Al4kEqvV2t6UCsBxEUHIelOIRSKR9KYIOgDguLiXORsSiUypDDa/yy9EIrHXe837qtUCW7IhmPwAmUzhSyNznXMgbqOKODKZHP1QQ4ZYTDBMr/xoCoUytOBQzhwkvfyrKZVqqbRXcZEEIeXNISGh893LzAxqBLVaK5H06hdEEDIMu1UoDRChBQ8ymZQgcL+nggTKEQh5BK+XkcvlIlFv5uDFMByACXkEDGPkcjmOY734FmgONDepLzeXtFhwAIiK8vQ4Mo5jcrkcw7y9fA5eLx2kgEcEvb7AfBMTBQgQIECAAAG3O4QgOwECBAgQIKAfQhDwAgQIECBAQD+EIOAFCBAgQICAfghBwAsQIECAAAH9EIKAFyBAgAABAvohBAEvQIAAAQIE9EP0tzz4t956pa+nIOAWwrJlT92cG23cuKGpqeHm3EvAbYGf9d0TFjoBXCQnp86YMde3vb8JeLO5ra+nIOCXCKu1Q3j3BNw0CC+bAC6MRv8V8AQTvQABAgQIENAPIQh4AQIECBAgoB9CEPACBAgQIEBAP4Qg4AUIECBAgIB+iP4WZHdbQKVSyeUKr5dpaWkJ4fKHH37YaDSeOnXq008/veFz88Xy5cvFYvHevXv37dsXoJtEIs3Pz2tubr548eJNmJWA0NDP3r1BgwaNGDEiIiLc6XTV19fv3r2npaX5JkxMQB/ihr+EN/mtvpkQBHwfoLCwqKio0OFwvPJK/8l1GT161KhRo06dOiUI+FsZ/endGz169B133IGOJRKpVqvNyMhYvXp1U5OfWukCBPwCIQh4Ab1CXFxceHh4bm5OWlp6X89FwC8IEol0zJjRAHD+/PkvvvhCo9H8+te/ViqVkydPXr9+fV/PTsDthM2bt4jFIqfT2dcTufEQBPzPhaysrIKCgqioaI+HrKys3Ldvn9lsBoDRo0enp6cCgFQqmTVr1qFDh0wmk0QiLS4uys3N0Wq1JOlpbm4+ePBg8KqwXq+/4447YmJiVCplc3PLhQsXysoOMgzNdoiIiJgyZXJsbHxDQ/327duXLl0qFotLS0tPnjx5XV8Kx0WLF/8mPj7eYrG89957NputpGR6VJT/FEwBfYVfwrun1WrFYgIAvv12t91ut9vt5879lJeXHxsbC4ABeK9rcAE3Fr38o2dmZhUXF0VEGBsa6nfu3Llw4UKVSnX48JGvv97ht38vX8K77voV10QvEokLCkYMHjxYr9e53WRTU+OBA2VXrly5Ac/lpkMQ8D8LiooKJ02aDAAk6VapVIMHD05LS3v//fdbW1vT0tIiIowAIBKJhwwZcubMWZPJNHfu7MzMLHStWEwkJiYmJCSsW7euqqqqx3vFxcXde++9EokEfYyNjY2Njc3IyFi79n2vlwEAnU734IP/RRASAEhOTl68eLFIFGJw5Z13To2PjydJ94cffmSz2QBg//59crkSAMaOHaNWq0MbVsANxC/k3dPpdGgX0t7ejjqYze0AQBAEhoFXkO99h17+0QcNGjRnzmwADF3+m9/cJxKJAvS/4S9hScm0oUOHAgDDMCqVRKVKS0lJ/eCDDyoqKoL/FrcIBAF/46FSqcaNGw8Au3fv3r9/v1qtXrJksV5vmDJl8ocffvT+++9PmjSZ6wfVaDRohd27d29ZWZler7///vtlMllWVlaPiyyGYSUl0yUSic1m27x5c1NTU15e3oQJE+Li4vLz844ePQoAd945lSAkDodj06ZNbrd77ty5KpUqhO81dOjQvLw8r5fZtGmzydTp5vzpp3PoYMSIfEHA9zl+Oe9ebW3tRx99xJ1OVlYWADQ0NHgF8d6n6M0fnSCISZMmAWAtLS2lpaUURc+aNSs6Oqq7/jf8JRSLiSFDBgPAwYMHd+/erVarf/3rheHhESNHFtyOAl5Ik7vxyMjIIAiCojxlZQcBwGq1fv/9MQBITk5G21IePB7Phg0fbNjwwcGDBxnGK5fLxWIxACgUih7vFRYWjizk3377bVVVlcvlOnDgQHV1NQDk5uYCgFgsTk/PAIADBw5cuXKloaFh+/btIXyp2NjYkpJpALBz585Lly6FMIKAm4Bf5ruHYfisWTPj4+MZhtm1a1cItxBwo9DLP3pSUhKSvtu3b6+rq2tqavz8820B+t/wl1Amk2IYjmaSk5NDkuSGDR+89957u3btDv5b3DoQNPgbj7CwMAAQi4k//emP3HaxmNBqNRaLhdff6XQ2N5uKigqnTJkUFhaO49ex6woPN6AD9E6zxwkJCWgaYWHhGIYBQG1t7dWzNV6vFzUGj/T0zhg6nU73/9k78/imyuz/n5ubfWnSNk33jUKhlFJ2BAQHR6uiIjIijoIjLoxfF3Dcxq864ziOo37np44Oo4466IjLKIKAsgkCCrIKWCi0ZemeLmn23CR3v78/nhJCm7bpTaGh3PfLl6/bu5ycJJec+zzPOZ/TpwslLiSX4L2n1+tvueWW3NxclmW++mrNRbpWOmiI8UtPTk4CAEEQGhsb0Z6WllaWZdFzZ1f6/SYkCOLUqZNDhw7LyMiYO3euIAjNzc0//1x+8OBP0fgfb0gBvv9BK0Y0TVdVVUU81AmtVvvb3/5Wq9X6/f4DBw40NTVNmzYtLa3bWamIhM9KoilK9FoKhTx8J9oQ8SMLAD6fz2AwTJgwYd++A06no6+XS1wALrV7Lzs7+9Zbb9Xr9R6P5/PPP29pkRr6DTAxfumh08JuKiGaNZd+vAk/++y/I0YMLy4uGTq0QKlUokX9goIhn3/+eTRvIa6QAnz/43A4AYDnha++WoOyeWUyXKFQAABFUV3PLy4u1mq1AMK//vUvn88HAFdddVWUr2W3dzSVysnJDjWYysnJAYD29nYAQOnTAJCenoGeYTMy0vs0UEOcPn167dq1S5Y8LJcrrr766s8//29fLUhcAC6pey8nJ+fOO+/Ecby+vv7zzz8flGVOFx0xfulOpwsAMAzLyspEg/K0tDR0A0ek329CtVqt0Wibm1uOH6/EcXl+fv4VV8zIysoqLCxUqVQR/xHFM9IafP9z+vRpnufUatWUKZcBYBqN5s47Fz711O/vvffu8KdGlUp17jMmlpWVqVAopkyZYjQaww3OmDHjmmuunThxUtfXcjjsSNYD5ZWgkqfc3FwAOH78OAD4/X40rJk+/fLMzEyLJfWGG24Q8aYaGxt9Pt/+/QcAYMSI4eglJOKNS+fewzDZ3LlzcRynKHL79u0JCcbU1DT0n4iXkOgvYvzSa2trSZIEgOuvvz4tLTUlxXLjjTf2cH6/34RDhw5dsuThpUuXFhQUcBzb0FBvs9kAgOM4hmGifyNxgjSC73+cTsfu3Xsuv/zysrKyX/ziCoVCiWEYw9Bff/0NmibyeNwAgOP4448//vnnn58+XXvVVTyGyW69dT4A8DxPkpRarQolpY8dO9ZkMtXV1R040jS9dQAAIABJREFUsL/TawmCsH79+oULFxoMhnvuuSe0v6mpCWWQAsDWrd/dccftBoPh3nvvBQCKIsVNkwLArl27JkwYr1SqrrnmmnfffU+qNo43Lp17Ly8vFz2LqFTqu+66K/zM559/XoR9if4ili+dpunt23dcd921Fovlt7+9HwAIgmAYGhW5daXfb8JTp046nY6kpOQFCxYwDKNQyFF26r59+3ie78OnEB9II/jzwnfffbdmzRo0I+Tz+SoqKt5779+hNJDDh3+urq6iaZrneY7j2tpaV61a7XDYaZqqq6v74IMPTp06CQD5+fmpqb1ryDQ2Nr799jsVFRUul5Om6ebm5u3bt3/wwYeh27Gm5vTHH3/c0NBAUVRTU9Py5R+GS0D0iWAwuHfvPgBIT08fPbpEnBGJ88olcu+ZzSni7Eicb2L80vfv37dq1arGxkaKIuvq6las+Jii6B7O79+bkCSpDz/8z969ex0OhyAIFEW1trauX79h27bt0b+F+AEbZDWjzz//vwPtQnyBYTKLxQIABOHz+/0AoNVqn3jiCQD46KOPamtrB9i/88xzz710YV7o/fffslobL8xrXSxI9975Mx63P3Tn40t/7LHHelayu8D+xCHDhxfddtudXfdLU/SDHEEQ7rrrN2q12m63//e/n5Nk8LrrrgMAkiSbm6WUY4nziHTvXYLE25ceb/5cYKQAP+gR1qxZc/PNc8xm80MPPYh2kSS1evVqiiLHjh07e/bsHi5etuyfDoeYvqISEtK9d0kSb196T/706wvFI9IU/SWBRqMZOXJkYmKiIAhOp7Oy8jhJUgCg1WoTExN7uLCtrY1l2QvlZv8jTdEPONK9dz6I8x+6/v3Sx4wZo1Ao29paw9Vs+sWfQYM0RX9JEwwGDx482HV/IBAIBAIX3h+JSwfp3rsE6d8v/eeffz5P/gx6BtsIviuBgL+1tTE7e0h3hRa9wjAUSXr1+mSkUSwCr9dtt7cOGTJC3OUAQNMBigoYDGbRFpzOdoLw5uQUiLZAkj6OY3W6sw/g1dXwwQcd208+CUlJvVhoa2vmODYjI0e0D4GAB8NAozH2fmoc0N7eSlHBrKx80RaCQa8g8FqteHnglpZGDMPS0rJEW/D7XTguV6vFdxJqbKzRanXJyeLbChOEQ6HQqFRnFfJXrwZUBpWRAUuW9G6hpqY6OTnFaOztHu0GQRAIwq5WGxQKtTgLF4C6upNGY2JiovhfCZ/PrlLplEqNuMt5nq+rO5GSkm4wiPwXKgg8QTg0GqNcLvLnmqappqbajIwctbr3fgoR4TgmEHDrdIky2dkB8AsvgN8PAHD11fDLX/ZiIRAgWlubcnIKUEdjETAMSZI+vd4srqY0xOAvkyPJQEPD6Vjm+liWIghnLE9CPp+noeF0LBZoOuj3u0RfDgBut6O5ufcGoD1AUYFg8Bwx85oaeOWVjv+6yJxHwOFotdmssfhAkj6SJGKxcCFxOttbW5tisUCSRDDoi8WCzdZst7fGYiEY9JKkPxYLzc0NLldM2sZ+v4umzxn5bdrUceN9+GFUFhobT/t8Udyj3SIQhJNl43pe12qt83hi+pUgCAfDiF+ZFgS+oeG03y/+juV5niCcLNtTXVzPMAzd0HCaJMXLGnIcSxBOjjunlO7NNzvutx9/7N1CIOCPOejQBOGMXWhk8Ad4CQkJCQmJSxApwEtISEhISAxCpAAvITFghDphSEhI9AtdeyJfygz+AO/z+b75ZlOoGbCERJxQU1OTlpa2cePGgXZEQmKQsGHDhiFDhgYCUl/BDgZ/gHe53K++uuzo0aMD7YiExDkcPnyYpmnU1FJCQiJ2bDYbRVFut3ugHYkXBlUdPMOQFNW5yJLjKADw+z0E4RRtFgD8fpfoMjmK8gMAQThF1zzQdFAQBNFvAQBomhQEPhYLDEPxPBduIRhUAHRUTwUCboLopdsSy9IoQ1W0DxzHAED0FtRqveh6mz7hclkZpnOKdTDoY1naZquJeMnRoz8BQHt7U3cnAADqitHDCb1C00GZDIvFAsexLEvbbOJr1jmOCQQ8sfjA85zf7woGvaE9wWAKuvdYloquNEMgCIfNFlNastfb7vNFWw5gsQyJ5bUkREDTNAAQRExFHzt37nr00d/t27e/+zb0Fw2DKsBTVMDn6zweYtkAAPh87q6H+gRBiK/zQZVdPl97jEWNsbwFmg7wPBfjh9DJh2BQFwrwBOH0+Xrpl8yyFMf1sw89I5crLkyAV6l0XV9ILnfIZJRGE7mC3O8nAUAQsO5OAACSJARB6OGEXpHJcByXxWIhEPDKZLharRNtAcNkcrkyFh/8fpdCoQqvzw5VGGMYHp1lTKFQxeCDQBAupVItl6vEWpA476Be8uj/ojl+vPLIkWMEQYiupI8fBlWA1+uT9PrOQhaBAAYAWm1SevpwcWaDQa/b3ZKaOlQmw8VZ4Pl6m609PX246ABPEA6CcKalDRN3OQAEg1WBACn6QwAAj6eNYYJmc15oT7iyjcUyJD29Fwsul5+mqVh8cDqbMAxLTMwUbeE8EVGLRqFolclIgyFyY1O5HEmmKLo7AQBYluF5rocTekUur8FxPBYLFBWQy1WxWJDJcKVSE4uFQMCjVGrDhZ5Coyscl0dpWaXSi/ZBEHiCcKnVhotFZGlAoGna6RSfN/rUU08tXLgwKUl8VKIoCgDC28uitsiKvgzGkUb99u3b582bL9qTOGHwr8Gjr5ZhehlcSkhcYJCShpRILzFoeOONNxcvXiriwn379n3xxRevvPLKBx8sj8UBn88HAC7X2TX4Rx55ZO7cuX0ygiYAKiqOxeJJnDCoRvARQQH+ou5aITEo4XkeAAZNR2qJC4jAcZF/0HieQ3kqohFtYdGiuzdu3OhwOAnC11cLb7zx9y1btgLAzz+Xi/Nh5cov8/PzqqurAKCmpo7nWWShoaG+qampB2sOh2Phwt8sWfLQtddei/YcOXIUACiKPPeqjjkAnuc4rpdkI5Q6w3Gs6O/ijAUmysQvDMPChXVDDP4AL5fLAYBlpRG8RHyBRvBoUlFCIno4jouYsYhSEWNJZoSO1cA+5xtZrS0bN2602x0AUF9/Sqfr29xwa6vVbrcDgNPZDgAeT5/FlZ9++n8vv/yy9vZWAPD5iG3bNgeDwW+/3XbiRDVNd5voCgC7du399ttvR44sGDeuEO2x29sAwOOxh1/F80MBcADw+102Wy+fD8ryczgaFIqYImx7e12UZ6rV+ogLl3EU4Bsa6tavXxMIBEaNGl1WNqvTk8uxY0e2bt1EUVRBwbDZs+dG3zkGx3EMw6Qpeol4A43gg0GpZleib8hkeFJSdtf9GFav0RgjHooSp7NRqzX1tbHQxo2bFi262+3uUJhhGKxPPowdO766+gTaPnny9KFD5TNm/DKUUEkQhF6v79UIQQSamtoqKqoAwOPx/uMf7+/cuQu1/8jOzpbLTYsW3f3GG69nZZ3Tdenw4Z8XLFgMAD/++FNVVePUqVMBgGF4ABAERfi7CMUjjcaYlNRr8p0doMVkSlepRLYmoukAQTgSE7OiTNvqLj8sXgI8z3OrVv13zpx5WVnZK1Ysr6g4UlIyJnTU7yfWrFm5YMHdFkvqypWf7t6984oremvoEwaO41KAl4g3UID/7rvvfD6fwSA+w1ziUgPDsPDGeuH75XJFxEPRI5cr+2Rhw4YNt9xya3hrlkAg2CcLJ0+eQv8WAIAg/Fu37vjlL2cRRHDBggVXX331U089VVdXl5GR0bMRr9e7ffsOtH3kSIVKpQo19yJJsqWlbf36DQ8++FBBQWH4VT/9dAhV1h04cODNN/85c+ZVANDc3AIALMuGv4tQnJXLFSpVLyl7CoUKAJRKjUolui8fCwAqlUZ0bTYiXpLsampOG42m/PwChUI5efLU8vLD4UedTodeb8jNzddotMOHj7TZ2vpkHMdxhpHW4CXiC/SbSNO01+vt9WQJifhk9erVnRqvEQQBAA0NDTNnznQ4epnNDgaDnRKkfvhh9759+++9995NmzY99thjDMNUV1f3bCQQCISWuoqLi30+Ai0WILxeL2pC30kAx+Fw7N27N/Tn2rVrjxw5IggCSovxeAbDv8p4GcF7PC6zuaOCJSXF4vGc801YLKk0TVdVHUtLyzh27JzBPcdxNlvHgo1KpepaDhEM+uVyeTDoF90skqL8JEn5fF6ZTOTzEOpd6PN5RJfJBQJ+kiRj6XdJ0xTP87FYCAQCLHuOD8GgHKCjPNrv9/l8vQrdMBzHxuIDmtCWy6O1oNFoRbdkPt+ERi0xlu1KSAwgKJwDQHp6+sSJE9etW0cQfqvVWl5evmPHjjfffHPZsmXbt2/PzMxMTk7uejkK3mq1Oisr69SpUwDQ3Ny6YcPGNWvWhM45evTozJkzQ38+99xzW7du/fbbb3U6XVNT08svv1xSUoIOqdXqefNuOXbsnAR4iqJQrcr7778/b948lJUFAEuXLv3kk09CpwmC8OCDDw4fPhwN/dvazuYBHD16lKIKAS4+CYR4CfCBQECl6vj4lEpVMHiObJZKpZ46dcbnn3+sUCgSEkxjxowPHSII37vvLkPbJSWlSUkRpjrlctxqbTh4cFcsHtbXN8ZyOQAcPPhjbDo3UFfXELMPMX0IAABwNvHk5MkUgElo++jRn+z2qMTO+sOHE1GeV1w8LiWlt/L8ASI07pECvMRFx/Dhw6+//vrXXnsNVaYBwN69e83mZJ1Ov337jocfXrpo0SIA+Prrr51O54wZM4YNG3bgwIGudjZv3gwA69at++CDD1CAJwiik5h8eCkpRVEvvPCCIAg7duy4/vrrt2zZ8s9//jN0VK/XJ51R50hJSQnpQFutVgDYtm3bxx9/fNddd4XvRGeWlZV98sknu3btOnToEABotZrwEfzy5ct9vme6C/CCIFRXV2u12pycnNDOYJCMh9KteAnwGo3G7e74FmmaUqvPWbqorT390097ly59Uq83bN/+7apVn912253okF5vWLz4IbQdcQTv9brkcnliYsr48ZeL842i/D6fPSkpW/QI3mZraWw8PX78tBhG8J5g0JOcnNP7qd1gtdY5ne0lJRNFWyAIJ8uSJtPZxTCn8+z9U1IyITe3lxF8bW0Vw9CFhaNF++D12gAgIcES5fkaTfxqUUkjeImLFL/ff+LEiRMnTjz77LOhee/k5GSVSiWTyTZv3szz/H/+8x8AqKmpAQCPx9PY2HjvvfcyDPOf//yHZdna2tpDhw7Nnz+/ra0NAMxmc0JCAgBYLJb29na/nwAAhUKBcqdQg7ja2trc3NwdO3agEfb3339fWFi4dOk5ZfeJiYmhgeIPP/wwbdo0p9MJAOvXrwcAmUx29913f/3113/+859XrVqFYjny/L333vv0008FQUCT+RZLClo427hxY0VFRQ9iFW63+7bbbkOPKQaDISUlhSTJQMDvdnt0Ot0NN9xQXFysVqtHjRqVl5cXDAazsrIsFssFS7uJlwBvMiVVVBxB2w6Hw2RKDD9aW3t66NDhaOe4cZPeffcfoUM4jqen96RrxjA0SqQ3GERKUMnlGMP4DIYE0Up2Xq8bAAwGo+gAj2GsIFCi3wIAKJUqmUwWiwWeJxnmnI9RE/YYptP1fsfK5Qqe52PxgWF8sXyVcQXP8ziOcxwnBXiJi4W33npr586d/+///T/05/79+/fu3Ws2mydOnKjT6TiOVatVPh8BZ6RHQs1b7Xb7t99+m5+f/89//vPjjz/ev38/juPz588nCGLYsGGlpaVDhw7V6XQvv/zS3Xffs2LFJwAwYcIEp9NZXV29c+fOffv2TZs2beXKlQ0NHbOYe/fuLSoqCs0fjBw58vjx41lZWUplR4FVSkrKhAkTDh486Pf7t23bBgClpaWHDx9evXq1y+Xavn07Og3DMLPZrNFoLBYLetoAgKyszCNHjj300ENNTU1HjhwZPTrymMTn81177bWVlZXLly9Xq9XV1dVoDZFlGYVCEAQlWkqgadrv7yyPr9frZ86cWVJSkpmZaTabjUajyWQ6U9TN2u1tDkezWl1uMBjMZrNMJtNqtSRJpqenp6WlRf99xUuAHzKkYN26Va2tzampaQcP7gutslutjSkplqys7PXr106ePCUhwXTgwN6srL4NZOVyuZRkJxFvcByn0+m8Xq8U4CUuCvx+/zvvvNPU1PSXv/wF7VmyZAnP83feeeerr76K9uj1ukAgiGFYKIkdwXFcY2Oj2Wx+8skn0SiZ53mapr///vuUlBSZTPbAAw9cc801SKkGPRxkZGR8+OGHt912208//TRlyhRBEJYsWVJWVoYMEgRhs9nQNoZhf/rTn+bPn3/ZZZdZLCkAUFRUlJycnJeXp9VqeZ5ft24dACxatOjw4cMAEIruN9988yOPPILGXUVFRaEAX1CQt2vXnn/+858YhqlUqqQwUe7Tp08DFADA2rVrn3rqqaamps2bN6P6uhB2e1tFxU+XXXblK6+8gt5pdXW11WrVarU1NTVWqzUxMbGlpeW7777797//HXrRaLjttts+++yz6M+PlwAvk+Hz5t2+Zs2XDMMMHz6itHQs2r98+Tt33bW4sLDI4bB//PEHDMNkZeXcdNMtfTKuUMilMjmJeCMU4KVSeImLgtdeew313Q6Nm5uamgBg3LhxoXPmzLnh3Xc/XLZs2aOPPtpVxAnF1xAvvvjiyZMnR44cCQBarbakpKS9vSNmz5gx49Zbby0sLFy3bt20adPQwL2pqSmU915eXp6amgoAixcvvvLKK+fNm/e73/0uJyfniiuuWLPm09GjJwLAW2+9xfP8008/DQAymey+++5bsmRJuAOjR4+eMWMG2v7FL36xY8eO7OzsKVMuKy4uQjsFQSBJMrzb+Ndff93aetvq1asffPDBcePG7d27t7i4uOfPTSaTFRUVFRUVAUD4o8Bzzz0HACRJEgThcrnCq2mUSpnf70hJyeN5Ae33eDw8z1ss0a5OIuIlwANAdnbu/fcv6bTzD394EW1MmTJ9ypTp4ixLdfAScQjP8zqdDqQ1+EuMvXt/3LNnJ8uy+fkFs2f/KjSljOhZ72sAqaqqQgEJAFpbOzLM0bNp+OJcSclIAMjMzBw6dOiJEyduuukmi8Xy1ltvRbT5t7/9TRCEKVOmhPZMnz79hhuu+eabzU888cQNN9wAAFlZWXPnzv373/+OTjh+/Dja4Hl+y5YtADBmzJj58+cjD3U6XSBAGI0JiYmJAIDjOI7j6enpAKDValUqVWhqwWKx2Gy28PL6Rx99lCTJJ554QqtVbd68NtxPVCuPIEmyoKBAJpMtXLjwo48+6vPn2AW1Wq1Wq81mc/jOYNDrdmvT0oYMkjr48wqO45JUrYRoqqqOv/32G//3fy+sWLG8UwFnLLAsiyS6pBH8pUNbW8uePTvvued/Hn74Mb+f2LNnZ/hRpPd17bU3LlnyuNXaFEpLigdOnDghCAJKYWtpaQGAkHi7Wn1Wry07O2vMmNLS0tKvv/7aZrOtXLkSRd9wtFptSkoKAASDwfT09Ouvvz50SCaTjRkzGs59aEAj9fDLcRwHAI7jjEbjxIkdicN6vT5iktOQIUNCR7XajqzbxYsXK5XK8LkHg8Hw0ksvJSUlyWSyiRPHhZL1OvHb3/723nvvveWWW958883uP6144ZII8HK5NEUvIRK327VmzRezZ8995JHfm0ym9evX9H5NdFAUZTKZAOCbb74JZdRLDG5cLufo0WMTEoxqtWb48KJQ6RCiZ72vgQVNUxcWFgLAPffcAwCjRo1Ch8LnjY3GhI0bv8nLy8vPz0e397Bhw/Ly8lCSPACoVKr3339//PiOUuenn3660xS3RqOGcwO82WzGMCz0GHHfffeh6W4AeOKJJyZMmNCz58gU+v/69etfe+01ABg7dqzX6+3uWgzD8vLyUlJS/vjHP7788svjx48PvXpiYuIbb7zxwQcfoHcX58TRFP35Qy6XlOwkRNLYWJ+fX5CZmQ0AU6ZMX778nf6yTJKkwWDAcfyLL77461//WlBQ0F+WJeKWESOKR4woDgT8ra0tR4+Wd5Lc7lnva8eOrWiJWqVSFxTkdTXOcazN1uL3+0S7R5I+m80ll0fo9HHiRCUAaLVqABAEAcMwo1EHAAqFQqHgjx07hPYDgNVa73DYwq/95ptVt9/+m6qqEwCQlpY6evTwJ59cumnTJgBwuWzoWoQgCCzLymQymvaF9o8ePeLtt9988823jh+vBACGIZcufeDFF1+pq6unaX/45ehDAIC6uhNILxYAbDYrAJSWjjp27JDZbDCbjQDg8dhPnYrcEJbnOZoO3Hnnr51O16233gQAN9xw9fTparSSZrO1HDvW0vPHiDrKnzhRgWYaRMBxDMOQTicBEFXhlcFgzMmJ8AMyqAK83+/q2gfJ7/fL5XK/39PWdkqcWXTXtreLb+vp87kBoK3tlOgyOUHgBUEQ/RYAwO93cRwbiwVBEADO8cHt1gJ0LGLZ7fVabS/TJCRJsGyMPvAAEL0FozFNre69U0UPjBgxctiw4Wi7pcXac01mn0AjeLVa7ff7KyoqpAB/6dDU1LB16yZBEFJSzsmZ6lnvy+fzulxOANBotCQZQVRKEIBlmYiHooRhGJ6P3Fzb5XJaLCm/+tVN+/YdAID58381YcKY7Oysm2+eHfaKAgAwDIU2wjGZTFOnTrZamzEMI8lAQoL+2muv3rRpCwCEOywIwsyZ0/Pz8xUKPLQfx2HChDFz5842Gg179x5ISNCPHVuSnZ1ZV1evVCo6vV80GUbTVEhIymw25eXlLlx4GzozMzN15swZOTmZ3X1QgsCzLFNWdiVyNeQY2ojmE0YrwhQVFC2dcqZhbrSLd911tRlUAV6p1Oh0SZ12sqwMx3Geh66HooRhSJL0abUm0fkOfj8NADpdkugAT9MBigqIfgsAoFAQMpk/FgskSfA8o9WelSgIb7qg1Rp1ul7mmeVyuyBgsfgQDHoAQKOJtg4+4likTygUSiSeVFFRvmXLxltvvSN0qLLy2E8/deT0jhw5Ui7vfHv4/QTHseXl+yJadrkcBoMWSTPde+89eXkRylvRb2VTU7No/5FAcnc+RAPDkBgmk8vrRVugKLK9vTWWkSVNB3C8FcfP3m8ORz6ABQCCQX95eUWvFgRBaG7uPLLsCwJFBVtb7RG7bkdk9OhJ3f17LywsKiws2rNn1/r1axcsWBTa37Pe1403zu35FX/8cUtGRk5e3rAoPexKS0t1QoJFp+v4N26320eOHLlly5bS0lKK4iZOnHTLLbc/+eSzAPDkk0+PHTu2oWFO+OUcx+7cuTkvrzAtLauT5S+/XA0Ad955p9frRZpjv/nN3Zs2bSkqGh0uQcZxrM12euLE6V2fy8ePv/yFF2DdunXTp09PTEwcObJk587dU6Zc0UnBzO/3HTjwQ2Fhicl0Vhm3tnZ2+DlXXnk9dA9NBx2OBrM5LzQHAABn9G0hIyNn/Phe6rRRmVxJycRO32D0BINet7slLW1YjEl2gyrAKxRqhaLzgwxNcwqFguMEvV5kXAkGvSTp0+kSRQvdeDw+ANDrxQd4ghBoOij6LQCAUmnDMFksFjiOYRg+3EK40I1Wa+q1qaNcruR58V8EANB0AMOwWCyIIBgMrF37pcfjWbBgkcVyNgzL5fLQP2CFQt51Ok4mk/E81p0YPsuyKpVarVYBgN3uoGkmlAEUgudZACEWOX0Mw2QyWSwWOI6J0ULsPrAsjuN4uIXQ2Ai1UIvGiEyGx+CDwLIUjsvDHzJEsHv3Tq1Wi8S2s7NzDhzYE360Z72v8w1Jkg6HM1wmsr29vb29fevWraWlpUeOHFmwYAG6RUtLS1FtW/SgrLr7778/JH2DUue63vM9M3t2R6heunSpy+UaO3Zsny6/1BhUAb475HIpi15CJBzHrljx77y8gvnzF3Z6Phs2bHho9j4i1dVHfT53cfG4iEdxXJGSkpqQYGxtbQMAiyU7Nze30zkuVzPPc8nJ4jt8l5fvw3G8Ox+iwW6vl8tVJlMf9LM6sW/fjuRky9ChfQsJ4bS1ndJqTQbD2VKixDOxT63WRvPuvv9+Q1paVlZWvjgHBIFvbT1pMqVFP3sUEaPR+OOP3+fk5Gm1ugMH9ubk5KH9SNGrO72vC8Nzzz338ccrnnzysaVLH0N7UCH7mjVr7r///tbW1uHDh6PCzuuuu667JPOeuemmm0LbV1xxxfLly6+44gpx3hYVFX3++efirr10uEQCvJyipAAvIYbKymNKpaqsbFa/W2ZZVi6Xa85Mg3QVs5QYfBQXj3Y47CtW/Jum6fz8guuv7wh4SNErOzs3ot7XhcFutzc3t/zlLy9v2rR14sSJf/7zn1GAb21t/dvf/gYASUlJqEStT2qp3SGTyVBDGonzx6US4AlC0hKREENzs7W+vvb55/8X/anV6p544tl+scyyrEKhCAV4qRr+EmHGjCtnzLiy086QoldEva8LA3rEdDgcW7Zs2bZt28MPP/zSSy8BQFNT01dffQUAOp1OJpN98803kyZNGhAPJfpKHAX4HiScfvzx+61bN4Wf/Pjjz+h00WZHy+XycCkiCYnoKSubdT6G79BlBC8FeImBBbVmFwSB4ziO4w4ePLh27VoAIEkSNXJF8/MhfRuJ+CdeAjyScJozZ15WVvaKFcsrKo6Erz9NnTr9ssumoe3a2poDB/ZEH91B0qKXiEsYhpHL5aEkI9SBQ0JioGhqarr88st27eooDAl1ZAGAQCCQm5s7bJj4/HyJASFelOx6lnDCMBmOy3FcjmHY9u1bZs26qTs7EZGU7CTiEDRFLwV4iTjB7Xb/4heXy88UhP3f//0fAPzyl79Egsp33XVXJ8lYifgnXkbwPUs4hTh4cH9+/hCj8axGoN9PrF7dkUuZk5NjMiV0ugSNk/x+QnQpMM+4ZKqoAAAgAElEQVSzDEO3tblEF7khYaPy8n2iLXAcw3FMW5tT3OUAEAwGaJqOpR6aZWlB4KzWs80Na2pMAB1p5JWVP3u9nZtHdcLv9wqCEFtNNgUACkVTlOfn5g4zmS5oTV30sCyL47gU4CUGCqQJgwoO3W43y7IWS0pqqsVqPau78NFHH02aNIkgiBEjRgyYoxJiiZcRfM8STgiO4/bu3S2ip5xcLo+ozSQhMYCgNfjc3Fx050sBXuICs3jx4oULF6LtEydOAEBmZsbQoUOHDx+OOrBlZmZmZGQgGfbp00U285QYQOJlBN+zhBPi+PGjKSkpnVbfdTr9woX39GDZ6WyXy+U8L5SWThbnGxIVSk0dKlroxmqtP3myorR0cgxCNw6CcKaliV8Dq6mpstmaRX8IAODxtDFM0GzOC+1pDhNYKyoak99bjfHx44domorFB6ezCcOwxMR+04sdQILBoFqtvu+++6ZNmzZnzhy73T7QHklcQpw4cWLnzp2hfqm1tbUAMGzYkL/85U8Air///e8bN25EDdO0Wq1MJktMvKCqOxL9QrwE+GgknI4cOVxYWCTCuFwu9YOXiC8CgQBN0yaTyWKxlJWVZWVlNTeL16OVuMQQ0FpV1/0cxzJMVCXB//jHmydOnEhOTmIYctu27d9+uxkADAZ9bu5QjSZBqVTk5ubeeOMshiFff/3VQCCgUMh6tYwavaBGKX19Swie5wCA42jRFliWAgCWjcUCfcZOuKK+CvV94Ti219ZlHNdhgWHEr8nCGZXoaM6XyfCIGovxEuC7k3BCAk9KpYphmJqa07Nmze7ZTkQUCoUU4CXiCrfbDQChjpN6vV4SupGIEo7j7PYIrQF4ng8E3BEPdbVQWVkBAIEAYbfX33HHAofDqdGoVSqV3+/y+11XXTVtxIh8ZGrkyBwAiMYsWtT3+RwyWUxlyV5vu+hrKYqGjulGbyw+uN3ntIzj+aEAOAAEAm67vXNLs04QhB8AnE6rQhFThHU4GqM8U63WR5zXjJcAL5PhESWcQgJPjY31JpMpMTG5ZzsRwXFcqoOXiCtsNhsAhIrgdTqdFOAlokQmw5OSIqgXY1i9RmOMeKgTn3762XfffQ8ATqfHbifdbg8ATJs2DQC0WpNabbjjjrtFOMZxHECNXp+UlCRS6o7nObe7Wa83K5Ui27QEAn6AhoSElIQEkWsKLEt5vbaEhLTwzgWhkbRGY0xK6lU/3w7QYjKld9fkrVdoOkAQjsTErChXdbtbPo6XAA/dSDiFBJ6GDBn68MOPi7OsVCpYluV5XnTzPgmJ/mXfvn0AgNKXAECr1UoBXiJKMAxTqSLEGNR3J+KhTrS3d4xBGxsb//SnP6POqkVFIwFALldGYyEiaIo+dgsKhUq0BZblAEChUIu2gGKqUqkO7yYXirNyuSK8i2ZE0IVKpUalEvmYwvMsAKhUmhi7yV0SAQ/H5QAgzdJLxAMPP/zwp59+ilLquhvBL1u27MCBAwPjn8QlABKZR6xfvx5tjB49eoDckThfxNEI/vyBFkJomhbXAUlCor/gef79999ftmwZGiWERvB6vd5qtdbU1ASDweLi4meeeebee++dOHHigDorMWjZsmUL2pDJZBRFqdXqSZMm3XLLLcFgW88XSlxcDKoAzzAkRXUuJg4GfUibyeVqwzAxg3iUjen3u0TPllCUHwAIwim6TI6mg4IgEIR4oRuaJgWBj8UCw1A8z4VbCAYVAAa0HQi4CYLv2QLL0hzHxuIDSi6N3oJarZfLlaJfrt8JBoMkSQKAIAharbagoADtRyP4p556qq2tbePGjV6vlyCIAfVUYtDCcdzp06fRdmFhYVVVVWpq6vfffw8AUoAfZAyqAE9RAZ+vc/plMBhAI3ins0Wh4EQbJ4heMid7gCQJAPD52kUHeETXdxc9NB3geS4WC119CAZ1oQBPEE6fr5fnJ5alOK6ffegZuVwRVwEeRXdEYmJiQkKH8CIK8C6Xy+v1oq4z0pK8xPng0Ucfraio8Hq9Fovls88+++STT6qqqkJyihKDjJ4CPKqj6JmiolH950ys6PVJen1nXVKns12hUACAyZSdnp4nwmzsQjc8X2+ztaenDx9AoZtgsCoQINPTh4u20FXoJinsw7ZYhqSn92LB5fLTNBWLDxe70E14gFcqzz556PV6n8/ndrtbWlo8Hg8AnDp1ChUdSUj0I01NTT/88ANN0+np6VdeeSWaq5cC/GClpwD/xRef9Hr9c8+91H/OnC9QgJcq5STOHwThRMsH4dB0kONYj+fstGd7+1kVfZkMCx2SywWHw+F0OgVB+Prr1QCwb9++iRPHf/75hwaDPtxCX2FZmudlsVjgOIbn+Vgs8DxHUYFYLAgCT1F+pIKCoOkEAA0AcBzr8UQ1u0aSvhh8EAAgGPTSdLTyKUZjPLZmIUkSZdihX0WDwQAAV1999QC7JXF+6CnAa7W6C+bHeQWtwUsBXuL8wbI0ktAKh+c5QeDDFbUI4qz4Bo6flQbTaNQAIAgCALS329DOQ4d+bmxsKioqFK3JBQCCwAuCEJsFAYCL0QLPR6uw1o0FVEB11gLP684c4qO0HL3KW0QX4MyzjlgLcUEof/6xxx4DANQp7v777x9InyTOGz0F+CeeePaC+XFeCWXRD7QjEoMWkymCsofD4WUY1mzODe1RKs+OIFUqTehQRkZeaD9Jng0hDMMrFOrk5N6lS7rDam3FcTzch75it9fL5aqIbzBKTp+u1WgSYvGhre2UVms0GMyhPWfqD0AuV0Zn+ZhOlyjaB0HgW1tP6vXJGo1RnIUQVVXHt2/f4vN509MzZ8+eG94bEwDef/8tq7VDv2zSpCnXXSdGu7MHQotEv/nNbwDAaDRiGCbpzA9WxCfZ8Tx3+PDB8eMn9ZcrDQ1169evCQQCo0aNLiub1Sll3e12rVu3qrW1JT09Y9682yN2o+kONBkVXvopITEghOfGhxpvA4DReDZsOBxnJ5ylVLtBhtvtWrPmi4UL70lJSd28+Zv169fcfvtd4Se4XM4nn/wDys+IUeQkIuiOUigUOI4DwNy5c81mcyjZU2KQEVWAp2lqw4Z1NTWnGObsIJhhWI5j+yvA8zy3atV/58yZl5WVvWLF8oqKIyE5egAQBOHjjz8oK5tVUDBs8+Zv9uzZNXNmHxaNpCl6iTghPGCHB/jp06fn5ubW19cDgNN5tgjQ55OK5QYVjY31+fkFmZnZADBlyvTly98JP0rTNIZhGs35Snmrq6s7duwYhCksGQyG66+//jy9nMSAE1WA37bt2/LyQ+fVj5qa00ajKT+/AAAmT556+PDB8ABfW3tKr9cXFo4AgGuuuR7VEUWPNEUvESegaSS9Xk8QRHiANxgMBQUFKMA7HA6VSsVxHMuyqGuFxKBhxIiRw4Z1VJG0tFjT08+pB3G5nIIgvPPOG16vNycn94YbbtbrDaGjO3ZsRfkZKpW6oCCvq3GOY222Fr/f192rP/bYUyRJJiQkqFTKY8ci/KSTpM9mc4muLEVJJFZrvcNhE22Booj2dg+SHxUBErutqzsRLjTbJ3ieo+mA3e4LlzbnuNEoXNpsLceOtXR/NQAARZEAcOJEBZomEQHqyOd0EqiFXa8YDMacnIKu+6P6EKuqjgNg1157/YED+wwGQ0nJmKNHf25qalywYFHfvO4ej8dlNqeg7ZQUi8fjDj/qdDq0Wu2XX37W0tKclZV97bU3hA5xHGeztaJtlUqFZuPDCQb9aKfb7fT5PCJ8oyg/SVI+n1e0lD1JBgHA5/OILpMLBPwkSYrzH0HTFM/zsVgIBAIse44PwaAcoCPXye/3+Xy9Ct0wHMfG4gN6tpPLo7Wg0WjDO0YMOEgv+eOPP54zZ06nexXlMwOAw+FQKpU4jrvdbknuZpChUCjR115RUb5ly8Zbb70j/CjD0Dk5ebNmzdbpdGvWfLlhw7rwEyiKQr8kAMCyETQnBAF4not4CAAEQdiy5TuZTJadneX1eiKexnE8hrFRBpWILwEAPM9250M0FjiOl8lYZEoESFef4zhxsmYAwPM8x/Ecx/D82R/8kDs9fMJdfGAEQWRKJs+zHMezLBPld4FesStRBXiCIEwm0+TJ0ziOO3Top3HjJo4ZM+7VV1/6+eeDubn5ffC6ewKBQEhHVqlUBYPnCNIFg8HKyuO3337njTfevGHDug0b1v3qV7ed8c337rvL0HZJSWlSkgG6gH5Jq6qOpKToRXtYXx9t577uOHjwx9h0bqCuriFmH3bFaAGgJrR18mQKQMcazdGjP9ntnWUEz5sPJ6I8r7h4XEpKb+X555NOc+wowCclJcG5dfAAYDAYkpKSXC5XfX29VqvVarVut9vn63Y0JnGREgwG1q790uPxLFiwyGI5J3UxKytn/vwFaHvy5KkrVvw7/Og11/Qyl/7jj1vS0rLy8iKrZXg8Hp7nS0tLR4wYUVlZWVo6ues5LS3VCQkWnU5kzh3HsTt3bs7OLkhLyxJtwWY7nZiYqVaL/K32+30HDvxQUFBkMolpPQoANB10OBrM5rzwOYDQdFtaWlZpaS/vzm5vq6j4qahobJ9yxcJB4itpacNizMOIKsBrtdpAIBAI+C2WVIfD7nDYExOTeJ4/ebI6ltcOR6PRuN0utE3TVKfPRa1WZ2fnDBs2AgAmT5760Udn73u93rB48UNoO+II3ut1eb17ASAra8j48ZeL8I2i/D6fPSkpW/QI3mZraWw8PX78tBhG8J5g0JOcnCPucgCwWuuczvaSEvHy5gThZFnSZMoI7XE6z94/JSUTcnN7eVytra1iGLqwUHxPC6/XBgAJCZYozz9/y5nRsGLFJy+99DeWZUPfOwrwKSkpADB16tTwkxMSEtRqdUJCgsfjCYncSVP0gwyOY1es+HdeXsH8+Qu7/hqg/Hm0Qo/jeP9OPjU3NwPAzTffPH369MbGWIcrEhcFUQX43Nz8ioryf/zj1SVLHscwWL78HZVKTZJBg6Hfci9NpqSKiiNo2+FwmEznPEIajWf/lMlk4YEWx/FO61idYBgaRX2ZTG4wiClxkcsxhvEZDAmiley8XjcAGAxG0QEew1hBoMT5j1AqVTKZLBYLPE8yDBZuQRP2GKbTheaYu0UuV/A8H4sPDOPDMCwWCxcSvV7P87zf70fVxnAmwA8ZMiQnJ6eoqCj85OLi4urqaqVSuXHjRqVSiQK8NIIfZFRWHlMqVWVlszrtt1ob0dLk5s3rFy36rdFo3L9/z4gRI/vrdZ1OZ3V1NQAsWLAg1AFBYtATVYC/8sqy5mar1+vWaLSjR48tLz8UCPgBYOLEy/rLjyFDCtatW9Xa2pyamnbw4L5Qhh267wsKhq5Zs7KpqSEzM3v//j0o2y56UJKdVCYncYHR63UA4PF4wgM8hmFKpRLl04XzwAMPPPDAA0hyRKlUms1mkLLoBx3Nzdb6+trnn/9f9KdWq0NyI8uXv3PXXYtHjixxu13/+c97HMcNGTL0mmtu6NFYtNA0PWnSJJS/YrFEO/slMQiIKsAnJiY98MBSq7UJAGbP/lVe3hCn05GVlV1YWNTrtVEik+Hz5t2+Zs2XDMMMHz6itHQs2o/u++zs3PnzF3z99Wq/35+bm3/DDXP6ZFwul6OuiP3lrYRENOh0egDwer2ZmR2TTAzDdF1FCuePf/zjv/71L5VK9a9//autra2pyXohHJW4UJSVzeo6fAeAP/zhRbQxdeqMqVNn9O+LVldXo/ZxRqOx93k2iUFEVAF+164dxcUlOTl5ACCTycaMGX8+XMnOzr3//iWddobu+9zc/P/5n0dEG1cqlVKAl7jAGAx6AEDNYxA1NTWh0XxE0MA9MzMzPT196tSpH3yw/Hw7KTHocbk60pvmzZs3sJ5IXGCiCvDffbf5u+82p6dnjBxZUlxckpgoMjtxAJECvMSFB42WwgO83W4fPrynZnpKpVKj0ZSUlADAqFGjPB7vtm3fz5u34Hy7KjGICd2B991338B6InGBiSrAG40mj8fd0tLc0tJ8kUZ6lUolBXiJC0ynEfzJkydXrlw5c+bMnq8aNmxYbm4uAGRkZABAW5tIzRAJCUQg0FG/mpeXN6COSFxoogrwjzzye6fTXlNzqqbmVG3t6fBIv3jxw+fbxX5BGsFLXHjQCN7t7lBtam5u5nm+197bhw8fRnUi06dPl8vlR45UnG8/JQY3IYFkqe/7pUa0coBJSeakJPOECZcJAn/48MHt27cQhK+lpfm8OtdX/H4XQXTuDI1ubrlc5nK1tbWdEmH2TBPPWtGO+XxuAGhrOyW6TA41/RTnP8Lvd3EcG4sFQRAAzvHB7dYCdJTF2+31Wm0vAk8kSbBsjD7wABC9BaMxTbRiRuwolUqVShUK8F6vFwCysnpRyQhVger1+oQEw+rVa99997y6KTHIQfnzCoVCoxGpuyJxkRJtgKcoqqbm1MmTVadOnfD5Onpa92MdfL+gVGp0uqROO1lWBgAajZbnZV2PRgPDkCTp02pNokWF/H4aAHS6JNEBnqYDFBUQ5z9CoSBkMn8sFkiS4HlGqz2rSaBSnU0I12qNOl0vQjdyuV0QsFh8CAY9ABB9y07RqtpdqaysKCwc0VeJbINBHwrwaOOvf/1r9JcbjQmNjVZBEETfORISBEFoNJrdu3eLlkaXuEiJ6tfqww/fbWys5/mOn2+dTj9y5Kji4tEorz5+UCjUCoW6006a5gBArdbwPOj1YkJLMOglSZ9Olyha6Mbj8QGAXi8+wBOEQNNBcf4jlEobhsliscBxDMPw4RbCxwNaranH9HAAALlcyfNCLD7QdADDsFgsiMPhsK9du+p3v/u9iAAfymFubW1Vq9Umk6nnS8JJSUmpra33+XxSQ0+JMASGibjgKHAcyzBkp70ul8NoTCguHtH1UFdQmxNxbqFGL7FY4Hmk4k6LtsCyFACwbCwW6DN2wvXwVUgWnuNYhmF7tsBxHRYYRuQPPscxAMAwZJSjSpkMx/EI9bdR/VrV19cCgFarKyoqLi4enZc35KIbT0hJdhKiWbnyk5Mnq5EIXV8xGAyhAH/y5MkhQ4b06fKrrrpy//6fnE6nFOAlQnAcZ7d3FkoCAJ7nAwF3+CGGYVeu/Gr//r06nTbiJV3x+11+v0ucY2gQ6PM5ZLKYWnd6ve2ir6UoGgA8njaG8cbig9t9Tss4nh8KgANAIOC22zsvBHcCKUw7nVaksSYahyNaRWG1Wp+YGEHRNaqXHzt2QnHx6Pz8AtFi7AOOWi0FeAmRzJt3BwC8+OIfOu1vbm6qrT2NttPT0+XyzhM8BOE1GPQtLdaGhtO///3T69dvHDFieEPD6ehfetSoEQBw/PgRmSxyt6heIcmgTIb16UU74fe7ZDK51yteFZ9lGZ/PE4sPBOH0egMulydsTwpAAgDQNNXQ0BSFDcHlsoemIfuKIAgE4QoEWIXCHuUl2dnnayCE47jZnNt1f01NvVZrCj+0f/+Bxx//AwBcfvm0iJd0wm6v1+kSNRqRT5Mcx548WWMwJJvNIjs88TzndDYlJKQolSLzAQMBoq6uwWhMDRc47xMMQ3k8rSZTevgCXyj2abUms7mXuUqZrN1qbUlKylSpOs8oRwlqgJKcnB39CD7i/qgC/OzZv+qDa3GJUqkiSZEzNhISEWlutu7a9T3avuyyyTje+dec4zi9Xrd//8Gqqoo9e/b6fD6e5/oU59Cc54kTlQkJIn8pkIVYgitKbMQw8dV6HMf6fO4e+pT3Cs/zGIaFx0uCUJ0J8HQ0704QwOVyeDwix6YAwPMchvWh43N2dt9ma/oC1nUtEu3HcXn4oaamjjzozMysbi7pDI4rojyzKzIZG6MFdLviuFK0BbmcAQC5XLwFlFUtl6sidpTHcXmv43IcV56xINIHtEygUKgvRDe5C0NDQ9369WsCgcCoUaPLymZ1emPvv/8WarUEAJMmTbnuutl9Mq5Wq0PFoBIS/cKECZMnTIjQczNEdfVRi8XicrnT0vJ5XgCA1NT0yy8vi/4lLJZyAMjIyO/TVeGUl+/DcXzUqAniLgcAu71eLleZTGm9n9oN+/btSE62DB0qvnVKW9sprdZkMJhDez76qGNDrzdE8+F8//2GIUOGZ2WJbG8tCHxr60mTKS36BM94YO/evWjjqquuGlhPJAaEeAnwPM+tWvXfOXPmZWVlr1ixvKLiSKjfDMLlcj755B9QC20RDzUqlSq0FCohccEYNWokAHz77bcEQQCAShVhTNADJlMihmF2e7TTwhISIWiaXrt2LQAolcp77rlnoN2RGADiZU29pua00WjKzy9QKJSTJ08tLz8cfpSmaQzDNBotjstxXC4iFUCtlqboJQaAadMu0+v15eXlqAi+rwFeoZAbDPrt27efH+8kBjPt7e21tbUAMH/+/IsuLVqiX4iXEbzH4zKbU9A26oscftTlcgqC8M47b3i93pyc3BtuuFmv7+iJ5PcTq1d/jrZzcnJMps7pISj5ORj0u92u8vJ9InzjeZZh6LY2l+h/JBRFAkB5+T7RFjiO4Timrc0p7nIACAYDNE2L+wQQLEsLAme1toX21NSYADqU1Ssrf/Z6e0lj9Pu9giDE4gOqDlIookmqAgDIzR1mMl3omrpwVCrV5ZdffuTIEY7jAKC0tLSvFpKSktavX38eXJMY5Ph8PgC45ZZbXn755YH2RWJgiJcRfCAQCA1ulEpVMHjOejnD0Dk5eXfcsejxx59WKlUbNqzrq31JqlYiRp555gVxObFms/nIkSMA8O677/7+97/v++XJwWDwq6++uummm8L3v/3224sXLxbhj8QlAloVWrp0KWpqIHEJEi8jeI1G43Z3rJHTNKVWnyOpmJWVM39+R0OtyZOnrljx79AhnU6/cGFPy0tOZ/uRI/szMrIEAUpLe0qJ6o5g0Ot2t6SmDhUtdGO11p88WVFaOjkGoRsHQTjT0oaJuxwAamqqbLZmcZ8AwuNpY5ig2ZwX2tMcJlVcVDQmv7cEpuPHD9E0FYsPTmcThmERKz7jlpSUjqmpkpISETeA2ZwMABs2bFi3bt3PP/88ZkxHbsrOnTt3797dj35KDDKQcmJy8kXTEkyi34mXAG8yJVVUHEHbDofDZDqnhBHlz2dmZgMAjuNyeQTJnp5Rq9XSGrzEgGCxWND/R40aJeLy6dOnbdq0BS2mXnbZZX6/HwmOOhyO9nbxeiASA0VV1fHt27f4fN709MzZs+cajecoG/ZcTBQlLpdr+vTps2bNAoA+KSdKDDLiZYp+yJACp9PR2tosCPzBg/tKSjqWKq3WRpqmPB73F1984na7BIHfv3/PiBF9rreR6uAlBor09HQAKCkp0feq5RuJ664rAwA0WKcoCmXUHz9+fNeuXYFAAK2zSlwsuN2uNWu+mD177iOP/N5kMq1fvyb8KComuvbaG5csedxqbQqNefrKoUOHjh07tnLlSpAC/KVNvAR4mQyfN+/2NWu+XLbsdYsltbR0LNq/fPk7bW2tI0eWTJ489T//ee/111/hef7qq2f11b5GI43gJQYG1ITbbDb3dmJkLBYznGkIBgBtbW0AUF5ejnQd0J8SFwuNjfX5+QWZmdlKpXLKlOlNTedokfZcTBQ9aHK+sbFRqVRKHeQuZeJlih4AsrNz779/Saedf/jDi2hj6tQZU6fOEG1crVazLMuyrFweR29Z4lJg9OjRFoulpKRE3OVqtdpoNHo8HSqtra2to0ePDj2ttra2Dh06tH8clTj/jBgxctiwjsKTlhZrevo52SQ9FxNFD5rX4TguKWkgS0gkBpxLJdqhrL1gMGgwGAbaF4lLi8TExBjH2WlpaaEA39LSAgChAI/+lLhYUCiUCgUAQEVF+ZYtG2+99Y7woz0XE23evN5mawUAtVpTWFjQ1TjLsq2tTR6P89ixn9EeHJf1qSqVogJyeXvEvmTRgEReGxtPt7VZRVug6WBrq0N0RjOqRz19ulJEqhaC53mGIdvanOEJECw7HoXL1tam8vJe3h1N0wBQWXlY9LtAtdk2mwu1sOuVhITE/PzCrvsvnQCvAgCSJKUAL3HRMXHixOrqarS9Y8eOX//61yRJormo5vBKBomLgWAwsHbtlx6PZ8GCRRbLOQLAPRcTqVQqtEelUkeMXhgGMhkulyscjg4jcrm8T3GOZWU43rdLwkEBXiaLyQLHUUjQTJwFVKgiLhcbwfMcz8twXBGuqBYqf0GfcM8W0EMGjitQPqwIOA54npXLFVEG+O5eaFAFeIYhKaqz4Hww6AMAmYwHALu9RaPp8yeO+gr7/S7Ruv8U5QcAgnCKLpOj6aAgCAQhXuiGpklB4GOxwDAUz3PhFoJBBUDHA1Mg4CaIXlp1sSzNcWwsPqA2ydFbUKv14S2hLlJef/31xMTE3bt3Hzx48MMPPxw7duzLL7+s0WgSEhKkAH9xwXHsihX/zssrmD9/Yddfg56LiX7xi1705H/8cYvFkp6XN0yj6Ujn1OsNxcXjonevpaU6IcGi04nsw8Zx7M6drZmZuWlpWaIt2GynExMz1WoxGakA4Pf7nM72vLxCk0lkfSBNBx2OBrM5L7zZTCiAWizpxcW99Mqz29sqKn4qLBzV6REtelBtdlrasMHTbCZ2aDro93f+6UfBFcM4AHjvvX+pVKpHHnmgT2bRY2kgIHI9DABoOgAAfr/4AC8IPIDQ9d1FD8MEeZ6PxYIgCJ18oChtWID3+P29dExHAT42H3gAiN6CXK4cBAHebDa/+eabR44cueOOOyoqKnbs2GGz2XJyctLT05uaohX1k4gHKiuPKZWqsrLOacJWa2NKimXIkIJ161a1tjanpqYdPLivUz+O6AmJeqHmHRKXLIMqwOt0iV2fPZ3O9oaGptTUXADYvHkHALz00mt9MoseplJS8kUvqLBsfWtrW2rq0BiFblJTxadT+f2s3x+MxUJXoZvwAhyzOTc1tRmKZXEAACAASURBVBcLDoeXpqlYfLgYhW76i9GjR8+aNauiomL//v0AkJWVlZ6e3tjY2OuFEvFDc7O1vr72+ef/F/2p1eqeeOJZAFi+/J277lqcnZ2LiokYhhk+fESomKiv0DSN4zjHcQqFyGlqicHBoArwPYBqRdxuN8/3Mo0sIRG3ICl7q9UKAO+99977779/8ODBgXZKog+Ulc3qOnyHsHKhiMVEfSUYDJaUlKAyuRhNSVzUxEsd/PkGrYW43W6nU/z8sITEwPLrX/966dKlAJCVlVVYWJiTk9PU1MSy7ED7JRFH8Dy/fv16s9n8/PPPX3PNNQPtjsRAcqkEeI1GDQCBQICm6WXLlqFldQmJiwsMw/72t7/J5fLbb79dLpfn5+ezLPvKK69IenYSIfx+fzAYLCoqevDBB5977rmBdkdiIImjKfpoRJhpmv7554OTJk3pq/FwOaeHH374pptuys7O7uF8r9eL47hOp+vrC0lcmlBUQBC4Tjs5jhEEniTFR1+OY7taeO65Z8vKriJJX0aGBQCeffbZ9va2jIz0JUse6mqB51kME2Lxgec5jmNisSAIPMvSsVkQOlngODWAArlHkp1rZyLCspRoH9CQgGHI6LOa1eqBqchFTeSuuOKKAXl1ibgiXgI8EmGeM2deVlb2ihXLKyqORMwg3bTp6/r62hgDPAD4/f6ez7/tttuMRuNnn33W1xeSuDTx+dpROWU4FBVgWcblirWSrZOFe+65De1MStJgGCYIwrJlb8lk2G233dg1qYphKI6TxegDxzGoEkTs5SxF+WP0gSR94eGZotIAjMh4dJaFQMDjconMckX4/W6/P9pqmvT0wiiLmPsXFODFNT6QGGTES4APiTADwOTJUw8fPtg1wFdVHW9pESmQ1CnAh5S9QwiC4PV6H3jggUWLFl111VWVlZVJSUk8z4drHUhIdEdycnbXdR+Ph+I4PpaqAbe7VRC4HqoGMjIyrFYrx3EcBz4fFBV1fq22NheO47FVLjTiuMpotIi20NDQotWaYvGhvb1WozHq9WeFVzWajn+YCoUyGssnTpw2GMypqXniHBAE3marMRotanVC1BcNQHSHMzJqUnqdBMRPgO9VhJkgfDt2bL3uuhvXrVsVvp/jOCTfCAAqlarrCCYY9AMATZMKhSIpKSkpKbGyssput/l8nvDT3n33vWeeeZaiKIPBMGHCOLfb7XA40tPTX3zxhTlzbiBJyufzig72JBkEAJ/PI7pMLhDwkyTZyec+QdMUz/OxWAgEAix7jg/BoBygYxXD7/f5fL0K3TAcx8biA3oyk8ujtaDRaEULWvUJDJN1/W4xDAPARFdXIguC0JOFoqIilFQPACdOnCwu7tyRFsMwDIvJB2QjNgvQ7xbCPu1oLWOYTLQPgoDFaOGCcfjwYQCQmm5IQPwE+J5FmAGEtWu/LCu7TqvVdrqQIHzvvrsMbZeUlCYlRV73qqj4KTHRdM01M2trGwDgwIEfFYpzco/XrFmF1CHWrPlq48b1qB2Tz+fbsmXT0KGZAFBfH2vB8cGDP4qN7x3U1TXE7MOuGC0A1IS2Tp5MAZiEto8e/cluj2oWtz98OBHlecXF41JSepGduqj5n//5H51Ot3btWgA4fvz4zTffPNAeSQwwr732GgBIFfASED8BvmcR5gMH9iYnpwwZMqy9vXPTDr3esHhxR25RxBG81+s6efLYqFETyst/1mq1X3zxxc6du5955oUXXvjz0qUd9abbtm3btWsv2m5rs4VfTpLsqFFjfT57UlK26BG8zdbS2Hh6/PhpMYzgPcGgJzk5R9zlAGC11jmd7SUlE0VbIAgny5ImU0Zoj9N59v4pKZmQm9vLCL62toph6MLC0aJ98HptAJCQEO10sUbT+YlwkDF37tybb775r3/964oVKyoqKgbaHYkLAerIEnE/yzKtra0AIAhcV93uaGBZWtyFcEaDPRYLPM8BAMNQoiVaaZqEbmTLo4RlKWQHOYMQBA1ac2FZhqJ6kexkGGQhiGEiy7VYlgYAigpGGTJkMjxcWDdEvAT4nkWYrdam6urj5eUHeV5gGPqVV55/6KHHdDo9AOA43qnlYicYhgYAnc6AfuvnzJl7332/BYCVK7989tk/oHO6js4xDNuzZ88rr7xSX1+v0+kZxmcwJIienfN63QBgMBhFB3gMYwWBMhiM4i4HAKVSJZPJYrHA8yTDYOEWwhMbdLre+/jI5Qqe52PxgWF8GIbFYmHwgWHYM888U1dX9+OPPw60LxIXAp7nnM4IE4qCwAeDnkDADwDBoCPiOb0SCLhFy3IjGTGCcOJ4LyGwZwjCLvpaiqIBwOttZ1kiFh+83tbwPwVhKAAOAMGgx+l09HwtQfgBwO1uUShiirAuV7RC1Gq1PmKmTrwE+O5EmJFE85w589Cf7e1t//3viocfflz0C4Uq36qqqr799tuysjIAOHHinClflUo1evToyZMnFxcX//DDD6HWnCE2bNhgsVgmTJgg2g0Jif5l3LhxH3zwwZ49e6ZM6XONicTFBY7jFsuQrvtrahp0ukQU4SyW/Ijn9IzNVqPXJ2u1Ih+gOY49ebImISHFYsno/ezIFjiHo95oTFOpRM69BQJEXV1DYmK60ZjU+9mRYBjS5WpOSsoKb2MRGt3pdIkWSy+fD463W60tyck5arVanA8kSXi9tpSUvChnMrobOsZLgJfJ8IgizCGJ5v56IY1GU1RUVFlZybLsF198UVZW9uWXX+7YsUOtVqtUKtR1+/bbb1++fDkA5OXlORyOt9/+19Sp4/773/W/+92jALB79+65c+fOnDlz48aN/eWVhESMTJw4keO4qVOnnjp1qqAgQrNwiUEE1l3LdgyToVwitVojrq27TIaL7gePJrEH1oJMJkf/F22B49geLMhkeK9NYNHTAI6L9+GMBcXg6SYXUYQ5JNGMSElJjWX4jkA9uIqKivx+v9fr/eMf/1hZWXnttde+/fbblZWVn3766euvv47ORGIRu3bt/uqrrw4dKl+wYOHu3bv/9Kc/URRVXV3d0tKSnj6YE7gkLiLGjBljNpvtdvvWrVulAH/JYrU2C4Kg1WoTE0W2fJUYTFyKRd5PP/30q6++ajQa7Xb7/2fvzOOjqs7G/9y5sy+ZSTKTfU8IIawCsglIXdhBBAVfBEXRKm3RatX+EG1fKm3V+taqrVJFrOKCsghoEAoIyA4CghCWbGSdTDL7Pnf9/XHDMMwkmcm9gYTkfD/8cefcc5557nBzn3vOeZb58+efP38eAObPn5+TkzNlypS1a9dqtS0rMLm5uUVFRd99t/3HH08xDDN69OhZs2adPn1aq9VWVVUNHz5806ZNXXopCEQLYrH46NGj/fr1+/7777taF0SXYTabAWD37t2JiTyroSN6Er3RwN95552PP/64Wq3etWtXSUkJAKxYsWLBggWRPXEc79u3b/BjRUUFAIhEopdffhkAGhoannzySYvFsm/fPlTwA9Hl5OXlTZ8+fffu3ZwzM6IXwuXo1IUWckb0YnqjgefIzs7mwt7y8vKee+65tpwUEhISAGDevJbw4vnz5//3v//9zW9+079//+Li4ubm5nHjxk2YMGHt2rVtfRFBEPv3H7i5ytSWlJTMnDnzP//5T1crgugY06ZNs1gsBw4IzzSAuClxudwAED2gBdE76EZ78MLxeGxud3gAA/dKazZXS6XX+DssWnT/hAkjvvlm+5NPPupyNbRVjkupFMvl8uXLn9ux43ulUvnGG38UiUR2e+3u3ZsZhpkwYRq3wr98+YtWa8OCBfMiJfz73x/98Y9/mTBh7Lp1G8eOHX3bbaPU6ig1bOrrG9544536+oa8vNwVK5bJZDKWZViWNZnKY/4xwvF4bDRNtSOBZVnuLcdstixe/IjJ1PzNN98MHdo3Odnw/ff73nnn/b/97ZWTJ3+aO3d2cIjdrgRocZc1m6uVyiixMX6/m6La0yEqLMsAQOwStNoUubwXJeUeO3ZsSkrK22+/jWqN9E6am5tFIlFSEv+8woieRI8y8FKpQqUKD42gKBEAKJVameyaiIX8/IT8/KI774xSL/lXv/r1hAm36/WJ+/d/39TUrNHoQ8/2798fx8Visfjnn89u27briSeWhA0nSfK//90LAHv3Hti798AHH3w8btxt27ZtBQCv12u12pKSDJ988umDD87nCtpybNr04RdfbACAH3441NhoXr/+c4LwBgLeyKuLEYqiCMIkEomOHz87cuSI0O/i+POfX92y5Zv//d+X8/Jyx4y5nSTJvn0LL168VFfXvHPn/uee+z0A3H33LIIghg8fNXBgSz5UmezqO5NSqVWpoqxSiMVmlsV4XwUA+HwOAFAoYg3jCQ106Q3gOP7iiy8+88wz1dXV2dmdFnuCuFkoL6/Izs5GaewQHD3KwEskcokk3HQRBA0ASqWOX1Kz4uKE3Nxcu91YXDx4wIDw6IjPP1+HYdhHH320ZMmSQ4eObNny3YMPPhja4csvvzx48JBOp+Ny3yYkJNTU1Mnlce+888758+dLSkqWLVv2zDPPnzr188iRIx966KGqqqrk5ORVqz4AgIKCgj59+nz33Xfr1m26fLl8yJD+9923EADKysqSkpKCnoCtUl5evnr16pUrV3IpqV944YWPP/7P73639Pe//8O//vWvJUuWnDx5ctCgQWKx+M0335w1a9b69ZsqKioefnhxIBDgiqa89trrs2bNmjbtnmBZKi4P/Nixv5g8efLvf//78ePHX7hwCqAloFGp1EWtXyUWSxmGDS0Z0lEIwothmBAJPZ6HHnropZde+r//+7+33367q3VBtMn582cLC4twPPwJvHr1u/X1LQlqRowYPWXKzA6JdTqdKSkpnaMi4uanRxn4Gw+Xx2DOnDmbNm3auXPnwoULKysrH3jggT59+jidzjfeeGPlypV6vX7Xrh1Lliw5efL0woUL33nnnV/84hfBXdJXX30VAD755JNPPvlk48aNe/bs6devn8ViWb58+dKlSyUSiV6vf/zxxwFAKpW+/PJflixZ8uyzzz788MMffvghJ8FkMtE0/eGHH5aWljY1NX3zzTcnTpw4cODAa6+9duzYsV27dplMpi+//LKpqfmTT74AgF//+tfvvvvuuXPn3nnnnRkzZvzud7/bunVrRUXFhAkT9u7dK5PJJkyYUFpaOnnyZLlc7vf73W73iBEjHnvssQED+hw4cOCFF17etm1bSkrKuHHj/vKXvwCsb+vHOXXq1JYtW06cOIHjeHJyssFg8Hqddrs9NfXbuLg4p9Mpk8k4FwetVpudnZ2enp6WlhZW9w/RUbRa7dNPP/3aa6/96le/Kioq6mp1EK1gsZi3bNn4zDO/jzTwNpv1hRde5mrB8YiB9vl86C8IEQQZ+E7AYDDMnj17586dLMv+4Q9/+OKLL/71r39VV1e/8sor8fHxL730kl6f+JvfPB4XlyIWi996661QH6j6+vqJEydyY7kAp9LSUo1G89RTT3EbaYWFhRcvXkxJSbZYrBcuXHj66acBYO3atWKxeNmyZU6nc9asWfX19RRFcX58w4YNu3DhgsFgAIA9e/YMHDgwISGhpqYGAC5dKuO+9Ny5cwCwceNGzkFh3759APCvf/3rzJkzd9xxh16v52r/lJaWVlVVPfXUU+++++6wYcMcDlNBQfY///lBTU3Nzp07//d//5cbHonH43niiSc+++yzuLi44cOH0zRdVlZmNBppmgZggw81giAiJahUqry8vMTERK1Wq9Vqg6mgpFKpXC5vbjbqdFq5XAMAGo1GLBbHxcXhOD5jxozrl5OgpuZySclmr9c7YMCgiROnCkw9cQN44YUX1q5de9ttt33xxRfJySitb/di/frPysoukmQrDisEQWAYxruAAsMwx44dv+22scIURPQckIHvHGbNmuVyuWiaXr58+fnz5994442UlBSpVLpu3bqJEyfW11enpaXefvtUDMMeeOCBdevWFRcXT548uaqq6uuvv3722We//vrrxYsXb9q0afLkyVu3bs3IyAi6yQwZMiQuLm7Dhs9cLuv773/+9ttvq9Vqt9v9/vvvr169Oi0tra6uJV/xr371q4MHD54+fRoAmpubFy9efPz48TNnzgDAhAkTysvL6urq77vvvqKiopUrVw4fPnzv3r0//PBD8FuKi4uLi4u5j9yyfG5ubm5ubmgJEwzD/v73v//lL385efLkn/70J4Vitu9KzQuapgFwi8WydevWlStXGo3GVatWPfLII6F1qUtLTxJEYMiQ8FyqNputpqamtra2qanJaDTW1tZaLBabzVZXV+d0OmmaVqvVPp+PpmmGaUky5fV6A4EASZJutxsA0tLSpk+f3qn/pS0wDL1x47pZs+7PyMhcu3bN2bNngnmUuy1qtXrz5s1LliyZPXv2q6/+acKE8V2tEeIq99//IAD8+c8vR56y2awsy65a9ZbT6czKyp4+/V61ugP+8CdPnrbZ7CgCHhGkGxn49udJR44cPHx4P0VRubn5M2fOCTUb3YGUlJTnn38eAAYMGPD888/v3r2boqixY8dyue5DmTBhwrp165555pnHHnvMbrc/+OCDd911F47jH3/88YoVKxITExcsWDBy5Mhg//fee4+iKIVCJJXCW2+91dDQsGTJEoZhKioqli5dWldXN3HixKNHj44cOfLtt9+22Wzp6ekzZszIz89/+eWXP//886eeeioQCPTv3/+xxx7+wx9WPPXUU0VFRQqFIjc3d/78+QzD3HPPPdu3b7/rrrtivNI5c+aMGTNm6dKlt99+O01PfOaZlvY777xzwoTs9evX+3y+SZMmff3114MGxVo1Lj4+Pj4+fvDgwVF7Wq11GIaFllVgGMbhcKij7v/zpbKyQqvV5ebmA8DIkWNOnTrR/Q08AAwePHjnzp2zZ89+6qnnpk6d9Mc//mnIkCGhvlculwvH8cj6y4guhCSJrKycqVNnqlSqzZs3bNu2de7cqz4933yzyWhsAACFQllcXBg5fPfuvQAwcGBf3hWZSdKP440Cat6zAHD58qX6+ss8x7MsRQXq6ky8S3dyC5mXLv0cuf0Rsw4MRRH19U2hsdMUNRJAAgANDTUnTkQp201RJAD8/PNxAVdB0zTZ0NAcY3+tNqGgoDiyvbsY+PbnSSaT8fDh/YsXL5FKpV9++enhw/tvv/3OLtS2HaZPny6RSCZPnozj+MqVKyM7TJw48X/+538eeOABANDpdHPmzOHapVJpYWEhAISluOdSTgbD/9avb9nzvuuuuzZv3rx9+/a3335bo9EYDAYcx/V6/enTp/Pz87lH+S9/+cv/+Z//+ec//zllypS4OPlnn304atQ4AHjxxRdpmvb7/Y8++ui0adMmTZrUoQolqampGzZsAIBQTQcPHnz8+M5f/vKXTz/9dG5ubuzSBCISia5rVk6Hw6bXG7hjgyHJ4bhaaMvjcTudDu5YqVRG/jGTJMEwtMvl4P3tXq+XZRneEr788os///lPH320dsSIEUqlMjk52W630zSdmJhYXV0tlUoTExOkUllOTjbLskZjo0ajSU1NyczM1Ol0UqnU5/Pp9YliMRsIkB5PgGVZiUSi0WgYhuFusGBCFYVCLhaLuSDsUGQymUKhuHy5TKVSl5aWaTRqzuuzozgcJplMyW3NcDQ2FgKkAoDb7d6//0RUCZculdvt3qqqKI/mtmBZxuEwKZVaqfSaVyIMw9p6MeVR8zAjI2vevJaMWyNHjlm79sNrBcZxha9kMrlc3sqbmdlsAQCtVtfq2digxWIZ79gTlmVdLqdEIgur990hCYEAI5HIeZtnmqY8HpdUKmu1fGosMAyNYaxUqgj9iw4ae7FYEvXnDQT8Pp9XJlNEzVrfFjRNkiTI5S01aqMilbZ+sd3FwLc/T7LZrIMG3RIXpwWAvn37mUyNbQrqBkycOFGn091xxx1jx7ayGZabm/v55593yhe9/vrrjz/+eGiuPQAI86vSaDTLli0DgMrKC6HtOI4//PDDVqt1zpw5nKebQN56660baNZvHJw7Ancslcp8vqtFpn/++fSOHd9yx+PGjW3rZZ33dCqEMt4jp027e9KkX5w5c+7ixTKz2apQyCQSidlsmTFjUiAQsFptgUCgoaGRYZj8/Gy/319dXXXs2DGvt+UygzZbqVQAYDRNccXKugHvAzwOAGVlZePHT+gqJcRi8c6dm1s9xW3JdUga5z+fnp4JADiOi8XXRLtNmBBlme2220YdPnxs2LBR/fvf0qHvDWI0XoyLS1KpeL4x0zS1f39jenp2SkoGbwlNTRXx8em801d4PC6rtTknp1Cn47lVQRA+i6VGr88JfUUIWuqkpNT+/aO4+5jNprNnfywsHMD7Rcfnc9rtxpSUPj2k2Ew78yQAKCrqX1TU3+v1NDYaf/75dOj03eNxb9r0JXeclZWl08WFSeacWc6fP43jvFdLKJIkTCZb7H+uX375SVyc5vTpo9zHQMAPAKdPH+VdD56mSZomTSZrWHt+fnrwW9rH5/MSBBHW+a67xtbWltXGVjaaogiWpevrTcGWykodQMvrxfnzPzmdgfYleDxOlmVjVLhVSDIAABJJrGWSs7P76HSCXl8UCoXdbuOOCSIQ+hc7cODg7Owc7rjVGXx1dbnX6+rXj+fTFgCczmaWZbTaZN4SLl06i+Oixx+fwFtCTc0llUqTmBj+UOOcTjweL8uy3JokAEilMqVSAQAkSbIsy70oXLp0FsNwg4G/F2TkDP5vfyv85hsAgD59+nz44d6oEi5cOJ2UlJaQYOCnAI8ZfIf+2Lm62A6HfceOkkceeUKr1R47drioqJVF13aYNWv6zJmzbrmF//2G6GF0FwPfzjwpSF1dza5d21mWNRi6e56m5OTuriEiRnS6hLNnz3DHFotFp7s6uVGp1CpVe/MMiUQqEuE8lmqDUJSHYWghEsRiMY4L0iE5OUkslkVKiF2m12tLTExqdY8wRkymcqVSF5pmKpgbWq1WjxsXPW0fw3jy8/tlZPBcZWJZprGxTKdLiT3JUofg6mIXFw+0220ff/wBTdN5eQWTJnXYbzQzk+fUGdEj6S4Gvp15UpDCwn6Fhf0OHz5QUrJlwYJHuEaVSr1w4eJ2JFutzWfOHOvXbzDv4BNutSQ5uYC370l9fXVZ2dnBg0fynsG73Ra325qS0offcACorLzQ1NQwePDI6F3bwOEwkaRPr88JtjQ0XD3br9+QqEv0nBe9EB0ineyuN3l5+Vu3bmxsbEhOTjlx4uhN4WGH6P4sX/5K6MdgXewxY8aPGYOiHhCdQ3cx8O3MkwDg0KH9SqVyyJBhAJCZmXX8+OEuUBHRKxGJ8Pvvn7958waSJPv2LRo8GK1/IhCIm4PuYuDbmidxW1NarfbgwX1ZWTlKper48SNZWTmxS5ZKZQZDKm9vRgDAcYlcruE9+QYAhUJpMKQKkSAWywQWTVGpNAkJgjYOJBJZmMdHaircf39QfnQJcXHxwc1afkilsbqVdiKZmdlPPvkUj4EajZaf03gQqVTBMIJqv+p0iUJuPACQyZQ4Lii3eUKCQa0Od47pEHK5OswpetgwsNsBAGJ07TQYUhWKGO7RNsHkco3A3+F6o9cnt79nFBWFQiOkfAOGYQZDKm/PMk6CXK7h7UIPADguNhhSebvQA4BIhMvlmjCXmpkzgStIVhzDRpNMJu8UoyP8WYdxice7A7W11SUlW7h50sSJU7lre+WV5YsW/TIzM/uHH74/depHgiByc/OnTbuH93o7AoFAIBC9gW5k4BEIBAKBQHQW3T2rNgKBQCAQCB4gA49AIBAIRA8EGXgEAoFAIHogyMAjEAgEAtEDQQYegUAgEIgeSHeJg+8sVqxY1tUqILoRf/zjX2/MF61e/S5XKQSB4Liu9x560CFC6du33wMPPBTZjmbwCAQCgUD0QJCBRyAQCASiB4IMPAKBQCAQPRBk4BEIBAKB6IEgA49AIBAIRA+kp3nR34wsWbIkKSnpzJkzX3/99Q34uuXLl4vF4r179+7bt6+dblKp7NZbhzc3N1+6dOkGaIXoErrbvTdo0KARI0YYDHqfz9/Q0PD993vM5uYboBii+3CD78meDTLwiNYZN27s2LFjz5w5gww84sYwbty4O+64gzuWSmVarbawsPCDDz4wmUxdqxgCcZOCDDziGjIyMvR6ff/+xQUFfbpaF0QvQiqVjR8/DgAuXLjw7bffxsXFPfjggyqVauLEiWvXru1q7RA3jg0bNorFuM/n62pFegLIwHdH4uPj77jjjrS0NLVa1dxsvnjx4sGDhxiGDnYwGAyTJk1MT880GhtKSkqefPJJsVi8efPm06dPd+iLRCJ80aKHMzMzHQ7H6tWr3W73tGnTU1KSO/uCEDcNXXXvabVasVgCALt3f+/xeDwez/nzpcOH35qeng6AAaCq1j0HHBePHDli8ODB8fG6QIAwmRoPHDh4+fJl7ux9980JW6Jv/5ZbtmyZVCo9ePCgRqPp06cPTdPl5eUlJdvy8/PHjRtrMBhsNtv33++5dOkiJ00qld1225j+/Yu1Wi1BkM3NzYcOHeqp65TIwHc7MjIyFi5cKJVKuY/p6enp6emFhYVr1nzEsgwA6HS6xx9/TCKRAkBubu6iRYtwnKez5JQpkzMzMwki8PnnX7jdbgDYv3+fQqECgNtvH6/RaDrnkhA3CV147+l0Ou4ha7fbuQ42mx0AJBIJhgGL7HsPYtq0qbfccgsAMAyjVkvV6oK8vPzPPvusoqIisnOMt9zo0aNEIpw7HjJkSFpaqsGQhGEYACQnJ8+de/+7775rtVoBYPbsWX37FnE9xWJJdnZ2VlbWxx9/XF1dfX0utytBBr57gWHYtGnTpVKp2+3esGGDyWQaPnz4nXfemZGRceutw48dOwYAU6ZMlkikXq93/fr1gUBgKe7LTAAAIABJREFU9uzZarWax3fdcsstw4cPZ1lm/foNTU0t25ylpee5gxEjbkUGvlfRtfdeXV3dF198EapOUVERABiNRhaZ9x6EWCwZMmQwABw6dOj777/XaDQPPjhfrzeMGjWyVQMf4y1H08wXX3zp83lnzbpHrzckJSWfPXt23759xcXFv/jFL3Acz83Ns1qtcXFxnHXfu3fvwYMH4+PjH330UblcXlRU1CMNPAqT614kJuq5FfLdu3dXV1f7/f4DBw7U1NQAQP/+/QFALBb36VMIAAcOHLh8+bLRaCwpKeHxRenp6dOmTQWAnTt3lpeXd+Y1IG5Ous+9h2Gie+6ZmZmZyTDMrl27hFwUorshl8swTAQAOTk5xcXFBEF8+ulnq1ev3rXr+8jOsd9y5eXl5eVl9fX1Z8+e41p++OEHs9l84MABboNJqVQAAEmSn3762aeffnbo0CGGYRUKhVgsBgClUnl9LreLQTP47oVen8AdcA/W4HFWVlZiYiIAJCbquXWnurq6K2drWZblGmOnT58WHzqdTidcbUQPoJvce2q1+r777svOzqYo8uuvNwe3ZhE9A7fbXV5eVlDQJy0tbfbs2SzLNjQ0/PTT6RMnfozsHPst19zcEk5JUVRoC8MwDMOKrsxkfT5fc3PTmDGjJ026OzFRLxL18CluD7+8m5fQVUluiRLHcQCQSMShjdwBvzVMl8sFAMOHD09ISBSmLKJH0YX3XmZm5hNPPJGdne1wONas+ai0tJTPBSC6N198sW79+q9KS88TBIFhGLeiM3fu3MienXjLcSiVyieeeGLkyFFKper48eMbN25sbGzkLa37gwx898JstnIHWVmZwcasrCy48kJqs9m4xtTUNO4gLS2Vx3toRUXFBx98QFGkSITffffdAtVG9AC6/N7Lysp6+OGH1Wp1dXX1v//9b6PRyPdSEN0XuVyu1eoaGozr13/1+ut/++yzz7nZeWFhoUwmC+vcWbdckP79+yuVSgD23//+9/bt28+ePatQKHhL6/4gA9+9sFjMXFoPzrmJi+jIzs4GAG424/F4uAffuHFj09PTk5KSp0+fzuOLamtrXS7XsWPHAaCoqC/3FYjeTNfeexgmmj17No7jgYB/z549cXHa5OQU7l8nXiOiyykoKHjqqaVPP/10fn4+TVM1NdVNTU0AQNM0SZJhnTvrlgtyZUkAy8hIl0gko0eP1mq1QgR2c9AefPeCZdmSkpKFCxdqNJrFixcH2+vq6jg3ZgDYtWv3gw/O12g0jz32GAAEAn4e+6AcBw4cGD58mFQqmzRp0vvvf4CijXszXXvv5eRkc49amUy+aNGi0J4rVqzgfVGI7kZ5eZnVaklISFywYAFJkhKJGAADgKNHjzIME9m/E285AKioqLrrLgbDRHPnzgMAhmH8/oBcLuupEUNoBt/tqK2tfe+9VWfPnrXZrARBNDQ07Nmz56OP/hO8+ysrKz799NOamppAIFBXV7dmzX9C85B0CJ/Pd+TIUQBITU0dNGhgp10D4uakC+89vd7QaZeB6Mb4/YH//OfjI0eOWCwWlmUDgUBjY2NJybbvv9/Tav9OvOUAwGRq3Lhxk8ViJojA5cuXP/roo/LyMgDIzc1NTu6BCb6wHhZjumLFsq5W4fqCYaKkpCQAcLtdHo8HAJRK5fPPPw8An3zySVVVVRfr18344x//emO+aPXqd+vra2/Md3UV6N7rENf13uvxDzoOdMvFSN++/R544KHIdrREf5PBsuyiRQ/L5XKz2bxu3Zd+v2/KlCkA4Pf7GxqQUxLiOoLuPcQNBt1yAkEG/qaD3bx58733ztLr9b/5za+5Jr8/sGnTpkDAf8stt8ycObOdwf/8578sFvMN0RPR80D3HuIG094t17Wa3RQgA3/zcfHixbfeeru4uDg+Pp5lWavVev58qd8f4E6tXr26nbEOh/1GqYnogaB7D3GDaeeWQ0QFGfibEp/Pd+LEich2r9fr9XpvvD6I3gO69xA3mLZuOURUepqTXSQul72y8mJR0WCZTM5PAkF43W5LfHw6l0KZBxaLqa7u8uDBI/kNBwCfz+n3u+Lj03lLMBpr7HZLv3638Jbg8dgoitBqr/qaHj8OL77Ycrx2LaREi1iuri4nSaKgoJi3Di5XMwCm0eh5S7iR1NVVeb3uwkL+4Qlut4Vh6Li4JN4SKisviESinJxC3hIcDhOOS9TqBN4SLlw4HRenS0vjn2vBZmuQy9UKRVyw5c03Yds2AICCAnjvvegSzpw5lpqaZTDwjKpnWdZmq1OpEmQyFT8JN4Bz507o9SnJyfyfElZrnVKpk8v5FBACAIahf/75x6ys/Ph4nn+hDEPb7Q1qtV4q5Zl/xu/3Xrz4c35+P7U6Lnrv1qCogNPZpNWm4Lgk2HjffeBwAAA8/DAsWBBFgtNpq6q61K/fLcHCjB0lEPB4PFYhRoej58/gSZK02cytRljGCE1TgYCXZVm+sZfg9/ttNrOQ8E2aJgMBQdMjn8/rcNiESKAogiR9oS1mMwRLgfh8rQwJw+NxEoSgtTWSDPD+DW88Ho/b5RK0Lk2SASFBQQDgcjm4RLMCdPALnAY4HDau1jtvCMIrFl/zrDx/vuXes1hikmCzmRMShETisYGAN/QNoxtit1tVKkEaBgIeIW8wLMvabGYhbxgsywYCXqWS/z1P07TNZqao8Jw5scMwTCDgZRgm9O9m3z4wmwEAxo2LLoEgCJvNLOQvl2FogQ98jp5v4BEIBOJm4cyZU/v27aYoql+/AZMmTRU4gUP0ctDdg0AgEN0Ck8n43XffLFjwyNKlv2tqavzxx6NdrRHi5qZHzeApiqCo8BVggvABQCDgwTCeCyYk6QeAQMDN+22ak+D3u3gvL1NUAID1+138hgMARREsywiRQNNkmASCEAO0bJUFAh6/P8o+CE1TDEML0YFhKAzDYpcgkShwPPpNfuTIwcOH91MUlZubP3PmnLCds9AkNiNGjJ4ypb1gsDCMxsa6urqMjIzYhyB6LWVlF/v06RsfnwgAt9466ujRQ7feOrqrlULcxPQoA+/3u12u5rDG2tr6//u/fy5d+sTAgf2ECLfb+VcV9HodAGCzNQjcP7bZGniP9fvdDEMLkRCpg9utAmgxXQ6HyWaLsu9Fkj6K6gQdCCJWCfHxaTgeJcu0yWQ8fHj/4sVLpFLpl19+evjw/ttvvzO0g81mfeGFlzmr39GXvL/+9Q29Pnnr1q0dGoXondA0HXR3wDCR3X6N08ylS+e5MrtisdhgaMWLbe3aL9LSMric7fxwOJx+PyuT8XwF51yd7HYLb58nhmFcLidBiCSS8MpyMUIQfgAwm01er4efBIoiPR4nRdWHzg0YJoNb8Ha5HA0NjvYluN1OADCZGiQSnq4nJOn3ep0MUxujyZDLFa26mPQoA69SxSuV4aWBTCb3t99uf/jhR5OTC/iJ9ftdDofJYMgViXg6K9F0TVNTc3JyAW8D7/FYPR57UlIev+EA4PUyXq+f948AAE5nM0X5EhKygi3x8VcvR6/PjprL2Wp1i0QBITpwL0k6XWqM/WOxxzabddCgW+LitADQt28/k+maNzmuZLVCoeShrcPhKC29kJIS5XGAQHAUFBQeOXLQZGpUq9VHjhzw+6/xXD10aH91dRUAqNWaYcMGRw7fv/9wWlrK+PH8o3UAAKBJ2HBobKxrbKzrWh3q6oRmsTWZrtGBplMApABgsZguXSqLRUJV1YXO1aEd9Prknm/gMQzDsHAbzE28aJrmbZ45IyES4cIl8DbwQQn8hgMA99WCJWChEkKvRiTCo5ZpjpTAQwcMEyQhkqKi/kVF/b1eT2Oj8eefT0dO31mWXbXqLafTmZWVPX36vWp1rIWnvv9+r8PhdLncHo9Hpeq+4VWIbkJ6euakSdM2bVqHYVhx8cCw1ECLFv2y/eEi0QsqlWbChGm8FTAaL8bFJalU8fyG0zS1f/+OoqLBKSk896RommpqqoiPT+cdqufxuI4f/2HIkFE6XSI/CQThs1hq9Pqc0FWE4FQ8J6dwwoQoQadms+ns2R9HjbpDLucZ7OfzOe12Y0pKHxQmFwUuPiey0jACEUpdXc2uXdtZljUYrgk6J0kiKytn6tSZKpVq8+YN27ZtnTv3Qe7UkSMHd+z4ljseN25s5PtNY2M9ADAM8+GH/xw0aIAA7c4IGAsAsHdviUAJAqmrqxI+qQrFaBwIkAUAbrdz7979sQwpLy8tLy8V9rXlsXe9/fapHX2hpyiqX7/+Q4YMA4CLF8/Hx3cs98DNE0CKuEH0fAMvkYgBgCSprlYE0Y04efL44cP7AWDy5Bn5+X0AoLCwX2Fhv8OHD5SUbFmw4JFgz4yMrHnzWhJbjBw5Zu3aD4OnsrKy77prMnecmpoqFoevK2i1B7kDuVyTl1fEQ0+fz8mybOTGU+wYjbUiEZaczN/Lz+OxiURihYJ/weza2kqlUp2YyD9dj9ttkUjkoSHaGo2OO5BKZbH8tlVVFxISkrRanul6WJZ1u81yuUYi4ZkvKxa8Xs+HH763ePESuVx+8OC+ESM67GHX4xOXITpEbzDwaAaPCGfo0FuHDr2VOz50aL9SqeSmTZmZWcePHw7tyfnPp6dnAgCO46EJW9LSMtLS2jOcWm2LEbJYHACSrKysdjq3is3WwDB0YmJmRweGSDDjOJ6Vlc9bgtlcLRbLdDqeOeAAwGis1Wi0QnQwmVilUheawVB9ZQVXKpXFIrmq6mJ8vD4jI5efAizLNDbSOl2KQsH/ZSsqcXHa2267/f33/ymTyYcPHzFgQCsb7e3S8zOTIjpEzzfwnCekkMRGiJ6NVqs9eHBfVlaOUqk6fvxIVlYO115fX2swJDkc9h07Sh555AmtVnvs2OGiog7k2Y2PjxeJRCzLLl++fM+ePStXrmxoaBgwYEBBAX83Q0TPZsSI0Twm7hwYhgw84hquu4G/cKF0z56dLpczNTV95szZwTkNh91u27p1Y2OjMTU17f7758vlioMH9+3atT20z3PPLVep1Lxjka8s0SMDj2id/v0HWSzmtWs/JAgiNzd/2rR7uPY1a1YtWvTL4uKBdrvt448/oGk6L69g0qTpsUseM2ZUScnG++9f6Ha77Xb7008/feTIkXnz5q1bt+76XAqiV4NhgOw7IpTra+DtdtvmzV8tXLjYYEjesePbkpLN8+cvCp5lWfbTTz+aOHFqfn6fHTu+PXz4wC9+cfeYMeNGjbqN61BVVXn8+GGVSg0CYpFFIpFIJKIotAePaJPx4+8YP/6OsMaXX/4zdzBmzPgxY8bzk6zXJ6rVarfb7fF4RCIRAHg8PMNzEYj2QTN4RBjX18DX1lbn5uZz+5ejR49bs2ZV6NmqqnK1Wl1YWAQAkyZN8/l8AIBhIhwXAQDD0Hv27OQ8loXEIgOASCSiaUEVOxAI3mg0msbGRrfbzbmDBAKolDVCEAzDRGb0AgCWBYahHA6TEOF+v4uiCL6K0QDg8zl568C9oHi99kCA53swlzzA47FhGM9JHcNQAOB2W0LDcVnWwCW68fvdDkcU3bxeOwC4XOZAgGc1OZomAMDpbAKIKTRCIpEplbrI9utr4IuKivv06csdG431qanXVBmyWi1KpXLDhi+MxoaMjMzJk69Z/Dxx4lhubh63pC8kFhlaDHxLZqVAIPCnP/3p+eef1+la+TkQiE5n4MCBZWVlZrNZq9UCgN/v72qNEDc7LJf9OgwMA4ZhWj0VOzRN8l4G4BLYURTJWwfumymK5F2KjctWTlGEAB24qyBCoxyDPwnDUFElc29IJBnAMN4Z/WhOQoz92wrIvL4GXiKRcvkBzp49vXPnd8EAYg6fz3f+fOn8+Q/NmHHvtm1bt23bOmfOA9wpmqaPHDn06KNPcB/biUV2u12ff/4xd5yXlxcfH14tkaYpkUjU0FB94sQBAKisrPrLX/6SkpIwZkys+Z4YhqZpsqEh1pepSAiCAIATJw7wTnTDMBRN0/X1/N/NAwE/RZHcj8AP7i+/uvpqjqry8niA/tzx2bM/Wq1R7nu/38uyjBAduL86HI81ojovr4h3aerO4u9//3ttbe3x48c5045m8AiBiES4Xp8d2Y5hIhwXt3oqRozGiypVgpBENwClGk2iXi8o0U1cnEFIohuAcq02WWCiG50uNTTRTTDLhVKp0+ujTg5NADUJCekCE90kJmZ2u0Q3YRHGPp93y5YNDodjwYJHkpKuibSRy+WZmVl9+hQBwMiRYz755GqEcWnpzwaDgdt9h3ZjkXEcT0trWRjQauM1mvDbIhDwi0SYWCzRaLQAIJcrAYBhWO5jLFAUQRBehUIrINGsOxDwaTT8JZCknyQDQuKhWZZlGDr2q46EIHwMQ8nlV9dOFIqrcckqlUajiZI+mnsxF6IDt3AXe8lqgWXIO4Xs7OwVK1ZMnTqVYRgMw5CBR1wnMAzFwSOuofMNfGiEMU1Ta9d+mJOTP2/ewkjbptVefU/kXOGCH8+cOVVYeLU2TDuxyAqFcvr0e9vRx2ptxnFco4kvLBwIAD4fAwAqlY77GAvcy1RycgHvDKn19dUOh7WwcCBvA+92W9xua0pKH37DAaCy8kJTU0PsVx2Jw2EiSZ9enxNsqai4ejY3t29utBjj0tKTBBEQooPVWodhWHx8evSu3YlBgwZxBwMHDkQBHYjrBHKyQ4RxfevBnz9/TiqVTZwYnrKxvr6WIAL5+QVmc3NdXQ3LsseOHea87QCAJMnKyoqCgqvGzOGwf/XVZ3a7jWWZjsYiAwCGYcHqRpy3HfK5Q9xIUlNTuffX/v37IwOPaIdz58689dbrr7/+ysaN60iyo85uKFct4hqu7x58Q0N9dXXVihXLuI9Kper551+CKxHGmZnZ8+Yt+OabTR6PJzs7d/r0WVy32tpqnU7HFUXmEBKLDAAiER502eDecHlXM0QgeCASie6++26CIJKTkzmHDAQiEo/HvXnz+gULHk1KSl6//vNDh8IrF0cFzeARoVxfAz9x4tSJE6dGtgcjjLOzc5cs+W3Y2by8gqVLnwtrFBKLjOOioEXnDpCBR9xgtm/fDgDLli1DM3hEW1itFrVak52dCwB9+xbX1Fzu0HAMw5B9R4RyfZfouwkYhgXX5DnTjpboEV2CVCr1er12uz16V0TvIykpmSCICxfO2e22c+fO5OTkdWg4crJDhNHzc9HDtYlu0Awe0YVIJBK73f7aa6/99a9/7WpdEN0OmUw+Zsz4L7/8VCKRxMXpuAJIQTZuXMe5GyuVyoEDW/dD8vk8R4/u4a0ATZMiUZ2A0CwWACoqzldXl/GWQNNUdTV/Hbgo9vPnT/H2iWZZlmGo6ur6UNcxihoLIAGAurqqo0cvty+BMzenTh0WiXh6RbAswzB0TU1DjP11On3fvq04L/cKA4/jVxPdIAOP6EK4ZHYWi6WrFUF0R6qqKn788cjTT7+gVmv27Pnvxo1fPPDAQ8GzqanpMpkMAKRSaavZHTAMw3FcSOIHr9culSrE4ijxrm3BsozPV6dSaZTKWANZW5PglMlUOM4zwJWiSL/fqFZrZTKehX0ZhvL73XK5JvQVIWjsFQpl1F/Y7/dZrc1abbxYzNPCcrHZSqU2RsdJlSo8AQxHrzDwaAaP6CZwD2iXy9XViiC6I1VVFQUFfXW6eAAYOnTE+++/E3p2zJhx7Q/HMEwslgoJQzUaL8bFJQlJdGM01qWkZKSkCEp0Ex+fLiTRTVOTMTMzT2CiG70+JzTRDX7F1icmJhcWJrcvwWw2Wa3NeXlFAhPdpKT06XaJbrohYrG4rq4l/xryokdcDwIBL8uGO3bQNMmyjN9/1Zxzj4mmJlNoYzvQNBUmoaMwDIVhrDAJNE2TQiSwLENRhDAJbJgEmpZzS6YMQ/v93liEUFSAtw7cc4Mk/bE/cENTQsVIRkZmScmWkSNHx8Xpjh8/kpGR1UEBGLdIjkBw9CgDzzAUTYcXGKAoQiwWb9261e/34DjOZfclyQ5kKqZpEgBIMhCaiqdDXJHg553ohrsuIVmmGYZi2dZTWMcsgQ6TQFEiAOmV4wBJRnm4MAzNsoJyZbMsw7JY7BJwXMJ7K65DuFzNkVoFAl6KIm22qxtpNO0DgL1796WnZ+/d+61eH9MkI1RCRyHJAE2LhEgAAJomCSImI9rGcCoQ8AjUwe93hZrnQCAFQMsJj00y6/U6bDZBkeIej93jidVBMjW1sKOB6YWF/SwW86effkSSZEZG1j333Neh4cjJDhFGjzLwXq8zssiSx+OVSMQsyzY1XZZIxFZrAwC43VazubpDwq3WWt6Kud0OADCbq3kbeI6O6hyK1+tkGEqIhEgdnE4VQMtanNVar1ZHCQAjCC9F0Z2rQ/vEx6fxmEjxIDExM/LZ6nAEaJpJTi4IthgMmQDAMIzVamMYVeipVrHbG1mWFpK5z2Sy4Tge9YvawWqtxXGZVpvEW0JNjVGp1AnRobm5SqHQqtUJwRaFouVtWyKRxiL50qUKjUafnJzDTwGWZZqaKrXaJLm89c3O1uDzxz569LjRo6Msxbf5fSiTHeJaepSBVyrjZLLwkrI4buGqyGu1qb/+9dJhw4YCgFweF3tJhkDA43KZExIyec/gSbKuqalZr8/mbeC9XofP50xMzOQ3HAA8HtLj8QkpROF2WykqoNOlBlvi4q7+IAkJ6Xp9lIeLxeLEMEKIDlz9xLg4Q4z9ebvqdBQME0X+32IYBoCFLiGE7smRJBl1dQHDMJbFhCxCYBiGYYIkcDIELoR0uoSQXztWyRgmEuBZjQmUcANAcfCIMHqUgReJxCJR+BWJxVLOddlmc3722efV1TUAgGEiiSRWH0uu9p9EIuP9t82ZGYlEztvA47iHk8BvOACIRGIMw4RJwMMkhLqIisUySTRjKhLhHfrlI8EwkcCr6FqSkq7Og1HVGQQCcV3pUQa+LbhYBbfbDQBcHjHkZIfoEgYOHHjbbbcdPHgQkIFHCKCtculc/LQQyUIkcAO7WkJLnFRnS2iZ3bEswzBR1km4WHyGoXnrEJSAYTGtyXDrdJHtvcLA47gIADweD1xJQYB2qhBdgk6ne//99/v37w/IwCP4wsWSRbZjGFAUYTKVCxHucpldLjO/sZxpdDiaAHxCdLDbjbzHBgIEANhs9YGAVYgOVmtN6EeGKeBsvNttNZmi5LFwuz0AYDZXSySCLGxTU2WMPeVydaueOr3CwHOvNi+//DKganKIroYLhQeAYOgmAtEhRCKRVttKKDb3oGv1VIw4HCa5XBPpyRQjDEMDVCqVcbxdMhmGcbmalUpdaAx6h/D5vAA1KlV8XJyWnwSKIj0eq1qdiONX7WNwfiyXq7XaKHaTYawARo1GH/xj7ygk6fd6HVptUoyumm05G/UKA8/lC7xw4QIAUBQFaAaP6DqCf/MoIz2CHxgmUip1rbVjLAutnooRh8MklSp4S+CieaVSpRAJLlezTKbineiGZXEAkMvVvHUgCJ/HY5XLNaEvGUHvKYlErlRG8QHyegMAoFDECUl04/U6FAqtwEQ3vaTYjAgA/H4/XJm7oz14RFchl7c8Hbh3TQQCgbhOXPcZ/IULpXv27HS5nKmp6TNnztZqr75VHTy4b9eu7aGdn3tuuUqlrqm5XFKy2ev1DhgwaOLEqZx5brUxRrjwtlADj5boEV1FcAaPCsMjrgNobRJxletr4O122+bNXy1cuNhgSN6x49uSks3z5y8Knh0zZtyoUbdxx1VVlcePH1ap1AxDb9y4btas+zMyMteuXXP27JmBA4e02hi7GtwSPTLwiO5AcAb/9ddfv/jii1999dU//vGP48ePd61WiO5AW3OeGIdjGMZ5XyMQHNfXwNfWVufm5qenZwLA6NHj1qxZFXoWw0ScfzvD0Hv27Jw790EAqKys0Gp1ubn5ADBy5JhTp04MHDik1cbY1eCm+1yAHFqiR3QtEonk3nvv3b59++nTp3/66acPPvjg7NmzXa0UolvQ6pwn9uEo0Q0ijOtr4IuKivv06csdG431qamtZ9w8ceJYbm4et3rvcNj0+pY8ZQZDksNhb6uRw+v1lJRs4Y7T09O12vC8pAQRCC3KGwj4AaC5ufHcuZMxXgVNkyTpt1jcvNPU+HxeADh37iRvCRRFUBRhsfAv1+HxuEiSiP2qIyFJP8PQJtPV4JPq6jiAliyhZWVnvd4oa85Op41lGSE6EIQPABoaTDH2z8zMi4vj73N0ndi0aVN+fn5lZeWlS5eam5sJgigvLy8o4J/JFdEzaHXO05HhKFUt4hqur4GXSKRcdrOzZ0/v3Pldq/crTdNHjhx69NEnuI9erze4SSmVyjjT2GojB8uyfn9LzCVJEhQVng6dpulQs8rN4CmKiuzZFgxD0TRDUSRv88ylOxAigaYphqFj17k1HRiWBSESaJpm2Wt0CN3piOUnZVmGZVmBOgB04Cq67VINl12xrq7OZrMxDLNq1ao33nijq5VCdBdC5zxBGhrquH1GkUik1baeEp9haJuNZxQ7AHi9XgAHQQhKU+PxuHjrwDC01+sViWw+H8+SVJw5cLkcvN91KCrg9XrtdqtYfDX2jGUTOJ90n89rs0UpvOR2OwHA4bD6fDzD5AIBr9frtdksMZoMiUSqVrdyS3S+gT958vjhw/sBYPLkGfn5fXw+75YtGxwOx4IFjyQlpUT2Ly392WAwBFeiFAqF3W7jjgkiwIUZtNrIoVKpFy5c3I4+VmtzaA55Lt1sfLx+8OCRMV4RV5o3ObmAd6ra+vrqsrKzgweP5G3g3W6L221NSenDbzgAVFZeaGpqiP2qI3E4TCTp0+tzgi0NIUW8+vUbkpsbRUJp6UmCCAjRwWqtwzBMSPGVdiAI4qefTowYMTqsXYiDZ1sMHTr04sWLJ0+etNlsAOBwOITLRPQMwuY8Qf77323V1VUAoFZrhg0bHDmQZZlAwH9KnREuAAAgAElEQVT69FFh3y+o6B8A1NZW1tbGmqHlOulQUXFeoIQwHSjqbq5ypslUd/p0WSzjz5//SaAGtbWx/g56ffKAAcMj2zvfwA8deuvQobdyxzRNrV37YU5O/rx5C9uybWfOnCos7Bf8qNMlnD17hju2WCw6XXxbjbETOYPvthM7RBeyffs31dVVYQZeoINnW3z++ec7d+786quvuI8uF//NF0QPI2zOE2TGjHu5yAsME6lUreSiEYlwqVQ2bNhY3l9tNlerVPEKRewV866BYehTpw7n5BQmJvJOdENbrXVxcQaplGeyHZ/PW1p6srBwoEbDM9ENSQYcjkadLlUslgYbg7P51NSsYcOipBKy260VFaUDB94qlfKewXtcLnNiYmaM04nQxYZr2vl9fYycP39OKpVNnDg1rL2+vtZgSJJKZSRJVlZWTJ06M3gqLy9/69aNjY0NyckpJ04c5R6mrTbGTugMHnnRI1rlwoVSo7E+sl2gg2c7iENq9aCkN4ggYXOeIImJUYooctULeRs2AHC7ZSqVWqXiKYFLdCOXK3jrQNOU1ytTqTS8E91wT3ulUsVbB4LwBQI2tTqu1UQ3Mplco4mS6Ibz9FKpNLwT3YjFGEm6NBqhiW6ur4FvaKivrq5asWIZ91GpVD3//EsAsGbNqkWLfpmZmV1bW63T6eLjE4NDRCL8/vvnb968gSTJvn2LBg++pa3G2EEzeET7uN2uvXt3TZkyY+vWjWGn2nHwFAiOX93xsVoF5c1G9Bgi5zyxg2EoRyfiGq6vgZ84cWrk9B0AXn75z9xBXl7B0qXPhZ3NzMx+8smnYmmMkVAvejSDR0TAbtmyYeLEKUplK6uC7Th4nj9/7scfj3DHxcXFYnH4u7bH46Zpqq090dBKU7W1ta12I8kAAFtXx39L0uVyYBgmZF+WJP0YJhKLq3lLCAT8zc2NHg//bQiC8OJ4Y2jCbYslFyAJAHw+z+nT0eMMWZZtaKi2WJr4qsAGAr7GRnNkQeq2GDRoBA+fm8g5T+xgGIYS3SBC6RW56ENXObi5OzLwvZxQV1Cr1ZyYaMjL69Pc3Er0XTsOngIRi3EAyMrKdLlcZrOZYZjQvSRE76TVOU/M8PThRfRUeoWBj5zBoyX6Xk6oK+jPP/908WLp6dMnGIYlSeK111b85je/C7o4tePg2a9f/379+rfzLRcv/uxy2duKGlAoVADw7rvveb3euXPnpqbmpqSEh5nYbA0MQycmZvK6SgCA06eP4jjeqodtjJjN1WKxTKdrJQQmRo4e3ZuYmFRQUMxbgslUrlTqNBp9sCXxyhRXoVDFEpexb9+2tLTsjIxokR5twLJMY2OZTpeiUPDf4b4BoBV6RCi9wsCHzuDREj0ijFmz7ucOmptN69atDc6fOFdQgQ6e7cA52Wk0Gu7gzTfffO211zpLOKIXghLdIMLoFQYezeARPAi6ggpx8GwHzq6LxWKpVAoAr7/++uOPP47y2SGiwrKMx9OKsyeGAcPQbrcgh81AwMv7LYFzK/H7Pbx14J7Mfr+boniWYuK8ZHw+p1jMO6sYCQBeryO0HjzLxnM7IAThc7t97Uvw+90A4PHYKSpKz7YgyQAAeDy2GLddxGKJXB6exRV6mIFnWTay1gLLMjk5Wdy7rUgk4m4giqJCXZyiiWXgWpeojivWIoF3opvO0IHtDAlsqASWxYIVhxmGjvrWFCmBlw4duAoME8X+mxsMyaHbn0FXUCEOnu1QVFR05syZ0GC5iooKZOARUWEYxuVqbvUUTdNtnYqRQMAdCLj5jb1inoWmdfD5+Od9CgQIAPB6HQCCqjV6vbbQjyyrBcChJULd0v5Yn88DAB6PlSAEWViXK9aEgHK5uucbeI/HFnlzezzeadMmvfXWKpIklUqF2+0BALfbbjKVd0h4c3MVb8WcTgcAmEzlvA08R0d1DsXjsdE0KURCpA42mwoggzs2m6uVyigZZAMBN0XRnatD+8THp7V633cHXn311WPHjqWlpTU2NnItPh/P931ErwLHxampfSPbMUzU1qkYMRovxsUlqVQdyyQWhKapsrJKnS4lJSWDt4Smpor4+HTecfAej+vy5ZrExEydjk8kAgAQhM9iqdHrc0Lj4IP+rxqNPjVV3/rIK5jNpvp6Y1JSHm+fXC59akpKn24dB3+DkcvVkQl9MMwG0CAWi0mS1GjiOAPv95Px8WkxiiUIn8dj0+lSeP/WgQAANMfHp/E28H6/y+9363Sp/IYDgMsV8Hi8sV91JF6vg6YJjeZqtg21+ur9o9Umx8dHmcKbzQ4AQogObrcFwzCVKiHG/hJJpzm9dzq5ublVVVUQEgSPKsQjhCASYWjzERFKjzLwYrE0NLkgh1TqBwCJROzzgU6nMxqNAOD1+mKf2HHLwjKZmncueolEDgByuUZINTkAj5DJqFgsxTCREAmBgJdhqFAJ0pAfWyZTyaPkdwIcF9M0LUQHr9eBYVi3nZTzIxhnjww8QggikQg52SFC6S1xt2KxZPTo0Wp1y7KPx+PpWn0QiCDSKy9KnIHfunXr3r17u1IhxM0JhqEZPOIaetQMvh3EYnFCQkJzc8sOPUnyr1iKQHQuQQPP3ZavvvpqUlLShAkTulInRBdht9u2bt3Y2GhMTU27//75HdrEFYlEKAAYEUpvmcFLJBKpVBrMFBYIBLpWHwQiCFcbHq7M4H0+X2NjI03Tb775Jqoy16tgWfbTTz8aNWrs7373YmKi/vDhAx0aLhKJGAYt0SOu0ltm8JyBD2YKQ5udiO5DcAbv9/sBwOfzeb3ey5cvP/vsswaDdsqUu7tUO8SNo6qqXK1WFxYWAcCkSdM6GlWBYZiQGFREz6N3GXi9viW8obm5uaysLDU1Nbgrj0B0FcEZPOca4vP5/H4/Z+wtligRt4iehNVqUSqVGzZ8YTQ2ZGRkTp48PfTs3r27mpubAEAmk+fn50QOxzCMJMlz507yVsDvdzU12SJdlWOEc/Grr+df1Idl2UDA3dx8TZKZDsGVrL18+VJokFuHYBiaILxms+vaOuODOHPZ1GQ8d87YvgSuXOylS2dDK0Z2CJomSdJvtbpjTHSj0WizsvIj26+7gb9woXTPnp0ulzM1NX3mzNlarS546uDBfbt2bQ/t/Nxzy1UqdatDVq9+t76+lus2YsToKVM6Vk5RLBbLZLLQpCILFixQq9W7d+/mf20IRGcQnMGfP39+2LBhZrNZJBJx+/F2+9WMHzRN835eIG4KfD7f+fOl8+c/NGPGvdu2bd22beucOQ8Ez7pcTpvNCgAKhdLv90YOF4kwhqFbPRUjJEkyDFAUxVcAC1dKIPIcz7IURTIMxrvwEudmSBAB3u4ILMtQFMmyvtCgp2B4AkWRUX9hiiIBIBDwCbgKmqZJgFiXcGSy1kOYrq+Bt9ttmzd/tXDhYoMheceOb0tKNs+fvyh4dsyYcaNG3cYdV1VVHj9+WKVStzXEZrO+8MLL3KOQRzz6o48+MmDAwJ07dwZbLl682Gp5UATiBiORSO66667Dhw/v2LHD4XAAgFwu53aRgnvwDMOkpKSsWrVqzpw5Xakr4noil8szM7P69CkCgJEjx3zyyYehZ2fMmN3+cBx/E8NEw4aN5a2A8EQ3+/fvyMkp7NpEN8eP/1BYOLBzE90E54ZpaVnDhmW1L8FsNp09++PAgbd2eaKb6+tkV1tbnZubn56eKZVKR48eV1dXG3qWy7uE42IMw/bs2Tl16j1tDSEIAsMwhULJ9efxWvTrX/968uTJoRMgh8OBduIR3YSdO3cWFxdz1h0AKIpyu90A4HS2GHi/3282mxsa+BeGR3R/tNqrllUkEnX0QScS4ShMDhHK9Z3BFxUV9+nTkjfRaKxPTU1vtduJE8dyc/O4pfhWh9hsVpZlV616y+l0ZmVlT59+r1rdkurE7/cfOLCXOzYYEjWa8BQo3HJKbW2FWCxxu69Jcezz+SorL0S9CooK+P0er5fhnabG5XICQGXlBd4SCMJHkn6vl78Hjd1uoSgylutti0DAwzCU0+kPtjQ2qgBaKpnW1FSwbJTgQ4/HRdOUEB38fhcA2Gyx+pYnJ6erVDdHVhyF4urLPk3TP/30EwD4/S3hHtyW/F//+teUlJT77rtPYM5jRPckP79g8+b1dXU16emZx44d5rztYkckwmgaGXjEVa6vgZdIpJz/0Nmzp3fu/G7u3Acj+9A0feTIoUcffaKdISRJZGXlTJ06U6VSbd68Ydu2rUFRBBE4ceIYd1xcXBwfHxcmn9s7MZmMGAZerxtagkkYTmxDQ03Uq+Bq2DidPAswwJVtoYaGWt6PZZZlWZZ1OPgHTTEMw7JMLNfbtg4MAGDY1Zcki0UfNPBNTQ04HmXHiGEoloXO0MEWtSdHXFz8jTHwLMtEJhHrUHGdUAPPsiyXsyEQ8HMSvF4PABiNxgceeCAzM/Oll5Y/+uijsSnGsqygAj+cDIHu2Z0ugWVFV/yP2BinrSzL8NaBu/E6JIFH4kscF8+bt+CbbzZ5PJ7s7Nzp02d1aLhIJIqstoXozXS+gT958vjhw/sBYPLkGfn5fXw+75YtGxwOx4IFjyQlpUT2Ly392WAwqFRXd1wih2RkZM2bt4A7O3LkmLVrr25NxcVpf//7P7Sjj9XafObMseHDxyoUyl27DgGAVCrl5kMURd92291RJ0PcdkhycgHvVLX19dVlZWfHjo3+XW3hdlvcbmtKSh9+wwGgsvJCU1PDqFF38JbgcJhI0qfX5wRbQoO0hw8fl5sbRUJp6UmCCAwZMpq3DlZrHYZh8fGtLwV1IRZLLUn6wxp9PidJEjGWxpFKr7k36uoqAaCsrNxorCcIX319HdfOMEx1dXVJyZZp08bHIpYgvCKRSGCBH5IM+P1O3sMpivB6O1zeKQyPx+rxXC1C6vOlAGgBgCQJk+lyDAJYl8tsMvF2HwMAcDiaHI5Y/cNTUwtjdIEOJTs7d8mS33Z0FEdw6oJAcHS+gR869NahQ2/ljmmaWrv2w5yc/HnzFrZl286cOVVY2C/4sdUhnP98enomAOA4HllRJka4eCSJRMIZeJZlq6qqCIIoKurYUhgCEYZGY2DZ8LmdzeahKDrG4jrZ2XmhHwMBBgBOnTrz1VdbXnxxudF4ze7Snj37YxTb2GjGcVxIgR+nsxnHJSqVLnrXNsDxeplMJUQHu71RJlMpFFcXY4Juwzgujk1yhVKp5a0Dy7J2u1Gl0kmlsXvm3uhtFAxDS/SIa7i+S/Tnz5+TSmUTJ04Na6+vrzUYkqRSGUmSlZUVU6fObH+Iw2HfsaPkkUee0Gq1x44dLioq5qcPt0MfDDsGgNtvv93r9aJoY4RAZLJWnvs4Lom9wI/BkBz68bvvWiJI7XanXK5xOq+JzHG53DKZOpYFIZFILBLhQsrzuN1WHJcIkYBhIrFYKkyCKUxC0F829qsTi2W8deCWviUSeXcudIRm8Igwrq+Bb2ior66uWrFiGfdRqVQ9//xLALBmzapFi36ZmZldW1ut0+ni4xPbH1JcPNBut3388Qc0TeflFUyaND3yu2IhPj4erjXwdXV1AODxeCQSSUlJyaxZs5D7EqJLiItrcR/Jy8urrKzkvOgBYM2aT4YMGR78yMEwjMfjQWmaeicMQ5nNrTiyiEQYTVNNTZVChLvdFo8nVh+XCMUYAHA6m0UingFKnB+Lw2FyOnmmyuHSkNtsDQThiNq5DR1YuLIbGGxkmGwAHAA8HltTU5Tfh4tutVhqQm0NDx2amy/H2F8mU2q1reyAX18DP3Hi1MjpOwC8/PKfuYO8vIKlS5+LZciYMePHjIlp07EdIg08h8vlKi0tnT179sWLFwsLCwV+CwLBg6FDh+bm5jY1Na1YsWLhwoXBdo/Hs3jx4qKiIpVKtXTp0ldffTXYjgx8b0Ukk6kiW3Ecpyi61VMx4vXaxWKpWMw/BxwASCQy3jqwLOvzOSQSGY7zNo0iAJBKFbx1YBjK73dLpYpQp6ugscdxSVTJfj8JAFKpUirlmROQpolAgJLJlDFu9LSVtq+3pKrl0Ol00JqBpyiKi4n3evkngUIghDB+/PjKykqv1/vf//438mxDQ0NiYuIrr7xy5MgRrpgsuld7LSKRSKtNjmyXSCQURbV6Kka8XrtcrhGS6AYAFIo43jrQNOXzOZRKHe9EN2KxCwBUqnitln+iG7/frVYnhlrN4GReLldrtVF044qVajR6IYluAgFvXFxSt050090oKipKTEyMNPAkSXKLS5zzHQLRVSiVV9/6U1NTg/MGp9OZmpoqFos/+eSTP/zhD4AMPCICHBcLyDKL6IH0LgOv1+tXrlw5d+7csPY1a9asW7cObrYysn6/v7xcUOgRohsik7XMG/7f//t/c+ZcjYTOzc0FgMzMzGnTpsGVyjQIRBCJRMwwDCoJjwjSu5boAeDJJ5+8cOHCypUrAUAikXAlPf72t79xpr2zZvC//e1v58+fP2LEiE6R1hb//ve/ly9f7nA4UA2SnkRwBq9Wq+fOnb1hw9fcR5WqZeePq6GAZvA9EiFVtbhiWiRJogcCgqPXGXgACGZ41mq1ZrMZQibuHS3A3Bbvvfee0Wj88ssvO0VaW5jNZo/H09DQkJmZeV2/CHEjCRp4uVyu0Vx15wnWRuIsPZrB90iEVNUSi3EAIAhCLm+9thiit9GjDLzHY3O7wyPaueeg2VwtlbZsvTudLdV8tVqNxWIJzTBaW1sWmW/rSsRCVeya0DRdXV0ZFOVy2QHAZCrnHYPHZUIN081kqgWA+vpLUmn0nQWPx0bTlJBsYlzi1VAJdrsSoCVziNlcrVRGyUXv97spSqAODADELkGrTeHtrdNVBJ/OCoUiPz8Px3GGYViWTU5ucVxCM/ieSrCqFr/hXAYwVEMLEaRHGXipVKFSJYQ1UpQIAJRKbTD1VWJiSy6IxYsf/eGHA9u37wh2JkmIlODxuH788ejtt98R4ws1y7I0TVMUExTl8RAAoFIlCCg24w0EvGG6+f0UAEgkqkidI5FI3CKRJ5aebeH3uxmGVCqvetjKZFfdFZVKrUoVJcmGWGxmWUyIDj6fAwAUCm2M/cVinmEqXUi/fv1++9vf/uMf/5DL5Xp94o8/Hli06MnTp08/++yzXAcuOs7l4l+YANE9aaeqFgAQRCCYx0YsbuXRzc3g/X4fV4+cBwzD0DTFezjnRU/TtBAJnaQDfwkURTEMQ1HktQ/8lmcdw9AUFeVBx7lBUJSgq2hNhzbBMAzHW7sl+H1990QikUsk4WtTBEEDgFKpC3kv5pa/sGXLXnrySVtBQYHV2pLjurKyWq0ONz9bt5Y8+OBDZnNzYmJMlolzZKVpJiiKKxKjVvM38G43SxC+MN18vgAAiMWKSJ0jkUqbMEwUS8+2oGmSJJlQCSHlUUCp1EWNyhaLpQzDCtGBILwYhgmR0P2RSqWvvPKK0WgcPHgwAGRmpmdnZ///9s47Popqe+Bne7ZlN7vZ9B5IAiQQQgsdBSLwBAEpShP1qYg/EJ+i7/lQQVSs2EUUUUAUC09AqiAdAgkQ0kjv2bTdbO9tfn/cOCzJJtnskgTD/X748JncmblzZvfunLnnnlJWVkZWo+FyuXQ6XaPxPDk85s6kg6paAPDDDzuqqioAgMfjDxs2pO3p6Oly8eLJgAB/L6TwKk8OAJSU5JWU5HnXh7cy5OZe8bKHVjJYrVOR4qiuLjt/vsSd869cOeulBCUl7n4O/v6BiYnD27b3KQXvJmw2m0qlomA5Pz+/yMhIUsGXlLj45pAtVKlUisVu/WzQ61t1dXVNTU23ro6jORzyEyTZs2fP5MmTJRJJ910X093weDwU1qFU1gHAV199lZ2d7XyAr68vVvB9jw6qagHAmDHjk5KSAYBOp0skLp5FmZlZABAeHhsZGeGZAGp1o48P32XeZXdwOBylpflBQWG+vh5G0jscDq1WxuEI28vc0ikWi6mysiQsLJrD8XBtzmaz6vUKHk/sPCcmt8XiwLi4TlwcdDpNXV1VdHSCx5nsrFaTwaD29Q1wc07YXsD93ajgaTTa0KFDSV3unGwoL8/FiyeakWu1LsrFvvXWWwwGY+3atW2PVygUR48efeKJJ26j5K1Aj3jnJTeLxbJo0aItW7Y89dRT3XddTA8TGBiYlpbm3MLj8Volr8X0ATququVclMslyM1IJJKEhHio4CkUo69vgDeJbkpL84VCcVBQmMc90GhmP79gj11n9HptZWWJv3+gUOh5opvmZpu/f6jzS8ZfntnA5wtCQjpZIpTLG+vqqgIDQ7xJdKNSEUFB4TjRjSeIRCJSr5POydBOHDyakbt0Wj558uSpU6dcHg8AdXV1Hsj2+eefHzx40J0j1Wo1ANy4cYNsMZlMBEG4Geyn1Wqjo6MzMzM9ELJPYrFYMjLS27Zv2/bFhg3/Qf+OHDnQ84K1hcfjkWYnTJ9BrVb9/PNulUpJEA4PqmqhhXnsZIchuUsVvEAgIBX8li1bpk6dirZdKng0I3f5s7FYLGQ7uUHqV8/coF588cUtW7a4c2RjYyMASKXSgQMHopA8dGk3FbxUKq2srHS5KtEBpaWlkyZNOnbsWNtdMpmsS13daRw9+vvlyxfatqPIpXXrNq5bt9HjQke3Fx6Pl5WV1dtSYG4zAwcmjRo1ZseOrz/88B2HwzF1qouqHB2AzMit1uwwdzN3qYKXSCRkvrD4+PiIiBaLVlsFn5mZ+e67HwCA1epCwZvNZqTXjx07JhaL9Xo9QRBJSUlo7/Xr190XyeFwyGQylUplMBikUmkHRxYUFCQlJY0fPx5Vuf3kk08KCgpQfnIk//r167/44otOr4j0cVejrbZs2XLmzJlp06b9+uuvmzZtItuXL18eEBCwYsWKLvV251BYeKO+3sXHTkYu0Wh0Go1OJlHoXQYPHozsN5g+xpgxE5599sV//es/s2fPJ59RbsJgYAWPuYVuX4MvLLxx6tRxrVYTHBw6a9ZcgUBI7rpw4cyJE0edD37hhf9yuTyXuZyqqysPHdpnMBgSEwenpc3wcmXiySefHDduHPknmfgJJaV3fojn5uYiS/umTW9HREQOGHDLMhh6+gNAfX29Tqe7ceNGXFxcU1MTAAiFwk5N9ARBfPzxx4sWLQoICPjuu+/++c9/vvfeewCgVHZUjvD8+fPOvgLIVLt169b333+fzMd37ty5lStXogOuXLkSFuZiSQz5bXVVwZOlUD766KMLF3wBWgr7nj179qGHHtq6deuUKVPmzZvXpT57HZ1Oe/r0ienTZx44sLfVrg4il+rqaisqytB2cHAwClK6tVuNxWKpri7zWDCjUUMQBAqzdIZKhbKysgMH/pec7MKb2hmTyUilUryRQa9XUql0jcbzvDo2m1WrVXsjg06n0GgMSqXaqUUC4AsAFou5urrWjT4IpVLucbl0giB0OqXBYGMw5G6eEh4e08O1p7GJHtOK7lXwKpVy376fly59XCIJPHbs4KFD+xYtWk7uHTNmfGrqWLRdUVGemZnO5fLAVS4nh8O+d++e2bPnh4WF79q1PS8vB3mTekxycnJy8s0enP3snPNAmc3mr7/+Gm2fOXPm3LlzbRU8UpDoRzV69GgU2gQAISEhHetpANi5c+dzzz0nkUiuXr1aUFBAEASqI+Js27dara2SSzun24uLiysuLgYAgiAUCoVKpULtx48fl0qloaGhADBz5sylS5euWPGYcyeFhYXvv/8+/OWp5yYWi4V8t7hw4QLAdHLXhAkTdu/e3tjYuGDBgpUrV3766ac9/HTzAmL//l/T0qY7e2OQdBC5VFcnPX/+DNpOTR1Fo7W+X7vdDkB4o9hQYh8KpbVSsViMAFBZWerjQ+NwOnLkQWHBt0MGD+tzIxm0WpVe73ngvsPhoFAoziNKp2P9peDdeoUiCFAqm9VqDyudA4DDYadQ1O6P6vDwGI+v5RnIZxsreAxJ9yr4mpqq6OhY5BQ6evT47du/dN5LoVBptBb9ferUcfTcdJnLqby8TCAQRkfHAsCoUWOysq56qeBbIRLdjKt2fsdvbGy8dOkS+Wdb25fFYiktLSWrzdrt9mvXrqFdQ4cO3bNnT2lpab9+/VqdVVZWFhsbW1NT8+KLLwJAcXHxhx9+iHah1wXnRfSVK1fKZLLvv78ZMEPupdFo9957L1LwAKBUKsvLW+Imm5ub9+zZ8/zzz+v1+oaGhrZa/I8//qiqqgKAmpqazj6em7gMJUC89NJLVCr1zz//fP/99//973+HhIS8/PLL7vfsDc3NzVwut0vpOa9dy0xPPwcA06bNVCjkYrEkJqa/TNbY9sgOIpeGDx81fPioDq5SVJSr1aqGDx/vvmCtUCrrHA67WNw62LKwsPq773anp2etX/+2XC7vYOEgO/syjUZzGSPrJnJ5FZ3OEgqDPO7h8uXTYnFAv35dcxlzprGxlMMR8vk3Y8N27mzZ4PH448aluT7NiTNnDsfExIeFRXsmAEE4GhpKhMIg95MsdR8EQaA3vFYgG5JOpzGbPc9yaLNZPD79rwQvnveAKspbrWaPbbQWiwkArFaTxzLYbGbUDxIGQRBsVJrdZrOazZ0sglitqAcjhUJ0fGT7MlgAwGw2uvlCSaXSXAYWdq+CT0gY2L9/PNqur5cGB4e6POzq1Yzo6BhkvXdpEVWrlf7+LYHdEkmAWq0iz7VYLLm5LUvdAoFv20mYXq8DgKamOgaj3aRmzo9HqbSKrOpx6NAtLtNyeVNdXbVzC1rGLisram5uPb+JiAiz2+2XL1/kcJhqtQIA6uqqKRRKTk7ujBkzDxz4Ta1WI2P+sWNHW51rNBq//XbbffelAUBFRblKpZLJmsxmvdVasXDhIrIi5Pff78jJySXPKisrvnr1pku8TNZYV1ddWFiEtnU6jd1uI+VvbGzJ19vcLNuzZ3dKSgpBODIzrzY1Nc2f/6DLYkKYo74AACAASURBVBVGo6a2tt1FB5FIgDpfvHhhYeGNjRs3zpw5XSy+JVLFaDQ4y9ApOp2+sLCwrq5OpVLJ5c1ms9nHh0GhAIPBlsubS0tL6+rqSkvLbDbbzp3fTp58b9se/Pz8XSb+TEkZkZIyAm3n5l4vKrqRnX3V4SCsVss772z4v/97HhmToLPIpd5iypQpAFBWVqZUKuvr65GpBnOX4HDYFQoX7+VIwSsUdS73uonBoDIYVJ0f51owBwDodAoazSs/AJ3O3XWQtpjNFgDQaGQ2m1dxpBpNg/OfBNEPgAYARqNaoWidEL0VOp0eAFSqeuQV4TFKpTtrTwAAPj48Pz8XD4HuVfAMBhMF+uflZR8/fsQ5KxOJ3W6/dOniY4+1xG27tIgaDAbS34TJZBmNN1/NjEbDwYMt5baSkoaIRHxwRUVFUQdyKhQ31XNhYQ6dTmezfQBgJzlNAACA+vqa4uKbCtVstiBHp7y8a3V1t/yiKBSK1WoAgJyca6GhLUquuDiPQoH09HMEQeTnXyct7VlZLnzxcnOvR0cHA0Bzs8xgMFZVlQNAZWVmevolcrrGYDjs9ptegfv27d2162Z5m4YGaXFx7rvvfggA9fV1CoUMAEj5SS1bWlry8MNLHntsCY/H++STLwFALq9PS7upLP/znw2xsdGPPbaESqUWFd38GMeMGXXx4k2BKyqKTKaW7+Uf/5i8c+f3H374wbJlD7e9NefPsC0VFVUnTpy+ciWrqUlOrjgAAI/HZbFYWq0O2XgkEv+IiLDo6IjJkycIBL4cDs1lt4MGpXSa2Xv27PloQyZr3LNn16pVL6A/pdIa9DZ57NihRx99SiAQeBC51E2gRaXCwkIAkMlkWMHfVdBoNH//yLbtLFYOALBYApd73UEur+Jy/dhsX89Ot9ttJSXlfL7Y3z/Ysx4cDrtCUevrK2EyPUy2YzDoKiurBYJAgcDDaH6r1axWNwiFwc6JrsmnLocj9PfvJEafSpVJpfUiUSiZH72rmM16rVYuFrsbB0+luq4fePsVvLPxMza2v9Fo2L//V7VavWTJowEBLqx8N27kSiQScsLk0iLKZrNVqpbFM4vF7Jw9wNdX8NJLr6JtKpXa1lapVMrz868NHz6+g5wDN25UktsZGXk7d+4oKSmhUqkMxjvOh4WHx6jV1pEjR6I8cWRysYiI+JCQW1YBORzO3LkPffzxlyyW77hxafX1NWVlBePGTaVQKGVl9QAQGzvoq6+++uuOXKyZBQdHIMMjg7FBq5XTaKzY2DCTCa1oOMRi8cqVK2fNWjhz5oJLl67l5eUpFIqdO/cAgEQiGTdu7J9/ngwMDBs3Lq2+/mUA0OmMYWHRr7++ceXK1cOHDweAI0da0ig2NMgAwM8vkM9v+QoqK+ucbZ4y2bOXLmWKxUGvvfbSmTMXAYDJZA4ZMuS11zZMm/YJWalnxIjxUVE37VFPPHHxu+++e/nl16KiosjGoqJsi8WSlDSi1c1qNJpvv/32wIHfGxoaiouLhULhlCmT4+Lio6IiBwwY0L9/fz8/P/TNKpV1FApFKHT38dHeuHeH7du/XL78yYEDk1Qq5Y4dX9vt9piYfndImBxabUW+Gk888QROZnCXQWmbkxsAkJHSZnO43OsmNBrD49OpVJuXPSCXERqN6XEPdLoVAOh0z3tA1cXodJZLozeNRu90Xk6jMf/qwUMZkImewfDx0p389it4Z+On3W7bteubqKjYhQuXtreWkJOT5ZyhyaVFVCgU5eXloAOam5uFwpuvZhQKpeNsQSg2lEajd2BcZTJvfpHl5eXV1TUqlTogIKBVsjC73TF//oJXXnnllVdeAYDFi1teRKqrayyWW0xSHA5n9OgxwcHBJpOJTmcgNUOnMygUismElmcsKEENn89vFS4vFApVKpXRaEICazSa+vr6jRvfjowMLS1tmXb7+/u//vrraDsqKkosFu/btw+Ny9WrV69bty4mJqa6uvqDDzajVQCDwXDkyLHdu3+KixuUmjoanAIC0SqD1WpFZiUAUCpVzp8VyvBTXV1No9GQi0B6enpKSgoAMBhbyJcTGu2W+hebNm3at2/ftGnT09LShEKhQqEoKysDcKSmjsjOLm5sbBSLxTk5OZcuXVIqlVKp1GazTZgwYdKkSevXr589ezab7fo7pdFoFAqlm+zkEkkgOX0HgFdeeRNtjBkzYcyYCd1xRY9xToEplUovXrx4/Pjx1157rRdFwvQ6LBYTcJlBjBPda6IvKMhnMllpaa3TNSDjJ5PJslqt5eVlM2bMIne5tIjGxMQeOLC3oaEuMDDo6tXLt9fDDm4tzfTnn3/CX/50rX4q2dnZVquVDFJXKpV0Ot1msymVylZByXw+HwA4HE7bHxtqKSwslEqlq1evBoBPPvkE7aJSqQ6HY/z48adPnyZPRFFwx4//6dyJs9s/8vOXSCTIk27ZsmXogN9++w3lMwcAs9mckZHpcBBoXUClUpELBGjZzGKxkC2t7gV1e/jw4d9/P6RSqcm7AwAGg9Gex65AIDh69Oj69etPnz6t0+l8fHzi4uKqqyvffPNdgiDQJyMUCh944AE+nx8SEvLwww87z/UxHePj40Oj0ZBPk9FoPHjw4AcffPDYY491a+0DzB0OnU5nMpk4hzGGpHsVfF2dtKqqYsOGllBpDoe7du06+Mv4GR4eWVNTJRQK/fxuumK5tIhSqbT58xft2/er1WqNj08YMmTo7ZUT+ZRRKBSCIFB6OKTgnQPSAOB///ufc6PFYhk1atS1a9dKS0u3bt0KAGKxOCgoaPr06bNmzQIANpvtrEePHDkyffp05AOP0pCNHTvWOR/ZokWLvv/++4kTJxYWFm7fvn3dunU8Hs9lrJ2zgkfbMTExWq2WTqej5YOIiAhyvZzNZisUirq6egAwmUwnT56cPn16WlpaVFRUYmIiSour1WrJYDznpLwNDQ1kHOAPP/ysUilHjhwZHd3iiuzj4+MqgW8LgwcPRp8YyY0b11QqVUJCskgk0ul0XC737xNKd2fh4+Nz8uTJOXPmKBQKg8Gg0+ksFkt5eTlW8H0Di8Vy/frVkSNHd/VELpeLFTyGpHsVfFrajLbTd3AyfsbE9HM2iiJcWkTDwyNXrFjdHULCXzP4xMTE3NwWRy2k4MmAtODgILPZgibTpM42m80CgcDPz+/w4cM2my0iIuL06dOk8oNbze8ZGVf/858N2dnZqBOkViMiIpwzxc6cOXPEiBFLly7dsWOHTCZramqiUqmkz7wzzgoe8eOPPwoEguDglsXpkSNHHj9+HAA4HM706dP37t174UI6El4mk1kslvr6+sjIyHHjxiFJfvzxx5EjR6Jza2trL126lJqaCgC7d+8mL1FSUmqz2R54YDZp8BAIBM2d+JO2hsNho6BEXqfFZTEdMmHCBLlcLhQKNRoNCs7EFeL7DEeP/l5VVeGRgufgYYAhuSPybvY6SGM5r/u2UvAJCf1feOF5tE02WiyW/v37+/v7o9X0wYMHO2t3AAgICEBL4ACQnZ0HAOfOnfv444/JAzgcjkAgAAAUKx8bG7t69Wo/Pz8kiVarRebxtgvSbbVjQkICqd0BAAUdiESiHTt2jB59c9Fdq9Uin77m5mYOhzN2bEuiIYfDUVpa6uvrGxMTo1AoxowZU1ZWVlxc/Nlnn3E4nH/+858AoFQqjUYjGUMIAOPHex7hjfEeCoWyb98+AMjPzwcA56ADzN+X9rImuwOPx8MKHkNyN5aLbQtS8M6Zn4uKiuLi4khdzmb7PP/88x999HFTU1Nubi5BEA6Hw263x8fH5+fn5+TkAMBzzz3XqluRSHTt2rWamhoqtcVVPiMjgyAI5wP8/PwAYPr06ZMmTUKeawAwYcKEjIyM11577Y033gCA0aNHnzx5csqUSSdOnE5MTExOTkbtHYCyvqxYsWLevHknT54k2+vq6pCmr6ysHD58OLo6Ki4ul8uXL1/+wAMPzJkzhyCIqqqqAwcOVFZWzpkzZ9asWdu2bVOp1Gy2j/PbxsKFC3fs6NInjbnNIJs8Uu3ffPPN4sWL8arH35oOsibn5l7XaNQAQKczgoMD257rcNg5HLZUWuNx4kKtVqnXW5lMDwsVoqSHzc1NFouLql3u4HA49Hql0ehwDlHrEujSjY11Go2H77t2u9VgUJlM4FwP3uGIQnHwarWiurqTfIgo+UpdXZXH7sBWq9lk0lgs5W7+nNlsrkTiIkgNK3gAVwp+7ty5O3bssFgsYrGYx+NyuVw6nc7j8ZqamiorKzUaDfrcORxOQkLCiRMn6HT6vfe2TrQiEAjKy8vnzJmzbdtWlH4BOehJJBKZTDZlypSwsDBkr+bz+XPnziVPfOqpp95///38/HzkEBAfH3/y5Mknnlh+4sTphQsXrlu3rtM7QveC3jliYm6mzDQYDGRUHpfLRe5yDz744LfffgsAvr43MwVpNBq0GP/AAw/MnDlzy5YtTz/9tE6n71LCOEx3ExoainwzKRTK6dOnq6qqsK/i35mOsiZfvZpRVVUBADwef9gw1zUI/P1FJSUl5eWFXsjQxVW3Nshk9TJZvXd9eCtDfb272bTclMFuD0cKXqmUl5e7VYHTm/zQCJnM3c/B3z8QK/h26devH5VKbWUJRwVYR48e/dJLz1OpFnDyHtdqtcj3LSIiQiwWf/bZZ20XxQFAKBQCgFQqHT9+IlLwFy5cAICHHnpo165dS5YsAYCgoCAAQLHpJMhubzQa0cwsISGBSqUmJPSnUChozt0pSA2j/319W9JW0Ol0q9VKKngejxcYGJiSkjJjxoydO3eyWKwnn3yS9OlTq9WVlZXkXUyaNAm1txfAhukV2Gy2UCjk8XgRERHnz5//9ddfX3ihtVML5u9CZualDrImL1/+ZMenX7hwPClpcHp65qRJ//BMgPr6Il/fAC7XwxQxdrvt3LljCQlDgoJcVLdys4empjI/v1AfHw99dPR6bWbm2eTkVKFQ3PnRrrBYjM3N1f7+Uc5x8GRcalRU3KRJcR33IJc35uVdSU29t+MQ7g4wGjUqVX1QUP87Lg7+78iAAQN+/vlnk8n0+++/k40XL14EgMWLFw8blqJS1YOTppwwYQKytAcEBKAW57hkksDAQABoamoi89ubTCY6nT5lypQ33ngDvS4MHTr00qVLpIMbws/Pz8/PT6vVotzyjz766IQJqQEBfrt3725rJ3DJ5MmTX375ZbRU7+vriwIE2Gy2xWIhI+DvueceFot19epVAFiyZMnp06cHDRpEZraXSqUnTpyIiYlBqp2cUricW2B6EV9f34CAADTY0tPTe1scjOdIpbUdZE12B5FIpNVqzWZzV0vNYvokfUrBG41qvb71ugsKGlEqpXp9R4s6EycOP3v2AgCwWCykAlE4uMWi1WrlANDcXJOSkpiXl6tWayoqKgCAwaD7+DjQYXQ6TS6vatVnYmJsfHz/oqJb7DlisWjMmCEWi7K5uWWuHBsb1Nzc2qD07LNPr1//1rp1/2UyGWazIiREbLfbpk4dA2Bqe6G2CAT05557kjwyNDS4traOzWbJZE179/6CGkUiNnlAYmKczWaSy6s4HAIF92dnXwWAsLBgq1Ull6sMhhZjEUEYybM0GjZAyyuOUinl8104/DtjNhtsNqs78reH3W4BoLjfA58vYbH6+BtJUFAQi8VClpXc3Nzy8vKoqKg7pG49pku0lzXZfQQCXwBQKpXINIi5y+lTCp5KpbdNDUijWaD9vIPOsFhsAODzeUjBozk6h8Oj0Rh2u5XB8Nmw4bUNG16Ljx/c2NgEAGKx2N8/QCgUBQUFMhgusjMmJAx4+ukn16xZ69zIZrPdyV8YGhoGABaLNSBAwmD4WK0mu93uceLDgICA2to6oVBYXFza0NCIJvT+/gFkh0888TjaYDB8nnnmqa1bt1VUVAGAj48POobHa7Fe8PlC8iyUkRFBp7M6zeBIpVIpFKo3eTRRfSf3e7gb9Nxbb71lNBoFAkFWVlZ+fn6/fv3WrFmzefPm3pYL0wugQJ6CggKs4DHQxxQ8i8VlsbitGu12KgDw+a6rijkTF5cIACKR2GAwkonkhEIJhyOwWAy+vhKUcTYsLBwpeBbLRyAIBICdO3f5+vqi7VYIhZJWLVwuz+WRrUhLa1lFCw0NEwgCdbpmm83izokuCQwMBrgeECApLi4FgJkzZz766KOJicNcumh+9NFnhYWlKCXAvHkL0UV9fQN4PJ5OpxsxYgwphlPEHPD5/oLOCmkyGFKCoHh8FwCgUNRSKF710Pe455570Mbo0aPz8/MJgti1axdS8AaDQaVStarph7nzaZU12X0iIsIBICMjY9KkSd9///3s2bNJzyHMXUjfn9+4D4oj53K5n3/+OdnYdnE9JCQEbZCOdVOnTh01ynVd8LYrYW6uYYeHhw8cOBCcvNu8YcGCBXPmzEpIaPENGTRo0OzZszsIwBCLxSiCf/DgwaiFQqH4+4vhr08JcwdChkvI5fKamhoAePbZZ1evxj53dxE8Hi8kJGT9+vUff/zxsmXLjhw50tsSYXqTPjWD9xIul0uj0ahUqsBpKnpLBRUAcFLwLh3rWtE2qMydIDcAoFAoGzdufPDBB+PiOvHYdIfly5dPmJCKqt+uXLnyxRdf7Pj44OBglEHP+fbZbDaVSsXOOy5RqRpsttaxvyaTzmazeONzYLNZAQg3exg8uD8KmQOA0tLcvXt/2LZtm5+fsLm5qb0eNBrt00+vSUiIe+WVlwDg0qWMxMRBPN4tZjCbzWK3e+k5YTMaNd704HA4DAa12XwzMbLJJAbgIfHkcreCsvR6pVzu8ZSGAACdrrmtl097eFyz1Uv279+fmpqKQmR37do1ZcoUFIuLuQvBCv4mFAoF6fi0tDQ/Pz8UMNZWi0dGRgKAUCh0GRrXCvJdgcFgxMfH02i0++93t95oYmLiiBEj3D++U5DASUlJKPKtA1JTU1Etk1sVvE9gYMDtEqaPQacz21pEqFSa9z4HBEG42cO4ceOXLVv83Xe7AMBsthYWlgCAUqlavnzl//63JybmZprFpiZZY2NjUlLijRtX//jjz+zs3HPnLjocjuzs3OnT73v66ScCAgJIe4/dbm1slJvN9ri4fl0SvqlJdvjw0fj4/hyOD5VKJwhaXl6+TCYPDg4MCwsTiboQi2WzmWm0WzxsSO8Kq9UmlboIKmtFXV2DwwEmUyd+oO1DGI1aJpNNo93yQKBQKJGREZ726SEOh12tdnHLBOEwGrWxsSGffbb51VffkMlkBw8eTEubcujQb0ymuxlXDAa1xWLs/Lh2BAMAvV6lVHr4IoU8n3Q6hdGo8awHlJ1Mq5UThMfJduwAoNHInD14CCIIGbyNRq1S2UmuQJ1ODQBqdaPR6GG6HlQ2V6WqB3Ar0Q2D4cPjuXiNwwr+FjgcDo1GQxlg2lPwiYmJALB27VpyKt8B/v7+AEChUKKiIi5dumS1Wt13+4qLi8vIyOjaDXQI+pG7Y3iYN29eZGRkeXm588F8Pj8wsLVLAQbh8tfFZDaZzWZvPAaUyjqHw+5+D5GRLVb6/PzSpibFsGHD6HTq5cuZL7zwXz8/v7lz56pUqnPnzhUUFJSXl8tksu+//xkAGhubkFuJUCg8cuTY0aN/hISElJaWIvuTStW8ePE/5fLmrKwsd1y3dDqdWq0OCAiYPHkEWikYMCCeyWTl5eWTBY0oFIq/v7/BYIiOjh4+fLhSqRQIBBaLhU6nm81mOp3O4XB4PJ6Pjw9KkGy3m5hMH5PJKpVKCwoKfH19a2tfBXgEAAoLi5KTXS+Q9QBMJpOMO+1JCMLezh4HQdgXLJgzadK4bdt2fPDBJ1evZi1cuEQoFGzfvqXjPk0mk8ViptOZqHOptE4k8msv74VCoTxw4PDy5YvJFrPZkpOTy2IxkAye3hdB3oWnPdgBgCC86cGB+nHOOuqEOz2TPXgow189ONw8ur0jsYK/BQ6HgxTwyy+/vGLFCnBlop84ceJLL720Zs0ad1bTBw0atHfvXrPZQBAmDofTu2lEkZe7OwoeAPz9/cvLy51v/733NlGpHo9XTE9Aekj89NNPNTU1U6ZMmTkzbenSf6J0xXv37iWjQAFgyJAhRUVFQqFQpVIhg83WrVvffffdoKCgY8eOvf7662+99RYAnDhxKi8vn06nP/LII2lpaUwmUywWz5s37+DBgzKZ7OGHHybzQ1y/fv3PP/8sKCj4+eef4+LiampqPvroI5VKtWvXDh6P/9577w0YMIDBYNhstrKysqKiIjabnZ+fn5GR4evrW1VVxWAwrFYrk8l0OBwajYYgCL1eLxAIWCyW1Wqi05lUKi0gIODxxx/X6XTnziXk5AAAREdHb936R6e/rJycjODgCJfZvtyBIAilspbLFbVy4+2VMA0qlSYSuSgbSKEUstkCtEskCn/33eSlSx9LTk4+c+Y8hUI5ePDUsmXL8vLy1q1bN3jw4LVr17byv5s6darRqC0rq1y/fsOIESOmTJkeEhISGhr6zDPP9O/fv7i4eOLEiUKhMD8/f8WKFfn5+Uql0uGgJyYmVlRUSCSS33777ccffxw9ekRKyvB58xakpKTU1NRQqdR+/frR6XS73X727NnS0tLS0tK3334bfV979uz55ZdfdDrdrFmz5s+fDwBvvvnG3Ln3jRw5Qa83i0Sizz77jMvlPv744wDQ2NhYVlam0WiuXLkycODAsWPHfvvttwMGDKiurp47d25oaKhCoZBKpUqlAgCamrSBgTFXrlxJT09ftmwZSlhisVgaGxtnzpy5detW9GSeP38+h8PZuHEjl8tNTU3l8/ksFis7O+vw4QP33ptWXl75/PPPo2wi5PhiswUiUSfuxA5HI0ClUBjiZaIbP7/QOz3RTWHhjVOnjmu1muDg0Fmz5goEN43DFy6cOXHiqPPBL7zw3+vXr7Zt5HJ527Z9IZXWoJaRI0dPnz4LugEOh4NSxZFlVNqqQ19f37ffftvNDqlU6ty5c6XSqpKSvNsop2cwGExwOxXd8OHDMzIynBV8XFw/q9VD2x2mZ0CjVyKRZGZmoo34+Ljly5d89dV2Go02derUo0ePxsTELFq06NSpUxcuXJgyZcqbb755+vTp+Pj4oqKiuXPnLliwAACWLFny6aefmkymtWvXfv751zEx0QsWLHz77bf/+OMPdKFvvvkGvTSsWbNGJBJJJBK1Wl1VVYUmPVwut6am5qmnnnr22WcBYNq0iWJxQL9+Az2+r8bGUg5HyOf7ky1PPglIwQuFwqlTp3baA4NhiY0dEBYW3emRLiEIR0NDiVAYxGZ3Fityx0ClUpOSkpYsWXLw4EEul/vII48UFRXJZLL9+/fv379fp9PNnz//5MmTEyZMoFKpX3311YkTJ9CJTz/9tEgkUiqVSqUyPz+f/NKDgoJWrVr1xRdfoJTbALB27Vr0akhe9OLFjIsXMz777IuYmBiUp0skErFYrJCQEJRTCwCuX78+fvx4uVx+/vx51PjHH3+8+uqrY8eO/f3337dt2zZo0KDMzCvR0dEVFRU+Pj6ff/55SkpKRkZGXl4eGmDoNZGcYf/0009GoxHVVGQwGCkpQy5fvkJW696xY8e9995bXV1NpVIPHTpkNpufeeaZ5ubmysrK999/PyAg4MyZM+gW/P39rVYrSm3y3Xc/AEBWVtbatWvJolwAUFBQcOKEFADMZrNSqWxsbJTJZGaz2Wg0kvKYTEaVSh4b+6fJZCJffwGAzWaTXlkMBuOvVweK85qpwWAwm802m5lOdwiF2a3eXMPDw1GdT3chuhOlUrFp02u1tdVms/nAgb27d3/rvNfhsNtsVvSvpKTohx++a6+RIIh3391oMOhRu91ud1+G5uamU6cOGgx6dw7evHnzvn37CIIwGAxhYWGRkZEmk8lgUNfVFdrtNvcv2ora2spTpw46HA6Pe9Bq5fX1xR6fThBEWVnBd999CQDXrl1z5/j09HShUKhUKskWlapBJqtwPubwYQKg5V95eed95udfzcq62DW5b6W5uUahqPWmh56ksDAnM/OsNz0oFFK5vNr94/Pz85OSkp5/vqXy4ebNm69fv7Rv388AsHLlSoIgDh8+XFlZSRCEw+HYtm0b2m7LL7+0ZEMaOnQoADzxxGPV1dWzZ8+eO3ducnIyKn64ZMmSd955Z9GiRZMmTRIIBPfee++aNWveeOONGTNmNDc3m0wmsrdLl06VlOR78zk0NJRoNDLnlieeaBl4Q4e61cPp04dqatwYo+3gcNjr6goNBpXHPfQA58//UVHh+imRl9fuBINUIRKJJDV1xKOPLktOTgaAyMjIYcOGvflmS2lvcrrv4+MzefLk9evXk7MgpKiCg4O//PLLFSsea3sJGo1Gbvv4+Dg76lIoFGcVOGjQoODgoPasjDweLzw8PDo62lntOde07N+/P9oQCAQpKSkjRoyIj49v78bJ2Yu/v79zJ1FRUfHxN91NIiMjn3vuOTpd+dez7lXnTphMZlhYWExMTExMzLC/GDBgQHBwUHR0dExMTFBQUExMTGhoqN+ttCdVxzz00ENdGhLdO4OvqamKjo4NDQ0HgNGjx2/f/qXzXgqFSqNRAcDhsJ86dXzBgsXtNVosFgqF0mkgu/eQFeHYbHa/fv0oFAqLxTIae2GNrTuIj++fn5+Pou86JTU1lcxLj/m7MHDgwJycnC+/bPmhoadbVFTEK6+8smrVKgCYPn062kWhUJDl0yXz5s3LysoaNmxYVlbW+vUvr1r1jEgU8ttvv6G96enpv/zyy4svvoizqdx2Ll26kJ5+zmazRUfHzpr1oDuevO6QkJDw8ccfFxcX8/n8oqKi0NDQkJCQd955x+FwaLXa1NTU/Pz85557bvnyub6+AQDMI0eOTJs2jSxLHRYWlpaW9vrrr+/atetf//rXhg0bACAtLe2XX37JaUcCJwAAD2ZJREFUzc3dtWsXSuz9wAOzzp0LS0wckp9fqFKpNm3aZDKZNm3a9Oyzz16+fFkkEuXm5q5aterChQuottbIkSO///57u91+5syZwYMHM5nMhIR4jUba3GzavPnjpqamJUuWJCQkVFdXb9++/dy5c2+++SZaOf3yyy93796dlpY2ffr0YcOGpaSkXL9+fePGjY8//virr75SW1v19dfbwsJaohiWLVtmNBrHjh1bV1c3fvz4oUOHfvTRR1FRUXPnzv39998lEsnYsWPFYvHx48d/+eUXFov1xhsbFIragwdPT5485cEHH6yoqPjwww8BXka9rVq16vHH59rtdrFYLBAIXDosu5+L3mg0kjVLnTGZtGp1Y0BATCsTfVfHA4Vw7Udwe7BaLXa7Hd1kbu7169evLV3q4v0uMzNdrVZNmTK9vcbGxoadO7fx+XyNRhMREXn//XN4vJbXSZvNVl1dibbZbLaPT+sgLq1WXV5emJCQ3NX4rtWr11Cp1I8+2mw2G/T6Zm+WQ+TyRqm0cvDgkR6vwRuNGqNRKxKFenY6ANTX16hUzQMGJHvcg16vbJVs58QJ5oIFLW/fWVnKyMhOVuirqkpsNmtsrOemWq1WRqFQeDz/zg8FAAAul89k9lpcX1FRrlarGj58fOeHtgNyshOLXSy4dsD169cnTpwoFovPnDmjUNTRaLTExOGdn9aGH3/80W6333ffOAbDRyj0XJdfvny6O0z0X38NADB0KFy71nkPZ84cvvNN9I2N9T/8sOPxx59mMpk//fR9VFTMxImT3T/9woXjoaFRUVH93T/l008/3bhx47Fjx5CpptNiM2q1GpXWdLnXzWIzer3+3//+96BBgx555JFWK4aeFZux2+0lJSUJCQlwu4vN1NfX02i0jIyMRYumarUsANiwAV59tZMe7pZiMwwGE9la8vKyjx8/gqbjrbDb7ZcuXXzssac6aLRaLRERUTNmzOJyufv2/Xr48AGyK71et2vXN2g7KWmISOQ6bVNh4fWuCv/44w8DQHb2ZfRnTU1dV3toRXZ2hpc+djU1tV7LcNnLHgAqya3ycglAS5mcgoLrKpWhp2Rwtw7joEEpEsldl5knOTkZrSMCgELh+bh9+OGHAcCb+HVMl1AqFYMHD/X1FQBAfPyAxsaG7r7iqlWrkGnHTQSdpqt0Ay6X++mnn3rfDwmNRkPa/baDvFbvv/9+Fgu0nQTH3YncfgV/7Vpmevo5AJg2bWZsbH+j0bB//69qtXrJkkcDAlxMAm7cyJVIJK0qJrVqDAuLWLhwCdoeNWoMqdEBgMfjP/nk/6FtFovVdvFGo1GWlOQnJg5nsTwMRzab9VqtXCQK99hjtqmpvqambNiwsR7P4A0GtdGoFos9j7iVSisVCllS0giPe9DpFDabSSi8GRyoUNwcP0lJwyMjOwnqqKgotFotcXGDPZZBo2kCAF9fd8Pxe2BZB4O5XSQkDEpIGGQw6Bsa6nNzs7s0fcdg2nL7FXxKyoiUlBYtYrfbdu36JioqduHCpe3ptpycrLi4AR03Iv95tJZPo9Ho9JtanEajBQd3ZLi2Wi0AwOXyPX7W0+kUq1XL5/uiXPQeoNGoAIDPF3is4CkUG0GY+XzPX5+ZTBaVSvWmB4fDZLVSnHtwtq5xuZ0nvabTGQ6HwxsZrFYthULxpgcM5g6ntrb6xImjBEFIJLe8yO7duwc9CTkcTlKSi/UOm81aW1vR2Oi5nc9ut1KptV6YhQkAKCsrqKoq6fTQ9nqw221VVZ7LgCLCCwqyPH5cEwThcNiqqqTOj2ubbRwAAwBqaysuX67suAfkk5+VlU6levjAJwiHw2GvrnbX/CYU+sfHJ7Vt714TfUFBPpPJSkub0apdKq2RSAKYTJbVai0vL5sx45aYt7aNarXq2LFDjz76lEAgyMhIT0jwfDEPg8Fg7mTi4gbExQ1ITz9/6ND+JUseJdujoqJRmDGDwXC58FRbW8nl8gUCDz20AUCnU7BYHI9zLxKEo6amwtdXyOV6WOGGIBx6vcrHh0ene+hdaLVa6utrBAKxx+vfKLMyhyNwfkUgXzi4XH6nq34Gg14ubxCLA9pzVugUm81sMul5PD83M9lxOK5dFrpXwdfVSauqKjZs+M9fQnDXrl0HANu3f7l8+ZPh4ZE1NVVCodDP7xZviLaNAwcmqVTKHTu+ttvtMTH97ruvC9lbjUaTQqG12awe3wWD4cPnS7xxdjAYDAqFVws4TCaHz/fK20Kr1SoUHmZ/RPj48Fr98uPjgcwI4E66a5VKaza7cBl1Hw7n7zR312h0KpVXnzmb7et+NiuXqNVaj6cyCC7Xz8selEoNg+Hh0xbB44lbjb25cyE2FgAg0L0sfwqFNjDQLR+RdqDw+RJvsg67w8WL5zgcTnLyMAAID4/IzEx33jtsWCcJ+/Lz80WioJgYz1ejvVTwVqslOzsnIiI+JqZrWY1JvFfwSqUiP//GwIFBAQEeOt+4VPDr14PRCAAwbpx/TEwnTr61tVXFxSXDh0eRYQhdBSl4LtfPy9xo3avg09JmtJ2+A8Arr7TEVsbE9GtbFdFl45gxE8aMmeCBDEajITc3+557Ok+F0R50OtNlIlL3UanUubnZc+Ys9LgHJpPNZHr1iJTJ5MXFRTNcfBvu0rYUb0wMvPRSF3qoq6vT6bx60fHx+TvVvmxoqJdKa93IwtIuXfIldklVVbWbuQvbg8327fygDikpKfbSGbitX/e0aTBtWhd6yMvLCQ7uyLW7YygUipcPAXcQCAQXLpyJiIjicLiZmZciIqK6dHpBwQ1f307KTHSMl/dIEERubnZsrOf1sSgUqpcyWK2W3NzsTl+GOoBGo7eVYfXqLvSg1xtyc7MnT+7KAL0VOp3F492G8B+cqhaDwWDuCAYNGtzcLN+16xuLxRIdHfuPfzzQ2xJh/t5gBY/BYDB3ChMm3Dthwr29LQWmj9C9iW7uBKxWq06nFQgEXq4jeoPZbDYY9H5+vVmV2Wg0WCwW51oAPY9er3M4HHy+tybfvwsGg95ms6Gw5t4CrYmQiaF6BY1GzWAwejdkUaVSstlsj2Nl/xao1SoWi+Wxc5n3EAShUim5XG4vZpey2+0ajZrP9/XYwc17/lI6wl6pReRM31fwGAwGg8HchfTy+wUGg8FgMJjuoI+vwVdXVx46tM9gMCQmDk5Lm+GlK2+nuCwU4VKG7hNMpVIeOLC3oaE+ODhk/vxFyF7XwzLk5GSdOfOnzWYbMCDxvvs6ulwPf0E9SU/eGh54iLt24OEHHR5vrvGqPOGdjd1u27x5U3l5qcVi/uabLTk5Wd16uYaGus2bN6nVKqPR8N13X50+faI9GbpPMIfD8emnHxQVFdhstkOH9p08+UfPy9DQUPf22xsUCrnVatmx4+uMjIs9L0Ov05O3hgce4q4dePhBh8dbe/y931s7pry8TCAQRkfHMhjMUaPGZGdndevlyEIRPj7s+PgBKpWyPRm6T7CKilIejxcXl0Cj0e677x8jRqT2vAwlJUX9+8f7+YnpdMaIEan5+bk9L0Ov05O3hgce4q4dePhBh8dbe/RlE71arfT3l6BtiSRArVZ16+VcFopwKUP3CaZQNHM4nF9//bG+vi4sLHzatPt7Xga73U785blJoVDR77+HZeh1evLW8MBD3LUDDz/o8Hhrj748gzcYDGQNeCaTZTR6k6jSXWprq48e/d1iMaNCES5l6D7BjEZjQcGNIUOGPvnkMwBw+PCBnpehX7+40tLixsYGvV536dJ5k8nY8zL0Oj1/a3jg3bUDDz/o8Hhrj76s4NlstsViQdsWi7lnwkPj4gasXPlcSsrIQ4f2tydD9wnm4+MTHh7Rv38Ci+UzatSYkpKinpchNDT8vvv+8b//7dm165uYmH4o+rmHZeh1ev7W8MC7awceftDh8dYefVnBC4Wi5mY52m5ubhYKPS+y5A4XL567fv0q2g4Pj1Ao5O3J0H2COReSolKpKM1CD8tgs9kGDBj09NNrVqx4NjAwGKX36WEZep2evDU88BB37cDDDzo83tqjLyv4mJhYhaK5oaGOIBxXr15OShrSrZcTCAQZGRcVimaTyUQWinApQ/cJFhvbTy6X1dZWEwSRkZEeF5fQ8zIYDPovvvhIo1FbLOYLF86kpIzoeRl6nZ68NTzwEHftwMMPOjze2qOPZ7Krqak6dGi/1WqNj09IS5vhZm1djzl79mRW1hWyUAQy2riUofsEq6qqOHx4v16vj4yMvv/+2b0iQ0ZG+tmzJ1ksn+HDR44ePR419rAMvU5P3hoeeIi7duDhBx0eby7p4woeg8FgMJi7k75sosdgMBgM5q4FK3gMBoPBYPogWMFjMBgMBtMHwQoeg8FgMJg+CFbwGAwGg8H0QbCCx2AwGAymD4IVPAaDwWAwfRCs4DEYDAaD6YP05XKxGAzmNvLee28YDPoFCxYPGJBYW1ut1Wr8/ERBQSG9LRcGg3ENnsFjMJguk55+7uefd1+9mtHbgmAwmHbBM3gMBuMW//rXvwGASqX1tiAYDMYtcC56DAbjFqSJ/uLFc7W11ajR31/yzDP/cjjsZ8+eKijIUyoVIpF4xIjUYcNGoQM2bvyvw+FYuvSxsrKS/PzcNWte6r07wGDuLvAMHoPBdI3k5BSTySiXy4KDQ4YMSQGAn37aXVxcQKPR/PzEjY0NBw/uU6lUkyffR55y4cK58vISOh0/cDCYngP/3jAYTNcYNmxUeXmpXC4LDQ0fNWpsZWV5cXEBnU5fvXotn++bn5/z668/Xrx4dvjwUQKBEJ3S0FA3ceLk8PDI3pUcg7mrwAoeg8F4RWVlOQDwePxr1zIBwOFwUChUh8NRWVmO5vcAMGJE6qRJU3pTSgzm7gMreAwG4xVqtQoAVCrl6dMnnNsNBj25HRGB5+4YTE+DFTwGg/EKX18BAAwaNHjevIfbO4ZCwRG5GExPgxU8BoPxEIvFAgBhYeEAUFlZrtfruFyeTNb0228/E4Rj/vzFIpG4t2XEYO5esILHYDBdhsvlAUBBQR6VSn3ggXlRUTGVleWffbZZLPZvaKi3223JycOwdsdgehdsN8NgMF0mNXVccHAoQRAqlRIAFi9ePmbMeC6X19TUIBb7T58+a9asB3tbRgzmbgcnusFgMBgMpg+CZ/AYDAaDwfRBsILHYDAYDKYPghU8BoPBYDB9EKzgMRgMBoPpg2AFj8FgMBhMHwQreAwGg8Fg+iBYwWMwGAwG0wfBCh6DwWAwmD7I/wO43AMJSJ1G4wAAAABJRU5ErkJggg==" /><!-- --></p> -<p>For DFOP with two-component error, a less erratic convergence is seen, but the variance of k2 again approximates zero.</p> -<pre class="r"><code>f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>To check if an increase in the number of iterations improves the fit, we repeat the fit with 1000 iterations for the burn in phase and 300 iterations for the second phase.</p> -<pre class="r"><code>f_parent_nlmixr_saem_dfop_tc_moreiter <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control_moreiter) -traceplot(f_parent_nlmixr_saem_dfop_tc_moreiter$nm)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>Here the fit looks very similar, but we will see below that it shows a higher AIC than the fit with 800 iterations in the burn in phase. Next we choose 10 000 iterations for the burn in phase and 1000 iterations for the second phase for comparison with saemix.</p> -<pre class="r"><code>f_parent_nlmixr_saem_dfop_tc_10k <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control_10k) -traceplot(f_parent_nlmixr_saem_dfop_tc_10k$nm)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOydeXxU1dn4nztzZ80kkz1khYQkZANcWGQRLFZQtCiKxVqt8tpaeKu276+1fdvaqvW12l3fWqsIWF/ASpFNjEDZZQuQCAkEEiD7MpnMvs/df3+c5GYyc2dyEyYb3O+HD5+bc8957rlz7r3PWZ7zPBjHcSAhISEhISFxYyEb7QpISEhISEhIRB9JwUtISEhISNyASApeQkJCQkLiBkRS8BISEhISEjcgkoKXkJCQkJC4AZEUvISEhISExA0IPtoVGBxbt3482lW4ucjOzrnjjvkjfFGplUeYrKycOXNGqJWlxh1hMjOz5869c4QvKrXyCJORkTVv3oLQ9HGm4C9dujDaVbi5wDBs5C8qtfKIwwGMkIKXGneEYVkWYKQVvNTKIwzD0AACCl6aopeQkJCQkLgBkRS8hISEhITEDYik4CUkJCQkJG5AJAUvISEhISFxAzLOjOyGg0ceeaSsrIz/k+NYn8/f0dFx8OBBo9HIp//gBz9ITk5Gx2vXrjUYDOj4jjtmL1lyLzquqanZsWPHqlWrcnJywl3uyy+/PHz4MACkpqatWbMaADiO+/Of/+x2uwdV7aysrDvvvDM7O9vlcjU1NR48eJiiyEFJuIHR6XQajZbjWLPZPITi0iMxNrnOZh1u1qxZk5qailp8bAoc71znAzDCv+cvf/lLHMePHDly9OjRCNmUStXMmTNMJtOVK1eiXgdpBB8Mhsm0Wm1BQcH3vve9wK98INnZfR/rzMysoV1o6tSy3itipaWlgypbUFD41FNPFRYWajSa1NTU2bPvePLJJ5RK1dBqcuMxZ87c//zPNatWrYqKNOmRGCNEt1klxh035ANw553zv/71rw/2fReJNILvgePYDz5YBwBKpTInJ2fhwgVyOX7vvffW19dTFBWUOTs768yZ0+g4Kyv4a/7ZZ7uVSgU6fvzxx3U6ndHYtWvXZyiFH5YFqorS0tLTp0+LrCqGYd/4xgM4jjc1Ne3YsSM1NfWxx1ZmZ2ffcsstfK0krh/pkZAYXT79dBuOy30+32hXRCL6ZGVlJScnl5aW5OcXDN9VJAXfA8cBP8Xa0tJis9keeeSRmJiYmTNnnDx5is9ms9kSEuKzs7PRnzExMfHx8VarNTExkc9jsfRNH9E0DQAEQfLCEVlZWfHx8QBw+XJdcXFRdnaWXq93OBxiqpqRkREbGwsABw4ccLlcLpertvbS9OnTp02belN9zYuKimbPnj1hQjpFkY2NjUePHrXZbABw5513FhRMBgCVSvnggw+eOnWqu7tbqVTNmze3tLREr9eTJGUymU6ePBl5Tkx6JEaFYW3WlJSUJUsWZ2ZmGwyd5eXlq1evxnF8586d1dXVYuo2ZUrRvHlzU1JSDYbO/fv3o75aRcXpffv2CuZPSEhYtGhRRkaGThdjMpnr6+tPnDjJsozI+qxY8UjglLJcjs+ePWv69OkJCfEEQRqNXcePn2hubh7EjzseGO73OpDrbCDxyGTyp59+Kjs72+FwrFu3zu1233//AxMmpA1KyBCQpuiFuXTpktfrhf5TrwDg8/lMJrNer0ffUzRWa29vH6z8qVOnAoDb7d67dw8AB4CVlpaILJuSkoIOLBYrOkArUmlpaQCj4JdmVJg7d87KlSsnTZokk4FOp5s+ffozzzyTlJQEAPn5+SkpqQAgl+O33HJLbGwcADz88EMLFixISkrGcYVWq504ceJjjz02ceJE8VeUHokRYFibNT4+/nvf++7kyflqtSo3N/fpp5+WywfxAZw2bdpjj30zOzsbFX/qqe+o1ZFWQLKyslavXl1WVpaYmKhUqjIzMxctWrRq1dMYJhtafe6/f+k999yTmpoql+M6nW7y5PzvfOc7kydPFn8LY5+RfK+j3kARuO++e7Ozs0mS+Pjjf6IZu2PHjn7+efnnn5e7XK6hyRSDpOCFYVnWarUCQGJiQtCptrY26P2Oo/9RingwTIZWXC5fvuR0Oltb2wCgtFR4cTeUmBgtOiBJMvAAx3GlUjmomoxTdDrdXXd9DQAOHTr0xhtv/uUvf7HZrDExMUuWLAaADz/8EI2wvV7vq6++2tBwLS4ubsqUIgA4cuTI66+//u677/r9fgzDioqKxF9UeiSGm+Fu1vvuu1ehUHq93o8++mjt2rV+v5//lA+IQqG45557ADCz2bxu3br33nvfarXjuCJcfgzD7r//AaVS6Xa7//GPf/zud787ePAgAGRlZc2cOWMI9cFxxS23TAeAkydP/va3v3377bfNZhOGYXfcMVvkLYx9RvK9jnoDReDWW2+dMWMGx7Fbt37a3d1jpXvp0uWqqsqqqsphXYKRFHxYvF4PAMTF6YPS0bcbDeMyMzMBoK1tcMO1vLzcmJgYAKitvQQAtbW1AJCRkZGQkDhASQAAkMtxAGBZhuNYlMIvCeO4fFA1GacUFhYqFAqapk6cOAkALpfr7NlKAMjNzRUcsFIUtWnT5k2bNp88eZJlOY1Gg+M4AGi12kFdV3okhpVhbVYcxwsKCgHg+PHjzc3NBoOhvLxcfN0mTZqk0+kAoLy8vKOjw2js2r37swj5k5KS0QTswYMHW1pa/H7/8ePHW1tbAQD15AZbH7VahbTLpEmTSkpKSJLctGnzunXrDhw4JP4uxjgj+V5HvYHCkZmZef/9SwFg//79165dG4KE60Fagw+LRqMFAKczeBEUfbuzs7MwDMvMzCBJsru7e1CSkS2V2+1Gz9OlS5fuvfdeDMPKykqPHTs2YHG/3w8AMplcJpOxLAsA/EiCpplIJW8U0JQdjit+9auXAtNxXKHXx4WuW/t8PpOpe+7cOUuW3JOUlCyTDbFfKz0Sw8qwNmtSUjIKrMCvnrS2tnEcJzLaQlJSIgBwHMfPzRgMXTRNI40SSnJyYu9VWvnE1tbWnJwcdJuDrY/b7b527Wp+fkFGRsbDDz/McVxnZ+f589VVVZVi6j8uGMn3OuoNFI6Cgh4bOmRhM8JICl4YDJMhIymr1RZ0ymIx+3y+9PT09PR0pVLV1NTED5vEgON4cXExAOh0ul//+teBp0R+zdFKMAAolUr0ZVeplABA0zQ/Q3tjI5fLAYAkybq6OsFTQWi12u9///tardbj8Zw9e7a9vX3evHkTJkwY1EWlR2K4GdZmVSh6vnUcx/EH4r/XfLbe0j0CBiwYmAXlR/cyhPr885+fFBVNKS2dmp8/WalUZmZmZmZmTp6ct2XLFjG3MPYZlfc6ig0UDpfLFRsbO2PGjNOnz1qtlsEWvx4kBS9MSUkxmjIVXExta2srLCycOXNmuAwRKCjIV6mEbXNSU9OSk1PMZlNkCWglGABSUlLQ1ZGNVXd3N8DAX5wbAGRKxrLcjh070S3LZHKFQgEABEGE5i8tLdVqtQDc+++/j0xavv71rw/2otIjMdwMa7MiS2wASE/PQGOyjIx08WM+1KvDMCwrKxON+SZMmIDqJojZ3NMiOTnZNht/nAMAJpNpCPVRq9Uajbaz03Dp0mW5HM/NzV24cEFWVlZhYaFKpRL8fcYdI/leR72BwtHQ0LBr164XXngexxX33HPPli2fDFbC9SAp+B4wDNLSJgCAUqnIzs7+2tfuAgCPx4MWgYJAX3PklmSw9tJTp04DALfb/fHHfSGT1Wr1d77zJABWVlZ65MiRyBI6OzvRLqx77723vLw8JSUF1eTcufODqsn4paGhgWUZtVo1Z84dp05VaDTqlStXTpw40Ww2vfvu3/kuuUqlksvlDMP0dsaxrKzMa9caZsyYodf3W0dfsGCBRqO1Wq1nz57hE6VHYoQZ1mb1eDwGgyE9Pf3OO+d3dnZQFP3AAw+Ir1tTU5Pf71er1ffff/+OHdsZhvvGN74RIb/FYjYajWlpaXfffbfFYunuNs2ceTuy7r506RIADLY++fn5jzzyCABs2rSpoaGhtbWlu7s7KyuLYZhQrwzjlJF5rxFRb6BwtLW1uVyuM2fOzp07t6hoysSJE1taWoYgZ2hICr4HDJOtXv39wBSGYfbu3Svo7BMNkuRyHIAb1NdcpVKhJZn6+vqgbdAGQ1d6erqYrzkA7Nmzd+XKb2ZkZHzve99DKd3dxpqawW3NHL9YrZaTJ0/Nnz9/8eLFd921UKFQYhhGUeTu3Z+j+TSHww4Acrn8Jz/5yZYtWxoamr7+dRbDZN/85koAYFnW7yfUahXa2AYAt956a3x8fHNzc38FLz0SI8pwN+uBAwe//e3HY2Njv/vd7wIAQfjFz7iSJHn48JH77rs3NTX1+99fDQBut5uiSIVCeJMCx3Hl5eVPPvlkbGzsM888w6e3t7efOdPzjA2qPteuXbVaLYmJSU888QRFUQoFjuzOTp8+jawubgBG5r1GRL2BInP8+PEZM25XKlVLlixZu/aDEZtXk6zog2EY2mQyXb5c98EHH1y8eFEwT2dnJ3qpLBbLoDY5FBUVIauc+vr6oFMoJSkpGY0aI3Pt2tXNmzfX19d7vV6LxVJZWbl+/YfjaLX1+jl48ODOnTuRKnW5XBcvXvzgg/W8vcy5c+fr6+tIkmRZlmEYo7Fr27btFouZJInm5uYPP/zw2rWrAJCbm5uWNrCvCemRGDGGtVkbGxs2bdrU2tpKEER7e/uGDf8IdGkyIGfOnN62bVtbWxtB+Jubmzdu3EQQkX7etra2v//9vYsXL9psVpIkOzs7Dx8+/OGH/+D18aDq4/cT//jHRxUVFRaLheM4giC6urrKy784dOiw+FsY+4zkex3dBoqMz+erqDgNAOnp6dOmTR2akCGAibETGTu8+urPR7sKNxelpdNWrPjWCF9UauURpqSk7NFHvz0y1xqtxsUwWWpqKgC43S6PxwMAWq32xRdfBID/+7//a2pqGoLMH//4x5E92Y1wfQQpKipdufKJaEkTyQ3wCo9YA0WFKVOKH3vsO6Hp0hS9hITEjQ/HcU8//ZRarTabzZ98ssXv9913330A4Pf7OzsNAxa/4esjEcSN0UCSgh9z3HrrrcuWLYuQ4Z13/hbo21zihkd6JKIBt3PnzuXLH0pOTn7uuR+gJL+f2L59O0H4R+MXjlSfqF5IYmiMtQdmKEgKfsxRX1+/bt26CBmQpYnEzYP0SESF+vr6t9/+35KSkoSEBI7jrFbr5cuX/H4ChvoLHzx4UKFQGo1dUa+PxFgg6g/MyCMp+DGH1+vl/ZZISID0SEQPn89XVVUVmj60X/j8+evdhRiuPhJjhOg+MCPPOFPwt98+iz8mSZJhaOQ9dEA4jmMYSi7HRUYL8Pt9GIapVGoxmVmWZVkax8VG9fB63UqlKkKkiv7CaY7j5HKRmVm/36tWawUdMly5AgwDAJCaCklJAAAMQ2EYJpOFfQzS0zPFXDe6DLmVATiavnlaWSOTCbj34ls5JQWSkwFEtXKGmOtGhcDGpSiSpimNJkZc0bHVuAqFMtweuRDho9m4Eyaki7ludBmbrcxxLMMMYyuzLCf6fY/UylevAk0DDKaV09LCtDI3brl27dKpUwdFZqYoorOzzu/3iMz/1Vcna2u/EpnZ67V3dtaxLCMy/5Ej5W1tjSIz2+1d3d1NIjM7nbbDhz93OGyCZ+PiOAAOgHvjjZ4Uk6nJbu8SKXxUaGi4fPLkYFvZLTL/uXMna2urRGb2eh2DauWjR79obW0QmXmQrWw/fPhzh8MqeDYhoaeV/+d/elJMpia73SBS+EjS2Fh38uQBkZlpmhxk4566eLFSZGbUuAxDi8w/qMZ1OIzd3WLfd5fLcfjw53a7RfBsUlJP4/7mNz0pJlPz2GxcnsbGuhMn9ovM3NvKLpH5z5+vuHBhuFr5yy/3tLRcE5l5CK1sswm3cmpqTyu//HJPisnUbLN1ihQeyLCM4Gtqzh09epCm6eLisiVLlqK+WG1tzYEDewmCmDy5YNmyh4O6RZHPSkhISEhISAyK6Du6MRoNe/bsfuKJVc8//+Pu7q7KytMA4PG4d+7c+tBDjz7//I89HvfJk/3iZ0Q+KyEhISEhITFYoq/gr16tLyiYkpCQhOOKmTPvqK29AABWq0Wni504MVej0U6ZUsIHvUdEPishISEhISExWKI/Rc8wDNfrHQ/DZHa7DQBSU9NIkqyrq50wIaO2tmbq1FsCi0Q46/f7jx8/go5TUpJ4J8MAYLdbaZpqbAwOLCgIyzJer8Nkci5btuKtt/44oLNAv99HUaRI4TRN+P0en49F3qHFYLEYSVLUfhiC8DAM7XKJ2hpLkn4A6OhoMZsFtu6wbCHq0lmtpsZGCwD4fA6ZDLdYguOf8sTExKWljZwF1rhm3759L774YnV19dC8Vd+EfPHF3vff/6CmRtj7r8SNQWtrW23thblzBx2/UeL6ib6Cz88vrKg4YTR26XS6iorjfr8PAFQq9dy5C7Zs2aRQKOLi4m+55fbAIhHOkiRRVdXj+r+kpCQhIY4/xTAMANvZ2SqyYizL2O2OlpbWixdrkpP1kTMzDAMAIoVzHMdxrNPpFjwFAcGk+USHw+ZyOSPI/PDDTe3tnb/61U9RZHG73SGyJgBgNncJ6hiOy0cK3um0o1tDwjEsbIji5OQJkoIXSWNj44ULF1iWFYxdLRFKS0trQ8PYcvkpEXV27Nj10Ucb//M//2u0K3IzEn0Fn5mZvWTJ/du3f4JhWEnJVLTfv6mpobKy4oc//KlOF3v48L+3bftnoOPcCGfj4vQ/+9mvBS/U0HDZZDLcccciAHA4HHV1dbNnzw5XK5omTaYmilICQFHR9PnzF0e+i3PnTqlU6pKSW8Xcss/nsNu7JkwoCN3a8dRTT6nV6vfffz8w8ejRL/LyirKyciPIXLt2k93umj9/scNhJElfSsokMTVxuexVVSemT58dFxcfepbXO5MmFcyfXwAAZnOzQqHR6wcOzCAxINy4CuswKnAcGxiuA0X1YBhR0U4ZhgYAlqVF5kfdbpGZUa0YhkJdXpFFRNeEBeBE1yTybSoCrs6CiNuMvL1K4sYm+g1P03RxcSkahdfXX05ISASApqaG/Pwp8fEJAHDbbbPWrv1rYJHIZ8WwYcOGV155xeEQGOaeOXNmx44dr732KvR+gkfyQ9zV1aVWi9qjOSAsyxoMhszMUdiYLiEeSc1HwOt1OJ3d/J8U5eM4rru7UbwEu12s2ziK8rGsbFDCzWaxgbo5jnO7rd3dg4jTKrImBEEAgM3W6fcLLJyxbD6AHAA8Hlt3d8/EG00TPp8rnEC1WpeQMKIfDZL0BtaHpkkAcDhEWVahDpbHY/f7PWLy0zTJsoxI4agmTqdJ5CIax3F+v1ukcJL0ia+Jz+cFAI/HhmECPTOOS0FTrQThcTjcAMAwFMuyEYQrFCqtVmBQF30F7/V61q//+zPPrFGr1SdOHJ01aw4AZGVll5fvmj17Tlxc/NmzFVlZOShzR0dbSkpquLPi8fl8NHINEMLnn3/+t7/9DSn4ked6PvdBZbdu3frUU0+5XC6FYmBfCjRNoyCk4w6S9BGEO+BPL8exLpdJTFk0DvP5HCQpyskUw1A0DSKFo6+D220OZ2nh97sBwOUy8b88QbhFCqcoH8vSIjN7vR4A8HrtGCbwzHNcMqohQXhcLi/0jH39EYTjuEqjiQt3Noqo1TocVwVeFwASE7PFlGVZ2m43xMamKBSieswGg1mhUIgUThBej8cSH58p6CEqFAxr1GrjRQr3eu0U5dPrRTmc8XjcAG1xcamxsQLfa36OUKPRJyZqAcDpNMrlipiYxHACBV2pDCsMw1BUn80Qx7EcxwWmRIQDAIYhRQZm5TiW40CkcCSTovzirWQYhhIvnONYkZnRx4SmScH8/JefYWiUAfV7IggPd0fR1wFxcfp58xauXfuOSqWeMWNWWdl0ACgsLLZYzJs2fUhRVFZWzoMPrkCZN2x47+mnnw13Vjx+vz+cKg2n+KPF/v37CwomqlQD5xwUobfjdrsJgqAoSoyCnzJlyksvvbRq1aooV2v4oWkyqPvPsmyEAUog6EcjCK9IN1gsyzAMJl642+0hCEqrFXarR9MEAHi9TtRAHMdRVKShVVBNOI4Tmdnv9wMAQXhlMsFnPgkpeH5gx3EMTXMsG1a4SsWOjIKXyxWBDt1kMjkAp1KJclOIZqEVCpXI/DKZXCaTi8yMJsZVKmHPYoLguEKkcIJw07RMZGaKogFAoVAL5uc/4ziuUKkUAIBhMrkcFyl8ZNBoYjWaPmtohUINgCUnTxRTlmGo7u7GuLhUlUonJn9HR5dcjosU7vM57XZDUlK2yFbGsMsxMQkihTud3QThEZnZ7XYCXNPr0+LjBXpmfCdTq9UjczGzuQXHlfHxg3ZKOCyDvFmz5qCBeyBz5tw5Z86dQYm/+tXrEc6KJ8JAOdCqf8DMQ+Bb3/rWihUP//d/Pyd4liRJHMebm5snTZoEAFu2bKmurl6yZL4YyddT7e7ubqNxXO421Gr1Wm2fCaTbTbpc3tTUPDFlkaVFfHy6SiXKL2Znp1GpVIkU7vM5V61ak5iYsmnTZsEMOl0SACQlTUSLMnV19Tpdkkjhg7S0cDQ1NSUkZMTFJYSe5XVATExiamoi9FhaqPX6CWKEjyQ0TUsrGhISw8e4nMUNJfAzceXKFYqiSktLAcDtdgeGBBD5NfnZz34WF6epqbmYkjLhnXfeiZyZYZj33/9gwoTEl1/+bejZ1tbWlpaW4uJij8cjk8mOHj365ZdfilTwoRcSfwsSw4Hb7RlwflhqIPFIv5WExLBygyh4ZI6L+MUvfuF2u/fu3QsAy5cvP3jwYEyMyDgHPezZs6ewcLLBYPR6B96qTpIkAIQL8shxHMuyfr+fYRiZTIb8A0cWSBBETk4OjuPp6f0mZAb1NeQv9Jvf/Obhhx8uKysTX1YiHGKaT0I80o8pITGs3DgKnv9Y0DR9/vx5g8GQnp7udDpDP8oDflZQEYoiw+V87rnnaJp+7733GIahKEqMzO7u7o8//ljMF83n83V3dwOAoIIf7Dfx9ddf1+l0koKPEje1gq+oOHHq1DGapnNzJy9b9ohS2S9gRGtrc3n5Tq/XW1Y2bfHipWLMIG7mH/MmQ2ro0WEcK/ijR49VVp5B++CDvhRGo7G+vj49PX2YviAtLS12ux0A3n77baTgw8FXIDc3l6Ko5cuXi79KVCrPsmzg9Mbosm7dux0dbeh41qw59923bHTrMxyMnV87uhiNhlOnjj3zzBqlUrlly6ZTp44tXHg3f5ZlmW3bPnnooUezsrI3btxw8WKwt0pBMAyTVLyExPAxjhX8mTNnd+z47N13e/5ExnQYhoXqRT4l9JTJZOru7i4tLf31r39NUdQbb7wRWMrn85lMppSUlKBSDoeDJEmO437yk5+IrC3qB6B19MiE0+uD1ff8mv3YGSfZbNaf/vRXaOQn0tD9OikvL29ra1u9enW0BI6dH3OEsdms06bdGhenB4ApU4qNxn770RsbG/T6+NzcyQAwe/bcc+eqxCj4m/bHHCNEnnQR7I4PYZ5GYhQZx82DYZjNZl+/fj36k6KowMF04Lcj9Dvy73//++rVqwDw5ptvzpgxo6mp6bXXXjt48GBQtkOHDi1ZsiT00levXuUD7qKUixcv1dUJOK4PGs+FmvRHgOM4q9VaX39VZP6gskjBj50BJUmSGIZpNFq5HJfLcZEbjq+TTz/9lH9CRoYbVWkVFZXeffcSr9fT2HjtwoXq4uJ+iz4Ohy05uacfnJKSivxXiuFG/bnGPmjS5d57v/HCCz/p6Gi/eLEmKAPqjr/00msvvfTakiUPiCkiMdYYxyN4AIyiqH379j3zzDOBqaGqPXQN/oUXXli6dOmf//xniqL8fr/b7Yb+A31+Ud/pDOsxPlDsF1/sT0v7AwD21FNPLVy4UDAPABw4cCAvb4B9U5988gl//Ic//OHTT7eePHkgnECe2bNnr1ixYvXq7wXl5DgOeQKor6/3erMBRm2/rM1m5TjuvffedjqdOTkTH3hguU4XO3Cx6ya6KuQmV0jt7a0HDuzlOC4lJTUw3ev1qnp9QSiVKuSoC+HxuNev/zs6zs8vSErq2wPpdNoxDE6fPizu4hzD0C0tHSK9lBCEH8MwkcKRD93W1k5xNQGWZVtbGzo7RXm+Q04OGhubRUoGgEuXvhLcq03T85G32vb2ptOnmwGAYWgMw2Syy+EEJiSkFBYKmOBEnnThu+Pii4RDcFZVYmQYTwre7bYGeuOiaT8AnD59ymCod7stAGAw1CuVSuRQzGJpMxjqe90AcSZTEwDY7Z3of4LwOZ1mg6G+qekKAKCzFOU3GOoBgKaJ3bu/SEtLAQCGoVBiIMi90RtvvNy/evbPP9+bkZFYWNi34Zim+63QEwTR6y6x22AgBW/zr399Gx34/V6bzUiSBE0TBkM9ctLZ1XXV7e7bFMAwDAptcv78+dhYzYoViwHAbG5hWdbttnR0XAYAp9NkMNQfO/Zvmn4KlXK5TAaDFR1TFOH1hh1vqdWxCQnRCTZDUWROzqSlS5fFxMTs3PnpF1989s1vfru3Ps53330LHQeFFGJZhmW548f/LfIqLMvIZM28szmjscPtdoYrzjA0hoFI4RzHeTxehmHD5b927RIAnDp1UKPRAADHsU1N9S0t18QJZwGgvv6KyJoAQHX1GUFVR9NfQzqgpeXa8eONvPAIs6nJyWlFRdPFXLqwsLiwsPjUqePl5bueeKLPjZJGo0FxIwGAJAm1WsOfwnE8Ly8fHaekpOr1sYGnOA4SEpLFXJrjWJ/PqVLp5HJRXy2zuVsux0QKp2mSJL0ajV5k78Hvb9NotDqdKO9AJOljWVqtFtWdpSjSZOqKjY1XKgWcZwV4stOiW/P73TKZTKkM23EP142OPOki2B0f8jyNxGgxnhS8ShUTOK+LXGJ5PD69Pk2pjAGAuLhUlUqlUKgAICYmXq9PQ3k4jouNTXa5zMhdl0YTj2EylUqr16ehNaQOfFwAACAASURBVHHk6FEuV6CwKzIZzjCMy+UBAAyTCcVikWGY/Ne/7rfxXaFQcxynVMY8+uiq3/zmlTvumAUAoRPR6BVVq2MFQ7wwDMN3dlmWU6m0KFZETEzC3r2HAaC52ThvXo8Todde++2ZM5W7d2/nfxCdLhGgXadLBOBUqhiz2YN+N70+LdB7jFodq9crAMDttsjlykC3U0HI5cpwpwZLVlbOypVPoOPZs+du3Ng3c65SqebP75n2SEhI0On6ejBWq8ntdubkTBZzCZZlPB6rRqPH8Z5qx8TEKpWqcMUNhha5HE9NDfbU3dTU/L3vrd669ZOEhD53oRRFKBQKtVobTlpiYioAZGZO0ul0ANDUVB8fnyzoqSoUv9/NMFRMjIDjmlAIwt/R0TxhQqZKpQk9y4/89PrEnBwMADwem1yOR1AwWu3A+0hPnjym1WpRjIns7JyzZ08Fno2PT+QnbC0WC4orgVCp1A88IGxbqtHEcBxXWDhA7GYE8nGWmJgl0ouR11uhUChECkc+ztLS8kX6OOvqak9JSc/OFuXFCPk4S0mJFFyKx+12mkxd2dl5er3Ak8PHi0pKSissTAMAs7lFoVANwYtRhEkXCNMdj1DE5/Nu3LgBHU+alBs4T+N2OwGgquq4mFpxHEfTREeHUeTqvtfrwTBMpHAUH6izs1tkXG+WZTo6mk0mg5jMDENzHNPa2iEuMwMA9fUXcFxwnmY26qMbDK1VVa0AQNMkhmFyeUM4gXp9Yn5+SWj6eFLwCkWP8kbwHXmtNh6lq1SxWq0Ww+QAoFLptNp49LoyDKNWx7pcZrlcDQAqlRYAw3GVz8dcvlwPAOjb19zcsm/f4eXLl/c+WxwAuFzuY8dOB63EsyxbX38laN4JxxUcByyLnThxqqmpbdEiFLAu+EliGNbt9iiVGsHYAN/97nfr6nomDDBMhuMqAEwmk9O07Nix4wBw+PCxe+65D2WwWh0mkxnJ4ThOLlegG1GrYwEwhUK9ceMnAKBQqLXa+MA+vkKh1mrVAOD12nFcKViTqIMMdjIzswFALpfjeJ/LUqVSNW/eQsFSNE35fF6RCp6mSZOJC9QBCoVKoVCGK261dguq/6tXm69evaZU9tPlPp9TocDVanVOzuTz58//7ne/+/jjjwMHfAkJSQCQmTlJr9cDQHPzlYSEJJE6YLCe7Do6mtPSMgU92fFdyvj4xJycqHmy0+v1J04czcmZpNXGnD1bkZPTU1UUTiIvb/Jnn23r6upMS5tQVXVazMwtACiVSmkD1WgRYdIFwnTHIxSRyWQorhgAaLVatVobeAoAAlMiwHEsQbBKpVpkBDy0ECNSONrUjEZNYvK73U4cV4gUTlEEy9IifQbTNOX1upVKVdBe0156qsdfnSBAJpNF8LIlON8D40vBRyBwrR156g5MD/0T2cfV1tY2NzcDwKFDhwDAarVu3ryZ38nG9cRWN//kJz8JUvAcxwU6yAtM//DDDyNXtbm5+cEHv3XmzAnBcLFms5k/7u7u3r59e2jN0YHBYNi1a1dqaioAsKhr2mtjuG7denSMIlONEZ+1Dod9377yVau+r9frz5w5VVQk0N+MOp2dYldVQxFcOESJVVVVn3zyyYYNG9Bs/IClbgBKS6dZLOaNG9eTJJmbO/n++x9E6SicRHb2xEcffXznzk8pipoypWj6dFFxljWa6ARalBgCESZdIEx3PPI8zaOPPi54Ia1WBwClpbeJqVXvPE2mSF/01dWn5XJcpPDBztMcO7Y3LS1T5OhisPM0lZXHcnMLBWf4+DBhKSnppaXpMNZ80Y8kka3lESzLbt366V133Y4y0DTd1tbW0NBw5UrPkuf/+3//L1CCz+cLlEaS5L/+9a9HHnlE3jtBJnih/fsP0DTd2toKAGvXrj106NDmzZtDc6LJGbd74GCIVqvVarXm5GQLXrG9vd1oNKItfAzDBFrL//73v0dXQQq+urp6wGuNACUlU+1220cffcAwTF5ePrLLHW6GtktwwJ2KN6oWj8yCBYsWLFgUlMiHk8jOnrh69QuDEijtgx9Fwk26oCkZwe740OZpJEaRca/gEfzAl2GYjo4OAHjrrbdaWlpMJhP0mM3/8Oc//9H99y9HeRiG+eKLLw4cOCAoLWhr2ZUrV1auXLlhw4aysrKZM2dCr+IMwmDoW6q5cOHCuXPn3nrrrQFrfvny5YqKilWrVh0+fLikpMTlCg755ff7jcbu9PTgBdTASQuuJ4oaQdP0mjX/ZbX2iySNbmcs6KS5cxfMnbtghC86hBtHM3ihBXl7YJRBcAviYPcl0jSNOpQSEiOJTCYXnHRBUzKC3fFwRSIjWdGPIuNYwQeuo+zbtw8ddHd3o/3ou3fv3r17N5/BbDb/5S/vIgX/3HM9kd9CI8meOXMmKSmpd9dcv1OvvPJKcXHx3r17OY4LXAUQBH2y29vbQxU2j9VqtdlsmzZteu+99x588MG7775bo9GEzvx3d5t++ctX16//CP0Z4VU5ceKE2+2uq+vZN7958+ZwgU1vHob2ZUGl7r777ldeeSXISU7UP1Wvvfbanj179u/fPXBWCYmoIjjpwk/JCHbHhzBPIzGKjGMFj+Bn3dGfEaK/u1zuAwcOAgDS3xBm3ps/DhqK0TRN07TD4aipqRE5SqupqeGvFcSXXx675577ZDJZQkKC1Wp97bXXwq3rA0BHh+Ho0aP8n93d3Y2NjSgl0OwArcTz2a5d69mgFdmZ7o3N9TjyMxqNLS39NjoHTdEHSXY4HKGJA2K325Hb45sSaWwnITGMjGNPdgik0YOcugjicDiff/6HgSmRPy4U1W+futls5jjuo48+uueee0TW7R//+Ee4U06nkyRJv9+PLOC2bt0aQU5V1blHH30UHbvd7nfffffRRx99/fXX+QyRNQRap7h5aGxsev7553kbyRFzdINma6K45H/DgyZvb9rbl5AYbsbxCB5N0ZMk2dTUxCdGXs4c1KeEYfoN00mSdLvdJEmKHxAfOXIk3Kny8j3oAE0GILuBCPA1r6qqWrRoEUVRgbMI/LHgDXZ3d0+dOvX73/++yGqPLjRN0jQR+CfHcX6/CwAIgvjss92PPPJwODe3DEMDwIkTx955551f/vKn8fHxTqczKSkRFQ+FZRmGoX0+59mzlTNnzuAXfUjSy1+dL4ucJjEM7fe70LHP58Lxvh8cuTAiCLffr+pNIcJdOqAUwXEsw1Acxw6YObB6JOn1+wXeX47ToW02NE34/WTvbVIRhMtkuFIpsKU+6pCkjyD65rQYhmRZ1uEw8uarEUDuenw+B986kWEYCsPYQNdYEUCPnNttFu1cnSMIj0jhJOljWUZkZjSN5/XaZTKBuBUcl4RGZQThcbm80PPMcxGE47gK+f+QuAkZFgVfU3Pu6NGDNE0XF5ctWdITkKC2tubAgb0EQUyeXLBs2cMKhcD+P5Ikz5+vmjVrjvhrEQQxb948NDtqMplWrlwZrbsIJcj//PVw6VJY15Ji6mC1WhWKHh8+77zzTmJiz14LwbUDt9t98eLFAfsQYwSCcDudfV8rv9/NssyXXx6orDxH0/TLL78xeXJmbu7E0IIvvfQ/t99+y/LlD3i9TgAwGls5zsswNE2TNpvwZjmaJgGYEycO3XPP8v37d5SWFqN05BgRAHw+V1BZiiJttk7k+89uN9B0n9MVn88FADabQSYjoGcvpcNmG0Bh+P0ehqEPHNg/ZUohw4jqO/r9BAC4XGaKElgA4rh8AHlv5XtuhGFoggirF9Xq2JFR8DRNol+pt1YMAPh8LjEKHkEQPgwbwAKmVziNYVzg5SLQ23twi9whzXFAUX7xwjmOFZmZIPwAQBBewZ+E43redP6X5DiGYSIJV6lYScHftERfwRuNhj17dj/77A9iY+M+/vijysrTM2fO8XjcO3dufeKJ/0hNTdu69eOTJ/vFmuTZu3d3S0uTSAXPv4q8+frMmTMjGLVFi1GfUWRZlqIoHMdRZf74xz/eemuPOWsEz/njxVRbq03QaPrcYHm9rMfjf+WV3586VVFQUAAAen1GWlp+aMHjx8+o1bHLlwPy25OcPDEtLV0mk8vlCsH8AGA0WhQKlU6nAgCdbgKfTa9v6q1MPJ945szJzs6uvLz8tLT8uLhUAEhJyY2L6/t0Ij908fFZaWkZAHD1amNsbHJa2gD7YjUavVyuWLPmx2vWfPc3v3kjcmaEy+VsaWlLSMiKixPwUMTv8dXpEtPSEgDAam3FcTWqsyAitdr1o9XqAz0qIq8gqal5YhQ82iEdHz9BpCe7zs5uhUKRmirK0RDaIZ2SMknkDum6unqdLkmk8MHukG5sbEpIyBD0ZMdPXcXEJKSmJsB1eLIbMaTNkKNI9Nfgr16tLyiYkpCQhOOKmTPvqK29AABWq0Wni504MVej0U6ZUtLdLeB6pa7uksFwXaNMq9U6rAZlo67aUR3a2tr4ytjtdpfLxdvWRViMHwuVFwOGYTKZnP+HdA9NM9C7kBF4NvBf0OwFSgQAjgtbBADDMAxN+Adel/+pAhP/8Ic/Nze3YhjIZHI0KRUkDVV13br1LS2tPp8fADBMFu7SgaU4jmMYlqLoATP3/kNXFxYe8EvK+t9mhDqMpi3OeHkyJSTGHdEfwQdGRMUwGXJtmJqaRpJkXV3thAkZtbU1oR4S3G7XkSMH7rvvG599ti0wnSCIysoKdBzkpRzNZY0kdrvtlVdeHjjfcEKShMvlgF7rwq6uLgDwenvc5ixbJuz0GwBQKYTdbmlttUOPl3KFwyFs6g8AWq0uOVnAZ/6oEG6XASJIwfN2lwPqj9AMgg5tkHyXyw29c8skSQLAhx9+mJKS8sADPX57Xn311ddee+0Xv/jFPffMjXxdm82m0Wj4GkaoZ2dn5/Hjx7/5zW9GFjjuGLGZA4mRxOu1u1x9HjlJ0gcARqOoqEsIu90g0l08SXplMrlI4egVQ6HFxOVn3W6L0SiqA4pWeUTWBO17stk6CMIaepZlc9Eqm8djNRqtAMCyDE0TRmNY92gqVYygn7voK/j8/MKKihNGY5dOp6uoOO73+wBApVLPnbtgy5ZNCoUiLi4ehawIgNu169PFi+8L3bdNEP7jx3t2iAXFGSNJAW8zw0pLS+uobzkjCL/X26djECgFAnYAhtLe3sof2+3W1tYmEBVnbMJYUPAi/RUKqmqRA0SUrbGx8d577/3FL34R7lroF0ZbEFETfPDBB7m5ubyCRzURsyDyjW98Y9asWaHp58+fLysrw/G+d3Pr1q0/+9nPblQFL43gbzBwXBW4EIOChgSmRIDjWI/HrlJpRYa5kssNMplcpHCaJvx+j0YTJ7pniSkUapHCCcLLMJTIzCiWjEoVo9UKhIDiq8df3et1ymQytTqs+14+vFZwurjaDILMzOwlS+7fvv0TDMNKSqaikIJNTQ2VlRU//OFPdbrYw4f/vW3bPx977Dt8kbNnK5KSUvLyCkym4Kn7uDj9z372a8ELbd36WdQrH5lR1+4AYLHYvN6eGLh8ohgLKa22r280aVLB/PkF0BOGRCMY1274GIIpZSCC+uC///u/m5ub0caHUB9/oVgslt27d99yy5Sg9La2tqtXr6J5EZ7KykqbzRYqNsIkgRil5XQ6XS6XUqnsnfRCwY1ct91229atW6dNm/bCCy+UlJT86U9/Yll2sA7yxgUymaTgb0CUSk3gFwnHlQBcbGyKmLIMQ3k8do1GL9IXvVzeKJfjIoX7fE6/36PTJYm0tMAwTKWKESmc47oJghWZGcOcAKDVxsfGClha8N0PpVIbG4uCzXhxXClSeCDRV/A0TRcXl6Ixen39ZRRiqKmpIT9/CgpOcNtts9au/WtgkY6O9vr6S9XVVSzLURT5u9+9+txzP46JEdXANxvd3d2hiWiuODKffTbS/aFwDMqUUiRIJX/00UcOh/lrX7sbAAwGA+8LaMeOHc8//3ygF6M9e/asWrXqxImDSqUqyN0vhEzRv/3229XV1Xl5k/jiQ5j2F8xjNpsTEhIClx4oiuI4jiCI1157be/evWPKF2Fd3aXDh/e7XM709Mxlyx7W6/uZ+K1b9y6KUAIAs2bNue++ZQMKFBnZXUJCYmhE/wXzej3r1//9mWfWqNXqEyeOou94VlZ2efmu2bPnxMXFnz1bkZWVgzKjwAYPPdTjxcVkMn7yycbnn/9J1Gt1YyNmeDdGxklDM6UUM0UPAARBbNmyvaioFAC2b9/+5ptvKhSKhISEzs7Ojo4OjuP4uS/0i/3pT28vXHjnrFnzI1+CZVm0D1MwG8dxvb6NgyvW2WnYtu2zH/3oR+Eq7Ha74+Pj7Xa7VqsNmhVA8/8DLs+PGHa7befOfz355DMpKWn79n1eXr7z8cefDsxgs1l/+tNfofCXo2u1JyEhgYj+exgXp583b+Hate+8//47xcWlZWXTAaCwsPiOO+Zt2vTh22//3mazPvjgCpR5w4b3jMauiPIkBiZwPX4sg0wp7733G9cjRFDbBSaiXwPNe5MkKWgxhzh06MvKyq8CzwYpVP7A4XBUV9dAmH6AxxNs/IJOHTly9L/+67/Cza/wbhVC+2diLO9GWOu3tbXk5k7OzMxWKpVz5tzZ3t4WeJYkSQzDNBqtXI7L5Xg4T0RBSDZ2NwNSsJlRZFimyGbNmhM6ATtnzp1z5twZlMgHNkCkpKRJw/chIBjdbuwR1pTS43Fv3vwPdJyXlxdoSkkQfoqirNY+o9xLl86RpAP6Y7H0rVx0dxsA4PDhnlCBfr+vtbUBAKqqjvv9xJ49+5YvX9bcfAUAOI71+311ddUAUF9fo1ZzNTWVANDe3mNn29XVXlV13Gw2OhwONIh3u11VVce7utoBoKbmjMGQ7HI5GYaqqjp++fKFgDp0chxns5kBoKrqhFKpCP05fD5vdfX5a9eusizr9/utVltV1XF0laamK1arCQDsdktV1fH29iaWZauqjqOCqAezfPnyzMyMH/3oB0FiGeYO9F53drZUVbUBAE2TGIbJ5WHtexMSkvPyisKdRRQVlRQU9JgsGAwd6emZgWdtNivHce+997bT6czJmfjAA8t1utje+tAtLc3oWK1WB8aAp2kKAKxWk0qlinx1AGBZ2uv1yuU2hUKURweapjiORU0wIATh9Xq9drtF5NwDx3E+n0ekcK/XSVF+HBeV2efzAoDL5RCcluO4JGRe7vN5bTYvALjdbhwnWDbsl1yhUOp0kqObm5RxvAYm7bHhGRcd5AimlDKZDNlqAIBGo1Wr+3oADMNQFNXc3LcFQKlUBWagaRrH8cDVXOQ15ezZr/gUHFcAgFqtPX/+4htv/GnBggXI6JTjOABMqVQBgEKhUqu16KMqlyt6ReFqtTbwSXM6XWq1FglUqdRqtdbpdOp0MWq1tr6+T4MiKx6kLVQqjUolYOOKYZjZbDGbLQDAsqxMhqnVWr+fAgCFQoHuSCaT85fj75qmKa/XbbXagn4rXjB/1+gsQXAymVyhUIfk7EHQrWRoHoUCAODixer9+/d885vfDjxLUWROzqSlS5fFxMTs3PnpF198xmfw+XwbN65Hx2VlU5OS+syMnU47ANTUnEET++IQdkoYjurq0+Izt7UNQnhnZ2tnZ+vA+fpoGThLL9euXRJMp+l7AJQA0NXVXl19VYyo5OQJZWW3D5xP4kZkHCt4CZ5xoeAjmFJqNNpHH31csFRjY11XV0fgDeblFZWWTkfHFRUVixYtamtri4/vM0ZNTOxna0pRdHp6NgAUF9/S3m4CgMmTi41GGwAwDKNUqnJzpwDApEkFpaW3nT1bAwApKT1+wRISkktLb4uN7bMmk8lkLKuwWOwAUFBQlpWVpVAotVpdaeltKlWfBm1oaK6oOKvXJwDAyy+//tZbb50/f95sNgdGBAjMT9N0bGxcaeltZrMZADIzJ8XFXQKA2Fh9aeltaWmHMQwrLb0NZXa5HFVVx1UqdVxcPJ/IwzuFS03NKC3NgJ69Eurrd3bm83l37frU4XA88cSq1NR+0rKyclaufAIdz549l9foAKDVap99tidAs1KpDNTlJ05UAsCtt85Vq8N2PnhYlrZaO+LiUkV61b1y5QKO43l5xWIyE4TH5TInJmaLXFw4d+5kRsbEtLTMgbMCeDxWkvQnJGSIyez1ei5fPjdlyjTBYTfq6gFARkbO7benAYDdbsBxpU6XFE4gXySU1tbm8vKdXq+3rGza4sVLBWcvgva8DMGUUmIUGccKXhrB84yLPVRDNqUM6r0EKnuz2ezz+YIc9Ibr7vBujPkMBBG8Qo/+/+CDD1Ai2hgZtO7+85//vLy8PPIVT5065fd70M71zz//fMmSJX/6058yMjLEh/xBTgkj78wcyY4dw9AbN66fNGnyypVPhr566KOfmZkNAHK5PFCpyGTyoPl8HhRPISYmVsxmAYahvF6zThcr0lWtXI7juCI2VtS+ZBzHKMoVGxsncgMVAKhUapHCOY6Qy0FkZvTbarUxgvn5H16lUsfGqgGAIOwKhUqk8EBYltm27ZOHHno0Kyt748YNFy8K+B+DkD0vQzCllFzVjiKSsavEOGOwvRleC/I+FvmthoGxhgMz84F56uvroX+8XWS4FyQ5jN1f33FVVVVzc7PIeiIOHToEAznvG0kuX65VKlWLFy8N0u4dHW0kSTgc9n/9a7PdbuM49syZU0VFJWJkIlHjont649HY2KDXx+fmTlYolLNnz62uPheaJ2jPy9BMKSVGkXE8gpcYp1ynKSXSyl6vd+LEif/xH/8BIa5mgjRloBpGZQPdzAXZq9fV1QWWRXMDp0/3W8flFTzfP0BlQxRVX63QKY7jKIrasWPHihUrZDKZYD0Ft8kJMsLrMp2dHS0tTa+++nP0p1Yb8+KLLwHAhg3vPf30syUlU+1220cffcAwTF5e/pIlD0QU1gPSEJKCHxUcDltycs9iVkpKKvJIFkio+/AIppQcx/G+wzFMJpf36X7kLhMZVA4Iw1Asy9I0LZeLyo+2oYgWTrMsS9OUTCb2kWNZRrxwhhlEZvR/mPyK3quzKAwHwzAYFkk4hmGCXiXGsYKXpuh5bsJPpNvtNpvNnZ2dEF6jI6xWK9Lo/Ia0/hn6dQ6+/PLL0GsF9R74X3vhwoWrV6/mTwVNpwfm5NX26dOnV65ceeHChbKyMsGrCM4KCOpyj8czkjp+8eKlixcvDU3nN8LMnbtg7twFQ5Asvcijgtfr5TcvKJUqZL0fgMCelwimlF6v549/7HkSSkunJif3LRnY7TYA7vjxfw+mdo2DupdBCb96dRDCm5uvNjeLMmbspV581gsXzgqmk2SPKWVra8Px41fEiEpOTisrmxGaPo4VvATPeNkHP1T6qTE0bkYIepgJ+pOm6QMHDgDAkSNHQvMIrsHz1NXVffXVV4GJFEXxv3ZXV5fRaORLoQASAZeAdes2oGO0+Q0NTSDiyrp4nX3tWkNZ2VR0vG3bNpPJFNjbGBdIvuhHEY1GgyKBAQBJEmp1P9NFwT0vEUwpVSrVAw/0RLqKjY0NjAoWE/MFx0Fh4VQxtWJZxuUyx8TE4/jAOycBoL29SSaTZ2TkiMlMUX6v1xEXlyLSeuDatdrExNQgo91w+P0uiiJiY5PFZCYIX0vLtaysPK1WwKAEx3sMQZKSUgsLVQDgdltkMjyCo/ug5usTJaY2YwSPx+Z2W/g/UZAiCejdOyset9uCzMjR7Jbf7wqXU63WjbVQ04Fz12jyfMWKFcj4HGEy9dtwjDy/AsDKlSsXLVoE/TXKkSPH1qx5HgC8Xi9N00FK2u12X7x4MTCFIAje64DQfEAfLS2tfn9PTjSrT9N0BPc16NLhOhxBRY4dOxXoP2f79u3Nzc2SgpcQT3x84sWLNejYYrEgP+I8gnteUIdA0JQSxxW33y4QOQl6d4uI1MEMQ8nlZGJiukhf9CaTQS7HRQr3+Zx2O5eWli3SlLKh4VJcXLxI4U5nN0F4UlJEZXa7nS0t15KT0wK3//Dwtg2xsfqMDD0AmM0cjisF48VFZjwpeKVSExPT93Pwm5UlBjvJqVRqYmIwAPB6bTKZYghBioYPlqXRAhX/Z5ACoCiCovwffrgBes3lqqurAzNs2rQ5SCZF9ejC6urzSAIfPLery+j3ewGAJP3vv//3mpqaoLI0TQZVgK8ey7IMQ3Mcx7JMd3eX292vn+Rw9Nn2o2VIjmNJ0s/fQpBYl8tFUX6KIgCAYSi+IEX50RUpys9XyWJBu+cZlMgwNH/MskpkPMswNEUhKwGWPyuITCYflbcJeSwQE0lBIurk5U3+7LNtXV2daWkTqqpO8yb0EdyHt7Q07dtXvmrV9/V6vXhTSolRZDwpeIVCHeisI8L+zpsNFKRcPEqlVqfTAoDJ1FlbW93Y2F5eXm4ymRYuXPijH/1o4sSJw1NNUfh8Tqezz2rd63UG6nsAaG29YjZnv/XWWxBmbSJUYSCtCQAWixUAHA6jy9U3FeRwdAGA02kyGtshBKfTFLRGgHzWAgDHcV6vg2VpkvTv3LnFarWFuymC8AIARZEWSwcAGI3NZnMir8V7K0mZzS0WiwkA+HDaFEWYzS1utxUAzOYeTyl+P4H6BgThQYkE4UE5AcDtTgeIAwCv14686AAATVN+f9iHRK2OFblLO7ogIztpBD8qyGTyRx99fOfOTymKmjKlaPr0W1E6sprMzhb4CAzNlBJAclU7aownBS8RDj4UikguXbr0zjuHdu7c2dbWcuXKNZlMNnXq1KysrL/97W9Lly4dXQWvVsfieF83zu0m7fZ+I2Obzff22+sHNewLii39wgs/e+GFF/g/Y2KSAUCnS9JoLMElAYxGe4SN+CqVDsPkHo/vyJGKCBVAC4oURaOAj1ptYmJidpDV69GjJ3bs2L906X2oMugqcrlyz55jW7fuAoDExGyUk58qkMtVKFGlisFxVWJiNsuyHNfTXABemgAAIABJREFU6dFo9ImJWgBwOrvkcmXg7FcQ4jd/Xyder53vuwAAQXgAwGRq4jhn+EL9sNs7eVd9kSFJH8sSRmNYB72BoF/bZGoUKZzjOJfLbDSKMm5Flt4ia4KWdazWdr/fGnqWZXMB5ADgdluNRiv0GHYQEXpvKlVMuKnd7OyJq1e/EJQY2X34kE0pJUYFScHfjOzYsWPjxpdkMllcXOw77/xl/vyvTZ8+fbQr1YNcrgicLg6dOq6tvbx+/XoYDEFLGFarze3uCw+DliFwXCk4J2Q0mkITA6qHYxh2+XLd5ct1EbI1NjYBQH39lcbGZgBQKNQqlTZIl5hM5gMHDj300MMQsDIik8mOHj1WX39FLperVD32zCRJ9d6XDCWyLIdhmEqlNRgMLKvuvS+FSqWAnm1LOF98FFEo1IH9DHSbWm1ChM4HD8cxbrdVpYoVuWwklxvkclyMZACgKL/f79JqE0Q7bwGlUitSOEG4aZoUmRnDPACg0cTFxAh4suOrx69Xer02mQxXq2PDCRz5VTaJsYOk4G9G3G73/Pnzf/CDHyxaNEulitHr00a7RoNgCFsGQmcIA+3YgzazBRFovhdKYLS6CDQ2NvL5w10Iwmx/D+NFp58JXpDd35glaJVNoVABgEYTp9MNrPwYhnK7rRqNeE92ChxXiJEMAD6f0+93xcQkiJ7MwFQqrUjhLEuzLCMyM/omh/tN+J6qUqnR6TQA4Pe7FAqVaOESNxfjWMFL22eHjFKpPHjwoFKpNJubR7suAxOk4Wy2sEvd4RCMxxp0luO4zZuDrfNCrx4EbxgvEqvVCgB+v/+vf/0r71AvQlVFCo/sU09CYhTBMCzIikVixJB8Dd6MZGRkDCZ+19hi165dgy0SqvYCl/DR8NdoNAa5sUOwLBtBa3711VeDGjR/+umnAHDt2rXf//73SNkPWOGgq1dWVq5Z859BpyiKQsf8VMG4APXRkTMDCQmJqCMp+JuRnBxRmzWjTkXFib/85c0//OF/Pv30nyKt5Pbt279y5Xeu87qhGvrMmTP8MdLrv/3tbwWrFHlFoLq6elDu4tHSQLhxf5DPXRCaezh9+vTmzR+HOvPx+/0syy5evFh8ZcYIgZ6LJCQkosg4nqK/GZDL5cPhpS4uTsB+Z7gxGg2nTh175pk1SqVyy5ZNp04dW7jw7gFLGQyGyGZuYghVk1991RctvrW1FQBaWoTDdQ84QB/UCJ6PdiNyLt1oNIZGxAmShrDb7RcvXuQX4yUkRguWZQK3gCInEBHcMPQvSwMATZMymaj8A/p4CATViqII8WFyGIYWXXOG4ziRmWmaAACaJsPkVyELXJa9XlcWw6Lga2rOHT16kKbp4uKyJUt6wgzX1tYcOLCXIIjJkwuWLXtYoeg3RSwmMnEQN8MSfHJyMnKGGl0mTBgF53Q2m3XatFvj4vQAMGVKsdHYJaZUVNaUg/zTBRE5JOv+/fsjCx/UCB6pZIvFIlLBNzQ0NDQ0hEoILd7S0rJs2fAG566ru3T48H6Xy5menrls2cN6fXzg2SG9whjc+I6Wbzp8PqfT2WdfgtQS78VBDIGeMCJDkn6ZTDYo4VZrm8icHMd6vbaIVrbBiKwJ6og7HF0UJbDDmWXz0WZIr9fBG/nSNIm2lQqiVusSEgSCMkdfwRuNhj17dj/77A9iY+M+/vijysrTM2fO8XjcO3dufeKJ/0hNTdu69eOTJ/uN3kRGJr4JKSgoGA4Fn5WVFXWZA1JUVFpUVOr1erq6DBcuVAc+AH6/78CBveg4LS1Nr+/b8+PxhHWjK57Irnzt9rBr4WIY1Awz8qm3adMmweUAt9vZ2FgHAAaDwDfoypULANDd3Qm9Hwi324ES0a/k8fR7/81m45Ur3QDg97tkMnmEiRCdTj+gP0673bZz57+efPKZlJS0ffs+Ly/f+fjjT/Nnr+cVlmYdbjDU6thAT/JKpZrj+rw4RIZlabvdEBubrFAIO1cPoqvLIpfjIoUThMfjsSYkZIreDNmk0cSLFO712inKL9Kxt8fjBmiLi0uNi4sPPctXT6OJS0zUAIDTaZTJcJ0uKZzAcLs/oq/gr16tLyiYkpCQBAAzZ95x+vTJmTPnWK0WnS524sRcAJgypaS1tTmwCB+ZGABmz5577lyVmK+DTifKWfG4JminwMyZM8+eFQ5ANCgUilFzAtje3nrgwF6O41JSUvlEhmE6O3vCTqvVqsBgjkFu7IZG5BH8559/cf2XEAlvRS84sU9RVEdHGwB4vQJddeRe1+/3AcBnn+1B+VEi+pXQXCiP2+1CZ2maxDCMIMJOVIhxCtnW1pKbOxn5IZ8z584NG94LPDu0Vxg93i5XFPpwEmMHFC2e/xPD5AAg0g0DmkVXKFQi88tkcplMLjIzmv9XKjXiPTvhuEKkcIJw0zQmMjOaeO/1hxEM/9WXy6/XlUX0FTzDMPz8IYbJUHyC1NQ0kiTr6monTMiorQ3u3UeITEwQRGVlj4+whISEwCBFvIPxGxg+xDIAJCcnf+c7346Kgnc6ba2tdgDweGxyucLhCGvGrNXqkpOjuUu+sLC4sLD41Knj5eW7nnhiFUqMidE9++xzgvm3b//8+i/a1RVpFmQkR5DIR024Zfvz52ucThIAJk7MDzrFcdz06XfgOH7y5DnoXVaIjY2//fb5AKDT6QEgyDXeiROnP/jg2wBgNjcrFOrrDBpUVFRSUDAFHRsMHenp/eYDI7zCfr9v9+4d6DgjIyM+vs/+w+m0A0BT05Xk5LB+Wng4jiUIj9nskMlEfbU8HpdMJqut/WrgrAAMQ1GU32Jxi9x8y3Gs0diB6j8gFEWwLN3dLWp7J4r53dR0JWgRs7ee09BHu7vbUFtrAACC8MpkMoWiM5zAuLj47Ow8MZeWuPGIvoLPzy+sqDhhNHbpdLqKiuNowKFSqefOXbBlyyaFQhEXF3/LLbcHFokQmThw8nbq1OmJiX0fAoIYejQ5DIume+SHH15WWflVa6uAJ/PrBHXfEDgud7sHvQVcEKvV1NjYMHA+gNTU9Ggp+JMnj2m1WtT02dk5Z8+eElMKPT/XyXjZHe50Otva2kCowoHR5QMT0QEKeB9EXV1dVVXV7bffHnpqCCgUSjTvc/Fi9f79e/hA4IgIrzDHcXwjUhSFFBh/EgB8Pm//RGE4jmMYlqZpmUysbwAULFFMZpalGYalaUq8dw2GYUQKR6GAxGdG/wvWhH8ueIHItgvDwgqPyhyYxDgl+go+MzN7yZL7t2//BMOwkpKpqC/f1NRQWVnxwx/+VKeLPXz439u2/fOxx/r2PkWITKzXx7/88huCF6qoODfkSt59990oRng4kpISUWASMfz2t6//+Mc/jrqCnz9//quv/gZFOAWArKzsadOEAzIOlry8orvuKoKesZ1mZDzZ6fX6EyeO5uRM0mpjzp6tyMmZJKbUOFHNUaPXKbrAenmgeR0KL8Qr+FD/dwCgUqlWr169f//+aPVvfD7vrl2fOhyOJ55YlZrabz4gwius0WiffPIZQYFnznwFALm5U6ZPnz3g1RmG6u5uTEzMEunJ7vz5CoVCUVoqqn/j8zntdkNaWr7Iydsvv9yTkZEjcmTcG0g0V0xmt9tZWXksP79ErxdwTof3frAnTMiaPj0LAMzmFoVCNdZiOkuMEaK/D56m6eLi0jVrfrR69Q/T0tITEhIBoKmpIT9/Snx8Ao7jt902q6mp3/AxPj7RYumxFQyNTDwczJ8/P3KGQW0kGyafeiUlJZmZfROhsbGxg73Q3Xff/corr0S5WkOltHRaUVHpxo3r//rXPzIMs2TJ/WJKjZfBd7RA97t79+7I2ZBlX2NjIxrxCyr46dOnV1ZWpqamrl79Iz4y/ZBhGHrjxvWJicnPPvtckHaHob7C0hr8zUB0p0slBkX0FbzX63n33becTgdJEidOHL3ttpkAkJWVfeXKZbO5myTJs2crsrJ6THY7OtpIksjLm2y1Wrq6OjmOrao6PXWqyMAnQ1er4TQlClANADiOR7ZEi43tWyzgS02YMCGKodhEqvOpU6eig6KioqBTL7300ssvvzxjxoxoVek6WbBg0Q9/+NMXX3xpxYpvaTSiDEZutk8DssKrr68PlyHwB7ly5QpyjSe4d27JkiV33XUXwzDbt392773Lfv7zn589e3bIBgeXL9cqlarFi5cGPZbX9woDDLRNUUJCYshEf4o+Lk4/b97CtWvfUanUM2bMKiubDgCFhcUWi3nTpg8pisrKynnwwRUoMx97WDAy8WBRKpUi/aOF051z5sxxOp01NTW/+MWLp09XvvfeB+EkTJ48+fz58+g4ISEBCczIyOA4LpzLFACQyWSBK6lBfw6t2l/72tcuXLgAAElJwZsoNBoNqp74S0iMLihQnqC/faS/g5bbAzeRByl4pVK5a9eu1tbWysrj3/3uc+fP17z55pt6vT4vL+/jjz8O7Q5GprOzo6Wl6dVXf47+1GpjXnzxJbi+V5jvGUuMCmJcF5Akef581axZc8QXkRg7DIujm1mz5vAPBM+cOXfOmXNnUCIfe1gwMnFkgrSdWq2O4mhv6tRSg6HPE0tcXJzT2S9kdaAvd5msryZ8rbKystrbg1flg+qck5PT3Nwcrg5yuTwwv6B212g0/GpCWlrwUvooboeLFjfbCH5AgrqwkePOxcXFlZWVTZigu3ixMiYm6eTJk6dPn+7s7MTxQb/4ixcvXbx4aWj69bzCer0epBH8KCHSdcHevbtbWprQ91xyWDLuGMf9r0C1ClFaNZ85cyaGYVOnTpXJZDk52egDBADr1q0Lyhn4iQyUxjuJy8sTMMAJGmQvWbIEAGJiYgBgwYIFkSupVqtDq22xWFJTUyPflBR27wYmcHI+QmcoPX1Cdnb2ypUr//znP3/yySf5+cHb8EYRqQ83KvCuCxQK5ezZc6urBWyW6+ouGQwdgyoSirQGP4qMY1/0d945X6NR+3w9O8VlMlmgJpPJZDKZLMjLWElJMY4rampqiouLS0tLa2trg2SmpqZiGDZz5kwAuPXWW/73f//3qaee+v/snXdYFNfawN/ZndneKEvvTQRRsaEo6lWxt9hjbDEmmn5jNKZ7veYmN718mujVGGsSY4ndJPYI1hhjiVgQkL7Uhe1t5vvjwDBuY0BE0P09PDyzM2fOnJkzc95T3rJ27dquXe07quha/v7+KpUKwzD0E8MwetaRthpinpKcnPzrr7/Se9AUenh4+LVr13x8fEQikbe3Nz3utxPMXbt2dcwTx3Har7KjIG+Pol2nq2I6qjSbW8BMrqV4sE1VSckNgUBg58VToykvKbmBTEZJ0nb3oYqSkkq0bbGY9HonTjERAoHUyyvoPhS5EUQiIXgE/APCjesChFarOX788IgRY/bs2dHoKVar9ebNLLQtFotRzSLMZhMAlJeXsCkVSdo0Gi1FlRMEK9VLs9nE4VhZZm42G3Q6LZdbynJlgaIonU7DMnODocZiMQGwSowMR9XqSovFiU4MSfqjsbdery0v1wCARlPD4eBuprp4PIFc7mQpth0LeKXSVyBoEPB2wuzbb789cuTI5s2bmTt79+7N4XAvX74cEBDw/vvvjxs3DgDGjh3bt2/fJUuWAACXy/Xx8aHH2d7e3mKxeMaMGcXFd/mRQBKdIIilS5c+99xztIBn4ri+KBKJ/ve//zlq4aFzeTxeSEjIm2++uWjRItr/sN0UvUJh79eQ2aVwFUFBJGqy/6MHCI8nlssbboSlVxMAkMvltbW1tLQIDw+7cye/BQvG5XJfeeW5Tz75vxbMs0nIZP5CoUAguMuBI58vfu+9z69duw4OJoUCgUQuxwFAq63kcgmh0OUUl9MwFa1AeHgYtCUBf/Xq3998880333zzoAvSGrhxXQAAANTu3duHDh3BbD3cnGIyGbdt+x5tJyYm+frK6UPIjzJLj0P1sApUQdOkzIuLm5C5SlWkUhU1nq6BJhhL5+XddLrfak0H4EGdOyNmmlxXWfn6+svlTpSp27GAh7vXmO0E6pQpUzp16mQn4HEcp/tu9Bx7cnJySkoKncn+/ftxHL98+RwAjB49uqyszHHcnJaWRlEUkvH1Y2gM7haxyJOut7e3m7DftGint2fPnq1QKMaPHw/Oxt9OlZI6duyINhwFfHscwRMEnyAaHjjyc8mGOXPmrF69mvZKO3LkqG+//Zal0iUbgoODevRopvpniyASyYVC4enT55g7d+3aV1pa12DZLWYThEAkEgCAXq/GcZ5I5MTrdStjtZpRHK36nxYAMJsNRmPjwzU0P2E2G+w88rpJb7NhbHKG+oAoJ08eW7Vq1YcfvsfUsHGF1WpimbnNZqYokmVis1kHAGaz3mh00uuiKAlqaqxWk9FohvrQbW4y53BwHs+JX3c3rgsA4Pz5Mz4+yqio2PJyFZtTRCLxkiXvom0M43C5DW3RoUO/A1D9+rEKZGyzWSoq7igUgSy9HVy9eoHL5XbsyEoVwGjU1NSolMpIlt4OTp8+EhoaFRLCyoGBRlNuMul9fVlZUel0mosXTycl9XQ67Obx6qo+LCy6X78IAKisLMBxnhuHJa6a+vYt4F977Z+LFr2FtmUymV6vZ3od79atGwBMnDjxl19+sYvDwXwcQUFBzAV1O3GOOrB0erlcXlNTg/4DwPTp0zt37oxWB8LCwj766KPPPvuMTgkACQkJGRkZaA9BEEqlcsCAASdOnGAWQyAQgMMSA0IikdARY53OE2AYRhvsOQp4tAQAAGFhYd7e3pcuYW1msHRf4HA4OM61Wm0AMHjw4K1bt7rpXTWVRi0n7wU2k/8oQUnJXXOAFy5coLfbftQWk0nHjDNmMmkBQKerrq526WnVDq22kmVKq9VEURb2OQOAXl8LANXVxY0KeIqi9PraJmXOMjGqRI2mwmp1Eo+AourijBkMmurqukdhs1lMJpfhlAQCCY/nJM6YQuF99epltO3ouqCoqPDGjWuXLl0gScpiMX/44bIXXnjVzSkYhtl1EZiHgF28AwDAMPQV46zTYxjGYZmYy8U5HA6OE+x90XM4XPaZc7lNSIz+u0/P4XBwnAMAXC6XfeZM2reA79atoeO2Zs2a0tLSiRMnop+0OExNTT169Cgt4O1kJIZhzzzzjMFgmDVr1saNGxu9Ii3+kTwWiUS9evUyGGowDJRK5cCBA2kBj0bbPXv2pAU8l8sVCoXr16+PjIykMwEAiUQiEomQmGeWEMOwgICAefPmrF79Lbq0ncHbkiVLuFxut27d+vfv//vvv9vd2qlTpzp0qHMerlAoevXqdflyexTwLks8YsSIgwcP0j8xDONwONOnT9648cfFixePHz9+/vz5LVgOHMfv34QI+9V9N8nazly3K0QiBXOloKbGBAAymZ+/f+NKf2hs5+UVxOOxWnIqLa0iCIJNzlA/tpNIvAHAzy/acdLOjlu3cqRSH39/lmO7CrNZ7+PTSLw+hFarycsr8PIKcTq2oyWTROLt7+8FAFVVBTjOl8n8HBMjXI3toqKi9+zZUVpa7O8fcOHCWVofvqioQKn0Gz9+MvpZXq768cdNL764yM0pHtos7ViLHgD4fH6vXnXeW5VKpbe3E+eOTi3N7DTShUIhWo939THQQzc6gbe3tysbNlQMdEqfPvbmgnblBwCxWIxG6nZXDw0NBQA65oTjCP7555+nyw8OI/jY2Fj6RBzH2+N0vXuGDrWf9ONwOKhfFRoa6vg8mwRtm8Dk/tltsymq+5h47QIMw1D4r/o/DgBQFNy90+UfAGAYJycnd9iw4QaD0X1i9L2wzPnEiYxz5y7Ur9/VnWU0mkiSclMSlpmjymWZGD0TDsd55ownyanf08htulIo43C4yHXBihWf+/n5064L1q1bpVI5X6V2dUqjld7me54PLe1pBK/TVTMn6HS6aoqyLVny4sSJMwGgpqa4urpBG6Ks7DYaE2s0FbQzGZNJB0AFBgYEB8tu3LgIABwOR6XKBgC1uhQlRj8tFiNFWdE2AOA47N27ddKkWQAkyqdLl+7791P1CSgMwywWk0qVbTLpgoMDqqqqDIZaADCZGhRNKYpUqbIrK+sKuX79N2iF2GzWk6TNYKi12SwqVbZaXQIAL720YOTI/ipVNr1mqdNVVVTc5UKnoiKPxzMBgMViAACR6K7aLC/PtdnUAMDh2AiCYzQ22PFrtZUqVTUqktXqbgFPIJC0oJvr69evHTt2SKOpDQwMHjt2glx+T6vC9AIEDS0m6Vay2ZkHBQWVlZXZ7WxqhuzH5RwOh+myximN2sK1O1B9ffDBB0899RT7ztP169cPHz5cVlZGz4Q1yrp167755hs3kRiXL/+PXC4ZN24CMJ7wuHHjkpKS6Dm5hw+nrgtoxwYIpdIfDd/dnOKhzdKeRvAEwReJ5PQfQfAxDAsLq/vIxWIZUy9DKJSLRHIA4POFGIYFBgYAgI+Pr0AgJghCJJKjxP3790W5CQRiAODxBOgnWnphXm7AgH/geF2cYxzn8XhCDgdDh5AOC5eLi0RyHOchvTAeT4BKRRcJwzCRSE5PUfbvPxBNNhIED8MwguDzeDy6JE8/PS80NFIkktPua8RimVB4V1RNoVBWXzwxALz++hLmUbrkb775+scffzB+/Hh6HoIg6Nvk4DiPeZt2fyynQ9mgVlfv2vXT2LET/vnPJQqFYv/+XWzOoltbu+mZESNGJCfbDyCQ2iPUD7XvZQQfFxfnmHmTvCOjZbPo6GiWiZtQuIeLvLy8JmkPoMSHDh2y269Wq7dt2wYAFEXFx8dnZJyiD+Xn5+fmutRA1ul0FRUVtD4m/cpVVVU5dSnIEr1ef/369Waf7sHDvdOeRvA8nogpb3i8Cg6nRiyua/elUqVI1GDpK5Mp0Ty8QCDlcDhIa2bEiJH79h3kcnGpVImUimfPniuVKgEAyV2BQIp+crnZOM5H2zRisRjNkuE4z98/2MvLGyUwGGoAAGWL4zykCsHniwBALGbqoXCkUmVMjNzfX+nl5R0WFqtU5gDA2LGPJSf3iI+PDw2NkEqVQqEcAMTiusxpSSOX+0ilvszySKW+KE23bj337TsokykBQCQS6vUGAJBIfKRSHwDo1q3uLvh8QA0pny+WSsUAYDLpCEJgd5v3iYKCO5GR0cHBoQDQp0/aunWr2JzVs2cPhWKrWq1et24dMi5A+Pj4OI6nFy58JSEh8n//W4/EfDMEfFhYWHh4+MmTJ0NCQhyPMnWDGwUVz3E1VyKRaLVau508Hq/RGfiHdQTvlPz8/MDAQKdajegJOKpP7t27d9asWVqtViAQ3Lhxo7i4xPEsALBYLHbZ9u7d+9at7JCQQDTV58b7b5NYtWrVe++914Jqnu2We/IeUV5erlS2RgP1UNKeRvBOYaqkOVUyt1spT0xMRMFXmtH6M3ObN2/etWvXHIsB9QZ46L/jVXg8nre3d1xcHJfLHTJkyPbt2+fMmbN06dKpU6cuW7bMTcHsFpW7detGe81jlqHNjgXj4xPoGAQlJUWBgQ2avRaL+cKFc+gvO/t6cXE+/detW2dvby8AMBjuEopGo76iQsXco9Npn3xyRmRkOADU1FQXF+c7+vmnxa1S6du/v73jZADw9vaWSEQ+Pj7Tp09ZtWol85DNZtPpGrzuIP+DjtALB+gFIAj7PnRwsBOXMmxqrbS0sLg4n71jV41GjZ5hVVV1RUUF86na/VVXV7DMEwCysq46DTG+du3Xy5a9gf4OHtzDPkNwJke7dOmC3PJbrdaFCxeeO3cOAEiS/Pvva0598gMAMmyx2WyPPfYYAJw//+eYMZPsXF2VlJRER0evWnVX5xLJYIqi0AtDF4aiqHuRTCaTybGQ98KUKVMeAj2MJlFaWhoYGLhv3z6r1Tpz5szbtxvCkF65ciUlJeV+P5CTJzN8fHzs/JS3CGp1zc8/s5rFvBfa0wieJSkpKWfPnkXbnTt3jo6+Syd23rx58+bNg3rjNFoTDeHePAapzYeFhSE3ebQjW/ooAPD5fH9//4MHDwoEgk8//RQN45Cp2919kbrL0Wr/jtey22DC5XK7d+9uJxUcDQTc3EvrQxA8NHC6evXSoUMHp0x5gj5kMBj27fsZbScldfb2vsslCxo3W636sLCQ/Pw6PxK1teqCgruCDtfWVmVnZ1mtVhzH1erymzevOMqhqKiIwsJijUYjk0nj4iJ///2kXQKr1azXawMD/TWaCqVSxvSWY7GYSZKUyaRBQYHXr9+cMWPK1atZmZln7HKgR/k4zsUwsFrtDfGZ/ub4fL7JZJo9e3pJSelvvx21S6lQyNXqhkmp27ezKipkdFTWRqmoUN28mc0mpVIZ6OXl23g6gMrKit27d7zyyhK0VsWkurrqtdfeqXfqwGrkQL+ijnJUp9Pp9XoAeOuttz7//HNfX9/u3ZNPnMicMeOZFStWQH3AXLPZXFlZGRgYSGeiVqv//PNPACgqKs7NzTMajcgjBTp69uzZgoICp8H6mHKdufPOnTsKhcLuY2fDPfYP7Lh+/fqZM2fs+ivtC71eTxCEq1mZL7/8ctasWXL5XauQGo3GZrNVV1dXVFRs3rx57Nix9JrXn3/+ee7cObVaLRZLnGb4008/9e/fH70bzSMz88zRoxlVVVW1tbVNcoXeKFar9fDh419/vbZbt+5dutSFXiRJ8l//+tfzzz8P4NLevak8PAKebiw6dep09uxZ9PPixTpNOsfV0/79+1++fJmOtYoYPny4+6sQBPHDDz+4idWxYsUKkiQDAgLQmANB27KzoWvXrrNnz3b6XtL3+NVXXz355JPM/XREO/pO2+BQ3mDQ7969vaamZsaMJ5kxxWUy+dKlHzg9JSfnelBQ4K1bt/v0GdS9+0FawAcGhvbseZf3/ri4xH790svLc69cuRgb25HL5QqFIoC7JkinTJlmMBg++ugjudwrOrqj4+ViYuKEQiFFcQcC3hi/AAAgAElEQVQOHAUAR4+eWbmybhzv5eWdltZbrVZv2LDxySefTErqHhAQxhTwCQkJFotFr9drtTqo03MGhcKH9pqAYJo/paen79u3b9Wqbz/4YLmjgA8NDVOrr9A/U1OHWCyW2lq20dMjIzsMHNgBACoq8ghCcO/Kktu2bbl164bTKQSz2YxhGMsQwDSOAr68vFyn00VERABDYEO9ODeZTBRF7dy5EwDWrl0bFham0+nefffdVatWzZgxgx7Z16/o161oLFiw4MKFC+goigCZk5OzdevWKVOmoAKg1XdaHl+5ciUtLQ3qA9VPmDChf//+n3/+eaO3o1ar582b9/XXX/v5+d24cWPHjh3NHsRbLJbffvuNaSfy2GOPFRQUAJgAnMiztg9JksOHD09KSqI/qOvXrxcUFISEhHTs2LG0tPSVV14JDw8fO3a03VnAqBpmhwm9h5cuXQ0KcmLln5eXN23atCFDhjjqariBoqhDhw4NGjQItfD79v2SmXmWLkZTMZlMt2/f9vHxcQwDNm/e0zt3/kxRFFOTV6VSLV++vGvXrgATmnE5p7R7Ae9+pEuDevF20/VM6Y4ONerJRCgUOup2AYBIJJRIxOBgXuW0VO6H1iEhIevXr3d2Fubr6/vPf/7ziy++kEqldjrkdJ8DxeDhcrlsvHG1JjabddOmbyMioqdOndmk2YXIyPATJzLgbiV2giCYmXh5eS1YsKA+faQrJbspU6ZERUV99NFHwcHBjkcVCkXfvn2HDBmCWnYAoLtZc+fOXbz4FbSNMufxeL1792aevmDBggEDBuTm5k6cONFms/F4hMlk5nA4Xbt2PXfuXFBQUElJiV6vj4iI+OOPP+Lj469fv+4mjgAAhIaGokDANA/Wlc3kyU8AwH/+847joerqKoqiVq36sra2NiwsfPToxySSuqEYRVEmU0PACGYlUhSF5jAsFjPyavfGG29cu3Zt/frvrFarzWbdu3fP1q1bAcBsNpeXl2/c+CMAXL16FQDKyspOnz4dGRmh0+kyMzOnTZuKrvLyyy8jT88VFZUAYLGYd+/eXVpaKpfL33777V27dgHA77//vmfPnm7dkpEePlKJIEkSTa688MILhw795u3tjfbo9fpLly79/vuJ1NTU5cuXDx06FDm+JEkbKjNNdvatHTt2XLt27ezZM0eOHEYTCSaTkaKsNpt9YlegaaeTJ09OmDD51q1bERF1ntHsqp4kbVZrncYAh+MucwzDHKdb7isWi5HpeIckrQBw8eK5kydPBgT4abVVZrN5z559zz33osFgnDVrxsqVX2o0VQCg19fu2bOzpqbyscces1jMAKDTqQHAYNDm5d0CgPz8XK22rstuNGoBYMmStwMC/JRKP9TC09TWVgKAVquh00O9v0Kdrpo5w0RR1OLFr8+f/3RsbEx29u1hw4b98svevn1TKYpCPXUAqK2t0Gqd9Ktqa2vT0gZt2bKhU6fE0aMf43Cw4OCQ1au/Rkc/++yLpUuXA8APP2waPbohEuNvvx3eu3cf6qzo9bV0CbXaagAwGDQURaLVc7PZoNUaoO6toJj3YgeOEwKB1Ml+Vye0X5yuxHfunJSff4eOlMD+XDsGDx7sdP+77y6Ry510JEUi0aJFiy5dutSkjqQrOBzOv//97y+++MJudN6rV6/8/Hxm4c+cOeNoRfZgycr6m8fjOw056h70NSIFCB6Ph8ZbY8aMYYoKqVQaFBRkNx/OfCBisRg5O0JnOdXYGDZs2KJFi5h76G7T9OnTw8PDkAUj6jmlp6ejOWQmnTt37ty5c3Fxsb+/P93JEIvFpaWlMplswYIFq1evHjly5Pbt21F7ZOcn0Q67GmzZKd+WxWIxh4VFjBw5ViwW79q1/cCBPfQSjE6n/fTT99F2p05JPj5OFrZOnz6CdBoKCnLLy1VHjuynKCo39+bt29fR5Mfevbt/+GFLTk4eMERdfn4Oh2MFgJKSgoyM33JybgAAHcwJCfg331yMltgtFsuGDesLC4sAAC2pnj17vKjoFtRr1V27duPdd5cCwOXLlyMjo/bv/8lg0N+8eb2iovz69euVlWVffvnhl19+mZeXffv23/7+frm5N3Nz7/IlfuvWbQDIysr67bfd2dl1kVdOnTpSH3nFybqAK27duoYeS2FhnbKn0XjXy3bnzu2MjFtssvL1DejUqTv7S987FotRp2sQRTabhaJIlaoQAKxWk05XderU2SeffBod1WprdLqq8+dPA8DevXtu3bpNUVRqaopMJisoKEJL7GazduTIsQBw6dLFCxfOxMfHQb2ABwCVShUWFjN79uPvvVfX9bx8+eqvvx5Bl6ZLUl2tLi+viImJ1OsbTJdVqnKCwFevXisW86OjoxIT4wGgpqZSp6s6ffrcpUtXUbLa2kqdTgYAy5b9d+LEcR06xKChYGFhQU5OblbWlcjIwIKCwry8fAzDPv10OXqrjx8/jk4vKrqDimE2m3k83meffU6bbBiNGrqEqGA6nVqn0wFIAcBiMaCjJGmjKBvzqdrB54sfTgEfGBjYuXPny5cv00028z8Cw7CwsLAbN/7w9g69l2shX2lOD4nFIl9fX7vE6P/HH3/83HPPHTp0yL0+oJuLMn9KpdJt27alp6czd44cOXLkyJFms1kmk3l5KWpqajt16sQy/1ajuLjozp3cZcveQD9FIvHixW+zOZGOC0wQhEAgQN/G0KFDc3Jy0P6YmBg0qetwYkNlTZky5bvvvkM1WO8CpeGoK4N1R/eCANCnT5/nnnsuJCSELoBdGl9f30GDBpGk5dq1G2imAS3gIafCKM/Ro0enpaXRYY2cvg92LxvLecIH4lckJCRs6tQZaDslJXXTpm/pQwKBcPLk6WjbLs5YWVkxuvEOHbrI5TIAkMu9a2quREbGAYC/fxBJ1t3J7du5tDNK2gahtlavVAYCgJeXL4YJ79y5yxcsqtB9+35FL4zVaqFPRE8yOjohJiYa6h9+VVWDRZxerw8Ojq6qqi4pqXP5kpV18/vvf+ZwuHq96YUXFs+d+8SCBfN9fPzRhVAONlvd/F9cXFJOTp27i/j4zjhOWiymsjLNF1989dlnH7ufWjMaDbdvZ/n7BwNAaGhMYmIC2s8M0AAAfn6BiYlSANBoyrlcwk2gAWSv25qIRApmeXg8EUlS//3vVwBQVlbt7x8jEDR0Tcxmyt8/RqXaAwBbt+5EO5cu/XjOnCdHjhy5d+9eABCLfTUaLQAcOHBo8+atmzdv5vP5ubkl9TnYLBbLunWbv/zyG9Rj/vbbt9HED4bhyJWhzWZbvvzlw4cPHTu2R6mMrKqqfvXVV//v//5v2rSxKGb3/v2Hb9y4gZS3MEzk7x8jFDao+Hh5haB8Vq1ad+dOyeHDh7Oysnx8fGpqcgFAJPKprraqVOUAQFGUn190vepVXZVdvJg1ZgwpEAhiYxMvXLiQn9/gr0Uk8qGdLX722RoAWLHiW73+cSTgxWJvf39vAKiouIPjPIWiyfoE7V7Ay2Syt956a+rUqW7SfPrppwkJ8e7zSUxMHDNmjJ2QtoPP59MtfqOglOi/XfM9derEDh0SWObjyKRJk5zu5/F4eXm3X3zxedSRbHb+94mhQ0c2Y/gOACiGHoZhQqEQCeaQkBCCIOg4V88///w///lPxxPRZxYZGZmbm4tqFsMwHMfR6JkpPnk8nslkcnxoc+bM+de//oWWgWnCwsLQOqLdgg7Tzc6RI0eOHNkjFnt/+umXdDJa4xIAOnbsOGXKFHo9kg4v4Vh+Jsx5WoGAbzS2FefzRUUFAIBsIO2cZuM4npCQ5PQsrbYW9d58ff1RLfP5AgzD5HIfABCJpE77NLSimVarFYkkAGCzkStXrjp48BdnBauT+mazxWy+K2Cul5cv6h84/Vh4PCHzaVsslpycPA6Hc/XqNaPRaDZbxGKZUhl4+PDh559/fs6cOW+88YZIlIcS+/j4SSTy+m1/DDOZTLoNG3b89NO2jz762GDQXbx4cfLkyWq1msvl0rEk6GcCAEVFJQDw/PMv0SGt7V4GsViqVEoBAMPMBMFvQW9U9wnUu0KOAeiI2ABw8ODBQ4cO2cUKuXDhQs+evUiSRPM3GRkZaJYFTcZ8+eWXRqORXgxFwYdIktTpdCKRiCRJ5A4BAP76668VK1a88MILS5cuXblyZWBg4Pffb0tPH1tTU7Nx48aXX35Zo9Hs378f6l8qpL+JOu5MfcbXXntt5MiRSJPjwIEDADBhwoSsrCzUd3zttdcqKipoZQuLxWJnHLtlyxYfH5+5c+eazeaqqirmWOLpp58+dOjQqVOnhg8fjop9585d3szukfsi4C9fvnjixBGr1dqxY6dhw0ZiGCcz88Thw3d9fosWvcXUflSrq/fs2VFaWhIYGDR58nRXcQucQo+VmRvMSezJkydbrebycpeeLgAgNjZ2z55GbHsOHz7sqC7hiqSkpCtXrqCRtL+/f3x8PO0aPS2tj1MNL/ewkdk4jhME7mamoT3i56cEAD6fHxISgmr2lVde4XK5ERERe/bsGTdunKsnExQUlJOTExgYmJubi6a7sTqnoRzUS1AqfcvLKwAAx3Gny9tyudxpFw2BHvLIkSPRN2+nekkQRHBwEFOdgulfLzg4mD6Fw+E4dX5uV4lWq5XZNPj6KpkN5YMCuS6vqVH/+uv+J5+cL5fLz507HR/Ptv+KngC6r4qKim3btslkMtS2MqW706iARUVFf/zxBwBs2rTJfdfcKUeOHPn4449jYmKcVr3jjA5aIqmpqbFYLPTRdevW3bx589ixYzNmzLh1q25gyiz5mTNnYmJCZTIhWhcoLCz85Zdf1qxZM3ny5NmzZ8tksk2bNqGUeXl5v/766xNPPG40Gt9++10AYLrZYW8e2dZA3w66l6qqqpycHKbstNls58+ft3vat25lv/rqq1C/mPLdd98xj+bm5tpstvz8OvMW+tQ7d+5oNJrz58/Tz99sNp8/f76ysvKLL74AgNramg8++LykRD127FgAuH79utVqRbVWUFAAAKgubDZbZmYm86L79u3bt28fswyXLl2it+mSIGbPnh0TE6PX65m9ll27dvXr1w8cFtpUKtXAgQOrqqo+/PBD5IvJ0UnGvdDyAl6lKjl4cO8zzzwvlcq+/37DH3+c7dmzT2pqWu/efVGC3Nyc8+dPM6U7RVGbN383dOjI6OjYX3/dd/p0xj/+ke4ieyeEhoaGh4fTHeFx48alpKTcDx1yll7JaOh58nfffffNN990o3vfgqCF6vsX96z16dOnV2ZmZlBQUMeOHZOTk5966qmBAweiQ2PGjJHJZHb6NTRoYhwdjYuLCwsLQ2KVw+H4+PhMmTIlOjqoR4+0kJCQiIiIjIwMN1LcqZUUOtSnT58DBw4899xzyPzaDqa5PHMEj7ZHjx69YsUKFHTY8Vy7d9hqtTI//jbSh1u3btWcOc8kJCSp1dUbNqyx2WxRUTHDho1u/EwAqG/9UZOn0WiQ2jka51EURdueOBVvWq2WbnaRYl2TWLZsWXl5uVOPRnB3mD6E0Wi0WCxM4WS1WlGs3kOHDn322Wd0GSorK+lGfPLkyc88M/fFF59evXo1AKSlpYnFYqFQWF5eXllZiSrx9ddfz87OTktLW7hw4TvvvDNnzuO00vg777zToUOHGTNmsDfDaZvk5eUBAEVR27Zt2759O/PQW2+95SpmR2Wlk+CBdnVNpxk+fLhWq3XsrhUUFCBZa7ORFov1r7/+QsqzTzzRYKnL7EEeOHAAGUGwvbe72bp1K1IdpRfgACA/P3/KlCnMwtCgaQl6mpDZO6ytrQW4J/O8lhc5t27diI3t4OXlAwA9e/Y+e/ZUz5596AjBJGk7duwQ0wYaAHJzsyUSSVxcPAAMGzaqqYYlvXv3Rm8PIjAwcOTI5kwF31fuXbqzn3VvNBZW+wLH8T59UgFg2rRp06ZNszt6/fp1V46ukGhHInbq1Kn090yPmJHvoFdffXXQoEG0NaodHA5n5syZ3bp1Q8EFmKAaCQsLO3HiRGpqaqNVTM8f0NvBwcF0xCBX6WloTywIV5e732vwb721nPmTdl2emto/NbW/szMaZ/78+VOnTkX3q9Vqkft3iqJoc4b7QXl5Odw9XcyE2fojSJI0GAzMrvOWLVuOHTuGts+dOxcWFkanpNPodDqtVmc0mpC+GEVRqJc2YMCArKwskUiUkZFRWFhYWFhotVpJkiwvL1+zZgM6V6VSvffeezNnzpwwYUJ7F/A0r7/+uuPO06dPO02MLBHcs2rVWrThtDeQk5NDu8cxGo1Go/HGjRtMeeGIVqu9x6eNOhlO3eN88sknTjM/cuSI484DBw589pl9i9ckWl7A22w2uveKYRy1+i5nzhcunIuMjLKLMlJVVSkSibZv/6GkpDgkJHT48Ibuv9lsvnLlL7Qtl8voZVcA0GprbTZrcXHD9EhlZRkAlJeXMnciSNKq0dRaLMU4zsp4zGw2kSTpmI9TLBaDXl9LkgXsZXBNTTXLmMQaTS19SqPlQaq2FEU5TUlRIcj6orZWXVxcCwBabTWXq9XpXC7lCoUili5QWgqDoVanq2b+JEmrXZQdJjgO1dV1bTRajautLUOK9ygGD4dDAkBlZT49IMYwMJm0FRV3LBYjhmEGg7q2VgUAZrPe8UISiYjP51RU3EE2VJWVBfQhtVoFAAaDOiEhXK0uAnsona6amaHZrAMAjaYcAGpqVMxDNpuTKWib7a56KSy8dbfAa0SM6/XqiooaALBazTab1WJx+Qz5fFHruCt2BH0y27dvP3ToEO0tAI3XKYqy035oTRwXBWpra0mSRGXbv//XQYOGMCdUsrKy6L5CTk4OHXuaJMni4hK73CwWC+pe6PX69evXHz16NDQ0lJ4boL0b0eP4adOmofTtkXvRB2IzjHZvO5qRkYGcH9A0urB17dq1Fln8cjrtZDQanRaY6TeF5saNG4cPHx4yZEizy9DyAj4mJu7MmUyVqlQikZw5k2E0NgzHbTbbmTOn5s61j9JtMBiysq5Nnz5rzJjHDhzYc+DAnokTp9Uf0jN8nHXx9ra3BLh5s8FQuKAgBwDy87N5PFf6xvbxwdyj0TShfVGpmpB5eXlJeXlJ4+nqxxkAoFIVMW/WNRgA5TSlzRaEBHxFRenNm7cdEzji58fWx1lLweFwCULA/AmAMfe4gSRtVquZyyW4XBRyBjmL5QEAjyek57RxnOBycYIQYBjnzTdfHT16tLe3V69ePeLi4hwv9P33G+RyOUEIbDazzWYhCD7tSggFGcJxnqvioavQP/l8AX2WQCBiHnJUOunUKdHb24e5Z+nS98vKGlp5HHfeQWTo+tVd3Waz2D1Vh3I+sAUdoVBYXa2GekezCCQOV6xY4XRM1jo4rsEj19RodF5QUHTu3B+02yUAqK6uppfMDxw4QDvTBIC9e/d7e981pDGZTHQrjzzyisViV4Jq+/btj5qH2palqavaFovl/r14ZWVlThVKnOLr6zt37twNGzYkJUU173ItL+CDg0OHDRu1c+ePGIYlJCTV1DTIyGvXriiVSkfPggKBIDQ0LDY2HgBSUlI3bmywsZHLFa58nN2+nVVeXtK79yB6T3R0502bto0dO9UxmDdSsvP2DkUxYBrl4sXTfL4gIYFVwGODoUatLg0IiGXpofPEiQPR0R1DQlgFu1Sp6mwf4+M7I/dqbtBo1Dk5efHxnZympOd0o6LiBw6MhzofZ0K5vMU8I947fL6YGRWwsrKaw6lhWUKr1Ww0asRiL5SDUIhszaUAIJf7MwQ8zueL5XJ/HM95/PFpCQndAODsWeexRJOT6y5tMNSaTHqZzI+uZZMJAwCxWOGieJhAIGUeEolkAODt7Q8AMpkv85BSaZ8Dny9AcQVpTpy4y7GuUOjcGT5BEGgKUCCQyOUSALBYDAQhaFO1TON0oaG4uBgA0PJ2m8VgMPz0009ODyF3Okw2bNjiPrfs7GxX09H3T7rn5+ft379Lr9d36tR56NCRds3XmTOZp0+ftFqtkZHRY8dORNZ9a9d+jSwmAKBXrz4jRoy9T2V7WGlSfMKJEyf+9tungwYNevbZea+99ur33/+ck5OTkpKSkpISEhLCRgun5QW81Wrt2DGxa9fuAHDjRpaXV0OUz8uXL8bFOdEeZzrvtPN11SRCQ0OdTnQ8UkRFRUya1PdBl6JNgPQuHb3aIVP4e8/fx8fnqaee6tmzJ8v0qCRIJ8DOGNqxOBiGDR069ObNm7Rxh53NWJcuSUxVXhqCINrReM9pPbSL9ebTp+3DENA0L4JcRkbGPRSnyZCkbceOH8ePnxwSErpp07qrVy8nJXWlj6pUJadPn3zqqWd5PN7WrZtPnz45YMBguLeIAx6aSkBAwF9//fXf//73gw8++Oabtcgl+aeffgoA165d69ixcVOslhfwer3u22+/eeqpZwUCQWbmiV696tQjLRZLTs5t5JCIBtnYREfH7Nq1rbAwPzg49Ny500jbzkM9ns+jmTC11u32t0ijw+Vy165d26TycDic3r17//7774mJicxDTL1I5K0Pw7D09HRvb29X1puu1L/biHa9U+wULPT6mvbb+v/558WWzbBJ08J3K1hY3ChY8HgiFEjajpyc23K5IjIyGgBSUlIvXrzAFPDV1VWdOyfLZHIA6NCho0pVCs2NOODhXpDJZO+//37v3l3Pnv1j9OjHUlJSzpw5c/PmTaaKvhtaXsDLZPK+fQf8738r+HxBjx69OnWqU04uKLijUCiQdj0NsrEJDQ2fOnXG3r07dTpdeHjk6NHjnWX8qNN+m8IHBZr+RSNm5tMTiUTsHRa1IH5+fkqlEsMwFMuEiVLZoOggFovNZnOjIe2bYfn9wOFwOMz1fpZ6ph4cwTAuepI2m8XuqdrhymC4pqaadt2NPBkwj8bHJ8bHJ+r1utLSkitXLtHDd1cRB0wm06FDB9C2n5+fQtFg3KXX30dTiIebysqymzdVABARERoVFc7jibKz//b1lfr6dlerVUjJFyEWy4KDwx1zuC+W2b169aEH7jRRUTEvvrjIbidtYxMeHvnss06ckXl4+MjKuhoXF98KATCQgHcUhAcOHPD29nZ2xv1l1qxZkydPdnqIaR6CPDUhzzxO/RlMn/7499//0B47fHy+hM9nurfSsJzm9WCHUCj18pICQEXFneZ5stPr9fS8EY/HNxjsAysAQGFh/uHDv1AUpVT6gduIAyRpKy4uqs+Nx+U2qChare1gwaVtYjYbNZq6eRoMw0wml86OXPWV272rWg/tCzcBxVscLpfL4XB4PJ6dLAwPd9LVbR1cRQAKDAx44YX5a9duMBqNkZGRtPv68PDwbt262alfIR3SNhgO2EM7QigU0jbMZrPJqfPQuLiOcXEdT5/O2L9/94wZT7qJOCAUip555gWnF9q//ze04Sriw8MEy3uUSCRsdPsDA8O6dw+De/BF7+k+t3VYxsNtF2zbtmX16q/o4KH3Gw6Hw+VyXfmJa1MQBPHuu68j8c/n88PCwtBSukwme+mllxwTg+s+e/t6T5pX2K5duzae6NGgsLD4zp38K1eu6PX63Nzcs2fPslTxUyi8KysbXO8pFF7Mo6dOnfzrrzpffqGhYVVVFQBQVFRAq9DbRRxgA0vtENS1bXegDjdLX+ajRrWSK7a23vB5eJhwFVDcZrPeuZOHtoVCAXOB3Gg0kCRZXc3KEanNZtXr9VxuNUEYAIAkrRwONmLEMH9/pdMcUBRtlpmbTHq9Xl9dXcleghoMOpaZ63S1Vqu5Pm4eBUBhWF3BMMx+QJCUlDh8+BAOx34/Gj1QFAnABQCDQV9drQcArVaH42aSdPmxEwRPInkwrWrzVAId7WAfNTIzM5OSFhiNxtu3b9sNGc+ePdurV69Gc4iKit6zZ0dpabG/f8CFC2dpDTuk+CyXyzMzT4SFRYhE4vPnz4SFRQBA8yIONHWIEhkZRsdpdQ+O40zPwS0Oh4ORJMVyUB4Q4F9UVGxnHTNu3Lj8/PyLF+31MV15125xPAK+3dBq70Tro9Pp6Om+pKTO3t72wubSpbMOJ7mhLoBYZWUZRUFh4S0/P6mbHNTqJmReUFDceCK6HMX5LJ0hIiiKkkjEwcH+tbUamUyIykxR9p6bfXzES5b88/hxe6uq0NDg/PxCm82GBHxpacGlS9lsrqtUBiYmdmNfzhakefMNzXbG7OPj8wCd57Qgv/76q5dXUZ8+fWbMmOLv7+/rG1RUVBQYGBgZGWlnoOEKDoc7efL0Xbu2WyyWDh3iu3Sp8/mBFJ8TEztXVlZs2vSt2WyOjIweNWocADQ74oAd7kVmUFCgUwHv7+9vtVqZ1derV69Tp065ucry5cvffvttABCLxXYe4BtlwIABTz/9xOnTf+7deyA2NjYzM9PRIUFsbKzZbEbx3+LiYjUaTUJCAh17hsvlbtmypaysbO7cuXRseAQKigEOj6Jjx45ZWVlNKqd7PAK+fbBs2TJX+lkPAWKxhF7A4/F4zF5wUVFeVVV5UhIrW3ObzVJdXSyT+SFvcTYbj88Xd+/ez1X6mzcvEwQvMpKVWabJpNNoKnx8QlnqhV28eCooKBwF9m4UrbbKYjFGRUVPmTJpwYIFzEMSyQ27xJ0798rOvhoSEmG3H6k008oNQUHh3bsHAIBaXYzjfInEpV1Nk+ZaXelIuvea4oqkpMRbt1g5VWTC1ElsErt370ZBvexITx9y6NDh5uX5QEhJSfn226mJiYnNVrIDgNDQ8AUL7BeAaMXn/v0H9e8/yO5oMyIOOI7gJRKJmygDkZFhTvfHxcWpVCqmgO/QoYN7AR8UFIS2o6OjL1++7JgGLd6haQB/f3+VSiWXy5FTRV9f37Cw0LS0Qb/8cigyMpJ5oTfffPP9999HVuk4jt+5cwfDsEGDBv7447p///szOpmvr69YLI6MjPzkk08GDO9jfWgAACAASURBVBjA7GEkJiYAQFpav2nTHn/++ee9vb3RwkpISEhWVhaHw3EWJ7k5eAR8+8Df3799ra02CS6XGxjoXBDyeHwM40ilToK5OWK1mg2GSolEijzZDR6cPniwu7CEXC6O4wTLzHEcs1g0UqmcveI3ny9gmTlJGs1m7Px5J970HCfPkS9IR60okUjMNPHn8wVSqQAATKZqgmBbEve40pF07zXFDSNHDt25s5EwzUw4HA5JkgMHDty6dSub9AKBQCqV0v6eXS2Rhoc7FyptllGjRrEbqLch5s2b98033wDA4MGDHZ390Xh5KRx3RkREbN26lRlFLCAgYPLkyXaRZAFAoVCgEAb0NI9CobDTSA0MDCwpKQGA06dPDx8+HHUaunfvfujQoXfeeWfRokXAWAZC0bd9fHxoB/Xz5s2zWq0qlcpoNKKdKGwVHbJ8xowZW7Zsob/E7t27z58//+uvv0ZzAFwuNy4uTqn0XblyRVJSl+7du3/yySfbt2/v2rXrM888M2PGjEWLfFoq8oBHya6t8xDLdQ9scHwBUGvl7e1l12xNnDjxjz/+uH8vjBsdSdprCkHwUlJSL11i6wRGKGzEIUFUVJ0X7tDQEADo06f31KlTp0yZ8uKLL4aGhr733nvM+0X6WUqlkhbky5YtY0YgFAqFEyZMcLwKUgUQi8UPxEHCo4BcLkdBArlcLo/H8/HxQUHZ7eByuU7XImfOnBkYGIiqKSwsDMOwhQsX0gocXC53wIABaNvX1zcxMfGdd955/fXXUQKpVGqn6tGhQwe0wTw0YsSIl19+uVu3urWqf//733R6DMOYhrUcDufDDz9cv379jz/+iF6/ZcuWpacPRod4PN6HH35IXwLx6aefdu7cGW2vXLkyJibmp5/Wh4aGAgAd3PyTTz6ZNGnSrFmzWvATbk8jeL1erdVWMX/abNayshw256J1DrW6hOWzs1iMFMU+cxIAysvz2CRGaLWVZWWsLEbMZn19+sYLg0L7VFcXGY1ONGkpKgJ16bTaqrIyNQCQpNVqtZhMLlenBAKJTPaoKzQ9WBzN4dA73K9fv9zcXDpEKQDweLzkZFbRE5qHKx1JcOs1xWw2nz9fFwxUoVBIpQ3Nd21tNel2LjIsLHTSpAkfffQJAMjl8oKCQplM8tFH/9FqqxctennRopcBYOnSpbRr2/DwsCtXrnbv3m3x4oWDBw8DgJqaKr1eCwBisUin05eU5D/33DM7d+5E6f/zn3+//fZSiqIEAj4AREVFVlVVFxUVjRs35vDho01dtW1Namqq8vOrAUCnq+Zy8Zoal0UVicS+vs2ZwG85MAD46quv0Hu7ZMmS/Px8Ly+vxYsX79mzZ/z48b1796ZjyPbs2ZOWuCEhIfSgGS1ao0NoOyAgIC4uDs2oP//88/PmzevcubNQKExMTAwNDUXiubS09Kuvvvrhhx/szFNfeuklHMdPnjwZGBgolUq1Wq3BYBg/fnxISAi9WI4cZEG9XwovLy+oXzJnChFk0vLGG29oNOUmkw6Fog4KCiIIgl4goG9BLBZbLJbU1FT7B4Rh3bt3Hzx48D09Zme0JwGP4zyhUMr4WY1hGHOPG0jSptfX8HgilhZTHA4XxwmWmVutJqNRJxRK2LuVJQg+y8zRXCiPJ2CTnqIwAODzxS4ikWB2VzcYajgcwk0AHhxv+dDydgHFPbgnKipq/fr1zz77rMFggLsH9KGhoYmJiX///Tf6+QCd1LrxmmKxmDMyTqDt+PiOPj4NKwUkSdIaRj17drt8+W+7iGq+vj58ft1NSSSixYtfjo2Nzs93uWaPQvoaDDocJ19//ZWvvlql09UOGNAnMjLUYjEvW/bf4uJ8jabB/tjPz4sgCLPZHB8fvXLlpyaT+caNW6tXr/P392mSaSWPxwsPD22GMkGzUaur8vNzoH50gWEuQ1n6+Pg9aAEPAJCcnIweaUBAQMeOHRMTE1GoiEmTJjGdLm/d+uNPP9VF2p05c+YHH3wAAL17905ISACAWbNm1dbWEgSRk5ODOr49eiQfOXJcIpGgyZugoKCdO3fSH0JAQMCLL764Y8cOOwGfmJjYvXv38+fPy2SyDz/88OTJk1999RU6Kyoqqlu3bkVFRbQmEPri9u3bt2TJksuXL2dkZDC/wYULFxIEwdyDttetW2e3HsTj8ZDSAJ/P12rvChWfmJgol7fACpoj7UnA83giHk/E+FnB4dSwjGNttZr1+hqRSM4ymhyXm43jfJaZGww1RqNOIvFtyuqshGXmXC4PAEQiObv0BACIRAqp1Mk6Fv0S8vliqVQMACaTjiAEDyoWuAc2cDic2bNnv/zyy44CHhhGwxMmTBg2bNgDKB8AuPWaIhZLlix51+lZubk3SkrqQsZlZp6ZN29ecXHx4cMNym5eXj4dOnQGAG9v77i4+FdeedbbO4QZbBAABg0adPz48RdeeKG6uvof//jHSy+95Ovr37//cJlMkZLSe+jQ0Ugjr7S01GbDR4+efPt2gxhOTu4jFArNZnPHjkkdOoT6+8dwONwnn3wmKSnp2LFMkiQ1Gk1cXNzNmzejoqJyclxOofXo0WPlypV20ydTp07NyspyqttFwxykNonw8Jh+/WLgHjzZtRrojY2KiuJyuW+//fbgwYORtL5x4wY66uvri1QrAEAkEvbu3XP+/KdXr16DBsdcLvf06boZoBdffHH8+PE6nY6e1kKSm178JgjCsZsrFAqFQiF9CQDg8/m0/6tJkyZxudxvv/0WubwMCwu7cOGCXfkxDJNIJCtXrty5c2dGRgbzEqNHjx49ejQzcX3Po4ddMZB+jFMDEKTqfz9oTwL+0QS9hW05goiHVmDcuHEbN24EADsnu7S8f/3119lEl7pPKBTeV6/WSTJHrylu8PJSJCYmKpVKHMc3bNiwa9cuJOCRSjPdcItEIldmor/99tuBAweSk5MDAwMBYNWqVfQziY2NpvXtAwICli9fDvWK0zKZTCQSderUadu2bWPHjmWG7khJSQGAP/744/PPP//Xv/7Vt2/f3NzccePGff755wRBjBo1av78+c89tyA3ty6+S0xMzMyZM9EULpNu3bqtWLFCqXTXe05JSXEq4AcN+sfRo8fAoQeA4/j9NPxuGYxGrV7fEFKIw6E4HKy2tsRk4r/yyjMAUFVVAAAGQxUAmM2awEBZnz69MjPPAEBtbVl0dOR7771ZXV3ZvXsCl8sVCoUoPUIsBrFYzNhDAYDBUKtWFwMAQXCZiREfffQvHMcPHDgQHR3p4+P15JOzJBKMw7HyeARKPGBAj6tXz2m1KrsTKYrS62sGDEhNSopDKUNDfYYNG8zhGB2vYrWaSZKkKDOXy3E8CgAWi8Fms9TfuwHdLEk6WVshyaB6Vxa1VVUNIYWcZosgCKFU6iQ4hUdstA9Qn9fDIwtt3PX4448z96OZvbi4uAflf7eoqMBsNkVFRVdVVZaWFlMUeeHC2aSkLixPl0qlV69ePXbsGJLKtGweOHCgSCTCcRx1bbdv3758+b9dZTJy5Egk3YFFqMDg4ODU1NQePXp4eXnx+fz09HStVhsTE22XTCaTLVy4EMOwhIQEDMMUCkWnTp3Wrl37888/Dx8+XCZrWC9buHDhggULFIq6ObP09PTjx49jGCaXy319fd99t272IjY29rfffuvb965Qzq5iDfv7+7/66quJiYmRkZG0VqBCoUDT2m0cDMMwjEv/jRiR/vXXnwsEIuZODOOi+U4Oh4th3DfffG3+/KegfkUSwzirVv1fWlrahQuZmZlHHE5s+EPdNQzDULgdu0ujv5CQ0ICAQBznjh49/Pvv144ePZLDwZ9+eu6+fTvoNHK5wvFElOF///vetGlT0J6YmNgfftgoEAidFYaDYTBhwvglS151WlQOh8ssHrpNF/eF2T1JV7dm9zAd8Yzg2wft4sNuEYxGrcFQy/xJkrbqalbuZdAUnFZbqdfXsElvtZoBSJaZ22xWAFCrS1hrWlAGQy3LzC0WI0m6K4lMVqfd7e/vXVtbDgAaTYXNZvjmm8//85+AZ56ZSxBWdDpFBaCOu8Ggqa7WAIDNZnGfOY8nFIvZjrntoANCOvWa0lTomar333//7bfftlqtt27dAoCQkBC5XF5W1riPmujoaDvlJjt4PF5mZmZJSQkylALXXv2RijWHw5FIJEql8sqVK/QhmUwqFAoNBsPGjRufeOIJAJDL5X5+fmKxODk5ecCAARs2bEhPTweAxYsXb9q0MTc3LyIiIj093dfXt1u3blwuFykG0i7nkHJWSkrK33//PWBA6ty5Tw4ZMtRkMpWVlT311FM7d+7s1KnTgQMHkpPbwZCMzxczl1HUak1SUqKXl32lmM1cDMOCgiK8vIJGjnwsISHZ1zcgJCS6rCxHIvFGQYkcz7LjqafmnD//Z3BwRHR0IgDgOM/VKZ06JXXokAAACkUAh8P18oLg4Bj3mWPYZaFQ1mgZELW1ZSaTLi1tSFraEKcJ/P2DqqpqUG4EUQtwXSr1VSicRL2iO6gCQUNIoeb5ovcI+PaBnQfEhxiKIm02C/MnAMXc4/5cACBJG8uYFhRFUhTbzJECFxLzLCFJW1NK7q4kfD4BAD17duvXrzdJWlFJbDaLWCx4//2lAOB4LkXVXZ2iKADSTeYk2QRHN3Y6krRfFKdeU5qKSCTCMGzbtm20dVx2djY0xVh0w4YNbJIFBgbSg3735REKhZcuXbJTmHrnnSVGIzl27ISIiAi6U3Lu3LmAgAC0yDpz5ky0UyKRJCTEl5WVoT56cnJybm7uwoULVSrVqVOnaBUKLy+v2NjYY8eOVVVVXL58Njm5BwB89dVXFEUdOXIEAF599VVkVfXQ4O/vf+PGjZiYOikbERHx3nvvsfxeaEJDQ3bv3t6tW2/krMaNamRmZqbBUKtWl9xLme+FTz75xGw2t/JFPQLeQ9tCKJQJhQ2uXWprDbW1Wl9fVvPPVqu5vDxXJvOz08NyRWFhMY/HZ5k5ah3Ye7IDuCYWe7HMvKZGZTYb3CQOCCgBgA8//GTAgH9oNDUAtxWKAJnMybCbFoUikcLXVwEAFRV5BCFoy3pYNIMGDbp58ybd6ANAUlLSmDFjmGvkrcmFCxfCwsIcFaP4fF6HDnG7d+/u06chLrarVZLvv99QVVXu7V0nniMiInbu3LlmzZpTp0516tQpLy/v448/XrBgATqd2ZWnVRDA9WR+uyY2NvbeM+HzefSDYuq7tTXsfHS2Dh4B39YhCBzc9kw9PAqg8d9DP5GDYRhTugNAhw4d9uzZA86mKFoBNxIIwzCnrlqcIhaL7IKkhYeHR0dHEwQRHh6+YsUKN+eiSkcq5R5cweVycRx3r9L4COIRG22d1NQ+mzatfYAhzD20BTp27Lh7926k4O3hIWDo0KFo9aFRUlNTd+/e3SKD3YcYDMMOHjzoaJz2iOMR8G0dHo8YNmywx2HtIw6Hw2E/XmyzkKSVqcRAklaKoiwWJ75vnZ4LAFarmcNxrhNnB0WRJGljmTmaHrBYTOztUW02K+uS29jfptVqRv+Z6UeMGGq1IhdAfKTjabNZLRYrsLhNDoeLNMwfeoYMca7d9ijjEfAePHhoJQwGTW1tg881vb6GJG0VFXfY58A83T0Wi5EkOU3K3I2dsR0URel01RUVrHQ5ESxLgnz51dSUms1OLEFIMgZZSOv16oqKOpsCq9VsNGodEyMEAomXF6uQhh4ePjwC3oMHD62EQCBhOj/W6SwajZ7WPnMPSVrV6hKpVEkQrOLBlJRUEATBMnOTSa/TVSoUwSxH8BiWIxIpWGau16stFoNczsrGSafTAhTIZH4unFHWFU8olHt7iwCgtlbF5RJisRNrKwTLCQ8PDyXtWMDz+QKxmK11OIZhBCFgP/8mEokJgq0bdgzjsmx0EBKJnMdjmzmXixMEW9UqDgeXSuWu7Hq7dgUUPiOgXp8ax3mOgb3bFHy+AIU5ZwOGcQhCwN5hsFAobsqzbWoty5pSywSO21gn5kqlclcV17Ur1NYCANBWYDjObyOTtFwuwSyJUCiVSPQsvUfbbFaCEPB4Qh7PPk6uUyQSGZeLs8wcgDKbBQKBiOXLI5HIRSIJy8zR/Dnr26SkUrlA4Dzzrl2hpgYAIDSUQJaTBCHAcR7r23wA8PkCx5DHrsEIQoBcu7BBKBS7au4cQZ8w++XOpjbUOM62MUGfsCvV6S5doKoKAIB26IDjvOZ9whhLi2EPHjx48ODBQzuiHfhF8uDBgwcPHjw0FY+A9+DBgwcPHh5CPALegwcPHjx4eAjxCHgPHjx48ODhIcQj4D148ODBg4eHEI+A9+DBgwcPHh5C2rQNtCNfffXxgy7Co0VsbIcRI1rbQ6qnlluZmJgOI0e2Ui17KreViY6OHTVqfCtf1FPLrUxkZPSYMRMc97czAV9dXfWgi/BooUOecVoXTy23MjqdptWu5ancVkardenF9v7hqeVWxs/P3+l+zxS9Bw8ePHjw8BDiEfAePHjw4MHDQ4hHwHvw4MGDBw8PIR4B78GDBw8ePDyEtDMlu/uERCIRCkUURVZUVDTj9GeffdbPz+/y5cs///xzi5fNkbfeegvH8ePHj584ccJpgs6dO/fq1Uup9DUYjMXFxUePHquoKG+Fgj2atHjtt/Lr9FDS1j5JBI/H79mzR3l5+c2bN1uhVA89ba2W22DD6xHwAAB9+qSmpvbR6/Uff9zurTvS0tIGDRqEtnk8vlwuj4uLW7NmjUqlerAF8+DhESctrV+/fv0uX77sEfAPH22z4fUI+IcKHo/fv38aAFy/fn3fvn0ymeyJJ54Qi8VDhw7dtGnTgy6dB1Zs374Dx7kGg+FBF8RDyxASEuLr65uYmBATE/ugy+LhvtBmG95HSMDHx8enpKQEBARaLOacnJwTJ05UV1cDQFpaWmxsNADw+bxx48adPn26rKyMx+P37ZuamJggl8vNZkt5efmpU6fY97u9vLwGDRoUFBQkkYjLyytu3LiRmXmKJG10AqVSOWzY0ODg0JKS4v379y9YsADH8V27dl26dKlJN8XhcOfMmR0aGlpTU7N27Vq5XI7jBAAcOXJUp9PpdLqsrGs9evQMDg4GwACoJmX+0HCPT7tDh/i+fVOVSr+SkuJDhw5Nnz5dIpGcOXP2119/cZr+Hmt/0qSJzIlHLhdPSenVpUsXLy+FyWRWqUozMjLz8vJa4Lk8SjyoT1Kr1Y4aNTogwLmZsoeWxdPw2vGoCPjU1D7p6UMBwGw2SSSSLl26xMTEfPfdd5WVlTExMUqlHwBwuXjXrl2vXv27rKxswoTxHTrEo3NxnAgPDw8LC9uwYcOdO3cavVZISMjMmTN5PB76GRwcHBwcHBcXt27ddxRFAoBCoXj66XkEwQOAyMjIOXPmcLnN1HYcMWJ4aGio2Wz6/vsftFqtQqFAvRC1Wo0SVFerAYAgCAwD6pGU7/f4tDt37vzYY+MBMHT67NmzuFyum/QtXvujRo1MTk4GAJIkJRKeRBITFRW9ZcuW27dvs7+LR5wH+EkCwMmTJ4RCMQAMGNBfKpW2zC15cMDT8DrySAh4iUQycOA/AODo0aMnT56USqVPPjnHy8t72LCh33//w3fffZeePpS5Bi+TyZB0P378eGZmppeX19y5cwUCQXx8fKMCHsOwUaNG83g8rVa7fft2lUrVo0ePwYMHh4SE9OzZ49y5cwAwYsRwguDp9fpt27aZTKYJEyZIJJJm3FdycnKPHj0oity2bXtZmQoACgsLf/jhB2Zx4uPjAaCkpIR6NMX7vT1tgiDS09MBsIqKil27dlmttnHjxgUGBrhK3+K1j+NE165dAODUqVNHjx6VSqVPPDHd11fZu3eKR8Cz5MF+kgBw7VoW2ujVq6dHwN8nPA2vUx4JM7m4uDiCIKxWS2bmKQDQaDTnz/8BAJGRkWhkZofFYtm8ecvmzVtOnTpFkpRQKMRxHABEIlGj1/Lx8UXTcUeOHLlz547RaMzIyMjPzweAxMREAMBxPDY2DgAyMjLy8vJKSkr279/fjJsKDg4eNWokABw6dCg7O9sxAYZxxo0bGxoaSpLk4cOHm3GJh4B7fNoRERGoCdi/f39RUZFKVbp37x436Vu89gUCPoZxUEkSEhLMZvPmzVvWrl17+PBR9nfxiNN2PkkP94+2U8ttquF9JEbwPj4+AIDjxDvvvM3cj+OEXC6rqamxS28wGMrLy1JT+wwblu7j48vhNKEb5OvrjTbQu0Vvh4WFoWL4+PhiGAYAhYWF9UcLKIpCO9kTG1unsKNQKByPSiSSSZMmhYeHW62Wn3/e9cgu2d7j0/bx8QYAiqIKCgrQnpKSUqvVijp8jrR47Wu12uzsWzExsUFBQRMmTKAoqri4+K+/Ll248Aeb8nuANvNJerivtJFabmsN7yMh4NGiqdlsvn79utNDdohEovnz54tEIp1Od/78+cLCwr59+wYEuJyYdQpzYgbN0qBrEQTO3Ik2mvGeAYBGo5FKpT169Dh79nxVVSW9PzQ0dMqUKRKJpKamZuvWrSUlJU3N+aHhHp82nYxRmxSbObcWrP0ffvgxPr5DYmJSTEw0j8dDK4vR0VFbt25lcwseaB7gJ+mh1fA0vEweiSn6ysoqACBJ6uefd/38888///zz7t17Dhw4eODAwaqqasf0iYmJIpEIgFq9evUvv/xy9epVoVDI8loVFXVhlMLCQumdYWFhAFBeXg4ASHUfAAIDg9BGUFBgkyYJELdv316zZo3VauFwuOnp6cxrzZ49WyKR3LlzZ/Xq1W3hJXuA3OPTRq8HhmEhIcFoT0BAAEEQrtK3eO0LBAK5XFFcXLJt208fffTxli3fo/FHXFwcn89neRePOA/8k/TQCjzwWm6bDe8jIeBv375NkjaBgN+nT28ATCgUzpo18/XXl8ybN5fZe+Pz+Xf39bCQkGCCIPr06SOXy5kZ9u/ff9iw4T179nK8VmVlBfJsgPQ7kLldeHg4AFy7dg0AdDodqvu0tH7BwcF+fv6jR49uxk0VFBRoNJpz584DQHx8B3QJDONMmDCBy+WaTMZjx47JZHJ//wD014xLPATc49POzc01Go0AMGrUqIAAf6XSb8yYMW7St3jtx8TEvPTSiy+//HJ0dLTNZs3Pv1NWVgYANpvNYrGwv5FHmQf7SXpoHTwNr1MeiSn6qqrKU6dO9+vXb+jQoQMHDiAIHoZhFot57959aLqmpkYNAFwud9GiRVu3br19O3fIEBLDOFOmTAUAkiSNRpNAwKc1YJOTkxUKRV5e3vnz5+yuRVHU/v37Z86cKZVKn3rqKXp/YWEh0uQEgMOHjzzxxHSpVDpv3jwAMJmMzZspAoCMjIwePbrzePxhw4b9739rIiLCUV+EzxfMmTOHmXLZsmXNyP8h4F6ettlsPnbs+IgRw/38/ObPXwAAWq3WYjEjSxtHWrz2s7NvVVVVenv7zJgxw2KxEASO1ELPnj1LkmQTnsIjzIP9JB9Z5xOtjKfhdcojMYIHgCNHjuzatQtNb2o0mqtXr65Z8y2tjnHx4l83blw3m80kSdpsNpWqdMeOnZWVFWazKS8v77vvvsvOvgUAkZGR/v6NO6woKCj45ptVV69era6uMpvNxcXFx44d++679XSLnJNze/Pmzfn5+SaTqbCwcN269UxXDE3CYDCcOXMWAAIDAzt3TvL1VTYvn4eYe3za586d3bFjR0FBgclkzMvL27Rps8lkdpO+ZWvfaDStX7/hzJkzlZWVFEWZTKbS0tL9+w8cPXqM/S14eICfZIvdg4fG8DS8jmDtyzx62bI3HnQR7hUM4/j5+QGAVqvR6XQAIBKJFi9eDAAbN27Mzc19wOW7m8TEzpMmPd7KF23BWr4fT/vVV19178mulctz7yQkdJo8+YnWuVYb/ITbZqW0FPHxiVOnzmjli3pquZXp0KHjtGmzHPc/ElP0bQqKoubMmS0QCCoqKn78cavRaBgxYgQAGI3G4uI2oZfxMNHWnnZbK48H8FTKo8GjWcseAd/6ULt27fp/9t48Por6fvx/zczO3snuJtkNCbkvAuEMYgQRKsWICHgV8IdoFf1atUr9fPBorfVsa7XtR/2UqlWhKhaQgh+IggoIWI4AIdwQCCQh97XJ3tecvz/eYbNsJpt3kFwwzwcPHrMzr3nNezK783ofr+Ouu+6Mi4t78slfol1+f+DLL78MBPwTJkyYN29ehJOXL/97W9vl1LS9Vhlsf+1I7bmiF5LBZ7B9SWT6gmvxKfffFH1NzYXNmzd6vd7Ro8cWFs5G+bmCnDp1fPv2bwOBQGZm9rx5d3fnxDQIZ34uD41GM2rUKJPJJIpie3t7Wdlpvz8AAFqt1mQyRTixubmZ47j+auaQn6JHXNm/9vjx42la2dzcFJpS44q0Z6C4xqfoEUPlJ9lb5Cn6UK7WpzzAU/SCwG/YsPbOO+cnJSWvWrXy5MnjY8aMDx71eNwbN/578eIlFkv8v/+9et++3dOn/7R/GjZQ+Hy+0tLSrvu9Xq/X6+3/9lzdXNm/9tGjR/uoPTIDiPyTvBa41p5yP43gz58v/89/dixZ8hgAnDp1/MiR0sWLHwoera2t/vLLL371q+cA4MCBfTU1F+bPX9Sjzra2Fo/HlZKSidMAQeA9nnaNxqBQSM8NhNHQUK1Q0BZLIo4wy/r9fpdeH4cZcVFZeTY21mIwROowBvH73TzP6nRYwoGAv77+wvDhqSqVRGael1+GQAAAYNYs+MlPAAA8HhtFKdTqwVsAo7291e12pKRk4Qj3/inXUJQiPr5PnnJV1dmYGLPBEIMjfBlPOTExVa2WeMqvvgqolPytt8LNNwMM4qfc3t7qcjlSU/vk4TY21pAkFR8/HEeYZQN+v7NXD9dkMhuN+A+X0emwhCM/3NdeA2SGCgthxgyAQfxwg/TxU64lCGLYsCQc4YtPOTZs/rg7V1JezQAAIABJREFUqqrKTaZYozEWR7hXT5lhAnV1VYmJKWq1RH2T3/8e3G4AgJkzYeZMAACv106S1GU85X4awTsctmAggdlsQXHnQSyWeIZhzpw5NWxY4qlTlwzuI2C3t7W2NuIbeLe7XanUYX5vmpsbVCo1poHnuIDb3a7Xx0qWrulKbW2FUqnENPCBgIdhfJivfobx19RUxMUNkzTw77wDTicAgNHYYeB9PgdNawbz28Fub2tubsA38G53u1KpxXzKLS31SqUK08BzHNPLp1ypUNCYBr6XTzlQU1MRFxcvaQPefRdQzq6oqA4D7/M5aFo9CJ+yw9He1FSH+eoXRaFXD7e5uYGmaUwDj37COl0MQUSqBRwEPVxMA88w3kDAg/nqZ1mmpqYiNtYi+XD/93+hrQ0AQKfrMPA+n5OmVYPw4QZxONobG2t7+ZQ12D/hBopSYBr4kKeMIw51dZUURWEa+F49ZfQTjomxSBr4v/0NWloAANTqoIF3KBTKwWvgvV5vMLOmUqny+S6ZDFGp1FOmTPvii89pmo6ONo4fPzF4yO12rV79KdrOyMgwmaKDhwIBH8dxpaV7cBogiiLHBerrmzH7bl6vx+NxYSoXBJ7n2YaGVhxh6CheUtncXI8jzPOsKIo1NXV4LeEA4MyZY5I59nl+MgAFAPX1F0pL6wCA4xiCICjqXHcKTaa4jIxcnEvLyMjIyAwq+snAazQau70jFTDDBMI6p1VVFYcO7f/Vr57T66N27ty6YcOaoL8ASZImU0efSKvVhfZ3OI7jeV6yB9QVURQCAUGp1JAkVg/d7/eRJIWpHKUNVas1mGM7l8tB00pM5SzrFwRepcIS5jjG4/EolWrJfOnBfqtC0XH1QEAkSYqm1d0pVCrlhOcyMjIyQ5J+MvBGY8zJk8fRdltbm9F4yVRkVVVFVtYItDM///oPP/xb8JBWq+tuPb6ioqy1tTEvLx+nARzHtLZWxcQkY1rKI0eKVSr1qFETcIR9Pofd3jRsWDbm9MAPP2wZNiwpKSkdR9jhaGYYn9mchiPsctlLS/dmZIyIjpYoZRgsrBAfn5iXlwgAVusFmtYYDD2n5+vK/v17i4t3cxyXnp45b949SuUlU2off/xefX1HidXrr598223zoKdIChkZmasPQRDcbs9At+IapZ/esBkZme3tbU1NDaIolJYeGDNmHNpfX1/LMIGkpOTy8jKrtYVhmJKS/UlJKTg6jx07vnXr933ZapluaW5uLC7e/fDDjz/11DKPx11cvDtMwGZrf+6537344usvvvj6rbfOgYuRFLNmzV269Jn6+rpghy8yx4+f+O677Vf+BmQGAfLDvRb44ov19933cM9yMn1APxl4kqTmz1+0ceP65cvftljix43rGBmvXPlBc3NTTs7IG2648fPP//nuu2/ZbO133PEzHJ1bt25/772P+rLVMt1is7WPHTshOtqgVmtGjBgZXH9BMAxDEIRGo6UoBUUpUE3GysoKg8GYnp5J08qCginHjh3BudC2bd///e8f9sk9yAw027fvWL5cfrhXOU6n0+VyD3QrrlH6L5NdcnLqY48tDdv5u9/9AW1MnnzT5Mk39VbnkMqjf1WRm5uXm5vn9XqamhpPnDgWlrfAZmsXRfGDD951Op0pKalz5tyl10dFjqSQkZEZPDAMc/Ro6fXXTw7bf+bM6Z07t7lczoSE4fPm3W0wSCwFhjG0yp1cZQzhVLWXV+ZP5gpSV1ezffu3oiiazZbQ/SzLpKSkzZ49T6fTbdy4fsuWogUL7osQSeF2u1au/ABtZ2dnx8QYgodcLicAHDiAVTxNFEVB4Kqr67GrwfoBCGzlgiDwNTUNOMJIvqbmfENDNY6wIPCiKFZWYlW8QNWxTp06TJISM3AcdxP6XdfVVR44UA0APM8RBEGSZd0pjIkxZ2ePxrm0zDXCt99+VV1dFWbg7Xbbxo3r7r//YbM5/rvvvt68eeOiRQ9iKJMN/IAxhA08yH3DgSYnZ2ROzsji4j2bN28KzVyUlJQSzI5ZUDBl1aoVEDGSgqbpUaM6CmvGxcVFR+tDDwGA2ZyA0x5B4L1eh0YTRVESQQRdaWmppyhFbCyWjyHHBfx+j15vwo+D1+miJb0duxIIeHie02oNPYsCMEygqanOZIpVKiXCH0iyo3labRT6u/l8DpJUqFS67hTqdFjxtZFHb5cxtpMZnJw5c7qxUSKIt7a2Oj09c/jwZACYPPmmYKe8J4ZYzdKriSFs4AlC/t4MGPv27dZqtShjQXJySklJcehR5D+PXgQURSkUNESMpFCp1DNnzpK8EOoHYMbiX4yVSIpgzEJxOm1KpQpTuc/ntNsb8WMl6uqqYmMtyckZOMK9jJVwNDXVJSamRkdLJMYJjupjYswZGWboiJVQGwzDcJR3R+TR2+WO7WQGHW63a9eu7bfdNreoaEPYodzcUdnZI9B2Y2N9QkJnEiFB4Jubm9C2UqlSKjt72BzHiaLgcjlwri4InN8fcLtdDINVqZ3nOVEUMZUHAh6/P+ByOSWnvroiimIg4MdU7vV6GKYXwuh/yYQlohiNRhEME3C5/ADg8/koiqOobpUrFLRGIxEgNoQNvMwAYjAY9u79ISUlTavVlZTsT0lJQ/vr62vR+vp3321+6KFfGAyGgweLc3NHAUBGRmZR0Yampob4+GGlpQcw8xXKDBIij94ud2wnM9gQN21aX1h4m1YrYS1oWonya5w8eWzbtm8WLOgsUOTz+T78cDnazssbExcXuspmF0XATBp2kdpeNbpXyqure6G8vv5Cff2F3rSlEl+0vFw6kohlbwFQAkBDQ01paTmOqri4+NGjr+u6XzbwMpdDXt7YtjbrqlUrGIZJT8+8/fY70P6VKz948MFHR40aY7fbPv30I57nMzKyUJhcMJKCZdkRI3KDkRQyQ4IIo7cej8oMZg4fLkFhrrNmzW1vt8bGmjMysltbmyWFfT7vpk3rHQ7H4sUPWSydc0IajebRR59E22Ej+K++2goAEydOxWmMIHDt7fXR0WalEithyblzJ0mSyswciSMcCHhcLmtMTDLmCP7IkX0JCSmYeXA9nnaG8ZtMWHmvvV5PWdmRnJyxUVHRXY8G05QlJqZMnGgBALu9kaLoqKi47hSiWVKJ/TitkZHpyrRpM6ZNmxG2MxgWMWXKtClTpoUdlYykkBkSRBi9RT7q8biXL/8ftJ2bOzI2tnNs53TaRVHcs2crZhsEgSfJC5g+EDzPAQCmclEURVGoqMDyiAQAQRCqqsqrq89jKgcQz57tNiF0F2E4fvyg5EoQx90MQANAdfX5PXsqAUAUBQCIsGwUG2sZOTLSbFl+/qT8/Elo+8SJo2fPnj52rFQQRJZl3nzz1SefXKbTdfjE8Dy3atWKtLTMhQvvD/NjJUmqu14dSh4aFYXlX8LzrNdr1eujVCp9z9IAKBAXU7lCQbCsKyoqGjOfKUEQKpUaU7koBigK9zbRX0+r1UnKB/+0SqUqKkoFAIGAXaFQYioPZQgbeNmLXkamTwkd22VmZnc3ekN0d5SmlVOnTkfbRqMxKqrzxa1SqQGgzwpC1lBUb6vJ4dcZO2syxWJXEnJzXO/qjMXHD5dMZR0cehqNMSkpBGBUk9NqsfxREHfeOR9ttLY2r1276qmnnkEf0dJbefkZpVJVWDgbX6HMwDKUDHwg4PH7OxMmCAIniqLDIT2VFAaKLPJ4bH6/C0ee41iSBEzlPM8AgNPZgjm2AAC/34WpnGF8PM9hCiPfDbe7XRQDXY+KogW10O93Oxwe6HBR8UVQTtMqrVZ2h75GCR3bRRi9RT6qVCpvvHG6pH6VSk0QBKaB53m2pUXE96Bsb2+laRpTOfKgjI/PwBzbXbhQbjTGYnpQOp0tgYDHbMZKTe12O5GBl+w9BKeWDYaYlJQYALBaq2la9SM9KHsELb01NNRXV1e9+upv0E6tVvfssy/26XVlfiRDycDzPBsIdOY0RqHDoXsigCa+WNbPcVg2WBR5niewlQsAEAh4e5QMwnEMpvJe3SbL+tD/AQn7DgAiMvDBq4uiwPNsICB0p1CeJpFBlJWdkhy9yWO7qxKzOT44fIeLS2/JyamX94jlcKeBYigZeK3WGDqaRDXQLBasTjQKoDIaEzCLzdTXN6tUakzlqNiM2ZyGOb9XVnZGr4+1WPqk2AzABZNpuGT4ddBY6/UxFgvq/l9+sRmZa4ruRm/y2E5GZtAylAx8GHIcvIxMv1FYOFty9PYjx3YyVz3yW3oAGcL1OuXZYxkZGRkZme4Y2gZe7hrKyMjIyMhIMoSn6AFALmNw9cEwvkCgM1aC51kAcLlacc4VBB4AfD4Hw2A5PPI8y3G4yjmOAQC324ofKxEIuDGVs6xPEDhM4Yt5Lu0EwXU9KopxqIWBgMfl8kJHRLg/gnKFQqXRSCTckJGRGdIMdQMvc7XBcYzP1xnKeNFmYwU3otW+QMCL6e0oCDzPE71S7vPhVrYWRZFlA5jKUawEprDf7weAQMBLkpId3NiLsRIdVxdFnuNEQehWuUolyAZepu+Ql+EHiqFt4OXvzdWHVmsILap2ubESWKHSDQ3NSqUKO1bCabc34sdKnDlzVq+PxVTe22IzVVVVJlOiZLGZoGuKThcaK/Fji83IyMgMOYb2GrwgyAZeRkZGZvAiD8MGkP4bwdfUXNi8eaPX6x09emxh4eywYZDdbisq2tDU1JiQkDh//qLQYuHdQRAEyzJ91l4ZGRkZGZkhTD+N4AWB37Bh7axZc5cufaa+vi5YFxwhiuLnn//zhhumLlv2QmxsXHFxrwoLysjIyMjIyITTTyP4ysoKg8GYnp4JAAUFU44cKQ0tB15VdV6v1+fk5ALArbfe7vP5MNXKcz8yMjIygwqW9Yfm7eZ5FkB0u9txzhVFHgD8fhfm7CzPs6IoYCpnWT8AeDw2TDcaURQZxouvXBB4TGEUCOPzORVSFlgUjWjszTA+t9sHHYEwkf6GCgUtWXConwy8w2GLizOjbbPZ4nDYQ4+2t7dptdr169c0NjYkJSXPmjUneIhhmBMnjqJtgyFaq+1MNMuyjCiKDQ01OA0QBM7lcrJsA2YpKoYJCIKAqZxlfV6vUxBq8XPvOBw2zLIWPp+T41iWxfpG+v1eALBam9xuZ9ejopiEvjdOp72hwQkAbreNotwej3TmegDQaLQmU7dFiGVkZGTCYFm/x9NpilDd3tA9PeL3uwGwqm8gA4+pHHkDeL32HiWDZzCMD1s5qmeGJYwCYfx+F0lKRrp2eBmzbMfVBYEXRT6CcpVKN5AG3uv1qlQqtK1Uqny+S8KUfT5fWdnpRYsemDv3ri1birZsKbrnnnsvHvJ+/fX/oe0xY8bFxHTeAzJm5eUnetOQll412+XC/ypAc3MvlLe2Nra2NuLLNzY24AvX1FRI7uf5RGTgrdam8nJpmTAslgTZwMtcKURRQHGPFz+KcDHVQY8gOyEIHKY8KvGOKYxahawFjjx0xFhitkQAELFbEvk26ZCrC4BxmwRBkGS/RktJFg2Jj8/COZfn2ZaWSqMxAbMefFNTG0UpMJVfDIRJxxxclZef1+tj4+OxChL2tmZgVVW1yTTcaIxUM1Cni4mP76gZqFAojcYEHOWh9NOD12g0drsNbTNMIMyHTq1WJyenZGfnAkBBwZTPPlsRPBQdbXj++ZfQNkmSwXLIAFBUtFUUxalTC3EawHFMW1uN0ThcperZfQ8Ajh8vUalUI0aMxRH2+50OR4vFkoE587N377b09JzExFQcYaezlWF8cXEpOMJut+Po0QPjxhVERRm6HlVcnA9KS8ueOjUTANraamhaEx1t7k4h5h31KfJCzFWD1+twOjv7wSzrF0WxpaUSX4Pd3oQpybI+QSB7pdxqrcaUFEXR7W5vacHtDQAAZksCgQAA2GwNfr+t61FByAKgAMDjsbW0tKGdwYQHkqjVepNpOH47Za4m+snAG40xQce6trY2o/GS+F2DofNjmBUnCKI7j3qSJAFEhYLGa4JIkqRCocCUJwiCIEhMYYpSkCSpUND45pAkKWzlFEXhCyvQ/5HlSZJSKKjeKg9j//69xcW7OY5LT8+cN+8epfKStY8zZ07v3LnN5XImJAyfN+9ug8EIAB9//F59fS0SuP76ybfdNq/Hq8gVB64m1Gq9QqEKfqRpFQDExCTjnCsInN3eGBVlRiPCHmlstNI0jak8EPB6PG1G4/DQl08ECKJSqzViKvd67SzrMxiwhl8ejxugNjraEhUlWRCyo3kajSEmRgsATmczRdE6ncRAEIE5Wu1T5D76QNFPBj4jI7OoaENTU0N8/LDS0gNBDztUTDozM2vjxn/X1dUMH5588GAx8rbrEZIk5e/NQNHc3FhcvPvhhx9XKpVffPF5cfHu6dN/Gjxqt9s2blx3//0Pm83x33339ebNGxctehAAbLb25577HeoKDIa5AZl+hqJoiursTSLbg1nBGc1C07QKU54kKZKkMIXRxLhKpcE3hwoFjak8EHBzHIkpzLIcANC0WlI+2N1VKGiVigYAgiApSoGpXOZao58MPElS8+cv2rhxPcuyI0bkjhs3Ae1HxaSTk1MXLlz81Vdfejye1NT0OXPuxNGpVF7OuFPmimCztY8dOyE62gAAI0aMbG6+ZOK0trY6PT1z+PBkAJg8+aaVKz8AAIZhCILQaOQ3kYyMjEx/0H/OF8nJqY89tjRsJyomDQCpqemPP/50L1XK9eAHjNzcvNzcPK/X09TUeOLEsdDhOwDk5o7Kzh6Bthsb6xMShgOAzdYuiuIHH7zrdDpTUlLnzLlLr+9wmeR5rrr6AtrWaNRqdeccLMexAKLNZsVpFc9zXq+Xomw0jRVpyXEsAGAqDwS8Xq/XZmvDXzXw+TyYyj0eJ8cxCgWWMIqxcbnsPM93PSqKHbnofT6vzeYFALfbo1AwgtDtj52mlXq9nIteRuZqY2jnopcZWOrqarZv/1YURbPZErqfppU0DQBw8uSxbdu+WbDgPgBgWSYlJW327Hk6nW7jxvVbthSh/QDg8XhWrerwrBwzZmxMTKex8XjcAHDs2IHetKsXEQcAYLf3QnltbS+UNzTUYEZaXuQCvui5c6cl9/N8IfK1bmqqPXbsPI4qszkhLy8f/9IyMjJDgiFs4AlCHsEPMDk5I3NyRhYX79m8edPixQ+FHvL5vJs2rXc4HIsXP2SxDAOApKSUhQsXo6MFBVOCFh0AdDr9o48+ibaVSmWov94XX2wEgIkTp+K0h+dZm60hOtqiVGLFSpSXH6dpZXo6ls9HIOBxuayxscmY3gNHjuxLTEyNjx++YsXK9vb2Z599JoKw293Osn6TKRFHs9frLis7mps7VqeTGHYjR0sASExMnThxGADY7Q0KhUqvj+1O4eV5WcrIYCK/qAeKIWzgZQaQfft2a7Xa8eMnAkByckpJSXHoUZ7nVq1akZaWuXDh/cEJbeQ/jxbmw1z3KYpC0/hdoSgFACEZ9dcVjmN8vja9PgqzmhwKN8BUrlAQLOuKijLguweqVOqoKENx8f7m5ubXXns9gqQg+BkG9zYRWq1eUj64gKBSqaOi1AAQCNhoWt0r5TIyMlcBQ9iTmSAIALljODAYDIaDB/e1t7f5/f6Skv0pKWlof319LcMEyspOKZWqwsLZocvVDod93bp/2e02URQOHizOzR01ME3vd0RRlEcwMtc4gtCLnAEyV4ohPIInCEJ+bQ4UeXlj29qsq1atYBgmPT3z9tvvQPtRWERDQ311ddWrr/4G7dRqdc8+++KoUWPsdtunn37E83xGRtatt87pXv3VxhU38FZr29Sp07/99tvERKxZfRmZgaWuri4lBStbl8wVZEgbeHlpZyCZNm3GtGkzwnaisIjk5NTCwtldT5kyZdqUKdP6o3FXOyUlh0+cOFFbWysbeJm+gGGYo0dLr79+8mUclaS9vV028P3PUDLwgsChfNQXEeFigaAeQVkyOI7BzFSFkmb3SjnL+vFXZ3mexVQuCLwoipjCHMeg/7uRV1+8OofyaYiiGPk2SZIKzUwiM0iorLwAAAyDVXFLRqa3fPvtV9XVVd2Z8MhHJfF6vT0LyVxphpKB93qdLldr8CPLBgCgtfUCflyy09mMKcmyAVHk8XNTA0BbWy2+sMdjs1p7Mf2A2RJUpMhubwwEJBJZi2I28rrwem1Wa0dhIo5j/P4IiayjMF27ZbpDFEW73e73+0Pj+3EIBAKjRo365JNPbrrpptD9O3bsaGhoAoBly5YdOHBAzuYrc2U5c+Z0Y2P95R3tDrkzOiAMJQOv0USFpqFWKtUAYDQOp6ies0vyPOdwNEZFWVD66x5paLCqVCrMXNMM43G7202m4dgj+Ar8RNYej43jAgbDMBxht9sJUGcwxEvmLQlaAo3GGBOjAwCns4miVDqdqaswAudv28d0uFLee++9hYWFS5YswTlHFMX6+vqkpKQ+bhsuJ06cWL9+/eLFiwHg4MGDkyZN6tEqf/PNNwzDVFZW1tRcEknf1tb20EMP22w2ACgpKWEYJlinsa+RrC8QxmVM3soMKtxu165d22+7bW5R0YbeHpUZbAwlAx+WyJogOhJZ4xghNHfdm0TWZG8SWbOoJfhT9AqFElO53+/ieRZTGHWTu0tkHXL1oZfI+uDBg/jrzd98880dd9zR1tYWHR0NAHfdddeMGTOeeuqpvmxgD6BHc+LEiYKCgkOHDk2cODGy/FtvvYWWk8Lcjz/++GNk3RH95obSXX2BMC5j8lZmMCFu2rS+sPA2rVbyndDtUb/f9+9/r0bbSUnJMTGdMZlutxMAzp8/bTL1nJ1CFEWG8TU1tWEWBXC5HARBYibCEgSOZZnmZhvmpJcgCI2NtZjJKDmOEQS+oQGraDjHcQBw/vxpmpZYAOW4fJSrqrm5/tixOuhY/yUUim6zZkVHm9LTc7ruH0oGPgx5ZvJaA9+YuVwujuN8Ph8y8KdPn+5nB5/q6moAeP/9f2za9HVWVhZcbDwqBrpjx465c+dWVFRoNN2+8kRRRKY97K7RqyH0Y3p6+scff/zTn/50xYoVY8aMAbi+D25Iur5AGJc3eSszsBw+XFJcvBsAZs2a295ujY01Z2Rkt7ZKrGaWlOzv7mho2U+apkOzXKDvL2b9TFEUeJ5UKBSYNexJkiQIAjNTE8+DIHAKBY1vO1CZUBxJURQIonc5o7ov+0mEXZ3n2cgt6W6UG+mPWFZ2sscmjhw5ukeZPgI9JEEQBsE0skyfI4piqG1jGIamu/2hIqMYNI2iKK5bt+6//uu/0tLS+r6lUFdXl56e/te//uHNN/8iCEJcXFxXgcbGRrfbHWrgrdY2vT4+uCe08aHnhn30+/0XLlxA/Yk33nhj7ty5fWTgJesLhNLd5C3HceXlZWhbp9NptZ23zLIMALS2NuI0QBB4l8stiq007cSRZ1lGEHhM5Qzj83jcFNWEOQkniqLH48RU7vM5WDYAgCXs9/sAwG5vY5hA16OCEI/caDweV2urGwBcLgdF0QzTbd9XqVSH1uPuSn7+pPz8SWj7xImjZ8+ePnasVBBElmXefPPVJ59cptPp0dH6+rrujqpU6vnzF0nqRwJJSek46ZB5nm1pqYyJGa5S6XsUBoBjxw5QlAIz0bLP57TbG+PjszCnB3bv/jY+fnhKSiaOsNPZEgh4zOZ0HGG323no0O709ByjUaLOr+KiWTabE/LyEgDAaq1WKJRGI1bF4UtURTi2bt2/ejz/5Zff6O0lrxTo3e73+yVnOWSuMkRR/P777zmOEwShoaHhvvvumzVr1u9+97vuhCHEFnIc19TUVFlZ2T8G3uv1iqLodnu6DsHRCF5yKmLRooduuWXWW2+9hT6Kotjc3NydcJDQO+V5vry8/IrdxqVI1hcIbQjO5O3o0WNiYzsnb71etyiKp04d7k1DmnoWCaFXypHrIm47muqbmno1XVGHL1pVJf0cef4WACUAtLQ0njp1DkdVXNwwg6GH9aAgd945H220tjavXbvqqac68iujut7dHY0M6oXLIc0DQiQDr9Vi5fuUkbmCeDw2t7st+JFlfaIoNjef5zi2rKysvPzwpk2b33zznbi42Orq883NHcVUGhoazWazQkEBgM3W4HA0A0BLSxVJeuCiB0Z7e11QHsEwPkFgw3Z2B3pDtbRUdifgcDhEEYxGAwp5CK6dozU8h6P5008/cLncAODx2AFg8uSCr776IibGBACiKLjdntbWhmBjGMZ39uxZAHA6W0Jb6HResiLY3FwRlGGYwJYtW/R6BtkAt7utudkGAKLIcxzj97u7a7lKpTcaJbw4QydvMzOzu9YXCBJh8lan0z///EtomyTJ0DjVDRs2EwQxdWphdw0LhedZq7XaaEzEdBk5ceIQTStyc8fjCPv9Loej2WxOxxzb7du3PTU1a/jwNBxhl6s1EPDGxaXiCHs8riNHiseOnRQdLTHsDg5mUlMzp05NB4C2tlqaVkVHW7oKI67IUmawrvdlnBsbGwOygR8gIhn4Z599sd/acRnIHcOrEqVSo9N1TltRFE0QhMMRaG21AoBGY+R5wu8PABA0rQpKzpxZ8Nvf/vqRRx70eNrV6o5c9BqNAQmgeVeVKipUMwBQVKNCQYft7A6WDfj9Tp3OFFwhAwBBEGbOvO33v39lypTJv/zlc36/f+3azzWadgD44Yc9SObkyTIA8HgCzz//1PXXTwKAHTt2A0BFRdWYMZN//vP7q6qqPvpoOQDBMFywMUF/0lOnyuvqrCkpKe+//8Hjjz/2zTfbQ1t1/Hg5ACiVWp0uhmFQsgf+4l9Sq9MRAOD12khSoVZHdXdrCoVScn/o5K1kfYEgESZvQ1dnw0B6MJctCQItSXa3bCmhnCBwF1ApSoHWODENPGAvKiPlYcUXIgtDpNXZ0KtTAEBRFL5yfMzm+NABerCut+TRyITWjpLPy1MQAAAgAElEQVTpZy7fyU4Q+CNHSidO7JMFPxwkfYxlhjo0rQ4NhkS+Nk6nD3mhazTRNK0RRZEkKYVCpdfHAIDX67XZbDwPWq3B42nXaDoMPE1rkQAyyWq1/uLHDihKoVAow3Z2h8/nRAY+uEy7devWc+fOlZQcqqlpnDyZbm5u9Xq9Bw4cQcOsnTt3h57+ySefcxyHuqMXLlxAO3meLy4+UFZW9t///Ru/37927b9XrvwUhb0FLc2HH65wu31PP/30yy+/Xlg4G80BBKmurgcApVKr18e0t7dDyC9CqdTo9RoA8PudNK3CvM3uCNYXCNv/YyZvZa4FYmO7LWMo09dgGXiGCWzZUlRZeR45xSBYluN5Dt/A19Rc2Lx5o9frHT16bGHhbElnll4F0SIN8gj+WiCYJQM9bkEQmpqaDh48WFNTk5KSwjCMZEEXnucBIBAIdM2i5fP5SktL9fof5Z65Zs2a/fv3o1bdfffde/bsAYCZM2fee++9XYUrKysB4MSJE2H7kT0uKvoafeQ4rmtcO8/z6O42bNjgcrm6no5ORNvorq84kvUF4MdN3spcC2RlZYD8oh4gsAz8jh1bjx3rlSNMOILAb9iw9s475yclJa9atfLkyeNjxkisjfUqiFappAGgsbHRZIrkIypzFXDgQEeca3t7O8dxHMe5XK6DBw/+5S9/eeedd5Bh6zqXU1NTk5WVtWPHDqvVCpe+YjZs2PDAAw8UF+9QKjus6f/93/+tW7duzZo1+K0KBALooi+88ELo1VmW7e4Utzt8Ibyp6RLHroaGhuzs7DCZYPflL3/5S9ihTz75BAD8fn9wnNRHBr6wcLZkfYEfM3krc+0gRzUPCFgxIWfOnAYgZs2aExtrTkvLmDv37rS0DIWCfvDBRzEvU1lZYTAY09MzaVpZUDDl2LEjklfpVRAt+sY0NDTgnyIzFGEYdtmyZWh73LhxL77Y6Rryt7/9raioCG0jE+v1+uCiLd+zZ09CQsKbb77ZVeeaNWtEUfT5fADAcZzH4zl69OiOHTt61bBgqLrVakXT46EtwcThcIR+XLBggdFofOKJJ5zOzmAw1EGR5Pz58wDg9XpD5WVkBhvyCH5AwDLwbrfbaDQWFNyYn3+dy+XKz590//1LlErl0aOlmJdxOGxxcWa0bTZbHA57l0u4du3aPmvWXPymI+TvzVWPKArBp9x1ePr4448j48px3NGjR0eOnFRf39FNdLvdTU1NwQXvUCoqKuCiP9pLL7106623XkbV9qamJhT2FsaWLVt6pSeUo0ePOhyO999//8yZM8Gddrs9wqwADGVPFPn3ew0gj90HDKwpeq1W6/V6vV6PxRLf1mZta7OaTDGCIJw7dxbzMl6vN7iyqFSqfL6wNdFug2gdDvs773SMwMaMGRcT0+kJjKKPjhwppmn8MgZYAVFBWlp6MT1w9mwvlJ8/f/r8+dO9acspfNHDh/dK7ue4W9ETr6w8s2tXBY4qiyVh1CisJBJ9R+Q556ampqBzmdXaxrKcw+FEpwQX7JHk4cOHCwsvichCYjabzW7v7HFu27btzJkzKK+t1+tlWdZg6Azd9ng8x4+fmDJlCgBYrVbJcbOk1e8toVl9Dh8+vHLlygjCtbUShY44jhvkqSrladtrh9bW1p6FZK40WL//1NT0kyeP/e1vf1269BmCgJUrP1Cp1H6/LypKoqKJJBqNxm7vSKDNMIGwsJkIQbQajXbOnLvQtsEQHdoDKCk5BgCJiak5OWN6bIAgcC5Xm05n6i4iKIza2kqFgk5IwKoHw7I+r9cZHW3BfGGdO3cyLm6YyRSe4EwSn8/JcWxUFJYnqt/vrampSEnJVKslYoUpqmPCJi5uWE6OFgDc7jaKojWabp+jRjPwaepRZdsIBF3wkC13uZzvvfceXFzwDo4RX3rppRkzZvzqV7/avXs32rlz538eeOA+uDRdzLp167Zv3261WlUqVVFRkSAIVVVV27ZtGzEiAwDWrv3iscceW7169fz58xsbG8Mm2K8gYUPb4uLiCMKNjRJZ0kpKSgDknPAyg4KwFMsy/QOWgZ8xo7Chod7ptGs02rFjJxw7dtjr9QDApEk3YF7GaIw5efI42m5razMaL3GLixBEq1Qqu3PUR3l4jMbYxMSe04xzHENRbEwMbpaM5uZ6lUqNoxkAfD6H3Q7DhiVj5rk8d+6kwWDCVO5wNDOMz2zGEna57DU1FXFxw6KjJSp9Bbsf0dHGxEQjAFitAk1rDIZ4HOUDRY+vBuRYvnbt2vh4MwB8/PE/29ra4KKBD47gWZbduXPn/v37Dx/u8BhFa/AAUFtb+8MPP6BsuP/61798Pt9f/vIXgiAEQYiKirJarfX19cjABwIBnucXLlz43nvvoav0EWEGPnTGviuHDh3qunPv3r2ygZcZcNCwRzbwAwKWgTeZYp544lf19XUAMG/ePWlpGe3tbUlJyTk5IzEvk5GRWVS0oampIT5+WGnpgaAL/Y8Poh26q49Dnf379xYX7+Y4Lj09c968e8LSWUhGReKESoaBMydyzz33AMDZs2e3bPkGADZs+BLFxaF1666ZYj/88EP0ER1qbW11uVz/+c9/YmNjUYkaAPD5fOioXq8PVRLc+OGHH3puWX9RVyeRBtXhcBw/fnzs2LH93x4ZmSByRrIBBGvEuWfPLqfTkZKSBgAkSY4fP3HGjEJ86w4AJEnNn79o48b1y5e/bbHEjxs3Ae1fufKD5ubeJZcO0Sknuhkwmpsbi4t3P/zw4089tczjcaOEpkFQVOSsWXOXLn2mvr4OTd5I7rwiBAfiX3+9GQCCUe9ohB36ZkHfFhQ3DwDff7/L6XQG/ddCJcPc+tatW3f8+AkYUu8piqLGjRv37rvvDnRDZK5pZE+LAQRrBP/99999//13CQmJo0aNycsbYzJdTmai5OTUxx5bGrbzxwTRokyNMgOCzdY+duyE6GgDAIwYMTKslxaMigSAgoIpR46UjhkzXnJnnzYSucoHzT908dc7ffpMZWVVaOqYrvYbefCtWrVq3769X365agh1KBctWtTUVPzcc88lJsYUFt5SWVkpCAKqXSsjI3MtgGXgDQajw2FvbGxobGz48Zb+yiHP/AwYubl5ubl5Xq+nqanxxIlj06f/NPSoZFRkhFBJv9+3ffu3aDs+Pt5giAo5FJ6EDp/Gxga4NNB87do1AOB02t3uDqNeXn6upqYabbvd7lGjup2XqqiovP76GZfdmP4nJsbwzDN/WLTowUce+eXNN0/bvPk7juNiYkx6vX7jxnU6XWcpKb3egOkRIiPTIx6PzelsCX70eu0AcObMscZG3Kir9nbchCiBgJckSXzNAIBZXAoARFFwuVobG3vhPYDZErRW2NZW4/NJBBcIQhYABQBud1tjY0cODJb1+3zd5rpQq/UmU3gFZ8A08E8//Xx7u7Wy8nxl5fmqqopQS//oo0/haOgL5KWdAaeurmb79m9FUTSbLylmJRkVGSFUkuf5hoaOn7RarSJJIfTQZTfP7w8PV0Nudz6fNzgt/847f3c4On42DMNcuFAdQeEViX/rNxjGzzD+t956/ZVX/vDdd9vnzr3tuuvyS0uP2u328+fPZ2SkBSWveKkSmWsZlUob6rerUl0AAK+XwXHmFQTe5bLqdEaFIjxhsySNjVaKojDdhBnG7/M5oqPNmN7QBFGpVusxlfv9Lo5j9HqsQa/X6wGo1elioqMlIpiCzVOpdAYDsvTtJElptYauwohgbaowcMNkY2LiYmLirrvuBlEUjhwp3blzm9vtQiOkgUJegx9wcnJG5uSMLC7es3nzpsWLHwrul4yKjBAqqdPpH330SclL6PW9yB3bIzStBACTKS5o0sJqt1xNJCamTpyYCgCTJk3geSI+fuDTxTOMLxDo/IPzPAsALhdWhLQoCgDg8zkYBmtSh+dZghAwlXNcAADcbivmqx9ADAQ8mMoZxicIPKYw8iDxeu0kKdG1FcVY5DgVCHhcLi8A8DwHIEZQrlCoIsTB9gUKhSrUPKPAJYVCpdVKhPaEwfOsy2VVqXQqlR7nWhSloCgFjmYAIAinz+fQaAzYNQMJmtZgKuc4huc5TGFBIAFArdZLygedFmhardWqAcDrdSgUSkzloeAa+EAgUFl5/ty5M+fPl7tcHSMe/Dj4vgBFdcsj+AFh377dWq12/PiJAJCcnFJSckmUtmRUZORQyf4BZdvwer3Br8010kFUq7HGQ30NxzE+X6fHA5qeCd3TI4GAjyD8OJI8zxGEiKn8Yu/BjekRJopoyhRXuSgKmMKBgB8AAgEvJWWDRLGjJGDwLymKPM9HUq5SCf1s4MOgJO9Epl/AMvCffPJhbW118FWo0+lHjRqdlzcW+dUPHPIU/YBhMBj27v0hJSVNq9WVlOwPfhNQ3KNkVGR3oZL9id/vB4DW1tZrxK4PNrRaQ+g0o1KpBgCLJQPnXJ5nW1oqjcZhqBZwjzQ0tNA0janc53Pa7Y1mcxrm2O7MmbN6fSymcqezJRDwmM3pOMJut7OysspkSjQYJMr7khfnF3Q6k8ViAgCrtZqmVQbDMBzlA4L8ih5AsAx8dXUVAGi1upEj8/LyxqalZQyGyAc0RS9/ewaEvLyxbW3WVatWMAyTnp55++13oP3B4qEoKpJl2REjclFUZDBUMnTngHDw4EFUsl1GRqavGQzG4poFy8BPmHBdXt7Y9PRMksRcoOoPZCe7gWXatBnTpoV7lQfjHiWjIiV39j+CIAwtdzkZmaGO/KIeELAM/Lx59/R1O3Dw+RweT2dsFfK1cTharNZIns8I9PVyOJox+ygsGwDgcTQDgCjyANDWVoNfN8njabdasVrC86wgiJgtQa7pdnsTw0jkSBfFZOSh4/HYrVYHdDiG8Czb7aKmSqWNijLjXLqPuHa6/1lZGYEAK1k2RkZmqHPt/JAHFYO62FQYJKmgaXXwI0Upuu7sDkEQOC6gUCjRWT1CECRJUjiaAYDnGY5jaVqNb+ApisZULoqiKHKYwizLA4BCoexGnrh49Y4/Gs+zkW+zu+iL/kelUg25YfcNNxQ0NDRaLBaHw3Hu3LnIwgRBREVFRZZBkCQpCIJCoZDze8sMfpBll0fwA8JQMvAqlS7UvwY5dqnVUThxihzH+P1Onc6EWWxGoaikaTVmBKTP5wgEvNHRFuwYG9xmw8ViM5jCJGkHAL0+JnKxGbVabzDoAYBlfYO/2Azipz/96Y+psz4gjBs3trGxafr06YcPH+7RwFMUlZube/r0JUWEMzMzUT6+UGbOnLl169bXXnv1hRd+e4VbLCMzcDz44IOiKPzpT/K3+ooxiNbUe0tsbAzIHcOrneCSSnz8EOiFjBzZmQgvJyfrt7/9NUmSJEl+9tlnmZmZkc9VKBSosE0ow4ZJeEfn5GQDgE53SVeVJMmYGAm/axmZgQV/cn7nzp0HDx7s08ZcawxhAy872V0LKBQdk0y33377wLYEB7W6Y7HjzjvvfPPNVwHg7bffXrJkSVJSUmJiYqikwRCelIogiISEhLCdkjHEFosFurw3CwoKlixZ8uOaLyPTV0R+UX///fePPPIIz/Nnzpy1WvuwCvO1xpA38HJA8zVCdnY22liwYMGgHaoG5xv0en1MjAkA7rjjjtzcXOhij//xj38EewNBgr0ZBE3TcXFxXa9yMb3lJQpJkrzrrrtycnJ+3B3IyFxhdDpdj4P44uLiVatWoQTStbU9J6L/5JNPiouLexSTkQ28zOCC4xi/3xX8h1KMAUB0dEdqW4sldvDkxgp7c2VkdCQzEQQOADguELwRQejIPBodHQUAPB/YsWNrfn5oMgAxN7ez1Ft0dFRTU112dvjEvslkyssbAQAUBQZDZ4YyURTy88fccMP1wT3BqwsCz/Ns6F817B/D+EBGJgSGYQ4elLCgdrvts88+fuut11etWuH3Y31tVCpVjwaeZVmGYVpaWgDgs8/WdleBwmazvfHGG++///4vf/nLN954ozttPM+jqhMyQ97AO53dFtiRGYoEAm6brSH4DyUJJwiCYZzoic+de0tGxsCnVUeMHj0SAAiCyM3NBgBUJmfZsicXLpwniqLX6wjeyN13z546dTIA/PvfnwKAx2NPS7OoVJ1DdqVSOWZMdvDj4sULAgFbdLSapunrr58IF6cHJkwYk5oaP3bs6NTUYZ988l5QnuMYm61h7txbgjl8fD4XujTPc4GAN/SvGvbP47H1/Z9KZijx7bdfHTiwN2ynKIqff/7PG26YumzZC7GxccXFezC1RTbwX3311WeffRb8+MUXX+7a9YOk5O7du1944YUnnnjC6/Xa7XZJGQB4+umnb7jhBsy2Xd0MJS/6MNCLTB7BX2VoNEa1ujNajKY1AKDT6TIzx+bn55eWlt588+yCgp0XLtQ2NzcPXDM7mDSpICrKeO+9C+fP/1lCwnCzOQEA7r33/gkTJuzdu12vj7VY0pDk0qXPqlSGPXuKCwp+cu+9C6dPv9ViyUB3h3jttRfN5lQAIAhCFMWMjFyLJeMXv3iqoOCm5cv/DlCalZUZF2dWKrUWS+q77/5p/PjrRZF85JGHP/54BQAkJaVYLBl33ZWhUgEqlafTmSwWAwC0t9fRtCpCPgNMN6gzZ07v3LnN5XImJAyfN+9ug+GSSA273VZUtKGpqTEhIXH+/EWhxYRkhhZnzpxubJSYJ6+qOq/X63NycgHg1ltv9/lwJ34if8FWr1594cIFtB0VFeVyub78cuPs2fMEQXjyySfz8/MfeeQRdLStrXN5vra29uzZs+Xl50I9W/1+/+rVq9977z2CICZOnPjZZ5/l5eWhQ4FA4OOPVxqNmvvu65wnu+oZwgaeJEmapmUnu6sMkiRDJ5bQsHXEiBEURSMDT9Oqd9/93+nTf3LPPQOff+n5538dXPYuKSlpaWlZvvzvFEWj/AEkSYUmEkA5GDQa3Zo1a9Ee9B1++OGHW1oaR48eZTTGmM3mL7744qWXXho5chRF0bGx5p/+9Ja1a9chbaNHj6ZpmiQ7MkBER5s++ujj+fMXlJWV3X333Rcvyl9UTqG1DIIgCIL8kSkN7Hbbxo3r7r//YbM5/rvvvt68eeOiRQ8Gj6KxXWHh7MzM7O+++7q4eM/NN9/yYy4nM1C43a5du7bfdtvcoqINYYfa29u0Wu369WsaGxuSkpJnzZoTPMTzPMpoDgAajSq0e+f3ewmC8Pt9NptV8or793euBTz++C+KijZVVlbabNb16ze8//7799xz9z333ImOnjp1AgDS09PGjRu3ceOm/Px8juPuumveoUOH16z5V1ZW1p///Nc33vgTEj58+PDXX3+VmNgRffOf/+xeuvS/KIqcMWOWVotVzkAURZ/P012zw/B6nSzrVyiwhFFGMpfLEVyCvPS6Megd6Pd7bTYvALjdboqiRbHbnzBNK/V6iZJC/Wfga2oubN680ev1jh49trBwdljIeOTBQXdQFCmP4K8F5s6dCwDTpk2rqup4iWRl9a4brlQqGYbp7XU1Gk2EYcpzzz2Xnt5ZQeS6667bunUrdD9esVgssbGxYZ50c+fOff/991G2A61Wi5Yhd+/eHSpzxx13bN682WKx/OMf/wAAl+uSNIWFhYWFhYXBj33koFBbW52enjl8eDIATJ5808qVH4QevbyxHZqr6IvWylwu4qZN6wsLb9NqJfKF+Hy+srLTixY9MHfuXVu2FG3ZUnTPPfeiQ36/b9WqFWg7L29MXNwlQSIURVmtzceOHZC8ZOhU3MiRGUeOJNXW1h87dmDHjm0AUFFxLnhiWdkpk8m4YsXfzp49t3HjJlRa94sv1gPAsmXLfvvbZf/61+cAoFQq4+MtdXX127d/V1Awpr3dZrc7iosPAgDPC59+unLKlOu7NkOSxsbaxsZeJZfESjmKqKg4LbmfZW8BUAJAU1P9sWPlOKri4uJHj76u6/5+MvCCwG/YsPbOO+cnJSWvWrXy5MnjocXEIg8OIkAQsoG/JkDj+MWLFy9evBjt0Wh6NwOsVqvDDPyECROOHDkS+axRo0Yplcru/HUff/zxsKI1qJ1hJjzIXXfdNXfu3NBkyTExMTgRAXPmzLnpppscDon0w/1Gbu6o7OwRaLuxsT4hYXjo0QhjO5lBzuHDJcXFuwFg1qy57e3W2FhzRkZ2a6vE+pdarU5OTsnOzgWAgoIpn322InhIq9UuXfos2lYoFKE/gbq6SpIkLJbEgoKbu2lCR4dYp9P97Gf3ff/9D6dPl+flTTp4cCkAnDxZxrL01KlTAUCj+Wdu7sgbbphxww0z1q0r2rlzV1BFScnh11//n5qaul/+8pf33bdo/PjxU6bc2NpqKyi4+f77H/jmm2/uvLOjINb776+8++57U1N79uM5dOg/iYlpiYkpPUoCgNvdxjDemJhkHGGv133iRMnIkRMkM5IF3ypJSWkFBcMBwGarpyg6OtrSncLuuvX9ZOArKysMBmN6eiYAFBRMOXKkNNTARx4cRIAkCdnAX92g0bBOhzWlBgCPP75kxYrPuw7Wk5KS3G53TU1NcE9ycrKkgVepVAaDoaWlhSAIgiAi9CS6jtSvu+66V155JUKsWpjtX716NeaA+89//jMKIhooaFqJXjsnTx7btu2bBQvuCz0aYWzn83nXr1+DtpOTU0ymzolEj8cFAN0N7MIQRZFhfE1NbZgVXd1uJ0kSmMoFgWNZprnZhl0PXmhoqGlvb8UR5jhGEPiGhhYcYZ7nAOD8+dMKhcR8LMdNRC/tpqa6Y8fqAYBl/QRBKhTdDhwNBlNaWqTgyfz8Sfn5k9D2iRNHz549fexYqSCILMu8+earTz65TKfTB1UFz0IZnIIfCYI0maS7qgoFTRAERVEajXQW0aDPPE3TGo1mxIiszz//4quvvq6srAQAjuOOHTs+c+YtBEG4XK7o6Gik549/fMPn873yyktRUVF33nn3//t//2/v3r1arfbdd99Fv6lJkyZt2rRJo9Hu27fP6/WuXt3xJayrqxszZmxFRUVKSs+Wm6bp7podBsu6AThMYfSUVSq1pHzwO6hQ0BoNDQAej1KhUGIqD6WfDLzDYYuL6/DxMZstDsclDpARBgc+n/f7779D2/Hxw1CCVYTTaSMIsqWlsbz8RI8NEATB73fa7V7MXPQ+n4dh/DiaAYDjGIbxulwB7Fz0Ymtro9eLFcjBMD5B4Gw2F44wywYAoKamQqlUdj0qCHloacdqbSovbwUAv99Fkorm5m5fPXq9AbMD26fgJ8MaPjxRpVIyDIOmf5VKJUVRDMNoNBqFQhFq4C9Gk3fVMHzkyJGbN29WqVSSl77xxhv37t2r0+m6JqsxGo0vv/wy7l0BoEvggPMyuuKEju0yM7N9Pu+mTesdDsfixQ9ZLJek2IswtiMIIrgiS9P0pXaLAABJS9YVURR4nlQoFMgFoUeQ5wGmcp4HQeCQKcKRBwCKovBbThC4t3lRuUJSPtg6kuy4Os+zJBnpNjHfeIg775yPNlpbm9euXfXUU8+gj/X1tWazJTMza+PGf9fV1QwfnnzwYDFakcEhwloMz/PBnivKDFFYOOOll/74/PPPEwQxderU3bt3/+EPf/jrX//60UcftbS0jB49GgkjP3mj8S2KUowZc93evXubmppef/31YI85MzOzpaWlvr6+tbWjH3brrYX//d+PzZ//oNPpPHLkyID8pvqZfjLwXq83+C5TKlXIxSBIhMEBz/MNDR3+nGq1KuhABAAMEyBJIhDwhS1JSiKKIscFOE7ETBfP87wgCDiaAQDFGfM8bsCeKILf78Oce+B5VhRFlsUSRuHXXq87EJAY5QR/Y4GAH90axzEEQQQC3S5O9+qt1HfgG0KNRv3ee3+///6f33zzzTt27Jg5c+bKlSt//vOfu1yusBFwV/OMQBOMcXFx+fn5NptNpVJNnjw5dJb+Jz/5SUlJya9//WujEctTZOgSOrbjeW7VqhVpaZkLF97f1QpGGNup1Zr58xdJ6kdDw7y8fJzG8Dzb0lIZE5MUWpAiAkeP7qdpGlO5z+e02xvj47Mwpwf+859v4uOHJydn4Ag7nS2BgMdsTu9ZFMDtdh46tDs9PcdgkBgQB6d7LJaEvLwEALBaq2laZTBIpDS+gqxc+cGDDz6anJy6cOHir7760uPxpKamz5lzJ+bpBEFIhravWLHixIkTwfcSMvDJycNTUpKrq2uSk5P/+Mc/zpkzB0XELV++vKKiYvLkyaEa0JeNIIh//vOfYcqR//y3337LcdyTTz6Zmpp6zz3z1Grx/fffu+++xStWrLjjjjt6+WcYevSTgddoNHZ7R6wtwwS6htB0NzjQ66MeffRJSZ0VFWUkScbHJ02cOLXHBnAc09paFROTjFls5siRYpVKPWrUhJ5FAXw+h93eNGxYNmbv4YcftiQnZyQlYf3gkfuV2ZyGI+xy2UtL9+bmjpNc2gm+HYYPT5s4MQ0ArNYLg7zYDDIlXSckuhtpZWamp6XlwkX7PXr06Pj4eIqiFApF6Dw/st+SGmiatlgs8fHxaWlpXq93+fLlLMuOGDEiKDBixIiuSeiuesrKTimVqsLC2WH7f+TYTmZwYjbHB4fvAPC73/0BbaSmpj/++NOXodDvl6hJ/fHHH4dGvgX78ffeu2Ddug3vvPPO1KlTFyxY8NFHHwHA1q1bRVHE965Fb4CioiIAeOCBByZNmoS6cQsXLnzggZ+fPn2a47ju3GWuGvrp9ozGmJMnj6PttrY2o9EUejTy4CACCoViyJUQvWqIEPiwd+8P27d/Gyr8zDO/1en0H3/8Xn19h0vq9ddPvu22eZjX6vqtQGPEJUuW2O32L7/8MrifpjtmWVGfIFiAVaPRJCcnFxcXa7Var9c7adKkJUuW/O1v7x49ehwAjEZjMG9GcEzwpz/9yePxJCUldX03oeV5zMZfHTQ01FdXV7366m/QR61W99L2RbsAACAASURBVOyzL8KPHtvJXAuEplwM4vV6a2pq0Lza0qVLP/zww2C9iZdeevGVV15H3ehp06Z99NFHFosFBZhIJm+WBFV2KCoqIkly/PhOly+CIB544IF//vOfr7322muvvfbj7myw008GPiMjs6hoQ1NTQ3z8sNLSA0EPO9T9Ly8/Izk46BGKIgfW8+iaJXLgw5QpN91ww41ou6qqsqSkGE3G2mztzz33O2R6sUvrSttRpGTWrFkjRozYs2cP+vEDQGxsjEajBgCz2QwAJpMJAGbMmCEIQmNjIwDk5+fv2dORgSs4pk9MTEQGPj4+Pj4+/u67787LyzOZTOj0rrY8KysrLS0Nr/1XCYWFsyV/oT9+bCdz1RMdHd11DX7r1q0NDQ3ox3XjjTeeP3/+pptuQocoilKpOibJZs2alZGR8emnn95+++1OpxP9JHHIysqKjY1ta2sbPnx4WLQLmgYoKSn5MTc1JOgnA0+S1Pz5izZuXM+y7IgRuePGdUx9o+5/d4ODHlEoaI7j+qrRMt0TOfCBIEiKIgFAEPidO7chpwrk+NZbR1CtVtqJ3WAwqNXqmJiYsWPH5ufnf/vttwAQHR0dH29OSEgpLy+vr69fvnw5MsPLli0DgB9++OHtt99+4YUX5s6di0x7iMsSCQCTJ0/etWsXRVEURYVGlgeviALVCII4dOhQr+5CRuZahiTJrkGeKMs4MvxKpXLz5s0AwPPhA7a4uLiKigoASElJOXnyZNeKixHIyspqa2vrOquPev9Hjx71+/1///vfn3jiid6G3Q4V+m8FIjk59bHHlobtRN3/5OTUyxi+AwBFUXKY3IAQOSo6SGnpwfT0DDR7b7O1i6L4wQfvOp3OlJTUOXPu0uujJM8KBU3TdR1DR0VFWa1WZKenTp1aXV1dVlaWmJiIlvGys7MbGhoAILTC+siRI2fNmjVu3DiTyRTqBQYXDfzWrVslow/QQt2CBT+LjY1+6613r8EF+L5DTnRzLUBR1DfffGO320P9UmtrOxPI4KyFZ2dnnzt37rrrJNK5dAdahn/vvffC9t9yyy2ZmZkVFRV79+595plnxo0bN3PmTIfDYbfbceLjhxBD28WAJKWdM2X6mshR0Qie5/fv37dkyS/QR5ZlUlLSZs+ep9PpNm5cv2VLUfAsp9Px9tsdCSbHjBkbE9O5YodCjc+ePb5r1+buGnPjjeNttuaysrKHH77v7NnzAOcB4Ny5CgAoLz8mip3hiM8//2R5+RGTydDe3rJr12aO44xGw6xZM0mSPH78+J4930kab1EUY2NjUlISpk69IScny2RSRmhMKBUVZRUVZTiSFzmFL3r48D7J/RxXCEADQFXV2V27zuOoMpsTML3NZWQuA9Q/X7NmzeOPPw4AVqs1Kytr2LBOZ2qcRBfXXXddMOANk9TUVJIku9rstLS0p5566umnn0YpI5Gj38svv7x69eqampqrqQc/tA08RVHyFP1AESEqGnH69Amz2RzMkpGUlLJwYUceuoKCKcHElgCg0WjmzLkLbRsM0aFpMvftOwQA8fFJOTljIjRm2LBTAHDrrbMVCl6nMyoUquzs0UVFKfn5E7qO/k2mmGHDEnNyxigUitzcEX/96/9cuHAhKso4duyk7vQfP36EZf1eryM7ezSme925cyfj4uJNpm5LvITi8zk5jo2KisUR9vt9NTXnU1Iy1WqJ9Y5goFdcXHxOjgYA3O42iqI1Gum4fwC4jAQaMjL4oJ/Mb37zG2Tgjx496nA4QiftJTPjhvHCCy+88MILvbrufffdl5+fLzn9npmZCQAHDhwAgFWrVhmNxtWrV7e2tqI1+15dZTAjG3iZywEn8OH48SM5OZ2FnpD/PFq2D0sSQtPKiROls0OjshAmU2zkfDsmUywAJCen+XytMTGJKFR6+HDp2bYNG77U6XQmk+n6668TRUhMTElMTJkyZVrEO0ah0uKwYcmY7oHnz5+KjjZhpgm6GAyJJexyOWpqzsfFxUdHSzgcBRcfoqKMiYlGALBaBZpW93WotIxMZILRKMhjBgCSk5PRRH2YE9yVYvr06dOnT5c8hFblUG2LzZs3Iw8AAHA4HJdh4D0ez0MPPfrii89iZjvoN4ZwPXgAoChKnqIfEIJR0WHWvb6+lmECAMCybGVlRVZWZ4Fzh8O+bt2/7HabKAoHDxbn5o7CuRDS32NKmYSEhLi4OMy5taSkJOSLe//9/98jjzyIc4pMXyAXm7lmIACAZVkUqxK09C+++CKanA/ziekH0AJBeXl4KReXCytnaCjnzp17++23v/xy06FDh69M464cQ3sET5KkbOAHhB6jomtrq41GIxpYI0aNGmO32z799COe5zMysm69tRf1SEJLPksye/bslpaWri64MoMZgiBk+37tIAjCokWLXC7XiRMnMjIyNm/ePGLEiD/84Q8ej0fSubVPGTNmDOpfqtVq1OFABSclE/JEZunSpWhOwuFw8DzfR+UcL4+hbeDlEfxA0WNUdEZGVmgmLMSUKdN6nAmXBGfZ+1rLPCMjM1QI/ja/+eYbtJGenp6bmwsAP/nJTz777LP+N4o0TScmJtbX1//sZz/7/PPPAWD69Onbtm0L9e3vkX/84x9KpfL48Y4cbm+99e6LL/7+sccee+edd/qk0b1nKBl4lg2wrC/0I0WRfr/X67VHOAshCDwABAJunscqCi4IHM8zOJoBALXK63Xg2xiG8WEqR6WoMIX9fhf6XzLqRBQNF+fK/F6vHwAEgee4SLdJUUrM5L59Sv/P4Mn0AyRJylP0Vx8M4wsE3CEfvVFR4U7yiYnDXK5WAFAqSQAgCAZ9FEUBAHw+B8P4AAOeZ0SRR+f2CMcFAMDtbkMv6pkzb/788zULFty1Zs2aiRMnvPvum6NGbbNam4LaOI7z+91hykVRLCkp3bbte41G88orvw891NpqBYBPPvlErVZYLJbW1tabbrqR43iv10tRJE3TWq3W7w8AQCDgr6m50NzcLunfGgjMAVABwNmzZUVFpwEgEPCQJEnTnd6C06bdGNorUiiUGo1EcY2hZOADAU/o35phPCRJ+nweh0OidLEkHo8NU5LjWAARXzMAOJ1Y5SARfr+rV8oxhdH8ktvdzvNeqePRyMD7/S6Hox3t4nmOYSSFAQDU6qiBNfDopyiPzq9eZAN/tcHzjM/XuZLNsoGuM/BRUVokk5AQT9MKo1F38RQRABjGB4CVg5zneVEUQy8XAdR78Ps7Oh/jx+f5/XcUFIxXqVTz5t0WG2vQarVbtmxZtOhuJPDrX7+Sm5vz5z93WvEzZ8rvv//RmprOUb5SqeR5PipK7/cH/H5/WlrKhQs1b775V3T0T3/6C07DutAMYAGAtWvXrV37iqRERcVxvb6z26RSaYe8gdfrY/T6zgpLXq+gUNButz8hYUSEsxC9LTbT1NSuUqlxNEPvi82Ul1dER1sSEvqk2Ex1dV1cXKpksZmglYyKMickmGEoFJtByAb+qkR2srsq0WgMocbG42G7zsAPH55msWQAwKOPPpmdnYe24WLNQINhmEqlBwwaG1spShE8PTKo2ExcXCoKJX366V+j/fv27cvKytLpdPPmzVu7du369d8+8cQTAFBVdUGt1oQq/9nPHgy17suWLXv22WcPHz580003/fvfq5cs+cVLL71y7ty5wsLCqKio2NjY8vLyhIQEnU7HsizP8z5fx7REIOA/efJQTs7YqKho9HIL/SEUFsa0twMAPProo48+OgcA7PZGiqKjojrz8KemjsJZ1/j/2bvz+CjK+3Hgn52d2Xuzu0k2dwI5SEICBLkip0UpAblF1AJSvt5UK1W0th5fS1ut7a9a/Xpbb/CooiAW1IogcoTDKGA4AiTk3Jy7m81es7Nz/P6YuKRhE57FkIvP+4+8Zud55jPP7Gz22Zl5joFUwZ+LppWNjRFcB6MBCiv4wcHna3W7W0IvBSEgCGJjI9FoPLLWVltX0xN0wnF+UQwQBpe/XpubKwiDS5Lkdrc0NhINoylJkiSRHqZ8C9fhqGVZx7mpopgOoAQAj8fR2OgAAFEUeT4Quio9l1qtN5sjGN71YghVRfLg8NHR0Y8++qi8JiUl5cYbb+y7ogEAFBQUyAsPPfTQF198cffdd2/YsGHevHnNzfaWlj0nTpyQmwucOXNG7jfPMMyNN94YHx//xz/+kabp2bNnA8CCBXM/+OCtRYuWdhyVr6sZKzyeNo5rHT26wGwOMylwKEBiYuLYse2TAtO06gLO48Cu4BmG4TiiZ+pogMKqfTBhGI1ef/YbjaZVLMsePlw2adLEbraSSZLg8TjUaiNNE7W4VirrlUq64+66YrPVR0Xp2tpakpLSCW/CKRSgUulIggNAIODheY4ws0LhBQCtNkqvDzMwUah4KpVWDujzOSmK1mi6HPWZ8O26qELFzsrKGjdu3Jo1a6Kiuhx2qQ+NGDHi7bffnjdv3o4dO3bs2AEAkiQ9/PDDGzZsqKmpmTlzJs/zK1asSExMfOKJJzptS9P0z342tb/NP9u/ShMphqG9Xm9flwJdRPKlFVbzgwPDaBjm7FgFarUWAJqanB0fvcl27NgxdepUmqbffPPNAwcOpKSk3H//Go/HIYrUxIlXPP/889dee+3GjRsnT57cacN//vOf06dPz8rKOnbs5PHjJ1asuDk3N3fbtm3jxo3rajSFqVOHx8TENDc3NTY2hsYBPB+FWq07t9hhiSIvigJhZvk7WauNCps/9H+gUmkNBi0AsKybYdTEwfuGXGyFQmGxWEIN6funWbNmabVa+V767Nk/93r9H3/8sdPp3L179+nTp++4444XX3yxr8sYgYFdwev1+nMnKUKDSX/7RYx6kDwz0Lvvvrt8+fIvv/zyu+++e+CBBwCgqqrqyiuv/Pe//33VVVc9+eSTpaWlALB169YPP3ytqamptLT0iy++aG5ufvjhh3fs2LF27VqbzTZ69Ohdu3YVFRX96le/mjx58urVq1esuAUABIGaMWPG7NmzL7vssmnTpq1cuVL+xhg3bhzLsg6HIz09vampyW63U5TiwIGDR48e3bNnz4gRI+69995zC8zz/E/8QL722muTJk3qOK7DkSNHKioqFi5c2NUmP32nfU7+gX7ffff9/ve/7+uynAdN09u3b584ceJ99903c+bkU6dqvvlm91133VVSUqJUKleuXNnXBYzMwP7c5OQM27Tp3xzH9f44CegiYVl3x257PM8CgMvV4HCc/7Mqt5J1u5u93jDPL8/F8wEAweEg6vkq97R0OutIMsvF8flaCYPzPCeKImFm+fKira2R58M8eZWkZHmESr/f5XC0AYAgcKLY3WGqVDqDgWgY/J5lNpvgx4FL58+fHwgEbr/9dpvN9o9//AMA/vjHPz7++ONy7Q4ABw8efOONd5555mUAePzxxwHgm2++kacNlCTp7bffZln2vffeA4CdO3eGLtb/9re//e1vfwOAb7/99ttvv33qqafi4+MpilqwYMHGjRsbGxtvuUX+HSAIAkyc2P6kgGGYq6+++tlnnzUYDIcOHbr11lvvuuuuhx9++P777z9x4gTHcSUlh9LT21vgNjQ0CIJQXl7+5JNPbtq0SaFQ1NbWHjhw4I033hAE4b333tu69fPq6sqEhCHr168/ePBgWlrab3/72+XL2+dleOmllzZv3jxkyJAHH3xw6dKlixYtEMX29la1tbWffPLJvffeu2bNmtWrV8fHx/v9foCBOrFpVFQU+Wzufejyyy8/evTo8OHDd+/+YtKkidnZ2e+++y4AzJ8/v7CwsK9LF5mBXcGr1SoAYFkWK3iEBpzoaAsASJJUXV0t9/CMj4/nOE4emfzAgQMdM3Mc99BDf+q4RhTF06fDt1z75JNPutppY2OjQqFYt26d/HTv1VdfPTdPMBjMy8sLNWz+z3/+AwCffvopy7JFRUUcx4kif+2113/zzTcbNmz4+OOP6+rq9Hq91+v94x//uHPnTpvNVlZWJm8r/9QYPjzniiumb9u2DQB++OGHf/zjHykpKTt37mxqatq+fXtdXd2UKVN8Pl9DQ8P8+XOvu+6XSUnJHo+nqqqKpmmO4/7yl78cPHjwd7/7HceNG4gVvHwFf5EGnL8Y8vLaB9KOjra8+eaby5YtO3PmzLRpFzJIV98a6BW8GgB27NixYMGCvi4L6hkajbFjiyGVSgcAZnNidHTqebeVO0MajVZ5spnzqq6uVanUJJHhxz42FksyYTssgFKdzkwYXO4MSZjZ7XYBnIqKCj/ZTOgxrVZrio42QXtnyP442YzB0N4VatGi9rkE5TazweDFHXJYkqTztt05t/+eXM3LNbfRaFi16tcHDhwMTWAqB/zDH/4QNprL5er47Pm7776bPn16xww+nw8ADh06tGLFL+12h93efgsq1Ih427Zt27ZtA2iBAUiu4Ptnw7rzmjhxYklJSVlZ2fjxXc422W/1XgVfXV25Zcsmn883YsSomTOv7vQt2X1qV+R5AL/44gus4AcrbF43iFksFoqiRFH87ruIZ+mgaboPZ5J0uz1btmwlz2+zNRDm3Lz5U5Jsdrv9iy++3bdv365dX9vtDqPRpFarnU6nQqH44IMP0tP72ZxmFJWQkHDTTTf1dUEukMViufzyy/u6FBeil0YAFUXho4/enzVr3t1331dXV1taeoQ8tRvx8XEAUFtbGza1tLT0AmYOQP0QVvODUkpK8vXXL76wba+77rqOL0ODGd93332h+6sTJoyTF2677bYrr7xSo9GE/SCZzebY2Fiz2TRq1MgLK8xP1P1IzKH7HB0988wzs2bNevLJJ0VRzMkZFhMTY7FYsrOz8/LyCOdU7E1KpVKtVss3XFFv6qUKvqKi3GQyp6dnMoyqsHDS4cPfk6d2IyNj6Lx58yoqKs5NOnr06NixY9euXbtnz55AgGjUQ9RvYQU/WN18841dVUh6vV6nOzvuZGpqCgCsWHHjgQMHxowZc/PNNwNAYWGhRqPJyMjYuXPn559/npqa+sgjj9x6660AUFQ044UXnn7uuedWrVr14osvfvbZZ4cPH3766ac3bNgwZswYrVZ7zz33PPTQQ7/97W9PnDhRVlZ67Nj+77///o033tiyZcs999yj0WjkNncm09lB2VJSUhITIxhsJDs7+2c/+1nHNfn5+fDfn2eKom677TZ5OTo6TG+3v/71r+f2BlywYEFpaandbv/gg7dffvm5jRs3fvDBB+++++5bb70VUQl7h0KB/8J9o5du0btczthYq7xstca5XK2EqYIgNDW1391Sq9Udm2lwXEAUxXHjxn766adXXDEtPj4hKso4YsSIlJTkf/97y1tvvQ0ATzzxxBNPPBEXF/e73/3WajXqdBaz2eLxeFpbXU6n02QySZIEIJWVnSwoKJBnJpbV1lbQNFNeXmMw6CmKUigUgUCAZQPykzmPxwMAKhWjUqkBJJutlufZ1NTMYDDodrv37z84cuQIjUZdUXGG47hRo0ZRFBUTE8PzPMcFGEZ15szxqqqGmJijRqPB5WqT44uiKElSqNu3Vqvx+fw2m00QAiqVMiMjl6QhIcv6zpw5zXFU2DkMBGGKPAxWZeWZXbuqAcDtblYq1Trd2WdjBQUFHf8VaZoJG6qX4bfDYKXT6TZs2DB37lwAWLduXVVV1cMPP1xQUDBs2LCsrKzHH3/8yy+/vPbaa6+99tonnni8puZEXt5YrdZYUlIiSdLf//735cuXb9y4MT8/f8qUKQBQXV0NAIWFhUVFRY899ohGo7nzzjvlHalUquzs7OzsbACQu8U/9dRToWL4/W2BQCsAyP2grr766jlz5mRnZ99555133XWXzWbbtm3bjh07hg8f/n//93+LFi2qrq7y+fyZmZlLly7929/+Nn/+fI1Gw/O83LB/165dgiBMmDBh7969SqXyscfWPvzwHwDgiSeeWLhw4bx582bMmLFp06b6+vqcnJx777137NixL730klqtLioqkjsCAIBCodDpdBRFxcXFvfbaa+++++7/+3//D0Arj3Y6duzY/PyLeF44jjt0qGTChM4DEB09emTbts8DgUBm5rD5869hGKLWzfj/21d6qYL3+Xyh+zMqldrv9xGmejzuV155Tl4eObIgOrrzgE0TJoyYO7fo88+/kj9DoeY5eXm5kyZNOHToh6SkhLKy0/fe23nq0o56dkzsjtH62WjbLoAoAHj55VdefrnzSEyyL7/c1LHTbVxcYl7emF4qXTg6nY6iFHJjCzQoXXHFFTfeeOM333yzaNGilpaWr7/+esOGDaHr5pkzZzY1NWk0GkEICkJi6G62QqFYs2YNANxxxx2dAk6cOPHzzz8/dGhfV3skGavkqquuAoDNmzfLL1euXMmybDAYNBqNx48fv+eeO3fu3Ltjx9cmk+mWW25JSkqS/2sEQaAoqra2Njo6WqfTyV9Kc+bMWrv2z6+//sayZcsUCsXJkycB4Nlnn73mmmv+9Kc/jRo1yu/3P/roo8uWLZOb9I8ZU1BQcNkNN/xixowZLMvKtzHWrl27du3a2FjwE82y9lN9/vmnVVVnOlXwXq9n06YPly+/KS4u/sMP3927d9cVV1xFEk2n03W8fEK9ppcqeK1W29raPpMbxwU0Gi1hqsFgvO22u+TlTlfwtbVnnM6WsWOnTJ48Qx4LQpKklpYWp9M5ZMiQTs97GhrqbbZyAA1F0SqVWq/XmUwmt9utVNKCwFutVpvNJggCwzBtbW5JkmprKyiKSkvLCgbPDoXLMCqVilEoFHJwQRC8Xp8oinq9qrW1WaOxyI+aEhMT29ragkHeZIpSKBRyO9vW1lYA0Ol0Xq+3rOyI1ZoYExMnSZJcAIWCkqdRkiSJphlB4CVJ0mg0UVEmn8/p9boliejxFcv6zpw5OXRodtjL7tmz9XLb4dtvv23ZsllAdgVPst+LZ9Kkyz/8cH1sbOz5s6KByWAwvP322/KyXq//8ssvO2XoJw+VNRpNqCSLFs2999418q+QtLS0UB550PXU1P/qDZGRkV5R8UNKSm7HlUqlMtSXT6vVys3vH3/88V/+8sba2lOFhVeYTNEA0PEhRa85ceJYfX2Y8R4cDrvBYBwyJB0AcnLyqqsrCQOuWPGLxx//fz1YQkSolyp4szk61HTObrebzRbCVKVSmZiYHDamSqWmKMpo/K858sLOogYAWq3WYFB0M5uc1fpfM6p9/z2tVmvy8i7r7qh+5Pe7WlujOs4mFxsbF0qNj+/8SEwUvZmZw1NSLspscgwjjhkzMez7EJp8aOjQ9KlT02EgzCanUCgSE/td/y6EIkLYvkypVKalpTU1VV3s8nTD43F//fW22bPnbd78UaekuLh4juNOnDiakJB09OiRkSNHE8ZUqVQJCfhf3Ad6qYLPyMjcvPmjhgZbfHxCScn+0Cejrq7Gao3rKhUhhFAvkj75ZMPMmbPD3jlQqzWTJk3717/WMwwTFWUePXpsKMnv961b97q8PHRoekzM2euuQIANBrmSkt1Eu5ckng/U1TUS9pT2+bwKhYIwuCgKghC02ZoI5wwURaGurrK5uZ4ksyDwkiRUVxONdCkIAgCUlf1A02HmPuD5QgAGAOrrq0tKqgGA5zmFQqFUlncV0GSKzsrKO3d9L1XwFKVcsmTppk0bgsFgTk5uQUH7lfHrr7+0cuVtqalDwqYihBC6qL777mBx8S4AmDVrnsPREhNjzcgY1twcZhruM2fKv/123+rVvzUYjDt2/Oejj9674YYVchJFKZOS2m+1ms1mo7HjmDYSz/OdbrV2RZJEv79NrdYrlUQPBzkuoFB0vo/bFZ7nOM6n1ZoIG/15PG1qtcZgIBqfh+P8osh3M61fR8Eg5/N5dDp92Fs7oeKpVBr50FjWQ1GUPOpXWFpt+CYOvTfQTWrqkDvuuLvTykceeayb1O4ZDFHy8OAkFApKozGGpiU+L7M5hnxgRaWS0WiMhL8KAcBqTezqfJyLYdTEQ6cBTaus1sSuSr5wYXsLndwfnwaqVPo+f8rePb3eGBMTd/58AABAUZRGY6Qo0k+12RxDfviRnuXY2ASdLkwP5rAYJnwX7bBomrFaE7uaBnTBApBbWoQmNCH/uuxlgQDHsqSD1ikUiohObiAQFEi/HtpPLvkpCAQE8omqaTqC/t88LwYCQldj+CxYAG43AMCPXf1Brdb9xJM7Zsz4MWPax2j74YdDZWXHDh8uEUUpGOT++te1d921Rq9v/xifOVOelZUjP0IdM2bCK688GwqiVqvnzl0UNv6JEz+0tXmzs4nGGBBFweVqNBiiO8462I0DB3YplTRhcI7ze71OszmB8Bu1urrKaIwZNiz3/FkB/H5XMBiIiiL6snK5Wmtr65KS0sJ+uS1aBPIcatOmxWVnxwGA291CUUq9PuKR/AfwULXx8cnx8eEfz59LqaQtliTy4Onp2eSZVSpdN7+tzkX4aF+m04VvVRCWVqvLz++y0ftbb3VeExVlJQ/eJyI6yxQV2VkeOjSis6xVqSJozB/hWSa6BJF1f5bfeKPzGqOxn55lm63+0KHvp0+fRZI50pN74sQJrVY3dizRAGSRntwDB/br9abMTKKv/ohOLsv69+0rHj58VEy42X9ee63zmp49uQsXLpEXmpsb339/3a9/3d75SH6WmpKSumXLJ4WFE6OizAcP7ktJSes60ln19Q3fffcd8VlWRnSWy8pOqlSqsWM7d+cLK/KzfFCrNRJW8FqtibyvD8uy+/YV5+aODHuW//nPzmuMxgtsZTyAK3iEEEK9QH6Wmp093G5vWb/+jWAwmJKStmDBtX1dLnQeWMEjhBD6L1ZrfOjyHTo8S504cerEiVP7qFAoYv1qGBaE0CXE4bB7PO60tKEXI3h9fR1FKePjL0rvrDNnyqOjY0ymCB6fEeK4QG1tTVJSSj/p/f/TOZ32trY2ufd8j6uvEw5UPwAAIABJREFUt1GU4tyuyD2isrLCbLZ06tTdIziOq62t7oWzjBU8QgghNAj10mQzCCGEEOpN+AweIdQHqqsrt2zZ5PP5RowYNXPm1eR9QQFg3749xcW7eJ5PT8+cP3+xPA9T2IDkK0NaW52bN3/U0FCfmJi0ZMlSeeTsngp+5Mj3O3d+xfP88OEjiooijvNT3rQ+gWe5b8/yAKvg1679fV8X4dKSnz/q2mt/0cs7xbPcy/LyRixZsqw39yiKwkcfvb9w4ZKUlNR1614vLY1g3NPGxvri4l0337xKpVL961/ri4t3XXHFVWEDkq8MBZckaf36N2bOvDozc9gXX/y7uHj39Ok/76ngjY31n3326W233Wk0Rr377lvffrt//PiJPRW8H8Kz3Odnub//AEQIDT4VFeUmkzk9PZNhVIWFkw4f/p58W6fTMWrUZVFRJo1Gm5MzXJ6nKmxA8pUhZ86cNhgM2dm5SqWyqGjO+PGX92DwU6fKhg3LsVhiaJoZP/7yo0d/6MHg/RCe5T4/ywPsCh4hNAi4XM7Y2PYRWqzWOJerlXzb3Nz83Nx8n8/b0FD/ww+H5RlLwwYkXxnicNh1Ot2GDe/V19tSUlJnzZrbg8EFQegwkTQlV1o9FbwfwrPc52cZr+ARQr3N5/OFRuFWqdR+vy/SCLW11Z9//inHBazWuK4Ckq8M8fv9x48fKyi47Lbb7gSArVs392DwrKzs06dPNjY2eL2efft2s6y/B4P3Q3iW+/wsYwWPEOptWq02NJY7xwXkJk4Ryc4e/qtf3TNmzIQtWz7pKiD5yhCNRpOamjZsWK5arSksnHTqVFkPBk9OTi0qmvPxx++vW/daRkaWVqvrweD9EJ7lPj/LWMEjhHqb2Rxtt7fIy3a7PaKxRPbu3XXoUIm8nJqa5nC0dBWQfGWIyXT2JUVRFEX1YHCe54cPz1+16jd33LE6Pj7RYonuweD9EJ7lPj/LWMH/JKtXr3700Udnz55NknnVqlWPPvrookXh51wiyfAT81+y+vMb1Z/LdvFkZGQ6HPaGBpskiSUl+0eOLCDf1mQyHTiw1+Gwsyx78OA+eSC8sAHJV4ZkZma1tDTX1lZLknTgQHF2dm4PBvf5vC+88HRbm4vjAnv27JTncOup4P0QnuU+P8vYyA4h1NsoSrlkydJNmzYEg8GcnNyCgghm3svPH2W3t6xb9xrHcenpmXPmLOgqIPnKEKWSvv765Z9++rHX6x0yJH3u3IU9GDwqyjR58hWvvPKcWq0ZN27CiBEFPRi8H8Kz3OdneYANVdvfekivXr3abDYfOHDgs88+O2/mVatWxcXFHTlyZOPGjWEzWK1xNK30+/2trUQtJ88b8KcbHP3gI31je1MvnMTz6v1+8AihXnDpXsGvWbPGYDDs27f/1KmTV1999ZEjR7755hulki4snFBQUGCxmAMBrrGxYffuPZWVlaGtMjIyp06dkpiY0NTUvG3btgvbdW5uzpQpU61Wa3Nz87Zt20Lxr712cafveqvVWlQ0Mzk5tb7etmXLljvuuIOm6U2bNh0+fJgk4KWj+xMX6Rv7+9//XqVS7dmzx2g0Dhs2TBCE06dPb9myNTMzc+rUKVar1el0bt++4+TJMjmaSqWePHlSfn6eyWTiuGBzc/PevXtPnjx5YcdisViuvPLKpKQkg0Hf3NxSVla2Z89eURQA4NZbb01KSqqoKF+3bj0ATJ8+fdq0aQDw7LPPOhyO+PiEO+64HQA+/PCDY8eO/5T3EyE0CFy6FbzMbDYtWbJEo9EoFAoAmDPn6ssuuwwARFE0GFQGQ1ZGRuY777xTXl4OADk5udddt0RukZGamrp8+fIL2GNaWtqIESPkIMnJyb/4xQ3PP/9CW1tbuLKZb731FoZRAUB6evrKlSuVyjBtJsgDDmLdn7hOCN/YiRMvpyilvDx69OikpESrNU7+nMTHx1933ZIXXnjB4XAAwDXXLMzJyZVz0jQzZMiQtLS0t956q6qqKtIDSUlJufHGG+VROQEgOTk5OTk5Ozv79dffkCSxvLw8KSkpOTkZQAEgJScnydmSkpIcDof8UpKkM2cqI90vQmjwudQr+Nzc3JqamvLy8qqqKppmRo8uAIC9e/du377daDQuW7Y0NtZ6+eWF5eXlFEUVFf2coii73b5x40ZB4BcuXHgBk1Gazeb//OeLsrJTo0cXTJ06VaVSZ2RkHDp06Nycs2fPYhiVz+f78MMPA4HANddcYzAYfkrAwar7E3dufsI3VhDE9977l9/vW7hwQWysNS4uvrS0dOfOnXl5edOnT1cqlenpGQ6HIyoqSq7dv/766z179lgslptuukmj0eTm5kZawSsUijlz5qpUKo/Hs2HDhsbGxnHjxl111VUpKSnjx487cOBARUXF1KlT1WpNbGxsS0tzUlKyvGFSUlJpaWlSUhIA1NfX+/3+SN9DhNDgc6m3om9qanzjjTd27txZWVmp0ajlYf2HDh2al5fHcdz69e+8+uqr27ZtB4C0tDS5w8PWrVvr6uoaGho3b/70AvZYVVVVXLzP4bDv2PG1KIoAYDaHmVWapulhw7IBYPfu3ZWVlfX19Vu2bPkpAQex7k9cJ+Rv7OnTp0+fPlVXV1daelRe880337S0tOzevVu+Ya7TaQEgGAyuX//O+vXv7N27VxQlrVZL0zQA6HS6SA8kJiY2ISEeAL766quqqiqWZXfv3l1dXQ0A+fn5AFBTUxsMBgEgJSXZYrFotdqamhoAkKv25ORkAKioqIh0vwihQelSv4Kvrq4JNTP0eDynT5/KyhqWlJR0zTXXSJJks9kOHTpcUvItAMTExIQ2kRdsNhvPB2maiWiPoZvnkiTyPK9SqeS7vp3ExMTK62trazsW9dzMhAEHse5PXCfkb2xzc7O8wPN8xzWiKIqiRP3429jv9zc3N02aNLGo6OcxMbEUdeE/mmNjo38sUnVoZXV1dVpamvzxEwS+uroqMzMrJSVZrunLyk6YzebExASGYeLirAAQ9qYFQugSdKlX8DwvdHz53nvv5+bm5OePzMrKVKlU8hPQzMyMf/3rX2q1/FhUAjjb7yAY5COt4Am7LTAM3Sm/JElh66GB1Q/iIunmxHXKSf7GEtLpdLfffrtOp/N6vQcPHqytrZ08eXJCQsTPbjrqeErlciqV7a0BKioqMjOzkpNTOS4IALW1dampdTk5ufn5+RSl5PlgTU3tT9k1QmjQuNQr+I40Go1Wq7PZ6o8dO65U0unp6VdcMS0lJSU7O1utVjudTgAAUKSkpMjNs6Ojo7XaizVa5I+7g8TEJPlaMykp8adcHQ5i3Z+4QCDQMXOPv7H5+fk6nQ5Aevnll91uNwDMmDHjwkK1tDjkhbS0VKcztJwGHW4nlJef+fnPIT7eKkmiJIk2W73NVp+Tkzt+/HgAqKqqEgT+go8FITSYYIVxVlZW1t13/3r16tWZmZnyvdCmpiYAEAQhGAzW1tbJT17nzLk6MTEhNta6cOHCi1cYr9dbX18PAFOnTklOTo6Li587d+7F292A1v2J65S5x9/YH28JKFJSkhmGmThxoslkurBQdntLY2MjAMgN6+Ted0OGDAGAY8eOyXkaGxu9Xq9CQSUmJjY1NQeDnM1WDz8+hi8vxwfwCKF2eAV/1unTpxwOe3R0zPLly4PBIMPQAAoA2L9/vyiKbrf7hx9KCwoKYmOtt912OwDwPH8Bz+DJbdv21bJlS41G4y233AIAgQD7U+4kD2Ldn7hz8/fsG1tefmbGDFGhoK677noAEEWRZQMajdpoNEYaSpKkLVu23HjjjUaj8eabbw6tr62tPXDgQCjXmTNnRowYAQB1dXUAUF9vC+XEFnYIoRC8gj+LZQNvvvnWvn377Ha7JEmBQKChoWHLlq3bt++QM2zevPk///mirq6O4wJ1dXXvvPOOx+O9eOWpqChfv359dXV1IBCora19/fU35VsIqJPznrhOevaNbWxs+Oijj+32Fo4LVFZWvvHGG6dPnwKA9PT0+Pj4SKPV1NS8+OJLpaWlTqeD4zibzbZjx4433niz4y+VUC0uP2Lwer0ul0teaGxsuuADQQgNMjhUbT+lUFBxcXEA4PG4vV4vAOh0uvvvvx8A3n777TNnzvROMQbHULUd9ZM3tl/BoWoRGpTwFn0/JUnSypW/1Gg0LS0t77//L5b1y3PWsSwrP3NFFwbfWITQJQIr+J5x2WWXzZ8/v5sMzz33fGiWXzLSpk2bFi1aGBsbe9ddd8qrWDbw8ccfBwLsTygp6r039iJ8KhBCiBRW8D2jrKzs1Vdf7SaDyxXxPGZlZWXPPPN/eXl5FotFkiSHw3H8+DGWDZx/S9StXntjL8anAiGECGEF3zN8Pp/P5+vxsH6/v6SkpMfDot55Yy/SpwIhhEgMsEZ2Hdls1S6XY/jw0SSZBYF3ueqNRivDaEjynz59jGFUQ4ZkkWQOBLxer8NiSSHsanXkyIGkpLTYWKLBzrxeJ88HTCaizD6f99Sp0mHD8nW6MLOnLFgAcnVz881www0AAC5XI02r9HoLSfA+UV9f43S25OVdRpK5V85ysjzu/XkdOXIgMTHNau35s+z3e0+eLM3Kytfrw5zlRYvA4wEA+J//gaVLAdrPMqPXR5MERwgNGgP4Ct7v97pcDsLMkiQGAj69Pkyv6LDcbpdaTVRJAIAo8oGAD0CSu1+fl9PZEh1tJQzO8xzHkT4bFoSg09kSGju9k6+/Bnnc+quual8TDPb3acf8fq/L5STM/ONZJu3z5vG4VCo1YWZRFAKBCC7HW1vtFkssYeaIzjLP805niyB0HsNHtnMnyIP1/exn7WuCQX/H8ZURQpcI7AePEEIIDUJYwSOEEEKDEFbwA15xcfHAbUiBEELoIhlIz+A9Hofb3Rx66fU6BCFYX19GHsHhaJ/Kfe/eA//851tPP/0XkykqbE6O84tiZMEbGk6RZ25ra6qv58jzf/rp+yNGDNdoOjcLKC8/M3Xq7E8/fX/s2NEAwLIsALS0VHm9jaE8giDceef99977K0maLf+kc7ub6+vbmy8EgwGfr8vOWhqN0WJJIi9nj/vyy6927941ceKVfVgGhBAaiAZSBa9W6wDOtk1rbfVRlN9oJGqtJoqC1+vQas3y3DDffXf0iy++ampyp6Rkhs2vVDYxjIowOM+zfr/baIwlbGQHUK5WG7oJ7vF4TpwoGzduLACwrMfv9y5cuOyll57/xS+uC+V59tkXpk2bolBoAODw4RN6vWX8+HEU5QGo1estcvvqioozGRnpX3/9zebNW+fPnx8qnlqtNxqVAODzOSmK0WjCNMaW0bSK7Ih6jCiKknS2ldzhw0c+/3xbVw3KOpFnShVFnjC/JEmSJBJmlserF4QgYSt6eRPikoiSJBGXpPvDZDrsXQSCw1QoFBQ1kL4KEEIkBtJ/NcNoOnZ/UqkaFQrKYCDq/MPznNfr0GqNarUOAChK/hKk6+qas7Oz5e5t06dPv+eee+Shx5RKmqZVHYMfOXJk1KhRHWPu379/xIgRer3e73f5/W693tLxq7+tre33v//9448/Lk8e2tjYGBcXF+pHp1bruir5/v37t2/fvnbtWvlyXBCCHo9HFMW6uobnn//nAw88AAC7d+/+wx/+NGbMGHmSsYcf/gMAvPjii8uW3VBTU/vmmx8899zzixYt2rZt29ixY+UO32q1PrR3lUpnMOgAgGXbGEZN+B72Dr+/ta3t7H2aYJAFgKamCCZJa21tIMwZDLKSFIwoeHNzJWFOSZI8HntTE2nHDSA+THlMHqfT5veH6UUiilkASgDwep1NTXZ5Jc9zfr+7q4B9fp8GIXQxDKQK/lybN2+9667ffvvtt4IgeDwenuddLhdFUQkJCRqNpri4eMOGDXV1dS+++GJx8d6kJFN0dOrXX3/9pz/9acyYMQDw/PPPf/bZZ4sXL96/f/+xY8eKi4tramomT54cHR0NAI2NTUpl2WOPPXbkyBGHw1FTU/PnP//51KlTV1555YoVKzwez5QpU55++ully5ap1YqmppaxY6dv3769oqJi8uTJJpOprKzshRdeWLFiRWFhodfrTUtL27Bhw7x58857UDNnzjSbzRzHNTU1ydOiyI/Yn3nmmdbWVkmSEhMT6+vrOY47efJkx0nGXn/99euvv3bDhk82b/7sq6+2y1ONhYZzsdlsoij2/1YXGo2Rps/+jJNvIURHp5JsKwhBl6uBvB98fX2LSqUiDM5xPo/HTj7agUJRodOZCYNH1A/e43ED1ERFxRkMYSaep6j2U6zVmqKjdQDQ1tagVKq66QdPUUqS/SKEBpYBXMF7PJ4nn3zWaDRWV1dPmDABAKxW68mTJzmOe+CBB06dOrVr167m5mYAEEXxq6++uuGGax56aMj8+fPdbrfcU9zlcskjmlVUVBw/fjwYDJaXlw8fPjw5Ofm3v129bNlNhYWF+/btC+3x4YcfBoB169bt378/NzeX5/k1a9asXr162rQpt9/+y/r6+jvvvHPHjh2vvvoqTdMPPvggAFx//fVr164tKiriOK65uVmSpOLi4mCQB4B7773XZrO98847lZWVdXV1lZWVRUVFt9xyi8fjaWtrA4D09PS2trbGxqbKyjMA0NraCgAffPBBYmLilClTAMDh+K8LuJMnT27Y8NHmzZ/BjxOJdvTggw+K4iqAiCcp72VKJaNUMqGXct0j33c5L57nAIBh1IT5KYqiKCVhZvnGuFqtJb9FT9MqwuAs6xaEIGFmjgsCAMNous9P04xazQCAQkEplTRhcITQ4CENWJ98sgEA9Hr9m2++2emgsrL+a2yyq666ymAwAEBe3nB5jVarBQB5pSwp6ewtSoVCYbGYu3nTcnJyCgoKOq4xGPSh5cLCQrkCDikqKpLDLl++HADy83N/8YvrU1JSsrOzn3zySTkJAC6//PJOO3r++eevv/7aTivj4uKUyvCXXGlpad0UG8AFIAFIf/lL+3vY3HymtbWhT0/jefzqV7fHxsYQZg4GAzbbCZb1EOb//vu9R4+WEGb2+Vw22wlRFAjz79y5tbq6nDBza2tDU9MZwsxtba07dvzb5XKETbVYJPks//nP7Wuam8+0ttYTBkcIDRoD+Aq+ubkFALxe78qVKzslnT59uuPLr776Sl44duy4vOD3+wHAIw/pCQAANpsttCxJktPZ3SwgZWWdW9d7PN7Q8v79+zulfvHFF3LY9evXA8DRoyeOHj0hJz3xxBPw4034jncLZHfeeSfDMJ1WNjU1dVWw6urqboqNEELo0tHfn8h2g+Mi6GbWb8kPEboRDBK1rEYIIYQ6GsAVvMvV1tdFGKg63rpACCE0KA3gW/Ryp2d0Af7+979XVZW7XC6/39PQ0ETTjNysz2KxbN++PSoq/OA/CCGEBpABXMHjvWuZUqmMi4uTJKmhgbT/98iRI4uL1yclJYlicMSIfIMhSq/XK5VKj8fjdDqxgkcIoUHgolTw1dWVW7Zs8vl8I0aMmjnz6k7disKm7tu3p7h4F8/z6emZ8+cvVqnOP4BaV5OiEqIoqmMn8oFrwoQJf/3rXz0ez/3333/06FGSTRYvXvy73y0GgJaWSobRmkzxF7mMCCGEelvPP4MXReGjj96fNWve3XffV1dXW1p65LypjY31xcW7br551a9/vcbr9RQX7yLZkdTFDCsWi6Xjy5EjR56b5+c//3lubi4A6HS6+PjzVG/p6ekAkJKS0mm9Tte5YzFN03q9Hi4CpVLZMXJ0dPQLL7wgH1pmZubUqVNnz5597bXXwo897iZOnPjOO+/IfflycnLkjgahIVAQQggNej3/jV9RUW4ymdPTMxlGVVg46fDh78+b6nQ6Ro26LCrKpNFoc3KGt7Y6I92p1WqNjo5OTk4GgNtvv71j0oMPPlhUVLRmzb1qtbqgYNSoUaOWL1/+5JNPjh8/HgD+/Oc/v/rqq1arlabp+++/X6lUKpXKd955BwBouv32xs6dO3ft2rVz584bbrjh4MGDzz//vEqlYhgmLy9PzjBnzuzs7Kxf/nLFxo0b5VqWpulHH300bIUqD5PXkdwRbsaMGQAwbdo0APj3v/8dExMjrx89elRKStIjjzzi8Xh+85vfJCQkmEymxYsXr1q16oMPPtizZ8/TTz8tx7nvvvsAIDExAQDmzp27dOnSxYsXa7XalJSUm266CX7s/Y8QQuhS0PO36F0uZ2xs+zQqVmucy9V63tTc3Pzc3Hyfz9vQUP/DD4evuOKqUH6e56urK+VlrVar0ahDScFgkGHo0aMvKykpmTx50uuvv1peXvHaa68NH56jUCgef/yxzz777Lbbbp02bXJR0QxB4JctWxAfP0Srbb8OvuaahRs3brzhhiUajebo0SMAQNO0yWRcu/ZPs2fPfOCBe+vrGw8cKHn22acNBm1+fi4AvPDCswCQmTl0zpxZDQ0Nv/71bzIzM3/zm9WLFs1xuZoTEjIUCsUnn2wCgFGjRq1efVd2dlZJScmzzz7/17/+JTMzs6GhQa1Wz5pV9Nhjf6yvb/700y2BQMBisSxbtnTPnj2rV/9627ZtDz30u8WLF02YMDY6OnrGjKu2bv3s3nvvnjr1cpMp3uls+d//feh///ehvXuLrdZYp7MlPj42Pj4WQHI6W+Q3hKbpFStuCAZh/vw5TmfLXXet2rNndyDADR+evWnTxzfe2P6Dw+/3Op3ySABemg6KYpcjlapUar2+xwa/O3Lk+507v+J5fvjwEUVFnZ/dIIQQ6kE9X8H7fD61ur0aVqnUfr+PMLW2tnrbts8lSbJa40IrvV7PunWvycsjRxZER5+tbNxuF4AiNtaUmZm+evWthw/vB4Drr58viuIrrzyTlZVx+eWjAODkybPPCFyu0tByVBSzceM7ZWWHOxbPbNb9/OfTDx/eP2vWlQCwfPm1NM3Lkc+VlzcsNjZ6xIiMU6eOA0BTUwsAzJ171ZAhiZMmFR4+vD8pyRIX97MRI4alpw8BgKioVAA4fvz7a66ZBwALF85uamrOy8tRq9WLF88WRfGxx/6XpoVRo4YdPVrCcQGW9X788TqapisrKwEqQ/vV6ymfzxG2VG+++WJCQpxSqWxpqW1pqQUAng+IIl9a+q3JpBIEQT7j9fU1hw+Xhz2oTuLiEvPyxpDkPK/GxvrPPvv0ttvuNBqj3n33rW+/3T9+/MQeiYwQQuhcPV/Ba7Xa0D12jgtoNFrC1Ozs4dnZw4uLd2/Z8sny5f8jrzQao+6++/72stJKebJX2ccfb1UoFO+/v0EUxdCPBtnEcyoOQQja7TVmc6JK1d1t6sLC6TfdtAoAjh37XqVSZ2XldZ9ZXmBZd1tbs9U6VL4knTlzQTdbAcCBA1+npWWGNu9Q7LO3Lp566qm0tLT8/Hy3uyUYZKOjO7cACMvrbSstLcnPH2swtLeEf++98ZIERqMBAGi6/Uo9NTWzsDANAJzOWprWGI2xXQXsakzcC3DqVNmwYTkWSwwAjB9/+f79e7GCRwihi6fnK3izOTrUsM5ut5vNlvOm7t27S6fTjR49FgBSU9MOHiwO5acoymIJPwuW3JosKirMhFrn4nlOpWI0Gi35NCRKpVKrJZyfI8iyjFarI7/nzDCq7oNfc81ieYHj1AqFSFgSebYVtVoTyh92Q4ZhtFoGALxeFcOoiQ/zJxEEIdQuUqGgOra0EAShqam9j59KperYh0IQBGi/W0OyiyDLBjweN8cR9bAQBJ7nKcLggYCXZQNut4v8LAcCLGFwn88XDJJn9sh/w5ZEkqIAFD/uPQAAfr8/GBQpqsvgNM30zmcAIdSber6Cz8jI3Lz5o4YGW3x8QknJ/pEjR8vr6+pqrNa4sKkmk2nPnp1paUN1Ov3Bg/vS0oaSlp4ewP34LzVZWdn79u1pbGwwGAz79u1mWX8oyev1vPLKc/LyyJGjoqPPdsT3eNokSSop2R3JrmoiKpjDEUHwqqoIgtfWnqmtPRNJWSKYmf7EiSNh1wvCTAAGAGy2qpKS02HzdGK1Jubn98yDGIRQ/9HzFSRFKZcsWbpp04ZgMJiTk1tQcJm8/vXXX1q58rbU1CHnpubnj7LbW9ate43juPT0zDlzznOLO4SkuzzqJ5KTU4uK5nz88fsKhSIvb2TH1pd6vf7GG2+Wl7VajUaj6ZC0UaFQFBQUkuxCEHiXq558Pvjy8mM0zQwZMowkcyDg83ojmA/+hx8OJiSkWK2JJJm9XifPc4QDEvh83lOnSocNy9PpwrR/VCrb/6kTElILCmIAwOVqpGmmm/ngGQb/jxAahC7KFXBq6pA77ri708pHHnmsm9Rp066cNu3KiPYiSRLZNy3qF3ieHz48X34QU1Z2vOOTF6WSzsjICruVfJPGYumylcB/74ILBl0mk0WtJhqNgKYZlUpNGNzvb5Mkv8USQ36LXqvVEwanKIHj/ISZ5ZYoRqM5Kspybmron0Kr1VksOgAQBA/DaEwmouAIoUED+ymhXuLzeV944em2NhfHBfbs2TlmzPi+LhFCCA1m+Awb9ZKoKNPkyVe88spzarVm3LgJI0YU9HWJEEJoMMMKHvWeCRMmTpiAXeMQQqg34C16hBBCaBDCCh4hhBAahLCCRwghhAYhrOARQgihQWgAV/BdzQePEEIIoYHUij4Q8LKsJ/SS54MKhcLlaiTZVhRFAPB6nSzrJsnP80GKAsLggsABQFtbkzwGOAmWdRMG5zi/IPCEmX0+LwB4PA5JCpybKklxcglZ1uNyeQFAEHhJ8ncTnGHUOp2ZZNcIIYT6lYFUwYsiHwyyoZeSJAJAxzXdkC/3eZ4TRaJpSCRJFEWBOLjwY0lIK3hBCBIGF0VekiTCzDwfgPYB3cLOAifJJRSE9nfyvIdJUQP4Hg9CCF3KBlIFr9WatNqzc8cxjBoAYmOHkGzL81xz8xmTKZ5wNrmaGptarSEM7ve7WlvKc676AAAex0lEQVQbYmLSiAcxParXRxMGd7kaOc5PmNntbgWoMJsToqLCXHaHBjHV682xsWYAaGmpZBgt4RDoCCGEBhC8PkMIIYQGoYF0BY8uBaLIC8LZxyiSJJI/oRCEIADwPEdRYZ9QdBbRgxg5eDDIkk82E3oUcl6iKETyIIaD9gcxYfOrOzyI4YHoQYxSqWRIdo0QGkCwgkf9i9/f1tbWHHopV2YtLVXkEdramghzBoMBSeIjCm63k84HL0mS1+toaYmgrwdhSVg2AAAuV0Mg0HpuqihmASgBwOdrbWmxyyt5PtixgWonGo3RYkkiLydCaEDACh71L1ptlEp1tp1ERC0tBCHodNqiouJUKi1J/oaGFoZREQYPBLxud0tMTCrhFfypUxV6fXRsbBpJZo/HEQwGLBaiyeM9HndVVY3JlGA0ms5NDbWL1OnMsbEGAGhttdG02mCI6Sog4Q0PhNDAMoAreOwHPyhRFE1RZz+WCgUFoGAYDcm2ctVL0yry/BSlJMws30tgGA35LXqlkiYMTlFKhYL0MGk6AASHqVTSDENDhIeJEBo0BnYjO4WCtFsaQgghdEkZ2BU8QgghhMLCCh4hhBAahC7KM/jq6sotWzb5fL4RI0bNnHl1p2eWYVNPnDi2Y8eXbndbYmLy/PnXmEw4PCpCCCF04Xr+Cl4UhY8+en/WrHl3331fXV1taemR86a2tjo3bfpg/vxrfvObB8xm85Ytm3q8VAghhNAlpecr+IqKcpPJnJ6eyTCqwsJJhw9/f97Umpqq9PTM5ORUlUo1ceLU2lrSrsYIIYQQCqvnb9G7XM7YWKu8bLXGuVyt503Nzc0bNixHXllfX5eYmNzjpUIIIYQuKT1fwft8PrVaLS+rVGq/33feVIZRMQwAQGnp4S+//Oy665aF8re1uV588Rl5OS8vz2KJ6pDUKknS7t3/ISyYKAoUVUk44ZsgCABAGFySJEkSy8tJB0STJKmi4kRl5SmyzCIAlJWdJIwMAIcP7w/bgVAQrpTPeGXlqd27z4SCd9OxOzY2ITd3FMmuEUII9Ss9X8FrtdrWVqe8zHEBjUZLkur3+z75ZIPL5Vq+/H/i4hJC+VUq9dixE+RlqzXGaDR2TAKApCSikcJEUfD5XFqtkXDM7cZGm1KpjI0lmmaN5wMs6zUYLIS/HmpqKkwmi9FI1JAwEPAKAq/ThRmz7FwcxzY01MXGJoR+RXUUqsijoszy++b3uyiKVqv1XQXU66O6SkIIIdSf9XwFbzZHhxrW2e12s9ly3lRB4Nete23o0Mzrr7+x06WnRqOZMWNW2B2p1RqKojIycklKJU8XGx2dSjhdrMvlVKs1hMHl6WITEoYRjnFWU1MRExOfkpJOVpJGjvNbrUNJMrvdrQ0NdcnJQ8JOFxua2z062pqRYYVeny62tdW5efNHDQ31iYlJS5Ys7fTjDyGEUA/q+UZ2GRmZDoe9ocEmSWJJyf6RIwvk9XV1NRwXCJt6/PhRlUo9c+bVODLdICZJ0vr1b1x++ZQ1ax6MiYktLt7d1yVCCKHBrOev4ClKuWTJ0k2bNgSDwZyc3IKCy+T1r7/+0sqVt6WmDjk31Warq6o6s3bt7+WcOp3+/vsf7vGCob515sxpg8GQnZ0LAEVFc/x+f1+XCCGEBrOLMtBNauqQO+64u9PKRx55rKvUmTOvnjnz6otREtR/OBx2nU63YcN79fW2lJTUWbPmhpIEga+qqpSXtVqNRnN2WhSeDwJITmcLyS4Egff5fEqlk2GIfj3wfBAACIMHAj6fz+d02snvM/n9XsLgXm8bz3M0TZTZ5/MCgNvdKjcF7USSYuS2IH6/z+n0AYDH46VpThS7/GdnGJXBgI0tEBpsBvBscmhg8fv9x48fW7p0xbx5i7Zu3bx16+bFi2+Qk7xe77p1r8nLI0eOio4+W9l4vR4AOHx4fyS7skVUsNbWCILX1EQQ3GarttmqIylLJXnWU6eOhV0vCDMBGABoaKg5fPg0SSirNTE/fwz5rhFCAwJW8KiXaDSa1NS0YcNyAaCwcNLbb78WSjIYjHfffb+8TNNKmj7b0+GDDz4BgMLC6SS7EISg3V5jNid0nFG+G8ePf88wqqysfJLMLOtpa2uyWocSNqU8eHBnSkp6YiJRLw+3uyUYZKOjU0gye73u0tJv8/PHGAxh+lYole3/1CkpGYWFqQDgdNbStNpotHYVUKnE+eARGoSwgke9xGQ625+CoiiKojq+tFiiw25FUUoAhVZLVGHzPKdSMRqNlrCvBEVRSqWSMDgAz7KMVqsjnw+eYVSEwTlOrVCIxIcZBAC1WhM2f+gBAsMwWi0DAF6vimHUxIeJEBokcDY51EsyM7NaWppra6slSTpwoFhubYcQQugiwSt41EuUSvr665d/+unHXq93yJD0uXMX9nWJEEJoMMMKHvWeIUPSV636TV+XAiGELgl4ix4hhBAahLCCRwghhAahgXSLXhR5QeBDLyVJlCQpGGRJthWEIADwPNex8XY3JEkURSGi4MEgS96+WhCChMFFUSA/TJ7n5L9d5Nf8uHc+GOQBQJKk7g+TopSE0/MghBDqVwZSBe/ztbndzaGXcmXW0kI6SSsAtLU1EuYMBgOSJEQU3G6vIc/s9TpbWiTy/IQlYVkWAFpb6wMB57mpkjRMvmfj8zlbWhzySp7nWNbdVUCNxmixJJGXEyGEUD8xkCp4nS6qY/9mmlYBQGzsEJJtBSHodNqiouJVKs35cwPU17eoVGrC4IGAx+22x8SkEl7BnzxZrtdbYmOJhkDxeBzBYMBiSSTL3FZVVWs2JxqNYUYeDRVPp7PExhoBoLW1nqbVBkP4PujQ3g0dIYTQwDOQKniKoinqbIEVCkqhUDAMUYUt1200rSLPT1FKwsw8HwAAhtGQ36JXKhnC4BSlJD9MmmaB4DCVSpphaABQKBTkh4kQQmgAwUZ2CCGE0CCEFTxCCCE0CGEFjxBCCA1CA+kZPLoU8Dwnt2mQSZIIIHXTzr8juRclx/klSSTJL4qCIPCEweXOhCzrIZ8PnucDxCUPSpJImJnjfPJflg3z/ytJBnk+eJ4PsCwH7YcZ7CY4RdEqlZZk1wihAQQreNS/BAKetrb/6gwpSZLTGcEs7B6PnTAnz3MAQkTBW1vrCXNKkuTzuZzOCG6SEZaEZQPQPsOsJ9x+swCUAOD3u53O9rdCEPhAwNdVQI3GiBU8QoMPVvCof9FqzRqNMfSSYTQKhSIuLoNkW54POhwRzAff0GBXqVSEwSOdD/7UqQqDISYubihJZre7hePYmBii+eA9nraqqhqLJcloNJ+bGurZqNdb4uJMAOBw1DJMd/PBk9+TQAgNIFjBo/6Foqj/bhqiAADC0fQkSQIAiqIJ8ysUCoWCIswsV5xKJUPeGZJ8HEC5zydxSWggOEyKUiqVSojwMBFCgwY2skMIIYQGoYtyBV9dXbllyyafzzdixKiZM6/udMXTTerx46XZ2blKJd5XQAghhH6Snr+CF0Xho4/enzVr3t1331dXV1taeoQw1W5v+eSTj3iePyckuqTJN94RQghFpOcr+IqKcpPJnJ6eyTCqwsJJhw9/T5L64YfvvPzy/wUCRHOmoUsHNv9CCKEL0/M3w10uZ2xse3tdqzXO5WolSV2yZBkAPPbYI52isSy7e/fXP+aPMRqNHZMkSaqoOEFSKlEUfD6Xx8MRNjViWX8wyBEG5/kAy3r9flFuEUbCbm/kuMD58wEEAl5B4N1uop8+HMcCQF1dVUtLw7mpopgt/6RzOJorKuwA4Pe7KIq228NMPSfT66Pi43E2OYQQGnh6voL3+XxqtVpeVqnUfr+PPPVcwSB37NgP8vKwYTnR0Z4OSQEAaG4m6pcsSZIo8j4fS9gjKBjkeF5BHFwURcHvJ6qw5cJ4PG0s6yfJLM8H7/We5436MbMIAK2tLWGnvZekLLmC93rd8qEJAq9QKCiqrauAgiBgBY8QQgNRz1fwWq22tbX9ipDjAhqNljz1XEZj1N133x826a233lcoFIWF00lKxfNcc/OZ6OjUjhPOduP774vVak1e3mUkmf1+V2trQ0LCMMIOVDt3bh0yZFhKSjpJZperkeP8VutQksxud2tJyZ78/LFRUWF6SCt/nPo1NTWjsDADAFpaKhlGazLFkwRHCCE0gPT8M3izOdpub5GX7Xa72WwhT0UIIYRQj+j5Cj4jI9PhsDc02CRJLCnZP3Jkgby+rq6G4wJdpSLUFWxFjxBCF6Dnb9FTlHLJkqWbNm0IBoM5ObkFBe13uV9//aWVK29LTR0SNhVdCvbt21NcvIvn+fT0zPnzF6tUqvNugqOoIoTQhbkoQ8qkpg654467O6185JHHukmVPfTQny5GeVB/0NhYX1y86+abV6lUqn/9a31x8a4rrriqrwuFEEKDFo4Zh3qJ0+kYNeqyqCgTAOTkDG9sDNORDyGEUE/BCh71ktzc/NzcfJ/P29BQ/8MPhztevvN88OTJ9iEH9HqDTqcJJQWDHBB3hhQE3u32SFIzw3TZ8a+jYJCTJIkwOMf5vV6PUtlA/NRA8njaCIP7fC6e5wCIMsudS1tb7WEHhpKk+A6dIT0A4Ha3KZV+juuyKYNarYmKwuauCA02WMGjXlVbW71t2+eSJFmtcaGVPp/vww/flZdHjhwVHR3VIckrSXD06HeR7CSyewMuV5fj/JzLZosgeGNjXWNjXSRlqSHPWlFRFna9IMyUK/imJtvRo6dJQlmtifn5WMEjNNhgBY96VXb28Ozs4cXFu7ds+WT58v+RVxqNUQ888L/yMkVRHUfp+fjjrQDSlCkzSYLzPGe3V5vNSYSjHfzww0GVSpWTQ9SVg2XdLldjXFwG4WgHe/duGzIkKzl5KEnmtrZmjvPHxqaRZPZ42g4d2ldQMCHsfPA03T5W45AhWVOmZACA3V7NMJqoqLhzM8vIJ8BFCA0gWMGjXrJ37y6dTjd69FgASE1NO3iwOJSkUCi6GvJIoVAAKEKV1vlIFEXRNE2YX54onTCzUklTFEXTkc0HTxxcqVSSZ6blv93npyglTSsjDY4QGjTwlzvqJSaT6cCBvQ6HnWXZgwf3paUN7esSIYTQYIZX8KiX5OePsttb1q17jeO49PTMOXMWEG+KA90ghFDEsIJHvWfatCunTbuyr0uBEEKXhIFUwft8rR6PI/RSnk2uqamCZFt5uNPW1nri2eRYSeKJg4sA0NxcSZJZ5vHYm5qILkzl2eQISyLPUOd01rGs49xUSRoqP5TxeBxNTa0AIIo8zwcDAW9XATUaQzeNs3qBQqHAoWoRQugCDKQKnqZVWu3Z+eApSgkAHdd0Q54PXqXS0TTRIcvNowiDy/PBa7UG8vngGUZNGDwQ8AlCkDCzJCkAQK3Wa7X6cOmKTnv3+10UxXTT5pym1ST7RQgh1N8MpApepdKpVGerIrktsdFoJdmW5zmfz6XTmQg7UCmVp2laTRjc73exrNdgiCVvX61WGwiDi2KjJEmEmQEYANDpzGE7UIVuXqjVeqNRDwCBgJdhNMTB+wBewSOE0IXBVvQIIYTQIIQVPOrXFAoFXsAjhNAFwAoeIYQQGoSwgkf9Gj6DRwihC4MVPOr/sIJHCKGIYQWP+jWapgVB7OtSIITQwDOQusmhS0EwGAgG/aGXCoUkCILP10qyrSDwABAIeAQhSJJfFHlBoAiDB4MsAPj9LvLRDjjOTxic5wOiSHqYLOuR/9J0mJJIkkkuYTDI+nwsAIiiwPNcN8GVSkatDjtwAkJoAMMKHvUvHOdta2sOvRQEXhRFl6uRPILXS1RNAgDPBwEiC+5yNRHmlCRJnmE2kuBEmVk2AABer0MQfOH2awRQAgDLelwuu7xSEHiO85+bWabRGLGCR2jwuSgVfHV15ZYtm3w+34gRo2bOvLrT8C9hU7vfBF06dDqLVmsKvVSrdZIkxcdnkWwrzwdvsSR1HBCpG42NdoZREwaPdD74U6cqjMbY+Ph0ksxtbc3BoD8mhmg+eLe7raqqxmJJiYoKM5yRPMIjABgM0fHxFgBwOKppuvv54EnvSSCEBpCer+BFUfjoo/cXLlySkpK6bt3rpaVHRo4c3X1q95ugS4pCoVAolKGXNK2UJDFUaXVPzqZQUIT55b0RZpbrdYpSkv/6JC+JPO098WHKJTlPcIVCzggRHSZCaNDo+Qq+oqLcZDKnp2cCQGHhpO+/L+lYW4dN7X4TdClTqVSiKD311FMsyxYVFZWXl0dHR/t8PovF4vP5VCoVAHi93ujoaLVabbPVCYI7JsZmMsVUVlZmZWWxLKtQKCoqKsaOHcuybFNTU1RUlN/vF0XR5/OVlBQbDAaep5uamjQajdPp9Pv9PM8nJydHRUXxPG+z2QBAEISMjAye91dUnNBqT7IsazQaWZYdO3asx+PZu3dvdnZ2TExMY2OjSqVSKpV+v99isezZs2/XroN5eSOVSqXdbjcYDBaLJRgMSpJkNBobGhri4uIkSaqqqjKbzfHx5i+++M+VV85Sq9VKpZJl2YaGBo1GYzab5QWTyXT48GGlUpmamlpWdiIY9Pj90NxsHzp0aG1tLcMwgiDExMSUlZUJwir5Fr0kSeTNBRBCg0/PV/AulzM2tn1sc6s1zuVqPW9qN5sIgtDU1CAvq9VqhmE6JgGA2+0iKZUgBFk24PG4OY6o+ZUg8DwfJAweCHhYNuB2u8iv7QIBljC4z+cLBskze+S/YW+6SlKU/I0fCLBudwAA/H5/MChRVJfBaZrRaolud18k6elDAWDNmjUA8NBDD/VhSXrBH/7weE+EWQZgAYBHHnnkkUceC5vD7/drNJqe2BdCqP/q+Qre5/Op1e1TkKlUar/fd97UbjbxeNyvvPKcvDxyZEF09NlJ1bRaJjY2uqRkdySlq4noWByO5vNn+lFVVQTBa2oqamqIZoD9UQSZT5w4HHa9IBTJZ7yurrKkpJwkVFxcYl7eGPJd97gJE8bffPOKurqmxMREt9vN87xer7fb7UePHh05cmRUVJTP5xMEoa2tjeO4xMQEAD4YlPx+lmXZ1tbWlJQUjuMsFktzczPDMEqlsrGx0ev1Dh8+PBgMCgKn0+l8voBWq2UYxuPxmEwml8vlcrlMJpPH4wkGgzqdjuf5/9/e3cc2cZ9xAP/57PO7fbkEJ3GC8wZ5G4VAwATo2lRjalpAZBIEaUVoaKiIFgkY0rRJ+6NCndRK1dCkVt20iapVhboi1gIrrJo6BqEkLCwF8tIsIXUcSBODHb+c7Tg+n+39YWYQOM4l2HLsfD9/md/97uHih/D4zne/h6bpQGCqsDB/ZGSssrKS5/mKior+/n6fz2c0GgVBiEajarV6YGDAYDBUVFT4fL5AwFtUZHS7OZZlCwoKJicn5XK5y+XSaDSRSCQUCnm9Xp1OFw6HeZ4fGbGsXPkDt9sXDAYZhlEoFBzHud1umqYLCwvD4bDf7y8tLeU4juO4oqLC8fG7CoW6uNjo8/kcDgfDMBzHsSyr0+lOnqRDIUIIaW1t3bq1PBQK2Wx3pFKZUqkjhEgkEr1e/+gHZQDIVakv8CqVyu12xV7zfFCpVM26NckuGo12z5598X2VyofdSw0G465dO+rrRV3MD4cFj2dCpyukaVH9T4eHv6VpeXm5qNuvgkG/3+9k2aUib1bq6ekyGssMhmIxk/1+lyDwDFMkZnIg4Bsa6q+uXqFWa5/cKpU++BbWaDQ1NCwhhHg892QyuUbDzhRQLs9wu1iJRLJv3882bvyRmMmCwNvtI/n5S0XeE37zZqdcrhD5CSYQ4NzuieLiapHXadrb/15ZWWsyVYmZ7PHc4/mAwVAhZrLX6+nu/rqxcZNenyBxZ88Sl4sQQsxm86uvmgkhDoeVppUMI+rfGwDkjNQX+Ly8/L6+ntjrycnJvDx21q1JdpHJZFVViaus02n3eimWXSLmqASBD4U8DMOKbBcrk9FyuUJk8ECAjkanWbZA/CV6tVojMjhFhXk+IHJyrNu9TpeX8P7q+McPlUrDshpCSDjso2kVw4gKDgAAWST1T6NVVS1zOidttvFoNNLd/e+VKxti499/f5fngwm3zrQLAAAAzE/qz+ApStrW9sqZM6dDoVBtbV1Dw5rY+Acf/HHv3v0mU/mTW2faBQAAAOYnLQvdmEzlBw4cemwwfkNvwq0JB5Pzev1OJydyMkVJdTqDTCb23iKXi1OpRN1vTwihaaVOZxD/SJLTyRUXJ1iDLCGlUkvTYm945vmQ0+nleT7h1mPHSDBICCHNzQ9G1GpWKl3Qqxl6vV6Xa65Zlouc73JxCoXY95amFXPMsrewMC1ZDoVCTqc3GEyc5TfeINPThBDy/PMPRjQalqIWdJYBIB2y+NfebrcPDQ1u2SJqMkVJtdp88cFHR616PTP7PEIIITKZQqudw81ovb29JSUmkZPntIZoMBjs7b3V1PRswq1Hjjw+olaL/RkzxW6fHBz8b9qyPKrV6mafRwiZe5b7+nqKikpETp5Hltev35Rw6+HDj488ujIgACweWBEWAAAgB6HAAwAA5CBJNBrN9DHMUyAwxfM8wyR4Huzpeb0cRVEaTYKnyZ+ey+VUq9Xiv/0VLxwWOI7T6/UL/Jt18bI3y263S6VSpS/LOp0+9lQkAEBCWVzgAQAAYCa4RA8AAJCDsvUS39P0j7927Wpn5xVBECorl23fviPWkUx8l/rkf7Xb7Tp37q8224TRWNLW9kps2d1UBe/puXH58j8FQaivf6alZc5xnuZNywhkeTFkGQDSJZqFwmHh+PG3LJZhng+eOPGHnp4b4ve12caPH3/L43EHAlMffvinS5e+mimg+MG4SCTy7ru/GxwcEATh/PkzFy/+I4XBbbbxt98+5nQ6QiH+o4/+3NXVkcLgCxCyvBiyDADpk5Wf7uP942la3tS06datG+L3dbmcq1at0esZpVJVW1sfa3KTMKD4wbiRkWGtVltTUyeVSltatprNG1IY/PbtwerqWpYtkMlos3lDf39vCoMvQMjyYsgyAKRPVl6iT95yPrm6uhV1dSumpvw220Rv763m5s0zBZxr63pCiNM5qVarT5/+ZGJifOlS00svbUth8HA4HP3/HZESCRUrWqkKvgAhy4shywCQPll5Bp+85bwYY2N3vvzybzwfNBgKZwo419b1hJBAIDAw8G1Dw5r9+w8SQi5cOJfC4MuX1wwPD927Z/P7fdeufT09HUhh8AUIWV4MWQaA9MnKAq9SqeLLrT/Zcl6Mmpr611//RWPj+vPnz84UUPxgnFKpNJnKqqvrFAplU9Om27cHUxi8tNTU0rL1s8/+8vHHJ6qqlqtU6hQGX4CQ5cWQZQBIn6ws8Hl5+ZOTjtjrJ1vOJ9fRceXmze7Ya5OpzOl0zBRQ/GAcwzz8I0VRFEWlMLggCPX1K1577ciBA4eLiowsm5/C4AsQsrwYsgwA6ZOVBf5p+sczDNPV1eF0Tk5PT1+/fq2srGKmgPNoXb9s2XKHwz42dicajXZ1ddbU1KUw+NSU//33f89xHp4PXr16ubHRnMLgCxCyvBiyDADpk60r2d29O3r+/NlY//gXX9wivoknIaS9/eKNG//heb6yctnWra2xq6AJA4ofjBsdHblw4azf7y8vr9y27SepDd7V1dneflGhUK5bt37jxueSvBXzCL4AIcuLIcsAkCbZWuABAAAgiay8RA8AAADJocADAADkIBR4AACAHIQCDwAAkINQ4AEAAHIQCjwAAEAOQoEHAADIQSjwAAAAOSgr28UCQLq9885vp6b8u3btrq9/ZmzsjtfLsWx+cXFJpo8LAMTCGTwAzKKz88qpUye7u7syfSAAMAc4gweABI4e/TUhhKKkmT4QAJgnrEUPAAnEL9F3dFwZG7sTG1yyxHDw4NFIJNze/q+BgT6Xy5mfX2A2b1i7tik24c03fxOJRPbs+fl3393u7+89cuRXmfsJABY7nMEDQDKrVzdOTwccDrvRWNLQ0EgI+fTTk0NDA1KplGUL7t2zffHFGbfbvXlzS3yXq1evWCy3ZTL89wKQSfgNBIBk1q5tsliGHQ57aampqelZq9UyNDQgk8kOHfqlTqfv7+85ffqTjo72deuaGCYvtovNNt7cvNlkKs/skQMscijwADAHVquFEKLV6r755johJBKJSCRUJBKxWi2x83tCiNm84YUXfpzJowQAFHgAmBOPx00Icbtdly599ej41JQ//rqsDOfuAJmHAg8Ac6DXM4SQFStW7dz505nmSCR4/hYg81DgAUAUnucJIUuXmgghVqvF7/dpNFq7/f7nn5+KRiNtbbvz8wsyfYwA8BAKPADMQqPREkIGBvooimpt3VlRUWW1Wt5773hBwRKbbSIcFlavXovqDrDQ4EoaAMxiw4YfGo2l0WjU7XYRQnbv3rtp03Majfb+fVtBwZKXX96+ffuOTB8jADwOC90AAADkIJzBAwAA5CAUeAAAgByEAg8AAJCDUOABAAByEAo8AABADkKBBwAAyEEo8AAAADkIBR4AACAH/Q+9MsSemnIlFwAAAABJRU5ErkJggg==" /><!-- --></p> -<p>The AIC values are internally calculated using Gaussian quadrature.</p> -<pre class="r"><code>AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm, - f_parent_nlmixr_saem_dfop_tc_moreiter$nm, - f_parent_nlmixr_saem_dfop_tc_10k$nm)</code></pre> -<pre><code> df AIC -f_parent_nlmixr_saem_sfo_const$nm 5 798.71 -f_parent_nlmixr_saem_sfo_tc$nm 6 808.64 -f_parent_nlmixr_saem_dfop_const$nm 9 1995.96 -f_parent_nlmixr_saem_dfop_tc$nm 10 664.96 -f_parent_nlmixr_saem_dfop_tc_moreiter$nm 10 4464.93 -f_parent_nlmixr_saem_dfop_tc_10k$nm 10 Inf</code></pre> -<p>We can see that again, the DFOP/tc model shows the best goodness of fit. However, increasing the number of burn-in iterations from 800 to 1600 results in a higher AIC. If we further increase the number of iterations to 10 000 (burn-in) and 1000 (second phase), the AIC cannot be calculated for the nlmixr/saem fit, confirming that this fit does not converge properly with the SAEM algorithm.</p> </div> -<div id="comparison" class="section level3"> -<h3>Comparison</h3> -<p>The following table gives the AIC values obtained with the three packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> +<div id="comparison" class="section level2"> +<h2>Comparison</h2> +<p>The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains). Note that</p> <pre class="r"><code>AIC_all <- data.frame( check.names = FALSE, "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), "Error model" = c("const", "tc", "const", "tc"), nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), - nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC), - saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, - f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC), - nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC) + saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "lin"), + saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "is") ) kable(AIC_all)</code></pre> <table> @@ -1869,9 +1845,8 @@ kable(AIC_all)</code></pre> <th align="left">Degradation model</th> <th align="left">Error model</th> <th align="right">nlme</th> -<th align="right">nlmixr_focei</th> -<th align="right">saemix</th> -<th align="right">nlmixr_saem</th> +<th align="right">saemix_lin</th> +<th align="right">saemix_is</th> </tr> </thead> <tbody> @@ -1881,36 +1856,36 @@ kable(AIC_all)</code></pre> <td align="right">796.60</td> <td align="right">796.60</td> <td align="right">796.38</td> -<td align="right">798.71</td> </tr> <tr class="even"> <td align="left">SFO</td> <td align="left">tc</td> <td align="right">798.60</td> -<td align="right">798.64</td> +<td align="right">798.60</td> <td align="right">798.38</td> -<td align="right">808.64</td> </tr> <tr class="odd"> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">745.87</td> +<td align="right">671.98</td> <td align="right">705.75</td> -<td align="right">1995.96</td> </tr> <tr class="even"> <td align="left">DFOP</td> <td align="left">tc</td> <td align="right">671.91</td> -<td align="right">740.42</td> +<td align="right">665.11</td> <td align="right">665.65</td> -<td align="right">664.96</td> </tr> </tbody> </table> </div> </div> +<div id="conclusion" class="section level1"> +<h1>Conclusion</h1> +<p>A more detailed analysis of the dimethenamid dataset confirmed that the DFOP model provides the most appropriate description of the decline of the parent compound in these data. On the other hand, closer inspection of the results revealed that the variability of the k2 parameter across the population of soils is ill-defined. This coincides with the observation that this parameter cannot robustly be quantified in some for some of the soils.</p> +<p>Regarding the regulatory use of these data, it is claimed that an improved characterisation of the mean parameter values across the population is obtained using the nonlinear mixed-effects models presented here. However, attempts to quantify the variability of the slower rate constant of the biphasic decline of dimethenamid indicate that the data are not sufficient to characterise this variability to a satisfactory precision.</p> </div> <div id="references" class="section level1"> <h1>References</h1> diff --git a/vignettes/web_only/dimethenamid_2018.rmd b/vignettes/web_only/dimethenamid_2018.rmd index 7700fe35..f86d776e 100644 --- a/vignettes/web_only/dimethenamid_2018.rmd +++ b/vignettes/web_only/dimethenamid_2018.rmd @@ -1,7 +1,7 @@ --- title: Example evaluations of the dimethenamid data from 2018 author: Johannes Ranke -date: Last change 10 February 2022, built on `r format(Sys.Date(), format = "%d %b %Y")` +date: Last change 7 March 2022, built on `r Sys.setlocale("LC_TIME", "C"); format(Sys.Date(), format = "%d %b %Y")` output: html_document: toc: true @@ -30,14 +30,18 @@ opts_chunk$set( # Introduction -During the preparation of the journal article on nonlinear mixed-effects models -in degradation kinetics [@ranke2021] and the analysis of the dimethenamid -degradation data analysed therein, a need for a more detailed analysis using -not only nlme and saemix, but also nlmixr for fitting the mixed-effects models -was identified, as many model variants do not converge when fitted with nlme, -and not all relevant error models can be fitted with saemix. +A first analysis of the data analysed here was presented in a recent journal +article on nonlinear mixed-effects models in degradation kinetics [@ranke2021]. +That analysis was based on the `nlme` package and a development version +of the `saemix` package that was unpublished at the time. Meanwhile, version +3.0 of the `saemix` package is available from the CRAN repository. Also, it +turned out that there was an error in the handling of the Borstel data in the +mkin package at the time, leading to the duplication of a few data points from +that soil. The dataset in the mkin package has been corrected, and the interface +to `saemix` in the mkin package has been updated to use the released version. -This vignette is an attempt to satisfy this need. +This vignette is intended to present an up to date analysis of the data, using the +corrected dataset and released versions of `mkin` and `saemix`. # Data @@ -234,9 +238,8 @@ the mkin package. The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every -fit. As we will compare the SAEM implementation of saemix to the results -obtained using the nlmixr package later, we define control settings that -work well for all the parent data fits shown in this vignette. +fit. We define control settings that work well for all the parent data fits +shown in this vignette. ```{r saemix_control, results='hide'} library(saemix) @@ -256,7 +259,7 @@ f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TR plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") ``` -Obviously the default number of iterations is sufficient to reach convergence. +Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model. ```{r f_parent_saemix_sfo_tc, results = 'hide', dependson = "saemix_control"} @@ -268,28 +271,35 @@ plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous. -```{r f_parent_saemix_dfop_const, results = 'hide', dependson = "saemix_control"} +```{r f_parent_saemix_dfop_const, results = 'show', dependson = "saemix_control"} f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") +print(f_parent_saemix_dfop_const) ``` -This is improved when the DFOP model is fitted with the two-component error -model. Convergence of the variance of k2 is enhanced, it remains more or less -stable already after 200 iterations of the first phase. +While the other parameters converge to credible values, the variance of k2 +(`omega2.k2`) converges to a very small value. The printout of the `saem.mmkin` +model shows that the estimated standard deviation of k2 across the population +of soils (`SD.k2`) is ill-defined, indicating overparameterisation of this model. -```{r f_parent_saemix_dfop_tc, results = 'hide', dependson = "saemix_control"} +When the DFOP model is fitted with the two-component error model, we also +observe that the estimated variance of k2 becomes very small, while being +ill-defined, as illustrated by the excessive confidence interval of `SD.k2`. + +```{r f_parent_saemix_dfop_tc, results = 'show', dependson = "saemix_control"} f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control_moreiter, transformations = "saemix") plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence") +print(f_parent_saemix_dfop_tc) ``` Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with an error message. This is -related to the variance of k2 approximating zero. This has been submitted +related to the variance of k2 approximating zero and has been submitted as a [bug to the saemix package](https://github.com/saemixdevelopment/saemixextension/issues/29), as the algorithm does not converge in this case. @@ -329,7 +339,6 @@ AIC_parent_saemix_methods <- c( ) print(AIC_parent_saemix_methods) ``` - The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value. In order to illustrate that the comparison of the three method @@ -349,142 +358,11 @@ AIC_parent_saemix_methods_defaults <- c( print(AIC_parent_saemix_methods_defaults) ``` -### nlmixr - -In the last years, a lot of effort has been put into the nlmixr package which -is designed for pharmacokinetics, where nonlinear mixed-effects models are -routinely used, but which can also be used for related data like chemical -degradation data. A current development branch of the mkin package provides -an interface between mkin and nlmixr. Here, we check if we get equivalent -results when using a refined version of the First Order Conditional Estimation -(FOCE) algorithm used in nlme, namely the First Order Conditional Estimation -with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr. - -First, the focei algorithm is used for the four model combinations. - -```{r f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE} -library(nlmixr) -f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") -f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") -f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") -f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei") -``` - -For the SFO model with constant variance, the AIC values are the same, for the -DFOP model, there are significant differences between the AIC values. These -may be caused by different solutions that are found, but also by the fact that -the AIC values for the nlmixr fits are calculated based on Gaussian quadrature, -not on linearisation. - -```{r AIC_parent_nlmixr_nlme, cache = FALSE} -aic_nlmixr_focei <- sapply( - list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), - AIC) -aic_nlme <- sapply( - list(f_parent_nlme_sfo_const, NA, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc), - function(x) if (is.na(x[1])) NA else AIC(x)) -aic_nlme_nlmixr_focei <- data.frame( - "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), - "Error model" = rep(c("constant variance", "two-component"), 2), - "AIC (nlme)" = aic_nlme, - "AIC (nlmixr with FOCEI)" = aic_nlmixr_focei, - check.names = FALSE -) -print(aic_nlme_nlmixr_focei) -``` - -Secondly, we use the SAEM estimation routine and check the convergence plots. The -control parameters, which were also used for the saemix fits, are defined beforehand. - -```{r nlmixr_saem_control} -nlmixr_saem_control_800 <- saemControl(logLik = TRUE, - nBurn = 800, nEm = 300, nmc = 15) -nlmixr_saem_control_moreiter <- saemControl(logLik = TRUE, - nBurn = 1600, nEm = 300, nmc = 15) -nlmixr_saem_control_10k <- saemControl(logLik = TRUE, - nBurn = 10000, nEm = 1000, nmc = 15) -``` - -Then we fit SFO with constant variance - -```{r f_parent_nlmixr_saem_sfo_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} -f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_sfo_const$nm) -``` - -and SFO with two-component error. - -```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} -f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_sfo_tc$nm) -``` - -For DFOP with constant variance, the convergence plots show considerable -instability of the fit, which indicates overparameterisation which was already -observed above for this model combination. Also note that the variance of k2 -approximates zero, which was already observed in the saemix fits of the DFOP -model. +## Comparison -```{r f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} -f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_dfop_const$nm) -``` - -For DFOP with two-component error, a less erratic convergence is seen, but the -variance of k2 again approximates zero. - -```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} -f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control_800) -traceplot(f_parent_nlmixr_saem_dfop_tc$nm) -``` - -To check if an increase in the number of iterations improves the fit, we repeat -the fit with 1000 iterations for the burn in phase and 300 iterations for the -second phase. - -```{r f_parent_nlmixr_saem_dfop_tc_1k, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} -f_parent_nlmixr_saem_dfop_tc_moreiter <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control_moreiter) -traceplot(f_parent_nlmixr_saem_dfop_tc_moreiter$nm) -``` - -Here the fit looks very similar, but we will see below that it shows a higher AIC -than the fit with 800 iterations in the burn in phase. Next we choose -10 000 iterations for the burn in phase and 1000 iterations for the second -phase for comparison with saemix. - -```{r f_parent_nlmixr_saem_dfop_tc_10k, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} -f_parent_nlmixr_saem_dfop_tc_10k <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control_10k) -traceplot(f_parent_nlmixr_saem_dfop_tc_10k$nm) -``` - -The AIC values are internally calculated using Gaussian quadrature. - -```{r AIC_parent_nlmixr_saem, cache = FALSE} -AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm, - f_parent_nlmixr_saem_dfop_tc_moreiter$nm, - f_parent_nlmixr_saem_dfop_tc_10k$nm) -``` - -We can see that again, the DFOP/tc model shows the best goodness of fit. -However, increasing the number of burn-in iterations from 800 to 1600 results -in a higher AIC. If we further increase the number of iterations to 10 000 -(burn-in) and 1000 (second phase), the AIC cannot be calculated for the -nlmixr/saem fit, confirming that this fit does not converge properly with -the SAEM algorithm. - -### Comparison - -The following table gives the AIC values obtained with the three packages using -the same control parameters (800 iterations burn-in, 300 iterations second -phase, 15 chains). +The following table gives the AIC values obtained with both backend packages +using the same control parameters (800 iterations burn-in, 300 iterations +second phase, 15 chains). Note that ```{r AIC_all, cache = FALSE} AIC_all <- data.frame( @@ -492,16 +370,30 @@ AIC_all <- data.frame( "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), "Error model" = c("const", "tc", "const", "tc"), nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), - nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC), - saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, - f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC), - nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC) + saemix_lin = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "lin"), + saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "is") ) kable(AIC_all) ``` +# Conclusion + +A more detailed analysis of the dimethenamid dataset confirmed that the DFOP +model provides the most appropriate description of the decline of the parent +compound in these data. On the other hand, closer inspection of the results +revealed that the variability of the k2 parameter across the population of +soils is ill-defined. This coincides with the observation that this parameter +cannot robustly be quantified in some for some of the soils. + +Regarding the regulatory use of these data, it is claimed that an improved +characterisation of the mean parameter values across the population is +obtained using the nonlinear mixed-effects models presented here. However, +attempts to quantify the variability of the slower rate constant of the +biphasic decline of dimethenamid indicate that the data are not sufficient +to characterise this variability to a satisfactory precision. + # References <!-- vim: set foldmethod=syntax: --> |
