Reversibility of glyphosate sorption in pampean loess-derived soil profiles of central Argentina.

There is a growing concern about glyphosate's behavior in the environment. Herbicide behavior in soils greatly depends on adsorption-desorption phenomena, which have shown great variability in soils, although the reversibility of this process has been less examined. The adsorption-desorption behavior of glyphosate was measured on representative soil profiles of the northeast region of Argentinean Pampas, a semi-arid crop cultivating region. Two soil profiles samples (P1 and P2, both Entic Haplustoll) were collected and segmented into depth increments of 0-10, 10-20, 20-40, 40-60, 60-80, and 80-100 cm. Batch adsorption/48 h-desorption isotherms were performed in a controlled setup. Soil samples had a high sand content (77-92%), and a low content of clay (<3%), but markedly differing in the available P content, especially in the upper layers of soil profiles (0-40 cm, P1 range 133-170 ppm; P2 range 7-43 ppm). Adsorption-desorption isotherms showed a similar range of variation, between 150 and 1400 L kg -1for KFads and 450-1400 L kg -1for KFdes, without appreciable evidence of hysteresis (0.95 ± 0.05). Sorption capacity parameters showed a distinct behavior with depth, P1 exhibiting a U-shape with minimum values at intermediate depths (20-60 cm), while P2 decreases sharply between 0 and 40 cm. General linear models considering the specific surface area (SSA) of each sample and the spatial correlation structure for soil profiles showed a main positive association of KFads and KFdes with the soil organic matter, together with a positive association with iron content (KFads), and a negative association of KFdes with available P content. These results indicate high adsorption extents and sorption reversibility of glyphosate to sandy loam soils of the region, which implies the potential for the herbicide to be available for leaching or degradation under a scenario of intensive use.

Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study.

Age-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980-2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.

The ontogenetic allometry of body morphology and chemical composition in dairy goat wethers.

We studied the ontogenetic growth of goat wethers (castrated male goats) of the Saanen and Swiss Alpine breeds based on a large range of intraspecific body mass (BM). The body parts and the chemical constituents of the empty body were described by the allometric function by using BM and the empty body mass (EBM) as the predictors for morphological traits and chemical composition, respectively. We fitted the allometric scaling function by applying the SAS NLMIXED procedure, but to evaluate assumptions regarding variances in morphological and compositional traits, we combined the scaling function with homoscedastic (MOD1), and the heteroscedastic exponential (MOD2) and power-of-the-mean (MOD3) variance functions. We also predicted the ontogenetic growth by using the traditional log-log transformation and back-transformed results into the arithmetic scale (MOD4). We obtained predictions from MOD4 in the arithmetic scale by a two-step process, and evaluated MOD1, MOD2 and MOD3 by a model selection framework, and compared MOD4 with MOD1, MOD2 and MOD3 based on goodness-of-fit measures. Based on information criteria for model selection, heterogeneous variance functions were more likely to describe 10 over 36 traits with a low level of model selection uncertainty. One trait was predicted by averaging the MOD1 and MOD2 variance functions; and nine traits were better described by averaging the MOD2 and MOD3 variance functions. The predictions for other 16 traits were averaged from MOD1, MOD2 and MOD3. However, MOD4 better described 11 traits according to the goodness-of-fit measures. Depending on the variable being analyzed, the body parts and the chemical amounts exhibited the three types of allometric behavior with respect to BM and EBM, that is, positive, negative and isometric ontogenetic growth. Reference BMs, that is, 20, 27, 35 and 45 kg, were used to compute the net protein and energy requirements based on the first derivative of the scaling function, and the results were presented in reference to the EBM and EBM0.75. Both the net protein and energy requirements scaled to EBM0.75 increased from 20 to 45 kg of BM.

Superensemble forecasts of dengue outbreaks.

In recent years, a number of systems capable of predicting future infectious disease incidence have been developed. As more of these systems are operationalized, it is important that the forecasts generated by these different approaches be formally reconciled so that individual forecast error and bias are reduced. Here we present a first example of such multi-system, or superensemble, forecast. We develop three distinct systems for predicting dengue, which are applied retrospectively to forecast outbreak characteristics in San Juan, Puerto Rico. We then use Bayesian averaging methods to combine the predictions from these systems and create superensemble forecasts. We demonstrate that on average, the superensemble approach produces more accurate forecasts than those made from any of the individual forecasting systems.

Improving electroencephalographic source localization of epileptogenic zones with time-frequency analysis.

The combination of recently developed methods for electroencephalographic (EEG) space-time-frequency analysis can provide noninvasive functional neuroimages necessary for obtaining an accurate localization of the epileptogenic zone. The aim of this study was to determine if time-frequency (TF) analysis, followed by EEG source localization, would improve the detection and identification of epileptogenic and related activity. Seventeen patients with refractory frontal lobe epilepsy (FLE) were studied using video EEG recording. TF analysis identified the first epileptogenic EEG changes. Using the Bayesian model averaging (BMA) approach, we compared brain electromagnetic tomographic (BET) images, constructed from the TF domain, with BET images constructed from the time domain only. We determined if the localization identified by BET images was concordant with the localization from medical history and video EEG recording. TF analysis provided a clear display of subtle EEG features, including EEG lateralization, and more concordant and delimited epileptogenic zones, compared with time-domain source analysis. In conclusion, EEG TF analysis improves source localization. After a thorough validation, this methodology could become a useful noninvasive tool for localizing the epileptogenic zone in clinical practice.