Linear mixed-effect models for correlated response to process electroencephalogram recordings.

A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them.

Recommendations for increasing yield of the edible Pinus pinea L. pine nuts.

In Pinus pinea, cone to pine nut yield (total pine nut weight expressed as percentage of cone weight), an important crop trait, is decreasing worldwide. This phenomenon is of great concern, since the nuts of this species are highly demanded. Cone weight, seed and pine nut morphometry, and pine nut yield were monitored in a non-native area in Chile for 10 years.

Influence of Climatic Variables on Incidence of Whitefly-Transmitted Begomovirus in Soybean and Bean Crops in North-Western Argentina.

Over the last 20 years, begomoviruses have emerged as devastating pathogens, limiting the production of different crops worldwide. Weather conditions increase vector populations, with negative effects on crop production. In this work we evaluate the relationship between the incidence of begomovirus and weather before and during the crop cycle.

Curtobacterium, A Foliar Pathogen Isolated from Maize in Central Argentina.

Plant pathogens, such as fungi, bacteria, and viruses, can cause serious damage to crops and significantly reduce yield and quality. Bacterial diseases of agronomic crops, however, have been little studied. The present study aims to isolate and identify bacteria recovered from symptomatic maize (Zea mays) leaves collected from field samples in the province of Cordoba, Argentina.

Unveiling spatial variability in herbicide soil sorption using Bayesian digital mapping.

Regional mapping herbicide sorption to soil is essential for risk assessment. However, conducting analytical quantification of adsorption coefficient (K ) in large-scale studies is too costly; therefore, a research question arises on goodness of K spatial prediction from sampling. The application of a spatial Bayesian regression (BR) is a newer technique in agricultural and natural resources sciences that allows converting spatially discrete samples into maps covering continuous spatial domains.

A spatially based quantile regression forest model for mapping rural land values.

Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting economic values. New machine learning methods, like tree-based regression models, have been proposed as an alternative to linear regression to predict economic values from ancillary variables, since these algorithms are able to handle non-normality and non-linearity in the data.

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that quantify immune cells from tumor biopsies using gene expression data apply computational deconvolution methods that present multicollinearity and estimation errors resulting in the overestimation or underestimation of the diversity of infiltrating immune cells and their quantity. To overcome such limitations, we developed MIXTURE, a new ν-support vector regression-based noise constrained recursive feature selection algorithm based on validated immune cell molecular signatures.

Site-specific data on herbicide soil retention and ancillary environmental variables.

This article presents original geospatial data on soil adsorption coefficient (Kd) for two widely used herbicides in agriculture, glyphosate and atrazine. Besides Kds, the dataset includes site-specific soil data: pH, total nitrogen, total organic carbon, Na, K, Ca, Mg, Zn, Mn, Cu, cation exchange capacity, percentage of sand, silt and clay, water holding capacity, aluminum and iron oxides, as well as climatic and topographic variables. The quantification of herbicides soil retention was made on a sample of soils selected by Conditionated Latin Hypercube method to capture the underlying edaphoclimatic variability in Cordoba, Argentina.

Introgression of peanut smut resistance from landraces to elite peanut cultivars (Arachis hypogaea L.).

Smut disease caused by the fungal pathogen Thecaphora frezii Carranza & Lindquist is threatening the peanut production in Argentina. Fungicides commonly used in the peanut crop have shown little or no effect controlling the disease, making it a priority to obtain peanut varieties resistant to smut. In this study, recombinant inbred lines (RILs) were developed from three crosses between three susceptible peanut elite cultivars (Arachis hypogaea L.

Fitting milk production curves through nonlinear mixed models.

The aim of this work was to fit and compare three non-linear models (Wood, Milkbot and diphasic) to model lactation curves from two approaches: with and without cow random effect. Knowing the behaviour of lactation curves is critical for decision-making in a dairy farm. Knowledge of the model of milk production progress along each lactation is necessary not only at the mean population level (dairy farm), but also at individual level (cow-lactation).

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach.

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied.

Comparison of algorithms to infer genetic population structure from unlinked molecular markers.

Identifying population genetic structure (PGS) is crucial for breeding and conservation. Several clustering algorithms are available to identify the underlying PGS to be used with genetic data of maize genotypes. In this work, six methods to identify PGS from unlinked molecular marker data were compared using simulated and experimental data consisting of multilocus-biallelic genotypes.

The multi-reference contrast method: facilitating set enrichment analysis.

Set enrichment analysis (SEA) is used to identify enriched biological categories/terms within high-throughput differential expression experiments. This is done by evaluating the proportion of differentially expressed genes against a background reference (BR). However, the choice of the "appropriate" BR is a perplexing problem and results will depend on it.

Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: assessing experimental effects in a melanoma cell study.

Motivation: Difference in-gel electrophoresis (DIGE)-based protein expression analysis allows assessing the relative expression of proteins in two biological samples differently labeled (Cy5, Cy3 CyDyes). In the same gel, a reference sample is also used (Cy2 CyDye) for spot matching during image analysis and volume normalization. The standard statistical techniques to identify differentially expressed (DE) proteins are the calculation of fold-changes and the comparison of treatment means by the t-test.

Molecular kinetics modeling in hemodialysis: on-line molecular monitoring and spectral analysis.

The knowledge of the underlying molecular kinetics is a key point for the development of a dialysis treatment as well as for patient monitoring. In this work, we propose a kinetic inference method that is general enough to be used on different molecular types measured in the spent dialysate. It estimates the number and significance of the compartments involved in the overall process of dialysis by means of a spectral deconvolution technique, characterizing therefore the kinetic behavior of the patient.

Improving cluster visualization in self-organizing maps: application in gene expression data analysis.

Cluster analysis is one of the crucial steps in gene expression pattern (GEP) analysis. It leads to the discovery or identification of temporal patterns and coexpressed genes. GEP analysis involves highly dimensional multivariate data which demand appropriate tools.

Changes in the Concentration of an Allexivirus During the Crop Cycle of Two Garlic Cultivars.

Garlic can be infected by a number of viruses, including allexiviruses. The coat protein sequence of an Allexivirus was detected in Argentina and deposited in the EMBL database as Garlic mite-borne filamentous virus (accession number X98991); it has high homology with Garlic virus A (GarV-A). For reliable virus detection, plants should be sampled when virus titer is high to reduce the risk of identifying infected plants as healthy.

Oil quality and sugar content of peanuts (Arachis hypogaea) grown in Argentina: their relationship with climatic variables and seed yield.

The ratio of oleic to linoleic acids (O/L) and the tocopherol content are important features in determining peanut (Arachis hypogaea) seed shelf life. Soluble carbohydrates are known to be important precursors in roasted peanut flavor. The chemical qualities of Argentine grain are different from those of other countries, but no previous studies that associate grain quality and environmental parameters have been performed.