The association of physical activity duration and intensity on emotional intelligence in 10-13 year-old Children.

Previous studies have shown that Physical Activity (PA) has a positive association with emotional health and intelligence in adolescents but none have focused on the relationship of PA duration and intensity on Emotional Intelligence (EI). The purpose of this study was to cross-sectionally assess the association of PA measures on overall EI and its domains in a cohort of 2 029 adolescents aged 10-13 years of age in the National Longitudinal Survey for Children and Youth (NLSCY) from Canada. Multivariable linear regression analysis of EI was adjusted for age, sex, annual household income, and health status.

: An R package for the analysis of very low frequency variants in DNA sequences.

Here, we introduce , an R package to determine the distribution of very low frequency variants (VLFs) in nucleotide and amino acid sequences for the analysis of errors in DNA sequence records. The package allows users to assess VLFs in aligned and trimmed protein-coding sequences by automatically calculating the frequency of nucleotides or amino acids in each sequence position and outputting those that occur under a user-specified frequency (default of = 0.001).

Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data.

Motivation: Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data.

Identification of five important genes to predict glioblastoma subtypes.

Background: Glioblastoma (GBM), the most common and aggressive primary brain tumour in adults, has been classified into three subtypes: classical, mesenchymal, and proneural. While the original classification relied on an 840 gene-set, further clarification on true GBM subtypes uses a 150-gene signature to accurately classify GBM into the three subtypes. We hypothesized whether a machine learning approach could be used to identify a smaller gene-set to accurately predict GBM subtype.

Trajectories of Symptom Severity in Children with Autism: Variability and Turning Points through the Transition to School.

This study examined the trajectories of autistic symptom severity in an inception cohort of 187 children with ASD assessed across four time points from diagnosis to age 10. Trajectory groups were derived using multivariate cluster analysis. A two trajectory/cluster solution was selected.

The impact of different diagnostic criteria on the association of sarcopenia with injurious falls in the CLSA.

Background: Sarcopenia definitions recommend different combinations of variables (lean mass, strength, and physical function) and different methods of adjusting lean mass. The purpose of this paper was to address the gaps in the literature regarding how differences in the operationalization of sarcopenia impact the association between sarcopenia and injurious falls.

Methods: Participants included 9936 individuals from the Canadian Longitudinal Study on Aging aged ≥65 years at baseline (2012-2015), with complete data for sarcopenia-related variables, injurious falls, and covariates.

Do Different Ascertainment Techniques Identify the Same Individuals as Sarcopenic in the Canadian Longitudinal Study on Aging?

Background/objectives: Sarcopenia is associated with poor health outcomes such as disability, institutionalization, and mortality. Efforts to manage sarcopenia clinically have been hindered by challenges in determining how to ascertain sarcopenia status correctly. The objective of this project was to assess the agreement between the different methods of ascertaining sarcopenia recommended by expert groups.

Reliability of transcranial magnetic stimulation measures of afferent inhibition.

Short-latency afferent inhibition (SAI) and long-latency afferent inhibition (LAI) are well-known transcranial magnetic stimulation (TMS) paradigms used to probe the sensorimotor system. To date, there is a paucity of research examining the reliability of these neurophysiological measures. This information is required to validate the utility of afferent inhibition as a biomarker of neural function.

A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data.

Background: High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within the data, cluster analysis provides an intuitive alternative.

Flexible High-Dimensional Unsupervised Learning with Missing Data.

The mixture of factor analyzers (MFA) model is a famous mixture model-based approach for unsupervised learning with high-dimensional data. It can be useful, inter alia, in situations where the data dimensionality far exceeds the number of observations. In recent years, the MFA model has been extended to non-Gaussian mixtures to account for clusters with heavier tail weight and/or asymmetry.

Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches.

Objective: The objective of this study was to compare the performance of several commonly used machine learning methods to traditional statistical methods for predicting emergency department and hospital utilization among patients receiving publicly-funded home care services.

Study Design And Setting: We conducted a population-based retrospective cohort study of publicly-funded home care recipients in the Hamilton-Niagara-Haldimand-Brant region of southern Ontario, Canada between 2014 and 2016. Gradient boosted trees, neural networks, and random forests were tested against two variations of logistic regression for predicting three outcomes related to emergency department and hospital utilization within six months of a comprehensive home care clinical assessment.

Muscle Androgen Receptor Content but Not Systemic Hormones Is Associated With Resistance Training-Induced Skeletal Muscle Hypertrophy in Healthy, Young Men.

The factors that underpin heterogeneity in muscle hypertrophy following resistance exercise training (RET) remain largely unknown. We examined circulating hormones, intramuscular hormones, and intramuscular hormone-related variables in resistance-trained men before and after 12 weeks of RET. Backward elimination and principal component regression evaluated the statistical significance of proposed circulating anabolic hormones (e.

Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm.

Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection.

Two-way learning with one-way supervision for gene expression data.

Background: A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues.

Parsimonious mixtures of multivariate contaminated normal distributions.

A mixture of multivariate contaminated normal distributions is developed for model-based clustering. In addition to the parameters of the classical normal mixture, our contaminated mixture has, for each cluster, a parameter controlling the proportion of mild outliers and one specifying the degree of contamination. Crucially, these parameters do not have to be specified a priori, adding a flexibility to our approach.

Mixtures of multivariate power exponential distributions.

An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness have received much attention in the model-based clustering literature recently, we investigate the use of a distribution that can deal with both varying tail-weight and peakedness of data. A family of parsimonious models is proposed using an eigen-decomposition of the scale matrix.

Metabolic and co-expression network-based analyses associated with nitrate response in rice.

Background: Understanding gene expression and metabolic re-programming that occur in response to limiting nitrogen (N) conditions in crop plants is crucial for the ongoing progress towards the development of varieties with improved nitrogen use efficiency (NUE). To unravel new details on the molecular and metabolic responses to N availability in a major food crop, we conducted analyses on a weighted gene co-expression network and metabolic profile data obtained from leaves and roots of rice plants adapted to sufficient and limiting N as well as after shifting them to limiting (reduction) and sufficient (induction) N conditions.

Results: A gene co-expression network representing clusters of rice genes with similar expression patterns across four nitrogen conditions and two tissue types was generated.

Nitrogen limitation and high density responses in rice suggest a role for ethylene under high density stress.

Background: High density stress, also known as intraspecies competition, causes significant yield losses in a wide variety of crop plants. At the same time, increases in density tolerance through selective breeding and the concomitant ability to plant crops at a higher population density has been one of the most important factors in the development of high yielding modern cultivars.

Results: Physiological changes underlying high density stress were examined in Oryza sativa plants over the course of a life cycle by assessing differences in gene expression and metabolism.

Mixtures of Shifted AsymmetricLaplace Distributions.

A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the generalized inverse Gaussian distribution. This approach is mathematically elegant and relatively computationally straightforward.

Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas.

Background: The discovery of genetic networks and cis-acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome (Zea mays L.) has facilitated in silico searches for regulatory motifs.

Genome-wide expression profiling of maize in response to individual and combined water and nitrogen stresses.

Background: Water and nitrogen are two of the most critical inputs required to achieve the high yield potential of modern corn varieties. Under most agricultural settings however they are often scarce and costly. Fortunately, tremendous progress has been made in the past decades in terms of modeling to assist growers in the decision making process and many tools are now available to achieve more sustainable practices both environmentally and economically.

Caloric restriction induces changes in insulin and body weight measurements that are inversely associated with subsequent weight regain.

Background: Successful weight maintenance following weight loss is challenging for many people. Identifying predictors of longer-term success will help target clinical resources more effectively. To date, focus has been predominantly on the identification of predictors of weight loss.

Detection of HIV-1 CXCR4 tropism and resistance in treatment experienced subjects receiving CCR5 antagonist-Vicriviroc.

Background: Vicriviroc (VCV), a small-molecule antagonist of the C-C chemokine receptor 5 (CCR5), blocks HIV's entry into CD4+ cells. Small studies have suggested that resistance to CCR5 antagonists is slow to develop.

Objectives: To examine resistance to VCV in isolates from treatment experienced patients who experienced virologic failure in two phase 3 trials.

The optimization of pyridazinone series of glucan synthase inhibitors.

A detailed structure-activity relationship study of a novel series of pyridazine-based small molecule glucan synthase inhibitors is described. The optimization of the PK profile of this series led to the discovery of compound 11g, which demonstrated in vivo potency ip in a lethal fungal infection model.