Transport lattice models of heat transport in skin with spatially heterogeneous,temperature-dependent perfusion

Background  

Investigation of bioheat transfer problems requires the evaluation of temporal and spatial distributions of temperature. This class of problems has been traditionally addressed using the Pennes bioheat equation. Transport of heat by conduction, and by temperature-dependent, spatially heterogeneous blood perfusion is modeled here using a transport lattice approach.     

Altered sense of Agency in children with spastic cerebral palsy

Background  

Children diagnosed with spastic Cerebral Palsy (CP) often show perceptual and cognitive problems, which may contribute to their functional deficit. Here we investigated if altered ability to determine whether an observed movement is performed by themselves (sense of agency) contributes to the motor deficit in children with CP.     

A discriminative method for protein remote homology detection and fold recognition combining Top-<Emphasis Type="Italic">n</Emphasis>-grams and latent semantic analysis

Background  

Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.     

Reducing bias through process inventory dataset normalization

Purpose  

This paper explores a computational method to resolve some of the problems of external normalization in the life cycle impact assessment (LCIA) process of midpoint characterized impacts. Problems inherent to external normalization (per capita per year for a defined region) that reduce the ability to accurately calculate the most significant impact categories include

Constraint Logic Programming approach to protein structure prediction

Background  

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems.     

Tooth loss and its relationship with protein intake by elderly Brazilians—A structural equation modelling approach

Objective

This study aimed at assessing the relationship between self‐perceived tooth loss and wearing dentures, on the one hand, and the consumption of protein, on the other hand, among the elderly population of Botucatu, SP. Food consumption tends to decrease with ageing, especially protein intake, and one of the causes could be the precariousness of oral health. Several risk factors associated with deficient dietary protein intake have been identified, namely greater physical dependence, reduced caloric intake and food insecurity, but no studies have analysed whether tooth loss and prostheses interfere with protein intake.

Methods

An interview was conducted among 365 elderly individuals, in which we examined oral health‐related quality of life (OHRQoL) as the only latent variable, in a 24‐hour nutritional assessment dietary recall repeated 3 times, conducted in person by a trained nutritionist and also performed an analysis of nutritional needs using the Nutrition Data System Research (NDSR) Program.

Results

The structural equation model, performed using Stata v.14, showed that lack of teeth (standardised coefficient [SC] = 0.21, P < .001), and prosthesis use (SC = ?0.21, P < .001) was associated with OHRQoL. Lack of teeth had a direct effect on the consumption of animal protein (SC = 0.08, P = .02), a strong total effect on animal protein intake (SC = 0.51, P = .04) and a medium effect on total protein intake (SC = 0.20, P = .03), adjusted for confounders (depression and medical problems).

Conclusion

Tooth loss had a strong and significant total effect on animal protein intake and a medium effect on total protein intake among elderly Brazilians.     

Production of <Emphasis Type="Italic">in vitro</Emphasis> amplified DNA pseudolibraries and high-throughput cDNA target amplification

Background  

Many structural biology- and high-throughput laboratories experience the acquisition of multiple cDNAs from different sources as a rather time- and resource-consuming procedure. The techniques presented here solve these problems.     

Prediction of protein-protein interaction sites using an ensemble method

Background  

Prediction of protein-protein interaction sites is one of the most challenging and intriguing problems in the field of computational biology. Although much progress has been achieved by using various machine learning methods and a variety of available features, the problem is still far from being solved.     

Effects of dependence in high-dimensional multiple testing problems

Background  

We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices.     

Building multiclass classifiers for remote homology detection and fold recognition

Background  

Protein remote homology detection and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for solving these problems. These methods are primarily used to solve binary classification problems and they have not been extensively used to solve the more general multiclass remote homology prediction and fold recognition problems.     

An evolutionary method for learning HMM structure: prediction of protein secondary structure

Background  

The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs).     

Revealing divergent evolution, identifying circular permutations and detecting active-sites by protein structure comparison

Background  

Protein structure comparison is one of the most important problems in computational biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and protein docking.     

A genetic approach for building different alphabets for peptide and protein classification

Background  

In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems.     

MolTalk – a programming library for protein structures and structure analysis

Background  

Two of the mostly unsolved but increasingly urgent problems for modern biologists are a) to quickly and easily analyse protein structures and b) to comprehensively mine the wealth of information, which is distributed along with the 3D co-ordinates by the Protein Data Bank (PDB). Tools which address this issue need to be highly flexible and powerful but at the same time must be freely available and easy to learn.     

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Background  

Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data.     

Subgroups of older adults with osteoarthritis based upon differing comorbid symptom presentations and potential underlying pain mechanisms

Introduction  

Although people with knee and hip osteoarthritis (OA) seek treatment because of pain, many of these individuals have commonly co-occurring symptoms (for example, fatigue, sleep problems, mood disorders). The purpose of this study was to characterize adults with OA by identifying subgroups with the above comorbid symptoms along with illness burden (a composite measure of somatic symptoms) to begin to examine whether subsets may have differing underlying pain mechanisms.     

Genotyping and inflated type I error rate in genome-wide association case/control studies

Background  

One common goal of a case/control genome wide association study (GWAS) is to find SNPs associated with a disease. Traditionally, the first step in such studies is to assign a genotype to each SNP in each subject, based on a statistic summarizing fluorescence measurements. When the distributions of the summary statistics are not well separated by genotype, the act of genotype assignment can lead to more potential problems than acknowledged by the literature.     

Functional complementation of RNA interference mutants in trypanosomes

Background  

In many eukaryotic cells, double-stranded RNA (dsRNA) triggers RNA interference (RNAi), the specific degradation of RNA of homologous sequence. RNAi is now a major tool for reverse-genetics projects, including large-scale high-throughput screens. Recent reports have questioned the specificity of RNAi, raising problems in interpretation of RNAi-based experiments.