How simple can a model of an empty viral capsid be? Charge distributions in viral capsids

We investigate and quantify salient features of the charge distributions on viral capsids. Our analysis combines the experimentally determined capsid geometry with simple models for ionization of amino acids, thus yielding the detailed description of spatial distribution for positive and negative charge across the capsid wall. The obtained data is processed in order to extract the mean radii of distributions, surface charge densities and dipole moment densities. The results are evaluated and examined in light of previously proposed models of capsid charge distributions, which are shown to have to some extent limited value when applied to real viruses.Comment: 10 pages, 10 figures; accepted for publication in Journal of Biological Physic

Calibrative approaches to protein solubility modeling of a mutant series using physicochemical descriptors

A set of physicochemical properties describing a protein of known structure is employed for a calibrative approach to protein solubility. Common hydrodynamic and electrophoretic properties routinely measured in the bio-analytical laboratory such as zeta potential, dipole moment, the second osmotic virial coefficient are first estimated in silico as a function a pH and solution ionic strength starting with the protein crystal structure. The utility of these descriptors in understanding the solubility of a series of ribonuclease Sa mutants is investigated. A simple two parameter model was trained using solubility data of the wild type protein measured at a restricted number of solution pHs. Solubility estimates of the mutants demonstrate that zeta potential and dipole moment may be used to rationalize solubility trends over a wide pH range. Additionally a calibrative model based on the protein’s second osmotic virial coefficient, B22 was developed. A modified DVLO type potential along with a simplified representation of the protein allowed for efficient computation of the second viral coefficient. The standard error of prediction for both models was on the order of 0.3 log S units. These results are very encouraging and demonstrate that these models may be trained with a small number of samples and employed extrapolatively for estimating mutant solubilities

Income in Adult Survivors of Childhood Cancer.

INTRODUCTION: Little is known about the impact of childhood cancer on the personal income of survivors. We compared income between survivors and siblings, and determined factors associated with income. METHODS: As part of the Swiss Childhood Cancer Survivor Study (SCCSS), a questionnaire was sent to survivors, aged ≥18 years, registered in the Swiss Childhood Cancer Registry (SCCR), diagnosed at age 4'500 CHF), even after we adjusted for socio-demographic and educational factors (OR = 0.46, p<0.001). Older age, male sex, personal and parental education, and number of working hours were associated with high income. Survivors of leukemia (OR = 0.40, p<0.001), lymphoma (OR = 0.63, p = 0.040), CNS tumors (OR = 0.22, p<0.001), bone tumors (OR = 0.24, p = 0.003) had a lower income than siblings. Survivors who had cranial irradiation, had a lower income than survivors who had no cranial irradiation (OR = 0.48, p = 0.006). DISCUSSION: Even after adjusting for socio-demographic characteristics, education and working hours, survivors of various diagnostic groups have lower incomes than siblings. Further research needs to identify the underlying causes

Parental stress before, during, and after pediatric stem cell transplantation: a review article

Goals of work: Pediatric stem cell transplantation (SCT) is a stressful treatment for children with relapsed or high-risk malignancies, immune deficiencies and certain blood diseases. Parents of children undergoing SCT can experience ongoing stress related to the SCT period. The aim of this article was to present a literature review of articles on parental distress and adaptation before, during, and after SCT and to identify risk and protective factors. Materials and methods: The review was conducted systematically by using PubMed, Web of Science, PsychInfo, and Picarta databases. Eighteen articles met our inclusion criteria: publishing date between January 1, 1990 and January 1, 2009; studies concerning parents of children undergoing SCT; studies examining the psychological adjustment and/or stress reactions of parents as primary outcomes and studies available in English. Main results: Highest levels of parental stress are reported in the period preceding SCT and during the acute phase. Stress levels decrease steadily after discharge in most parents. However, in a subgroup of parents, stress levels still remain elevated post-SCT. Parents most at risk in the longer term display highest levels of stress during the acute phase of the SCT. Conclusions: Psychosocial assessment before SCT, during the acute phase and in the longer term, is necessary to identify parents in need for support and follow-up care

Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

Evaluating the quality of the ontology-based auto-generated questions

An ontology is a knowledge representation structure which has been used in Virtual Learning Environments (VLEs) to describe educational courses by capturing the concepts and the relationships between them. Several ontology-based question generators used ontologies to auto-generate questions, which aimed to assess students' at different levels in Bloom's taxonomy. However, the evaluation of the questions was confined to measuring the qualitative satisfaction of domain experts and students. None of the question generators tested the questions on students and analysed the quality of the auto-generated questions by examining the question's difficulty, and the question's ability to discriminate between high ability and low ability students. The lack of quantitative analysis resulted in having no evidence on the quality of questions, and how the quality is a�affected by the ontology-based generation strategies, and the level of question in Bloom's taxonomy (determined by the question's stem templates). This paper presents an experiment carried out to address the drawbacks mentioned above by achieving two objectives. First, it assesses the auto-generated questions' difficulty, discrimination, and reliability using two statistical methods: Classical Test Theory (CTT) and Item Response Theory (IRT). Second, it studies the effect of the ontology-based generation strategies and the level of the questions in Bloom's taxonomy on the quality of the questions. This will provide guidance for developers and researchers working in the field of ontology-based question generators, and help building a prediction model using machine learning techniques

The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery

The International Human Epigenome Consortium (IHEC) coordinates the generation of a catalog of high-resolution reference epigenomes of major primary human cell types. The studies now presented (see the Cell Press IHEC web portal at http://www.cell.com/consortium/IHEC) highlight the coordinated achievements of IHEC teams to gather and interpret comprehensive epigenomic datasets to gain insights in the epigenetic control of cell states relevant for human health and disease

Mapping autism risk loci using genetic linkage and chromosomal rearrangements.

International audienceAutism spectrum disorders (ASDs) are common, heritable neurodevelopmental conditions. The genetic architecture of ASDs is complex, requiring large samples to overcome heterogeneity. Here we broaden coverage and sample size relative to other studies of ASDs by using Affymetrix 10K SNP arrays and 1,181 [corrected] families with at least two affected individuals, performing the largest linkage scan to date while also analyzing copy number variation in these families. Linkage and copy number variation analyses implicate chromosome 11p12-p13 and neurexins, respectively, among other candidate loci. Neurexins team with previously implicated neuroligins for glutamatergic synaptogenesis, highlighting glutamate-related genes as promising candidates for contributing to ASDs

The effects of Δ9-tetrahydrocannabinol on the dopamine system

Δ(9)-tetrahydrocannabinol (THC), the main psychoactive ingredient in cannabis, is a pressing concern to global mental health. Patterns of use are changing drastically due to legalisation, availability of synthetic analogues (‘spice’), cannavaping and aggrandizements in the purported therapeutic effects of cannabis. Many of THC’s reinforcing effects are mediated by the dopamine system. Due to complex cannabinoid-dopamine interactions there is conflicting evidence from human and animal research fields. Acute THC causes increased dopamine release and neuron activity, whilst long-term use is associated with blunting of the dopamine system. Future research must examine the long-term and developmental dopaminergic effects of the drug