Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures

In this paper we describe a novel strategy for exploring the conformational space of proteins and show that this leads to better models for proteins the structure of which is not amenable to template based methods. Our strategy is based on the assumption that the energy global minimum of homologous proteins must correspond to similar conformations, while the precise profiles of their energy landscape, and consequently the positions of the local minima, are likely to be different. In line with this hypothesis, we apply a replica exchange Monte Carlo simulation protocol that, rather than using different parameters for each parallel simulation, uses the sequences of homologous proteins. We show that our results are competitive with respect to alternative methods, including those producing the best model for each of the analyzed targets in the CASP10 (10th Critical Assessment of techniques for protein Structure Prediction) experiment free modeling category

Evaluating The Relationship Between Short- and Long-Term Neural Adaptations to Motor Skill Acquisition and Retention

Attempting to understand the neurophysiological underpinnings of learned behaviors and the process of learning itself has yielded interesting findings relating to what happens in the brain and across the nervous system when learning a new skill. The nervous system displays several structural, functional and neurochemical adaptations to motor learning which have been highlighted through the use of neuroimaging techniques such as fMRI, EEG and TMS. This review attempts to outline the neural adaptations governing the acquisition and retention of motor skills, as well as build a timeline for these adaptations following Fitt’s model of motor learning (Fitts and Posner 1967). As one moves across the stages of learning (cognitive, associative, autonomous) the nervous system displays an initial increase in activity and plasticity in the frontal associative regions, motor cortical regions, parietal cortices, sensorimotor striatum, associative striatum, cerebral cortices and nuclei and hippocampus (Doyon et al., 2008), as well as the basal ganglia thalamocortical loops, medial cerebellum, anterior cingulate cortex, inferior frontal gyrus and the visual and parietal cortical areas (Seidler 2011). These neuro-plastic adaptations and activation patterns cement and refine themselves in later stages, indicating a more efficient circuitry and decreased cognitive load when performing the skill (Poldrack et al., 2005). In terms of practical applications of these findings, manipulation of the training principles involved in specific contexts of motor skill learning such as training specificity, duration and intensity, may yield improved neural adaptations and in turn performance on the skill in question

PepComposer: computational design of peptides binding to a given protein surface

There is a wide interest in designing peptides able to bind to a specific region of a protein with the aim of interfering with a known interaction or as starting point for the design of inhibitors. Here we describe PepComposer, a new pipeline for the computational design of peptides binding to a given protein surface. PepComposer only requires the target protein structure and an approximate definition of the binding site as input. We first retrieve a set of peptide backbone scaffolds from monomeric proteins that harbor the same backbone arrangement as the binding site of the protein of interest. Next, we design optimal sequences for the identified peptide scaffolds. The method is fully automatic and available as a web server at http://biocomputing.it/pepcomposer/webserver

Implementation of the Control System for the LHCb Muon Detector

The Muon Detector of LHCb will be equipped with 1368 Multi- Wire Proportional Chambers and 24 Triple-GEM Detectors. Within the Framework of the CERN Control System Project, using PVSS as the main tool, we are developing an instrument to manage the Muon System of LHCb. Adjustment and monitoring of High and Low Voltage power supplies, on-line diagnostics and ne tuning of the Front-End read-out devices, data acquisition from the gas system and the monitoring of pressure and temperature of the experimental hall are being implemented. The system will also look after long term data archiving and alert handling. The Control System performance is currently under evaluation in a cosmic ray station. Built as a nal quality control of the LHCb Multi-Wire Proportional Chambers, allowing acquisition of data from as many as 600 Front-End readout channels, the cosmic ray station is fully managed by means of a Control System prototype

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration

Vagus nerve stimulation: Clinical experience in drug-resistant pediatric epileptic patients

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ELMB Microcontroller Firmware and SCADA Integration for the LHCb Muon Detector Readout Control System

The LHCb system requires high efficiency muon detection into LHC bunch crossing: 95% into a 25 ns time window. To reach such efficiency many parameters of the detector readout apparatus have to be calibrated and adjusted and its channels must be aligned in time. In addition, essential characteristics must be monitored to guarantee a good working condition of the apparatus (to avoid loss of efficiency and to minimize systematic errors). As the number of the muon readout parameters is extremely high (∼700000 registers), a system able to process information in parallel is required: 122000 readout channels will be controlled by about 600 microcontrollers and 6 computers. The complexity of such an apparatus requires the use of a distributed system. For this a Supervisory Control And Data Acquisition (SCADA) based system is being developed to control the entire detector readout equipment. Moreover, a Finite State Machine (FSM) implementation is being developed to integrate the Detector Readout Control (DRC) into the LHC Experiment Control System (ECS)