Modeling the functional genomics of autism using human neurons.

Human neural progenitors from a variety of sources present new opportunities to model aspects of human neuropsychiatric disease in vitro. Such in vitro models provide the advantages of a human genetic background combined with rapid and easy manipulation, making them highly useful adjuncts to animal models. Here, we examined whether a human neuronal culture system could be utilized to assess the transcriptional program involved in human neural differentiation and to model some of the molecular features of a neurodevelopmental disorder, such as autism. Primary normal human neuronal progenitors (NHNPs) were differentiated into a post-mitotic neuronal state through addition of specific growth factors and whole-genome gene expression was examined throughout a time course of neuronal differentiation. After 4 weeks of differentiation, a significant number of genes associated with autism spectrum disorders (ASDs) are either induced or repressed. This includes the ASD susceptibility gene neurexin 1, which showed a distinct pattern from neurexin 3 in vitro, and which we validated in vivo in fetal human brain. Using weighted gene co-expression network analysis, we visualized the network structure of transcriptional regulation, demonstrating via this unbiased analysis that a significant number of ASD candidate genes are coordinately regulated during the differentiation process. As NHNPs are genetically tractable and manipulable, they can be used to study both the effects of mutations in multiple ASD candidate genes on neuronal differentiation and gene expression in combination with the effects of potential therapeutic molecules. These data also provide a step towards better understanding of the signaling pathways disrupted in ASD

Designing metal hemispheres on silicon ultrathin film solar cells for plasmonic light trapping

We systematically investigate the design of two-dimensional silver (Ag) hemisphere arrays on crystalline silicon (c-Si) ultrathin film solar cells for plasmonic light trapping. The absorption in ultrathin films is governed by the excitation of Fabry-Perot TEMm modes.We demonstrate that metal hemispheres can enhance absorption in the films by (1) coupling light to c-Si film waveguide modes and (2) exciting localized surface plasmon resonances (LSPRs). We show that hemisphere arrays allow light to couple to fundamental TEm and TMm waveguide modes in c-Si film as well as higher-order versions of these modes. The near-field light concentration of LSPRs also may increase absorption in the c-Si film, though these resonances are associated with significant parasitic absorption in the metal. We illustrate how Ag plasmonic hemispheres may be utilized for light trapping with 22% enhancement in short-circuit current density compared with that of a bare 100 nm thick c-Si ultrathin film solar cell. © 2014 Optical Society of America

Machine Learning in Astronomy: A Case Study in Quasar-Star Classification

We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We provide a careful scrutiny of approaches available in the literature and have highlighted the pitfalls in those approaches based on the nature of data used for the study. The aim is to investigate the appropriateness of the application of certain ML methods. The manuscript argues convincingly in favor of the efficacy of asymmetric AdaBoost to classify photometric data. The paper presents a critical review of existing study and puts forward an application of asymmetric AdaBoost, as an offspring of that exercise.Comment: 10 pages, 8 figure

On Identifying the Optimal Number of Population Clusters via the Deviance Information Criterion

Inferring population structure using Bayesian clustering programs often requires a priori specification of the number of subpopulations, , from which the sample has been drawn. Here, we explore the utility of a common Bayesian model selection criterion, the Deviance Information Criterion (DIC), for estimating . We evaluate the accuracy of DIC, as well as other popular approaches, on datasets generated by coalescent simulations under various demographic scenarios. We find that DIC outperforms competing methods in many genetic contexts, validating its application in assessing population structure

Detection violent behaviors: A survey

Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n ∘ 039334; Funding Reference: POCI-01-0247-FEDER-039334]. This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/202

Shannon and Renyi Entropies to Classify Effects of Mild Traumatic Brain Injury on Postural Sway

Background: Mild Traumatic Brain Injury (mTBI) has been identified as a major public and military health concern both in the United States and worldwide. Characterizing the effects of mTBI on postural sway could be an important tool for assessing recovery from the injury. Methodology/Principal Findings: We assess postural sway by motion of the center of pressure (COP). Methods for data reduction include calculation of area of COP and fractal analysis of COP motion time courses. We found that fractal scaling appears applicable to sway power above about 0.5 Hz, thus fractal characterization is only quantifying the secondary effects (a small fraction of total power) in the sway time series, and is not effective in quantifying long-term effects of mTBI on postural sway. We also found that the area of COP sensitively depends on the length of data series over which the COP is obtained. These weaknesses motivated us to use instead Shannon and Renyi entropies to assess postural instability following mTBI. These entropy measures have a number of appealing properties, including capacity for determination of the optimal length of the time series for analysis and a new interpretation of the area of COP. Conclusions: Entropy analysis can readily detect postural instability in athletes at least 10 days post-concussion so that it appears promising as a sensitive measure of effects of mTBI on postural sway

The s ---> d gamma decay in and beyond the Standard Model

The New Physics sensitivity of the s ---> d gamma transition and its accessibility through hadronic processes are thoroughly investigated. Firstly, the Standard Model predictions for the direct CP-violating observables in radiative K decays are systematically improved. Besides, the magnetic contribution to epsilon prime is estimated and found subleading, even in the presence of New Physics, and a new strategy to resolve its electroweak versus QCD penguin fraction is identified. Secondly, the signatures of a series of New Physics scenarios, characterized as model-independently as possible in terms of their underlying dynamics, are investigated by combining the information from all the FCNC transitions in the s ---> d sector.Comment: 54 pages, 14 eps figure

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

Physiological Roles of ArcA, Crp, and EtrA and Their Interactive Control on Aerobic and Anaerobic Respiration in Shewanella oneidensis

In the genome of Shewanella oneidensis, genes encoding the global regulators ArcA, Crp, and EtrA have been identified. All these proteins deviate from their counterparts in E. coli significantly in terms of functionality and regulon. It is worth investigating the involvement and relationship of these global regulators in aerobic and anaerobic respiration in S. oneidensis. In this study, the impact of the transcriptional factors ArcA, Crp, and EtrA on aerobic and anaerobic respiration in S. oneidensis were assessed. While all these proteins appeared to be functional in vivo, the importance of individual proteins in these two major biological processes differed. The ArcA transcriptional factor was critical in aerobic respiration while the Crp protein was indispensible in anaerobic respiration. Using a newly developed reporter system, it was found that expression of arcA and etrA was not influenced by growth conditions but transcription of crp was induced by removal of oxygen. An analysis of the impact of each protein on transcription of the others revealed that Crp expression was independent of the other factors whereas ArcA repressed both etrA and its own transcription while EtrA also repressed arcA transcription. Transcriptional levels of arcA in the wild type, crp, and etrA strains under either aerobic or anaerobic conditions were further validated by quantitative immunoblotting with a polyclonal antibody against ArcA. This extensive survey demonstrated that all these three global regulators are functional in S. oneidensis. In addition, the reporter system constructed in this study will facilitate in vivo transcriptional analysis of targeted promoters