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I1 Introduction to the 2015 Brainhack Proceedings
R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller
A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface
AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro
A2 Advancing open science through...
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Objective: To investigate effects of early HIV infection and combination antiretroviral therapy (cART) on intrinsic brain activity by using amplitude of low-frequency fluctuation (ALFF) analysis.
Patients and methods: Forty-nine HIV patients, including 26 with cART (HIV+/cART+) and 23 treatment-naïve (HIV+/cART–), and 25 matched healthy controls (H...
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... The results of this study suggest that for many antibiotics, it may be possible to build diagnostically valid classifiers for the prediction of antimicrobial resistance directly from the genome sequence data. Paired with new rapid sequencing technologies, these classifiers could provide clinically actionable data (63). We also attempted to build classifiers for the prediction of other clinical phenotypes. ...
Klebsiella pneumoniae is a major human pathogen responsible for high morbidity and mortality rates. The emergence and spread of strains resistant to multiple antimicrobial agents and documented large nosocomial outbreaks are especially concerning. To develop new therapeutic strategies for K. pneumoniae, it is imperative to understand the population genomic structure of strains causing human infections. To address this knowledge gap, we sequenced the genomes of 1,777 extended-spectrum beta-lactamase-producing K. pneumoniae strains cultured from patients in the 2,000-bed Houston Methodist Hospital system between September 2011 and May 2015, representing a comprehensive, population-based strain sample. Strains of largely uncharacterized clonal group 307 (CG307) caused more infections than those of well-studied epidemic CG258. Strains varied markedly in gene content and had an extensive array of small and very large plasmids, often containing antimicrobial resistance genes. Some patients with multiple strains cultured over time were infected with genetically distinct clones. We identified 15 strains expressing the New Delhi metallo-beta-lactamase 1 (NDM-1) enzyme that confers broad resistance to nearly all beta-lactam antibiotics. Transcriptome sequencing analysis of 10 phylogenetically diverse strains showed that the global transcriptome of each strain was unique and highly variable. Experimental mouse infection provided new information about immunological parameters of host-pathogen interaction. We exploited the large data set to develop whole-genome sequence-based classifiers that accurately predict clinical antimicrobial resistance for 12 of the 16 antibiotics tested. We conclude that analysis of large, comprehensive, population-based strain samples can assist understanding of the molecular diversity of these organisms and contribute to enhanced translational research.
The hackathons related systematic literature review findings, from the past 15 years, show that academics mostly focus on giving overview, describing and explaining the events. It also seems to be quite popular to present an overview of event arrangements. The research, which appears to be missing, is the new style of hackathons, which were a requirement for online hackathons and remote events because of the social distancing, e.g., what covid-19 did demand from global society. By core design, hackathons have always been social gatherings and innovation events. Especially in these exceptional times with social distancing requirements, hackathon organizers turned towards online models. As this is not the original way these events have been organized, it was hard to get the best practices to how to proceed as there are few to none general guidelines and models for online/remote hackathons. By studying the current academic literature, our systematic literature review concludes that contemporary academic literature does not give too much guidance either. This review concludes that more practical, on-site, and educational problem-based development research studies about online and remote hackathon events are needed. Especially research on means and tools to utilize to organize.
In this paper, a random-forest-based method was proposed for the classification and localization of Attention-Deficit/Hyperactivity Disorder (ADHD), a common neurodevelopmental disorder among children. Experimental data were magnetic resonance imaging (MRI) from the public case-control dataset of 3D images for ADHD-200. Each MRI image was a 3D-tensor of 121×145×121 size. All 3D matrices (MRI) were segmented into the slices from each of three orthogonal directions. Each slice from the same position of the same direction in the training set was converted into a vector, and all these vectors were composed into a designed matrix to train the random forest classification algorithm; then, the well-trained RF classifier was exploited to give a prediction label in correspondence direction and position. Diagnosis and location results can be obtained upon the intersection of these three prediction matrices. The performance of our proposed method was illustrated on the dataset from New York University (NYU), Kennedy Krieger Institute (KKI) and full datasets; the results show that the proposed methods can archive more accuracy identification in discrimination of ADHD, and can be extended to the other practices of diagnosis. Moreover, another suspected region was found at the first time.
We present Boutiques, a system to automatically publish, integrate and execute command-line applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitate the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.
