456 research outputs found

    Stochastic Evolution with Slow Learning..

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    This paper studies the extent to which diffusion approximations provide a reliable guide to equilibrium selection results in finite games. It is shown that they do for a class of finite games with weak learning provided that limits are taken in a certain order. The paper also shows that making mutation rates small does not in general select a unique equilibrium but making selection strong does.

    METHYL PARATHION RESIDUES IN PROTECTIVE APPAREL FABRIC: EFFECT OF RESIDUAL SOILS ON DECONTAMINATION

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    This study evaluated the contribution of oily and particulate soil residue to pesticide residue removal. 100% cotton and 65% polyester/35% cotton, were artificially soiled with a standard soil. The fabrics were laundered with the same substrate fabric without soil. Initial methyl parathion contamination was not dependent on the soil level or fiber content of the fabric. Residues remaining after laundering were affected by soiling level. Pesticide residues were greater when the fabric had a heavy soil build-up even though the initial contamination had been lower. Based on these findings, protective apparel should be kept as clean as possible, with daily laundering, for the presence of soil residue affected decontamination of the fabrics

    Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

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    Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons

    An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

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    This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al.[Background]: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. [Results]: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. [Conclusions]: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.This work was supported by funds provided through the Gene Partnership and the Manton Center for Orphan Disease Research at Boston Children’s Hospital and the Center for Biomedical Informatics at Harvard Medical School and by generous donations in-kind of genomic sequencing services by Life Technologies (Carlsbad, CA, USA) and Complete Genomics (Mountain View, CA, USA).Peer Reviewe
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