17 research outputs found

    Federated Learning on Heterogenous Data using Chest CT

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    Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

    Evolving Open Access: The PLoS Story

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    国际知名开放获取期刊PLoS ONE主编Kristen Fisher Ratan介绍了以PLoS ONE为代表的巨型开放期刊的运营模式和论文的影响力评价模式。</span

    Letter from Guest Content Editor

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    A New Research Economy: Socio-technical framework to open up lines of credit in the academic community

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    Journal articles have been the gold standard for research and scholarly communication. Specifically, measurements of publication and citation, particularly in high-impact journals, have long been the key means of accruing credit for researchers. In turn, these credits become the currency through which researchers acquire funding and achieve professional success. But, like global trade, tying in to a fixed standard limits wealth distribution and innovation.  It is time for the research community to attribute credit for contributions that reflect and drive collaborative innovation, rewarding behaviors that produce better research outcomes

    N-acetylcysteine Targets 5 Lipoxygenase-Derived, Toxic Lipids and Can Synergize with PGE2 to Inhibit Ferroptosis and Improve Outcomes Following Hemorrhagic Stroke in Mice

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    OBJECTIVES: N-acetylcysteine (NAC) is a clinically approved thiol-containing redox modulatory compound currently in trials for many neurological and psychiatric disorders. Although generically labeled as an antioxidant , poor understanding of its site(s) of action is a barrier to its use in neurological practice. Here, we examined the efficacy and mechanism of action of NAC in rodent models of hemorrhagic stroke. METHODS: Hemin was used to model ferroptosis and hemorrhagic stroke in cultured neurons. Striatal infusion of collagenase was used to model intracerebral hemorrhage (ICH) in mice and rats. Chemical biology, targeted lipidomics, 5-lipoxygenase (ALOX5) knockout mice and viral-gene transfer were used to gain insight into the pharmacological targets and mechanism of action of NAC. RESULTS: NAC prevented hemin-induced ferroptosis by neutralizing toxic lipids generated via arachidonate dependent ALOX5 activity. NAC efficacy required increases in glutathione and is correlated with suppression of reactive lipids by glutathione dependent enzymes such as glutathione-S-transferase. Accordingly, its protective effects were mimicked by chemical or molecular lipid peroxidation inhibitors. NAC delivered post-injury reduced neuronal death and improved functional recovery at least 7 days following ICH in mice, and can synergize with clinically-approved prostaglandin E2 (PGE2 ). INTERPRETATION: NAC is a promising, protective therapy for ICH, which acted to inhibit toxic arachidonic acid (AA) products of nuclear ALOX5 that synergized with exogenously delivered protective PGE2 in vitro and in vivo. The findings provide novel insight into a target for NAC, beyond the generic characterization as an antioxidant, resulting in neuroprotection and offer a feasible combinatorial strategy to optimize efficacy and safety in dosing of NAC for treatment of neurological disorders involving ferroptosis such as ICH. This article is protected by copyright. All rights reserved

    FAIR Resources and Training for Researchers - TAG C

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    TBD: Implementing guidance/training for researchers on repositories and FAI
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