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Abstract Hydrogen sulfide (H2S) is a noxious, potentially poisonous, but necessary gas produced from sulfur metabolism in humans. In Down Syndrome (DS), the production of H2S is elevated and associated with degraded mitochondrial function. Therefore, removing H2S from the body as a stable oxide could be an approach to reducing the deleterious effects of H2S in DS. In this report we describe the catalytic oxidation of hydrogen sulfide (H2S) to polysulfides (HS2+n−) and thiosulfate (S2O32−) by poly(ethylene glycol) hydrophilic carbon clusters (PEG‐HCCs) and poly(ethylene glycol) oxidized activated charcoal (PEG‐OACs), examples of oxidized carbon nanozymes (OCNs). We show that OCNs oxidize H2S to polysulfides and S2O32−in a dose‐dependent manner. The reaction is dependent on O2and the presence of quinone groups on the OCNs. In DS donor lymphocytes we found that OCNs increased polysulfide production, proliferation, and afforded protection against additional toxic levels of H2S compared to untreated DS lymphocytes. Finally, in Dp16 and Ts65DN murine models of DS, we found that OCNs restored osteoclast differentiation. This new action suggests potential facile translation into the clinic for conditions involving excess H2S exemplified by DS.
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A Gibbs Sampler for a Class of Random Convex Polytopes
We present a Gibbs sampler for the Dempster–Shafer (DS) approach to statistical inference for categorical distributions. The DS framework extends the Bayesian approach, allows in particular the use of partial prior information, and yields three-valued uncertainty assessments representing probabilities “for,” “against,” and “don’t know” about formal assertions of interest. The proposed algorithm targets the distribution of a class of random convex polytopes which encapsulate the DS inference. The sampler relies on an equivalence between the iterative constraints of the vertex configuration and the nonnegativity of cycles in a fully connected directed graph. Illustrations include the testing of independence in 2 × 2 contingency tables and parameter estimation of the linkage model.
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- PAR ID:
- 10225161
- Date Published:
- Journal Name:
- Journal of the American Statistical Association
- ISSN:
- 0162-1459
- Page Range / eLocation ID:
- 1 to 12
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/01621459.2021.1881523
