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Order-Restricted 贝叶斯 (Bayesian) Ordinal 回归 (Regression) for the 模型 (Model)ing of Neuron Degeneration in Caenorhabditis elegans
Order-Restricted Bayesian Ordinal Regression for the Modeling of Neuron Degeneration in Caenorhabditis elegans

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Neuron degeneration is the underlying mechanism for the development of many diseases. Quantifying the association between increasing levels of toxic exposure and progressive neuronal damage is a critical component of understanding this development. We investigate this association by analyzing a novel dataset of ordinal neuronal damage scores derived from a series of toxicological assays of C. elegans, including variables such as toxicant concentration, maternal treatment, and direct chemical exposure. We propose a computationally efficient parameter-constrained Bayesian ordinal regression that captures the monotonic association between neuron damage scores and corresponding treatments. Power analysis via simulation studies reinforces the advantages of our model over standard alternatives used in existing work by practitioners. Analysis of the novel C. elegans assays indicates that maternal toxicity increases susceptibility in progeny, with the offspring generation exhibiting amplified neuronal damage upon later-life rotenone exposure even under mild parental developmental treatment.

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