Bayesian Life Test Planning for the Log-Location-Scale Family of Distributions

This paper describes Bayesian methods for life test planning with censored data from a log-location-scale distribution, when prior information of the distribution parameters is available. We use a Bayesian criterion based on the estimation precision of a distribution quantile. A large sample normal approximation gives a simplified, easy-tointerpret, yet valid approach to this planning problem, where in general no closed form solutions are available. To illustrate this approach, we present numerical investigations using the Weibull distribution with Type II censoring. We also assess the effects of prior distribution choice. A simulation approach of the same Bayesian problem is also presented as a tool for visualization and validation. The validation results generally are consistent with those from the large sample approximation approach

Modeling autosomal dominant Alzheimer's disease with machine learning

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2  = 0.95), fluorodeoxyglucose (R2  = 0.93), and atrophy (R2  = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions

Comparative phylogeography of reef fishes from the Gulf of Aden to the Arabian Sea reveals two cryptic lineages

Arabian Sea is a heterogeneous region with high coral cover and warm stable conditions at the western end (Djibouti), in contrast to sparse coral cover, cooler temperatures, and upwelling at the eastern end (southern Oman). We tested for barriers to dispersal across this region (including the Gulf of Aden and Gulf of Oman), using mitochondrial DNA surveys of 11 reef fishes. Study species included seven taxa from six families with broad distributions across the Indo-Pacific and four species restricted to the Arabian Sea (and adjacent areas). Nine species showed no significant genetic partitions, indicating connectivity among contrasting environments spread across 2000 km. One butterflyfish (Chaetodon melannotus) and a snapper (Lutjanus kasmira) showed phylogenetic divergences of d = 0.008 and 0.048, respectively, possibly indicating cryptic species within these broadly distributed taxa. These genetic partitions at the western periphery of the Indo-Pacific reflect similar partitions recently discovered at the eastern periphery of the Indo-Pacific (the Hawaiian and the Marquesan Archipelagos), indicating that these disjunctive habitats at the ends of the range may serve as evolutionary incubators for coral reef organisms. © 2017 Springer-Verlag Berlin HeidelbergTh

Final report for theoretical and experimental investigation of large-signal traveling-wave tubes

http://deepblue.lib.umich.edu/bitstream/2027.42/6048/5/bac6312.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/6048/4/bac6312.0001.001.tx

Accelerated Degradation Tests: Modeling and Analysis

High reliability systems generally require individual system components having extremely high reliability over long periods of time. Short product development times require reliability tests to be conducted with severe time constraints. Frequently few or no failures occur during such tests, even with acceleration. Thus, it is difficult to assess reliability with traditional life tests that record only failure times. For some components, degradation measures can be taken over time. A relationship between component failure and amount of degradation makes it possible to use degradation models and data to make inferences and predictions about a failure-time distribution. This paper describes degradation reliability models that correspond to physical-failure mechanisms. We explain the connection between degradation reliability models and failuretime reliability models. Acceleration is modeled by having an acceleration model that describes the effect that temperature (or another acceleratin..