A unifying representation for a class of dependent random measures

We present a general construction for dependent random measures based on thinning Poisson processes on an augmented space. The framework is not restricted to dependent versions of a specific nonparametric model, but can be applied to all models that can be represented using completely random measures. Several existing dependent random measures can be seen as specific cases of this framework. Interesting properties of the resulting measures are derived and the efficacy of the framework is demonstrated by constructing a covariate-dependent latent feature model and topic model that obtain superior predictive performance

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

The role of complement activity in the sensitivity of Salmonella O48 strains with sialic acid-containing lipopolysaccharides to the bactericidal action of normal bovine serum

Sialic acids are important constituents of animal tissue glycoconjugates and are also present in the antigens of some bacterial strains. Capsular polysaccharides with sialic acid (NeuAc) have been extensively studied with regard to sensitivity to the bactericidal action of serum, whereas little is known in this regard about lipopolysaccharides (LPS) which contain NeuAc. Strains of Salmonella 048, able to infect animals and containing the same structures of LPS with NeuAc, were examined for their susceptibility to the bactericidal action of normal bovine serum (NBS). The strains showed varied sensitivity to the bactericidal action of NBS, which indicates that the expression of LPS containing NeuAc residues is not critical for the strains' resistance to the serum's activity. In this study the mechanisms of complement activation responsible for killing serum-sensitive Salmonella 048 rods by NBS were also established. Three such mechanisms were distinguished: activation of the classi- cal/lectin pathways, important (decisive) in the bactericidal mechanism of complement activation, parallel activation of the classical/lectin and alternative pathways, and independent activation of the classical and lectin or the alternative pathway

The 800-Pound Grouper in the Room: Asymptotic Body Size and Invasiveness of Marine Aquarium Fishes

The global trade in aquatic wildlife destined for home aquaria not only has the potential to be a positive force for conservation, but also has a number of potential risks. The greatest and most documented risk is the potential to translocate species that will become invasive in a new habitat. Although propagule pressure can influence species invasiveness, a high percentage of documented marine aquarium fish that are invasive in the US are uncommon in the trade. Here, the covariation of size with species invasiveness was assessed using a web scraper to collect size, price, life history characteristics, and behavior data from five internet retail stores for 775 species of fish. Fish that routinely exceed 100. cm in total length are traded, nevertheless are typically sold at sizes much smaller than their theoretical maximum. No economic benefit from the sale of species that will outgrow tanks and have a high risk of being released was found. Large fish, including groupers that can achieve weights of 800 pounds, will continue to enter the trade because the growth of aquaculture for commercial food markets is making it easier to acquire these species that also have appealing small life stages, making it easier and less expensive to bring these species into the aquarium trade. The entire trade should consider taking concerted action to limit the trade in fish that are likely to become invasive

Statistical Deconvolution for Inference of Infection Time Series.

Accurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from observed data. Existing estimators can be sensitive to model misspecification and censored observations; many analysts have instead used methods that exhibit strong bias. We develop an estimator with a regularization scheme to cope with stochastic delays, which we term the robust incidence deconvolution estimator. We compare the method to existing estimators in a simulation study, measuring accuracy in a variety of experimental conditions. We then use the method to study COVID-19 records in the United States, highlighting its stability in the face of misspecification and right censoring. To implement the robust incidence deconvolution estimator, we release incidental, a ready-to-use R implementation of our estimator that can aid ongoing efforts to monitor the COVID-19 pandemic