Succession of gut microbial community structure for newborns is highly influenced by early life factors. Many preterm infants cared for in the NICU are exposed to parent–infant separation, stress, and pain from medical care procedures. The purpose of the study was to investigate the impact of early life stress on the trajectory of gut microbial structure. Stool samples from very preterm infants were collected weekly for 6 weeks. NICU stress exposure data were collected daily for 6 weeks. V4 region of the 16S rRNA gene was amplified by PCR and sequenced. Zero‐inflated beta regression model with random effects was used to assess the impact of stress on gut microbiome trajectories. Week of sampling was significant for Escherichia, Staphylococcus, Enterococcus, Bifidobacterium, Proteus, Streptococcus, Clostridium butyricum, and Clostridium perfringens. Antibiotic usage was significant for Proteus, Citrobacter, and C. perfringens. Gender was significant for Proteus. Stress exposure occurring 1 and 2 weeks prior to sampling had a significant effect on Proteus and Veillonella. NICU stress exposure had a significant effect on Proteus and Veillonella. An overall dominance of Gammaproteobacteria was found. Findings suggest early life NICU stress may significantly influence the developing gut microbiome, which is important to NICU practice and future microbiome research.
Survival of Gulf Stream (GS) warm core rings (WCRs) was investigated using a census consisting of a total of 961 rings formed during the period 1980-2017. Kaplan-Meier survival probability and Cox hazard proportional models were used for the analysis. The survival analysis was performed for rings formed in four 5 • zones between 75 • W and 55 • W. The radius, latitude, and distance from the shelf-break of a WCR at formation all had a significant effect on the survival of WCRs. A pattern of higher survival was observed in WCRs formed in Zone 2 (70 •-65 • W) or Zone 3 (65 •-60 • W) and then demised in Zone 1 (75 •-70 • W). Survival probability of the WCRs increased to more than 70% for those formed within a latitude band from 39.5 • to 41.5 • N. Survival probability is reduced when the WCRs are formed near the New England Seamounts. Plain Language Summary The Gulf Stream produces warm core rings in the Western Atlantic Ocean due to its meandering nature. These warm core rings have physical, chemical, and biological impacts on shelf and slope sea regions of the Western North Atlantic. This region is one of the most highly productive fishing areas in the world, and there is a need to understand the Warm Core Ring influence on different food web systems. We use data from a 38-yearlong (1980-2017) warm core ring census to investigate the survival probability of these warm core rings. After using multiple survival analysis techniques (a popular analysis technique in the medical and health sciences), we observed a high survival probability in WCRs formed within the 7 •-65 • W longitudinal band. Also, the warm core rings which demised within the 75 •-70 • W longitudinal band exhibited higher survival. The effect of the New England Seamount Chain (NESC) on WCR survival probabilities was revealed through a Cox proportional hazard model which showed that the further east a ring was formed from the NESC, the higher the survival probability. These findings are very important as precursors to understand the effect of WCRs on shelf-slope processes in the Western North Atlantic.
Black-box variational inference (BBVI) now sees widespread use in machine learning and statistics as a fast yet flexible alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, stochastic optimization methods for BBVI remain unreliable and require substantial expertise and hand-tuning to apply effectively. In this paper, we propose Robust, Automated, and Accurate BBVI (RAABBVI), a framework for reliable BBVI optimization. RAABBVI is based on rigorously justified automation techniques, includes just a small number of intuitive tuning parameters, and detects inaccurate estimates of the optimal variational approximation. RAABBVI adaptively decreases the learning rate by detecting convergence of the fixed-learning-rate iterates, then estimates the symmetrized Kullback-Leiber (KL) divergence between the current variational approximation and the optimal one. It also employs a novel optimization termination criterion that enables the user to balance desired accuracy against computational cost by comparing (i) the predicted relative decrease in the symmetrized KL divergence if a smaller learning were used and (ii) the predicted computation required to converge with the smaller learning rate. We validate the robustness and accuracy of RAABBVI through carefully designed simulation studies and on a diverse set of real-world model and data examples.
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