Search for: All records

Abstract It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer’s disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study. 

Abstract Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model. 

Allotetraploid cotton (Gossypium) species represents a model system for the study of plant polyploidy, molecular evolution, and domestication. Here, chromosome-scale genome sequences were obtained and assembled for two recently described wild species of tetraploid cotton,Gossypium ekmanianum[(AD)₆,Ge] andGossypium stephensii[(AD)₇,Gs], and one early form of domesticatedGossypium hirsutum, racepunctatum[(AD)₁,Ghp]. Based on phylogenomic analysis, we provide a dated whole-genome level perspective for the evolution of the tetraploidGossypiumclade and resolved the evolutionary relationships ofGs,Ge, and domesticatedG. hirsutum. We describe genomic structural variation that arose duringGossypiumevolution and describe its correlates—including phenotypic differentiation, genetic isolation, and genetic convergence—that contributed to cotton biodiversity and cotton domestication. Presence/absence variation is prominent in causing cotton genomic structural variations. A presence/absence variation-derived gene encoding a phosphopeptide-binding protein is implicated in increasing fiber length during cotton domestication. The relatively unimprovedGhpoffers the potential for gene discovery related to adaptation to environmental challenges. Expanded gene families enoyl-CoA δ isomerase 3 and RAP2-7 may have contributed to abiotic stress tolerance, possibly by targeting plant hormone-associated biochemical pathways. Our results generate a genomic context for a better understanding of cotton evolution and for agriculture. 

As the use of Integrase inhibitor (INSTI)-class antiretroviral medications becomes more common to maintain long-term viral suppression, early reports suggest the potential for CNS side-effects when starting or switching to an INSTI-based regimen. In a population already at higher risk for developing mood and anxiety disorders, these drugs may have significant effects on PTSD scale symptom scores, particularly in women with HIV (WWH). A total of 551 participants were included after completing ≥ 1 WIHS study visits before and after starting/switching to an INSTI-based ART regimen. Of these, 14% were ART naïve, the remainder switched from primarily a protease inhibitor (PI) or non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimen. Using multivariable linear mixed effects models, we compared PTSD Civilian Checklist subscale scores before and after a “start/switch” to dolutegravir (DTG), raltegravir (RAL), or elvitegravir (EVG). Start/switch to EVG improved re-experiencing subscale symptoms (P’s < 0.05). Switching to EVG improved symptoms of avoidance (P = 0.01). Starting RAL improved arousal subscale symptoms (P = 0.03); however, switching to RAL worsened re-experiencing subscale symptoms (P < 0.005). Starting DTG worsened avoidance subscale symptoms (P = 0.03), whereas switching to DTG did not change subscale or overall PTSD symptoms (P’s > 0.08). In WWH, an EVG-based ART regimen is associated with improved PTSD symptoms, in both treatment naïve patients and those switching from other ART. While a RAL-based regimen was associated with better PTSD symptoms than in treatment naïve patients, switching onto a RAL-based regimen was associated with worse PTSD symptoms. DTG-based regimens either did not affect, or worsened symptoms, in both naïve and switch patients. Further studies are needed to determine mechanisms underlying differential effects of EVG, RAL and DTG on stress symptoms in WWH.