Special Article: Physical Activity, Physical Fitness, and Cardiovascular Risk Factors in Childhood

In adults, physical activity and exercise training are associated with reduced cardiovascular morbidity and mortality, a reduced likelihood of developing adverse cardiovascular risk factors, and improved insulin sensitivity. In childhood, participation in appropriate physical activity may prevent the development of cardiovascular risk factors in the future and complement treatment of existing cardiovascular risk factors, including hypertension, dyslipidemia, and overweight. Exercise in children can also significantly improve insulin sensitivity independent of weight loss. These e fects are mediated in overweight children by increases in lean body mass relative to fat mass and associated improvements in inflammatory mediators, endothelial function, and the associated adverse hormonal milieu

Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

The agrin gene codes for a family of basal lamina proteins that differ in function and distribution

We isolated two cDNAs that encode isoforms of agrin, the basal lamina protein that mediates the motor neuron-induced aggregation of acetylcholine receptors on muscle fibers at the neuromuscular junction. Both proteins are the result of alternative splicing of the product of the agrin gene, but, unlike agrin, they are inactive in standard acetylcholine receptor aggregation assays. They lack one (agrin-related protein 1) or two (agrin-related protein 2) regions in agrin that are required for its activity. Expression studies provide evidence that both proteins are present in the nervous system and muscle and that, in muscle, myofibers and Schwann cells synthesize the agrin-related proteins while the axon terminals of motor neurons are the sole source of agrin

Agrin isoforms and their role in synaptogenesis

Agrin is thought to mediate the motor neuron-induced aggregation of synaptic proteins on the surface of muscle fibers at neuromuscular junctions. Recent experiments provide direct evidence in support of this hypothesis, reveal the nature of agrin immunoreactivity at sites other than neuromuscular junctions, and have resulted in findings that are consistent with the possibility that agrin plays a role in synaptogenesis throughout the nervous system

The Computational Power of Optimization in Online Learning

We consider the fundamental problem of prediction with expert advice where the experts are "optimizable": there is a black-box optimization oracle that can be used to compute, in constant time, the leading expert in retrospect at any point in time. In this setting, we give a novel online algorithm that attains vanishing regret with respect to $N$ experts in total $\widetilde{O}(\sqrt{N})$ computation time. We also give a lower bound showing that this running time cannot be improved (up to log factors) in the oracle model, thereby exhibiting a quadratic speedup as compared to the standard, oracle-free setting where the required time for vanishing regret is $\widetilde{\Theta}(N)$. These results demonstrate an exponential gap between the power of optimization in online learning and its power in statistical learning: in the latter, an optimization oracle---i.e., an efficient empirical risk minimizer---allows to learn a finite hypothesis class of size $N$ in time $O(\log{N})$. We also study the implications of our results to learning in repeated zero-sum games, in a setting where the players have access to oracles that compute, in constant time, their best-response to any mixed strategy of their opponent. We show that the runtime required for approximating the minimax value of the game in this setting is $\widetilde{\Theta}(\sqrt{N})$, yielding again a quadratic improvement upon the oracle-free setting, where $\widetilde{\Theta}(N)$ is known to be tight

Informative Group Testing for Multiplex Assays

Infectious disease testing frequently takes advantage of two tools–group testing and multiplex assays–to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. (2017), there was no research available to understand how best to apply these tools simultaneously. This recent work focused on applications where each individual is considered to be identical in terms of the probability of disease. However, risk-factor information, such as past behavior and presence of symptoms, is very often available on each individual to allow one to estimate individual-specific probabilities. The purpose of our paper is to propose the first group testing algorithms for multiplex assays that take advantage of individual risk-factor information as expressed by these probabilities. We show that our methods significantly reduce the number of tests required while preserving accuracy. Throughout this paper, we focus on applying our methods with the Aptima Combo 2 Assay that is used worldwide for chlamydia and gonorrhea screening

Estimating the prevalence of two or more diseases using outcomes from multiplex group testing

When screening a population for infectious diseases, pooling individual specimens (e.g., blood, swabs, urine, etc.) can provide enormous cost savings when compared to testing specimens individually. In the biostatistics literature, testing pools of specimens is commonly known as group testing or pooled testing. Although estimating a population-level prevalence with group testing data has received a large amount of attention, most of this work has focused on applications involving a single disease, such as human immunodeficiency virus. Modern methods of screening now involve testing pools and individuals for multiple diseases simultaneously through the use of multiplex assays. Hou et al. (2017, Biometrics, 73, 656–665) and Hou et al. (2020, Biostatistics, 21, 417–431) recently proposed group testing protocols for multiplex assays and derived relevant case identification characteristics, including the expected number of tests and those which quantify classification accuracy. In this article, we describe Bayesian methods to estimate population-level disease probabilities from implementing these protocols or any other multiplex group testing protocol which might be carried out in practice. Our estimation methods can be used with multiplex assays for two or more diseases while incorporating the possibility of test misclassification for each disease. We use chlamydia and gonorrhea testing data collected at the State Hygienic Laboratory at the University of Iowa to illustrate our work. We also provide an online R resource practitioners can use to implement the methods in this article

Nature Connectedness Moderates the Effect of Nature Exposure on Explicit and Implicit Measures of Emotion

Previous research indicates that both short-term and long-term exposure to natural environments is associated with higher levels of emotional well-being. However, less research has examined whether person-related factors may impact the salutogenic effects of nature. In the current study, we examined whether trait-level nature connectedness moderates the effect of exposure to nature on explicit and implicit measures of affect. Participants (n = 89) completed baseline measurements of trait nature connectedness and affective state. Approximately two weeks later, participants viewed a lab-based immersive simulation of either a natural or built environment and then again completed measures of affective state. Findings indicated that trait nature connectedness moderated the effect of nature on affect, with more positive outcomes of nature exposure observed among those high in nature connectedness. These findings suggest that interacting with nature may be especially beneficial for those who already feel a strong sense of connectedness to the natural environment