Humans operating in extreme environments often conduct their operations at the edges of the limits of human performance. Sometimes, they are required to push these limits to previously unattained levels. As a result, their margins for error in execution are much smaller than that found in the general public. These same small margins for error that impact execution may also impact risk, safety, health, and even survival. Thus, humans operating in extreme environments have a need for greater refinement in their preparation, training, fitness, and medical care. Precision medicine (PM) is uniquely suited to address the needs of those engaged in these extreme operations because of its depth of molecular analysis, derived precision countermeasures, and ability to match each individual (and his or her specific molecular phenotype) with any given operating context (environment). Herein, we present an overview of a systems approach to PM in extreme environments, which affords clinicians one method to contextualize the inputs, processes, and outputs that can form the basis of a formal practice. For the sake of brevity, this overview is focused on molecular dynamics, while providing only a brief introduction to the also important physiologic and behavioral phenotypes in PM. Moreover, rather than a full review, it highlights important concepts, while using only selected citations to illustrate those concepts. It further explores, by demonstration, the basic principles of using functionally characterized molecular networks to guide the practical application of PM in extreme environments. At its core, PM in extreme environments is about attention to incremental gains and losses in molecular network efficiency that can scale to produce notable changes in health and performance. The aim of this overview is to provide a conceptual overview of one approach to PM in extreme environments, coupled with a selected suite of practical considerations for molecular profiling and countermeasures.

As professors, we seek not only to impart knowledge about issues and concepts in American politics but also to engage and inspire students to become more knowledgeable and more active in politics. This article explains how a student-run exit poll conducted on Election Day 2016 accomplished both goals. Seven faculty members from four universities pooled our students and carried out an exit poll in the District of Columbia, Maryland, Virginia, and Ohio. By the time the polls closed, our students had spoken to more than 2,300 respondents, providing a memorable experience and creating a shared dataset that served as the centerpiece for many final class projects. Through this project, students gained hands-on experience in survey design, sampling, research ethics, polling, and data analysis.

In September 2016, the annual meeting of the International Union for Quaternary Research’s Loess and Pedostratigraphy Focus Group, traditionally referred to as a LoessFest, met in Eau Claire, Wisconsin, USA. The 2016 LoessFest focused on “thin” loess deposits and loess transportation surfaces. This LoessFest included 75 registered participants from 10 countries. Almost half of the participants were from outside the United States, and 18 of the participants were students. This review is the introduction to the special issue for Quaternary Research that originated from presentations and discussions at the 2016 LoessFest. This introduction highlights current understanding and ongoing work on loess in various regions of the world and provides brief summaries of some of the current approaches/strategies used to study loess deposits.

We examined the prospective associations of objective and subjective measures of stress during pregnancy with infant stress reactivity and regulation, an early-life predictor of psychopathology. In a racially and ethnically diverse low-income sample of 151 mother–infant dyads, maternal reports of stressful life events (SLE) and perceived stress (PS) were collected serially over gestation and the early postpartum period. Infant reactivity and regulation at 6 months of age was assessed via maternal report of temperament (negativity, surgency, and regulation) and infant parasympathetic nervous system physiology (respiratory sinus arrhythmia [RSA]) during the Still Face Paradigm. Regression models predicting infant temperament showed higher maternal prenatal PS predicted lower surgency and self-regulation but not negativity. Regression models predicting infant physiology showed higher numbers of SLE during gestation predicted greater RSA reactivity and weaker recovery. Tests of interactions revealed SLE predicted RSA reactivity only at moderate to high levels of PS. Thus, findings suggest objective and subjective measures of maternal prenatal stress uniquely predict infant behavior and physiology, adjusting for key pre- and postnatal covariates, and advance the limited evidence for such prenatal programming within high-risk populations. Assessing multiple levels of maternal stress and offspring stress reactivity and regulation provides a richer picture of intergenerational transmission of adversity.

The analysis of multilayer networks is among the most active areas of network science, and there are several methods to detect dense “communities” of nodes in multilayer networks. One way to define a community is as a set of nodes that trap a diffusion-like dynamical process (usually a random walk) for a long time. In this view, communities are sets of nodes that create bottlenecks to the spreading of a dynamical process on a network. We analyze the local behavior of different random walks on multiplex networks (which are multilayer networks in which different layers correspond to different types of edges) and show that they have very different bottlenecks, which correspond to rather different notions of what it means for a set of nodes to be a good community. This has direct implications for the behavior of community-detection methods that are based on these random walks.