Active students enjoy better health (overall and mental) and are happier than their inactive peers. This provides a clear rationale for providing students with opportunities to be active at university. The data provide a baseline to monitor changes in physical activity patterns.

Assessing inter-individual variability in responses to xenobiotics remains a substantial challenge, both in drug development with respect to pharmaceuticals and in public health with respect to environmental chemicals. Although approaches exist to characterize pharmacokinetic variability, there are no methods to routinely address pharmacodynamic variability. In this study, we aimed to demonstrate the feasibility of characterizing inter-individual variability in a human in vitro model. Specifically, we hypothesized that genetic variability across a population of iPSC-derived cardiomyocytes translates into reproducible variability in both baseline phenotypes and drug responses. We measured baseline and drug-related effects in iPSC-derived cardiomyocytes from 27 healthy donors on kinetic Ca2+ flux and high-content live cell imaging. Cells were treated in concentration-response with cardiotoxic drugs: isoproterenol (β-adrenergic receptor agonist/positive inotrope), propranolol (β-adrenergic receptor antagonist/negative inotrope), and cisapride (hERG channel inhibitor/QT prolongation). Cells from four of the 27 donors were further evaluated in terms of baseline and treatment-related gene expression. Reproducibility of phenotypic responses was evaluated across batches and time. iPSC-derived cardiomyocytes exhibited reproducible donor-specific differences in baseline function and drug-induced effects. We demonstrate the feasibility of using a panel of population-based organotypic cells from healthy donors as an animal replacement experimental model. This model can be used to rapidly screen drugs and chemicals for inter-individual variability in cardiotoxicity. This approach demonstrates the feasibility of quantifying inter-individual variability in xenobiotic responses and can be expanded to other cell types for which in vitro populations can be derived from iPSCs.

“Thorough QT/corrected QT (QTc)” (TQT) studies are cornerstones of clinical cardiovascular safety assessment. However, TQT studies are resource intensive, and preclinical models predictive of the threshold of regulatory concern are lacking. We hypothesized that an in vitro model using induced pluripotent stem cell (iPSC)‐derived cardiomyocytes from a diverse sample of human subjects can serve as a “TQT study in a dish.” For 10 positive and 3 negative control drugs, in vitro concentration‐QTc, computed using a population Bayesian model, accurately predicted known in vivo concentration‐QTc. Moreover, predictions of the percent confidence that the regulatory threshold of 10 ms QTc prolongation would be breached were also consistent with in vivo evidence. This “TQT study in a dish,” consisting of a population‐based iPSC‐derived cardiomyocyte model and Bayesian concentration‐QTc modeling, has several advantages over existing in vitro platforms, including higher throughput, lower cost, and the ability to accurately predict the in vivo concentration range below the threshold of regulatory concern.

The received view is that psychology has undergone several scientific revolutions similar to those that occurred in the physical sciences. Of these, this paper will consider the cognitive revolution. Because the arguments in favor of the existence of a cognitive revolution are cast using the concepts and terms of revolutionary science, we will examine the cognitive revolution using accounts of revolutionary science advanced by five influential philosophers of science. Specifically, we will draw from the philosophical positions of Popper, Kuhn, Lakatos, Laudan, and Gross for the purpose of discussion. We conclude that no substantive revolution took place according to these accounts. This conclusion is based on data gathered from some of the major participants in the "cognitive revolution" and on a general scholarly survey of the literature. We argue that the so-called cognitive revolution is best characterized as a socio-rhetorical phenomenon.Key words: scientific revolution, cognitive revolution, fallibilism, paradigms, research programs, research traditions, rhetoric of science The received view is that psychology has undergone a few key scientific revolutions, similar to the scientific revolutions that have occurred in the physical sciences (Baars, 1986;Gardner, 1985).' Histories of psychology, for example, typically depict two revolutions: behaviorism's overthrow of mentalism in the first quarter of the 20th century, and in the second quarter of the century, cognitive psychology's overthrow of behaviorism (Buss, 1978; see Hergenhahn, 1997, p. 553 ff.). This paper will examine the latter of the two revolutions, what is generally called the cognitive revolution.We examine the cognitive revolution according to accounts of revolutionary science provided by five key philosophers of science. We conclude that no such substantive revolution took place, at least according to the accounts of revolutionary science provided by these ' See the Appendix for quotations relevant to this "received view," as it pertains to the cognitive revolution.

A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes–Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity.