Why is lead-contaminated water a matter of public concern but contaminated housing is not? Catherine Fennell explores infrastructure and the politics of solidarity.

Obesity and its associated health issues are reaching epidemic levels worldwide. There are many reasons for this increase in metabolic diseases including environmental and lifestyle changes. However, there are also many biological mechanisms that shift the energy homeostasis of a system to a dysregulated state, often resulting in obesity or diabetes. One of these mechanisms that contribute to the rise of blood glucose levels and eventual failing of insulin producing beta-cells in the pancreas is hepatic gluconeogenesis. The hormones that regulate this process include insulin and glucagon. Glucagon acts through the secondary messenger cAMP, stimulating the cAMP Response Element Binding Protein (CREB) and its associated co- activators, the CRTCs to enhance gluconeogenic enzyme transcription. It has been clear that CRTC2 plays a role in this process, but this dissertation provides evidence for overlapping roles of CRTC2 and CRTC3, explaining past observations regarding modest phenotypes of CRTC2 null mice. Additionally, we show here that chronic expression of a liver expressed, constitutively active CRTC2 through an adeno-associated virus that upregulated hepatic gluconeogenesis is able to lead to an insulin resistant phenotype that is eventually compensated for by the upregulation of IRS2, a CREB target gene

This paper critically analyzes Sherrilyn Roush’s (Tracking truth: knowledge, evidence and science, 2005) definition of evidence and especially her powerful defence that in the ideal, a claim should be probable to be evidence for anything. We suggest that Roush treats not one sense of ‘evidence’ but three: relevance, leveraging and grounds for knowledge; and that different parts of her argument fare differently with respect to different senses. For relevance, we argue that probable evidence is sufficient but not necessary for Roush’s own two criteria of evidence to be met. With respect to grounds for knowledge, we agree that high probability evidence is indeed ideal for the central reason Roush gives: When believing a hypothesis on the basis of e it is desirable that e be probable. But we maintain that her further argument that Bayesians need probable evidence to warrant the method they recommend for belief revision rests on a mistaken interpretation of Bayesian conditionalization. Moreover, we argue that attempts to reconcile Roush’s arguments with Bayesianism fail. For leveraging, which we agree is a matter of great importance, the requirement that evidence be probable suffices for leveraging to the probability of the hypothesis if either one of Roush’s two criteria for evidence are met. Insisting on both then seems excessive. To finish, we show how evidence, as Roush defines it, can fail to track the hypothesis. This can remedied by adding a requirement that evidence be probable, suggesting another rationale for taking probable evidence as ideal—but only for a grounds-for-knowledge sense of evidence.

Machine learning (ML) is an application of computational and statistical techniques to allow computers to learn and predict without explicit programming. In recent years, with the increasing availability of large scale and low-cost computing power, ML capacity has expanded vastly and has begun to change how many industries operate. The ability of machines to analyze large, complex datasets and to detect patterns beyond the scope of the human mind provides a powerful opportunity for application in a healthcare setting. ML has introduced new approaches to many dimensions of medicine including, but not limited to, Pathology, Radiology, drug development, enhancing existing clinical predictive tools, and the management of many diseases including cancer and autoimmune diseases. Currently, ML remains in its infancy but has already started to make an impact in various healthcare disciplines. This research project aimed to provide the foundational training and understanding of the modern approaches to ML and develop the skill set necessary to use available healthcare data to develop and deploy new ML models to assist in the delivery of future healthcare.

We are interested in the free energies of transferring nonpolar solutes into aqueous NaCl solutions with salt concentrations upwards of 2 M, the Hofmeister regime. We use the semi-explicit assembly (SEA) computational model to represent these electrolyte solutions. We find good agreement with experiments (Setschenow coefficients) on 43 nonpolar and polar solutes and with TIP3P explicit-solvent simulations. Besides being much faster than explicit solvent calculations, SEA is more accurate than the PB models we tested, successfully capturing even subtle salt effects in both the polar and nonpolar components of solvation. We find that the salt effects are mainly due to changes in the cost of forming nonpolar cavities in aqueous NaCl solutions, and not mainly due to solute-ion electrostatic interactions.

Prior studies have reported that bilingualism enhancescognitive ability due to the regular conflict management oftwo language systems (Bialystok, 2015). Here, we explorewhether infant bilingualism improves cognitive ability at 9.5months. Twenty-four monolingual English and 23 bilingualFrench-English infants were first trained to predict a rewardon the right based on a set of tone-shape rule structure (AABpattern). Infants were later trained to predict a differentreward on the left based on another set of new rule structure(ABB pattern). Correct anticipation of reward locationsindicates successful learning. If bilingualism improvesinfants’ cognitive skills, bilingual infants would be better atlearning a new pattern-reward association. However, we didnot find evidence that bilinguals looked at the correct locationmore than monolinguals or learned the new pattern-rewardassociation faster. Thus, our results suggest bilingualism maynot enhance cognitive ability at 9.5 months, as least using thecurrent paradigm.

We present a simple optimization strategy for incorporating experimental dielectric response information on neat liquids in classical molecular models of alcohol. Using this strategy, we determine simple and transferable hydroxyl modulation rules that, when applied to an existing molecular parameter set, result in a newly dielectric corrected (DC) parameter set. We applied these rules to the general Amber force field (GAFF) to form an initial set of GAFF-DC parameters, and we found this to lead to significant improvement in the calculated dielectric constant and hydration free energy values for a wide variety of small molecule alcohol models. Tests of the GAFF-DC parameters in the SAMPL4 blind prediction event for hydration show these changes improve agreement with experiment. Surprisingly, these simple modifications also outperform detailed quantum mechanical electric field calculations using a self-consistent reaction field environment coupling term. This work provides a potential benchmark for future developments in methods for representing condensed-phase environments in electronic structure calculations.

Previous work describes a computational solvation model called semi-explicit assembly (SEA). The SEA water model computes the free energies of solvation of nonpolar and polar solutes in water with good efficiency and accuracy. However, SEA gives systematic

We describe here some tests we made in the SAMPL5 communal event of 'Semi-Explicit Assembly' (SEA), a recent method for computing solvation free energies. We combined the prospective tests of SAMPL5 with followup retrospective calculations, to improve two technical aspects of the field variant of SEA. First, SEA uses an approximate analytical surface around the solute on which a water potential is computed. We have improved and simplified the mathematical model of that surface. Second, some of the solutes in SAMPL5 were large enough to need a way to treat solvating waters interacting with 'buried atoms', i.e. interior atoms of the solute. We improved SEA with a buried-atom correction. We also compare SEA to Thermodynamic Integration molecular dynamics simulations, so that we can sort out force field errors.