Does Pollution Increase School Absences?

We examine the effect of air pollution on school absences using unique administrative data for elementary and middle school children in the 39 largest school districts in Texas. These data are merged with information from monitors maintained by the Environmental Protection Agency. To control for potentially confounding factors, we adopt a difference-in-difference-in differences strategy, and control for persistent characteristics of schools, years, and attendance periods in order to focus on variations in pollution within school-year-attendance period cells. We find that high levels of carbon monoxide (CO) significantly increase absences, even when they are below federal air quality standards.

Prompt Detection of Fast Optical Bursts with the Vera C. Rubin Observatory

The transient optical sky has remained largely unexplored on very short timescales. While there have been some experiments searching for optical transients from minutes to years, none have had the capability to distinguish millisecond Fast Optical Bursts (FOB). Such very fast transients could be the optical counterparts of Fast Radio Bursts (FRB), the prompt emission from $\gamma$-Ray Bursts (GRB), or other previously unknown phenomena. Here, we investigate a novel approach to the serendipitous detection of FOBs, which relies on searching for anomalous spatial images. In particular, due to their short duration, the seeing distorted images of FOBs should look characteristically different than those of steady sources in a standard optical exposure of finite duration. We apply this idea to simulated observations with the Vera C. Rubin Observatory, produced by tracing individual photons through a turbulent atmosphere, and down through the optics and camera of the Rubin telescope. We compare these simulated images to steady-source star simulations in 15 s integrations, the nominal Rubin exposure time. We report the classification accuracy results of a Neural Network classifier for distinguishing FOBs from steady sources. From this classifier, we derive constraints in duration-intensity parameter space for unambiguously identifying FOBs in Rubin observations. We conclude with estimates of the total number of detections of FOB counterparts to FRBs expected during the 10-year Rubin Legacy Survey of Space and Time (LSST).Comment: 7 pages, 4 figures, submitted to the Astrophysical Journa

Direct Autocrine Action of Insulin on β-Cells: Does It Make Physiological Sense?

In recent years there has been a growing interest in the possibility of a direct autocrine effect of insulin on the pancreatic β-cell. Indeed, there have been numerous intriguing articles and several eloquent reviews written on the subject (1–3); however, the concept is still controversial. Although many in vitro experiments, a few transgenic mouse studies, and some human investigations would be supportive of the notion, there exist different insights, other studies, and circumstantial evidence that question the concept. Therefore, the idea of autocrine action of insulin remains a conundrum. Here we outline a series of thoughts, insights, and alternative interpretations of the available experimental evidence. We ask, how convincing are these, and what are the confusing issues? We agree that there is a clear contribution of certain downstream elements in the insulin signaling pathway for β-cell function and survival, but the question of whether insulin itself is actually the physiologically relevant ligand that triggers this signal transduction remains unsettled

The Medical Informatics Group: Ongoing Research

Two current research projects within the Medical Informatics Group are described. The first, the Diabetes Data Management Project, has as its major goal the effective analysis, display, and summarization of information relevant to the care of insulin-dependent diabetics. These goals are achieved through the use of quantitative and qualitative modeling techniques, object-oriented graphical display methods, and natural language generation programs. The second research activity, the Hypertext Medical Handbook Project, emphasizes many aspects of electronic publishing and biomedical communication. In particular, the project explores machine-assisted information retrieval by combining user feedback with Bayesian inference networks

Medications for type 2 diabetes: how will we be treating patients in 50 years?

The past 50 years have seen the development of many new options for treating and preventing type 2 diabetes. Despite this success, the individual and societal burden of the disease continues unabated. Thus, the next 50 years will be critical if we are going to quell the major non-communicable disease of our time. The knowledge we will gain in the next few years from clinical studies will inform treatment guidelines with regard to which agents to use in whom and whether more aggressive approaches can slow the development of hyperglycaemia in those at high risk. Beyond that, we anticipate identification of novel targets and techniques for therapeutic intervention. These advances will lead to more personalised approaches to treatment. Most importantly, we will need to focus our political and economic efforts on enhancing and implementing public health approaches aimed at prevention of diabetes and its co-morbidities. This is one of a series of commentaries under the banner ‘50 years forward’, giving personal opinions on future perspectives in diabetes, to celebrate the 50th anniversary of Diabetologia (1965–2015)

Individual Differences in Learning Social and Non-Social Network Structures

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between non-social bits of information? Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. We examined individual differences in the ability to learn community structure of networks composed of social versus non-social stimuli. Although participants were able to learn community structure of both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of non-social community structure. Taken together, our results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes

Neprilysin Deficiency Protects Against Fat-Induced Insulin Secretory Dysfunction by Maintaining Calcium Influx

Neprilysin contributes to free fatty acid (FFA)-induced cellular dysfunction in nonislet tissues in type 2 diabetes. Here, we show for the first time that with prolonged FFA exposure, islet neprilysin is upregulated and this is associated with reduced insulin pre-mRNA and ATP levels, oxidative/nitrative stress, impaired potassium and calcium channel activities, and decreased glucose-stimulated insulin secretion (GSIS). Genetic ablation of neprilysin specifically protects against FFA-induced impairment of calcium influx and GSIS in vitro and in vivo but does not ameliorate other FFA-induced defects. Importantly, adenoviral overexpression of neprilysin in islets cultured without FFA reproduces the defects in both calcium influx and GSIS, suggesting that upregulation of neprilysin per se mediates insulin secretory dysfunction and that the mechanism for protection conferred by neprilysin deletion involves prevention of reduced calcium influx. Our findings highlight the critical nature of calcium signaling for normal insulin secretion and suggest that interventions to inhibit neprilysin may improve β-cell function in obese humans with type 2 diabetes

Elevated Depression Symptoms, Antidepressant Medicine Use, and Risk of Developing Diabetes During the Diabetes Prevention Program

OBJECTIVE—To assess the association between elevated depression symptoms or antidepressant medicine use on entry to the Diabetes Prevention Program (DPP) and during the study and the risk of developing diabetes during the study. RESEARCH DESIGN AND METHODS—DPP participants (n = 3,187) in three treatment arms (intensive lifestyle [ILS], metformin [MET], and placebo [PLB]) completed the Beck Depression Inventory (BDI) and reported their use of antidepressant medication at randomization and throughout the study (average duration in study 3.2 years). RESULTS—When other factors associated with the risk of developing diabetes were controlled, elevated BDI scores at baseline or during the study were not associated with diabetes risk in any arm. Baseline antidepressant use was associated with diabetes risk in the PLB (hazard ratio 2.25 [95% CI 1.38–3.66]) and ILS (3.48 [1.93–6.28]) arms. Continuous antidepressant use during the study (compared with no use) was also associated with diabetes risk in the same arms (PLB 2.60 [1.37–4.94]; ILS 3.39 [1.61–7.13]), as was intermittent antidepressant use during the study in the ILS arm (2.07 [1.18–3.62]). Among MET arm participants, antidepressant use was not associated with developing diabetes. CONCLUSIONS—A strong and statistically significant association between antidepressant use and diabetes risk in the PLB and ILS arms was not accounted for by measured confounders or mediators. If future research finds that antidepressant use independently predicts diabetes risk, efforts to minimize the negative effects of antidepressant agents on glycemic control should be pursued