The largest ever Sri Lankan dengue outbreak of 2017 provides an opportunity for investigating the relative contributions of climatological, epidemiological and sociological drivers on the epidemic patterns of this clinically important vector-borne disease. To do so, we develop a climatologically driven disease transmission framework for dengue virus using spatially resolved temperature and precipitation data as well as the time-series susceptible-infected-recovered (SIR) model. From this framework, we first demonstrate that the distinct climatological patterns encountered across the island play an important role in establishing the typical yearly temporal dynamics of dengue, but alone are unable to account for the epidemic case numbers observed in Sri Lanka during 2017. Using a simplified two-strain SIR model, we demonstrate that the re-introduction of a dengue virus serotype that had been largely absent from the island in previous years may have played an important role in driving the epidemic, and provide a discussion of the possible roles for extreme weather events and human mobility patterns on the outbreak dynamics. Lastly, we provide estimates for the future burden of dengue across Sri Lanka using the Coupled Model Intercomparison Phase 5 climate projections. Critically, we demonstrate that climatological and serological factors can act synergistically to yield greater projected case numbers than would be expected from the presence of a single driver alone. Altogether, this work provides a holistic framework for teasing apart and analysing the various complex drivers of vector-borne disease outbreak dynamics.A social system is susceptible to perturbation when its collective properties depend sensitively on a few pivotal components. Using the information geometry of minimal models from statistical physics, we develop an approach to identify pivotal components to which coarse-grained, or aggregate, properties are sensitive. As an example, we introduce our approach on a reduced toy model with a median voter who always votes in the majority. The sensitivity of majority-minority divisions to changing voter behaviour pinpoints the unique role of the median. More generally, the sensitivity identifies pivotal components that precisely determine collective outcomes generated by a complex network of interactions. Using perturbations to target pivotal components in the models, we analyse datasets from political voting, finance and Twitter. Across these systems, we find remarkable variety, from systems dominated by a median-like component to those whose components behave more equally. In the context of political institutions such as courts or legislatures, our methodology can help describe how changes in voters map to new collective voting outcomes. For economic indices, differing system response reflects varying fiscal conditions across time. Thus, our information-geometric approach provides a principled, quantitative framework that may help assess the robustness of collective outcomes to targeted perturbation and compare social institutions, or even biological networks, with one another and across time.The need for consortial programs to provide advanced education in food animal veterinary production medicine has been recognized and lauded for nearly three decades. This article describes one effort to create a dairy production medicine curriculum funded by a United States Department of Agriculture (USDA) Higher Education Challenge Grant. This National Center of Excellence in Dairy Production Medicine Education for Veterinarians is housed at the Dairy Education Center of the University of Minnesota and the project was a collaboration of the University of Minnesota, the University of Illinois, the University of Georgia, and Kansas State University. The article reviews the need for innovative ways to educate students who will optimally serve the dairy industry, provides a broad overview of the process of developing and delivering the eight-week dairy production medicine curriculum, and describes the challenges faced and lessons learned as a result of offering such a program.Between 2012 and 2014, three cohorts of senior veterinary students participated in an 8-week dairy production medicine course created by the National Center of Excellence in Dairy Production Medicine Education for Veterinarians. One goal of this course is to better prepare veterinary students to serve the increasingly complex needs of the dairy industry. In this article, we describe the assessment methods and student performance outcomes of those first three cohorts. A combination of assessment methods was used, including pre- and post-testing; instructor observations and scores on individual and group projects, including a final integrative project; and peer evaluation. Student feedback, collected via anonymous survey, provided insight into students' perceptions about the course and their learning. Performance and feedback suggest that the course was successful in preparing students for careers using skills in dairy production medicine. Pre- and post-testing was conducted for most topic modules in the course. The mean (median) pre- and post-test scores were 47% (50% ) and 83% (88%), respectively. The mean improvement in score was significant (p less then .002) for all modules and cohorts.  https://www.selleckchem.com/products/ly333531.html  Students indicated a moderate or high degree of confidence in performing dairy production medicine skills after each module. Of students in cohorts 1, 2, and 3, respectively, 55%, 75%, and 82% felt they could provide dairy production medicine services (e.g., records analysis, problem investigation, protocol and standard operating procedure design) either alone or with some mentoring, immediately after graduation. In addition, assessment results and student feedback enabled timely course modifications during these first three cohorts.The 8-week dairy production medicine course at the National Center of Excellence in Dairy Production Medicine Education for Veterinarians is designed to equip senior veterinary students with the knowledge and skills needed to serve the dairy industry. Course developers identified 59 topics of importance for dairy production medicine veterinarians. Students (N = 50) were surveyed before and after the course to determine their perceptions of (a) the importance of the 59 topics for their intended positions and (b) their knowledge and skill in those areas. We expected the course to affirm or strengthen perceptions of importance and increase confidence. Students rated 57 of the topics as moderately or very important before the course. Ratings were unchanged (56 topics) or increased (3 topics) after the course. Before the course, students believed they had a lot of knowledge and skill in just one area animal behavior and handling. At the end of the course, students believed they had a lot of knowledge and skill in 21 areas; confidence ratings were higher for 47 of the 59 topics.