A First Exposure to Statistical Mechanics for Life Scientists

Statistical mechanics is one of the most powerful and elegant tools in the quantitative sciences. One key virtue of statistical mechanics is that it is designed to examine large systems with many interacting degrees of freedom, providing a clue that it might have some bearing on the analysis of the molecules of living matter. As a result of data on biological systems becoming increasingly quantitative, there is a concomitant demand that the models set forth to describe biological systems be themselves quantitative. We describe how statistical mechanics is part of the quantitative toolkit that is needed to respond to such data. The power of statistical mechanics is not limited to traditional physical and chemical problems and there are a host of interesting ways in which these ideas can be applied in biology. This article reports on our efforts to teach statistical mechanics to life science students and provides a framework for others interested in bringing these tools to a nontraditional audience in the life sciences.Comment: 27 pages, 16 figures. Submitted to American Journal of Physic

Cross-sectional and longitudinal risk of physical impairment in a cohort of postmenopausal women who experience physical and verbal abuse.

BackgroundExposure to interpersonal violence, namely verbal and physical abuse, is a highly prevalent threat to women's health and well-being. Among older, post-menopausal women, several researchers have characterized a possible bi-directional relationship of abuse exposure and diminished physical functioning. However, studies that prospectively examine the relationship between interpersonal abuse exposure and physical functioning across multiple years of observation are lacking. To address this literature gap, we prospectively evaluate the association between abuse exposure and physical functioning in a large, national cohort of post-menopausal women across 12 years of follow-up observation.MethodsMultivariable logistic regression was used to measure the adjusted association between experiencing abuse and physical function score at baseline in 154,902 Women's Health Initiative (WHI) participants. Multilevel modeling, where the trajectories of decline in physical function were modeled as a function of time-varying abuse exposure, was used to evaluate the contribution of abuse to trajectories of physical function scores over time.ResultAbuse was prevalent among WHI participants, with 11 % of our study population reporting baseline exposure. Verbal abuse was the most commonly reported abuse type (10 %), followed by combined physical and verbal abuse (1 %), followed by physical abuse in the absence of verbal abuse (0.2 %). Abuse exposure (all types) was associated with diminished physical functioning, with women exposed to combined physical and verbal abuse presenting baseline physical functioning scores consistent with non-abused women 20 years senior. Results did not reveal a differential rate of decline over time in physical functioning based on abuse exposure.ConclusionsTaken together, our findings suggest a need for increased awareness of the prevalence of abuse exposure among postmenopausal women; they also underscore the importance of clinician's vigilance in their efforts toward the prevention, early detection and effective intervention with abuse exposure, including verbal abuse exposure, in post-menopausal women. Given our findings related to abuse exposure and women's diminished physical functioning at WHI baseline, our work illuminates a need for further study, particularly the investigation of this association in younger, pre-menopausal women so that the temporal ordering if this relationship may be better understood

Unpacking the black box of improvement

During the Salzburg Global Seminar Session 565-Better Health Care: How do we learn about improvement, participants discussed the need to unpack the black box of improvement. The black box refers to the fact that when quality improvement interventions are described or evaluated, there is a tendency to assume a simple, linear path between the intervention and the outcomes it yields. It is also assumed that it is enough to evaluate the results without understanding the process of by which the improvement took place. However, quality improvement interventions are complex, nonlinear and evolve in response to local settings. To accurately assess the effectiveness of quality improvement and disseminate the learning, there must be a greater understanding of the complexity of quality improvement work. To remain consistent with the language used in Salzburg, we refer to this as unpacking the black box of improvement. To illustrate the complexity of improvement, this article introduces four quality improvement case studies. In unpacking the black box, we present and demonstrate how Cynefin framework from complexity theory can be used to categorize and evaluate quality improvement interventions. Many quality improvement projects are implemented in complex contexts, necessitating an approach defined as probesense- respond. In this approach, teams experiment, learn and adapt their changes to their local setting. Quality improvement professionals intuitively use the probe-sense-respond approach in their work but document and evaluate their projects using language for simple or complicated' contexts, rather than the complex contexts in which they work. As a result, evaluations tend to ask 'How can we attribute outcomes to the intervention, rather than 'What were the adaptations that took place. By unpacking the black box of improvement, improvers can more accurately document and describe their interventions, allowing evaluators to ask the right questions and more adequately evaluate quality improvement interventions.Fil: Ramaswamy, Rohit. University of North Carolina; Estados UnidosFil: Reed, Julie. Nihr Clarch Northwest London; Estados UnidosFil: Livesley, Nigel. Institute for Healthcare Improvement; Estados UnidosFil: Boguslavsky, Victor. University Research Co; Estados UnidosFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Sax, Sylvia. University of Heidelberg; AlemaniaFil: Houleymata, Diarra. Applying Science to Strengthen and Improve Systems Project,; MalíFil: Kimble, Leighann. University Research Co; Estados UnidosFil: Parry, Gareth. Institute of Healthcare Improvement; Estados Unido

Mapeando o processo de pesquisa colaborativa

Despite significant federal investments in the production of high-quality education research, the direct use of that research in policy and practice is not evident. Some education researchers are increasingly employing collaborative research models that use structures and processes to integrate practitioners into the research process in an effort to produce more relevant and useful work. This article presents and describes the logic model developed by researchers at American Institutes for Research (AIR) to guide their work on the Regional Educational Laboratory Midwest. Under this program, AIR researchers have developed eight research alliances. The alliance members, who represent districts, state education agencies, and other organizations with a vested interest in education, partner with researchers to develop three- to five-year research agendas. These agendas drive the research and technical assistance projects that the alliance members and AIR researchers do together. It contributes to the emergent literature on research-practice partnerships, providing a theory-based approach to the work that others might model, build upon, or revisit.A pesar de las inversiones federales significativas en la producción de investigaciones en educación de alta calidad, el uso directo de que la investigación en la política y la práctica no es evidente. Algunos investigadores en el área de educación están empleando cada vez más modelos de investigación en colaboración que utilizan estructuras y procesos para integrar los profesionales en el proceso de investigación, en un esfuerzo para producir un trabajo más relevante y útil. Este artículo presenta y describe el modelo lógico desarrollado por investigadores de la American Institutes for Research (AIR) para orientar su trabajo en el Laboratorio Educativo Regional del Medio Oeste. Bajo este programa, los investigadores de AIR han desarrollado ocho alianzas de investigación. Los miembros de la alianza, que representan a los distritos, las agencias estatales de educación y otras organizaciones interesados en la educación, forman un equipo con investigadores para desarrollar programas de investigación de tres a cinco años de duración. Estos programas de investigación y de asistencia técnica orientan a los miembros de la alianza e investigadores AIR en su colaboración. Este trabajo contribuye a la literatura sobre las asociaciones de investigación-práctica, proporcionando un enfoque teórico que otros podrían modelar, utilizar o modificar.Apesar dos investimentos federais significativos na produção de pesquisas em educação de alta qualidade, o uso direto de investigação na política e na prática não é clara. Alguns pesquisadores no campo da educação estão cada vez mais usando modelos colaborativos de pesquisa que utilizem estruturas e processos para integrar os profissionais no processo de pesquisa em um esforço para produzir um trabalho mais relevante e útil. Este artigo apresenta e descreve o modelo lógico desenvolvido por pesquisadores do Instituto Americano de Pesquisa (AIR) para orientar o seu trabalho no Laboratório Educacional Regional do Centro-Oeste. No âmbito deste programa, os pesquisadores de AIR desenvolveram oito parcerias de investigação. Os membros da aliança, que representam distritos, agências estaduais de educação e outras organizações interessadas em educação, formaram parcerias com pesquisadores para desenvolver programas de investigação  de três a cinco anos de duração. Estes programas de pesquisa e assistência técnica orientou a colaboração dos membros da aliança e pesquisadores da AIR. Este artigo contribui para a literatura sobre parcerias de investigação e prática, proporcionando uma abordagem teórica de modelagem que outros poderiam construir ou modificar

Scavenging vs hunting affects behavioral traits of an opportunistic carnivore

Background. Human-induced changes to ecosystems transform the availability of resources to predators, including altering prey populations and increasing access to anthropogenic foods. Opportunistic predators are likely to respond to altered food resources by changing the proportion of food they hunt versus scavenge. These shifts in foraging behavior will affect species interactions through multiple pathways, including by changing other aspects of predator behavior such as boldness, innovation, and social structure. Methods. To understand how foraging behavior impacts predator behavior, we conducted a controlled experiment to simulate hunting by introducing a prey model to captive coyotes (Canis latrans) and compared their behavior to coyotes that continued to scavenge over one year. We used focal observations to construct behavioral budgets, and conducted novel object, puzzle box, and conspecific tests to evaluate boldness, innovation, and response to conspecifics. Results. We documented increased time spent resting by hunting coyotes paired with decreased time spent active. Hunting coyotes increased boldness and persistence but there were no changes in innovation. Our results illustrate how foraging behavior can impact other aspects of behavior, with potential ecological consequences to predator ecology, predator-prey dynamics, and human-wildlife conflict; however, the captive nature of our study limits specific conclusions related to wild predators. We conclude that human-induced behavioral changes could have cascading ecological implications that are not fully understood