295 research outputs found

    Teaching Health Impact and Behavior with Infographics

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    The use of Infographics can be a tool that not only allows for the communication of empirical health data in an understandable format, but encourages the health administration student to present evidence-based research in a creative manner. The purpose of this paper is to describe a learning exercise that implements Infographics to demonstrate an impact of a health issue and/or encourage a health behavior change. This learning exercise is developed to increase student knowledge and visual literacy skills with respect to presenting, in a concise format, a well-researched and referenced health issue and/or a health behavior change. Specifically, the exercise was designed to: (a) curate health statistics and reference information for the selected health issue; (b) identify media resources and apply copyright and fair use in a proper manner; (c) evaluate internet resources for credibility and accuracy; and (d) utilize Infographic tools to communicate one\u27s visual viewpoint. At the conclusion of the course, students reflected on the effective visual aspects of their Infographics and the points that were challenging to communicate using this medium. The benefits of this applied learning approach for students and the faculty instructor are discussed

    Trial By Fire: Gaming and Badging in an FYE Program

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    The ecology and evolution of wildlife cancers: Applications for management and conservation

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    Evolutionary Applications published by John Wiley & Sons Ltd Ecological and evolutionary concepts have been widely adopted to understand host–pathogen dynamics, and more recently, integrated into wildlife disease management. Cancer is a ubiquitous disease that affects most metazoan species; however, the role of oncogenic phenomena in eco-evolutionary processes and its implications for wildlife management and conservation remains undeveloped. Despite the pervasive nature of cancer across taxa, our ability to detect its occurrence, progression and prevalence in wildlife populations is constrained due to logistic and diagnostic limitations, which suggests that most cancers in the wild are unreported and understudied. Nevertheless, an increasing number of virus-associated and directly transmissible cancers in terrestrial and aquatic environments have been detected. Furthermore, anthropogenic activities and sudden environmental changes are increasingly associated with cancer incidence in wildlife. This highlights the need to upscale surveillance efforts, collection of critical data and developing novel approaches for studying the emergence and evolution of cancers in the wild. Here, we discuss the relevance of malignant cells as important agents of selection and offer a holistic framework to understand the interplay of ecological, epidemiological and evolutionary dynamics of cancer in wildlife. We use a directly transmissible cancer (devil facial tumour disease) as a model system to reveal the potential evolutionary dynamics and broader ecological effects of cancer epidemics in wildlife. We provide further examples of tumour–host interactions and trade-offs that may lead to changes in life histories, and epidemiological and population dynamics. Within this framework, we explore immunological strategies at the individual level as well as transgenerational adaptations at the population level. Then, we highlight the need to integrate multiple disciplines to undertake comparative cancer research at the human–domestic–wildlife interface and their environments. Finally, we suggest strategies for screening cancer incidence in wildlife and discuss how to integrate ecological and evolutionary concepts in the management of current and future cancer epizootics

    Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol

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    Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state-of-the-art tools used to inform dosing in drug development. In particular, we used pharmacokinetic-pharmacodynamic (PK-PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be tested experimentally. Through simulations, we show that, when learning from retrospective trial data, more than 100 patients are needed to reach an accuracy within the range of what is achieved with a standard dosing solution. However, embedding a model of drug effect within the RL algorithm improves accuracy by reducing errors to target by 90% through learning to take dosing actions maximizing long-term benefit. Data residual variability impacts accuracy while the algorithm efficiently coped with up to 50% interindividual variability in the PK and 25% in the PD model's parameters. We illustrate how extending the state definition of the RL agent with meaningful variables is key to achieve high accuracy of optimal dosing policy. These results suggest that RL constitutes an attractive approach for precision dosing when rich data are available or when complemented with synthetic data from model-based tools used in model-informed drug development

    A Review Of Wind-Assisted Ship Propulsion For Sustainable Commercial Shipping: Latest Developments And Future Stakes

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    With the current global warming crisis and contemporary concerns for sustainability, the transport industry is developing and implementing novel solutions to reduce greenhouse gases. With close to 90% of the world’s goods relying on maritime transportation, responsible for 3% of global energy-related carbon dioxide (CO2) emissions in 2019, there is a vital emphasis on reducing emissions. The latest legislation from the International Maritime Organisation has imposed even tougher sulphur oxide targets. On the other hand, emission intensity for CO2 will need to be decreased by 70% in 2050, compared to 2008 figures. While operating measures and fuel alternatives are suitable in the short-term to meet these novel regulatory constraints, as the use of fossil fuels tapers off, the long-terms solution appears to reside in wind-assisted ships. Consequently, this study aims to identify viable solutions that could reduce emissions, focusing on three prominent technologies, namely sails, rotors and kites. Furthermore, this review provides guidance on the benefits and risks associated with each technology and recommends guidelines for performance prediction and associated constraints. Ultimately, future stakes in wind-assisted propulsion are highlighted, including the need for full-scale validation, the challenge in assessing environmental and economic impact, and the structural issues associated with wind-assisted propulsion systems
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