The design of surveillance strategies is often a compromise between science, feasibility, and available resources, especially when sampling is based at fixed locations, such as slaughter-houses. Advances in animal identification, movement recording and traceability should provide data that can facilitate the development, design and interpretation of surveillance activities. Here, for the first time since the introduction of electronic identification of sheep, the utility of a statutory sheep movement database to inform the design and interpretation of slaughter-house based surveillance activities has been investigated. Scottish sheep movement records for 2015-2018 were analyzed in combination with several other data sources. Patterns of off-farm movements of Scottish sheep to slaughter were described and the spatial distribution of several distinct slaughter populations, throughputs and catchment areas for Scottish slaughterhouses were determined. These were used to evaluate the coverage of a convenience-sample slaughter-house based survey for antimicrobial resistance (AMR). In addition, non-slaughter sheep movements within and between Scottish regions were described and inter-and intra-regional movement matrices were produced. There is potential at a number of levels for bias in spatially-associated factors for ovine surveillance activities based at Scottish slaughterhouses. The first is intrinsic because the slaughtered in Scotland population differs from the overall Scottish sheep slaughter population. Other levels will be survey-dependent and occur when the catchment area differs from the slaughtered in Scotland population and when the sampled sheep differ from the catchment area. These are both observed in the AMR survey. Furthermore, the Scottish non-slaughter sheep population is dynamic. Inter-regional movements vary seasonally, driven by the sheep calendar year, structure of the Scottish sheep industry and management practices. These sheep movement data provide a valuable resource for surveillance purposes, despite a number of challenges and limitations that were encountered. They can be used to identify and characterize the spatial origin of relevant populations and so inform the interpretation of existing slaughterhouse-based surveillance activities. They can be used to improve future design by exploring the feasibility and cost:benefit of alternative sampling strategies. Further development could also contribute to other surveillance activities, such as situational awareness and resource allocation, for the benefit of stakeholders.
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http://dx.doi.org/10.3389/fvets.2020.00205 | DOI Listing |
Neurobiol Aging
January 2025
Department of Psychology, The Pennsylvania State University, University Park, PA 16802, United States. Electronic address:
The growing population of older adults emphasizes the need to develop interventions that prevent or delay some of the cognitive decline that accompanies aging. In particular, as memory impairment is the foremost cognitive deficit affecting older adults, it is vital to develop interventions that improve memory function. This study addressed the problem of false memories in aging by training older adults to use details of past events during memory retrieval to distinguish targets from related lures.
View Article and Find Full Text PDFJMIR Form Res
December 2024
Department of Communication, Stanford University, Stanford, US.
Background: Contrary to popular concerns about the harmful effects of media use on mental health, research on this relationship is ambiguous, stalling advances in theory, interventions, and policy. Scientific explorations of the relationship between media and mental health have mostly found null or small associations, with the results often blamed on the use of cross-sectional study designs or imprecise measures of media use and mental health.
Objective: This exploratory empirical demonstration aimed to answer whether mental health effects are associated with media use experiences by (1) redirecting research investments to granular and intensive longitudinal recordings of digital experiences to build models of media use and mental health for single individuals over the course of one entire year, (2) using new metrics of fragmented media use to propose explanations of mental health effects that will advance person-specific theorizing in media psychology, and (3) identifying combinations of media behaviors and mental health symptoms that may be more useful for studying media effects than single measures of dosage and affect or assessments of clinical symptoms related to specific disorders.
J Strength Cond Res
December 2024
School of Kinesiology, University of Michigan, Ann Arbor, Michigan.
Motlagh, JG and Lipps, DB. The contribution of muscular fatigue and shoulder biomechanics to shoulder injury incidence during the bench press exercise: A narrative review. J Strength Cond Res 38(12): 2147-2163, 2024-Participation in competitive powerlifting has rapidly grown over the past two decades.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Haemodialysis, Fuyong People's Hospital of Baoan District, Shenzhen, Guangdong Province, China.
Objective: Blood urea nitrogen (BUN) is a commonly used biomarker for assessing kidney function and neuroendocrine activity. Previous studies have indicated that elevated BUN levels are associated with increased mortality in various critically ill patient populations. The focus of this study was to investigate the relationship between BUN and 28-day mortality in intensive care patients.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Structural and Molecular Biology, University College London, London, United Kingdom.
Previous studies have highlighted the inherent subjectivity, complexity, and challenges associated with research quality leading to fragmented findings. We identified determinants of research publication quality in terms of research activities and the use of information and communication technologies by employing an interdisciplinary approach. We conducted web-based surveys among academic scientists and applied machine learning techniques to model behaviors during and after the COVID-19 pandemic.
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