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The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso- and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s. The data are publicly available through biodiversity.aq and the Ocean Biogeographic Information System. The archive includes tracking data from over 70 contributors across 12 national Antarctic programs, and includes data from 17 predator species, 4060 individual animals, and over 2.9 million observed locations.
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http://dx.doi.org/10.1038/s41597-020-0406-x | DOI Listing |
J Behav Health Serv Res
September 2025
Department of Health Policy and Management, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 W. Markham St., Little Rock, AR, USA.
Telehealth is increasingly a standard and routine clinical option, indicating a changing outlook for SUD treatment from in-person to the more convenient option of telehealth. As populations across geographies increasingly prefer telehealth, more research is warranted that focuses on how where a person lives is associated with telehealth availability. The authors used the Mental Health and Addiction Treatment Tracking Repository (MATTR 2024) to identify telehealth availability among all known licensed SUD treatment facilities in the USA (N = 10,492 facilities).
View Article and Find Full Text PDFArch Cardiovasc Dis
September 2025
CIC INSERM 1410, 97410 Saint-Pierre, France; Department of Cardiology, La Réunion University Hospital, 97400 Saint-Denis, France. Electronic address:
Background: Artificial intelligence has emerged as a promising tool to optimize patient care in the field of cardiovascular medicine. However, data on its adoption and utilization by healthcare professionals are scarce.
Aim: To explore the factors that support or hinder the adoption of artificial intelligence in cardiology in France.
Med Eng Phys
October 2025
Ansys Inc., Houston, TX 77094, USA.
Introduction: Benchtop and animal models have traditionally been used to study the propagation of Onyx Liquid Embolic Systems (Onyx) used in the treatment of brain arteriovenous malformations (AVM). However, such models are costly, do not provide sufficient detail to elucidate how variations in Onyx viscosity alter flow dynamics, and rely on some trial-and-error, resulting in elongated timelines for product development.
Objectives: The goal of this study was to leverage Computational Fluid Dynamics (CFD) simulations to predict the behavior of different Onyx formulations.
J Med Internet Res
September 2025
Artificial Intelligence and Mathematical Modeling Lab, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Background: The H5N1 avian influenza A virus represents a serious threat to both animal and human health, with the potential to escalate into a global pandemic. Effective monitoring of social media during H5N1 avian influenza outbreaks could potentially offer critical insights to guide public health strategies. Social media platforms like Reddit, with their diverse and region-specific communities, provide a rich source of data that can reveal collective attitudes, concerns, and behavioral trends in real time.
View Article and Find Full Text PDFJMIR Res Protoc
September 2025
School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Background: Electronic health records (EHRs) have been linked to information overload, which can lead to cognitive fatigue, a precursor to burnout. This can cause health care providers to miss critical information and make clinical errors, leading to delays in care delivery. This challenge is particularly pronounced in medical intensive care units (ICUs), where patients are critically ill and their EHRs contain extensive and complex data.
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