Foresight science is a systematic approach to generate future predictions for planning and management by drawing upon analytical and predictive tools to understand the past and present, while providing insights about the future. To illustrate the application of foresight science in conservation, we present three case studies: identification of emerging risks to conservation, conservation of at-risk species, and aid in the development of management strategies for multiple stressors. We highlight barriers to mainstreaming foresight science in conservation including knowledge accessibility/organization, communication across diverse stakeholders/decision makers, and organizational capacity. Finally, we investigate opportunities for mainstreaming foresight science including continued advocacy to showcase its application, incorporating emerging technologies (i.e., artificial intelligence) to increase capacity/decrease costs, and increasing education/training in foresight science via specialized courses and curricula for trainees and practicing professionals. We argue that failure to mainstream foresight science will hinder the ability to achieve future conservation objectives in the Anthropocene.
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http://dx.doi.org/10.1007/s13280-022-01786-0 | DOI Listing |
Eur Radiol Exp
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)-in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.
Methods: In this six-center retrospective study, clinical and imaging data from 445 consecutive IVT-treated AIS patients were collected (01/2018-06/2023).
Mayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFCan Commun Dis Rep
January 2025
Data, Surveillance and Foresight Branch, Public Health Agency of Canada, Ottawa, ON.
Background: Antimicrobial resistance (AMR) is associated with significant human and financial costs, particularly among vulnerable populations like older adults living in long-term care homes (LTCHs). Urinary tract infection (UTI) is the leading indication for antibiotic use in this population, with some estimates suggesting that up to 70% of these prescriptions may be avoidable.
Objective: The purpose of this study is to develop and test novel behavioural science-informed antimicrobial stewardship (AMS) quality improvement strategies in Canadian LTCHs, which aim to decrease unnecessary testing and treatment for residents who lack the minimum clinical signs and symptoms of UTI.
J Cell Sci
January 2025
Sorbonne Université, INSERM, CNRS, Institut de la Vision, F-75012 Paris, France.
Signal transduction downstream of axon guidance molecules is essential for steering developing axons. Second messengers including cAMP are key molecules shared by a multitude of signaling pathways and are required for a wide range of cellular processes including axon pathfinding. Yet, how these signaling molecules achieve specificity for each of their downstream pathways remains elusive.
View Article and Find Full Text PDFPLOS Glob Public Health
January 2025
Global Strategy Lab, Dahdaleh Institute for Global Health Research, York University, Toronto, Ontario, Canada.
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