The influence of kinship on animal cooperation is often unclear. Cooperating Asiatic lion coalitions are linearly hierarchical; male partners appropriate resources disproportionately. To investigate how kinship affect coalitionary dynamics, we combined microsatellite based genetic inferences with long-term genealogical records to measure relatedness between coalition partners of free-ranging lions in Gir, India. Large coalitions had higher likelihood of having sibling partners, while pairs were primarily unrelated. Fitness computations incorporating genetic relatedness revealed that low-ranking males in large coalitions were typically related to the dominant males and had fitness indices higher than single males, contrary to the previous understanding of this system based on indices derived from behavioural metrics alone. This demonstrates the indirect benefits to (related) males in large coalitions. Dominant males were found to 'lose less' if they lost mating opportunities to related partners versus unrelated males. From observations on territorial conflicts we show that while unrelated males cooperate, kin-selected benefits are ultimately essential for the maintenance of large coalitions. Although large coalitions maximised fitness as a group, demographic parameters limited their prevalence by restricting kin availability. Such demographic and behavioural constraints condition two-male coalitions to be the most attainable compromise for Gir lions.
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http://dx.doi.org/10.1038/s41598-020-74247-x | DOI Listing |
Lancet Planet Health
January 2025
Center for Climate Change Communication, Department of Communication, George Mason University, Fairfax, VA, USA.
Ambitious policies are urgently needed to protect human health from the impacts of climate change. Civil society, including researchers and advocates, can help advance such policies by communicating with policy makers. In this scoping review, we examined what is known about effectively communicating with policy makers to encourage them to act on public health, climate change, or their nexus.
View Article and Find Full Text PDFSci Rep
January 2025
School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology (IUST), P.O. Box 16844-13114, Tehran, Iran.
Surfactant chemistry can affect the phenolic foam (PF) properties by controlling the collision and combination of the created bubbles during foam production. The study was accomplished using two surfactant families, nonionic: polysorbate (Tween80) and anionic: sodium and ammonium lauryl sulfates (SLS30 and ALS70) and sodium laureth sulfate (SLES270) to manufacture PF foams. Tween80 and SLS30 resulted in foams with the lowest and highest densities, 20.
View Article and Find Full Text PDFOpen Forum Infect Dis
January 2025
Sorbonne Université, INSERM, Institut Pierre Louis d'Épidémiologie et de Santé Publique, Paris, France.
Background: We evaluated 1-year engagement in pre-exposure prophylaxis (PrEP) care and associated factors among gay, bisexual, and other men who have sex with men (GBMSM) in a large cohort of oral PrEP users in the Paris region, France.
Methods: We included in this analysis cisgender GBMSM enrolled in the ANRS PREVENIR cohort study from 3 May 2017 to 28 February 2019. We categorized 1-year PrEP engagement into 4 categories: high (consistent visits, attendance, and prescription refills at months 3, 6, 9, and 12), low (missed visits or no prescription refills), disengagement (PrEP discontinuation), and lost to follow-up.
NPJ Digit Med
January 2025
Department of Biomedical Data Science, Stanford, CA, USA.
Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. We introduce CLinical Entity Augmented Retrieval (CLEAR), a RAG pipeline that retrieves information using entities. We compared CLEAR to embedding RAG and full-note approaches for extracting 18 variables using six LLMs across 20,000 clinical notes.
View Article and Find Full Text PDFMayo 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.
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