Pair housing is one of the most important components of behavioral management for caged macaques; however, it can result in aggression and injury if partners are incompatible. Knowing when to proceed and when to stop social introductions can be challenging, and can have consequences for the partners. We examined whether behavior early in social introductions predicted success (i.e., partners remained cohoused with full contact for at least 28 days) in 724 female-female and 477 male-male rhesus macaque pairs. We took cage side one-zero focal observations on pairs during the first 2 days of full contact, recording social and aggressive behaviors. The majority of pairs (79.6% of female and 83.0% of male) were successful. The most common behaviors exhibited by pairs during these observations were maintaining proximity, tandem threats, and anxiety. Mounting was also relatively common in male pairs. Grooming and close social contact (e.g., touching) were not common in our study. Several behaviors observed on Day 1 significantly predicted pairing success. For females, these included proximity, tandem threat, rump present, mount, and groom. Day 1 predictors of success for male pairs included proximity, tandem threat, rump present, mount, and social contact. Fewer behaviors predicted success on Day 2. Maintaining proximity on Day 2 predicted success for both sexes, but tandem threat predicted success only for females. Behaviors that predicted incompatibility for females on Day 1 included displace, grimace, threat, bite, and other aggressive contacts. Day 1 predictors of separation for male pairs were displaced, grimace, and abnormal behavior. The only Day 2 behavior that correlated with incompatibility was grimace, which was predictive for males. Interestingly, aggression did not predict incompatibility for male pairs. Identifying behaviors exhibited by monkeys early in the pair introduction that are predictive of long-term compatibility can shape pairing decisions, reducing later stress and potential injury.
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http://dx.doi.org/10.1002/ajp.23081 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFPLoS One
January 2025
SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka.
This study explores the integration of sexual and reproductive health (SRH) education in Sri Lanka, utilizing the Health Belief Model (HBM) to predict the perceived quality of SRH education among non-state undergraduate students. In many Asian countries, including Sri Lanka, cultural resistance and skepticism often challenge SRH education initiatives. The research is based on a questionnaire survey, examining factors influencing the perceived quality of SRH education, such as cultural norms, embarrassment, attitudes, awareness, and institutional support.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the S2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model.
View Article and Find Full Text PDFCurr Res Transl Med
January 2025
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
View Article and Find Full Text PDFJMIR Hum Factors
January 2025
School of Nursing, National Taipei University of Nursing and Health Sciences, Room B631, No. 365, Ming-te Road, Peitou District, Taipei City, 11219, Taiwan, 886 2 28227101 ext 3186.
Background: Colonoscopy is the standard diagnostic method for colorectal cancer. Patients usually receive written and verbal instructions for bowel preparation (BP) before the procedure. Failure to understand the importance of BP can lead to inadequate BP in 25%-30% of patients.
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