Whether or not evolutionary lineages in general show a tendency to increase in body size has often been discussed. This tendency has been dubbed "Cope's rule" but because Cope never hypothesized it, we suggest renaming it after Depéret, who formulated it clearly in 1907. Depéret's rule has traditionally been studied using fossil data, but more recently a number of studies have used present-day species. While several paleontological studies of Cenozoic placental mammals have found support for increasing body size, most studies of extant placentals have failed to detect such a trend. Here, we present a method to combine information from present-day species with fossil data in a Bayesian phylogenetic framework. We apply the method to body mass estimates of a large number of extant and extinct mammal species, and find strong support for Depéret's rule. The tendency for size increase appears to be driven not by evolution toward larger size in established species, but by processes related to the emergence of new species. Our analysis shows that complementary data from extant and extinct species can greatly improve inference of macroevolutionary processes.
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http://dx.doi.org/10.1093/sysbio/syv075 | DOI Listing |
Int J Med Inform
January 2025
Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address:
Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
View Article and Find Full Text PDFISA Trans
January 2025
Toronto Metropolitan University, Toronto, Canada. Electronic address:
This research introduces an innovative approach to optimal control for a class of linear systems with input saturation. It leverages the synergy of Takagi-Sugeno (T-S) fuzzy models and reinforcement learning (RL) techniques. To enhance interpretability and analytical accessibility, our approach applies T-S models to approximate the value function and generate optimal control laws while incorporating prior knowledge.
View Article and Find Full Text PDFPharmaceutics
January 2025
Department of Chemical and Pharmaceutical Sciences, University of Trieste, Piazzale Europa 1, 34127 Trieste, Italy.
This study investigates the preparation of coamorphous systems composed entirely of active pharmaceutical ingredients (APIs), namely praziquantel, niclosamide, and mebendazole. The objective was to formulate and characterize binary and ternary coamorphous systems to evaluate their structural, thermal, and stability properties. Ten different mixtures (binary and ternary) were designed through a mixture design approach and prepared using a sustainable, one-step neat grinding process in a lab-scale vibrational mill.
View Article and Find Full Text PDFJ Clin Med
January 2025
Experimental Laboratory for Auxo-Endocrinological Research, Istituto Auxologico Italiano, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), 28824 Piancavallo-Verbania, Italy.
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