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AbstractVocal production learning (the capacity to learn to produce vocalizations) is a multidimensional trait that involves different learning mechanisms during different temporal and socioecological contexts. Key outstanding questions are whether vocal production learning begins during the embryonic stage and whether mothers play an active role in this through pupil-directed vocalization behaviors. We examined variation in vocal copy similarity (an indicator of learning) in eight species from the songbird family Maluridae, using comparative and experimental approaches. We found that (1) incubating females from all species vocalized inside the nest and produced call types including a signature "B element" that was structurally similar to their nestlings' begging call; (2) in a prenatal playback experiment using superb fairy wrens (), embryos showed a stronger heart rate response to playbacks of the B element than to another call element (A); and (3) mothers that produced slower calls had offspring with greater similarity between their begging call and the mother's B element vocalization. We conclude that malurid mothers display behaviors concordant with pupil-directed vocalizations and may actively influence their offspring's early life through sound learning shaped by maternal call tempo.
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http://dx.doi.org/10.1086/728105 | DOI Listing |
Vet Res Commun
September 2025
College of Veterinary Medicine, Vietnam National University of Agriculture, 100000, Hanoi, Vietnam.
African swine fever (ASF) is a contagious viral disease that affects domestic pigs and Eurasian wild boars, causing significant economic losses to the global pig industry. Since its first outbreak in February 2019, ASF has had a profound impact on the Vietnamese pig sector. This review presents a comprehensive analysis of ASF outbreaks in Vietnam from 2019 to 2024, focusing on outbreak dynamics, control strategies, economic impact, and key lessons learned.
View Article and Find Full Text PDFMol Divers
September 2025
Department of Biotechnology, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492001, India.
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors.
View Article and Find Full Text PDFFunct Integr Genomics
September 2025
Department of Plastic Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Keloid scarring and Metabolic Syndrome (MS) are distinct conditions marked by chronic inflammation and tissue dysregulation, suggesting shared pathogenic mechanisms. Identifying common regulatory genes could unveil novel therapeutic targets. Methods.
View Article and Find Full Text PDFChaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
View Article and Find Full Text PDFRadiol Artif Intell
September 2025
Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China.
Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023).
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