Performance on different cognitive tasks could either be positively correlated in an individual as a measure of general intelligence or costs related to specific aspects of cognition could give rise to specialized cognitive phenotypes. Social living offers the potential for individual specialization in learning and a cooperative group can benefit from a diversity of learning phenotypes. However, there is little empirical data regarding the nature of such interindividual variation in learning abilities in honeybees, a classic model of animal cognition. We tested for the presence of variation in learning abilities in the honeybee, Apis mellifera, and whether any component of learning has an influence on wing damage, a proxy for performance and survival. Our results show considerable interindividual variation in different types of learning abilities. At the individual level, while landmark and olfactory learning abilities are negatively correlated, olfactory learning shows a positive association with maneuverability performance, a measure which in turn shows a positive influence on wing damage, a proxy for survival. We discuss our results in the context of cognitive diversity and specialization in a social group.
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http://dx.doi.org/10.1016/j.beproc.2019.103918 | DOI Listing |
Bioinformatics
January 2025
Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
Sci Rep
January 2025
Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.
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January 2025
Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
This study retrospectively collected clinical data from 480 patients with connective tissue diseases (CTDs) at Nanjing First Hospital between August 2019 and December 2023 to develop and validate a multi-classification machine learning (ML) model for assessing depression risk. Addressing the limitations of traditional assessment tools, six ML models were constructed using univariate analysis and the LASSO algorithm, with the categorical boosting (Catboost) model emerging as the best performer, demonstrating strong predictive ability across different depression severity levels (none_F1 = 0.879, mild_F1 = 0.
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January 2025
Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Mogadishu, Somalia.
In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.
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January 2025
Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Korea.
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.
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