Individual differences in the speed of information processing have been hypothesized to give rise to individual differences in general intelligence. Consistent with this hypothesis, reaction times (RTs) and latencies of event-related potential have been shown to be moderately associated with intelligence. These associations have been explained either in terms of individual differences in some brain-wide property such as myelination, the speed of neural oscillations, or white-matter tract integrity, or in terms of individual differences in specific processes such as the signal-to-noise ratio in evidence accumulation, executive control, or the cholinergic system. Here we show in a sample of 122 participants, who completed a battery of RT tasks at 2 laboratory sessions while an EEG was recorded, that more intelligent individuals have a higher speed of higher-order information processing that explains about 80% of the variance in general intelligence. Our results do not support the notion that individuals with higher levels of general intelligence show advantages in some brain-wide property. Instead, they suggest that more intelligent individuals benefit from a more efficient transmission of information from frontal attention and working memory processes to temporal-parietal processes of memory storage. (PsycINFO Database Record
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http://dx.doi.org/10.1037/xge0000325 | 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 PDFJ Chem Theory Comput
January 2025
Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China.
Traditional machine learning methods face significant challenges in predicting the properties of highly symmetric molecules. In this study, we developed a machine learning model based on graph neural networks (GNNs) to accurately and swiftly predict the thermodynamic and photochemical properties of fullerenols, such as C(OH) ( = 1 to 30). First, we established a global method for generating fullerenol isomers through isomer fingerprinting, which can generate all possible isomers or produce diverse structural types on demand.
View Article and Find Full Text PDFPLoS One
January 2025
School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.
Background: Previous studies have shown that both the composite dietary antioxidant index (CDAI) and sex are strongly associated with a variety of cardiovascular diseases, but sex differences between CDAI and hyperlipidemia are unknown.
Objective: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) to investigate the sex differences between CDAI and hyperlipidemia.
Method: We calculated the CDAI of the six dietary antioxidants using data from NHANES, explored the relationship between CDAI and the prevalence of hyperlipidemia using multivariate logistic regression analysis, and analyzed for potential nonlinear associations using restricted cubic spline.
PLoS One
January 2025
Biology Department, Faculty of Science, Islamic University of Madinah, Madinah, Saudi Arabia.
This study presents a novel approach to modeling breast cancer dynamics, one of the most significant health threats to women worldwide. Utilizing a piecewise mathematical framework, we incorporate both deterministic and stochastic elements of cancer progression. The model is divided into three distinct phases: (1) initial growth, characterized by a constant-order Caputo proportional operator (CPC), (2) intermediate growth, modeled by a variable-order CPC, and (3) advanced stages, capturing stochastic fluctuations in cancer cell populations using a stochastic operator.
View Article and Find Full Text PDFJ Healthc Manag
January 2025
Division of Hospital Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, and Rocky Mountain Regional VA Medical Center, Aurora, Colorado.
Goal: To evaluate long-term outcomes of Better Together Physician Coaching, a digital life-coaching program to improve resident well-being.
Methods: We performed a secondary analysis of survey data from the pilot program implementation between January 2021 and June 2022. An intention-to-treat analysis was completed for baseline versus post-6 months and baseline versus post-12 months for all outcome measures.
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