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Putting the prime in priming: Using prime processing behavior to predict target structural processing.
Psychon Bull Rev
January 2025
University of California, Santa Barbara, CA, USA.
Structural priming effects are widespread and heavily relied upon to assess structural representation and processing. Whether these effects are caused by error-driven implicit learning, residual activation, a combination of these, or some other learning mechanism remains to be established. The current study used preexisting data and a novel data analysis approach that links processing at the prime to later processing at the target to better understand the nature of structural priming.
View Article and Find Full Text PDFImpaired spatial coding of the hippocampus in a dentate gyrus hypoplasia mouse model.
Proc Natl Acad Sci U S A
February 2025
Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218.
The hippocampal dentate gyrus (DG) is thought to orthogonalize inputs from the entorhinal cortex (pattern separation) and relay this information to the CA3 region. In turn, attractor dynamics in CA3 perform a pattern completion or error correction operation before sending its output to CA1. In a mouse model of congenital hypoplasia of the DG, a deficiency in the (Wls) gene, specifically in cells expressing , which targets neuronal progenitors, led to an almost total absence of dentate granule cells and modestly impaired performance in spatial tasks.
View Article and Find Full Text PDFAbstract visual reasoning based on algebraic methods.
Sci Rep
January 2025
School of Computer Science and Technology, Donghua University, Shanghai, 201620, China.
Extracting high-order abstract patterns from complex high-dimensional data forms the foundation of human cognitive abilities. Abstract visual reasoning involves identifying abstract patterns embedded within composite images, considered a core competency of machine intelligence. Traditional neuro-symbolic methods often infer unknown objects through data fitting, without fully exploring the abstract patterns within composite images and the sequential sensitivity of visual sequences.
View Article and Find Full Text PDFAccurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part III: partitioning into torsional groups.
Acta Crystallogr B Struct Sci Cryst Eng Mater
February 2025
Department of Chemical Engineering, Sargent Centre for Process Systems Engineering, Institute for Molecular Science and Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
We present an approach to reduce this computational cost substantially, based on the partitioning of the molecule into geometrically separated torsional groups, with the dependence of the intramolecular energy and atomic point charges and dependent degrees of freedom on molecular conformation being computed as a linear combination of the contributions of these groups. This can lead to large savings in computational cost without a significant impact on accuracy, as demonstrated in the cases of N-acetyl-para-aminophenol (paracetamol) and methyl 4-hydroxybenzoate (methyl paraben). The approach is also applied successfully to two larger molecules, benzyl [4-(4-methyl-5-[(4-methylphenyl)sulfonyl]-1,3-thiazol-2-yl)phenyl]carbamate (molecule XX from the fifth CSP blind test) and (2S)-2-[4-(3-fluorobenzyloxy)benzylamino]propionamide (safinamide), for which we conduct the first reported CSP study.
View Article and Find Full Text PDFAdaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.
Interdiscip Sci
January 2025
School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. However, drug-drug interactions (DDIs) can sometimes lead to adverse reactions, potentially endangering lives. Therefore, developing efficient and accurate DDI prediction methods is crucial for elucidating drug mechanisms and preventing side effects.
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