Results 51 to 60 of about 80,683 (273)

Psychometric Properties of the Persian Word Pairs Task to Evaluate Declarative Memory

Basic and Clinical Neuroscience, 2022
Introduction: According to the declarative/procedural (DP) model, the semantic aspect of language depends on the brain structures responsible for declarative memory. The word pairs task is a common tool to evaluate declarative memory.
Maryam Malekian, Yalda Kazemi, Talieh Zarifian   +2 more
doaj  

CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities

Advanced Science, EarlyView.
CELLama is created, a framework that harnesses language models to convert cellular data into “sentences” that represent gene expression and metadata, enabling a universal embedding of cells. Unlike most single‐cell foundation models, CELLama supports scalable analysis and offers flexible applications including spatial transcriptomics.
Jeongbin Park, Sumin Kim, Jiwon Kim, Dongjoo Lee, Sungwoo Bae, Haenara Shin, Daeseung Lee, Hongyoon Choi   +7 more
wiley   +1 more source

Perceived Stress as a Mediator between Episodic and Semantic Memory in Hypertensive and Normotensive Individuals: A Neuropsychological Perspective

Nature-Nurture Journal of Psychology
Background: Hypertension is not only a leading cardiovascular risk factor but also significantly influences cognitive functioning, particularly episodic and semantic memory.
Maryum Anees, Aisha Tauqeer
doaj   +1 more source

Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases

Advanced Science, EarlyView.
This schematic integrates the eight statistically significant causal relationships identified between 1,366 brain imaging‐derived phenotypes (IDPs) and 18 autoimmune inflammatory diseases (AIDs). Arrows indicate the direction of causality inferred from bidirectional two‐sample MR analyses.
Jinbin Chen, Xin Wang, Haifeng Ding, Bosheng Zheng, Keni Zeng, Chuying Hu, Jiayi Liu, Xiao Zhu, Haibing Yu   +8 more
wiley   +1 more source

S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning

Advanced Science, EarlyView.
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu, Penglei Wang, Gaoyuan Xu, Jitao Lu, Qinke Peng, Danyang Wu, Hequan Sun   +6 more
wiley   +1 more source

Retrieval and Monitoring Processes during Visual Working Memory: An ERP Study of the Benefit of Visual Semantics

Frontiers in Psychology, 2017
In this study, we examined electrophysiological indices of episodic remembering whilst participants recalled novel shapes, with and without semantic content, within a visual working memory paradigm.
Elizabeth Orme, Louise A. Brown, Leigh M. Riby   +2 more
doaj   +1 more source

CACLENS: A Multitask Deep Learning System for Enzyme Discovery

Advanced Science, EarlyView.
CACLENS, a multimodal and multi‐task deep learning framework integrating cross‐attention, contrastive learning, and customized gate control, enables reaction type classification, EC number prediction, and reaction feasibility assessment. CACLENS accelerates functional enzyme discovery and identifies efficient Zearalenone (ZEN)‐degrading enzymes.
Xilong Yi, Yingzhu Tan, Huikang Lin, Guoqing Zhang, Ye Tian, Aibo Wu   +5 more
wiley   +1 more source

MicrobeDiscover: A Knowledge Graph–Enabled AI Framework for Identifying Microbes for Inorganic Nanomaterial Biosynthesis

Advanced Science, EarlyView.
Microbial synthesis of nanomaterials (NMs) is eco‐friendly, but the screening of microorganisms is limited by inefficient traditional methods (currently only involving∽400 microorganisms/90 NMs). We propose AI framework MicrobeDiscover, integrating a knowledge graph of microbe‐NM interactions.
Ludi Wang, Hexing Han, Yufeng Liu, Zhiyuan Ning, Yujie Ma, Haidan Wang, Jinling Xu, Qiansheng Huang, Wenjuan Cui, Yuanchun Zhou, Yang Gao, Bin Wang, Yi Du   +12 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Episodic and semantic memory impairments in (very) early Alzheimer’s disease: The diagnostic accuracy of paired-associate learning formats

Cogent Psychology, 2016
Paired-associate learning (PAL) paradigms measure memory processes sensitive to the medial temporal lobe, which shows atrophy in early Alzheimer’s disease (AD).
Pauline E.J. Spaan
doaj   +1 more source