The human visual system is able to extract summary statistics from sets of similar items, but the underlying neural mechanism remains poorly understood. Using functional magnetic resonance imaging (fMRI) and an encoding model, we examined how the neural representation of ensemble coding is constructed by manipulating the task-relevance of ensemble features. We found a gradual increase in orientation-selective responses to the mean orientation of multiple stimuli along the visual hierarchy only when these orientations were task-relevant. Such responses to the ensemble orientation were present in the extrastriate area, V3, even when the mean orientation was not task-relevant, indicating that the ensemble representation can co-exist with the task-relevant individual feature representation. Ensemble orientations were also represented in frontal regions, but those representations were robust only when each mean orientation was linked to a motor response dimension. Together, our findings suggest that the neural representation of the ensemble percept is formed by pooling signals at multiple levels of the visual processing stream.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118680 | DOI Listing |
Spatial transcriptomics data analysis integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer cell-type dynamics, and detect gene expression patterns within tissues. However, the current spatial transcriptomics analysis neglects the multiscale cell-cell interactions that are crucial in biology. To fill this gap, we propose multiscale cell-cell interactive spatial transcriptomics (MCIST) analysis.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
View Article and Find Full Text PDFAtten Percept Psychophys
January 2025
Department of Philosophy, University of York, York, UK.
Here we report four experiments that explore the nature of perceptual averaging. We examine the evidence that participants recover and store a representation of the mean value of a set of perceptual features that are distributed across the optic array. The extant evidence shows that participants are particularly accurate in estimating the relevant mean value, but we ask whether this might be due to processes that reflect assessing featural similarity rather than computing an average.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China.
Drug-induced liver injury (DILI) is a major challenge in drug development, often leading to clinical trial failures and market withdrawals due to liver toxicity. This study presents StackDILI, a computational framework designed to accelerate toxicity assessment by predicting DILI risk. StackDILI integrates multiple molecular descriptors to extract structural and physicochemical features, including the constitution, pharmacophore, MACCS, and E-state descriptors.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Kennewick, WA 99338, United States.
Objective: This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding ("eponymic condition") and the mean of all symptom embeddings associated with a disease ("ensemble mean").
Materials And Methods: Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were translated into vector representations in a high-dimensional space.
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