Paper List
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PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell represen...
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Beyond Bayesian Inference: The Correlation Integral Likelihood Framework and Gradient Flow Methods for Deterministic Sampling
This paper addresses the core challenge of calibrating complex biological models (e.g., PDEs, agent-based models) with incomplete, noisy, or heterogen...
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Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
This paper addresses the core challenge of detecting viral variants in wastewater sequencing data without reference genomes or labeled annotations, ov...
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
Influence of Object Affordance on Action Language Understanding: Evidence from Dynamic Causal Modeling Analysis
Centre for Linguistic Science and Technology (CLST), Indian Institute of Technology Guwahati | Neural Engineering Lab, Department of Bio Sciences and Bio Engineering, IIT Guwahati | Biomimetic Robotics and Artificial Intelligence Lab (BRAIL), Department of Mechanical Engineering, IIT Guwahati
The 30-Second View
IN SHORT: This study addresses the core challenge of moving beyond correlational evidence to establish the *causal direction* and *temporal dynamics* of how object affordance processing in sensorimotor brain regions drives the comprehension of action-related language.
Innovation (TL;DR)
- Methodology Applies Dynamic Causal Modeling (DCM) to EEG data to infer *directed, causal connectivity* between key brain regions during affordance-language processing, moving beyond traditional correlational analyses.
- Biology Identifies a specific feedforward causal architecture where the Ventral Premotor Cortex (PMv) acts as a driver, causally influencing the Inferior Parietal Lobule (IPL) and Posterior Superior Temporal Gyrus (pSTG) during action language comprehension.
- Theory Provides direct, mechanistic evidence supporting grounded/embodied cognition theories by showing that affordance-related motor regions (PMv) actively *drive* semantic hubs (pSTG, IPL), rather than merely co-activating with them.
Key conclusions
- Bayesian Model Selection identified a dominant model (M6, exceedance probability = 0.91) featuring strong modulatory influences from PMv to IPL (mean coupling strength = 0.28 Hz ± 0.05) and PMv to pSTG, establishing a causal feedforward pathway from motor to semantic regions.
- The video+text condition significantly strengthened the causal influence from PMv to IPL and pSTG compared to the text-only condition, demonstrating that multimodal (visual+linguistic) affordance cues amplify the driving role of premotor cortex.
- Source localization (LORETA) and DCM together delineate a core left-hemisphere network (LOC, pSTg, PMv, IPL) where visual input (LOC) feeds into premotor affordance processing (PMv), which in turn causally drives semantic integration in parietal (IPL) and temporal (pSTG) hubs.
Abstract: This study investigates the causal neural dynamics by which affordance representations influence action language comprehension. In this study, 18 participants observed stimuli displayed in two conditions during the experiment: text-only (e.g., ‘Hit with a hammer’) and video+text (visual clips with matching phrases). EEG data were recorded from 32 channels and analyzed for event-related potentials and source localization using LORETA, which identified four left-hemisphere regions of interest: the Lateral Occipital Cortex (LOC), Posterior Superior Temporal Gyrus (pSTG), Ventral Premotor Cortex (PMv), and Inferior Parietal Lobule (IPL). A space of dynamic causal modeling (DCM) was constructed with driving inputs to LOC and pSTG, and multiple connectivity configurations were tested. Bayesian Model Selection revealed a dominant model in which PMv causally influenced IPL and pSTG, reflecting a feedforward architecture from affordance-related motor regions to semantic hubs. Bayesian Model Averaging further confirmed strong endogenous connections from LOC to PMv and IPL, and significant modulation from PMv to IPL. These findings provide direct evidence that affordance processing in premotor regions drives action language understanding by engaging downstream parietal and temporal areas. The results support grounded cognition theories and offer a mechanistic account of how sensorimotor information contributes to linguistic comprehension.