SCD
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Related Articles from SNS
SCD-dependent lipid metabolism licenses alternative macrophage activation and macrophage plasticity
Lipid metabolic reprogramming accompanies macrophage activation, yet our understanding of why macrophages profoundly reshape their lipid composition remains unclear. Here, we identify stearoyl-CoA desaturase (SCD) as a lipid-metabolic checkpoint required for the acquisition of the alternatively activated macrophage (AAM) cell state. We show that SCD maintains lipid desaturation balance by ensuring the conversion of newly synthesized saturated long-fatty acids (SFAs) into monounsaturated...
Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes
new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention. Existing connectome-based deep learning models can improve classification performance but often provide limited insight into disease-related functional and structural connectivity changes. This paper proposes an atlas-knowledge-guided...
SemDINO: A DINOv3-Driven Network for Cross-Temporal Semantic Alignment in Change Detection
arXiv:2606.09772v1 Announce Type: new Abstract: Semantic change detection (SCD) aims to simultaneously locate land-cover changes and identify semantic categories before and after transition. However, existing methods suffer from insufficient cross-temporal alignment, weak multi-scale representation, and poor robustness to pseudo-changes caused by illumination, season, and registration noise. To address these issues, we propose a novel end-to-end semantic change detection network named...
idSCD: Identifying Training Datasets through Semantic Correlation Descriptors
Announce Type: new Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model's learned semantic correlation structure: incidental regularities that are predictive within a dataset, but not causal for the underlying task, can be internalized during training. We use this insight to study dataset-level membership inference, moving beyond existing methods that rely on behavioral or...
TriAlignGR: Triangular Multitask Alignment with Multimodal Deep Interest Mining for Generative Recommendation
arXiv:2605.05249v3 Announce Type: replace Abstract: We introduce TriAlignGR, a unified multitask-multimodal framework for generative recommendation that establishes two-stage multimodal semantic propagation: (i) encoding visual semantics directly into SIDs via multimodal embeddings, and (ii) enabling the model to decode these semantics through visual description tasks. Existing Semantic ID (SID) pipelines suffer from two fundamental but underexplored problems: \textbf{SID Content Degradation...