Fuse, Antisurge, 4A, 5mm x20mm
- Known as:
- Fuse, Antisurge, 4A, 5mm x20mm
- Catalog number:
- rvth5015
- Product Quantity:
- EUR
- Category:
- -
- Supplier:
- Diasource
- Gene target:
- Fuse Antisurge 4A 5mm x20mm
Related genes to: Fuse, Antisurge, 4A, 5mm x20mm
- Gene:
- FUSE NIH gene
- Name:
- polykaryocytosis promoter
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 10
- Locus Type:
- phenotype only
- Date approved:
- 2001-06-22
- Date modifiied:
- 2012-10-02
Related products to: Fuse, Antisurge, 4A, 5mm x20mm
Related articles to: Fuse, Antisurge, 4A, 5mm x20mm
- Dexterous in-hand manipulation requires robotic hands to estimate object-state reliably under frequent occlusions, contact-rich interactions, and fast dynamics. Tactile sensing provides high-frequency, contact-specific feedback, although extracting useful representations from raw tactile signals and integrating them with vision and proprioception remains challenging. In this article, we present TouchWGNN, a multimodal dexterous manipulation framework that explicitly models tactile signals as a spatio-temporal graph. We first develop a low-cost distributed tactile sensor array for a five-fingered robotic hand, enabling real-time acquisition of normal forces from 113 sensing points distributed across key contact regions. We then construct a tactile graph in which taxels (or active contact points) are nodes with force features and 3D coordinates and edges encode spatial proximity and local force variation. A graph-based spatial encoder captures instantaneous contact geometry, and a temporal module models its evolution over time to refine object-state estimates. Finally, we fuse the tactile estimate with vision (point-cloud-based pose) and proprioception (joint states) for policy learning with reinforcement learning. Experiments on two in-hand manipulation tasks, cube reorientation and Baoding ball swapping, demonstrate that integrating raw tactile feedback with vision and proprioception improves manipulation performance compared with unimodal baselines. - Source: PubMed
Publication date: 2026/06/26
Ning YuZhao FuQiangLiu Qian - Accurate pathological subtyping and epidermal growth factor receptor (EGFR) mutation profiling are critical for personalized non-small cell lung cancer (NSCLC) management. However, traditional invasive biopsies possess inherent limitations in dynamic monitoring and capturing tumor heterogeneity. While dual-modal positron emission tomography/computed tomography (PET/CT) imaging provides valuable non-invasive phenotypic insights, deep learning models that jointly fuse these modalities for simultaneous prediction while maintaining clinical interpretability remain scarce. Therefore, this study proposes an integrated dual-modal PET/CT radiomics framework for the simultaneous prediction of pathological subtypes and EGFR mutation status in NSCLC. - Source: PubMed
Publication date: 2026/06/24
Jiang FanZhang Nan-FengGao YiChen XinLiu En-TaoMou Tian - Early diagnosis of Alzheimer's disease (AD), especially accurate identification at the mild cognitive impairment (MCI) stage, is crucial for slowing disease progression. Although deep learning has achieved promising performance in AD diagnosis, existing multimodal models often operate as "black boxes," lacking the transparency required for clinical practice and failing to explicitly model deep interactions between imaging and clinical features. To address these limitations, this study proposes an interpretable multimodal framework, namely the Compact Biomarker Kolmogorov-Arnold Network (CBKAN). Specifically, we introduce EHCTNet with disease-specific attention to extract features from 3D MRI data, and innovatively constrain the encoder to output a set of compact biomarkers instead of traditional high-dimensional abstract vectors, mimicking the diagnostic logic of clinicians (e.g., judging brain atrophy). In addition, a hybrid feature Transformer is used to fuse these imaging biomarkers with clinical and genetic data, explicitly capturing complementary relationships across modalities. Finally, the Kolmogorov-Arnold Network (KAN) is adopted as the classifier to effectively model the highly nonlinear characteristics of AD progression. Experiments on 800 subjects from the ADNI dataset show that CBKAN achieves 91.1% accuracy and an F1-score of 0.910 in the AD/MCI/CN classification task, significantly outperforming existing mainstream methods. Statistical analyses validate the effectiveness of each component of the model. The proposed model provides a potentially interpretable and high-performing decision-support framework for early Alzheimer's disease diagnosis, although its cross-cohort generalizability and real-world clinical utility require further validation in independent external datasets. - Source: PubMed
Publication date: 2026/07/10
Tan DaiqingSong LianYingTang YanHan Aiqing - The World Health Organization (WHO) recommends six domains for a whole-school approach to promote physical activity (PA) levels. This approach aims to help children achieve 30 of the recommended 60 minutes of daily moderate-to-vigorous physical activity (MVPA). Evidence on PA practice and policy in school settings is limited, particularly in urban areas with high deprivation, where children's PA levels are lowest. - Source: PubMed
Publication date: 2026/07/10
Benkhelfa AminaRam BinaGullett NancyCunningham MarkAzar Sharabiani Mansour TaghaviPallan Mirandavan Sluijs EstherSummerbell CarolynHillsdon MelvynSiddiqui NadiaSaxena Sonia - Personalized Federated Continual Learning (PFCL) requires that the server not only effectively integrate temporal knowledge accumulated across past tasks and spatial knowledge distributed among heterogeneous clients, but also ensure strong personalized performance of the global model for each client. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and enable coarse-to-fine personalization. Furthermore, most approaches rely on local client-side personalization, increasing computational load and failing to address the risks posed by malicious or low-quality clients. To this end, we propose FedMGP+, which utilizes multi-granularity prompts to address these challenges, namely coarse-grained global prompt and fine-grained local prompt. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. Visualization results and theoretical analyses further reveal that coarse-grained prompts primarily guide regional attention, whereas fine-grained prompts enrich object-level representations within those regions. Building upon this, Personalized Selective Prompt Fusion is designed to exclusively fuse coarse-grained knowledge on the server to generate client-specific global prompts, reducing local overhead and resisting poisoning attacks from malicious clients. Extensive experiments demonstrate that the proposed FedMGP+ effectively mitigates forgetting, significantly enhances personalized performance, and provides robust defense against poisoning and gradient leakage attacks. - Source: PubMed
Publication date: 2026/07/09
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