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Neuronal guidance factor Sema3A inhibits neurite ingrowth and prevents chondrocyte hypertrophy in the degeneration of knee cartilage in mice,monkeys and humans

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Bone Research 2025年第1期13卷 75-90页

作者： Shishu Huang Dashuang Gao Zhenxia Li Hongchen He Xi Yu Xuanhe You Diwei Wu Ze Du Jiancheng Zeng Xiaojun Shi Qinshen Hu Yong Nie Zhong Zhang Zeyu Luo Duan Wang Zhihe Zhao Lingli Li Guanglin Wang Liping Wang Zongke Zhou Di Chen Fan Yang Department of Orthopedics Surgery and Orthopedic Research Institute National Clinical Research Center for GeriatricsWest China HospitalSichuan UniversityChengduChina The Guangdong Provincial Key Laboratory of Brain Connectome and Behavior CAS Key Laboratory of Brain Connectome and Manipulationthe Brain Cognition and Brain Disease Institute(BCBDI)Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research InstitutionsShenzhenChina University of Chinese Academy of Sciences BeijingChina The seventh Clinical Medical School of Guangzhou University of Chinese Medicine Bao’an District TCM HospitalShenzhenGuangdongChina Department of Orthodontics Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineCollege of StomatologyShanghai Jiao Tong UniversityNational Center for StomatologyNational Clinical Research Center for Oral DiseasesShanghaiChina Rehabilitation Medicine Center West China HospitalSichuan UniversityChengduChina State Key Laboratory of Oral Diseases National Clinical Research Center for Oral DiseasesWest China Hospital of StomatologySichuan UniversityChengduChina Department of Nursing West China HospitalSichuan UniversityChengduChina Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina

Osteoarthritis(OA)is a degenerative joint disease accompanied with the loss of cartilage and consequent nociceptive *** articular cartilage maintains at aneural *** guidance factor Semaphorin 3A(Sema3A)is a membrane-associated secreted protein with chemorepulsive properties for ***,the role of Sema3A in articular cartilage is still not *** the present studies,we investigated the functions of Sema3A in OA development in mice,non-human primates,and patients with ***3A has a protective effect on cartilage degradation,validated by the organoid culture in vitro and confirmed in chondrocyte-specific Sema3A conditional knockout *** demonstrated that Sema3A is a key molecule in maintaining cartilage homeostasis from chondrocyte hypertrophy via activating the PI3K *** potential usage of Sema3A for OA treatment was validated in mouse and Rhesus macaque OA models through intra-articular injection of Sema3A,and also in patients by administering Sema3A containing platelet-rich plasma into the knee *** studies demonstrated that Sema3A exerts a critical role in inhibiting neurite ingrowth and preventing chondrocyte hypertrophy in cartilage,and could be potentially used for OA treatment.

关键词： Sema3A cartilage hypertrophy

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Multi-Level and Energy-Efficient Partial Computation Offloading in Heterogeneous Edge Intelligence

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IEEE Internet of Things Journal 2025年

作者： Xu, Baoyu Ruan, Yancheng Qiu, Chenghu He, Shuibing Shu, Feng Kang, Xiaoyang Zhang, Lihua Academy for Engineering and Technology Shanghai200433 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China University of Sydney School of Electrical and Information Engineering Australia Fudan University Laboratory for Neural Interface and Brain Computer Interface MOE Frontiers Center for Brain Science Academy for Engineering and Technology Shanghai200433 China Fudan University Academy for Engineering and Technology Shanghai200433 China

Due to the diversity of edge devices and applications, edge systems are heterogeneous and have been applied in artificial intelligence fields such as smart factories and intelligent transportation, which is called heterogeneous edge intelligence. Many studies employ computation offloading to transfer processing data from resource-scarce edge devices to resource-rich edge servers. These studies primarily focus on the overall resource consumption of homogeneous edge systems, neglecting the system heterogeneity and the details of resource consumption. In this article, we construct a system model from a parallel perspective for the heterogeneous edge system with different processors, memory, and applications, which perceives the cost of energy and delay from three levels: system, application, and component. A hybrid metaheuristic algorithm combined with a greedy rule, hybrid mutation, and whale optimization algorithm (GHMWOA) is proposed to realize partial computation offloading. A partial offloading architecture of heterogeneous edge intelligence is proposed to validate our model and algorithm with real-world hardware and software. Experiment results not only show GHMWOA outperforms multiple classical optimization algorithms in minimizing energy consumption, but also discover on which system component energy consumption depends, and how properties of application and system influence the cost of energy. © 2014 IEEE.

关键词： Heuristic algorithms

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Multi-Site rs-fMRI Domain Alignment for Autism Spectrum Disorder Auxiliary Diagnosis Based on Hyperbolic Space

arXiv

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arXiv 2025年

作者： Luo, Yiqian Chen, Qiurong Li, Fali Xu, Peng Zhang, Yangsong Laboratory for Brain Science and Artificial Intelligence School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China Laboratory for Brain Science and Artificial Intelligence School of Computer Science and Technology China School of Medicine Southwest University of Science and Technology Mianyang621010 China MOE Key Laboratory for Neuroinformation Clinical Hospital of Chengdu Brain Science Institute University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China

Increasing the volume of training data can enable the auxiliary diagnostic algorithms for Autism Spectrum Disorder (ASD) to learn more accurate and stable models. However, due to the significant heterogeneity and domain shift in rs-fMRI data across different sites, the accuracy of auxiliary diagnosis remains unsatisfactory. Moreover, there has been limited exploration of multi-source domain adaptation models on ASD recognition, and the interpretability of models is often poor. To address these challenges, we proposed a domain-adaptive algorithm based on hyperbolic space embedding. Hyperbolic space is naturally suited for representing the topology of complex networks such as brain functional networks. Therefore, we embedded the brain functional network into hyperbolic space and constructed the corresponding hyperbolic space community network to effectively extract latent representations. To address the heterogeneity of data across different sites and the issue of domain shift, we introduce a constraint loss function, Hyperbolic Maximum Mean Discrepancy (HMMD), to align the marginal distributions in the hyperbolic space. Additionally, we employ class prototype alignment to to mitigate discrepancies in conditional distributions across domains. Experimental results demonstrated that the proposed algorithm shows superior classification performance for ASD compared with baseline models, and is more robust to multi-site heterogeneity. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search

arXiv

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arXiv 2025年

作者： Liu, Shengcai Lv, Haoze Wang, Zhiyuan Tang, Ke Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. This work presents UNiCS, a novel unified neural-guided cascaded solver for solving large-scale TSP instances. UNiCS comprises a local search (LS) phase and a population-based search (PBS) phase, both guided by a learning component called unified neural guidance (UNG). Specifically, UNG guides solution generation across both phases and determines appropriate phase transition timing to effectively combine the complementary strengths of LS and PBS. While trained only on simple distributions with relatively small-scale TSP instances, UNiCS generalizes effectively to challenging TSP benchmarks containing much larger instances (10,000-71,009 nodes) with diverse node distributions entirely unseen during training. Experimental results on the large-scale TSP instances demonstrate that UNiCS consistently outperforms state-of-the-art methods, with its advantage remaining consistent across various runtime budgets. Copyright © 2025, The Authors. All rights reserved.

关键词： Budget control

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Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression

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Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2025年第1期26卷 26-38页

作者： Chunxiao DAI Yaoyang FU Xuanwei LI Meihua LIN Yinbo LI Xiao LI Keke HUANG Chengcheng ZHOU Jian XIE Qingwei ZHAO Shaohua HU Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Department of Clinical Psychology Hangzhou First People’s HospitalHangzhou 310006China Department of Clinical Pharmacy the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Fuyang Third People’s Hospital Hangzhou 311402China Nanhu Brain-Computer Interface Institute Hangzhou 311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003China School of Brain Science and Brain Medicine and MOE Frontier Science Center for Brain Science and Brain-Machine IntegrationZhejiang University School of MedicineHangzhou 310012China Brain Research Institute of Zhejiang University Hangzhou 310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310003China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003China

Objective:Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains *** study aimed to validate the efficacy and safety of vortioxetine in bipolar ***:Patients with bipolarⅡdepression were enrolled in this prospective,two-center,randomized,12-week pilot *** main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale(MADRS)of≥50%.All eligible patients initially received four weeks of lurasidone *** who responded well continued to receive this kind of ***,no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight *** comprehensively comparing the results of MADRS over a period of 4-12 weeks,a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar ***:Thirty-seven patients responded to lurasidone monotherapy,and 60 patients were randomly assigned to the valproate or vortioxetine group for eight *** two weeks of combined valproate or vortioxetine treatment,the MADRS score in the vortioxetine group was significantly lower than that in the valproate *** was no difference in the MADRS scores between the two groups at 8 and 12 *** incidence of side effects did not significantly differ between the valproate and vortioxetine ***,three patients in the vortioxetine group appeared to switch to mania or ***:This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage,while disease progression should be monitored closely for the risk of switching to mania.

关键词： BipolarⅡdepression Lurasidone Vortioxetine Combination

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Domain-Agnostic Co-Evolution of Generalizable Parallel Algorithm Portfolios

arXiv

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arXiv 2025年

作者： Wang, Zhiyuan Liu, Shengcai Yang, Peng Tang, Ke The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers. Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population to eventually obtain PAPs with good generalization. Yet, when applied to a specific problem class, these approaches have a major limitation. They require practitioners to provide instance generators specially tailored to the problem class, which is often non-trivial to design. This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE), for binary optimization problems where decision variables take values of 0 or 1. The key innovation of DACE lies in its neural network-based domain-agnostic instance representation and generation mechanism that delimitates the need for domain-specific instance generators. The strong generality of DACE is validated across three real-world binary optimization problems: the complementary influence maximization problem (CIMP), the compiler arguments optimization problem (CAOP), and the contamination control problem (CCP). Given only a small set of training instances from these classes, DACE, without requiring any domain knowledge, constructs PAPs with better generalization performance than existing approaches on all three classes, despite their use of domain-specific instance generators. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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Large Language Model-Based Benchmarking Experiment Settings for Evolutionary Multi-Objective Optimization

arXiv

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arXiv 2025年

作者： Pang, Lie Meng Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

When we manually design an evolutionary optimization algorithm, we implicitly or explicitly assume a set of target optimization problems. In the case of automated algorithm design, target optimization problems are usually explicitly shown. Recently, the use of large language models (LLMs) for the design of evolutionary multi-objective optimization (EMO) algorithms have been examined in some studies. In those studies, target multi-objective problems are not always explicitly shown. It is well known in the EMO community that the performance evaluation results of EMO algorithms depend on not only test problems but also many other factors such as performance indicators, reference point, termination condition, and population size. Thus, it is likely that the designed EMO algorithms by LLMs depends on those factors. In this paper, we try to examine the implicit assumption about the performance comparison of EMO algorithms in LLMs. For this purpose, we ask LLMs to design a benchmarking experiment of EMO algorithms. Our experiments show that LLMs often suggest classical benchmark settings: Performance examination of NSGA-II, MOEA/D and NSGA-III on ZDT, DTLZ and WFG by HV and IGD under the standard parameter specifications. Copyright © 2025, The Authors. All rights reserved.

关键词： Population statistics

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Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

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science Bulletin 2025年第4期70卷 573-586页

作者： Jujiao Kang Liu Yang Tianye Jia Wei Zhang Lin-Bo Wang Yu-Jie Zhao Jia You Yue-Ting Deng Yi-Jun Ge Wei-Shi Liu Yi Zhang Yi-Lin Chen Xiao-Yu He Barbara J.Sahakian Yucheng TYang Xing-Ming Zhao Jin-Tai Yu Jianfeng Feng Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence(ISTBI) Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghai 200433China Department of Neurology and National Center for Neurological Disorders Huashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical CollegeFudan UniversityShanghai 200433China Department of Psychiatry University of CambridgeCambridge CB20SZUK Department of Computer Science University of WarwickCoventry CV47ALUK School of Data Science Fudan UniversityShanghai 200438China

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential *** data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline,we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident *** regression was then used to assess associations between depression-related proteins and brain structures,genetic factors,and stress-related *** analysis identified 157 proteins associated with incident depression(P<1.71×10^(-5)),including novel associations with proteins such as GAST,PLAUR,LRRN1,BCAN,and ***,higher expression levels of GDF15(P=6.18×10-26)and PLAUR(P=2.88×10^(-14))were linked to an increased risk of depression,whereas higher levels of LRRN1(P=4.28×10^(-11))and ITGA11(P=3.68×10^(-9))were associated with a decreased *** of the 157 proteins is correlated with brain regions implicated in depression,including the hippocampus and middle temporal ***,these protein alterations were strongly correlated with stress-related events,including self-harm events,adult,and childhood *** pathway enrichment analysis highlighted the critical roles of the immune *** and TNF emerged as key proteins in the protein-protein interaction ***3A2,newly linked to incident depression(P=4.35×10^(-10)),was confirmed as a causal factor through Mendelian randomization *** summary,our research identified the proteomic signatures associated with the onset of depression,highlighting its potential for early intervention and tailored therapeutic avenues.

关键词： Plasma proteins Depression brain structure

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Pose as a Modality: A Psychology-Inspired Network for Personality Recognition with a New Multimodal Dataset

arXiv

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arXiv 2025年

作者： Tang, Bin Pan, Ke-Qi Zheng, Miao Zhou, Ning Sui, Jia-Lu Zhu, Dandan Deng, Cheng-Long Kuai, Shu-Guang School of Computer Science and Technology Shanghai Institute of Artificial Intelligence for Education Lab of Artificial Intelligence for Education East China Normal University Shanghai China School of Psychology and Cognitive Science Institute of Brain and Education Innovation Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention Shanghai Key Laboratory of Brain Functional Genomics Key Laboratory of Brain Functional Genomics East China Normal University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China NYU-ECNU Institute of Brain and Cognitive Science New York University Shanghai Shanghai China

In recent years, predicting Big Five personality traits from multimodal data has received significant attention in artificial intelligence (AI). However, existing computational models often fail to achieve satisfactory performance. Psychological research has shown a strong correlation between pose and personality traits, yet previous research has largely ignored pose data in computational models. To address this gap, we develop a novel multimodal dataset that incorporates full-body pose data. The dataset includes video recordings of 287 participants completing a virtual interview with 36 questions, along with self-reported Big Five personality scores as labels. To effectively utilize this multimodal data, we introduce the Psychology-Inspired Network (PINet), which consists of three key modules: Multimodal Feature Awareness (MFA), Multimodal Feature Interaction (MFI), and Psychology-Informed Modality Correlation Loss (PIMC Loss). The MFA module leverages the Vision Mamba Block to capture comprehensive visual features related to personality, while the MFI module efficiently fuses the multimodal features. The PIMC Loss, grounded in psychological theory, guides the model to emphasize different modalities for different personality dimensions. Experimental results show that the PINet outperforms several state-of-the-art baseline models. Furthermore, the three modules of PINet contribute almost equally to the model’s overall performance. Incorporating pose data significantly enhances the model’s performance, with the pose modality ranking mid-level in importance among the five modalities. These findings address the existing gap in personality-related datasets that lack full-body pose data and provide a new approach for improving the accuracy of personality prediction models, highlighting the importance of integrating psychological insights into AI frameworks. Copyright © 2025, The Authors. All rights reserved.

关键词： Psychoacoustic

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Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT

arXiv

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arXiv 2025年

作者： Mei, Zihao Li, Jianhao Zhang, Bolin Wang, Chong Guo, Lijun Li, Guoqi Qian, Jiangbo Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Beijing100045 China

With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neural networks (SNNs) can encode information through spikes, they demonstrate great potential in promoting energy efficiency. Based on the binary characteristics of SNNs, we first propose a novel supervised hashing method named Spikinghash with a hierarchical lightweight structure. Spiking WaveMixer (SWM) is deployed in shallow layers, utilizing a multilevel 3D discrete wavelet transform (3D-DWT) to decouple spatiotemporal features into various low-frequency and high-frequency components, and then employing efficient spectral feature fusion. SWM can effectively capture the temporal dependencies and local spatial features. Spiking Self-Attention (SSA) is deployed in deeper layers to further extract global spatiotemporal information. We also design a hash layer utilizing binary characteristic of SNNs, which integrates information over multiple time steps to generate final hash codes. Furthermore, we propose a new dynamic soft similarity loss for SNNs, which utilizes membrane potentials to construct a learnable similarity matrix as soft labels to fully capture the similarity differences between classes and compensate information loss in SNNs, thereby improving retrieval performance. Experiments on multiple datasets demonstrate that Spikinghash can achieve state-of-the-art results with low energy consumption and fewer parameters. Copyright © 2025, The Authors. All rights reserved.

关键词： Discrete wavelet transforms

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