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Restoration of FMRP expression in adult V1 neurons rescues visual deficits in a mouse model of fragile X syndrome

Protein & Cell 2022年第3期13卷 203-219页

作者： Chaojuan Yang Yonglu Tian Feng Su Yangzhen Wang Mengna Liu Hongyi Wang Yaxuan Cui Peijiang Yuan Xiangning Li Anan Li Hui Gong Qingming Luo Desheng Zhu Peng Cao Yunbo Liu Xunli Wang Min-hua Luo Fuqiang Xu Wei Xiong Liecheng Wang Xiang-yao Li Chen Zhang State Key Laboratory of Membrane Biology PKU-IDG/McGovern Institute for Brain ResearchSchool of Life SciencesPeking UniversityBeijing 100871China School of Basic Medical Sciences Beijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing 100069China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China School of Life Sciences Tsinghua UniversityBeijing 100084China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Brain and Cognitive Sciences Institute of BiophysicsChinese Academy of Sciences(CAS)Beijing 100101China Institute of Laboratory Animal Science Peking Union Medical College/Chinese Academy of Medical ScienceBeijing 100021China Laboratory Animal Center Fujian University of Tradition Chinese MedicineFuzhou 350122China State Key Laboratory of Virology CAS Center for Excellence in Brain Science andIntelligence TechnologyWuhan Institute of VirologyCASWuhan 430071China Center for Brain Science Key Laboratory of Magnetic Resonance in Biological Systems and State Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsWuhan Institute of Physics and MathematicsCASCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesWuhan 430071China Department of Physiology Anhui Medical UniversityHefei 230032China Department of Physiology Institute of Neuroscience and Collaborative Innovation Center for Brain ScienceSchool of MedicineZhejiang UniversityHangzhou 310058China

Many people affected by fragile X syndrome(FXS)and autism spectrum disorders have sensory processing deficits,such as hypersensitivity to auditory,tactile,and visual *** FXS in humans,loss of Fmr1 in rodents also cause sensory,behavioral,and cognitive ***,the neural mechanisms underlying sensory impairment,especially vision impairment,remain *** remains elusive whether the visual processing deficits originate from corrupted inputs,impaired perception in the primary sensory cortex,or altered integration in the higher cortex,and there is no effective *** this study,we used a genetic knockout mouse model(Fmr1^(KO)),in vivo imaging,and behavioral measurements to show that the loss of Fmr1 impaired signal processing in the primary visual cortex(V1).Specifically,Fmr1^(KO) mice showed enhanced responses to low-intensity stimuli but normal responses to high-intensity *** abnormality was accompanied by enhancements in local network connectivity in V1 microcircuits and increased dendritic complexity of V1 *** effects were ameliorated by the acute application of GABAA receptor activators,which enhanced the activity of inhibitory neurons,or by reintroducing Fmr1 gene expression in knockout V1 neurons in both juvenile and young-adult ***,V1 plays an important role in the visual abnormalities of Fmr1^(KO) mice and it could be possible to rescue the sensory disturbances in developed FXS and autism patients.

关键词： autism spectrum disorder calcium imaging fragile X syndrome primary visual cortex visual hypersensitivity

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Donghua Ni, Jiangqun Alasbali, Nada Nafea, Ibtehal Liu, Qingliang Ahmad, Jawad Boulila, Wadii Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518052 China King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Taibah University College of Computer Science and Engineering Medina41477 Saudi Arabia Qingdao University of Science and Technology School of Mathematics and Physics Qingdao266061 China Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers' facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate. © 1975-2011 IEEE.

关键词： Anonymity

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One-Point Sampling for Distributed Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Wen, Guanghui Xie, Lihua Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Department of Mathematics Southeast University Nanjing210096 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed bandit convex optimization problem with time-varying constraints. In this problem, the global loss function is the average of all the local convex loss functions, which are unknown beforehand. Each agent iteratively makes its own decision subject to time-varying inequality constraints which can be violated but are fulfilled in the long run. For a uniformly jointly strongly connected time-varying directed graph, a distributed bandit online primal–dual projection algorithm with one-point sampling is proposed. We show that sublinear dynamic network regret and network cumulative constraint violation are achieved if the path–length of the benchmark also increases in a sublinear manner. In addition, an O(T3/4+g) static network regret bound and an O(T1−g/2) network cumulative constraint violation bound are established, where T is the total number of iterations and g ∈ (0, 1/4) is a trade-off parameter. Moreover, a reduced static network regret bound O(T2/3+4g/3) is established for strongly convex local loss functions. Finally, a numerical example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Speech-Vision Based Multi-Modal AI Control of a Magnetic Anchored and Actuated Endoscope

Speech-Vision Based Multi-Modal AI Control of a Magnetic Anc...

引用

2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Jixiu Huang, Yisen Ng, Wing Yin Cheng, Truman Wu, Xixin Dou, Qi Meng, Helen Heng, Pheng Ann Liu, Yunhui Chan, Shannon Melissa Navarro-Alarcon, David Hang Ng, Calvin Sze Yan Chiu, Philip Wai Li, Zheng The Department of Surgery The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Mechanical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong The Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Surgery ChowYuk Ho Technology Centre of Innovative Medicine Li Ka Shing Institute of Health Science Multiscale Medical Robotics Center Ltd. The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781665481090

In minimally invasive surgery (MIS), controlling the endoscope view is crucial for the operation. Many robotic endoscope holders were developed aiming to address this prob-lem,. These systems rely on joystick, foot pedal, simple voice command, etc. to control the robot. These methods requires surgeons extra effort and are not intuitive enough. In this paper, we propose a speech-vision based multi-modal AI approach, which integrates deep learning based instrument detection, automatic speech recognition and robot visual servo control. Surgeons could communicate with the endoscope by speech to indicate their view preference, such as the instrument to be tracked. The instrument is detected by the deep learning neural network. Then the endoscope takes the detected instrument as the target and follows it with the visual servo controller. This method is applied to a magnetic anchored and guided endoscope and evaluated experimentally. Preliminary results demonstrated this approach is effective and requires little efforts for the surgeon to control the endoscope view intuitively. © 2022 IEEE.

关键词： Endoscopy

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Semiconductor Manufacturing Industry: Assessment, Challenges, and Future Trends

Semiconductor Manufacturing Industry: Assessment, Challenges...

引用

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Yasmin Musa Khalid Tantawi Maged Mikhail Jeffrey Ma Dalal Alharthi Larry Flatt Lyn Potter Department of Career Readiness Motlow State Community College Smyrna TN Department of Engineering Management and Technology University of Tennessee at Chattanooga TN Department of Engineering Technology Purdue University Northwest Hammond IN Department of Mechanical Engineering Saint Louis University Saint Louis MO College of Applied Science and Technology University of Arizona Tuscan AZ Director of the Robotics and Automation Center Motlow State Community College McMinnville TN Dept. of Engineering Systems Tech. Chattanooga State Community College Chattanooga State Community College Chattanooga TN

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

In this work we evaluate the state of the semiconductor manufacturing industry and its challenges and trends. Future trends in the industry are analyzed from three perspectives: the evolution of Industry 4.0, the advances in semiconductor materials, and the impact of the Covid-19 Pandemic. The semiconductor manufacturing industry witnessed an acute decline in the United States and other regions in the two decades prior to the CoVid-19 pandemic. The decline was only uncovered after the chip shortage of 2021 that resulted from the severe supply chain disruption. Trends in the industry were analyzed from three perspectives: Industry 4.0, advances in materials, and the Post-pandemic era. As a result of the evolution of the fourth generation of industry (Industry 4.0), trends in semiconductor manufacturing include robotization, which caused the industry to become the largest market for industrial robotics since 2020, and an all-time peak globalization. The semiconductor industry is a very globalized industry with corporates from different parts of the world taking part in the production of the final product. Although some materials such as carbon and Gallium Nitride show promising trends to replace silicon as the material of choice. It will likely not be before two or three decades when a semiconductor material will be able to replace silicon. Challenges for the industry include the shortage of the trained-workforce, and the added inter-country restrictions that may hinder the globalization of the industry.

关键词： Industries COVID-19 Pandemics Service robots Supply chains Globalization Semiconductor device manufacture Market research Silicon Fourth Industrial Revolution

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Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

Learning-based Control for PMSM Using Distributed Gaussian P...

引用

Annual Conference of Industrial Electronics Society

作者： Zhenxiao Yin Xiaobing Dai Zewen Yang Yang Shen Georges Hattab Hang Zhao Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Chair of Information-oriented Control Technical University of Munich Munich Germany Center for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong China

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

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Rethinking specificity in SBDD: leveraging delta score and energy-guided diffusion 24

Rethinking specificity in SBDD: leveraging delta score and e...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Bowen Gao Minsi Ren Yuyan Ni Yanwen Huang Bo Qiang Zhi-Ming Ma Wei-Ying Ma Yanyan Lan Institute for AI Industry Research (AIR) Tsinghua University Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Academy of Mathematics and Systems Science Chinese Academy of Sciences Department of Pharmaceutical Science Peking University Institute for AI Industry Research (AIR) Tsinghua University and Beijing Frontier Research Center for Biological Structure Tsinghua University Beijing China and Beijing Academy of Artificial Intelligence (BAAI)

In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score. However, further study shows that the existing molecular generative methods and docking scores both have lacked consideration in terms of specificity, which means that generated molecules bind to almost every protein pocket with high affinity. To address this, we introduce the Delta Score, a new metric for evaluating the specificity of molecular binding. To further incorporate this insight for generation, we develop an innovative energy-guided approach using contrastive learning, with active compounds as decoys, to direct generative models toward creating molecules with high specificity. Our empirical results show that this method not only enhances the delta score but also maintains or improves traditional docking scores, successfully bridging the gap between SBDD and real-world needs.

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

引用

arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Extracting Semantic Knowledge from GANs with Unsupervised Learning

arXiv

引用

arXiv 2022年

作者： Xu, Jianjin Zhang, Zhaoxiang Hu, Xiaolin The School of Mathematics and Computer Science Panzhihua University The Department of Computer Science and Technology Tsinghua University China The Insitute of Automation Chinese Academy of Sciences The Centre for Artificial Intelligence and Robotics Hong Kong Innovation and Science Insitute Chinese Academy of Sciences Hong Kong The Department of Computer Science and Technology Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems THU-Bosch JCML Center THBI IDG/McGovern Institute for Brain Research BNRist Tsinghua University Beijing China Beijing China

Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations learned by generative models and in particular, Generative Adversarial Networks (GANs). Previous works on the interpretation of GANs reveal that GANs encode semantics in feature maps in a linearly separable form. In this work, we further find that GAN’s features can be well clustered with the linear separability assumption. We propose a novel clustering algorithm, named KLiSH, which leverages the linear separability to cluster GAN’s features. KLiSH succeeds in extracting fine-grained semantics of GANs trained on datasets of various objects, e.g., car, portrait, animals, and so on. With KLiSH, we can sample images from GANs along with their segmentation masks and synthesize paired image-segmentation datasets. Using the synthesized datasets, we enable two downstream applications. First, we train semantic segmentation networks on these datasets and test them on real images, realizing unsupervised semantic segmentation. Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations. © 2022, CC BY-SA.

关键词： Unsupervised learning

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Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation

arXiv

引用

arXiv 2024年

作者： Feng, Yifan Huang, Jiangang Du, Shaoyi Ying, Shihui Yong, Jun-Hai Li, Yipeng Ding, Guiguang Ji, Rongrong Gao, Yue School of Software BNRist THUIBCS BLBCI Tsinghua University Beijing100084 China Institute of Artificial Intelligence and Robotics College of Artificial Intelligence Xi’an Jiaotong University Xi’an710049 China The Department of Mathematics School of Science Shanghai University Shanghai200444 China The Department of Automation Tsinghua University Beijing100084 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Institute of Artificial Intelligence Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Xiamen University 361005 China

We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features. Traditional YOLO models, while powerful, have limitations in their neck designs that restrict the integration of cross-level features and the exploitation of high-order feature interrelationships. To address these challenges, we propose the Hypergraph Computation Empowered Semantic Collecting and Scattering (HGC-SCS) framework, which transposes visual feature maps into a semantic space and constructs a hypergraph for high-order message propagation. This enables the model to acquire both semantic and structural information, advancing beyond conventional feature-focused learning. Hyper-YOLO incorporates the proposed Mixed Aggregation Network (MANet) in its backbone for enhanced feature extraction and introduces the Hypergraph-Based Cross-Level and Cross-Position Representation Network (HyperC2Net) in its neck. HyperC2Net operates across five scales and breaks free from traditional grid structures, allowing for sophisticated high-order interactions across levels and positions. This synergy of components positions Hyper-YOLO as a state-of-the-art architecture in various scale models, as evidenced by its superior performance on the COCO dataset. Specifically, HyperYOLO-N significantly outperforms the advanced YOLOv8-N and YOLOv9-T with 12% APval and 9% APval improvements. The source codes are at https://***/iMoonLab/Hyper-YOLO. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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