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Chitosan-based thermosensitive hydrogel with long-term release of murine nerve growth factor for neurotrophic keratopathy

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Neural Regeneration Research 2024年第3期19卷 680-686页

作者： Jie Wu Yulei Huang Hanrui Yu Kaixiu Li Shifeng Zhang Guoqing Qiao Xiao Liu Hongmei Duan Yifei Huang Kwok-Fai So Zhaoyang Yang Xiaoguang Li Liqiang Wang Department of Ophthalmology the Third Medical CenterChinese PLA General HospitalBeijingChina The PLA Medical College Department of NephrologyChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesBeijingChina Department of Ophthalmology Hainan Hospital of Chinese PLA General HospitalSanyaHainan ProvinceChina Medical School of Chinese PLA BeijingChina Department of Neurobiology School of Basic Medical SciencesCapital Medical UniversityBeijingChina Guangdong-Hongkong-Macao Institute of CNS Regeneration Ministry of Education CNS Regeneration Collaborative Joint LaboratoryJinan UniversityGuangzhouGuangdong ProvinceChina Bioland Laboratory(Guangzhou Regenerative Medicine and Health Guangdong Laboratory) GuangzhouGuangdong ProvinceChina Department of Ophthalmology and State Key Laboratory of Brain and Cognitive Sciences The University of Hong KongHong Kong Special Administrative RegionChina Center for Brain Science and Brain-Inspired Intelligence Guangdong-Hong Kong-Macao Greater Bay AreaGuangzhouGuangdong ProvinceChina Co-innovation Center of Neuroregeneration Nantong UniversityNantongJiangsu ProvinceChina School of Engineering Medicine Beijing Key Laboratory for Biomaterials and Neural RegenerationBeihang UniversityBeijingChina Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang UniversityBeijingChina

Neurotrophic keratopathy is a persistent defect of the corneal epithelium,with or without stromal ulceration,due to corneal nerve deficiency caused by a variety of *** treatment options for neurotrophic keratopathy are *** this study,an ophthalmic solution was constructed from a chitosan-based thermosensitive hydrogel with long-term release of murine nerve growth factor(CTH-mNGF).Its effectiveness was evaluated in corneal denervation(CD)mice and patients with neurotrophic *** the preclinical setting,CTH-mNGF was assessed in a murine corneal denervation ***-mNGF was transparent,thermosensitive,and ensured sustained release of mNGF for over 20 hours on the ocular surface,maintaining the local mNGF concentration around 1300 pg/mL in *** denervation mice treated with CTH-mNGF for 10 days showed a significant increase in corneal nerve area and total corneal nerve length compared with non-treated and CTH treated mice.A subsequent clinical trial of CTH-mNGF was conducted in patients with stage 2 or 3 neurotrophic *** received topical CTH-mNGF twice daily for 8 *** sodium images,Schirmer’s test,intraocular pressure,Cochet-Bonnet corneal perception test,and best corrected visual acuity were *** total,six patients(total of seven eyes)diagnosed with neurotrophic keratopathy were *** 8 weeks of CTH-mNGF treatment,all participants showed a decreased area of corneal epithelial defect,as stained by ***,six out of seven eyes had fluorescence staining scores<***,best corrected visual acuity,intraocular pressure,Schirmer’s test and Cochet-Bonnet corneal perception test results showed no significant *** increase in corneal nerve density was observed by in vivo confocal microscopy after 8 weeks of CTH-mNGF treatment in three out of seven *** study demonstrates that CTH-mNGF is transparent,thermosensitive,and has sustained-release *** effecti

关键词： chitosan corneal reinnervation murine nerve growth factor neurotrophic keratopathy thermosensitive hydrogel

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Scientific Large Language Models: A Survey on Biological & Chemical Domains

arXiv

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arXiv 2024年

作者： Zhang, Qiang Ding, Keyan Lyu, Tianwen Wang, Xinda Yin, Qingyu Zhang, Yiwen Yu, Jing Wang, Yuhao Li, Xiaotong Xiang, Zhuoyi Feng, Kehua Zhuang, Xiang Wang, Zeyuan Qin, Ming Zhang, Mengyao Zhang, Jinlu Cui, Jiyu Huang, Tao Yan, Pengju Xu, Renjun Chen, Hongyang Li, Xiaolin Fan, Xiaohui Xing, Huabin Chen, Huajun College of Computer Science and Technology Zhejiang University China Hangzhou Innovation Center Zhejiang University China College of Pharmaceutical Sciences Zhejiang University China College of Chemical and Biological Engineering Zhejiang University China Hangzhou Institute of Medicine Chinese Academy of Sciences China Center for Data Science Zhejiang University China Zhejiang Lab China

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of "scientific language", whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs. © 2024, CC BY-NC-ND.

关键词： Genome

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Design of Healthcare Blockchain Systems to Support Accessible Edge Computing

Design of Healthcare Blockchain Systems to Support Accessibl...

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Information Technology: IoT and Smart City (ITIoTSC), International Conference on

作者： Yongqi Cai Jiyu Jiao Ziqiang Ma Jiali Chen Yajie Lan Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education School of Electronic and Electrical Engineering Ningxia University Yinchuan China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education School of Information Engineering Ningxia University Yinchuan China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

Edge computing is an extension of IoT evolution and cloud computing technology development, which is closer to data sources and can provide low-latency and high-bandwidth service support to end users. In this paper, we propose a blockchain-enabled offloading method for edge computing to address the data silos and high latency of service delivery in the healthcare industry, using a cloud-edge-chain three-layer underlying architecture model to achieve a joint design of task offloading and blockchain updates to improve the quality of healthcare services, and blockchain technology to provide authentication for edge service nodes to improve system security, data traceability, and user identity management. The results show that compared with the traditional reinforcement learning Q-learning algorithm, the computational offloading method of the DRLO algorithm designed in this paper converges faster and can effectively reduce the total system processing latency under the same conditions, with a 26% reduction in system processing latency and a 9.2% increase in reward value. Meanwhile, with the change of local terminal computing resources, the algorithm proposed in this paper can still keep the total system latency at a low level.

关键词： Cloud computing Heuristic algorithms Computational modeling Soft sensors Medical services Computer architecture Blockchains

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Dynamic time prediction for electric vehicle charging based on charging pattern recognition

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Frontiers of Information Technology & Electronic engineering 2023年第2期24卷 299-313页

作者： Chunxi LI Yingying FU Xiangke CUI Quanbo GE Logistics Engineering College Shanghai Maritime UniversityShanghai200135China School of Economics and Management Beijing Jiaotong UniversityBeijing102603China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Big Data Analysis Technology(B-DAT) Nanjing University of Information Science and TechnologyNanjing210044China

Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety *** is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from *** to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low *** response to the above problems,data drivers and machine learning theories are *** the basis of fully considering the different electric vehicle battery management system(BMS)charging modes,a charging time prediction method with charging mode recognition is ***,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging ***,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking ***,the data run by the actual engineering system are verified for the proposed charging time prediction *** results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.

关键词： Charging mode Charging time Random forest Long short-term memory(LSTM) Simplified particle swarm optimization(SPSO)

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Robust Indoor Person Re-Identification with Multimodal Training

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IEEE Internet of Things Journal 2025年

作者： Su, Can Xue, Xinlei Ma, Lei Zhang, Xiaolong Yan, Wei Bian, Kaigui Peking University School of Computer Science AI Innovation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China Peking University School of Computer Science Beijing100871 China Peking University Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology National Key Laboratory for Multimedia Information Processing Beijing100871 China Beihang University Beijing100191 China

Existing person re-identification (ReID) methods mainly rely on images and videos to match persons across cameras, yet visual data captured by cameras are vulnerable to environmental interferences (e.g. illumination and occlusion) or personal appearance changes, leading to performance degradation under such scenes. Meanwhile, the popularization of Wi-Fi networks has allowed probe requests to be captured for mobile sensing applications such as crowd counting and trajectory estimation. However, the MAC address randomization technique adopted by modern devices breaks the association of probe requests and adversely affects the functionality of these applications. In this paper, we propose MaRPA, the first multimodal training approach that incorporates both videos and Wi-Fi probe requests to simultaneously promote tasks of probe requests association and person ReID. MaRPA first distinguishes among pairwise probe request frames through a contrastive learning model. It then matches video and probe request sequences by exploring their similarities from the position and the vision aspects. Matched videos and probe requests provide complementary information and generate more robust features for both tasks. To evaluate MaRPA, we contribute a new dataset containing synchronous videos and probe requests data for probe requests association and person ReID. Experimental results demonstrate the effectiveness of our approach. For probe requests association, it achieves > 85% discrimination accuracy and > 0.90 V-measure score;for person ReID, it achieves 75.8% mAP and 90.6% Rank-1, improving state-of-the-art video-based ReID methods by over 40% © 2014 IEEE.

关键词： Wi-Fi

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data-Driven Vulnerable Community Identification During Compound Disasters

Data-Driven Vulnerable Community Identification During Compo...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Jainil Anilkumar Patel Mohammadreza Akbari Lor Shu-Ching Chen Mei-Ling Shyu Steven Luis School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA Data Science and Analytics Innovation Center (dSAIC) University of Missouri-Kansas City Kansas City MO USA Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

This paper aims to introduce research directions to identify vulnerable communities during compound disasters, such as the intersection of hurricanes and pandemics like COVID-19. We suggest integrating mobility, socioeconomic, and geographic factors to develop a comprehensive vulnerability index. By analyzing mobility data, U.S. Census socioeconomic data, disaster data, pandemic data, and geographic data, we extract information like hospital visits, business closures, increased travel distances, decreased inbound movement to commercial census block groups (CBGs), decreased outbound from home CBGs, and changes in mobility trends to various categories of stores, to develop a vulnerability index that integrates all of these factors. This index aims to capture and analyze the unique impacts of natural disasters on communities, especially those exacerbated by simultaneous disaster scenarios. We also propose a model design that can capture the spatial and temporal nature of the data and can be trained to perform the dual task of vulnerability index calculation in a disaster scenario along with predicting pandemic growth features in case of a compound disaster.

关键词： Pandemics Hospitals Disasters Predictive models Market research data models Real-time systems Indexes Compounds Socioeconomics

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Efficient Batch Verification and Privacy-Preserving data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： data aggregation

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Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

arXiv

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arXiv 2024年

作者： Takayama, Masayuki Okuda, Tadahisa Pham, Thong Ikenoue, Tatsuyoshi Fukuma, Shingo Shimizu, Shohei Sannai, Akiyoshi Data Science and AI Innovation Research Promotion Center Shiga University Japan Department of Health Data Science Tokyo Medical University Graduate School of Medicine Human Health Sciences Kyoto University Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Japan Graduate School of Medicine Human Health Sciences Kyoto University Department of Epidemiology Infectious Disease Control and Prevention Hiroshima University Graduate school of Biomedical and Health Sciences Japan Faculty of Data Science Shiga University Graduate School of Medicine Human Health Sciences Kyoto University Institute for the Advanced Study of Human Biology Kyoto University Center for Advanced Intelligence Project RIKEN Japan Department of Physics Kyoto University Data Science and AI Innovation Research Promotion Center Shiga University Graduate School of Engineering The University of Tokyo Center for Advanced Intelligence Project RIKEN Research Development Center for Large Language Models National Institute of Informatics Japan

In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is significant for creating consistent meaningful causal models, despite the challenges in systematic acquisition of the background knowledge. To overcome these challenges, this paper proposes a novel methodology for causal inference, in which SCD methods and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through "statistical causal prompting (SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result with prior knowledge from LLM-KBCI to approach the ground truth, and that the SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, by using an unpublished real-world dataset, we have demonstrated that the background knowledge provided by the LLM can improve SCD on this dataset, even if this dataset has never been included in the training data of the LLM. The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains. © 2024, CC BY.

关键词： Computational linguistics

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Real-time partitioned scheduling: Exploiting the inter-resource affinity for task allocation on multiprocessors

Real-time partitioned scheduling: Exploiting the inter-resou...

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作者： Akram, Naveed Li, Jianxin Bai, Yan Zhang, Yangyang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Engineering and Technology University of Washington Tacoma WA United States

Real-time edge computing is forging its place in cloud computing rapidly, and requirements for high-performance edge devices are becoming increasingly complex. Multiprocessor edge devices are an attractive choice to meet these higher performance requirements. However, multiprocessor devices encounter inherent challenges when handling on-chip shared resources. The concurrent access to these resources by the tasks requesting more than one shared resource and running on multiple processors may face huge blocking times, which can lead to missed hard real-time deadlines and cause a catastrophic system failure. To reduce the task blocking time, we propose a task allocation algorithm that takes advantage of inter-resource affinity and allocates all the tasks accessing multiple shared resources having inter-resource affinity on the same processor of the multiprocessor. The proposed algorithm reduces global resources and remote blocking, which subsequently increase schedulability of task sets and reduce the processor utilization. In our experiments, we compare the proposed task allocation algorithm SRTA with existing well-known task allocation strategies SPA, ROP, and blocking-agnostic FFD. The experimental results reveal that the SRTA, on the average, can allocate 1.80, 2.34, and 3.14 times more task sets as compared to ROP, SPA, and FFD, respectively and reduces the number of globally shared resources significantly. © 2019 John Wiley & Sons, Ltd.

关键词： Scheduling

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A review of big data technology and its application in cancer care

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Computers in Biology and Medicine 2024年 176卷 108577-108577页

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Hua, Dianbo Liu, Fengchun Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future. © 2024 Elsevier Ltd

关键词： Big data

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