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A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

Science China(Technological Sciences) 2024年第2期67卷 449-463页

作者： LIU YunQing QIN ChengJin LIU JinLei JIN YanRui LI ZhiYuan ZHAO LiQun LIU ChengLiang School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Key Labof Artificial Intelligence(Ministryof Education) AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology Shanghai First Peoples Hospital Affiliated to Shanghai Jiao Tong UniversityShanghai 200080China

Although 12-lead electrocardiograms(ECGs)provide a wide range of spatiotemporal characteristics,interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical ***,we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead *** model was trained,validated,and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong *** experiments revealed that our approach had a classification accuracy of 94.41%in the classification task of seven types of rhythms,which was markedly superior to related single-lead and 12-lead ECG classification ***,the average receiver operating characteristic area under the curve reached a value of 0.940,and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91,respectively,with specificity values of 0.996 and *** employing our boosting method,we were able to improve the accuracy to 94.85%.To investigate the performance degradation of the proposed neural network in some classes,an ECG cardiologist was enlisted to review questionable ECGs;this process provides a promising direction for network performance ***,the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’heart conditions based on real-time 12-lead ECG or grade their disease severity in advance.

关键词： computerized ECG interpretation large-scale 12-lead clinical ECG database lightweight neural network

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Encoding biological metaverse: Advancements and challenges in neural fields from macroscopic to microscopic

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The Innovation 2024年第3期5卷 30-32页

作者： Yantong Cai Wenbo Hu Yao Pei Hao Zhao Guangchuang Yu Dermatology Hospital Southern Medical UniversityGuangzhou 266003China Department of Bioinformatics School of Basic Medical SciencesSouthern Medical UniversityGuangzhou 510515China Tencent ai Lab Shenzhen 518071China State Key Laboratory of Emerging Infectious Diseases School of Public HealthThe University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health(D24H) Hong Kong Science ParkHong Kong SARChina Institute for ai Industry Research Tsinghua UniversityBeijing 100000China

Neural fields can efficiently encode three-dimensional(3D)scenes,providing a bridge between two-dimensional(2D)images and virtual *** method becomes a trendsetter in bringing the metaverse into vivo *** has initially captured the attention of macroscopic biology,as demonstrated by computed tomography and magnetic resonance imaging,which provide a 3D field of view for diagnostic biological ***,it has also opened up new research opportunities in microscopic imaging,such as achieving clearer de novo protein structure *** this method to the field of biology is particularly significant,as it is refining the approach to studying biological ***,many biologists have yet to fully appreciate the distinctive meaning of neural fields in transforming 2D images into 3D *** article discusses the application of neural fields in both microscopic and macroscopic biological images and their practical uses in biomedicine,highlighting the broad prospects of neural fields in the future biological *** stand at the threshold of an exciting new era,where the advancements in neural field technology herald the dawn of exploring the mysteries of life in innovative ways.

关键词： neural bringing innovative

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Defect Detection Model Using Time Series Data Augmentation and Transformation

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Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of ai Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： Defect detection time series deep learning data augmentation data transformation

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Design,synthesis and biological evaluation of fluorescent derivatives of ursolic acid in living cells

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Chinese Chemical Letters 2024年第5期35卷 287-289页

作者： Wenyi Mei Lijuan Xie Xiaodong Zhang Cunjian Shi Fengzhi Wang Qiqi Fu Zhenjiang Zhao Honglin Li Yufang Xu Zhuo Chen Shanghai Key Laboratory of New Drug Design State Key Laboratory of Bioreactor EngineeringSchool of PharmacyEast China University of Science and TechnologyShanghai 200237China Innovation Center for ai and Drug Discovery East China Normal UniversityShanghai 200062China Lingang Laboratory Shanghai 200031China

Ursolic acid(UA)is a naturally occurring ursane triterpenoid,which exhibits a wide range of unique biological *** clarify its mechanism of action(MOA),a series of fluorescent derivatives of UA(5a-c)were designed and synthesized by conjugation with 7-nitrobenzo-2-oxa-1,3-diazole(NBD)*** them,5c exhibited similar anti-proliferative activity with UA against HCT116 cells(half maximal inhibitory concentration(IC_(50))=9.21±0.50μmol/L).Cell imaging experiment indicated that 5c was rapidly taken up in HCT116 cells in a dose and time-dependent ***,5c was found to localize in endoplasmic reticulum(ER),lysosomes,and mitochondria,but not in nucleus of HCT116 cells by confocal microscopy *** MOA proved that UA induced autophagy with a unique intracellular distribution mechanism involving ER and *** all,our work provides new clues for revealing the molecular mechanism of UA as an antitumor agent.

关键词： Pentacyclic triterpenoid Ursolic acid Fluorescent derivatives Autophagy Apoptosis

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大模型驱动的具身智能:发展与挑战

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中国科学:信息科学 2024年

作者：白辰甲许华哲李学龙中国电信人工智能研究院(Tele ai) 中国电信人工智能研究院(Teleai) 清华大学交叉信息学院

大模型驱动的具身智能是涵盖人工智能、机器人学和认知科学的交叉领域,重点研究如何将大模型的感知、推理和逻辑思维能力与具身智能相结合,提升现有模仿学习、强化学习、模型预测控制等具身智能框架的数据效率和泛化能力.近年来,随着大... 详细信息

大模型驱动的具身智能是涵盖人工智能、机器人学和认知科学的交叉领域,重点研究如何将大模型的感知、推理和逻辑思维能力与具身智能相结合,提升现有模仿学习、强化学习、模型预测控制等具身智能框架的数据效率和泛化能力.近年来,随着大模型能力的不断提升,以及具身智能中示教数据、仿真平台、任务集合的不断完善,大模型和具身智能的结合将成为人工智能的下一个浪潮,有望成为人工智能迈向实体机器人的重要突破口.本文围绕大模型驱动的具身智能这一研究领域,从3个方面进行了系统的调研、分析和展望.首先,回顾了大模型和具身智能的相关技术背景,以及具身智能现有的学习框架.其次,按照大模型赋能具身智能的方式,将现有研究分为大模型驱动的环境感知、大模型驱动的任务规划、大模型驱动的基础策略、大模型驱动的奖励函数、大模型驱动的数据生成等5类范式.最后,总结了大模型驱动的具身智能中存在的挑战,对可行的技术路线进行展望,为相关研究人员提供参考,进一步推动国家人工智能发展战略.

关键词：具身智能大模型环境感知任务规划基础策略

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ai for organic and polymer synthesis

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Science China Chemistry 2024年第8期67卷 2461-2496页

作者： Xin Hong Qi Yang Kuangbiao Liao Jianfeng Pei Mao Chen Fanyang Mo Hua Lu Wen-Bin Zhang Haisen Zhou Jiaxiao Chen Lebin Su Shuo-Qing Zhang Siyuan Liu Xu Huang Yi-Zhou Sun Yuxiang Wang Zexi Zhang Zhunzhun Yu Sanzhong Luo Xue-Feng Fu Shu-Li You Center of Chemistry for Frontier Technologies Department of ChemistryZhejiang UniversityHangzhou 310027China Center of Basic Molecular Science Department of ChemistryTsinghua UniversityBeijing 100084China Guangzhou National Laboratory Guangzhou 510005China Center for Quantitative Biology Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular ScienceFudan UniversityShanghai 200433China School of Materials Science and Engineering Peking UniversityBeijing 100871China Beijing National Laboratory for Molecular Sciences Center for Soft Matter Science and EngineeringKey Laboratory of Polymer Chemistry and Physics of Ministry of EducationCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China ai for Science(ai4S)-Preferred Program Shenzhen Graduate SchoolPeking UniversityShenzhen 518055China State Key Laboratory of Chemical Biology Shanghai Institute of Organic ChemistryUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200032China Department of Chemical Sciences National Natural Science Foundation of ChinaBeijing 100085China State Key Laboratory of Organometallic Chemistry Shanghai Institute of Organic ChemistryChinese Academy of SciencesShanghai 200032China

Recent years have witnessed the transformative impact from the integration of artificial intelligence with organic and polymer synthesis. This synergy offers innovative and intelligent solutions to a range of classic problems in synthetic chemistry. These exciting advancements include the prediction of molecular property, multi-step retrosynthetic pathway planning, elucidation of the structure-performance relationship of single-step transformation, establishment of the quantitative linkage between polymer structures and their functions, design and optimization of polymerization process, prediction of the structure and sequence of biological macromolecules, as well as automated and intelligent synthesis platforms. Chemists can now explore synthetic chemistry with unprecedented precision and efficiency, creating novel reactions, catalysts, and polymer materials under the datadriven paradigm. Despite these thrilling developments, the field of artificial intelligence(ai) synthetic chemistry is still in its infancy, facing challenges and limitations in terms of data openness, model interpretability, as well as software and hardware support. This review aims to provide an overview of the current progress, key challenges, and future development suggestions in the interdisciplinary field between ai and synthetic chemistry. It is hoped that this overview will offer readers a comprehensive understanding of this emerging field, inspiring and promoting further scientific research and development.

关键词： organic synthesis polymer synthesis machine learning prediction chemical database automated synthesis

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ai Fairness-From Machine Learning to Federated Learning

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1203-1215页

作者： Lalit Mohan Patnaik Wenfeng Wang Consciousness Studies Program School of HumanitiesNational Institute of Advanced StudiesBangalore560012India Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of TechnologyShanghai201418China Research Center of Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi830011China Applied Nonlinear Science Lab Anand International College of EngineeringJaipur391320India London Institute of Technology The ASE-London CTI of SCOLondonCR26EQUK Sino-Indian Joint Research Center of ai and Robotics The IMT InstituteBhubaneswar752054India

This article reviews the theory of fairness in ai-frommachine learning to federated learning,where the constraints on precision ai fairness and perspective solutions are also *** a reliable and quantitative evaluation of ai fairness,many associated concepts have been proposed,formulated and ***,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure *** privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating ai fairness are *** imbalance between algorithms’utility and humanization has further reinforced *** for federated learning systems,these constraints on precision ai fairness still *** solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.

关键词： Formulation evaluation classification constraints imbalance biases

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Transforming organic chemistry research paradigms:Moving from manual efforts to the intersection of automation and artificial intelligence

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National Science Open 2024年第2期3卷 98-127页

作者： Chengchun Liu Yuntian Chen Fanyang Mo School of Materials Science and Engineering Peking UniversityBeijing 100871China Ningbo Institute of Digital Twin Eastern Institute of TechnologyNingbo 315000China ai for Science(ai4S)-Preferred Program Peking University Shenzhen Graduate SchoolShenzhen 518055China

Organic chemistry is undergoing a major paradigm shift,moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence(ai).This transformative shift is being driven by technological advances,the ever-increasing demand for greater research efficiency and accuracy,and the burgeoning growth of interdisciplinary *** models,supported by computational power and algorithms,are drastically reshaping synthetic planning and introducing groundbreaking ways to tackle complex molecular *** addition,autonomous robotic systems are rapidly accelerating the pace of discovery by performing tedious tasks with unprecedented speed and *** article examines the multiple opportunities and challenges presented by this paradigm shift and explores its far-reaching *** provides valuable insights into the future trajectory of organic chemistry research,which is increasingly defined by the synergistic interaction of automation and ai.

关键词： organic chemistry automation platform artificial intelligence algorithms

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Effect of Adhesive Type on the Quality of Coconut Shell Charcoal Briquettes Prepared by the Screw Extruder Machine

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Journal of Renewable Materials 2024年第2期12卷 381-396页

作者： Samsudin Anis Deni Fajar Fitriyana Aldias Bahatmaka Muhammad Choirul Anwar Arsyad Zanadin Ramadhan Fajar Chairul Anam Raffanel Adi Permana Ahmad Jazilussurur Hakim Natalino Fonseca Da Silva Guterres Mateus De Sousa Da Silva Department of Mechanical Engineering Faculty of EngineeringUniversitas Negeri SemarangGunung patiSemarang50229Indonesia Arka Tama Indonesia Co.Ltd.KarawangWest Java41382Indonesia Department of Mechanical Engineering Dili Institute of TechnologyAi-meti Laran StreetDiliTimor Leste

Indonesia is one of the largest coconut-producing countries in the *** utilization of coconut shell waste into briquettes will increase the selling value and become a great export ***,the effect of adhesives on the quality of coconut shell charcoal briquettes made using screw extruder machine has not been widely *** study aims to determine the effect of adhesive type on the quality of coconut shell charcoal *** process of fabricating briquettes in this study included crushing,mixing,blending,pressing,and *** the mixing process,3 types of adhesives were used,namely tapioca flour(Briquette_1),cassava flour(Briquette_2),and modified cassava flour(Briquette_3)with a concentration of 5%of the weight of coconut shell charcoal *** quality of the resulting briquettes and commercial briquettes will be evaluated by moisture content,ash content,volatile matter,fixed carbon,calorific value,density,compressive,and drop test *** results of this research showed that the type of adhesive had a significant effect on the quality of the briquettes *** Briquette_1 had better quality than commercial briquettes(Briquette_4)and other briquette *** test results showed that Briquette_1 produced briquettes with better compressive strength and friability than the other specimens,at 6.95 N/mm^(2) and 4.44%,*** moisture content,ash content,fixed carbon,and calorific value of Briquette_1 have met the requirements set by the Indonesian National Standard(SNI)number ***,the volatile matter content and density of Briquette_1 are by the standards of Japan and the United States America(USA).

关键词： Coconut charcoal briquettes adhesive mixing blending pressing

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Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

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Advances in Breast Cancer Research 2024年第2期13卷 11-26页

作者： Chien-Hui Wu Min-Hsiang Chang Hsin-Hsiu Tsai Yi-Ting Peng Department of Anatomical Pathology Far Eastern Memorial Hospital New Taipei ai Lab Quanta Computer Inc. Taoyuan

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

关键词： Ki67 Heterogeneity Breast Cancer Digital Image Analysis

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