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What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?

Journal of Integrative Agriculture 2020年第7期19卷 1789-1801页

作者： WANG Xiu-juan KANG Meng-zhen FAN Xing-rong YANG Li-li ZHANG Bao-gui HUANG San-wen Philippe DE REFFYE WANG Fei-yue The State Key Laboratory of Management and Control for Cormplex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China Bejjing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBejing 100190P.R.China Innovation Center for Parallel Agriculture Qingdao Academy of Intelligent IndustriesQingdao 266109P.R.China School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing 400067P.R.China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083P.R.China College of Land Science and Technology China Agricultural UniversityBeijing 100193P.R.China Agricultural Genomes Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhen 518124P.R.China AMAP University MontpellierCIRADCNRSINRAIRDMontpellier 34000France The School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049P.R.China

To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.

关键词： cucumber biomass production functional-structural plant model source-sink ratio fruit-setting PSO heterosis

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Probabilistic learning vector quantization on manifold of symmetric positive definite matrices

arXiv

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arXiv 2021年

作者： Sonna, Fengzhen Tang Feng, Haifeng Tino, Peter Si, Bailu Ji, Daxiong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of computer Science University of Birmingham BirminghamB15 2TT United Kingdom School of Systems Science Beijing Normal University Beijing100875 China Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data, and motor imagery EEG data demonstrate the superior performance of the proposed method. © 2021, CC BY-NC-SA.

关键词： Learning algorithms

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Apolipoprotein A-Ⅳ and its derived peptide, T55-121,improve glycemic control and increase energy expenditure

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Life Metabolism 2024年第4期 42-53页

作者： Zhen Cao Lei Lei Ziyun Zhou Shimeng Xu Linlin Wang Weikang Gong Qi Zhang Bin Pan Gaoxin Zhang Quan Yuan Liujuan Cui Min Zheng Tao Xu You Wang Shuyan Zhang Pingsheng Liu National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences University of Chinese Academy of Sciences The State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia Medica Chinese Academy of Medical Sciences and Peking Union Medical College Luxembourg Centre for Systems Biomedicine University of Luxembourg Guangzhou Regenerative Medicine and Health Guangdong Laboratory Bioland Laboratory Department of Computer Science School of Computing National University of Singapore School of Basic Medical Sciences Southwest Medical University The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases National Clinical Research Center for Infectious Diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases The First Affiliated Hospital College of Medicine Zhejiang University Beijing Key Laboratory of Emerging Infectious Diseases Institute of Infectious Diseases Beijing Ditan Hospital Capital Medical University Beijing Institute of Infectious Diseases National Center for Infectious Diseases Beijing Ditan Hospital Capital Medical University National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases

It is crucial to understand the glucose control within our bodies. Bariatric/metabolic surgeries, including laparoscopic sleeve gastrectomy(LSG) and Roux-en-Y gastric bypass(RYGB), provide an avenue for exploring the potential key factors involved in maintaining glucose homeostasis since these surgeries have shown promising results in improving glycemic control among patients with severe type 2 diabetes(T2D). For the first time, a markedly altered population of serum proteins in patients after LSG was discovered and analyzed through proteomics. Apolipoprotein A-Ⅳ(apoA-Ⅳ) was revealed to be increased dramatically in diabetic obese patients following LSG, and a similar effect was observed in patients after RYGB surgery. Moreover, recombinant apoA-Ⅳ protein treatment was proven to enhance insulin secretion in isolated human islets. These results showed that apoA-Ⅳ may play a crucial role in glycemic control in humans, potentially through enhancing insulin secretion in human islets. ApoA-Ⅳ was further shown to enhance energy expenditure and improve glucose tolerance in diabetic rodents, through stimulating glucose-dependent insulin secretion in pancreatic β cells, partially via Gαs-coupled GPCR/cAMP(G protein-coupled receptor/cyclic adenosine monophosphate) ***, T55-121, truncated peptide 55-121 of apoA-Ⅳ, was discovered to mediate the function of apoA-Ⅳ. These collective findings contribute to our understanding of the relationship between apoA-Ⅳ and glycemic control, highlighting its potential as a biomarker or therapeutic target in managing and improving glucose regulation.

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Generating adjacency-constrained subgoals in hierarchical reinforcement learning 20

Generating adjacency-constrained subgoals in hierarchical re...

引用

Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Tianren Zhang Shangqi Guo Tian Tan Xiaolin Hu Feng Chen Department of Automation Tsinghua University Department of Civil and Environmental Engineering Stanford University Department of Computer Science and Technology Tsinghua University and Beijing National Research Center for Information Science and Technology and State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University and Beijing Innovation Center for Future Chip and LSBDPA Beijing Key Laboratory

ISBN: (纸本)9781713829546

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is often large. Searching in a large goal space poses difficulties for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that the proposed adjacency constraint preserves the optimal hierarchical policy in deterministic MDPs, and show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks show that incorporating the adjacency constraint improves the performance of state-of-the-art HRL approaches in both deterministic and stochastic environments.

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Comparative Study of Four Speed Controllers of Brushless DC Motors for Industrial Applications

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IFAC-PapersOnLine 2020年第5期53卷 59-64页

作者： Tariku Sinshaw Tamir Gang Xiong Zhen Shen Xiaoyan Gong Sheng Liu Ehtisham Lodhi Li Wan Xisong Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan 528225 China

Direct current (DC) motors are one of the most important kind of motors and are widely used in robotic and industrial applications. Recently, there have been significant efforts to develop direct current (DC) motors in an attempt to control speed of motors. However, conventional controlling approaches perform undesirably in terms of stability and quick response. Therefore, this paper presents a hybrid intelligent controller configuration for optimized speed control of brushless direct current (BLDC) motors in a factory supervisory control data acquisition (SCADA) system. We compare this hybrid intelligent controller with a conventional PID controller, fuzzy logic controller (FLC), and artificial neural network model reference controller (ANNMRC) in MATLAB , and the results show that the hybrid (neuro-fuzzy) controller performs superior in terms of stability, speed trajectory tracking capability, fast response, and simplicity for implementation.

关键词： BLDC motor fuzzy logic controller (FLC) artificial neural network model reference controller (ANNMRC) hybrid (neuro-fuzzy) control

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A large-scale transcriptional study reveals inhibition of COVID-19 related cytokine storm by traditional Chinese medicines

引用

Science Bulletin 2021年第9期66卷 884-888,M0003页

作者： Yifei Dai Weijie Qiang Yu Gui Xue Tan Tianli Pei Kequan Lin Siwei Cai Liang Sun Guochen Ning Jianxun Wang Hongyan Guo Yimin Sun Jing Cheng Lan Xie Xun Lan Dong Wang State Key Laboratory of Southwestern Chinese Medicine Resources School of Basic Medical SciencesChengdu University of Traditional Chinese MedicineChengdu 611137China Department of Basic Medical Sciences School of MedicineTsinghua UniversityBeijing 100084China Institute of Medicinal Plant Development Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100193China School of Pharmacy Chengdu University of Traditional Chinese MedicineChengdu 611137China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China Department of Electronic and Computer Engineering College of EngineeringDrexel UniversityPhiladelphia 19104USA Dongli District Jinqiao Street Community Health Service Center Tianjin 300300China Department of Biomedical Engineering School of MedicineTsinghua UniversityBeijing 100084China School of Life Sciences Beijing University of Chinese MedicineBeijing 100029China National Engineering Research Center for Beijing Biochip Technology Beijing 102206China State Key Laboratory of Membrane Biology School of MedicineTsinghua UniversityBeijing 100084China Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Hangzhou 310003China Medical Systems Biology Research Center School of MedicineTsinghua UniversityBeijing 100084China

The application of traditional Chinese medicine(TCM)has made great contributions to the fight against the epidemic of coronavirus disease-2019(COVID-19).Despite the remarkable therapeutic effects of TCM,the molecular mechanisms of TCM formulae inhibiting COVID-19 are still not fully ***,we cφm-bined the automated high throughput sequencing-based high throughput screening(HTS^(2))assay with bioinformatics and computer-aided drug design(CADD)to investigate the molecular mechanisms of TCM-mediated therapeutic effects on COVID-19-related cytokine storm(Fig.1a).

关键词：细胞因子风暴六君子汤理中汤诊疗方案黄连解毒汤中药提取物肺炎分子机制

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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A meta-inspired termite queen algorithm for global optimization and engineering design problems

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Engineering Applications of Artificial Intelligence 2022年 111卷

作者： Peng Chen Shihua Zhou Qiang Zhang Nikola Kasabov Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1010 New Zealand Intelligent Systems Research Center Ulster University Londonderry BT52 1SA UK

This paper proposes a novel bio-inspired termite queen algorithm (TQA) to solve optimization problems by simulating the division of labor in termite populations under a queen’s rule. TQA is benchmarked on a set of 23 functions to test its performance at solving global optimization problems, and applied to six real-world engineering design problems to verify its reliability and effectiveness. Comparative simulation studies with other algorithms are conducted, from whose results it is observed that TQA satisfactorily solves global optimization problems and engineering design problems.

关键词： Termite queen algorithm Benchmarked functions Loss minimization Metaheuristic technique Engineering design problems

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Generating adjacency-constrained subgoals in hierarchical reinforcement learning

arXiv

引用

arXiv 2020年

作者： Zhang, Tianren Guo, Shangqi Tan, Tian Hu, Xiaolin Chen, Feng Department of Automation Tsinghua University China Department of Civil and Environmental Engineering Stanford University United States Department of Computer Science and Technology Tsinghua University China Beijing National Research Center for Information Science and Technology China State Key Laboratory of Intelligent Technology and Systems Beijing Innovation Center for Future Chip China LSBDPA Beijing Key Laboratory China

关键词： Stochastic systems

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