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Assessment of the aroma profiles of 15 commercial Chinese jujubes using GC-IMS

Journal of Future Foods 2025年

作者： Jianxin Song Junguo Wang Fangyuan Wei Tong Zhang Xin Li Xiaoya Pan Mengyuan Zhao Chao Zhang Xiaodong Wang Junran Chen School of Functional Food and Wine Shenyang Pharmaceutical University Shenyang 110016 China Institute of Agri-Food Processing and Nutrition Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Vegetable Postharvest Processing Ministry of Agriculture and Rural Affairs Beijing 100097 China School of Biological Science and Food Engineering Chuzhou University Chuzhou 239000 China College of Food Science and Engineering Stata Key Laboratory of Food Nutrition and Safety Tianjin University of Science and Technology Tianjin 300457 China

Jujube is the homology of medicine and food, which popular for its characteristic flavor. Aroma is one of the most key factors that determining the quality of commercial Chinese jujube. In this study, gas chromatography-ion mobility spectrometry (GC-IMS) was applied for aroma characterization of 15 varieties of commercial Chinese jujubes. Results showed a total of 29 aroma compositions were identified, and aldehydes (10.42%−22.87%), ketones (26.45%−42.06%), alcohols (2.42%−7.30%), and esters (16.41%−26.86%) were common in 15 varieties of commercial Chinese jujubes. 3-Hydroxy-2-butanone, methyl acetate, benzaldehyde, 2,3-butanedione and 2-methylbutyric acid contributed great to the aroma profiles of different commercial Chinese jujubes. Moreover, significant ( P <0.05) differences of aroma profiles were found among 15 varieties of commercial Chinese jujubes, and it can be well distinguished by 3D-topographic, characteristic fingerprint and principal component analysis (PCA) results. In conclusion, JSBZ (Ji Shan Ban Zao), HMDZ (Ha Mi Da Zao) and HPZ (Hu Ping Zao) respectively presented significantly different aroma profile characteristics. GC-IMS instrument and PCA statistical method were superior and effective in assessing the differences of aroma profiles of different commercial Chinese jujubes.

关键词： Commercial jujube Aroma profile Variety GC-IMS PCA

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Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Single-cell transcriptome analysis of in vivo and in vitro human pancreas development

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Genes & Diseases 2025年

作者： Xiaofei Zhang Hongyan Yi Zhuo Ma Yinsuo Zhao Yanlin Ma Eli Song Tao Xu Key Laboratory of Molecular Biophysics of the Ministry of Education College of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei 430074 China National Laboratory of Biomacromolecules CAS Center for Excellence in Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research The First Affiliated Hospital of Hainan Medical University Hainan Medical University Haikou Hainan 571199 China Guangzhou Laboratory Guangzhou Guangdong 510005 China Shandong First Medical University & Shandong Academy of Medical Sciences Jinan Shandong 250117 China

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Extracting Photometric Redshift from Galaxy Flux and Image Data using Neural Networks in the CSST Survey

arXiv

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arXiv 2021年

作者： Zhou, Xingchen Gong, Yan Meng, Xian-Min Cao, Ye Chen, Xuelei Chen, Zhu Du, Wei Fu, Liping Luo, Zhijian Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Science Center for China Space Station Telescope National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China Key Laboratory for Computational Astrophysics National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Beijing100101 China Centre for High Energy Physics Peking University Beijing100871 China Shanghai Key Lab for Astrophysics Shanghai Normal University Shanghai200234 China

The accuracy of galaxy photometric redshift (photo-z) can significantly affect the analysis of weak gravitational lensing measurements, especially for future high-precision surveys. In this work, we try to extract photo-z information from both galaxy flux and image data expected to be obtained by China Space Station Telescope (CSST) using neural networks. We generate mock galaxy images based on the observational images from the Advanced Camera for Surveys of Hubble Space Telescope (HST-ACS) and COSMOS catalogs, considering the CSST instrumental effects. Galaxy flux data are then measured directly from these images by aperture photometry. The Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) are constructed to predict photo-z from fluxes and images, respectively. We also propose to use an efficient hybrid network, which combines the MLP and CNN, by employing the transfer learning techniques to investigate the improvement of the result with both flux and image data included. We find that the photo-z accuracy and outlier fraction can achieve σNMAD = 0.023 and η = 1.43% for the MLP using flux data only, and σNMAD = 0.025 and η = 1.21% for the CNN using image data only. The result can be further improved in high efficiency as σNMAD = 0.020 and η = 0.90% for the hybrid transfer network. These approaches result in similar galaxy median and mean redshifts 0.8 and 0.9, respectively, for the redshift range from 0 to 4. This indicates that our networks can effectively and properly extract photo-z information from the CSST galaxy flux and image data. Copyright © 2021, The Authors. All rights reserved.

关键词： Image enhancement

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Synthesis of novel nanomaterials and their application in efficient removal of radionuclides

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science China Chemistry 2019年第8期62卷 933-967页

作者： Xiangxue Wang Long Chen Lin Wang Qiaohui Fan Duoqiang Pan Jiaxing Li Fangting Chi Yi Xie Shujun Yu Chengliang Xiao Feng Luo Jun Wang Xiaolin Wang Changlun Chen Wangsuo Wu Weiqun Shi Shuao Wang Xiangke Wang Key Laboratory of Resources and Environmental Systems Optimization Ministry of EducationCollege of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China China Academy of Engineering Physics Mianyang 621900China State Key L aboratory of Radiation Medicine and Protection School of Radiation Medicine and ProtectionCollaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education InstitutionsSoochow UniversitySuzhou 215123China Laboratory of Nuclear Energy Chemistry and Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Key Laboratory of Petroleum Resources Gansu ProvinceNorthwest Institute of Eco-Environment and Resources. Chinese Academy of SciencesLanzhou 730000China Radiochemistry Laboratory School of Nuclear Science and TechnologyLanzhou UniversityLanzhou 730000China CAS Key Laboratory of Photovoltaic and Energy Conservation Materials. Institute of Plasma Physics Chinese Academy of Sciences Hefei 230031China Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory Southwest University of Science and Technology Mianyang 621010China State Key Laboratory of Nuclear Resources and Environment School of BiologyChemistry and Material Science East China University of TechnologyFuzhou 344000China College of Materials Science and Chemical Engineering Harbin Engineering UniversityHarbin 150001China China Academy of Engineering Physics Mianyang 621900China

With the development of nuclear energy, large amounts of radionuclides are inevitably released into the natural environment. It is necessary to eliminate radionuclides from wastewater for the protection of environment. Nanomaterials have been considered as the potential candidates for the effective and selective removal of radionuclides from aqueous solutions under complicated conditions because of their high specific surface area, large amounts of binding sites, abundant functional groups, pore-size controllable and easily surface modification. This review mainly summarized the recent studies for the synthesis, fabrication and surface modification of novel nanomaterials and their applications in the efficient elimination and solidification of radionuclides,and discussed the interaction mechanisms from batch experiments, spectroscopy analysis and theoretical calculations. The sorption capacities with other materials, advantages and disadvantages of different nanomaterials are compared, and at last the perspective of the novel nanomaterials is summarized.

关键词： nanomaterials radionuclides removal preconcentration interaction mechanism

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Research on weather classification of atmospheric complex pollution: Progress and prospect

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Atmospheric Environment 2025年

作者： Yongxin Yan Hong Wang Xiaoling Zhang Xiaoyu Yan Yan Nie Junling Li Haijie Zhang Yanqin Ren Hong Li State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing 100012 China Fujian Key Laboratory of Severe Weather and Key Laboratory of Straits Severe Weather China Meteorological Administration Fuzhou 350008 China Plateau Atmosphere and Environment Key Laboratory of Sichuan Province School of Atmospheric Sciences Chengdu University of Information Technology Chengdu 610225 China College of Earth Science Jilin University Changchun 130061 China

Understanding the atmospheric circulation causes of atmospheric complex pollution is crucial for unraveling its intricate formation mechanisms. This study summarizes the progress of weather classification research in atmospheric pollution, focusing on the history, the characteristics and applicability of different research methods, the research conclusions on weather classification for different types of air pollution and their corresponding mechanisms, and points out the challenges of current research and the directions for future research. Driven by technological development and research needs, the research has generally gone through four stages of development, and the current objective classification method, especially the T-mode principal component analysis, show the superior performance. Local generation patterns contributing to PM 2.5 pollution are characterized by stagnation conditions, while those contributing to O 3 pollution are linked to high pressure which enhances the photochemical reaction, and low pressure accompanied by inversion. PM 2.5 and O 3 complex pollution, SO 2 pollution, and NOx pollution are all mainly affected by the continental and/or subtropical high. The current dominant patterns of CO pollution are unclear. Regional transport patterns of PM 2.5 pollution are generally cold fronts and high pressure, while those of O 3 pollution are mostly low pressure. Future expectations for weather classification research include in depth studies on the dominant patterns of multi-pollutants complex pollution, and expanding the application of weather modeling in pollution forecasting, thereby promoting the research results in this field to play a greater role in the research on the causes and the control of atmospheric complex pollution.

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VRAS: A virtual rehearsal assistant system for live performance 2nd

VRAS: A virtual rehearsal assistant system for live performa...

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2nd International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2018

作者： Wu, Yufeng Ding, Gangyi Li, Hongsong Xue, Tong Jiao, Di Huang, Tianyu Zhang, Longfei Zhang, Fuquan Xu, Lin Digital Performance and Simulation Key Laboratory School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783030045845

The VR technologies are widely adopted for training purposes by providing the users with educational virtual experience. In this work, we propose an immersive VR system that help the choreographers and the dancers to facilitate their dance rehearsal experience. The system integrates motion capture devices and head-mounted displays (HMDs). The motions of the dancers, their partners, and the choreographers are captured and projected into a virtual dancing scene in an interactive frame rate. The dancers who are wearing the HMDs, are allowed to observe the synthesized virtual performances within a virtual stage space from several selected third-person views. These are the audience’s view, the dancing partner’s view, and the choreographer’s view. Such synthesized external self-images augment dancer’s perception of their dance performance and their understanding of the choreography. Feedbacks from the participants indicate the proposed system is effective and the preliminary experimental results agree with our observations. © Springer Nature Switzerland AG 2019.

关键词： Helmet mounted displays

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In-situ characterization on the fracture behavior of three dimensional polymer nanocomposites reinforced by CNT sponge

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Composites science and technology 2022年 217卷

作者： Qing Ma Bin Hao Peng-Cheng Ma Laboratory of Environmental Science and Technology The Xinjiang Technical Institute of Physics and Chemistry Key Laboratory of Functional Materials and Devices for Special Environments Chinese Academy of Sciences Urumqi 830011 China Center of Material Science and Opto-electronic Technology University of Chinese Academy of Science Beijing 100049 China

This paper reported the fracture behavior of polymer-based nanocomposites reinforced by carbon nanotubes (CNTs). In order to solve the problem associated with the dispersion and re-agglomeration of CNTs, three dimensional CNT sponge was prepared and incorporated into the epoxy through the monomer infusion process. The fracture toughness and critical energy release rate of the nanocomposites were enhanced by more than 100% and 250%, respectively. The toughening mechanism behind such observation was revealed by studying the in-situ crack initiation and propagation in the bulky nanocomposites with assistance of micro-mechanical testing device in a scanning electronic microscope chamber. The results showed that the intrinsic fracture behaviors, such as void generation, as well as the extrinsic toughening features arising from crack deflection and bifurcation, were responsible for the improved fracture properties of nanocomposites.

关键词： A: Carbon nanotubes A: Nano composites B: Fracture D: Fractography In-situ characterization

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Distribution-Induced Bidirectional Generative Adversarial Network for Graph Representation Learning

Distribution-Induced Bidirectional Generative Adversarial Ne...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shuai Zheng Zhenfeng Zhu Xingxing Zhang Zhizhe Liu Jian Cheng Yao Zhao Institute of Information Science Beijing Jiaotong University China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China NLPR Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many computer vision tasks. However, most existing algorithms ignore the existence of inherent data distribution and even noises. This may significantly increase the phenomenon of over-fitting and deteriorate the testing accuracy. In this paper, we propose a Distribution-induced Bidirectional Generative Adversarial Network (named DBGAN) for graph representation learning. Instead of the widely used Gaussian assumption, the prior distribution of latent representation in our DBGAN is estimated in a structure-aware way, which implicitly bridges the graph and content spaces by prototype learning. Thus discriminative and robust representations are generated for all nodes. Furthermore, to improve their generalization ability while preserving representation ability, the sample-level and distribution-level consistency are well balanced via a bidirectional adversarial learning framework. An extensive group of experiments is then carefully designed and presented, demonstrating that our DBGAN obtains remarkably more favorable trade-off between representation and robustness, and meanwhile is dimension-efficient, over currently available alternatives in various tasks.

关键词： Generative adversarial networks Robustness Data models Gallium nitride Generators Task analysis Gaussian distribution

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