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Preparation and Structure-Property Regulation Mechanism of Reversible Thermochromic Polydiacetylene

Chinese Journal of Polymer Science 2024年第8期42卷 1111-1121页

作者： Zhi-Qing Ge Shuo Yan Zu-Xiong Pan Shan Lei Xuan-Zhi Mao Chu Zhao Mo-Zhen Wang Xue-Wu Ge Key Laboratory of Precision and intelligent Chemistry Department of Polymer Science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China Marshall Laboratory of Biomedical engineering International Cancer CenterLaboratory of Evolutionary Theranostics(LET)School of Biomedical EngineeringShenzhen University Medical SchoolShenzhen UniversityShenzhen 518055China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China

The thermochromic mechanism and the structure-property regulation principle of reversible thermochromic polydiacetylene(PDA)materials have always been a challenging *** this work,a series of diacetylene monomers(m-PCDA)containing phenyl and amide or carboxyl groups were synthesized from 10,12-pentacosadiynoic acid(PCDA)through the esterification or amidation *** effects of the number and the distribution of the functional groups in m-PCDA molecules on their solid-state polymerization capability,and the thermochromic mechanism of their corresponding polymers(m-PDA)were investigated and discussed in *** results show that the m-PCDA monomers containing both benzene ring and groups that can form hydrogen bonding interactions have strong intermolecular interaction,and are easy to carry out the solid phase polymerization under 254-nm UV irradiation to obtain the corresponding new thermochromic m-PDA *** thermochromic behavior of m-PDA depends on its melting *** initial color-change temperature(blue to red)is determined by the onset melting tem perature,and the temperature range in which reversible color recovery can be achieved by repeat heating-cooling treatment is determined by its melting *** to the proposed thermochromic mechanism of PDA,various new PDA materials with precise thermochromic temperatu res and reversible thermochromic temperature ra nges can be designed and synthesized through the appropriate introduction of benzene ring and groups that can form hydrogen bonding interactions into the molecular structure of linear diacetylene *** work provides a perspective to the precise molecular structure design and the property regulation of the reversible thermochromic PDA materials.

关键词： Polydiacetylene Thermochromism Hydrogen bonding Chemical modification

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Privacy-Preserving Federated Deep Learning Diagnostic Method for Multi-Stage Diseases

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Computer Modeling in engineering & Sciences 2024年第6期139卷 3085-3099页

作者： Jinbo Yang Hai Huang Lailai Yin Jiaxing Qu Wanjuan Xie School of Computer Science and Technology Harbin University of Science and TechnologyHarbin150080China Shanghai Futures Information Technology Co. Ltd.Shanghai201201China Heilongjiang Province Cyberspace research center Harbin150001China Information Network engineering and research center South China University of TechnologyGuangzhou510641China

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources,including clinical symptoms,physical signs,biochemical test results,imaging findings,pathological examination data,and even genetic *** applying machine learning modeling to predict and diagnose multi-stage diseases,several challenges need to be ***,the model needs to handle multimodal data,as the data used by doctors for diagnosis includes image data,natural language data,and structured ***,privacy of patients’data needs to be protected,as these data contain the most sensitive and private ***,considering the practicality of the model,the computational requirements should not be too *** address these challenges,this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage *** method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an *** also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm,providing accelerated support for homomorphic encryption in ***,this paper designs and conducts experiments to evaluate the proposed *** experimental results show that in privacy-preserving federated deep learning diagnostic modeling,the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection,and has higher modeling speed compared to similar algorithms.

关键词： Vertical federation homomorphic encryption deep neural network intelligent diagnosis machine learning and big data

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HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 991-1000页

作者： Li Xiao Cheng-Wu Wang Ying Deng Yi-Jing Yang Jing Lu Jun-Feng Yan Qing-Hua Peng School of Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina School of Informatics Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Key Laboratory for Prevention and treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial engineering and Technological research center for Prevention and treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine and Protecting Visual Function Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome ***:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic *** Algorithm(GA)was used for feature selection to obtain the optimal feature *** Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was *** performance of the model was compared with other optimization *** models were evaluated with accuracy,precision,recall,and F1 score as ***:data on 970 cases that met screening requirements were *** Vector Machine(SVM)was the best basic classification *** accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical *** model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic *** model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization *** with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of *** provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

关键词： traditional Chinese medicine diabetic retinopathy Harris Hawk Optimization Support Vector Machine syndrome differentiation

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CBioProfiler:A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 153-165页

作者： Xiaoping Liu Zisong Wang Hongjie Shi Sheng Li Xinghuan Wang Department of Urology Zhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Pathology Zhongnan Hospital of Wuhan UniversityWuhan 430071China Cancer Precision Diagnosis and treatment and Translational Medicine Hubei engineering research center Zhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Biological Repositories Zhongnan Hospital of Wuhan UniversityWuhan 430071China

Cancer is a leading cause of death worldwide,and the identification of biomarkers and subtypes that can predict the long-term survival of cancer patients is essential for their risk stratification,treatment,and ***,there are currently no standardized tools for exploring cancer biomarkers or *** this study,we introduced Cancer Biomarker and subtype Profiler(CBioProfiler),a web server and standalone application that includes two pipelines for analyzing cancer biomarkers and *** cancer biomarker pipeline consists of five modules for identifying and annotating cancer survival-related biomarkers using multiple survival-related machine learning *** cancer subtype pipeline includes three modules for data preprocessing,subtype identification using multiple unsupervised machine learning methods,and subtype evaluation and *** also includes CuratedCancerPrognosisdata,a novel R package that integrates reviewed and curated gene expression and clinical data from 268 *** studies cover 43 common blood and solid tumors and draw upon 47,686 clinical *** web server is available at https://***/and https://***/CBioProfiler/,and the standalone app and source code can be found at https://***/liuxiaoping2020/CBioProfiler.

关键词： CBioProfiler Cancer biomarker Cancer subtype Machine learning Web app

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An efficient online histogram publication method for data streams with local differential privacy

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Frontiers of Information Technology & Electronic engineering 2024年第8期25卷 1096-1109页

作者： Tao TAO Funan ZHANG Xiujun WANG Xiao ZHENG Xin ZHAO School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China Anhui engineering research center for intelligent Applications and Security of Industrial Internet Maanshan 243032China engineering research Institute Anhui University of TechnologyMaanshan 243032China Shengli No.1 Middle School of Dongying City Dongying 257000China

Many areas are now experiencing data streams that contain privacy-sensitive *** the sharing and release of these data are of great commercial value,if these data are released directly,the private user information in the data will be ***,how to continuously generate publishable histograms(meeting privacy protection requirements)based on sliding data stream windows has become a critical issue,especially when sending data to an untrusted third *** histogram publication methods are unsatisfactory in terms of time and storage costs,because they must cache all elements in the current sliding window(sW).Our work addresses this drawback by designing an efficient online histogram publication(EOHP)method for local differential privacy data ***,in the EOHP method,the data collector first crafts a histogram of the current SW using an approximate counting ***,the data collector reduces the privacy budget by using the optimized budget absorption mechanism and adds appropriate noise to the approximate histogram,making it possible to publish the histogram while retaining satisfactory data *** experimental results on two different real datasets show that the EOHP algorithm significantly reduces the time and storage costs and improves data utility compared to other existing algorithms.

关键词： data stream Differential privacy Sliding windows Approximate counting

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intelligent fault diagnosis methods toward gas turbine: A review

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Chinese Journal of Aeronautics 2024年第4期37卷 93-120页

作者： Xiaofeng LIU Yingjie CHEN Liuqi XIONG Jianhua WANG Chenshuang LUO Liming ZHANG Kehuan WANG School of Transportation Science and engineering Beihang UniversityBeijing 100191China Systems engineering research Institute China State Shipbuilding CorporationBeijing 100094China

Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

关键词： Fault diagnosis Health management Gas turbine Artificial intelligence intelligent diagnosis method

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Fully automatic AI segmentation of oral surgery-related tissues based on cone beam computed tomography images

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International Journal of Oral Science 2024年第3期16卷 413-424页

作者： Yu Liu Rui Xie Lifeng Wang Hongpeng Liu Chen Liu Yimin Zhao Shizhu Bai Wenyong Liu Beijing Yakebot Technology Co. Ltd.BeijingChina School of Mechanical engineering and Automation Beihang UniversityBeijingChina State Key Laboratory of Oral&Maxillofacial Reconstruction and Regeneration National Clinical Research Center for Oral DiseasesShaanxi Key Laboratory of StomatologyDigital CenterSchool of StomatologyThe Fourth Military Medical UniversityXi’anChina Key Laboratory of Biomechanics and Mechanobiology of the Ministry of Education Beijing Advanced Innovation Center for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina

Accurate segmentation of oral surgery-related tissues from cone beam computed tomography(CBCT)images can significantly accelerate treatment planning and improve surgical *** this paper,we propose a fully automated tissue segmentation system for dental implant ***,we propose an image preprocessing method based on data distribution histograms,which can adaptively process CBCT images with different *** on this,we use the bone segmentation network to obtain the segmentation results of alveolar bone,teeth,and maxillary *** use the tooth and mandibular regions as the ROI regions of tooth segmentation and mandibular nerve tube segmentation to achieve the corresponding *** tooth segmentation results can obtain the order information of the *** corresponding experimental results show that our method can achieve higher segmentation accuracy and efficiency compared to existing *** average Dice scores on the tooth,alveolar bone,maxillary sinus,and mandibular canal segmentation tasks were 96.5%,95.4%,93.6%,and 94.8%,*** results demonstrate that it can accelerate the development of digital dentistry.

关键词： surgery CBCT beam

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Long-term functional maintenance of primary hepatocytes in vitro using macroporous hydrogels engineered through liquid-liquid phase separation

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Nano research 2024年第3期17卷 1725-1736页

作者： Yang Sun Sheng Yin Jian Cui Zhongxia Wang Yueying Han Ding Ma Shuo Wang Junhua Wu Yi Cao Chunping Jiang Xiaosong Gu Jinan Microecological Biomedicine Shandong Laboratory Jinan 250021China Collaborative Innovation center of Advanced Microstructures National Laboratory of Solid State MicrostructureKey Laboratory of Intelligent Optical Sensing and ManipulationMinistry of EducationDepartment of PhysicsNanjing UniversityNanjing 210093China Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital of Nanjing University Medical SchoolNanjing 210093China National Institute of Healthcare data Science at Nanjing University Nanjing UniversityNanjing 210093China

Preserving the functionality of hepatocytes in vitro poses a significant challenge in liver tissue engineering and bioartificial liver,as these cells rapidly lose their metabolic and functional characteristics after *** by the macroporous structures found in native liver tissues,here we develop synthetic hydrogel scaffolds that closely mimic the liver’s structural organization through the phase separation between polyethylene glycol(PEG)and *** hydrogels exhibit interconnected macroporous structures and appropriate mechanical properties,providing an optimal microenvironment conducive to hepatocyte adhesion and the formation of sizable *** to two-dimensional hepatocyte cultures,enhanced functionalities of hepatocytes cultured in our macroporous hydrogels were observed for 14 days,as evidenced by quantitative reverse-transcription–polymerase chain reactions(qRT-PCR),immunofluorescence,and enzyme linked immunosorbent assay(ELISA)*** sequencing data further confirmed the establishment of cell–cell interactions among hepatocytes when cultured in our ***,these hepatocytes maintained a protein expression lineage that closely resembled freshly isolated hepatocytes,particularly in the Notch and tumor necrosis factor(TNF)signaling *** results suggest that the macroporous hydrogels are attractive scaffolds for liver tissue engineering.

关键词： liver tissue engineering phase separation hepatocytes macroporous hydrogel

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NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

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National Science Review 2024年第05期11卷 202-216页

作者： Weijie Zheng Huawei Mu Zhiyi Chen Jiajun Liu Debin Xia Yuxiao Cheng Qi Jing Pak-Ming Lau Jin Tang Guo-Qiang Bi Feng Wu Hao Wang AHU-IAI AI Joint Laboratory Anhui University Anhui Province Key Laboratory of Biomedical Imaging and intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science Center National engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of China Division of Life Sciences and Medicine University of Science and Technology of China Interdisciplinary center for Brain Information Brain Cognition and Brain Disease InstituteShenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning–based high-efficiency,high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

关键词： whole-brain imagingneuronal activity trace mapping deep learning quantitative analysis

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Multi-UAV Collaborative Mission PlanningMethod for Self-Organized Sensor data Acquisition

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Computers, Materials and Continua 2024年第1期81卷 1529-1563页

作者： Yang, Shijie Yuan, Jiateng Zhang, Zhipeng Chen, Zhibo Zhang, Hanchao Cui, Xiaohui School of Information Science and Technology Beijing Forestry University Beijing 100083 China engineering research center for Forestry-Oriented intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083 China State Key Laboratory of Efficient Production of Forest Resources Beijing 100083 China Division of Biostatistics Department of Population Heath Grossman School of Medicine New York University New York NY 10016 United States

In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominantmethod. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data *** regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAVmission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstr

关键词： Unmanned aerial vehicles (UAV)

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