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Mechanisms of immune checkpoint inhibitormediated liver injury

Acta Pharmaceutica Sinica B 2021年第12期11卷 3727-3739页

作者： Layla Shojaie Myra Ali Andrea Iorga Lily Dara Division of Gastrointestinal and Liver Diseases Department of MedicineKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCA 90033USA Research center for Liver Disease Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCA 90033USA Keck School of Medicine University of Southern CaliforniaLos AngelesCA 90033USA U.S.Food and Drug Administration Center for Devices and Radiological HealthSilver SpringMD 20993USA umbc center for accelerated real time analytics University of MarylandBaltimore CountyBaltimoreMD 21250USA

The immune checkpoints,cytotoxic T-lymphocyte-associated antigen 4(CTLA-4) and programmed cell death protein-1/ligand-1(PD-1/PD-L1) are vital contributors to immune resulation and *** immune checkpoint inhibitors(ICIs) have revolutionized cancer therapy;however,they come with the cost of immune related adverse events involving multiple organs such as the *** to its constant expo sure to foreign antigens,the liver has evolved a high capacity for immune tolerance,therefore,blockade of the immune checkpoints can result in aberrant immune activation affecting the liver in up to 20% of patients depending on the agent(s) used and underlying *** type of hepatotoxicity is termed immune mediated liver injury from checkpoint inhibitors(ILICI) and is more common when CTLA4 and PD-1/PDL1 are used in *** underlying mechanisms of this unique type of hepatotoxicity are not fully understood;however,the contribution of CD8^(+) cytotoxic T lymphocytes,various CD4^(+) T cells populations,cytokines,and the secondary activation of the innate immune system leading to liver injury have all been *** review summarizes our current understanding of the underlying mechanisms of liver injury in immunotherapy using animal models of ILICI and available patient data from clinical studies.

关键词： Hepatotoxicity DILI Immunotherapy Cell death Hepatocyte CTLA-4 PD-1 PD-L1 Necrosis Apoptosis

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Massive data clustering by multi-scale psychological observations

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National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data analytics Xi'an Jiaotong University Department of Mathematics and Statistics University of Ottawa center for Statistics and Machine Learning Princeton University Industrial Artificial Intelligent center Pazhou Laboratory

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

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Understanding celiac disease monitoring patterns and outcomes after diagnosis:A multinational,retrospective chart review study

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World Journal of Gastroenterology 2021年第20期27卷 2603-2614页

作者： Knut EA Lundin Ciaran P Kelly David S Sanders Kristina Chen Sheena Kayaniyil Sisi Wang Rajvi J Wani Caitlin Barrett Shakira Yoosuf Ellen S Pettersen Robert Sambrook Daniel A Leffler K.G.Jebsen Coeliac Disease Research Centre University of OsloOslo 0450Norway Department of Gastroenterology Oslo University Hospital RikshospitaletOslo 0372Norway Celiac center Beth Israel Deaconess Medical center Celiac Research Program Harvard Medical SchoolBostonMA 02115United States Royal Hallamshire Hospital University of SheffieldSheffield S102TNUnited Kingdom Takeda Pharmaceuticals International Co. CambridgeMA 02139United States real World Evidence Strategy and analytics ICON plc.TorontoON L7N 3G2Canada real World Evidence Strategy and analytics ICON plc.VancouverBC V6B 1P1Canada

BACKGROUND Long-term outcomes and monitoring patterns in real-world practice are largely unknown among patients with celiac *** To understand patterns of follow-up and management of patients with celiac disease,and to characterize symptoms and villous atrophy after *** A retrospective chart review study was performed using medical chart data of patients diagnosed with celiac *** gastroenterology referral centers,with substantial expertise in celiac disease,participated in the United Kingdom,United States,and *** and clinical data were collected from medical *** analyses were conducted on patients with biopsyconfirmed celiac disease,diagnosed between 2008 and 2012,with at least one follow-up visit before December 31,*** demographic and clinical characteristics,biopsy/serology tests and results,symptoms,and comorbidities were captured at diagnosis and for each clinic visit occurring within the study period(i.e.,before the study end date of December 31,2017).RESULTS A total of 300 patients were included in this study[72%female;mean age at diagnosis:38.9 years,standard deviation(SD)17.2].Patients were followed-up for a mean of 29.9 mo(SD 22.1)and there were,on average,three follow-up visits per patient during the study *** two-thirds(68.4%)of patients were recorded as having ongoing gastrointestinal symptoms and 11.0%had ongoing symptoms and enteropathy during *** 80%of patients were referred to a dietician at least once during the follow-up ***(50.0%)of the patients underwent at least one follow-up duodenal biopsy and 36.6%had continued villous *** of monitoring varied between *** were conducted more frequently in Norway and patients in the United States had a longer follow-up *** This real-world study demonstrates variable follow-up of patients with celiac disease despite most patients continuing to have abnormal histology

关键词： Celiac disease Outcomes research Endoscopy real-world General practice Villous atrophy

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Deep Reinforcement Learning Based real-time AC Optimal Power Flow Considering Uncertainties

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1098-1109页

作者： Yuhao Zhou Wei-Jen Lee Ruisheng Diao Di Shi IEEE Energy Systems Research center in the Electrical Engineering Department University of Texasat ArlingtonArlington TX76019USA AI&System analytics Groupat GEIRINA San JoseCA95134USA

Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources(RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive realtime alternating current(AC) optimal power flow(OPF) solutions considering the uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning(DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization(PPO) algorithm is applied to train and test the artificial intelligence(AI) agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N-1 topology changes validate the effectiveness of the proposed method and demonstrate its great potential in promoting sustainable energy integration into the power system.

关键词： Alternating current(AC)optimal power flow(OPF) deep learning deep reinforcement learning(DRL) renewable integration proximal policy optimization

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Generating geologically realistic 3D reservoir facies models using deep learning of sedimentary architecture with generative adversarial networks

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Petroleum Science 2019年第3期16卷 541-549页

作者： Tuan-Feng Zhang Peter Tilke Emilien Dupont Ling-Chen Zhu Lin Liang William Bailey Applied Math and Data analytics Schlumberger-Doll Research Cambridge MAUSA Schlumberger Technology Innovation center Menlo Park CA USA Reservoir Geosciences Schlumberger-Doll Research Cambridge MA USA

This paper proposes a novel approach for generating 3-dimensional complex geological facies models based on deep generative *** can reproduce a wide range of conceptual geological models while possessing the flexibility necessary to honor constraints such as well *** with existing geostatistics-based modeling methods,our approach produces realistic subsurface facies architecture in 3D using a state-of-the-art deep learning method called generative adversarial networks(GANs).GANs couple a generator with a discriminator,and each uses a deep convolutional neural *** networks are trained in an adversarial manner until the generator can create "fake" images that the discriminator cannot distinguish from "real" *** extend the original GAN approach to 3D geological modeling at the reservoir *** GANs are trained using a library of 3D facies *** the GANs have been trained,they can generate a variety of geologically realistic facies models constrained by well data *** geomodelling approach using GANs has been tested on models of both complex fluvial depositional systems and carbonate reservoirs that exhibit progradational and aggradational *** results demonstrate that this deep learning-driven modeling approach can capture more realistic facies architectures and associations than existing geostatistical modeling methods,which often fail to reproduce heterogeneous nonstationary sedimentary facies with apparent depositional trend.

关键词： Geological facies Geomodeling Data conditioning Generative adversarial networks

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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research center for Supply Chain Big Data analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Measurement Manifolds Shape Clustering algorithms Synthetic data

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Recommendation system with minimized transaction data

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Data Science and Management 2021年第4期4卷 40-45页

作者： Yujeong Hwangbo Kyoung Jun Lee Baek Jeong Kyung Yang Park Big Data Research center Kyung Hee UniversitySeoul02447South Korea Department of Big Data analytics Kyung Hee UniversitySeoul02447South Korea Harex InfoTech Seoul02447South Korea

This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to *** service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of *** study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment ***,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction *** recommendation methodology was verified with the real transaction *** on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.

关键词： User-centric payment Recommendation service Artificial intelligence Extrapolative collaborative filtering

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Experiments and Analyses of Anonymization Mechanisms for Trajectory Data Publishing

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Journal of Computer Science & Technology 2022年第5期37卷 1026-1048页

作者： She Sun Shuai Ma Jing-He Song Wen-Hai Yue Xue-Lian Lin Tiejun Ma State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang UniversityBeijing 100191China Department of Decision analytics and Risk Southampton Business SchoolUniversity of Southampton Southampton SO171BJU.K

With the advancing of location-detection technologies and the increasing popularity of mobile phones and other location-aware devices,trajectory data is continuously *** large-scale trajectories provide opportunities for various applications,the locations in trajectories pose a threat to individual ***,there has been an interesting debate on the reidentifiability of individuals in the Science *** main finding of Sánchez et *** exactly opposite to that of De Montjoye et al.,which raises the first question:"what is the true situation of the privacy preservation for trajectories in terms of reidentification?''Furthermore,it is known that anonymization typically causes a decline of data utility,and anonymization mechanisms need to consider the trade-off between privacy and *** raises the second question:"what is the true situation of the utility of anonymized trajectories?''To answer these two questions,we conduct a systematic experimental study,using three real-life trajectory datasets,five existing anonymization mechanisms(i.e.,identifier anonymization,grid-based anonymization,dummy trajectories,k-anonymity andε-differential privacy),and two practical applications(i.e.,travel time estimation and window range queries).Our findings reveal the true situation of the privacy preservation for trajectories in terms of reidentification and the true situation of the utility of anonymized trajectories,and essentially close the debate between De Montjoye et *** Sánchez et *** the best of our knowledge,this study is among the first systematic evaluation and analysis of anonymized trajectories on the individual privacy in terms of unicity and on the utility in terms of practical applications.

关键词： anonymization privacy reidentification trajectory utility

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An empirical behavioral order‑driven model with price limit rules

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Financial Innovation 2021年第1期7卷 1733-1756页

作者： Gao‑Feng Gu Xiong Xiong Hai‑Chuan Xu Wei Zhang Yongjie Zhang Wei Chen Wei‑Xing Zhou School of Business East China University of Science and TechnologyShanghaiChina Research center for Econophysics East China University of Science and TechnologyShanghaiChina College of Management and Economics Tianjin UniversityTianjinChina China center for Social Computing and analytics Tianjin UniversityTianjinChina Shenzhen Stock Exchange ShenzhenChina School of Mathematics East China University of Science and TechnologyShanghaiChina

We propose an empirical behavioral order-driven(EBOD)model with price limit rules,which consists of an order placement process and an order cancellation *** the ingredients of the model are determined based on the empirical microscopic regularities in the order flows of stocks traded on the Shenzhen Stock *** model can reproduce the main stylized facts in real *** experiments unveil that asymmetric setting of price limits will cause the stock price to diverge exponentially when the up price limit is higher than the down price limit and to vanish vice *** also find that asymmetric price limits have little influence on the correlation structure of the return series and the volatility series,but cause remarkable changes in the average returns and the tail exponents of *** EBOD model provides a suitable computational experiment platform for academics,market participants,and policy makers.

关键词： Econophysics Order-driven model Agent-based model Asymmetric price limit Stylized facts Limit order book

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Trend Analysis and Intervention Effect Starting Point Detection of COVID-19 Epidemics Using Recalibrated time Series Models—Worldwide,2020

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China CDC weekly 2021年第20期3卷 417-422页

作者： Shu Li Chen Chen Shuyin Cao Kejia Hu Hao Lei Xiaolin Xu Qinchuan Wang Changzheng Yuan Sicong Wang Sisi Wang Junlin Jia Yuanqing Ye Xifeng Wu center of Clinical Big Data and analytics Second Affiliated Hospital and Department of Big Data Health Science School of Public HealthZhejiang University School of MedicineHangzhouZhejiangChina National Institute for Data Science in Health and Medicine HangzhouZhejiang UniversityZhejiangChina Department of Surgical Oncology Affiliated Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina.

Objective:This study aimed to identify a model for short-term coronavirus disease 2019(COVID-19)trend prediction and intervention ***:We compared the autoregressive integrated moving average(ARIMA)model and Holt exponential smoothing(Holt)model on predicting the number of cumulative COVID-19 cases in *** on the mean absolute percentage error(MAPE)value,the optimal model was selected and further tested using data from the United States,Italy and Republic of *** intervention effect starting time points and abnormal trend changes were detected by observing the pattern of differences between the predicted and real ***:The recalibrated ARIMA model with a 5-day prediction time span has the best model performance with MAPEs ranged between 2%and 5%.The intervention effects started to show on February 7 in the mainland of China,March 5 in Republic of Korea and April 27 in Italy,but have not been detected in the US as of May *** abnormal trends were detected in Korea and Italy,but the overall epidemic trends were stable since the effect starting ***:The recalibrated ARIMA model can detect the intervention effects starting points and abnormal trend changes;thus to provide valuable information support for epidemic trend analysis and intervention evaluation.

关键词： starting absolute smoothing

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