Slicer4minute_SoniaPujol_2013

合集下载

人工智能英文参考文献(最新120个)

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享，供大家借鉴参考。

人工智能英文参考文献一：[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二：[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? [J]. ,2020,25(4).[47]Savage Rock H,van Assen Marly,Martin Simon S,Sahbaee Pooyan,Griffith Lewis P,Giovagnoli Dante,Sperl Jonathan I,Hopfgartner Christian,K?rgel Rainer,Schoepf U Joseph. Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[48]Brzezicki Maksymilian A,Bridger Nicholas E,Kobeti? Matthew D,Ostrowski Maciej,Grabowski Waldemar,Gill Simran S,Neumann Sandra. Artificial intelligence outperforms human students in conducting neurosurgical audits.[J]. Clinical neurology and neurosurgery,2020,192.[49]Lockhart Mark E,Smith Andrew D. Fatty Liver Disease: Artificial Intelligence Takes on the Challenge.[J]. Radiology,2020,295(2).[50]Wood Edward H,Korot Edward,Storey Philip P,Muscat Stephanie,Williams George A,Drenser Kimberly A. The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence.[J]. Current opinion in ophthalmology,2020,31(3).[51]Ho Dean,Quake Stephen R,McCabe Edward R B,Chng Wee Joo,Chow Edward K,Ding Xianting,Gelb Bruce D,Ginsburg Geoffrey S,Hassenstab Jason,Ho Chih-Ming,Mobley William C,Nolan Garry P,Rosen Steven T,Tan Patrick,Yen Yun,Zarrinpar Ali. Enabling Technologies for Personalized and Precision Medicine.[J]. Trends in biotechnology,2020,38(5).[52]Fischer Andreas M,Varga-Szemes Akos,van Assen Marly,Griffith L Parkwood,Sahbaee Pooyan,Sperl Jonathan I,Nance John W,Schoepf U Joseph. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.[J]. AJR. American journal ofroentgenology,2020,214(5).[53]Moore William,Ko Jane,Gozansky Elliott. Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation.[J]. Journal of thoracic imaging,2020,35(3).[54]Hwang Eui Jin,Park Chang Min. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. Cardiovascular research,2020,116(6).[59]Fischer Andreas M,Eid Marwen,De Cecco Carlo N,Gulsun Mehmet A,van Assen Marly,Nance John W,Sahbaee Pooyan,De Santis Domenico,Bauer Maximilian J,Jacobs Brian E,Varga-Szemes Akos,Kabakus Ismail M,Sharma Puneet,Jackson Logan J,Schoepf U Joseph. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[60]Ghosh Adarsh,Kandasamy Devasenathipathy. Interpretable Artificial Intelligence: Why and When.[J]. AJR. American journal of roentgenology,2020,214(5).[61]M.Rosario González-Rodríguez,M.Carmen Díaz-Fernández,Carmen Pacheco Gómez. Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions[J]. Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. Shlobin,Kartik Kesavabhotla,Zachary A Smith,Nader S Dahdaleh. Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions[J]. Clinical Neurology and Neurosurgery,2020,192.[71]Mei Yang,Runze Zhou,Xiangjun Qiu,Xiangfei Feng,Jian Sun,Qunshan Wang,Qiufen Lu,Pengpai Zhang,Bo Liu,Wei Li,Mu Chen,Yan Zhao,Binfeng Mo,Xin Zhou,Xi Zhang,Yingxue Hua,Jin Guo,Fangfang Bi,Yajun Cao,Feng Ling,Shengming Shi,Yi-Gang Li. Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals[J]. Environment International,2020,139.[72]Fatemehalsadat Madaeni,Rachid Lhissou,Karem Chokmani,Sebastien Raymond,Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review[J]. Cold Regions Science and Technology,2020,174.[73]Steve Chukwuebuka Arum,David Grace,Paul Daniel Mitchell. A review of wireless communication using high-altitude platforms for extended coverage and capacity[J]. Computer Communications,2020,157.[74]Yong-Hong Kuo,Nicholas B. Chan,Janny M.Y. Leung,Helen Meng,Anthony Man-Cho So,Kelvin K.F. Tsoi,Colin A. Graham. An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department[J]. International Journal of Medical Informatics,2020,139.[75]Matteo Terzi,Gian Antonio Susto,Pratik Chaudhari. Directional adversarial training for cost sensitive deep learning classification applications[J]. Engineering Applications of Artificial Intelligence,2020,91.[76]Arman Kilic. Artificial Intelligence and Machine Learning in Cardiovascular Health Care[J]. The Annals of Thoracic Surgery,2020,109(5).[77]Hossein Azarmdel,Ahmad Jahanbakhshi,Seyed Saeid Mohtasebi,Alfredo Rosado Mu?oz. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)[J]. Postharvest Biology and Technology,2020,166.[78]Wafaa Wardah,Abdollah Dehzangi,Ghazaleh Taherzadeh,Mahmood A. Rashid,M.G.M. Khan,Tatsuhiko Tsunoda,Alok Sharma. Predicting protein-peptide binding sites with a deep convolutional neural network[J]. Journal of Theoretical Biology,2020,496.[79]Francisco F.X. Vasconcelos,Róger M. Sarmento,Pedro P. Rebou?as Filho,Victor Hugo C. de Albuquerque. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis[J]. Engineering Applications of Artificial Intelligence,2020,91.[80]Masaaki Konishi. Bioethanol production estimated from volatile compositions in hydrolysates of lignocellulosic biomass by deep learning[J]. Journal of Bioscience and Bioengineering,2020,129(6).人工智能英文参考文献三：[81]J. Kwon,K. Kim. Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography[J]. Journal of Heart and Lung Transplantation,2020,39(4).[82]C. Maathuis,W. Pieters,J. van den Berg. Decision support model for effects estimation and proportionality assessment for targeting in cyber operations[J]. Defence Technology,2020.[83]Samer Ellahham. Artificial Intelligence in Diabetes Care[J]. The American Journal of Medicine,2020.[84]Yi-Ting Hsieh,Lee-Ming Chuang,Yi-Der Jiang,Tien-Jyun Chang,Chung-May Yang,Chang-Hao Yang,Li-Wei Chan,Tzu-Yun Kao,Ta-Ching Chen,Hsuan-Chieh Lin,Chin-Han Tsai,Mingke Chen. Application of deep learning image assessment software VeriSee? for diabetic retinopathy screening[J]. Journal of the Formosan Medical Association,2020.[85]Emre ARTUN,Burak KULGA. Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference[J]. Petroleum Exploration and Development Online,2020,47(2).[86]Alberto Arenal,Cristina Armu?a,Claudio Feijoo,Sergio Ramos,Zimu Xu,Ana Moreno. Innovation ecosystems theory revisited: The case of artificial intelligence in China[J]. Telecommunications Policy,2020.[87]T. Som,M. Dwivedi,C. Dubey,A. Sharma. Parametric Studies on Artificial Intelligence Techniques for Battery SOC Management and Optimization of Renewable Power[J]. Procedia Computer Science,2020,167.[88]Bushra Kidwai,Nadesh RK. Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems[J]. Procedia Computer Science,2020,167.[89]Asl? Bozda?,Ye?im Dokuz,?znur Begüm G?k?ek. Spatial prediction of PM 10 concentration using machine learning algorithms in Ankara, Turkey[J]. Environmental Pollution,2020.[90]K.P. Smith,J.E. Kirby. Image analysis and artificial intelligence in infectious disease diagnostics[J]. Clinical Microbiology and Infection,2020.[91]Alklih Mohamad YOUSEF,Ghahfarokhi Payam KAVOUSI,Marwan ALNUAIMI,Yara ALATRACH. Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East[J]. Petroleum Exploration and Development Online,2020,47(2).[92]Omer F. Ahmad,Danail Stoyanov,Laurence B. Lovat. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[93]Sanne A. Hoogenboom,Ulas Bagci,Michael B. Wallace. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[94]Douglas K. Rex. Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[95]Neal Shahidi,Michael J. Bourke. Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[96]Michael Byrne. Artificial intelligence in gastroenterology[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[97]Piet C. de Groen. Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[98]Robin Zachariah,Andrew Ninh,William Karnes. Artificial intelligence for colon polyp detection: Why should we embrace this?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[99]Alexandra T. Greenhill,Bethany R. Edmunds. A primer of artificial intelligence in medicine[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[100]Tomohiro Tada,Toshiaki Hirasawa,Toshiyuki Yoshio. The role for artificial intelligence in evaluation of upper GI cancer[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[101]Yahui Jiang,Meng Yang,Shuhao Wang,Xiangchun Li,Yan Sun. Emerging role of deep learning‐based artificial intelligence in tumor pathology[J]. Cancer Communications,2020,40(4).[102]Kristopher D. Knott,Andreas Seraphim,Joao B. Augusto,Hui Xue,Liza Chacko,Nay Aung,Steffen E. Petersen,Jackie A. Cooper,Charlotte Manisty,Anish N. Bhuva,Tushar Kotecha,Christos V. Bourantas,Rhodri H. Davies,Louise A.E. Brown,Sven Plein,Marianna Fontana,Peter Kellman,James C. Moon. The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence–Based Approach Using Perfusion Mapping[J]. Circulation,2020,141(16).[103]Muhammad Asad,Ahmed Moustafa,Takayuki Ito. FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning[J]. Applied Sciences,2020,10(8).[104]Wu Wenzhi,Zhang Yan,Wang Pu,Zhang Li,Wang Guixiang,Lei Guanghui,Xiao Qiang,Cao Xiaochen,Bian Yueran,Xie Simiao,Huang Fei,Luo Na,Zhang Jingyuan,Luo Mingyan. Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy.[J]. Journal of medical virology,2020.[105]. Eyenuk Fulfills Contract for Artificial Intelligence Grading of Retinal Images[J]. Telecomworldwire,2020.[106]Kim Tae Woo,Duhachek Adam. Artificial Intelligence and Persuasion: A Construal-Level Account.[J]. Psychological science,2020,31(4).[107]McCall Becky. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread.[J]. The Lancet. Digital health,2020,2(4).[108]Alca?iz Mariano,Chicchi Giglioli Irene A,Sirera Marian,Minissi Eleonora,Abad Luis. [Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence].[J]. Medicina,2020,80 Suppl 2.[109]Cong Lei,Feng Wanbing,Yao Zhigang,Zhou Xiaoming,Xiao Wei. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer.[J]. Journal of Cancer,2020,11(12).[110]Wang Fengdan,Gu Xiao,Chen Shi,Liu Yongliang,Shen Qing,Pan Hui,Shi Lei,Jin Zhengyu. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.[J]. PeerJ,2020,8.[111]Hu Wenmo,Yang Huayu,Xu Haifeng,Mao Yilei. Radiomics based on artificial intelligence in liver diseases: where we are?[J]. Gastroenterology report,2020,8(2).[112]Batayneh Wafa,Abdulhay Enas,Alothman Mohammad. Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques.[J]. Heliyon,2020,6(4).[113]Aydin Emrah,Türkmen ?nan Utku,Namli G?zde,?ztürk ?i?dem,Esen Ay?e B,Eray Y Nur,Ero?lu Egemen,Akova Fatih. A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children.[J]. Pediatric surgery international,2020.[114]Ellahham Samer. Artificial Intelligence in Diabetes Care.[J]. The Americanjournal of medicine,2020.[115]David J. Winkel,Thomas J. Weikert,Hanns-Christian Breit,Guillaume Chabin,Eli Gibson,Tobias J. Heye,Dorin Comaniciu,Daniel T. Boll. Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation[J]. European Journal of Radiology,2020,126.[116]Binjie Fu,Guoshu Wang,Mingyue Wu,Wangjia Li,Yineng Zheng,Zhigang Chu,Fajin Lv. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study[J]. European Journal of Radiology,2020,126.[117]Georgios N. Kouziokas. A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting[J]. Engineering Applications of Artificial Intelligence,2020,92.[118]Qingsong Ruan,Zilin Wang,Yaping Zhou,Dayong Lv. A new investor sentiment indicator ( ISI ) based on artificial intelligence: A powerful return predictor in China[J]. Economic Modelling,2020,88.[119]Mohamed Abdel-Basset,Weiping Ding,Laila Abdel-Fatah. The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model[J]. Information Fusion,2020,61.[120]Federico Caobelli. Artificial intelligence in medical imaging: Game over for radiologists?[J]. European Journal of Radiology,2020,126.以上就是关于人工智能参考文献的分享，希望对你有所帮助。

硅烷偶联剂KH550_改性白炭黑及其在环氧树脂中的应用

硅烷偶联剂KH550改性白炭黑及其在环氧树脂中的应用赵志明，李文琼，靳朝辉，于朝生（东北林业大学化学化工与资源利用学院，东北林业大学森林植物生态学教育部重点实验室，黑龙江哈尔滨150040）摘要：利用硅烷偶联剂KH550对白炭黑纳米粉体进行表面接枝改性，并制备改性白炭黑（mSiO 2）/环氧树脂（EP ）浇铸体，利用傅里叶变换红外光谱（FTIR ）、X 射线衍射（XRD ）、粒度分析、拉伸性能测试、热重分析（TG ）、扫描电镜（SEM ）等手段对改性前后的白炭黑粒、mSiO 2/EP 浇铸体进行表征分析，探究了KH550对白炭黑的改性效果以及mSiO 2用量对浇铸体力学性能、耐热性和结构的影响。

结果表明：以异丙醇作为分散剂，当KH550质量分数为20%，反应温度为90℃，反应时间为5h ，在醇、水混合溶剂中可以实现KH550对白炭黑的表面改性；当改性白炭黑用量为8%(wt.)时，浇铸体综合性能最好，拉伸强度为41.29MPa ，较纯EP 提升了95.2%；其最大分解速率时的温度为377℃，较纯EP 提升了14℃。

关键词：KH550；白炭黑；改性；环氧树脂；拉伸强度中图分类号：TQ 127.2Study on Surface Modiﬁ cation of Silica with KH550 and Its Application in Epoxy ResinZHAO Zhi-ming, LI Wen-qiong, JIN Zhao-hui, YU Chao-sheng（ College of Chemistry, Chemical Engineering and Resource Utilization, Northeast Forestry University; Key Laboratory of ForestPlant Ecology, Ministry of Education, Northeast Forestry University, Harbin 150040, Heilongjiang, China )Abstract: The silane coupling agent KH550 was used to modify the silica by surface grafting and to prepare modiﬁ ed silica (mSiO 2)/epoxy resin (EP) casts. The silica pellets and mSiO 2/EP casts before and after modiﬁcation were characterised by means of Fourier transform infrared spectroscopy (FTIR), X-ray diﬀ raction (XRD), particle size analysis, tensile properties testing, thermogravimetric analysis (TG) and scanning electron microscopy (SEM). The eﬀ ect of KH550 on the modiﬁ cation of silica and the eﬀ ect of mSiO 2 dosage on the mechanical properties, heat resistance and structure of the cast body were investigated. The results show that the surface modiﬁ cation of silica by KH550 can be achieved in a mixed solvent of alcohol and water when the mass fraction of KH550 is 20%, the reaction temperature is 90°C and the reaction time is 5h, using isopropanol as the dispersant. Furthermore, the mechanical properties and thermal stability of the mSiO 2/EP composites were improved by the KH550 modiﬁ cation. When the amount of mSiO 2 was 8% (wt.), the tensile strength of the mSiO 2/EP composite exhibited 41.29MPa, which resulted in an increase of tensile strength by 95.2%, and a maximum decomposition rate temperature of 377°C, which is 14°C higher than that of pure EP materials.Key words: KH550; silica; modiﬁ cation; EP; tensile strength 作者简介：赵志明，硕士研究生，主要从事功能材料研究工作。

IGCS19-0405 ：产品说明书

abnormal vaginal bleeding requiring intervention had no statis-tical difference between VP and WVP patients group (p=0.3074)as other complications as well(table1).Median of related days of vaginal bleeding after the procedure were 7.4days(SD8.75)in VP group and7.34days(SD8.52)in WVP group,with no statistical difference(p=0.912). Conclusions Insert a vaginal pack or not,after LEEP,do not affect the number of postoperative gynecologic intervention due to vaginal bleeding or the amount of postoperative bleed-ing days.Previous pregnancies,hormonal status,cytology or LEEP specimen characteristics did not affect the disclosure. We also could not find any risk factor associated to abnormal bleeding.Based on that,the use of vaginal pack can be omit-ted with no further complications.IGCS19-0405382LATERALLY EXTENDED ENDOPELVIC RESECTION(LEER) AND NEOVAGINE,PATIENT WITH RECTALADENOCARCINOMA AND RECURRENCE IN CERVIX,VAGINA AND PELVIC WALL:A PURPOSE OF A CASE1J Torres*,2J Saenz,3O Suescun,3M Medina,4L Trujillo.1Especialista en entrenamiento–Universidad Militar Nueva Granada–Instituto Nacional de Cancerologia,Department of Gynecologic Oncology,Bogota D.C.,Colombia;2Especialista en entrenamiento–Universidad Militar Nueva Granada–Instituto Nacional de Cancerologia,Department of Gynecologic Oncology,Bogota D.C,Colombia;3Instituto Nacional de Cancerologia, Department of Gynecologic Oncology,Bogota D.C,Colombia;4Instituto Nacional de Cancerologia,Department of Gynecologic Oncology,Bogota D.C.,Colombia10.1136/ijgc-2019-IGCS.382Objectives Exenteration is used to treat cancers of the lower and middle female genital tract in the irradiated pelvis. Höckel described laterally extended endopelvic resection (LEER)as an approach in which the resection line extends to the pelvic side wall.Methods A49-year-old patient diagnosed with rectal adenocar-cinoma10years ago,managed with chemotherapy plus radio-therapy.T umor relapse at3years,management with low abdominoperineal resection and definitive colostomy.Second relapse4years later,compromising the posterior aspect of the coccyx and right side of the pelvis with irresecability criteria, management was decided with chemotherapy with capecita-bine,oxaliplatin and bevacizumab.New relapse at2years in the cervix,vagina and pelvic wall.Images without distance disease,type LEER management with extension of pelvic floor margins and resection of muscle pubococcygeus and right lat-eral iliococcygeus with neovagina(Singapore flap)and non-continent urinary derivation with bilateral cutaneous ureteros-tomy,achieving adequate lateral margin with curative intent. During follow-up with favorable evolution.Results LEER combines at least two procedures:total mesorec-tal excision,total mesometrial resection or total mesovesical resection.It may even require resection of the pelvic wall, internal obturator muscle,pubococcygeus,iliococcygeus,coccy-geus or internal iliac vessels.In combination with neovagina, it would offer better results in non-gynecological cancer relapses.Conclusions LEER with neovagina can be offered as a new therapy to a selected subset of patients with relapse in adja-cent gynecological organs with good oncological,functional and aesthetic results.Symptom Management–Supportive Cancer CareIGCS19-0706383PHOTOBIOMODULATION AND MANUAL LYMPHDRAINAGE FOR NIPPLE NECROSIS TREATMENT INBREAST CANCER:A CASE REPORT1J Baiocchi,2L Campanholi,3G Baiocchi*.1Oncofisio,Physical Therapy,Sao Paulo,Brazil;2CESCAGE,Physical Therapy,Ponta Grossa,Brazil;3AC Camargo Cancer Center, Gynecologic Oncology,Sao Paulo,Brazil10.1136/ijgc-2019-IGCS.383Objectives Recently,breast reconstruction after mastectomywith nipple preservation became an option of breast cancer surgery.Despite its efficacy and aesthetic superiority,the nip-ple preservation is associated with several complications in the postoperative period.The photobiomodulation therapy,for-merly known as low-intensity laser therapy,demonstrated tis-sue promotion repair by cellular repair biostimulation, angiogenesis and anti-inflammatory effects.These characteris-tics suggest a potential role for repair of chronic wounds andmay be applicable in necrosis treatment.Our aim was toreport the effects of the physiotherapeutic intervention through photobiomodulation therapy in a patient with nipple necrosis after risk reducing mastectomy.Methods We report a case of a breast cancer surgery with nip-ple necrosis treated with low-level laser therapy.The patientwas a36-year-old women who developed skin nipple necrosisin the right breast after bilateral reconstructive mastectomy.She had6sessions of low-level laser therapy.Results A female subject developed a nipple necrosis of morethan40%on the right breast after mastectomy and recon-struction.She was referred to Physical Therapy(PT)and thePT sessions were composed by manual lymph drainage,man-ual therapy for de AWS,exercises of strength and flexibility, followed by LLLT with laser660nm,2joules per point atevery1cm.Therapy was implemented for12times in total,from May2016to June2016.A re-evaluation was performed monthly from July13,2016to November2017.After18 months of follow-up,the sustained effects of LLLT were found.Conclusions Low-level laser therapy is effective for the skin cicatrization after nipple necrosis.IGCS19-0446384CONTRACEPTION AND FERTILITY COUNSELING INPATIENTS RECEIVING CHEMOTHERAPY1A Elnaggar*,2A Calfee,1LB Daily,2T Hasley,1T Tillmanns.1West Cancer Center and Research Institute,Gynecologic Oncology,Memphis,USA;2University of Tennessee Health Science Center,Obstetrics and Gynecology,Mempis,USA10.1136/ijgc-2019-IGCS.384Objectives Cancer care advances allow more patients to pursue fertility.Unfortunately,treatments may have detrimental effectson fertility and fetus should pregnancy occur.This study examines physician documentation and patient perceptions of fertility and contraception counseling. on December 24, 2023 by guest. Protected by copyright./ Int J Gynecol Cancer: first published as 10.1136/ijgc-2019-IGCS.384 on 18 September 2019. Downloaded fromMethods IRB approval obtained for a cross-sectional study of men and women,ages18–50,with newly diagnosed malig-nancy between May2017and2018.Prior sterilization,secon-dary or synchronous cancer,or prior chemotherapy were exclusionary.Consented patients received a survey regarding perception on receipt and quality of,counseling.Demographic, sexual,and social information was obtained.Differences were evaluated using chi-square tests.Results Fifty-three of179patients identified participated. Majority were women(75v25%).Patients were more likely to have perceived counseling for contraception and fertility than documented.The majority perceived counseling as suffi-cient regarding contraception and fertility.Men were more likely than women to be perceive counsel-ing regarding fertility(85v43%,p=0.010).However,both felt fertility counseling to be sufficient with similar rates of documentation.Caucasians were more likely to perceive receipt of fertility counseling(68v29%)and to perceive it to be sufficient(70v40%),then African Americans,with the same rate of documentation(35%).Conclusions Significant discrepancies in perception counsel-ing regarding contraception and fertility were seen.Gen-der and race were important factors for the perception of fertility counseling,while only race was a factor to qual-ity of perceived counseling.These differences occurred despite equal rates of physician documentation,across all groups.IGCS19-0430385WHO ARE YOU CALLING OLD?PRACTICE PATTERNS AND MANAGEMENT OF NONAGENARIANS PRESENTINGTO A GYNECOLOGIC ONCOLOGIST FOR INITIALCONSULTATIONE Ryan*,B Margolis,B Pothuri.New York University Langone Health,Obstetrics and Gynecology,New York,USA10.1136/ijgc-2019-IGCS.385Objectives T o describe the practice patterns and treatment of nonagenarians who initiated care with a gynecologic oncologist.Methods Retrospective chart review of women aged90or older who presented to a gynecologic oncologist between10/ 09and12/18at an urban academic medical center.Descrip-tive statistics utilized for variables of interest.Results We identified34nonagenarians(median age92,range 90–98):10(29%)had benign disease,8(24%)pre-malignancy or suspected malignancy,and16(47%)malignancy.Of these, 79%had age and/or functional status discussed in the care plan.Of the8with suspected malignancy,5declined further workup.The cancer distribution revealed5(31%)vulvar,5 (31%)uterine,4(25%)ovarian,1(6%)vaginal and1(6%) cervical bined,37%had stage I disease;6% stage3;6%stage4;13%recurrent;and25%unstaged.All received treatment plans:7(47%)with palliative intent and8 (53%)with curative intent.In the curative group,7under-went surgery(1adjuvant chemotherapy)and1chemotherapy/radiation.In the palliative group,4underwent radiation,1 chemotherapy and2declined/unknown.Overall,13(87%) completed the proposed treatment.T reatment-related complica-tions included1superficial skin infection and1thirty-day readmission.Conclusions Nonagenarians often presented with vulvar or endometrial cancer and87%successfully completed treatmentwith minimal adverse effects or toxicity.Age and/or functionalstatus were considered in the care plan for79%of women,but it did not preclude treatments that had the potential to preserve meaningful quality of life and/or cure patients oftheir disease.IGCS19-0646386RISK FACTORS COMPREHENSIVE GERIATRICASSESSMENT FOR EARLY DEATH IN ELDERLY PATIENTSWITH GYNECOLOGICAL CANCER.A PROSPECTIVECOHORT STUDY1J Sales*,2C Azevedo,2C santos,3L sales,4M Bezerra,5G Bezerra,4Z cavalcanti,6MJ Mello.1IMIP,Geriatric Oncology,Recife,Brazil;2IMIP,Oncology,Recife,Brazil;3FPS,Medical Course,Recife,Brazil;4IMIP,geriatric,Recife,Brazil;5HMV,oncology,caruaru,Brazil;6IMIP,post graduation,Recife,Brazil10.1136/ijgc-2019-IGCS.386Objectives T o determine risk factors for early death identifiedthe Comprehensive Geriatric Assessment(CGA)in elderly patients with gynecological cancer(EPGC).Methods Prospective cohort study.Participants with a recent diagnosis of cancer were from eight community hospitals andone cancer center in Northeast Brazil and were recruited dur-ing their first medical appointment at the outpatient oncologic clinic.A basal CGA was done before the treatment decision (ADL,Charlson Comorbidity Index-CCI,Karnofsky Perform-ance status–KPS,GDS15,IPAQ,MMSE,MNA,MNA-SF,PS,PPS,Polipharmacy,TUG).During the follow up of12 months,information about the treatments performed,the tar-geted interventions and early death was collected.Overall sur-vival was estimated using the Kaplan–Meier method,and survival curves were compared using the Log rank test for cat-egorical variables.A multivariate Cox proportional hazardsmodel was used.Results From2015–2017,84EPGC,mean age69,6±7,9;range60–96),were enrolled,25%were metastatic disease.tumor site:40,4%cervical uterine,36,9%endometrial,20,2%ovary and2,3vulva.Nine(10.7%)ECP died in less than12 months of follow-up.In our multivariate model,controlled byage,site of cancer and cancer stage,the remaining significantrisk factors were malnutrition/nonutrition determined byMNA-SF(HR3.70,95%CI1.81–5.99,p<0.001),Katz index(HR 3.60,CI 1.56–3.81,p<0.001)CCI>2(HR2,74,CI1.0.74–10.20,p=0.013)and Polipharmacy(HR2.65,CI0.71–9.81,p<0.001).Conclusions The CGA at admission identified risk factors (Nutritional risk,polypharmacy,functionality for Katz indexand comorbidity index)for premature death in EPGC.They can help to plan a personalized care. on December 24, 2023 by guest. Protected by copyright./ Int J Gynecol Cancer: first published as 10.1136/ijgc-2019-IGCS.384 on 18 September 2019. Downloaded from。

软件工程英文参考文献(优秀范文105个)

软件工程英文参考文献(优秀范文105个)软件工程英文参考文献一：[1]Carine Khalil,Sabine Khalil. Exploring knowledge management in agile software developmentorganizations[J]. International Entrepreneurship and Management Journal,2020,16(4).[2]Kevin A. Gary,Ruben Acuna,Alexandra Mehlhase,Robert Heinrichs,Sohum Sohoni. SCALING TO MEET THE ONLINE DEMAND IN SOFTWARE ENGINEERING[J]. International Journal on Innovations in Online Education,2020,4(1).[3]Hosseini Hadi,Zirakjou Abbas,GoodarziVahabodin,Mousavi Seyyed Mohammad,Khonakdar Hossein Ali,Zamanlui Soheila. Lightweight aerogels based on bacterial cellulose/silver nanoparticles/polyaniline with tuning morphology of polyaniline and application in soft tissue engineering.[J]. International journal of biological macromolecules,2020,152.[4]Dylan G. Kelly,Patrick Seeling. Introducing underrepresented high school students to software engineering: Using the micro:bit microcontroller to program connected autonomous cars[J]. Computer Applications in Engineering Education,2020,28(3).[5]. Soft Computing; Research Conducted at School of Computing Science and Engineering Has Updated OurKnowledge about Soft Computing (Indeterminate Likert scale: feedback based on neutrosophy, its distance measures and clustering algorithm)[J]. News of Science,2020.[6]. Engineering; New Engineering Findings from Hanyang University Outlined (Can-based Aging Monitoring Technique for Automotive Asics With Efficient Soft Error Resilience)[J]. Journal of Transportation,2020.[7]. Engineering - Software Engineering; New Findings from University of Michigan in the Area of Software Engineering Reported (Multi-criteria Test Cases Selection for Model Transformations)[J]. Journal of Transportation,2020.[8]Tamas Galli,Francisco Chiclana,Francois Siewe. Software Product Quality Models, Developments, Trends, and Evaluation[J]. SN Computer Science,2020,1(2).[9]. Infotech; Infotech Joins BIM for Bridges and Structures Transportation Pooled Fund Project as an Official Software Advisor[J]. Computer Technology Journal,2020.[10]. Engineering; Study Findings from Beijing Jiaotong University Provide New Insights into Engineering (Analyzing Software Rejuvenation Techniques In a Virtualized System: Service Provider and User Views)[J]. Computer Technology Journal,2020.[11]. Soft Computing; Data on Soft Computing Reported by Researchers at Sakarya University (An exponential jerk system, its fractional-order form with dynamical analysis and engineering application)[J]. Computer Technology Journal,2020.[12]. Engineering; Studies from Henan University Yield New Data on Engineering (Extracting Phrases As Software Features From Overlapping Sentence Clusters In Product Descriptions)[J]. Computer Technology Journal,2020.[13]. Engineering; Data from Nanjing University of Aeronautics and Astronautics Provide New Insights into Engineering (A Systematic Study to Improve the Requirements Engineering Process in the Domain of Global Software Development)[J]. Computer Technology Journal,2020.[14]. Soft Computing; Investigators at Air Force Engineering University Report Findings in Soft Computing (Evidential model for intuitionistic fuzzy multi-attribute group decision making)[J]. Computer Technology Journal,2020.[15]. Engineering; Researchers from COMSATS University Islamabad Describe Findings in Engineering (A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks)[J]. Computer Technology Journal,2020.[16]Pedro Delgado-Pérez,Francisco Chicano. An Experimental and Practical Study on the EquivalentMutant Connection: An Evolutionary Approach[J]. Information and Software Technology,2020.[17]Koehler Leman Julia,Weitzner Brian D,Renfrew P Douglas,Lewis Steven M,Moretti Rocco,Watkins Andrew M,Mulligan Vikram Khipple,Lyskov Sergey,Adolf-Bryfogle Jared,Labonte Jason W,Krys Justyna,Bystroff Christopher,Schief William,Gront Dominik,Schueler-Furman Ora,Baker David,Bradley Philip,Dunbrack Roland,Kortemme Tanja,Leaver-Fay Andrew,StraussCharlie E M,Meiler Jens,Kuhlman Brian,Gray JeffreyJ,Bonneau Richard. Better together: Elements of successful scientific software development in a distributed collaborative munity.[J]. PLoS putational biology,2020,16(5).[18]. Mathematics; Data on Mathematics Reported by Researchers at Thapar Institute of Engineering and Technology (Algorithms Based on COPRAS and Aggregation Operators with New Information Measures forPossibility Intuitionistic Fuzzy Soft Decision-Making)[J]. Journal of Mathematics,2020.[19]. Engineering - Medical and Biological Engineering; Reports from Heriot-Watt University Describe Recent Advances in Medical and Biological Engineering (ANovel Palpation-based Method for Tumor Nodule Quantification In Soft Tissue-putational Framework and Experimental Validation)[J]. Journal ofEngineering,2020.[20]. Engineering - Industrial Engineering; Studies from Xi'an Jiaotong University Have Provided New Data on Industrial Engineering (Dc Voltage Control Strategy of Three-terminal Medium-voltage Power Electronic Transformer-based Soft Normally Open Points)[J]. Journal of Engineering,2020.[21]. Engineering; Reports from Hohai University Add New Data to Findings in Engineering (Soft Error Resilience of Deep Residual Networks for Object Recognition)[J]. Journal of Engineering,2020.[22]. Engineering - Mechanical Engineering; Study Data from K.N. Toosi University of Technology Update Understanding of Mechanical Engineering (Coupled Directional Dilation-Damage Approach to Model the Cyclic-Undrained Response of Soft Clay under Pure Principal Stress Axes Rotation)[J]. Journal of Engineering,2020.[23]. Soft Computing; Researchers from Abes Engineering College Report Details of New Studies and Findings in the Area of Soft Computing (An intelligent personalized web blog searching technique using fuzzy-based feedback recurrent neural network)[J]. Network Weekly News,2020.[24]. Engineering; Studies from University of Alexandria in the Area of Engineering Reported (Software Defined Network-Based Management for Enhanced 5G Network Services)[J]. Network Weekly News,2020.[25]. Soft Computing; Data on Soft Computing Discussed by Researchers at Department of Electrical and Communication Engineering [A metaheuristicoptimization model for spectral allocation incognitive networks based on ant colony algorithm (M-ACO)][J]. Computer Technology Journal,2020.[26]. Engineering - Software Engineering; Complutense University Madrid Reports Findings in Software Engineering (Recolibry Suite: a Set of Intelligent Tools for the Development of Remender Systems)[J]. Computer Technology Journal,2020.[27]. Engineering - Software Engineering; Data on Software Engineering Reported by Researchers at Gautam Buddha University (A novel quality prediction modelfor ponent based software system using ACO-NM optimized extreme learning machine)[J]. Computer Technology Journal,2020.[28]. Soft Computing; New Soft Computing Study Findings Recently Were Reported by Researchers at University College of Engineering (A novel QIM-DCT based fusion approach for classification of remote sensing images via PSO and SVM models)[J]. Computer Technology Journal,2020.[29]Morshedloo Fatemeh,Khoshfetrat Ali Baradar,Kazemi Davoud,Ahmadian Mehri. Gelatin improves peroxidase-mediated alginate hydrogel characteristics as a potential injectable hydrogel for soft tissueengineering applications.[J]. Journal of biomedical materials research. Part B, Applied biomaterials,2020.[30]Jung-Chieh Lee,Chung-Yang Chen. Exploring the team dynamic learning process in software process tailoring performance[J]. Journal of Enterprise Information Management,2020,33(3).[31]. Soft Computing; Study Results from Velammal Engineering College in the Area of Soft Computing Reported (Efficient routing in UASN during the thermohaline environment condition to improve the propagation delay and throughput)[J]. Mathematics Week,2020.[32]. Soft Matter; Findings from School of Materials Science and Engineering Provide New Insights into Soft Matter (A practical guide to active colloids: choosing synthetic model systems for soft matter physics research)[J]. Physics Week,2020.[33]Julio César Puche-Regaliza,Alfredo Jiménez,Pablo Arranz-Val. Diagnosis of Software Projects Based on the Viable System Model[J]. Systemic Practice and Action Research,2020,33(1).[34]Meinert Edward,Milne-Ives Madison,Surodina Svitlana,Lam Ching. Agile requirements engineering and software planning for a digital health platform to engage the effects of isolation caused by social distancing: A case study and feasibility study protocol.[J]. JMIR public health and surveillance,2020.[35]. Engineering - Civil Engineering; Studies Conducted at Shandong Jianzhu University on Civil Engineering Recently Published (Seismic Response Analysis and Control of Frame Structures with Soft First Storey under Near-Fault Ground Motions)[J]. Journal of Engineering,2020.软件工程英文参考文献二：[36]Chao-ze Lu,Guo-sun Zeng,Ying-jie Xie. Bigraph specification of software architecture and evolution analysis in mobile puting environment[J]. Future Generation Computer Systems,2020,108.[37]Ompal Singh, Saurabh Panwar, P. K. Kapur.. Determining Software Time-to-Market and Testing Stop Time when Release Time is a Change-Point[J]. International Journal of Mathematical, Engineering and Management Sciences,2020,5(2).[38]Ayushi Verma,Neetu Sardana,Sangeeta Lal. Developer Remendation for Stack Exchange Software EngineeringQ&A Website based on K-Means clustering and Developer Social Network Metric[J]. Procedia ComputerScience,2020,167.[39]Jagdeep Singh,Sachin Bagga,Ranjodh Kaur. Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques[J]. Procedia Computer Science,2020,167.[40]. Engineering - Software Engineering; Studies from Concordia University Update Current Data on SoftwareEngineering (On the impact of using trivial packages: an empirical case study on npm and PyPI)[J]. Computer Technology Journal,2020.[41]. Engineering - Software Engineering; Study Findings from University of Alberta Broaden Understanding of Software Engineering (Building the perfect game - an empirical study of game modifications)[J]. Computer Technology Journal,2020.[42]. Engineering - Software Engineering; Investigators at National Research Council (CNR) Detail Findings in Software Engineering [A Framework for Quantitative Modeling and Analysis of Highly (Re)Configurable Systems][J]. Computer Technology Journal,2020.[43]. Engineering - Knowledge Engineering; Data from University of Paris Saclay Provide New Insights into Knowledge Engineering (Dynamic monitoring of software use with recurrent neural networks)[J]. Computer Technology Journal,2020.[44]. Engineering - Circuits Research; Findings from Federal University Santa Maria Yields New Data on Circuits Research (A New Cpfsk Demodulation Approach for Software Defined Radio)[J]. Computer Technology Journal,2020.[45]. Soft Computing; Investigators from Lovely Professional University Release New Data on Soft Computing (An intensify Harris Hawks optimizer fornumerical and engineering optimization problems)[J]. Computer Technology Journal,2020.[46]. GlobalLogic Inc.; GlobalLogic Acquires Meelogic Consulting AG, a European Healthcare and Automotive-Focused Software Engineering Services Firm[J]. Computer Technology Journal,2020.[47]. Engineering - Circuits and Systems Research; Data on Circuits and Systems Research Described by Researchers at Northeastern University (Softcharge: Software Defined Multi-device Wireless Charging Over Large Surfaces)[J]. Telemunications Weekly,2020.[48]. Soft Computing; Researchers from Department of Electrical and Communication Engineering Report on Findings in Soft Computing (Dynamic Histogram Equalization for contrast enhancement for digital images)[J]. Technology News Focus,2020.[49]Mohamed Ellithey Barghoth,Akram Salah,Manal A. Ismail. A Comprehensive Software Project Management Framework[J]. Journal of Computer and Communications,2020,08(03).[50]. Soft Computing; Researchers from Air Force Engineering University Describe Findings in Soft Computing (Random orthocenter strategy in interior search algorithm and its engineering application)[J]. Journal of Mathematics,2020.[51]. Soft Computing; Study Findings on Soft Computing Are Outlined in Reports from Department of MechanicalEngineering (Constrained design optimization of selected mechanical system ponents using Rao algorithms)[J]. Mathematics Week,2020.[52]Iqbal Javed,Ahmad Rodina B,Khan Muzafar,Fazal-E-Amin,Alyahya Sultan,Nizam Nasir Mohd Hairul,Akhunzada Adnan,Shoaib Muhammad. Requirements engineering issues causing software development outsourcing failure.[J]. PloS one,2020,15(4).[53]Raymond C.Z. Cohen,Simon M. Harrison,Paul W. Cleary. Dive Mechanic: Bringing 3D virtual experimentation using biomechanical modelling to elite level diving with the Workspace workflow engine[J]. Mathematics and Computers in Simulation,2020,175.[54]Emelie Engstr?m,Margaret-Anne Storey,Per Runeson,Martin H?st,Maria Teresa Baldassarre. How software engineering research aligns with design science: a review[J]. Empirical SoftwareEngineering,2020(prepublish).[55]Christian Lettner,Michael Moser,Josef Pichler. An integrated approach for power transformer modeling and manufacturing[J]. Procedia Manufacturing,2020,42.[56]. Engineering - Mechanical Engineering; New Findings from Leibniz University Hannover Update Understanding of Mechanical Engineering (A finite element for soft tissue deformation based on the absolute nodal coordinate formulation)[J]. Computer Technology Journal,2020.[57]. Science - Social Science; Studies fromUniversity of Burgos Yield New Information about Social Science (Diagnosis of Software Projects Based on the Viable System Model)[J]. Computer Technology Journal,2020.[58]. Technology - Powder Technology; Investigators at Research Center Pharmaceutical Engineering GmbH Discuss Findings in Powder Technology [Extended Validation and Verification of Xps/avl-fire (Tm), a Computational Cfd-dem Software Platform][J]. Computer Technology Journal,2020.[59]Guadalupe-Isaura Trujillo-Tzanahua,Ulises Juárez-Martínez,Alberto-Alfonso Aguilar-Lasserre,María-Karen Cortés-Verdín,Catherine Azzaro-Pantel.Multiple software product lines to configure applications of internet of things[J]. IETSoftware,2020,14(2).[60]Eduardo Juárez,Rocio Aldeco-Pérez,Jose.Manuel Velázquez. Academic approach to transform organisations: one engineer at a time[J]. IET Software,2020,14(2).[61]Dennys García-López,Marco Segura-Morales,Edson Loza-Aguirre. Improving the quality and quantity of functional and non-functional requirements obtained during requirements elicitation stage for the development of e-merce mobile applications: an alternative reference process model[J]. IETSoftware,2020,14(2).[62]. Guest Editorial: Software Engineering Applications to Solve Organisations Issues[J]. IET Software,2020,14(2).[63]?,?. Engine ControlUnit ? ? ?[J]. ,2020,47(4).[64]. Engineering - Software Engineering; Study Data from Nanjing University Update Understanding of Software Engineering (Identifying Failure-causing Schemas In the Presence of Multiple Faults)[J]. Mathematics Week,2020.[65]. Energy - Renewable Energy; Researchers from Institute of Electrical Engineering Detail New Studies and Findings in the Area of Renewable Energy (A Local Control Strategy for Distributed Energy Fluctuation Suppression Based on Soft Open Point)[J]. Journal of Mathematics,2020.[66]Ahmed Zeraoui,Mahfoud Benzerzour,WalidMaherzi,Raid Mansi,Nor-Edine Abriak. New software for the optimization of the formulation and the treatment of dredged sediments for utilization in civil engineering[J]. Journal of Soils andSediments,2020(prepublish).[67]. Engineering - Concurrent Engineering; Reports from Delhi Technological University Add New Data to Findings in Concurrent Engineering (Systematic literature review of sentiment analysis on Twitter using soft puting techniques)[J]. Journal of Engineering,2020.[68]. Engineering; New Findings from Future University in Egypt in the Area of Engineering Reported (Decision support system for optimum soft clay improvement technique for highway construction projects)[J]. Journal of Engineering,2020.[69]Erica Mour?o,Jo?o Felipe Pimentel,LeonardoMurta,Marcos Kalinowski,Emilia Mendes,Claes Wohlin. On the performance of hybrid search strategies for systematic literature reviews in softwareengineering[J]. Information and SoftwareTechnology,2020,123.[70]. Soft Computing; Researchers from Anna University Discuss Findings in Soft Computing (A novel fuzzy mechanism for risk assessment in software projects)[J]. News of Science,2020.软件工程英文参考文献三：[71]. Software and Systems Research; New Software and Systems Research Study Results from ChalmersUniversity of Technology Described (Why and How To Balance Alignment and Diversity of Requirements Engineering Practices In Automotive)[J]. Journal of Transportation,2020.[72]Anupama Kaushik,Devendra Kr. Tayal,Kalpana Yadav.A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO[J]. Arabian Journal for Science and Engineering,2020,45(6).[73]Subhrata Das,Adarsh Anand,Mohini Agarwal,Mangey Ram. Release Time Problem Incorporating the Effect of Imperfect Debugging and Fault Generation: An Analysis for Multi-Upgraded Software System[J]. International Journal of Reliability, Quality and Safety Engineering,2020,27(02).[74]Saerom Lee,Hyunmi Baek,Sehwan Oh. The role of openness in open collaboration: A focus onopen‐source software development projects[J]. ETRI Journal,2020,42(2).[75]. Soft Computing; Study Results from Computer Science and Engineering Broaden Understanding of Soft Computing (Efficient attribute selection technique for leukaemia prediction using microarray gene data)[J]. Computer Technology Journal,2020.[76]. Engineering - Computational Engineering; Findings from University of Cincinnati in the Area of Computational Engineering Described (Exploratory Metamorphic Testing for Scientific Software)[J]. Computer Technology Journal,2020.[77]. Organizational and End User Computing; Data from Gyeongnam National University of Science and Technology Advance Knowledge in Organizational and End User Computing (A Contingent Approach to Facilitating Conflict Resolution in Software Development Outsourcing Projects)[J]. Computer Technology Journal,2020.[78]. Soft Computing; Findings from Department of Industrial Engineering in the Area of Soft Computing Reported (Analysis of fuzzy supply chain performance based on different buyback contract configurations)[J]. Computer Technology Journal,2020.[79]Hana M kaouar,Bechir Zalila,Jér?me Hugues,Mohamed Jmaiel. A formal approach to AADL model-based software engineering[J]. International Journal on SoftwareTools for Technology Transfer,2020,22(5).[80]Riesch Michael,Nguyen Tien Dat,Jirauschek Christian. bertha: Project skeleton for scientific software.[J]. PloS one,2020,15(3).[81]. Computers; Findings from Department of Computer Sciences and Engineering Reveals New Findings on Computers (An assessment of software definednetworking approach in surveillance using sparse optimization algorithm)[J]. TelemunicationsWeekly,2020.[82]Luigi Ranghetti,Mirco Boschetti,FrancescoNutini,Lorenzo Busetto. “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data[J]. Computers and Geosciences,2020,139.[83]Mathie Najberg,Muhammad Haji Mansor,ThéodoreT aillé,Céline Bouré,Rodolfo Molina-Pe?a,Frank Boury,José Luis Cenis,Emmanuel Garcion,CarmenAlvarez-Lorenzo. Aerogel sponges of silk fibroin, hyaluronic acid and heparin for soft tissueengineering: Composition-properties relationship[J]. Carbohydrate Polymers,2020,237.[84]Isonkobong Udousoro. Effective Requirement Engineering Process Model in Software Engineering[J]. Software Engineering,2020,8(1).[85]. Soft Computing; Research Conducted at Department of Computer Sciences and Engineering Has Updated Our Knowledge about Soft Computing [Hyperparameter tuning in convolutional neural networks for domain adaptation in sentiment classification (HTCNN-DASC)][J]. Network Weekly News,2020.[86]. Engineering - Software Engineering; Data on Software Engineering Discussed by Researchers at Universita della Svizzera italiana (Investigating Types and Survivability of Performance Bugs In Mobile Apps)[J]. Computer Technology Journal,2020.[87]. Engineering - Software Engineering; Findings from Nanjing University Broaden Understanding of Software Engineering (Boosting Crash-inducing Change Localization With Rank-performance-based Feature Subset Selection)[J]. Computer Technology Journal,2020.[88]. Engineering - Software Engineering; Study Data from Queen's University Belfast Update Knowledge of Software Engineering (Practical relevance of software engineering research: synthesizing the munity's voice)[J]. Computer Technology Journal,2020.[89]. Engineering - Software Engineering; Researchers from Concordia University Detail New Studies and Findings in the Area of Software Engineering (MSRBot: Using bots to answer questions from software repositories)[J]. Computer Technology Journal,2020.[90]Anonymous. DBTA LIVE[J]. Database Trends and Applications,2020,34(2).[91]Tachanun KANGWANTRAKOOL,Kobkrit VIRIYAYUDHAKORN,Thanaruk THEERAMUNKONG. Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models[J]. IEICE Transactions on Information andSystems,2020,E103.D(4).[92]Reza Mohammadi,Reza Javidan,NegarRikhtegar,Manijeh Keshtgari. An intelligent multicast traffic engineering method over software defined networks[J]. Journal of High Speed Networks,2020,26(1).[93]. Engineering - Civil Engineering; HohaiUniversity Researchers Detail New Studies and Findings in the Area of Civil Engineering (An Experimental Study on Settlement due to the Mutual Embedding of Miscellaneous Fill and Soft Soil)[J]. Journal of Engineering,2020.[94]. Engineering - Biomechanical Engineering; Researchers from Washington University St. LouisDetail New Studies and Findings in the Area of Biomechanical Engineering (Estimation of Anisotropic Material Properties of Soft Tissue By Mri ofUltrasound-induced Shear Waves)[J]. Journal of Engineering,2020.[95]. Engineering - Rock Engineering; Reports from University of Alicante Add New Data to Findings in Rock Engineering (Evaluation of Strength and Deformability of Soft Sedimentary Rocks In Dry and Saturated Conditions Through Needle Penetration and Point Load Tests: a Comparative ...)[J]. Journal of Engineering,2020.[96]. Computers; Study Findings from Department of Electrical and Communication Engineering Broaden Understanding of Computers [Improved energy efficient design in software defined wireless electroencephalography sensor networks (WESN) using distributed ...][J]. Network Weekly News,2020.[97]Mouro Erica,Pimentel Joo Felipe,MurtaLeonardo,Kalinowski Marcos,Mendes Emilia,Wohlin Claes. On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in SoftwareEngineering[J]. Information and SoftwareTechnology,2020(prepublish).[98]Osuna Enrique,Rodrguez Luis-Felipe,Gutierrez-Garcia J. Octavio,Castro Luis A.. Development of putational models of emotions: A software engineering perspective[J]. Cognitive Systems Research,2020,60(C).[99]Sharifzadeh Bahador,Kalbasi Rasool,Jahangiri Mehdi,Toghraie Davood,Karimipour Arash. Computer modeling of pulsatile blood flow in elastic arteryusing a software program for application in biomedical engineering[J]. Computer Methods and Programs in Biomedicine,2020.[100]Shen Xiaoning,Guo Yinan,Li Aimin. Cooperative coevolution with an improved resource allocation for large-scale multi-objective software projectscheduling[J]. Applied Soft Computing,2020,88(C).[101]Jung Jaesoon,Kook Junghwan,Goo Seongyeol,Wang Semyung. Corrigendum to Sound transmission analysis of plate structures using the finite element method and elementary radiator approach with radiator error index [Advances in Engineering Software 112 (2017 115][J]. Advances in Engineering Software,2020,140(C).[102]Zhang Chenyi,Pang Jun. Preface for the special issue of the 12th International Symposium on Theoretical Aspects of Software Engineering (TASE2018[J]. Science of Computer Programming,2020,187(C).[103]Karras Oliver,Schneider Kurt,Fricker Samuel A.. Representing software project vision by means of video: A quality model for vision videos[J]. Journal of Systems and Software,2020,162(C).[104]Sutanto Juliana,Jiang Qiqi,Tan Chuan-Hoo. The contingent role of interproject connectedness in cultivating open source software projects[J]. The Journal of Strategic InformationSystems,2020(prepublish).[105]Weiner Iddo,Feldman Yael,Shahar Noam,Yacoby Iftach,Tuller Tamir. CSO A sequence optimization software for engineering chloroplast expression in Chlamydomonas reinhardtii[J]. AlgalResearch,2020,46(C).。

肺部磨玻璃结节的诊治策略

肺部磨玻璃结节的诊治策略王群【摘要】肺部磨玻璃结节(ground glass nodule, GGN)是一种影像学表现,可能是肺部恶性肿瘤或良性病变.目前对于肺部磨玻璃结节的诊疗仍存在争议.2017年Fleischner协会和美国国立综合癌症网络(National Comprehensive Cancer Network, NCCN)都更新了GGN诊疗的指南,与之前的版本相比,手术或活检的指征更严,随访的间隔时间更长.临床工作中,GGN的大小、实性成分大小、动态随访变化和CT值都是判断手术介入时机的因素.GGN的诊疗中还存在一些误区:抗生素的使用、正电子发射型计算机断层显像(positron emission tomography-computed tomography, PET-CT)检查、贴近胸膜的纯GGN和进入GGN的血管都是值得注意的问题.总之,GGN是一种发展缓慢的病灶,可以安全地进行随访.%Pulmonary ground glass nodule (GGN) is a term of radiological manifestation, which may be malignant or benign. The management for pulmonary GGN remains controversial. Both Fleischner society and National Comprehensive Cancer Network (NCCN) panel updated the guideline for the management of GGN in 2017. Compared with previous ver-sions, the indication for surgery or biopsy is stricter, and the recommended follow-up interval is prolonged. In clinical practice, the size of GGN component, the size of consolidation component, dynamic change during follow-up and computed tomog-raphy (CT) value are the four factors that help surgeons to decide the timing of surgery. There are some misunderstandings for the management of GGN, such as the administration of antibiotics, the use of positron emission tomography-computed tomography (PET-CT), pure GGN adjacent to visceral pleura, and GGN with penetrating vessel. In conclusion, GGN is a kind of slowly growing lesion, which can be followed up safely.【期刊名称】《中国肺癌杂志》【年(卷),期】2018(021)003【总页数】3页(P160-162)【关键词】肺肿瘤;肺部磨玻璃结节;诊断【作者】王群【作者单位】200032 上海,复旦大学附属中山医院胸外科【正文语种】中文肺部磨玻璃结节（ground glass nodule, GGN）是指计算机断层扫描（computed tomography, CT）上边界清楚或不清楚的肺内密度增高影，其病变密度不足以掩盖其中走行的血管和支气管影。

Guideline for Structural Health Monitoring F08b

SAMCO Final Report 2006 F08b Guideline for Structural Health Monitoring
CONTENTS
1 2 3 3.1 3.1.1 3.1.1.1 3.1.1.2 3.1.2 3.1.2.1 3.1.2.2 3.1.3 3.2 3.3 3.3.1 3.3.2 3.3.2.1 3.3.2.2 3.3.2.3 3.3.2.4 3.3.2.5 3.3.2.6 3.3.2.7 3.3.2.8 3.3.2.9 3.3.3 3.3.4 3.3.5 3.3.5.1 4 4.1 4.2 4.2.1 4.2.2 4.2.3 4.2.4 4.2.4.1 4.2.4.2 4.2.4.3 4.2.5 Introduction........................................................................................ 5 Objectives and outline of the guideline............................................ 6 Analysis of actions ............................................................................ 7 Classification of actions ........................................................................7 Type of actions .........................................................................................7 Static loads.........................................................................................7 Dynamic loads....................................................................................7 Character of actions .................................................................................8 Dead loads .........................................................................................8 Live loads ...........................................................................................8 Loads and load effects .............................................................................8 Objectives and approach to action analysis ........................................8 Determination of actions based on dimension, duration and local effect .......................................................................................................9 Measurands for characterisation of actions ..............................................9 Determination of actions.........................................................................10 Monitoring pattern ............................................................................10 Wind loads .......................................................................................11 Wave loads and swell loads .............................................................11 Traffic loads......................................................................................11 Loading by displacements ................................................................12 Weight loads ....................................................................................12 Impact and collision loads; vibrations ...............................................12 Temperature loads ...........................................................................13 Effects caused by physical - chemical processes .............................13 Load combinations .................................................................................13 Use and analysis of measurement data..................................................14 Load models...........................................................................................14 Calibration of load models ................................................................15 Diagnostic of structures.................................................................. 16 Preamble ...............................................................................................16 Structural Condition Analysis .............................................................16 Description of design and construction of the structure...........................16 Determination of threshold values for position stability, serviceability and load bearing capacity.......................................................................17 Structural identification ...........................................................................18 Application of NDT techniques ...............................................................19 Steel structures ................................................................................19 Reinforced and prestressed structures .............................................19 Masonry structures...........................................................................20 Field tests...............................................................................................20

Development and Applications of CRISPR-Cas9 for Genome Engineering

Leading EdgeReviewDevelopment and Applications ofCRISPR-Cas9for Genome EngineeringPatrick D.Hsu,1,2,3Eric nder,1and Feng Zhang1,2,*1Broad Institute of MIT and Harvard,7Cambridge Center,Cambridge,MA02141,USA2McGovern Institute for Brain Research,Department of Brain and Cognitive Sciences,Department of Biological Engineering, Massachusetts Institute of Technology,Cambridge,MA02139,USA3Department of Molecular and Cellular Biology,Harvard University,Cambridge,MA02138,USA*Correspondence:zhang@/10.1016/j.cell.2014.05.010Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9are enabling the systematic interrogation of mammalian genome function.Analogous to the search function in modern word processors,Cas9can be guided to speciﬁc locations within complex genomes by a short RNA search ing this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice.Cas9-mediated genetic perturbation is simple and scalable,empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. In this Review,we describe the development and applications of Cas9for a variety of research or translational applications while highlighting challenges as well as future directions.Derived from a remarkable microbial defense system,Cas9is driving innovative applications from basic biology to biotechnology and medicine.IntroductionThe development of recombinant DNA technology in the1970s marked the beginning of a new era for biology.For theﬁrst time,molecular biologists gained the ability to manipulate DNA molecules,making it possible to study genes and harness them to develop novel medicine and biotechnology.Recent advances in genome engineering technologies are sparking a new revolution in biological research.Rather than studying DNA taken out of the context of the genome,researchers can now directly edit or modulate the function of DNA sequences in their endogenous context in virtually any organism of choice, enabling them to elucidate the functional organization of the genome at the systems level,as well as identify causal genetic variations.Broadly speaking,genome engineering refers to the process of making targeted modiﬁcations to the genome,its contexts (e.g.,epigenetic marks),or its outputs(e.g.,transcripts).The ability to do so easily and efﬁciently in eukaryotic and especially mammalian cells holds immense promise to transform basic sci-ence,biotechnology,and medicine(Figure1).For life sciences research,technologies that can delete,insert, and modify the DNA sequences of cells or organisms enable dis-secting the function of speciﬁc genes and regulatory elements. Multiplexed editing could further allow the interrogation of gene or protein networks at a larger scale.Similarly,manipu-lating transcriptional regulation or chromatin states at particular loci can reveal how genetic material is organized and utilized within a cell,illuminating relationships between the architecture of the genome and its functions.In biotechnology,precise manipulation of genetic building blocks and regulatory machin-ery also facilitates the reverse engineering or reconstruction of useful biological systems,for example,by enhancing biofuel production pathways in industrially relevant organisms or by creating infection-resistant crops.Additionally,genome engi-neering is stimulating a new generation of drug development processes and medical therapeutics.Perturbation of multiple genes simultaneously could model the additive effects that un-derlie complex polygenic disorders,leading to new drug targets, while genome editing could directly correct harmful mutations in the context of human gene therapy(Tebas et al.,2014). Eukaryotic genomes contain billions of DNA bases and are difﬁcult to manipulate.One of the breakthroughs in genome manipulation has been the development of gene targeting by homologous recombination(HR),which integrates exogenous repair templates that contain sequence homology to the donor site(Figure2A)(Capecchi,1989).HR-mediated targeting has facilitated the generation of knockin and knockout animal models via manipulation of germline competent stem cells, dramatically advancing many areas of biological research.How-ever,although HR-mediated gene targeting produces highly pre-cise alterations,the desired recombination events occur extremely infrequently(1in106–109cells)(Capecchi,1989),pre-senting enormous challenges for large-scale applications of gene-targeting experiments.To overcome these challenges,a series of programmable nuclease-based genome editing technologies havebeen1262Cell157,June5,2014ª2014Elsevier Inc.developed in recent years,enabling targeted and efﬁcient modi-ﬁcation of a variety of eukaryotic and particularly mammalian species.Of the current generation of genome editing technolo-gies,the most rapidly developing is the class of RNA-guided endonucleases known as Cas9from the microbial adaptive im-mune system CRISPR (clustered regularly interspaced short palindromic repeats),which can be easily targeted to virtually any genomic location of choice by a short RNA guide.Here,we review the development and applications of the CRISPR-associated endonuclease Cas9as a platform technology for achieving targeted perturbation of endogenous genomic ele-ments and also discuss challenges and future avenues for inno-vation.Programmable Nucleases as Tools for Efﬁcient and Precise Genome EditingA series of studies by Haber and Jasin (Rudin et al.,1989;Plessis et al.,1992;Rouet et al.,1994;Choulika et al.,1995;Bibikova et al.,2001;Bibikova et al.,2003)led to the realization that tar-geted DNA double-strand breaks (DSBs)could greatly stimulate genome editing through HR-mediated recombination events.Subsequently,Carroll and Chandrasegaran demonstrated the potential of designer nucleases based on zinc ﬁnger proteins for efﬁcient,locus-speciﬁc HR (Bibikova et al.,2001,2003).Moreover,it was shown in the absence of an exogenous homol-ogy repair template that localized DSBs can induce insertions or deletion mutations (indels)via the error-prone nonhomologous end-joining (NHEJ)repair pathway (Figure 2A)(Bibikova et al.,2002).These early genome editing studies established DSB-induced HR and NHEJ as powerful pathways for the versatileand precise modiﬁcation of eukaryotic genomes.To achieve effective genome editing via introduction of site-speciﬁc DNA DSBs,four major classes of customizable DNA-binding proteins have been engineered so far:meganucleases derived from microbial mobile genetic elements (Smith et al.,2006),zinc ﬁnger (ZF)nucleases based on eukaryotic transcrip-tion factors (Urnov et al.,2005;Miller et al.,2007),transcription activator-like effectors (TALEs)from Xanthomonas bacteria (Christian et al.,2010;Miller et al.,2011;Boch et al.,2009;Mos-cou and Bogdanove,2009),and most recently the RNA-guided DNA endonuclease Cas9from the type II bacterial adaptive im-mune system CRISPR (Cong et al.,2013;Mali et al.,2013a ).Meganuclease,ZF,and TALE proteins all recognize speciﬁc DNA sequences through protein-DNA interactions.Although meganucleases integrate its nuclease and DNA-binding domains,ZF and TALE proteins consist of individual modules targeting 3or 1nucleotides (nt)of DNA,respectively (Figure 2B).ZFs and TALEs can be assembled in desired combi-nations and attached to the nuclease domain of FokI to direct nucleolytic activity toward speciﬁc genomic loci.Each of these platforms,however,has unique limitations.Meganucleases have not been widely adopted as a genome engineering platform due to lack of clear correspondence between meganuclease protein residues and their target DNA sequence speciﬁcity.ZF domains,on the other hand,exhibit context-dependent binding preference due to crosstalk between adjacent modules when assembled into a larger array (Maeder et al.,2008).Although multiple strategies have been developed to account for these limitations (Gonzaelz et al.,2010;Sander et al.,2011),assembly of functional ZFPs with the desired DNA binding speciﬁcity remains a major challenge that requires an extensive screening process.Similarly,although TALE DNA-binding monomers are for the most part modular,they can still suffer from context-dependent speciﬁcity (Juillerat et al.,2014),and their repetitive sequences render construction of novel TALE arrays labor intensive and costly.Given the challenges associated with engineering of modular DNA-binding proteins,new modes of recognition would signiﬁ-cantly simplify the development of custom nucleases.The CRISPR nuclease Cas9is targeted by a short guide RNA that recognizes the target DNA via Watson-Crick base pairing (Figure 2C).The guide sequence within these CRISPR RNAs typically corresponds to phage sequences,constituting the nat-ural mechanism for CRISPR antiviral defense,but can be easily replaced by a sequence of interest to retarget the Cas9nuclease.Multiplexed targeting by Cas9can now be achieved at unprecedented scale by introducing a battery of short guideFigure 1.Applications of Genome EngineeringGenetic and epigenetic control of cells with genome engineering technologies is enabling a broad range of applications from basic biology to biotechnology and medicine.(Clockwise from top)Causal genetic mutations or epigenetic variants associated with altered biological function or disease phenotypes can now be rapidly and efﬁciently recapitulated in animal or cellular models (Animal models,Genetic variation).Manipulating biological circuits could also facilitate the generation of useful synthetic materials,such as algae-derived,silica-based diatoms for oral drug delivery (Materials).Additionally,precise genetic engineering of important agricultural crops could confer resistance to envi-ronmental deprivation or pathogenic infection,improving food security while avoiding the introduction of foreign DNA (Food).Sustainable and cost-effec-tive biofuels are attractive sources for renewable energy,which could be achieved by creating efﬁcient metabolic pathways for ethanol production in algae or corn (Fuel).Direct in vivo correction of genetic or epigenetic defects in somatic tissue would be permanent genetic solutions that address the root cause of genetically encoded disorders (Gene surgery).Finally,engineering cells to optimize high yield generation of drug precursors in bacterial factories could signiﬁcantly reduce the cost and accessibility of useful therapeutics (Drug development).Cell 157,June 5,2014ª2014Elsevier Inc.1263RNAs rather than a library of large,bulky proteins.The ease of Cas9targeting,its high efﬁciency as a site-speciﬁc nuclease,and the possibility for highly multiplexed modiﬁcations have opened up a broad range of biological applications across basic research to biotechnology and medicine.The utility of customizable DNA-binding domains extends far beyond genome editing with site-speciﬁc endonucleases.Fusing them to modular,sequence-agnostic functional effector domains allows ﬂexible recruitment of desired perturbations,such as transcriptional activation,to a locus of interest (Xu and Bestor,1997;Beerli et al.,2000a;Konermann et al.,2013;Maeder et al.,2013a;Mendenhall et al.,2013).In fact,any modular enzymatic component can,in principle,be substituted,allowing facile additions to the genome engineering toolbox.Integration of genome-and epigenome-modifying enzymes with inducible protein regulation further allows precise temporal control of dynamic processes (Beerli et al.,2000b;Konermann et al.,2013).CRISPR-Cas9:From Yogurt to Genome EditingThe recent development of the Cas9endonuclease for genome editing draws upon more than a decade of basic research into understanding the biological function of the mysterious repetitive elements now known as CRISPR (Figure 3),which are found throughout the bacterial and archaeal diversity.CRISPR loci typically consist of a clustered set of CRISPR-associated (Cas)genes and the signature CRISPR array—a series of repeat sequences (direct repeats)interspaced by variable sequences (spacers)corresponding to sequences within foreign genetic elements (protospacers)(Figure 4).Whereas Cas genes are translated into proteins,most CRISPR arrays are ﬁrst tran-scribed as a single RNA before subsequent processing into shorter CRISPR RNAs (crRNAs),which direct the nucleolytic activity of certain Cas enzymes to degrade target nucleic acids.The CRISPR story began in 1987.While studying the iap enzyme involved in isozyme conversion of alkaline phosphatase in E.coli ,Nakata and colleagues reported a curious set of 29nt repeats downstream of the iap gene (Ishino et al.,1987).Unlike most repetitive elements,which typically take the form of tandem repeats like TALE repeat monomers,these 29nt repeats were interspaced by ﬁve intervening 32nt nonrepetitive sequences.Over the next 10years,as more microbial genomes were sequenced,additional repeat elements were reported from genomes of different bacterial and archaeal strains.Mojica and colleagues eventually classiﬁed interspaced repeat sequences as a unique family of clustered repeat elements present in >40%of sequenced bacteria and 90%of archaea (Mojica et al.,2000).These early ﬁndings began to stimulate interest in such micro-bial repeat elements.By 2002,Jansen and Mojica coined the acronym CRISPR to unify the description of microbial genomic loci consisting of an interspaced repeat array (Jansen et al.,2002;Barrangou and van der Oost,2013).At the same time,several clusters of signature CRISPR-associated (cas )genes were identiﬁed to be well conserved and typically adjacent to the repeat elements (Jansen et al.,2002),serving as a basis for the eventual classiﬁcation of three different types of CRISPR systems (types I–III)(Haft et al.,2005;Makarova et al.,2011b ).Types I and III CRISPR loci contain multiple Cas proteins,now known to form complexes with crRNA (CASCADE complex for type I;Cmr or Csm RAMP complexes for type III)to facilitate the recognition and destruction of target nucleic acids (BrounsFigure 2.Genome Editing Technologies Exploit Endogenous DNA Repair Machinery(A)DNA double-strand breaks (DSBs)are typically repaired by nonhomologous end-joining (NHEJ)or homology-directed repair (HDR).In the error-prone NHEJ pathway,Ku heterodimers bind to DSB ends and serve as a molecular scaffold for associated repair proteins.Indels are introduced when the complementary strands undergo end resection and misaligned repair due to micro-homology,eventually leading to frameshift muta-tions and gene knockout.Alternatively,Rad51proteins may bind DSB ends during the initial phase of HDR,recruiting accessory factors that direct genomic recombination with homology arms on an exogenous repair template.Bypassing the matching sister chromatid facilitates the introduction of precise gene modiﬁcations.(B)Zinc ﬁnger (ZF)proteins and transcription activator-like effectors (TALEs)are naturally occurring DNA-binding domains that can be modularly assembled to target speciﬁc se-quences.ZF and TALE domains each recognize 3and 1bp of DNA,respectively.Such DNA-binding proteins can be fused to the FokI endonuclease to generate programmable site-speciﬁc nucleases.(C)The Cas9nuclease from the microbial CRISPR adaptive immune system is localized to speciﬁc DNA sequences via the guide sequence on its guide RNA (red),directly base-pairing with the DNA target.Binding of a protospacer-adjacent motif (PAM,blue)downstream of the target locus helps to direct Cas9-mediated DSBs.1264Cell 157,June 5,2014ª2014Elsevier Inc.et al.,2008;Hale et al.,2009)(Figure 4).In contrast,the type II system has a signiﬁcantly reduced number of Cas proteins.However,despite increasingly detailed mapping and annotation of CRISPR loci across many microbial species,their biological signiﬁcance remained elusive.A key turning point came in 2005,when systematic analysis of the spacer sequences separating the individual direct repeats suggested their extrachromosomal and phage-associated ori-gins (Mojica et al.,2005;Pourcel et al.,2005;Bolotin et al.,2005).This insight was tremendously exciting,especially given previous studies showing that CRISPR loci are transcribed (Tang et al.,2002)and that viruses are unable to infect archaeal cells carrying spacers corresponding to their own genomes (Mojica et al.,2005).Together,these ﬁndings led to the specula-tion that CRISPR arrays serve as an immune memory and defense mechanism,and individual spacers facilitate defense against bacteriophage infection by exploiting Watson-Crick base-pairing between nucleic acids (Mojica et al.,2005;Pourcel et al.,2005).Despite these compelling realizations that CRISPR loci might be involved in microbial immunity,the speciﬁc mech-anism of how the spacers act to mediate viral defense remained a challenging puzzle.Several hypotheses were raised,including thoughts that CRISPR spacers act as small RNA guides to degrade viral transcripts in a RNAi-like mechanism (Makarova et al.,2006)or that CRISPR spacers direct Cas enzymes to cleave viral DNA at spacer-matching regions (Bolotin et al.,2005).Working with the dairy production bacterial strain Strepto-coccus thermophilus at the food ingredient company Danisco,Horvath and colleagues uncovered the ﬁrst experimental evidence for the natural role of a type II CRISPR system as an adaptive immunity system,demonstrating a nucleic-acid-based immune system in which CRISPR spacers dictate target speci-ﬁcity while Cas enzymes control spacer acquisition and phage defense (Barrangou et al.,2007).A rapid series of studies illumi-nating the mechanisms of CRISPR defense followed shortly and helped to establish the mechanism as well as function of all three types of CRISPR loci in adaptive immunity.By studying the type I CRISPR locus of Escherichia coli ,van der Oost and colleagues showed that CRISPR arrays are transcribed and converted into small crRNAs containing individual spacers to guide Cas nuclease activity (Brouns et al.,2008).In the same year,CRISPR-mediated defense by a type III-A CRISPR system from Staphylococcus epidermidis was demonstrated to block plasmid conjugation,establishing the target of Cas enzyme activity as DNA rather than RNA (Marrafﬁni andSontheimer,Figure 3.Key Studies Characterizing and Engineering CRISPR SystemsCas9has also been referred to as Cas5,Csx12,and Csn1in literature prior to 2012.For clarity,we exclusively adopt the Cas9nomenclature throughout this Review.CRISPR,clustered regularly interspaced short palindromic repeats;Cas,CRISPR-associated;crRNA,CRISPR RNA;DSB,double-strand break;tracrRNA,trans -activating CRISPR RNA.Cell 157,June 5,2014ª2014Elsevier Inc.12652008),although later investigation of a different type III-B system from Pyrococcus furiosus also revealed crRNA-directed RNA cleavage activity(Hale et al.,2009,2012).As the pace of CRISPR research accelerated,researchers quickly unraveled many details of each type of CRISPR system (Figure4).Building on an earlier speculation that protospacer-adjacent motifs(PAMs)may direct the type II Cas9nuclease to cleave DNA(Bolotin et al.,2005),Moineau and colleagues high-lighted the importance of PAM sequences by demonstrating that PAM mutations in phage genomes circumvented CRISPR inter-ference(Deveau et al.,2008).Additionally,for types I and II,the lack of PAM within the direct repeat sequence within the CRISPR array prevents self-targeting by the CRISPR system.In type III systems,however,mismatches between the50end of the crRNA and the DNA target are required for plasmid interference(Marraf-ﬁni and Sontheimer,2010).By2010,just3years after theﬁrst experimental evidence for CRISPR in bacterial immunity,the basic function and mecha-nisms of CRISPR systems were becoming clear.A variety of groups had begun to harness the natural CRISPR system for various biotechnological applications,including the generation of phage-resistant dairy cultures(Quiberoni et al.,2010)and phylogenetic classiﬁcation of bacterial strains(Horvath et al., 2008,2009).However,genome editing applications had not yet been explored.Around this time,two studies characterizing the functional mechanisms of the native type II CRISPR system elucidated the basic components that proved vital for engineering a simple RNA-programmable DNA endonuclease for genome editing. First,Moineau and colleagues used genetic studies in Strepto-coccus thermophilus to reveal that Cas9(formerly called Cas5,Csn1,or Csx12)is the only enzyme within the cas gene cluster that mediates target DNA cleavage(Garneau et al.,2010).Next,Charpentier and colleagues revealed a key component in the biogenesis and processing of crRNA in type II CRISPR systems—a noncoding trans-activating crRNA(tracrRNA)that hybridizes with crRNA to facilitate RNA-guided targeting of Cas9(Deltcheva et al.,2011).This dual RNA hybrid,together with Cas9and endogenous RNase III,is required for processing the CRISPR array transcript into mature crRNAs(Deltcheva et al.,2011).These two studies suggested that there are at least three components(Cas9, the mature crRNA,and tracrRNA)that are essential for recon-stituting the type II CRISPR nuclease system.Given the increasing importance of programmable site-speciﬁc nucleases based on ZFs and TALEs for enhancing eukaryotic genome editing,it was tantalizing to think that perhaps Cas9could be developed into an RNA-guided genome editing system. From this point,the race to harness Cas9for genome editing wason.Figure4.Natural Mechanisms of Microbial CRISPR Systems in Adaptive Immunity Following invasion of the cell by foreign genetic elements from bacteriophages or plasmids(step 1:phage infection),certain CRISPR-associated (Cas)enzymes acquire spacers from the exoge-nous protospacer sequences and install them into the CRISPR locus within the prokaryotic genome (step2:spacer acquisition).These spacers are segregated between direct repeats that allow the CRISPR system to mediate self and nonself recognition.The CRISPR array is a noncoding RNA transcript that is enzymatically maturated through distinct pathways that are unique to each type of CRISPR system(step3:crRNA biogenesis and processing).In types I and III CRISPR,the pre-crRNA transcript is cleaved within the repeats by CRISPR-asso-ciated ribonucleases,releasing multiple small crRNAs.Type III crRNA intermediates are further processed at the30end by yet-to-be-identiﬁed RNases to produce the fully mature transcript.In type II CRISPR,an associated trans-activating CRISPR RNA(tracrRNA)hybridizes with the direct repeats,forming an RNA duplex that is cleaved and processed by endogenous RNase III and other unknown nucleases.Maturated crRNAs from type I and III CRISPR systems are then loaded onto effector protein complexes for target recognition and degradation.In type II systems, crRNA-tracrRNA hybrids complex with Cas9to mediate interference.Both type I and III CRISPR systems use multi-protein interference modules to facilitate target recognition.In type I CRISPR,the Cascade com-plex is loaded with a crRNA molecule,constituting a catalytically inert surveillance complex that rec-ognizes target DNA.The Cas3nuclease is then recruited to the Cascade-bound R loop,mediatingtarget degradation.In type III CRISPR,crRNAs associate either with Csm or Cmr complexes that bind and cleave DNA and RNA substrates,respectively.In contrast,the type II system requires only the Cas9nuclease to degrade DNA matching its dual guide RNA consisting of a crRNA-tracrRNA hybrid.1266Cell157,June5,2014ª2014Elsevier Inc.In2011,Siksnys and colleaguesﬁrst demonstrated that the type II CRISPR system is transferrable,in that transplantation of the type II CRISPR locus from Streptococcus thermophilus into Escherichia coli is able to reconstitute CRISPR interference in a different bacterial strain(Sapranauskas et al.,2011).By 2012,biochemical characterizations by the groups of Charpent-ier,Doudna,and Siksnys showed that puriﬁed Cas9from Strep-tococcus thermophilus or Streptococcus pyogenes can be guided by crRNAs to cleave target DNA in vitro(Jinek et al., 2012;Gasiunas et al.,2012),in agreement with previous bacte-rial studies(Garneau et al.,2010;Deltcheva et al.,2011;Sapra-nauskas et al.,2011).Furthermore,a single guide RNA(sgRNA) can be constructed by fusing a crRNA containing the targeting guide sequence to a tracrRNA that facilitates DNA cleavage by Cas9in vitro(Jinek et al.,2012).In2013,a pair of studies simultaneously showed how to suc-cessfully engineer type II CRISPR systems from Streptococcus thermophilus(Cong et al.,2013)and Streptococcus pyogenes (Cong et al.,2013;Mali et al.,2013a)to accomplish genome editing in mammalian cells.Heterologous expression of mature crRNA-tracrRNA hybrids(Cong et al.,2013)as well as sgRNAs (Cong et al.,2013;Mali et al.,2013a)directs Cas9cleavage within the mammalian cellular genome to stimulate NHEJ or HDR-mediated genome editing.Multiple guide RNAs can also be used to target several genes at once.Since these initial studies,Cas9has been used by thousands of laboratories for genome editing applications in a variety of experimental model systems(Sander and Joung,2014).The rapid adoption of the Cas9technology was also greatly accelerated through a com-bination of open-source distributors such as Addgene,as well as a number of online user forums such as http://www. and . Structural Organization and Domain Architecture ofCas9The family of Cas9proteins is characterized by two signature nuclease domains,RuvC and HNH,each named based on homology to known nuclease domain structures(Figure2C). Though HNH is a single nuclease domain,the full RuvC domain is divided into three subdomains across the linear protein sequence,with RuvC I near the N-terminal region of Cas9and RuvC II/IIIﬂanking the HNH domain near the middle of the pro-tein.Recently,a pair of structural studies shed light on the struc-tural mechanism of RNA-guided DNA cleavage by Cas9. First,single-particle EM reconstructions of the Streptococcus pyogenes Cas9(SpCas9)revealed a large structural rearrange-ment between apo-Cas9unbound to nucleic acid and Cas9in complex with crRNA and tracrRNA,forming a central channel to accommodate the RNA-DNA heteroduplex(Jinek et al., 2014).Second,a high-resolution structure of SpCas9in complex with sgRNA and the complementary strand of target DNA further revealed the domain organization to comprise of an a-helical recognition(REC)lobe and a nuclease(NUC)lobe consisting of the HNH domain,assembled RuvC subdomains,and a PAM-interacting(PI)C-terminal region(Nishimasu et al.,2014) (Figure5A and Movie S1).Together,these two studies support the model that SpCas9 unbound to target DNA or guide RNA exhibits an autoinhibited conformation in which the HNH domain active site is blocked by the RuvC domain and is positioned away from the REC lobe (Jinek et al.,2014).Binding of the RNA-DNA heteroduplex would additionally be sterically inhibited by the orientation of the C-ter-minal domain.As a result,apo-Cas9likely cannot bind nor cleave target DNA.Like many ribonucleoprotein complexes,the guide RNA serves as a scaffold around which Cas9can fold and orga-nize its various domains(Nishimasu et al.,2014).The crystal structure of SpCas9in complex with an sgRNA and target DNA also revealed how the REC lobe facilitates target binding.An arginine-rich bridge helix(BH)within the REC lobe is responsible for contacting the308–12nt of the RNA-DNA het-eroduplex(Nishimasu et al.,2014),which correspond with the seed sequence identiﬁed through guide sequence mutation ex-periments(Jinek et al.,2012;Cong et al.,2013;Fu et al.,2013; Hsu et al.,2013;Pattanayak et al.,2013;Mali et al.,2013b). The SpCas9structure also provides a useful scaffold for engi-neering or refactoring of Cas9and sgRNA.Because the REC2 domain of SpCas9is poorly conserved in shorter orthologs, domain recombination or truncation is a promising approach for minimizing Cas9size.SpCas9mutants lacking REC2retain roughly50%of wild-type cleavage activity,which could be partly attributed to their weaker expression levels(Nishimasu et al., 2014).Introducing combinations of orthologous domain re-combination,truncation,and peptide linkers could facilitate the generation of a suite of Cas9mutant variants optimized for different parameters such as DNA binding,DNA cleavage,or overall protein size.Metagenomic,Structural,and Functional Diversity of Cas9Cas9is exclusively associated with the type II CRISPR locus and serves as the signature type II gene.Based on the diversity of associated Cas genes,type II CRISPR loci are further subdivided into three subtypes(IIA–IIC)(Figure5B)(Makarova et al.,2011a; Chylinski et al.,2013).Type II CRISPR loci mostly consist of the cas9,cas1,and cas2genes,as well as a CRISPR array and tracrRNA.Type IIC CRISPR systems contain only this minimal set of cas genes,whereas types IIA and IIB have an additional signature csn2or cas4gene,respectively(Chylinski et al.,2013). Subtype classiﬁcation of type II CRISPR loci is based on the architecture and organization of each CRISPR locus.For example,type IIA and IIB loci usually consist of four cas genes, whereas type IIC loci only contain three cas genes.However, this classiﬁcation does not reﬂect the structural diversity of Cas9proteins,which exhibit sequence homology and length variability irrespective of the subtype classiﬁcation of their parental CRISPR locus.Of>1,000Cas9nucleases identiﬁed from sequence databases(UniProt)based on homology,protein length is rather heterogeneous,roughly ranging from900to1600 amino acids(Figure5C).The length distribution of most Cas9 proteins can be divided into two populations centered around 1,100and1,350amino acids in length.It is worth noting that a third population of large Cas9proteins belonging to subtype IIA,formerly called Csx12,typically contain around1500amino acids.Despite the apparent diversity of protein length,all Cas9pro-teins share similar domain architecture(Makarova et al.,2011a;Cell157,June5,2014ª2014Elsevier Inc.1267。

机器人辅助腹腔镜与传统腹腔镜行结直肠癌手术的安全性和有效性比较

李书原，楼征，张卫
（海军军医大学第一附属医院肛肠外科上海 200433）
摘要结直肠癌是最常见的消化道恶性肿瘤之一，严重威胁着患者的身体健康。外科手术是治疗结直肠癌的有效方法。目前，微创手术因创伤小、恢复快等优点，成为许多患者的首选。传统腹腔镜手术在技术上存在一定的难度，它需要外科医生拥有丰富的腹腔镜操作经验。而达芬奇机器人拥有高清的 3D 镜头、灵活的机械臂以及更符合人体工程学的操作方式，在设计上优于腹腔镜。但多项研究结果显示，机器人结直肠手术的围手术期结果、远期结果与传统腹腔镜相比未见明显优势，而费用却明显增加。目前，机器人结直肠手术已被证实是安全可行的，其便于进行体内肠吻合，并可以缩短学习曲线。达芬奇机器人可在手术空间狭小、解剖复杂的盆腔内操作。在面对内脏肥胖、骨盆狭窄、低位肿瘤等病例时，它的视觉系统也有助于辨识解剖层次，更好的保留盆腔自主神经，可能会促进泌尿与性功能的术后恢复。随着各领域的新技术不断发展和融合，以及外科医生机器人手术经验的积累，相信机器人手术在未来会拥有更广阔的应用前景。
使用 PubMed 数据库搜索 2015 年至 2020 年 4 月期间的“robotic”、“robot”“laparoscopic”、 “robot-assisted”、“colorectal”、“colonel”、 “rectal”等术语。文章的参考部分也被搜索并添加到相关研究。因为机器人手术是相对较新源自的技术，所以多中心随机研究的数量有限。
虽然机器人结肠癌手术已经在多个国家和地区广泛开展，但是缺乏有力的临床证据。本文通过收录的 10 篇高质量文献来探讨机器人在结肠癌手术中的安全性及有效性（见表 1）[13-22]。
2.1 术中和围手术期效果
部分研究表明，机器人辅助腹腔镜行结肠

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Check off ONLY the box with the Description “MRML Scene” and click OK
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
The “Add data into the scene” table appears. Image Courtesy of Dr. Alexandra Golby, Brigham and Women’ s Hospital, Boston, MA.. Click Choose Directory to Add
Select the scene hemispheric_white_matter.vtk, and under the Display tab check the option Clip
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Sonia Pujol, Ph.D. Surgical Planning Laboratory Harvard University
Slicer4Minute Tutorial
First, click on Load Data
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Select the Models module
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Under the Display tab, locate the option Opacity and lower the opacity of Skin.vtk
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
Slicer4Minute Tutorial
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Select the scene Skin.vtk again, and under the Display tab slightly increase the opacity
Hospital, Boston, MA..
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Next, click the viewing mode menu and select Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA.. the Conventional Widescreen option
Slicer displays the elements of the slicer4minute scene, which contains the MR volume of the brain and a series of 3D surface models Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s
Slicer4Minute Tutorial
ቤተ መጻሕፍቲ ባይዱ
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Scroll down the Models module and select the tab Clipping, and check off the options for Green Slice Clipping in the Negative space
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Locate and select the file Slicer4minute and click Choose
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Use the slider of the axial and coronal slices to expose the optic chiasm
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Click on the pin icon in the top left corner of the Red axial slice to display the slice viewer menu, then click on the eye icon to display the axial slice in the 3D Viewer
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
The Models module GUI displays the list of models loaded in the slicer4minute scene, their color, and the value of their opacity (between 0.0 an 1.0)
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Sonia Pujol, Ph.D.
NA-MIC ARR 2012-2014
Slicer4Minute Tutorial
Image Courtesy of Dr. Alexandra Golby, Brigham and Women’s Hospital, Boston, MA..
Under the Display tab, uncheck the option for Visible. The white matter surface, as well as the left and right optic nerves, appear in the 3D viewer