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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

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AI-Driven Ultrasound Detection of Ovarian Cancer that Generalizes: An International Multicentre Validation Study

SSRN

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SSRN 2024年

作者： Christiansen, Filip Konuk, Emir Ganeshan, Adithya Raju Welch, Robert Huix, Joana Palés Czekierdowski, Artur Leone, Francesco Paolo Giuseppe Haak, Lucia Anna Fruscio, Robert Gaurilcikas, Adrius Franchi, Dorella Fischerova, Daniela Mor, Elisa Savelli, Luca Pascual, Maria Àngela Kudla, Marek Guerriero, Stefano Buonomo, Francesca Liuba, Karina Montik, Nina Alcázar, Juan Luis Domali, Ekaterini Pangilinan, Nelinda Catherine P. Carella, Chiara Munaretto, Maria Šašková, Petra Verri, Debora Visenzi, Chiara Herman, Pawel Smith, Kevin Epstein, Elisabeth Department of Clinical Science and Education Södersjukhuset Karolinska Institutet Stockholm Sweden School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Science for Life Laboratory Stockholm Sweden Sweden Pathology Clinical Pharmacology and Safety Sciences AstraZeneca R&D Gothenburg Sweden Department of Obstetrics and Gynecology Södersjukhuset Stockholm Sweden Digital Futures KTH Royal Institute of Technology Stockholm Sweden First Department of Gynecological Oncology and Gynecology Medical University of Lublin Lublin Poland Unit of Obstetrics & Gynecology Department of Biomedical and Clinical Sciences Luigi Sacco University Hospital University of Milan Milan Italy Institute for the Care of Mother and Child Prague Czech Republic Third Faculty of Medicine Charles University Prague Czech Republic Department of Obstetrics and Gynecology IRCCS San Gerardo Dei Tintori Monza Italy Department of Medicine and Surgery University of Milano-Bicocca Milan Italy Department of Obstetrics and Gynaecology Lithuanian University of Health Sciences Kaunas Lithuania Unit of Preventive Gynecology European Institute of Oncology IRCCS Milan Italy Gynecologic Oncology Centre Department of Gynecology Obstetrics and Neonatology First Faculty of Medicine Charles University General University Hospital in Prague Prague Czech Republic Fondazione Poliambulanza Istituto Ospedaliero Brescia Italy Obstetrics and Gynecology Unit Forlì and Faenza Hospitals AUSL Romagna Italy Department of Obstetrics Gynecology and Reproduction Hospital Universitario Dexeus Barcelona Spain Department of Perinatology and Oncological Gynecology Faculty of Medical Sciences Medical University of Silesia Katowice Poland Centro Integrato di Procreazione Medicalmente Assistita e Diagnostica Ostetrico-Ginecologica Azienda Ospedaliero Universitaria-Policlinico Duilio Casula Monserrato University of Cagliari Cagliari Italy Institute for Maternal and Chi

Background: A critical shortage of expert ultrasound examiners has raised concerns of unnecessary interventions and delayed cancer diagnoses. Artificial intelligence (AI)-driven diagnostic support has the potential to alleviate this burden and improve patient outcomes. Deep learning, applied to ultrasound images, has recently demonstrated promising results in ovarian cancer detection;however, external validation is lacking. Our aim was to develop a deep neural network (DNN) model for ovarian cancer detection and evaluate its robustness and ability to generalize across different patient populations in a large multicentre setting. We also sought to assess an AI-assisted triage strategy and compare it to current practice in a retrospective ***: We retrospectively collected 17119 ultrasound images from 3652 women with an ovarian lesion (2224 benign, 1428 malignant) from 20 centres in 8 countries. A total of 2718 cases were externally reviewed by a minimum of 7 expert and 6 non-expert examiners. Using a leave-one-centre-out cross-validation scheme, for each centre in turn, we trained a transformer-based DNN model using data from the remaining 19 centres and compared the models' performance with that of expert and non-expert examiners in terms of accuracy, sensitivity, specificity, and F1 score. Furthermore, we retrospectively simulated and assessed how these models could be used in AI-assisted ***: Our models demonstrated robust performance across centres, ultrasound systems, and different histological diagnoses, with an overall area under the receiver operating characteristic curve (AUC) of 0·929 (95% CI, 0·919–0·938), and F1 score of 83·66% (95% CI, 82·25–85·04) on cases from unseen centres. They outperformed both expert and non-expert examiners, with F1 scores of 79·85% (95% CI, 78·32–81·33;Δ = 3·82 [95% CI, 2·42–5·24, p Interpretation: Our models exhibit strong generalization and outperform both expert and non-expert examiners in diagnostic

关键词： Deep neural networks

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The Large Area Detector onboard the eXTP mission

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Proceedings of SPIE - The International Society for Optical engineering 2018年 10699卷

作者： Feroci, Marco Ahangarianabhari, Mahdi Ambrosi, Giovanni Ambrosino, Filippo Argan, Andrea Barbera, Marco Bayer, Joerg Bellutti, Pierluigi Bertucci, Bruna Bertuccio, Giuseppe Borghi, Giacomo Bozzo, Enrico Cadeaux, Franck Campana, Riccardo Ceraudo, Francesco Chen, Tianxiang Cirrincione, Daniela De Rosa, Alessandra Del Monte, Ettore DI Cosimo, Sergio DIebold, Sebastian Evangelista, Yuri Fan, Qingmei Favre, Yannick Ficorella, Francesco Fuschino, Fabio Gevin, Olivier Grassi, Marco Hong, Bin Mao, Hanqi Karas, Vladimir Kennedy, Tom Labanti, Claudio Limousin, Olivier Lo Cicero, Ugo Lu, Fangjun Luo, Tao Malcovati, Piero Martindale, Adrian Meuris, Aline Michalska, Malgrozata Morbidini, Alfredo Muleri, Fabio Orleanski, Piotr Paltani, Stephane Pan, Teng Perinati, Emanuele Picciotto, Antonino Pohl, Martin Rashvskaia, Irina Santangelo, Andrea Schanne, Stéphane Skup, Kornad Svoboda, Jiri Tenzer, Chris Vacchi, Andrea Walton, Dave Winter, Berend Wu, Xin Xu, Yupeng Zampa, Gianluigi Zampa, Nicola Zane, Silvia Zdziarski, Andrzej Zhang, Long Zhang, Shu Zhang, Shuangnan Zhang, Wenda Zhang, Xiaoli Zhou, Yupeng Zorzi, Nicola INAF Istituto di Astrofisica e Planetologia Spaziali Via Fosso del Cavaliere 100 Rome00133 Italy INFN Sezione Roma Tor Vergata Via della Ricerca Scientifica Rome00133 Italy Politecnico di Milano Polo di Como Via Anzani 41 Como Italy INFN Sezione di Perugia Via Alessandro Pascoli 23c Perugia06123 Italy Università di Palermo Dipartimento di Fisica e Chmica Via Archirafi 36 Palermo90123 Italy INAF Osservatorio Astronomico di Palermo Piazza del Parlamento 1 Palermo90134 Italy Institut fur Astronomie und Astrophysik Eberhard Karls Universitat Tuebingen72076 Germany Fondazione Bruno Kessler Via Sommarive Povo Trento38123 Italy University of Perugia Dip. Fisica e Geologia P.zza Università Perugia06123 Italy Department of Astronomy University of Geneva Chemin d'Ecogia 16 Versoix1290 Switzerland Department of Nuclear and Particle Physics University of Geneva CH-1211 Switzerland INAF Osservatorio di Astrofisica e Scienza Dello Spazio di Bologna Via P. Gobetti 101 Bologna Italy CEA Paris-Saclay DRF/IRFU Gif sur Yvette91191 France Key Laboratory for Particle Astrophysics Institute of High Energy Physics CAS Beijing100049 China University of Udine Via delle Scienze 206 Udine33100 Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Via Ferrata 5 Pavia27100 Italy North Night Vision Technology Co. Ltd Nanjing211106 China Astronomical Institute Czech Academy of Sciences Bocni II 1401 PragueCZ-14100 Czech Republic Mullard Space Science Laboratory UCL Holmbury St Mary Dorking SurreyRH56NT United Kingdom Space Research Centre Department of Physics and Astronomy University of Leicester LeicesterLE17RH United Kingdom Space Research Center Polish Academy of Sciences Bartycka 18a Warszawa00-716 Poland TIFPA Istituto Nazionale di Fisica Nucleare Via Sommarive 14 Povo Trento38123 Italy Nicolaus Copernicus Astronomical Center Polish Academy of Sciences Bartycka 18 WarszawaPL-00-716 Poland INFN

ISBN: (纸本)9781510619517

The eXTP (enhanced X-ray Timing and Polarimetry) mission is a major project of the Chinese Academy of Sciences (CAS) and China National Space Administration (CNSA) currently performing an extended phase A study and proposed for a launch by 2025 in a low-earth orbit. The eXTP scientific payload envisages a suite of instruments (Spectroscopy Focusing Array, Polarimetry Focusing Array, Large Area Detector and Wide Field Monitor) offering unprecedented simultaneous wide-band X-ray spectral, timing and polarimetry sensitivity. A large European consortium is contributing to the eXTP study and it is expected to provide key hardware elements, including a Large Area Detector (LAD). The LAD instrument for eXTP is based on the design originally proposed for the LOFT mission within the ESA context. The eXTP/LAD envisages a deployed 3.4 m2 effective area in the 2-30 keV energy range, achieved through the technology of the large-area Silicon Drift Detectors - offering a spectral resolution of up to 200 eV FWHM at 6 keV - and of capillary plate collimators - limiting the field of view to about 1 degree. In this paper we provide an overview of the LAD instrument design, including new elements with respect to the earlier LOFT configuration. © 2018 SPIE.

关键词： X ray detectors Buildings Earth (planet) Ellipsometry Gamma rays Orbits Polarimeters Silicon detectors Space telescopes X rays Capillary plate Chinese Academy of Sciences Large area detector Scientific payloads Silicon Drift Detector Space administration Timing X ray astronomy

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Association of PM2.5 Reduction with Improved Kidney Function:A Nationwide Quasiexperiment among Chinese Adults

健康数据科学（英文）

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健康数据科学（英文） 2022年第3期 25-33页

作者： Yiqun Han Tao Xue Frank J.Kelly Yixuan Zheng Yao Yao Jiajianghui Li Jiwei Li Chun Fan Pengfei Li Tong Zhu Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijingChina Environmental Research Group MRC Centre for Environment and HealthImperial College LondonLondonUK Center of Air Quality Simulation and System Analysis Chinese Academy of Environmental Planning100012 BeijingChina China Center for Health Development Studies Peking UniversityBeijingChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Computer Center Peking University and Peng Cheng LaboratoryChina Advanced Institute of Information Technology Peking UniversityChina BIC-ESAT and SKL-ESPC College of Environmental Sciences and EngineeringCenter for Environment and HealthPeking UniversityBeijing 100871China

*** evidence from human studies has revealed the adverse impact of ambient fine particles(PM2.5)on health outcomes related to metabolic disorders and distant *** exposure to ambient PM2.5 leads to kidney impairment remains *** rapid air quality improvement driven by the clean air actions in China since 2013 provides an opportunity for a quasiexperiment to investigate the beneficial effect of PM2.5 reduction on kidney *** on two repeated nationwide surveys of the same population of 5115 adults in 2011 and 2015,we conducted a difference-in-difference *** in long-term exposure to ambient PM2.5 were associated with changes in kidney function biomarkers,including estimated glomerular filtration rate by serum creatinine(GFRscr)or cystatin C(GFRcys),blood urea nitrogen(BUN),and uric acid(UA).*** a 10μg/m3 reduction in PM2.5,a significant improvement was observed for multiple kidney functional biomarkers,including GFRscr,BUN and UA,with a change of 0.42(95％confidence interval[CI]:0.06,0.78)mL/min/1.73m2,-0.38(-0.64,-0.12)mg/dL,and-0.06(-0.12,-0.00)mg/dL,respectively.A lower socioeconomic status,indicated by rural residence or low educational level,enhanced the adverse effect of PM2.5 on kidney *** results support a significant nephrotoxicity of PM2.5 based on multiple serum biomarkers and indicate a beneficial effect of improved air quality on kidney function.

关键词： association chinese improved function kidney with adults among nationwide quasiexperiment

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The Impact of Genomic Variation on Function (IGVF) Consortium

arXiv

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arXiv 2023年

作者： Shendure, Jay Ahituv, Nadav Kircher, Martin Agarwal, Vikram Blair, Andrew Chalkiadakis, Theofilos Chardon, Florence M. Dash, Pyaree M. Deng, Chengyu Hamazaki, Nobuhiko Keukeleire, Pia Kubo, Connor Lalanne, Jean-Benoît Maass, Thorben Martin, Beth McDiarmid, Troy Nobuhara, Mai Page, Nicholas F. Regalado, Sam Schubach, Max Sims, Jasmine Ushiki, Aki Zhao, Jingjing Starita, Lea M. Fowler, Douglas M. Best, Sabrina M. Boyle, Gabe Camp, Nathan Casadei, Silvia Da, Estelle Y. Dawood, Moez Dawson, Samantha C. Fayer, Shawn Hamm, Audrey James, Richard G. Jarvik, Gail P. McEwen, Abbye E. Moore, Nick Muffley, Lara A. Pendyala, Sriram Popp, Nicholas A. Post, Mason Rubin, Alan F. Shendure, Jay Smith, Nahum T. Rubin, Alan F. Stone, Jeremy Tejura, Malvika Wang, Ziyu R. Wheelock, Melinda K. Woo, Ivan Zapp, Brendan D. Engreitz, Jesse M. Quertermous, Thomas Amgalan, Dulguun Aradhana, Aradhana Arana, Sophia M. Bassik, Michael C. Bauman, Julia R. Bhattacharya, Asmita Cai, Xiangmeng Shawn Chen, Ziwei Conley, Stephanie Deshpande, Salil Doughty, Benjamin R. Du, Peter P. Gifford, Casey Greenleaf, William J. Gschwind, Andreas R. Guo, Katherine Isobe, Sarasa Jagoda, Evelyn Jain, Nimit Jones, Hank Kang, Helen Y. Kim, Samuel H. Kim, YeEun Klemm, Sandy Kundaje, Anshul Kundu, Soumya Lago-Docampo, Mauro Lee-Yow, Yannick C. Levin-Konigsberg, Roni Li, Daniel Y. Lindenhofer, Dominik Rosa Ma, X. Marinov, Georgi K. Martyn, Gabriella E. Metzl-Raz, Eyal Monteiro, Joao P. Montgomery, Michael T. Mualim, Kristy S. Munger, Chad Munson, Glen Nguyen, Tri C. Nguyen, Trieu Palmisano, Brian T. Pampari, Anusri Park, Chong Y. Rabinovitch, Marlene Ramste, Markus Ray, Judhajeet Roy, Kevin R. Rubio, Oriane M. Schaepe, Julia M. Schnitzler, Gavin Schreiber, Jacob Sharma, Disha Sheth, Maya U. Shi, Huitong Singh, Vasundhara Sinha, Riya Steinmetz, Lars M. Tan, Jason Tan, Anthony Tycko, Josh Valbuena, Raeline C. Pour Amiri, Valeh Valiollah van Kooten, Mariëlle J.F.M. Vaughan-Jackson, Alun Venida, Anthony Weldy, Chad S. Yao, David Zeng, Tony Zhou, Ronghao Vidal, Marc Calderw Department of Genome Sciences University of Washington SeattleWA United States Department of Bioengineering and Therapeutic Sciences Institute for Human Genetics University of California San Francisco San FranciscoCA United States Institute of Human Genetics University Medical Center Schleswig-Holstein University of Lübeck Lübeck23562 Germany Exploratory Diagnostic Sciences Berlin Institute of Health Charité-Universitätsmedizin Berlin Berlin10117 Germany Brotman Baty Institute for Precision Medicine SeattleWA United States Center of immunotherapy and Immunity Seattle Children's Research Institute SeattleWA United States Bioinformatics Division WEHI ParkvilleVIC Australia Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Laboratory Medicine and Pathology University of Washington SeattleWA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Basic Science and Engineering Initiative Stanford Children’s Health Betty Irene Moore Children’s Heart Center StanfordCA United States The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard CambridgeMA United States Division of Cardiovascular Medicine School of Medicine Stanford University United States Department of Computer Science Stanford University School of Medicine StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Department of Pediatrics Stanford University School of Medicine StanfordCA United States Institute for Stem Cell Biology and Regenerative Medicine Stanford University School of Medicine StanfordCA United States Division of Pediatric Cardiology Cardiovascular Institute Stanford University School of Medicine Stanford University United States Genome Biology Unit Heidelberg Germany Department of Biology Stanford University StanfordCA United States Stanford Genome Techn

Our genomes influence nearly every aspect of human biology from molecular and cellular functions to phenotypes in health and disease. Human genetics studies have now associated hundreds of thousands of differences in our DNA sequence ("genomic variation") with disease risk and other phenotypes, many of which could reveal novel mechanisms of human biology and uncover the basis of genetic predispositions to diseases, thereby guiding the development of new diagnostics and therapeutics. Yet, understanding how genomic variation alters genome function to influence phenotype has proven challenging. To unlock these insights, we need a systematic and comprehensive catalog of genome function and the molecular and cellular effects of genomic variants. Toward this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations, and predictive modeling to investigate the relationships among genomic variation, genome function, and phenotypes. Through systematic comparisons and benchmarking of experimental and computational methods, we aim to create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how both coding and noncoding variants may connect through gene regulatory and protein interaction networks. These experimental data, computational predictions, and accompanying standards and pipelines will be integrated into an open resource that will catalyze community efforts to explore genome function and the impact of genetic variation on human biology and disease across populations. © 2023, CC BY.

关键词： Genome

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High-depth African genomes inform human migration and health (vol 586, pg 741, 2020)

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NATURE 2021年第7856期592卷 E26-E26页

作者： Choudhury, Ananyo Aron, Shaun Botigue, Laura R. Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Muzny, Donna Metcalf, Ginger Gibbs, Richard A. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Rotimi, Charles Ramsay, Michele Choudhury, Ananyo Choudhury, Ananyo Aron, Shaun Botigue, Laura Sengupta, Dhriti Botha, Gerrit Bensellak, Taoufik Wells, Gordon Kumuthini, Judit Shriner, Daniel Fakim, Yasmina J. Ghoorah, Anisah W. Dareng, Eileen Odia, Trust Falola, Oluwadamilare Adebiyi, Ezekiel Hazelhurst, Scott Mazandu, Gaston Nyangiri, Oscar A. Mbiyavanga, Mamana Benkahla, Alia Kassim, Samar K. Mulder, Nicola Adebamowo, Sally N. Chimusa, Emile R. Rotimi, Charles Ramsay, Michele Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Adebamowo, Clement Agongo, Godfred Boua, Romuald P. Oduro, Abraham Sorgho, Hermann Landoure, Guida Cisse, Lassana Diarra, Salimata Samassekou, Oumar Anabwani, Gabriel Matshaba, Mogomotsi Joloba, Moses Kekitiinwa, Adeodata Mardon, Graeme Mpoloka, Sununguko W. Kyobe, Samuel Mlotshwa, Busisiwe Mwesigwa, Savannah Retshabile, Gaone Williams, Lesedi Wonkam, Ambroise Moussa, Ahmed Adu, Dwomoa Ojo, Akinlolu Burke, David Salako, Babatunde O. Matovu, Enock Bucheton, Bruno Hertz-Fowler, Christiane Koffi, Mathurin Macleod, Annette Mumba-Ngoyi, Dieudonne Noyes, Harry Nyangiri, Oscar A. Simo, Gustave Simuunza, Martin Awadalla, Philip Bruat, Vanessa Gbeha, Elias Adeyemo, Adebowale A. Lombard, Zane Hanchard, Neil A. Sydney Brenner Institute for Molecular Bioscience Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Division of Human Genetics National Health Laboratory Service and School of Pathology Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Navrongo Health Research Centre Navrongo Ghana Clinical Research Unit of Nanoro Institut de Recherche en Sciences de la Sante Bobo-Dioulasso Burkina Faso Center for Research in Agricultural Genomics (CRAG) Plant and Animal Genomics Program CSIC-IRTA-UAB-UB Barcelona Spain Computational Biology Division and H3ABioNet Department of Integrative Biomedical Sciences IDM University of Cape Town Cape Town South Africa System and Data Engineering Team Abdelmalek Essaadi University ENSA Tangier Morocco Centre for Proteomic and Genomic Research (CPGR) Cape Town South Africa South African National Bioinformatics Institute University of the Western Cape Bellville South Africa Africa Health Research Institute Durban South Africa Center for Research on Genomics and Global Health National Human Genome Research Institute National Institutes of Health Bethesda MD USA Department of Agriculture and Food Science Faculty of Agriculture University of Mauritius Reduit Mauritius Department of Digital Technologies Faculty of Information Communication & Digital Technologies University of Mauritius Reduit Mauritius Department of Public Health and Primary Care University of Cambridge Cambridge UK Institute of Human Virology Nigeria Abuja Nigeria Department of Epidemiology and Public Health University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA University of Maryland Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine University of Maryland Baltimore Baltimore MD USA Institute of Human Virology and Greeneba

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A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency

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Npj mental health research 2022年第1期1卷 19页

作者： Adam S Miner Scott L Fleming Albert Haque Jason A Fries Tim Althoff Denise E Wilfley W Stewart Agras Arnold Milstein Jeff Hancock Steven M Asch Shannon Wiltsey Stirman Bruce A Arnow Nigam H Shah Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. aminer@stanford.edu. Center for Biomedical Informatics Research Stanford University Stanford CA USA. aminer@stanford.edu. Department of Biomedical Data Science Stanford University Stanford CA USA. Department of Computer Science Stanford University Stanford CA USA. Center for Biomedical Informatics Research Stanford University Stanford CA USA. Allen School of Computer Science & Engineering University of Washington Seattle WA USA. Departments of Psychiatry Medicine Pediatrics and Psychological & Brain Sciences Washington University in St. Louis St. Louis MO USA. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Clinical Excellence Research Center Stanford University Stanford CA USA. Department of Communication Stanford University Stanford CA USA. VA Palo Alto Health Care System Palo Alto CA USA. Division of Primary Care and Population Health Stanford University School of Medicine Stanford CA USA. National Center for Posttraumatic Stress Disorders Dissemination and Training Division VA Palo Alto Healthcare System Menlo Park CA USA. Technology and Digital Solutions Stanford Healthcare Stanford CA USA.

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.

关键词： Translational research Human behaviour

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CEPC Technical Design Report

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Radiation Detection Technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi Yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-Yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi Dong Jianin AGH University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakow Anhui University HefeiAnhui Applied Physics Department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu University Institute of ScienceVaranasi Bandirma Onyedi Eylul University Bandirma Beihang University Beijing Beijing Computational Science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of Technology Beijing Beijing Normal University Beijing Brown University ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad University Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo University Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central China Normal University WuhanHubei Central South University ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan China academy of engineering physics MianyangSichuan China building materials academy Beijing China Center of Advanced Science and Technology Beijing China Institute of Atomic Energy Beijing China Jiliang University HangzhouZhejiang China Nuclear Instrument CO. LTDBeijing China University of Geosciences Beijing China University of Geosciences WuhanHubei China University of Mining and Technology XuzhouJiangsu China West Normal University NanchongSichuan Chongqing University Department of PhysicsChongqing Chulalongkorn University Bangkok City University of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell University IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu University DalianLiaoning Dalian University of Technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East China University Of Science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by China,fostered through extensive collaboration with international *** complex comprises four accelerators:a 30 GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 30 MW per beam,it can achieve a luminosity of 5×10^(34)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13 ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-2030s.

关键词： initiated exceeding precise

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HARNESSING digital PATHOLOGY AND CAUSAL LEARNING TO IMPROVE EOSINOPHILIC ESOPHAGITIS DIETARY TREATMENT ASSIGNMENT

arXiv

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arXiv 2023年

作者： Aknin, Eliel Larey, Ariel Caldwell, Julie M. Collins, Margaret H. Rothenberg, Marc E. Savir, Yonatan Abonia, Juan P. Aceves, Seema S. Arva, Nicoleta C. Chehade, Mirna Dellon, Evan S. Gonsalves, Nirmala Gupta, Sandeep K. Leung, John Peterson, Kathryn A. Shoda, Tetsuo Spergel, Jonathan M. Department of Physiology Biophysics and System Biology Faculty of Medicine Technion Israel Institute of Technology Haifa Israel Faculty of Industrial Engineering Technion Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion Israel Institute of Technology Haifa Israel Division of Allergy and Immunology Cincinnati Children's Hospital Medical Center CincinnatiOH United States Dept. of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Division of Pathology and Laboratory Medicine Cincinnati Children's Hospital Medical Center CincinnatiOH United States Division of Allergy and Immunology University of California at San Diego Rady Children's Hospital San DiegoCA United States Dept. of Pathology and Laboratory Ann and Robert H Lurie Children's Hospital Northwestern University Feinberg School of Medicine ChicagoIL United States Mount Sinai Center for Eosinophilic Disorders Icahn School of Medicine at Mount Sinai New YorkNY United States Division of Gastroenterology and Hepatology University of North Carolina School of Medicine Chapel HillNC United States Division of Gastroenterology and Hepatology Northwestern University Feinberg School of Medicine ChicagoIL United States Division of Pediatric Gastroenterology Hepatology and Nutrition Children's of Alabama University of Alabama at Birmingham BirminghamAL United States Boston Specialists BostonMA United States Division of Gastroenterology University of Utah Salt Lake CityUT United States Division of Allergy and Immunology Children's Hospital of Philadelphia Dept. of Pediatrics Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of EoE relies on counting eosinophils in histological slides, a manual and time-consuming task that limits the ability to extract complex patient-dependent features. The treatment of EoE includes medication and food elimination. A personalized food elimination plan is crucial for engagement and efficiency, but previous attempts failed to produce significant results. In this work, on the one hand, we utilize AI for inferring histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This study illustrates the significance of AI in enhancing treatment planning by analyzing molecular features' distribution in histological slides through causal learning. Our approach can be harnessed for other conditions that rely on histology for diagnosis and treatment. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Author Correction: Mapping annual 10-m soybean cropland with spatiotemporal sample migration

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Scientific data 2024年第1期11卷 565页

作者： Hongchi Zhang Zihang Lou Dailiang Peng Bing Zhang Wang Luo Jianxi Huang Xiaoyang Zhang Le Yu Fumin Wang Linsheng Huang Guohua Liu Shuang Gao Jinkang Hu Songlin Yang Enhui Cheng Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. University of Chinese Academy of Sciences Beijing 100094 China. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. pengdl@***. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. pengdl@***. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. University of Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. Jiangxi Nuclearindustry Surveying and Mapping Institute Group Co. Ltd Nanchang 330038 China. College of Land Science and Technology China Agricultural University Beijing 100083 China. Geospatial Sciences Center of Excellence Department of Geography Geospatial Sciences South Dakota State University Brookings SD 57007 USA. Department of Earth System Science Tsinghua University Beijing 100084 China. Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou 310058 China. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei 230601 China. Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai 200120 China.

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