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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

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Nature metabolism 2025年 2025 May 13页

作者： Anne Loft Margo P Emont Ada Weinstock Adeline Divoux Adhideb Ghosh Allon Wagner Ann V Hertzel Babukrishna Maniyadath Bart Deplancke Boxiang Liu Camilla Scheele Carey Lumeng Changhai Ding Chenkai Ma Christian Wolfrum Clarissa Strieder-Barboza Congru Li Danh D Truong David A Bernlohr Elisabet Stener-Victorin Erin E Kershaw Esti Yeger-Lotem Farnaz Shamsi Hannah X Hui Henrique Camara Jiawei Zhong Joanna Kalucka Joseph A Ludwig Julie A Semon Jutta Jalkanen Katie L Whytock Kyle D Dumont Lauren M Sparks Lindsey A Muir Lingzhao Fang Lucas Massier Luis R Saraiva Marc D Beyer Marc G Jeschke Marcelo A Mori Mariana Boroni Martin J Walsh Mary-Elizabeth Patti Matthew D Lynes Matthias Blüher Mikael Rydén Natnael Hamda Nicole L Solimini Niklas Mejhert Peng Gao Rana K Gupta Rinki Murphy Saeed Pirouzpanah Silvia Corvera Su'an Tang Swapan K Das Søren F Schmidt Tao Zhang Theodore M Nelson Timothy E O'Sullivan Vissarion Efthymiou Wenjing Wang Yihan Tong Yu-Hua Tseng Susanne Mandrup Evan D Rosen Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark. anlo@bmb.sdu.dk. Section of Endocrinology Diabetes and Metabolism University of Chicago Chicago IL USA. margo@uchicago.edu. Department of Medicine Section of Genetic Medicine University of Chicago Chicago IL USA. Translational Research Institute AdventHealth Orlando FL USA. Department of Health Sciences and Technology Institute of Food Nutrition and Health ETH Zurich Schwerzenbach Switzerland. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Molecular and Cell Biology University of California Berkeley Berkeley CA USA. Center for Computational Biology University of California Berkeley Berkeley CA USA. Department of Biochemistry Molecular Biology and Biophysics Institute on the Biology of Aging and Metabolism The University of Minnesota-Twin Cities Minneapolis MN USA. Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark. Laboratory of Systems Biology and Genetics Institute of Bioengineering School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland. Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Singapore Singapore. Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Genome Institute of Singapore (GIS) Agency for Science Technology and Research (A*STAR) Singapore Singapore. Precision Medicine Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Cardiovascular-Metabolic Disease Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapor

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The Human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data.

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Cross vendor test-retest validation of diffusion tensor analysis along the perivascular space (DTI-ALPS) method for evaluating glymphatic system function in vascular cognitive impairment and dementia (VCID)

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Xiaodan Liu Giuseppe Barisano Xingfeng Shao Kay Jann John M Ringman Hanzhang Lu Konstantinos Arfanakis Caprihan Arvind Charles Decarli Brian T. Gold Pauline Maillard Clandia L. Satizabal Elyas Fadaee Mohamad Habes Lara Stables Herpreet Singh Bruce Fischl Andre J. van der Kouwe Kristin Schwab Karl G. Helmer Steven M. Greenberg Danny J.J. Wang University of Southern California Los Angeles CA USA University of California San Francisco San Francisco CA USA Zilkha Neurogenetic Institute University of Southern California Los Angeles CA USA Correspondece Stanford University Stanford CA USA Laboratory of Neuro Imaging USC Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Los Angeles CA USA Laboratory of FMRI Technology (LOFT) Mark & Mary Stevens Neuroimaging and Informatics Institute University of Southern California Los Angeles CA USA Department of Neurology Keck School of Medicine at USC Los Angeles CA USA Department of Radiology Johns Hopkins University School of Medicine Baltimore MD USA Department of Biomedical Engineering Illinois Institute of Technology Chicago IL USA Department of Diagnostic Radiology and Nuclear Medicine Rush UniversityRush University Medical Center Chicago IL USA The Mind Research Network Albuquerque NM USA University of California at Davis Davis CA USA University of Kentucky Lexington KY USA University of California Davis Davis CA USA Population Health Sciences and Glenn Biggs Institute for Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio TX USA Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core Glenn Biggs Institute for Neurodegenerative Diseases University of Texas Health Science Center at San Antonio San Antonio TX USA University of Texas Health San Antonio San Antonio TX USA Memory and Aging Center UCSF Weill Institute for Neurosciences University of California San Francisco San Francisco CA USA Massachusetts General Hospital Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA Department of Radiology Harvard Medical School Boston MA USA Department of Radiology Massachusetts General Hospital Boston MA USA Department of Computer Science and AI Lab Massachusetts

Background Glymphatic system (GS) is a recently discovered brain-wide perivascular fluid transport system in the cerebral nervous system (CNS)(Benveniste H et al., 2019) The impairment of glymphatic transport has been demonstrated to be associated with several neurological diseases, in particular the vascular cognitive impairment and dementia (VCID)(Tang J et al., 2022). The diffusion tensor analysis along the perivascular space (DTI-ALPS) was proposed to non-invasively evaluate the GS clearance dysfunction which has been used to investigate the GS in various pathologies(Taoka T et al., 2017, Hsu JL et al., 2022). However, studies on the cross-vendor and test-retest reliability of DTI-ALPS method are lacking. The current study aimed to perform cross-vendor, inter-rater and test-retest validations of DTI-ALPS method by using a cohort from the MarkVCID consortium. Method Fifty participants’ DTI data from the MarkVCID consortium (consists of 7 sites) were included in this study: 15 participants were scanned on four MRI scanners; 35 participants underwent two MR scans within 14 days. The DTI data were acquired used a single shell, b = 1000s/mm2, 40-direction with a voxel size of 2.02.02.0mm 3 and six b = 0 s/mm 2 on 3T MR scanners from seven sites, including two Siemens systems, one Philips system and one GE system. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation (Figure 1). The mean ALPS (mALPS) index was obtained by the average of bilateral ALPS index and was used for testing the inter-rater, cross-vendor and test-retest reliability. The association between mALPS index and Montreal Cognitive assessment (MoCA) scores were evaluated using a general linear model with age and gender as covariate. Result The Bland-Altman plot and scatterplot illustrated the validation results of the mALPS index analyzed using DSI studio and FSL pipelines (Figure 2). The mALPS index demonstrated favorable inter-scanner reprodu

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Mental health care needs of caregivers of people with Alzheimer's disease from online forum analysis

Npj mental health research

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Npj mental health research 2024年第1期3卷 54页

作者： Jiyeong Kim Zhuo Ran Cai Michael L Chen Shawheen J Rezaei Sonia Onyeka Carolyn I Rodriguez Tina Hernandez-Boussard Vladimir Filkov Rachel A Whitmer Eleni Linos Yong K Choi Department of Medicine Stanford Center for Digital Health Stanford University Stanford CA USA. jykim3@stanford.edu. Department of Dermatology School of Medicine Stanford University Stanford CA USA. jykim3@stanford.edu. Department of Medicine Stanford Center for Digital Health Stanford University Stanford CA USA. Department of Dermatology School of Medicine Stanford University Stanford CA USA. Department of Psychiatry and Behavioral Sciences School of Medicine Stanford University Stanford CA USA. Veterans Affairs Palo Alto Health Care System Palo Alto CA USA. Department of Medicine Biomedical Informatics Stanford University Stanford CA USA. Department of Computer Science College of Engineering University of California Davis Davis CA USA. Department of Public Health Sciences School of Medicine University of California Davis Davis CA USA. Department of Health Information Management School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh PA USA.

Informal caregivers of people with Alzheimer's disease and related dementias (ADRD) are at risk of poor mental health. This study aimed to investigate the feasibility and validity of studying caregivers' mental stressors using online caregiving forum data (March 2018-February 2022) and natural language processing and machine learning (NLP/ML). NLP/ML topic modeling generated eight prominent topics, which we compared with qualitatively defined themes and the existing caregiving framework to assess validity. Among a total of 60,182 posts, 5848 were mental distress-related; for the ADRD patients (symptoms, medication, relocation, care duty share, diagnosis, conversation strategy) and the caregivers (caregiving burden and support). While we observed novel topics from NLP/ML-defined topics, mostly those were aligned with the existing framework. For feasibility assessment, qualitative title screening was done. The findings shed new light on the potential of NLP/ML text analysis of the online forum for informal caregivers to prepare tailored support for this vulnerable population.

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FAMU: Study of the energy dependent transfer rate Λµp→µO

arXiv

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arXiv 2018年

作者： Mocchiutti, E. Bonvicini, V. Danailov, M. Furlanetto, E. Gadedjisso-Tossou, K.S. Guffanti, D. Pizzolotto, C. Rachevski, A. Stoychev, L. Vallazza, E. Zampa, G. Niemela, J. Ishida, K. Adamczak, A. Baccolo, G. Benocci, R. Bertoni, R. Bonesini, M. Chignoli, F. Clemenza, M. Curioni, A. Maggi, V. Mazza, R. Moretti, M. Nastasi, M. Previtali, E. Bakalov, D. Danev, P. Stoilov, M. Baldazzi, G. Campana, R. D'Antone, I. Furini, M. Fuschino, F. Labanti, C. Margotti, A. Meneghini, S. Morgante, G. Rignanese, L.P. Rossi, P.L. Zuffa, M. Cervi, T. de Bari, A. Menegolli, A. de Vecchi, C. Nardò, R. Rossella, M. Tomaselli, A. Colace, L. de Vincenzi, M. Iaciofano, A. Somma, F. Tortora, L. Ramponi, R. Vacchi, A. Sezione di Trieste via A. Valerio 2 Trieste34127 Italy Elettra-Sincrotrone Trieste S.C.p.A. SS14 Km 163.5 BasovizzaTrieste34149 Italy Mathematics and Informatics Department Udine University via delle Scienze 206 Udine Italy Abdus Salam International Centre for Theoretical Physics Strada Costiera 11 Trieste Italy Département de physique Université de Lomé 01 BP 1515 Lomé Togo Gran Sasso Science Institute - INFN-LNGS via F. Crispi 7 L’Aquila Italy RIKEN-RAL RIKEN Nishina Center for Accelerator-Based Science 2-1 Hirosawa Wako Saitama351-0198 Japan Institute of Nuclear Physics Polish Academy of Sciences Radzikowskiego 152 KrakówPL31342 Poland Sezione di Milano Bicocca piazza della Scienza 3 Milano Italy Università di Milano Bicocca Dip. di Fisica "G. Occhialini" piazza della Scienza 3 Milano Italy Università di Milano Bicocca Dip. di Scienze dell’Ambiente e della Terra piazza della Scienza 1 Miano Italy Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences blvd. Tsarigradsko ch. 72 Sofia1142 Bulgaria Sezione di Bologna viale Berti Pichat 6/2 Bologna Italy Department of Physics and Astronomy University of Bologna viale Berti Pichat 6/2 Bologna Italy INAF-IAFS Bologna Area della Ricerca via P. Gobetti 101 Bologna Italy Department of Physics University of Pavia via Bassi 6 Pavia Italy Sezione di Pavia via Bassi 6 Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia via Ferrata 5 Pavia Italy Sezione di Roma Tre via della Vasca Navale 84 Roma Italy Dipartimento di Ingegneria Università degli Studi Roma Tre via V. Volterra 62 Roma Italy Dipartimento di Matematica e Fisica Università di Roma Tre via della Vasca Navale 84 Roma Italy Dipartimento di Scienze Università di Roma Tre viale G. Marconi 446 Roma Italy Sezione di Milano Politecnico piazza Leonardo da Vinci 32 Milano20133 Italy

The main goal of the FAMU experiment is the measurement of the hyperfine splitting (hfs) in the 1S state of muonic hydrogen ∆Ehfs(µ−p)1S. The physical process behind this experiment is the following: µp are formed in a mixture of hydrogen and a higher-Z gas. When absorbing a photon at resonance-energy ∆Ehfs ≈ 0.182 eV, in subsequent collisions with the surrounding H2 molecules, the µp is quickly de-excited and accelerated by ∼ 2/3 of the excitation energy. The observable is the time distribution of the K-lines X-rays emitted from the µZ formed by muon transfer (µp) + Z → (µZ)∗ + p, a reaction whose rate depends on the µp kinetic energy. The maximal response, to the tuned laser wavelength, of the time distribution of X-ray from K-lines of the (µZ)∗ cascade indicate the resonance. During the preparatory phase of the FAMU experiment, several measurements have been performed both to validate the methodology and to prepare the best configuration of target and detectors for the spectroscopic measurement. We present here the crucial study of the energy dependence of the transfer rate from muonic hydrogen to oxygen (Λµp→µO), precisely measured for the first time. Copyright © 2018, The Authors. All rights reserved.

关键词： Kinetic energy

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First FAMU observation of muon transfer from µp atoms to higher-Z elements

arXiv

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arXiv 2017年

作者： Mocchiutti, E. Bonvicini, V. Carbone, R. Danailov, M. Furlanetto, E. Gadedjisso-Tossou, K.S. Guffanti, D. Pizzolotto, C. Rachevski, A. Stoychev, L. Vallazza, E. Zampa, G. Niemela, J. Ishida, K. Adamczak, A. Baccolo, G. Benocci, R. Bertoni, R. Bonesini, M. Chignoli, F. Clemenza, M. Curioni, A. Maggi, V. Mazza, R. Moretti, M. Nastasi, M. Previtali, E. Bakalov, D. Danev, P. Stoilov, M. Baldazzi, G. Campana, G. D'Antone, I. Furini, M. Fuschino, F. Labanti, C. Margotti, A. Meneghini, S. Morgante, G. Rignanese, L.P. Rossi, P.L. Zuffa, M. Cervi, T. De Bari, A. Menegolli, A. De Vecchi, C. Nardò, R. Rossella, M. Tomaselli, A. Colace, L. De Vincenzi, M. Iaciofano, A. Somma, F. Tortora, L. Ramponi, R. Vacchi, A. Sezione di Trieste via A. Valerio 2 Trieste34127 Italy Elettra-Sincrotrone Trieste S.C.p.A. SS14 Km 163.5 Basovizza Trieste34149 Italy Abdus Salam International Centre for Theoretical Physics Strada Costiera 11 Trieste Italy Département de physique Université de Lomé 01 BP 1515 Lomé Togo Gran Sasso Science Institute INFN-LNGS via F. Crispi 7 L'Aquila Italy RIKEN-RAL RIKEN Nishina Center for Accelerator-Based Science 2-1 Hirosawa Wako Saitama351-0198 Japan Institute of Nuclear Physics Polish Academy of Sciences Radzikowskiego 152 KrakówPL31342 Poland Sezione di Milano Bicocca piazza della Scienza 3 Milano Italy Universitá di Milano Bicocca Dip. di Fisica "G. Occhialini" piazza della Scienza 3 Milano Italy Universitá di Milano Bicocca Dip. di Scienze dell'Ambiente e della Terra piazza della Scienza 1 Miano Italy Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences blvd. Tsarigradsko ch. 72 Sofia1142 Bulgaria Sezione di Bologna viale Berti Pichat 6/2 Bologna Italy Department of Physics and Astronomy University of Bologna viale Berti Pichat 6/2 Bologna Italy INAF-IAFS Bologna Area della Ricerca via P. Gobetti 101 Bologna Italy Department of Physics University of Pavia via Bassi 6 Pavia Italy Sezione di Pavia via Bassi 6 Pavia Italy Department of Electrical Computer and Biomedical Engineering University of Pavia via Ferrata 5 Pavia Italy Sezione di Roma Tre via della Vasca Navale 84 Roma Italy Dipartimento di Ingegneria Università degli Studi Roma Tre via V. Volterra 62 Roma Italy Dipartimento di Matematica e Fisica Università di Roma Tre via della Vasca Navale 84 Roma Italy Dipartimento di Scienze Università di Roma Tre viale G. Marconi 446 Roma Italy Sezione di Milano Politecnico piazza Leonardo da Vinci 32 Milano20133 Italy Mathematics and Informatics Department Udine University via delle Scienze 206 Udine Italy

The FAMU experiment aims to accurately measure the hyperfine splitting of the ground state of the muonic hydrogen atom. A measurement of the transfer rate of muons from hydrogen to heavier gases is necessary for this purpose. In June 2014, within a preliminary experiment, a pressurized gas-target was exposed to the pulsed low-energy muon beam at the RIKEN RAL muon facility (Rutherford Appleton Laboratory, UK). The main goal of the test was the characterization of both the noise induced by the pulsed beam and the X-ray detectors. The apparatus, to some extent rudimental, has served admirably to this task. Technical results have been published that prove the validity of the choices made and pave the way for the next steps. This paper presents the results of physical relevance of measurements of the muon transfer rate to carbon dioxide, oxygen, and argon from non-thermalized excited µp atoms. The analysis methodology and the approach to the systematics errors are useful for the subsequent study of the transfer rate as function of the kinetic energy of the µp currently under way. Copyright © 2017, The Authors. All rights reserved.

关键词： Oxygen

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Designing ecosystems of intelligence from first principles

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Collective Intelligence 2024年第1期3卷

作者： Karl J Friston Maxwell JD Ramstead Alex B Kiefer Alexander Tschantz Christopher L Buckley Mahault Albarracin Riddhi J Pitliya Conor Heins Brennan Klein Beren Millidge Dalton AR Sakthivadivel Toby St Clere Smithe Magnus Koudahl Safae Essafi Tremblay Capm Petersen Kaiser Fung Jason G Fox Steven Swanson Dan Mapes Gabriel René VERSES Research Lab Los Angeles CA USA (KJF MJDR ABK AT CLB MA RJP CH BK BM DARS TSCS MK SET CP KF JGF SS DM GR) Wellcome Centre for Human Neuroimaging University College London London UK (KJF MJDR) Department of Philosophy Monash University Melbourne VIC Australia (ABK) Department of Informatics University of Sussex Brighton UK (CLB) Department of Computer Science Université du Québec à Montréal Montréal QC Canada (MA) Department of Experimental Psychology University of Oxford Oxford UK (RJP TSCS) Department of Collective Behaviour Max Planck Institute of Animal Behavior Konstanz Germany (CH) Department of Biology University of Konstanz Konstanz Germany (CH) Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany (CH) Network Science Institute Northeastern University Boston MA USA (BK) Brain Network Dynamics Unit University of Oxford Oxford UK (BM) Department of Mathematics Stony Brook University New York NY USA (DARS) Department of Physics and Astronomy Stony Brook University New York NY USA (DARS) Department of Biomedical Engineering Stony Brook University New York NY USA (DARS) Topos Institute Berkeley CA USA (TSCS) Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands (MK) Department of Philosophy Université du Québec à Montréal Montréal QC Canada (SET)

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.

关键词： Active inference free energy principle artificial intelligence belief updating belief propagation

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The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

引用

arXiv 2023年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rodriguez, Andres Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Shah, Lubdha M. Sheth, Nakul Shinohara, Russel Taki Tu, Wenxin Viswanathan, Karthik Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Wiggins, Walter Zapaishchykova, Anna Aboian, Mariam Bornhorst, Miriam de Blank, Peter Deutsch, Michelle Fouladi, Maryam Hoffman, Lindsey Kann, Benjamin Lazow, Margot Mikael, Leonie Nabavizadeh, Ali Packer, Roger Resnick, Adam Rood, Brian Vossough, Arastoo Bakas, Spyridon Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Radiol

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2023, CC BY.

关键词： Tumors

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Correction: Variability of temperature measurements recorded by a wearable device by biological sex

引用

Biology of sex differences 2023年第1期14卷 82页

作者： Lauryn Keeler Bruce Patrick Kasl Severine Soltani Varun K Viswanath Wendy Hartogensis Stephan Dilchert Frederick M Hecht Anoushka Chowdhary Claudine Anglo Leena Pandya Subhasis Dasgupta Ilkay Altintas Amarnath Gupta Ashley E Mason Benjamin L Smarr Department of Biomedical Informatics UC San Diego Health University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. Bioinformatics and Systems Biology University of California San Diego San Diego CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Osher Center for Integrative Health University of California San Francisco San Francisco CA USA. Department of Management Zicklin School of Business Baruch College The City University of New York New York NY USA. San Diego Supercomputer Center University of California San Diego San Diego CA USA. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. Gene Lay Department of Bioengineering University of California San Diego 9500 Gilman Dr La Jolla San Diego CA USA. bsmarr@eng.ucsd.edu. Halıcıoğlu Data Science Institute University of California San Diego San Diego CA USA. bsmarr@eng.ucsd.edu.

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