检索结果-内蒙古大学图书馆

Comparative Analysis of Genome-Scale Gene Regulatory Networks in Human Hematopoietic Stem and Myeloid Progenitor Fractions

引用

BLOOD 2022年 140卷 2846-2848页

作者： Subramanian, Shruthi Thoms, Julie A., I Huang, Yizhou Jacquelin, Sebastien Shen, Sylvie Song, Emma Joshi, Swapna Brownlee, Chris Woll, Petter S. Fajardo, Diego Chacon Beck, Dominik Curtis, David J. Yehson, Kenneth Antonenas, Vicki O' Brien, Tracey Trickett, Annette Powell, Jason Pitson, Stuart M. Gandhi, Maher K. Cornejo, Paola Wong, Emily Lane, Steven W. Gottgens, Bertie Rokny, Hamid Alinejad Wong, Jason W. H. Pimanda, John E. Univ New South wales Sch Clin Med Sydney NSW Australia Univ New South Wales Sch Med Sci Randwick NSW Australia Univ Technol Sydney Ctr Hlth Technol Sydney NSW Australia Univ Technol Sydney Sch Biomed Engn Sydney NSW Australia Mater Res Macrophage Biol Lab Brisbane Qld Australia Sydney Childrens Hosp Sydney NSW Australia Prince Wales Hosp Bone Marrow Transplant Lab NSW Hlth Pathol Sydney NSW Australia Univ New South Wales Mark Wainwright Analyt Ctr Sydney NSW Australia Karolinska Inst Ctr Hematol & Regenerat Med Dept Med MedH Huddinge Sweden Monash Univ Australian Ctr Blood Dis Melbourne Vic Australia Westmead Hosp Sydney NSW Australia Westmead Hosp Sydney Cellular Therapies Lab Sydney NSW Australia Univ South Australia Ctr Canc Biol SA Pathol Adelaide SA Australia Univ Queensland Mater Res Brisbane Qld Australia Victor Chang Cardiac Res Inst Sydney NSW Australia Queensland Inst Med Res Canc Program Brisbane Qld Australia Univ Cambridge Stem Cell Inst Cambridge England Univ New South Wales Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW Australia Univ Hong Kong Li Ka Shing Fac Med Sch Biomed Sci Hong Kong Peoples R China Prince Wales Hosp Haematol Dept Sydney NSW Australia Univ New South Wales Sch Med Sci Sydney NSW Australia

来源：评论

学校读者我要写书评

暂无评论

Accelerated chemical science with AI

DIGITAL DISCOVERY

引用

DIGITAL DISCOVERY 2024年第1期3卷 23-33页

作者： Back, Seoin Aspuru-Guzik, Alan Ceriotti, Michele Gryn'ova, Ganna Grzybowski, Bartosz Gu, Geun Ho Hein, Jason Hippalgaonkar, Kedar Hormazabal, Rodrigo Jung, Yousung Kim, Seonah Kim, Woo Youn Moosavi, Seyed Mohamad Noh, Juhwan Park, Changyoung Schrier, Joshua Schwaller, Philippe Tsuda, Koji Vegge, Tejs von Lilienfeld, O. Anatole Walsh, Aron Sogang Univ Inst Emergent Mat Dept Chem & Biomol Engn Seoul South Korea Univ Toronto Dept Chem Comp Sci St George Campus Toronto ON Canada Accelerat Consortium Toronto ON M5S 1M1 Canada Vector Inst Articial Intelligence Toronto ON M5S 1M1 Canada Ecole Polytech Fed Lausanne Lab Computat Sci & Modeling COSMO Lausanne Switzerland Heidelberg Inst Theoret Studies HITS gGmbH D-69118 Heidelberg Germany Heidelberg Univ Interdisciplinary Ctr Sci Comp D-69120 Heidelberg Germany Inst Basic Sci IBS Ctr Algorithm & Robotized Synth CARS Ulsan South Korea Polish Acad Sci Inst Organ Chem Warsaw Poland Ulsan Natl Inst Sci & Technol Dept Chem Ulsan South Korea Korea Inst Energy Technol KENTECH Dept Energy Engn Naju 58330 South Korea Univ British Columbia Dept Chem Vancouver BC V6T 1Z1 Canada Nanyang Technol Univ Sch Mat Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore Agcy Sci Technol & Res Inst Mat Res & Engn 2 Fusionopolis Way08-03 Singapore 138634 Singapore LG AI Res Seoul South Korea Korea Adv Inst Sci & Technol Dept Chem & Biomol Engn Daejeon South Korea Seoul Natl Univ Sch Chem & Biol Engn Interdisciplinary Program Artificial Intelligence 1 Gwanak Ro Seoul 08826 South Korea Colorado State Univ Dept Chem 1301 Ctr Ave Ft Collins CO 80523 USA Korea Adv Inst Sci & Technol Dept Chem Daejeon South Korea Univ Toronto Chem Engn & Appl Chem Toronto ON M5S 3E5 Canada Korea Res Inst Chem Technol Chem Data Driven Res Ctr Daejeon 34114 South Korea Fordham Univ Dept Chem The Bronx NY 10458 USA Ecole Polytech Fed Lausanne Lab Artificial Chem Intelligence LIAC Lausanne Switzerland Ecole Polytech Fed Lausanne Natl Ctr Competence Res NCCR Catalysis Lausanne Switzerland Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 2778561 Japan Natl Inst Mat Sci Ctr Basic Res Mat Tsukuba Ibaraki 3050044 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Tech Univ Denmark Dept Energy Convers & Storage 301 Anker Engelunds Vej DK-2800 Copenhagen Denmar

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions. The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, and academic bodies were provided.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study

引用

SCIENTIFIC REPORTS 2021年第1期11卷 1-9页

作者： Cao, Bo Liu, Yang S. Selvitella, Alessandro Librenza-Garcia, Diego Passos, Ives Cavalcante Sawalha, Jeffrey Ballester, Pedro Chen, Jianshan Dong, Shimiao Wang, Fei Kapczinski, Flavio Dursun, Serdar M. Li, Xin-Min Greiner, Russell Greenshaw, Andrew Univ Alberta Fac Med & Dent Dept Psychiat Edmonton AB Canada Univ Alberta Fac Sci Dept Comp Sci Edmonton AB Canada Purdue Univ Dept Math Sci Ft Wayne IN USA Univ Washington US eSci Inst Seattle WA 98195 USA McMaster Univ Dept Psychiat & Behav Neurosci Hamilton ON Canada Univ Fed Rio Grande do Sul Hosp Clin Porto Alegre Lab Mol Psychiat Programa Posgrad Psiquiatria & Ciencias Comportam Porto Alegre RS Brazil McMaster Univ Neurosci Grad Program Hamilton ON Canada Nanjing Med Univ Dept Psychiat Early Intervent Unit Affiliated Brain Hosp Nanjing Jiangsu Peoples R China Amii Alberta Machine Learning Inst Edmonton AB Canada

The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate

引用

NATURE MICROBIOLOGY 2019年第12期4卷 2109-2117页

作者： Lopatkin, Allison J. Stokes, Jonathan M. Zheng, Erica J. Yang, Jason H. Takahashi, Melissa K. You, Lingchong Collins, James J. MIT Inst Med Engn & Sci 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Infect Dis & Microbiome Program Cambridge MA 02142 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA MIT Machine Learning Pharmaceut Discovery & Synth Con 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Program Chem Biol Cambridge MA 02138 USA Calif State Univ Northridge Dept Biol Northridge CA 91330 USA Duke Univ Dept Biomed Engn Durham NC 27706 USA MIT Synthet Biol Ctr 77 Massachusetts Ave Cambridge MA 02139 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA

Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy(1-3). However, the two are interrelated as bacterial growth inherently imposes a metabolic burden(4);thus, determining individual contributions from each is challenging(5,6). Indeed, faster growth is often correlated with increased antibiotic efficacy(7,8);however, the concurrent role of metabolism in that relationship has not been well characterized. As a result, a clear understanding of the interdependence between growth and metabolism, and their implications for antibiotic efficacy, are lacking(9). Here, we measured growth and metabolism in parallel across a broad range of coupled and uncoupled conditions to determine their relative contribution to antibiotic lethality. We show that when growth and metabolism are uncoupled, antibiotic lethality uniformly depends on the bacterial metabolic state at the time of treatment, rather than growth rate. We further reveal a critical metabolic threshold below which antibiotic lethality is negligible. These findings were general for a wide range of conditions, including nine representative bactericidal drugs and a diverse range of Gram-positive and Gram-negative species (Escherichia coli, Acinetobacter baumannii and Staphylococcus aureus). This study provides a cohesive metabolic-dependent basis for antibiotic-mediated cell death, with implications for current treatment strategies and future drug development.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LtU-ILI: AN ALL-IN-ONE FRAMEWORK FOR IMPLICIT INFERENCE IN ASTROPHYSICS AND COSMOLOGY

arXiv

引用

arXiv 2024年

作者： Ho, Matthew Bartlett, Deaglan J. Chartier, Nicolas Cuesta-Lazaro, Carolina Ding, Simon Lapel, Axel Lemos, Pablo Lovell, Christopher C. Makinen, T. Lucas Modi, Chirag Pandya, Viraj Pandey, Shivam Perez, Lucia A. Wandelt, Benjamin Bryan, Greg L. UMR 7095 98 bis bd Arago ParisF-75014 France Center for the Gravitational-Wave Universe Astronomy Program Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Center for Astrophysics Harvard & Smithsonian 60 Garden St CambridgeMA02138 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Université de Montréal Montréal Canada Mila - Quebec Artificial Intelligence Institute Montréal Canada Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning Montréal Canada Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Institute of Cosmology and Gravitation University of Portsmouth Burnaby Road PortsmouthPO1 3FX United Kingdom & Astrophysics Group Imperial College London Blackett Laboratory Prince Consort Road LondonSW7 2AZ United Kingdom Center for Computational Mathematics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States 4 place Jussieu ParisF-75252 Cedex 5 France

This paper presents the learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schemata, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable and is designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry;inferring cosmology from matter power spectra and halo point clouds;characterizing progenitors in gravitational wave signals;capturing physical dust parameters from galaxy colors and luminosities;and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://***/maho3/ltu-ili. © 2024, CC BY.

关键词： Cosmology

来源：评论

学校读者我要写书评

暂无评论

In the Era of Deep learning, Why Reconstruct an Image at All?

引用

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY 2021年第1期18卷 170-173页

作者： Chung, Caroline Kalpathy-Cramer, Jayashree Knopp, Michael V. Jaffray, David A. Univ Texas MD Anderson Canc Ctr Imaging Technol & Innovat Radiat Oncol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Data Governance Program Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr MR Coordinating Comm Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dynam Contrast Enhanced MRI Comm & Quantitat Imag Houston TX 77030 USA Massachusetts Gen Hosp Dept Radiol Athinoula A Martinos Ctr Biomed Imaging Quantitat Translat Imaging Med Lab Charlestown MA USA Massachusetts Gen Hosp Dept Radiol Athinoula A Martinos Ctr Biomed Imaging Ctr Machine Learning Charlestown MA USA Ohio State Univ Wexner Med Ctr Wright Ctr Innovat Biomed Imaging ResDiv Imaging SciDept Radiol Columbus OH 43210 USA Ohio State Univ Wexner Med Ctr Wright Ctr Innovat Biomed Imaging Columbus OH 43210 USA Ohio State Univ Wexner Med Ctr Wright Ctr Innovat Biomed Imaging ImagingDiv Imaging SciDept Radiol Columbus OH 43210 USA Ohio State Univ Wright Ctr Innovat Biomed Imaging Alliance Clin Trials Oncol Imaging CommDiv Imaging SciDept RadiolWexner M Columbus OH 43210 USA

来源：评论

学校读者我要写书评

暂无评论

Protecting scientific integrity in an age of generative AI

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2024年第22期121卷 e2407886121-e2407886121页

作者： Blau, Wolfgang Cerf, Vinton G. Enriquez, Juan Francisco, Joseph S. Gasser, Urs Gray, Mary L. Greaves, Mark Grosz, Barbara J. Jamieson, Kathleen Hall Haug, Gerald H. Hennessy, John L. Horvitz, Eric Kaiser, David I. London, Alex John Lovell-Badge, Robin McNutt, Marcia K. Minow, Martha Mitchell, Tom M. Ness, Susan Parthasarathy, Shobita Perlmutter, Saul Press, William H. Wing, Jeannette M. Witherell, Michael Brunswick Grp London WC2A 3ED England Google LLC Google Global Networking Reston VA 20190 USA Excel Venture Management Boston MA 02116 USA Univ Penn Dept Earth & Environm Sci Philadelphia PA 19104 USA Univ Penn Dept Chem Philadelphia PA 19104 USA Tech Univ Munich Sch Social Sci & Technol D-80333 Munich Germany Microsoft Res New England Lab Cambridge MA 02142 USA Indiana Univ Luddy Sch Informat Comp & Engn Bloomington IN 47408 USA Schmidt Sci Washington DC 20008 USA Harvard Univ Harvard Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA Univ Penn Annenberg Publ Policy Ctr Philadelphia PA 19104 USA German Natl Acad Sci Leopoldina D-06108 Halle Germany Stanford Univ Knight Hennessy Scholars Stanford CA 94305 USA Off Chief Sci Officer Microsoft Redmond WA 98052 USA MIT Program Sci Technol & Soc Cambridge MA 02139 USA MIT Dept Phys Cambridge MA 02139 USA Carnegie Mellon Univ Dept Philosophy Pittsburgh PA 15213 USA Francis Crick Inst Lab Stem Cell Biol & Dev Genet London NW1 1AT England Natl Acad Sci Washington DC 20001 USA Harvard Univ Cambridge MA 02138 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Michigan Sci Technol & Publ Policy Program Ann Arbor MI 48109 USA Univ Calif Berkeley Dept Phys Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Phys Div Berkeley CA 94720 USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Columbia Univ Res & Comp Sci Dept New York NY 10027 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

来源：评论

学校读者我要写书评

暂无评论

Longitudinal clinical and functional outcome in distinct cognitive subgroups of first-episode psychosis: a cluster analysis

引用

PSYCHOLOGICAL MEDICINE 2023年第6期53卷 2317-2327页

作者： Oomen, Priscilla P. Begemann, Marieke J. H. Brand, Bodyl A. de Haan, Lieuwe Veling, Wim Koops, Sanne van Os, Jim Smit, Filip Bakker, P. Roberto van Beveren, Nico Boonstra, Nynke Guloksuz, Sinan Kikkert, Martijn Lokkerbol, Joran Marcelis, Machteld Rosema, Bram-Sieben de Beer, Franciska Gangadin, Shiral S. Geraets, Chris N. W. van't Hag, Erna Haveman, Yudith van der Heijden, Inge Voppel, Alban E. Willemse, Elske van Amelsvoort, Therese Bak, Maarten Batalla, Albert Been, Agaath van den Bosch, Marinte van den Brink, Truus Faber, Gunnar Grootens, Koen P. de Jonge, Martin Knegtering, Rikus Kurkamp, Jorg Mahabir, Amrita Pijnenborg, Gerdina H. M. Staring, Tonnie Veen, Natalie Veerman, Selene Wiersma, Sybren Graveland, Ellen Hoornaar, Joelle Sommer, Iris E. C. Univ Groningen Univ Med Ctr Groningen Dept Biomed Sci Cells & Syst Groningen Netherlands Univ Groningen Univ Med Ctr Groningen Dept Psychiat Groningen Netherlands Amsterdam UMC Dept Early Psychosis Acad Med Ctr Amsterdam Netherlands Univ Groningen Univ Med Ctr Groningen Dept Psychiat Groningen Netherlands Univ Med Ctr Utrecht Dept Psychiat UMC Utrecht Brain Ctr Utrecht Netherlands Maastricht Univ Sch Mental Hlth & Neurosci MheNS Dept Psychiat & Neuropsychol Med Ctr Maastricht Netherlands Kings Coll London Kings Hlth Partners Dept Psychosis Studies Inst Psychiat Psychol & Neurosci London England Univ Amsterdam Amsterdam Publ Hlth Res Inst Dept Epidemiol & Biostat Med Ctr Locat VUmc Amsterdam Netherlands Vrije Univ Amsterdam Publ Hlth Res Inst Dept Clin Neuro & Dev Psychol Amsterdam Netherlands Trimbos Inst Netherlands Inst Mental Hlth Ctr Econ Evaluat & Machine Learning Utrecht Netherlands Arkin Mental Hlth Care Dept Res Amsterdam Netherlands Antes Ctr Mental Hlth Care Rotterdam Netherlands Erasmus MC Dept Neurosci Rotterdam Netherlands Erasmus MC Dept Psychiat Rotterdam Netherlands Univ Appl Sci NHL Stenden Leeuwarden Netherlands KieN VIP Mental Hlth Care Serv Leeuwarden Netherlands Yale Univ Dept Psychiat Sch Med New Haven CT USA Maastricht Univ Med Ctr Sch Mental Hlth & Neurosci Dept Psychiat & Neuropsychol EURON Maastricht Netherlands Inst Mental Hlth Care Eindhoven GGzE Eindhoven Netherlands Janssen Cilag BV Breda Netherlands Mondriaan Mental Hlth Care Heerlen Netherlands Dimence Inst Mental Hlth Deventer Netherlands GGZ Centraal Early Intervent Team Amersfoort Netherlands Mental Hlth Inst Yulius Dordrecht Netherlands Reinier Arkel Inst Mental Hlth Care sHertogenbosch Netherlands Pro Persona Mental Hlth Program Psychosis & Severe Mental Illness Wolfheze Netherlands Lentis Psychiat Inst Lentis Res Groningen Netherlands Mediant ABC Twente Ctr Youth Psychosis Enschede Netherlands GGNet Early Psychosis Team Apeldoorn N

Background Cognitive deficits may be characteristic for only a subgroup of first-episode psychosis (FEP) and the link with clinical and functional outcomes is less profound than previously thought. This study aimed to identify cognitive subgroups in a large sample of FEP using a clustering approach with healthy controls as a reference group, subsequently linking cognitive subgroups to clinical and functional outcomes. Methods 204 FEP patients were included. Hierarchical cluster analysis was performed using baseline brief assessment of cognition in schizophrenia (BACS). Cognitive subgroups were compared to 40 controls and linked to longitudinal clinical and functional outcomes (PANSS, GAF, self-reported WHODAS 2.0) up to 12-month follow-up. Results Three distinct cognitive clusters emerged: relative to controls, we found one cluster with preserved cognition (n = 76), one moderately impaired cluster (n = 74) and one severely impaired cluster (n = 54). Patients with severely impaired cognition had more severe clinical symptoms at baseline, 6- and 12-month follow-up as compared to patients with preserved cognition. General functioning (GAF) in the severely impaired cluster was significantly lower than in those with preserved cognition at baseline and showed trend-level effects at 6- and 12-month follow-up. No significant differences in self-reported functional outcome (WHODAS 2.0) were present. Conclusions Current results demonstrate the existence of three distinct cognitive subgroups, corresponding with clinical outcome at baseline, 6- and 12-month follow-up. Importantly, the cognitively preserved subgroup was larger than the severely impaired group. Early identification of discrete cognitive profiles can offer valuable information about the clinical outcome but may not be relevant in predicting self-reported functional outcomes.

关键词： Clustering cognition FEP functional outcome global functioning psychosis

来源：评论

学校读者我要写书评

暂无评论

Reconstructed Single-Cell Fate Trajectories Define Lineage Plasticity Windows during Differentiation of Human PSC-Derived Distal Lung Progenitors

引用

CELL STEM CELL 2020年第4期26卷 593-+页

作者： Hurley, Killian Ding, Jun Villacorta-Martin, Carlos Herriges, Michael J. Jacob, Anjali Vedaie, Marall Alysandratos, Konstantinos D. Sun, Yuliang L. Lin, Chieh Werder, Rhiannon B. Huang, Jessie Wilson, Andrew A. Mithal, Aditya Mostoslavsky, Gustavo Oglesby, Irene Caballero, Ignacio S. Guttentag, Susan H. Ahangari, Farida Kaminski, Naftali Rodriguez-Fraticelli, Alejo Camargo, Fernando Bar-Joseph, Ziv Kotton, Darrell N. Boston Univ Ctr Regenerat Med Boston MA 02118 USA Boston Med Ctr Boston MA 02118 USA Boston Univ Pulm Ctr Sch Med Boston MA 02118 USA Boston Univ Dept Med Sch Med Boston MA 02118 USA Beaumont Hosp Royal Coll Surg Ireland Educ & Res Ctr Dept Med Dublin Ireland Royal Coll Surgeons Ireland Tissue Engn Res Grp Dublin Ireland Carnegie Mellon Univ Sch Comp Sci Computat Biol Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Sch Comp Sci Machine Learning Dept Pittsburgh PA 15217 USA Vanderbilt Univ Dept Pediat Monroe Carell Jr Childrens Hosp Nashville TN 37232 USA Yale Univ Pulm Crit Care & Sleep Med Sch Med New Haven CT 16520 USA Boston Childrens Hosp Stem Cell Program Boston MA 02115 USA Harvard Univ Dept Stem Cell & Regenerat Biol Cambridge MA 02138 USA Harvard Stem Cell Inst Boston MA 02115 USA

Alveolar epithelial type 2 cells (AEC2s) are the facultative progenitors responsible for maintaining lung alveoli throughout life but are difficult to isolate from patients. Here, we engineer AEC2s from human pluripotent stem cells (PSCs) in vitro and use time-series single-cell RNA sequencing with lentiviral bar-coding to profile the kinetics of their differentiation in comparison to primary fetal and adult AEC2 benchmarks. We observe bifurcating cell-fate trajectories as primordial lung progenitors differentiate in vitro, with some progeny reaching their AEC2 fate target, while others diverge to alternative non-lung endo-dermal fates. We develop a Continuous State Hidden-Markov model to identify the timing and type of signals, such as overexuberant Wnt responses, that induce some early multipotent NKX2-1(+) progenitors to lose lung fate. Finally, we find that this initial developmental plasticity is regulatable and subsides over time, ultimately resulting in PSC-derived AEC2s that exhibit a stable phenotype and nearly limitless self-renewal capacity.

关键词： Alveolar epithelial type 2 cells induced pluripotent stem cells lungprogenitors directed differentiation cell fate cellular plasticity heterogeneity single cell RNA sequencing DNA barcoding hidden Markov model

来源：评论

学校读者我要写书评

暂无评论

Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior

引用

NATURE COMMUNICATIONS 2020年第1期11卷 4014-4014页

作者： Li, Yuanning Ward, Michael J. Richardson, R. Mark G'Sell, Max Ghuman, Avniel Singh Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA 15260 USA Carnegie Mellon Univ Program Neural Computat & Machine Learning Pittsburgh PA 15213 USA Univ Pittsburgh Dept Neurol Surg Pittsburgh PA 15260 USA Massachusetts Gen Hosp Dept Neurosurg Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA Carnegie Mellon Univ Dept Stat Pittsburgh PA 15213 USA Univ Calif San Francisco Dept Neurol Surg San Francisco CA 94143 USA

Perception reflects not only sensory inputs, but also the endogenous state when these inputs enter the brain. Prior studies show that endogenous neural states influence stimulus processing through non-specific, global mechanisms, such as spontaneous fluctuations of arousal. It is unclear if endogenous activity influences circuit and stimulus-specific processing and behavior as well. Here we use intracranial recordings from 30 pre-surgical epilepsy patients to show that patterns of endogenous activity are related to the strength of trial-by-trial neural tuning in different visual category-selective neural circuits. The same aspects of the endogenous activity that relate to tuning in a particular neural circuit also correlate to behavioral reaction times only for stimuli from the category that circuit is selective for. These results suggest that endogenous activity can modulate neural tuning and influence behavior in a circuit- and stimulus-specific manner, reflecting a potential mechanism by which endogenous neural states facilitate and bias perception. Endogenous brain states influence perception. In this manuscript the authors use human intracranial recordings to provide mechanistic insight into this process by showing that endogenous brain activity facilitates neural tuning and behavior in a stimulus and circuit specific manner.

关键词： Perception Neural decoding Object vision

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：