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Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma

Cell reports. Medicine 2025年第6期6卷 102188页

作者： Josephine Yates Camille Mathey-Andrews Jihye Park Amanda Garza Andréanne Gagné Samantha Hoffman Kevin Bi Breanna Titchen Connor Hennessey Joshua Remland Matthew Carnes Erin Shannon Sabrina Camp Siddhi Balamurali Shweta Kiran Cavale Zhixin Li Akhouri Kishore Raghawan Agnieszka Kraft Genevieve Boland Andrew J Aguirre Nilay S Sethi Valentina Boeva Eliezer M Van Allen Institute for Machine Learning Department of Computer Science ETH Zürich Zurich Switzerland ETH AI Center ETH Zurich Zurich Switzerland Swiss Institute for Bioinformatics (SIB) Lausanne Switzerland Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA. Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Surgery Massachusetts General Hospital Boston MA USA. Cancer Program Broad Institute of MIT and Harvard Cambridge MA USA. Cancer Program Broad Institute of MIT and Harvard Cambridge MA USA Division of Medical Sciences Harvard University Boston MA USA. Penn Medicine University of Pennsylvania Philadelphia PA USA. Swiss Institute for Bioinformatics (SIB) Lausanne Switzerland. Department of Surgery Division of Gastrointestinal and Surgical Oncology Massachusetts General Hospital Harvard Medical School Boston MA USA. Division of Medical Sciences Harvard University Boston MA USA Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA. Cochin Institute Inserm U1016 CNRS UMR 8104 Paris Descartes University UMR-S1016 Paris 75014 France. Electronic address: valentina.boeva@inf.ethz.ch. Parker Institute for Cancer Immunotherapy Dana-Farber Cancer Institute Boston MA USA. Electronic address: eliezerm_vanallen@dfci.harvard.edu.

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we perform multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing along with spatial profiling. We recover tumor microenvironmental features previously described to associate with therapy response. We subsequently identify five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which are associated with differential epigenetic plasticity and clinical outcomes, and for which we infer candidate transcription factor regulons. Furthermore, we reveal diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predict their significant interactions with microenvironmental cell types. We validate our findings in three external single-cell RNA sequencing (RNA-seq) and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.

关键词： bioinformatics computational biology epigenetics esophageal adenocarcinoma gastrointestinal cancer oncology single cell spatial transcriptomics transcriptomics

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machine learning enables new insights into genetic contributions to liver fat accumulation

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CELL GENOMICS 2021年第3期1卷 100066-100066页

作者： Haas, Mary E. Pirruccello, James P. Friedman, Samuel N. Wang, Minxian Emdin, Connor A. Ajmera, Veeral H. Simon, Tracey G. Homburger, Julian R. Guo, Xiuqing Budoff, Matthew Corey, Kathleen E. Zhou, Alicia Y. Philippakis, Anthony Ellinor, Patrick T. Loomba, Rohit Batra, Puneet Khera, Amit V. Broad Inst MIT & Harvard Program Med & Populat Genet Cambridge MA 02142 USA Massachusetts Gen Hosp Dept Mol Biol Dept Med Boston MA 02114 USA Massachusetts Gen Hosp Dept Med Ctr Genom Med Boston MA 02114 USA Harvard Med Sch Dept Med Boston MA 02114 USA Massachusetts Gen Hosp Dept Med Cardiol Div Boston MA 02114 USA Broad Inst MIT & Harvard Machine Learning Hlth Cambridge MA 02142 USA Univ Calif San Diego NAFLD Res Ctr Dept Med La Jolla CA 92093 USA Massachusetts Gen Hosp Dept Med Div Gastroenterol Liver Ctr Boston MA 02114 USA Color Hlth Burlingame CA 94010 USA Broad Inst MIT & Harvard Eric & Wendy Schmidt Ctr Cambridge MA 02142 USA Harbor UCLA Med Ctr Lundquist Inst Biomed Innovat Torrance CA 90502 USA Harbor UCLA Med Ctr Dept Pediat Torrance CA 90502 USA

Excess liver fat, called hepatic steatosis, is a leading risk factor for end-stage liver disease and cardiometabolic diseases but often remains undiagnosed in clinical practice because of the need for direct imaging assessments. We developed an abdominal MRI-based machine-learning algorithm to accurately estimate liver fat (correlation coefficients, 0.97-0.99) from a truth dataset of 4,511 middle-aged UK Biobank participants, enabling quantification in 32,192 additional individuals. 17% of participants had predicted liver fat levels indicative of steatosis, and liver fat could not have been reliably estimated based on clinical factors such as BMI. A genome-wide association study of common genetic variants and liver fat replicated three known associations and identified five newly associated variants in or near the MTARC1, ADH1B, TRIB1, GPAM, and MAST3 genes (p < 3 3 10(-8)). A polygenic score integrating these eight genetic variants was strongly associated with future risk of chronic liver disease (hazard ratio > 1.32 per SD score, p < 9 3 10(-17)). Rare inactivating variants in the APOB or MTTP genes were identified in 0.8% of individuals with steatosis and conferred more than 6-fold risk (p < 2 3 10(-5)), highlighting a molecular subtype of hepatic steatosis characterized by defective secretion of apolipoprotein B-containing lipoproteins. We demonstrate that our imaging-based machine-learning model accurately estimates liver fat and may be useful in epidemiological and genetic studies of hepatic steatosis.

关键词： liver fat hepatic steatosis machine learning MRI GWAS whole-exome sequencing polygenic risk score nonalcoholic fatty liver disease NAFLD

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CIRCUIT Summer program: A Computational Neuroscience Outreach Experience for High-Achieving Undergraduates via Sponsored Research 8

CIRCUIT Summer Program: A Computational Neuroscience Outreac...

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8th IEEE Integrated STEM Education Conference (ISEC)

作者： Encarnacion, Marysol Bishop, Caitlyn Downs, Joseph Drenkow, Nathan Matelsky, Jordan K. Rivlin, Patricia K. Wester, Brock Gray-Roncal, William Johns Hopkins Bloomberg Sch Publ Hlth Int Hlth Baltimore MD 21205 USA CIRCUIT Summer Program Baltimore MD 21218 USA JHU APL REDD Baltimore MD USA CIRCUIT Visualizat Team Baltimore MD USA CIRCUIT Summer Machine Learning Team Baltimore MD USA Janelia Res Campus Project F1yEM Neuronal Tracing Ashburn VA USA MICrONS Boise ID USA JHU Comp Sci Baltimore MD USA

ISBN: (纸本)9781538633090

programs that focus on student outreach are often disjoint from sponsored research efforts, despite the mutually beneficial opportunities that are possible with a combined approach. We designed and piloted a program to simultaneously meet the needs of underserved students and a large-scale sponsored research goal. Our program trained undergraduates to produce neuron maps for a major connectomics effort (i.e., single synapse brain maps), while providing these students with the resources and mentors to conduct novel research. Students were recruited from Johns Hopkins University to participate in a ten-week summer program. These students were trained in computational research, scientific communication skills and methods to map electron microscopy volumes. The students also had regular exposure to mentors and opportunities for guided, small group, independent discovery. A learning-for-Use model was leveraged to provide the students with the tools, skills, and knowledge to pursue their research questions, while an Affinity Research Group model was adapted to provide students with mentorship in conducting cutting-edge research. A focus was placed on recruiting students who had limited opportunities and access to similar experiences. program metrics demonstrated a substantial increase in knowledge (e.g., neuroscience, graph theory, machine learning, and scientific communication), while students also showed an overall increase in awareness and responsiveness to computational research after the program. Ultimately, the program positively impacted students' career choices and research readiness, and successfully achieved sponsor goals in a compact timeframe. This framework for combining outreach with sponsored research can be broadly leveraged for other programs across domains.

关键词： learning-for-Use Affinity Research Model Computational neuroscience Connectomics Outreach

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MMLSpark: Unifying machine learning ecosystems at massive scales

arXiv

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arXiv 2018年

作者： Hamilton, Mark Raghunathan, Sudarshan Matiach, Ilya Schonhoffer, Andrew Raman, Anand Barzilay, Eli Rajendran, Karthik Banda, Dalitso Hong, Casey Jisoo Knoertzer, Manon Brodsky, Ben Thigpen, Minsoo Mahajan, Janhavi Suresh Cochrane, Courtney Eswaran, Abhiram Green, Ari Microsoft Applied AI CambridgeMA United States Microsoft Applied AI RedmondWA United States Microsoft Azure Machine Learning CambridgeMA United States Microsoft AI Development Acceleration Program CambridgeMA United States

We introduce Microsoft machine learning for Apache Spark (MMLSpark), an open-source library that expands the Apache Spark distributed computing library to tackle problems in deep learning, micro-service orchestration, gradient boosting, model interpretability, and other areas of modern machine learning. We also present a novel machine learning deployment system called Spark Serving that can deploy Apache Spark programs as distributed, sub-millisecond latency web services with significantly greater flexibility and lower latencies than existing frameworks. Spark Serving generalizes beyond just map-style computations and allows distributed aggregations, joins, and shuffles and allows users to leverage the same cluster for both training and deployment. Our contributions allow easy composition across machine learning frameworks, compute modes (batch, streaming, and RESTful web serving) and cluster types (static, elastic, and serverless). We demonstrate the value of MMLSpark by creating a method for deep object detection capable of learning without human labeled data and demonstrate its effectiveness for Snow Leopard conservation. We also demonstrate its ability to create large-scale image search engines. Copyright © 2018, The Authors. All rights reserved.

关键词： Object detection

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Sentiment analysis in organizational work: Towards an ontology of people analytics

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EXPERT SYSTEMS 2018年第5期35卷

作者： Gelbard, Roy Ramon-Gonen, Roni Carmeli, Abraham Bittmann, Ran M. Talyansky, Roman Bar Ilan Univ Grad Sch Business Adm Informat Syst Program Ramat Gan Israel Tel Aviv Univ Coller Sch Management Tel Aviv Israel Univ Surrey Business Sch Guildford Surrey England SAP Labs Machine Learning Ctr Raanana Israel

The present paper proposes a conceptual ontology to evaluate human factors by modelling their key performance indicators and defining these indicators' explanatory factors, manifestations, and diverse corresponding digital footprints. Our methodology incorporates 6 main human resource constructs: performance, engagement, leadership, workplace dynamics, organizational developmental support, and learning and knowledge creation. Using sentiment analysis, we introduce a potential way to evaluate several components of the proposed human factors ontology. We use the Enron email corpus as a test case, to demonstrate how digital footprints can predict such phenomena. In so doing, we hope to encourage further research applying data mining techniques to allow real-time, less costly, and more reliable assessments of human factor patterns and trends.

关键词： human resource management key performance indicators people analytics sentiment analysis workforce analytics

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EMPRESS. XII. Statistics on the Dynamics and Gas Mass Fraction of Extremely Metal-poor Galaxies

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ASTROPHYSICAL JOURNAL 2024年第1期961卷 49-49页

作者： Xu, Yi Ouchi, Masami Isobe, Yuki Nakajima, Kimihiko Ozaki, Shinobu Bouche, Nicolas F. Wise, John H. Emsellem, Eric Kusakabe, Haruka Hattori, Takashi Nagao, Tohru Chiaki, Gen Fukushima, Hajime Harikane, Yuichi Hayashi, Kohei Hirai, Yutaka Kim, Ji Hoon Maseda, Michael V. Nagamine, Kentaro Shibuya, Takatoshi Sugahara, Yuma Yajima, Hidenobu Aoyama, Shohei Fujimoto, Seiji Fukushima, Keita Hatano, Shun Inoue, Akio K. Ishigaki, Tsuyoshi Kawasaki, Masahiro Kojima, Takashi Komiyama, Yutaka Koyama, Shuhei Koyama, Yusei Lee, Chien-Hsiu Matsumoto, Akinori Mawatari, Ken Moriya, Takashi J. Motohara, Kentaro Murai, Kai Nishigaki, Moka Onodera, Masato Ono, Yoshiaki Rauch, Michael Saito, Tomoki Sasaki, Rin Suzuki, Akihiro Takeuchi, Tsutomu T. Umeda, Hiroya Umemura, Masayuki Watanabe, Kuria Yabe, Kiyoto Zhang, Yechi Univ Tokyo Inst Cosm Ray Res 5-1-5 Kashiwanoha Kashiwa Chiba 2778582 Japan Univ Tokyo Grad Sch Sci Dept Astron 7-3-1 Hongo Tokyo 1130033 Japan Natl Astron Observ Japan 2-21-1 Osawa Mitaka Tokyo 1818588 Japan Univ Tokyo Kavli Inst Phys & Math Universe WPI Kashiwa Chiba 2778583 Japan Univ Tokyo Grad Sch Sci Dept Phys 7-3-1 Hongo Tokyo 1130033 Japan Univ Lyon1 Univ Lyon ENS Lyon Ctr Rech Astrophys LyonCNRSUMR5574 F-69230 St Genis Laval France Georgia Inst Technol Sch Phys Ctr Relativist Astrophys Atlanta GA 30332 USA European Southern Observ Karl Schwarzschild Str 2 D-85748 Garching Germany Univ Geneva Observ Geneve 51 Ch Maillettes CH-1290 Versoix Switzerland Natl Inst Nat Sci Natl Astron Observ Japan Subaru Telescope 650 North Aohoku Pl Hilo HI 96720 USA Ehime Univ Res Ctr Space & Cosm Evolut Bunkyo Cho 2-5 Matsuyama Ehime 7908577 Japan Univ Tsukuba Ctr Computat Sci Ten Nodai 1-1-1 Tsukuba Ibaraki 3058577 Japan UCL Dept Phys & Astron Gower St London WC1E 6BT England Ichinoseki Coll Natl Inst Technol Hagisho Ichinoseki 0218511 Japan Tohoku Univ Astron Inst 6-3 AobaAoba Ku Sendai Miyagi 9808578 Japan Univ Notre Dame Dept Phys & Astron 225 Nieuwland Sci Hall Notre Dame IN 46556 USA Seoul Natl Univ Dept Phys & Astron Astron Program 1 Gwanak Ro Seoul 08826 South Korea Seoul Natl Univ SNU Astron Res Ctr 1 Gwanak Ro Seoul 08826 South Korea Univ Wisconsin Dept Astron 475 N Charter St Madison WI 53706 USA Osaka Univ Grad Sch Sci Dept Earth & Space Sci Theoret Astrophys 1-1 Machikaneyama Toyonaka Osaka 5600043 Japan Univ Nevada Dept Phys & Astron 4505 S Maryland Pkwy Las Vegas NV 89154 USA Kitami Inst Technol 165 Koen Cho Kitami Hokkaido 0908507 Japan Waseda Univ Fac Sci & Engn Waseda Res Inst Sci & Engn 3-4-1 OkuboShinjuku Tokyo 1698555 Japan Chiba Univ Inst Management & Informat Technol Inage Ward 1-33 Yayoi Cho Chiba 2638522 Japan Cosm Dawn Ctr DAWN Jagtvej 128 DK-2200 Copenhagen N Denmark Univ Copenhagen

We present the demography of the dynamics and gas mass fraction of 33 extremely metal-poor galaxies (EMPGs) with metallicities of 0.015-0.195 Z(circle dot) and low stellar masses of 10(4)-10(8)M(circle dot) in the local universe. We conduct deep optical integral field spectroscopy (IFS) for the low-mass EMPGs with the medium-high resolution (R = 7500) grism of the 8 m Subaru FOCAS IFU instrument by the EMPRESS 3D survey, and investigate the H alpha emission of the EMPGs. Exploiting the resolution high enough for the low-mass galaxies, we derive gas dynamics with the H alpha lines by the fitting of three-dimensional disk models. We obtain an average maximum rotation velocity (v(rot)) of 15 +/- 3 km s(-1) and an average intrinsic velocity dispersion (sigma(0)) of 27 +/- 10 km s(-1) for 15 spatially resolved EMPGs out of 33 EMPGs, and find that all 15 EMPGs have v(rot)/sigma(0) < 1 suggesting dispersion-dominated systems. There is a clear decreasing trend of v(rot)/sigma(0) with the decreasing stellar mass and metallicity. We derive the gas mass fraction (f(gas)) for all 33 EMPGs, and find no clear dependence on stellar mass and metallicity. These v(rot)/sigma(0) and f(gas) trends should be compared with young high-z galaxies observed by the forthcoming JWST IFS programs to understand the physical origins of the EMPGs in the local universe.

关键词： Dwarf galaxies (416) Galaxy dynamics (591) Galaxy kinematics (602) Unified Astronomy Thesaurus concepts: Galaxy evolution (594)

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Semi-Supervised Techniques for Mining learning Outcomes and Prerequisites 17

Semi-Supervised Techniques for Mining Learning Outcomes and ...

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23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Labutov, Igor Huang, Yun Brusilovsky, Peter He, Daqing Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Sch Informat Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781450348874

Educational content of today no longer only resides in textbooks and classroom;more and more learning material is found in a free, accessible form on the Internet. Our long-standing vision is to transform this web of educational content into an adaptive, web-scale "textbook", that can guide its readers to most relevant "pages" according to their learning goal and current knowledge. In this paper, we address one core, long-standing problem towards this goal: identifying outcome and prerequisite concepts within a piece of educational content (e.g., a tutorial). Specifically, we propose a novel approach that leverages textbooks as a source of distant supervision, but learns a model that can generalize to arbitrary documents (such as those on the web). As such, our model can Lake advantage of any existing textbook, without requiring expert annotation. At the task of predicting outcome and prerequisite concepts, we demonstrate improvements over a number of baselines on six textbooks, especially in the regime of little to no ground-truth labels available(1). Finally, we demonstrate the utility of a model learned using our approach at the task of identifying prerequisite documents for adaptive content recommendation - an important step towards our vision of the "web as a textbook".

关键词： semi-supervised machine learning graphical models adaptive hypermedia open corpus adaptive hypermedia educational data mining

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Annotating unknown species of urban microorganisms on a global scale unveils novel functional diversity and local environment association

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ENVIRONMENTAL RESEARCH 2022年 207卷 112183页

作者： Wu, Jun Danko, David Afshinnekoo, Ebrahim Bezdan, Daniela Bhattacharyya, Malay Castro-Nallar, Eduardo Chmielarczyk, Agnieszka Hazrin-Chong, Nur Hazlin Deng, Youping Dias-Neto, Emmanuel Frolova, Alina Mason-Buck, Gabriella Iraola, Gregorio Jang, Soojin Labaj, Pawel Lee, Patrick K. H. Nieto-Caballero, Marina Osuolale, Olayinka O. Ouzounis, Christos A. Perlin, Michael H. Prithiviraj, Bharath Rascovan, Nicolas Rozanska, Anna Schriml, Lynn M. Semmler, Torsten Suzuki, Haruo Ugalde, Juan A. Young, Ben Werner, Johannes Zambrano, Maria Mercedes Zhao, Yongxiang Mason, Christopher Shi, Tieliu East China Normal Univ Ctr Bioinformat & Computat Biol Shanghai 200241 Peoples R China East China Normal Univ Sch Life Sci Inst Biomed Sci Shanghai 200241 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing 100083 Peoples R China Capital Med Univ Beijing 100083 Peoples R China Weill Cornell Med New York NY 10065 USA Bin Talal Bin Abdulaziz Alsaud Inst Computat Biom New York NY USA Indian Stat Inst Machine Intelligence Unit Kolkata India Indian Stat Inst Ctr Artificial Intelligence & Machine Learning Kolkata India Univ Andres Bello Fac Ciencias Vida Ctr Bioinformat & Integrat Biol Buenos Aires DF Argentina Jagiellonian Univ Fac Med Dept Microbiol Krakow Poland Univ Kebangsaan Malaysia Fac Sci & Technol Dept Biol Sci & Biotechnol Ukm Bangi 43600 Selangor Malaysia Univ Hawaii John A Burns Sch Mecidine Honolulu HI 96822 USA Univ Sao Paulo AC Camargo Canc Ctr Med Genom Grp Sao Paulo Brazil Univ Sao Paulo LIM 27 Fac Med Sao Paulo Brazil Natl Acad Sci Ukraine Inst Mol Biol & Genet Kiev Ukraine Kings Coll London Dept Analyt Environm & Forens Sci London England Inst Pasteur Montevideo Microbial Genom Lab Montevideo Uruguay Univ Mayor Ctr Integrat Biol Santiago Chile Wellcome Sanger Inst Hinxton England Inst Pasteur Korea Seongnam South Korea Jagiellonian Univ Malopolska Ctr Biotechnol Krakow Poland City Univ Hong Kong Sch Energy & Environm Hong Kong Peoples R China Univ Colorado Civil Environm & Architectural Dept Boulder CO 80303 USA Elizade Univ Dept Biol Sci Appl Environm Metagen & Infect Dis Res AEMIDR Ilara Mokin Nigeria Ctr Res & Technol Hellas BCPL CPERI GR-57001 Thessaloniki Greece Univ Louisville Dept Biol Program Dis Evolut Louisville KY 40292 USA Reckitt Hlth Montvale NJ USA Aix Marseille Univ AP HM IHU Mediterranee Infect IRD Marseille France Univ Maryland Inst Genome Sci Sch Med College Pk MD 20742 USA Robert Koch Inst Berlin Berlin Germany Keio Univ Fac Environm & Informat Studie

In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The cooccurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment.

关键词： Urban environmental microbiome Metagenomic analysis Novel species uncovering Functional diversity Biosynthetic gene clusters

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Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex

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CELL 2017年第7期171卷 1663-+页

作者： Murugan, Malavika Jang, Hee Jae Park, Michelle Miller, Ellia M. Cox, Julia Taliaferro, Joshua P. Parker, Nathan F. Bhave, Varun Hur, Hong Liang, Yupu Nectow, Alexander R. Pillow, Jonathan W. Witten, Ilana B. Princeton Univ Princeton Neurosci Inst Princeton NJ 08544 USA Princeton Univ Dept Psychol Princeton NJ 08544 USA Princeton Univ Ctr Stat & Machine Learning Princeton NJ 08544 USA Rockefeller Univ CCTS Bioinformat Program New York NY 10065 USA

Social behaviors are crucial to all mammals. Although the prelimbic cortex (PL, part of medial prefrontal cortex) has been implicated in social behavior, it is not clear which neurons are relevant or how they contribute. We found that PL contains anatomically and molecularly distinct subpopulations that target three downstream regions that have been implicated in social behavior: the nucleus accumbens (NAc), amygdala, and ventral tegmental area. Activation of NAc-projecting PL neurons (PL-NAc), but not the other subpopulations, decreased the preference for a social target. To determine what information PL-NAc neurons convey, we selectively recorded from them and found that individual neurons were active during social investigation, but only in specific spatial locations. Spatially specific manipulation of these neurons bidirectionally regulated the formation of a social-spatial association. Thus, the unexpected combination of social and spatial information within the PL-NAc may contribute to social behavior by supporting social-spatial learning.

关键词： social behavior medial prefrontal cortex nucleus accumbens calcium imaging optogenetics

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The SCUBA-2 Large eXtragalactic Survey: 850 μm map, catalogue and the bright-end number counts of the XMM-LSS field

arXiv

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arXiv 2023年

作者： Garratt, T.K. Geach, J.E. Tamura, Y. Coppin, K.E.K. Franco, M. Ao, Y. Chen, C.-C. Cheng, C. Clements, D.L. Dai, Y.S. Dannerbauer, H. Greve, T.R. Hatsukade, B. Hwang, H.S. Jiang, L. Kohno, K. Koprowski, M.P. Michalowski, M.J. Sawicki, M. Scott, D. Shim, H. Takeuchi, T.T. Wang, W.-H. Xue, Y.Q. Yang, C. Centre for Astrophysics Research University of Hertfordshire HatfieldAL10 9AB United Kingdom Division of Particle and Astrophysical Science Nagoya University Aichi Nagoya464-8602 Japan Purple Mountain Observatory Key Laboratory for Radio Astronomy Chinese Academy of Sciences 10 Yuanhua Road Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Anhui Hefei230026 China Academia Sinica Institute of Astronomy and Astrophysics No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Chinese Academy of Sciences South America Center for Astronomy National Astronomical Observatories CAS Beijing100101 China Imperial College London Blackett Lab Prince Consort Road LondonSW7 2AZ United Kingdom National Astronomical Observatories Chinese Academy of Sciences 20A Datun RoadChaoyang District Beijing100101 China Tenerife La LagunaE-38205 Spain Universidad de La Laguna Dpto. Astrofísica Tenerife La LagunaE-38206 Spain National Space Institute DTU Space Technical University of Denmark Elektrovej 327 Kgs. LyngbyDK-2800 Denmark Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Tokyo Mitaka181-0015 Japan Astronomy Program Department of Physics and Astronomy Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of SNU Astronomy Research Center Astronomy Program Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Kavli Institute for Astronomy and Astrophysics Peking University No. 5 Yiheyuan Road Haidian District Beijing100871 China Institute of Astronomy Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University Grudziadzka 5 Torun87-100 Poland Astronomical Observatory Institute Faculty of Physics Adam Mickiewicz University ul. Sloneczna 36 Poznań60-286 Poland Department of Astronomy and P

We present 850 μm imaging of the XMM-LSS field observed for 170 hours as part of the James Clerk Maxwell Telescope SCUBA-2 Large eXtragalactic Survey (S2LXS). S2LXS XMM-LSS maps an area of 9 deg2, reaching a moderate depth of 1σ 4 mJy beam−1. This is the largest contiguous area of extragalactic sky mapped by JCMT at 850 μm to date. The wide area of the S2LXS XMM-LSS survey allows us to probe the ultra-bright (S850μm & 15 mJy), yet rare submillimetre population. We present the S2LXS XMM-LSS catalogue, which comprises 40 sources detected at >5σ significance, with deboosted flux densities in the range of 7 mJy to 48 mJy. We robustly measure the bright-end of the 850 μm number counts at flux densities >7 mJy, reducing the Poisson errors compared to existing measurements. The S2LXS XMM-LSS observed number counts show the characteristic upturn at bright fluxes, expected to be motivated by local sources of submillimetre emission and high-redshift strongly lensed galaxies. We find that the observed 850 μm number counts are best reproduced by model predictions that include either strong lensing or source blending from a 15 arcsec beam, indicating that both may make an important contribution to the observed over-abundance of bright single dish 850 μm selected sources. We make the S2LXS XMM-LSS 850 μm map and >5σ catalogue presented here publicly available. Copyright © 2023, The Authors. All rights reserved.

关键词： Blending

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