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38th International Cosmic Ray Conference, ICRC 2023

作者： Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, d. Bernardini, E. Besson, d.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, d. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, d. Choi, S. Clark, B.A. Classen, l. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, d.F. dave, P. de Clercq, C. delaunay, J.J. delgado, d. deng, S. deoskar, K. desai, A. desiati, P. de Vries, K.d. de Wasseige, G. deYoung, T. diaz, A. díaz-Vélez, J.C. dittmer, M. domi, A. dujmovic, H. duVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, d. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.l. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, l. Fox, d. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, l. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, d. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, l. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, l. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.d. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Jeong, M. III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen CopenhagenDK-2100 Denmark Dept. of Physics TU Dortmund University DortmundD-44221 Germany Dept. of Physics and Astronomy M

Neutron stars with very strong magnetic fields are known as magnetars. There are multiple theories that predict magnetars may be able to emit high-energy (HE) neutrinos through hadronic processes by accelerating cosmic rays to high energies. A subclass of magnetars known as soft gamma-ray repeaters (SGRs) can produce giant flares that can result in the production of HE neutrinos. Some magnetars also exhibit bursting activity during which they may emit HE neutrinos. Here we describe our time-integrated search for neutrino emission from magnetars listed in the McGill Online Magnetar Catalog and three newly discovered magnetars SGR 1830-0645, Swift J1555.5-5402, and NGC 253. SGR 1830-0645 and Swift J1555.2-5402 were discovered in 2020 and 2021 respectively by SWIFT after emitting short bursts. A very bright short gamma-ray burst that is believed to be a magnetar giant flare has been localized to NGC 253. We use 14 years of well-reconstructed muon-neutrino candidate events collected by the IceCube Neutrino Observatory to look for significant clustering in the direction of magnetars. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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A Two-Component lateral distribution Function for the Reconstruction of Air-Shower Events in IceTop

A Two-Component Lateral Distribution Function for the Recons...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Soldin, dennis Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, d. Bernardini, E. Besson, d.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, d. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, d. Choi, S. Clark, B.A. Classen, l. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, d.F. dave, P. de Clercq, C. delaunay, J.J. delgado, d. deng, S. deoskar, K. desai, A. desiati, P. de Vries, K.d. de Wasseige, G. deYoung, T. diaz, A. díaz-Vélez, J.C. dittmer, M. domi, A. dujmovic, H. duVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, d. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.l. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, l. Fox, d. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, l. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, d. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, l. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, l. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.d. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen CopenhagenDK-2100 Denmark Dept. of Physics TU Dortmund University DortmundD-44221 Germany Dept. of Physics and Astro

The surface component of the IceCube Neutrino Observatory, IceTop, consists of an array of ice-Cherenkov tanks measuring the electromagnetic signal as well as low-energy (∼ GeV) muons from cosmic-ray air showers. In addition, accompanying high-energy (above a few 100 GeV) muons can be observed in coincidence in the deep in-ice detector. A combined measurement of the low- and high-energy muon content is of particular interest for tests of hadronic interaction models as well as for cosmic-ray mass discrimination. However, since IceTop does not feature dedicated muon detectors, an estimation of the low-energy muon component of individual air showers is challenging. In this work, a two-component lateral distribution function (ldF), using separate descriptions for the electromagnetic and muon lateral distributions of the detector signals, is introduced as a new approach for the estimation of low-energy muons in air showers on an event-by-event basis. The principle of the air-shower reconstruction using the two-component ldF, as well as its reconstruction performance with respect to primary energy and number of low-energy muons will be discussed. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Analysis of Friedreich's ataxia patient clinical data reveals importance of accurate GAA repeat determination in disease prognosis and gender differences in cardiac measures

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Informatics in Medicine Unlocked 2019年 17卷

作者： Ghorbani, Mohammadmersad Pousset, Françoise Tucker, Allan Swift, Stephen Giunti, Paola Parkinson, Michael Gilbert, david liu, XiaoHui Payne, Annette Dept. of Computer Science College of Engineering Design and Physical Brunel University London United Kingdom Département de Cardiologie La Pitié Salpêtrière Paris France Department of Molecular Neuroscience Institute of Neurology UCL London United Kingdom

Friedreich's ataxia (FRdA) is a rare autosomal recessive inherited neurodegenerative disease which is the result of a triplet repeat expansion in the intronic region of the frataxin FXN gene resulting in depleted frataxin protein expression. disease onset is usually in childhood and causes progressive damage to the nervous system resulting in progressive disability. This work uses computer aided classification techniques to identify which measures of the disease progression, including accurate determination of the shortest allele repeat length, are the most informative when trying to predict likely disease progression and prognosis. Further we investigate the possibility of a gender difference in the progression of the disease. Our results highlight the importance of accurate determination GAA repeat length in any clinical predictions showing that the number of repeats is the best prognostic tool in FRdA and is strongly linked to the age at onset disease. Further that there are possible gender dependent differences in cardiac measurements recorded from patients of similar age of onset and GAA repeat length. © 2019

关键词： Clinical prognosis computerised classification Friedrich's ataxia Intelligent data analysis Neurology Nucleotide repeat

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IceCube search for neutrinos from GRB 221009A

IceCube search for neutrinos from GRB 221009A

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38th International Cosmic Ray Conference, ICRC 2023

作者： Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, d. Bernardini, E. Besson, d.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, d. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, d. Choi, S. Clark, B.A. Classen, l. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, d.F. dave, P. de Clercq, C. delaunay, J.J. delgado, d. deng, S. deoskar, K. desai, A. desiati, P. de Vries, K.d. de Wasseige, G. deYoung, T. diaz, A. díaz-Vélez, J.C. dittmer, M. domi, A. dujmovic, H. duVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, d. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.l. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, l. Fox, d. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, l. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, d. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, l. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, l. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.d. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Jeong, M. III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen CopenhagenDK-2100 Denmark Dept. of Physics TU Dortmund University DortmundD-44221 Germany Dept. of Physics and Astro

GRB 221009A is the brightest Gamma Ray Burst (GRB) ever observed. The observed extremely high flux of high and very-high-energy photons provide a unique opportunity to probe the predicted neutrino counterpart to the electromagnetic emission. We have used a variety of methods to search for neutrinos in coincidence with the GRB over several time windows during the precursor, prompt and afterglow phases of the GRB. MeV scale neutrinos are studied using photo-multiplier rate scalers which are normally used to search for galactic core-collapse supernovae neutrinos. GeV neutrinos are searched starting with deepCore triggers. These events don’t have directional localization, but instead can indicate an excess in the rate of events. 10 GeV - 1 TeV and >TeV neutrinos are searched using traditional neutrino point source methods which take into account the direction and time of events with deepCore and the entire IceCube detector respectively. The >TeV results include both a fast-response analysis conducted by IceCube in real-time with time windows of T0 - 1 to T0 + 2 hours and T0 ± 1 day around the time of GRB 221009A, as well as an offline analysis with 3 new time windows up to a time window of T0 - 1 to T0 + 14 days, the longest time period we consider. The combination of observations by IceCube covers 9 orders of magnitude in neutrino energy, from MeV to PeV, placing upper limits across the range for predicted neutrino emission. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Supernovae

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Photocurrent imaging of multi-memristive charge density wave switching in two-dimensional 1T-TaS2

arXiv

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arXiv 2020年

作者： Patel, Tarun Okamoto, Junichi dekker, Tina Yang, Bowen Gao, Jingjing luo, Xuan lu, Wenjian Sun, Yuping Tsen, Adam W. Institute for Quantum Computing Department of Physics and Astronomy Department of Electrical and Computer Engineering Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics University of Freiburg FreiburgD-79104 Germany Key Laboratory of Materials Physics Institute of Solid State Physics Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China High Magnetic Field Laboratory Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Centre of Advanced Microstructures Nanjing University Nanjing210093 China

Transport studies of atomically thin 1T-TaS2 have demonstrated the presence of intermediate resistance states across the nearly commensurate (NC) to commensurate (C) charge density wave (CdW) transition, which can be further switched electrically. While this presents exciting opportunities for the material in memristor applications, the switching mechanism has remained elusive and could be potentially attributed to the formation of inhomogeneous C and NC domains across the 1T-TaS2 flake. Here, we present simultaneous electrical driving and scanning photocurrent imaging of CdWs in ultrathin 1T-TaS2 using a vertical heterostructure geometry. While micron-sized CdW domains form upon changing temperature, electrically driven transitions result in largely uniform changes, indicating that states of intermediate resistance for the latter likely correspond to true metastable CdW states in between the NC and C phases, which we then explain by a free energy analysis. Additionally, we are able to perform repeatable and bidirectional switching across the multiple CdW states without changing sample temperature, demonstrating that atomically thin 1T-TaS2 can be further used as a robust and reversible multi-memristor material. Copyright © 2020, The Authors. All rights reserved.

关键词： Memristors

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The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study

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The lancet Planetary Health 2022年第4期6卷 e350-e358页

作者： Wang, Zengmiao liu, Yonghong li, Yapin Wang, Guangze lourenço, José Kraemer, Moritz He, Qixin Cazelles, Bernard li, Yidan Wang, Ruixue Gao, dongqi li, Yuchun Song, Wenjing Sun, dingwei dong, lu Pybus, Oliver G Stenseth, Nils Chr Tian, Huaiyu State Key Laboratory of Remote Sensing Science Center for Global Change and Public Health College of Global Change and Earth System Science Beijing Normal University Beijing China Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering College of Life Sciences Beijing Normal University Beijing China School of National Security and Emergency Management Beijing Normal University Beijing China Central Theater Center for Disease Control and Prevention of PLA Beijing China Hainan Center for Disease Control and Prevention Haikou China Department of Zoology University of Oxford UK Oxford United Kingdom Harvard Medical School Harvard University Boston MA United States Boston Children's Hospital Boston MA United States Department of Ecology and Evolution University of Chicago Chicago IL United States Institut de Biologie de l’École Normale Supérieure Unité Mixte de Recherche 8197 Centre National de la Recherche Scientifique et École Normale Supérieure Paris France Unité Mixte Internationnale 209 Mathematical and Computational Modeling of Complex Systems Institut de Recherche pour le Développement et Sorbonne Université Bondy France Centre for Ecological and Evolutionary Synthesis Department of Biosciences University of Oslo Oslo Norway Biosystems and Integrative Sciences Institute University of Lisbon Lisbon Portugal Department of Pathobiology and Population Science The Royal Veterinary College UK London United Kingdom

Background: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. Methods: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984–2010 and the WorldClim dataset. A temperature-dependent R₀ model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. Findings: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from –5·6% (95% CI –4·5 to –6·6) to –9·2% (95% CI –7·6 to –10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. Interpretation: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. Funding: S

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The status of the human gene catalogue

arXiv

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arXiv 2023年

作者： Amaral, Paulo Carbonell-Sala, Silvia de la Vega, Francisco M. Faial, Tiago Frankish, Adam Gingeras, Thomas Guigo, Roderic Harrow, Jennifer l. Hatzigeorgiou, Artemis G. Johnson, Rory Murphy, Terence d. Pertea, Mihaela Pruitt, Kim d. Pujar, Shashikant Takahashi, Hazuki Ulitsky, Igor Varabyou, Ales Wells, Christine A. Yandell, Mark Carninci, Piero Salzberg, Steven l. INSPER Institute of Education and Research SP São Paulo Brazil Dr. Aiguader 88 Catalonia Barcelona08003 Spain Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Tempus Labs Inc. ChicagoIL United States Nature Genetics San FranciscoCA United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Department of Functional Genomics Cold Spring Harbor Laboratory Cold Spring HarborNY United States Catalonia Barcelona Spain Centre for Genomics Research Discovery Sciences AstraZeneca Da Vinci Building. Melbourn Science Park RoystonSG8 6HB United Kingdom Universithy of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Hellenic Pasteur Institute Athens Greece School of Biology and Environmental Science University College Dublin DublinD04 V1W8 Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin DublinD04 V1W8 Ireland Department of Medical Oncology Inselspital Bern University Hospital University of Bern Bern3010 Switzerland Department for BioMedical Research University of Bern Bern3008 Switzerland National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD20894 United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Transcriptome Technology RIKEN Center for Integrative Medical Sciences Kanagawa Yokohama230-0045 Japan Department of Immunology and Regenerative Biology Department of Molecular Neuroscience Weizmann Institute of Science Rehovot76100 Israel Department of Computer Science Johns Hopkins University BaltimoreMD United States Stem Cell Systems Department of Anatomy and Physiology Faculty of Medicine Dentistry and Health Scien

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants. © 2023, CC BY.

关键词： Genes

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State of the climate in 2022

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Bulletin of the American Meteorological Society 2023年第9期104卷 1-516页

作者： Blunden, Jessica Adusumilli, Susheel Agyakwah, W. Abida, A. Ades, Melanie Adler, Robert Alfaro, Eric J. Allan, Richard P. Allgood, Adam Alves, lincoln M. Amador, Jorge A. Anderson, John Andrade, B. Anneville, Orlane Aono, Yasuyuki Arguez, Anthony Arosio, Carlo Atkinson, C. Augustine, John A. Avalos, Grinia Azorin-Molina, Cesar Backensto, Stacia A. Bader, Stephan Baez, Julian Baiman, Rebecca Ballinger, Thomas J. Banwell, Alison F. Bardin, M.Yu. Barichivich, Jonathan Barnes, John E. Barreira, Sandra Beadling, Rebecca l. Beck, Hylke E. Becker, Emily J. Bekele, E. Bellido, Guillem Martín Bellouin, Nicolas Benedetti, Angela Benestad, Rasmus Berne, Christine Berner, logan T. Bernhard, Germar H. Bhatt, Uma S. Bhuiyan, A.E. Bigalke, Siiri Biló, Tiago Bissolli, Peter Bjerke Jarle, W. Blagrave, Kevin Blake, Eric S. Blenkinsop, Stephen Bochníček, Oliver Bock, Olivier Bodin, Xavier Bosilovich, Michael Boucher, Olivier Bozkurt, deniz Brettschneider, Brian Bringas, Francis G. Bringas, Francis Buechler, dennis Buehler, Stefan A. Bukunt, Brandon Calderón, Blanca Camargo, Suzana J. Campbell, Jayaka Campos, diego Carrea, laura Carter, Brendan R. Cetinić, Ivona Chambers, don P. Chan, duo Chandler, Elise Chang, Kai-lan Chen, Hua Chen, lin Cheng, lijing Cheng, Vincent Y.S. Chomiak, leah Christiansen, Hanne H. Christy, John R. Chung, Eui-Seok Ciasto, laura M. Clarke, leonardo Clem, Kyle R. Clingan, Scott Coelho, Caio A.S. Cohen, Judah l. Coldewey-Egbers, Melanie Colwell, Steve Cooper, Owen R. Cornes, Richard C. Correa, Kris Costa, Felipe Covey, Curt Coy, lawrence Créatux, Jean-François Crhova, lenka Crimmins, Theresa Cronin, Meghan F. Cropper, Thomas Crotwell, Molly Culpepper, Joshua Cunha, Ana P. Cusicanqui, diego datta, Rajashree T. davis, Sean M. de Bock, Veerle de Jeu, Richard A.M. de laat, Jos decharme, Bertrand degenstein, doug delaloye, Reynald demircan, Mesut derksen, Chris deus, Ricardo dhurmea, K.R. diamond, Howard J. dirkse, S. divine, dmitry dokulil, Martin T. donat, Markus G. dong, Shenfu dorigo, Wouter A. drost Jensen, C Agence Nationale de l’Aviation Civile et de la Météorologie Moroni Comoros European Centre for Medium-Range Weather Forecasts Reading United Kingdom CMNS-Earth System Science Interdisciplinary Center University of Maryland College ParkMD United States Scripps-Institution of Oceanography University of California La Jolla San DiegoCA United States NOAA NWS National Centers for Environmental Prediction Climate Prediction Center College ParkMD United States Kawerak Inc. NomeAK United States Servicio Meteorológico Nacional Buenos Aires Argentina European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research School of Physics University of Costa Rica San José Costa Rica Department of Meteorology National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Hampton University HamptonVA United States Seychelles Meteorological Authority Mahe Seychelles CARRTEL Université Savoie Mont Blanc Chambéry France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information AshevilleNC United States University of Bremen Bremen Germany Met Office Hadley Center Exeter United Kingdom NOAA Global Monitoring Laboratory BoulderCO United States Servicio Nacional de Meteorología e Hidrología del Perú Lima Peru Valencia Spain National Park Service FairbanksAK United States Federal Office of Meteorology and Climatology MeteoSwiss Switzerland Universidad Católica Nuestra Señora de la Asunción Asunción Paraguay Department of Atmospheric and Oceanic Sciences University of Colorado Boulder BoulderCO United States International Arctic Research Center University of Alaska Fairbanks FairbanksAK United States University of Colorado Boulder BoulderCO United States Yu. A. Izrael Institute of Global Climate and Ecology Institute of Geography Russian Academy of

Earth’s global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the "triple dip" la Niña conditions that prevailed in the eastern Pacific Ocean nearly continuously from mid-2020 through all of 2022;by regional phenomena such as the positive winter and summer North Atlantic Oscillation that impacted weather in parts the Northern Hemisphere and the negative Indian Ocean dipole that impacted weather in parts of the Southern Hemisphere;and by more localized systems such as high-pressure heat domes that caused extreme heat in different areas of the world. Underlying all these natural short-term variabilities are long-term climate trends due to continuous increases since the beginning of the Industrial Revolution in the atmospheric concentrations of Earth’s major greenhouse gases. In 2022, the annual global average carbon dioxide concentration in the atmosphere rose to 417.1±0.1 ppm, which is 50% greater than the pre-industrial level. Global mean tropospheric methane abundance was 165% higher than its pre-industrial level, and nitrous oxide was 24% higher. All three gases set new record-high atmospheric concentration levels in 2022. Sea-surface temperature patterns in the tropical Pacific characteristic of la Niña and attendant atmospheric patterns tend to mitigate atmospheric heat gain at the global scale, but the annual global surface temperature across land and oceans was still among the six highest in records dating as far back as the mid-1800s. It was the warmest la Niña year on record. Many areas observed record or near-record heat. Europe as a whole observed its second-warmest year on record, with sixteen individual countries observing record warmth at the national scale. Records were shattered across the continent during the summer months as heatwaves plagued the region. On 18 July, 104 stations in France broke their all-time records. One day later, England recorded a temperature of 40°C

关键词： Atmospheric temperature

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diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

arXiv

引用

arXiv 2021年

作者： Fyles, Martyn Vihta, Karina-doris Sudre, Carole H. long, Harry das, Rajenki Jay, Caroline Wingfield, Tom Cumming, Fergus Green, William Hadjipantelis, Pantelis Kirk, Joni Steves, Claire J. Ourselin, Sebastien Medley, Graham F. Fearon, Elizabeth House, Thomas Department of Mathematics University of Manchester Manchester United Kingdom The Alan Turing Institute for Data Science and Artificial Intelligence LondonNW1 2DB United Kingdom United Kingdom Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Engineering University of Oxford Oxford United Kingdom National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance The University of Oxford Oxford United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom MRC Unit for Lifelong Health and Ageing University College London London United Kingdom Department of Computer Science University of Manchester Oxford Road ManchesterM13 9PL United Kingdom Department of Clinical Sciences and International Public Health Liverpool School of Tropical Medicine LiverpoolL3 5QA United Kingdom Tropical and Infectious Disease Unit Liverpool University Hospitals NHS Foundation Trust LiverpoolL7 8XP United Kingdom WHO Collaborating Centre on Tuberculosis and Social Medicine Department of Global Public Health Karolinska Institutet Stockholm171 77 Sweden Department of Twin Research Genetic Epidemiology King’s College London London United Kingdom Department of Ageing and Health Guy’s and St Thomas’ NHS Foundation Trust Centre for the Mathematical Modelling of Infectious Disease London School of Hygiene and Tropical Medicine LondonWC1E 7HT United Kingdom Department of Global Health and Development London School of Hygiene and Tropical Medicine LondonWC1E 7HT United Kingdom Institute for Global Health University College London WC1E 6BT United Kingdom IBM Research Hartree Centre DaresburyWA4 4AD United Kingdom

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVId-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVId-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom

关键词： Coronavirus

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Trust evaluation mechanism for user recruitment in mobile crowd-sensing in the internet of things

arXiv

引用

arXiv 2019年

作者： Truong, Nguyen Binh lee, Gyu Myoung Um, Tai-Won Mackay, Michael Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science Liverpool John Moores University LiverpoolL3 3AF United Kingdom Department of Information and Communication Engineering Chosun University Gwangju Korea Republic of

Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high-quality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism

关键词： Internet of things

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