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A Versatile Strategy for Screening Custom-Designed Warhead-Armed Cyclic Peptide Inhibitors

Angewandte Chemie 2022年第7期135卷

作者： Deokhee Kang Do-Wook Kim Joo-Chan Kim Prof. Hee-Sung Park Department of Chemistry Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon 34141 Korea Contribution: Conceptualization (equal) Data curation (equal) Methodology (equal) Validation (equal) Writing - original draft (equal) Contribution: Conceptualization (equal) Data curation (equal) Methodology (equal) Validation (equal) Writing - original draft (supporting) Contribution: Formal analysis (supporting) Methodology (supporting) Visualization (supporting) Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Funding acquisition (lead) Investigation (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - original draft (lead) Writing - review & editing (lead)

Demand for peptide-based pharmaceuticals has been steadily increasing, but only limited success has been achieved to date. To expedite peptide-based drug discovery, we developed a general scheme for cell-based screening of cyclic peptide inhibitors armed with a user-designed warhead. We combined unnatural amino acid incorporation and split intein-mediated peptide cyclization techniques and integrated a yeast-based colorimetric screening assay to generate a new scheme that we call the custom-designed warhead-armed cyclic peptide screening platform (CWCPS). This strategy successfully discovered a potent inhibitor, CY5-6Q, that targets human histone deacetylase 8 (HDAC8) with a K D value of 15 nM. This approach can be a versatile and general platform for discovering cyclic peptide inhibitors.

关键词： Cyclic Peptide Inhibitor Genetic Code Expansion Warhead-Bearing Unnatural Amino Acid

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Taking the pulse of COVID-19:a spatiotemporal perspective

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International Journal of Digital Earth 2020年第10期13卷 1186-1211页

作者： Chaowei Yang Dexuan Sha Qian Liu Yun Li Hai Lan Weihe Wendy Guan Tao Hu Zhenlong Li Zhiran Zhang John Hoot Thompson Zifu Wang David Wong Shiyang Ruan Manzhu Yu Douglas Richardson Luyao Zhang Ruizhi Hou You Zhoua Cheng Zhong Yifei Tian Fayez Beaini Kyla Carte Colin Flynn Wei Liu Dieter Pfoser Shuming Bao Mei Li Haoyuan Zhang Chunbo Liu Jie Jiang Shihong Du Liang Zhao Mingyue Lu Lin Li Huan Zhou Andrew Ding NSF Spatiotemporal Innovation Center George Mason Univ.FairfaxVAUSA Department of Geography and GeoInformation Science George Mason Univ.FairfaxVAUSA Department of Geographical Sciences University of MarylandCollege ParkMDUSA Center for Geographic Analysis Harvard UniversityCambridgeMAUSA Geoinformation and Big Data Research Lab Department of GeographyUniversity of South CarolinaColumbiaSCUSA Chinese Academy of Surveying and Mapping BeijingPeople’s Republic of China School of Resource and Environmental Sciences Wuhan UniversityWuhanPeople’s Republic of China Cloud Computeand Storage OperationsInformation Technology ServicesGeorge Mason UniversityFairfaxVAUSA Department of Geography Pennsylvania State UniversityState CollegePAUSA School of Business Management East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences East China Normal UniversityShanghaiPeople’s Republic of China Department of Psychology George Mason UniversityFairfaxVAUSA mDepartment of Computer Science George Mason UniversityFairfaxVAUSA Department of Information Technology George Mason UniversityFairfaxVAUSA Department of Biology George Mason UniversityFairfaxVAUSA College of Land Science and Technology China Agricultural UniversityBeijingPeople’s Republic of China China Data Institute Ann ArborMIUSA Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and ArchitectureBeijingPeople’s Republic of China School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjingPeople’s Republic of China School of Geodesy and Geomatics Wuhan UniversityWuhanPeople’s Republic of China wFaculty of Environment University of WaterlooWaterlooCanada

The sudden outbreak of the Coronavirus disease(COVID-19)swept across the world in early 2020,triggering the lockdowns of several billion people across many countries,including China,Spain,India,the U.K.,Italy,France,Germany,Brazil,Russia,and the *** transmission of the virus accelerated rapidly with the most confirmed cases in the U.S.,India,Russia,and *** response to this national and global emergency,the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis,for supporting research,saving lives,and protecting the health of global *** perspective paper presents our collective view on the global health emergency and our effort in collecting,analyzing,and sharing relevant data on global policy and government responses,human mobility,environmental impact,socioeconomical impact;in developing research capabilities and mitigation measures with global scientists,promoting collaborative research on outbreak dynamics,and reflecting on the dynamic responses from human societies.

关键词： Big data Earth system emergency geospatial sciences epidemics applications

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Measurement of the photon polarization in Λb0→Λγ decays

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Physical Review D 2022年第5期105卷 L051104-L051104页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellán Beteta F. Abudinén T. Ackernley B. Adeva M. Adinolfi H. Afsharnia C. Agapopoulou C. A. Aidala S. Aiola Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero Z. Aliouche G. Alkhazov P. Alvarez Cartelle S. Amato J. L. Amey Y. Amhis L. An L. Anderlini A. Andreianov M. Andreotti F. Archilli A. Artamonov M. Artuso K. Arzymatov E. Aslanides M. Atzeni B. Audurier S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura W. Baldini J. Baptista Leite M. Barbetti R. J. Barlow S. Barsuk W. Barter M. Bartolini F. Baryshnikov J. M. Basels S. Bashir G. Bassi B. Batsukh A. Battig A. Bay A. Beck M. Becker F. Bedeschi I. Bediaga A. Beiter V. Belavin S. Belin V. Bellee K. Belous I. Belov I. Belyaev G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet D. Berninghoff H. C. Bernstein C. Bertella A. Bertolin C. Betancourt F. Betti Ia. Bezshyiko S. Bhasin J. Bhom L. Bian M. S. Bieker S. Bifani P. Billoir M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti M. Bjørn M. P. Blago T. Blake F. Blanc S. Blusk D. Bobulska J. A. Boelhauve O. Boente Garcia T. Boettcher A. Boldyrev A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato J. T. Borsuk S. A. Bouchiba T. J. V. Bowcock A. Boyer C. Bozzi M. J. Bradley S. Braun A. Brea Rodriguez J. Brodzicka A. Brossa Gonzalo D. Brundu A. Buonaura L. Buonincontri A. T. Burke C. Burr A. Bursche A. Butkevich J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese L. Calefice L. Calero Diaz S. Cali R. Calladine M. Calvi M. Calvo Gomez P. Camargo Magalhaes P. Campana A. F. Campoverde Quezada S. Capelli L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini I. Carli P. Carniti L. Carus K. Carvalho Akiba A. Casais Vidal G. Casse M. Cattaneo G. Cavallero S. Celani J. Cerasoli D. Cervenkov A. J. Chadwick M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis C. A. Chavez Barajas M. Chefdeville C. Chen S. Chen A. Chernov V. Chobanova S. Cholak M. Chrzaszcz A. Chubykin V. Chulikov P. Ciambrone M. F. Cicala X. Cid Vidal G. Ci Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France University of Michigan Ann Arbor United States (associated with Syracuse University Syracuse NY United States) INFN Sezione di Milano Milano Italy University of Cincinnati Cincinnati OH United States Fakultät Physik Technische Universität Dortmund Dortmund Germany European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom ICCUB Universitat de Barcelona Barcelona Spain Department of Physics and Astronomy University of Manchester Manchester United Kingdom Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI) Gatchina Russia Cavendish Laboratory University of Cambridge Cambridge United Kingdom Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France INFN Sezione di Firenze Firenze Italy INFN Sezione di Ferrara Ferrara Italy Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI) Protvino Russia Protvino Russia Syracuse University Syracuse NY United States Yandex School of Data Analysis Moscow Russia Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France Institu

The photon polarization in b→sγ transitions is measured for the first time in radiative b -baryon decays exploiting the unique spin structure of Λb0→Λγ decays. A data sample corresponding to an integrated luminosity of 6 fb−1 collected by the LHCb experiment in pp collisions at a center-of-mass energy of 13 TeV is used. The photon polarization is measured to be αγ=0.82−0.26−0.13+0.17+0.04, where the first uncertainty is statistical and the second systematic. This result is in agreement with the Standard Model prediction and previous measurements in b -meson decays. Charge-parity breaking effects are studied for the first time in this observable and found to be consistent with CP symmetry.

关键词： Flavor changing neutral currents Particle decays Bottom baryons Bottom quark Hyperons Photons CP symmetry

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Learning to scan: A deep reinforcement learning approach for personalized scanning in CT imaging

arXiv

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arXiv 2020年

作者： Shen, Ziju Wang, Yufei Wu, Dufan Yang, Xu Dong, Bin Academy for Advanced Interdisciplinary Studies Peking University China Computer Science Department Carnegie Mellon University United States Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School United States Department of Mathematics University of California Santa Barbara United States Beijing International Center for Mathematical Researchs Center for Data Science Institute for Artificial Intelligence Peking University China

Computed Tomography (CT) takes X-ray measurements on the subjects to reconstruct tomographic images. As X-ray is radioactive, it is desirable to control the total amount of dose of X-ray for safety concerns. Therefore, we can only select a limited number of measurement angles and assign each of them limited amount of dose. Traditional methods such as compressed sensing usually randomly select the angles and equally distribute the allowed dose on them. In most CT reconstruction models, the emphasize is on designing effective image representations, while much less emphasize is on improving the scanning strategy. The simple scanning strategy of random angle selection and equal dose distribution performs well in general, but they may not be ideal for each individual subject. It is more desirable to design a personalized scanning strategy for each subject to obtain better reconstruction result. In this paper, we propose to use Reinforcement Learning (RL) to learn a personalized scanning policy to select the angles and the dose at each chosen angle for each individual subject. We first formulate the CT scanning process as an Markov Decision Process (MDP), and then use modern deep RL methods to solve it. The learned personalized scanning strategy not only leads to better reconstruction results, but also shows strong generalization to be combined with different reconstruction algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Reinforcement learning

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Prediction of solar energetic events impacting space weather conditions

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Advances in Space Research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Probability-turbulence divergence: A tunable allotaxonometric instrument for comparing heavy-tailed categorical distributions

arXiv

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arXiv 2020年

作者： Dodds, Peter Sheridan Minot, Joshua R. Arnold, Michael V. Alshaabi, Thayer Adams, Jane Lydia Reagan, Andrew J. Danforth, Christopher M. Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT05401 United States Vermont Advanced Computing Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Santa Fe Institute 1399 Hyde Park Rd Santa FeNM87501 United States MassMutual Data Science AmherstMA01002 United States Howard Hughes Medical Institute University of California Berkeley BerkeleyCA94720 United States Data Visualization Lab Khoury College of Computer Sciences Northeastern University BostonMA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States

Real-world complex systems often comprise many distinct types of elements as well as many more types of networked interactions between elements. When the relative abundances of types can be measured well, we further observe heavy-tailed categorical distributions for type frequencies. For the comparison of type frequency distributions of two systems or a system with itself at different time points in time—a facet of allotaxonometry—a great range of probability divergences are available. Here, we introduce and explore ‘probability-turbulence divergence’, a tunable, straightforward, and interpretable instrument for comparing normalizable categorical frequency distributions. We model probability-turbulence divergence (PTD) after rank-turbulence divergence (RTD). While probability-turbulence divergence is more limited in application than rank-turbulence divergence, it is more sensitive to changes in type frequency. We build allotaxonographs to display probability turbulence, incorporating a way to visually accommodate zero probabilities for ‘exclusive types’ which are types that appear in only one system. We explore comparisons of example distributions taken from literature, social media, and ecology. We show how probability-turbulence divergence either explicitly or functionally generalizes many existing kinds of distances and measures, including, as special cases, L(p) norms, the Sørensen-Dice coefficient (the F1 statistic), and the Hellinger distance. We discuss similarities with the generalized entropies of Rényi and Tsallis, and the diversity indices (or Hill numbers) from ecology. We close with thoughts on open problems concerning the optimization of the tuning of rank- and probability-turbulence divergence. Copyright © 2020, The Authors. All rights reserved.

关键词： Normal distribution

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Lorentzian geometry and variability reduction in airplane boarding: Slow passengers first outperforms random boarding

arXiv

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arXiv 2020年

作者： Erland, Sveinung Kaupužs, Jevgenijs Steiner, Albert Bachmat, Eitan Department of Maritime Studies Western Norway University of Applied Sciences HaugesundN-5528 Norway Faculty of Materials Science and Applied Chemistry Institute of Technical Physics Riga Technical University RigaLV-1048 Latvia Institute of Mathematical Sciences and Information Technologies University of Liepaja LiepajaLV-3401 Latvia Institute of Data Analysis and Process Design Zurich University of Applied Sciences ZHAW Winterthur8401 Switzerland Department of Computer Science Ben-Gurion University Beer-Sheva84105 Israel

Airlines use different boarding policies to organize the queue of passengers waiting to enter the airplane. We analyze three policies in the many-passenger limit by a geometric representation of the queue position and row designation of each passenger, and apply a Lorentzian metric to calculate the total boarding time. The boarding time is governed by the time each passenger needs to clear the aisle, and the added time is determined by the aisle-clearing time distribution through an effective aisle-clearing time parameter. The non-organized queues under the common random boarding policy are characterized by large effective aisle-clearing time. We show that, subject to a mathematical assumption which we have verified by extensive numerical computations in all realistic cases, the average total boarding time is always reduced when slow passengers are separated from faster passengers and the slow group is allowed to enter the airplane first. This is a universal result that holds for any combination of the three main governing parameters: the ratio between effective aisle-clearing times of the fast and the slow group, the fraction of slow passenger, and the congestion of passengers in the aisle. Separation into groups based on aisle-clearing time allows for more synchronized seating, but the result is non-trivial, as the similar fast-first policy — where the two groups enter the airplane in reverse order — is inferior to random boarding for a range of parameter settings. The asymptotic results conform well with discrete-event simulations with realistic number of passengers, and both the slow-first and the fast-first policies have the ability to perform unboundedly better than random boarding. Parameters based on empirical data, with hand luggage as criteria for separating passengers into the slow group, give a 13% reduction in total boarding time for slow first compared to random boarding. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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High-Performance Thermoelectric α-Ag9Ga1−xTe6Compounds with Ultralow Lattice Thermal Conductivity Originating from Ag9Te2Motifs

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Angewandte Chemie 2022年第36期134卷 e202208281-e202208281页

作者： Yingfei Tang Yimeng Yu Na Zhao Keke Liu Haijie Chen Constantinos C. Stoumpos Yixuan Shi Shuo Chen Lingxiao Yu Jinsong Wu Qingjie Zhang Xianli Su Xinfeng Tang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan 430070 China International School of Materials Science and Engineering Wuhan University of Technology Wuhan 430070 China Contribution: Conceptualization (equal) Data curation (lead) Formal analysis (lead) Investigation (equal) Methodology (equal) Visualization (equal) Writing - original draft (lead) Nanostructure Research Center Wuhan University of Technology Wuhan 430070 China Contribution: Data curation (equal) Software (equal) Visualization (supporting) Contribution: Investigation (equal) Methodology (equal) Contribution: Investigation (equal) Methodology (equal) Visualization (equal) State Key Laboratory for Modification of Chemical Fibers and Polymer Materials Institute of Functional Materials College of Materials Science and Engineering Donghua University Shanghai 201620 China Contribution: Methodology (equal) Visualization (equal) Writing - review & editing (equal) Department of Materials Science and Technology Voutes Campus University of Crete Iraklio Heraklion 70013 Greece Contribution: Methodology (equal) Visualization (equal) Contribution: Investigation (equal) Visualization (equal) Contribution: Conceptualization (equal) Investigation (equal) Contribution: Methodology (equal) Contribution: Conceptualization (equal) Methodology (equal) Contribution: Conceptualization (equal) Investigation (equal) Methodology (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Funding acquisition (lead) Methodology (equal) Supervision (equal) Writing - review & editing (equal)

We have determined the complex atomic structure of high-temperature α-Ag 9 GaTe 6 phase with a hexagonal lattice ( P 6 3 mc space group, a = b =8.2766 Å, c =13.4349 Å). The structure has outer [GaTe 4 ] 5− tetrahedrons and inner [Ag 9 Te 2 ] 5+ clusters. All of the Ag ions are disorderly distributed in the lattice. Seven types of the Ag atoms constitute the cage-like [Ag 9 Te 2 ] 5+ clusters. The highly disordered Ag ions vibrate in-harmonically, producing strong coupling between low frequency optical phonons and acoustic phonons. This in conjunction with a low sound velocity of 1354 m s −1 leads to an ultralow thermal conductivity of 0.20 W m −1 K −1 at 673 K. Meanwhile, the deficiency of Ga in Ag 9 Ga 1− x Te 6 compounds effectively optimizes the electronic transport properties. Ag 9 Ga 0.91 Te 6 attains a highest power factor of 0.40 mW m −1 K −2 at 673 K. All these contribute to a much-improved ZT value of 1.13 at 623 K for Ag 9 Ga 0.95 Te 6 , which is 41 % higher than that of the pristine Ag 9 GaTe 6 sample.

关键词： α-Ag9GaTe6 Crystal Structure Ga Deficiency Thermoelectrics Ultralow Lattice Thermal Conductivity

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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