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Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

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REMOTE SENSING OF ENVIRONMENT 2025年 323卷

作者： Orndahl, Kathleen M. Berner, Logan T. Macander, Matthew J. Arndal, Marie F. Alexander, Heather D. Humphreys, Elyn R. Loranty, Michael M. Ludwig, Sarah M. Nyman, Johanna Juutinen, Sari Aurela, Mika Mikola, Juha Mack, Michelle C. Rose, Melissa Vankoughnett, Mathew R. Iversen, Colleen M. Kumar, Jitendra Salmon, Verity G. Yang, Dedi Grogan, Paul Danby, Ryan K. Scott, Neal A. Olofsson, Johan Siewert, Matthias B. Deschamps, Lucas Maire, Vincent Levesque, Esther Gauthier, Gilles Boudreau, Stephane Gaspard, Anna Bret-Harte, M. Syndonia Raynolds, Martha K. Walker, Donald A. Michelsen, Anders Kumpula, Timo Villoslad, Miguel Ylanne, Henni Luoto, Miska Virtanen, Tarmo Greaves, Heather E. Forbes, Bruce C. Heim, Ramona J. Hoelzel, Norbert Epstein, Howard Bunn, Andrew G. Holmes, Robert Max Natali, Susan M. Virkkala, Anna-Maria Goetz, Scott J. No Arizona Univ Sch Informat Comp & Cyber Syst 1295 Knoles Dr Flagstaff AZ 86011 USA ABR Inc Environm Res & Serv Fairbanks AK USA Aarhus Univ Dept Ecosci Aarhus Denmark Auburn Univ Coll Forestry Wildlife & Environm Auburn AL USA Carleton Univ Dept Geog & Environm Studies Ottawa ON Canada Colgate Univ Dept Geog Hamilton NY USA Columbia Univ Dept Earth & Environm Sci Palisades NY USA Cornell Univ Jeb E Brooks Sch Publ Policy Ithaca NY USA Finnish Meteorol Inst Helsinki Finland Nat Resources Inst Finland Bioecon & Environm Unit Helsinki Finland No Arizona Univ Ctr Ecosyst Sci & Soc Flagstaff AZ USA No Arizona Univ Dept Biol Sci Flagstaff AZ USA Nova Scotia Community Coll Appl Res Middleton NS Canada Oak Ridge Natl Lab Climate Change Sci Inst Oak Ridge TN USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Queens Univ Dept Biol Kingston ON Canada Queens Univ Dept Geog & Planning Kingston ON Canada Umea Univ Dept Ecol & Environm Sci Umea Sweden Univ Quebec Trois Rivieres Dept Sci Environm Trois Rivieres PQ Canada Univ Quebec Trois Rivieres Dept Sci Environm Trois Rivieres PQ Canada Univ Quebec Trois Rivieres Ctr Etud Nord Trois Rivieres PQ Canada Univ Laval Ctr Etud Nord Quebec City PQ Canada Univ Laval Dept Biol Quebec City PQ Canada Univ Alaska Fairbanks Inst Arctic Biol Fairbanks AK USA Univ Copenhagen Dept Biol Copenhagen Denmark Univ Eastern Finland Dept Geog & Hist Studies Joensuu Finland Univ Eastern Finland Sch Forest Sci Joensuu Finland Univ Helsinki Dept Geosci & Geog Helsinki Finland Univ Helsinki Ecosyst & Environm Res Programme Helsinki Finland Univ Idaho Dept Nat Resources & Soc Moscow ID USA Univ Lapland Arctic Ctr Rovaniemi Finland Univ Munster Inst Landscape Ecol Munster Germany Univ Virginia Dept Environm Sci Charlottesville VA USA Western Washington Univ Dept Environm Sci Bellingham WA USA Woodwell Climate Res Ctr Falmouth MA US

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m(-2)) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to similar to 6000 g m(-2) (mean approximate to 350 g m(-2)), while predicted values ranged from 0 to similar to 4000 g m(-2) (mean approximate to 275 g m(-2)), resulting in model validation root-mean-squared-error (RMSE) approximate to 400 g m(-2) and R-2 approximate to 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

关键词： Pan Arctic Plant biomass Woody plant dominance Vegetation distribution Remote sensing Climate change Landsat

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Prevalence of antibiotic-resistant E. coli in broilers challenged with a multi-resistant E. coli strain and received ampicillin, an organic acid-based feed additive or a synbiotic preparation

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POULTRY SCIENCE 2019年第6期98卷 2598-2607页

作者： Roth, Nataliya Hofacre, Charles Zitz, Ulrike Mathis, Greg F. Moder, Karl Doupovec, Barbara Berghouse, Roy Domig, Konrad J. BOKU Univ Nat Resources & Life Sci Inst Food Sci Dept Food Sci & Technol A-1190 Vienna Austria Univ Georgia Poultry Diagnost & Res Ctr Dept Populat Hlth Athens GA 30602 USA BOKU Univ Nat Resources & Life Sci Inst Appl Stat & Comp A-1190 Vienna Austria BIOMIN Holding GmbH A-3131 Getzersdorf Austria Southern Poultry Res Grp Inc Athens GA 30607 USA

The aim of this study was to evaluate the effect of ampicillin, an organic acid-based feed additive and a synbiotic preparation on the prevalence of antibiotic-resistant E. coli in the ceca of broilers. A total of 2000 broiler chickens (Ross 708) were randomly assigned to 5 groups with 8 replicates. The negative control group was the only group that was not subjected to avian pathogenic E. coli challenge, while all the other 4 groups received a multi-resistant E. coli strain that was resistant to ampicillin, cephalexin, and nalidixic acid as an oral challenge. The second group served as a challenge control, and the third group received the antibiotic ampicillin via water for 5 d. The fourth group received a feed additive based on organic acids and cinnamaldehyde, and the fifth group received a synbiotic preparation via feed and water. On day 17 and 38 of the trial, cecal samples from 3 birds from each of the 40 pens were obtained, and the E. coli counts and abundances of antibiotic-resistant E. coli were determined. Oral challenge with an avian pathogenic E. coli strain did not influence the performance, and there was no significant difference in growth performance between groups. The total E. coli count was lower (P < 0.05) in the group supplemented with the synbiotic than in the challenge control group on day 38 of the trial. Administration of an antibiotic for 5 d led to a significant increase in the abundance of E. coli strains resistant to ampicillin, amoxicillin-clavulanic acid, cefoxitin, and ceftriaxone. There was no increase in the abundance of antibiotic-resistant E. coli observed in the groups that received feed supplemented with an organic acid/cinnamaldehyde-based feed additive or a synbiotic. Moreover, the effects of the tested feed additives on the prevalence of resistant E. coli are demonstrated by the lower ceftriaxone minimal inhibitory concentration values for this group than for the antibiotic group. Additionally, the synbiotic group exhibited lo

关键词： feed additive poultry antibiotic resistance APEC E. coli challenge

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Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge

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AGRICULTURAL AND FOREST METEOROLOGY 2024年 345卷

作者： Wheeler, Kathryn I. Dietze, Michael C. Lebauer, David Peters, Jody A. Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, Mckalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob H. Zachmann, Luke Boston Univ Dept Earth & Environm 685 Commonwealth Ave Boston MA 02215 USA Univ Corp Atmospher Res Cooperat Programs Advancement Earth Syst Sci CPAES Boulder CO 80305 USA NOAA Environm Res Labs 3300 Mitchell LaneSuite 175 Boulder CO 80301 USA MIT Dept Civil & Environm Engn 15 Vassar St Cambridge MA 02139 USA UNIV ARIZONA ARIZONA AGR EXPT STN TUCSON AZ 85721 USA Univ Notre Dame Dept Biol Sci 100 Galvin Life Sci Notre Dame IN 46556 USA No Arizona Univ Sch Informat Comp & Cyber Syst 1295 Knoles Dr Flagstaff AZ 86011 USA No Arizona Univ Ctr Ecosyst Sci & Soc POB 5640 Flagstaff AZ 86011 USA Michigan State Univ Dept Comp Sci & Engn 428 South Shaw Lane E Lansing MI 48824 USA Virginia Polytech Inst & State Univ Dept Forest Resources & Environm Conservat 310 West Campus Dr Blacksburg VA 24061 USA Virginia Tech Dept Biol Sci 926 West Campus Dr Blacksburg VA 24061 USA Univ Michigan CIGLR 440 Church St Ann Arbor MI 48109 USA Univ Michigan Sch Environm & Sustainabil 440 Church St Ann Arbor MI 48109 USA Univ Calif Santa Cruz Inst Marine Sci 1156 High St Santa Cruz CA 95064 USA Univ Calif Santa Cruz Baskin Sch Engn Dept Appl Math & Stat 1156 High St Santa Cruz CA 95060 USA Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Univ Wisconsin Madison Dept Forest & Wildlife Ecol 1630 Linden Dr Madison WI 53706 USA Pacific Northwest Natl Lab Earth & Biol Sci Directorate Div Biol Sci 902 Battelle Blvd Richland WA 99354 USA North Carolina State Univ Ctr Geospatial Analyt Raleigh NC 27695 USA Georgetown Univ Dept Biol 37th & O St NW Washington DC 20057 USA Univ Calif Santa Cruz Dept Environm Studies 1156 High St Santa Cruz CA 95064 USA Univ Colorado Boulder Dept Informat Sci 1111 Engn Dr Boulder CO 80309 USA Oregon State Univ Dept Microbiol 2820 SW Campus Way Corvallis OR 97331 USA Conservat Sci Partners Inc 11050 Pioneer TrailSuite 202 Truckee CA 96161 USA

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.

关键词： Phenology Ecological forecasting Deciduous broadleaf Budburst Community challenge Forests

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Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill comparison

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2024年第7期105卷 E1170-E1203页

作者： Bushuk, Mitchell Ali, Sahara Bailey, David A. Bao, Qing Batte, Lauriane Bhatt, Uma S. Blanchard-Wrigglesworth, Edward Blockley, Ed Cawley, Gavin Chi, Junhwa Counillon, Francois Coulombe, Philippe Goulet Cullather, Richard I. Diebold, Francis X. Dirkson, Arlan Exarchou, Eleftheria Goebel, Maximilian Gregory, William Guemas, Virginie Hamilton, Lawrence He, Bian Horvath, Sean Ionita, Monica Kay, Jennifer E. Kim, Eliot Kimura, Noriaki Kondrashov, Dmitri Labe, Zachary M. Lee, Woosung Lee, Younjoo J. Li, Cuihua Li, Xuewei Lin, Yongcheng Liu, Yanyun Maslowski, Wieslaw Massonnet, Francois Meier, Walter N. Merryfield, William J. Myint, Hannah Navarro, Juan C. Acosta Petty, Alek Qiao, Fangli Schroeder, David Schweiger, Axel Shu, Qi Sigmond, Michael Steele, Michael Stroeve, Julienne Sun, Nico Tietsche, Steffen Tsamados, Michel Wang, Keguang Wang, Jianwu Wang, Wanqiu Wang, Yiguo Wang, Yun Williams, James Yang, Qinghua Yuan, Xiaojun Zhang, Jinlun Zhang, Yongfei NOAA Geophys Fluid Dynam Lab Princeton NJ 08540 USA Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD USA Natl Ctr Atmospher Res Boulder CO USA Chinese Acad Sci Inst Atmospher Phys State Key Lab Numer Modeling Atmospher Sci & Geoph Beijing Peoples R China Meteo France Direct Climatol & Serv Climat Toulouse France Univ Alaska Fairbanks Dept Atmospher Sci Geophys Inst Fairbanks AK USA Univ Washington Dept Atmospher Sci Seattle WA USA Met Off Exeter England Univ East Anglia Sch Comp Sci Norwich England Pukyong Natl Univ Div Data Informat Sci Busan South Korea Nansen Environm & Remote Sensing Ctr Bergen Norway Bjerknes Ctr Climate Res Bergen Norway Univ Quebec Montreal Dept Sci Econ Montreal PQ Canada NASA Goddard Space Flight Ctr Global Modeling & Assimilat Off Greenbelt MD USA Univ Tokyo Atmosphere & Ocean Res Inst Kashiwa Japan Univ Penn Dept Econ Philadelphia PA USA Univ Penn Dept Finance Philadelphia PA USA Univ Penn Dept Stat Philadelphia PA USA Environm & Climate Change Canada Meteorol Res Div Dorval PQ Canada Barcelona Supercomp Ctr Barcelona Spain Bocconi Univ Milan Italy Princeton Univ Atmospher & Ocean Sci Program Princeton NJ USA Univ Toulouse Meteo France CNRM CNRS Toulouse France Univ New Hampshire Dept Sociol Durham NH USA Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Bremerhaven Germany Stefan Cel Mare Univ Suceava Fac Forestry Suceava Romania Univ Colorado Boulder Dept Atmospher & Ocean Sci Boulder CO USA Univ Colorado Boulder CIRES Boulder CO USA Univ Calif Los Angeles Dept Atmospher & Ocean Sci Los Angeles CA USA Environm & Climate Change Canada Canadian Ctr Climate Modelling & Anal Victoria BC Canada Naval Postgrad Sch Dept Oceanog Monterey CA USA Columbia Univ Lamont Doherty Earth Observ Palisades NY USA Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Zhuhai Peoples R China Southern Marine Sci & Engn Guangdong

This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001-20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

关键词： Arctic Sea ice Statistical techniques Climate prediction General circulation models Model evaluation/ performance

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General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

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CLINICAL PHARMACOLOGY & THERAPEUTICS 2020年第1期107卷 102-111页

作者： Li, Zhihua Mirams, Gary R. Yoshinaga, Takashi Ridder, Bradley J. Han, Xiaomei Chen, Janell E. Stockbridge, Norman L. Wisialowski, Todd A. Damiano, Bruce Severi, Stefano Morissette, Pierre Kowey, Peter R. Holbrook, Mark Smith, Godfrey Rasmusson, Randall L. Liu, Michael Song, Zhen Qu, Zhilin Leishman, Derek J. Steidl-Nichols, Jill Rodriguez, Blanca Bueno-Orovio, Alfonso Zhou, Xin Passini, Elisa Edwards, Andrew G. Morotti, Stefano Ni, Haibo Grandi, Eleonora Clancy, Colleen E. Vandenberg, Jamie Hill, Adam Nakamura, Mikiko Singer, Thomas Polonchuk, Liudmila Greiter-Wilke, Andrea Wang, Ken Nave, Stephane Fullerton, Aaron Sobie, Eric A. Paci, Michelangelo Tshinanu, Flora Musuamba Strauss, David G. US FDA Div Appl Regulatory Sci Off Clin Pharmacol Off Translat SciCtr Drug Evaluat & Res Silver Spring MD 20993 USA Univ Nottingham Sch Math Sci Ctr Math Med & Biol Nottingham England Eisai & Co Ltd Global Cardiovasc Assessment Tokyo Japan US FDA Div Biometr 4 Off Biostat Off Translat SciCtr Drug Evaluat & Res Silver Spring MD USA US FDA Div Cardiovasc & Renal Prod Off Drug Evaluat 1 Off New DrugsCtr Drug Evaluat & Res Silver Spring MD USA Pfizer Inc Global Safety Pharmacol Groton CT 06340 USA Nonclin Safety Janssen Res & Dev Global Safety Pharmacol Raritan NJ USA Univ Bologna Dept Elect Elect & Informat Engn Cesena Italy Merck & Co Inc Dept Safety Assessment Kenilworth NJ USA Merck & Co Inc Lab Anim Resources Kenilworth NJ USA Main Line Hlth & Sidney Kimmel Med Coll Philadelphia PA USA Certara Ltd Sheffield S Yorkshire England Clyde Biosci Ltd Glasgow Lanark Scotland Glasgow Univ Inst Cardiovasc & Med Sci Glasgow Lanark Scotland SUNY Buffalo Jacobs Sch Med & Biomed Sci Dept Physiol & Biophys Buffalo NY USA Univ Calif Los Angeles Dept Med Div Cardiol Los Angeles CA 90024 USA Eli Lilly & Co Dept Toxicol & Pathol Indianapolis IN 46285 USA Covance Inc Madison WI USA Univ Oxford Dept Comp Sci Oxford England Univ Calif Davis Dept Pharmacol Davis CA 95616 USA Victor Chang Cardiac Res Inst Darlinghurst NSW Australia UNSW Sydney St Vincents Clin Sch Darlinghurst NSW Australia Chugai Pharmaceut Co Ltd Clin Pharmacol Dept Tokyo Japan F Hoffmann La Roche Ltd Roche Innovat Ctr Basel Roche Pharma Res & Early Dev Pharmaceut Sci Basel Switzerland F Hoffmann La Roche Ltd Safety Sci Licensing & Early Dev Roche Prod Dev Basel Switzerland Genentech Inc Dept Safety Assessment San Francisco CA 94080 USA Icahn Sch Med Mt Sinai New York NY 10029 USA Tampere Univ Fac Med & Hlth Technol BioMediTech Tampere Finland Fed Agcy Med & Hlth Prod Brussels Belgium European Medicines Agcy Amsterdam Netherlands

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.

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Materials Acceleration Platforms (MAPs) Accelerating Materials Research and Development to Meet Urgent Societal Challenges

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ADVANCED MATERIALS 2024年第45期36卷 e2407791页

作者： Stier, Simon P. Kreisbeck, Christoph Ihssen, Holger Popp, Matthias Albert Hauch, Jens Malek, Kourosh Reynaud, Marine Goumans, T. P. M. Carlsson, Johan Todorov, Ilian Gold, Lukas Raeder, Andreas Wenzel, Wolfgang Bandesha, Shahbaz Tareq Jacques, Philippe Garcia-Moreno, Francisco Arcelus, Oier Friederich, Pascal Clark, Simon Maglione, Mario Laukkanen, Anssi Castelli, Ivano Eligio Carrasco, Javier Cabanas, Montserrat Casas Stein, Helge Soeren Ozcan, Ozlem Elbert, David Reuter, Karsten Scheurer, Christoph Demura, Masahiko Han, Sang Soo Vegge, Tejs Nakamae, Sawako Fabrizio, Monica Kozdras, Mark Fraunhofer ISC TLZ RT Dept Digital Transformat Neunerplatz 2 D-97082 Wurzburg Germany Aixelo Inc Cambridge MA 02141 USA Helmholtz Assoc Rue Trone 98 B-1050 Brussels Belgium Forschungszentrum Julich Inst Mat Elect & Energy Technol i MEET Helmholtz Inst Erlangen Nurnberg Renewable Energy D-91058 Erlangen Germany Forschungszentrum Julich Inst Energy & Climate Res IEK Theory & Computat Energy Mat IEK-13 D-52428 Julich Germany Basque Res & Technol Alliance BRTA Parque Tecnol Alava Ctr Invest Cooperat Energias Alternat CIC EnergiGU Albert Einstein 48 Vitoria 01510 Spain Software Chem & Mat BV Boelelaan 1083 NL-1081 HV Amsterdam Netherlands Dassault Syst Deutschland GmbH D-51063 Cologne Germany Sci & Technol Facil Council Sci Comp Dept Daresbury Lab Warrington WA4 4AD England Karlsruhe Inst Technol Inst Nanotechnol INT Hermann von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany EMIRI AISBL Rue Ransbeek 310 B-1120 Brussels Belgium Helmholtz Zent Berlin Materialien & Energie Inst Appl Mat Hahn-Meitner-Pl 1 D-14109 Berlin Germany SINTEF Ind New Energy Solut Sem Saelands Vei 12 N-7034 Trondheim Norway Univ Bordeaux Inst Chim Matiere Condensee Bordeaux ICMCB UMR 5026 CNRS 87 Ave Docteur Schweitzer F-33608 Pessac France VTT Tech Res Ctr Finland Ltd Espoo 02044 Finland Tech Univ Denmark Dept Energy Convers & Storage DK-2800 Lyngby Denmark IKERBASQUE Basque Fdn Sci Plaza Euskadi 5 Bilbao 48009 Spain Tech Univ Munich TUM Digital Catalysis Lichtenbergstr 4 D-85748 Garching Germany BAM Fed Inst Mat Res & Testing Unter Eichen 87 D-12205 Berlin Germany Johns Hopkins Univ Hopkins Extreme Mat Inst Baltimore MD 21218 USA Max Planck Gesellschaf Fritz Haber Inst Faradayweg 4-6 D-14195 Berlin Germany Natl Inst Mat Sci NIMS 1-2-1 Sengen Tsukuba Ibaraki 3050044 Japan Korea Inst Sci & Technol KIST Dept Life Sci 5 Hwarangno 14-gilSeongbuk-gu Seoul 136 791 South Korea Univ Paris Saclay CEA Saclay CEA CNRSServ Phys Etat Condense F-91191 Gif Sur Yve

Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economic prosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance computing through Materials Acceleration Platforms, MAPs. In this perspective a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome ***

关键词： advanced materials artificial intelligence autonomous labs materials acceleration platforms societal challenges

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The Importance of Consistent Global Forest Aboveground Biomass Product Validation

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SURVEYS IN GEOPHYSICS 2019年第4期40卷 979-999页

作者： Duncanson, L. Armston, J. Disney, M. Avitabile, V. Barbier, N. Calders, K. Carter, S. Chave, J. Herold, M. Crowther, T. W. Falkowski, M. Kellner, J. R. Labriere, N. Lucas, R. MacBean, N. McRoberts, R. E. Meyer, V. Naesset, E. Nickeson, J. E. Paul, K. I. Phillips, O. L. Rejou-Mechain, M. Roman, M. Roxburgh, S. Saatchi, S. Schepaschenko, D. Scipal, K. Siqueira, P. R. Whitehurst, A. Williams, M. Univ Maryland Dept Geog Sci 2181 Lefrak Hall College Pk MD 20742 USA UCL Dept Geog Gower St London WC1E 6BT England European Commiss JRC Via E Fermi 2749 I-21027 Ispra Italy Montpellier Univ INRA CNRS AMAPIRDCIRAD TA A51-PS2 F-34398 Montpellier 5 France Univ Ghent CAVElab Computat & Appl Vegetat Ecol Room A2-089Coupure Links 653 B-9000 Ghent Belgium Wageningen Univ & Res Lab Geoinformat Sci & Remote Sensing Droevendaalsesteeg 3 NL-6708 PB Wageningen Netherlands Univ Toulouse Paul Sabatier Lab Evolut & Diversit Biol UMR 5174 CNRS 118 Route Narbonne F-31062 Toulouse 9 France Swiss Fed Inst Technol Inst Integrat Biol Univ Str 16 CH-8006 Zurich Switzerland Colorado State Univ Dept Ecosyst Sci & Sustainabil Ft Collins CO 80523 USA Brown Univ Inst Brown Environm & Soc Providence RI 02912 USA Brown Univ Dept Ecol & Evolutionary Biol Providence RI 02912 USA Aberystwyth Univ Earth Observat & Ecosyst Dynam Res Grp DGES Aberystwyth SY23 3DB Dyfed Wales Indiana Univ Dept Geog 701 E Kirkwood Ave Bloomington IN 47405 USA US Forest Serv USDA Northern Res Stn 1992 Folwell Ave St Paul MN 55108 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Norwegian Univ Life Sci Fac Environm Sci & Nat Resource Management NMBU POB 5003 N-1432 As Norway NASA Goddard Space Flight Ctr Sci Syst & Applicat Inc 10210 Greenbelt Rd 600 Lanham MD 20706 USA CSIRO Land & Water GPO Box 1700 Canberra ACT 2601 Australia Univ Leeds Sch Geog Leeds LS2 9JT W Yorkshire England Int Inst Appl Syst Anal Schlosspl 1 A-2361 Laxenburg Austria European Space Agcy Estec Keplerlaan 1 NL-2201 AZ Noordwijk Netherlands Univ Massachusetts Dept Elect & Comp Engn 201 Marcus Hall100 Nat Resources Rd Amherst MA 01003 USA Arctic Slope Fed Tech Serv 7000 Muirkirk Meadows Dr 100 Laurel MD 20707 USA Univ Edinburgh Sch GeoSci Drummond St Edinburgh EH8 9XP Midlothian Scotland Univ Space Res Assoc Earth Space Inst Columbia MD USA

Several upcoming satellite missions have core science requirements to produce data for accurate forest aboveground biomass mapping. Largely because of these mission datasets, the number of available biomass products is expected to greatly increase over the coming decade. Despite the recognized importance of biomass mapping for a wide range of science, policy and management applications, there remains no community accepted standard for satellite-based biomass map validation. The Committee on Earth Observing Satellites (CEOS) is developing a protocol to fill this need in advance of the next generation of biomass-relevant satellites, and this paper presents a review of biomass validation practices from a CEOS perspective. We outline the wide range of anticipated user requirements for product accuracy assessment and provide recommendations for the validation of biomass products. These recommendations include the collection of new, high-quality in situ data and the use of airborne lidar biomass maps as tools toward transparent multi-resolution validation. Adoption of community-vetted validation standards and practices will facilitate the uptake of the next generation of biomass products.

关键词： Map validation Biomass mapping Reference data Committee on Earth Observing Satellites Remote sensing Lidar

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Genome resources for climate-resilient cowpea, an essential crop for food security

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PLANT JOURNAL 2017年第5期89卷 1042-1054页

作者： Munoz-Amatriain, Maria Mirebrahim, Hamid Xu, Pei Wanamaker, Steve I. Luo, MingCheng Alhakami, Hind Alpert, Matthew Atokple, Ibrahim Batieno, Benoit J. Boukar, Ousmane Bozdag, Serdar Cisse, Ndiaga Drabo, Issa Ehlers, Jeffrey D. Farmer, Andrew Fatokun, Christian Gu, Yong Q. Guo, Yi-Ning Huynh, Bao-Lam Jackson, Scott A. Kusi, Francis Lawley, Cynthia T. Lucas, Mitchell R. Ma, Yaqin Timko, Michael P. Wu, Jiajie You, Frank Barkley, Noelle A. Roberts, Philip A. Lonardi, Stefano Close, Timothy J. Univ Calif Riverside Dept Bot & Plant Sci Riverside CA USA Univ Calif Riverside Dept Comp Sci & Engn Riverside CA USA ZAAS Inst Vegetables Hangzhou 310021 Peoples R China Univ Calif Davis Dept Plant Sci Davis CA USA Savanna Agr Res Inst Council Sci & Ind Res Tamale Ghana Inst Environm & Rech Agr Saria Burkina Faso Int Inst Trop Agr Kano Nigeria Marquette Univ Dept Math Stat & Comp Sci Milwaukee WI USA Inst Senegalais Rech Agr Thies Senegal Bill Melinda Gates Fdn Seattle WA USA Natl Ctr Genome Resources Santa Fe NM USA Int Inst Trop Agr Ibadan Nigeria USDA ARS Western Reg Res Ctr Albany CA USA Univ Calif Riverside Dept Nematol Riverside CA USA Univ Georgia Ctr Appl Genet Technol Athens GA USA Illumina Inc San Francisco CA USA Univ Virginia Dept Biol Charlottesville VA USA Agr & Agri Food Canada Morden MB Canada USDA ARS Plant Genet Resources Conservat Unit Griffin GA USA

Cowpea (Vigna unguiculata L. Walp.) is a legume crop that is resilient to hot and drought-prone climates, and a primary source of protein in sub-Saharan Africa and other parts of the developing world. However, genome resources for cowpea have lagged behind most other major crops. Here we describe foundational genome resources and their application to the analysis of germplasm currently in use in West African breeding programs. resources developed from the African cultivar IT97K-499-35 include a whole-genome shotgun (WGS) assembly, a bacterial artificial chromosome (BAC) physical map, and assembled sequences from 4355 BACs. These resources and WGS sequences of an additional 36 diverse cowpea accessions supported the development of a genotyping assay for 51 128 SNPs, which was then applied to five bi-parental RIL populations to produce a consensus genetic map containing 37 372 SNPs. This genetic map enabled the anchoring of 100 Mb of WGS and 420 Mb of BAC sequences, an exploration of genetic diversity along each linkage group, and clarification of macrosynteny between cowpea and common bean. The SNP assay enabled a diversity analysis of materials from West African breeding programs. Two major subpopulations exist within those materials, one of which has significant parentage from South and East Africa and more diversity. There are genomic regions of high differentiation between subpopulations, one of which coincides with a cluster of nodulin genes. The new resources and knowledge help to define goals and accelerate the breeding of improved varieties to address food security issues related to limited-input small-holder farming and climate stress.

关键词： BAC sequencing consensus genetic map cowpea genetic anchoring iSelect genotyping array Phaseolus vulgaris L. synteny Vigna unguiculata L. Walp. West Africa WGS sequencing

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Wastewater-Based Epidemiology: Global Collaborative to Maximize Contributions in the Fight Against COVID-19

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020年第13期54卷 7754-7757页

作者： Bivins, Aaron North, Devin Ahmad, Arslan Ahmed, Warish Alm, Eric Been, Frederic Bhattacharya, Prosun Bijlsma, Lubertus Boehm, Alexandria B. Brown, Joe Buttiglieri, Gianluigi Calabro, Vincenza Carducci, Annalaura Castiglioni, Sara Gurol, Zeynep Cetecioglu Chakraborty, Sudip Costa, Federico Curcio, Stefano de los Reyes, Francis L., III Vela, Jeseth Delgado Farkas, Kata Fernandez-Casi, Xavier Gerba, Charles Gerrity, Daniel Girones, Rosina Gonzalez, Raul Haramoto, Eiji Harris, Angela Holden, Patricia A. Islam, Md. Tahmidul Jones, Davey L. Kasprzyk-Hordern, Barbara Kitajima, Masaaki Kotlarz, Nadine Kumar, Manish Kuroda, Keisuke La Rosa, Giuseppina Malpei, Francesca Mautus, Mariana McLellan, Sandra L. Medema, Gertjan Meschke, John Scott Mueller, Jochen Newton, Ryan J. Nilsson, David Noble, Rachel T. van Nuijs, Alexander Peccia, Jordan Perkins, T. Alex Pickering, Amy J. Rose, Joan Sanchez, Gloria Smith, Adam Stadler, Lauren Stauber, Christine Thomas, Kevin van der Voorn, Tom Wigginton, Krista Zhu, Kevin Bibby, Kyle Univ Notre Dame Dept Civil & Environm Engn & Earth Sci Notre Dame IN 46556 USA KTH Royal Inst Technol Dept Sustainable Dev Environm Sci & Engn SE-10044 Stockholm Sweden KWR Water Res Inst NL-3433 PE Nieuwegein Netherlands CSIRO Land & Water Ecosci Precinct Dutton Pk Qld 4102 Australia MIT Dept Biol Engn Cambridge MA 02142 USA KWR Water Res Inst Water Qual & Heath NL-3433 PE Nieuwegein Netherlands Univ Jaume 1 Analyt Chem Publ Hlth & Environm Castellon De La Plana 12071 Spain Stanford Univ Dept Civil & Environm Engn Stanford CA 94305 USA Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA Catalan Inst Water Res ICRA E-17003 Girona Spain Univ Calabria Dept Comp Engn Modeling Elect & Syst Lab Transport Phenomena & Biotechnol I-87036 Cosenza Italy Univ Pisa Dept Biol I-56126 Pisa Italy Ist Ric Farmacol Mario Negri IRCCS Dept Environm Hlth Sci I-20156 Milan Italy KTH Royal Inst Technol Dept Chem Engn SE-11428 Stockholm Sweden Univ Fed Bahia Inst Saude Colet BR-40110040 Salvador BA Brazil North Carolina State Univ Dept Civil Construct & Environm Engn Raleigh NC 27607 USA Howard Univ Dept Civil & Environm Engn Washington DC 20059 USA Bangor Univ Sch Ocean Sci Anglesey LL59 5AB Wales Ecole Polytech Fed Lausanne EPFL Sch Architecture Civil & Environm Engn ENAC Environm Chem Lab CH-1015 Lausanne Switzerland Univ Arizona Dept Environm Sci Tucson AZ 85745 USA Southern Nevada Water Author Appl Res & Dev Ctr Las Vegas NV 89106 USA Univ Barcelona Dept Genet Microbiol Stat Barcelona 08028 Spain Hampton Rd Sanitat Dist Virginia Beach VA 23455 USA Univ Yamanashi Interdisciplinary Ctr River Basin Environm Kofu Yamanashi 4008511 Japan Boron Biol Inc Raleigh NC 27607 USA Univ Calif Santa Barbara Bren Sch Environm Sci & Management Santa Barbara CA 93106 USA Bangor Univ Sch Nat Sci Bangor LL57 2UW Gwynedd Wales Univ Bath Dept Chem Bath BA2 7AY Avon England Hokkaido Univ Div Environm Engn Kita Ku Sapporo Hokkaido 0608628 Japan No

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Geomagnetically induced currents: Science, engineering, and applications readiness

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SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND applICATIONS 2017年第7期15卷 828-856页

作者： Pulkkinen, A. Bernabeu, E. Thomson, A. Viljanen, A. Pirjola, R. Boteler, D. Eichner, J. Cilliers, P. J. Welling, D. Savani, N. P. Weigel, R. S. Love, J. J. Balch, C. Ngwira, C. M. Crowley, G. Schultz, A. Kataoka, R. Anderson, B. Fugate, D. Simpson, J. J. MacAlester, M. NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA PJM Valley Forge PA USA British Geol Survey Nottingham England Finnish Meteorol Inst Helsinki Finland Nat Resources Canada Ottawa ON Canada Munich Re Munich Germany South African Natl Space Agcy Pretoria South Africa Univ Michigan Dept Atmospher Ocean & Space Sci Ann Arbor MI 48109 USA Univ Maryland Baltimore Cty Goddard Planetary Heliophys Inst Baltimore MD 21228 USA George Mason Univ Dept Phys & Astron Fairfax VA 22030 USA US Geol Survey Golden CO USA NOAA Space Weather Predict Ctr Boulder CO USA Catholic Univ Amer Inst Astrophys & Computat Sci Washington DC 20064 USA Atmospher & Space Technol Res Associates LLC Boulder CO USA Oregon State Univ Inst Energy Resources & Resilience Corvallis OR 97331 USA Natl Inst Polar Res Tokyo Japan Johns Hopkins Univ Appl Phys Lab Laurel MD USA Elect Res & Management Inc Cabot PA USA Univ Utah Elect & Comp Engn Dept Salt Lake City UT USA Fed Emergency Management Assoc Washington DC USA

This paper is the primary deliverable of the very first NASA Living With a Star Institute Working Group, Geomagnetically Induced Currents (GIC) Working Group. The paper provides a broad overview of the current status and future challenges pertaining to the science, engineering, and applications of the GIC problem. Science is understood here as the basic space and Earth sciences research that allows improved understanding and physics-based modeling of the physical processes behind GIC. Engineering, in turn, is understood here as the "impact" aspect of GIC. applications are understood as the models, tools, and activities that can provide actionable information to entities such as power systems operators for mitigating the effects of GIC and government agencies for managing any potential consequences from GIC impact to critical infrastructure. applications can be considered the ultimate goal of our GIC work. In assessing the status of the field, we quantify the readiness of various applications in the mitigation context. We use the applications Readiness Level (ARL) concept to carry out the quantification.

关键词： geomagnetically induced currents space weather status of the field

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