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Annual IEEE Symposium on computer-Based Medical systems

作者： Mario Merone Paolo Soda Department of Engineering University Campus Bio-Medico of Rome Unit of Computer Systems and Bioinformatics Italy

ISBN: (纸本)9781467390378

The development of computer-aided diagnosis (CAD) systems for antinuclear autoantibodies tests in indirect immunofluorescence using HEp-2 cells has attracted growing research efforts in the last years. Although in this field many CAD solutions extract information from objects detected within the images, cell segmentation is an issue far from being solved. This work introduces a segmentation pipeline based on an active contour method, as none in the literature on HEp-2 cell segmentation does. Our proposal can detect objects whose boundaries are not necessarily defined by gradient, and this choice plays a relevant role for HEp-2 images with different fluorescence intensities and different staining patterns. The performances of the approach is tested not only on a public benchmark dataset with 18 images, but also on other 24 images that we made publicly available. Furthermore, its performances are compared with those provided by other state-of-the-art segmentation methods.

关键词： Image segmentation Active contours Pipelines Pattern recognition Biomedical imaging Immune system Design automation

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Ground-based hyperspectral remote sensing for weed management in crop production

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International Journal of Agricultural and Biological engineering 2016年第2期9卷 98-109页

作者： Yanbo Huang Matthew A.Lee Steven J.Thomson Krishna N.Reddy United States Department of Agriculture Agricultural Research ServiceCrop Production Systems Research UnitPO Box 350Stoneville MS 38776USA Department of Electrical and Computer Engineering Mississippi State UniversityMississippi State MS 39762USA United States Department of Agriculture National Institute of Food and AgricultureWashington DC 20024USA

Agricultural remote sensing has been developed and applied in monitoring soil,crop growth,weed infestation,insects,diseases and water status in farm fields to provide data and information to guide agricultural management *** agriculture has been implemented through prescription mapping of crop fields at different scales with the data remotely sensed from space-borne,airborne and ground-based ***-based remote sensing techniques offer portability,flexibility and controllability in applications for precision *** weed management,crop injury from off-target herbicide spray drift and herbicide resistance in weeds are two important *** precision weed management,ground-based hyperspectral remote sensing techniques were developed for detection of crop injury from dicamba and differentiation between glyphosate resistant and sensitive *** research presents the techniques for ground-based hyperspectral remote sensing for these two *** illustrate the advantages of ground-based hyperspectral remote sensing for precision weed management.

关键词： ground-based remote sensing hyperspectral crop injury herbicide resistance precision agriculture

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Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

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Scientific Data 2017年第1期4卷 170140页

作者： St John-Williams, Lisa Blach, Colette Toledo, Jon B. Rotroff, Daniel M. Kim, Sungeun Klavins, Kristaps Baillie, Rebecca Han, Xianlin Mahmoudiandehkordi, Siamak Jack, John Massaro, Tyler J. Lucas, Joseph E. Louie, Gregory Motsinger-Reif, Alison A. Risacher, Shannon L. Saykin, Andrew J. Kastenmüller, Gabi Arnold, Matthias Koal, Therese Moseley, M. Arthur Mangravite, Lara M. Peters, Mette A. Tenenbaum, Jessica D. Thompson, J. Will Kaddurah-Daouk, Rima Proteomics and Metabolomics Shared Resource Center for Genomic and Computational Biology Duke University Durham 27710 NC United States Duke Molecular Physiology Institute Duke University Durham 27701 NC United States Department of Pathology and Laboratory Medicine University of Pennsylvania Philadelphia 19104 PA United States Department of Neurology Houston Methodist Hospital Houston 77030 TX United States Bioinformatics Research Center Department of Statistics North Carolina State University Raleigh 27607 NC United States Department of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center Indiana University School of Medicine Indianapolis 46202 IN United States Department of Electrical and Computer Engineering State University of New York Oswego 13126 NY United States BIOCRATES Life Sciences AG Innsbruck 6020 Austria Rosa and Co LLC San Carlos 94070 CA United States Sanford Burnham Prebys Medical Discovery Institute Orlando 32827 FL United States Department of Psychiatry and Behavioral Sciences Duke Institute for Brain Sciences Duke University Durham 27710 NC United States Duke Social Sciences Research Institute Duke University Durham 27708 NC United States Institute of Bioinformatics and Systems Biology Helmholtz Zentrum Mönchen German Research Center for Environmental Health Neuherberg D-85764 Germany German Center for Diabetes Research Neuherberg D-85764 Germany Sage Bionetworks Seattle 98109 WA United States Department of Biostatistics and Bioinformatics Duke University Durham 27710 NC United States

Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non-targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes. © The Author(s) 2017.

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

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Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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Corrigendum to: More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis

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Chemical senses 2021年第46期46卷 bjab050页

作者： Valentina Parma Kathrin Ohla Maria G Veldhuizen Masha Y Niv Christine E Kelly Alyssa J Bakke Keiland W Cooper Cédric Bouysset Nicola Pirastu Michele Dibattista Rishemjit Kaur Marco Tullio Liuzza Marta Y Pepino Veronika Schöpf Veronica Pereda-Loth Shannon B Olsson Richard C Gerkin Paloma Rohlfs Domínguez Javier Albayay Michael C Farruggia Surabhi Bhutani Alexander W Fjaeldstad Ritesh Kumar Anna Menini Moustafa Bensafi Mari Sandell Iordanis Konstantinidis Antonella Di Pizio Federica Genovese Lina Öztürk Thierry Thomas-Danguin Johannes Frasnelli Sanne Boesveldt Özlem Saatci Luis R Saraiva Cailu Lin Jérôme Golebiowski Liang-Dar Hwang Mehmet Hakan Ozdener Maria Dolors Guàrdia Christophe Laudamiel Marina Ritchie Jan Havlícek Denis Pierron Eugeni Roura Marta Navarro Alissa A Nolden Juyun Lim Katherine L Whitcroft Lauren R Colquitt Camille Ferdenzi Evelyn V Brindha Aytug Altundag Alberto Macchi Alexia Nunez-Parra Zara M Patel Sébastien Fiorucci Carl M Philpott Barry C Smith Johan N Lundström Carla Mucignat Jane K Parker Mirjam van den Brink Michael Schmuker Florian Ph S Fischmeister Thomas Heinbockel Vonnie D C Shields Farhoud Faraji Enrique Santamaría William E A Fredborg Gabriella Morini Jonas K Olofsson Maryam Jalessi Noam Karni Anna D'Errico Rafieh Alizadeh Robert Pellegrino Pablo Meyer Caroline Huart Ben Chen Graciela M Soler Mohammed K Alwashahi Antje Welge-Lüssen Jessica Freiherr Jasper H B de Groot Hadar Klein Masako Okamoto Preet Bano Singh Julien W Hsieh Danielle R Reed Thomas Hummel Steven D Munger John E Hayes Department of Psychology Temple University Philadelphia PA USA. Institute of Neuroscience and Medicine (INM-3) Research Center Jülich Wilhelm-Johnen-Straße Jülich Germany. Department of Anatomy Faculty of Medicine Mersin University Çiftlikköy Campus Yenişehir Mersin Turkey. Institute of Biochemistry Food Science and Nutrition The Hebrew University of Jerusalem Rehovot Israel. AbScent Andover Hampshire UK. Department of Food Science The Pennsylvania State University Erickson Food Science Building University Park PA USA. Center for the Neurobiology of Learning and Memory University of California and Qureshey Research Laboratory Irvine CA USA. Institut de Chimie de Nice UMR CNRS 7272 Université Côte d'Azur Avenue Valrose Nice France. Centre for Global Health Research Usher Institute The University of Edinburgh Old Medical School Teviot Place Edinburgh UK. Department of Basic Medical Sciences Neuroscience and Sensory Organs Università degli Studi di Bari A. Moro P.zza G. Cesare Bari Italy. CSIR-Central Scientific Instruments Organisation Chandigarh India. Department of Medical and Surgical Sciences "Magna Graecia" University of Catanzaro Viale Europa (Loc. Germaneto) Catanzaro Italy. Department of Food Science and Human Nutrition and Division of Nutritional Sciences University of Illinois at Urbana Champaign Urbana IL USA. Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna Währinger Gürtel Vienna Austria. Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthese UMR 5288 CNRS Universitéde Toulouse Toulouse France. National Centre for Biological Sciences Tata Institute of Fundamental Research GKVK Campus Bengaluru India. School of Life Sciences Arizona State University Tempe AZ USA. Department of Psychology and Anthropology University of Extremadura Avenida de la Universidad s/n Cáceres Spain. Department of General Psychology University of Padova Via Venezia Padova Italy. Department of Psychiatry

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NWChem: Past, present, and future

arXiv

引用

arXiv 2020年

作者： Aprà, E. Bylaska, E.J. de Jong, W.A. Govind, N. Kowalski, K. Straatsma, T.P. Valiev, M. van Dam, H.J.J. Alexeev, Y. Anchell, J. Anisimov, V. Aquino, F.W. Atta-Fynn, R. Autschbach, J. Bauman, N.P. Becca, J.C. Bernholdt, D.E. Bhaskaran-Nair, K. Bogatko, S. Borowski, P. Boschen, J. Brabec, J. Bruner, A. Cauët, E. Chen, Y. Chuev, G.N. Cramer, C.J. Daily, J. Deegan, M.J.O. Dunning, T.H. Dupuis, M. Dyall, K.G. Fann, G.I. Fischer, S.A. Fonari, A. Früchtl, H. Gagliardi, L. Garza, J. Gawande, N. Ghosh, S. Glaesemann, K. Götz, A.W. Hammond, J. Helms, V. Hermes, E.D. Hirao, K. Hirata, S. Jacquelin, M. Jensen, L. Johnson, B.G. Jónsson, H. Kendall, R.A. Klemm, M. Kobayashi, R. Konkov, V. Krishnamoorthy, S. Krishnan, M. Lin, Z. Lins, R.D. Littlefield, R.J. Logsdail, A.J. Lopata, K. Ma, W. Marenich, A.V. Martin del Campo, J. Mejia-Rodriguez, D. Moore, J.E. Mullin, J.M. Nakajima, T. Nascimento, D.R. Nichols, J.A. Nichols, P.J. Nieplocha, J. Otero-de-la-Roza, A. Palmer, B. Panyala, A. Pirojsirikul, T. Peng, B. Peverati, R. Pittner, J. Pollack, L. Richard, R.M. Sadayappan, P. Schatz, G.C. Shelton, W.A. Silverstein, D.W. Smith, D.M.A. Soares, T.A. Song, D. Swart, M. Taylor, H.L. Thomas, G.S. Tipparaju, V. Truhlar, D.G. Tsemekhman, K. van Voorhis, T. Vázquez-Mayagoitia, Á. Verma, P. Villa, O. Vishnu, A. Vogiatzis, K.D. Wang, D. Weare, J.H. Williamson, M.J. Windus, T.L. Woliński, K. Wong, A.T. Wu, Q. Yang, C. Yu, Q. Zacharias, M. Zhang, Z. Zhao, Y. Harrison, R.J. Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Brookhaven National Laboratory UptonNY11973 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Intel Corporation Santa ClaraCA95054 United States QSimulate CambridgeMA02139 United States Department of Physics University of Texas at Arlington ArlingtonTX76019 United States Department of Chemistry University at Buffalo State University of New York BuffaloNY14260 United States Department of Chemistry Pennsylvania State University University ParkPA16802 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Washington University St. LouisMO63130 United States 4G Clinical WellesleyMA02481 United States Faculty of Chemistry Maria Curie-Sklodowska University in Lublin Lublin20-031 Poland Department of Chemistry Iowa State University AmesIA50011 United States J. Heyrovský Institute of Physical Chemistry Academy of Sciences of the Czech Republic Prague 818223 Czech Republic Department of Chemistry and Physics University of Tennessee at Martin MartinTN38238 United States Université libre de Bruxelles BrusselsB-1050 Belgium Facebook Menlo ParkCA94025 United States Institute of Theoretical and Experimental Biophysics Russian Academy of Science Pushchino Moscow142290 Russia Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota MinneapolisMN55455 United States SKAO Jodrell Bank Observatory MacclesfieldSK11 9DL United Kingdom Department of Chemistry University of Washington SeattleWA98195 United States Dirac Solutions PortlandOR97229 United States Chemistry Division U. S. Naval Research Laboratory WashingtonDC20375 United States School of Chemistry and Biochemis

Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the last few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach and outlook. Copyright © 2020, The Authors. All rights reserved.

关键词： Computational chemistry

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Generalized integrated interleaving BCH codes

Generalized integrated interleaving BCH codes

引用

IEEE International Symposium on Information Theory

作者： Yingquan Wu Department of Engineering University Campus Bio-Medico of Rome Unit of Computer Systems and Bioinformatics Italy Laboratory of Sciences and Techniques of Automatic control and computer engineering (Lab-STA) University of Sfax Sfax Tunisia

ISBN: (纸本)9781509018079

Tang and Koetter proposed generalized integrated interleaved Reed-Solomon codes in which each code of the second layer belongs to different subcode of the first layer code. However, the original scheme is inapplicable to binary BCH codes since its integrating coefficients are general finite field elements. In this work we propose a novel generalized integrated interleaving scheme over binary BCH codes, derive a lower bound on the minimum distance, and further present an algebraic encoding and decoding algorithm.

关键词： Encoding Decoding Redundancy Systematics Generators Power demand

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Future CSP in South Africa – A review of generation mix models, their assumptions, methods, results and implications

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AIP Conference Proceedings 2018年第1期2033卷

作者： D. Frank Duvenhage Omotoyosi O. Craig Alan C. Brent William H. L. Stafford 1Ph.D. candidates Engineering Management Stellenbosch University Department of Industrial Engineering Private Bag XI Matieland 7602 Stellenbosch South Africa 2Extraordinary Professor Department of Industrial Engineering and the Centre for Renewable and Sustainable Energy Studies Stellenbosch University South Africa 3Professor Sustainable Energy Systems Engineering and Computer Science Victoria University of Wellington New Zealand 4Extraordinary Associate Professor Department of Industrial Engineering Stellenbosch University South Africa 5Senior Researcher Green Economy Solutions Natural Resources and the Environment Unit Council for Scientific and Industrial Research South Africa

South Africa has experienced great uncertainty in its electricity supply sector. Many energy system modeling efforts have been undertaken. This is to determine the most appropriate energy mix and manage uncertainty. We review all available, relevant studies and summarize their results, modeling approaches, and input assumptions. From this, we try to understand their impact on CSP development in South Africa.

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Maging linear and circular polarization features in leaves with complete mueller matrix polarimetry

arXiv

引用

arXiv 2018年

作者： Patty, C.H. Lucas Luo, David A. Snik, Frans Ariese, Freek Buma, Wybren Jan Ten Kate, Inge Loes Van Spanning, Rob J.M. Sparks, William B. Germer, Thomas A. Garab, Gyozo Kudenov, Michael W. Molecular Cell Physiology VU Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Optical Sensing Lab Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands LaserLaB VU Amsterdam De Boelelaan 1083 Amsterdam1081 HV Netherlands HIMS Photonics group University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Department of Earth Sciences Utrecht University Budapestlaan 4 Utrecht3584 CD Netherlands Systems Bioinformatics VU Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Senior Science Division National Institute of Standards and Technology 100 Bureau Drive GaithersburgMD20899 United States Institute of Plant Biology Biological Research Centre Hungarian Academy of Sciences P.O. Box 521 SzegedH-6701 Hungary Department of Physics Faculty of Science University of Ostrava Chittussiho 10 Slezská Ostrava Czech Republic

Spectropolarimetry of intact plant leaves allows to probe the molecular architecture of vegetation photosynthesis in a non-invasive and non-destructive way and, as such, can offer a wealth of physiological information. In addition to the molecular signals due to the photosynthetic machinery, the cell structure and its arrangement within a leaf can create and modify polarization signals. Using Mueller matrix polarimetry with rotating retarder modulation, we have visualized spatial variations in polarization in transmission around the chlorophyll a absorbance band from 650 nm to 710 nm. We show linear and circular polarization measurements of maple leaves and cultivated maize leaves and discuss the corresponding Mueller matrices and the Mueller matrix decompositions, which show distinct features in diattenuation, polarizance, retardance and depolarization. Importantly, while normal leaf tissue shows a typical split signal with both a negative and a positive peak in the induced fractional circular polarization and circular dichroism, the signals close to the veins only display a negative band. The results are similar to the negative band as reported earlier for single macrodomains. We discuss the possible role of the chloroplast orientation around the veins as a cause of this phenomenon. Systematic artefacts are ruled out as three independent measurements by different instruments gave similar results. These results provide better insight into circular polarization measurements on whole leaves and options for vegetation remote sensing using circular polarization. Copyright © 2018, The Authors. All rights reserved.

关键词： Dichroism

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