Université Bourgogne Franche-Comté

We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under activedevelopment, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.

Abstract. By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.

Context. We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system. Aims. A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results. Methods. The raw data collected with the Gaia instruments during the first 22 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into this second data release, which represents a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products. Results. Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the G BP (330–680 nm) and G RP (630–1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14 000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia -CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent. Conclusions. Gaia DR2 represents a major achievement for the Gaia mission, delivering on the long standing promise to provide parallaxes and proper motions for over 1 billion stars, and representing a first step in the availability of complementary radial velocity and source astrophysical information for a sample of stars in the Gaia survey which covers a very substantial fraction of the volume of our galaxy.

Abstract The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy , which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.

Gaia is a cornerstone mission in the science programme of the EuropeanSpace Agency (ESA). The spacecraft construction was approved in 2006, following a study in which the original interferometric concept was changed to a direct-imaging approach. Both the spacecraft and the payload were built by European industry. The involvement of the scientific community focusses on data processing for which the international Gaia Data Processing and Analysis Consortium (DPAC) was selected in 2007. Gaia was launched on 19 December 2013 and arrived at its operating point, the second Lagrange point of the Sun-Earth-Moon system, a few weeks later. The commissioning of the spacecraft and payload was completed on 19 July 2014. The nominal five-year mission started with four weeks of special, ecliptic-pole scanning and subsequently transferred into full-sky scanning mode. We recall the scientific goals of Gaia and give a description of the as-built spacecraft that is currently (mid-2016) being operated to achieve these goals. We pay special attention to the payload module, the performance of which is closely related to the scientific performance of the mission. We provide a summary of the commissioning activities and findings, followed by a description of the routine operational mode. We summarise scientific performance estimates on the basis of in-orbit operations. Several intermediate Gaia data releases are planned and the data can be retrieved from the Gaia Archive, which is available through the Gaia home page.

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
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\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
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\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
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\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
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\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

This paper describes the Seventh Data Release of the Sloan Digital Sky Survey (SDSS), marking the completion of the original goals of the SDSS and the end of the phase known as SDSS-II. It includes 11,663 deg2 of imaging data, with most of the ~2000 deg2 increment over the previous data release lying in regions of low Galactic latitude. The catalog contains five-band photometry for 357 million distinct objects. The survey also includes repeat photometry on a 120° long, 2fdg5 wide stripe along the celestial equator in the Southern Galactic Cap, with some regions covered by as many as 90 individual imaging runs. We include a co-addition of the best of these data, going roughly 2 mag fainter than the main survey over 250 deg2. The survey has completed spectroscopy over 9380 deg2; the spectroscopy is now complete over a large contiguous area of the Northern Galactic Cap, closing the gap that was present in previous data releases. There are over 1.6 million spectra in total, including 930,000 galaxies, 120,000 quasars, and 460,000 stars. The data release includes improved stellar photometry at low Galactic latitude. The astrometry has all been recalibrated with the second version of the USNO CCD Astrograph Catalog, reducing the rms statistical errors at the bright end to 45 milliarcseconds per coordinate. We further quantify a systematic error in bright galaxy photometry due to poor sky determination; this problem is less severe than previously reported for the majority of galaxies. Finally, we describe a series of improvements to the spectroscopic reductions, including better flat fielding and improved wavelength calibration at the blue end, better processing of objects with extremely strong narrow emission lines, and an improved determination of stellar metallicities.

Full list of authors: Price-Whelan, Adrian M.; Lim, Pey Lian; Earl, Nicholas; Starkman, Nathaniel; Bradley, Larry; Shupe, David L.; Patil, Aarya A.; Corrales, Lia; Brasseur, C. E.; Noethe, Maximilian; Donath, Axel; Tollerud, Erik; Morris, Brett M.; Ginsburg, Adam; Vaher, Eero; Weaver, Benjamin A.; Tocknell, James; Jamieson, William; van Kerkwijk, Marten H.; Robitaille, Thomas P.; Merry, Bruce; Bachetti, Matteo; Gunther, H. Moritz; Aldcroft, Thomas L.; Alvarado-Montes, Jaime A.; Archibald, Anne M.; Bodi, Attila; Bapat, Shreyas; Barentsen, Geert; Bazan, Juanjo; Biswas, Manish; Boquien, Mederic; Burke, D. J.; Cara, Daria; Cara, Mihai; Conroy, Kyle E.; Conseil, Simon; Craig, Matthew W.; Cross, Robert M.; Cruz, Kelle L.; D'Eugenio, Francesco; Dencheva, Nadia; Devillepoix, Hadrien A. R.; Dietrich, Jorg P.; Eigenbrot, Arthur Davis; Erben, Thomas; Ferreira, Leonardo; Foreman-Mackey, Daniel; Fox, Ryan; Freij, Nabil; Garg, Suyog; Geda, Robel; Glattly, Lauren; Gondhalekar, Yash; Gordon, Karl D.; Grant, David; Greenfield, Perry; Groener, Austen M.; Guest, Steve; Gurovich, Sebastian; Handberg, Rasmus; Hart, Akeem; Hatfield-Dodds, Zac; Homeier, Derek; Hosseinzadeh, Griffin; Jenness, Tim; Jones, Craig K.; Joseph, Prajwel; Kalmbach, J. Bryce; Karamehmetoglu, Emir; Kaluszynski, Mikolaj; Kelley, Michael S. P.; Kern, Nicholas; Kerzendorf, Wolfgang E.; Koch, Eric W.; Kulumani, Shankar; Lee, Antony; Ly, Chun; Ma, Zhiyuan; MacBride, Conor; Maljaars, Jakob M.; Muna, Demitri; Murphy, N. A.; Norman, Henrik; O'Steen, Richard; Oman, Kyle A.; Pacifici, Camilla; Pascual, Sergio; Pascual-Granado, J.; Patil, Rohit R.; Perren, Gabriel, I; Pickering, Timothy E.; Rastogi, Tanuj; Roulston, Benjamin R.; Ryan, Daniel F.; Rykoff, Eli S.; Sabater, Jose; Sakurikar, Parikshit; Salgado, Jesus; Sanghi, Aniket; Saunders, Nicholas; Savchenko, Volodymyr; Schwardt, Ludwig; Seifert-Eckert, Michael; Shih, Albert Y.; Jain, Anany Shrey; Shukla, Gyanendra; Sick, Jonathan; Simpson, Chris; Singanamalla, Sudheesh; Singer, Leo P.; Singhal, Jaladh; Sinha, Manodeep; Sipocz, Brigitta M.; Spitler, Lee R.; Stansby, David; Streicher, Ole; Sumak, Jani; Swinbank, John D.; Taranu, Dan S.; Tewary, Nikita; Tremblay, Grant R.; De Val-Borro, Miguel; Vasovic, Zlatan; Van Kooten, Samuel J.; Verma, Shresth; Cardoso, Jose Vinicius de Miranda; Williams, Peter K. G.; Wilson, Tom J.; Winkel, Benjamin; Wood-Vasey, W. M.; Xue, Rui; Yoachim, Peter; Zhang, Chen; Zonca, Andrea; Astropy Project Contributors; TARDIS Collaboration; Astropy Coordination Comm.--This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Context. We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. Aims. A summary of the contents of Gaia EDR3 is presented, accompanied by a discussion on the differences with respect to Gaia DR2 and an overview of the main limitations which are present in the survey. Recommendations are made on the responsible use of Gaia EDR3 results. Methods. The raw data collected with the Gaia instruments during the first 34 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium and turned into this early third data release, which represents a major advance with respect to Gaia DR2 in terms of astrometric and photometric precision, accuracy, and homogeneity. Results. Gaia EDR3 contains celestial positions and the apparent brightness in G for approximately 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the ( G BP − G RP ) colour are also available. The passbands for G , G BP , and G RP are provided as part of the release. For ease of use, the 7 million radial velocities from Gaia DR2 are included in this release, after the removal of a small number of spurious values. New radial velocities will appear as part of Gaia DR3. Finally, Gaia EDR3 represents an updated materialisation of the celestial reference frame (CRF) in the optical, the Gaia -CRF3, which is based solely on extragalactic sources. The creation of the source list for Gaia EDR3 includes enhancements that make it more robust with respect to high proper motion stars, and the disturbing effects of spurious and partially resolved sources. The source list is largely the same as that for Gaia DR2, but it does feature new sources and there are some notable changes. The source list will not change for Gaia DR3. Conclusions. Gaia EDR3 represents a significant advance over Gaia DR2, with parallax precisions increased by 30 per cent, proper motion precisions increased by a factor of 2, and the systematic errors in the astrometry suppressed by 30–40% for the parallaxes and by a factor ~2.5 for the proper motions. The photometry also features increased precision, but above all much better homogeneity across colour, magnitude, and celestial position. A single passband for G , G BP , and G RP is valid over the entire magnitude and colour range, with no systematics above the 1% level

BACKGROUND: Contemporary data for causes of vision impairment and blindness form an important basis of recommendations in public health policies. Refreshment of the Global Vision Database with recently published data sources permitted modelling of cause of vision loss data from 1990 to 2015, further disaggregation by cause, and forecasts to 2020. METHODS: In this systematic review and meta-analysis, we analysed published and unpublished population-based data for the causes of vision impairment and blindness from 1980 to 2014. We identified population-based studies published before July 8, 2014, by searching online databases with no language restrictions (MEDLINE from Jan 1, 1946, and Embase from Jan 1, 1974, and the WHO Library Database). We fitted a series of regression models to estimate the proportion of moderate or severe vision impairment (defined as presenting visual acuity of <6/18 but ≥3/60 in the better eye) and blindness (presenting visual acuity of <3/60 in the better eye) by cause, age, region, and year. FINDINGS: We identified 288 studies of 3 983 541 participants contributing data from 98 countries. Among the global population with moderate or severe vision impairment in 2015 (216·6 million [80% uncertainty interval 98·5 million to 359·1 million]), the leading causes were uncorrected refractive error (116·3 million [49·4 million to 202·1 million]), cataract (52·6 million [18·2 million to 109·6 million]), age-related macular degeneration (8·4 million [0·9 million to 29·5 million]), glaucoma (4·0 million [0·6 million to 13·3 million]), and diabetic retinopathy (2·6 million [0·2 million to 9·9 million]). Among the global population who were blind in 2015 (36·0 million [12·9 million to 65·4 million]), the leading causes were cataract (12·6 million [3·4 million to 28·7 million]), uncorrected refractive error (7·4 million [2·4 million to 14·8 million]), and glaucoma (2·9 million [0·4 million to 9·9 million]). By 2020, among the global population with moderate or severe vision impairment (237·1 million [101·5 million to 399·0 million]), the number of people affected by uncorrected refractive error is anticipated to rise to 127·7 million (51·0 million to 225·3 million), by cataract to 57·1 million (17·9 million to 124·1 million), by age-related macular degeneration to 8·8 million (0·8 million to 32·1 million), by glaucoma to 4·5 million (0·5 million to 15·4 million), and by diabetic retinopathy to 3·2 million (0·2 million to 12·9 million). By 2020, among the global population who are blind (38·5 million [13·2 million to 70·9 million]), the number of patients blind because of cataract is anticipated to rise to 13·4 million (3·3 million to 31·6 million), because of uncorrected refractive error to 8·0 million (2·5 million to 16·3 million), and because of glaucoma to 3·2 million (0·4 million to 11·0 million). Cataract and uncorrected refractive error combined contributed to 55% of blindness and 77% of vision impairment in adults aged 50 years and older in 2015. World regions varied markedly in the causes of blindness and vision impairment in this age group, with a low prevalence of cataract (<22% for blindness and 14·1-15·9% for vision impairment) and a high prevalence of age-related macular degeneration (>14% of blindness) as causes in the high-income subregions. Blindness and vision impairment at all ages in 2015 due to diabetic retinopathy (odds ratio 2·52 [1·48-3·73]) and cataract (1·21 [1·17-1·25]) were more common among women than among men, whereas blindness and vision impairment due to glaucoma (0·71 [0·57-0·86]) and corneal opacity (0·54 [0·43-0·66]) were more common among men than among women, with no sex difference related to age-related macular degeneration (0·91 [0·70-1·14]). INTERPRETATION: The number of people affected by the common causes of vision loss has increased substantially as the population increases and ages. Preventable vision loss due to cataract (reversible with surgery) and refractive error (reversible with spectacle correction) continue to cause most cases of blindness and moderate or severe vision impairment in adults aged 50 years and older. A large scale-up of eye care provision to cope with the increasing numbers is needed to address avoidable vision loss. FUNDING: Brien Holden Vision Institute.

A Weyl semimetal is a new state of matter that hosts Weyl fermions as emergent quasiparticles and admits a topological classification that protects Fermi arc surface states on the boundary of a bulk sample. This unusual electronic structure has deep analogies with particle physics and leads to unique topological properties. We report the experimental discovery of a Weyl semimetal, tantalum arsenide (TaAs). Using photoemission spectroscopy, we directly observe Fermi arcs on the surface, as well as the Weyl fermion cones and Weyl nodes in the bulk of TaAs single crystals. We find that Fermi arcs terminate on the Weyl fermion nodes, consistent with their topological character. Our work opens the field for the experimental study of Weyl fermions in physics and materials science.

BACKGROUND: Many causes of vision impairment can be prevented or treated. With an ageing global population, the demands for eye health services are increasing. We estimated the prevalence and relative contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020. We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error. METHODS: We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals [UIs]) of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness (<3/60 or less than 10° visual field around central fixation) by cause, age, region, and year. Because of data sparsity at younger ages, our analysis focused on adults aged 50 years and older. FINDINGS: Global crude prevalence of avoidable vision impairment and blindness in adults aged 50 years and older did not change between 2010 and 2019 (percentage change -0·2% [95% UI -1·5 to 1·0]; 2019 prevalence 9·58 cases per 1000 people [95% IU 8·51 to 10·8], 2010 prevalence 96·0 cases per 1000 people [86·0 to 107·0]). Age-standardised prevalence of avoidable blindness decreased by -15·4% [-16·8 to -14·3], while avoidable MSVI showed no change (0·5% [-0·8 to 1·6]). However, the number of cases increased for both avoidable blindness (10·8% [8·9 to 12·4]) and MSVI (31·5% [30·0 to 33·1]). The leading global causes of blindness in those aged 50 years and older in 2020 were cataract (15·2 million cases [9% IU 12·7-18·0]), followed by glaucoma (3·6 million cases [2·8-4·4]), undercorrected refractive error (2·3 million cases [1·8-2·8]), age-related macular degeneration (1·8 million cases [1·3-2·4]), and diabetic retinopathy (0·86 million cases [0·59-1·23]). Leading causes of MSVI were undercorrected refractive error (86·1 million cases [74·2-101·0]) and cataract (78·8 million cases [67·2-91·4]). INTERPRETATION: Results suggest eye care services contributed to the observed reduction of age-standardised rates of avoidable blindness but not of MSVI, and that the target in an ageing global population was not reached. FUNDING: Brien Holden Vision Institute, Fondation Théa, The Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg.

Abstract We present optical light curves, redshifts, and classifications for spectroscopically confirmed Type Ia supernovae (SNe Ia) discovered by the Pan-STARRS1 (PS1) Medium Deep Survey. We detail improvements to the PS1 SN photometry, astrometry, and calibration that reduce the systematic uncertainties in the PS1 SN Ia distances. We combine the subset of PS1 SNe Ia (0.03 &lt; z &lt; 0.68) with useful distance estimates of SNe Ia from the Sloan Digital Sky Survey (SDSS), SNLS, and various low- z and Hubble Space Telescope samples to form the largest combined sample of SNe Ia, consisting of a total of SNe Ia in the range of 0.01 &lt; z &lt; 2.3, which we call the “Pantheon Sample.” When combining Planck 2015 cosmic microwave background (CMB) measurements with the Pantheon SN sample, we find and for the w CDM model. When the SN and CMB constraints are combined with constraints from BAO and local H 0 measurements, the analysis yields the most precise measurement of dark energy to date: and for the CDM model. Tension with a cosmological constant previously seen in an analysis of PS1 and low- z SNe has diminished after an increase of 2× in the statistics of the PS1 sample, improved calibration and photometry, and stricter light-curve quality cuts. We find that the systematic uncertainties in our measurements of dark energy are almost as large as the statistical uncertainties, primarily due to limitations of modeling the low-redshift sample. This must be addressed for future progress in using SNe Ia to measure dark energy.

Citation: Alam, S., Albareti, F. D., Prieto, C. A., Anders, F., Anderson, S. F., Anderton, T., . . . Zhu, G. T. (2015). THE ELEVENTH AND TWELFTH DATA RELEASES OF THE SLOAN DIGITAL SKY SURVEY: FINAL DATA FROM SDSS-III. Astrophysical Journal Supplement Series, 219(1), 27. doi:10.1088/0067-0049/219/1/12

Although there are many methods of fabricating nanofibres, electrospinning is perhaps the most versatile process. Materials such as polymer, composites, ceramic and metal nanofibres have been fabricated using electrospinning directly or through post-spinning processes. However, what makes electrospinning different from other nanofibre fabrication processes is its ability to form various fibre assemblies. This will certainly enhance the performance of products made from nanofibres and allow application specific modifications. It is therefore vital for us to understand the various parameters and processes that allow us to fabricate the desired fibre assemblies. Fibre assemblies that can be fabricated include nonwoven fibre mesh, aligned fibre mesh, patterned fibre mesh, random three-dimensional structures and sub-micron spring and convoluted fibres. Nevertheless, more studies are required to understand and precisely control the actual mechanics in the formation of various electrospun fibrous assemblies.

We introduce the Illustris Project, a series of large-scale hydrodynamical simulations of galaxy formation. The highest resolution simulation, Illustris-1, covers a volume of (106.5 Mpc)³, has a dark mass resolution of 6.26 × 10⁶M⊙, and an initial baryonic matter mass resolution of 1.26 × 10⁶M⊙. At z = 0 gravitational forces are softened on scales of 710 pc, and the smallest hydrodynamical gas cells have an extent of 48 pc. We follow the dynamical evolution of 2 × 1820³ resolution elements and in addition passively evolve 1820³ Monte Carlo tracer particles reaching a total particle count of more than 18 billion. The galaxy formation model includes: Primordial and metal-line cooling with self-shielding corrections, stellar evolution, stellar feedback, gas recycling, chemical enrichment, supermassive black hole growth, and feedback from active galactic nuclei. Here we describe the simulation suite, and contrast basic predictions of our model for the present-day galaxy population with observations of the local universe. At z = 0 our simulation volume contains about 40 000 well-resolved galaxies covering a diverse range of morphologies and colours including early-type, late-type and irregular galaxies. The simulation reproduces reasonably well the cosmic star formation rate density, the galaxy luminosity function, and baryon conversion efficiency at z = 0. It also qualitatively captures the impact of galaxy environment on the red fractions of galaxies. The internal velocity structure of selected well-resolved disc galaxies obeys the stellar and baryonic Tully-Fisher relation together with flat circular velocity curves. In the well-resolved regime, the simulation reproduces the observed mix of early-type and late-type galaxies. Our model predicts a halo mass dependent impact of baryonic effects on the halo mass function and the masses of haloes caused by feedback from supernova and active galactic nuclei.

BACKGROUND: Global and regional prevalence estimates for blindness and vision impairment are important for the development of public health policies. We aimed to provide global estimates, trends, and projections of global blindness and vision impairment. METHODS: We did a systematic review and meta-analysis of population-based datasets relevant to global vision impairment and blindness that were published between 1980 and 2015. We fitted hierarchical models to estimate the prevalence (by age, country, and sex), in 2015, of mild visual impairment (presenting visual acuity worse than 6/12 to 6/18 inclusive), moderate to severe visual impairment (presenting visual acuity worse than 6/18 to 3/60 inclusive), blindness (presenting visual acuity worse than 3/60), and functional presbyopia (defined as presenting near vision worse than N6 or N8 at 40 cm when best-corrected distance visual acuity was better than 6/12). FINDINGS: Globally, of the 7·33 billion people alive in 2015, an estimated 36·0 million (80% uncertainty interval [UI] 12·9-65·4) were blind (crude prevalence 0·48%; 80% UI 0·17-0·87; 56% female), 216·6 million (80% UI 98·5-359·1) people had moderate to severe visual impairment (2·95%, 80% UI 1·34-4·89; 55% female), and 188·5 million (80% UI 64·5-350·2) had mild visual impairment (2·57%, 80% UI 0·88-4·77; 54% female). Functional presbyopia affected an estimated 1094·7 million (80% UI 581·1-1686·5) people aged 35 years and older, with 666·7 million (80% UI 364·9-997·6) being aged 50 years or older. The estimated number of blind people increased by 17·6%, from 30·6 million (80% UI 9·9-57·3) in 1990 to 36·0 million (80% UI 12·9-65·4) in 2015. This change was attributable to three factors, namely an increase because of population growth (38·4%), population ageing after accounting for population growth (34·6%), and reduction in age-specific prevalence (-36·7%). The number of people with moderate and severe visual impairment also increased, from 159·9 million (80% UI 68·3-270·0) in 1990 to 216·6 million (80% UI 98·5-359·1) in 2015. INTERPRETATION: There is an ongoing reduction in the age-standardised prevalence of blindness and visual impairment, yet the growth and ageing of the world's population is causing a substantial increase in number of people affected. These observations, plus a very large contribution from uncorrected presbyopia, highlight the need to scale up vision impairment alleviation efforts at all levels. FUNDING: Brien Holden Vision Institute.

Chemically modified graphene and polyaniline (PANI) nanofiber composites were prepared by in situ polymerization of aniline monomer in the presence of graphene oxide under acid conditions. The obtained graphene oxide/PANI composites with different mass ratios were reduced to graphene using hydrazine followed by reoxidation and reprotonation of the reduced PANI to give the graphene/PANI nanocomposites. The morphology, composition, and electronic structure of the composites together with pure polyaniline fibers (PANI-F), graphene oxide (GO), and graphene (GR) were characterized using X-ray diffraction (XRD), solid-state 13C NMR, FT-IR, scanning electron microscope (SEM), transmission electron microscope (TEM), thermogravimetric analysis (TGA), and X-ray photoelectron spectroscopy (XPS). It was found that the chemically modified graphene and the PANI nanofibers formed a uniform nanocomposite with the PANI fibers absorbed on the graphene surface and/or filled between the graphene sheets. Such uniform structure together with the observed high conductivities afforded high specific capacitance and good cycling stability during the charge−discharge process when used as supercapacitor electrodes. A specific capacitance of as high as 480 F/g at a current density of 0.1 A/g was achieved over a PANI-doped graphene composite. The research data revealed that high specific capacitance and good cycling stability can be achieved either by doping chemically modified graphenes with PANI or by doping the bulky PANIs with graphene/graphene oxide.

The global energy transition towards a carbon neutral society requires a profound transformation of electricity generation and consumption, as well as of electric power systems. Hydrogen has an important potential to accelerate the process of scaling up clean and renewable energy, however its integration in power systems remains little studied. This paper reviews the current progress and outlook of hydrogen technologies and their application in power systems for hydrogen production, re-electrification and storage. The characteristics of electrolysers and fuel cells are demonstrated with experimental data and the deployments of hydrogen for energy storage, power-to-gas, co- and tri-generation and transportation are investigated using examples from worldwide projects. The current techno-economic status of these technologies and applications is presented, in which cost, efficiency and durability are identified as the main critical aspects. This is also confirmed by the results of a statistical analysis of the literature. Finally, conclusions show that continuous efforts on performance improvements, scale ramp-up, technical prospects and political support are required to enable a cost-competitive hydrogen economy.

We present an analysis of the host properties of 85 224 emission-line galaxies selected from the Sloan Digital Sky Survey. We show that Seyferts and low-ionization narrow emission-line regions (LINERs) form clearly separated branches on the standard optical diagnostic diagrams. We derive a new empirical classification scheme which cleanly separates star-forming galaxies, composite active galactic nucleus-H II (AGN-H II) galaxies, Seyferts and LINERs and we study the host galaxy properties of these different classes of objects. LINERs are older, more massive, less dusty, less concentrated, and they have higher velocity dispersions and lower [O III] luminosities than Seyfert galaxies have. Seyferts and LINERs are most strongly distinguished by their [O III] luminosities. We then consider the quantity L[O III]/ 4 , which is an indicator of the black hole accretion rate relative to the Eddington rate. Remarkably, we find that at fixed L[O III]/ 4 , all differences between Seyfert and LINER host properties disappear. LINERs and Seyferts form a continuous sequence, with LINERs dominant at low L/L EDD and Seyferts dominant at high L/L EDD . These results suggest that the majority of LINERs are AGN and that the Seyfert/LINER dichotomy is analogous to the high/low-state models and show that pure LINERs require a harder ionizing radiation field with lower ionization parameter than required by Seyfert galaxies, consistent with the low and high X-ray binary states.