University of the Western Cape

We present cosmological parameter results from the final full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction. Compared to the 2015 results, improved measurements of large-scale polarization allow the reionization optical depth to be measured with higher precision, leading to significant gains in the precision of other correlated parameters. Improved modelling of the small-scale polarization leads to more robust constraints on many parameters, with residual modelling uncertainties estimated to affect them only at the 0.5 σ level. We find good consistency with the standard spatially-flat 6-parameter ΛCDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted “base ΛCDM” in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density Ω c h 2 = 0.120 ± 0.001, baryon density Ω b h 2 = 0.0224 ± 0.0001, scalar spectral index n s = 0.965 ± 0.004, and optical depth τ = 0.054 ± 0.007 (in this abstract we quote 68% confidence regions on measured parameters and 95% on upper limits). The angular acoustic scale is measured to 0.03% precision, with 100 θ * = 1.0411 ± 0.0003. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-ΛCDM cosmology, the inferred (model-dependent) late-Universe parameters are: Hubble constant H 0 = (67.4 ± 0.5) km s −1 Mpc −1 ; matter density parameter Ω m = 0.315 ± 0.007; and matter fluctuation amplitude σ 8 = 0.811 ± 0.006. We find no compelling evidence for extensions to the base-ΛCDM model. Combining with baryon acoustic oscillation (BAO) measurements (and considering single-parameter extensions) we constrain the effective extra relativistic degrees of freedom to be N eff = 2.99 ± 0.17, in agreement with the Standard Model prediction N eff = 3.046, and find that the neutrino mass is tightly constrained to ∑ m ν < 0.12 eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base ΛCDM at over 2 σ , which pulls some parameters that affect the lensing amplitude away from the ΛCDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. The joint constraint with BAO measurements on spatial curvature is consistent with a flat universe, Ω K = 0.001 ± 0.002. Also combining with Type Ia supernovae (SNe), the dark-energy equation of state parameter is measured to be w 0 = −1.03 ± 0.03, consistent with a cosmological constant. We find no evidence for deviations from a purely power-law primordial spectrum, and combining with data from BAO, BICEP2, and Keck Array data, we place a limit on the tensor-to-scalar ratio r 0.002 < 0.06. Standard big-bang nucleosynthesis predictions for the helium and deuterium abundances for the base-ΛCDM cosmology are in excellent agreement with observations. The Planck base-ΛCDM results are in good agreement with BAO, SNe, and some galaxy lensing observations, but in slight tension with the Dark Energy Survey’s combined-probe results including galaxy clustering (which prefers lower fluctuation amplitudes or matter density parameters), and in significant, 3.6 σ , tension with local measurements of the Hubble constant (which prefer a higher value). Simple model extensions that can partially resolve these tensions are not favoured by the Planck data.

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,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\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,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\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,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\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 study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5' and 3' boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

We report on the implications for cosmic inflation of the 2018 release of the Planck cosmic microwave background (CMB) anisotropy measurements. The results are fully consistent with those reported using the data from the two previous Planck cosmological releases, but have smaller uncertainties thanks to improvements in the characterization of polarization at low and high multipoles. Planck temperature, polarization, and lensing data determine the spectral index of scalar perturbations to be n s = 0.9649 ± 0.0042 at 68% CL. We find no evidence for a scale dependence of n s , either as a running or as a running of the running. The Universe is found to be consistent with spatial flatness with a precision of 0.4% at 95% CL by combining Planck with a compilation of baryon acoustic oscillation data. The Planck 95% CL upper limit on the tensor-to-scalar ratio, r 0.002 < 0.10, is further tightened by combining with the BICEP2/Keck Array BK15 data to obtain r 0.002 < 0.056. In the framework of standard single-field inflationary models with Einstein gravity, these results imply that: (a) the predictions of slow-roll models with a concave potential, V ″( ϕ ) < 0, are increasingly favoured by the data; and (b) based on two different methods for reconstructing the inflaton potential, we find no evidence for dynamics beyond slow roll. Three different methods for the non-parametric reconstruction of the primordial power spectrum consistently confirm a pure power law in the range of comoving scales 0.005 Mpc −1 ≲ k ≲ 0.2 Mpc −1 . A complementary analysis also finds no evidence for theoretically motivated parameterized features in the Planck power spectra. For the case of oscillatory features that are logarithmic or linear in k , this result is further strengthened by a new combined analysis including the Planck bispectrum data. The new Planck polarization data provide a stringent test of the adiabaticity of the initial conditions for the cosmological fluctuations. In correlated, mixed adiabatic and isocurvature models, the non-adiabatic contribution to the observed CMB temperature variance is constrained to 1.3%, 1.7%, and 1.7% at 95% CL for cold dark matter, neutrino density, and neutrino velocity, respectively. Planck power spectra plus lensing set constraints on the amplitude of compensated cold dark matter-baryon isocurvature perturbations that are consistent with current complementary measurements. The polarization data also provide improved constraints on inflationary models that predict a small statistically anisotropic quadupolar modulation of the primordial fluctuations. However, the polarization data do not support physical models for a scale-dependent dipolar modulation. All these findings support the key predictions of the standard single-field inflationary models, which will be further tested by future cosmological observations.

BACKGROUND: Coronaviruses (CoVs) primarily cause enzootic infections in birds and mammals but, in the last few decades, have shown to be capable of infecting humans as well. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and, more recently, Middle-East respiratory syndrome (MERS) has demonstrated the lethality of CoVs when they cross the species barrier and infect humans. A renewed interest in coronaviral research has led to the discovery of several novel human CoVs and since then much progress has been made in understanding the CoV life cycle. The CoV envelope (E) protein is a small, integral membrane protein involved in several aspects of the virus' life cycle, such as assembly, budding, envelope formation, and pathogenesis. Recent studies have expanded on its structural motifs and topology, its functions as an ion-channelling viroporin, and its interactions with both other CoV proteins and host cell proteins. MAIN BODY: This review aims to establish the current knowledge on CoV E by highlighting the recent progress that has been made and comparing it to previous knowledge. It also compares E to other viral proteins of a similar nature to speculate the relevance of these new findings. Good progress has been made but much still remains unknown and this review has identified some gaps in the current knowledge and made suggestions for consideration in future research. CONCLUSIONS: The most progress has been made on SARS-CoV E, highlighting specific structural requirements for its functions in the CoV life cycle as well as mechanisms behind its pathogenesis. Data shows that E is involved in critical aspects of the viral life cycle and that CoVs lacking E make promising vaccine candidates. The high mortality rate of certain CoVs, along with their ease of transmission, underpins the need for more research into CoV molecular biology which can aid in the production of effective anti-coronaviral agents for both human CoVs and enzootic CoVs.

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7.
\n
\nAims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release.
\n
\nMethods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue.
\n
\nResults. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the Hipparcos and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ~3000 Cepheid and RR Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr-1 for the proper motions. A systematic component of ~0.3 mas should be added to the parallax uncertainties. For the subset of ~94 000 Hipparcos stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr-1. For the secondary astrometric data set, the typical uncertainty of the positions is ~10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ~0.03 mag over the magnitude range 5 to 20.7.
\n
\nConclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data.

Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05 <z< 0.4), and three years from SNLS (0.2 <z< 1), and it totals 740 spectroscopically confirmed type Ia supernovae with high-quality light curves.

Abstract The evidentiary basis of the currently accepted classification of living amphibians is discussed and shown not to warrant the degree of authority conferred on it by use and tradition. A new taxonomy of living amphibians is proposed to correct the deficiencies of the old one. This new taxonomy is based on the largest phylogenetic analysis of living Amphibia so far accomplished. We combined the comparative anatomical character evidence of Haas (2003) with DNA sequences from the mitochondrial transcription unit H1 (12S and 16S ribosomal RNA and tRNAValine genes, ≈ 2,400 bp of mitochondrial sequences) and the nuclear genes histone H3, rhodopsin, tyrosinase, and seven in absentia, and the large ribosomal subunit 28S (≈ 2,300 bp of nuclear sequences; ca. 1.8 million base pairs; x̄ = 3.7 kb/terminal). The dataset includes 532 terminals sampled from 522 species representative of the global diversity of amphibians as well as seven of the closest living relatives of amphibians for outgroup comparisons. The primary purpose of our taxon sampling strategy was to provide strong tests of the monophyly of all “family-group” taxa. All currently recognized nominal families and subfamilies were sampled, with the exception of Protohynobiinae (Hynobiidae). Many of the currently recognized genera were also sampled. Although we discuss the monophyly of genera, and provide remedies for nonmonophyly where possible, we also make recommendations for future research. A parsimony analysis was performed under Direct Optimization, which simultaneously optimizes nucleotide homology (alignment) and tree costs, using the same set of assumptions throughout the analysis. Multiple search algorithms were run in the program POY over a period of seven months of computing time on the AMNH Parallel Computing Cluster. Results demonstrate that the following major taxonomic groups, as currently recognized, are nonmonophyletic: Ichthyophiidae (paraphyletic with respect to Uraeotyphlidae), Caeciliidae (paraphyletic with respect to Typhlonectidae and Scolecomorphidae), Salamandroidea (paraphyletic with respect to Sirenidae), Leiopelmatanura (paraphyletic with respect to Ascaphidae), Discoglossanura (paraphyletic with respect to Bombinatoridae), Mesobatrachia (paraphyletic with respect to Neobatrachia), Pipanura (paraphyletic with respect to Bombinatoridae and Discoglossidae/Alytidae), Hyloidea (in the sense of containing Heleophrynidae; paraphyletic with respect to Ranoidea), Leptodactylidae (polyphyletic, with Batrachophrynidae forming the sister taxon of Myobatrachidae + Limnodynastidae, and broadly paraphyletic with respect to Hemiphractinae, Rhinodermatidae, Hylidae, Allophrynidae, Centrolenidae, Brachycephalidae, Dendrobatidae, and Bufonidae), Microhylidae (polyphyletic, with Brevicipitinae being the sister taxon of Hemisotidae), Microhylinae (poly/paraphyletic with respect to the remaining non-brevicipitine microhylids), Hyperoliidae (para/polyphyletic, with Leptopelinae forming the sister taxon of Arthroleptidae + Astylosternidae), Astylosternidae (paraphyletic with respect to Arthroleptinae), Ranidae (paraphyletic with respect to Rhacophoridae and Mantellidae). In addition, many subsidiary taxa are demonstrated to be nonmonophyletic, such as (1) Eleutherodactylus with respect to Brachycephalus; (2) Rana (sensu Dubois, 1992), which is polyphyletic, with various elements falling far from each other on the tree; and (3) Bufo, with respect to several nominal bufonid genera. A new taxonomy of living amphibians is proposed, and the evidence for this is presented to promote further investigation and data acquisition bearing on the evolutionary history of amphibians. The taxonomy provided is consistent with the International Code of Zoological Nomenclature (ICZN, 1999). Salient features of the new taxonomy are (1) the three major groups of living amphibians, caecilians/Gymnophiona, salamanders/Caudata, and frogs/Anura, form a monophyletic group, to which we restrict the name Amphibia; (2) Gymnophiona forms the sister taxon of Batrachia (salamanders + frogs) and is composed of two groups, Rhinatrematidae and Stegokrotaphia; (3) Stegokrotaphia is composed of two families, Ichthyophiidae (including Uraeotyphlidae) and Caeciliidae (including Scolecomorphidae and Typhlonectidae, which are regarded as subfamilies); (4) Batrachia is a highly corroborated monophyletic group, composed of two taxa, Caudata (salamanders) and Anura (frogs); (5) Caudata is composed of two taxa, Cryptobranchoidei (Cryptobranchidae and Hynobiidae) and Diadectosalamandroidei new taxon (all other salamanders); (6) Diadectosalamandroidei is composed of two taxa, Hydatinosalamandroidei new taxon (composed of Perennibranchia and Treptobranchia new taxon) and Plethosalamandroidei new taxon; (7) Perennibranchia is composed of Proteidae and Sirenidae; (8) Treptobranchia new taxon is composed of two taxa, Ambystomatidae (including Dicamptodontidae) and Salamandridae; (9) Plethosalamandroidei new taxon is composed of Rhyacotritonidae and Xenosalamandroidei new taxon; (10) Xenosalamandroidei is composed of Plethodontidae and Amphiumidae; (11) Anura is monophyletic and composed of two clades, Leiopelmatidae (including Ascaphidae) and Lalagobatrachia new taxon (all other frogs); (12) Lalagobatrachia is composed of two clades, Xenoanura (Pipidae and Rhinophrynidae) and Sokolanura new taxon (all other lalagobatrachians); (13) Bombinatoridae and Alytidae (former Discoglossidae) are each others' closest relatives and in a clade called Costata, which, excluding Leiopelmatidae and Xenoanura, forms the sister taxon of all other frogs, Acosmanura; (14) Acosmanura is composed of two clades, Anomocoela (= Pelobatoidea of other authors) and Neobatrachia; (15) Anomocoela contains Pelobatoidea (Pelobatidae and Megophryidae) and Pelodytoidea (Pelodytidae and Scaphiopodidae), and forms the sister taxon of Neobatrachia, together forming Acosmanura; (16) Neobatrachia is composed of two clades, Heleophrynidae, and all other neobatrachians, Phthanobatrachia new taxon; (17) Phthanobatrachia is composed of two major units, Hyloides and Ranoides; (18) Hyloides comprises Sooglossidae (including Nasikabatrachidae) and Notogaeanura new taxon (the remaining hyloids); (19) Notogaeanura contains two taxa, Australobatrachia new taxon and Nobleobatrachia new taxon; (20) Australobatrachia is a clade composed of Batrachophrynidae and its sister taxon, Myobatrachoidea (Myobatrachidae and Limnodynastidae), which forms the sister taxon of all other hyloids, excluding sooglossids; (21) Nobleobatrachia new taxon, is dominated at its base by frogs of a treefrog morphotype, several with intercalary phalangeal cartilages—Hemiphractus (Hemiphractidae) forms the sister taxon of the remaining members of this group, here termed Meridianura new taxon; (22) Meridianura comprises Brachycephalidae (former Eleutherodactylinae + Brachycephalus) and Cladophrynia new taxon; (23) Cladophrynia is composed of two groups, Cryptobatrachidae (composed of Cryptobatrachus and Stefania, previously a fragment of the polyphyletic Hemiphractinae) and Tinctanura new taxon; (24) Tinctanura is composed of Amphignathodontidae (Gastrotheca and Flectonotus, another fragment of the polyphyletic Hemiphractinae) and Athesphatanura new taxon; (25) Athesphatanura is composed of Hylidae (Hylinae, Pelodryadinae, and Phyllomedusinae, and excluding former Hemiphractinae, whose inclusion would have rendered this taxon polyphyletic) and Leptodactyliformes new taxon; (26) Leptodactyliformes is composed of Diphyabatrachia new taxon (composed of Centrolenidae [including Allophryne] and Leptodactylidae, sensu stricto, including Leptodactylus and relatives) and Chthonobatrachia new taxon; (27) Chthonobatrachia is composed of a reformulated Ceratophryidae (which excludes such genera as Odontophrynus and Proceratophrys and includes other taxa, such as Telmatobius) and Hesticobatrachia new taxon; (28) Hesticobatrachia is composed of a reformulated Cycloramphidae (which includes Rhinoderma) and Agastorophrynia new taxon; (29) Agastorophrynia is composed of Bufonidae (which is partially revised) and Dendrobatoidea (Dendrobatidae and Thoropidae); (30) Ranoides new taxon forms the sister taxon of Hyloides and is composed of two major monophyletic components, Allodapanura new taxon (microhylids, hyperoliids, and allies) and Natatanura new taxon (ranids and allies); (31) Allodapanura is composed of Microhylidae (which is partially revised) and Afrobatrachia new taxon; (32) Afrobatrachia is composed of Xenosyneunitanura new taxon (the “strange-bedfellows” Brevicipitidae [formerly in Microhylidae] and Hemisotidae) and a more normal-looking group of frogs, Laurentobatrachia new taxon (Hyperoliidae and Arthroleptidae, which includes Leptopelinae and former Astylosternidae); (33) Natatanura new taxon is composed of two taxa, the African Ptychadenidae and the worldwide Victoranura new taxon; (34) Victoranura is composed of Ceratobatrachidae and Telmatobatrachia new taxon; (35) Telmatobatrachia is composed of Micrixalidae and a worldwide group of ranoids, Ametrobatrachia new taxon; (36) Ametrobatrachia is composed of Africanura new taxon and Saukrobatrachia new taxon; (37) Africanura is composed of two taxa: Phrynobatrachidae (Phrynobatrachus, including Dimorphognathus and Phrynodon as synonyms) and Pyxicephaloidea; (38) Pyxicephaloidea is composed of Petropedetidae (Conraua, Indirana, Arthroleptides, and Petropedetes), and Pyxicephalidae (including a number of African genera, e.g. Amietia [including Afrana], Arthroleptella, Pyxicephalus, Strongylopus, and Tomopterna); and (39) Saukrobatrachia new taxon is the sister taxon of Africanura and is composed of Dicroglossidae and Aglaioanura new taxon, which is, in turn, composed of Rhacophoroidea (Mantellidae and Rhacophoridae) and Ranoidea (Nyctibatrachidae and Ranidae, sensu stricto). Many generic revisions are made either to render a monophyle

In the original version, the bounds given in Eqs. (87a) and (87b) on the contribution to the early-time optical depth, (15,30), contained a numerical error in deriving the 95th percentile from the Monte Carlo samples. The corrected 95% upper bounds are: τ(15,30) &amp;lt; 0:018 (lowE, flat τ(15, 30), FlexKnot), (1) τ(15, 30) &amp;lt; 0:023 (lowE, flat knot, FlexKnot): (2) These bounds are a factor of 3 larger than the originally reported results. Consequently, the new bounds do not significantly improve upon previous results from Planck data presented in Millea &amp;amp; Bouchet (2018) as was stated, but are instead comparable. Equations (1) and (2) give results that are now similar to those of Heinrich &amp;amp; Hu (2021), who used the same Planck 2018 data to derive a 95% upper bound of 0.020 using the principal component analysis (PCA) model and uniform priors on the PCA mode amplitudes.

The European Space Agency’s Planck satellite, which was dedicated to studying the early Universe and its subsequent evolution, was launched on 14 May 2009. It scanned the microwave and submillimetre sky continuously between 12 August 2009 and 23 October 2013, producing deep, high-resolution, all-sky maps in nine frequency bands from 30 to 857 GHz. This paper presents the cosmological legacy of Planck , which currently provides our strongest constraints on the parameters of the standard cosmological model and some of the tightest limits available on deviations from that model. The 6-parameter ΛCDM model continues to provide an excellent fit to the cosmic microwave background data at high and low redshift, describing the cosmological information in over a billion map pixels with just six parameters. With 18 peaks in the temperature and polarization angular power spectra constrained well, Planck measures five of the six parameters to better than 1% (simultaneously), with the best-determined parameter ( θ * ) now known to 0.03%. We describe the multi-component sky as seen by Planck , the success of the ΛCDM model, and the connection to lower-redshift probes of structure formation. We also give a comprehensive summary of the major changes introduced in this 2018 release. The Planck data, alone and in combination with other probes, provide stringent constraints on our models of the early Universe and the large-scale structure within which all astrophysical objects form and evolve. We discuss some lessons learned from the Planck mission, and highlight areas ripe for further experimental advances.

Assessing Biodiversity Declines Understanding human impact on biodiversity depends on sound quantitative projection. Pereira et al. (p. 1496 , published online 26 October) review quantitative scenarios that have been developed for four main areas of concern: species extinctions, species abundances and community structure, habitat loss and degradation, and shifts in the distribution of species and biomes. Declines in biodiversity are projected for the whole of the 21st century in all scenarios, but with a wide range of variation. Hoffmann et al. (p. 1503 , published online 26 October) draw on the results of five decades' worth of data collection, managed by the International Union for Conservation of Nature Species Survival Commission. A comprehensive synthesis of the conservation status of the world's vertebrates, based on an analysis of 25,780 species (approximately half of total vertebrate diversity), is presented: Approximately 20% of all vertebrate species are at risk of extinction in the wild, and 11% of threatened birds and 17% of threatened mammals have moved closer to extinction over time. Despite these trends, overall declines would have been significantly worse in the absence of conservation actions.

Modeling galaxy formation in a cosmological context presents one of the greatest challenges in astrophysics today due to the vast range of scales and numerous physical processes involved. Here we review the current status of models that employ two leading techniques to simulate the physics of galaxy formation: semianalytic models and numerical hydrodynamic simulations. We focus on a set of observational targets that describe the evolution of the global and structural properties of galaxies from roughly cosmic high noon (z ∼ 2–3) to the present. Although minor discrepancies remain, overall, models show remarkable convergence among different methods and make predictions that are in qualitative agreement with observations. Modelers have converged on a core set of physical processes that are critical for shaping galaxy properties. This core set includes cosmological accretion, strong stellar-driven winds that are more efficient at low masses, black hole feedback that preferentially suppresses star formation at high masses, and structural and morphological evolution through merging and environmental processes. However, all cosmological models currently adopt phenomenological implementations of many of these core processes, which must be tuned to observations. Many details of how these diverse processes interact within a hierarchical structure formation setting remain poorly understood. Emerging multiscale simulations are helping to bridge the gap between stellar and cosmological scales, placing models on a firmer, more physically grounded footing. Concurrently, upcoming telescope facilities will provide new challenges and constraints for models, particularly by directly constraining inflows and outflows through observations of gas in and around galaxies.

Many introduced plant species rely on mutualisms in their new habitats to overcome barriers to establishment and to become naturalized and, in some cases, invasive. Mutualisms involving animal-mediated pollination and seed dispersal, and symbioses between plant roots and microbiota often facilitate invasions. The spread of many alien plants, particularly woody ones, depends on pollinator mutualisms. Most alien plants are well served by generalist pollinators (insects and birds), and pollinator limitation does not appear to be a major barrier for the spread of introduced plants (special conditions relating to Ficus and orchids are described). Seeds of many of the most notorious plant invaders are dispersed by animals, mainly birds and mammals. Our review supports the view that tightly coevolved, plant-vertebrate seed dispersal systems are extremely rare. Vertebrate-dispersed plants are generally not limited reproductively by the lack of dispersers. Most mycorrhizal plants form associations with arbuscular mycorrhizal fungi which, because of their low specificity, do not seem to play a major role in facilitating or hindering plant invasions (except possibly on remote islands such as the Galapagos which are poor in arbuscular mycorrhizal fungi). The lack of symbionts has, however, been a major barrier for many ectomycorrhizal plants, notably for Pinus spp. in parts of the southern hemisphere. The roles of nitrogen-fixing associations between legumes and rhizobia and between actinorhizal plants and Frankia spp. in promoting or hindering invasions have been virtually ignored in the invasions literature. Symbionts required to induce nitrogen fixation in many plants are extremely widespread, but intentional introductions of symbionts have altered the invasibility of many, if not most, systems. Some of the world's worst invasive alien species only invaded after the introduction of symbionts. Mutualisms in the new environment sometimes re-unite the same species that form partnerships in the native range of the plant. Very often, however, different species are involved, emphasizing the diffuse nature of many (most) mutualisms. Mutualisms in new habitats usually duplicate functions or strategies that exist in the natural range of the plant. Occasionally, mutualisms forge totally novel combinations, with profound implications for the behaviour of the introduced plant in the new environment (examples are seed dispersal mutualisms involving wind-dispersed pines and cockatoos in Australia; and mycorrhizal associations involving plant roots and fungi). Many ecosystems are becoming more susceptible to invasion by introduced plants because: (a) they contain an increasing array of potential mutualistic partners (e.g. generalist frugivores and pollinators, mycorrhizal fungi with wide host ranges, rhizobia strains with infectivity across genera); and (b) conditions conductive for the establishment of various alien/alien synergisms are becoming more abundant. Incorporating perspectives on mutualisms in screening protocols will improve (but not perfect) our ability to predict whether a given plant species could invade a particular habitat.

The International Strawberry Sequencing Consortium reports the draft genome of the woodland strawberry (Fragaria vesca). The genome of this diploid species should serve as a reference genome for the Fragaria genus, as the cultivated strawberry (Fragaria × ananassa) is an octoploid where F. vesca is predicted to be a subgenome donor. The woodland strawberry, Fragaria vesca (2n = 2x = 14), is a versatile experimental plant system. This diminutive herbaceous perennial has a small genome (240 Mb), is amenable to genetic transformation and shares substantial sequence identity with the cultivated strawberry (Fragaria × ananassa) and other economically important rosaceous plants. Here we report the draft F. vesca genome, which was sequenced to ×39 coverage using second-generation technology, assembled de novo and then anchored to the genetic linkage map into seven pseudochromosomes. This diploid strawberry sequence lacks the large genome duplications seen in other rosids. Gene prediction modeling identified 34,809 genes, with most being supported by transcriptome mapping. Genes critical to valuable horticultural traits including flavor, nutritional value and flowering time were identified. Macrosyntenic relationships between Fragaria and Prunus predict a hypothetical ancestral Rosaceae genome that had nine chromosomes. New phylogenetic analysis of 154 protein-coding genes suggests that assignment of Populus to Malvidae, rather than Fabidae, is warranted.

The standard Λ Cold Dark Matter (ΛCDM) cosmological model provides a good description of a wide range of astrophysical and cosmological data. However, there are a few big open questions that make the standard model look like an approximation to a more realistic scenario yet to be found. In this paper, we list a few important goals that need to be addressed in the next decade, taking into account the current discordances between the different cosmological probes, such as the disagreement in the value of the Hubble constant H0, the σ8–S8 tension, and other less statistically significant anomalies. While these discordances can still be in part the result of systematic errors, their persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the necessity for new physics or generalisations beyond the standard model. In this paper, we focus on the 5.0σ tension between the Planck CMB estimate of the Hubble constant H0 and the SH0ES collaboration measurements. After showing the H0 evaluations made from different teams using different methods and geometric calibrations, we list a few interesting new physics models that could alleviate this tension and discuss how the next decade's experiments will be crucial. Moreover, we focus on the tension of the Planck CMB data with weak lensing measurements and redshift surveys, about the value of the matter energy density Ωm, and the amplitude or rate of the growth of structure (σ8,fσ8). We list a few interesting models proposed for alleviating this tension, and we discuss the importance of trying to fit a full array of data with a single model and not just one parameter at a time. Additionally, we present a wide range of other less discussed anomalies at a statistical significance level lower than the H0–S8 tensions which may also constitute hints towards new physics, and we discuss possible generic theoretical approaches that can collectively explain the non-standard nature of these signals. Finally, we give an overview of upgraded experiments and next-generation space missions and facilities on Earth that will be of crucial importance to address all these open questions.

Concerns about secondary use of data and limited opportunities for benefit-sharing have focused attention on the tension that Indigenous communities feel between (1) protecting Indigenous rights and interests in Indigenous data (including traditional knowledges) and (2) supporting open data, machine learning, broad data sharing, and big data initiatives. The International Indigenous Data Sovereignty Interest Group (within the Research Data Alliance) is a network of nation-state based Indigenous data sovereignty networks and individuals that developed the ‘CARE Principles for Indigenous Data Governance’ (Collective Benefit, Authority to Control, Responsibility, and Ethics) in consultation with Indigenous Peoples, scholars, non-profit organizations, and governments. The CARE Principles are people– and purpose-oriented, reflecting the crucial role of data in advancing innovation, governance, and self-determination among Indigenous Peoples. The Principles complement the existing data-centric approach represented in the ‘FAIR Guiding Principles for scientific data management and stewardship’ (Findable, Accessible, Interoperable, Reusable). The CARE Principles build upon earlier work by the Te Mana Raraunga Maori Data Sovereignty Network, US Indigenous Data Sovereignty Network, Maiam nayri Wingara Aboriginal and Torres Strait Islander Data Sovereignty Collective, and numerous Indigenous Peoples, nations, and communities. The goal is that stewards and other users of Indigenous data will ‘Be FAIR and CARE.’ In this first formal publication of the CARE Principles, we articulate their rationale, describe their relation to the FAIR Principles, and present examples of their application.

ABSTRACT We present the COSMOS2015 24 catalog, which contains precise photometric redshifts and stellar masses for more than half a million objects over the 2deg 2 COSMOS field. Including new images from the UltraVISTA-DR2 survey, Y-band images from Subaru/Hyper-Suprime-Cam, and infrared data from the Spitzer Large Area Survey with the Hyper-Suprime-Cam Spitzer legacy program, this near-infrared-selected catalog is highly optimized for the study of galaxy evolution and environments in the early universe. To maximize catalog completeness for bluer objects and at higher redshifts, objects have been detected on a χ 2 sum of the and z ++ images. The catalog contains objects in the 1.5 deg 2 UltraVISTA-DR2 region and objects are detected in the “ultra-deep stripes” (0.62 deg 2 ) at (3 σ , 3″, AB magnitude). Through a comparison with the zCOSMOS-bright spectroscopic redshifts, we measure a photometric redshift precision of = 0.007 and a catastrophic failure fraction of %. At , using the unique database of spectroscopic redshifts in COSMOS, we find = 0.021 and . The deepest regions reach a 90% completeness limit of to z = 4. Detailed comparisons of the color distributions, number counts, and clustering show excellent agreement with the literature in the same mass ranges. COSMOS2015 represents a unique, publicly available, valuable resource with which to investigate the evolution of galaxies within their environment back to the earliest stages of the history of the universe. The COSMOS2015 catalog is distributed via anonymous ftp and through the usual astronomical archive systems (CDS, ESO Phase 3, IRSA).

We describe the legacy Planck cosmic microwave background (CMB) likelihoods derived from the 2018 data release. The overall approach is similar in spirit to the one retained for the 2013 and 2015 data release, with a hybrid method using different approximations at low ( ℓ &lt; 30) and high ( ℓ ≥ 30) multipoles, implementing several methodological and data-analysis refinements compared to previous releases. With more realistic simulations, and better correction and modelling of systematic effects, we can now make full use of the CMB polarization observed in the High Frequency Instrument (HFI) channels. The low-multipole EE cross-spectra from the 100 GHz and 143 GHz data give a constraint on the ΛCDM reionization optical-depth parameter τ to better than 15% (in combination with the TT low- ℓ data and the high- ℓ temperature and polarization data), tightening constraints on all parameters with posterior distributions correlated with τ . We also update the weaker constraint on τ from the joint TEB likelihood using the Low Frequency Instrument (LFI) channels, which was used in 2015 as part of our baseline analysis. At higher multipoles, the CMB temperature spectrum and likelihood are very similar to previous releases. A better model of the temperature-to-polarization leakage and corrections for the effective calibrations of the polarization channels (i.e., the polarization efficiencies) allow us to make full use of polarization spectra, improving the ΛCDM constraints on the parameters θ MC , ω c , ω b , and H 0 by more than 30%, and n s by more than 20% compared to TT-only constraints. Extensive tests on the robustness of the modelling of the polarization data demonstrate good consistency, with some residual modelling uncertainties. At high multipoles, we are now limited mainly by the accuracy of the polarization efficiency modelling. Using our various tests, simulations, and comparison between different high-multipole likelihood implementations, we estimate the consistency of the results to be better than the 0.5 σ level on the ΛCDM parameters, as well as classical single-parameter extensions for the joint likelihood (to be compared to the 0.3 σ levels we achieved in 2015 for the temperature data alone on ΛCDM only). Minor curiosities already present in the previous releases remain, such as the differences between the best-fit ΛCDM parameters for the ℓ &lt; 800 and ℓ &gt; 800 ranges of the power spectrum, or the preference for more smoothing of the power-spectrum peaks than predicted in ΛCDM fits. These are shown to be driven by the temperature power spectrum and are not significantly modified by the inclusion of the polarization data. Overall, the legacy Planck CMB likelihoods provide a robust tool for constraining the cosmological model and represent a reference for future CMB observations.

We introduce the Simba simulations, the next generation of the Mufasa cosmological galaxy formation simulations run with Gizmo's meshless finite mass hydrodynamics. Simba includes updates to Mufasa's sub-resolution star formation and feedback prescriptions, and introduces black hole growth via the torque-limited accretion model of Angls-Alczar et al. (2017a) from cold gas and Bondi accretion from hot gas, along with black hole feedback via kinetic bipolar outflows and X-ray energy. Ejection velocities are taken to be 10 3 km s -1 at high Eddington ratios, increasing to 8000 km s -1 at Eddington ratios below 2%, with a constant momentum input of 20L/c. Simba further includes an on-the-fly dust production, growth, and destruction model. Our Simba run with (100h -1 Mpc) 3 and 1024 3 gas elements reproduces numerous observables, including galaxy stellar mass functions at z = 0 -6, the stellar mass-star formation rate main sequence, H i and H 2 fractions, the mass-metallicity relation at z 0, 2, star-forming galaxy sizes, hot gas fractions in massive halos, and z = 0 galaxy dust properties. However, Simba also yields an insufficiently sharp truncation of the z = 0 mass function, and too-large sizes for low-mass quenched galaxies. We show that Simba's jet feedback is primarily responsible for quenching massive galaxies.