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Institution

University of Arizona

EducationTucson, Arizona, United States
About: University of Arizona is a education organization based out in Tucson, Arizona, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 63805 authors who have published 155998 publications receiving 6854915 citations. The organization is also known as: UA & U of A.
Topics: Population, Galaxy, Star formation, Redshift, Planet


Papers
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Journal ArticleDOI
Karen Wynn1
TL;DR: This article examined how and when children come to understand the way in which counting determines numerosity and learn the meanings of the number words and found that it takes children a long time (on the order of a year) to learn how the counting system represents numerosity.

777 citations

Journal ArticleDOI
01 Jun 2008-Chest
TL;DR: This chapter about antithrombotic therapy in atrial fibrillation (AF) is part of the American College of Chest Physicians Evidence-Based Guidelines Clinical Practice Guidelines (8th Edition).

777 citations

Journal ArticleDOI
TL;DR: Thousands of genes with significantly lower diversity in cultivated but not wild rice are identified, which represent candidate regions selected during domestication and should be valuable for breeding and for identifying agronomically important genes in rice.
Abstract: Rice is a staple crop that has undergone substantial phenotypic and physiological changes during domestication. Here we resequenced the genomes of 40 cultivated accessions selected from the major groups of rice and 10 accessions of their wild progenitors (Oryza rufipogon and Oryza nivara) to >15 × raw data coverage. We investigated genome-wide variation patterns in rice and obtained 6.5 million high-quality single nucleotide polymorphisms (SNPs) after excluding sites with missing data in any accession. Using these population SNP data, we identified thousands of genes with significantly lower diversity in cultivated but not wild rice, which represent candidate regions selected during domestication. Some of these variants are associated with important biological features, whereas others have yet to be functionally characterized. The molecular markers we have identified should be valuable for breeding and for identifying agronomically important genes in rice.

776 citations

Journal ArticleDOI
TL;DR: The DEEP2 Galaxy Redshift Survey (DEEP2) as discussed by the authors is the largest high-precision redshift survey of galaxies at z ~ 1 completed to date, covering an area of 2.8 deg^2 divided into four separate fields observed to a limiting apparent magnitude of R_(AB) = 24.1.
Abstract: We describe the design and data analysis of the DEEP2 Galaxy Redshift Survey, the densest and largest high-precision redshift survey of galaxies at z ~ 1 completed to date. The survey was designed to conduct a comprehensive census of massive galaxies, their properties, environments, and large-scale structure down to absolute magnitude M_B = −20 at z ~ 1 via ~90 nights of observation on the Keck telescope. The survey covers an area of 2.8 deg^2 divided into four separate fields observed to a limiting apparent magnitude of R_(AB) = 24.1. Objects with z ≾0.7 are readily identifiable using BRI photometry and rejected in three of the four DEEP2 fields, allowing galaxies with z > 0.7 to be targeted ~2.5 times more efficiently than in a purely magnitude-limited sample. Approximately 60% of eligible targets are chosen for spectroscopy, yielding nearly 53,000 spectra and more than 38,000 reliable redshift measurements. Most of the targets that fail to yield secure redshifts are blue objects that lie beyond z ~ 1.45, where the [O ii] 3727 A doublet lies in the infrared. The DEIMOS 1200 line mm^(−1) grating used for the survey delivers high spectral resolution (R ~ 6000), accurate and secure redshifts, and unique internal kinematic information. Extensive ancillary data are available in the DEEP2 fields, particularly in the Extended Groth Strip, which has evolved into one of the richest multiwavelength regions on the sky. This paper is intended as a handbook for users of the DEEP2 Data Release 4, which includes all DEEP2 spectra and redshifts, as well as for the DEEP2 DEIMOS data reduction pipelines. Extensive details are provided on object selection, mask design, biases in target selection and redshift measurements, the spec2d two-dimensional data-reduction pipeline, the spec1d automated redshift pipeline, and the zspec visual redshift verification process, along with examples of instrumental signatures or other artifacts that in some cases remain after data reduction. Redshift errors and catastrophic failure rates are assessed through more than 2000 objects with duplicate observations. Sky subtraction is essentially photon-limited even under bright OH sky lines; we describe the strategies that permitted this, based on high image stability, accurate wavelength solutions, and powerful B-spline modeling methods. We also investigate the impact of targets that appear to be single objects in ground-based targeting imaging but prove to be composite in Hubble Space Telescope data; they constitute several percent of targets at z ~ 1, approaching ~5%–10% at z > 1.5. Summary data are given that demonstrate the superiority of DEEP2 over other deep high-precision redshift surveys at z ~ 1 in terms of redshift accuracy, sample number density, and amount of spectral information. We also provide an overview of the scientific highlights of the DEEP2 survey thus far.

776 citations

Journal ArticleDOI
John L. Bowman1, Takayuki Kohchi2, Katsuyuki T. Yamato3, Jerry Jenkins4, Shengqiang Shu4, Kimitsune Ishizaki5, Shohei Yamaoka2, Ryuichi Nishihama2, Yasukazu Nakamura6, Frédéric Berger7, Catherine Adam4, Shiori S Aki8, Felix Althoff9, Takashi Araki2, Mario A. Arteaga-Vazquez10, Sureshkumar Balasubrmanian1, Kerrie Barry4, Diane Bauer4, Christian R. Boehm11, Liam N. Briginshaw1, Juan Caballero-Pérez12, Bruno Catarino13, Feng Chen14, Shota Chiyoda2, Mansi Chovatia4, Kevin M. Davies15, Mihails Delmans11, Taku Demura8, Tom Dierschke1, Tom Dierschke9, Liam Dolan13, Ana E. Dorantes-Acosta10, D. Magnus Eklund16, D. Magnus Eklund1, Stevie N. Florent1, Eduardo Flores-Sandoval1, Asao Fujiyama6, Hideya Fukuzawa2, Bence Galik, Daniel Grimanelli17, Jane Grimwood4, Ueli Grossniklaus18, Takahiro Hamada19, Jim Haseloff11, Alexander J. Hetherington13, Asuka Higo2, Yuki Hirakawa20, Yuki Hirakawa1, Hope Hundley4, Yoko Ikeda21, Keisuke Inoue2, Shin-ichiro Inoue20, Sakiko Ishida2, Qidong Jia14, Mitsuru Kakita20, Takehiko Kanazawa19, Takehiko Kanazawa22, Yosuke Kawai23, Tomokazu Kawashima24, Tomokazu Kawashima25, Megan Kennedy4, Keita Kinose2, Toshinori Kinoshita20, Yuji Kohara6, Eri Koide2, Kenji Komatsu26, Sarah Kopischke9, Minoru Kubo8, Junko Kyozuka23, Ulf Lagercrantz16, Shih-Shun Lin27, Erika Lindquist4, Anna Lipzen4, Chia-Wei Lu27, Efraín De Luna, Robert A. Martienssen28, Naoki Minamino19, Naoki Minamino22, Masaharu Mizutani5, Miya Mizutani2, Nobuyoshi Mochizuki2, Isabel Monte29, Rebecca A. Mosher30, Hideki Nagasaki, Hirofumi Nakagami31, Satoshi Naramoto23, Kazuhiko Nishitani23, Misato Ohtani8, Takashi Okamoto32, Masaki Okumura20, Jeremy Phillips4, Bernardo Pollak11, Anke Reinders33, Moritz Rövekamp18, Ryosuke Sano8, Shinichiro Sawa34, Marc W. Schmid18, Makoto Shirakawa2, Roberto Solano29, Alexander Spunde4, Noriyuki Suetsugu2, Sumio Sugano19, Akifumi Sugiyama2, Rui Sun2, Yutaka Suzuki19, Mizuki Takenaka35, Daisuke Takezawa36, Hirokazu Tomogane2, Masayuki Tsuzuki19, Takashi Ueda22, Masaaki Umeda8, John M. Ward33, Yuichiro Watanabe19, Kazufumi Yazaki2, Ryusuke Yokoyama23, Yoshihiro Yoshitake2, Izumi Yotsui, Sabine Zachgo9, Jeremy Schmutz4 
05 Oct 2017-Cell
TL;DR: Compared with other sequenced land plants, M. polymorpha exhibits low genetic redundancy in most regulatory pathways, with this portion of its genome resembling that predicted for the ancestral land plant.

774 citations


Authors

Showing all 64388 results

NameH-indexPapersCitations
Simon D. M. White189795231645
Julie E. Buring186950132967
David H. Weinberg183700171424
Richard Peto183683231434
Xiaohui Fan183878168522
Dennis S. Charney179802122408
Daniel J. Eisenstein179672151720
David Haussler172488224960
Carlos S. Frenk165799140345
Jian-Kang Zhu161550105551
Tobin J. Marks1591621111604
Todd Adams1541866143110
Jane A. Cauley15191499933
Wei Zheng1511929120209
Daniel L. Schacter14959290148
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023205
2022987
20217,005
20207,325
20196,716
20186,375