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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
01 Jun 1993
TL;DR: Polysaccharide-specific staining techniques reveal the existence and high abundance of a class of large, discrete, transparent particles in seawater and diatom cultures formed from dissolved exopolymers exuded by phytoplankton and bacteria, suggesting that they may alter the distributions and microenvironments of marine microbes in nature.
Abstract: Polysaccharide-specific staining techniques reveal the existence and high abundance of a class of large, discrete, transparent particles in seawater and diatom cultures formed from dissolved exopolymers exuded by phytoplankton and bacteria. Transparent exopolymer particles (TEP), ranged from 28 to 5000 particles ml−1 and 3 to 100s μm in longest dimension at five coastal stations off California. A high percentage of seemingly free-living bacteria (28–68%) were attached to these transparent sheets and films, suggesting that they may alter the distributions and microenvironments of marine microbes in nature. Preliminary coagulation experiments demonstrated that TEP are major agents in the aggregation of diatoms and in the formation of marine snow. The existence of microbial exudates acting as large, discrete particles, rather than as dissolved molecules or as coating on other particles, suggests that the transformation of dissolved organic matter into particulate form in the sea can occur via a rapid abiotic pathway as well as through conventional microbial uptake. The existence of these particles has far reaching implications for food web structure, microbial processes, carbon cycling and particulate flux in the ocean.

866 citations

Journal ArticleDOI
TL;DR: In this paper, the design, fabrication, and testing of several metamaterials that exhibit double negative medium properties at X band frequencies are reported, and the extraction of the effective permittivity and permeability for these metammaterials from reflection and transmission data at normal incidence is treated.
Abstract: The design, fabrication, and testing of several metamaterials that exhibit double negative (DNG) medium properties at X band frequencies are reported. DNG media are materials in which the permittivity and permeability are both negative. Simulation and experimental results are given that demonstrate the realization of DNG metamaterials matched to free-space. The extraction of the effective permittivity and permeability for these metamaterials from reflection and transmission data at normal incidence is treated. It is shown that the metamaterials studied exhibit DNG properties in the frequency range of interest.

865 citations

Journal ArticleDOI
28 May 2015-PeerJ
TL;DR: VirSorter is a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses.
Abstract: Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter's prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in "reverse" to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.

863 citations

Journal ArticleDOI
01 Mar 1999-Blood
TL;DR: It is demonstrated that FN-mediated adhesion confers a survival advantage for myeloma cells acutely exposed to cytotoxic drugs by inhibiting drug-induced apoptosis, which may explain how some cells survive initial drug exposure and eventually express classical mechanisms of drug resistance such as MDR1 overexpression.

862 citations

Journal ArticleDOI
TL;DR: A new grading scale that will be used by several family medicine and primary care journals and allowing readers to learn one taxonomy that will apply to many sources of evidence is developed, called the Strength of Recommendation Taxonomy.
Abstract: A large number of taxonomies are used to rate the quality of an individual study and the strength of a recommendation based on a body of evidence. We have developed a new grading scale that will be used by several family medicine and primary care journals (required or optional), with the goal of allowing readers to learn one taxonomy that will apply to many sources of evidence. Our scale is called the Strength of Recommendation Taxonomy. It addresses the quality, quantity, and consistency of evidence and allows authors to rate individual studies or bodies of evidence. The taxonomy is built around the information mastery framework, which emphasizes the use of patient-oriented outcomes that measure changes in morbidity or mortality. An A-level recommendation is based on consistent and good quality patient-oriented evidence; a B-level recommendation is based on inconsistent or limited quality patient-oriented evidence; and a C-level recommendation is based on consensus, usual practice, opinion, disease-oriented evidence, or case series for studies of diagnosis, treatment, prevention, or screening. Levels of evidence from 1 to 3 for individual studies also are defined. We hope that consistent use of this taxonomy will improve the ability of authors and readers to communicate about the translation of research into practice.

861 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