scispace - formally typeset
Search or ask a question
Institution

United States Department of Agriculture

GovernmentWashington D.C., District of Columbia, United States
About: United States Department of Agriculture is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Virus. The organization has 50600 authors who have published 90831 publications receiving 3496586 citations. The organization is also known as: USDA & Agriculture Department.
Topics: Population, Virus, Soil water, Gene, Salmonella


Papers
More filters
Journal ArticleDOI
TL;DR: In order to make experimental studies comparable and statistically meaningful, the article recommends the following formula: per cent control = 100(X - Y)/X, which eliminates errors due to deaths in the control sample which were not due to the insecticide.
Abstract: There are several statistical methods used in biology (entomology) for computing the effectiveness of an insecticide, based on relating the number of dead insects in the treated plat to the number of live ones in the untreated plat. In order to make experimental studies comparable and statistically meaningful, the article recommends the following formula: per cent control = 100(X - Y)/X, where X = % living in the untreated check sample and Y = % living in the treated sample. Calculation using this method eliminates errors due to deaths in the control sample which were not due to the insecticide. An example based on treatments of San Jose scale includes computation of probable errors for X and Y, and the significance of the difference between the two counts. Common biometric convention holds that when the difference between the results of two experiments is greater than three times its probable error, the results are significant and due to the treatment applied.

11,700 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present guidelines for watershed model evaluation based on the review results and project-specific considerations, including single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude.
Abstract: Watershed models are powerful tools for simulating the effect of watershed processes and management on soil and water resources. However, no comprehensive guidance is available to facilitate model evaluation in terms of the accuracy of simulated data compared to measured flow and constituent values. Thus, the objectives of this research were to: (1) determine recommended model evaluation techniques (statistical and graphical), (2) review reported ranges of values and corresponding performance ratings for the recommended statistics, and (3) establish guidelines for model evaluation based on the review results and project-specific considerations; all of these objectives focus on simulation of streamflow and transport of sediment and nutrients. These objectives were achieved with a thorough review of relevant literature on model application and recommended model evaluation methods. Based on this analysis, we recommend that three quantitative statistics, Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR), in addition to the graphical techniques, be used in model evaluation. The following model evaluation performance ratings were established for each recommended statistic. In general, model simulation can be judged as satisfactory if NSE > 0.50 and RSR < 0.70, and if PBIAS + 25% for streamflow, PBIAS + 55% for sediment, and PBIAS + 70% for N and P. For PBIAS, constituent-specific performance ratings were determined based on uncertainty of measured data. Additional considerations related to model evaluation guidelines are also discussed. These considerations include: single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude. A case study illustrating the application of the model evaluation guidelines is also provided.

9,386 citations

Journal ArticleDOI
Evan Bolyen1, Jai Ram Rideout1, Matthew R. Dillon1, Nicholas A. Bokulich1, Christian C. Abnet2, Gabriel A. Al-Ghalith3, Harriet Alexander4, Harriet Alexander5, Eric J. Alm6, Manimozhiyan Arumugam7, Francesco Asnicar8, Yang Bai9, Jordan E. Bisanz10, Kyle Bittinger11, Asker Daniel Brejnrod7, Colin J. Brislawn12, C. Titus Brown4, Benjamin J. Callahan13, Andrés Mauricio Caraballo-Rodríguez14, John Chase1, Emily K. Cope1, Ricardo Silva14, Christian Diener15, Pieter C. Dorrestein14, Gavin M. Douglas16, Daniel M. Durall17, Claire Duvallet6, Christian F. Edwardson, Madeleine Ernst14, Madeleine Ernst18, Mehrbod Estaki17, Jennifer Fouquier19, Julia M. Gauglitz14, Sean M. Gibbons20, Sean M. Gibbons15, Deanna L. Gibson17, Antonio Gonzalez14, Kestrel Gorlick1, Jiarong Guo21, Benjamin Hillmann3, Susan Holmes22, Hannes Holste14, Curtis Huttenhower23, Curtis Huttenhower24, Gavin A. Huttley25, Stefan Janssen26, Alan K. Jarmusch14, Lingjing Jiang14, Benjamin D. Kaehler25, Benjamin D. Kaehler27, Kyo Bin Kang14, Kyo Bin Kang28, Christopher R. Keefe1, Paul Keim1, Scott T. Kelley29, Dan Knights3, Irina Koester14, Tomasz Kosciolek14, Jorden Kreps1, Morgan G. I. Langille16, Joslynn S. Lee30, Ruth E. Ley31, Ruth E. Ley32, Yong-Xin Liu, Erikka Loftfield2, Catherine A. Lozupone19, Massoud Maher14, Clarisse Marotz14, Bryan D Martin20, Daniel McDonald14, Lauren J. McIver23, Lauren J. McIver24, Alexey V. Melnik14, Jessica L. Metcalf33, Sydney C. Morgan17, Jamie Morton14, Ahmad Turan Naimey1, Jose A. Navas-Molina34, Jose A. Navas-Molina14, Louis-Félix Nothias14, Stephanie B. Orchanian, Talima Pearson1, Samuel L. Peoples35, Samuel L. Peoples20, Daniel Petras14, Mary L. Preuss36, Elmar Pruesse19, Lasse Buur Rasmussen7, Adam R. Rivers37, Michael S. Robeson38, Patrick Rosenthal36, Nicola Segata8, Michael Shaffer19, Arron Shiffer1, Rashmi Sinha2, Se Jin Song14, John R. Spear39, Austin D. Swafford, Luke R. Thompson40, Luke R. Thompson41, Pedro J. Torres29, Pauline Trinh20, Anupriya Tripathi14, Peter J. Turnbaugh10, Sabah Ul-Hasan42, Justin J. J. van der Hooft43, Fernando Vargas, Yoshiki Vázquez-Baeza14, Emily Vogtmann2, Max von Hippel44, William A. Walters32, Yunhu Wan2, Mingxun Wang14, Jonathan Warren45, Kyle C. Weber46, Kyle C. Weber37, Charles H. D. Williamson1, Amy D. Willis20, Zhenjiang Zech Xu14, Jesse R. Zaneveld20, Yilong Zhang47, Qiyun Zhu14, Rob Knight14, J. Gregory Caporaso1 
TL;DR: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and R.K.P. and partial support was also provided by the following: grants NIH U54CA143925 and U54MD012388.
Abstract: QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.

8,821 citations

Journal ArticleDOI
TL;DR: Two new diets may prove to be a better choice than AIN-76A for long-term as well as short-term studies with laboratory rodents because of a better balance of essential nutrients.
Abstract: For sixteen years, the American Institute of Nutrition Rodent Diets, AIN-76 and AIN-76A, have been used extensively around the world. Because of numerous nutritional and technical problems encountered with the diet during this period, it was revised. Two new formulations were derived: AIN-93G for growth, pregnancy and lactation, and AIN-93M for adult maintenance. Some major differences in the new formulation of AIN-93G compared with AIN-76A are as follows: 7 g soybean oil/100 g diet was substituted for 5 g corn oil/100 g diet to increase the amount of linolenic acid; cornstarch was substituted for sucrose; the amount of phosphorus was reduced to help eliminate the problem of kidney calcification in female rats; L-cystine was substituted for DL-methionine as the amino acid supplement for casein, known to be deficient in the sulfur amino acids; manganese concentration was lowered to one-fifth the amount in the old diet; the amounts of vitamin E, vitamin K and vitamin B-12 were increased; and molybdenum, silicon, fluoride, nickel, boron, lithium and vanadium were added to the mineral mix. For the AIN-93M maintenance diet, the amount of fat was lowered to 40 g/kg diet from 70 g/kg diet, and the amount of casein to 140 g/kg from 200 g/kg in the AIN-93G diet. Because of a better balance of essential nutrients, the AIN-93 diets may prove to be a better choice than AIN-76A for long-term as well as short-term studies with laboratory rodents.

7,946 citations

Journal ArticleDOI
TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
Abstract: De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

6,369 citations


Authors

Showing all 50637 results

NameH-indexPapersCitations
Walter C. Willett3342399413322
Eric S. Lander301826525976
Patrick O. Brown183755200985
Peter W.F. Wilson181680139852
Roderick T. Bronson169679107702
Stanley B. Prusiner16874597528
Donald G. Truhlar1651518157965
Robert G. Webster15884390776
Joseph R. Ecker14838194860
James B. Meigs147574115899
Peter B. Jones145185794641
Michael F. Holick145767107937
David A. Jackson136109568352
Alicja Wolk13577866239
John F. Thompson132142095894
Network Information
Related Institutions (5)
Agricultural Research Service
58.6K papers, 2.1M citations

96% related

Kansas State University
46.9K papers, 1.4M citations

91% related

University of Guelph
50.5K papers, 1.7M citations

90% related

Institut national de la recherche agronomique
68.3K papers, 3.2M citations

90% related

University of Georgia
93.6K papers, 3.7M citations

90% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202310
2022118
20212,792
20202,708
20192,670
20182,624