United States Department of Agriculture

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.

A method of computing the effectiveness of an 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.

Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

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.

Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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.

AIN-93 Purified Diets for Laboratory Rodents: Final Report of the American Institute of Nutrition Ad Hoc Writing Committee on the Reformulation of the AIN-76A Rodent Diet

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.

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

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.