The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

Loukas Martinou - One of the best experts on this subject based on the ideXlab platform.

  • T
    internal, 2017
    Co-Authors: Loukas Martinou
    Abstract:

    A cross-functional or departmental project whose objective is to develop a Tool that will enhance a department’s or R&D’s functional/operational efficiency. Examples: Development of a method (e.g. UV Testing method) Development of an organizational process (e.g. Update of C-process, KICs, etc.) Development

Christopher J Petzold - One of the best experts on this subject based on the ideXlab platform.

  • a rapid Methods Development workflow for high throughput quantitative proteomic applications
    PLOS ONE, 2019
    Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J Petzold
    Abstract:

    Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

  • A rapid Methods Development workflow for high-throughput quantitative proteomic applications - Fig 4
    2019
    Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J Petzold
    Abstract:

    (A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.

Mitchell G Thompson - One of the best experts on this subject based on the ideXlab platform.

  • a rapid Methods Development workflow for high throughput quantitative proteomic applications
    PLOS ONE, 2019
    Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J Petzold
    Abstract:

    Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

  • A rapid Methods Development workflow for high-throughput quantitative proteomic applications - Fig 4
    2019
    Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J Petzold
    Abstract:

    (A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.

Paul D Adams - One of the best experts on this subject based on the ideXlab platform.

  • a rapid Methods Development workflow for high throughput quantitative proteomic applications
    PLOS ONE, 2019
    Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jennifer Gin, Jay D Keasling, Paul D Adams, Christopher J Petzold
    Abstract:

    Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific Methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition Methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

  • A rapid Methods Development workflow for high-throughput quantitative proteomic applications - Fig 4
    2019
    Co-Authors: Yan Chen, Mitchell G Thompson, William A Sharpless, Leanne Jade G Chan, Jay D Keasling, Paul D Adams, Jennifer W. Gin, Christopher J Petzold
    Abstract:

    (A) Central carbon pathways (glycolysis, lysine degradation, aromatic monomer degradation pathways, and tricarboxylic acid (TCA) cycle) in P. putida; (B-E) comparison of the relative protein abundances of P. putida grown on 10 mM of glucose, p-coumarate, and 5-aminovalerate carbon sources in MOPS media. The error bar shows the standard deviation of measured peak area of three biological replicates. Statistical significance of p-coumarate and 5-aminovalerate against glucose were calculated by moderated t-test with the limma package in R, and resulting p-values were adjusted using the Benjamini-Hochberg (BH) method. *, **, and *** indicate adjusted P < 0.05, 0.01 and 0.001, respectively.

Sally Hopewell - One of the best experts on this subject based on the ideXlab platform.

  • Cochrane Methods - twenty years experience in developing systematic review Methods.
    Systematic Reviews, 2013
    Co-Authors: Jackie Chandler, Sally Hopewell
    Abstract:

    : This year, The Cochrane Collaboration reached its 20th anniversary. It has played a pivotal role in the scientific Development of systematic reviewing and in the Development of review Methods to synthesize research evidence, primarily from randomized trials, to answer questions about the effects of healthcare interventions. We introduce a series of articles, which form this special issue describing the Development of systematic review Methods within The Cochrane Collaboration. We also discuss the impact of Cochrane Review Methods, and acknowledge the breadth and depth of Methods Development within The Cochrane Collaboration as part of the wider context of evidence synthesis. We conclude by considering the future Development of Methods for Cochrane Reviews.