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Related Concept Videos

Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Automated Imaging and Analysis for the Quantification of Fluorescently Labeled Macropinosomes
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apQuant: Accurate Label-Free Quantification by Quality Filtering.

Johannes Doblmann1, Frederico Dusberger1, Richard Imre1,2

  • 1Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria.

Journal of Proteome Research
|October 24, 2018
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Summary
This summary is machine-generated.

This study introduces a bioinformatics approach to enhance label-free quantification in shotgun proteomics. The method uses Percolator for peptide quality assessment, yielding more accurate and reliable quantitative results with a controlled false discovery rate.

Keywords:
computational proteomicsdata analysis pipelinelabel-free quantificationmass spectrometryquantitative proteomics

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Label-free quantification (LFQ) is a vital technique in shotgun proteomics for analyzing large sample sets.
  • LFQ offers high dynamic range and sensitivity without the need for isotopic labeling.
  • Current LFQ algorithms can be limited in accuracy and reliability.

Purpose of the Study:

  • To develop and present a novel bioinformatics approach to significantly improve the accuracy and reliability of label-free quantification in shotgun proteomics.
  • To introduce Percolator for robust peptide quality assessment within the LFQ workflow.
  • To provide a freely accessible tool for the proteomics community.

Main Methods:

  • Implementation of a bioinformatics pipeline integrating Percolator for peptide quality assessment.
  • Utilizing false discovery rate (FDR) control for accurate and reliable quantitative result extraction.
  • Benchmarking the developed approach against existing algorithms using public proteomics datasets.

Main Results:

  • The developed bioinformatics approach demonstrates a significant improvement in label-free quantification accuracy and reliability.
  • Percolator-based peptide quality assessment effectively enhances the extraction of dependable quantitative data.
  • Benchmarking results show superior performance compared to currently available label-free quantification algorithms.

Conclusions:

  • The presented bioinformatics approach offers a substantial advancement for label-free quantification in shotgun proteomics.
  • Accurate peptide quality assessment is crucial for reliable quantitative proteomics.
  • The tool, apQuant, is available as a free node for Proteome Discoverer, facilitating its adoption.