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Related Concept Videos

Quality Assurance01:19

Quality Assurance

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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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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
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For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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SOFIevaluator: a strategy for the quantitative quality assessment of SOFI data.

Benjamien Moeyaert1,2, Wim Vandenberg1, Peter Dedecker1,3

  • 1Laboratory for NanoBiology, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Heverlee, Belgium.

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We developed SOFIevaluator, a new algorithm to assess super-resolution optical fluctuation imaging (SOFI) data quality. This tool helps evaluate image fidelity and identify optimal fluorescent proteins for SOFI applications.

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Area of Science:

  • Optical microscopy
  • Biophysics
  • Computational imaging

Background:

  • Super-resolution fluorescence imaging surpasses the diffraction limit of light.
  • Super-resolution optical fluctuation imaging (SOFI) uses computational analysis of blinking events for higher resolution.
  • Assessing the accuracy of SOFI images compared to sample structure is challenging.

Purpose of the Study:

  • To introduce SOFIevaluator, an unbiased, parameter-free algorithm for quantifying SOFI image quality.
  • To demonstrate SOFIevaluator's utility in selecting suitable fluorescent proteins for SOFI.

Main Methods:

  • Development of SOFIevaluator algorithm.
  • Application of SOFIevaluator to analyze simulated and experimental SOFI data.
  • Evaluation of fluorescent protein performance under various imaging conditions using SOFIevaluator.

Main Results:

  • SOFIevaluator provides objective metrics for SOFI data quality assessment.
  • The algorithm successfully quantifies image fidelity and resolution.
  • SOFIevaluator identified specific fluorescent proteins demonstrating superior performance in SOFI.

Conclusions:

  • SOFIevaluator offers a reliable method for evaluating SOFI image quality.
  • This tool aids researchers in optimizing SOFI experiments and data interpretation.
  • SOFIevaluator facilitates the selection of optimal fluorescent probes for advanced microscopy.