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Related Concept Videos

Quality Assurance01:19

Quality Assurance

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...
Quality Control01:05

Quality Control

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.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Testing Water Quality01:14

Testing Water Quality

When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
Nursing Assessment01:29

Nursing Assessment

The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
The nurse collects all aspects of the patient's health in the initial assessment, establishing priorities for ongoing focused assessments and...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

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Related Experiment Video

Updated: Jun 21, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education
10:07

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

Applying Undertaker to quality assessment.

John G Archie1, Martin Paluszewski, Kevin Karplus

  • 1Department of Biomolecular Engineering, University of California, Santa Cruz, California 95064, USA.

Proteins
|July 30, 2009
PubMed
Summary
This summary is machine-generated.

The study evaluated three protein model quality assessment functions in CASP8. Functions incorporating more data, particularly consensus terms, significantly improved model ranking and accuracy estimation.

Related Experiment Videos

Last Updated: Jun 21, 2026

Using Learning Outcome Measures to assess Doctoral Nursing Education
10:07

Using Learning Outcome Measures to assess Doctoral Nursing Education

Published on: June 21, 2010

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Protein structure prediction is crucial for understanding biological function.
  • Accurate quality assessment of predicted protein models is essential for reliable analysis.
  • CASP (Critical Assessment of protein Structure Prediction) provides a benchmark for evaluating prediction methods.

Purpose of the Study:

  • To test and compare three protein model quality assessment functions developed by the group.
  • To evaluate the performance of these functions in ranking models and estimating accuracy (GDT_TS).
  • To assess the impact of incorporating different types of terms (distance constraints, single-model terms, consensus terms) on assessment quality.

Main Methods:

  • Three quality assessment functions were tested: SAM-T08-MQAO (distance constraints only), SAM-T08-MQAU (distance constraints + single-model terms), and SAM-T08-MQAC (single-model + consensus terms).
  • Functions were analyzed for their ability to rank models for a single target and estimate GDT_TS.
  • The functions were optimized for the ranking problem, suitable for metaserver applications.

Main Results:

  • Functions incorporating more terms demonstrated superior performance in the CASP8 assessment.
  • The SAM-T08-MQAC function, using consensus terms, significantly outperformed the single-model functions.
  • The SAM-T08-MQAU function showed substantial improvement over the SAM-T08-MQAO function.

Conclusions:

  • The inclusion of consensus terms and multiple data sources enhances the accuracy of protein model quality assessment.
  • The tested functions, particularly MQAC, show promise for improving protein structure prediction evaluation.
  • The findings suggest that more comprehensive quality assessment functions are beneficial for both model ranking and accuracy estimation.