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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Consider a man with a mass of 70 kg seated in a chair connected to a pin support through a member BC. If the man maintains an upright position, the task is to determine the horizontal and vertical reactions of the chair on the man when the member makes a 45° angle with the horizontal. At this moment, the man has a speed of 5 m/s, increasing at a rate of 1 m/s².
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Variance01:15

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 The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Titrimetric Analysis Based on Reaction Types01:01

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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Updated: Sep 3, 2025

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
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tmVar 3.0: an improved variant concept recognition and normalization tool.

Chih-Hsuan Wei1, Alexis Allot1, Kevin Riehle2

  • 1National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Bioinformatics (Oxford, England)
|July 29, 2022
PubMed
Summary
This summary is machine-generated.

tmVar 3.0 enhances automated variant recognition and normalization in scientific literature. This improved system achieves over 90% F-measure, offering broader entity coverage and advanced normalization for genetic variant data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Automated text-mining tools are crucial for large-scale extraction of genetic variant information from scientific literature.
  • Existing tools have limitations in recognition scope and precision for variant data.

Purpose of the Study:

  • To introduce tmVar 3.0, an advanced system for variant recognition and normalization.
  • To improve the accuracy and scope of automated extraction of genetic variants from biomedical texts.

Main Methods:

  • tmVar 3.0 was developed to recognize a wider range of variant entities, including allele and copy number variants.
  • The system groups variant mentions by genomic position for enhanced accuracy.
  • Advanced normalization options, such as ClinGen Allele Registry identifiers, are incorporated.

Main Results:

  • tmVar 3.0 demonstrates state-of-the-art performance with over 90% F-measure for both variant recognition and normalization.
  • Evaluation on three independent benchmarking datasets confirmed its high accuracy.
  • The system successfully handles diverse variant types and improves data consolidation.

Conclusions:

  • tmVar 3.0 significantly advances automated variant information extraction from scientific literature.
  • The tool offers improved precision and broader applicability compared to previous systems.
  • tmVar 3.0 is freely available, along with annotations for PubMed and PMC datasets.