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Related Concept Videos

What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
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The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
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Variation: Normal Distribution, Range, and Standard Deviation02:32

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Variation01:19

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ
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Quantifying spatio-temporal variation of invasion spread.

Joshua Goldstein1, Jaewoo Park2, Murali Haran2

  • 11 Social and Data Analytics Laboratory, Virginia Tech , 900 N Glebe Rd, Arlington, VA 22203 , USA.

Proceedings. Biological Sciences
|April 10, 2019
PubMed
Summary

Understanding invasive insect spread is crucial for ecological management. This study introduces a novel statistical method to map invasion speed and direction, aiding prediction and control efforts.

Keywords:
Gaussian processgypsy mothhemlock woolly adelgidinvasive speciesspatial gradients

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Area of Science:

  • Ecology
  • Statistical Modeling
  • Invasive Species Research

Background:

  • Invasive species pose significant environmental and ecological risks.
  • Predicting and managing invasions requires understanding spread patterns and drivers.
  • Current methods may lack detailed spatial and temporal resolution for spread dynamics.

Purpose of the Study:

  • To develop and validate a novel statistical framework for characterizing local invasive species spread.
  • To quantify the speed and direction of invasion with associated statistical uncertainties.
  • To link local spread rates to environmental factors for improved invasion management.

Main Methods:

  • Utilized a novel combination of statistical methods, including Gaussian process fitting to invasion waiting times.
  • Characterized the vector field of spread to determine local spread properties.
  • Employed simulated data and historical records of invasive insects for method validation.

Main Results:

  • The method accurately estimates the speed and direction of spread at specific locations.
  • Simulations using a stratified diffusion model confirmed the method's accuracy.
  • Demonstrated the linkage between local spread rates and environmental covariates using case studies.

Conclusions:

  • The developed method provides a robust tool for analyzing and predicting invasive species spread.
  • This approach enhances our ability to manage ecological invasions by identifying key drivers of spread.
  • An accompanying R-package is available to facilitate the application of these methods to new datasets.