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相关概念视频

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

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相关实验视频

Updated: Jul 7, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

在随机网络中扩展的出现.

Barabasi1, Albert

  • 1Department of Physics, University of Notre Dame, Notre Dame, IN 46556, USA.

Science (New York, N.Y.)
|October 16, 1999
PubMed
概括

像万维网这样的大型网络,由于持续增长和优先附加,表现出无尺度的特性. 这表明,强大的自我组织原则控制着网络的发展.

科学领域:

  • 复杂系统科学 复杂系统科学
  • 网络理论 网络理论
  • 统计物理学的统计物理.

背景情况:

  • 许多大型系统,包括遗传网络和万维网,都表现出复杂的拓.
  • 这些网络的一个共同特征是顶点连接的无尺度功率定律分布.

研究的目的:

  • 确定负责复杂网络中无尺度属性的基本机制.
  • 开发一个模型来解释静态无尺度分布的出现.

主要方法:

  • 该研究提出了一个基于两个关键机制的模型:通过添加新的顶点来持续扩展网络.
  • 该模型包含优先连接,新顶点连接到已经连接良好的现有顶点.

主要成果:

  • 拟议的模型成功地复制了在复杂网络中观察到的静态无尺度分布.
  • 这些发现表明,网络发展是由通用的自我组织现象驱动的.

结论:

  • 大型网络的无尺度性质是简单,强大的自我组织机制的结果.
  • 这些机制独立于个别网络系统的具体细节运作,突出了网络增长的普遍原则.

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