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Related Concept Videos

Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Random Sampling Method01:09

Random Sampling Method

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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...
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Stratified Sampling Method01:16

Stratified Sampling Method

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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. 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.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure 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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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An Unbiased Approach of Sampling TEM Sections in Neuroscience
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A Framework to Simplify Combined Sampling Strategies in Rosetta.

Justin R Porter1, Brian D Weitzner2, Oliver F Lange3

  • 1School of Medicine, Washington University in St. Louis, Missouri, Washington, United States of America.

Plos One
|September 19, 2015
PubMed
Summary
This summary is machine-generated.

Computational structural biology searches macromolecule configurations. The Broker extension for Rosetta simplifies incorporating diverse constraints for biomolecular design and modeling.

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Area of Science:

  • Computational structural biology
  • Macromolecular modeling
  • Biomolecular design

Background:

  • Searching conformational space for low-energy macromolecule configurations is crucial.
  • High dimensionality of conformational space necessitates integrating prior knowledge into sampling algorithms.
  • Integrating multiple constraints into existing methods can be challenging.

Purpose of the Study:

  • To develop a framework for streamlining the incorporation of diverse constraints in macromolecular modeling.
  • To enhance the Rosetta macromolecular modeling suite with a flexible constraint integration system.

Main Methods:

  • Developed the Broker, an extension of the Rosetta suite.
  • The Broker combines small, independent modules, each implementing different constraint sets.
  • Demonstrated the Broker's expressiveness through code vignettes.

Main Results:

  • The Broker can express a wide range of protocols using constraints.
  • The framework enables rapid protocol development for biomolecular design and structural modeling.
  • Exposes Rosetta's core functionality to a wider user community.

Conclusions:

  • The Broker facilitates the integration of diverse constraints in computational biology.
  • This framework accelerates protocol development in macromolecular design and modeling.
  • Enhances accessibility to Rosetta's capabilities for various computational biology tasks.