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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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...
Sampling Plans01:23

Sampling Plans

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...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Convenience Sampling Method00:55

Convenience 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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...

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Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Let's get honest about sampling.

David L Mobley1

  • 1Department of Chemistry, University of New Orleans, New Orleans, LA 70148, USA. dmobley@gmail.com

Journal of Computer-Aided Molecular Design
|November 25, 2011
PubMed
Summary
This summary is machine-generated.

Molecular simulations offer benefits for biomolecular design, but force field accuracy and reproducibility are key challenges. Careful evaluation and honest assessment of simulation results, particularly convergence, are crucial for reliable scientific discovery.

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Area of Science:

  • Computational chemistry
  • Biomolecular modeling
  • Drug discovery

Background:

  • Molecular simulations are increasingly used in computational and molecular design, particularly for biomolecular binding and interactions.
  • The accuracy of molecular force fields is a significant concern, with uncertainty regarding the required level of accuracy for practical benefits in discovery settings.
  • Current simulation tools and force fields show potential for real-world applications despite inherent limitations.

Purpose of the Study:

  • To assess the current state and potential benefits of molecular simulations in biomolecular design.
  • To highlight the challenges related to force field accuracy, reproducibility, and interpretation of simulation results.
  • To advocate for improved honesty in assessment and careful evaluation of simulation outcomes, focusing on convergence.

Main Methods:

  • Review and critical analysis of current molecular simulation tools and force fields.
  • Discussion of the implications of force field accuracy and simulation reproducibility in discovery settings.
  • Emphasis on the importance of result interpretation and convergence assessment.

Main Results:

  • Despite limitations, current molecular simulation tools and force fields offer potential benefits for various applications.
  • Simulation tools can produce irreproducible results that are often poorly interpreted.
  • The level of force field accuracy needed for discovery payoffs is often unclear.

Conclusions:

  • Continued progress in molecular simulations requires greater honesty in assessing simulation capabilities and limitations.
  • Careful evaluation of simulation results, with a specific focus on convergence, is essential for reliable scientific discovery.
  • Addressing issues of force field accuracy and reproducibility will enhance the utility of molecular simulations in biomolecular design.