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Related Concept Videos

Upsampling01:22

Upsampling

367
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...
367
Sampling Theorem01:15

Sampling Theorem

891
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.
891
Bandpass Sampling01:17

Bandpass Sampling

293
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
293
Aliasing01:18

Aliasing

304
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
304
Sampling Methods: Overview01:06

Sampling Methods: Overview

710
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...
710
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

595
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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Test Samples for Optimizing STORM Super-Resolution Microscopy
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Presampling Factors.

G V Iyengar1

  • 1Nuclear Research Center, D-517 Juelich, Federal Republic of Germany.

Journal of Research of the National Bureau of Standards (1977)
|August 4, 2021
PubMed
Summary
This summary is machine-generated.

Selecting human samples for trace element studies is challenging. Proper sample collection protocols are crucial for accurate analysis and meaningful public health insights.

Keywords:
biological systemsbiological variationsbiomedicalhuman tissues and body fluidsinternal contaminationintrinsic errorspost mortem changesprecision and accuracypresampling factorsreference valuessamplingtrace element analysisvalid samples

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Area of Science:

  • Human Physiology
  • Analytical Chemistry
  • Public Health

Background:

  • Trace element studies in human subjects face significant challenges in sample selection.
  • Practical limitations restrict clinically relevant specimens to whole blood, hair, nails, urine, and feces.
  • Autopsy samples offer organ diversity but are limited to activity monitoring, not clinical diagnosis.

Purpose of the Study:

  • To highlight the difficulties in obtaining valid human samples for trace element analysis.
  • To emphasize the importance of analytical and biological validity in specimen selection.
  • To address presampling factors influencing sample integrity.

Main Methods:

  • Discussion of common human sample types (blood, hair, urine, feces, autopsy organs).
  • Identification of presampling factors affecting sample validity, including biological variations and postmortem changes.
  • Emphasis on the need for well-defined sample protocols for accurate characterization.

Main Results:

  • Procuring "valid" samples requires consideration of both analytical and biological aspects.
  • Presampling factors like biological variations and contamination can alter sample status.
  • Inadequate sample protocols render analytical efforts ineffective.

Conclusions:

  • Effective trace element studies necessitate careful consideration of sample validity.
  • Addressing presampling factors and implementing standardized protocols are essential.
  • Interdisciplinary collaboration and teamwork are vital for advancing public health research using human samples.