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Related Concept Videos

pre-mRNA Processing02:01

pre-mRNA Processing

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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a “cap” to the 5’ end of the growing transcript. In this process, a 5’ phosphate is replaced by modified guanosine that has a methyl group attached to it (7-Methyl...
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In eukaryotic cells, transcripts made by RNA polymerase are modified and processed before exiting the nucleus. Unprocessed RNA is called precursor mRNA or pre-mRNA to distinguish it from mature mRNA.
Once about 20-40 ribonucleotides have been joined together by RNA polymerase, a group of enzymes adds a cap to the 5' end of the growing transcript. In this process, a 5' phosphate is replaced by modified guanosine that has a methyl group attached (7-methyl guanosine). This 5' cap helps...
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Chromatin Structure Regulates pre-mRNA Processing02:41

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In eukaryotic cells, nascent mRNA transcripts need to undergo many post-transcriptional modifications to reach the cell cytoplasm and translate into functional proteins. For a long time, transcription and pre-mRNA processing were considered two independent events that occur sequentially in the cell. However, it has now been well established that transcription and pre-mRNA processing are two simultaneous processes that are precisely regulated inside the cell.
The chromatin structure, especially...
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Pre-mRNA Processing: RNA Splicing01:36

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The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

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The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
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IR Spectrum01:19

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
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A Multimodal Wide-Field Fourier-Transform Raman Microscope
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A Novel Pre-Processing Algorithm Based on the Wavelet Transform for Raman Spectrum.

Yang Xi1, Yuee Li1, Zhizhen Duan1

  • 1School of Information Science & Engineering, Lanzhou University, China.

Applied Spectroscopy
|July 5, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new wavelet transform algorithm for automatic Raman signal extraction. The method effectively removes noise and corrects baseline drift, improving spectral analysis with minimal user intervention.

Keywords:
Raman spectroscopybaseline correctiondenoisingderivative spectrawavelet transform

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Area of Science:

  • Spectroscopy
  • Signal Processing
  • Chemometrics

Background:

  • Raman spectra acquisition is hindered by noise and fluorescent background.
  • These issues blur spectral data, complicating detection and imaging.
  • Automated signal extraction is crucial for efficient Raman analysis.

Purpose of the Study:

  • To develop a novel algorithm for automatic Raman signal extraction.
  • To address challenges of noise and baseline drift in Raman spectra.
  • To enhance the accuracy and efficiency of Raman detection and imaging.

Main Methods:

  • A wavelet transform-based algorithm integrating denoising and baseline correction.
  • Improved conventional-scale correlation for denoising.
  • Continuous wavelet transform with Haar wavelet for baseline correction, including peak detection and background subtraction.

Main Results:

  • The algorithm demonstrated satisfactory denoising performance on simulated and experimental Raman spectra.
  • Effective background correction capabilities were observed.
  • The method enables automatic signal extraction with minimal human intervention.

Conclusions:

  • The proposed wavelet transform algorithm offers an effective solution for noise and baseline correction in Raman spectroscopy.
  • This automated approach enhances the reliability of Raman spectral analysis.
  • The algorithm has significant potential for improving Raman detection and imaging applications.