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The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
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Related Experiment Video

Updated: May 6, 2026

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
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FiPhA: an open-source platform for fiber photometry analysis.

Matthew F Bridge1, Leslie R Wilson2, Sambit Panda2

  • 1Social & Scientific Systems, Inc., a DLH Holdings Corp. Company, Durham, North Carolina, United States.

Neurophotonics
|February 26, 2024
PubMed
Summary
This summary is machine-generated.

Fiber photometry analysis (FiPhA) streamlines neural data processing for behavioral neuroscience. This user-friendly R Shiny app simplifies complex fiber photometry data analysis, saving researchers time and effort.

Keywords:
R, Shinycalcium imagingevent processingfiber photometry

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Area of Science:

  • Behavioral Neuroscience
  • Neuroimaging Techniques
  • Data Analysis Software

Background:

  • Fiber photometry (FP) is crucial for monitoring neural activity and neurotransmitter release in awake animals.
  • Analyzing FP data is often labor-intensive and time-consuming, hindering research progress.

Purpose of the Study:

  • To develop a general-purpose application, Fiber Photometry Analysis (FiPhA), for streamlined FP data analysis.
  • To create a versatile pipeline compatible with various FP methods like spectrally resolved, camera-based, and lock-in demodulation.

Main Methods:

  • FiPhA was developed using the R Shiny framework, ensuring a user-friendly interface.
  • The application incorporates interactive visualization, quality control, and batch processing features.
  • It enables event-triggered average processing and filtering of behavioral events.

Main Results:

  • FiPhA significantly simplifies and accelerates the analysis of fiber photometry data.
  • The app provides interactive visualizations and robust quality control measures.
  • Batch processing capabilities enhance efficiency for large datasets.

Conclusions:

  • FiPhA offers a valuable, user-friendly solution for analyzing discrete, event-based FP data in behavioral neuroscience.
  • It reduces the need for custom analysis pipelines, promoting standardization in FP data investigation.
  • This tool empowers researchers to efficiently analyze complex neural activity data.