Region Of Interest Selection
Region Of Interest Selection - Related Videos
09:00
A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions
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2025
Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.
01:03
IR Frequency Region: Fingerprint Region
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2024
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1. The...
06:48
Natural Selection - Concepts
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2019
Fitness Widespread variation of phenotypes in natural populations provides the raw material for evolution, which is the change in the inherited traits of populations over successive generations. Natural selection is one of the main mechanisms of evolution and requires variable traits to be heritable and associated with differential survival and/or reproductive success. Phenotypes that correlate with greater success will have more offspring that survive to reproduce in the next generation, and...
03:14
Artificial Selection - Concepts
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2019
Natural Selection and Adaptive Evolution In natural settings, adaptive evolution takes place by natural selection, a process in which differences in traits lead to differences in survival and reproductive success between individuals. This happens because individuals with certain phenotypes have a higher probability of survival and/or produce more surviving offspring than individuals with other phenotypes. A classic example of this was demonstrated by Rosemary and Peter Grant studying the...
02:40
The Eukaryotic Promoter Region
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2020
The eukaryotic promoter region is a segment of DNA located upstream of a gene. It contains an RNA polymerase binding site, a transcription start site, and several cis-regulatory sequences. The proximal promoter region is located in the vicinity of the gene and has cis-regulatory sequences and the core promoter. The core promoter is the binding site for RNA polymerase and is usually located between -35 and +35 nucleotides from the transcription start site. The distal promoter regions are...
FAQs
These criteria determine which pixels or voxels belong to the biological feature being measured. Anatomical boundaries can define a structure’s physical extent, while signal intensity can distinguish labeled or brighter regions from surrounding areas. Morphology adds shape-related guidance. Choosing criteria that match the experimental question helps focus measurements on relevant cellular or tissue features rather than unrelated background.
Software-assisted segmentation can help identify regions according to consistent rules across many images, whereas manual selection allows researchers to use visual judgment when boundaries or morphology are difficult to characterize. Either approach can be appropriate, but the selection method should follow predefined experimental criteria. Consistent application is especially important when comparing samples, time points, or treatment conditions.
Consistency limits variation caused by analyzing different areas rather than by genuine biological differences. Applying the same selection criteria across samples makes measurements such as fluorescence, cell number, area, or intensity more comparable. This is important in experiments involving multiple time points or treatment conditions, where inconsistent boundaries could obscure or exaggerate changes in cellular structures or tissue features.
Selected pixels or voxels can provide measurements including fluorescence, cell number, area, and intensity. The most useful measurement depends on the feature under study and the experimental question. For example, intensity can characterize a signal, while area can describe the extent of a structure. Extracting values only from the selected region helps exclude irrelevant background from the analysis.
First, define the experimental criteria for identifying the relevant biological feature. Next, locate the area using anatomical boundaries, signal intensity, morphology, or a combination of these cues. Select it manually or with software-assisted segmentation, then extract the intended measurements from its pixels or voxels. Applying the same documented approach to every sample supports meaningful comparisons.
In microscopy and image-based biology, this approach supports quantitative analysis of cellular structures, tissue features, and experimental responses. Researchers can focus measurements on biologically relevant areas while excluding unrelated background. The resulting values help compare samples, time points, and treatment conditions, provided that the selection criteria remain consistent and the analyzed regions are documented clearly.