Fiber Gram Alignment
Fiber Gram Alignment - Related Videos
07:12
Microengineering 3D Collagen Hydrogels with Long-Range Fiber Alignment
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Cited by 3 •
2022
This protocol demonstrates the use of a microfluidic channel with changing geometry along the fluid flow direction to generate extensional strain (stretching) to align fibers in a 3D collagen hydrogel (<250 µm in thickness). The resulting alignment extends across several millimeters and is influenced by the extensional strain rate.
08:28
Microfluidic Dry-spinning and Characterization of Regenerated Silk Fibroin Fibers
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Cited by 6 •
2017
A protocol for the microfluidic spinning and microstructure characterization of regenerated silk fibroin monofilament is presented.
11:07
Preparation of Aligned Steel Fiber Reinforced Cementitious Composite and Its Flexural Behavior
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Cited by 19 •
2018
This protocol describes an approach for manufacturing aligned steel fiber reinforced cementitious composite by applying a uniform electromagnetic field. Aligned steel fiber reinforced cementitious composite exhibits superior mechanical properties to ordinary fiber reinforced concrete.
11:36
Assembly, Loading, and Alignment of an Analytical Ultracentrifuge Sample Cell
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Cited by 21 •
2009
The analytical ultracentrifuge (AUC) sample cell holds sample and reference buffer and during experiments and is exposed to high vacuum and rotor speeds up to 60,000 rpm. This video will demonstrate the rigorous attention to detail necessary for assembly, loading and alignment of this very important component of an AUC experiment.
09:31
Gram Staining of Bacteria from Environmental Sources
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2023
Source: Laboratories of Dr. Ian Pepper and Dr. Charles Gerba - The University of Arizona Demonstrating Author: Luisa Ikner The spectrum of research in environmental microbiology is broad in scope and application potential. Whether the work is bench-scale with known bacterial isolates, or in the field collecting soil or water samples containing unknown bacterial isolates, the ability to quickly and visually discern culturable populations of interest remains of great import to environmental...
FAQs
The approach establishes correspondence by comparing multiple properties of fiber organization rather than relying on spatial location alone. It considers where fibers are positioned, how they are oriented, and how they connect with other structures. Combining these features helps identify related pathways across brain images, specimens, or datasets and supports a more meaningful transformation into shared coordinates.
Preserving anatomical relationships prevents the alignment from treating fiber pathways as isolated points or unrelated features. The estimated transformation must improve correspondence while retaining the organization that links pathways to one another. This matters because structural connectivity depends on those relationships, and their preservation makes comparisons of neural architecture more interpretable across individuals or experimental conditions.
Spatial position, fiber orientation, and connectivity patterns each contribute different information to the result. Position describes where a pathway occurs, orientation captures its directional organization, and connectivity reflects its structural relationships. Considering these features together can distinguish pathways that occupy similar regions but differ in direction or connections, improving correspondence between complex neural fiber architectures.
A typical workflow begins by comparing fiber organization across the selected images, specimens, or datasets. The analysis then uses spatial, orientational, and connectivity information to estimate transformations between corresponding structures. Those transformations place the data in a common coordinate framework while maintaining anatomical relationships, creating a basis for subsequent comparisons of white-matter or structural connectivity patterns.
Researchers may use the approach when they need to compare white-matter tract organization across individuals, experimental conditions, or imaging datasets. It is also relevant to multimodal registration, where information from different types of brain data must be related spatially. By improving correspondence between fiber architectures, the method supports investigations of structure in relation to brain function, development, or disease-related change.
Aligned fiber data can support comparisons of structural connectivity and pathway organization across brains or experimental groups. A common coordinate framework makes corresponding structures easier to examine and relate to one another. In neuroscience, this can help connect microscopic fiber organization with broader questions about brain function, development, and changes associated with disease, without losing the underlying anatomical context.