Multiscale Measurements
Multiscale Measurements - Related Videos
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Cited by 27 •
2013
Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.
06:14
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
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Cited by 1 •
2018
Presented is an easy method to fabricate nano-micro multiscale structures, for functional surfaces, by aggregating nanofibers fabricated using an anodic aluminum oxide filter.
09:38
Strain Sensing Based on Multiscale Composite Materials Reinforced with Graphene Nanoplatelets
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Cited by 4 •
2016
The integration of conductive nanoparticles, such as graphene nanoplatelets, into glass fiber composite materials creates an intrinsic electrical network susceptible to strain. Here, different methods to obtain strain sensors based on the addition of graphene nanoplatelets into the epoxy matrix or as a coating on glass fabrics are proposed.
11:19
Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry
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Cited by 34 •
2016
We present a set of techniques to characterize the viscoelastic mechanical properties of brain at the micro-, meso-, and macro-scales.
07:22
Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
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Cited by 7 •
2017
Optimized sampling protocols and the development of new wipe materials can be facilitated by standardized measurements of collection efficiency from wipe-sampling. Our approach for sampling trace explosives uses an automated device to control speed, force, and distance during wipe-sampling followed by extraction of collected explosives.
FAQs
Connection depends on aligning observations across levels rather than interpreting each measurement in isolation. Gene expression and cell signaling can be examined alongside tissue development, physiological measurements, and organism-level behavior. This alignment helps investigators ask how activity at a smaller scale relates to changes at larger scales, producing a more integrated account of biological function.
Different biological methods resolve different features in space and time. Combining them allows molecular or cellular events to be considered alongside tissue, organ, or whole-organism changes, instead of treating those observations as unrelated. The resulting cross-scale relationships can expose patterns that a method with only one spatial or temporal resolution would not capture.
Single-scale measurements show behavior within one level of organization, but they may not reveal how that behavior affects another level. For example, molecular changes may be difficult to interpret without tissue or organism-level observations. Multiscale integration addresses this gap by linking measurements across levels, which can support more predictive models of development, disease progression, and organismal function.
Depending on the biological question, investigators may combine molecular assays, microscopy, physiological measurements, and observations of whole-organism behavior. These approaches contribute complementary views: assays characterize molecular activity, microscopy examines structures or cells, physiological measurements capture function, and organism-level observations place those findings in a broader biological context. Together, they provide connected evidence across scales.
They can be used to relate gene expression and cell signaling to changes in tissues, organs, or the whole organism. In tissue development, this can connect molecular activity with larger-scale formation; in disease research, it can associate molecular or cellular changes with progression at higher levels. The value lies in examining these processes as linked biological events rather than isolated observations.
Integration can reveal cross-scale relationships that remain hidden when data are collected at only one level. It may also support predictive models of complex biological systems by connecting molecular and cellular observations with tissue, organ, and organismal function. These outcomes help researchers interpret biological activity in context, including its relevance to development, disease progression, and overall function.