Data Fusion
Data Fusion - Related Videos
02:45
Nuclear Fusion
0 Views •
2020
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons. A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
10:45
A Faster, High Resolution, mtPA-GFP-based Mitochondrial Fusion Assay Acquiring Kinetic Data of Multiple Cells in Parallel Using Confocal Microscopy
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Cited by 20 •
2012
Mitochondrial fusion was measured by tracking the equilibration of photoconverted matrix-targeted GFP across the mitochondrial network over time. Thus far, only one cell could be subjected to an hour long kinetic analysis at a time. We present a method that simultaneously measures multiple cells, thereby speeding up the data collection process.
09:19
Analysis of SNARE-mediated Membrane Fusion Using an Enzymatic Cell Fusion Assay
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2012
We have developed a cell fusion assay that quantifies SNARE-mediated membrane fusion events by activated expression of β-galactosidase.
06:10
Live Imaging of Mouse Secondary Palate Fusion
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Cited by 11 •
2017
Here, we present a protocol for live imaging of mouse secondary palate fusion using confocal microscopy. This protocol can be used in combination with a variety of fluorescent reporter mouse lines, and with pathway inhibitors for mechanistic insight. This protocol can be adapted for live imaging in other developmental systems.
14:59
Method for Measurement of Viral Fusion Kinetics at the Single Particle Level
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Cited by 11 •
2009
We present an in vitro, two-color fluorescence assay to visualize the fusion of single virus particles with a fluid target bilayer. By labeling viral particles with fluorophores that differentially stain the viral membrane and its interior, we are able to monitor the kinetics of hemifusion and pore formation.
FAQs
Before integration, researchers preprocess each dataset and align its geographic and temporal references. This may require reconciling differences in spatial scale, observation timing, and data format so that satellite imagery, sensor measurements, geographic information, and field observations can be compared appropriately. Careful alignment matters because uncorrected mismatches can weaken the combined representation of an environmental system or event.
Data fusion can use statistical, model-based, or machine-learning approaches, depending on the information being combined and the desired representation. These approaches must account for uncertainty rather than treating every observation as equally exact. That consideration helps produce results that are more reliable when sources differ in spatial resolution, timing, or other measurement characteristics.
Combining observations across space and time can expose environmental patterns that remain hidden in an individual dataset. The resulting integrated representation may improve forecasting and support analysis of changing conditions because information from one source can complement gaps in another. This is particularly relevant when researchers need to interpret broad environmental dynamics rather than isolated measurements.
A practical workflow begins by collecting the relevant source data, then preprocessing and aligning them before integration. Researchers may combine satellite imagery, sensor measurements, geographic information, and field observations, selecting a statistical, model-based, or machine-learning method that fits the task. The integrated output can then be examined for environmental patterns, forecasts, or evidence relevant to management decisions.
In land-cover mapping, fusion connects observations that describe the same landscape from different perspectives. Satellite imagery can be considered alongside geographic information and field observations, while sensor measurements add complementary environmental evidence. The combined information can support a more complete map than one source alone, helping researchers characterize land patterns and inform resource-management work.
Environmental monitoring uses fusion to connect measurements across locations and times in pollution, weather, climate, and ecosystem studies. Its value lies not only in adding data, but in relating observations so researchers can identify patterns, improve forecasts, and assess conditions at useful scales. These outputs can guide hazard response as well as broader resource-management decisions.