Digital Analyzer
Digital Analyzer - Related Videos
08:06
Use of a Low-flow Digital Anesthesia System for Mice and Rats
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Cited by 4 •
2016
Here, we present a protocol to more safely and efficiently administer anesthetic gas to mice using a digital, low flow anesthesia system utilizing a syringe-driven direct injection vaporizer.
10:55
Digital Microfluidics for Automated Proteomic Processing
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Cited by 30 •
2009
Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL - mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.
14:02
Measuring Engagement of Spectators of Social Digital Games
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2021
We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.
08:41
Analyzing Dendritic Morphology in Columns and Layers
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2017
Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.
01:30
Analyzing Changes in Working Capital
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2025
Working capital is a key indicator of a company’s short-term financial strength and operational efficiency. Defined as current assets minus current liabilities, it measures the firm's capacity to manage day-to-day financial obligations. In financial analysis, shifts in working capital are especially significant when reviewing the cash flow statement.In the operating section of the cash flow statement, changes in working capital explain how operational decisions affect cash availability. When...
FAQs
It may capture optical, electrical, or image-based signals, then convert those measurements into quantitative data. The signal type depends on how the biological assay produces evidence of an antibody, antigen, cytokine, or pathogen. This flexibility lets one analytical approach support different assay formats while maintaining objective measurement across samples.
Calibration helps separate relevant signal from background so reported values more closely represent assay-associated information rather than background contributions. In practice, this supports quantitative comparisons among samples and contributes to reproducible interpretation, especially when intensity or concentration is the reported measurement for a study design.
Digital analysis applies computational analysis to captured signals rather than relying only on visual or otherwise subjective scoring. It can reduce observer-dependent variation and make measurements more consistent across samples. That consistency is valuable when immunology or infection studies compare antibody, antigen, cytokine, or pathogen-detection results.
Depending on the assay and signal, the system can report intensity, concentration, or particle number. These outputs translate captured biological evidence into numerical values that can be compared across samples. The available measurement depends on the assay's signal and analytical purpose, allowing researchers to interpret detection results quantitatively rather than only descriptively.
A typical workflow begins by capturing the assay's optical, electrical, or image-based signal. The analyzer then uses calibration and computational analysis to distinguish relevant signal from background before converting the result into a reported measurement. When integrated with automated workflows, these stages can support standardized processing and higher-throughput testing across many samples.
This approach is useful when studies need objective, reproducible measurements from antibody, antigen, cytokine, or pathogen-detection assays. In immunology and infection research, standardized digital outputs can strengthen comparisons among samples and support both experimental studies and diagnostics. Automated integration becomes particularly relevant when laboratories need higher throughput without abandoning consistent data analysis.