Automated Acquisition
Automated Acquisition - Related Videos
09:42
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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2025
Here, we describe a protocol to record and analyze respiratory electromyography (EMG) signals. It includes the anatomic references for placing the EMG electrodes over several respiratory muscles, removing electrocardiographic noise from the EMG signals, and acquiring the EMG root mean square (RMS) and onset timing of activity.
08:06
Automated Acoustic Dispensing for the Serial Dilution of Peptide Agonists in Potency Determination Assays
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Cited by 1 •
2016
Peptide adsorption to plasticware during traditional tip-based serial dilutions can significantly impact potency determination and confound the understanding of structure-activity relationships used for lead identification and lead optimization phases of drug discovery. Here methods for automated acoustic non-contact serial dilution of peptide samples are described.
07:56
User-friendly, High-throughput, and Fully Automated Data Acquisition Software for Single-particle Cryo-electron Microscopy
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Cited by 16 •
2021
Single-particle cryo-electron microscopy demands a suitable software package and user-friendly pipeline for high-throughput automatic data acquisition. Here, we present the application of a fully automated image acquisition software package, Latitude-S, and a practical pipeline for data collection of vitrified biomolecules under low-dose conditions.
02:51
Light Acquisition
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2026
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the development of increasingly larger leaves and taller plants, to avoid shading by their neighbors, along with the elaboration of root architecture and mechanisms to transport water and...
01:28
Acquisition of Stock and Assets
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2025
Acquiring stock or assets is a critical strategy for business expansion, enabling organizations to strengthen their competitive position, diversify operations, or enter new markets. This process is tailored to the specific objectives of the acquiring company, with stock, asset, and mixed acquisitions offering distinct advantages and challenges. Stock acquisitions involve purchasing a target company's equity, granting the acquirer control over its operations, assets, and liabilities. This...
FAQs
Automated acquisition connects programmed experimental settings with instrument actions, allowing software to trigger measurements at specified time points, positions, wavelengths, or sample conditions. This coordination creates a repeatable sequence rather than relying on an operator to initiate each measurement. As a result, experiments can follow the same acquisition logic across many samples or observations.
Metadata records the conditions and settings associated with each measurement, helping researchers interpret results in context. Linking measurements to factors such as acquisition time, position, wavelength, or sample condition supports comparisons across samples and experimental stages. This record also strengthens reproducibility because later analyses can distinguish biological differences from changes in data-collection conditions.
The programmed time points, spatial positions, wavelengths, and sample conditions determine what the system measures and when it measures it. Changing these variables can alter the biological features captured in images or other readouts. Careful selection therefore matters for detecting changes across cells, tissues, organisms, or samples while maintaining consistent comparisons throughout the experiment.
Manual collection depends more heavily on repeated operator actions, whereas automated acquisition applies programmed settings across the experiment. This reduces variation introduced by inconsistent timing, positioning, or measurement decisions and makes extended or large-scale studies more practical. The resulting standardization can improve reproducibility and reveal biological patterns that sporadic manual measurements may overlook.
A typical workflow begins by selecting the biological samples and instrument, such as a microscope, plate reader, or sensor. Researchers then program the measurement settings and define the required time points, positions, wavelengths, or sample conditions. The system performs the scheduled acquisitions while recording metadata, creating a structured dataset for later quantitative analysis.
The approach is particularly useful for high-throughput screening, time-lapse imaging, and longitudinal experiments. It can collect comparable measurements from many samples or follow biological changes over extended periods without requiring continuous manual intervention. These capabilities support quantitative studies of cells, tissues, and organisms and help researchers examine patterns across scale or time.
Automated acquisition produces organized measurements, images, or other experimental data linked to their collection conditions. Such datasets can support quantitative analysis of cells, tissues, and organisms, while repeated observations reveal changes over time or across sample locations. By standardizing collection across experiments, the method helps researchers assess biological variation and identify patterns that isolated measurements might miss.