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Lateralization01:28

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Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
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The lateral view of the cranium is dominated by temporal, sphenoid, and ethmoid bones.
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Design Example: Traverse Angle Computations01:25

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Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
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Improving Lateral Flow Assay Performance Using Computational Modeling.

David Gasperino1, Ted Baughman1, Helen V Hsieh1

  • 1Intellectual Ventures Laboratory, Bellevue, Washington 98007, USA.

Annual Review of Analytical Chemistry (Palo Alto, Calif.)
|March 30, 2018
PubMed
Summary
This summary is machine-generated.

Lateral flow assays (LFAs) are crucial for global healthcare, but their design lacks mechanistic understanding. This review explores LFA principles and theory for improved, predictive computational models and future high-fidelity diagnostic assays.

Keywords:
computational modelinglateral flow assayoptimizationreaction theorytransport theory

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Area of Science:

  • Biomedical Engineering
  • Diagnostic Technologies
  • Point-of-Care Testing

Background:

  • Lateral flow assays (LFAs) have revolutionized global healthcare by enabling diagnostics in resource-limited settings.
  • Their success is driven by performance, field utility, and low cost, motivating advanced material and reagent development.

Purpose of the Study:

  • To review the fundamental principles governing LFAs.
  • To examine the historical development of theories predicting LFA performance.
  • To highlight the need for mechanistic understanding to guide LFA design.

Main Methods:

  • Literature review of LFA principles and theoretical advancements.
  • Analysis of the integration of theory into computational models.
  • Discussion of performance quantification and predictive validation criteria.

Main Results:

  • LFAs have significantly impacted global health diagnostics.
  • A gap exists between LFA technological advancement and mechanistic understanding.
  • Theoretical frameworks are evolving for LFA performance prediction.

Conclusions:

  • Integrating computational design with LFA development offers a path to future low-cost, high-fidelity assays.
  • Further research into LFA mechanisms is critical for informed design and optimization.
  • Validated predictive models will accelerate the development of next-generation diagnostic tools.