Real-world Modeling
Real-world Modeling - Related Videos
09:13
A Real-world What-Where-When Memory Test
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Cited by 3 •
2017
The Real-World What-Where-When memory test is a novel episodic memory test, in which participants need to recall which objects have been hidden in which locations on which of two distinct occasions. It is easy to run and is sensitive to normal cognitive aging.
02:12
Real-Time Imaging of Bacterial Infection in a Mouse Model Using Bioluminescence
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2026
Source: Valentine, M. E., et al., Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection. J. Vis. Exp. (2020)This video demonstrates the use of a bioluminescence-based imaging approach to monitor bacterial infection in a live mouse model. Sequential bioluminescent and X-ray imaging enables real-time visualization and anatomical localization of infection spread throughout the host.
05:12
Implanting an On-Head Electrode System in a Rat Model for Real-Time Seizure Detection
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2025
This video demonstrates the method of implanting an on-head electrode system in an anesthetized rat for real-time seizure detection. The procedure includes placing stimulation and recording electrodes, securing them with dental cement, and connecting them to a head-mounted amplifier.
11:32
Following in Real Time the Impact of Pneumococcal Virulence Factors in an Acute Mouse Pneumonia Model Using Bioluminescent Bacteria
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Cited by 18 •
2014
Streptococcus pneumoniae is the leading pathogen causing severe community-acquired pneumonia and responsible for over 2 million deaths worldwide. The impact of bacterial factors implicated in fitness or virulence can be monitored in real-time in an acute mouse pneumonia or bacteremia model using bioluminescent bacteria.
08:16
Movement Retraining using Real-time Feedback of Performance
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Cited by 4 •
2013
Retraining abnormal movement patterns following injury or disease is a key component of physical rehabilitation. Recent advances in technology have permitted accurate assessment of movement during a variety of tasks, with near instantaneous quantification of results. This provides new opportunities for modification of faulty movement patterns in real time.
FAQs
A model begins by selecting variables that represent measurable features of a situation, then expressing relationships among them with equations, functions, graphs, or simulations. Observations and measurements help determine which quantities matter, while stated assumptions define how the representation should be interpreted. This choice connects a practical question to a form that can be analyzed mathematically.
The mathematical form depends on how the situation is being represented and analyzed. Equations can express relationships among quantities, functions can describe how one quantity depends on another, graphs can display patterns, and simulations can represent behavior across scenarios. Using these structures gives modelers different ways to explain relationships, examine behavior, and estimate possible outcomes.
Assumptions make a complex system manageable by specifying which relationships or conditions the model will represent. They also establish the limits of interpretation, because predictions apply to the simplified situation rather than automatically describing every aspect of reality. Stating assumptions openly allows modelers to examine whether the resulting explanation remains appropriate for the practical question.
Modelers compare the model's predictions with observed data from the situation it represents. Agreement can support the selected relationships and assumptions, while disagreement may indicate that the structure, measurements, or simplifications need reconsideration. Repeating this comparison helps refine the model and reveals limitations, so its estimates are interpreted with evidence rather than treated as certain.
A typical process starts with observations or measurements, identifies relevant variables and relationships, and represents them using a mathematical structure. Modelers then analyze the representation, test scenarios, and estimate outcomes. Finally, they compare predictions with observed data, assess accuracy, and revise assumptions or relationships when the evidence exposes weaknesses in the model.
Applications described for this approach include population change, finance, engineering, environmental science, and public health. In each area, mathematical representations can support analysis of a practical situation, comparison of possible scenarios, or estimation of outcomes. These applications show how mathematical reasoning can contribute to decisions that depend on relationships, measurements, and evidence.
A model can explain relationships within a practical situation, predict or estimate possible outcomes, and allow different scenarios to be examined. Its results can support decisions when users also consider the assumptions and limitations behind the representation. Comparing estimates with observations provides a basis for judging how much confidence to place in the model's conclusions.