Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

53.1K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
53.1K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

595
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
595
Variability: Analysis01:11

Variability: Analysis

954
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
954
Modeling with Differential Equations01:25

Modeling with Differential Equations

334
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
334
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Elderly patients with multimorbidity in the home setting: umbrella review on therapeutic non-adherence causes.

European review for medical and pharmacological sciences·2023
Same author

Early-versus-Late Endovascular Stroke Treatment: Similar Frequencies of Nonrevascularization and Postprocedural Cerebrovascular Complications in a Large Single-Center Cohort Study.

AJNR. American journal of neuroradiology·2023
Same author

Predictors of Endovascular Treatment Procedural Complications in Acute Ischemic Stroke: A Single-Center Cohort Study.

AJNR. American journal of neuroradiology·2022
Same author

2-pentadecyl-2-oxazoline prevents cognitive and social behaviour impairments in the Amyloid β-induced Alzheimer-like mice model: Bring the α2 adrenergic receptor back into play.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2022
Same author

The comparison of DNA mixture profiles with multiple persons of interest.

Forensic science international. Genetics·2021
Same author

Correlation between ASPECTS and Core Volume on CT Perfusion: Impact of Time since Stroke Onset and Presence of Large-Vessel Occlusion.

AJNR. American journal of neuroradiology·2021

Related Experiment Video

Updated: May 1, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Mutation models for DVI analysis.

F Ricciardi1, K Slooten2

  • 1University of Florence, Italy.

Forensic Science International. Genetics
|March 29, 2014
PubMed
Summary

This study evaluates computational methods for DNA data in disaster victim identification (DVI). It aims to optimize likelihood ratio calculations, minimizing errors in identifying victims during mass casualty events.

Keywords:
Disaster victim identificationMutationsNull allelesPaternity testing

More Related Videos

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

11.5K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

22.2K

Related Experiment Videos

Last Updated: May 1, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
07:38

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

11.5K
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

22.2K

Area of Science:

  • Forensic Science
  • Computational Biology
  • Genetics

Background:

  • DNA data is vital for personal identification in forensic applications like disaster victim identification (DVI).
  • Effective computational methods are needed to manage large-scale events with numerous victims.
  • Accurate likelihood ratios and posterior odds are essential for robust identification hypotheses.

Purpose of the Study:

  • To evaluate various computational methods for likelihood ratio computation in DVI.
  • To minimize identification error rates, including false negatives and false positives.
  • To determine the most appropriate approach for DNA profile analysis in DVI scenarios, considering complex factors.

Main Methods:

  • Simulation of DNA profiles to test different computational strategies.
  • Evaluation of alternative mutation models for DNA profile analysis.
  • Assessment of methods to handle complicating factors like mutations and null alleles.

Main Results:

  • Proposed and evaluated several computational methods to address challenges in DVI.
  • Focused on optimizing likelihood ratio calculations for improved accuracy.
  • Investigated strategies to manage limited and fragmentary data on mutations and null alleles.

Conclusions:

  • Suggests the most appropriate methods for likelihood ratio computation in DVI cases.
  • Highlights the importance of efficient handling of mutations and null alleles for accurate victim identification.
  • Aims to reduce identification errors in mass casualty events through advanced computational approaches.