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

Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.0K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
9.0K
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

38.4K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
38.4K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Confidence Intervals01:21

Confidence Intervals

10.8K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.8K
Improper Integrals: Infinite Intervals01:29

Improper Integrals: Infinite Intervals

118
An integral is classified as improper due to an infinite interval when at least one of its limits of integration extends to positive or negative infinity. In such cases, the region under the curve is unbounded, and standard techniques for evaluating definite integrals are not directly applicable. Instead, the improper integral is defined through a limiting process that allows one to determine whether the accumulated area remains finite despite the infinite domain.Application to Exponential...
118
What are Estimates?01:06

What are Estimates?

8.9K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Temperature Unlocks Inheritance: Indications of Vertical Transmission of Usutu Virus in <i>Culex pipiens</i> Bioform Molestus.

Vector borne and zoonotic diseases (Larchmont, N.Y.)·2026
Same author

[Mosquitoes, climate change and new diseases].

Lakartidningen·2026
Same author

Comparison of pre-mortem and post-mortem blood concentrations of analgesic and sedative drugs in intensive care patients.

Forensic science international·2025
Same author

First Outbreak of African Swine Fever in Sweden: Local Epidemiology, Surveillance, and Eradication Strategies.

Transboundary and emerging diseases·2025
Same author

European <i>Culex pipiens</i> Populations Carry Different Strains of <i>Wolbachia pipientis</i>.

Insects·2024
Same author

Transcriptome sequencing data provide a solid base to understand the phylogenetic relationships, biogeography and reticulated evolution of the genus Zamia L. (Cycadales: Zamiaceae).

Annals of botany·2024

Related Experiment Video

Updated: Feb 16, 2026

Derivation of Leptomeninges Explant Cultures from Postmortem Human Brain Donors
05:18

Derivation of Leptomeninges Explant Cultures from Postmortem Human Brain Donors

Published on: January 21, 2017

8.7K

Quantifying human decomposition in an indoor setting and implications for postmortem interval estimation.

Ann-Sofie Ceciliason1, M Gunnar Andersson2, Anders Lindström3

  • 1Department of Surgical Sciences, Uppsala University, University Hospital, SE-751 85 Uppsala, Sweden; Department of Forensic Medicine, The National Board of Forensic Medicine, Box 1024, SE-751 40 Uppsala, Sweden.

Forensic Science International
|January 7, 2018
PubMed
Summary

Estimating the postmortem interval (PMI) for indoor decomposition using the Total Body Score (TBS) shows moderate to low precision. Modifying the scoring method and considering seasonal and insect factors can improve accuracy for forensic investigations.

Keywords:
Accumulated degree daysDecomposition stagesForensic taphonomyIndoorPost-mortem interval estimation

More Related Videos

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem
08:16

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Published on: December 30, 2015

15.8K
Field Postmortem Rabies Rapid Immunochromatographic Diagnostic Test for Resource-Limited Settings with Further Molecular Applications
07:40

Field Postmortem Rabies Rapid Immunochromatographic Diagnostic Test for Resource-Limited Settings with Further Molecular Applications

Published on: June 29, 2020

14.6K

Related Experiment Videos

Last Updated: Feb 16, 2026

Derivation of Leptomeninges Explant Cultures from Postmortem Human Brain Donors
05:18

Derivation of Leptomeninges Explant Cultures from Postmortem Human Brain Donors

Published on: January 21, 2017

8.7K
High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem
08:16

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Published on: December 30, 2015

15.8K
Field Postmortem Rabies Rapid Immunochromatographic Diagnostic Test for Resource-Limited Settings with Further Molecular Applications
07:40

Field Postmortem Rabies Rapid Immunochromatographic Diagnostic Test for Resource-Limited Settings with Further Molecular Applications

Published on: June 29, 2020

14.6K

Area of Science:

  • Forensic Science
  • Thanatology
  • Decomposition Studies

Background:

  • Accurate postmortem interval (PMI) estimation is crucial in forensic investigations.
  • Decomposition rates vary significantly based on environmental factors, especially indoors.
  • Existing methods for PMI estimation require refinement for indoor environments.

Purpose of the Study:

  • To evaluate the accuracy and precision of the Total Body Score (TBS) for estimating PMI in indoor decomposition.
  • To identify factors influencing TBS and explore potential improvements for indoor settings.
  • To assess the impact of Accumulated Degree-Days (ADD) and insect activity on TBS.

Main Methods:

  • Prospective data collection from 140 forensic cases with known death dates.
  • Scoring of decomposing human remains using the Total Body Score (TBS) scale.
  • Analysis of variance in TBS explained by Accumulated Degree-Days (ADD), with and without blowfly larvae presence.

Main Results:

  • Approximately 45% (with larvae) to 66% (without larvae) of TBS variance was attributable to ADD in the model.
  • Precision of ADD/PMI estimation from TBS was moderate to low.
  • Significant seasonal differences and the influence of insect activity on TBS were observed.
  • Desiccation and insect infestations may lead to PMI under- or overestimation, respectively.

Conclusions:

  • The current TBS model shows limitations in precision for indoor decomposition PMI estimation.
  • Subgroup analysis and potential modifications to the TBS scoring method are needed for indoor settings.
  • Accounting for seasonal variations, insect activity, and desiccation is essential for improving PMI accuracy.