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

Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

249
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
249
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

278
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
278
Basic Equation for Pressure Field01:13

Basic Equation for Pressure Field

607
The basic equation for a pressure field in fluid mechanics captures the balance of forces within any segment of fluid, providing a foundational understanding of how pressure changes within fluids under various forces. Generally, two main types of forces act on any part of a fluid: surface forces and body forces. Surface forces arise from pressure differences across points within the fluid, which result in net forces that can vary depending on the local pressure gradient. Body forces, on the...
607
What are Estimates?01:06

What are Estimates?

8.8K
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...
8.8K
What is Natural Selection?01:32

What is Natural Selection?

129.6K
Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
129.6K
Vapor Pressure02:34

Vapor Pressure

40.9K
When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase molecules move randomly about, they will occasionally collide with the surface of the condensed phase, and in some cases, these collisions will result in the molecules re-entering the condensed phase. The change from the gas phase to the liquid is called condensation. When the rate of condensation becomes equal to the rate of vaporization, neither the amount of the liquid nor the amount of the vapor...
40.9K

You might also read

Related Articles

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

Sort by
Same author

Occupational exposures and maternal mental health across the perinatal period: a systematic review.

Best practice & research. Clinical obstetrics & gynaecology·2026
Same author

A Diagnostic Challenge: From Suspected Ischemic Stroke to Confirmed Herpetic Encephalitis.

Clinical case reports·2026
Same author

Target product profiles of laboratory and data analytical frameworks for genotyping to monitor antimalarial efficacy.

PLOS global public health·2026
Same author

Pre-pregnancy body mass index and biomarkers of inflammation at birth.

International journal of obesity (2005)·2026
Same author

Maternal prenatal stress and infant temperament at one year: exploring the mediating role of postpartum depression in the French EDEN cohort.

Archives of women's mental health·2026
Same author

From non-specific biomarker to targeted action: transdiagnostic and sex-specific drivers of high-CRP status in severe mental illness across the FondaMental Advanced Centers of Expertise (FACE) cohorts.

Brain, behavior, and immunity·2026

Related Experiment Video

Updated: Feb 8, 2026

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
09:37

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

7.1K

Challenges in estimating insecticide selection pressures from mosquito field data.

Susana Barbosa1, William C Black, Ian Hastings

  • 1Molecular and Biochemical Parasitology Group, Liverpool School of Tropical Medicine, Liverpool, United Kingdom. sbarbosa@liv.ac.uk

Plos Neglected Tropical Diseases
|November 10, 2011
PubMed
Summary

Quantifying insecticide resistance allele spread is crucial for controlling Aedes aegypti. Maximum likelihood models provide estimates but struggle with sparse data and environmental variations, impacting resistance predictions.

More Related Videos

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

9.1K
Protocols for Testing the Toxicity of Novel Insecticidal Chemistries to Mosquitoes
09:32

Protocols for Testing the Toxicity of Novel Insecticidal Chemistries to Mosquitoes

Published on: February 13, 2019

16.4K

Related Experiment Videos

Last Updated: Feb 8, 2026

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
09:37

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

7.1K
Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

9.1K
Protocols for Testing the Toxicity of Novel Insecticidal Chemistries to Mosquitoes
09:32

Protocols for Testing the Toxicity of Novel Insecticidal Chemistries to Mosquitoes

Published on: February 13, 2019

16.4K

Area of Science:

  • Vector-borne disease control
  • Population genetics
  • Insecticide resistance

Background:

  • Insecticide resistance threatens dengue and malaria vector control efforts.
  • Accurate quantification of resistance allele selection, dominance, and initial frequencies is vital for predicting spread.

Purpose of the Study:

  • To investigate challenges in estimating selection and dominance coefficients for insecticide resistance alleles.
  • To assess the performance of maximum likelihood (ML) models under data sparsity and heterogeneity.
  • To analyze pyrethroid resistance in Mexican Aedes aegypti populations.

Main Methods:

  • Utilized maximum likelihood (ML) methodology to fit genetic models to field data.
  • Investigated the impact of sparse data and spatial/temporal heterogeneity on ML model accuracy.
  • Analyzed published data on pyrethroid resistance, focusing on the Ile1,016 mutation in Aedes aegypti.

Main Results:

  • ML models accurately estimated coefficients under ideal conditions.
  • Estimates were less reliable with sparse data during resistance allele frequency increase.
  • Spatial and temporal heterogeneity significantly impacted the accuracy of coefficient estimates.
  • Analysis of Mexican Aedes aegypti data yielded expected selection and initial allele frequency estimates, with incomplete dominance.

Conclusions:

  • While ML models offer insights, their reliability is compromised by data limitations and environmental variability.
  • Estimates for insecticide resistance in Aedes aegypti populations require cautious interpretation due to heterogeneity.
  • Further research is needed to refine models for predicting resistance spread in complex natural environments.