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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.0K
VSEPR Theory for Determination of Electron Pair Geometries
46.0K
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
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.2K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.4K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.4K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

10.9K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
10.9K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

14.8K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
14.8K

You might also read

Related Articles

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

Sort by
Same author

Self-moving multi-sensor AI-based robotic technology for road crack inspection.

Frontiers in robotics and AI·2026
Same author

Fine-grained pixel-level crack mapping in earthen heritage structures.

Scientific reports·2026
Same author

Enhancing crack detection and severity assessment in historical Tabiya basins using U-Net and adaptive thresholding.

Frontiers in artificial intelligence·2026
Same author

Enhancing weed detection through knowledge distillation and attention mechanism.

Frontiers in robotics and AI·2025
Same author

Adaptive Model Predictive Control for 4WD-4WS Mobile Robot: A Multivariate Gaussian Mixture Model-Ant Colony Optimization for Robust Trajectory Tracking and Obstacle Avoidance.

Sensors (Basel, Switzerland)·2025
Same author

FloraNER: A new dataset for species and morphological terms named entity recognition in French botanical text.

Data in brief·2024

Related Experiment Video

Updated: Feb 9, 2026

Multiplexed Fluorescent Immunohistochemical Staining of Four Endometrial Immune Cell Types in Recurrent Miscarriage
05:16

Multiplexed Fluorescent Immunohistochemical Staining of Four Endometrial Immune Cell Types in Recurrent Miscarriage

Published on: August 4, 2021

3.8K

Comprehensive miscarriage dataset for an early miscarriage prediction.

Hiba Asri1, Hajar Mousannif2, Hassan Al Moatassime1

  • 1OSER Laboratory, Cadi Ayyad University, Marrakech, Morocco.

Data in Brief
|June 13, 2018
PubMed
Summary

This study identifies risk factors for miscarriage using real-time data from a mobile app. The system collects data from pregnant women to predict and assess miscarriage risks effectively.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

3.2K

Related Experiment Videos

Last Updated: Feb 9, 2026

Multiplexed Fluorescent Immunohistochemical Staining of Four Endometrial Immune Cell Types in Recurrent Miscarriage
05:16

Multiplexed Fluorescent Immunohistochemical Staining of Four Endometrial Immune Cell Types in Recurrent Miscarriage

Published on: August 4, 2021

3.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

3.2K

Area of Science:

  • Reproductive Health
  • Medical Informatics
  • Mobile Health

Background:

  • Miscarriage is a significant concern in reproductive health.
  • Accurate prediction of miscarriage risk is crucial for timely intervention.
  • Existing methods for risk assessment may lack continuous, real-time data.

Purpose of the Study:

  • To identify key risk factors for predicting miscarriage.
  • To develop and validate a system for real-time miscarriage risk assessment.
  • To leverage mobile health technology for improved prenatal monitoring.

Main Methods:

  • Utilizing an Android mobile application to collect real-time data from pregnant women.
  • Integrating data from both the mobile phone and connected healthcare sensors.
  • Automated data collection at 60-second intervals during active app usage.
  • Employing real-world data from actual pregnant women for system validation.

Main Results:

  • The system successfully collected and processed real-time data streams.
  • The proposed system demonstrated effectiveness in assessing miscarriage risk factors.
  • Validation using real-world data confirmed the system's performance.

Conclusions:

  • Real-time data collection via mobile applications can enhance miscarriage risk prediction.
  • The integration of mobile phone and healthcare sensor data offers a comprehensive approach.
  • This technology holds potential for improving prenatal care and reducing miscarriage rates.