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

Regression Analysis01:11

Regression Analysis

6.1K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.1K
Response Surface Methodology01:16

Response Surface Methodology

288
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
288
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.5K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.5K
The Calvin Benson Cycle01:46

The Calvin Benson Cycle

4.8K
Ribulose 1,5- bisphosphate carboxylase/oxygenase (RuBisCo) is a critical enzyme that catalyzes carbon dioxide assimilation during photosynthesis. However, it is an inefficient enzyme, having an extremely slow catalytic rate. A typical enzyme can process about a thousand molecules per second; however, RuBisCo fixes only around three-carbon dioxides per second. Photosynthetic cells compensate for this slow rate by synthesizing very high amounts of RuBisCo, making it the most abundant single...
4.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

89
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
89
Interpreting R Charts01:22

Interpreting R Charts

120
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
120

You might also read

Related Articles

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

Sort by
Same author

Stop LCNP: High dose corticosteroid therapy for late radiation-associated lower cranial neuropathy: A report of the phase I dose finding trial and parallel prospective data registry.

medRxiv : the preprint server for health sciences·2026
Same author

Patient-Centered Approach to Surgical Prevention of Ovarian Cancer: A Nonrandomized Clinical Trial.

JAMA network open·2026
Same author

Magnetic Resonance Guided Radiation Therapy for High-Grade Glioma in Pregnancy: Planning, Validation, and Adaptive Fetal Dose Monitoring.

Advances in radiation oncology·2026
Same author

Cerebral amyloid angiopathy-related inflammation (CAA-ri): an updated systematic review and meta-analysis.

Journal of neurology·2026
Same author

Retrospective analysis of dose and dose-averaged LET combined effect on local tumour control in adenoid cystic carcinoma treated with carbon-ion radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026
Same author

Stroke or Seizure? Diagnostic Role of Neuroimaging in Acute Neurologic Mimics.

NeuroSci·2026

Related Experiment Video

Updated: Sep 19, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.4K

Quantifying Sensitivity of Carbon RBE Models to Reference Parameter Variations.

Shannon Hartzell1, Fada Guan2, Giuseppe Magro3

  • 1Department of Radiation Oncology, Mayo Clinic Florida, Jacksonville, Flordia.

Radiation Research
|June 1, 2025
PubMed
Summary

This study found that input parameters significantly impact carbon-ion radiotherapy relative biological effectiveness (RBE) calculations, comparable to cell line and RBE model choice. Understanding parameter sensitivity is crucial for accurate RBE predictions.

More Related Videos

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.0K
Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

12.0K

Related Experiment Videos

Last Updated: Sep 19, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

8.4K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.0K
Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

12.0K

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Relative biological effectiveness (RBE) models are essential for carbon-ion radiotherapy treatment planning.
  • Key RBE models include microdosimetric kinetic model (MKM), stochastic MKM (SMKM), repair-misrepair-fixation (RMF), and local effect model I (LEM).
  • Variations in input parameters and cell lines can significantly influence RBE predictions.

Purpose of the Study:

  • To compare the sensitivity of different RBE models (MKM, SMKM, RMF, LEM) to variations in input biological and reference parameters.
  • To assess the impact of cell line selection and inter/intra-cell line variability on RBE calculations.
  • To evaluate the robustness of RBE models used in carbon-ion radiotherapy.

Main Methods:

  • Utilized Monte Carlo simulations of carbon-ion beams incident on a phantom.
  • Scored input parameters for RBE models, including kinetic energy, microdosimetric spectra, double-strand break yield, and physical dose.
  • Quantified model sensitivity by introducing statistical uncertainty into parameters and sampling RBE, using various cell lines and datasets.

Main Results:

  • Variability in RBE due to measurement/estimation uncertainty was substantial (25-30% at 1-σ level) across models.
  • Inter-cell line variability (avg. 27% for microdosimetric models) was comparable to intra-cell line variability.
  • Input parameter selection was found to be as important as cell line or RBE model choice.

Conclusions:

  • The choice of input parameters significantly influences RBE calculations, on par with the selection of cell line and RBE model.
  • Substantial variation in RBE exists within each model based solely on reference parameters.
  • Further computational and in-vitro research is needed to improve RBE model robustness and accuracy in carbon-ion radiotherapy.