Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Statistical Analysis System (SAS)

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...

Statistical Package for the Social Sciences (SPSS)

Statistical Package for the Social Sciences (SPSS)

The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
SPSS streamlines the process from data preparation to analysis and reporting. It is characterized by its user-friendly interface, which conceals...

F Distribution

F Distribution

The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...

Identifying Statistically Significant Differences: The F-Test

Identifying Statistically Significant Differences: The F-Test

The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...

Supercritical Fluid Chromatography

Supercritical Fluid Chromatography

Supercritical fluid chromatography (SFC) provides a beneficial substitute for gas chromatography (GC) and liquid chromatography (LC) for certain samples because it merges the top attributes of both techniques. SFC allows the separation and analysis of compounds that GC or LC does not easily manage. These compounds are traditionally nonvolatile or thermally unstable, making GC unsuitable and lacking functional groups required for HPLC analysis.
SFC utilizes a supercritical fluid mobile phase,...

SFG Algebra

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...

You might also read

Related Articles

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

Sort by

Same author

Descriptive analysis of recovery-related outcomes following cognitive behavioural therapy in a Japanese clinical sample using definitions from the Talking Therapies for Anxiety and Depression programme.

Behavioural and cognitive psychotherapy·2026

Same author

Data-Driven Cutoff Selection for the Patient Health Questionnaire-9 Depression Screening Tool.

JAMA network open·2024

Same author

A Model of Burnout Among Healthcare Professionals.

Journal of general internal medicine·2023

Same author

Utility of PERMA-profiler in association with burnout of internists amid COVID-19 pandemic: A cross-sectional study.

Asian journal of psychiatry·2023

Same author

Impact of solid fuel usage on respiratory symptoms among reproductive aged women: a cross-sectional study in Sri Lanka.

BMC public health·2022

Same author

Burnout and Turnover Intention in Critical Care Professionals During the COVID-19 Pandemic in Japan: A Cross-sectional Survey.

Annals of the American Thoracic Society·2022

Same journal

[Development of novel therapeutics for multiple myeloma and improvement of drug lag].

Nihon rinsho. Japanese journal of clinical medicine·2019

Same journal

[Clinical pharmacy services to patients of immunomodulatory drugs].

Nihon rinsho. Japanese journal of clinical medicine·2019

Same journal

[Therapeutic drug monitoring of the new anti-myeloma drugs in the treatment of multiple myeloma].

Nihon rinsho. Japanese journal of clinical medicine·2019

Same journal

[Prognostic value of minimal residual disease assessment using next-generation sequencing in multiple myeloma].

Nihon rinsho. Japanese journal of clinical medicine·2019

Same journal

[The evaluation of minimal residual disease in multiple myeloma by an allele-specific oligonucleotide real-time PCR].

Nihon rinsho. Japanese journal of clinical medicine·2019

Same journal

[Evaluation of minimal residual disease in myeloma by multiparametric flow cytometry].

Nihon rinsho. Japanese journal of clinical medicine·2019

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

FSL Constructs: A Simple Method for Modifying Cell/Virion Surfaces with a Range of Biological Markers Without Affecting their Viability

FSL Constructs: A Simple Method for Modifying Cell/Virion Surfaces with a Range of Biological Markers Without Affecting their Viability

Published on: August 5, 2011

[FSS and SAS].

Yoshiyuki Muramatsu¹, Kumiko Muramatsu, Ichiro Majima

¹School of Health Sciences, Faculty of Medicine, Niigata University.

Nihon Rinsho. Japanese Journal of Clinical Medicine

|September 23, 2009

Summary

This summary is machine-generated.

Obesity often causes obstructive sleep apnea syndrome (OSAS), impacting the circulatory system and leading to diseases like hypertension. Upper airway resistance syndrome (UARS) shares similarities but requires distinct classification and study.

More Related Videos

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

Related Experiment Videos

Last Updated: Jun 20, 2026

FSL Constructs: A Simple Method for Modifying Cell/Virion Surfaces with a Range of Biological Markers Without Affecting their Viability

FSL Constructs: A Simple Method for Modifying Cell/Virion Surfaces with a Range of Biological Markers Without Affecting their Viability

Published on: August 5, 2011

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

Area of Science:

Sleep Medicine
Respiratory Medicine
Cardiology

Context:

Obstructive Sleep Apnea Syndrome (OSAS) is linked to obesity, causing upper airway abnormalities.
OSAS significantly impacts the circulatory system, contributing to hypertension and cardiovascular diseases.
Obesity is a primary driver of OSAS, necessitating psychosomatic treatment approaches.

Purpose:

To differentiate between OSAS and Upper Airway Resistance Syndrome (UARS).
To explore the relationship between UARS, functional somatic syndrome (FSS), and unidentified somatic symptoms.
To highlight the need for respiratory event monitoring in patients with FSS and unexplained symptoms.

Summary:

OSAS, driven by obesity, involves upper airway dysfunction and is associated with significant cardiovascular complications.
UARS presents with similar upper airway resistance during sleep but does not meet OSAS diagnostic criteria.
UARS patients exhibit symptoms resembling FSS, with potential nocturnal hypoxemia, warranting further sleep-disordered breathing investigation.

Impact:

Clarifies the distinction between OSAS and UARS, aiding in accurate diagnosis and management.
Suggests a potential link between UARS, FSS, and sleep-disordered breathing, expanding diagnostic considerations.
Emphasizes the importance of evaluating sleep respiratory events in patients with unexplained somatic symptoms and potential UARS.