Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Prevalence of Enterovirus genus respiratory infections and co-infections: a cross-sectional study in a Northeast Italian Hospital (2023-2025).

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Seasonality and environmental determinants of exhaled nitric oxide in individuals with and without chronic respiratory diseases.

Environmental epidemiology (Philadelphia, Pa.)·2026
Same author

The coherent structures of EVP fluid flow past a circular cylinder.

Theoretical and computational fluid dynamics·2026
Same author

A comprehensive longitudinal analysis of the cellular immune response specific to the spike protein in healthcare workers vaccinated against SARS-CoV-2- ORCHESTRA Project.

Frontiers in immunology·2025
Same author

Use of long-acting muscarinic antagonists for severe asthma: insights from clinicians in the SHARP network.

Respiratory research·2025
Same author

Inside the anger: development and validation of a new questionnaire.

BMC psychiatry·2025

相关实验视频

Updated: May 8, 2026

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA
12:02

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

12.4K

一种基于物理模式的新方法,用于归因空气生物学数据集.

Sofia Tagliaferro1, Adrián Corrochano2, Pierpaolo Marchetti1

  • 1Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy.

PloS one
|November 19, 2024
PubMed
概括

差距单值分解 (GSVD) 显示了与空气生物学数据移动平均值归算相比较的准确性. 无论采用的方法如何,花粉的变化和位置都会对归算错误产生重大影响.

更多相关视频

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

10.6K
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.4K

相关实验视频

Last Updated: May 8, 2026

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA
12:02

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

12.4K
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

10.6K
Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model
07:39

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

3.4K

科学领域:

  • 航空生物学 航空生物学
  • 数据科学数据科学数据科学
  • 环境监测 环境监测

背景情况:

  • 对于空气生物学数据集的归算方法准确性存在有限的研究.
  • 准确的数据归算对于可靠的空气生物学分析至关重要.

研究的目的:

  • 为了评估Gappy单值值分解 (GSVD) 的有效性,用于空气生物学数据的归算.
  • 为了比较GSVD性能与传统的移动平均线插值方法.
  • 确定影响花粉数据集中归算精度的因素.

主要方法:

  • 一项模拟研究使用来自意大利东北部两个监测站 (2022) 的完整花粉数据.
  • 随机生成不同比例 (5-25%) 和差距长度 (3-10天) 的缺失数据.
  • 使用GSVD和移动平均数算法计算4800个时间序列;通过根平均平方误差 (RMSE) 评估准确性.

主要成果:

  • GSVD 显示出与移动平均值方法相比较的归算精度.
  • 在不同的数据类型中,GSVD表现出强大的泛化能力.
  • 无论采用何种方法,花粉变异性和监测站的位置都是归算错误的主要驱动因素.
  • 高的花粉度变化和缺失的数据分布对准确性产生了负面影响.

结论:

  • 一种新的数据驱动归算方法 (GSVD) 已成功引入并对空气生物学数据进行验证.
  • 这些发现支持使用GSVD作为统计归算方法的可行替代方案.
  • 需要进一步的研究来增强归算方法,以改善空气生物学数据的重建.