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

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

503
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
503
Fineness of Cement01:15

Fineness of Cement

533
The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
Direct...
533
Fineness Modulus01:19

Fineness Modulus

1.5K
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
1.5K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

6.3K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
6.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.9K

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

Influence of Both La Nina and Island Isolation During COVID-19 on the Epidemiology of Infectious Diseases in New Caledonia.

Epidemiologia (Basel, Switzerland)·2026
Same author

Contrasting population structures of reef-building corals and their algal symbionts inform adaptive potential across the western Pacific.

Current biology : CB·2026
Same author

The Effect of Increased Temperature on Dengue Virus in the Vector <i>Aedes aegypti</i> from New Caledonia.

Tropical medicine and infectious disease·2026
Same author

Coral Genetic Structure in the Western Indian Ocean Mirrors Ocean Circulation and Thermal Stress History.

Evolutionary applications·2026
Same author

Chromosome-Level Assembly and Annotation of the Grey Reef Shark (Carcharhinus amblyrhynchos) Genome.

Genome biology and evolution·2025
Same author

Evidence of Fine-Scale Genetic Structure in Tiger Sharks (<i>Galeocerdo cuvier</i>) Highlights the Importance of Stratified Sampling Regimes.

Evolutionary applications·2025

相关实验视频

Updated: Feb 13, 2026

Determining the Mechanical Strength of Ultra-Fine-Grained Metals
05:04

Determining the Mechanical Strength of Ultra-Fine-Grained Metals

Published on: November 22, 2021

2.6K

使用神经网络细粒度分配未知的海洋eDNA序列.

Sébastien Villon1,2, Morgan Mangeas1,3, Véronique Berteaux-Lecellier1,3

  • 1ENTROPIE, CNRS, Institute of Research for Development (IRD), University of New Caledonia, University of Reunion, IFREMER, Promenade Roger-Laroque, 98848 Noumea Cedex, New Caledonia, France.

Biology
|February 12, 2026
PubMed
概括

一个新的AI深度神经网络改善了环境DNA (eDNA) 的metabarcoding准确性,用于物种识别. 该工具增强了分类学赋值,特别是当参考数据库不完整时,有助于生物多样性监测.

关键词:
生物多样性监测 生物多样性监测生物信息学是一种生物信息学.卷积神经网络CNNCNN是一个神经网络.深度学习算法深度学习算法生态调查 - 生态调查环境 DNA DNA 环境 DNA鱼类的多样性 鱼类的多样性

更多相关视频

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
09:13

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

Published on: April 1, 2017

14.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

相关实验视频

Last Updated: Feb 13, 2026

Determining the Mechanical Strength of Ultra-Fine-Grained Metals
05:04

Determining the Mechanical Strength of Ultra-Fine-Grained Metals

Published on: November 22, 2021

2.6K
Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
09:13

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

Published on: April 1, 2017

14.2K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

科学领域:

  • 生态生态学 生态生态学
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 环境DNA (eDNA) 的元编码能够在不同的环境中同时检测物种.
  • 当前生物信息学工具在参考数据库中缺少物种时,难以准确地进行分类学赋值.
  • 现有的方法往往忽略了关键的核酸定位信息.

研究的目的:

  • 开发一种新的深度神经网络架构,以进行增强的eDNA元编码分析.
  • 提高分类学赋值的准确性,特别是对于代表性不足的物种.
  • 为了解决eDNA数据当前生物信息学工具的局限性.

主要方法:

  • 提出了一个深度的神经架构,利用短序的核酸身份和位置模式.
  • 使用NCBI GenBank序列进行了in-silico验证.
  • 将新方法与最先进的工具 (Obitools,Kraken2,Lolo) 和嵌入方法进行比较.

主要成果:

  • 实现了高分类准确度:94.7%的属级别和86.5%的家族级别.
  • 显著优于现有的基于参考的管道.
  • 经过有限的训练数据证明了稳定性,并通过序列对齐提高了性能.

结论:

  • 由人工智能驱动的eDNA元编码为现有的分类学赋值工具提供了强大的补充.
  • 该方法对于不完整的参考数据库和非物种级别的分辨率特别有价值.
  • 提高生物多样性监测和生态系统管理的能力.