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

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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

Beyond the Continuum Theory: Conductance Scaling and Correlated Imaging in Atom-Scale Artificial Ion Channels.

ACS nano·2026
Same author

A Fully Endovascular Neural Interface.

bioRxiv : the preprint server for biology·2026
Same author

Nanopore event detection in a simple and adaptive way.

bioRxiv : the preprint server for biology·2026
Same author

Defects and defect-mediated engineering of two-dimensional materials: challenges and open questions.

Beilstein journal of nanotechnology·2026
Same author

Uncovering Hidden Protein Conformations with High Bandwidth Nanopore Measurements.

Nano letters·2026
Same author

Uncovering hidden protein conformations with high bandwidth nanopore measurements.

ArXiv·2025

相关实验视频

Updated: Jul 1, 2026

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.3K

在单分子生物物理数据中使用单指令,多线程gpGPU架构进行时间域事件检测.

Boyan Penkov1, David Niedzwiecki2, Nicolae Lari1

  • 1Department of Electrical Engineering, Columbia University, New York, NY, 10027.

Computer physics communications
|May 13, 2024
PubMed
概括

我们开发了三种基于图形处理器 (GPU) 的算法,以加速从单分子测量中分析时间域数据. 这种方法显著改善了纳米孔和单分子场效应晶体管 (smFET) 数据集中的关键特征的检测.

更多相关视频

An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics
09:52

An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics

Published on: September 15, 2020

3.1K
Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
06:48

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

3.5K

相关实验视频

Last Updated: Jul 1, 2026

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
07:19

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

10.3K
An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics
09:52

An In Vitro Single-Molecule Imaging Assay for the Analysis of Cap-Dependent Translation Kinetics

Published on: September 15, 2020

3.1K
Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
06:48

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

3.5K

科学领域:

  • 计算生物学 计算生物学
  • 数据科学数据科学数据科学
  • 生物物理学的生物物理.

背景情况:

  • 具有离散幅度级别的时间域数据通常表明系统的潜态.
  • 分析有序的数据会对计算效率和硬件加速产生序列限制.
  • 单分子测量 (纳米孔,smFETs) 可以产生大量的时间排序的数据集.

研究的目的:

  • 开发先进的算法,以高效地分析时间域数据.
  • 为了利用通用图形处理单元 (gpGPU) 来加速特征检测.
  • 克服大规模生物数据集串行处理的局限性.

主要方法:

  • 实施了三种基于gpGPU的新算法,用于时间序列分析.
  • 将算法应用于纳米孔和单分子场效应晶体管 (smFET) 数据.
  • 使用单指令多数据 (SIMD) 架构进行并行处理.

主要成果:

  • 在突出特征检测速度方面实现了250倍的改进.
  • 在terabyte规模的数据集中显示了事件检测的显著加速.
  • 实现高通量单分子数据的高效分析.

结论:

  • 基于gpGPU的算法为分析有序的时间域数据提供了重大进步.
  • 开发的方法克服了生物数据分析中的串行处理瓶.
  • 自由可用的代码有助于在科学研究中得到更广泛的应用.