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Infection

Infection

When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...

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Updated: Jun 23, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

spread.gl:在高性能浏览器应用程序中可视化病原体的传播.

Yimin Li¹, Nena Bollen¹, Samuel L Hong¹

¹Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium.

medRxiv : the preprint server for health sciences

|June 17, 2024

概括

此摘要是机器生成的。

spread.gl 是一个新的工具,用于使用植物地理数据可视化病原体传播历史. 它活化了经过时间的地理分布,并整合了环境数据以获得更深入的见解.

关键词:

数据可视化数据可视化环境因素环境因素地理空间扩散植物动力学学.植物地理学植物地理学

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

相关实验视频

Last Updated: Jun 23, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

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Published on: March 16, 2019

Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

科学领域:

* 进化生物学进化生物学
* 计算生物学 * 计算生物学
* 流行病学流行病学

背景情况:

* 植物地理分析利用分子序列的位置数据来重建病原体传播历史.
* 解释复杂的植物地理结果往往需要专门的可视化工具.
* 现有的方法可能缺乏用于直观和全面可视化时空数据的功能.

研究的目的:

* 推出 spread.gl,这是一个开源的,基于浏览器的应用程序,用于可视化族谱推理结果.
* 为了使分立和连续的植物地理数据的直观和用户友好的可视化.
* 促进对影响病原体传播模式的环境因素的探索.

主要方法:

* 开发了spread.gl作为一个功能丰富,开源的可视化应用程序.
* 能够结合地理,遗传学和环境数据层.
* 实现了流动画功能,可视化病原体在时间上的地理分布.
* 展示了多种病原体分散示例的功能,包括大规模的SARS-CoV-2分析.

主要成果:

* spread.gl提供了流,直观和用户友好的类地推理可视化.
* 该应用程序有效地激活了病原体随着时间的推移的地理分散.
*用户可以整合和探索多个数据层,包括环境数据,以及遗传信息.
* 证明了大规模的SARS-CoV-2数据集的成功可视化,其中包含超过17000个序列.

结论:

* spread.gl增强了复杂的植物地理分析的解释性.
* 有助于探索病原体传播的环境驱动因素.
* 提供了一个强大的平台来生成假设,指导进一步的统计分析.
* 在可视化病原体进化和传播方面取得了重大进展.