微生物去除对河曲线曲的反应:物理信息的神经网络模型的机制和发展
Haolan Wang1, Dawei Wang1, Bo Zhao1
1State Key Laboratory of Water Cycle and Water Security, Ministry of Education, College of Environment, Hohai University, Nanjing, 210098, PR China.
Journal of environmental management
|March 12, 2026
概括
河曲线的曲性通过创造多样化的微生物息地来增强的去除. 这项研究揭示了环境异质性和微生物协作如何推动这一过程,这对于河流建设和自我净化至关重要.
科学领域:
- 环境科学 环境科学
- 微生物学 微生物学
- 河流工程 河流工程
背景情况:
- 河流的曲性影响了流动动力学和息地的多样性.
- 了解河流曲的生物地化学过程是有效的河流建设和自我净化的关键.
- 河的空间异质性给预测建模带来了挑战.
研究的目的:
- 为了研究微生物群落影响去除的机制,以应对河曲线的曲性.
- 探索功能基因和酶在流驱动的去除中的作用.
- 开发一个预测模型,用于河曲线中去除气.
主要方法:
- 室内实验研究微生物社区对曲性的反应.
- 功能基因 (narG,nirK/S,hzo) 和酶合成的分析.
- 结构方程建模用于评估微生物种类,基因表达和去除之间的关系.
- 开发一个物理信息神经网络 (PINN),集成CNN和MLP进行预测.
主要成果:
- 阴性增加了通过增强环境异质性的去除.
- 环境异质性促进微生物在转化过程中的协作.
- 去除率取决于微生物基因丰富度和环境对基因表达的影响.
- 开发的PINN模型准确地预测了河流曲处的去除 (RMSE ~0.67).
结论:
- 河曲线是影响微生物通过环境异质性和微生物相互作用去除的关键因素.
- 这项研究阐明了河曲线中微生物去除的基本机制.
- 开发的PINN模型提供了一个可行的工具,用于预测去除,并为河流建设决策提供信息.
更多相关视频
12:32Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales
Published on: November 25, 2020
7.1K
09:38Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems
Published on: October 29, 2016
11.1K
相关概念视频
Typical Model Studies
669
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
669
Rapidly Varying Flow
606
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
606
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
382
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
382
