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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
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Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content

Hong Cheng1,2, Julie Sanchez Medina2,3, Jianqiang Zhou2,4

  • 1Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, People's Republic of China.

Environmental Science & Technology
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

A new model predicts anaerobic membrane bioreactor performance using microbial cell counts. This tool forecasts chemical oxygen demand removal and methane production 3.5 days ahead, aiding efficient operation.

Keywords:
HNA and LNA cellsanaerobic membrane bioreactorflow cytometrymicrobial diversitypredictive model

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Area of Science:

  • Environmental microbiology
  • Biotechnology
  • Wastewater treatment

Background:

  • Anaerobic membrane bioreactors (AnMBRs) require efficient monitoring for optimal performance.
  • Predicting reactor outcomes like chemical oxygen demand (COD) removal and methane production is crucial for operational control.

Purpose of the Study:

  • To develop a predictive model for AnMBR performance using microbial cell abundance data.
  • To establish a rapid monitoring tool for facilitating AnMBR operation.

Main Methods:

  • Development of a linear regression model incorporating a time-lagging mode.
  • Utilizing low nucleic acid (LNA) cell numbers and the high nucleic acid (HNA) to LNA cell ratio as input data.
  • Training and validation using pilot-scale AnMBR data and flow cytometry (FCM).

Main Results:

  • The model accurately predicted COD removal efficiency and methane production 3.5 days in advance.
  • Validation on an independent reactor confirmed the model's predictive accuracy.
  • Correlation analysis identified specific HNA and LNA genera contributing to prediction accuracy.

Conclusions:

  • Routine enumeration of HNA and LNA cells can be used with the developed model for rapid AnMBR performance prediction.
  • This approach offers a fast and effective method for monitoring and managing AnMBR systems.
  • The findings support the integration of microbial monitoring into wastewater treatment operational strategies.