Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Time-Series Graph

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

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...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Application of antibiotics for the selective isolation of previously uncultured species from activated sludge.

Microbiology spectrum·2026

Same author

<i>Desulfatiglans-</i>related bacteria associated with conductive mineral particles in marine subsurface sediments.

mBio·2026

Same author

The Microflora Danica atlas of Danish environmental microbiomes.

Nature·2025

Same author

Sewer microbiomes shape microbial community composition and dynamics of wastewater treatment plants.

The ISME journal·2025

Same author

Ecophysiology and niche differentiation of three genera of polyphosphate-accumulating bacteria in a full-scale wastewater treatment plant.

mSystems·2025

Same author

Genome-resolved long-read sequencing expands known microbial diversity across terrestrial habitats.

Nature microbiology·2025

Same journal

Chlorinated VSLSs Surpass HCFCs in CFC-11-Equivalent Emissions for Ozone Layer Depletion in China.

Nature communications·2026

Same journal

Vegetation browning patterns under compound soil and atmospheric dryness in northern permafrost ecosystems.

Nature communications·2026

Same journal

Voltage imaging of CA1 pyramidal cells and SST+ interneurons reveals stability and plasticity mechanisms of spatial firing.

Nature communications·2026

Same journal

Radical-omics reveals the hydrogen-abstraction pathway of isoprene oxidation.

Nature communications·2026

Same journal

Toughening elastomer via sequentially activated multi-pathway energy dissipation.

Nature communications·2026

Same journal

De novo EHMT2 variants cause an autosomal dominant EHMT2-related Kleefstra syndrome via loss of G9a methyltransferase activity.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Predicting microbial community structure and temporal dynamics by using graph neural network models.

Kasper Skytte Andersen¹, Kai Zhao², Alexander de Linde Agerskov²

¹Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.

Nature Communications

|October 14, 2025

Summary

This summary is machine-generated.

Predicting microbial species abundance is crucial for ecosystem management. A new graph neural network model accurately forecasts these dynamics in wastewater treatment plants (WWTPs) using historical data.

More Related Videos

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Related Experiment Videos

Last Updated: Jan 15, 2026

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Area of Science:

Microbiology
Bioinformatics
Machine Learning

Background:

Managing microbial ecosystems requires understanding species abundance dynamics.
Process-critical bacteria in wastewater treatment plants (WWTPs) are vital for pollutant removal.
Unpredictable fluctuations in microbial species abundance pose challenges for process stability and optimization.

Purpose of the Study:

To develop a predictive model for microbial species abundance dynamics.
To forecast future abundance patterns using only historical relative abundance data.
To assess the model's accuracy and applicability across different microbial datasets.

Main Methods:

Developed a graph neural network (GNN)-based model.
Trained and tested the model on longitudinal time-series data from 24 Danish WWTPs.
Validated the approach on a human gut microbiome dataset.

Main Results:

The GNN model accurately predicts species dynamics up to 10 time points (2-4 months) ahead.
Predictions extended to 20 time points (8 months) in some cases.
The "mc-prediction" workflow demonstrated suitability for diverse longitudinal microbial datasets.

Conclusions:

The developed GNN model offers a robust method for forecasting microbial abundance dynamics.
This predictive capability is essential for managing microbial ecosystems and optimizing processes like WWTPs.
The "mc-prediction" workflow shows broad applicability to various longitudinal microbial studies.