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Related Concept Videos

Cohesion01:07

Cohesion

60.1K
Cohesion is the attraction between molecules of the same type, such as water molecules. Water molecules have an overall neutral charge but are polar molecule. An oxygen atom in one water molecule has a partial negative charge that can bind to a hydrogen atom with a partial positive charge in a second water molecule, forming a hydrogen bond. Each water molecule can form up to four hydrogen bonds with other water molecules. Hydrogen bonds are responsible for water's cohesive nature.
On a...
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Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
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Microbial Growth Measurement: Direct Methods01:23

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Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
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Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria
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Cohesion: a method for quantifying the connectivity of microbial communities.

Cristina M Herren1, Katherine D McMahon2,3

  • 1Freshwater and Marine Sciences Program, University of Wisconsin-Madison, Madison, WI, USA.

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|July 22, 2017
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Summary

Predicting microbial community dynamics can be improved by measuring community cohesion, a new metric for connectivity. This approach outperforms models using only environmental data, enhancing ecological predictions.

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Area of Science:

  • Microbial Ecology
  • Ecological Modeling
  • Data Science

Background:

  • Predicting microbial community dynamics is challenging despite abundant data.
  • Current models primarily use environmental parameters, neglecting community complexity.
  • Ecological theory suggests community structure influences dynamics.

Purpose of the Study:

  • To introduce a new metric, community cohesion, to quantify microbial community connectivity.
  • To test if incorporating community cohesion improves predictions of microbial dynamics.
  • To assess the utility of cohesion metrics across diverse long-term ecological datasets.

Main Methods:

  • Developed and applied community cohesion metrics to six long-term microbial datasets (10+ years).
  • Validated the approach using datasets with absolute taxon abundances.
  • Compared cohesion-based model performance against models using only environmental data.

Main Results:

  • Community cohesion strongly predicts Bray-Curtis dissimilarity in phytoplankton communities (R²=0.47).
  • Cohesion metrics outperformed models based solely on environmental parameters.
  • Consistent results observed across five phytoplankton and one bacterial dataset.

Conclusions:

  • Community cohesion is a valuable metric for understanding and predicting microbial community dynamics.
  • Incorporating community complexity, via cohesion, enhances predictive power in microbial ecology.
  • Cohesion metrics offer a novel tool for analyzing microbial ecology and time-series data.