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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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Genomic Imprinting and Inheritance02:30

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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Large Insert Environmental Genomic Library Production
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Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Tristan Cordier1, Anders Lanzén2, Laure Apothéloz-Perret-Gentil1

  • 1University of Geneva, Department of Genetics and Evolution, 1211 Geneva, Switzerland.

Trends in Microbiology
|December 18, 2018
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Environmental genomics, combining microbial data and machine learning, offers a powerful new approach for biomonitoring. This method addresses challenges in identifying microbes and understanding their ecological roles, advancing ecosystem science.

Keywords:
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Area of Science:

  • Environmental science
  • Microbial ecology
  • Genomics

Background:

  • Genomics is increasingly used in diagnostics and biotechnology.
  • Environmental genomics has primarily replaced morphological identification for known bioindicator taxa.
  • Microbial diversity is crucial for ecosystem function but underutilized in biomonitoring due to identification and functional knowledge gaps.

Purpose of the Study:

  • To highlight the potential of combining environmental genomics with machine learning for biomonitoring.
  • To address limitations in current microbial biomonitoring approaches.
  • To advance the understanding of microbial ecology in environmental contexts.

Main Methods:

  • Leveraging massive environmental genomics data.
  • Applying machine learning algorithms to microbial datasets.
  • Integrating genomic data with ecological function knowledge.

Main Results:

  • Demonstrates the power of integrating large-scale genomic data with machine learning.
  • Provides a framework for overcoming challenges in microbial identification for biomonitoring.
  • Highlights the potential to significantly improve our understanding of microbial ecology.

Conclusions:

  • Environmental genomics coupled with machine learning presents a transformative tool for biomonitoring.
  • This integrated approach can fill critical knowledge gaps in microbial ecology.
  • It paves the way for more effective and comprehensive environmental assessments.