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Related Concept Videos

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

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The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
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Wind Turbine Machine Models01:24

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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Translation01:31

Translation

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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
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Initiation of Translation02:33

Initiation of Translation

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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
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Termination of Translation01:44

Termination of Translation

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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Found In Translation: a machine learning model for mouse-to-human inference.

Rachelly Normand1, Wenfei Du2, Mayan Briller1

  • 1Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.

Nature Methods
|November 28, 2018
PubMed
Summary
This summary is machine-generated.

Found In Translation (FIT) is a new statistical method that uses gene expression data to better predict human disease from mouse models. This approach improves translational research by increasing gene overlap by 20-50% and identifying novel disease-associated genes.

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Area of Science:

  • Translational research
  • Bioinformatics
  • Genomics

Background:

  • Cross-species differences impede translational research and clinical trial success.
  • Current interpretation of animal models does not systematically incorporate species-specific knowledge.
  • Bridging the gap between animal models and human conditions remains a significant challenge.

Purpose of the Study:

  • To introduce Found In Translation (FIT), a statistical methodology for extrapolating mouse experimental results to human conditions.
  • To leverage public gene expression data for improved cross-species analysis.
  • To enhance the predictive power of animal models in disease research.

Main Methods:

  • Developed a statistical methodology (FIT) utilizing public gene expression data.
  • Applied FIT to mouse models across 28 diverse human diseases.
  • Compared FIT predictions against direct cross-species extrapolation.

Main Results:

  • FIT predictions showed improved overlap of differentially expressed genes (20-50%) compared to direct extrapolation.
  • Identified novel disease-associated genes through FIT predictions, with one validated experimentally.
  • FIT successfully highlighted relevant biological signals and reduced false leads in translational studies.

Conclusions:

  • FIT offers a cost-effective statistical approach to enhance the interpretation of mouse models for human diseases.
  • This methodology improves the reliability of translational research by accounting for cross-species gene expression differences.
  • FIT facilitates the identification of potential therapeutic targets and biomarkers with greater accuracy.