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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

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Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Mesh Analysis with Current Sources01:10

Mesh Analysis with Current Sources

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Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law...
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The Bone Matrix01:18

The Bone Matrix

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Bone contains a relatively small number of cells entrenched in a matrix of collagen fibers that provide an adherent surface for inorganic salt crystals. Both components of the matrix, organic and inorganic, contribute to the unusual properties of bone. Without collagen, bones would be brittle and shatter easily. Without mineral crystals, bones would flex and provide little support. This can be observed by an experiment: when the minerals of a bone are dissolved by soaking the bone in...
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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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FullMeSH: improving large-scale MeSH indexing with full text.

Suyang Dai1, Ronghui You1, Zhiyong Lu2

  • 1School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)
|October 10, 2019
PubMed
Summary
This summary is machine-generated.

FullMeSH improves biomedical article indexing by utilizing full-text content, outperforming existing methods like DeepMeSH and MeSHLabeler. This enhanced Medical Subject Heading (MeSH) indexing facilitates better knowledge discovery from scientific literature.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Natural Language Processing

Background:

  • The rapid growth of biomedical literature necessitates efficient automatic indexing using Medical Subject Headings (MeSH).
  • Existing large-scale MeSH indexing methods (e.g., DeepMeSH, MeSHLabeler) are limited by their reliance on only article titles and abstracts.
  • This limitation hinders comprehensive knowledge discovery and hypothesis generation.

Purpose of the Study:

  • To introduce FullMeSH, a novel large-scale MeSH indexing method that leverages the full text of biomedical articles.
  • To address the limitations of previous methods by incorporating more extensive textual information.
  • To improve the accuracy and efficiency of automatic MeSH indexing for biomedical literature.

Main Methods:

  • FullMeSH segments full-text articles into sections with normalized titles to analyze contributions.
  • It employs a 'learning to rank' framework to integrate evidence from different sections, combining sparse and deep semantic representations.
  • An Attention-based Convolutional Neural Network is trained for each section to enhance performance on infrequent MeSH headings.

Main Results:

  • FullMeSH was trained on 1.4 million full-text articles from the PubMed Central Open Access subset.
  • Achieved a Micro F-measure of 66.76% on a test set, surpassing DeepMeSH by 3.3% and MeSHLabeler by 6.4%.
  • Demonstrated an average improvement of 4.7% over DeepMeSH for indexing frequently used MeSH headings (Check Tags).

Conclusions:

  • FullMeSH represents a significant advancement in large-scale MeSH indexing by effectively utilizing full-text article content.
  • The method's section-based analysis and attention-based CNNs improve indexing accuracy, particularly for less common MeSH terms.
  • FullMeSH enhances the potential for hypothesis generation and knowledge discovery from the ever-expanding biomedical literature.