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Related Concept Videos

Atomic Mass01:52

Atomic Mass

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Atoms — and the protons, neutrons, and electrons that compose them — are extremely small. For example, a carbon atom weighs less than 2 × 10−23 g. When describing the properties of tiny objects such as atoms, we use appropriately small units of measure, such as the atomic mass unit (amu). The amu was originally defined based on hydrogen, the lightest element, then later in terms of oxygen. Since 1961, it has been defined with regard to the most abundant isotope of carbon, atoms of which...
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Molar Mass01:54

Molar Mass

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The identity of a substance is defined not only by the types of atoms or ions it contains but by the quantity of each type of atom or ion. For example, water, H2O, and hydrogen peroxide, H2O2, are alike in that their respective molecules are composed of hydrogen and oxygen atoms. However, because a hydrogen peroxide molecule contains two oxygen atoms, as opposed to the water molecule, which has only one, the two substances exhibit very different properties.
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Formula Mass and Mole Concepts of Compounds02:56

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Formula Mass of Covalent Compounds
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Mass and Weight01:19

Mass and Weight

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Mass and weight are often used interchangeably in everyday conversation. For example,  medical records often show our weight in kilograms, but never in the correct units of newtons. In physics, however, there is an important distinction. Weight is the pull of the Earth on an object. It depends on the distance from the center of the Earth. Weight dramatically varies if we leave the Earth's surface, unlike mass, which does not vary with location. On the Moon, for example, the...
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Mass Concreting01:22

Mass Concreting

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Mass concreting refers to the process of placing large volumes of concrete, such as in gravity dams. The heat generated during the cement hydration process and differential cooling rates within the concrete mass can lead to a temperature gradient, which can result in thermal cracks in the concrete mass.
To reduce the risk of such cracking, the concrete mix may incorporate low-heat cement and pozzolans to reduce the temperature rise. Pre-cooled angular aggregates and water-reducing admixtures...
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Atomic Number and Mass Number01:12

Atomic Number and Mass Number

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The number of protons in the nucleus of an atom is its atomic number (Z). This is the defining trait of an element. Its value determines the identity of the atom. For example, any atom that contains six protons is the element carbon and has the atomic number 6, regardless of how many neutrons or electrons it may have. A neutral atom must contain the same number of positive and negative charges, so the number of protons equals the number of electrons. This means that the atomic number also...
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Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics
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Mass spectra alignment using virtual lock-masses.

Francis Brochu1,2, Pier-Luc Plante3, Alexandre Drouin4,5

  • 1Big Data Research Center, Université Laval, Québec, Qc, Canada. francis.brochu.2@ulaval.ca.

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|June 13, 2019
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Summary
This summary is machine-generated.

This study introduces algorithms to correct spectral variations in high-throughput mass spectrometry, improving data comparability for large-scale metabolomics analysis and machine learning applications.

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Spectroscopy

Background:

  • High-throughput mass spectrometry, utilizing rapid ionization techniques like Laser Diode Thermal Desorption (LDTD) and Direct Analysis in Real Time (DART), is crucial for large-scale metabolomics.
  • Variations in spectra due to environmental, protocol, or instrument factors hinder automated comparative analysis of biological samples.

Purpose of the Study:

  • To develop and present a sequence of algorithms for correcting spectral variations.
  • To enhance the comparability of spectra within large metabolomics datasets.

Main Methods:

  • A pipeline of algorithms was designed to identify common peaks across multiple spectra.
  • Spectrum-specific corrections were computed based on these common peaks.

Main Results:

  • The proposed algorithms effectively correct minor discrepancies between spectra.
  • Increased comparability was demonstrated within large datasets of mass spectrometry data.

Conclusions:

  • The developed algorithms facilitate more accurate and automated comparative analysis of metabolomics data.
  • This approach enhances the utility of high-throughput mass spectrometry for large-scale population studies and machine learning.