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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Normal Stress01:19

Normal Stress

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Normal stress is a type of stress that occurs when forces act perpendicular, or normal, to a material's cross-sectional area. This stress often arises in structures when subjected to axial loading, which is the application of force along the axis of an object. A practical example of this can be found in bridge truss members.
When a rod is under axial loading, the internal forces and corresponding stress are normal to the plane of the section, so it is termed normal stress. It's important to...
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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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Atomic Weight01:25

Atomic Weight

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Protons and neutrons have approximately the same mass, about 1.67 × 10-24 grams. Scientists arbitrarily define this amount of mass as one atomic mass unit (amu) or one Dalton. Electrons are much smaller in mass than protons, weighing only 9.11 × 10-28 grams, or about 1/1800 of an atomic mass unit. As a result, they do not contribute much to an element's overall atomic mass. This means that, when considering atomic mass, it is customary to ignore the mass of any electrons and...
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Normal Distribution01:11

Normal Distribution

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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
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Related Experiment Video

Updated: Jan 27, 2026

Purification of High Molecular Weight Genomic DNA from Powdery Mildew for Long-Read Sequencing
06:56

Purification of High Molecular Weight Genomic DNA from Powdery Mildew for Long-Read Sequencing

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Improving in-silico normalization using read weights.

Dilip A Durai1,2, Marcel H Schulz3,4,5

  • 1Cluster of Excellence on Multimodal Computing and Interaction (MMCI) and Max Planck Insitute for Informatics (MPII), Saarland University, Saarbrücken, Germany.

Scientific Reports
|March 28, 2019
PubMed
Summary
This summary is machine-generated.

New ORNA algorithms (ORNA-Q and ORNA-K) improve in-silico read normalization for large sequencing datasets. These methods enhance computational efficiency and memory savings for genome assembly pipelines, particularly for RNA-seq data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • De novo assemblers are crucial for analyzing diverse genomics data like single-cell, metagenomics, and RNA-seq.
  • Assembling large datasets from modern sequencing technologies is computationally demanding.
  • In-silico read normalization can reduce redundancy, speeding up assembly and saving memory.

Purpose of the Study:

  • To introduce extensions to the ORNA read normalization method, named ORNA-Q and ORNA-K.
  • To improve in-silico read normalization using weighted set multi-cover optimization.
  • To leverage base quality scores (ORNA-Q) or k-mer abundances (ORNA-K) for enhanced normalization.

Main Methods:

  • Developed weighted set multi-cover optimization formulations for read normalization.
  • Designed efficient heuristic algorithms to solve the ORNA-Q and ORNA-K formulations.
  • Applied and evaluated ORNA-Q and ORNA-K on human RNA-seq datasets.

Main Results:

  • ORNA-Q and ORNA-K achieved similar or higher read reduction compared to existing methods.
  • These novel methods assembled comparable or greater numbers of full-length transcripts.
  • The algorithms demonstrated improved performance in speed and memory efficiency for assembly pipelines.

Conclusions:

  • ORNA-Q and ORNA-K represent significant advancements in in-silico read normalization.
  • These methods effectively reduce redundancy in large sequencing datasets while preserving essential connectivity information.
  • The ORNA v2.0 implementation offers a powerful tool for accelerating and improving genome assembly, especially for RNA-seq data analysis.