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Related Concept Videos

Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Bacterial and archaeal cells exhibit remarkable diversity in shape and structure, critical in their adaptability and functionality. Among bacteria, the most commonly observed shapes include cocci and bacilli. Cocci are spherical and may exist singly or in groupings such as pairs (diplococci), chains (streptococci), clusters (staphylococci), or tetrads. Bacilli, in contrast, are rod-shaped and can also occur as single cells, in pairs, or chains, depending on their environmental and genetic...
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Equilibrium calculations for systems involving multiple equilibria are often complex. For example, to calculate the solubility of a sparingly soluble salt in an aqueous solution in the presence of a common ion, one must consider all the equilibria in this solution. Calculations for these systems can be complicated and tedious, so a systematic approach with a series of steps is often helpful. The process is detailed below.
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Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.

Isar Nassiri1,2, Matthew N McCall1,3

  • 1Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.

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This study introduces a novel cell morphology enrichment analysis to link gene expression changes with cell shape alterations. The method predicts morphological effects from transcriptomic data, advancing our understanding of cellular responses to compounds.

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Area of Science:

  • * Computational Biology and Bioinformatics
  • * Cellular Imaging and Phenomics

Background:

  • * Cellular morphology changes in response to small molecule compounds are key indicators of cellular function and molecular mechanisms.
  • * Image-based cell profiling links morphological features to cellular alterations, but lacks explicit models connecting transcriptomics to morphology.
  • * The Library of Integrated Network-based Cellular Signatures (LINCS) dataset offers a rich resource for exploring the relationship between gene expression and cell morphology.

Purpose of the Study:

  • * To develop and validate a computational method for assessing the association between transcriptomic alterations and cell morphology.
  • * To enable prediction of cellular morphological changes based on transcriptomic data.
  • * To provide a framework for understanding the interdependence of transcription and cell morphology.

Main Methods:

  • * Development of a cell morphology enrichment analysis approach.
  • * Application of the method to analyze cell morphological changes in response to compound perturbations using LINCS data.
  • * Validation using a human bone osteosarcoma cell line.

Main Results:

  • * The proposed method effectively assesses the link between transcriptomic changes and observed cell morphology.
  • * The approach can predict associated morphological phenotypes for novel transcriptomic queries.
  • * Demonstrated utility in a specific cell line model.

Conclusions:

  • * Cell morphology enrichment analysis provides a robust model to link transcriptomic data with cellular morphology.
  • * This method enhances the interpretation of image-based cell profiling and transcriptomic studies.
  • * The approach facilitates prediction of compound-induced morphological effects from gene expression profiles.