Grid Curves
Grid Curves - Related Videos
05:05
Preparation of High-Temperature Sample Grids for Cryo-EM
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Cited by 4 •
2021
This paper provides a detailed protocol for preparing sample grids at temperatures as high as 70 °C, prior to plunge freezing for cryo-EM experiments.
06:53
Fabrication of Monolayer Graphene-Coated Grids for Cryoelectron Microscopy
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Cited by 6 •
2023
Here, we describe a protocol for applying a single monolayer of graphene to electron microscopy grids and how to prepare them for use in cryoEM structure determination.
07:43
Calibration Curves
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2023
Source: Laboratory of Dr. B. Jill Venton - University of Virginia Calibration curves are used to understand the instrumental response to an analyte and predict the concentration in an unknown sample. Generally, a set of standard samples are made at various concentrations with a range than includes the unknown of interest and the instrumental response at each concentration is recorded. For more accuracy and to understand the error, the response at each concentration can be repeated so an error...
08:49
Mapping the Cellular Distribution of an Optogenetic Protein Using a Light-Stimulation Grid
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2024
This protocol involves transfecting cAMP sensors and bPAC-nLuc, an optogenetic protein, to accurately track its cellular distribution and response to light stimulation. The innovative approach of creating a cAMP response map using a point scanning system holds the potential for advancing research with optogenetic proteins in different fields.
07:52
Cryo-Electron Microscopy Screening Automation Across Multiple Grids Using Smart Leginon
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Cited by 3 •
2023
Cryo-electron microscopy (cryoEM) multi-grid screening is often a tedious process that demands hours of attention. This protocol shows how to set up a standard Leginon collection and Smart Leginon Autoscreen to automate this process. This protocol can be applied to the majority of cryoEM holey foil grids.
FAQs
Changing the coordinates of plotted points changes the curve’s position, scale, or shape on the grid. Comparing the original and modified plots helps reveal how horizontal or vertical changes affect features such as intercepts, slope, symmetry, and curvature. This visual comparison connects coordinate values with the geometric behavior produced by the underlying rule.
Slope describes how the curve changes as coordinates vary, while intercepts identify where it meets a coordinate reference line. Examining both gives a concise way to compare curves and relate their graphical features to equations. On a grid, these characteristics can often be estimated visually before a more exact symbolic calculation is attempted.
Symmetry shows whether parts of a curve correspond in a balanced way across the grid, whereas curvature describes how the curve bends or changes direction. These features make broad patterns easier to recognize than a list of ordered pairs. Identifying them supports comparisons among graphs and helps students connect visual structure with the rules generating the curves.
Begin with the rule that determines the relationship between coordinates, then select suitable coordinate values and calculate or identify the corresponding ordered pairs. Plot those points carefully on the regularly spaced grid, connect them according to the rule, and inspect the resulting shape. Finally, check visible features such as intercepts, slope, symmetry, and curvature for consistency.
A graph allows an equation to be examined through position, scale, and shape rather than symbols alone. Students can compare curves, estimate values, and identify how coordinate changes correspond to visible features. This approach is especially useful when two relationships have different graphical behavior, because the grid makes similarities and differences easier to inspect directly.
Grid curves are useful when measured relationships need to be represented visually or when a mathematical model must be compared with observed patterns. Plotting the relevant coordinate pairs can expose trends, shape, and possible differences between relationships. In mathematics, this supports analytic geometry, data visualization, modeling, and the analysis of measured relationships.