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RNA folding problem: Is it possible to accurately predict the secondary, tertiary and quaternary structure of a polyribonucleic acid sequence based on its sequence and environment? Protein design : Is it possible to design highly active enzymes de novo for any desired reaction?
A mass spectrum is a histogram plot of intensity vs. mass-to-charge ratio (m/z) in a chemical sample, [1] usually acquired using an instrument called a mass spectrometer. Not all mass spectra of a given substance are the same; for example, some mass spectrometers break the analyte molecules into fragments ; others observe the intact molecular ...
This histogram shows the number of cases per unit interval as the height of each block, so that the area of each block is equal to the number of people in the survey who fall into its category. The area under the curve represents the total number of cases (124 million). This type of histogram shows absolute numbers, with Q in thousands.
Sturges's rule [1] is a method to choose the number of bins for a histogram.Given observations, Sturges's rule suggests using ^ = + bins in the histogram. This rule is widely employed in data analysis software including Python [2] and R, where it is the default bin selection method.
Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18th century.
This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file.
The lines always slope and start from top-left to bottom-right. For a structure receiving a very homogeneous dose (100% of the volume receiving exactly 10 Gy, for example) the cumulative DVH will appear as a horizontal line at the top of the graph, at 100% volume as plotted vertically, with a vertical drop at 10 Gy on the horizontal axis.
A v-optimal histogram is based on the concept of minimizing a quantity which is called the weighted variance in this context. [1] This is defined as = =, where the histogram consists of J bins or buckets, n j is the number of items contained in the jth bin and where V j is the variance between the values associated with the items in the jth bin.