Abstract
Distance metrics are broadly used in different research areas and applications, such as bio-informatics, data mining and many other fields. However, there are some metrics, like pq-gram and Edit Distance used specifically for data with a hierarchical structure. Other metrics used for non-hierarchical data are the geometric and Hamming metrics. We have applied these metrics to The Health Improvement Network (THIN) database which has some hierarchical data. The THIN data has to be converted into a tree-like structure for the first group of metrics. For the second group of metrics, the data are converted into a frequency table or matrix, then for all metrics, all distances are found and normalised. Based on this particular data set, our research question: which of these metrics is useful for THIN data?. This paper compares the metrics, particularly the pqgram metric on finding the similarities of patients’ data. It also investigates the similar patients who have the same close distances as well as the metrics suitability for clustering the whole patient population. Our results show that the two groups of metrics perform differently as they represent different structures of the data. Nevertheless, all the metrics could represent some similar data of patients as well as discriminate sufficiently well in clustering the patient population using k-means clustering algorithm.
Highlights
For most capillaries the primary method of fluid exchange between the plasma and the tissues is defined by the Starling Hypothesis as interpreted in 1997 by CC Michel (1997) and Weinbaum (1998) often dubbed the “revised Starling hypothesis.” With this interpretation, in the steady state, the oncotic pressure ( ) difference resisting the hydrostatic pressure (P) is across the endothelial glycocalyx rather than the whole vessel wall: Jv = LpA [(Plumen − PeGlx) − σ] (1)Where the Jv is the volumetric fluid flux, Lp the hydraulic conductivity and A the capillary surface area
Where ∅ is the partition coefficient defined as the relative free space between the solute (Fs) and the water (Fw): Fs ∅=
Regardless, clearly the stains are affecting the structure, and adding to the size of the GAGs when bound
Summary
The different hydraulic conductivities across tissues are of physiological importance, and allow normal processes such as an immune response to manipulate the molecular size ratio of transported molecules (Owen-Woods et al, 2020) Until this point the remaining unknown was to confirm the pore size of the eGlx. A study of electron micrographs and a limited number of Pt/C replicate micrographs from freezeetched frog mesenteric and pulmonary vessels indicated a spacing between the eGlx fibers of 19.5 nm (Squire et al, 2001). There is an issue between linking the ≈20 nm spacing repeatedly observed in perfusion fixed resin embedded electron microscopy and the expected 7–8 nm pore size to exclude albumin, as the required fiber thickness would be too large to fit with our understanding of eGlx composition. There are a multitude of unknowns and as such the subsequent interpretation of the electron microscopy is not claimed as definitive, but instead claims to fit current understanding consistently and offers a promising alternative on previous interpretations
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