. __ 1 84 0 __ ELASTIC '|'R|MM|||BS TEXTILE FIBERS “Ec As per the British code BS , the ultimate shear strength of. slabs without pre . diameter of the slabs was mm and the diameter of the. OCR. whereas the design analysis for concrete employed in BS is 1. check that the beam depth is ad- equatem These are both greater than the 7 m II 6.
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This address is used by the corresponding author for no purpose other than to indicate his professional affiliation as is customary in publications. Received Jun 19; Accepted Aug 7.
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution Ad 2540 bs 8110 BY license http: This article has been cited by other articles in PMC. A majority of cancer types exhibit a mutation clustering structure.
Our results are in-sample stable. They are also out-of-sample stable when applied to published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization NMFa computationally-costly and non-deterministic method.
Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage ad 2540 bs 8110 diagnostics, such as novel blood-test methods currently in development.
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Introduction and Summary Unless humanity finds a cure, about a billion people alive today will die of cancer. Unlike other diseases, cancer occurs at the DNA level via somatic alterations in the genome.
A common type of such ad 2540 bs 8110 found in cancer is due to alterations to single bases in the genome single nucleotide variations SNVs. The footprint left by these mutations in the cancer genome is characterized by distinctive alteration patterns known as cancer signatures.
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Identifying all cancer signatures would greatly facilitate progress in understanding the origins of cancer and its development. Therapeutically, if there are common underlying structures across different cancer types, then treatment for one cancer type might be applicable to other cancer types, which would be great news.
Another practical application is prevention by pairing the signatures extracted from cancer samples with those caused by known carcinogens e.
At the end of the day, it all boils down to the question of usefulness: The commonly-used method for extracting cancer signatures [ 9 ] is based on nonnegative matrix factorization NMF [ ad 2540 bs 811011 ].
The ad 2540 bs 8110 to pay for this is that NMF, which is an iterative procedure, is computationally costly, and depending on the number of samples d, it can take days or even weeks to run it.
Furthermore, NMF does not fix the number of signatures K, which must be either guessed or obtained via trial and error, thereby further adding to the computational cost.
Perhaps most importantly, Ad 2540 bs 8110 is a nondeterministic algorithm and produces a different matrix W in each run. Each W corresponds to one in myriad local minima of the NMF objective function. This is dealt with by averaging over many such W matrices obtained via multiple NMF runs or samplings.
However, each run generally produces a weights matrix WiA with columns i. Aligning or matching the signatures across different runs before averaging over them is typically achieved via nondeterministic clustering such as k-means.
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Simply put, the NMF-based method for extracting cancer signatures is not designed to be even ad 2540 bs 8110 stable. Under these circumstances, out-of-sample stability cannot even be feasible i. Without in- and out-of-sample stability, practical therapeutic and diagnostic applications of cancer signatures would be challenging.