Most endangered species exist today in small populations, many of which are isolated. Evolution in such populations is largely governed by genetic drift. Empirical evidence for drift affecting striking phenotypes based on substantial genetic data are rare. Approximately 37% of tigers (Panthera tigris) in the Similipal Tiger Reserve (in eastern India) are pseudomelanistic, characterized by wide, merged stripes. Camera trap data across the tiger range revealed the presence of pseudomelanistic tigers only in Similipal. We investigated the genetic basis for pseudomelanism and examined the role of drift in driving this phenotype's frequency. Whole-genome data and pedigree-based association analyses from captive tigers revealed that pseudomelanism cosegregates with a conserved and functionally important coding alteration in Transmembrane Aminopeptidase Q (Taqpep), a gene responsible for similar traits in other felid species. Noninvasive sampling of tigers revealed a high frequency of the Taqpep p.H454Y mutation in Similipal (12 individuals, allele frequency = 0.58) and absence from all other tiger populations (395 individuals). Population genetic analyses confirmed few (minimal number) tigers in Similipal, and its genetic isolation, with poor geneflow. Pairwise FST (0.33) at the mutation site was high but not an outlier. Similipal tigers had low diversity at 81 single nucleotide polymorphisms (mean heterozygosity = 0.28, SD = 0.27). Simulations were consistent with founding events and drift as possible drivers for the observed stark difference of allele frequency. Our results highlight the role of stochastic processes in the evolution of rare phenotypes. We highlight an unusual evolutionary trajectory in a small and isolated population of an endangered species.
Due to the huge difference in performance between the computer memory and processor , the virtual memory management plays a vital role in system performance .A Cache memory is the fast memory which is used to compensate the speed difference between the memory and processor. This paper gives an adaptive replacement policy over the traditional policy which has low overhead, better performance and is easy to implement. Simulations show that our algorithm performs better than Least-Recently-Used (LRU), First-In-First-Out (FIFO) and Clock with Adaptive Replacement (CAR).
At present usage of computational intelligence became the ultimate need of the heavy engineering industries. Digitization can be achieved in these sectors by scanning the hard copy images. When older documents are digitized are not of very high fidelity and therefore the accuracy, reliability of the estimates of components such as equipment and materials after digitization are remarkably low since (Piping and Instrumentation Diagrams) P&IDs come in various shapes and sizes, with varying levels of quality along with myriad smaller challenges such as low resolution of images, high intra project diagram variation along with no standardization in the engineering sector for diagram representation to name a few, digitizing P&IDs remains a challenging problem. In this study an end to end pipeline is proposed for automatically digitizing engineering diagrams which would involve automatic recognition, classification and extraction of diagram components from images and scans of engineering drawings such as P&IDs and automatically generating digitized drawings automatically from this obtained data. This would be done using image processing algorithms such as template matching, canny edge detection and the sliding window method. Then the lines would be obtained from the P&ID using canny edge detection and sliding window approach, the text would be recognized using an aspect ratio calculation. Finally, all the extracted components of the P&ID are associated with the closest texts present and the components mapped to each other. By the way of using such pipelines as proposed the diagrams are consistently of high quality, other smaller problems such as mis-spelling and valuable time churn are solved or minimized to large extent and paving the way for application of big data technologies such as machine learning analytics on these diagrams resulting in further efficiencies in operational processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.