scholarly journals Euclidean Distance Analysis Enables Nucleotide Skew Analysis in Viral Genomes

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Formijn van Hemert ◽  
Maarten Jebbink ◽  
Andries van der Ark ◽  
Frits Scholer ◽  
Ben Berkhout

Nucleotide skew analysis is a versatile method to study the nucleotide composition of RNA/DNA molecules, in particular to reveal characteristic sequence signatures. For instance, skew analysis of the nucleotide bias of several viral RNA genomes indicated that it is enriched in the unpaired, single-stranded genome regions, thus creating an even more striking virus-specific signature. The comparison of skew graphs for many virus isolates or families is difficult, time-consuming, and nonquantitative. Here, we present a procedure for a more simple identification of similarities and dissimilarities between nucleotide skew data of coronavirus, flavivirus, picornavirus, and HIV-1 RNA genomes. Window and step sizes were normalized to correct for differences in length of the viral genome. Cumulative skew data are converted into pairwise Euclidean distance matrices, which can be presented as neighbor-joining trees. We present skew value trees for the four virus families and show that closely related viruses are placed in small clusters. Importantly, the skew value trees are similar to the trees constructed by a “classical” model of evolutionary nucleotide substitution. Thus, we conclude that the simple calculation of Euclidean distances between nucleotide skew data allows an easy and quantitative comparison of characteristic sequence signatures of virus genomes. These results indicate that the Euclidean distance analysis of nucleotide skew data forms a nice addition to the virology toolbox.

Author(s):  
Sulthan Fikri Mu'afa ◽  
Nurissaidah Ulinnuha

AbstractEast Java Province is one of the provinces that has the largest agricultural resources in Indonesia. The Government of East Java needs to produce superior commodities in each region. This study aims to group districts in East Java Province based on variable types of agriculture with the hybrid hierarchical clustering via mutual cluster method that combines the merging of bottom-up clustering advantages and top-down clustering advantages. Mutual cluster is a grouping with the largest distance between small groups of the shortest distance for each point outside the group. In this research, the calculation uses Euclidean distance. The data used in this study are from the East Java Central Statistics Agency (BPS) in 2017. The division calculation is obtained by finding the minimum  (standard deviation of intra cluster) value and the maximum  (standard deviation of inter clusters) value and using the analysis of variance calculation. The grouping results obtained were nine groups with  value of 725.934,  value of 1.475.978 and  value of 7,908.Keywords: agriculture; Hybrid Hierarchical Clustering; mutual cluster; Euclidean distance; analysis of variance. AbstrakProvinsi Jawa Timur merupakan salah satu provinsi yang memiliki sumber daya pertanian terbesar di Indonesia. Pemerintah Jawa Timur perlu mengembangkan komoditi unggulan di tiap daerah di Jawa Timur. Penelitian ini bertujuan untuk mengelompokkan kabupaten di Provinsi Jawa Timur berdasarkan variabel jenis pertanian dengan metode hybrid hierarchical clustering via mutual cluster yaitu menggabungkan kelebihan bottom-up clustering dan kelebihan top-down clustering. Mutual cluster yakni pengelompokkan dengan jarak terbesar antara bagian dalam kelompok yang kecil dari jarak yang terpendek kepada tiap titik di luar kelompok. Dalam penelitian ini, perhitungan jarak menggunakan jarak Euclidean. Data yang digunakan dalam penelitian ini dari Badan Pusat Statistik Jawa Timur tahun 2017. Perhitungan pembagian didapat dengan mencari nilai (simpangan baku dalam klaster) yang minimal dan nilai  (simpangan baku antar klaster) yang maksimal, serta digunakan perhitungan analyze of varians. Hasil pengelompokkan yang diperoleh didapatkan sebanyak sembilan kelompok dengan nilai  sebesar 725.934, nilai sebesar 1.475.978 dan nilai  sebesar 7,908.Kata Kunci: pertanian; Hybrid Hierarchical Clustering; mutual cluster; jarak Euclid; analisis variansi.


2016 ◽  
Vol 38 (2) ◽  
pp. 107-137 ◽  
Author(s):  
Gabriel M Sanchez ◽  
Jon M Erlandson ◽  
Nicholas Tripcevich

In America’s Far West, chipped stone crescents dating between approximately 12,000 to 8000 cal BP are often found associated with Western Stemmed Tradition points. Crescent function is debated, but scholars have suggested that they are closely associated with wetland habitats, an association that has never been systematically investigated. Using a geographic information system-based Euclidean distance analysis, we compared a sample of 100 geolocated crescent-bearing sites in Washington, Oregon, Idaho, Nevada, Utah, and California with reconstructed paleoshorelines. We confirmed a strong association of crescents with wetlands—94 of the 100 sites and approximately 99% of crescents themselves were located within 10 km of reconstructed paleoshorelines. Our results provide quantitative and region-wide support for a strong association of crescents with terminal Pleistocene and early Holocene wetland habitats. The diversity of aquatic habitats crescents are associated with, along with their morphology, suggests an association with faunal rather than plant resources, possibly birds of the Pacific Flyway.


2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Dewi Murni ◽  
UMIE LESTARI ◽  
SRI ENDAH INDRIWATI ◽  
ACHMAD EFENDI ◽  
NANI MARYANI ◽  
...  

Abstract. Murni D, Lestari U, Indriwati SE, Efendi A, Maryani N, Amin M. 2020. Morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia. Biodiversitas 21: 933-940. This study aimed to describe the morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia. In this study, 125 buffaloes from six regions were investigated based on 11 morphometric characters. Morphometric diversity was analyzed using multivariate discriminant analysis. The Euclidean genetic distances were used to estimate the phenotypic relationship among the buffalo populations. The indigenous buffaloes of Banten have high morphometric diversity, with a coefficient from 2.83 to 41.43%. The body length and chest circumference can be used as a morphometric marker to determine potential indigenous buffaloes as their high correlation coefficient value (0.506). The Serang district buffaloes have the highest mean of body length and chest circumference, which shows that this population is potential compared to the populations from other regions. The morphometric of buffalo population from Serang City, Cilegon City, Serang District, and Pandeglang District tend to be homogenous. Meanwhile, Lebak and Tangerang District population tends to heterogeneous. According to Euclidean distance analysis, the proximate phenotypic relationship was between Serang and Pandeglang District's buffalo populations. Our results indicated that morphometric diversity and phenotypic relationships of the populations were related to geographical origins and can be used to determine the potential indigenous of buffaloes.


Heritage ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 1330-1343
Author(s):  
Francisco J. Fortes ◽  
Luisa M. Cabalín ◽  
Javier J. Laserna

This paper reports the use of an advanced statistical algorithm for the recognition and classification of a set of 30 archaeological metallic objects from the Museum of Malaga. In-situ laser-induced breakdown spectrometry (LIBS) analysis was performed using a portable analyzer. The coordinate-obtaining method provided the statistical weights of each element in the sample. A comparative study between the coordinate-obtaining method and the linear correlation method is also discussed in order to corroborate the applicability of the proposed approach to the field of cultural heritage. The possibility of fast identification based on the simultaneous comparison of all the spectra in the reference LIBS library while allowing the analysis of heterogeneous materials is the main advantage of the method. In addition, statistical analysis (Euclidean distance analysis and binary diagrams) suggested that differentiating between archaeological sites is feasible.


1993 ◽  
Vol 113 (3) ◽  
pp. 252-258
Author(s):  
Kiyoto Nishijima ◽  
Itaru Tsuneyasu ◽  
Hiraku Nakahodo ◽  
Masaharu Minakami

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.


2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1580-1584
Author(s):  
Radhika Kulkarni ◽  
Kumar Gaurav Chhabra ◽  
Gargi Nimbulkar ◽  
Amit Reche

To decrease the chance of spread of highly infectious coronavirus disease, the complete lockdown has been taking place in India as well as many other countries of the world. At this difficult time, telehealth can play a major role as it is ideal for the treatment and management of infectious diseases, thus fulfilling the purpose of ‘social distancing’. Telehealth can be beneficial to those who are at higher risk of getting infected and also to the health care providers by decreasing the exposure as well as the workload of health care providers. Telehealth uses computer technology to convey clinical data for diagnosis, treatment as well as management of the disease. Tele-dentistry is telemedicine in dental practice which can also be helpful in the current national emergency. Within the dental practice, teledentistry is widely used in disciplines like preventive dentistry, orthodontics, endodontics, oral surgery, periodontal conditions, early dental caries detection, and education. Patients, oral medication and diagnosis. Some of the main modes and methods used in teledentistry are electronic health records, electronic referral systems, image scanning, teleconvention and telediagnosis. All applications used in teledentistry aim to improve efficiency, provide access to an ineligible population, improve quality of care, and reduce the burden of oral disease. This article provides a review of the use of telemedicine and teledentistry in the time of coronavirus disease.


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