Selected Ordovician Trilobites from the Lake St. John District of Quebec and Their Bearing on Systematics

1995 ◽  
Vol 69 (S42) ◽  
pp. 1-19 ◽  
Author(s):  
Pierre J. Lespérance ◽  
Sylvain Desbiens
Keyword(s):  
Phylogenetic Analysis ◽  
Phylogenetic Analyses ◽  
Large Data ◽  
Minimal Length ◽  
Large Data Sets ◽  
Morphological Data ◽  
Eye Position ◽  
Data Sets ◽  
Close Relationship ◽  
The Status

The thorax of Hypodicranotus has ten segments and a spine on the eighth. The ages of Erratencrinurus s.l. spicatus and Erratencrinurus (Erratencrinurus?) vigilans in the Lake St. John district do not confirm their temporal roles leading to subgenera of Erratencrinurus, as has been recently suggested. Phylogenetic analyses of large data sets of species previously referred to Encrinuroides and Physemataspis yield a minimal length cladogram containing 18 species. Encrinuroides is restricted to four species, two of which have biogeographic affinities with Iapetus. These results lead to three clades, named the Walencrinuroides n. gen. clade, Frencrinuroides n. gen. clade, and finally the Physemataspis clade, with an enlarged concept of the genus with the erection of Physemataspis (Prophysemataspis) n. subgen. These last three clades are restricted to North America and Scotland, with alternating predominance of one region. Walencrinuroides s.l. gelaisi n. gen. n. sp. is described. New morphological data on Erratencrinurus s.l. spicatus confirm its close relationship with the clades discussed above. Data are insufficient for phylogenetic analysis of selected cheirurine species here surveyed. Eye position, glabellar segmentation, and pygidial shape differentiate the genera Ceraurus and Gabriceraurus; emended diagnoses of these genera are presented. Ceraurus globulobatus and C. matranseris are distinct, but morphologically close to one another. The status of Gabriceraurus dentatus can be stabilized on its extant types.

scholarly journals Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

2020 ◽  
Author(s):  
Christiane Scherer ◽  
James Grover ◽  
Darby Kammeraad ◽  
Gabe Rudy ◽  
Andreas Scherer
Keyword(s):  
Principal Component Analysis ◽  
Population Genetics ◽  
Phylogenetic Analysis ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Whole Genomes

AbstractSince the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year.SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

Molecular and morphological phylogenetic analysis and taxonomic revision of the genus Orleanesia (Laeliinae, Epidendroideae, Orchidaceae)

Phytotaxa ◽  
2019 ◽  
Vol 392 (1) ◽  
pp. 1
Author(s):  
GABRIEL F. GONÇALVES ◽  
ANNA VICTORIA S. R. MAUAD ◽  
GIULIANA TAQUES ◽  
ERIC C. SMIDT ◽  
FÁBIO DE BARROS
Keyword(s):  
Phylogenetic Analysis ◽  
Dna Sequences ◽  
Sequence Data ◽  
Phylogenetic Analyses ◽  
Taxonomic Revision ◽  
Sister Group ◽  
Morphological Data ◽  
Data Sets ◽  
New Synonyms ◽  
Nuclear Its

In order to evaluate the monophyly of the genus Orleanesia (Orchidaceae) and to assess its position within Laeliinae, a phylogenetic analysis was performed using molecular (nuclear ITS and plastid matK DNA sequences) and morphological data. A taxonomic revision of Orleanesia was also performed, with a description of the genus and its species using fresh living plants and 115 exsiccates from 31 herbaria. All phylogenetic analyses were highly congruent, and thus the sequence data from all three data sets were combined. The resulting phylogeny corroborated the monophyly of Orleanesia, with two strongly supported clades, and confirmed Caularthron as its sister group. Character analysis was not very informative due to a high degree of homoplasy. Two lectotypifications and three new synonyms were proposed for the genus, thereby reducing the number of accepted species to six. Although none of the species of Orleanesia are considered endangered, it is clear that some populations are threatened with deforestation and habitat reduction.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

2018 ◽  
Vol 2018 (6) ◽  
pp. 38-39
Author(s):  
Austa Parker ◽  
Yan Qu ◽  
David Hokanson ◽  
Jeff Soller ◽  
Eric Dickenson ◽  
...  
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Statistical Tools

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

Correlations of random classifiers on large data sets

2021 ◽  
Author(s):  
Věra Kůrková ◽  
Marcello Sanguineti
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets
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