scholarly journals Genetic Stability and Evolution of the sigB Allele, Used for Listeria Sensu Stricto Subtyping and Phylogenetic Inference

2017 ◽  
Vol 83 (12) ◽  
Author(s):  
Jingqiu Liao ◽  
Martin Wiedmann ◽  
Jasna Kovac
Keyword(s):  
Homologous Recombination ◽  
Positive Selection ◽  
Molecular Clock ◽  
Genetic Stability ◽  
Sensu Stricto ◽  
Whole Genome Sequence ◽  
Data Set ◽  
Content Type ◽  
Lineage Iv

ABSTRACT Sequencing of single genes remains an important tool that allows the rapid classification of bacteria. Sequencing of a portion of sigB, which encodes a stress-responsive alternative sigma factor, has emerged as a commonly used molecular tool for the initial characterization of diverse Listeria isolates. In this study, evolutionary approaches were used to assess the validity of sigB allelic typing for Listeria. For a data set of 4,280 isolates, sigB allelic typing showed a Simpson's index of diversity of 0.96. Analyses of 164 sigB allelic types (ATs) found among the 6 Listeria sensu stricto species, representing these 4,280 isolates, indicate that neither frequent homologous recombination nor positive selection significantly contributed to the evolution of sigB, confirming its genetic stability. The molecular clock test provided evidence for unequal evolution rates across clades; Listeria welshimeri displayed the lowest sigB diversity and was the only species in which sigB evolved in a clocklike manner, implying a unique natural history. Among the four L. monocytogenes lineages, sigB evolution followed a molecular clock only in lineage IV. Moreover, sigB displayed a significant negative Tajima D value in lineage II, suggesting a recent population bottleneck followed by lineage expansion. The absence of positive selection along with the violation of the molecular clock suggested a nearly neutral mechanism of Listeria sensu stricto sigB evolution. While comparison with a whole-genome sequence-based phylogeny revealed that the sigB phylogeny did not correctly reflect the ancestry of L. monocytogenes lineage IV, the availability of a large sigB AT database allowed accurate species classification. IMPORTANCE sigB allelic typing has been widely used for species delineation and subtyping of Listeria. However, an informative evaluation of this method from an evolutionary perspective was missing. Our data indicate that the genetic stability of sigB is affected by neither frequent homologous recombination nor positive selection, which supports that sigB allelic typing provides reliable subtyping and classification of Listeria sensu stricto strains. However, multigene data are required for accurate phylogeny reconstruction of Listeria. This study thus contributes to a better understanding of the evolution of sigB and confirms the robustness of the sigB subtyping system for Listeria.

scholarly journals A surrogate-based generic classifier for Chinese TV series reviews

2017 ◽  
Vol 45 (2) ◽  
pp. 66-74
Author(s):  
Yufeng Ma ◽  
Long Xia ◽  
Wenqi Shen ◽  
Mi Zhou ◽  
Weiguo Fan
Keyword(s):  
Design Methodology ◽  
Experimental Result ◽  
Digital Information ◽  
Data Set ◽  
Content Type ◽  
Category Labels ◽  
Information Supply Chain ◽  
Feature Selection Techniques ◽  
Practical Implications

Purpose The purpose of this paper is automatic classification of TV series reviews based on generic categories. Design/methodology/approach What the authors mainly applied is using surrogate instead of specific roles or actors’ name in reviews to make reviews more generic. Besides, feature selection techniques and different kinds of classifiers are incorporated. Findings With roles’ and actors’ names replaced by generic tags, the experimental result showed that it can generalize well to agnostic TV series as compared with reviews keeping the original names. Research limitations/implications The model presented in this paper must be built on top of an already existed knowledge base like Baidu Encyclopedia. Such database takes lots of work. Practical implications Like in digital information supply chain, if reviews are part of the information to be transported or exchanged, then the model presented in this paper can help automatically identify individual review according to different requirements and help the information sharing. Originality/value One originality is that the authors proposed the surrogate-based approach to make reviews more generic. Besides, they also built a review data set of hot Chinese TV series, which includes eight generic category labels for each review.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Arcobacter vandammei sp. nov., isolated from the rectal mucus of a healthy pig

2021 ◽  
Vol 71 (11) ◽  
Author(s):  
Pieter-Jan Kerkhof ◽  
Stephen L. W. On ◽  
Kurt Houf
Keyword(s):  
Gene Sequence ◽  
Whole Genome Sequence ◽  
Rrna Gene ◽  
Phenotypic Analysis ◽  
Sequence Comparisons ◽  
Content Type ◽  
Link Type ◽  
Intestinal Mucus ◽  
Protein Encoding

A study on the polyphasic taxonomic classification of an Arcobacter strain, R-73987T, isolated from the rectal mucus of a porcine intestinal tract, was performed. Phylogenetic analysis based on the 16S rRNA gene sequence revealed that the strain could be assigned to the genus Arcobacter and suggested that strain R-73987T belongs to a novel undescribed species. Comparative analysis of the rpoB gene sequence confirmed the findings. Arcobacter faecis LMG 28519T was identified as its closest neighbour in a multigene analysis based on 107 protein- encoding genes. Further, whole-genome sequence comparisons by means of average nucleotide identity and in silico DNA–DNA hybridization between the genome of strain R-73987T and the genomes of validly named Arcobacter species resulted in values below 95–96 and 70  %, respectively. In addition, a phenotypic analysis further corroborated the conclusion that strain R-73987T represents a novel Arcobacter species, for which the name Arcobacter vandammei sp. nov. is proposed. The type strain is R-73987T (=LMG 31429T=CCUG 75005T). This appears to be the first Arcobacter species recovered from porcine intestinal mucus.

Classification of body shape characteristics of women's torsos using angles

2010 ◽  
Vol 22 (4) ◽  
pp. 297-311 ◽  
Author(s):  
Wookyung Lee ◽  
Haruki Imaoka
Keyword(s):  
Body Shape ◽  
Three Dimensional ◽  
The Body ◽  
Data Set ◽  
Content Type ◽  
Body Sizes ◽  
Body Shapes ◽  
Three Body ◽  
Shape Characteristics

PurposeThe purpose of this paper is to classify body shapes using angular defects instead of sizes.Design/methodology/approachA large amount of dimensional data from a national anthropometry survey was analysed, and a basic pattern and its polyhedron were also used to create a three‐dimensional body shape from three body sizes. Using this method, the sizes were converted into nine angular defects.FindingsThe authors could define the factors explaining body shape characteristics and classify the body shapes into four groups. The four groups could be characterised by two pattern making difficulties of the upper and lower parts of the body as well as by two proportions, of waist girth to bust girth and bust girth to back length. Furthermore, depending on the age, the authors could understand body shape by the angle made.Originality/valueUsing a polyhedron model, the angles could be calculated using an enormous existing data set of sizes. An angular defect serves as an index to indicate the degree of difficulty for developing a flat pattern. If an angular defect of the bust is large, it is difficult to make a paper pattern of a bust dart. On the other hand, if an angular defect of the waist is large, it is easy to make a paper pattern of a waist dart. Thus, each body shape could be simultaneously characterized by two difficulty indices and two proportions of sizes.

scholarly journals Listeria cossartiae sp. nov., Listeria immobilis sp. nov., Listeria portnoyi sp. nov. and Listeria rustica sp. nov., isolated from agricultural water and natural environments

2021 ◽  
Vol 71 (5) ◽  
Author(s):  
Catharine R. Carlin ◽  
Jingqiu Liao ◽  
Dan Weller ◽  
Xiaodong Guo ◽  
Renato Orsi ◽  
...  
Keyword(s):  
Type Strain ◽  
Type Species ◽  
Novel Species ◽  
Sensu Stricto ◽  
Whole Genome Sequence ◽  
Natural Environments ◽  
The Novel ◽  
Agricultural Water ◽  
Content Type ◽  
Link Type

A total of 27 Listeria isolates that could not be classified to the species level were obtained from soil samples from different locations in the contiguous United States and an agricultural water sample from New York. Whole-genome sequence-based average nucleotide identity blast (ANIb) showed that the 27 isolates form five distinct clusters; for each cluster, all draft genomes showed ANI values of <95 % similarity to each other and any currently described Listeria species, indicating that each cluster represents a novel species. Of the five novel species, three cluster with the Listeria sensu stricto clade and two cluster with sensu lato. One of the novel sensu stricto species, designated L. cossartiae sp. nov., contains two subclusters with an average ANI similarity of 94.9%, which were designated as subspecies. The proposed three novel sensu stricto species (including two subspecies) are Listeria farberi sp. nov. (type strain FSL L7-0091T=CCUG 74668T=LMG 31917T; maximum ANI 91.9 % to L. innocua ), Listeria immobilis sp. nov. (type strain FSL L7-1519T=CCUG 74666T=LMG 31920T; maximum ANI 87.4 % to L. ivanovii subsp. londoniensis ) and Listeria cossartiae sp. nov. [subsp. cossartiae (type strain FSL L7-1447T=CCUG 74667T=LMG 31919T; maximum ANI 93.4 % to L. marthii ) and subsp. cayugensis (type strain FSL L7-0993T=CCUG 74670T=LMG 31918T; maximum ANI 94.7 % to L. marthii ). The two proposed novel sensu lato species are Listeria portnoyi sp. nov. (type strain FSL L7-1582T=CCUG 74671T=LMG 31921T; maximum ANI value of 88.9 % to L. cornellensis and 89.2 % to L. newyorkensis ) and Listeria rustica sp. nov. (type strain FSL W9-0585T=CCUG 74665T=LMG 31922T; maximum ANI value of 88.7 % to L. cornellensis and 88.9 % to L . newyorkensis ). L. immobilis is the first sensu stricto species isolated to date that is non-motile. All five of the novel species are non-haemolytic and negative for phosphatidylinositol-specific phospholipase C activity; the draft genomes lack the virulence genes found in Listeria pathogenicity island 1 (LIPI-1), and the internalin genes inlA and inlB, indicating that they are non-pathogenic.

Chachezhania sediminis sp. nov., isolated from marine sediment

2021 ◽  
Vol 71 (7) ◽  
Author(s):  
Jihye Baek ◽  
Jong-Hwa Kim ◽  
Jung-Hoon Yoon ◽  
Jung-Sook Lee ◽  
Ampaitip Sukhoom ◽  
...  
Keyword(s):  
Marine Sediment ◽  
Type Species ◽  
Phylogenetic Analyses ◽  
Whole Genome Sequence ◽  
Rrna Gene ◽  
Whole Genome ◽  
Content Type ◽  
Link Type ◽  
The Republic

A Gram-stain-negative, aerobic, non-motile, rod-shaped bacterial strain (CAU 1508T) was isolated from marine sediment collected in the Republic of Korea. Growth was observed at 10–45 °C (optimum, 30 °C), pH 4.0–11.0 (optimum, pH 6.0–8.0) and with 0–8.0 % (w/v) NaCl (optimum, 2–4 %). The isolate formed a monophyletic clade in the phylogenetic analyses using 16S rRNA gene and whole-genome sequences, exhibiting the highest similarity to Chachezhania antarctica SM1703T (96.5 %), and representing a distinct branch within the genus Chachezhania (family Rhodobacteraceae ). Its whole genome sequence was 5.59 Mb long, with a G+C content of 65.7 mol% and 2183 predicted genes belonging to six functional categories. The average nucleotide identity and digital DNA–DNA hybridization values between CAU 1508T and C. antarctica SM1703T were 79.1 and 22.2 %, respectively. The predominant cellular fatty acids were C19 : 0 cyclo ω8c and summed feature 8 (C18 : 1  ω7c/C18 : 1  ω6c). The major polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylcholine, phosphatidylethanolamine, two unidentified phospholipids and one unidentified aminophospholipid. The sole isoprenoid quinone was ubiquinone 10. Phenotypic phylogenetic properties supported the classification of CAU 1508T as representing a novel species of the genus Chachezhania , with the proposed name Chachezhania sediminis sp. nov. The type strain is CAU 1508T (=KCTC 62999T=NBRC 113697T).

CNN-based minor fabric defects detection

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhijie Wen ◽  
Qikun Zhao ◽  
Lining Tong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Design Methodology ◽  
Estimation Algorithm ◽  
Data Set ◽  
Content Type ◽  
Novel Method ◽  
Self Similar ◽  
Better Than

PurposeThe purpose of this paper is to present a novel method for minor fabric defects detection.Design/methodology/approachThis paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.FindingsThis algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.Originality/valueTraditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

scholarly journals The Emergence of Successful Streptococcus pyogenes Lineages through Convergent Pathways of Capsule Loss and Recombination Directing High Toxin Expression

mBio ◽  
2019 ◽  
Vol 10 (6) ◽  
Author(s):  
Claire E. Turner ◽  
Matthew T. G. Holden ◽  
Beth Blane ◽  
Carolyne Horner ◽  
Sharon J. Peacock ◽  
...  
Keyword(s):  
Hyaluronic Acid ◽  
Gene Transfer ◽  
Homologous Recombination ◽  
Streptococcus Pyogenes ◽  
High Expression ◽  
Data Set ◽  
Altered Expression ◽  
Content Type ◽  
Hyaluronic Acid Capsule ◽  
Inactivating Mutations

ABSTRACT Gene transfer and homologous recombination in Streptococcus pyogenes has the potential to trigger the emergence of pandemic lineages, as exemplified by lineages of emm1 and emm89 that emerged in the 1980s and 2000s, respectively. Although near-identical replacement gene transfer events in the nga (NADase) and slo (streptolysin O) loci conferring high expression of these toxins underpinned the success of these lineages, extension to other emm genotype lineages is unreported. The emergent emm89 lineage was characterized by five regions of homologous recombination additional to nga-slo, including complete loss of the hyaluronic acid capsule synthesis locus hasABC, a genetic trait replicated in two other leading emm types and recapitulated by other emm types by inactivating mutations. We hypothesized that other leading genotypes may have undergone similar recombination events. We analyzed a longitudinal data set of genomes from 344 clinical invasive disease isolates representative of locations across England, dating from 2001 to 2011, and an international collection of S. pyogenes genomes representing 54 different genotypes and found frequent evidence of recombination events at the nga-slo locus predicted to confer higher toxin genotype. We identified multiple associations between recombination at this locus and inactivating mutations within hasAB, suggesting convergent evolutionary pathways in successful genotypes. This included common genotypes emm28 and emm87. The combination of no or low capsule and high expression of nga and slo may underpin the success of many emergent S. pyogenes lineages of different genotypes, triggering new pandemics, and could change the way S. pyogenes causes disease. IMPORTANCE Streptococcus pyogenes is a genetically diverse pathogen, with over 200 different genotypes defined by emm typing, but only a minority of these genotypes are responsible for the majority of human infection in high-income countries. Two prevalent genotypes associated with disease rose to international dominance following recombination of a toxin locus that conferred increased expression. Here, we found that recombination of this locus and promoter has occurred in other diverse genotypes, events that may allow these genotypes to expand in the population. We identified an association between the loss of hyaluronic acid capsule synthesis and high toxin expression, which we propose may be associated with an adaptive advantage. As S. pyogenes pathogenesis depends both on capsule and toxin production, new variants with altered expression may result in abrupt changes in the molecular epidemiology of this pathogen in the human population over time.

scholarly journals Neisseria Adhesin A Variation and Revised Nomenclature Scheme

2014 ◽  
Vol 21 (7) ◽  
pp. 966-971 ◽  
Author(s):  
Stefania Bambini ◽  
Matteo De Chiara ◽  
Alessandro Muzzi ◽  
Marirosa Mora ◽  
Jay Lucidarme ◽  
...  
Keyword(s):  
Amino Acid Sequences ◽  
Data Set ◽  
Nomenclature System ◽  
Content Type ◽  
Named Groups ◽  
Protein Variants ◽  
Adhesion And Invasion ◽  
Multicomponent Vaccine ◽  
Host Tissues

ABSTRACTNeisseriaadhesin A (NadA), involved in the adhesion and invasion ofNeisseria meningitidisinto host tissues, is one of the major components of Bexsero, a novel multicomponent vaccine licensed for protection against meningococcal serogroup B in Europe, Australia, and Canada. NadA has been identified in approximately 30% of clinical isolates and in a much lower proportion of carrier isolates. Three protein variants were originally identified in invasive meningococci and named NadA-1, NadA-2, and NadA-3, whereas most carrier isolates either lacked the gene or harbored a different variant, NadA-4. Further analysis of isolates belonging to the sequence type 213 (ST-213) clonal complex identified NadA-5, which was structurally similar to NadA-4, but more distantly related to NadA-1, -2, and -3. At the time of this writing, more than 89 distinctnadAallele sequences and 43 distinct peptides have been described. Here, we present a revised nomenclature system, taking into account the complete data set, which is compatible with previous classification schemes and is expandable. The main features of this new scheme include (i) the grouping of the previously named NadA-2 and NadA-3 variants into a single NadA-2/3 variant, (ii) the grouping of the previously assigned NadA-4 and NadA-5 variants into a single NadA-4/5 variant, (iii) the introduction of an additional variant (NadA-6), and (iv) the classification of the variants into two main groups, named groups I and II. To facilitate querying of the sequences and submission of new allele sequences, the nucleotide and amino acid sequences are available athttp://pubmlst.org/neisseria/NadA/.

scholarly journals Genome sequence-based criteria for demarcation and definition of species in the genus Rickettsia

2020 ◽  
Vol 70 (3) ◽  
pp. 1738-1750 ◽  
Author(s):  
Awa Diop ◽  
Khalid El Karkouri ◽  
Didier Raoult ◽  
Pierre-Edouard Fournier
Keyword(s):  
Genome Sequence ◽  
Bacterial Species ◽  
Whole Genome Sequence ◽  
Current Standard ◽  
Phenotypic Properties ◽  
Species Definition ◽  
Content Type ◽  
Link Type ◽  
Reliable Classification

Over recent years, genomic information has increasingly been used for prokaryotic species definition and classification. Genome sequence-based alternatives to the gold standard DNA–DNA hybridization (DDH) relatedness have been developed, notably average nucleotide identity (ANI), which is one of the most useful measurements for species delineation in the genomic era. However, the strictly intracellar lifestyle, the few measurable phenotypic properties and the low level of genetic heterogeneity made the current standard genomic criteria for bacterial species definition inapplicable to Rickettsia species. We evaluated a range of whole genome sequence (WGS)-based taxonomic parameters to develop guidelines for the classification of Rickettsia isolates at genus and species levels. By comparing the degree of similarity of 74 WGSs from 31 Rickettsia species and 61 WGSs from members of three closely related genera also belonging to the order Rickettsiales ( Orientia , 11 genomes; Ehrlichia , 22 genomes; and Anaplasma , 28 genomes) using digital DDH (dDDh) and ANI by orthology (OrthoANI) parameters, we demonstrated that WGS-based taxonomic information, which is easy to obtain and use, can serve for reliable classification of isolates within the Rickettsia genus and species. To be classified as a member of the genus Rickettsia , a bacterial isolate should exhibit OrthoANI values with any Rickettsia species with a validly published name of ≥83.63 %. To be classified as a new Rickettsia species, an isolate should not exhibit more than any of the following degrees of genomic relatedness levels with the most closely related species: >92.30 and >99.19 % for the dDDH and OrthoANI values, respectively. When applied to four rickettsial isolates of uncertain status, the above-described thresholds enabled their classification as new species in one case. Thus, we propose WGS-based guidelines to efficiently delineate Rickettsia species, with OrthoANI and dDDH being the most accurate for classification at the genus and species levels, respectively.

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