An approach to the classification of lactobacilli using computer-aided numerical analysis

1968 ◽  
Vol 14 (4) ◽  
pp. 313-318 ◽  
Author(s):  
Patricia L. Seyfried
Keyword(s):  
Numerical Analysis ◽  
Electronic Computer ◽  
Computer Aided ◽  
Genus Lactobacillus ◽  
Taxonomic Groups

An electronic computer was used to analyze the data obtained for 82 strains of streptococci, lactobacilli, and propionibacteria. The lactobacilli were divided into three major taxonomic groups which correspond to the subgenera Thermobacterium, Streptobacterium, and Betabacterium described by Orla-Jensen (14). Three clusters observed among the streptococcal strains also correlate with Sherman's (19) classification of pyogenic, fecal, and lactic groups of streptococci. The results are in agreement with present classifications. No justification for including the genus Lactobacillus in the same family as genus Propionibacterium was found.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Olivia M Gearner ◽  
Marcin J Kamiński ◽  
Kojun Kanda ◽  
Kali Swichtenberg ◽  
Aaron D Smith
Keyword(s):  
Dna Sequences ◽  
Species Group ◽  
Phylogenetic Placement ◽  
Darkling Beetles ◽  
Species Groups ◽  
Taxonomic Groups ◽  
Inference Methods ◽  
A New Species ◽  
Coleoptera Tenebrionidae

Abstract Sepidiini is a speciose tribe of desert-inhabiting darkling beetles, which contains a number of poorly defined taxonomic groups and is in need of revision at all taxonomic levels. In this study, two previously unrecognized lineages were discovered, based on morphological traits, among the extremely speciose genera Psammodes Kirby, 1819 (164 species and subspecies) and Ocnodes Fåhraeus, 1870 (144 species and subspecies), namely the Psammodes spinosus species-group and Ocnodes humeralis species-group. In order to test their phylogenetic placement, a phylogeny of the tribe was reconstructed based on analyses of DNA sequences from six nonoverlapping genetic loci (CAD, wg, COI JP, COI BC, COII, and 28S) using Bayesian and maximum likelihood inference methods. The aforementioned, morphologically defined, species-groups were recovered as distinct and well-supported lineages within Molurina + Phanerotomeina and are interpreted as independent genera, respectively, Tibiocnodes Gearner & Kamiński gen. nov. and Tuberocnodes Gearner & Kamiński gen. nov. A new species, Tuberocnodes synhimboides Gearner & Kamiński sp. nov., is also described. Furthermore, as the recovered phylogenetic placement of Tibiocnodes and Tuberocnodes undermines the monophyly of Molurina and Phanerotomeina, an analysis of the available diagnostic characters for those subtribes is also performed. As a consequence, Phanerotomeina is considered as a synonym of the newly redefined Molurina sens. nov. Finally, spectrograms of vibrations produced by substrate tapping of two Molurina species, Toktokkus vialis (Burchell, 1822) and T. synhimboides, are presented.

Fr612 INFLUENCE OF COMPUTER AIDED DIAGNOSIS WITH AUGMENTED VISUALIZATION ON ENDOSCOPIC CLASSIFICATION OF COLORECTAL POLYPS

2021 ◽  
Vol 160 (6) ◽  
pp. S-376
Author(s):  
Eladio Rodriguez-Diaz ◽  
Gyorgy Baffy Wai-Kit Lo ◽  
Hiroshi Mashimo ◽  
Aparna Repaka ◽  
Alexander Goldowsky ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Colorectal Polyps ◽  
Computer Aided ◽  
Endoscopic Classification ◽  
Aided Diagnosis

Computer-aided RAPD fingerprinting of accessions from the ryegrass–fescue complex

1997 ◽  
Vol 129 (3) ◽  
pp. 257-265 ◽  
Author(s):  
G. šIFFELOVÁ ◽  
M. PAVELKOVÁ ◽  
A. KLABOUCHOVÁ ◽  
I. WIESNER ◽  
V. NAšINEC
Keyword(s):  
Festuca Arundinacea ◽  
Dna Isolation ◽  
Similarity Matrix ◽  
Rapd Profile ◽  
Average Linkage ◽  
Computer Image Processing ◽  
Computer Aided ◽  
Primary Output ◽  
Taxonomic Groups ◽  
Linkage Cluster

RAPD (Randomly Amplified Polymorphic DNA) assay of 32 cultivar accessions from the ryegrass–fescue (Lolium–Festuca) complex was accomplished using ten decamer primers to assess (i) the power of RAPD technology to discriminate between individual commercial accessions and to produce cultivar fingerprinting, (ii) the degree of relatedness of accessions based on RAPD profiles in comparison with other existing classifications, and (iii) the possibility of automation of RAPD technology.The variation of the correlation coefficient r as the primary output from the automated RAPD-profile processing summarizes variability derived from DNA isolation, the RAPD reaction, and final computer-image processing of RAPD profiles. The AII (Accession Identity Interval) of r for accession Festuca arundinacea cv. Lekora was determined experimentally and the value obtained was accepted as a valid interval for all the other accessions studied. In order to evaluate the discrimination potential of all ten primers together, a pooled-similarity matrix was computed. Employing this approach, we achieved 100% discrimination between all 35 accessions when using all ten primers. A dendrogram for all 35 accessions was obtained using average linkage cluster analysis (UPGMA – Unweighted Pair Group Method with Arithmetic Means). This procedure successfully produced smaller groups of higher taxonomic homogeneity. The relationships between the Lolium–Festuca accessions were also revealed by principal coordinate analysis (PCO) based on absorbance profiles from the RAPD assay. Again, all accessions were well separated, recognising even subspecies relationships. In general, PCO analysis confirmed the inferences made from the UPGMA method.We successfully applied the computer-aided system of RAPD assay, based on an IBM PC computer, for discrimination of cultivars as well as for description of DNA-based relationships of accessions from various taxonomic groups of the Lolium–Festuca complex.

scholarly journals Segmentation and Classification of Breast Cancer Tumour

2018 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Gokalp Cinarer ◽  
Bulent Gursel Emiroglu ◽  
Ahmet Hasim Yurttakal
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Wavelet Approximation ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

Computer-aided Diagnosis for the Detection and Classification of Lung Cancers on Chest Radiographs

2006 ◽  
Vol 13 (8) ◽  
pp. 995-1003 ◽  
Author(s):  
Junji Shiraishi ◽  
Hiroyuki Abe ◽  
Feng Li ◽  
Roger Engelmann ◽  
Heber MacMahon ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Chest Radiographs ◽  
Lung Cancers ◽  
Computer Aided ◽  
Aided Diagnosis
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