scholarly journals Detecting outlying variables in multigroup data: A comparison of different loading similarity coefficients

2020 ◽  
pp. e3233 ◽  
Author(s):  
Sopiko Gvaladze ◽  
Kim De Roover ◽  
Francis Tuerlinckx ◽  
Eva Ceulemans
Keyword(s):  
Similarity Coefficients ◽  
Multigroup Data

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

scholarly journals Microsatellite-based Genotyping of the Commercial Eucalyptus Clones Cultivated in China

2011 ◽  
Vol 60 (1-6) ◽  
pp. 216-223 ◽  
Author(s):  
F. Li ◽  
S. Gan ◽  
Z. Zhang ◽  
Q. Weng ◽  
D. Xiang ◽  
...  
Keyword(s):  
Microsatellite Markers ◽  
Marker Locus ◽  
Genetic Variations ◽  
Clonal Forestry ◽  
Similarity Coefficients ◽  
Multilocus Genotypes ◽  
Legitimate Interests

AbstractA proper identification of clones is necessary in clonal forestry and will help to protect the legitimate interests of breeders, growers and industry. Twenty-four of theEucalyptusclones most widely cultivated in China were analyzed using a set of 24 microsatellite markers to develop their DNA-based fingerprints and exploit the genetic variations. A total of 286 alleles were detected, averaging at 11.9 alleles per marker locus. All the microsatellites were polymorphic among the clones investigated. The observed heterozygosity (Ho) varied with locus between 0.500 and 1.000 with a mean of 0.885. The 24 clones could be uniquely fingerprinted based on their multilocus genotypes at a minimum of three loci (Embra169, Embra72 and Embra2). The dendrogram constructed from the genotypic similarity coefficients separated the 24 clones into three groups, matching essentially the historically known or speculated clonal origins. Clones T13, Guanglin-5 and Guanglin-9 turned out to be full siblings of cross DH32 while the DH201-2 sampled here appeared to be mislabelled.

scholarly journals Multi-Omic Meta-Analysis of Transcriptomes and the Bibliome Uncovers Novel Hypoxia-Inducible Genes

Biomedicines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 582
Author(s):  
Yoko Ono ◽  
Hidemasa Bono
Keyword(s):  
Meta Analysis ◽  
Hypoxia Inducible Factor ◽  
Data Driven ◽  
Similarity Coefficients ◽  
Hypoxia Inducible Factor 1 ◽  
Response System ◽  
Inducible Genes ◽  
The Relationship ◽  
G Protein Coupled ◽  
Sufficient Oxygen

Hypoxia is a condition in which cells, tissues, or organisms are deprived of sufficient oxygen supply. Aerobic organisms have a hypoxic response system, represented by hypoxia-inducible factor 1-α (HIF1A), to adapt to this condition. Due to publication bias, there has been little focus on genes other than well-known signature hypoxia-inducible genes. Therefore, in this study, we performed a meta-analysis to identify novel hypoxia-inducible genes. We searched publicly available transcriptome databases to obtain hypoxia-related experimental data, retrieved the metadata, and manually curated it. We selected the genes that are differentially expressed by hypoxic stimulation, and evaluated their relevance in hypoxia by performing enrichment analyses. Next, we performed a bibliometric analysis using gene2pubmed data to examine genes that have not been well studied in relation to hypoxia. Gene2pubmed data provides information about the relationship between genes and publications. We calculated and evaluated the number of reports and similarity coefficients of each gene to HIF1A, which is a representative gene in hypoxia studies. In this data-driven study, we report that several genes that were not known to be associated with hypoxia, including the G protein-coupled receptor 146 gene, are upregulated by hypoxic stimulation.

Phylogenetic relationships of five morphological groups of hexaploid wheat (Triticum aestivum L. em Thell.) based on RAPD analysis

Genome ◽  
2000 ◽  
Vol 43 (4) ◽  
pp. 724-727 ◽  
Author(s):  
Wenguang Cao ◽  
G Scoles ◽  
P Hucl ◽  
R N Chibbar
Keyword(s):  
Hexaploid Wheat ◽  
Phylogenetic Relationships ◽  
Genetic Similarity ◽  
Rapd Analysis ◽  
Genetic Relationships ◽  
Random Amplified Polymorphic Dna ◽  
Triticum Aestivum L ◽  
Similarity Coefficients ◽  
Morphological Classification ◽  
Wild Wheat

The genetic relationships among the five groups of hexaploid wheat: common, spelta, macha, vavilovii, and semi-wild wheat (SWW) are not clear. Random amplified polymorphic DNA (RAPD) analysis was used to assess phylogenetic relationships among these five morphological groups of hexaploid wheat. RAPD data were analyzed using the NTSYS-PC computer program to generate Jaccard genetic similarity coefficients. A dendrogram based on RAPD analysis grouped 15 accessions into five distinct clusters. These results are in agreement with those based on morphological classification, suggesting that common wheat is most closely related to SWW, followed by spelta, vavilovii, and macha.Key words: RAPD, macha, spelta, vavilovii, semi-wild wheat, phylogenetic relationships.

scholarly journals Genetic Diversity of Salvia Species Assessed by ISSR and RAPD Markers

2016 ◽  
Vol 44 (2) ◽  
pp. 431-436 ◽  
Author(s):  
Masoumeh YOUSEFIAZARKHANIAN ◽  
Ali ASGHARI ◽  
Jafar AHMADI ◽  
Behvar ASGHARI ◽  
Ali Ashraf JAFARI
Keyword(s):  
Genetic Diversity ◽  
Genetic Polymorphisms ◽  
Genomic Dna ◽  
Rapd Markers ◽  
Genetic Relationships ◽  
Genetic Variations ◽  
Molecular Techniques ◽  
Wild Populations ◽  
Similarity Coefficients ◽  
The World

The genus Salvia includes an enormous assemblage of nearly 1,000 species dispersed around the world. Due to possible threats to this genus, there is an immediate requirement to evaluate the diversity of its wild populations. ISSR and RAPD molecular techniques were used to evaluate the genetic relationships among twenty-one ecotypes of eight Salvia species. Amplification of genomic DNA using 23 primers (15 RAPD and eight ISSR) produced 280 bands, of which 91% were polymorphic. The results of marker parameters showed no clear difference between two marker systems. It was generally observed that both ISSR and RAPD markers had similar efficiency in detecting genetic polymorphisms with remarkable ability to differentiate the closely related ecotypes of Salvia. Nei’s similarity coefficients for these techniques ranged from 0.48 to 0.98. Based on the results of clustering, PCoA and AMOVA, the genetic diversity between and within species was confirmed. So, conservation and domestication of the genus Salvia must be due to levels of genetic variations.

Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

2021 ◽  
Author(s):  
Sang-Heon Lim ◽  
Young Jae Kim ◽  
Yeon-Ho Park ◽  
Doojin Kim ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
External Validation ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Similarity Coefficients ◽  
Internal Validation ◽  
Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

scholarly journals Molecular Characterization and Similarity Relationships among Flue-Cured Tobacco (Nicotiana tabacum L.) Genotypes Using Simple Sequence Repeat Markers

2012 ◽  
Vol 40 (2) ◽  
pp. 247
Author(s):  
Soheila GHOLIZADEH ◽  
Reza DARVISHZADEH ◽  
Babak ABDOLLAHI MANDOULAKANI ◽  
Iraj BERNOUSI ◽  
Seyed Reza ALAVI ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Molecular Genetic ◽  
Genetic Distances ◽  
Similarity Coefficients ◽  
Allele Number ◽  
Nicotiana Tabacum L ◽  
Ssr Loci ◽  
Actual Degree ◽  
Similarity Relationships

Characterization of genetic diversity has long been a major goal in tobacco breeding programs. Information on genetic diversity is essential for a rational use of genetic resources. In the present study, the genetic variation among 72 flue-cured tobacco genotypes was evaluated using microsatellite markers (SSRs). A set of 104 alleles was generated at 30 SSR loci. The mean number of alleles per locus (na) and the effective allele number (ne) were 3.467 and 2.358, respectively. The expected heterozygosity ranged from 0.29 to 0.75 with average of 0.54. Several methods were used to construct the similarity matrices and dendrograms. The co-phenetic correlation coefficient, which is a measure of the correlation between the similarities represented on the dendrograms and the actual degree of similarity, was calculated for each dendrogram. Among the different methods, the highest value (r=0.76368) was observed for the UPGMA created based on Jaccard’s similarity coefficients. The genetic similarity among the tobacco genotypes calculated by using Jaccard’s similarity coefficient ranged from 0.08 to 0.84, suggesting the presence of high molecular genetic variability among the studied tobacco genotypes. Based on UPGMA clustering method all studied flue-cured tobacco genotypes, except for ‘Glustinusa Rasht’, were placed in three distinct groups. We observed an obvious heterotic pattern in the studied flue-cured germplasm corresponding to genetic distances and classification dendrogram, which persuades exploitation of heterosis in flue-cured tobaccos.

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