scholarly journals Identification and DUS Testing of Rice Varieties through Microsatellite Markers

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Pourabed ◽  
Mohammad Reza Jazayeri Noushabadi ◽  
Seyed Hossein Jamali ◽  
Naser Moheb Alipour ◽  
Abbas Zareyan ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Microsatellite Markers ◽  
Morphological Characteristics ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Rice Varieties ◽  
Local Varieties ◽  
Improved Varieties ◽  
Discriminating Power ◽  
Dus Testing

Identification and registration of new rice varieties are very important to be free from environmental effects and using molecular markers that are more reliable. The objectives of this study were, first, the identification and distinction of 40 rice varieties consisting of local varieties of Iran, improved varieties, and IRRI varieties using PIC, and discriminating power, second, cluster analysis based on Dice similarity coefficient and UPGMA algorithm, and, third, determining the ability of microsatellite markers to separate varieties utilizing the best combination of markers. For this research, 12 microsatellite markers were used. In total, 83 polymorphic alleles (6.91 alleles per locus) were found. In addition, the variation of PIC was calculated from 0.52 to 0.9. The results of cluster analysis showed the complete discrimination of varieties from each other except for IR58025A and IR58025B. Moreover, cluster analysis could detect the most of the improved varieties from local varieties. Based on the best combination of markers analysis, five pair primers together have shown the same results of all markers for detection among all varieties. Considering the results of this research, we can propose that microsatellite markers can be used as a complementary tool for morphological characteristics in DUS tests.

scholarly journals Genetic variability of wheat germplasm represented in the south Pannonian region

Genetika ◽  
2017 ◽  
Vol 49 (3) ◽  
pp. 831-842
Author(s):  
Ivana Rukavina ◽  
Sonja Petrovic ◽  
Tihomir Cupic ◽  
Sonja Vila ◽  
Suncica Guberac ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Morphological Characteristics ◽  
Triticum Aestivum L ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Valuable Resource ◽  
Wheat Varieties ◽  
Breeding Programs ◽  
Significant Variability ◽  
Winter Wheat Varieties

In this study, genetic variability was investigated among 50 winter wheat varieties (Triticum aestivum L.) which are grown in parts of Croatia, Hungary, Serbia and Slovenia according to 22 morphological characteristics used for DUS (distinctness, uniformity and stability) testing. The average Dice similarity coefficient was 0.371. The determined similarity coefficient was in range 0.083 - 0.776. A significant variability of 6.21% in the breeding programs according to period was determined as well as significant variability of 3.10% between breeding programs. The UPGMA clustering divided investigated varieties into four main clusters. Based on data analysis, most distant varieties with best morphological characteristics were found which will provide valuable resource of new parent's combinations in future breeding programs. This paper also provided valuable assessment of morphological characteristics to define distinctness criteria in the DUS examination of wheat.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning

2021 ◽  
pp. 002203452110053
Author(s):  
H. Wang ◽  
J. Minnema ◽  
K.J. Batenburg ◽  
T. Forouzanfar ◽  
F.J. Hu ◽  
...  
Keyword(s):  
Gold Standard ◽  
Orthodontic Treatment ◽  
3D Models ◽  
Similarity Coefficient ◽  
Patient Specific ◽  
Dice Similarity Coefficient ◽  
Surface Deviations ◽  
Surface Deviation ◽  
Binary Segmentation ◽  
Time Required

Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto-segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma

2021 ◽  
Vol 17 ◽  
Author(s):  
Yisong He ◽  
Shengyuan Zhang ◽  
Yong Luo ◽  
Hang Yu ◽  
Yuchuan Fu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Similarity Coefficient ◽  
Relative Volume ◽  
Dice Similarity Coefficient ◽  
Intermediate Risk ◽  
Segmentation Methods ◽  
Accuracy And Stability ◽  
Volume Difference

Background: Manual segment target volumes were time-consuming and inter-observer variability couldn’t be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem. Objective: To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively. Methods and Materials: A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test. Results: The average Dice similarity coefficient (±standard deviation) given by the deep-learning-based and Atlas-based auto-segmentation were 0.84(±0.03) and 0.74(±0.04) for CTV2, 0.79(±0.02) and 0.68(±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55mm and 9.34±3.39mm for CTV2, 7.09±2.27mm and 14.33±3.98mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p<0.01). Conclusions: Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

scholarly journals DNA Fingerprinting and Diversity Analysis of BRRI Hybrid Varieties and their Corresponding Parents

2011 ◽  
Vol 21 (2) ◽  
pp. 189-198
Author(s):  
M.M. Islam ◽  
M.E. Hoque ◽  
S.M.H.A. Rabbi ◽  
M.S. Ali
Keyword(s):  
Microsatellite Markers ◽  
Dna Fingerprinting ◽  
Hybrid Rice ◽  
Similarity Coefficient ◽  
Diversity Analysis ◽  
Allele Number ◽  
Information Contents ◽  
Polymorphic Bands ◽  
Parental Lines ◽  
Genetic Closeness

DNA fingerprinting and genetic diversity of four Bangladesh Rice Research Institute (BRRI) hybrid varieties and their parental lines were carried out. A total of 73 microsatellite markers were tested for screening the genotypes. Among the 73 amplified products, 37% had polymorphic bands showing 81 alleles. The number of alleles per locus ranged from two (RM10) to eight (RM327), where average allele number was 4.333. The Polymorphism Information Contents (PIC) lied between 0.337 (RM10) and 0.852 (RM327). RM327 was the most robust marker providing the highest PIC value (0.852). Pair-wise genetic dissimilarity coefficient interaction showed that BRRI hybrids two was the most genetically distant from each other whereas BRRI hybrids one, three, four and their respective parents were very close. Cluster analysis based on Dice’s similarity coefficient UPGMA system grouped BRRI hybrid and their parental lines into four major clusters at 0.41 cut off similarity coefficient. Four BRRI hybrid varieties grouped into four distinct clusters along with their component lines indicating their genetic closeness. Key words: Hybrid rice, Diversity analysis, Microsatellite markers, DNA fingerprinting   D. O. I. 10.3329/ptcb.v21i2.10242   Plant Tissue Cult. & Biotech. 21(2): 189-198, 2011 (December)

scholarly journals Semi-Siamese U-Net for separation of lung and heart bioimpedance images: A simulation study of thorax EIT

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246071
Author(s):  
Yen-Fen Ko ◽  
Kuo-Sheng Cheng
Keyword(s):  
Mean Absolute Error ◽  
Lung Ventilation ◽  
Semantic Segmentation ◽  
Absolute Error ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Linear Transformations ◽  
Impedance Tomography ◽  
Performance Improvements ◽  
The Individual

Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely distinguishable. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is based on the state-of-the-art U-Net, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.

scholarly journals Application of Multi-Scale Fusion Attention U-Net to Segment the Thyroid Gland on CT Localization Images for Radiotherapy

2021 ◽  
Author(s):  
Xiaobo Wen ◽  
Biao Zhao ◽  
Meifang Yuan ◽  
Jinzhi Li ◽  
Mengzhen Sun ◽  
...  
Keyword(s):  
Thyroid Gland ◽  
Clinical Work ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Training Set ◽  
Data Set ◽  
Test Set ◽  
Noise Interference ◽  
Multi Scale ◽  
Validation Set

Abstract Objectives: To explore the performance of Multi-scale Fusion Attention U-net (MSFA-U-net) in thyroid gland segmentation on CT localization images for radiotherapy. Methods: CT localization images for radiotherapy of 80 patients with breast cancer or head and neck tumors were selected; label images were manually delineated by experienced radiologists. The data set was randomly divided into the training set (n=60), the validation set (n=10), and the test set (n=10). Data expansion was performed in the training set, and the performance of the MSFA-U-net model was evaluated using the evaluation indicators Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). Results: With the MSFA-U-net model, the DSC, JSC, PPV, SE, and HD indexes of the segmented thyroid gland in the test set were 0.8967±0.0935, 0.8219±0.1115, 0.9065±0.0940, 0.8979±0.1104, and 2.3922±0.5423, respectively. Compared with U-net, HR-net, and Attention U-net, MSFA-U-net showed that DSC increased by 0.052, 0.0376, and 0.0346 respectively; JSC increased by 0.0569, 0.0805, and 0.0433, respectively; SE increased by 0.0361, 0.1091, and 0.0831, respectively; and HD increased by −0.208, −0.1952, and −0.0548, respectively. The test set image results showed that the thyroid edges segmented by the MSFA-U-net model were closer to the standard thyroid delineated by the experts, in comparison with those segmented by the other three models. Moreover, the edges were smoother, over-anti-noise interference was stronger, and oversegmentation and undersegmentation were reduced. Conclusion: The MSFA-U-net model can meet basic clinical requirements and improve the efficiency of physicians' clinical work.

scholarly journals The Identification of Rice Varieties Used in Central Sulawesi

2021 ◽  
Vol 232 ◽  
pp. 03013
Author(s):  
Femmi Norfahmi ◽  
Komalawati Komalawati ◽  
Muh. Afif Juradi ◽  
Mardiana Mardiana ◽  
F.F. Munier
Keyword(s):  
Secondary Data ◽  
Rice Production ◽  
Rice Varieties ◽  
Local Varieties ◽  
Rice Productivity ◽  
Pests And Diseases ◽  
High Yielding Varieties ◽  
Central Sulawesi

Central Sulawesi’s rice productivity in 2019 was lower compared to that in 2018. One of the problems for the low productivity of paddy in Central Sulawesi is the application of low quality of seeds. Ministry of Agriculture through Central Sulawesi AIAT has introduced a numbers of new high yielding varieties (HYV) to increase rice production and productivity. To support the dissemination of new HYV, it is important to study the rice varieties that mostly used by farmers in Central Sulawesi. The objectives of this study are to identify the rice varieties and the preferred characteristics of rice varieties that farmers usually used in Central Sulawesi. This study used primary and secondary data. Data were analyzed descriptively and presented in tables and graphs. The results show that most farmers in Central Sulawesi use Mekongga, Ciherang, and Cisantana varieties, and local varieties such as Peluncur, Dewi, Ntabone and others. Farmers generally prefer varieties which tend to produce higher yields and resistant to pests and diseases. To maintain the availability of the varieties in Central Sulawesi, it is important to train farmers to become breeders.

scholarly journals LOW ASSOCIATION OF Bph17 ALLELE IN LANDRACES AND IMPROVED VARIETIES OF RICE RESISTANT TO BROWN PLANTHOPPER

2017 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Wage Ratna Rohaeni ◽  
Untung Susanto ◽  
Aida F.V. Yuningsih
Keyword(s):  
Brown Planthopper ◽  
Gene Marker ◽  
Specific Gene ◽  
Rice Varieties ◽  
Improved Varieties ◽  
Recessive Genes ◽  
Check Variety ◽  
Rice Genotypes ◽  
Brown Planthopper Resistance ◽  
Dominant Genes

<p>Resistance traits to brown planthopper on rice varieties are controlled by dominant and recessive genes called Bph/bph. Bph17 is one of dominant genes that control rice resistance to brown planthopper.  Marker of Bph17 allele can be used as a tool of marker assisted selection (MAS) in breeding activity. Association of Bph17 allele and resistance to brown planthopper in Indonesian landraces and new-improved varieties of rice is not clearly known. The study aimed to determine the association of Bph17 allele in landraces and new-improved varieties of rice resistant to brown planthopper. Twenty-one rice genotypes were used in the study, consisting of 13 landraces, 5 improved varieties, 3 popular varieties and a check variety Rathu Heenati. Two simple sequence repeat markers linked to Bph17 allele were used, i.e. RM8213 and RM5953. The results showed that association of Bph17 allele in landraces and new-improved varieties of rice resistant to brown planthopper resistance was very low (r = -0.019 and -0.023, respectively). The presence of Bph17 allele did not constantly express resistance to brown planthopper. The study suggests that Bph17 allele cannot be used as a tool of MAS for evaluating resistance of landraces and new-improved varieties of rice to brown planthopper. Further research is needed to obtain a specific gene marker that can be used as a tool of MAS and applicable for Indonesian differential rice varieties. </p>

scholarly journals Berlian, SOJ A3 and Jawa Local Rice Varieties Phylogenetically Distinct From Oryza sativa Indica and Oryza sativa Japonica

el–Hayah ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 112-125
Author(s):  
Yudrik Lathif ◽  
Riri Wiyanti Retnaningtyas ◽  
Dwi Listyorini ◽  
Suharti Suharti
Keyword(s):  
Oryza Sativa ◽  
Phylogenetic Tree ◽  
Dna Barcode ◽  
Rbcl Gene ◽  
Tree Reconstruction ◽  
Rice Varieties ◽  
Local Varieties ◽  
Precise Position ◽  
Total Dna ◽  
Taxonomical Position

The genetic resources identification of Indonesian local rice varieties is a crucial work should be done to conserve our native germplasm. This research aimed to know the taxonomical position of East Java local rice varieties including Jawa (JW), Berlian (BR), and SOJ A3 (SJ) using DNA barcode based on rbcL gene. Total DNA of each sample was isolated from leaves. A pair of forward 5'-ATG TCA CCA CAA ACA SJA AC-3' and reverse 5'-TCG GTA CCT GCA GTA GC-3' primers were used to amplify fragments of rbcL gene resulting in 751bp, 755bp, and 754bp fragments from BR, SJ, and JW varieties, respectively. Phylogenetic tree reconstruction revealed that our three local varieties were forming a cluster separated from the widely cultivated subspecies Oryza sativa Indica and Oryza sativa Japonica. However, further studies are necessary to reveal a more precise position of the local varieties in a phylogenetic tree on the species level.

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