Similarity Coefficient Values Research Articles

Keragaman Fenetik Pisang Lokal yang Dimanfaatkan oleh Masyarakat Sentani Kabupaten Jayapura, Papua

Banana plants are a food source that has good prospects. This is because bananas are very popular with all groups, and are easy to grow in tropical areas. There are many varieties of wild and cultivated bananas that grow in Indonesia, one of which grows around Lake Sentani, causing differences in morphological characters between types and between varieties of bananas. The purpose of this study was to determine the phenetic diversity of local Sentani bananas based on morphological characters. The research was conducted in June-October 2022 in five villages around Lake Sentani, namely Ayapo, Yahim, Kampung Harapan, Doyo and Netar using the exploration method and interviewing key informants. For morphological identification using banana descriptor guidelines from the International Plant Genetic Resources Institute (IPGRI). Morphological character data were analyzed in a quantitative descriptive manner using the NTSYs 2.01 application program. The results showed that there were 15 local banana cultivars based on the knowledge of the Sentani people. Based on the phenogram, the local Sentani bananas are grouped into two groups based on the character of the shape, size, color, taste, and color of the organs of the leaves, stems, flowers and fruit. The local banana cultivars have wide phenetic diversity with similarity coefficient values ranging from 20-73%. The closest similarity value is found in Hoyombi and Wabulu cultivars with a similarity value of 73%. The furthest similarity value was found in the Rawo and Anakhola cultivars in group A, and the Olukhu and Honggambu cultivars in group B with a value of 53%.  Key words: diversity; characters; morphology; bananas; Sentani.Â

Explore highly relevant questions in the Baumann skin type questionnaire through the digital skin analyzer: A retrospective single-center study in South Korea.

Baumann skin type questionnaire (BSTQ) has been widely used for evaluating skin types in dermatology. However, it requires excessive assessment time and lacks sufficient clinical validation for the Asian population. We aimed to establish optimized BSTQ based on dermatological assessment of the Asian population. This was a single-center retrospective study, where the patient completed a modified BSTQ and a digital photography examination. The answers to four question groups for evaluating skin properties, including oily versus dry (O-D), sensitive versus resistant (S-R), pigmented versus non-pigmented (P-N), and wrinkled versus tight (W-T) were compared with the measurements. Highly relevant questions are selected using two different strategies and used to determine the threshold level, which was compared with skin-type measurement. In O-D, S-R, P-N, and W-T, 3-5 out of 6, 2-6 out of 9, 3-6 out of 7, and 4-9 out of 11 questions were selected, respectively. As a result, skin type scores from two strategies and measurements showed similar Pearson correlation coefficient values compared to modified BSTQ (for O-D and sebum, 0.236/0.266 vs. 0.232; for O-D and porphyrin, 0.230/0.267 vs. 0.230; for S-R and redness, 0.157/0.175 vs. 0.095; for S-R and porphyrin, 0.061 vs. 0.051; for P-N and melanin pigmentation, 0.156/0.208 vs. 0.150; for W-T and wrinkle, 0.265/0.269 vs. 0.217). Two strategies for optimizing BSTQ are proposed and validated for Asian patients. Compared to the BSTQ, our methods show comparable performance with a significantly reduced number of questions.

De-Identification Technique with Facial Deformation in Head CT Images.

Head CT, which includes the facial region, can visualize faces using 3D reconstruction, raising concern that individuals may be identified. We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as "original images" and the others as "reference images." Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were < 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.

Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI

Background and objectiveTraditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy. Residual learning can facilitate network training, Bi-Directional Convolutional LSTM (BDC-LSTM) can exploit interlayer spatial dependencies, and HDC can expand the receptive domain without losing resolution. MethodsIn this paper, we propose a segmentation method by combining BDC-LSTM and U-Net to segment the corpus callosum from multiple angles of brain images based on computed tomography (CT) and magnetic resonance imaging (MRI) in which two types of sequence, namely T2-weighted imaging as well as the Fluid Attenuated Inversion Recovery (Flair), were utilized. The two-dimensional slice sequences are segmented in the cross-sectional plane, and the segmentation results are combined to obtain the final results. Encoding, BDC- LSTM, and decoding include convolutional neural networks. The coding part uses asymmetric convolutional layers of different sizes and dilated convolutions to get multi-slice information and extend the convolutional layers' perceptual field. ResultsThis paper uses BDC-LSTM between the encoding and decoding parts of the algorithm. On the image segmentation of the brain in multiple cerebral infarcts dataset, accuracy rates of 0.876, 0.881, 0.887, and 0.912 were attained for the intersection of union (IOU), dice similarity coefficient (DS), sensitivity (SE), and predictive positivity value (PPV). The experimental findings demonstrate that the algorithm outperforms its rivals in accuracy. ConclusionThis paper obtained segmentation results for three images using three models, ConvLSTM, Pyramid-LSTM, and BDC-LSTM, and compared them to verify that BDC-LSTM is the best method to perform the segmentation task for faster and more accurate detection of 3D medical images. We improve the convolutional neural network segmentation method to obtain medical images with high segmentation accuracy by solving the over-segmentation problem.

Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation.

The aim of the study is to demonstrate whether radiomics based on an automatic segmentation method is feasible for predicting molecular subtypes. This retrospective study included 516 patients with confirmed breast cancer. An automatic segmentation-3-dimensional UNet-based Convolutional Neural Networks, trained on our in-house data set-was applied to segment the regions of interest. A set of 1316 radiomics features per region of interest was extracted. Eighteen cross-combination radiomics methods-with 6 feature selection methods and 3 classifiers-were used for model selection. Model classification performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. The average dice similarity coefficient value of the automatic segmentation was 0.89. The radiomics models were predictive of 4 molecular subtypes with the best average: AUC = 0.8623, accuracy = 0.6596, sensitivity = 0.6383, and specificity = 0.8775. For luminal versus nonluminal subtypes, AUC = 0.8788 (95% confidence interval [CI], 0.8505-0.9071), accuracy = 0.7756, sensitivity = 0.7973, and specificity = 0.7466. For human epidermal growth factor receptor 2 (HER2)-enriched versus non-HER2-enriched subtypes, AUC = 0.8676 (95% CI, 0.8370-0.8982), accuracy = 0.7737, sensitivity = 0.8859, and specificity = 0.7283. For triple-negative breast cancer versus non-triple-negative breast cancer subtypes, AUC = 0.9335 (95% CI, 0.9027-0.9643), accuracy = 0.9110, sensitivity = 0.4444, and specificity = 0.9865. Radiomics based on automatic segmentation of magnetic resonance imaging can predict breast cancer of 4 molecular subtypes noninvasively and is potentially applicable in large samples.

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images.

To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.

A dual attention-guided 3D convolution network for automatic segmentation of prostate and tumor

BackgroundIn middle-aged and older men, prostate cancer (PCa) is a common tumor disease with a mortality rate second only to lung cancer. The automatic and accurate segmentation of the prostate and tumor in magnetic resonance imaging (MRI) images can help doctors diagnose malignancies more efficiently. T2 weighted imaging (T2W) is now used in the majority of studies on prostate MRI image segmentation; however, diffusion-weighted imaging (DWI) is more valuable in the diagnosis of PCa. The morphological differences between the prostate and tumor regions are minimal, the tumor size is uncertain, the border between the tumor and surrounding tissue is hazy, and the categories separating normal regions from tumors are uneven. Consequently, it is challenging to segment prostate and tumor on DWI images. MethodsFor the segmentation of prostate and tumor regions on DWI images, this study offers a dual attention-guided 3D convolutional neural network (3D DAG-Net). A visual attention method is built into the encoder step to obtain the features of various receptive fields and deliver more detailed contextual information. A multiscale attention technique is proposed at the decoder stage to fuse multiscale features to acquire finer global and local details. To resolve the class discrepancies between the prostate, tumor, and background regions in segmentation tasks, we propose a hybrid loss function for handling class imbalance. ResultsWe tested the algorithm on DWI images of PCa obtained from a nearby hospital, demonstrating the uniqueness and effectiveness of the method. Dice similarity coefficient (DSC) values for prostate and tumor DWI segmentation were 92.28% and 88.73%, respectively. ConclusionWe present a unique dual-attention mechanism 3D segmentation network architecture for quantitative assessment of prostate and tumor volumes on DWI. The automatic segmentation results produced by our technology were highly correlated and consistent with expert manual segmentation findings.

First report of 'Candidatus phytoplasma asteris' (16SrI) from Cassia fistula showing symptoms of flat stem and witches'-broom in India.

Cassia fistula commonly known as 'golden shower tree' is a deciduous tree with a greenish-gray bark and complex leaves with lovely clusters of yellow blossoms that is also utilized for several purposes in traditional medicine offer therapeutic characteristics (Pawar et al., 2017). Random spotting of flat stem symptoms along with unopened flower beds was observed in C. fistula plant during March 2022 in IISER (Indian Institute of Science Education and Research), Thiruvananthapuram, Kerala, India and during May 2022 in SKUAST (Sher-e-Kashmir University of Agricultural Sciences and Technology), Jammu, which were suggestive of phytoplasma infection (Fig. 1 a-e). Surge of leaf hoppers was also observed in and around the tree. The leaf samples were collected from 3 individual C. fistula trees showing suspected symptoms of phytoplasma and one sample from asymptomatic plant of both the states. Leafhopper (LH) species were collected using sweep net method from both the locations. DNA was extracted using CTAB (Cetyl trimethyl ammonium bromide) method and nested universal PCR primers P1/P7 and R16F2n/R16R2 for the 16S rRNA gene (Deng and Hiruki 1991; Gundersen and Lee 1996) and secAfor1/secArev3 and SecAfor2/ SecArev3 for SecA gene (Hodgetts et al. 2008) were employed for the analysis of the phytoplasma strain association. The symptomatic plants and leaf hopper species showed positive bands of 1.2kb and 480bp for 16S rRNA and SecA gene respectively along with. Purified PCR products of both the genes (16Sr RNA and sec A) were ligated into pGEM ®T vector and cloned in Escherichia coli (DH5-α) were sequenced at Agri Genome labs, Kerala, India. The comparative sequence analysis using the BLASTn tool results showed 16S rRNA sequences acquired from plant samples (GenBank Acc. No. OP950857, OP950858) and the leafhoppers Hishimonus phycitis (OP538583) and Orosius albicinctus (OP538584) of Kerala had the minimum of 99.84% of similarity with Bitter gourd little leaf phytoplasma from Myanmar and maximum sequence identity (100%) with the Rapeseed phyllody phytoplasma strain from Taiwan. The sequences of phytoplasma strains from Jammu trees (Genbank Acc. No. OP801671 & OP801672) and H. phycitis (OP801673) shared 100% similarity with each other as well as with North American grapevine yellows and a minimum of 97.65% with Beta vulgaris phytoplasma from Poland. The pairwise comparison results were completely supported by the corresponding phylogenetic sequence analysis of 16S rRNA and SecA gene sequences of all the isolates in the study which clustered with 16SrI-B subgroup related strains. Virtual RFLP analysis through iPhyClassifer results that were derived from in silico digestions of R16F2n/R2 region of 16S rRNA gene using 17 restriction endonucleases enzymes indicated that all the samples produced similar virtual RFLP profiles identical to the reference strain of 16SrI-B phytoplasma subgroup (aster yellows: Acc. No. M30790) with a similarity coefficient value of 1.0. To the best of our knowledge, this is the first report of the phytoplasma association of 'Ca. P. asteris' (16SrI-B) subgroup with Cassia fistula in the world.

COVID-SegNet: encoder-decoder-based architecture for COVID-19 lesion segmentation in chest X-ray.

The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world's health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder-decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module.

An optimized segmentation convolutional neural network with dynamic energy loss function for 3D reconstruction of lumbar spine MR images

3D reconstruction for lumbar spine based on segmentation of Magnetic Resonance (MR) images is meaningful for diagnosis of degenerative lumbar spine diseases. However, spine MR images with unbalanced pixel distribution often cause the segmentation performance of Convolutional Neural Network (CNN) reduced. Designing a composite loss function for CNN is an effective way to enhance the segmentation capacity, yet composition loss values with fixed weight may still cause underfitting in CNN training. In this study, we designed a composite loss function with a dynamic weight, called Dynamic Energy Loss, for spine MR images segmentation. In our loss function, the weight percentage of different loss values could be dynamically adjusted during training, thus CNN could fast converge in earlier training stage and focus on detail learning in the later stage. Two datasets were used in control experiments, and the U-net CNN model with our proposed loss function achieved superior performance with Dice similarity coefficient values of 0.9484 and 0.8284 respectively, which were also verified by the Pearson correlation, Bland-Altman, and intra-class correlation coefficient analysis. Furthermore, to improve the 3D reconstruction based on the segmentation results, we proposed a filling algorithm to generate contextually related slices by computing the pixel-level difference between adjacent slices of segmented images, which could enhance the structural information of tissues between slices, and improve the performance of 3D lumbar spine model rendering. Our methods could help radiologists to build a 3D lumbar spine graphical model accurately for diagnosis while reducing burden of manual image reading.

A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images.

Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature.

Encapsulation Studies on closo-Dicarbadodecaborane Isomers in Neutral Tetrahedral Palladium(II) Cages.

The encapsulation of icosahedral closo-dicarbadodecaborane (o-, m-, and p-carboranes, Cb) as guest molecules at the intrinsic cavities of the three isostructural tetrahedral cages [{Pd3(NiPr)3PO}4(Cl-AN)6] (1), [{Pd3(NiPr)3PO}4(Br-AN)6] (2), and [{Pd3(NiPr)3PO}4(H-AN)6] (3) was studied. The formation of definite host-guest assemblies was probed with mass spectrometry, IR, and NMR spectral analysis. 2D DOSY 1H NMR of the Cb⊂Cage systems showed similar diffusion coefficient (D) values for the host and guest species, signifying the encapsulation of these guests inside the cage assemblies. The hydrodynamic radius (RH) derived from the D values of the host and guest species further confirmed the encapsulation of the Cb isomers at the cage pockets. The single-molecule energy optimization of the host-guest assemblies indicated the preferential binding of o-Cb as a guest inside the cages (1-3). The stabilization of these Cb guests inside these cages was further attributed to various possible nonclassical C-H···X-type interactions.

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.

Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.

Molecular Fingerprinting and Phytochemical Investigation of Syzygium cumini L. from Different Agro-Ecological Zones of India

Syzygium cumini L. (ver Jamun; BlackBerry) is a native, evergreen multipurpose tree species of India. Besides being a fruit tree and for agroforestry in different regions, it is medicinally important too. This study aimed to determine genetic diversity using molecular and phytochemical markers in sixteen genotypes of Indian S. cumini from different agro-ecological zones. The present study used a combination of ISSR markers and the HPLC technique to explore these genotypes. The results showed a wide genetic diversity range based on the similarity coefficient values observed in S. cumini sixteen accessions from different sites. Four primary phenolic acids were discovered in all the accessions; caffeic acid (CA) was found in high concentrations. The intraspecific association between molecular and phytochemical characteristics was the primary goal of this investigation. By employing gene-specific markers for the route of secondary metabolites (polyphenols) production, it further investigated the progressive research of diversity analysis of polyphenol content in S. cumini accessions, which may also expand its nutraceutical and pharmaceutical utilization.

LDANet: Automatic lung parenchyma segmentation from CT images

Automatic segmentation of the lung parenchyma from computed tomography (CT) images is helpful for the subsequent diagnosis and treatment of patients. In this paper, based on a deep learning algorithm, a lung dense attention network (LDANet) is proposed with two mechanisms: residual spatial attention (RSA) and gated channel attention (GCA). RSA is utilized to weight the spatial information of the lung parenchyma and suppress feature activation in irrelevant regions, while the weights of each channel are adaptively calibrated using GCA to implicitly predict potential key features. Then, a dual attention guidance module (DAGM) is designed to maximize the integration of the advantages of both mechanisms. In addition, LDANet introduces a lightweight dense block (LDB) that reuses feature information and a positioned transpose block (PTB) that realizes accurate positioning and gradually restores the image resolution until the predicted segmentation map is generated. Experiments are conducted on two public datasets, LIDC-IDRI and COVID-19 CT Segmentation, on which LDANet achieves Dice similarity coefficient values of 0.98430 and 0.98319, respectively, outperforming a state-of-the-art lung segmentation model. Additionally, the effectiveness of the main components of LDANet is demonstrated through ablation experiments.

Assessment of genetic diversity in cowpea (Vigna unguiculata L.) genotypes by using RAPD markers

The present study was carried out at the department of Plant Biotechnology, College of Agricultural Biotechnology Latur, which aimed to study or assess the genetic diversity in eight different Cowpea genotypes by using the RAPD markers. This is important for cowpea genetic improvement and for the overall management of cowpea germplasm. Eight Cowpea genotypes namely GDVC-2, GC-3, GC-4, GC-6, Gangotri, Pusa Komal, Arka Garima, and VU-5 were assessed by using two RAPD markers (RPI-07 and RPI-08). In RAPD analysis, total of 20 amplicons were generated, among them 19 were polymorphic with 88.8% average polymorphism. RPI-7 and RPI-8 showed 88.8% and 100% polymorphism respectively. The similarity coefficient values ranged from 0.350% to 0.950% indicating high degree of genetic variation. The highest genetic similarity i.e. 0.857% was recorded between Pusa Komal and GC-6 and least genetic similarity i.e. 0.125% was observed in between VU-5 and GC-4. Two screened primers are found to be polymorphic (94.4%) with high polymorphic information content (PIC) which efficiently discriminating the varieties at molecular level.

A two-stage CNN method for MRI image segmentation of prostate with lesion

Prostate magnetic resonance imaging (MRI) is widely used in the diagnosis of prostate cancer and other prostate diseases. The automatic segmentation of images from prostate MRI plays an important role in the auxiliary diagnosis of prostate diseases. Currently, there are two commonly used methods for automatic segmentation of prostate MRI, which are 2D image segmentation and 3D image segmentation. In this paper, a two-stage CNN method for MRI image segmentation of prostate with lesion is proposed. At the first stage, we used a CNN model incorporating the Squeeze-Excitation module to discriminate whether the image contains prostate or not. At the second stage, we proposed a Residual-Attention U-Net for segmentation of images containing prostate. Eventually, the 3D prostate MRI segmentation results are obtained and fully automated segmentation is accomplished. We evaluated our proposed method and other common 2D and 3D segmentation methods on the test dataset and compared their results based on Dice Similarity Coefficient (DSC) value. Our method performed the best and achieved the DSC metric value of 0.860.

Molecular Characterization of Different Soybean [Glycine max (L). Merril] genotypes by RAPD Markers

The present investigation was carried out on molecular characterization of different soybean genotypes by using RAPD markers. Genetic diversity of 10 soybean genotypes was studied using Random Amplified polymorphic DNA (RAPD). 12 RAPD primers were used for screening 10 soybean genotypes from which total 334 fragments were amplified. It was observed that 41.10% bands were polymorphic and 58.88% were monomorphic. The percent of polymorphic amplicons in banding pattern was calculated and it was highest in OPA-01 (67.85%) followed by OPA-11 (66.66%). Polymorphism information content (PIC) was calculated and highest PIC value was observed in OPA-01 (0.26) and lowest was observed in OPA-08 and OPA-05 (0.03). Maximum amplicons were produced in OPA-01 (56) and minimum in OPA-08 (11). Genetic relationship between soybean genotypes was determined on the basis of Jaccard’s pair wise similarity coefficient values and dendrogram was generated by UPGMA (Unweighted Pair Group Method with Arithmetic Mean) cluster analysis using dice’s similarity matrix through NT-SYS pc software. The dendrogram of RAPD analysis shows 8 clusters and sub clusters were revealed. The value of similarity coefficient ranged from 0.19 to 0.71. The maximum similarity percentage i.e. 71% was found between KDS-726 and KDS-922 genotypes. The minimum similarity i.e. 19% was found between IVT-33 and IVT-13. According to this information it is concluded that the KDS-726 and KDS-922 genotypes showed minimum genetic diversity as compare to the IVT-33 and IVT-13 genotypes. Therefore, understanding the genetic diversity of soybean genotypes is essential to broaden the genetic base and to further utilize in the MAS breeding program.

Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy

ObjectiveTo develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network model under clinically relevant conditions. MethodsIntraoperative videos of robot-assisted radical prostatectomy performed by the posterior approach from 3 institutions were obtained between 2019 and 2020. Using SV-VD dissection videos, semantic segmentation of the seminal vesicle-vas deferens area was performed using a convolutional neural network-based approach. The dataset was split into training and test data in a 10:3 ratio. The average time required by 6 novice urologists to correctly recognize the SV-VD was compared using intraoperative videos with and without segmentation masks generated by the convolutional neural network model, which was evaluated with the test data using the Dice similarity coefficient. Training and test datasets were compared using the Mann–Whitney U-test and chi-square test. Time required to recognize the SV-VD was evaluated using the Mann–Whitney U-test. ResultsFrom 26 patient videos, 1 040 images were created (520 SV-VD annotated images and 520 SV-VD non-displayed images). The convolutional neural network model had a Dice similarity coefficient value of 0.73 in the test data. Compared with original videos, videos with the generated segmentation mask promoted significantly faster seminal vesicle and vas deferens recognition (P < .001). ConclusionThe convolutional neural network model provides accurate recognition of the SV-VD in the posterior approach RARP, which may be helpful, especially for novice urologists.

Computer-Aided Breast Surgery Framework Using a Markerless Augmented Reality Method.

This study proposes a markerless Augmented Reality (AR) surgical framework for breast lesion removal using a depth sensor and 3D breast Computed Tomography (CT) images. A patient mesh in the real coordinate system is acquired through a patient 3D scan using a depth sensor for registration. The patient mesh on the virtual coordinate system is obtained by contrast-based skin segmentation in 3D mesh generated from breast CT scans. Then, the nipple area is detected based on the gradient in the segmented skin area. The region of interest (ROI) is set based on the detection result to select the vertices in the virtual coordinate system. The mesh on the real and virtual coordinate systems is first aligned by matching the center of mass, and the Iterative Closest Point (ICP) method is applied to perform more precise registration. Experimental results of 20 patients' data showed 98.35 ± 0.71% skin segmentation accuracy in terms of Dice Similarity Coefficient (DSC) value, 2.79 ± 1.54 mm nipple detection error, and 4.69 ± 1.95 mm registration error. Experiments using phantom and patient data also confirmed high accuracy in AR visualization. The proposed method in this study showed that the 3D AR visualization of medical data on the patient's body is possible by using a single depth sensor without having to use markers.