Similarity Coefficient Values Research Articles

Morphological and molecular diversity analysis vis-a-vis phylogenetic correlation among pomegranate (Punica granatum L.) varieties

Abstract Identification of different pomegranate (Punica granatum L.) genotypes using morphological descriptors and further substantiated through molecular means is essential for a breeding program. Characterization using molecular markers has an advantage over its morphological counterpart due to its low expense, quick and reliable results, and the requirement of less sophisticated equipment. In this study, eight different pomegranate cultivars were characterized based on their morphological and molecular characters in sub-tropical regions between 2018 and 2019. Morphological experiments were conducted in a randomized block design replicated thrice for each cultivar, whereas RAPD markers were employed for molecular studies. Principal component analysis revealed significant morphological variation (79.61%) among the cultivars. A high level of polymorphism, similar to PCA, was observed with RAPD analysis (80.49%). Some discrepancies between morphological and molecular results were noted in UPGMA cluster analysis. However, eight primers (total = 41) distinctively separated the Bhagwa variety from others, with the maximum similarity coefficient value (ca. 0.54) and superior fruit quality distinguished in PCA analysis with the maximum mean Euclidean distance (ca. 16). Hence, RAPD markers could be used along with morphological markers for a quick screening of germplasms in further crop improvement programs.

A Prostate Boundary Localization and Edge Denoising Algorithm

This research aimed at presenting a two-step method for prostate segmentation in TRUS images. The research used a prostate boundary localization and prostate edge denoising approach. The proposed method contribution is the use of the optimized Hodge’s method as the boundary operator and the use of the Bidirectional Exponential moving average to perform edge denoising. The results showed that the proposed method is effective in completing the prostate segmentation task. (1) The prostate region is effectively initialized and localized. (2) The recovery of noise points is accomplished and the segmentation result being consistent with the general shape of the prostate. The experimental results showed that this method can improve the overall segmentation accuracy. The process uses a combination of traditional and unsupervised methods, eliminating the need to rely on large data sets compared to current deep learning methods. The proposed method achieved excellent segmentation accuracy, with the Dice similarity coefficient (DICE) value of 0.9679, an average Intersection over Union (IoU) value of 0.9377, and an average False Positive Rate (FPR) of 0.0399. The results obtained from this study have significant implications for clinical practice. Accurate prostate segmentation is crucial for various applications, including radiation therapy planning, image-guided interventions, and computer-aided diagnosis. The proposed method has the potential to improve these applications by providing more precise and reliable prostate segmentations. However, it is important to acknowledge some limitations of this study. First, the proposed method was evaluated on a limited dataset, which may not fully represent the diversity of prostate images encountered in clinical practice. Further validation on larger datasets is necessary to assess its generalizability. Additionally, the proposed method relied on manual annotations for training, which can introduce inter-observer variability. Incorporating automated or semi-automated annotation techniques could enhance the robustness of the method

Keragaman Pepaya (Carica papaya L.) di Kotamadya Pontianak Berdasarkan Karakter Morfologi

Papaya (Carica papaya L.) is a plant that is widely cultivated in Indonesia. Pontianak City is one of the centers for the production of papaya cultivation, with a production of 12,593 tons in 2019. This study aims to determine the diversity and kinship of papaya in Pontianak City based on its morphological characters. The research was conducted from September to October 2021 in Pontianak City. A total of 32 individuals were sampled from six districts using the cruising method. Consanguinity was analyzed on 28 morphological characters using the UPGMA method with the NTSYS ver. 2.0. The results indicated that the 32 papaya accessions exhibited a similarity coefficient value of 0.35, which represents a 35% level of similarity. The papaya plants in Pontianak City exhibited a low level of similarity but a high level of diversity.

A deep inverse convolutional neural network-based semantic classification method for land cover remote sensing images

The imbalance of land cover categories is a common problem. Some categories appear less frequently in the image, while others may occupy the vast majority of the proportion. This imbalance can lead the classifier to tend to predict categories with higher frequency of occurrence, while the recognition effect on minority categories is poor. In view of the difficulty of land cover remote sensing image multi-target semantic classification, a semantic classification method of land cover remote sensing image based on depth deconvolution neural network is proposed. In this method, the land cover remote sensing image semantic segmentation algorithm based on depth deconvolution neural network is used to segment the land cover remote sensing image with multi-target semantic segmentation; Four semantic features of color, texture, shape and size in land cover remote sensing image are extracted by using the semantic feature extraction method of remote sensing image based on improved sequential clustering algorithm; The classification and recognition method of remote sensing image semantic features based on random forest algorithm is adopted to classify and identify four semantic feature types of land cover remote sensing image, and realize the semantic classification of land cover remote sensing image. The experimental results show that after this method classifies the multi-target semantic types of land cover remote sensing images, the average values of Dice similarity coefficient and Hausdorff distance are 0.9877 and 0.9911 respectively, which can accurately classify the multi-target semantic types of land cover remote sensing images.

Homogeneity pursuit and variable selection in regression models for multivariate abundance data.

When building regression models for multivariate abundance data in ecology, it is important to allow for the fact that the species are correlated with each other. Moreover, there is often evidence species exhibit some degree of homogeneity in their responses to each environmental predictor, and that most species are informed by only a subset of predictors. We propose a generalized estimating equation(GEE) approach for simultaneous homogeneity pursuit (ie, grouping species with similar coefficient values while allowing differing groups for different covariates) and variable selection in regression models for multivariate abundance data. Using GEEs allows us to straightforwardly account for between-response correlations through a (reduced-rank) working correlation matrix. We augment the GEE with both adaptive fused lasso- and adaptive lasso-type penalties, which aim to cluster the species-specific coefficients within each covariate and encourage differing levels of sparsity across the covariates, respectively. Numerical studies demonstrate the strong finite sample performance of the proposed method relative to several existing approaches for modeling multivariate abundance data. Applying the proposed method to presence-absence records collected along the Great Barrier Reef in Australia reveals both a substantial degree of homogeneity and sparsity in species-environmental relationships. We show this leads to a more parsimonious model for understanding the environmental drivers of seabed biodiversity, and results in stronger out-of-sample predictive performance relative to methods that do not accommodate such features.

Deep learning methods for early detection of Alzheimer’s disease using structural MR images: a survey

In this paper, we present an extensive review of the most recent works on Alzheimer’s disease (AD) prediction, focusing on Moderate Cognitive Impairment (MCI) conversion prediction. We aimed to identify the most useful brain-magnetic resonance imaging (MRI) biomarkers and deep learning frameworks used for prediction. To achieve this, we analyzed more than 130 studies and reviewed 7 articles. A closer examination revealed that the hippocampus is an important region of interest (ROI) affected early by AD, and many related features help detect the disease in its early stages. However, when considered alone, this ROI is not sufficient to ensure high prediction performance. Therefore, several other brain regions can also provide additional information to improve prediction accuracy. Concerning state-of-the-art deep neural networks, the U-Net represents the most efficient architecture for hippocampus segmentation. The RESU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) value, equal to 94%.For MCI conversion prediction, the best results were obtained by two models identifying significant landmarks from the entire brain for classification. The multi-stream convolutional neural network achieved the best Area Under the Curve (AUC) and specificity of 94.39% and 99.70%, respectively. Finally, a region ensemble model delivered the highest accuracy of 85.90%, highlighting the need for further research to address this challenging problem.

Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7MHz wide field swept source FDML optical coherence tomography.

The posterior segment of the human eye complex contains two discrete microstructure and vasculature network systems, namely, the retina and choroid. We present a single segmentation framework technique for segmenting the entire layers present in the chorio-retinal complex of the human eye using optical coherence tomography (OCT) images. This automatic program is based on the graph theory method. This single program is capable of segmenting seven layers of the retina and choroid scleral interface. The graph theory was utilized to find the probability matrix and subsequent boundaries of different layers. The program was also implemented to segment angiographic maps of different chorio-retinal layers using "segmentation matrices." The method was tested and successfully validated on OCT images from six normal human eyes as well as eyes with non-exudative age-related macular degeneration (AMD). The thickness of microstructure and microvasculature for different layers located in the chorio-retinal segment of the eye was also generated and compared. A decent efficiency in terms of processing time, sensitivity, and accuracy was observed compared to the manual segmentation and other existing methods. The proposed method automatically segments whole OCT images of chorio-retinal complex with augmented probability maps generation in OCT volume dataset. We have also evaluated the segmentation results using quantitative metrics such as Dice coefficient and Hausdorff distance This method realizes a mean descent Dice similarity coefficient (DSC) value of 0.82 (range, 0.816-0.864) for RPE and CSI layer.

Accurate malocclusion tooth segmentation method based on a level set with adaptive edge feature enhancement

ObjectiveThis study aimed to accurately segment teeth under complex oral conditions, including complex structural interference among adjacent teeth or malocclusion conditions, such as tooth rotation and displacement caused by dental crowding. Study designCone-beam computed tomography (CBCT) images were obtained from 19 patients with complex oral conditions, and a three-step solution was proposed. This study used a global convex level-set model to extract bony tissue and developed a flexible curve extraction method for separating neighbouring teeth under complex structural interference. In addition, a local level-set model with adaptive edge feature enhancement was proposed to segment individual teeth precisely. This model adaptively enhances edge features based on the structure of the root boundary and accurately distinguishes between the close-contact root and alveolar bone resulting from tooth rotation or displacement. ResultsThe experimental results showed that the average Dice similarity coefficient values for incisors, canines, premolars, and molars were 93.30%, 93.47%, 93.24%, and 93.89%, respectively, and the average tooth centroid distances were 0.66, 0.61, 0.87, and 0.80 mm, respectively. ConclusionThe proposed method can effectively segment teeth without relying on highly precise annotated datasets, yielding satisfactory results even under complex structural interference between adjacent teeth or tooth rotation and displacement caused by dental crowding. It is more robust than the other methods and provides valuable data for further research and clinical practice.

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI-guided radiation therapy.

Cine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image quality. To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images. This study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images. Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20ms for a typical input image size of 512 × 512. Taking advantage of the prior high-quality positioning and planning MR images, the deep learning-based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real-time MRgRT.

Morphological Characteristics and Similarity Analysis of Cassava (Manihot esculenta Crantz) in Wonosobo, Temanggung, and Magelang Regencies

The diversity of cassava in Wonosobo, Temanggung and Magelang districts must be investigated. The objective of the study was to analyze the diversity of cassava based on morphological characteristics. The cassava of each accession was planted in Bandongan, Magelang Regency. Morphological characters observed after six months of planting included apical leaf color, apical leaf hair, leaf lobe shape, petiole color, leaf color, number of leaf lobes, leaf length, leaf width, petiole length, vein color, petiole orientation, color epidermis of stem, color of outer appearance of stem, distance between nodes, stem growth, color of mature branch tip, branching type, tuber, tuber shape, outer tuber skin color, tuber flesh color, and tuber inner skin color. Analysis of the morphological characteristics of cassava is presented in the form of a dendrogram using the MVSP (Multi Variate Statistical Package) version 3.22. The results of the study obtained 39 accessions of cassava which showed different morphological characteristics. The lowest morphological similarity (0.735) was found in Group I only consisted of 2 acquisitions that did not have tubers, namely Red Vegetables and Green Vegetables and Group II ( Groups III and IV) have a similarity of 0.92. The highest similarity was in groups IIIa and IIIb and Groups IVa and IVb have a similarity coefficient value of 0.95. The morphological diversity of 39 accessions is a source of cassava germplasm: 37 accessions are harvested for tubers and 2 accessions areharvested explicitly for leaves as vegetables. This research was useful for the development of cassava-based food industry bioproducts.

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

This study aimed to investigate the effectiveness and practicality of using models like convolutional neural network and transformer in detecting and precise segmenting meningioma from magnetic resonance images. The retrospective study on T1-weighted and contrast-enhanced images of 523 meningioma patients from 3 centers between 2010 and 2020. A total of 373 cases split 8:2 for training and validation. Three independent test sets were built based on the remaining 150 cases. Six convolutional neural network detection models trained via transfer learning were evaluated using 4 metrics and receiver operating characteristic analysis. Detected images were used for segmentation. Three segmentation models were trained for meningioma segmentation and were evaluated via 4 metrics. In 3 test sets, intraclass consistency values were used to evaluate the consistency of detection and segmentation models with manually annotated results from 3 different levels of radiologists. The average accuracies of the detection model in the 3 test sets were 97.3%, 93.5%, and 96.0%, respectively. The model of segmentation showed mean Dice similarity coefficient values of 0.884, 0.834, and 0.892, respectively. Intraclass consistency values showed that the results of detection and segmentation models were highly consistent with those of intermediate and senior radiologists and lowly consistent with those of junior radiologists. The proposed deep learning system exhibits advanced performance comparable with intermediate and senior radiologists in meningioma detection and segmentation. This system could potentially significantly improve the efficiency of the detection and segmentation of meningiomas.

HUBUNGAN KEKERABATAN Nepenthes HIBRID HASIL PERSILANGAN ALAMI ANTARA Nepenthes mirabillis (Lour.) Druce DAN Nepenthes reinwardtiana Miq BERDASARKAN KARAKTER MORFOLOGI

Nepenthes is a carnivorous plant that can cross-pollinate the same or different species so that it can produce hybrid Nepenthes. An analysis of the relationship between Nepenthes species and their hybrids can be carried out by observing the morphological characters. The purpose of this study was to determine the morphological characteristics and the relationship between Nepenthes hybrids and their parents. This study used Nepenthes hybrid resulting from a cross between N. mirabillis and N. reinwardtiana which were found in Entikong District, Sanggau Regency. The relationships were analyzed using the NTSys program with the UPGMA method. Nepenthes reinwardtiana does not have variations in pitcher color or shape. Nepenthes mirabillis and N. mirabillis X N. reinwardtiana had variations in pitcher color and shape. Nepenthes hybrid has a characters inherited from both parents. Nepenthes reinwardtiana, N. mirabillis, and N. mirabillis X N. reinwardtiana had a similarity level of 0.46 and were divided into three clusters. The relationship between N. mirabillis and N. mirabillis X N. reinwardtiana has the greatest similarity coefficient value of 0.77, while N. reinwardtiana and N. mirabillis X N. reinwardtiana have a similarity coefficient value of 0.64. Morphological characters show clear differences in the pitcher. The relationships of Nepenthes hybrid is closer to N. mirabillis.

Genetic Diversity of Bananas (Musa spp.) in Pesawaran District, Lampung Province Based on Sequence Related Amplified Polymorphism (SRAP) Marker

Banana (Musa spp.) is a leading commodity that makes a major contribution to national fruit production. One of the districts in Lampung Province which has a significant contribution to Indonesia's banana production is Pesawaran Regency. The molecular marker used in this study is Sequence-Related Amplified Polymorphism (SRAP). This study aims to determine the genetic diversity of bananas, determine the suitable combination of SRAP primers to determine the genetic diversity of bananas and determine the similarity relationship of bananas in the Pesawaran District. The total genomic DNA was extracted using DNeasy Plant Mini Kit (Geneaid) according to the protocol from the manufacturer followed by their amplification using SRAP primers. Similarity analysis was carried out based on similarity index values and dendrogram construction using the NTSYS 2.0 program. Research results show that a total of 204 loci (167 polymorphic loci and 37 monomorphic loci) were obtained from 9 selected primer combinations. The genetic diversity of 19 banana cultivars originating from Pesawaran Regency resulted in low genetic diversity values (0-0.27). Banana kinship based on Sequence Related Amplified Polymorphism (SRAP) markers divided 19 banana cultivar samples and 4 wild banana samples into 2 large clusters with similarity coefficient values between 0.70-0.97.

Development of a test method for the dynamic drapability of fabrics using reciprocating motion

This study aimed to develop a method for measuring the dynamic drapability of fabrics using a reciprocating motion device that mimics the movement created by shaking a piece of fabric. Five types of fabrics were selected based on their drape coefficients and weights, and the lengths of fabrics were determined based on skirt length (50 cm) and dress length (100 cm). The width and speed of the reciprocating motion were considered as the experimental variables. Results of reciprocating motion-based fabric movement analysis revealed that the number of nodes, the position of the first node, amplitude, total length, and area are significant factors for measuring dynamic drapability. It was found that the longer fabrics had more nodes, enabling measurement of various types of fabrics. Furthermore, it was found that a wider reciprocation width under the same speed and a faster reciprocation speed with the same width favorably demonstrated dynamic drapability, resulting in proportionality between the reciprocating motion and the force transmitted to the fabric. Movement analysis of seven selected conditions and additional samples showed that the optimal condition was a 100-mm reciprocation width and a 150-rpm speed and that the first node’s position is the most significant factor for a dynamic drapability. To verify the proposed measurement method, measuring four pairs of fabrics with similar drape coefficients but different shapes showed that as fabric weight increased, the node moved downward, despite similar drape coefficient values. This confirms that the proposed method can overcome the limitations of the existing drape coefficient.

Amorphophallus muelleri blume diversity based on morphological characteristics in South Sulawesi

Porang is an alternative carbohydrate source plant that contains the highest glucomannan among other Amorphophallus species in Indonesia. Amorphophallus muelleri Blume is an important species of the genus Amorphophallus, which belongs to the same subspecies A. bulbifer and A. yuloensis. A. muelleri Blume has good disease resistance and heat tolerance tendency, and high KGM content. Kinship relationships can be used as a basis for assembling more potential genotypes. To find out the kinship between existing varieties we must exploration and identify. This research was carried out using qualitative methods analysed in the numerical taxonomy and multivariate analysis system version 2.02i (NTSYS) program. The genetic materials used in this study were explored from wild growing habitat in agroforestry areas in South Sulawesi, Indonesia. The results obtained that the MR6 and BL10 genotypes which had a similarity coefficient value of 1.00, between genotypes MR4, MR9, MR10, BT4 and BT10 had a similarity coefficient value of 1.00, between genotypes BT5 and BT7 have a similarity coefficient value of 1.00, and between genotypes BT1, BT2, BT9 have a similarity coefficient value of 1.00. Furthermore, BL1, BL4, and BL6 contribute significantly of the variation as showed by PC1.

A Hybrid Generative Adversarial network with Quantum U-NET for 3D spine X-ray image registration

The registration of Two Dimensional (2D) to Three Dimensional (3D) spine x-ray image is essential in medical field for the diagnosis of scoliosis. In the surgerical procedure of scoliosis, Computed tomography (CT) imagery and intraoperative projective X-rays are used with the requirement of implant localization. The 3D reconstruction is challenged with the presence of implant projection which affects the registration capture range. In this paper, the novel inpainting algorithm Generative Adversarial Network with Quantum Unet (GAN-Q-Unet) model is developed to resolve the issues of registration capture range. The generator network learns to produce data with the output of discriminator network. In GAN-Q-Unet, the discriminator network is replaced with the encoder-decoder network namely U-net which improves the quality of the network by providing more semantic information. In addition to that, the layers of U-net are replaced with quantum layers in which encoding and transformation is applied based on non-linearity. Initially, the bone regions of the spine image are enhanced with synthetic data generation. After enhancement, the proposed inpainting is applied for 3D image registration. With GAN-Q-Unet approach, better 3D reconstruction of spine image is obtained from 2D spine image. The proposed algorithm is used for automatic transformation of spine image with varying dimensions in the routine examination of clinical applications. By using the proposed approach, the Peak Signal to Noise Ratio (PSNR), Structural Similarity index Measure (SSIM), Dice Similarity coefficient (DSC) and Loss values obtained are 28.1767, 0.9300, 2.9674, and 0.9107 respectively. The registration performance of GAN-Q-Unet is compared with the state-of-the-art techniques and it shows the efficiency of the proposed 3D image registration.

First report of a 'Candidatus Phytoplasma aurantifolia'-related strain associated with faba bean phyllody symptoms in India.

Vicia faba L. commonly known as broad bean or faba bean is one of the most widely grown protein rich legume crops. Out of more than 50 faba bean-producing countries, about 90% production is concentrated in the Asian, European Union (EU), and African region (FAO, 2020). Owing to its high nutritional value, both the fresh pods and dry seeds are consumed. During March 2022, some plants with little leaf and phyllody symptoms such as leaf-like floral structures were observed in the experimental fields of Indian Agricultural Research Institute (IARI), New Delhi (Fig. 1 a, b, c). The twig samples were collected from two individual symptomatic and one from asymptomatic plant. DNA was extracted using CTAB (cetyl trimethyl ammonium bromide) method (Ahrens and Seemüller, 1992; Marzachi et al. 1998) and examined for the association of phytoplasma through nested PCR using the universal primers P1/P7 and R16F2n/R16R2 targeting the 16SrRNA gene (Deng and Hiruki 1991; Gundersen and Lee 1996) and the other set of primers secAfor1/secArev3 and secAfor2/secArev3 targeting secA gene (Hodgetts et al. 2008). The DNA from symptomatic plants resulted the amplicons of 1200bp and 840bp specific to 16S rRNA and secA gene respectively. The gel purified PCR products were cloned into pGEM®-T Easy Vector system (Promega) and outsourced for Sanger sequencing at Agri Genome Labs, Kerala, India. The resultant 16S rRNA sequences (GenBank Acc. No. OP978231, OP978232) and secA sequences (ON715392 and ON715393) were examined through NCBI BLASTn analysis. The 16S rRNA sequences of the V. faba strains shared a minimum of 99.85% similarity with the phytoplasma strain causing little leaf and phyllody disease of sesame in India (MW622017) and a maximum of 100% sequence identity with the Vigna radiata phyllody and necrosis phytoplasma strain of Jodhpur (OP935760) India, whereas the secA gene sequences showed 100% identity with Tephrosia purpurea witches'-broom phytoplasma (MW603929) from China and a minimum of 91.14% similarity with 'Candidatus Phytoplasma aurantifolia' (MW020541) from India. The pairwise comparison results were completely in support of the corresponding phylogenetic sequence analysis results of 16SrRNA and secA gene sequences of faba bean strains in comparison with other strains retrieved from GenBank database, wherein the faba bean strains got clustered with 16SrII-D subgroup related strains (Fig. 2 a and b). Virtual RFLP analysis through iPhyClassifer tool through in silico digestion of R16F2n/R2 region of 16S rRNA gene of the faba bean strain using 17 restriction endonuclease enzymes resulted in the RFLP profiles similar to that of the profile of phytoplasma subgroup 16SrII-D (Y10097: papaya yellow crinkle) used as reference strain with a similarity coefficient value of 1.0. All the results of this investigation confirmed the association of 'Candidatus phytoplasma aurantifolia' (16SrII-D) with the diseased faba bean plants in this study. Previous reports of phytoplasma infecting faba bean include a group 16SrIII strain detected in Spain in 2004 (Castro and Romero), a subgroup 16SrII-D strain detected in Sudan in 2012 (Alfaro-Fernandez et al.), a group 16SrII strain detected in Saudi Arabia in 2014 (Al-Saleh and Amer), and subgroup 16SrIII-J strains detected in Egypt in 2014 (Hamed et al.) and in Peru in 2021 (Torres-Suarez et al.). To the best of our knowledge, these findings, document the first report of the association of 'Candidatus Phytoplasma aurantifolia' (subgroup 16SrII-D) with faba bean plants in India. This report necessitates further research on the status of distribution of this phytoplasma strain in other locations and hosts in the country so as to develop possible strategies to contain its further spread and management of the disease.

Genetic diversity of ex-situ conserved Arabica cofee (Coffea arabica L.) accessions in Ethiopia as revealed by simple sequence repeats markers

So far, there has been limited use of molecular markers in arabica coffee breeding program in Ethiopia. The objectives of this study were to explore the effectiveness of simple sequence repeats markers (SSRs) in detecting polymor- phism and to assess the extent of genetic diversity and relationships among ex-situconserved Arabica coffee accessions. Sixty-two forest coffee accessions planted in the experimental plot of the Ethiopian coffee breeding program were evaluated using 14 SSR markers. These markers amplified a total of 100 alleles, varying from four to ten alleles per locus, with an average of 7.2 across all loci. The rate of polymorphism ranged from 75 to 100, with a mean value of 96.4 across the accessions. The polymorphic information content (PIC) varied from 0.26 to 0.92, with a mean value of 0.70. The genetic similarity coefficient values between 72% possible pair-wise combinations ranged from 0.18 to 0.50, with overall mean value of 0.44.The unweighted pair group method with arithmetic mean (UPGMA) dendrogram based on Jaccard’s genetic similarity grouped the accessions into five main clusters and two singletons at <0.47 similarity coefficient value. These results indicate the effectiveness of the SSR markers in detecting polymorphism and the presence of a high level of genetic diversity and distant relatedness among the studied coffee accessions. The observed diversity could be exploited in the future coffee breeding program to develop heterotic hybrid coffee varieties through crossing of divergent parental lines. The highly informative SSRs markers can be also used in genetic analysis of Arabica coffee germplasm conserved in the field gene bank at Jimma Agricultural Research Center (JARC) to establish core collections for effective conservation, management and utilization purposes

Use of retrotransposon based iPBS markers for determination of genetic relationship among some Chestnut Cultivars (Castanea sativa Mill.) in Türkiye.

The aim of this study was to reveal the genetic relationships among some economically important chestnut cultivars for Türkiye by using retrotransposon-based inter primer binding site (iPBS) markers. In this study, a total of 19 iPBS markers were used to determine the genetic relationships among 11 chestnut cultivars (Castanea sativa Mill.). In the study, chestnut cultivars named Hacıömer, Osmanoğlu, Sarıaşlama, Erfelek, Kemer, Işıklar, Şekerci, Siyah Bursa, Tülü, Bouche De Betizac and Marigoule were the preferred cultivars utilised. Using the online marker efficiency calculator (iMEC), some indices of polymorphism, such as the mean heterozygosity, polymorphism information content, marker index and discriminating power, were determined. In addition, the size ranges of alleles, number of average alleles, number of total alleles, number of polymorphic alleles, and polymorphism rate were determined at a successful level. The chestnut cultivars of Hacıömer and Şekerci were determined to be the most similar cultivars with a similarity coefficient value of 0.924, and they formed a subgroup together with the chestnut cultivars Osmanoğlu and Erfelek, showing close similarity with these two cultivars. The use of iPBS markers in chestnuts in Türkiye was carried out for the first time in this study. The power of iPBS markers to evaluate the genetic relationship for our preferred chestnut cultivars was revealed. For this reason, it has emerged that it will be useful in the molecular characterization of both genotypes in natural chestnut populations and chestnut breeding materials such as varieties and cultivars in chestnut breeding programs.

Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI

Background and Objective:Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging. Methods:This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism. Results:The results of our proposed network’s prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively. Conclusions:Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.