Structural variation among assembled genomes facilitates development of rapid and low-cost NOR-linked markers and NOR-telomere junction mapping in Arabidopsis.

KEY MESSAGE: Genome-wide structural variants we identified and new NOR-linked markers we developed would be useful for future genome-wide association studies (GWAS), and for new gene/trait mapping purposes. Bioinformatic alignment of the assembled genomes of Col-0 and Sha ecotypes of Arabidopsis thaliana revealed ~ 13,000 genome-wide structural variants involving simple insertions or deletions and repeat contractions or expansions. Using some of these structural variants, we developed new, rapid, and low-cost PCR-based molecular markers that are genetically linked to the nucleolus organizer regions (NORs). A. thaliana has two NORs, one each on chromosome 2 (NOR2) and chromosome 4 (NOR4). Both NORs are ~ 4 Mb each, and hundreds of 45S ribosomal RNA (rRNA) genes are tandemly arrayed at these loci. Using previously characterized recombinant inbred lines (RILs) derived from Sha x Col-0 crosses, we validated the utility of the newly developed NOR-linked markers in genetically mapping rRNA genes and the associated telomeres to either NOR2 or NOR4. Lastly, we sequenced Sha genome using Oxford Nanopore Technology (ONT) and used the data to obtain sequences of NOR-telomere junctions, and with the help of RILs, we mapped them as new genetic markers to their respective NORs (NOR2-TEL2N and NOR4-TEL4N). The structural variants obtained from this study would serve as valuable data for genome-wide association studies (GWAS), and to rapidly design more genome-wide genetic (molecular) markers for new gene/trait mapping purposes.

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification.

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

Comparison of Connective Tissue Graft and Platelet Rich Fibrin as Matrices in A Novel Papillary Augmentation Access: A Randomized Controlled Clinical Trial.

OBJECTIVE: The present study was a randomized controlled clinical trial with the aim of evaluating the increase in papillary height and reduction in black triangle height obtained by the novel papillary augmentation access with either connective tissue graft (CTG) or platelet-rich fibrin (PRF) as matrix from baseline to 6 months. MATERIALS AND METHODS: A total number of 20 sites with Nordland and Tarnow's Class I, I-II and II interdental papillary loss were recruited into the study. The sites were randomly allocated with 10 sites per group into either: Group A (control group) or Group B (test group) which utilizes the same technique with CTG or PRF as matrix, respectively. The clinical parameters such as PPD (probing pocket depth) and CAL (clinical attachment level) at the surgical site, plaque score (FMPS), bleeding scores (FMBS), interdental papillary height (PH) and black triangle height (BTH), along with visual analog score by dentist (VAS-D) and by patient (VAS-P) were evaluated at baseline and at 6 months. RESULTS: In both CTG and PRF groups, all the primary and secondary outcome variables such as PH, BTH, VAS-D and VAS-P showed statistically significant improvement from baseline to 6 months (p ≤0.05) within the group. On comparison at 6 months, there were no differences in the papillary height between the groups. However, the BTH has significantly reduced in the CTG group than the PRF group. Similarly, the VAS-D significantly improved in the CTG group than the PRF group (p = 0.010) at 6 months. CONCLUSION: The study demonstrates that the proposed papillary augmentation access with CTG and PRF was successful in managing the unaesthetic interdental papillary loss, with CTG showing better results in terms of reduction of black triangle than PRF.