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The primary causes of low sesame production are a lack of high-yielding varieties, an inability to adjust to environmental challenges, and a poor response to enhanced crop management. The potential for improving sesame has been investigated using a variety of breeding approaches, including mutation and recombination breeding; however, these methods have only produced a minor increase in productivity. Recombination and mutant breeding have recently been proposed as novel, cutting-edge approaches to overcome the limitations of conventional breeding practices. This study was conducted to assess the recurrent mutagenesis efficacy of sesame crosses, followed by the isolation and evaluation of superior breeding lines that could be released as varieties in the future. In the F4M4 generation, the populations were grown in a randomized block design (RBD), and the efficacy of recurrent mutagenesis was assessed in terms of variability parameters such as the mean, standard deviation, and coefficient of variation. During the F5M5 generation, the materials were grown in three separate trials in Compact Family Block Design to evaluate the between-family and within-family variations, followed by the isolation of improved breeding lines based on per se performance. The improved breeding lines were further advanced to the F6M6 generation for evaluation in three separate trials in an RBD. In the F6M6 generation, ANOVA, correlation, regression, path coefficient, and Mahalanobis’s D 2 analysis were computed, followed by the construction of selection indices to select superior lines. The results showed the importance of three or four EMS treatment cycles for sesame improvement in the F4M4 generation. Twenty of the top lines based on per se performance (with a selection intensity of 27%) from each category in the F5M5 generation were carried forward to the F6M6 generation. In the F6M6 generation, the top six breeding lines (with a selection intensity of 30%) from each category, for a total of 18 breeding lines, were chosen based on these index scores. These high-yielding micromutant lines could be subsequently released as varieties through multiplication trials with standard checks. After a thorough evaluation, these lines have the potential to replace the current varieties and increase production, productivity, and adoption of sesame in India.

Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew's correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background and objectives Over the years, several treatment options have been developed for neovascular age-related macular degeneration (AMD), the most notable being intravitreal injections of anti-vascular endothelial growth factor drugs. The rationale for treating neovascular AMD is to preserve and improve central vision, enhance the quality of life for affected individuals, stabilize or improve vision, and prevent further structural damage to the macula. The objective of the present study was to evaluate the clinical course of different disease types of neovascular age-related macular degeneration and their treatment response to anti-vascular endothelial growth factor (anti-VEGF) injections. Methods This prospective observational study was conducted at a tertiary care referral hospital in Eastern India during October 2019 and September 2021. Patients diagnosed with neovascular AMD attending our Outpatient department and retina clinic were recruited for the study. An experienced ophthalmologist examined all patients, meeting the inclusion criteria. The clinical profile, including initial best corrected visual acuity (BCVA), ophthalmoscopic, fluorescein angiographic, and optical coherence tomography (OCT) findings of different patterns of neovascular AMD, were collected and analyzed. Patients were subjected to intravitreal Ranibizumab every month for three months and then on a when-required basis. Visual outcomes were recorded at each follow-up, and a comparison was done between initial and final visual acuity. Descriptive statistics were used for analysis, with p< 0.05 taken as statistically significant. Results A total of 72 patients were included in the study. Fundus fluorescein angiography revealed that 52.78% were classic, 15.28% were minimally classic, and 31.94% were of occult variety. 41.66% of lesions were subfoveal in location, 47.22% were juxtafoveal, and 11.11% lesions were extrafoveal in location. The mean BCVA was Log MAR (Logarithm of the Minimum Angle of Resolution) 1.061±0.25. The average number of intravitreal Ranibizumab injections given to each eye was five. BCVA of patients after the third injection was log MAR 0.818±0.296. There was a significant improvement in mean BCVA from baseline 1.061±0.254 to 0.787±0.317 after the study (p-valve: p<0.05). After the first injection, 49 patients (68.05%) experienced an initial improvement of at least one line, 20 patients (27.77%) did not exhibit any improvement, and 3 patients (4.16%) had a decline of one line in Snellen's visual acuity chart. Over the follow-up period,10 showed improvement in 1 line in the Snellen chart after subsequent injection. At the end of the study, six patients showed no change, and four patients showed deterioration after the completion of injections. No adverse events were noted during the study period. Conclusions Intravitreal Ranibizumab is effective in improving visual outcomes in treatment-naïve individuals with neovascular age-related macular degeneration. The decision for repeat intravitreal anti-VEGF injection should be based on OCT findings of subretinal fluid, pigment epithelial detachment, and cystoid macular edema as an indicator of disease activity. This can also lessen the number of intravitreal injections and morbidity in these patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Dalai et al.)