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Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease. [ABSTRACT FROM AUTHOR]

Stevia rebaudiana Bertoni is a perennial herb, widely used as a natural sweetener around the globe. The key compounds responsible for its sweetness includes stevioside and rebaudioside-A. In order to improve these steviol glycosides, the present study was initiated to study the effect of induced mutagenesis on growth parameters, steviol glycosides and nuclear DNA content in Stevia rebaudiana Bertoni using ten doses of gamma-rays (5-100 kR). Healthy seeds of 'Madhuguna' variety of Stevia rebaudiana Bertoni developed and maintained at stevia breeding farm, Agrotechnology division, CSIR-Institute of Himalayan Bioresource Technology, Palampur (HP), India were irradiated with ten doses of gamma rays (600 seeds each/dose) ranging from 5 kR to 100 kR (i.e., 5, 10, 15, 20, 30, 40, 50, 60, 80 and 100 kR) using Co60 gamma irradiation chamber at CCS Haryana Agricultural University, Hisar, (Haryana), India. Significant variations were recorded for all the seedling traits studied while major impact was noticed on the seedling after reaching the cotyledonary stage and doses above 40 kR showed absolute mortality of the seedlings. Based on probit analysis, the optimum LD50 dose lies in the range of 20-23 kR. Glycosidic profiling of 296 mutants using high-performance liquid chromatography showed decreased total steviol glycoside content with increased radiation dose. Doses 5 kR and 10 kR, were found to be effective in increasing the overall glycosidic content. A total of 72 promising mutants were also screened for increased rebaudioside-A stevioside ratio. Comparison of nuclear DNA content using flow cytometry revealed a similar decrease in the total nuclear DNA content with increase in dosage of gamma rays. The average genome size at 5, 10, 15, 20 and 30 kR treatments were 2.72, 2.69, 2.68, 2.70 and 2.66 pg as compared to 2.72 pg in control. Mild dose of gamma rays (5 and 10 kR) in stevia were found to be effective in improving the mean steviol glycoside content and may be used in future stevia mutation programmes. [ABSTRACT FROM AUTHOR]

In surveys, successive sampling is generally considered to study the same characteristic from one occasion to another so that the change in characteristics over time can be further studied. This paper presents a class of estimators using a novel approach to estimate population mean on two occasions under successive sampling. We have carried out numerical study and simulation study to validate the significance of the proposed class and its members. We have used an optimum unmatched proportion of each class or estimator to compute their respective mean square error during simulation, while other studies considered some fixed value for unmatched units. For large population, the suggested estimation technique is always better for survey practitioners than others. [ABSTRACT FROM AUTHOR]

This study aimed to estimate the relationship between fish size and otolith size of four creek-associated fishes (Anodontostoma chacunda, Epinephelus diacanthus, Johnius glaucus and Mugil cephalus) collected from the north-eastern Arabian Sea from November 2021 to November 2022. The numerical relations were established using regression between fish size (Total length-TL and Body weight-BW) and otolith dimensions (Length-OL, Weight-Owt, Area-OA, Width-OW and Perimeter-OP). All regressions between the fish size and otolith morphometric measurements were highly correlated (R2 > 0.8), which indicates the robustness of the model. Taxonomists can use findings from this study for species confirmation and reconstruction of past species assemblages. These findings might provide insight into predicting the prey size in food and feeding studies to explore the food web dynamics. [ABSTRACT FROM AUTHOR]