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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]

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]

In this article, we propose logarithmic type direct and synthetic estima- tors for the estimation of domain mean under simple random sampling. The properties such as bias and mean square error of the proposed direct and synthetic estimators are obtained up to first order approximation. The efficiency conditions are obtained under which the proposed direct and synthetic estimators outperform their conventional counterparts. The performance of the proposed direct and synthetic estimators is examined with the help of comprehensive computational study using real and artificially drawn populations. Some appropriate suggestions are also provided to the surveyors. [ABSTRACT FROM AUTHOR]

New forms of interaction made possible by developments in special educational technologies can now help students with dyscalculia. Artificial intelligence (AI) has emerged as a promising tool in recent decades, particularly between 2001 and 2010, offering avenues to enhance the quality of education for individuals with dyscalculia. Therefore, the implementation of AI becomes crucial in addressing the needs of students with dyscalculia. Content analysis techniques were used to examine the literature covering the influence of AI on dyscalculia and its potential to assist instructors in promoting education for individuals with dyscalculia. The study sought to create a foundation for a more inclusive dyscalculia education in the future through in-depth studies. AI integration has had a big impact on educational institutions as well as people who struggle with dyscalculia. This paper highlights the importance of AI in improving the educational outcomes of students affected by dyscalculia.