Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

MicroRNAs (miRNAs) play critical roles in gene expression and numerous human diseases. The success of miRNA biogenesis is largely determined by the primary miRNA (pri-miRNA) processing by the DROSHA-DGCR8 complex, called Microprocessor. Here, we analysed the high-throughput pri-miRNA processing assays and secondary structures of pri-miRNAs to investigate the roles of bulges in the pri-miRNA processing. We found that bulges in multiple places control both the cleavage efficiency and accuracy of pri-miRNA processing. These bulges were shown to act on Microprocessor via its catalytic subunit, DROSHA, and function in a position and strand-dependent manner. Interestingly, we discovered that the enriched and conserved bulges, called midB, can correct DROSHA orientation on pri-miRNAs, thereby enhancing production of miRNAs. The revealed functions of the bulges help improve our understanding of pri-miRNA processing and suggest their potential roles in miRNA biogenesis regulation.

Small bowel capsule endoscopy (SBCE) remains the gold standard for practice for the diagnosis of small bowel disorders. A rather challenging task, for those who start to use this diagnostic modality, is the recognition of the typical anatomic landmarks and the distinction of normal small bowel anatomy from abnormal findings. The reader of SBCE images may also often encounter unusual views of the normal anatomy as well as various artifacts that need to be distinguished from pathologic findings. Experience gained through standard endoscopy is invaluable to the interpretation of capsule examinations; however, formalized training and credentialing in reading competency are essential.