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.

The accurate \"base pairing\" in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar\'s advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub.

The guava fruit fly, Bactrocera correcta, is one of the most destructive pests in the genus Bactrocera and detects environmental odorants mainly through antennal olfactory sensilla phenotypes with nanopores. However, it is unclear whether there are naturally occurring abnormal antennal olfactory sensilla phenotypes that affect olfaction. Here, we found that there were abnormal bulges besides nanopores on the surface of trichoid and basiconic olfactory sensilla in the antennal flagellum of long-term laboratory rearing colony (LTC), and that nanopore number in these olfactory sensilla was also remarkably reduced. Notably, the electroantennogram (EAG) responses of LTC insects to methyl eugenol or β-caryophyllene were inhibited, and their behavioral responses elicited by the same odorants were also impaired. These results revealed naturally occurring abnormal antennal olfactory sensilla phenotypes which were involved in olfactory deficit in B. correcta, providing a platform to further study nanopore-targeted pest control technologies in the future.

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.