Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.

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.

Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops.

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.

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition.

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate "base pairing" prediction, also known as "RNA secondary structure prediction". As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA's pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern's context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Cross-cultural differences in pedestrian behaviors in relation to values: A comparison of five countries.

This study compared pedestrian behaviors in five countries (Estonia, Greece, Kosovo, Russia, and Turkey) and investigated the relationships between these behaviors and values in each country. The study participants were 131 pedestrians for Estonia, 249 for Greece, 112 for Kosovo, 176 for Russia, and 145 for Turkey. The principal component analyses revealed that the four-factor structure of the Pedestrian Behavior Scale (PBS) was highly consistent across the five countries. ANCOVA results revealed significant differences between countries on the PBS items and scale scores. Specifically, Greek and Turkish participants reported transgressive pedestrian behaviors more frequently than Estonian, Kosovar, and Russian pedestrians while Kosovar participants reported transgressive pedestrian behaviors less frequently than Estonian pedestrians. In addition, Turkish and Russian pedestrians reported lapses and aggressive behaviors more frequently than Estonian, Greek, and Kosovar pedestrians. Finally, Turkish and Estonian pedestrians reported positive behaviors more frequently than Kosovar pedestrians. Unexpectedly, the regression analyses showed that values have varying effects on pedestrian behavior in the five countries. That is, context or country may determine the effect of values on pedestrian behaviors. The results are discussed in relation to the previous literature.

A novel method for the generation of multi-block computational structured grids from medical imaging of arterial bifurcations.

In this study a description of a new approach, for the generation of multi-block structured computational grids on patient-specific bifurcation geometries is presented. The structured grid generation technique is applied to data obtained by medical imaging examination, resulting in a surface conforming, high quality, multi-block structured grid of the branching geometry. As a case study application a patient specific abdominal aorta bifurcation is selected. For the evaluation of the grid produced by the novel method, a grid convergence study and a comparison between the grid produced by the method and unstructured grids produced by commercial meshing software are carried out.

A methodology to generate structured computational grids from DICOM data: application to a patient-specific abdominal aortic aneurysm (AAA) model.

This study presents the generation of a multi-block structured grid on a real abdominal aortic aneurysm (AAA) acquired from Digital Imaging and Communication in Medicine (DICOM) data. With the use of a computed tomography exam (or medical images in standard DICOM format), the shape of a human organ is extracted and a structured computational grid is created. The structured grid generation is done by utilising Floater's and Gopalsamy et al.'s algorithm. The proposed methodology is applied to the AAA case, but it may also be applied to other human organs, enabling the scientist to develop an advanced patient-specific model. More importantly, the proposed methodology provides a precise reconstruction of the human organs, which is required in an AAA, where small variations in the geometry may alter the flow field, the stresses exerted on the walls and finally the rupture risk of the aneurysm.