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.

Selenium, Selenoproteins and 10-year Cardiovascular Risk: Results from the ATTICA Study.

BACKGROUND: Selenium (Se) is an essential trace element that is involved in several pathophysiological functions. The relationship of Se with cardiovascular disease remains inconclusive, especially regarding the role of different selenospecies. OBJECTIVE: The present study assessed the levels of Se distribution in plasma selenoproteins, namely glutathione peroxidase 3 (GPx3), selenoprotein P (SelP) and selenoalbumin (SeAlb) and total Se in selenoproteins in relation to 10-year cardiovascular risk in the ATTICA prospective study. METHODS: A sub-sample from the ATTICA Study's database, consisting of 278 subjects (114 women and 164 men) with data on Se and selenoproteins levels, was considered. SeGPx3, SelP, and SeAlb in human plasma were simultaneously determined by high-performance liquid chromatography (HPLC) coupled with inductively coupled plasma mass spectrometry (ICP-MS) at baseline. The duration of the follow-up was 8.74 ±2.36 years (mean± standard deviation) and cardiovascular outcomes were recorded. Cox proportional hazards models were applied with total Se or selenoprotein Se as independent variables adjusted for several covariates. RESULTS: Total Se in selenoproteins was positively related to 10-year relative risk of cardiovascular disease (Hazard Ratios of 3rd vs 2nd tertile 10.02, 95% CI:1.15, 92.34). Subjects with high Se but low SeGPx3, as identified by discordant percentiles in the distribution of SeGPx3 and Se, had a higher cardiovascular risk. CONCLUSION: The differentiated effects of circulating selenoproteins on cardiovascular disease risk in the present study, suggest the importance of redox regulation by specific selenoproteins.

Citizen Science to improve healthy and active living among adolescents in four European countries: a protocol of the cluster randomised controlled trial of the Science Engagement to Empower aDolescentS (SEEDS) project.

INTRODUCTION: Improving healthy lifestyles of adolescents is challenging. Citizen Science is a way to engage them in the design and delivery of interventions, and may also increase their interest in science, technology, engineering and mathematics (STEM). The Science Engagement to Empower aDolescentS (SEEDS) project aims to use an equity-lens, and engage and empower boys and girls from deprived areas by designing and cocreating interventions to promote healthy lifestyles, and to seed interest in STEM. METHODS AND ANALYSIS: SEEDS is a cluster randomised controlled trial in four countries (Greece, the Netherlands, Spain and the UK). Each country will recruit six to eight high schools from lower socioeconomic neighbourhoods. Adolescents aged 13-15 years are the target population. High schools will be randomised into intervention or control group. Each country will select 15 adolescents from intervention schools called ambassadors, who will be involved throughout the project.In each country, focus groups with ambassadors and stakeholders will focus on physical activity, snacking behaviour and STEM. The input from focus groups will be used to shape Makeathon events, cocreation events where adolescents and stakeholders will develop the interventions. The resultant intervention will be implemented in the intervention schools during 6 months. In total, we aim to recruit 720 adolescents who will complete questionnaires related to healthy lifestyles and STEM outcomes at baseline (November 2021) and after the 6 months (June 2022). ETHICS AND DISSEMINATION: The four countries obtained approval from their corresponding Ethics Committees (Greece: Bioethics Committee of Harokopio University; the Netherlands: The Medical Research Ethics Committee of the Erasmus Medical Center; Spain: The Drug Research Ethics Committee of the Pere Virgili Health Research Institute; UK: Sport and Health Sciences Ethics Committee of the University of Exeter). Informed consent will be collected from adolescents and their parents in line with General Data Protection Regulation legislation. The findings will be disseminated by conference presentations, publications in scientific peer-reviewed journals and during (local) stakeholders and public events. Lessons learnt and the main results will also be used to provide policy recommendations. TRIAL REGISTRATION NUMBER: NCT05002049.

Noise-assisted data processing with empirical mode decomposition in biomedical signals.

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.