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.

E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures.

Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.

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.

Patient-centered approaches for patients with systemic autoimmune rheumatic diseases: development and evolution.

INTRODUCTION: European Reference Networks (ERNs) are dedicated to rare complex diseases. Systemic autoimmune rheumatic diseases (SARDs) comprise a group of disorders, some of which are rare, complex, and chronic, characterized by relapsing-remitting course and requiring targeted treatments for long periods; SARDs are also associated with various co-morbidities and therefore health-care infrastructures, at the highest level of expertise are required. AREAS COVERED: For the current work, literature on the basic characteristics of a center of excellence dedicated to SARDs, its advantages over the existing health infrastructures in order to improve health and social care, its contribution to the education of health-care workers, and the related research opportunities are presented. In addition, our experience, vision, and initiatives as a new member of the ERNs are reported. EXPERT OPINION: A restructure in healthcare policy and resource allocation, based on centers of expertise, is necessary to improve the medical care of patients with SARDs.

Knowledge Discovery on IoT-Enabled mHealth Applications.

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

An Enhanced Device-Transparent Real-Time Teleconsultation Environment for Radiologists.

This paper describes a novel web-based platform promoting real-time advanced teleconsultation services on medical imaging. Principles of heterogeneous workflow management systems and state-of-the-art technologies such as the microservices architectural pattern, peer-to-peer networking, and the single-page application concept are combined to build a scalable and extensible platform to aid collaboration among geographically distributed healthcare professionals. The real-time communication capabilities are based on the webRTC protocol to enable direct communication among clients. This paper discusses the conceptual and technical details of the system, emphasizing on its innovative elements.

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective.

In this review, the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources paves the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic diseases, as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features, and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools supports the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues that arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed toward a roadmap in cohort harmonization for clinical medicine.

A DICOM Based Collaborative Platform for Real-Time Medical Teleconsultation on Medical Images.

The paper deals with the design of a Web-based platform for real-time medical teleconsultation on medical images. The proposed platform combines the principles of heterogeneous Workflow Management Systems (WfMSs), the peer-to-peer networking architecture and the SPA (Single-Page Application) concept, to facilitate medical collaboration among healthcare professionals geographically distributed. The presented work leverages state-of-the-art features of the web to support peer-to-peer communication using the WebRTC (Web Real Time Communication) protocol and client-side data processing for creating an integrated collaboration environment. The paper discusses the technical details of implementation and presents the operation of the platform in practice along with some initial results.

Utilizing a Homecare Platform for Remote Monitoring of Patients with Idiopathic Pulmonary Fibrosis.

Homecare and home telemonitoring are a focal point of emerging healthcare schemes, with proven benefits for both patients, caregivers and providers, including reduction of healthcare costs and improved patients' quality of life, especially in the case of chronic disease management. Studies have evaluated solutions for remote monitoring of chronic patients based on technologies that allow daily symptom and vital signs monitoring, tailored to the needs of specific diseases. In this work, we present an affordable home telemonitoring system for patients with idiopathic pulmonary fibrosis (IPF), based on an application for mobile devices and Bluetooth-enabled sensors for pulse oximetry and blood pressure measurements. Besides monitoring of vital signs, the system incorporates communication via videoconferencing and emergency response, with support from a helpdesk service. A pilot study was conducted, in order to verify the proposed solution's feasibility. The results support the utilization of the system for effective monitoring of patients with IPF.

Automated integration of wireless biosignal collection devices for patient-centred decision-making in point-of-care systems.

The proper acquisition of biosignals data from various biosensor devices and their remote accessibility are still issues that prevent the wide adoption of point-of-care systems in the routine of monitoring chronic patients. This Letter presents an advanced framework for enabling patient monitoring that utilises a cloud computing infrastructure for data management and analysis. The framework introduces also a local mechanism for uniform biosignals collection from wearables and biosignal sensors, and decision support modules, in order to enable prompt and essential decisions. A prototype smartphone application and the related cloud modules have been implemented for demonstrating the value of the proposed framework. Initial results regarding the performance of the system and the effectiveness in data management and decision-making have been quite encouraging.

DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators.

microRNAs (miRNAs) are small non-coding RNAs that actively fine-tune gene expression. The accurate characterization of the mechanisms underlying miRNA transcription regulation will further expand our knowledge regarding their implication in homeostatic and pathobiological networks. Aim of DIANA-miRGen v3.0 (http://www.microrna.gr/mirgen) is to provide for the first time accurate cell-line-specific miRNA gene transcription start sites (TSSs), coupled with genome-wide maps of transcription factor (TF) binding sites in order to unveil the mechanisms of miRNA transcription regulation. To this end, more than 7.3 billion RNA-, ChIP- and DNase-Seq next generation sequencing reads were analyzed/assembled and combined with state-of-the-art miRNA TSS prediction and TF binding site identification algorithms. The new database schema and web interface facilitates user interaction, provides advanced queries and innate connection with other DIANA resources for miRNA target identification and pathway analysis. The database currently supports 276 miRNA TSSs that correspond to 428 precursors and >19M binding sites of 202 TFs on a genome-wide scale in nine cell-lines and six tissues of Homo sapiens and Mus musculus.

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts.

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads.

Real-time medical collaboration services over the web.

The gradual shift in modern medical practice, from working alone clinical doctors to MDTs (Multi-Disciplinary Teams), raises the need of online real-time collaboration among geographically distributed medical personnel. The paper presents a Web-based platform, featuring an efficient medical data management and exchange, for hosting real-time collaborative services. The presented work leverages state-of-the-art features of the web (technologies and APIs) to support client-side medical data processing. Moreover, to address the typical bandwidth bottleneck and known scalability issues of centralized data sharing, an indirect RPC (Remote Process Call) scheme is introduced through object synchronization over the WebRTC paradigm.