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.

This paper describes a novel web-based platform promoting real-time advanced teleconsultation services on medical imaging. Principles of heterogeneous Workflow Management Systems (WFMSs) and state-of-the-art technologies such as the microservices architectural pattern, peer-to-peer networking and the Single-Page Application (SPA) concept are combined to build a platform to aid collaboration among geographically distributed healthcare professionals. The real time communication capabilities are based on the webRTC protocol. The paper discusses the conceptual and technical details of the system, emphasizing on its innovative elements. The operation of the system in practice along with some initial results are also presented.

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.

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.