Obstructive sleep apnea (OSA) is a chronic disease characterized by intermittent hypoxemia during sleep related to snoring. It affects the quality of life and increases the risk of severe health conditions, including cardiovascular diseases. The gold standard for diagnosing OSA is polysomnography (PSG), which requires an overnight hospital stay while physically connected to 10-15 measurement channels. PSG is costly, inconvenient, and requires the involvement of a sleep technologist. Such as, over 80% of affected individuals remain undiagnosed. Therefore, cost-effective and non-invasive screening methods for OSA play a fundamental role in improving people’s file quality. Approaches based on deep learning techniques have achieved evaluable results. However, such results are not reproducible due to the lack of code and dataset, making it difficult to evaluate the impact of these methods on first-level diagnosis scenarios.In this work, we face apnea detection as a classification image problem. The introduced method exploits the Mel-spectrograms of snoring and VGG19, an architecture based on Convolutional Neural Networks (CNN), to detect apnea. We test our approach on a public dataset that stores data related to polysomnography with simultaneous audio recordings for sleep apnea studies. On this dataset, our methods archive 95, 4% of accuracy. The analysis of the performance values shows that our method reaches competitive results.

The examined paper presents a simulation infrastructure for scalable disaggregated memory systems called DRackSim. The authors have uploaded their artefacts to Zenodo, which ensures long-term retention of the artefact. This paper can thus receive the Artifacts Available badge. The tool has been built successfully with some interaction with authors. The paper can earn the Artifacts Functional badge. Due to the lack of material regarding the data used, the experiments can not be replicated and the artefacts cannot obtain the Artifacts Reproduced – Evaluated badge. Furthermore, since the artefact is also available on GitHub, the paper is assigned the Artifacts Reusable badge.

Collective Adaptive Systems often consist of many heterogeneous components typically organised in groups. These entities interact with each other by adapting their behaviour to pursue individual or collective goals. In these systems, the distribution of these entities determines a space that can be either physical or logical. The former is defined in terms of a physical relation among components. The latter depends on logical relations, such as being part of the same group. In this context, specification and verification of spatial properties play a fundamental role in supporting the design of systems and predicting their behaviour. For this reason, different tools and techniques have been proposed to specify and verify the properties of space, mainly described as graphs. Therefore, the approaches generally use model spatial relations to describe a form of proximity among pairs of entities. Unfortunately, these graph-based models do not permit considering relations among more than two entities that may arise when one is interested in describing aspects of space by involving interactions among groups of entities. In this work, we propose a spatial logic interpreted on simplicial complexes. These are topological objects, able to represent surfaces and volumes efficiently that generalise graphs with higher-order edges. We discuss how the satisfaction of logical formulas can be verified by a correct and complete model checking algorithm, which is linear to the dimension of the simplicial complex and logical formula. The expressiveness of the proposed logic is studied in terms of the spatial variants of classical bisimulation and branching bisimulation relations defined over simplicial complexes.

BACKGROUND: Protein-protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection. RESULTS: We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar. CONCLUSIONS: The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions.

BACKGROUND: The ability to compare RNA secondary structures is important in understanding their biological function and for grouping similar organisms into families by looking at evolutionarily conserved sequences such as 16S rRNA. Most comparison methods and benchmarks in the literature focus on pseudoknot-free structures due to the difficulty of mapping pseudoknots in classical tree representations. Some approaches exist that permit to cluster pseudoknotted RNAs but there is not a general framework for evaluating their performance. RESULTS: We introduce an evaluation framework based on a similarity/dissimilarity measure obtained by a comparison method and agglomerative clustering. Their combination automatically partition a set of molecules into groups. To illustrate the framework we define and make available a benchmark of pseudoknotted (16S and 23S) and pseudoknot-free (5S) rRNA secondary structures belonging to Archaea, Bacteria and Eukaryota. We also consider five different comparison methods from the literature that are able to manage pseudoknots. For each method we clusterize the molecules in the benchmark to obtain the taxa at the rank phylum according to the European Nucleotide Archive curated taxonomy. We compute appropriate metrics for each method and we compare their suitability to reconstruct the taxa.

SUMMARY: Current methods for comparing RNA secondary structures are based on tree representations and exploit edit distance or alignment algorithms. Most of them can only process structures without pseudoknots. To overcome this limitation, we introduce ASPRAlign, a Java tool that aligns particular algebraic tree representations of RNA. These trees neglect the primary sequence and can handle structures with arbitrary pseudoknots. A measure of comparison, called ASPRA distance, is computed with a worst-case time complexity of O(n2) where n is the number of nucleotides of the longer structure. AVAILABILITY AND IMPLEMENTATION: ASPRAlign is implemented in Java and source code is released under the GNU GPLv3 license. Code and documentation are freely available at https://github.com/bdslab/aspralign. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation. RESULTS: In this work, we introduce an algebraic language to represent RNA secondary structures with arbitrary pseudoknots. Each structure is associated with a unique algebraic RNA tree that is derived from a tree grammar having concatenation, nesting and crossing as operators. From an algebraic RNA tree, an abstraction is defined in which the primary structure is neglected. The resulting structural RNA tree allows us to define a new measure of similarity calculated exploiting classical tree alignment. CONCLUSIONS: The tree grammar with its operators permit to uniquely represent any RNA secondary structure as a tree. Structural RNA trees allow us to perform comparison of RNA secondary structures with arbitrary pseudoknots without taking into account the primary structure.