A Spatial Logic for a Simplicial Complex Model

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 to support the design of a system and predict its behaviour. For this reasons, different tools and techniques have been proposed to specify and verify the properties of space. However, these approaches are mainly based on graphs. These are used to model spatial relations, describing 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 \emph{multi-dimensional} aspects of space. In this work, we propose a spatial logic interpreted on \emph{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 \emph{bisimulation} and \emph{branching bisimulation} relations defined over simplicial complexes

Human Activity Recognition using a Semantic Ontology-Based Framework

In the last years, the extensive use of smart objects embedded in the physical world, in order to monitor and record physical or environmental conditions, has increased rapidly. In this scenario, heterogeneous devices are connected together into a network. Data generated from such system are usually stored in a database, which often shows a lack of semantic information and relationship among devices. Moreover, this set can be incomplete, unreliable, incorrect and noisy. So, it turns out to be important both the integration of information and the interoperability of applications. For this reason, ontologies are becoming widely used to describe the domain and achieve efficient interoperability of information system. An example of the described situation could be represented by Ambient Assisted Living context, which intends to enable older or disabled people to remain living independently longer in their own house. In this contest, human activity recognition plays a main role because it could be considered as starting point to facilitate assistance and care for elderly. Due to the nature of human behavior, it is necessary to manage the time and spatial restrictions. So, we propose a framework that implements a novel methodology based on the integration of an ontology for representing contextual knowledge and a Complex Event Processing engine for supporting timed reasoning. Moreover, it is an infrastructure where knowledge, organized in conceptual spaces (based on its meaning) can be semantically queried, discovered, and shared across applications. In our framework, benefits deriving from the implementation of a domain ontology are exploited into different levels of abstrac- tion. Thereafter, reasoning techniques represent a preprocessing method to prepare data for the final temporal analysis. The results, presented in this paper, have been obtained applying the methodology into AALISABETH, an Ambient Assisted Living project aimed to monitor the lifestyle of old people, not suffering from major chronic diseases or severe disabilities

Topological Classification of RNA Structures via Intersection Graph

We introduce a new algebraic representation of RNA secondary structures as composition of hairpins and we define an appropriate abstract algebraic representation. Moreover, we propose a novel method to classify the RNA structures based on two topological invariants, the genus and the number of crossing. Starting from the classic arc representation of RNA secondary structures, the proposed method takes advantage of the algebraic representation to easy obtain an interaction graph of RNA molecule through an appropriate procedure. Each vertex of the graph is a loop and each edge represents the interaction between the two loops, thus the cardinality of edges is the number of crossing of the RNA molecule. Through the definition and application of a new procedure, the intersection graph of the RNA shape is obtained. The cardinality of the resulting graph corresponds to the crossing number of the shape associated to the RNA molecule. The aforementioned crossing number is a topological invariant, as well as the genus. Both do not uniquely identify an RNA graph, but the crossing number permits to add a term which is proportional to the standard free energy of the RNA molecule. Thus, a more precise free-energy parametrization can be obtained. Finally, our method is validated over a subset of real RNA structures from Pseudobase++ databases, and we classify the RNA structures according to their topological genus and crossing number

ASPRAlign: a tool for the alignment of RNA secondary structures with arbitrary pseudoknots

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 (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

An algebraic language for RNA pseudoknots comparison

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

A formal language for classifying RNA secondary structures

We introduce a formal language for representing RNA secondary structures as interactions of loops towards a topological shape language. A base loop is an hairpin. All the other loops, such as bulge, helix, inner loop and multiple loop, are compositions of hairpins. Two loops are sequentially connected by base pair weak interactions. We introduce a set of operators to manipulate loops and interactions between loops. The grammar of the resulting language of RNA secondary structure allows us to generate both pseudoknot free and pseudoknotted RNA secondary structures starting from the RNA sequences. Moreover, we can represent a pseudoknot free RNA secondary structure in a “canonical form”, thus we have a way to tell whether two given structures differ by a loop. We will investigate the characterization of the higher order language corresponding to the loops interactions

Hierarchical representation for PPI sites prediction

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