Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions

RNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA-RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA-RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors

A SPATIAL LOGIC FOR SIMPLICIAL MODELS

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

Automatic generation of pseudoknotted RNAs taxonomy

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

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

A Virtual PEP for Web Optimization over a Satellite-Terrestrial Backhaul

The availability of network softwarization and virtualization technology in the field of telecommunications has opened the door to a radical review of the applications, protocols, and deployment models. In this evolving framework, old assumptions and constraints specific to satellite communications must be carefully re-assessed. To this aim, we revisit the role of the performance enhancing proxy (PEP), replaced by a chain of custom virtual network functions properly enabled to optimize common web traffic performance over a backhaul dynamically enabled with a supplementary satellite link. The resulting virtual PEP (vPEP) is compliant with the breakthrough virtualization and slicing paradigms and can fruitfully exploit the advanced features of the most recent IETF technologies such as QUIC and MPTCP

Testing the Dispersion of Nanoparticles in a Nanocomposite with an Ultra-Low Fill Content Using a Novel Non-Destructive Evaluation Technique

A non-destructive evaluation (NDE) technique capable of testing the dispersion of nanoparticles in a nanocomposite would be of great use to the industry to check the quality of the products made and to ensure compliance with their specifications. Very few NDE techniques found in the literature can evaluate the level of dispersion of the nanoparticles in the whole nanocomposite. Here, a recently developed NDE technique based on pulsed phase thermography (PPT) in transmission mode was used to assess the particle dispersion in ultra-low, less than 0.05 wt%, Ag enriched polymeric based nanocomposite manufactured with an innovative nano-coating fragmentation technique. The phasegrams obtained with the presented technique clearly showed clusters or bundles of Ag nanoparticles when present, down to the size of 6 µm. Therefore, the new NDE approach can be applied to verify that the expected levels of dispersion are met in the production process

Breakdown of the mean-field approximation in a wealth distribution model

One of the key socioeconomic phenomena to explain is the distribution of wealth. Bouchaud and M\'ezard have proposed an interesting model of economy [Bouchaud and M\'ezard (2000)] based on trade and investments of agents. In the mean-field approximation, the model produces a stationary wealth distribution with a power-law tail. In this paper we examine characteristic time scales of the model and show that for any finite number of agents, the validity of the mean-field result is time-limited and the model in fact has no stationary wealth distribution. Further analysis suggests that for heterogeneous agents, the limitations are even stronger. We conclude with general implications of the presented results.Comment: 11 pages, 3 figure

Abiraterone acetate in metastatic castration-resistant prostate cancer after chemotherapy. A retrospective “Real Life” analysis of activity and safety

Abiraterone acetate (AA) is a potent, selective androge (CYP17) biosynthesis inhibitor, which showed to improve overall survival (HR = 0.646) in mCRPC patients progressing after docetaxel. In this retrospective analysis we assessed the safety and efficacy of AA in patients affected with mCRPC progressing after chemotherapy, treated in the normal clinical practice, in several Italian Oncologic Units, after the approval of the drug from the Italian Drug Agency (AIFA)

Financial development and economic growth : long-run equilibrium and transitional dynamics

We analyze the impact of financial development on economic growth. Differently from previous studies that focus mainly on balanced growth path outcomes, we also analyze the transitional dynamics of our model economy by using a finance-extended Uzawa-Lucas framework where financial intermediation affects both human and physical capital accumulation. We show that, under certain rather general conditions, economic growth may turn out to be non-monotonically related to financial development (as suggested by the most recent empirical evidence) and that too much finance may be detrimental to growth. We also show that the degree of financial development may affect the speed of convergence, which suggests that finance may play a crucial role in determining the length of the recovery process associated with exogenous shocks. Moreover, in a special case of the model, we observe that, under a realistic set of parameters, social welfare decreases with financial development, meaning that even when finance positively affects economic growth the short term costs associated with financial activities more than compensate their long run benefits