A Markovian model for the spread of the SARS-CoV-2 virus.

We propose a Markovian stochastic approach to model the spread of a SARS-CoV-2-like infection within a closed group of humans. The model takes the form of a Partially Observable Markov Decision Process (POMDP), whose states are given by the number of subjects in different health conditions. The model also exposes the different parameters that have an impact on the spread of the disease and the various decision variables that can be used to control it (e.g, social distancing, number of tests administered to single out infected subjects). The model describes the stochastic phenomena that underlie the spread of the epidemic and captures, in the form of deterministic parameters, some fundamental limitations in the availability of resources (hospital beds and test swabs). The model lends itself to different uses. For a given control policy, it is possible to verify if it satisfies an analytical property on the stochastic evolution of the state (e.g., to compute probability that the hospital beds will reach a fill level, or that a specified percentage of the population will die). If the control policy is not given, it is possible to apply POMDP techniques to identify an optimal control policy that fulfils some specified probabilistic goals. Whilst the paper primarily aims at the model description, we show with numeric examples some of its potential applications.

Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †.

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

Combining Haptic and Bang-Bang Braking Actions for Passive Robotic Walker Path Following.

Robotic walkers are a promising solution for physical and cognitive support to older adults. This paper proposes a low cost path following strategy combining the advantages of a simple mechanical braking guidance, such as safety, passivity, and a low cost, and the ones of a vibrotactile haptic guidance, such as comfort and portability. The user is guided by providing indications on the directions of motion using the haptic interface so that he/she can autonomously and comfortably follow the planned path. However, whenever the user significantly departs from the path (for instance, s/he gets too close to obstacles), the braking system kicks in to safely steer the user back along the proper direction. The formal correctnesses of the hybrid strategy ruling the combination of the two guidance systems is proved theoretically. Moreover, a comprehensive experimental study with users aged 64 to 100, including also psychological evaluations, has been performed. The hybrid combination of the braking and the haptic guidance systems is shown to outperform the two individual approaches in isolation. The combination of the two retains the same level of the users' perceived comfort typical of the haptic-only guidance while ensuring the adequate path following performance typical of the braking-only guidance. In particular, the combined approach produces a mean path following error equal to 41% of the mean path following error ensured by the haptic-only approach. Conversely, thanks to the haptic feedback, the combined approach halves the activation time and the number of interventions needed in the braking-only approach.

FEDRO: a software tool for the automatic discovery of candidate ORFs in plants with c →u RNA editing.

BACKGROUND: RNA editing is an important mechanism for gene expression in plants organelles. It alters the direct transfer of genetic information from DNA to proteins, due to the introduction of differences between RNAs and the corresponding coding DNA sequences. Software tools successful for the search of genes in other organisms not always are able to correctly perform this task in plants organellar genomes. Moreover, the available software tools predicting RNA editing events utilise algorithms that do not account for events which may generate a novel start codon. RESULTS: We present FEDRO, a Java software tool implementing a novel strategy to generate candidate Open Reading Frames (ORFs) resulting from Cytidine to Uridine (c→u) editing substitutions which occur in the mitochondrial genome (mtDNA) of a given input plant. The goal is to predict putative proteins of plants mitochondria that have not been yet annotated. In order to validate the generated ORFs, a screening is performed by checking for sequence similarity or presence in active transcripts of the same or similar organisms. We illustrate the functionalities of our framework on a model organism. CONCLUSIONS: The proposed tool may be used also on other organisms and genomes. FEDRO is publicly available at http://math.unipa.it/rombo/FEDRO .

IP6K gene identification in plant genomes by tag searching.

BACKGROUND: Plants have played a special role in inositol polyphosphate (IP) research since in plant seeds was discovered the first IP, the fully phosphorylated inositol ring of phytic acid (IP6). It is now known that phytic acid is further metabolized by the IP6 Kinases (IP6Ks) to generate IP containing pyro-phosphate moiety. The IP6K are evolutionary conserved enzymes identified in several mammalian, fungi and amoebae species. Although IP6K has not yet been identified in plant chromosomes, there are many clues suggesting its presences in vegetal cells. RESULTS: In this paper we propose a new approach to search for the plant IP6K gene, that lead to the identification in plant genome of a nucleotide sequence corresponding to a specific tag of the IP6K family. Such a tag has been found in all IP6K genes identified up to now, as well as in all genes belonging to the Inositol Polyphosphate Kinases superfamily (IPK). The tag sequence corresponds to the inositol-binding site of the enzyme, and it can be considered as characterizing all IPK genes. To this aim we applied a technique based on motif discovery. We exploited DLSME, a software recently proposed, which allows for the motif structure to be only partially specified by the user. First we applied the new method on mitochondrial DNA (mtDNA) of plants, where such a gene could have been nested, possibly encrypted and hidden by virtue of the editing and/or trans-splicing processes. Then we looked for the gene in nuclear genome of two model plants, Arabidopsis thaliana and Oryza sativa. CONCLUSIONS: The analysis we conducted in plant mitochondria provided the negative, though we argue relevant, result that IP6K does not actually occur in vegetable mtDNA. Very interestingly, the tag search in nuclear genomes lead us to identify a promising sequence in chromosome 5 of Oryza sativa. Further analyses are in course to confirm that this sequence actually corresponds to IP6K mammalian gene.

Biological network querying techniques: analysis and comparison.

Research in systems biology has made available large amounts of data about interactions among cell building blocks (e.g., proteins, genes). To properly look up these data and mine useful information, the design and development of automatic tools has become crucial. These tools leverage Biological Networks as a formal model to encode molecular interactions. Biological networks can be fed as input to graph-based techniques useful to infer new information about cellular activity and evolutive processes of the species. In this context, a rather interesting family of techniques is that of network querying. Network querying tools search a whole biological network to identify conserved occurrences of a given query module for transferring biological knowledge. Indeed, inasmuch as the query network generally encodes a well-characterized functional module, its occurrences in the queried network suggest that the latter (and, as such, the corresponding organism) features the function encoded by the former. The aim of this paper is that of analyzing and comparing tools devised to query biological networks. This analysis is intended to help in understanding problems and research issues, state of the art and opportunities for researchers working in this area.

Asymmetric comparison and querying of biological networks.

Comparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to guide the alignment process to the second input network (called Slave), so that generated results preferably retain the structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite automaton, called alignment model, which is then fed with (a linearization of) the Slave for the purpose of extracting, via the Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying, and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant.

A technique to search for functional similarities in protein-protein interaction networks.

We describe a method to search for similarities across protein-protein interaction networks of different organisms. The technique core consists in computing a maximum weight matching of bipartite graphs resulting from comparing the neighbourhoods of proteins belonging to different networks. Both quantitative and reliability information are exploited. We tested the method on the networks of S. cerevisiae, D. melanogaster and C. elegans. The experiments showed that the technique is able to detect functional orthologs when the sole sequence similarity does not prove itself sufficient. They also demonstrated the capability of our approach in discovering common biological processes involving uncharacterised proteins.

CooPPS: a system for the cooperative prediction of protein structures.

Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.