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The current COVID-19 pandemic presents unprecedented new challenges to public health and medical care delivery. To control viral transmission, social distancing measures have been implemented all over the world, interrupting the access to routine medical care for many individuals with neurological diseases. Cognitive disorders are common in many neurological conditions, e.g., stroke, traumatic brain injury, Alzheimer's disease, and other types of dementia, Parkinson's disease and parkinsonian syndromes, and multiple sclerosis, and should be addressed by cognitive rehabilitation interventions. To be effective, cognitive rehabilitation programs must be intensive and prolonged over time; however, the current virus containment measures are hampering their implementation. Moreover, the reduced access to cognitive rehabilitation might worsen the relationship between the patient and the healthcare professional. Urgent measures to address issues connected to COVID-19 pandemic are, therefore, needed. Remote communication technologies are increasingly regarded as potential effective options to support health care interventions, including neurorehabilitation and cognitive rehabilitation. Among them, telemedicine, virtual reality, augmented reality, and serious games could be in the forefront of these efforts. We will briefly review current evidence-based recommendations on the efficacy of cognitive rehabilitation and offer a perspective on the role of tele- and virtual rehabilitation to achieve adequate cognitive stimulation in the era of social distancing related to COVID-19 pandemic. In particular, we will discuss issues related to their diffusion and propose a roadmap to address them. Methodological and technological improvements might lead to a paradigm shift to promote the delivery of cognitive rehabilitation to people with reduced mobility and in remote regions.

Background Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with an overall 5-year survival rate of less than 8%. New evidence indicates that PDAC cells release pro-inflammatory metabolites that induce a marked alteration of normal hematopoiesis, favoring the expansion and accumulation of myeloid-derived suppressor cells (MDSCs). We report here that PDAC patients show increased levels of both circulating and tumor-infiltrating MDSC-like cells. Methods The frequency of MDSC subsets in the peripheral blood was determined by flow cytometry in three independent cohorts of PDAC patients (total analyzed patients, n = 117). Frequency of circulating MDSCs was correlated with overall survival of PDAC patients. We also analyzed the frequency of tumor-infiltrating MDSC and the immune landscape in fresh biopsies. Purified myeloid cell subsets were tested in vitro for their T-cell suppressive capacity. Results Correlation with clinical data revealed that MDSC frequency was significantly associated with a shorter patients’ overall survival and metastatic disease. However, the immunosuppressive activity of purified MDSCs was detectable only in some patients and mainly limited to the monocytic subset. A transcriptome analysis of the immunosuppressive M-MDSCs highlighted a distinct gene signature in which STAT3 was crucial for monocyte re-programming. Suppressive M-MDSCs can be characterized as circulating STAT3/arginase1-expressing CD14+ cells. Conclusion MDSC analysis aids in defining the immune landscape of PDAC patients for a more appropriate diagnosis, stratification and treatment.

Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing overfitting risk netDx is a machine learning method to integrate multi-modal patient data and build a patient classifier. Patient data are converted into networks of patient similarity, which is intuitive to clinicians who also use patient similarity for medical diagnosis. Features passing selection are integrated, and new patients are assigned to the class with the greatest profile similarity. netDx has excellent performance, outperforming most machine-learning methods in binary cancer survival prediction. It handles missing data – a common problem in real-world data – without requiring imputation. netDx also has excellent interpretability, with native support to group genes into pathways for mechanistic insight into predictive features. The netDx Bioconductor package provides multiple workflows for users to build custom patient classifiers. It provides turnkey functions for one-step predictor generation from multi-modal data, including feature selection over multiple train/test data splits. Workflows offer versatility with custom feature design, choice of similarity metric; speed is improved by parallel execution. Built-in functions and examples allow users to compute model performance metrics such as AUROC, AUPR, and accuracy. netDx uses RCy3 to visualize top-scoring pathways and the final integrated patient network in Cytoscape. Advanced users can build more complex predictor designs with functional building blocks used in the default design. Finally, the netDx Bioconductor package provides a novel workflow for pathway-based patient classification from sparse genetic data.

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was “Methods, tools and platforms for Personalised Medicine in the Big Data Era”, but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

Software applications for biological networks analysis rely on graphs to model the structure interactions. A great part of them requires searching for subgraphs in a target graph or in collections of graphs. Even though very efficient algorithms have been defined to solve such a subgraph isomorphisms problem, the complexity of current real biological networks make their sequential execution time prohibitive. On the other hand, parallel architectures, from multi-core to manycore, have become pervasive to deal with the problem of the data size. Nevertheless, the sequential nature of the graph searching algorithms makes their implementation for parallel architectures very challenging. This paper presents three different parallel solutions for the graph searching problem. The first two target the exact search for multi-core CPUs and manycore GPUs, respectively. The third one targets the approximate search for GPUs, which handles node, edge, and node label mismatches. The paper shows how different techniques have been developed in all the solutions to reduce the search space complexity. The paper shows the performance of the proposed solutions on representative biological networks containing antiviral chemical compounds and protein interactions networks.

The amount of biological data is increasing and their analysis is becoming one of the most challenging topics in the information sciences. Before starting the analysis it is important to remove unwanted variability due to some factors such as: year of sequencing, laboratory conditions and use of different protocols. This is a crucial step because if the variability is not evaluated before starting the analysis of interest, the results may be undesirable and the conclusion can not be true. The literature suggests to use some valid mathematical models, but experience shows that applying these to high-throughput data with a non-uniform study design is not straightforward and in many cases it may introduce a false signal. Therefore it is necessary to develop models that allow to remove the effects that can negatively influence the study preserving biological meaning. In this paper we report a new case study related lymphoma methylation data and we propose a suitable pipeline for its analysis.

Background and purpose While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD. Methods Amyloid PET results using a new processing imaging algorithm (MRI-Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave-one-out cross-validation was carried out to validate the results. Results Forty-four consecutive patients with clinical evidence of dementia, were retrospectively evaluated. Amyloid PET scan was positive in 26/44 patients with dementia. After integration with 18F-FDG PET, clinical data and CSF protein levels, 22 of them were classified as AD, the remaining 4 as vascular or frontotemporal dementia. Amyloid and FDG PET, CDR 1, CSF Tau, and p-tau levels showed the best true positive and true negative rates (amyloid PET: AUC = .85, sensitivity .91, specificity .79). A SUVR value of 1.006 in the inferior frontal cortex and of 1.03 in the precuneus region was the best cutoff SUVR value and showed a good correlation with the diagnosis of AD. Thirteen of 44 amyloid PET positive patients have been enrolled in clinical trials using antiamyloid approaches. Conclusions Amyloid PET using SPM-normalized SUVR analysis showed high predictive power for the differential diagnosis of AD.

Purpose The regional quantification of amyloid burden is crucial for the clinical diagnosis of Alzheimer’s disease [1]. The best method to evaluate regional amyloid deposition in PET is through the use MR imaging for brain space normalization. However, since MR imaging is not always available in the clinical practice, a MR-less methodology is needed in order to compute semi-quantitative and analyze regional amyloid burden. Methods Forty-four patients with clinical evidence of dementia, underwent 18F-Florbetaben PET (FBB-PET), FDG-PET, neuropsychological assessment and cerebrospinal fluid analysis. We implemented a methodology that uses SPM12 to import and normalize the FBB-PET images in Montreal Neurological Institute (MNI) space without MRI, and the Automated Anatomical Labeling (AAL) atlas [2] in order to extract regional uptake from normalized FBB-PET. SUVR has been computed by using cerebellum as control region (‘ Cerebelum _ 4 _ 5 ’ of AAL atlas). We then computed Receiver Operating Characteristic (ROC) curve in order to find best thresholds for identifying clinical subgroups in our patients. Results Semi-quantitative evaluation of FBB-PET images and ROC analysis stated that SUVR value of 1,006 in the bilateral inferior frontal cortex and a SUVR of 1.03 in the precuneus region were the best cutoff (AUC 0.883 and 0.826, respectively). Box-Plot analysis showed a trend distribution of elevated SUVR levels in bilateral frontal cortex, angular girus, occipital, parietal, precuneus and paracentral lobule, among AD patients. Conclusion MR-less methodology based on Tissue Probability Map and AAL atlas provide regional quantification of amyloid burden. The ROC analysis is able to retrieve useful thresholds for the classification of AD versus non-AD thus providing a tool in clinical practice. We will perform the study on a larger sample in order to confirm the results.

Aim The aim of this article was to review and conduct a meta-analysis of the paediatric literature on the neurology of coeliac disease. Method We conducted a review of paediatric studies published in English assessing neurological illness in coeliac disease identified through a MEDLINE search (1950–2009). Calculation of computed relative risk, odds ratio, and risk difference was performed using the fixed effect method if applicable. Results Fifteen studies were analysed (11 772 participants). The meta-analysis showed that (1) the relative risk of epilepsy in individuals with coeliac disease, and of coeliac disease in individuals with epilepsy, compared with the general population, was 2.1 and 1.7, respectively, and the risk difference was close to zero, indicating that it was probably a chance association; and (2) the relative risk of headache in individuals with the disease compared with comparison groups was 3.2. In two studies, cerebellar ataxia was documented in 2.7 to 5.4% of participants; in two further studies, the risk of cerebellar dysfunction was zero. Two studies found an association between coeliac disease and peripheral neuropathy. Brain white matter lesions were recorded in two other studies. An association between autism and coeliac disease is disputed. Interpretation Children with coeliac disease are at risk of developing neurological complications, but the risk is lower than in adulthood. The discrepancy might be due to short disease duration, early elimination of gluten from the diet, stricter adherence to diet, or different susceptibility to immune-mediated disorders.

Boolean models are gaining an increasing interest for reproducing dynamic behaviours, understanding processes, and predicting emerging properties of cellular signalling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modelling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signalling. Even though several approaches and software have been recently proposed for logic modelling of biological systems, they are limited to specific modelling contexts and they lack of automation in analysing biological properties such as complex attractors, molecule vulnerability, dose response. This paper presents a design and verification platform based on SystemC that applies methodologies and tools well established in the electronic-design automation (EDA) field such as assertion-based verification (ABV) and mutation analysis, which allow complex attractors (i.e., protein oscillations) and robustness/sensitivity of the signalling networks to be simulated and analysed. The paper reports the results obtained by applying such verification techniques for the analysis of the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues.

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/ . Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p-values expressing the reliability of each group of predictions. Finally, It is also possible to specify a list of genes tracking down all the drugs that may have an indirect influence on them based on a multi-drug, multi-target, multi-pathway analysis, which aims to discover drugs for future follow-up studies.

Protein–protein interaction (PPI) networks available in public repositories usually represent relationships between proteins within the cell. They ignore the specific set of tissues or tumors where the interactions take place. Indeed, proteins can form tissue-selective complexes, while they remain inactive in other tissues. For these reasons, a great attention has been recently paid to tissue-specific PPI networks, in which nodes are proteins of the global PPI network whose corresponding genes are preferentially expressed in specific tissues. In this paper, we present SPECTRA, a knowledge base to build and compare tissue or tumor-specific PPI networks. SPECTRA integrates gene expression and protein interaction data from the most authoritative online repositories. We also provide tools for visualizing and comparing such networks, in order to identify the expression and interaction changes of proteins across tissues, or between the normal and pathological states of the same tissue. SPECTRA is available as a web server at http://alpha.dmi.unict.it/spectra.

Background: Vitis vinifera (Grapevine) is the most important fruit species in the modern world. Wine and table grapes sales contribute significantly to the economy of major wine producing countries. The most relevant goals in wine production concern quality and safety. In order to significantly improve the achievement of these objectives and to gain biological knowledge about cultivars, a genomic approach is the most reliable strategy. The recent grapevine genome sequencing offers the opportunity to study the potential roles of genes and microRNAs in fruit maturation and other physiological and pathological processes. Although several systems allowing the analysis of plant genomes have been reported, none of them has been designed specifically for the functional analysis of grapevine genomes of cultivars under environmental stress in connection with microRNA data. Description: Here we introduce a novel knowledge base, called BIOWINE, designed for the functional analysis of Vitis vinifera genomes of cultivars present in Sicily. The system allows the analysis of RNA-seq experiments of two different cultivars, namely Nero d'Avola and Nerello Mascalese. Samples were taken under different climatic conditions of phenological phases, diseases, and geographic locations. The BIOWINE web interface is equipped with data analysis modules for grapevine genomes. In particular users may analyze the current genome assembly together with the RNA-seq data through a customized version of GBrowse. The web interface allows users to perform gene set enrichment by exploiting third-party databases. Conclusions: BIOWINE is a knowledge base implementing a set of bioinformatics tools for the analysis of grapevine genomes. The system aims to increase our understanding of the grapevine varieties and species of Sicilian products focusing on adaptability to different climatic conditions, phenological phases, diseases, and geographic locations. This work has been partially founded by Programma Operativo Fondo Europeo per lo Sviluppo Regionale (PO-FESR 2007-2013), Linea di intervento 4.1.1.2. Grant number: CUP G23F11000840004. AP, RG, and AF were also partially supported by ``Progetto di Ricerca GNCS - INDAM 2014´´. Sí

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithm. GASOLINE starts with the identification of sets of similar nodes, called seeds of the alignment. Alignments are then extended in a greedy manner and finally refined. Both the identification of seeds and the extension of alignments are performed through an iterative Gibbs sampling strategy. GASOLINE is a Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape.

Biological applications, from genomics to ecology, deal with graphs that represents the structure of interactions. Analyzing such data requires searching for subgraphs in collections of graphs. This task is computationally expensive. Even though multicore architectures, from commodity computers to more advanced symmetric multiprocessing (SMP), offer scalable computing power, currently published software implementations for indexing and graph matching are fundamentally sequential. As a consequence, such software implementations (i) do not fully exploit available parallel computing power and (ii) they do not scale with respect to the size of graphs in the database. We present GRAPES, software for parallel searching on databases of large biological graphs. GRAPES implements a parallel version of well-established graph searching algorithms, and introduces new strategies which naturally lead to a faster parallel searching system especially for large graphs. GRAPES decomposes graphs into subcomponents that can be efficiently searched in parallel. We show the performance of GRAPES on representative biological datasets containing antiviral chemical compounds, DNA, RNA, proteins, protein contact maps and protein interactions networks.

Clustering is a widely used unsupervised data mining technique. It allows to identify structures in collections of objects by grouping them into classes, named clusters, in such a way that similarity of objects within any cluster is maximized and similarity of objects belonging to different clusters is minimized. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction driven by the density. The basic structure of density-based clustering presents some common drawbacks: (i) parameters have to be set; (ii) the behavior of the algorithm is sensitive to the density of the starting object; and (iii) adjacent clusters of different densities could not be properly identified. In this paper, we address all the above problems. Our method, based on the concept of space stratification, efficiently identifies the different densities in the dataset and, accordingly, ranks the objects of the original space. Next, it exploits such a knowledge by projecting the original data into a space with one more dimension. It performs a density based clustering taking into account the reverse-nearest-neighbor of the objects. Our method also reduces the number of input parameters by giving a guideline to set them in a suitable way. Experimental results indicate that our algorithm is able to deal with clusters of different densities and outperforms the most popular algorithms DBSCAN and OPTICS in all the standard benchmark datasets.

Motivations. Recent data coming from the comparison of genomes of different individuals in human species and of different genotypes in plants has led interesting findings about the differences among individuals, ecotypes or genotypes. Cross-species conservation analysis revealed that many of the genes potentially encoded by novel sequences are conserved across a number of mammal and might be biologically functional and thus may be related to differences in gene networks between human individuals. This strongly suggests that genetics and transcriptomics must be performed in the context of individual genomes. NGS technologies provide for the first time the opportunity to study the complexity of individual-specific sequences. However a full genome assembly still presents problems due to highly repetitive sequences which cannot be easily solved with current technologies. Methods. The first step in our workflow is de novo assembly based on de bruijn graph assembly plus an error detection and correction step based on comparison with datasets of annotated proteins. This has been implemented in order to overcome limitations of current assembly methods which rely uniquely on sequence data and thus they do not prevent frameshift or overassembly errors. The platform determines if the new genes and transcript isoforms are potentially functional and if mutations disrupting the functionality of the original gene models present in the reference genome are compensated by the new isoform. Those data are integrated and linked to expression profiles, annotation functions and network data. This allows determining if metabolic pathways are affected or modified by the expression of transcripts alternative to those expressed in the reference genotype or by the expression of novel genes. On the algorithmic viewpoint, innovative approaches contributing to efficiently carry out the comparison of reconstructed transcriptomes with reference genome and quantify the transcriptome and proteome diversity will be proposed based on: (i) Machine learning techniques to genome reassembling; (ii) Functional enrichment based on non parametric statistical tests; (iii) Gene similarity based on common miRNA targeting and RNA editing function; (iv) Probabilistic generative models for network analysis. On the computational viewpoint, we propose an innovative infrastructure, based on grid/cloud computing and efficient intra-node accelerators (i.e., GP-GPUs and FPGAs). Since complex analysis pipeline made of several stages are characterized by heterogeneous computational requirements, we developed a middleware infrastructure where specific schedulers and task migration agents will orchestrate task allocation both across nodes and within nodes. The orchestration will be performed by matching application computational kernels characteristics (obtained through off-line profiling) with computational capabilities of nodes. Moreover, since transcriptome reconstruction requires the capability of processing many biological samples for statistical and comparative reasons and current frameworks are not optimized for multi-sample analysis, rather they run various samples sequentially, we designed techniques for efficient sample-level allocation on computational nodes. See Figure 1 for a description of the platform. Results. The solution we propose here improves the existing solutions in the following two directions. First, efficient algorithms are applied for genome reconstruction and identification. Second, these algorithms are implemented in an pipeline analysis framework, where the processing of multiple samples is optimized to better exploit computational resources. The infrastructure makes possible for bioinformaticians, through a web service interface, to build workflows and execute them on a grid/cloud computing platform in a easy to use and programming-friendly environment.

The Annual Meeting of the Bioinformatics Italian Society (BITS) is an important event for scientists and professionals working in the field of Bioinformatics or using the software tools developed within this scientific discipline. The meeting provides a thorough view of how and in which directions the Italian scientific community is investing financial and human resources, and an opportunity to establish and strengthen scientific collaborations.

In this paper, we propose a protocol for dynamic reconfiguration of ad-hoc wireless networks with movable base stations in presence of obstacles. Hosts are assigned to base stations according to a probabilistic throughput function based on both the quality of the signal and the base station load. In order to optimize space coverage, base stations cluster hosts using a distributed clustering algorithm. Obstacles may interfere with transmission and obstruct base stations and hosts movement. To overcome this problem, we perform base stations repositioning making use of a motion planning algorithm on the visibility graph based on an extension of the bottleneck matching technique. We implemented the protocol on top of the NS2 simulator as an extension of the AODV. We tested it using both Random Way Point and Reference Point Group mobility models properly adapted to deal with obstacles. Experimental analysis shows that the protocol ensures the total space coverage together with a good throughput on the realistic model (Reference Point Group) outperforming both the standard AODV and DSR.

Advanced research requires intensive interaction among a multitude of actors, often possessing different expertise and usually working at a distance from each other. The field of collaborative research aims to establish suitable models and technologies to properly support these interactions. In this article, we first present the reasons for an interest of Bioinformatics in this context by also suggesting some research domains that could benefit from collaborative research. We then review the principles and some of the most relevant applications of social networking, with a special attention to networks supporting scientific collaboration, by also highlighting some critical issues, such as identification of users and standardization of formats. We then introduce some systems for collaborative document creation, including wiki systems and tools for ontology development, and review some of the most interesting biological wikis. We also review the principles of Collaborative Development Environments for software and show some examples in Bioinformatics. Finally, we present the principles and some examples of Learning Management Systems. In conclusion, we try to devise some of the goals to be achieved in the short term for the exploitation of these technologies.

Clustering is a widely used unsupervised data mining technique. In density-based clustering, a cluster is defined as a connected dense component and grows in the direction set by the density. In this paper we present a software system called DBStrata that implements the density-based clustering architecture together with several extensions able to boost the clustering performances and to efficiently identify outliers.

Nowadays, business process reuse is very important and necessary inside large organizations that continually increase their process collections. Therefore, an efficient system to manage and search for concrete and relevant processes is necessary. Here we overcome to this problem proposing business process model retrieval based on a Graph Indexing Method. It takes into account a measure similarity between two graphs and provides a ranking of business process retrieved.

Background: There are only a few series in the literature on acute disseminated encephalomyelitis (ADEM) in children. Objectives and Methods: the aims of this study were to perform (i) a prospective clinical/imaging study (1992-2009) on ADEM in children consecutively referred to our institution in Catania, Italy, and (ii) to undertake a systematic review and meta-analysis of published ADEM pediatric cohorts (> 10 cases). Results: We identified 17 patients with ADEM (incidence < 10 years of age = 1.1 per 100 000 person-years). 15 previously published cohorts were compared with our cohort: (i) systematically reviewed (750 cases); and (ii) meta-analyzed (492/750 cases). The 17 patients had the following characteristics: (a) male-to-female ratio, 1.4 (vs. 1.2-1.3 in previous cohorts); (b) mean age at presentation, 3.6 years (vs. 7.1 years in previous cohorts); (c) specific preceding triggering factor, 88 % (vs. 69-79 % in previous cohorts); (d) the most common initial signs were ataxia, seizures, headache, and thalamic syndrome; (e) brain imaging revealed > 3 lesions in 100 % (vs. 92 % in previous cohorts); (f) the outcome was good in 94 % (vs. 70-75 % in previous cohorts); and (g) 12 % relapsed once (vs. 18 % in previous cohorts). Conclusions: ADEM is generally a benign condition that mosly affects boys more than girls and rarely recurs.

miRo is a web-based knowledge base that provides users with miRNA–phenotype associations in humans. It integrates data from various online sources, such as databases of miRNAs, ontologies, diseases and targets, into a unified database equipped with an intuitive and flexible query interface and data mining facilities. The main goal of miRo is the establishment of a knowledge base which allows non-trivial analysis through sophisticated mining techniques and the introduction of a new layer of associations between genes and phenotypes inferred based on miRNAs annotations. Furthermore, a specificity function applied to validated data highlights the most significant associations. The miRo web site is available at: http://ferrolab.dmi.unict.it/miro. Database URL: http://ferrolab.dmi.unict.it/miro

Summary: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. Availability: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch Contact: ferro@dmi.unict.it

We present NetMatchStar, a Cytoscape app to find all the occurrences of a query graph in a network and check for its significance as a motif with respect to seven different random models. The query can be uploaded or built from scratch using Cytoscape facilities. The app significantly enhances the previous NetMatch in style, performance and functionality. Notably NetMatchStar allows queries with wildcards.

Due to their ballistic precision, apoptosis induction by protons could be a strategy to specifically eliminate neoplastic cells. To characterize the cellular and molecular effects of these hadrons, we performed dose-response and time-course experiments by exposing different cell lines (PC3, Ca301D, MCF7) to increasing doses of protons and examining them with FACS, RT-PCR, and electron spin resonance (ESR). Irradiation with a dose of 10 Gy of a 26,7 Mev proton beam altered cell structures such as membranes, caused DNA double strand breaks, and significantly increased intracellular levels of hydroxyl ions, are active oxygen species (ROS). This modified the transcriptome of irradiated cells, activated the mitochondrial (intrinsic) pathway of apoptosis, and resulted in cycle arrest at the G2/M boundary. The number of necrotic cells within the irradiated cell population did not significantly increase with respect to the controls. The effects of irradiation with 20 Gy were qualitatively as well as quantitatively similar, but exposure to 40 Gy caused massive necrosis. Similar experiments with photons demonstrated that they induce apoptosis in a significantly lower number of cells and in a temporally delayed manner. These data advance our knowledge on the cellular and molecular effects of proton irradiation and could be useful for improving current hadrontherapy protocols.

Ontologies play a central role in the development of the semantic web, as they provide precise definitions of shared terms in web resources. One important web ontology language is DAML+OIL; it has a formal semantics and a reasoning support through a mapping to the expressive description logic SHOQ(D) with the addition of inverse roles. In this paper, we present a probabilistic extension of SHOQ(D), called P-SHOQ(D), to allow for dealing with probabilistic ontologies in the semantic web. The description logic P-SHOQ(D) is based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning. It allows to express rich probabilistic knowledge about concepts and instances, as well as default knowledge about concepts. We also present sound and complete reasoning techniques for P-SHOQ(D), which are based on reductions to classical reasoning in SHOQ(D) and to linear programming, and which show in particular that reasoning in P-SHOQ(D) is decidable.