Pierangelo Veltri

The XML web: a first study

Although originally designed for large-scale electronic publishing, XML plays an increasingly important role in the exchange of data on the Web. In fact, it is expected that XML will become the lingua franca of the Web, eventually replacing HTML. Not surprisingly, there has been a great deal of interest on XML both in industry and in academia. Nevertheless, to date no comprehensive study on the XML Web (i.e., the subset of the Web made of XML documents only) nor on its contents has been made. This paper is the first attempt at describing the XML Web and the documents contained in it. Our results are drawn from a sample of a repository of the publicly available XML documents on the Web, consisting of about 200,000 documents. Our results show that, despite its short history, XML already permeates the Web, both in terms of generic domains and geographically. Also, our results about the contents of the XML Web provide valuable input for the design of algorithms, tools and systems that use XML in one form or another.

Views in a Large Scale XML Repository

Abstract. We are interested in defining and querying views in a huge and highly heterogeneous XML repository (Web scale). In this context, view definitions are very large, involving lots of sources, and there is no apparent limitation to their size. This raises interesting problems that we address in the paper: (i) how to distribute views over several machines without having a negative impact on the query translation process; (ii) how to quickly select the relevant part of a view given a query; (iii) how to minimize the cost of communicating potentially large queries to the machines where they will be evaluated. The solution that we propose is based on a simple view definition language that allows for automatic generation of views. The language maps paths in the view abstract DTD to paths in the concrete source DTDs. It enables a distributed implementation of the view system that is scalable both in terms of data and load. In particular, the query translation algorithm is shown to have a good (linear) complexity.

Studying the XML Web: Gathering Statistics from an XML Sample

XML has emerged as the language for exchanging data on the web and has attracted considerable interest both in industry and in academia. Nevertheless, to date, little is known about the XML documents published on the web. This paper presents a comprehensive analysis of a sample of about 200,000 XML documents on the web, and is the first study of its kind. We study the distribution of XML documents across the web in several ways; moreover, we provided a detailed characterization of the structure of real XML documents. Our results provide valuable input to the design of algorithms, tools and systems that use XML in one form or another.

A peroxisome proliferator-activated receptor gamma (PPARG) polymorphism is associated with zoledronic acid-related osteonecrosis of the jaw in multiple myeloma patients: analysis by DMET microarray profiling

The aminobisphosphonate zoledronic acid (ZA) is the most important antiresorptive agent for the treatment of multiple myeloma (MM)-related bone disease (BD). Osteonecrosis of the jaw (ONJ) is an important complication of ZA-treated MM patients (Vannucchi et al, 2005; Filleul et al, 2010). So far, the mechanism of ONJ pathogenesis has not been clearly elucidated. Recently, a genetic susceptibility to ONJ has been suggested and a polymorphism of the cytochrome P450 CYP2C8 has been associated with ZA-related ONJ in MM (Sarasquete et al, 2008). To further investigate the genetic bases of ONJ, we genotyped in a case-control study a cohort of 19 MM patients treated with ZA who developed [nine cases, median age 66 years (range: 63–79)] or not [10 matched controls, median age 69 years (range: 63–84)] ONJ. We used the novel Affymetrix DMET™ plus platform (Affymetrix, Santa Clara, CA, USA), which interrogates 1936 genetic variations in 225 genes associated with phase I–II drug metabolism, disposition and transport (Deeken, 2009). The study protocol was approved by our University Hospital Bioethical Committee and informed consent was obtained from each patient. All patients received ZA according to the conventional dose and administration schedule; the ONJ group received 20 ± standard deviation (SD) 5·1 treatment courses and the control group underwent 15·1 ± 4·2 courses. MM patients were homogeneous on clinical and pathological characteristics at diagnosis and on their response to treatment. ONJ was diagnosed by clinical examination and imaging, including radiographs and/or computed tomography or magnetic resonance imaging. Peripheral blood was collected and used for DNA extraction. Genotypes were determined for each single nucleotide polymorphism (SNP) site of the 1931 of all interrogated SNPs and for the five Copy Number Variations (CNVs) included in DMET™ Plus GeneChip. Pharmacogenomic profiles were generated by Affymetrix DMET™ Console software®. Statistical analysis was performed by two-tailed Fisher’s exact test. No correction for multiple comparisons was performed. Results are therefore to be interpreted as hypothesis generating. Eight SNPs were significantly (P ≤ 0·05) associated with ONJ occurrence. Table I shows these SNPs, the reference and variant allele and the genotype and allele frequencies. All alleles were in Hardy-Weinberg equilibrium. The four genes correlated to the eight statistically relevant SNPs were PPARG (peroxisome proliferator-activated receptor gamma), ABP1{amiloride binding protein 1 [amine oxidase (copper-containing)]}, CHST11 [carbohydrate (chondroitin 4) sulfotransferase 11] and CROT (carnitine O-octanoyltransferase). The different distribution of SNP alleles and genotypes between ONJ patients and control cases are reported in Table II. The SNP rs1152003, mapping in PPARG, showed the strongest association with ONJ. We detected a highly significant (P = 0·0055) differential occurrence of the C/C homozygous (HOM) genotype in 77·7% of ONJ cases (7/9) versus only 10% of controls (1/10) (Fig 1A). Moreover, homozygous and heterozygous genotypes for the C variant were differently distributed between ONJ patients and the control group (Table II). The frequency of the C variant allele in the PPARG SNP underlines a highly significant association of the C allele with the ONJ group (P = 0·0064, Table II). No clinical association has been previously reported for these SNPs. Table I SNP polymorphisms associated with ONJ in MM patients. Table II Allele and genotype frequencies of polymorphisms in MM patients. Fig 1 SNP rs1152003 genotype clustering of MM patients. (A) The red colour symbols are MM patients with ONJ. Blue colour symbols are matched MM control patients. Genotypes are identified as homozygote reference allele , heterozygote (•) and homozygote ... Direct nucleotide sequencing was carried out on patient specimens to further confirm the presence of genetic variations, using an Applied Biosystems ABI 3100 Genetic Analyser. We found a concordance rate of 100% between DMET genotyping and sequence analysis (Fig 1B). The rs1152003 SNP maps in the 3′UTR region of PPARG, at position 12477055 of chromosome 3 (Genome Build 37.1). Although no clinical correlation has been reported for the rs1152003 variant, polymorphisms in PPARG have been associated with increased risk of a variety of diseases (Dallongeville et al, 2009). PPARG is located in the human chromosome 3, band 3p25. Chromosomal abnormalities, such as 3p deletion, have been identified in several hematologic malignancies. PPARG is involved in adipocyte differentiation and in angiogenesis (Rosen & Spiegelman, 2001). Recently, the PPARG pathway has been recognized as key mechanism for bone remodelling. It acts on mesenchymal stem cell differentiation by increasing adipogenesis but also inhibiting osteoblast and osteoclast formation. Moreover, PPARG polymorphisms correlate with the bone mass density (Ackert-Bicknell et al, 2008). However, a recent study on a wide cohort of Korean individuals, with idiopathic, steroid-induced or alcohol-induced osteonecrosis of the femoral head, failed to demonstrate a significant correlation with three common PPARG polymorphisms (Kim et al, 2007). Interestingly, modulation of PPARG activity within the bone marrow microenvironment has been recently shown to interfere with cytokines such as IL6, which is involved with a central role in the pathogenesis of MM (Wang et al, 2004), suggesting also that PPARG may represent a valuable therapeutic target in MM (Garcia-Bates et al, 2008). The present study also showed that three SNPs identified in ABP1 were associated with ONJ and were in linkage disequilibrium (data not shown). ABP1 encodes a membrane glycoprotein that is expressed in many epithelial and haematopoietic tissues. Moreover, a further three ONJ-associated SNPs map to CHST11, which was recently described as a factor required for proper chondroitin sulfation and cartilage morphogenesis. Expression of the chondroitin sulfotransferase genes is crucial for the correct mammalian bone morphogenesis. Finally, the ONJ-associated rs2097937 maps to CROT, whose protein is involved in the trans-esterification of acyl-CoA molecules. Our findings indicate that genetic polymorphisms are involved in the pathogenesis of ONJ in MM patients. The highly significant association of ONJ with the rs1152003 SNP polymorphism in PPARG strongly suggests this genetic variant as candidate biomarker for the identification of MM patients at risk of ONJ if treated with ZA. In fact, the C/C genotype demonstrated an odds ratio of 31·5 (95% confidence interval, 2·35–422·32) for developing ONJ following ZA treatment. Differently from the recent report (Sarasquete et al, 2008), where the study was based on the 500K Affymetrix high density array, we used the DMET platform that interrogates only highly selective SNPs associated with drug toxicity and has the advantage of avoiding an extremely high number of comparisons, which requires statistical corrections and large patient cohorts. We propose the rs1152003 C/C genotype as a candidate genetic biomarker for ONJ, which warrants validation in larger series.

Adult cardiac stem cells are multipotent and robustly myogenic: c-kit expression is necessary but not sufficient for their identification

Multipotent adult resident cardiac stem cells (CSCs) were first identified by the expression of c-kit, the stem cell factor receptor. However, in the adult myocardium c-kit alone cannot distinguish CSCs from other c-kit-expressing (c-kitpos) cells. The adult heart indeed contains a heterogeneous mixture of c-kitpos cells, mainly composed of mast and endothelial/progenitor cells. This heterogeneity of cardiac c-kitpos cells has generated confusion and controversy about the existence and role of CSCs in the adult heart. Here, to unravel CSC identity within the heterogeneous c-kit-expressing cardiac cell population, c-kitpos cardiac cells were separated through CD45-positive or -negative sorting followed by c-kitpos sorting. The blood/endothelial lineage-committed (Lineagepos) CD45posc-kitpos cardiac cells were compared to CD45neg(Lineageneg/Linneg) c-kitpos cardiac cells for stemness and myogenic properties in vitro and in vivo. The majority (~90%) of the resident c-kitpos cardiac cells are blood/endothelial lineage-committed CD45posCD31posc-kitpos cells. In contrast, the LinnegCD45negc-kitpos cardiac cell cohort, which represents ⩽10% of the total c-kitpos cells, contain all the cardiac cells with the properties of adult multipotent CSCs. These characteristics are absent from the c-kitneg and the blood/endothelial lineage-committed c-kitpos cardiac cells. Single Linnegc-kitpos cell-derived clones, which represent only 1–2% of total c-kitpos myocardial cells, when stimulated with TGF-β/Wnt molecules, acquire full transcriptome and protein expression, sarcomere organisation, spontaneous contraction and electrophysiological properties of differentiated cardiomyocytes (CMs). Genetically tagged cloned progeny of one Linnegc-kitpos cell when injected into the infarcted myocardium, results in significant regeneration of new CMs, arterioles and capillaries, derived from the injected cells. The CSC’s myogenic regenerative capacity is dependent on commitment to the CM lineage through activation of the SMAD2 pathway. Such regeneration was not apparent when blood/endothelial lineage-committed c-kitpos cardiac cells were injected. Thus, among the cardiac c-kitpos cell cohort only a very small fraction has the phenotype and the differentiation/regenerative potential characteristics of true multipotent CSCs.

Algorithms and tools for analysis and management of mass spectrometry data

Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed; (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data; (iii) databases querying to identify peptides; and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics.

A time series approach for clustering mass spectrometry data

Abstract Advanced statistical techniques and data mining methods have been recognized as a powerful support for mass spectrometry (MS) data analysis. Particularly, due to its unsupervised learning nature, data clustering methods have attracted increasing interest for exploring, identifying, and discriminating pathological cases from MS clinical samples. Supporting biomarker discovery in protein profiles has drawn special attention from biologists and clinicians. However, the huge amount of information contained in a single sample, that is, the high-dimensionality of MS data makes the effective identification of biomarkers a challenging problem. In this paper, we present a data mining approach for the analysis of MS data, in which the mining phase is developed as a task of clustering of MS data. Under the natural assumption of modeling MS data as time series, we propose a new representation model of MS data which allows for significantly reducing the high-dimensionality of such data, while preserving the relevant features. Besides the reduction of high-dimensionality (which typically affects effectiveness and efficiency of computational methods), the proposed representation model of MS data also alleviates the critical task of preprocessing the raw spectra in the whole process of MS data analysis. We evaluated our MS data clustering approach to publicly available proteomic datasets, and experimental results have shown the effectiveness of the proposed approach that can be used to aid clinicians in studying and formulating diagnosis of pathological states.

The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments

BackgroundIsotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.ResultsWe designed a method for improving the data processing and peptide identification in sample sets subjected to ICAT labeling and LC-MS/MS analysis, based on cross validating MS/MS results. Such a method has been implemented in a tool, called EIPeptiDi, which boosts the ICAT data analysis software improving peptide identification throughout the input data set. Heavy/Light (H/L) pairs quantified but not identified by the MS/MS routine, are assigned to peptide sequences identified in other samples, by using similarity criteria based on chromatographic retention time and Heavy/Light mass attributes. EIPeptiDi significantly improves the number of identified peptides per sample, proving that the proposed method has a considerable impact on the protein identification process and, consequently, on the amount of potentially critical information in clinical studies. The EIPeptiDi tool is available at http://bioingegneria.unicz.it/~veltri/projects/eipeptidi/ with a demo data set.ConclusionEIPeptiDi significantly increases the number of peptides identified and quantified in analyzed samples, thus reducing the number of unassigned H/L pairs and allowing a better comparative analysis of sample data sets.

A framework for the atrial fibrillation prediction in electrophysiological studies

BACKGROUND AND OBJECTIVE Cardiac arrhythmias are disorders in terms of speed or rhythm in the hearts electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures. METHODS We gathered data signals collected by a General Electric Healthcares CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation. RESULTS A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS.

Algorithms and databases in bioinformatics: towards a proteomic ontology

Classification of bioinformatics tools and databases, as well as modelling of biological processes, can help the design of complex in silico experiments. After presenting a survey of bioinformatics algorithms and biological databases, we describe a first design of an ontology for the Proteomics domain. The ontology can be used either to better understand biological processes or to enhance design of distributed bioinformatics applications.