I progek di ricerca. Associazioni di ricerca * I progek infrastru

Dimensione: px
Iniziare la visualizzazioe della pagina:

Download "I progek di ricerca. Associazioni di ricerca * I progek infrastru

Transcript

1 Doing scien0fic research: context and guidelines Nicole<a Dessì 8 /4/2014 Il contesto opera0vo La ricerca Associazioni di ricerca * I progek di ricerca Sono gli strumen0 per finanziare la ricerca - Europei Molto complessi e difficili. Coivolgono partners di 2 o 3 nazioni. - Nazionali PRIN Unità base + unità locali (annuale) FIRB- Propos0 da giovani ricercatori - Locali Università (CAR, annuale, riservato ai ricercatori akvi) Regione Sardegna (annuali) * Esempio per l informa0ca I progek di ricerca Prevedono una traccia ar0colata delle ricerche da svolgere da parte di una o piu unità (che includono i do<orandi) coordinate da un responsabile. Hanno un piano finanziario che puo prevedere l acquisizione di: - Apparecchiature - Personale (ricercatori a tempo, assegnis0, borsis&, do<orandi,contrak) - Rimborso spese missioni, congressi etc I progek infrastru<urali Sono gli strumen0 per finanziare l acquisizione di apparecchiature o la realizzazione di specifici servizi. Non includono borse o assegni per la ricerca, ma contrak. Non mirano alla produzione scien0fica, ma alla realizzazione di un obiekvo 1

2 I progek di ricerca Il Dipar0mento e, gerarchicamente anche l Ateneo dal MIUR, viene valutato e anche finanziato sulla base dei fondi per la ricerca che ha avuto la capacità di acquisire. I progek sono giudica0 da almeno due revisori anonimi qualifica0, a volte anche non italiani. Come si conduce una ricerca (1) Passo per passo con piccoli avanzamen0 rispe<o a quello che finora si è fa<o i quel se<ore (Stato dell arte). Quasi sempre le idee nuove vengono leggendo i lavori già fak. Con0nuo up- date degli argomen0. Ad esempio, in informa0ca, un lavoro di due anni prima puo essere già vecchio, a meno che non si trak di una pietra miliare del se<ore. I Come si conduce una ricerca (2) ACQUISIRE LO STATO DELL ARTE Significa avere un quadro di riferimento del progredire delle ricerche in un se<ore. I lavori di rassegna inquadrano il problema e fanno risparmiare tempo. Serve a valutare quanto e come inves0re in un argomento di ricerca (individuazione problemi aper0). Tenere traccia di quanto acquisito perché u0le per le future pubblicazioni Come si conduce una ricerca (3) Sono necessarie almeno 3 componen0: - L idea base innova0va rispe<o allo stato dell arte - La verifica o realizzazione proto0pale (fakbilità di tale idea) - La vendita (pubblicazione dei risulta0) di tale idea Le aree scien0fico disciplinari 01 Matema0ca e Informa0ca 02- Scienze Fisiche 03- Scienze Chimiche 04- Scienze della Terra 05- Scienze Biologiche 06- Scienze Mediche 07- Agraria e Veterinaria Ingegneria Civile e Archite<ura 09- Ingegneria Industriale e dell Informazione 10- Scienze dell an0chità, filologico le<erarie e storico ar0s0che 11- Scienze storiche, filosofiche,pedagogiche e psicologiche 12- Scienze Giuridiche 13- Scienze Economiche e Sta0s0che 14- Scienze Poli0che e Sociali Classificazione delle aree rispe<o alla ricerca Aree NON Bibliometriche : Aree Bibliometriche :

3 Aree Bibliometriche Aree NON Bibliometriche 01 Matema0ca e Informa0ca 02- Scienze Fisiche 03- Scienze Chimiche 04- Scienze della Terra 05- Scienze Biologiche 06- Scienze Mediche 07- Agraria e Veterinaria 09- Ingegneria Industriale e dell Informazione 08 Ingegneria Civile e Archite<ura 10- Scienze dell an0chità, filologico le<erarie e storico ar0s0che 11- Scienze storiche, filosofiche,pedagogiche e psicologiche 12- Scienze Giuridiche 13- Scienze Economiche e Sta0s0che 14- Scienze Poli0che e Sociali Diversificazione della produzione scien0fica (cosa conta) Aree NON bibliometriche a)numero di libri (dota0 di ISBN) b) numero di ar0coli su rivista e di capitoli su libro (con ISBN) c) Numero di ar0coli su riviste appartenen0 alla classe A. h<p://www.anvur.org/index.php? op0on=com_content&view=ar0cle&id=254&itemid=315&lang=it Diversificazione della produzione scien0fica (cosa conta) Aree NON bibliometriche - La produzione scien0fica non è sogge<a a revisione di esper0 (tranne che per le riviste) - L autore delle monografie è spesso l editore delle stesse. - Non si valuta il livello di diffusione del prodo<o scien0fico. Pubblicazioni PREVALENTEMENTE in ITALIANO Diversificazione della produzione scien0fica (cosa conta) Aree Bibliometriche Numero di ar0coli su riviste contenute nelle principali banche da0 internazionali (ISI e SCOPUS) Numero totale di citazioni ricevute riferite alla produzione scien0fica complessiva ed all età accademica H- index (Indice di Hirsch contemporaneo) Un autore ha H index N se N sue pubblicazioni hanno ricevuto N citazioni. Diversificazione della produzione scien0fica (cosa conta) Aree Bibliometriche - La produzione scien0fica è sogge<a a revisione di esper0 (Peer Review) - L editore ed i revisori possono rifiutare la pubblicazione. - Si valuta il livello di diffusione del prodo<o scien0fico Pubblicazioni ESCLUSIVAMENTE in INGLESE 3

4 Tipologia di una pubblicazione - A<o di Congresso Da 6 a 14 pagine (limite definito dalla call del congresso, Per le riviste non esiste limite) Comprende: TITOLO AUTORI (in ordine alfabe0co per Mat e INF, in ordine di Importanza per Bio e Med (principal inves0gator etc ) ABSTRACT Parole chiave (Keywords) Tipologia di una pubblicazione - A<o di Congresso rispe<a un iter specifico: - Call For Papers e Important Dates (diffusione ele<ronica) - Invio Lavoro (in forma ele<ronica es. EASY CHAIR) - Peer Review - Comunicazione Giudizio revisori (acce<azione/rifiuto) - Recepire modifiche suggerite dai revisori - Iscrizione a Congresso - Presentazione (15 in inglese) - Pubblicazione dei Proceedings Assicurarsi che il Congresso sia citato su ISI/Scopus/ A<o di Congresso - A<o di Congresso In Matema0ca,Fisica,Chimica e alcuni se<ori Bio i lavori presenta0 ai congressi hanno importanza trascurabile. In Informa0ca, alcuni Congressi sono considera0 della stessa importanza dei lavori su rivista, specie quelli che compaiono in collane (esempio LNCS,Lecture Notes In Computer Science) o in Congressi che si svolgono da mol0 anni (VLDB,DEXA etc ) All interno dei Congressi si tengono workshops su argomen0 specifici. Assicurarsi che i lavori allo workshop siano pubblica0 nei Proceedings del Congresso e non a parte. Ar0colo su Rivista - Ar0colo su rivista E un ar0colo che presenta un lavoro completo, a volte una extended version di un lavoro presentato ad un congresso - Invio Lavoro - Comunicazione Giudizio revisori (acce<azione con modifiche/rifiuto); ci me<e anche 1 anno o piu. - Inserimento modifiche e nuova revisione - Proofs e pubblicazione Assicurarsi la rivista su ISI/Scopus e valutarne l importanza (es.quar0le SCIMAGO ovvero elenchi specifici di se<ore) Tipologia di una pubblicazione - LIBRI (argomen0 di ricerca, con ISBN) - ScriK da un solo autore (rari, monografie) - Raccolta di ar0coli di autori diversi effe<uata da uno o piu editors. H- index Esempio. L autore X ha H- index 3 se almeno 3 delle sue pubblicazioni sono citate ciascuna 3 volte. Problema del conteggio delle cita0ons. Normalizzazione rispe<o all età accademica (la prima pubblicazione) Le cita0ons sono un parametro di valutazione concorsuale. 4

5 Stru<ura di una pubblicazione (1) Una pubblicazione ha la seguente stru<ura standard: Stru<ura di una pubblicazione - Titolo e autori con affiliazione - Abstract (breve, che riassume il lavoro) - Introduc0on - Related Work - Sec0on 1. - Sec0on 2. - Conclusions /Future work - Aknowledgements - References Titolo,Autori Abstract esempio.. BioCloud Search EnGene: Surfing Biological Data on the Cloud Nicole<a Dessì, Emanuele Pascariello, Gabriele Milia, Barbara Pes Università degli Studi di Cagliari, Dipar0mento di Matema0ca e Informa0ca, Via Ospedale 72, Cagliari, Italy Abstract. The massive produc0on and spread of biomedical data around the web introduces new challenges related to iden0fy computa0onal approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud applica0on that facilitates searching. Keywords: Biomedical data explora0on, Cloud compu0ng, Data searching, Data integra0on, Dataspaces, Pay- as- you- go data querying. Stru<ura di una pubblicazione (2) - Introduc0on Presenta l inquadramento del lavoro, cioè cosa è stato fa<o in precedenza(, le mo0vazioni e gli aspek innova0vi del lavoro che si presenta e in che cosa si differenzia dai preceden0. Termina con una brevissima sintesi su come il lavoro è stru<urato. 1 IntroducGon The massive produc0on and spread of biomedical data around the web introduces new challenges related to iden0fy computa0onal approaches for their management and exploita0on. These challenges mainly result from three issues: - Biomedical data are typical of the category of big data [1]. The term big data refers to the ever increasing amount of informa0on that organiza0ons are storing, processing and analyzing, owning the growing number of informa0on sources in use [2]. Fine dell introduzione.. The paper is organized as follows. Sec0on 2 provides background concepts and mo0vates the adop0on of dataspace and cloud paradigms. Sec0ons 3 details the architectural aspects of BSE. The system func0onali0es are described in sec0on 4. Finally, sec0on 5 presents conclusions. Referenze 5

6 Comprehensive review of semantic similarity measures. Suggestions concerning the best uses of semantic similarity measures tailored to different contexts. Assessment with biological features. Critical discussion of common issues. Outline of future direction of research. 1. Cannataro M, Guzzi PH, Veltri P. Protein Interaction Data: technologies, databases and algorithms. ACM Comput Sur 2010;43: Baclawski K, Niu T. Ontologies for Bioinformatics (Computational Molecular Biology). Cambridge, MA: The MIT Press, Maurizio Atzori University of Cagliari Nicoletta Dessì University of Cagliari 1 The work of Dr. Atzori has been done within the project Unstructured Data Integration for Dataspaces (U-DID) founded by RAS PO Sardegna FSE L.R.7/2007 BRIEFINGS IN BIOINFORMATICS. page 1 of 17 Submitted: 5th August 2011; Received (in revised form): 30th September 2011 Corresponding author. Pietro H. Guzzi, Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Viale Europa (Loc. Germaneto), Catanzaro, Italy. *These authors contributed equally to this work Pietro H. Guzzi is an Assistant Professor of Computer Engineering at the University Magna Græcia of Catanzaro, Italy, since He received his PhD in Biomedical Engineering in 2008, from Magna Græcia University of Catanzaro. He received his Laurea degree in Computer Engineering in 2004 from the University of Calabria, Rende, Italy. His research interests comprise bioinformatics, the analysis of proteomics data, and the analysis of protein interaction networks. Pietro is an ACM member and serves the scientific community as reviewer for many conferences. He is associate editor of Information Science journal, and of SIGBioinformatics Record. Marco Mina is a Ph.D. student at the Department of Information Engineering, University of Padova, Italy, since He received the bachelor degree and the master degree in Computer Science and Engineering from the University of Padova, Italy, in 2009 and 2007, respectively. His research interests comprise bioinformatics, in particular the analysis of protein interaction networks and the integration of heterogeneous data. Concettina Guerra is a professor at the Department of Information Engineering of the University of Padova, Italy, and at the College of Computing of the Georgia Institute of Technology, Atlanta, GA, USA. Her research activity is in the areas of Computational Biology, Bioinformatics and Computer Vision. Her recent interests fall in the domains of protein classification, recognition and docking and of comparative analysis of biological networks. She has been on the faculty of the University of Rome, Italy and of Purdue University, USA, for over a decade. She has visited extensively with US Institutions, including Rensseleaer Polytechnic and Carnegie Mellon University. Dr Guerra is a founding member of the steering committee of the International Symposium on 3D Data Processing Visualization and Transmission, that she co-chaired in She was Co-Director of the CIME School on Mathematical Methods for Protein Structure Analysis and Design (2000) and chairman of the fifth IEEE International Workshop on Computer Architectures for Machine Perception (2000), general chairman of the 10th International Conference on Research in Computational Molecular Biology, RECOMB06 and Co-Director of the series of Lipari Schools in Bioinformatics and Computational Biology. Mario Cannataro is Associate Professor of Computer Engineering at the Magna Græcia University of Catanzaro, Department of Medical and Surgical Sciences, and an Associate Researcher at ICAR-CNR, Italy. He worked on parallel computing, massively parallel architectures, parallel implementation of logic programs and cellular automata. His current research explores bioinformatics, computational proteomics and genomics, medical informatics, grid and parallel computing and adaptive web systems. Dr Cannataro has published three books and more than 150 papers in international journals and conference proceedings. He is a Senior Member of ACM and a member of IEEE Computer Society and BITS (Italian Bioinformatics Society). Dr. Cannataro is a co-founder and a member of Exeura (www.exeura.com) and EasyAnalysis (www.easyanalysis.it). ß The Author Published by Oxford University Press. For Permissions, please 3. Harris MA, Clark J, Ireland A, etal. The gene ontology (go) database and informatics resource. Nucleic AcidsRes 2004;32: du Plessis L, Škunca N, Dessimoz C. The what, where, how and why of gene ontology, a primer for bioinformaticians. Brief Bioinform 2011; doi: /bib/bbr Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009;37: Pesquita C, Faria D, Falcao AO, et al. Semantic similarity in biomedical ontologies. PLoSComput Biol 2009;5:e Pesquita C, Pessoa D, Faria D, et al. CESSM: Collaborative Evaluation of Semantic Similarity Measures, JB2009: Challenges in Bioinformatics Wang J, Zhou X, Zhu J, et al. Revealing and avoiding bias in semantic similarity scores for protein pairs. BMC Bioinformatics 2010;11: Ali W, Deane CM. Functionally guided alignment of protein interaction networks for module detection. Bioinformatics 2009;25: Cho Y-R, Hwang W, Ramanathan M, et al. Semantic integration to identify overlapping functional modules in protein interaction networks. BMC bioinformatics 2007;8: Popescu M, Keller JM, Mitchell JA. Fuzzy measures on the Gene Ontology for gene product similarity. IEEE/ACM Trans Comput Biol Bioinform 2006;3: Martin D, Brun C, Remy E, et al. GOToolBox: functional analysis of gene datasets based on Gene Ontology. Genome Biol 2004;5:R Benabderrahmane S, Smail-Tabbone M, Poch O, et al. IntelliGO: a new vector- based semantic similarity measure including annotation origin. BMC Bioinformatics 2010; 1: Huang DW, Sherman BT, Tan Q, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 2007;8:R Mistry M, Pavlidis P. Gene Ontology term overlap as a measure of gene functional similarity. BMC Bioinformatics 2008;9: Al-Mubaid H, Nagar A. Comparison of four similarity measures based on GO annotations for Gene Clustering. Report no. 3, 2008 IEEE Symposium on Computers and Communications, 6 9 July Morocco: Marrakech. 17. Pesquita C, Faria D, Bastos H, et al. Metrics for GO based protein semantic sim- ilarity: a systematic evaluation. BMC Bioinformatics 2008;9(Suppl 5):S Gentleman A. Visualizing GO Distances Using Bioconductor. html (10 October 2011, date last accessed). 19. Ye P, Peyser BD, Pan X, et al. Gene function prediction from congruent synthetic lethal interactions in yeast. Mol Syst Biol 2005;1: Sheehan B, Quigley A, Gaudin B, et al. A relation based measure of semantic similarity for Gene Ontology annotations. BMC Bioinformatics 2008;9: Lee HK, Hsu AK, Sajdak J, et al. Coexpression analysis of human genes across many microarray data sets. Genome Res 2004;14: doi: /bib/bbr066 Functions Measures Input data csbl.go [60] SS measures, Resnik, Lin, JiangConrath, Genes and Clustering GRaSM, simrel, Kappa Proteins based on SS Statistics, Cosine, annotations Weighted Jaccard, Czekanowski-Dice GOSemSim [61] SS measures Resnik, Lin, Jiang, simrel, GO Terms G-SESAME GOvis [62] SS measures simlp, simui Entrez gene IDs, Gene ontology Web server Functions Measures FuSSiMeG [47] SS measures, statistical tests Resnik, Lin, JiangCon- rath, GraSM ProteInOn [17] SSmeasures, searchfor Resnik, Lin, assigned GO Terms and JiangCon- rath, annotated proteins, simgic, GraSM, representative of simui GO Terms xldb.di.fc.ul.pt/tools/proteinon/ FunSimMat [63] SS measures, disease-related simrel, Lin, genes prioritization Resnik, JiangConrath GOToolBox [12] SSmeasures, clustering Si, Sp, SCD G-SESAME [25] SSmeasures, clustering G-SESAME None of these toolsrequiresinput annotations or GOs Autori in ordine ALFABETICO Dataspaces: where structure and schema meet Maurizio Atzori and Nicoletta Dessì Abstract. In this chapter we investigate the crucial problem that poses the bases to the concept of dataspaces: the need for human interaction/intervention in the process of organizing (getting the structure of) unstructured data. We survey the existing techniques behind dataspaces to overcome that need, exploring the structure of a dataspace along three dimensions: dataspace profiling, querying and searching and application domain.wewillfurther explore existing projects focusing on dataspaces, induction of data structure from documents, and data models where data schema and documents structure overlaps will be reviewed, such as Apache Hadoop, Cassandra on Amazon Dynamo, Google BigTable model and other DHT-based flexible data structures, Google Fusion Tables, imemex, U-DID, WebTables and Yahoo! SearchMonkey. 1 Introduction Data integration has emerged over the last few years as a challenge to improving search in vast collections of structured data that yield heterogeneity at scale unseen before. Current information systems and IT infrastructures are mainly based on the exchange of strongly-structured data and on wellestablished standards (database, XML files and other known data formats). Nevertheless, enterprise and personal data handled everyday are mostly unstructured (estimates range from 80 to 95%), i.e., their contents do not follow Stru<ura di una pubblicazione (3) - Sec0ons. Sono i vari paragrafi che descrivono per pun0 il lavoro svolto - Conclusions / Future work. Tirano le conclusioni ed eventuali sviluppi futuri - Aknowledgements - References Bibliografia estesa riferita con numeri all interno del paper (es [1]) Tu<o il paper è forma<ato secondo quanto richiesto dall editore. page 14 of 17 Guzzi et al. Ordine degli autori NON allfabe0co Esempio da PubMed Briefings in Bioinformatics Advance Access published December 2, 2011 Semantic similarity analysis of protein data: assessment with biological features and issues Pietro H. Guzzi*, Marco Mina*, Concettina Guerra and Mario Cannataro Abstract Theintegration ofproteomics datawithbiologicalknowledgeis arecent trendinbioinformatics. Alotofbiologicalinformation is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale.thiswork, after the definitionofmainconceptof suchanalysis, presents a systematicdiscussionandcomparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented. Keywords: Semantic similarity measures; protein data; biological features Downloaded from by guest on March 6, 2014 similarity. However, most of groupwise approaches do not take into account term specificity and behave poorly. SimGIC is the only groupwise measure competing with pairwise approaches. Actually, Resnik is one of the most considered semantic similarity measure, always included in assessment works and behaving properly most of the times. More recent approaches based on term specificity such as G-SESAME, simgic, simic and TCSS seem to outperform Resnik in several cases, but with the exception of simgic they have not been included in many assessment or comparison works. Anyhow, we believe they represent the next generation of semantic similarity measures that should be used. All of them offer improvements over Resnik in different directions, resolving some of the issues presented above. TOOLS ANDAPPLICATIONS FOR THE SEMANTIC ANALYSIS This section presents some existing tools implementing SS measures. The current scenario is characterized from the absence of a tool that implements all the SS measures or that is easily extendible. Considering the distribution, tools are mainly available as web servers (Table 6) or as packages for the R platform (Table 5). However, FuSSiMeG, ProteInOn, FunSimMat, csbl.go and SemSim together cover almost all the similarity measures. In general, tools are based on GO and annotation corpora. Some tools, such as the web servers, include their own copy of annotation corpora and GO, offering user-friendly and ready-to-go solutions. However, they rely on maintainers for updated data, and generally do not offer many possibilities of customization or extension. On the contrary, other tools such as stand-alone R-packages, are generally more flexible and often easily extendable, but they require the intervention of expert users. Usually they require the user to provide annotations and ontologies as input data in more or less common formats. While this enables the full control over data used and guarantees the possibility to use most-updated data, the preparation of input datasets may result in an error-prone waste of time. A possible future direction may regard the development of a comprehensive platform for the integrated semantic analysis of protein interaction networks. Table 5: Packages for R Table 6: Web servers for calculation of semantic similarity measures CONCLUSIONS SS measures, i.e. the quantification of the similarity of two or more terms belonging to the same ontology, is a well established field. The application of SS to proteins as well as to protein interaction data is still a novel field, and there exist many open problems and challenges that should be addressed. In this work, we presented a survey of main SS measures based on GO and the main issues discussed in the scientific community regarding: (i) the assessment of SSs in terms of biological features and (ii) the biases on the calculation of SSs that arise in the biological field. Downloaded from by guest on March 6, 2014 Semantic similarity analysis of protein data page 15 of 17 The several assessments reported in this work provide a clear vision of the extent to which SS measures correlate with other biological features and similarity measures. Furthermore, we identified some critical points and issues regarding current measures that may stimulate discussion and research in the future. We concluded that Resnik, one of the most considered SS measures, behaves properly most of the times. More recent approaches based on term specificity such as G-SESAME, simgic, simic and TCSS seem to outperform Resnik in several cases. We believe they represent the next generation of SS measures that should be used, since all of them offer improvements over Resnik in different directions, resolving some of the issues presented above. Finally, we point the attention to another problem that is emerging. Recently, semantic similarity measures have been used as input or validation data in several genome-wide and proteome-wide applications (i.e. PPI networks alignment problems), requiring the computation of semantic similarity between whole proteomes. Considering as an example the yeast organism, containing more than 5000 proteins, these applications require the calculation of more than 25 millions of protein similarities. So far, there is only one freely available tool, GS2 [64], that efficiently generates proteome-wide SS scores. Further work is necessary to design faster solutions for the calculation of semantic similarity measures. SUPPLEMENTARY DATA Supplementary data are available online at bib.oxfordjournals.org/. Key Points Downloaded from by guest on March 6, 2014 Materiale Supplementare Altro Schema Bio- Med e comunque per le scienze sperimentali: - Introduc0on - Methods - Results - Discussion References 6

7 Received on September 20, 2005; revised on January 16, 2006; accepted on February 3, 2006 Advance Access publication February 21, 2006 Associate Editor: Chris Stoeckert ABSTRACT Motivation: Pathway modeling requires the integration of multiple data including prior knowledge. In this study, we quantitatively assess the application of Gene Ontology (GO)-derived similarity measures for the characterization of direct and indirect interactions within human regulatory pathways. The characterization would help the integration of prior pathway knowledge for the modeling. Results: Our analysis indicates information content-based measures outperform graph structure-based measures for stratifying protein interactions. Measures in terms of GO biological process and molecular function annotations can be used alone or together for the validation of protein interactions involved in the pathways. However, GO cellular component-derived measures may not have the ability to separate true positives from noise. Furthermore, we demonstrate that the functional similarity of proteins within known regulatory pathways decays rapidly as the path length between two proteins increases. Several logistic regression models are built to estimate the confidence of both direct and indirect interactions within a pathway, which may be used to score putative pathways inferred from a scaffold of molecular interactions. Contact: The function of a biological system relies on a combinatory effect of many semantic elements, which interact non-linearly. We need to take a global view of the entire biological network, at many levels of abstraction, to manage complex biological states such as disease. Biological pathways and networks are built upon the identification of protein interactions. Traditionally, information about protein protein interactions is collected from small-scale screening. The accuracy of each interaction is often validated with multiple experiments. With the development of high-throughput methods such as the two-hybrid assay and protein chip technology, the information within interaction databases has increased tremendously (Drewes and Bouwmeester, 2003). In addition, a number of computational methods have been developed for the prediction of protein protein interactions based on protein structure and/or genomic information (Valencia and Pazos, 2002). The increased coverage of the protein protein interaction map provides deeper insight into the global properties of the interaction networks. However, interaction data To whom correspondence should be addressed. Vol. 22 no , pages doi: /bioinformatics/btl042 derived from large-scale assays and computational methods are often very noisy. Thus, it is essential to develop strategies to validate putative protein interactions such that pathways can be rebuilt from a scaffold of reliable molecular interactions (Chen and Xu, 2003). Various genomic features exist in sequence, structure, functional annotation and expression-level databases which may be used for interaction prediction and validation (Valencia and Pazos, 2002). Recently, Lu et al. (2005) have evaluated the predictive power of 16 features, ranging from coexpression relationships to similar phylogenetic profiles. Among those features, semantic similarity between two proteins has the dominant performance in discriminating true interactions from noise. The maximum predictive power is approached by integrating only a few features including the functional similarity of protein pairs. Semantic similarity is traditionally assessed as a function of the shared annotation of proteins in a controlled vocabulary system, such as Gene Ontology (GO) (Sprinzak et al., 2003). GO terms and their relationships are represented in the form of directed acyclic graphs (DAGs). The ontology provides computationally accessible semantics about the gene functions they describe. GO comprises three categories: molecular function (MF), biological process (BP) and cellular component (CC). MF describes activities at the molecular level, andabpisaccomplished byone ormore assemblies ofmf (Ashburner et al., 2000). Although interacting proteins often participate in the same BP, they are less likely to have the same MF. Jansen et al. calculate the similarity of a protein pair by identifying the set of GO terms shared by the two sets of protein annotations (2003). Their method can only use annotations derived from BP subontology, but not MF subontology. In addition, even though two annotations are different, they can be closely related via their common ancestors in DAG. Traditional methods also fail to take into account the specificity of GO terms. Although some proteins share the same GO terms, these terms may be too general to verify the functional association of the annotated proteins. There are two strategies that can be used to overcome these limitations. The first strategy is based on the graph structure of GO. For each protein we may obtain an induced graph which includes the specific set of GO annotations for the protein and all parents of those GO terms. The similarity between two induced graphs can then be used to estimate the similarity between two proteins (Gentleman, 2005, repository/devel/vignette/govis.pdf). The second strategy is based on the assumption that the more information two terms Ó The Author Published by Oxford University Press. All rights reserved. For Permissions, please A realistic classification method must have an AUC larger than 0.5. Curves from different cross-validation runs are averaged by sampling at fixed thresholds, and standard deviations are used to visualize the variability across the runs (Fawcett, 2003). We use the ROC and ROCR libraries in R to draw the graph and calculate the AUCs (Sing et al., 2004). Multiple logistic regression is effective when the response variable is dichotomous and the input variables are continuous, categorical or dichotomous. It is a commonly used model for the prediction of true protein protein interactions (Bader et al., 2004; Lin et al., 2005). The form of the model is p log ¼ b 1 p 0 þ b 1X1 þ b 2X2 þ... þ b kxk ð4þ where p is the probability of a putative interaction to be true and X 1, X2,..., Xk are independent variables such as semantic similarity measures. Logistic regression thus forms a predictor variable log[p/(1 p)] which is a linear combination of the explanatory variables. The values of this predictor variable are then transformed into probabilities by a logistic function. We use the glm function in R to perform the logistic regression. Likelihood ratio test is applied to see if a model including a given independent variable provides more information than a model without this variable. The generalization error and performance of each logistic regression model is estimated by 10-fold cross-validation and ROC curve analysis. Experimentally determined human protein protein interactions have been collected in the Biomolecular Interaction Network Database (BIND) (Bader et al., 2003). Interaction data in BIND are organized into low-throughput (LTP) and high-throughput (HTP) sections based on the number of records in the same publication. HTP data are imported from papers that have more than 40 interaction results arising from the same experimental design and methodology. Examples include those derived from exhaustive 2-hybrid hybridizations, immunoprecipitations and microarray methods. LTP interactions are manually curated from papers with less than 40 interaction results identified by the same method. They include not only data identified by traditional small scale screening, but also two-hybrid assay and other newer approaches. Recently, an approach based on evolutionary cross-species comparisons has emerged for the completion of protein interaction maps (Matthews et al., 2001). Human protein protein interactions may be predicted from lower eukaryotic protein interaction maps through the identification of orthologous genes between different species (Lehner and Fraser, 2004; Brown and Jurisica, 2005). We compare the reliability of the three human protein interaction datasets using Resnik measures. Experimental datasets (LTP and HTP) are downloaded from BIND, and the orthology-inferred dataset (Ortho) is from the core dataset computed by Lehner and Fraser. The reliability of each dataset is estimated by the fraction of interactions with scores more than the defined threshold over all protein protein interactions with corresponding measures available. For BP, MF and CC-derived measures, a different threshold is chosen to achieve maximum accuracy in discriminating true and false interactions for our training dataset described in Section 2.2. The accuracy is the weighted average of true positive and true negative rates. For the logistic regression model, 0.5 is used as the threshold. KEGG Markup Language (KGML) facilitates computational analysis and modeling of protein pathways and networks (Kanehisa et al., 2004). Currently, there are approximately 30 human regulatory pathways with KGML files available. For each pathway, we calculate the semantic similarity values for proteins within the same complex, neighboring proteins and protein pairs with different distance in the pathway. Neighboring pairs represent proteins that directly interact with each other, while distant pairs represent proteins Assessment of semantic similarity measures that interact indirectly through various numbers of bridge proteins. The distance of two proteins is defined as the length of their shortest path in the pathway. Mean similarity values are calculated for each category of protein pairs. Permutation test is used to see how often random chance would generate a mean similarity at least as high as the observed value. For each category, the same number of random pairs is picked from all proteins in the pathways, and the mean similarity value is calculated and compared with the original mean similarity. This process is repeated 1000 times, and the P-value is defined as the frequency that the random dataset generates mean similarity value equal or higher than the original value. In addition, the mean similarity (y) is fitted against the distance (x) with exponential distribution such that the rate of decay may be estimated by mean life of the distribution. X.Guo et al. share, the more similar they are. The shared information is indicated by the information content of the terms that subsume them in DAG. The information content is defined as the frequency of each term, or any of its children, occurring in an annotated dataset. Less frequently occurring terms are said to be more informative. Given the information content of each term, several measures may be calculated to estimate the semantic similarity between annotated proteins (Lord et al., 2003b). Recently, both approaches have been applied in the analysis of protein interactome (Brown and Jurisica, 2005; Chen and Xu, 2004). However, a systematic evaluation of their performance remains to be done. Given the large amount of protein interaction data, we can build a comprehensive scaffold of interactions. One popular paradigm for cellular modeling involves rebuilding pathways from this scaffold. The mining usually uses global data pertaining to molecular and cellular states such as gene expression profiles and protein post-translational modifications. The active subnetworks extracted from the large interaction scaffold may represent concrete hypotheses as to the underlying mechanisms governing the observed state change (Ideker and Lauffenburger, 2003). However, the noisy nature of both high-throughput interactions and state measurements makes pathway modeling extremely difficult. The integration of prior pathway knowledge would increase the reliability of newly inferred pathways. KEGG (Kyoto Encyclopedia of Genes and Genomes) includes current knowledge on molecular interaction networks such as pathways and complexes (Kanehisa et al., 2004). Characterization of KEGG pathways may help us to develop new methods for the pathway modeling. In this study, we quantitatively assess the application of GO-based similarity methods in human protein protein interaction and pathway analysis. First, receiver operating characteristic (ROC) analysis is used to assess the ability of GO graph structure and information content-based methods to stratify protein interactions. For each method, there are three measures in terms of BP, MF or CC annotations. We investigate the possibility to integrate the three measures by logistic regression for performance improvement. Based on the logistic regression model, we then estimate the reliability of several protein protein interaction datasets. More importantly, we characterize semantic similarity of proteins within human regulatory pathways. Several logistic regression models are built to validate indirect protein interactions in a pathway. These models may be used to infer or rank putative pathways given the scaffold of protein interactions. Graph similarity-based measures are estimated using GOstats package of Bioconductor (Gentleman, 2005). Each protein is associated with an induced graph that is obtained by taking the most specific GO terms annotated with the protein and by finding all parents of those terms until the root node has been obtained. Two methods, union-intersection (UI) and longest shared path (LP), are used to calculate the between-graph similarity. The first method uses the number of nodes two induced graphs share divided by the total number of nodes in two graphs. The resulting similarity values are bounded between 0 and 1 with more similar proteins having values near 1. The second method,lp, adopts the depth of the longest path shared by two inducedgraphs asthe similarity score. The largerthe depththe moresimilar two proteins are. If two proteins are both quite specific and similar, they should have long shared path and thus high similarity score. 968 X.Guo et al. Information content-based measures are implemented using a locally installed GO database. We use the associations between GO terms and UniProt-Human (Bairoch et al., 2005) proteins to calculate the information content p(t) which is the frequency of each GO term or any child term occurring within the corpus. Both is-a and part-of links are used to define the child term. Given the information content, we have applied the three measures tocalculatethe semanticsimilaritybetweenterms. Thefirstmeasure (Resnik) is solely based on the information content of shared parents of the two terms. If there is more than one shared parent, the minimum information content is taken. Then the similarity score is derived as shown in Equation (1). simðt1 t2þ ¼ ln ð1þ where S(t1, t2) is the set of parent terms shared by t1 and t2 (Resnik, 1999). Two other measures use not only the information content of the shared parents, but also that of the query terms. Given query terms t1 and t2, the Lin s similarity is defined as 2 ln simðt1 t2þ ¼ ð2þ ln pðt1þþln pðt2þ where p(t1), p(t2) and p(t) are information content values for t1, t2 and their parents, respectively (Lin, 1998). Lin s method generates normalized similarity values between 0 and 1. In contrast, Jiang s method uses the same components for the calculation, but generates semantic distance which can vary between infinity and 0 (Jiang and Conrath, 1997). simðt1 t2þ ¼2 ln lnpðt1þ lnpðt2þ Given those measures, the semantic similarity between two proteins could be derived accordingly. If a protein is annotated with several GO terms, the maximum similarity between all terms is taken as the between protein similarity. All five methods (UI, LP, Resnik, Lin and Jiang) are based on the April 2005 release of GO database. The mappings from Gene IDs to GO IDs can be restricted based on evidence codes. We drop those annotations inferred from physical interaction (IPI) to avoid circular reference. In addition, the annotations associated with BP unknown (GO: ), MF unknown (GO: ) and CC unknown (GO: ) are eliminated from our analysis. These five methods are assessed for their ability to stratify human protein protein interactions. Each method generates three sets of similarity values corresponding to BP, MF and CC categories of GO. The positive dataset is assembled from KEGG. It comprises pairwise interactions among proteins of the same complex and interactions of neighboring proteins within human regulatory pathways. After discarding proteins with indirect interaction effect, the interaction nature of neighboring proteins includes activation, inhibition, binding/association, dissociation, state change, phosphorylation, dephosphorylation, glycosylation, ubiquitination and methylation. As to the negative dataset, we randomly choose two distinct human proteins from Entrez Gene database as a non-interacting protein pair. This is valid since the chance of identifying protein protein interactions at random is very small (0.024% based on the two-hybrid data by Utez et al., 2000). An ROC curve depicts relative trade-offs between sensitivity and specificity of certain method for different values of the threshold. Sensitivity is defined as the ability to identify a true positive in a dataset. Specificity is defined as the ability to identify a true negative in a dataset. The area under an ROC curve (AUC) is generally used as a measure of the performance. It denotes the probability that the classification method will rank a randomly chosen positive instance higher than a randomly chosen negative instance. Random guessing generates the diagonal line y ¼ x, which has an AUC of Ashburner,M. et al. (2000) Gene Ontology: tool for the unification of biology. Nat. Genet., 25, Bader,G.D. et al. (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res., 31, Bader,J.S. et al. (2004) Gaining confidence in high-throughput protein interaction networks. Nat. Biotechnol., 22, Bairoch,A. et al. (2005) The Universal Protein Resource (UniProt). Nucleic Acids Res., 33, D154 D159. Brown,K.R. and Jurisica,I. (2005) Online predicted human interaction database. Bioinformatics, 21, Chen,Y. and Xu,D. (2003) Computational analyses of high-throughput protein protein interaction data. Curr. Protein Pept. Sci., 4, Chen,Y. and Xu,D. (2004) Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae. Nucleic Acids. Res., 32, Deane,C.M. et al. (2002) Protein interactions: two methods for the assessment of the reliability of high-throughput observations. Mol. Cell Proteomics, 1, Drewes,G. and Bouwmeester,T. (2003) Global approaches to protein protein interactions. Curr. Opin. Cell Biol., 15, ð3þ BIOINFORMATICS ORIGINAL PAPER Systems biology Assessing semantic similarity measures for the characterization of human regulatory pathways Xiang Guo 1,, Rongxiang Liu 2, Craig D. Shriver 3, Hai Hu 1 and Michael N. Liebman 1 1 Windber Research Institute, Windber, PA 15963, USA, 2 GlaxoSmithKline Pharmaceutical R&D, King of Prussia, PA 19420, USA and 3 Walter Reed Army Medical Center, Washington, DC 20307, USA 1 INTRODUCTION Downloaded from by guest on March 6, METHODS 2.1 Estimation of semantic similarity 2.2 ROC curve analysis min t 2 Sðt1 t2þ fpðtþg min t 2 Sðt1 t2þ fpðtþg min t 2 Sðt1 t2þ fpðtþg Downloaded from by guest on March 6, Logistic regression 2.4 Reliability estimation 2.5 Regulatory pathway analysis 3 RESULTS 3.1 Performance of semantic similarity measures for stratifying protein protein interactions We assemble proteins within a complex or neighboring to each other in KEGG regulatory pathways as the positive protein protein interaction dataset (total number 1649). Among them, there are 1500 protein pairs with BP annotations, 1425 pairs with MF annotations and 1255 pairs with CC annotations available for both proteins. The negative dataset with the same number of protein pairs is built by randomly choosing human proteins from Entrez Gene. As shown by the ROC curve analysis, similarity measures based on BP annotation have the highest ability to stratify protein protein interactions (Figs 1 and 2). MF-derived measures follow, and CC-derived measures have the worst discriminating power. Since GO associations with evidence code TAS (Traceable Author Statement) are regarded as the most accurate, we investigate if the performance can be improved by restricting GO annotations to TAS only. Interestingly, no significant improvement is achieved while less protein pairs have similarity values available. While the information on subcellular localizations can be used to define robust negative controls for protein interactions, our analysis indicates that localization-based similarity measures may not have the ability to separate true protein interactions from noise. The reason may be 2-fold. In contrast to the existence of over 9000 BP terms and over 7000 MF terms, the total number of CC terms is only around This subontology is much less complete and specific compared with the MF and BP subontologies, thus it may not be expressive enough to validate protein protein interactions. The other possible reason is related to the bias in link type usage among the different subontologies. GO terms are placed within a structure of relationships with the link type of is-a between parent and children as well as the type of part-of between part and whole. Generally, only the is-a links are considered for similarity measures (Resnik, 1999), but the omission of the part-of links would result in orphan terms which make the semantic comparison impossible. Our similarity measures consider two links equally, which may not be optimal. The ratio of part-of links versus is-a links is 17% in BP category and there are only 2 part-of links in MF category, but the ratio increases to 70% in CC category. The high percentage of part-of relationships may make the CC-derived measurement less accurate than the other measures. In all three GO categories, the information theoretic methods consistently perform better than graph structure-based methods (Fig. 2). Among the five methods, UI has the worst performance Downloaded from by guest on March 6, 2014 expected by chance in terms of BP. In contrast, similarity values of remote protein pairs are not different from those of random pairs in terms of MF and CC. As we know, a series of different functional steps comprise a pathway. Neighboring proteins perform one functional step, while distant proteins may play different functional roles in different cellular location. Our results are consistent with the pathway biology. In addition, CC-derived similarity values decrease in a stepwise pattern, since two or three sequential functional steps are likely to occur in the same cellular compartment. The distance-dependent similarity fits an exponential decay model. The rate of decay is characterized by the mean life, which is the distance needed for the similarity to be reduced by a factor of e. BP, MF and CC-derived similarity values decay rapidly with mean lives of 1.51, 2.42 and 0.81, respectively. Our study has shown that the logistic regression model can be used to separate direct interacting proteins from random protein pairs (Fig. 3). The reliability of a putative interaction may be estimated by this model. Similarly, indirect interacting proteins within a putative pathway may also be validated based on their semantic similarity. Following the same procedure, we have created three models using BP and MF-derived measures to assign confidence scores to protein pairs with distance of 2, 3 or 4 in a pathway. The 10-fold cross-validation shows that the prediction errors of these models are 26.9, 30.5 and 33.5%. Three models have AUC estimates of 0.82 ± 0.03, 0.79 ± 0.06 and 0.77 ± 0.06, respectively. These models may be used together to validate putative pathways by scoring both direct and indirect interactions in the pathway. 4 DISCUSSION Although various functional similarity measures have been used in the interactome analysis, a systematic evaluation of their performance has not been reported. Our results demonstrate that information content-based measures have better performance than GO structure-based measures for the validation of protein interactions involved in human regulatory pathways. Among them, Resnik s approach seems to have the best performance. Measures in terms of either MF or BP can be used to stratify protein interactions. However, CC-derived measures may not be sensitive enough for this purpose. The application of semantic similarity measures relies on the completeness and accuracy of GO annotation. Most of the proteins included in KEGG pathways have accurate and detailed annotation. However, there may be considerable amount of incorrect or underannotated proteins in other databases. The performance of semantic similarity measures may be decreased when applied to a poorly annotated dataset. For example, if two proteins are annotated by a non-specific term signal transducer activity (GO: )only, Lin similarity will be 1, Jiang distance will be 0, while UI, LP and Resnik measures generate low similarity scores. Therefore, in the case of under annotation, Lin and Jiang measures are more likely to generate false positives while more false negatives may be seen in other three measures. As the use of GO improves, the performance of those measures should improve when applied to experimental datasets. Brown and Jurisica (2005) have recently adopted information content-based method to validate their protein interaction datasets. However, their method does not separate the three GO categories. The semantic similarity is determined by the maximum similarity from the set of all GO term pairs between interacting proteins. Our results show that BP-based measures produce higher similarity values than MF and CC-based measures (Fig. 4). If there are BP annotations available for a protein pair, then the similarity value derived from the method of Brown and Jurisica is most likely equal to our BP-based similarity value. Currently, BP annotation is the most comprehensive among the three GO categories. In our dataset, if an MF-based measure is defined for a protein pair, there is a 93% chance that a BP-based measure is also defined. Thus, information included in the MF annotation still remains largely unexplored by the method of Brown and Jurisica. Our results demonstrate that MFderived measures can be used alone or integrated with BP-derived measures for the interactome analysis. Our KEGG pathway analysis indicates that protein pairs with short path length have significantly higher semantic similarity values than expected by chance alone. These protein pairs can be separated from random protein pairs by logistic regression models. Current pathway modeling methods score candidate subnetworks based on various evidence including semantic similarity estimates for each protein interaction (Sharan et al., 2005). However, information about proteins, which interact indirectly through other bridge proteins, has not been utilized for pathway modeling. We propose to calculate confidence scores of not only direct interactions but also indirect interactions for the validation of putative pathways. The logistic regression model is our first step in this direction. Future work may include integration of more genomic features such as mrna coexpression, and the development of a probabilistic model to score the candidate subnetworks based on the confidence values assigned to different protein pairs. We believe that new methods incorporating semantic similarity of proteins that interact directly and indirectly will greatly aid the extraction of active pathways and thus improve the interpretation of intriguing biological phenomenon. ACKNOWLEDGEMENTS We thank Dr Chen Yu of Monsanto Company for stimulating discussions and Nicholas Jacob, President of Windber Research Institute, for continuing support. Conflict of Interest: none declared. REFERENCES Downloaded from by guest on March 6,

Data Alignment and (Geo)Referencing (sometimes Registration process)

Data Alignment and (Geo)Referencing (sometimes Registration process) Data Alignment and (Geo)Referencing (sometimes Registration process) All data aquired from a scan position are refered to an intrinsic reference system (even if more than one scan has been performed) Data

Dettagli

Informazioni su questo libro

Informazioni su questo libro Informazioni su questo libro Si tratta della copia digitale di un libro che per generazioni è stato conservata negli scaffali di una biblioteca prima di essere digitalizzato da Google nell ambito del progetto

Dettagli

Process automation Grazie a oltre trent anni di presenza nel settore e all esperienza maturata in ambito nazionale e internazionale, Elsag Datamat ha acquisito un profondo know-how dei processi industriali,

Dettagli

Gi-Gi Art. 859 - User's Guide Istruzioni d'uso

Gi-Gi Art. 859 - User's Guide Istruzioni d'uso doc.4.12-06/03 Gi-Gi Art. 859 - User's Guide Istruzioni d'uso A belaying plate that can be used in many different conditions Una piastrina d'assicurazione che può essere utilizzata in condizioni diverse.

Dettagli

e-spare Parts User Manual Peg Perego Service Site Peg Perego [Dicembre 2011]

e-spare Parts User Manual Peg Perego Service Site Peg Perego [Dicembre 2011] Peg Perego Service Site Peg Perego [Dicembre 2011] 2 Esegui il login: ecco la nuova Home page per il portale servizi. Log in: welcome to the new Peg Perego Service site. Scegli il servizio selezionando

Dettagli

Narrare i gruppi. Rivista semestrale pubblicata on-line dal 2006 Indirizzo web: www.narrareigruppi.it - Direttore responsabile: Giuseppe Licari

Narrare i gruppi. Rivista semestrale pubblicata on-line dal 2006 Indirizzo web: www.narrareigruppi.it - Direttore responsabile: Giuseppe Licari Narrare i gruppi Etnografia dell interazione quotidiana Prospettive cliniche e sociali ISSN: 2281-8960 Narrare i gruppi. Etnografia dell'interazione quotidiana. Prospettive cliniche e sociali è una Rivista

Dettagli

APPLICATION FORM 1. YOUR MOTIVATION/ LA TUA MOTIVAZIONE

APPLICATION FORM 1. YOUR MOTIVATION/ LA TUA MOTIVAZIONE APPLICATION FORM Thank you for your interest in our project. We would like to understand better your motivation in taking part in this specific project. So please, read carefully the form, answer the questions

Dettagli

Il test valuta la capacità di pensare?

Il test valuta la capacità di pensare? Il test valuta la capacità di pensare? Per favore compili il seguente questionario senza farsi aiutare da altri. Cognome e Nome Data di Nascita / / Quanti anni scolastici ha frequentato? Maschio Femmina

Dettagli

Business Process Management

Business Process Management Business Process Management Come si organizza un progetto di BPM 1 INDICE Organizzazione di un progetto di Business Process Management Tipo di intervento Struttura del progetto BPM Process Performance

Dettagli

Ministero della Salute Direzione Generale della Ricerca Scientifica e Tecnologica Bando Giovani Ricercatori - 2007 FULL PROJECT FORM

Ministero della Salute Direzione Generale della Ricerca Scientifica e Tecnologica Bando Giovani Ricercatori - 2007 FULL PROJECT FORM ALLEGATO 2 FULL PROJECT FORM FORM 1 FORM 1 General information about the project PROJECT SCIENTIFIC COORDINATOR TITLE OF THE PROJECT (max 90 characters) TOTAL BUDGET OF THE PROJECT FUNDING REQUIRED TO

Dettagli

INTERNET e RETI di CALCOLATORI A.A. 2014/2015 Capitolo 4 DHCP Dynamic Host Configuration Protocol Fausto Marcantoni fausto.marcantoni@unicam.

INTERNET e RETI di CALCOLATORI A.A. 2014/2015 Capitolo 4 DHCP Dynamic Host Configuration Protocol Fausto Marcantoni fausto.marcantoni@unicam. Laurea in INFORMATICA INTERNET e RETI di CALCOLATORI A.A. 2014/2015 Capitolo 4 Dynamic Host Configuration Protocol fausto.marcantoni@unicam.it Prima di iniziare... Gli indirizzi IP privati possono essere

Dettagli

ncdna Per ncdna si intende il DNA intronico, intergenico e altre zone non codificanti del genoma.

ncdna Per ncdna si intende il DNA intronico, intergenico e altre zone non codificanti del genoma. ncdna Per ncdna si intende il DNA intronico, intergenico e altre zone non codificanti del genoma. ncdna è caratteristico degli eucarioti: Sequenze codificanti 1.5% del genoma umano Introni in media 95-97%

Dettagli

Zeroshell come client OpenVPN

Zeroshell come client OpenVPN Zeroshell come client OpenVPN (di un server OpenVpn Linux) Le funzionalità di stabilire connessioni VPN di Zeroshell vede come scenario solito Zeroshell sia come client sia come server e per scelta architetturale,

Dettagli

Virtualizzazione con Microsoft Tecnologie e Licensing

Virtualizzazione con Microsoft Tecnologie e Licensing Microsoft Virtualizzazione con Microsoft Tecnologie e Licensing Profile Redirezione dei documenti Offline files Server Presentation Management Desktop Windows Vista Enterprise Centralized Desktop Application

Dettagli

Aggiornamenti CIO Rivista ufficiale del Club Italiano Osteosintesi

Aggiornamenti CIO Rivista ufficiale del Club Italiano Osteosintesi Aggiornamenti CIO Rivista ufficiale del Club Italiano Osteosintesi Istruzioni per gli Autori Informazioni generali Aggiornamenti CIO è la rivista ufficiale del Club Italiano Osteosintesi e pubblica articoli

Dettagli

Prodotto Isi Web Knowledge

Prodotto Isi Web Knowledge Guida pratica all uso di: Web of Science Prodotto Isi Web Knowledge acura di Liana Taverniti Biblioteca ISG INMP INMP I prodotti ISI Web of Knowledge sono basi di dati di alta qualità di ricerca alle quali

Dettagli

MODULO DI ISCRIZIONE - ENROLMENT FORM

MODULO DI ISCRIZIONE - ENROLMENT FORM Under the Patronage of Comune di Portofino Regione Liguria 1ST INTERNATIONAL OPERA SINGING COMPETITION OF PORTOFINO from 27th to 31st July 2015 MODULO DI ISCRIZIONE - ENROLMENT FORM Direzione artistica

Dettagli

PMI. Management Maturity Model, OPM3 Second Edition 2008

PMI. Management Maturity Model, OPM3 Second Edition 2008 Nuovi standard PMI, certificazioni professionali e non solo Milano, 20 marzo 2009 PMI Organizational Project Management Maturity Model, OPM3 Second Edition 2008 Andrea Caccamese, PMP Prince2 Practitioner

Dettagli

Il Form C cartaceo ed elettronico

Il Form C cartaceo ed elettronico Il Form C cartaceo ed elettronico Giusy Lo Grasso Roma, 9 luglio 2012 Reporting DURANTE IL PROGETTO VENGONO RICHIESTI PERIODIC REPORT entro 60 giorni dalla fine del periodo indicato all Art 4 del GA DELIVERABLES

Dettagli

COMINCIAMO A SENTIRCI UNA FAMIGLIA

COMINCIAMO A SENTIRCI UNA FAMIGLIA COMINCIAMO A SENTIRCI UNA FAMIGLIA IL PRIMO GIORNO CON LA FAMIGLIA OSPITANTE FIRST DAY WITH THE HOST FAMILY Questa serie di domande, a cui gli studenti risponderanno insieme alle loro famiglie, vuole aiutare

Dettagli

I sistemi di valutazione delle pubblicazioni scientifiche. di Laura Colombo

I sistemi di valutazione delle pubblicazioni scientifiche. di Laura Colombo I sistemi di valutazione delle pubblicazioni scientifiche di Laura Colombo Ultimo aggiornamento: marzo 2011 Sommario L analisi bibliometrica Indicatori bibliometrici o Impact Factor o Immediacy Index o

Dettagli

group HIGH CURRENT MULTIPLEX NODE

group HIGH CURRENT MULTIPLEX NODE HIGH CURRENT MULTIPLEX NODE edizione/edition 04-2010 HIGH CURRENT MULTIPLEX NODE DESCRIZIONE GENERALE GENERAL DESCRIPTION L'unità di controllo COBO è una centralina elettronica Multiplex Slave ; la sua

Dettagli

Sistemi di gestione dei dati e dei processi aziendali. Information Technology General Controls

Sistemi di gestione dei dati e dei processi aziendali. Information Technology General Controls Information Technology General Controls Indice degli argomenti Introduzione agli ITGC ITGC e altre componenti del COSO Framework Sviluppo e manutenzione degli applicativi Gestione operativa delle infrastrutture

Dettagli

5 cabins (1 main deck+ 4 lower deck) Legno: essenza di rovere naturale Rigatino Wood: striped oak

5 cabins (1 main deck+ 4 lower deck) Legno: essenza di rovere naturale Rigatino Wood: striped oak Tipo: Type: 5 cabine (1 main deck+ 4 lower deck) 5 cabins (1 main deck+ 4 lower deck) Legno: essenza di rovere naturale Rigatino Wood: striped oak Tessuti: Dedar Fanfara, Paola Lenti Fabrics: Dedar Fanfara,

Dettagli

IT Service Management, le best practice per la gestione dei servizi

IT Service Management, le best practice per la gestione dei servizi Il Framework ITIL e gli Standard di PMI : : possibili sinergie Milano, Venerdì, 11 Luglio 2008 IT Service Management, le best practice per la gestione dei servizi Maxime Sottini Slide 1 Agenda Introduzione

Dettagli

Guida ai Parametri di negoziazione dei mercati regolamentati organizzati e gestiti da Borsa Italiana

Guida ai Parametri di negoziazione dei mercati regolamentati organizzati e gestiti da Borsa Italiana Guida ai Parametri di negoziazione dei mercati regolamentati organizzati e gestiti da Borsa Italiana Versione 04 1/28 INTRODUZIONE La Guida ai Parametri contiene la disciplina relativa ai limiti di variazione

Dettagli

Official Announcement Codice Italia Academy

Official Announcement Codice Italia Academy a c a d e m y Official Announcement Codice Italia Academy INITIATIVES FOR THE BIENNALE ARTE 2015 PROMOTED BY THE MIBACT DIREZIONE GENERALE ARTE E ARCHITETTURA CONTEMPORANEE E PERIFERIE URBANE cured by

Dettagli

Lezione 20: Strutture di controllo di robot. avanzati

Lezione 20: Strutture di controllo di robot. avanzati Robotica Mobile Lezione 20: Strutture di controllo di robot Come costruire un architettura BARCS L architettura BARCS Behavioural Architecture Robot Control System Strategie Strategie Obiettivi Obiettivi

Dettagli

PRESENT SIMPLE. Indicativo Presente = Presente Abituale. Tom s everyday life

PRESENT SIMPLE. Indicativo Presente = Presente Abituale. Tom s everyday life PRESENT SIMPLE Indicativo Presente = Presente Abituale Prerequisiti: - Pronomi personali soggetto e complemento - Aggettivi possessivi - Esprimere l ora - Presente indicativo dei verbi essere ed avere

Dettagli

Presentazioni multimediali relative al senso del tatto DIMENSIONI LIVELLO INIZIALE LIVELLO INTERMEDIO LIVELLO AVANZATO

Presentazioni multimediali relative al senso del tatto DIMENSIONI LIVELLO INIZIALE LIVELLO INTERMEDIO LIVELLO AVANZATO PERCORSO DI INSEGNAMENTO/APPRENDIMENTO TIPO DI UdP: SEMPLICE (monodisciplinare) ARTICOLATO (pluridisciplinare) Progetto didattico N. 1 Titolo : Let s investigate the world with our touch! Durata: Annuale

Dettagli

Principali prove meccaniche su materiali polimerici

Principali prove meccaniche su materiali polimerici modulo: Proprietà viscoelastiche e proprietà meccaniche dei polimeri Principali prove meccaniche su materiali polimerici R. Pantani Scheda tecnica di un materiale polimerico Standard per prove meccaniche

Dettagli

Catalogo Trattamento dell Aria - Collezione 2009

Catalogo Trattamento dell Aria - Collezione 2009 Catalogo Trattamento dell Aria - Collezione 2009 SECCOTECH & S 8 SeccoTech & Secco Tecnologia al servizio della deumidificazione Technology at dehumidification's service Potenti ed armoniosi Seccotech

Dettagli

Il vostro sogno diventa realtà... Your dream comes true... Close to Volterra,portions for sale of "typical tuscan"

Il vostro sogno diventa realtà... Your dream comes true... Close to Volterra,portions for sale of typical tuscan Il vostro sogno diventa realtà... Vicinanze di Volterra vendita di porzione di fabbricato "tipico Toscano" realizzate da recupero di casolare in bellissima posizione panoramica. Your dream comes true...

Dettagli

Present Perfect SCUOLA SECONDARIA I GRADO LORENZO GHIBERTI ISTITUTO COMPRENSIVO DI PELAGO CLASSI III C/D

Present Perfect SCUOLA SECONDARIA I GRADO LORENZO GHIBERTI ISTITUTO COMPRENSIVO DI PELAGO CLASSI III C/D SCUOLA SECONDARIA I GRADO LORENZO GHIBERTI ISTITUTO COMPRENSIVO DI PELAGO CLASSI III C/D Present Perfect Affirmative Forma intera I have played we have played you have played you have played he has played

Dettagli

Curriculum Vitae di ENRICO NARDELLI

Curriculum Vitae di ENRICO NARDELLI Curriculum Vitae di ENRICO NARDELLI (Versione Abbreviata) Ultimo Aggiornamento: 24 Febbraio 2011 1 Posizioni Enrico Nardelli si è laureato nel 1983 in Ingegneria Elettronica (110/110 con lode) presso l

Dettagli

Teoria della misurazione e misurabilità di grandezze non fisiche

Teoria della misurazione e misurabilità di grandezze non fisiche Teoria della misurazione e misurabilità di grandezze non fisiche Versione 12.6.05 Teoria della misurazione e misurabilità di grandezze non fisiche 1 Il contesto del discorso (dalla lezione introduttiva)

Dettagli

Proposition for a case-study identification process. 6/7 May 2008 Helsinki

Proposition for a case-study identification process. 6/7 May 2008 Helsinki Conférence des Régions Périphériques Maritimes d Europe Conference of Peripheral Maritime Regions of Europe ANALYSIS PARTICIPATION TO THE FP THROUGH A TERRITORIAL AND REGIONAL PERSPECTIVE MEETING WITH

Dettagli

RefWorks Guida all utente Versione 4.0

RefWorks Guida all utente Versione 4.0 Accesso a RefWorks per utenti registrati RefWorks Guida all utente Versione 4.0 Dalla pagina web www.refworks.com/refworks Inserire il proprio username (indirizzo e-mail) e password NB: Agli utenti remoti

Dettagli

DTT : DECODER, SWITCH- OFF PROCESS, CNID. Roberto de Martino Contents and Multimedia Department RAK/AGCOM

DTT : DECODER, SWITCH- OFF PROCESS, CNID. Roberto de Martino Contents and Multimedia Department RAK/AGCOM DTT : DECODER, SWITCH- OFF PROCESS, CNID Roberto de Martino Contents and Multimedia Department RAK/AGCOM List of contents 1. Introduction 2. Decoder 3. Switch-off process 4. CNID Introduction 1. Introduction

Dettagli

Section I - The Process of Project Risk Management in Selex-ES and the supporting tools

Section I - The Process of Project Risk Management in Selex-ES and the supporting tools Agenda Section I - The Process of Project Risk Management in Selex-ES and the supporting tools Section II - The Data Representation Problem and the construction of the Model Section III - Data Analysis

Dettagli

CMMI-Dev V1.3. Capability Maturity Model Integration for Software Development, Version 1.3. Roma, 2012 Ercole Colonese

CMMI-Dev V1.3. Capability Maturity Model Integration for Software Development, Version 1.3. Roma, 2012 Ercole Colonese CMMI-Dev V1.3 Capability Maturity Model Integration for Software Development, Version 1.3 Roma, 2012 Agenda Che cos è il CMMI Costellazione di modelli Approccio staged e continuous Aree di processo Goals

Dettagli

Editoriale VALUTAZIONE PER L E.C.M.: ANALISI DEI QUESTIONARI DI GRADIMENTO

Editoriale VALUTAZIONE PER L E.C.M.: ANALISI DEI QUESTIONARI DI GRADIMENTO Lo Spallanzani (2007) 21: 5-10 C. Beggi e Al. Editoriale VALUTAZIONE PER L E.C.M.: ANALISI DEI QUESTIONARI DI GRADIMENTO IL GRADIMENTO DEI DISCENTI, INDICATORE DI SODDISFAZIONE DELLE ATTIVITÀ FORMATIVE

Dettagli

INFRASTRUCTURE LICENSING WINDOWS SERVER. Microsoft licensing in ambienti virtualizzati. Acronimi

INFRASTRUCTURE LICENSING WINDOWS SERVER. Microsoft licensing in ambienti virtualizzati. Acronimi Microsoft licensing in ambienti virtualizzati Luca De Angelis Product marketing manager Luca.deangelis@microsoft.com Acronimi E Operating System Environment ML Management License CAL Client Access License

Dettagli

Luca Mambella Disaster recovery: dalle soluzioni tradizionali al cloud, come far evolvere le soluzioni contenendone i costi.

Luca Mambella Disaster recovery: dalle soluzioni tradizionali al cloud, come far evolvere le soluzioni contenendone i costi. Luca Mambella Disaster recovery: dalle soluzioni tradizionali al cloud, come far evolvere le soluzioni contenendone i costi. I modelli di sourcing Il mercato offre una varietà di modelli di sourcing, ispirati

Dettagli

PerformAzioni International Workshop Festival 22nd February 3rd May 2013 LIV Performing Arts Centre Bologna, Italy APPLICATION FORM AND BANK DETAILS

PerformAzioni International Workshop Festival 22nd February 3rd May 2013 LIV Performing Arts Centre Bologna, Italy APPLICATION FORM AND BANK DETAILS PerformAzioni International Workshop Festival 22nd February 3rd May 2013 LIV Performing Arts Centre Bologna, Italy APPLICATION FORM AND BANK DETAILS La domanda di partecipazione deve essere compilata e

Dettagli

1. Uno studente universitario, dopo aver superato tre esami, ha la media di 28. Nell esame successivo

1. Uno studente universitario, dopo aver superato tre esami, ha la media di 28. Nell esame successivo Prova di verifica classi quarte 1. Uno studente universitario, dopo aver superato tre esami, ha la media di 28. Nell esame successivo lo studente prende 20. Qual è la media dopo il quarto esame? A 26 24

Dettagli

Istruzione N. Versione. Ultima. modifica. Funzione. Data 18/12/2009. Firma. Approvato da: ASSEMBLAGGIO COLLAUDO TRAINING IMBALLO. service 07.

Istruzione N. Versione. Ultima. modifica. Funzione. Data 18/12/2009. Firma. Approvato da: ASSEMBLAGGIO COLLAUDO TRAINING IMBALLO. service 07. Istruzione N 62 Data creazione 18/ 12/2009 Versione N 00 Ultima modifica TIPO ISTRUZIONE ASSEMBLAGGIO COLLAUDO TRAINING MODIFICA TEST FUNZIONALE RIPARAZIONE/SOSTITUZIONE IMBALLO TITOLO DELL ISTRUZIONE

Dettagli

Managed Services e Unified Communication & Collaboration: verso il paradigma del Cloud Computing

Managed Services e Unified Communication & Collaboration: verso il paradigma del Cloud Computing Managed Services e Unified Communication & Collaboration: verso il paradigma del Cloud Computing Claudio Chiarenza (General Manager and Chief Strategy Officer) Italtel, Italtel logo and imss (Italtel Multi-Service

Dettagli

MS OFFICE COMMUNICATIONS SERVER 2007 IMPLEMENTING AND MAINTAINING AUDIO/VISUAL CONFERENCING AND WEB CONFERENCING

MS OFFICE COMMUNICATIONS SERVER 2007 IMPLEMENTING AND MAINTAINING AUDIO/VISUAL CONFERENCING AND WEB CONFERENCING MS OFFICE COMMUNICATIONS SERVER 2007 IMPLEMENTING AND MAINTAINING AUDIO/VISUAL CONFERENCING AND WEB CONFERENCING UN BUON MOTIVO PER [cod. E603] L obiettivo del corso è fornire le competenze e conoscenze

Dettagli

HIGH SPEED FIMER. HSF (High Speed Fimer)

HIGH SPEED FIMER. HSF (High Speed Fimer) HSF PROCESS HSF (High Speed Fimer) HIGH SPEED FIMER The development of Fimer HSF innovative process represent a revolution particularly on the welding process of low (and high) alloy steels as well as

Dettagli

Smobilizzo pro-soluto di Lettere di Credito Import

Smobilizzo pro-soluto di Lettere di Credito Import definizione L operazione presuppone l emissione di una lettera di credito IMPORT in favore dell esportatore estero, con termine di pagamento differito (es. 180 gg dalla data di spedizione con documenti

Dettagli

Per far sviluppare appieno la

Per far sviluppare appieno la 2008;25 (4): 30-32 30 Maria Benetton, Gian Domenico Giusti, Comitato Direttivo Aniarti Scrivere per una rivista. Suggerimenti per presentare un articolo scientifico Riassunto Obiettivo: il principale obiettivo

Dettagli

Mario Sbriccoli, Ercole Sori. Alberto Grohmann, Giacomina Nenci, UNIVERSITÀ DEGLI STUDI DELLA REPUBBLICA DI SAN MARINO CENTRO SAMMARINESE

Mario Sbriccoli, Ercole Sori. Alberto Grohmann, Giacomina Nenci, UNIVERSITÀ DEGLI STUDI DELLA REPUBBLICA DI SAN MARINO CENTRO SAMMARINESE copertina univ. 21 11-04-2005 16:30 Pagina 1 A State and its history in the volumes 1-20 (1993-1999) of the San Marino Center for Historical Studies The San Marino Centre for Historical Studies came into

Dettagli

Istruzioni per gli autori

Istruzioni per gli autori Istruzioni per gli autori Informazioni generali LO SCALPELLO OTODI Educational è la rivista ufficiale della Società Italiana degli Ortopedici e Traumatologi Ospedalieri d Italia (OTODI) e pubblica articoli

Dettagli

Come si prepara una presentazione

Come si prepara una presentazione Analisi Critica della Letteratura Scientifica 1 Come si prepara una presentazione Perché? 2 Esperienza: Si vedono spesso presentazioni di scarsa qualità Evidenza: Un lavoro ottimo, presentato in modo pessimo,

Dettagli

These data are only utilised to the purpose of obtaining anonymous statistics on the users of this site and to check correct functionality.

These data are only utilised to the purpose of obtaining anonymous statistics on the users of this site and to check correct functionality. Privacy INFORMATIVE ON PRIVACY (Art.13 D.lgs 30 giugno 2003, Law n.196) Dear Guest, we wish to inform, in accordance to the our Law # 196/Comma 13 June 30th 1996, Article related to the protection of all

Dettagli

WWW.TINYLOC.COM CUSTOMER SERVICE GPS/ RADIOTRACKING DOG COLLAR. T. (+34) 937 907 971 F. (+34) 937 571 329 sales@tinyloc.com

WWW.TINYLOC.COM CUSTOMER SERVICE GPS/ RADIOTRACKING DOG COLLAR. T. (+34) 937 907 971 F. (+34) 937 571 329 sales@tinyloc.com WWW.TINYLOC.COM CUSTOMER SERVICE T. (+34) 937 907 971 F. (+34) 937 571 329 sales@tinyloc.com GPS/ RADIOTRACKING DOG COLLAR MANUALE DI ISTRUZIONI ACCENSIONE / SPEGNERE DEL TAG HOUND Finder GPS Il TAG HOUND

Dettagli

Materia: INGLESE Data: 24/10/2004

Materia: INGLESE Data: 24/10/2004 ! VERBI CHE TERMINANO IN... COME COSTRUIRE IL SIMPLE PAST ESEMPIO e aggiungere -d live - lived date - dated consonante + y 1 vocale + 1 consonante (ma non w o y) cambiare y in i, poi aggiungere -ed raddoppiare

Dettagli

1. SIMPLE PRESENT. Il Simple Present viene anche detto presente abituale in quanto l azione viene compiuta abitualmente.

1. SIMPLE PRESENT. Il Simple Present viene anche detto presente abituale in quanto l azione viene compiuta abitualmente. 1. SIMPLE PRESENT 1. Quando si usa? Il Simple Present viene anche detto presente abituale in quanto l azione viene compiuta abitualmente. Quanto abitualmente? Questo ci viene spesso detto dalla presenza

Dettagli

LINEE GUIDA PER LA REVISIONE SISTEMATICA DI LETTERATURA

LINEE GUIDA PER LA REVISIONE SISTEMATICA DI LETTERATURA LINEE GUIDA PER LA REVISIONE SISTEMATICA DI LETTERATURA report tecnico nr. 03/08 v. 1 indice dei contenuti 1. Utilità delle RSL Pag. 1 2. Principali metodi Pag. 2 3. Esempi di RSL Pag. 3 4. Protocollo

Dettagli

L OPPORTUNITÀ DEL RECUPERO DI EFFICIENZA OPERATIVA PUÒ NASCONDERSI NEI DATI DELLA TUA AZIENDA?

L OPPORTUNITÀ DEL RECUPERO DI EFFICIENZA OPERATIVA PUÒ NASCONDERSI NEI DATI DELLA TUA AZIENDA? OSSERVATORIO IT GOVERNANCE L OPPORTUNITÀ DEL RECUPERO DI EFFICIENZA OPERATIVA PUÒ NASCONDERSI NEI DATI DELLA TUA AZIENDA? A cura del Professor Marcello La Rosa, Direttore Accademico (corporate programs

Dettagli

La materia di cui sono fa0 i sogni digitali. Gian Luigi Ferrari Dipar7mento di Informa7ca Universita di Pisa

La materia di cui sono fa0 i sogni digitali. Gian Luigi Ferrari Dipar7mento di Informa7ca Universita di Pisa La materia di cui sono fa0 i sogni digitali Gian Luigi Ferrari Dipar7mento di Informa7ca Universita di Pisa Presentazioni! Gian Luigi Ferrari o Informa7co! Di cosa mi occupo (ricerca) o Linguaggi di Programmazione!

Dettagli

Mod. VS/AM VALVOLE DI SFIORO E SICUREZZA RELIEF VALVES AND SAFETY DEVICES

Mod. VS/AM VALVOLE DI SFIORO E SICUREZZA RELIEF VALVES AND SAFETY DEVICES Mod VS/AM VALVOLE DI SFIORO E SICUREZZA RELIEF VALVES AND SAFETY DEVICES VALVOLE DI SFIORO E SICUREZZA RELIEF VALVES AND SAFETY DEVICES Mod VS/AM 65 1 2 VS/AM 65 STANDARD VS/AM 65 CON RACCORDI VS/AM 65

Dettagli

ITIL v3, il nuovo framework per l ITSM

ITIL v3, il nuovo framework per l ITSM ITIL v3, il nuovo framework per l ITSM ( a cura di Stefania Renna ITIL - IBM) Pag. 1 Alcune immagini contenute in questo documento fanno riferimento a documentazione prodotta da ITIL Intl che ne detiene

Dettagli

Rilascio dei Permessi Volo

Rilascio dei Permessi Volo R E P U B L I C O F S A N M A R I N O C I V I L A V I A T I O N A U T H O R I T Y SAN MARINO CIVIL AVIATION REGULATION Rilascio dei Permessi Volo SM-CAR PART 5 Approvazione: Ing. Marco Conti official of

Dettagli

LE NOVITÀ DELL EDIZIONE 2011 DELLO STANDARD ISO/IEC 20000-1 E LE CORRELAZIONI CON IL FRAMEWORK ITIL

LE NOVITÀ DELL EDIZIONE 2011 DELLO STANDARD ISO/IEC 20000-1 E LE CORRELAZIONI CON IL FRAMEWORK ITIL Care Colleghe, Cari Colleghi, prosegue la nuova serie di Newsletter legata agli Schemi di Certificazione di AICQ SICEV. Questa volta la pillola formativa si riferisce alle novità dell edizione 2011 dello

Dettagli

BOSCH EDC16/EDC16+/ME9

BOSCH EDC16/EDC16+/ME9 pag. 16 di 49 BOSCH EDC16/EDC16+/ME9 BOSCH EDC16/EDC16+/ME9 Identificare la zona dove sono poste le piazzole dove andremo a saldare il connettore. Le piazzole sono situate in tutte le centraline Bosch

Dettagli

I was not you were not he was not she was not it was not we were not you were not they were not. Was I not? Were you not? Was she not?

I was not you were not he was not she was not it was not we were not you were not they were not. Was I not? Were you not? Was she not? Il passato Grammar File 12 Past simple Il past simple inglese corrisponde al passato prossimo, al passato remoto e, in alcuni casi, all imperfetto italiano. Con l eccezione del verbo be, la forma del past

Dettagli

NEUROSCIENZE EEDUCAZIONE

NEUROSCIENZE EEDUCAZIONE NEUROSCIENZE EEDUCAZIONE AUDIZIONE PRESSO UFFICIO DI PRESIDENZA 7ª COMMISSIONE (Istruzione) SULL'AFFARE ASSEGNATO DISABILITÀ NELLA SCUOLA E CONTINUITÀ DIDATTICA DEGLI INSEGNANTI DI SOSTEGNO (ATTO N. 304)

Dettagli

Gli Investitori Globali Ritengono che le Azioni Saranno La Migliore Asset Class Nei Prossimi 10 Anni

Gli Investitori Globali Ritengono che le Azioni Saranno La Migliore Asset Class Nei Prossimi 10 Anni é Tempo di BILANCI Gli Investitori Hanno Sentimenti Diversi Verso le Azioni In tutto il mondo c è ottimismo sul potenziale di lungo periodo delle azioni. In effetti, i risultati dell ultimo sondaggio di

Dettagli

Presentazione per. «La governance dei progetti agili: esperienze a confronto»

Presentazione per. «La governance dei progetti agili: esperienze a confronto» Presentazione per «La governance dei progetti agili: esperienze a confronto» Pascal Jansen pascal.jansen@inspearit.com Evento «Agile Project Management» Firenze, 6 Marzo 2013 Agenda Due parole su inspearit

Dettagli

Manuale BDM - TRUCK -

Manuale BDM - TRUCK - Manuale BDM - TRUCK - FG Technology 1/38 EOBD2 Indice Index Premessa / Premise............................................. 3 Il modulo EOBD2 / The EOBD2 module........................... 4 Pin dell interfaccia

Dettagli

Progettare Qualità di Vita nell ambito delle Disabilità Intellettive ed Evolutive:

Progettare Qualità di Vita nell ambito delle Disabilità Intellettive ed Evolutive: Progettare Qualità di Vita nell ambito delle Disabilità Intellettive ed Evolutive: Dai diritti alla costruzione di un sistema di sostegni orientato al miglioramento della Qualità di Vita A cura di: Luigi

Dettagli

Catalogo Trattamento dell Aria - Collezione 2009

Catalogo Trattamento dell Aria - Collezione 2009 Catalogo Trattamento dell Aria - Collezione 2009 SECCOASCIUTT 16 SeccoAsciutto EL & SeccoAsciutto Thermo Piccolo e potente, deumidifica e asciuga Small and powerful, dehumidifies and dries Deumidificare

Dettagli

LEZIONE 4: PRESENT SIMPLE / PRESENT CONTINUOUS

LEZIONE 4: PRESENT SIMPLE / PRESENT CONTINUOUS LEZIONE 4: PRESENT SIMPLE / PRESENT CONTINUOUS TEMPO PRESENTE In italiano non vi sono differenze particolari tra le due frasi: MANGIO UNA MELA e STO MANGIANDO UNA MELA Entrambe le frasi si possono riferire

Dettagli

1. Che cos è. 2. A che cosa serve

1. Che cos è. 2. A che cosa serve 1. Che cos è Il Supplemento al diploma è una certificazione integrativa del titolo ufficiale conseguito al termine di un corso di studi in una università o in un istituto di istruzione superiore corrisponde

Dettagli

JUMP INTO THE PSS WORLD AND ENJOY THE DIVE EVOLUTION

JUMP INTO THE PSS WORLD AND ENJOY THE DIVE EVOLUTION JUMP INTO THE PSS WORLD AND ENJOY THE DIVE EVOLUTION PSS Worldwide is one of the most important diver training agencies in the world. It was created thanks to the passion of a few dedicated diving instructors

Dettagli

Diss. ETH No. 20918. Information Theoretic Modeling of Dynamical Systems: Estimation and Experimental Design. for the degree of Doctor of Sciences

Diss. ETH No. 20918. Information Theoretic Modeling of Dynamical Systems: Estimation and Experimental Design. for the degree of Doctor of Sciences Diss. ETH No. 20918 Information Theoretic Modeling of Dynamical Systems: Estimation and Experimental Design A dissertation submitted to ETH Zurich for the degree of Doctor of Sciences presented by Alberto

Dettagli

LABORATORIO di RICERCA BIBLIOGRAFICA SUI TEST

LABORATORIO di RICERCA BIBLIOGRAFICA SUI TEST LABORATORIO di RICERCA BIBLIOGRAFICA SUI TEST emanuela.canepa@unipd.it Biblioteca di psicologia Fabio Metelli Università degli Studi di Padova Materiale didattico: guida corso Casella della biblioteca

Dettagli

Web of Science SM QUICK REFERENCE GUIDE IN COSA CONSISTE WEB OF SCIENCE? General Search

Web of Science SM QUICK REFERENCE GUIDE IN COSA CONSISTE WEB OF SCIENCE? General Search T TMTMTt QUICK REFERENCE GUIDE Web of Science SM IN COSA CONSISTE WEB OF SCIENCE? Consente di effettuare ricerche in oltre 12.000 riviste e 148.000 atti di convegni nel campo delle scienze, delle scienze

Dettagli

INFORMATIVA EMITTENTI N. 22/2015

INFORMATIVA EMITTENTI N. 22/2015 INFORMATIVA EMITTENTI N. 22/2015 Data: 23/04/2015 Ora: 17:45 Mittente: UniCredit S.p.A. Oggetto: Pioneer Investments e Santander Asset Management: unite per creare un leader globale nell asset management

Dettagli

Per effettuare una chiamata in conferenza, seguire queste semplici istruzioni:

Per effettuare una chiamata in conferenza, seguire queste semplici istruzioni: premium access user guide powwownow per ogni occasione Making a Call Per effettuare una chiamata in conferenza, seguire queste semplici istruzioni: 1. Tell your fellow conference call participants what

Dettagli

U.O.C. di Chirurgia Endoscopica

U.O.C. di Chirurgia Endoscopica Congresso Nazionale Palermo 28/30 Ottobre 2010 Azienda Ospedaliera Universitaria Policlinico Seconda Università degli Studi di Napoli Dipartimento di Chirurgia generale e specialistica U.O.C. di Chirurgia

Dettagli

E INNOVAZIONE EFFICIENZA AND INNOVATION EFFICIENCY PER CRESCERE IN EDEFFICACIA TO GROW IN AND EFFECTIVENESS

E INNOVAZIONE EFFICIENZA AND INNOVATION EFFICIENCY PER CRESCERE IN EDEFFICACIA TO GROW IN AND EFFECTIVENESS Una società certificata di qualità UNI EN ISO 9001-2000 per: Progettazione ed erogazione di servizi di consulenza per l accesso ai programmi di finanziamento europei, nazionali e regionali. FIT Consulting

Dettagli

HTA: PRINCIPI, LOGICHE OPERATIVE ED ESPERIENZE. Cosa è l HTA?

HTA: PRINCIPI, LOGICHE OPERATIVE ED ESPERIENZE. Cosa è l HTA? HTA: PRINCIPI, LOGICHE OPERATIVE ED ESPERIENZE Cosa è l HTA? Marco Marchetti Unità di Valutazione delle Tecnologie Policlinico Universitario "Agostino Gemelli Università Cattolica del Sacro Cuore L Health

Dettagli

È un progetto di Project by Comune di Numana Ideazione

È un progetto di Project by Comune di Numana Ideazione LA CALETTA È proprio nell ambito del progetto NetCet che si è deciso di realizzare qui, in uno dei tratti di costa più belli della Riviera del Conero, un area di riabilitazione o pre-rilascio denominata

Dettagli

Il programma Interreg CENTRAL EUROPE

Il programma Interreg CENTRAL EUROPE Il primo bando del programma Interreg CENTRAL EUROPE, Verona, 20.02.2015 Il programma Interreg CENTRAL EUROPE Benedetta Pricolo, Punto di contatto nazionale, Regione del Veneto INFORMAZIONI DI BASE 246

Dettagli

AGLI OPERATORI DELLA STAMPA E attiva la procedura di accredito alle Serie WSK 2014. La richiesta di accredito deve pervenire entro il 9 febbraio 2014

AGLI OPERATORI DELLA STAMPA E attiva la procedura di accredito alle Serie WSK 2014. La richiesta di accredito deve pervenire entro il 9 febbraio 2014 AGLI OPERATORI DELLA STAMPA E attiva la procedura di accredito alle Serie WSK 2014. La richiesta di accredito deve pervenire entro il 9 febbraio 2014 Per la richiesta di accredito alla singola gara, le

Dettagli

SOL terra Marco Zanuso Jr, Christophe Mathieu 2014

SOL terra Marco Zanuso Jr, Christophe Mathieu 2014 SOL terra Marco Zanuso Jr, Christophe Mathieu 2014 MADE IN ITALY SOL - Marco Zanuso Jr, Christophe Mathieu 2013 Sol è un sistema basato interamente sull interpretazione di innovativi principi di calcolo

Dettagli

PANNELLO FRONTALE: QUERCIA STYLE CANNELLA PROFILO: PINO NERO PIANO DI SERVIZIO E ZOCCOLO: AGGLOMERATO MEROPE

PANNELLO FRONTALE: QUERCIA STYLE CANNELLA PROFILO: PINO NERO PIANO DI SERVIZIO E ZOCCOLO: AGGLOMERATO MEROPE Una proposta dall estetica esclusiva, in cui tutti gli elementi compositivi sono ispirati dalla geometria più pura e dalla massima essenzialità del disegno per un progetto caratterizzato da semplicità

Dettagli

DDS elettronica srl si riserva il diritto di apportare modifiche senza preavviso /we reserves the right to make changes without notice

DDS elettronica srl si riserva il diritto di apportare modifiche senza preavviso /we reserves the right to make changes without notice Maccarone Maccarone Maccarone integra 10 LED POWER TOP alta efficienza, in tecnologia FULL COLOR che permette di raggiungere colori e sfumature ad alta definizione. Ogni singolo led full color di Maccarone

Dettagli

MANUALE CASALINI M10. A number of this manual are strongly recommends you read and download manuale casalini m10 information in this manual.

MANUALE CASALINI M10. A number of this manual are strongly recommends you read and download manuale casalini m10 information in this manual. MANUALE CASALINI M10 A number of this manual are strongly recommends you read and download information in this manual. Although not all products are identical, even people who come from the same brand

Dettagli

CatalogoItalianBuffalo210x245esec_Layout 1 24/04/15 17.55 Pagina 1

CatalogoItalianBuffalo210x245esec_Layout 1 24/04/15 17.55 Pagina 1 CatalogoItalianBuffalo210x245esec_Layout 1 24/04/15 17.55 Pagina 1 CatalogoItalianBuffalo210x245esec_Layout 1 24/04/15 17.55 Pagina 2 BUFALO MEDITERRANEO ITALIANO Mediterranean Italian Buffalo RICONOSCIUTO

Dettagli

CALDO SGUARDO GRECO PDF

CALDO SGUARDO GRECO PDF CALDO SGUARDO GRECO PDF ==> Download: CALDO SGUARDO GRECO PDF CALDO SGUARDO GRECO PDF - Are you searching for Caldo Sguardo Greco Books? Now, you will be happy that at this time Caldo Sguardo Greco PDF

Dettagli

L Health Technology Assessment: tutti ne parlano, non. conoscono. Marco Marchetti

L Health Technology Assessment: tutti ne parlano, non. conoscono. Marco Marchetti L Health Technology Assessment: tutti ne parlano, non tutti lo conoscono Marco Marchetti L Health Technology Assessment La valutazione delle tecnologie sanitarie, è la complessiva e sistematica valutazione

Dettagli

MANUALE GRANDE PUNTO. A number of this manual are strongly recommends you read and download manuale grande punto information in this manual.

MANUALE GRANDE PUNTO. A number of this manual are strongly recommends you read and download manuale grande punto information in this manual. MANUALE GRANDE PUNTO A number of this manual are strongly recommends you read and download information in this manual. Although not all products are identical, even those that range from same brand name

Dettagli

Interfaccia Web per customizzare l interfaccia dei terminali e

Interfaccia Web per customizzare l interfaccia dei terminali e SIP - Session Initiation Protocol Il protocollo SIP (RFC 2543) è un protocollo di segnalazione e controllo in architettura peer-to-peer che opera al livello delle applicazioni e quindi sviluppato per stabilire

Dettagli

UNITA DI MISURA E TIPO DI IMBALLO

UNITA DI MISURA E TIPO DI IMBALLO Poste ALL. 15 Informazioni relative a FDA (Food and Drug Administration) FDA: e un ente che regolamenta, esamina, e autorizza l importazione negli Stati Uniti d America d articoli che possono avere effetti

Dettagli

Qual è l errore più comune tra i Trader sul Forex e come possiamo evitarlo? David Rodriguez, Quantitative Strategist drodriguez@dailyfx.

Qual è l errore più comune tra i Trader sul Forex e come possiamo evitarlo? David Rodriguez, Quantitative Strategist drodriguez@dailyfx. Qual è l errore più comune tra i Trader sul Forex e come possiamo evitarlo? David Rodriguez, Quantitative Strategist drodriguez@dailyfx.com Avvertenza di Rischio: Il Margin Trading su forex e/o CFD comporta

Dettagli