Measure of Global Specialization and Spatial Clustering for the Identification of "Specialized" Agglomeration

The intensity of regional specialization in specific activities, and conversely, the level of industrial concentration in specific locations, has been used as a complementary evidence for the existence and significance of externalities. Additionally, economists have mainly focused the debate on disentangling the sources of specialization and concentration processes according to three vectors: natural advantages, internal, and external scale economies. The arbitrariness of partitions plays a key role in capturing these effects, while the selection of the partition would have to reflect the actual characteristics of the economy. Thus, the identification of spatial boundaries to measure specialization becomes critical, since most likely the model will be adapted to different scales of distance, and be influenced by different types of externalities or economies of agglomeration, which are based on the mechanisms of interaction with particular requirements of spatial proximity. This work is based on the analysis of the spatial aspect of economic specialization supported by the manufacturing industry case. The main objective is to propose, for discrete and continuous space: i) a measure of global specialization; ii) a local disaggregation of the global measure; and iii) a spatial clustering method for the identification of specialized agglomerations.

Comparing Different Approaches for Clustering Categorical Data

There are different ways to do cluster analysis of categorical data in the literature and the choice among them is strongly related to the aim of the researcher, if we do not take into account time and economical constraints. Main approaches for clustering are usually distinguished into model-based and distance-based methods: the former assume that objects belonging to the same class are similar in the sense that their observed values come from the same probability distribution, whose parameters are unknown and need to be estimated; the latter evaluate distances among objects by a defined dissimilarity measure and, basing on it, allocate units to the closest group. In clustering, one may be interested in the classification of similar objects into groups, and one may be interested in finding observations that come from the same true homogeneous distribution. But do both of these aims lead to the same clustering? And how good are clustering methods designed to fulfil one of these aims in terms of the other? In order to answer, two approaches, namely a latent class model (mixture of multinomial distributions) and a partition around medoids one, are evaluated and compared by Adjusted Rand Index, Average Silhouette Width and Pearson-Gamma indexes in a fairly wide simulation study. Simulation outcomes are plotted in bi-dimensional graphs via Multidimensional Scaling; size of points is proportional to the number of points that overlap and different colours are used according to the cluster membership.

Progettazione e sviluppo di una versione distribuita di un algoritmo di subspace clustering

Il task del data mining si pone come obiettivo l'estrazione automatica di schemi significativi da grandi quantità di dati. Un esempio di schemi che possono essere cercati sono raggruppamenti significativi dei dati, si parla in questo caso di clustering. Gli algoritmi di clustering tradizionali mostrano grossi limiti in caso di dataset ad alta dimensionalità, composti cioè da oggetti descritti da un numero consistente di attributi. Di fronte a queste tipologie di dataset è necessario quindi adottare una diversa metodologia di analisi: il subspace clustering. Il subspace clustering consiste nella visita del reticolo di tutti i possibili sottospazi alla ricerca di gruppi signicativi (cluster). Una ricerca di questo tipo è un'operazione particolarmente costosa dal punto di vista computazionale. Diverse ottimizzazioni sono state proposte al fine di rendere gli algoritmi di subspace clustering più efficienti. In questo lavoro di tesi si è affrontato il problema da un punto di vista diverso: l'utilizzo della parallelizzazione al fine di ridurre il costo computazionale di un algoritmo di subspace clustering.

Clustering di ammassi di galassie con cataloghi otticamente selezionati

In questo lavoro di tesi si è studiato il clustering degli ammassi di galassie e la determinazione della posizione del picco BAO per ottenere vincoli sui parametri cosmologici. A tale scopo si è implementato un codice per la stima dell'errore tramite i metodi di jackknife e bootstrap. La misura del picco BAO confrontata con i modelli cosmologici, grazie all'errore stimato molto piccolo, è risultato in accordo con il modelli LambdaCDM, e permette di ottenere vincoli su alcuni parametri dei modelli cosmologici.

A clustering method for robust and reliable large scale functional and structural protein sequence annotation

Bioinformatics, in the last few decades, has played a fundamental role to give sense to the huge amount of data produced. Obtained the complete sequence of a genome, the major problem of knowing as much as possible of its coding regions, is crucial. Protein sequence annotation is challenging and, due to the size of the problem, only computational approaches can provide a feasible solution. As it has been recently pointed out by the Critical Assessment of Function Annotations (CAFA), most accurate methods are those based on the transfer-by-homology approach and the most incisive contribution is given by cross-genome comparisons. In the present thesis it is described a non-hierarchical sequence clustering method for protein automatic large-scale annotation, called “The Bologna Annotation Resource Plus” (BAR+). The method is based on an all-against-all alignment of more than 13 millions protein sequences characterized by a very stringent metric. BAR+ can safely transfer functional features (Gene Ontology and Pfam terms) inside clusters by means of a statistical validation, even in the case of multi-domain proteins. Within BAR+ clusters it is also possible to transfer the three dimensional structure (when a template is available). This is possible by the way of cluster-specific HMM profiles that can be used to calculate reliable template-to-target alignments even in the case of distantly related proteins (sequence identity < 30%). Other BAR+ based applications have been developed during my doctorate including the prediction of Magnesium binding sites in human proteins, the ABC transporters superfamily classification and the functional prediction (GO terms) of the CAFA targets. Remarkably, in the CAFA assessment, BAR+ placed among the ten most accurate methods. At present, as a web server for the functional and structural protein sequence annotation, BAR+ is freely available at http://bar.biocomp.unibo.it/bar2.0.

Genexpressionsanalyse der fötalen Wachstumsfuge des Rindes

Erkrankungen des Skelettapparats wie beispielsweise die Osteoporose oder Arthrose gehören neben den Herz-Kreislauferkrankungen und Tumoren zu den Häufigsten Erkrankungen des Menschen. Ein besseres Verständnis der Bildung und des Erhalts von Knochen- oder Knorpelgewebe ist deshalb von besonderer Bedeutung. Viele bisherige Ansätze zur Identifizierung hierfür relevanter Gene, deren Produkte und Interaktionen beruhen auf der Untersuchung pathologischer Situationen. Daher ist die Funktion vieler Gene nur im Zusammenhang mit Krankheiten beschrieben. Untersuchungen, die die Genaktivität bei der Normalentwicklung von knochen- und knorpelbildenden Geweben zum Ziel haben, sind dagegen weit weniger oft durchgeführt worden. rnEines der entwicklungsphysiologisch interessantesten Gewebe ist die Epiphysenfuge der Röhrenknochen. In dieser sogenannten Wachstumsfuge ist insbesondere beim fötalen Gewebe eine sehr hohe Aktivität derjenigen Gene zu erwarten, die an der Knochen- und Knorpelbildung beteiligt sind. In der vorliegenden Arbeit wurde daher aus der Epiphysenfuge von Kälberknochen RNA isoliert und eine cDNA-Bibliothek konstruiert. Von dieser wurden ca. 4000 Klone im Rahmen eines klassischen EST-Projekts sequenziert. Durch die Analyse konnte ein ungefähr 900 Gene umfassendes Expressionsprofil erstellt werden und viele Transkripte für Komponenten der regulatorischen und strukturbildenden Bestandteile der Knochen- und Knorpelentwicklung identifiziert werden. Neben den typischen Genen für Komponenten der Knochenentwicklung sind auch deutlich Bestandteile für embryonale Entwicklungsprozesse vertreten. Zu ersten gehören in erster Linie die Kollagene, allen voran Kollagen II alpha 1, das mit Abstand höchst exprimierte Gen in der fötalen Wachstumsfuge. Nach den ribosomalen Proteinen stellen die Kollagene mit ca. 10 % aller auswertbaren Sequenzen die zweitgrößte Gengruppe im erstellten Expressionsprofil dar. Proteoglykane und andere niedrig exprimierte regulatorische Elemente, wie Transkriptionsfaktoren, konnten im EST-Projekt aufgrund der geringen Abdeckung nur in sehr geringer Kopienzahl gefunden werden. Allerdings förderte die EST-Analyse mehrere interessante, bisher nicht bekannte Transkripte zutage, die detaillierter untersucht wurden. Dazu gehören Transkripte die, die dem LOC618319 zugeordnet werden konnten. Neben den bisher beschriebenen drei Exonbereichen konnte ein weiteres Exon im 3‘-UTR identifiziert werden. Im abgeleiteten Protein, das mindestens 121 AS lang ist, wurden ein Signalpeptid und eine Transmembrandomäne nachgewiesen. In Verbindung mit einer möglichen Glykosylierung ist das Genprodukt in die Gruppe der Proteoglykane einzuordnen. Leicht abweichend von den typischen Strukturen knochen- und knorpelspezifischer Proteoglykane ist eine mögliche Funktion dieses Genprodukts bei der Interaktion mit Integrinen und der Zell-Zellinteraktion, aber auch bei der Signaltransduktion denkbar. rnDie EST-Sequenzierungen von ca. 4000 cDNA-Klonen können aber in der Regel nur einen Bruchteil der möglichen Transkripte des untersuchten Gewebes abdecken. Mit den neuen Sequenziertechnologien des „Next Generation Sequencing“ bestehen völlig neue Möglichkeiten, komplette Transkriptome mit sehr hoher Abdeckung zu sequenzieren und zu analysieren. Zur Unterstützung der EST-Daten und zur deutlichen Verbreiterung der Datenbasis wurde das Transkriptom der bovinen fötalen Wachstumsfuge sowohl mit Hilfe der Roche-454/FLX- als auch der Illumina-Solexa-Technologie sequenziert. Bei der Auswertung der ca. 40000 454- und 75 Millionen Illumina-Sequenzen wurden Verfahren zur allgemeinen Handhabung, der Qualitätskontrolle, dem „Clustern“, der Annotation und quantitativen Auswertung von großen Mengen an Sequenzdaten etabliert. Beim Vergleich der Hochdurchsatz Blast-Analysen im klassischen „Read-Count“-Ansatz mit dem erstellten EST-Expressionsprofil konnten gute Überstimmungen gezeigt werden. Abweichungen zwischen den einzelnen Methoden konnten nicht in allen Fällen methodisch erklärt werden. In einigen Fällen sind Korrelationen zwischen Transkriptlänge und „Read“-Verteilung zu erkennen. Obwohl schon simple Methoden wie die Normierung auf RPKM („reads per kilo base transkript per million mappable reads“) eine Verbesserung der Interpretation ermöglichen, konnten messtechnisch durch die Art der Sequenzierung bedingte systematische Fehler nicht immer ausgeräumt werden. Besonders wichtig ist daher die geeignete Normalisierung der Daten beim Vergleich verschieden generierter Datensätze. rnDie hier diskutierten Ergebnisse aus den verschiedenen Analysen zeigen die neuen Sequenziertechnologien als gute Ergänzung und potentiellen Ersatz für etablierte Methoden zur Genexpressionsanalyse.rn

Sviluppo di algoritmi di grid data clustering basati su metodi statistici

Lo scopo del clustering è quindi quello di individuare strutture nei dati significative, ed è proprio dalla seguente definizione che è iniziata questa attività di tesi , fornendo un approccio innovativo ed inesplorato al cluster, ovvero non ricercando la relazione ma ragionando su cosa non lo sia. Osservando un insieme di dati ,cosa rappresenta la non relazione? Una domanda difficile da porsi , che ha intrinsecamente la sua risposta, ovvero l’indipendenza di ogni singolo dato da tutti gli altri. La ricerca quindi dell’indipendenza tra i dati ha portato il nostro pensiero all’approccio statistico ai dati , in quanto essa è ben descritta e dimostrata in statistica. Ogni punto in un dataset, per essere considerato “privo di collegamenti/relazioni” , significa che la stessa probabilità di essere presente in ogni elemento spaziale dell’intero dataset. Matematicamente parlando , ogni punto P in uno spazio S ha la stessa probabilità di cadere in una regione R ; il che vuol dire che tale punto può CASUALMENTE essere all’interno di una qualsiasi regione del dataset. Da questa assunzione inizia il lavoro di tesi, diviso in più parti. Il secondo capitolo analizza lo stato dell’arte del clustering, raffrontato alla crescente problematica della mole di dati, che con l’avvento della diffusione della rete ha visto incrementare esponenzialmente la grandezza delle basi di conoscenza sia in termini di attributi (dimensioni) che in termini di quantità di dati (Big Data). Il terzo capitolo richiama i concetti teorico-statistici utilizzati dagli algoritimi statistici implementati. Nel quarto capitolo vi sono i dettagli relativi all’implementazione degli algoritmi , ove sono descritte le varie fasi di investigazione ,le motivazioni sulle scelte architetturali e le considerazioni che hanno portato all’esclusione di una delle 3 versioni implementate. Nel quinto capitolo gli algoritmi 2 e 3 sono confrontati con alcuni algoritmi presenti in letteratura, per dimostrare le potenzialità e le problematiche dell’algoritmo sviluppato , tali test sono a livello qualitativo , in quanto l’obbiettivo del lavoro di tesi è dimostrare come un approccio statistico può rivelarsi un’arma vincente e non quello di fornire un nuovo algoritmo utilizzabile nelle varie problematiche di clustering. Nel sesto capitolo saranno tratte le conclusioni sul lavoro svolto e saranno elencati i possibili interventi futuri dai quali la ricerca appena iniziata del clustering statistico potrebbe crescere.

Hier di Nicole Brossard: Proposta di traduzione di un romanzo eterolingue

L’objectif de ce mémoire est la proposition de traduction en italien du premier chapitre de Hier, roman écrit par l’auteure québécoise Nicole Brossard. Ce travail est divisé en six chapitres. Dans le premier chapitre je présente les caractéristiques fondamentales de la traduction littéraire et ensuite de la littérature postcoloniale e féminine. Le deuxième chapitre se focalise sur le thème de l’hétérolinguisme et les stratégies de traduction possible. Le troisième chapitre est dédié a la traduction au Québec. Dans le quatrième chapitre je présente l’auteure e le roman, en particulier la structure de l’œuvre et les thématiques traitées, mais aussi le style de l’auteure e sa conception de l’écriture. Le cinquième chapitre est dédié à la traduction de la première partie du roman. Finalement, dans le dernier chapitre je présente la méthodologie traductive e plusieurs exemples des problèmes rencontrés en phase de traduction et les stratégies adoptés pour les résoudre.

Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

Premature Osteoblast Clustering by Enamel Matrix Proteins Induces Osteoblast Differentiation through Up-Regulation of Connexin 43 and N-Cadherin

In recent years, enamel matrix derivative (EMD) has garnered much interest in the dental field for its apparent bioactivity that stimulates regeneration of periodontal tissues including periodontal ligament, cementum and alveolar bone. Despite its widespread use, the underlying cellular mechanisms remain unclear and an understanding of its biological interactions could identify new strategies for tissue engineering. Previous in vitro research has demonstrated that EMD promotes premature osteoblast clustering at early time points. The aim of the present study was to evaluate the influence of cell clustering on vital osteoblast cell-cell communication and adhesion molecules, connexin 43 (cx43) and N-cadherin (N-cad) as assessed by immunofluorescence imaging, real-time PCR and Western blot analysis. In addition, differentiation markers of osteoblasts were quantified using alkaline phosphatase, osteocalcin and von Kossa staining. EMD significantly increased the expression of connexin 43 and N-cadherin at early time points ranging from 2 to 5 days. Protein expression was localized to cell membranes when compared to control groups. Alkaline phosphatase activity was also significantly increased on EMD-coated samples at 3, 5 and 7 days post seeding. Interestingly, higher activity was localized to cell cluster regions. There was a 3 fold increase in osteocalcin and bone sialoprotein mRNA levels for osteoblasts cultured on EMD-coated culture dishes. Moreover, EMD significantly increased extracellular mineral deposition in cell clusters as assessed through von Kossa staining at 5, 7, 10 and 14 days post seeding. We conclude that EMD up-regulates the expression of vital osteoblast cell-cell communication and adhesion molecules, which enhances the differentiation and mineralization activity of osteoblasts. These findings provide further support for the clinical evidence that EMD increases the speed and quality of new bone formation in vivo.

Does published orthodontic research account for clustering effects during statistical data analysis?

In orthodontics, multiple site observations within patients or multiple observations collected at consecutive time points are often encountered. Clustered designs require larger sample sizes compared to individual randomized trials and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this study to assess to what degree clustering effects are considered during design and data analysis in the three major orthodontic journals. The contents of the most recent 24 issues of the American Journal of Orthodontics and Dentofacial Orthopedics (AJODO), Angle Orthodontist (AO), and European Journal of Orthodontics (EJO) from December 2010 backwards were hand searched. Articles with clustering effects and whether the authors accounted for clustering effects were identified. Additionally, information was collected on: involvement of a statistician, single or multicenter study, number of authors in the publication, geographical area, and statistical significance. From the 1584 articles, after exclusions, 1062 were assessed for clustering effects from which 250 (23.5 per cent) were considered to have clustering effects in the design (kappa = 0.92, 95 per cent CI: 0.67-0.99 for inter rater agreement). From the studies with clustering effects only, 63 (25.20 per cent) had indicated accounting for clustering effects. There was evidence that the studies published in the AO have higher odds of accounting for clustering effects [AO versus AJODO: odds ratio (OR) = 2.17, 95 per cent confidence interval (CI): 1.06-4.43, P = 0.03; EJO versus AJODO: OR = 1.90, 95 per cent CI: 0.84-4.24, non-significant; and EJO versus AO: OR = 1.15, 95 per cent CI: 0.57-2.33, non-significant). The results of this study indicate that only about a quarter of the studies with clustering effects account for this in statistical data analysis.