Confronto di metodi statistici per la misura dell'espressione differenziale in dati di RNA sequencing

L’RNA sequencing (RNA-­‐Seq) è una tecnica per l’analisi del trascrittoma e la sua quantificazione, basata sulle recenti tecnologie Next-­‐Generation Sequencing (NGS). Lo sviluppo tecnologico ha infatti permesso di ottenere piattaforme di sequenziamento che generano dati ad alto throughput e con costi molto inferiori rispetto ai sequenziatori tradizionali. La prima parte di questa tesi offre una panoramica sulle più diffuse piattaforme commerciali di sequenziamento NGS (454 Genome Sequencer di Roche, Genome Analyzer di Illumina, SOLiD di Applied Biosystems), valutandone le caratteristiche tecniche e le prestazioni. I dati grezzi che i sequenziatori permettono di ottenere sono le read, cioè sequenze che identificano l’ordine in cui si susseguono le basi azotate che compongono il gene. In un esperimento RNA-­‐Seq, l’espressione genica viene misurata in termini di count, cioè del numero di read mappate sui geni di un genoma o trascrittoma di riferimento. I count sono dunque somme di variabili aleatorie (l’assegnazione delle read a ciascun gene) e sono descrivibili tramite modelli statistici. I principali modelli utilizzati in letteratura sono il modello binomiale, il modello di Poisson e il modello Binomiale Negativo. La descrizione statistica dei dati di RNA-­‐Seq è oggetto di studio molto recente e non esiste ancora una descrizione comune. In questa tesi si è quindi cercato di organizzare le informazioni in un modello generale della distribuzione dei dati, stabilendo una notazione comune nella descrizione dei lavori dei diversi autori. Una delle più interessanti applicazioni di RNA-­‐Seq è l’analisi dell’espressione differenziale, cioè l’identificazione dei geni che presentano significative differenze del loro livello di espressione fra due o più condizioni sperimentali (interne o esterne alla cellula). In esperimenti RNA-­‐Seq, ciò significa valutare se le differenze osservate nei count delle diverse condizioni sperimentali siano o meno significative in senso statistico. Sono molti gli autori e i gruppi di ricerca che hanno sviluppato proposte di metodi di analisi differenziale, che implementano i modelli di distribuzione dei dati sopra citati. In questa tesi sono stati considerati i metodi DEGSeq, PoissonSeq (che implementano il modello di Poisson), edgeR e DESeq (che implementano il modello Binomiale Negativo). Ciascun metodo è stato testato su due data set pubblici valutandone le prestazioni in termini di precisione e sensitività. EdgeR è risultato il migliore, anche se tutti i diversi metodi hanno ottenuto risultati molto simili fra loro. Studi futuri con presenza di repliche biologiche potranno fornire indicazioni statisticamente più significative sulla bontà dei metodiope

Metapopulation epidemic models with heterogeneous mixing and travel behaviour.

BACKGROUND: Determining the pandemic potential of an emerging infectious disease and how it depends on the various epidemic and population aspects is critical for the preparation of an adequate response aimed at its control. The complex interplay between population movements in space and non-homogeneous mixing patterns have so far hindered the fundamental understanding of the conditions for spatial invasion through a general theoretical framework. To address this issue, we present an analytical modelling approach taking into account such interplay under general conditions of mobility and interactions, in the simplifying assumption of two population classes. METHODS: We describe a spatially structured population with non-homogeneous mixing and travel behaviour through a multi-host stochastic epidemic metapopulation model. Different population partitions, mixing patterns and mobility structures are considered, along with a specific application for the study of the role of age partition in the early spread of the 2009 H1N1 pandemic influenza. RESULTS: We provide a complete mathematical formulation of the model and derive a semi-analytical expression of the threshold condition for global invasion of an emerging infectious disease in the metapopulation system. A rich solution space is found that depends on the social partition of the population, the pattern of contacts across groups and their relative social activity, the travel attitude of each class, and the topological and traffic features of the mobility network. Reducing the activity of the less social group and reducing the cross-group mixing are predicted to be the most efficient strategies for controlling the pandemic potential in the case the less active group constitutes the majority of travellers. If instead traveling is dominated by the more social class, our model predicts the existence of an optimal across-groups mixing that maximises the pandemic potential of the disease, whereas the impact of variations in the activity of each group is less important. CONCLUSIONS: The proposed modelling approach introduces a theoretical framework for the study of infectious diseases spread in a population with two layers of heterogeneity relevant for the local transmission and the spatial propagation of the disease. It can be used for pandemic preparedness studies to identify adequate interventions and quantitatively estimate the corresponding required effort, as well as in an emerging epidemic situation to assess the pandemic potential of the pathogen from population and early outbreak data

Ex-ante impact of pest des petits ruminant control on micro and macro socioeconomic indicators in Senegal: A system dynamics modelling approach.

Vaccination is considered as the main tool for the Global Control and Eradication Strategy for peste des petits ruminants (PPR), and the efficacity of the PPR-vaccine in conferring long-life immunity has been established. Despite this, previous studies asserted that vaccination can be expensive and consequently, the effectiveness of disease control may not necessarily translate to overall profit for farmers. Also, the consequences of PPR control on socioeconomic indicators like food and nutrition security at a macro-national level have not been explored thoroughly. Therefore, this study seeks to assess ex-ante the impact of PPR control strategies on farm-level profitability and the socioeconomic consequences concerning food and nutrition security at a national level in Senegal. A bi-level system dynamics model, compartmentalised into five modules consisting of integrated production-epidemiological, economics, disease control, marketing, and policy modules, was developed with the STELLA Architect software, validated, and simulated for 30 years at a weekly timestep. The model was parameterised with data from household surveys from pastoral areas in Northern Senegal and relevant existing data. Nine vaccination scenarios were examined considering different vaccination parameters (vaccination coverage, vaccine wastage, and the provision of government subsidies). The findings indicate that compared to a no-vaccination scenario, all the vaccination scenarios for both 26.5% (actual vaccination coverage) and 70% (expected vaccination coverage) resulted in statistically significant differences in the gross margin earnings and the potential per capita consumption for the supply of mutton and goat meat. At the prevailing vaccination coverage (with or without the provision of government subsidies), farm households will earn an average gross margin of $69.43 (annually) more than without vaccination, and the average per capita consumption for mutton and goat meat will increase by 1.13kg/person/year. When the vaccination coverage is increased to the prescribed threshold for PPR eradication (i.e., 70$72.23 annually and the per capita consumption will increase by 1.23kg/person/year compared to the baseline (without vaccination). This study's findings offer an empirical justification for a sustainable approach to PPR eradication. The information on the socioeconomic benefits of vaccination can be promoted via sensitization campaigns to stimulate farmers' uptake of the practice. This study can inform investment in PPR control

The topology of a discussion: the #occupy case

We analyse a large sample of the Twitter activity developed around the social movement 'Occupy Wall Street' to study the complex interactions between the human communication activity and the semantic content of a discussion. We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users In the first instance, we find out that discussion topics (#hashtags) present a high heterogeneity, with the distinct role of the communication hubs where most the 'opinion traffic' passes through. In the second case, the self-organization process of users activity leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Moreover the network presents a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when a certain set of topics are censored and/or accounts are removed. Analysing the characteristics the @Users-#Hashtags network we can distinguish three phases of the discussion about the movement. Each phase corresponds to specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of specific #hashtags in the discussion. Keywords: Twitter, Network analysisComment: 13 pages, 9 figure

Entrenamiento de redes neuronales

En la línea Redes Neuronales, dirigida por el Lic. Carlos Kavka, del Proyecto Sistemas Inteligentes para Scheduling y Control, dirigido por el Prof. Raúl Gallard, se está trabajando en temas vinculados con el desarrollo de nuevos algoritmos de entrenamiento de redes neuronales adecuados para su aplicación en distintos contextos. En particular se consideran dos sublíneas de trabajo: - Evolución de redes neuronales para la obtención de arquitecturas adecuadas para problemas difíciles. - Paralelización de redes neuronalesEje: Sistemas inteligentes. Metaheurísticas.Red de Universidades con Carreras en Informática (RedUNCI

Monitor ES como una herramienta de acercamiento a las redes de computadoras

En este trabajo presentamos las conclusiones de la primera etapa planifi cada en un proyecto educativo de nuestra Facultad. Como objetivos generales nos propusimos desarrollar una herramienta educativa que estuviese dirigida a los alumnos avanzados de nuestra carrera, a través de la curr ícula corriente. La idea es ofrecerles un ámbito de discusi ón y desarrollo disciplinar en este área, no fuertemente abordada en nuestro Departamento. De esta manera también contribuir a la formación de tesistas, becarios y pasantes interesados en esta tem ática. Concretamente, en este trabajo presentamos el diseño de MonitorES, un Monitor de Estado de Servidores en una red de computadoras, trabajando en un ambiente TCP=IP. La motivaci ón principal del desarrollo de esta primera etapa estuvo basada en el siguiente concepto: para diseñar efi cientemente aplicaciones distribuidas, primero hay que familiarizarse y conocer la performance de la red sobre la que se implementar a la aplicaci ón. Una consecuencia inmediata del proyecto fue la formaci ón de recursos humanos en el área altamente cr ítica de redes, produciendo un acercamiento amigable de usuarios no expertos en la tem ática.Eje: Tecnología Informática aplicada en Educación (TIE)Red de Universidades con Carreras en Informática (RedUNCI