Environmental weathering of natural and artificial stones used in historical architecture: influence of microstructure and new restoration methods

For some study cases (the Cathedral of Modena, Italy, XII-XIV century; the Ducal Palace in Mantua, Italy, XVI century; the church of San Francesco in Fano, Italy, XIV-XIX century), considered as representative of the use of natural and artificial stones in historical architecture, the complex interaction between environ-mental aggressiveness, materials’ microstructural characteristics and degradation was investigated. From the results of such analyses, it was found that materials microstructure plays a fundamental role in the actual extent to which weathering mechanisms affect natural and artificial stones. Consequently, the need of taking into account the important role of material microstructure, when evaluating the environmental aggressiveness to natural and artificial stones, was highlighted. Therefore, a possible quantification of the role of microstructure on the resistance to environmental attack was investigated. By exposing stone samples, with significantly different microstructural features, to slightly acidic aqueous solutions, simulating clean and acid rain, a good correlation between weight losses and the product of carbonate content and specific surface area (defined as the “vulnerable specific surface area”) was found. Alongside the evaluation of stone vulnerability, the development of a new consolidant for weathered carbonate stones was undertaken. The use of hydroxya-patite, formed by reacting the calcite of the stone with an aqueous solution of di-ammonium hydrogen phosphate, was found to be a promising consolidating tech-nique for carbonates stones. Indeed, significant increases in the mechanical prop-erties can be achieved after the treatment, which has the advantage of simply con-sisting in a non-hazardous aqueous solution, able to penetrate deeply into the stone (> 2 cm) and bring significant strengthening after just 2 days of reaction. Furthermore, the stone sorptivity is not eliminated after treatment, so that water and water vapor exchanges between the stone and the environment are not com-pletely blocked.

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.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.