La diagnostica molecolare nel laboratorio di patologia clinica veterinaria

La prima parte del nostro studio riguarda la tecnica LAMP (Loop-mediated isothermal amplification), una tecnica di amplificazione isotermica recentemente inventata (Notomi et al., 2000). Essa presenta notevoli vantaggi rispetto alle tradizionali PCR: non necessita di strumentazioni sofisticate come i termociclatori, può essere eseguita da personale non specializzato, è una tecnica altamente sensibile e specifica ed è molto tollerante agli inibitori. Tutte queste caratteristiche fanno sì che essa possa essere utilizzata al di fuori dei laboratori diagnostici, come POCT (Point of care testing), con il vantaggio di non dover gestire la spedizione del campione e di avere in tempi molto brevi risultati paragonabili a quelli ottenuti con la tradizionale PCR. Sono state prese in considerazione malattie infettive sostenute da batteri che richiedono tempi molto lunghi per la coltivazione o che non sono addirittura coltivabili. Sono stati disegnati dei saggi per la diagnosi di patologie virali che necessitano di diagnosi tempestiva. Altri test messi a punto riguardano malattie genetiche del cane e due batteri d’interesse agro-alimentare. Tutte le prove sono state condotte con tecnica real-time per diminuire il rischio di cross-contaminazione pur riuscendo a comprendere in maniera approfondita l’andamento delle reazioni. Infine è stato messo a punto un metodo di visualizzazione colorimetrico utilizzabile con tutti i saggi messi a punto, che svincola completamente la reazione LAMP dall’esecuzione in un laboratorio specializzato. Il secondo capitolo riguarda lo studio dal punto di vista molecolare di un soggetto che presenza totale assenza di attività mieloperossidasica all’analisi di citochimica automatica (ADVIA® 2120 Hematology System). Lo studio è stato condotto attraverso amplificazione e confronto dei prodotti di PCR ottenuti sul soggetto patologico e su due soggetti con fenotipo wild-type. Si è poi provveduto al sequenziamento dei prodotti di PCR su sequenziatore automatico al fine di ricercare la mutazione responsabile della carenza di MPO nel soggetto indicato.

Automated Analysis Methods for the Assessment of Bicycle Infrastructure and Demand

A first phase of the research activity has been related to the study of the state of art of the infrastructures for cycling, bicycle use and methods for evaluation. In this part, the candidate has studied the "bicycle system" in countries with high bicycle use and in particular in the Netherlands. Has been carried out an evaluation of the questionnaires of the survey conducted within the European project BICY on mobility in general in 13 cities of the participating countries. The questionnaire was designed, tested and implemented, and was later validated by a test in Bologna. The results were corrected with information on demographic situation and compared with official data. The cycling infrastructure analysis was conducted on the basis of information from the OpenStreetMap database. The activity consisted in programming algorithms in Python that allow to extract data from the database infrastructure for a region, to sort and filter cycling infrastructure calculating some attributes, such as the length of the arcs paths. The results obtained were compared with official data where available. The structure of the thesis is as follows: 1. Introduction: description of the state of cycling in several advanced countries, description of methods of analysis and their importance to implement appropriate policies for cycling. Supply and demand of bicycle infrastructures. 2. Survey on mobility: it gives details of the investigation developed and the method of evaluation. The results obtained are presented and compared with official data. 3. Analysis cycling infrastructure based on information from the database of OpenStreetMap: describes the methods and algorithms developed during the PhD. The results obtained by the algorithms are compared with official data. 4. Discussion: The above results are discussed and compared. In particular the cycle demand is compared with the length of cycle networks within a city. 5. Conclusions

Discrete Event Systems based Design Patterns for Diagnosability Analysis of Automated Manufacturing Systems

The main goal of this thesis is to facilitate the process of industrial automated systems development applying formal methods to ensure the reliability of systems. A new formulation of distributed diagnosability problem in terms of Discrete Event Systems theory and automata framework is presented, which is then used to enforce the desired property of the system, rather then just verifying it. This approach tackles the state explosion problem with modeling patterns and new algorithms, aimed for verification of diagnosability property in the context of the distributed diagnosability problem. The concepts are validated with a newly developed software tool.

The notion of equivalence and lexical cohesion in the translation of political speeches

Abstract This dissertation investigates the notion of equivalence with particular reference to lexical cohesion in the translation of political speeches. Lexical cohesion poses a particular challenge to the translators of political speeches and thus preserving lexical cohesion elements as one of the major elements of cohesion is undoubtedly crucial to their translation equivalence. We rely on Halliday’s (1994) classification of lexical cohesion which comprises: repetition, synonymy, antonymy, meronymy and hyponymy. Other traditional models of lexical cohesion are examined. We include Grammatical Parallelism for its role in creating textual semantic unity which is what cohesion is all about. The study shed light on the function of lexical cohesion elements as rhetorical device. The study also deals with lexical problems resulting from the transfer of lexical cohesion elements from the SL into the TL, which is often beset by many problems that most often result from the differences between languages. Three key issues are identified as being fundamental to equivalence and lexical cohesion in the translation of political speeches: sociosemiotic approach, register analysis, rhetoric, and poetic function. The study also investigates the lexical cohesion elements in the translation of political speeches from English into Arabic, Italian and French in relation to ideology, and its control, through bias and distortion. The findings are discussed, implications examined and topics for further research suggested.

Procedures of quality control and data analysis of multi-site ground-based observations for the absolute flux calibration of Gaia

The Gaia space mission is a major project for the European astronomical community. As challenging as it is, the processing and analysis of the huge data-flow incoming from Gaia is the subject of thorough study and preparatory work by the DPAC (Data Processing and Analysis Consortium), in charge of all aspects of the Gaia data reduction. This PhD Thesis was carried out in the framework of the DPAC, within the team based in Bologna. The task of the Bologna team is to define the calibration model and to build a grid of spectro-photometric standard stars (SPSS) suitable for the absolute flux calibration of the Gaia G-band photometry and the BP/RP spectrophotometry. Such a flux calibration can be performed by repeatedly observing each SPSS during the life-time of the Gaia mission and by comparing the observed Gaia spectra to the spectra obtained by our ground-based observations. Due to both the different observing sites involved and the huge amount of frames expected (≃100000), it is essential to maintain the maximum homogeneity in data quality, acquisition and treatment, and a particular care has to be used to test the capabilities of each telescope/instrument combination (through the “instrument familiarization plan”), to devise methods to keep under control, and eventually to correct for, the typical instrumental effects that can affect the high precision required for the Gaia SPSS grid (a few % with respect to Vega). I contributed to the ground-based survey of Gaia SPSS in many respects: with the observations, the instrument familiarization plan, the data reduction and analysis activities (both photometry and spectroscopy), and to the maintenance of the data archives. However, the field I was personally responsible for was photometry and in particular relative photometry for the production of short-term light curves. In this context I defined and tested a semi-automated pipeline which allows for the pre-reduction of imaging SPSS data and the production of aperture photometry catalogues ready to be used for further analysis. A series of semi-automated quality control criteria are included in the pipeline at various levels, from pre-reduction, to aperture photometry, to light curves production and analysis.

New computational biology tools for the systematic analysis of the structure and expression of human genes

From the late 1980s, the automation of sequencing techniques and the computer spread gave rise to a flourishing number of new molecular structures and sequences and to proliferation of new databases in which to store them. Here are presented three computational approaches able to analyse the massive amount of publicly avalilable data in order to answer to important biological questions. The first strategy studies the incorrect assignment of the first AUG codon in a messenger RNA (mRNA), due to the incomplete determination of its 5' end sequence. An extension of the mRNA 5' coding region was identified in 477 in human loci, out of all human known mRNAs analysed, using an automated expressed sequence tag (EST)-based approach. Proof-of-concept confirmation was obtained by in vitro cloning and sequencing for GNB2L1, QARS and TDP2 and the consequences for the functional studies are discussed. The second approach analyses the codon bias, the phenomenon in which distinct synonymous codons are used with different frequencies, and, following integration with a gene expression profile, estimates the total number of codons present across all the expressed mRNAs (named here "codonome value") in a given biological condition. Systematic analyses across different pathological and normal human tissues and multiple species shows a surprisingly tight correlation between the codon bias and the codonome bias. The third approach is useful to studies the expression of human autism spectrum disorder (ASD) implicated genes. ASD implicated genes sharing microRNA response elements (MREs) for the same microRNA are co-expressed in brain samples from healthy and ASD affected individuals. The different expression of a recently identified long non coding RNA which have four MREs for the same microRNA could disrupt the equilibrium in this network, but further analyses and experiments are needed.

Numerical and experimental analysis of 3D printed smart structures and systems

Three dimensional (3D) printers of continuous fiber reinforced composites, such as MarkTwo (MT) by Markforged, can be used to manufacture such structures. To date, research works devoted to the study and application of flexible elements and CMs realized with MT printer are only a few and very recent. A good numerical and/or analytical tool for the mechanical behavior analysis of the new composites is still missing. In addition, there is still a gap in obtaining the material properties used (e.g. elastic modulus) as it is usually unknown and sensitive to printing parameters used (e.g. infill density), making the numerical simulation inaccurate. Consequently, the aim of this thesis is to present several work developed. The first is a preliminary investigation on the tensile and flexural response of Straight Beam Flexures (SBF) realized with MT printer and featuring different interlayer fiber volume-fraction and orientation, as well as different laminate position within the sample. The second is to develop a numerical analysis within the Carrera' s Unified Formulation (CUF) framework, based on component-wise (CW) approach, including a novel preprocessing tool that has been developed to account all regions printed in an easy and time efficient way. Among its benefits, the CUF-CW approach enables building an accurate database for collecting first natural frequencies modes results, then predicting Young' s modulus based on an inverse problem formulation. To validate the tool, the numerical results are compared to the experimental natural frequencies evaluated using a digital image correlation method. Further, we take the CUF-CW model and use static condensation to analyze smart structures which can be decomposed into a large number of similar components. Third, the potentiality of MT in combination with topology optimization and compliant joints design (CJD) is investigated for the realization of automated machinery mechanisms subjected to inertial loads.

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.