Molecular genetics of inherited cystic kidney diseases: new diagnostic approaches

Background. Hhereditary cystic kidney diseases are a heterogeneous spectrum of disorders leading to renal failure. Clinical features and family history can help to distinguish the recessive from dominant diseases but the differential diagnosis is difficult due the phenotypic overlap. The molecular diagnosis is often the only way to characterize the different forms. A conventional molecular screening is suitable for small genes but is expensive and time-consuming for large size genes. Next Generation Sequencing (NGS) technologies enables massively parallel sequencing of nucleic acid fragments. Purpose. The first purpose was to validate a diagnostic algorithm useful to drive the genetic screening. The second aim was to validate a NGS protocol of PKHD1 gene. Methods. DNAs from 50 patients were submitted to conventional screening of NPHP1, NPHP5, UMOD, REN and HNF1B genes. 5 patients with known mutations in PKHD1 were submitted to NGS to validate the new method and a not genotyped proband with his parents were analyzed for a diagnostic application. Results. The conventional molecular screening detected 8 mutations: 1) the novel p.E48K of REN in a patient with cystic nephropathy, hyperuricemia, hyperkalemia and anemia; 2) p.R489X of NPHP5 in a patient with Senior Loken Syndrome; 3) pR295C of HNF1B in a patient with renal failure and diabetes.; 4) the NPHP1 deletion in 3 patients with medullar cysts; 5) the HNF1B deletion in a patient with medullar cysts and renal hypoplasia and in a diabetic patient with liver disease. The NGS of PKHD1 detected all known mutations and two additional variants during the validation. The diagnostic NGS analysis identified the patient’s compound heterozygosity with a maternal frameshift mutation and a paternal missense mutation besides a not transmitted paternal missense mutation. Conclusions. The results confirm the validity of our diagnostic algorithm and suggest the possibility to introduce this NGS protocol to clinical practice.

Engineering in Transition. Approaches, strategies and technologies for implementing system innovation towards sustainability

With the aim to provide people with sustainable options, engineers are ethically required to hold the safety, health and welfare of the public paramount and to satisfy society's need for sustainable development. The global crisis and related sustainability challenges are calling for a fundamental change in culture, structures and practices. Sustainability Transitions (ST) have been recognized as promising frameworks for radical system innovation towards sustainability. In order to enhance the effectiveness of transformative processes, both the adoption of a transdisciplinary approach and the experimentation of practices are crucial. The evolution of approaches towards ST provides a series of inspiring cases which allow to identify advances in making sustainability transitions happen. In this framework, the thesis has emphasized the role of Transition Engineering (TE). TE adopts a transdisciplinary approach for engineering to face the sustainability challenges and address the risks of un-sustainability. With this purpose, a definition of Transition Technologies is provided as a valid instruments to contribute to ST. In the empirical section, several transition initiatives have been analysed especially at the urban level. As a consequence, the model of living-lab of sustainability has crucially emerged. Living-labs are environments in which innovative technologies and services are co-created with users active participation. In this framework, university can play a key role as learning organization. The core of the thesis has concerned the experimental application of transition approach within the School of Engineering and Architecture of University of Bologna at Terracini Campus. The final vision is to realize a living-lab of sustainability. Particularly, a Transition Team has been established and several transition experiments have been conducted. The final result is not only the improvement of sustainability and resilience of the Terracini Campus, but the demonstration that university can generate solutions and strategies that tackle the complex, dynamic factors fuelling the global crisis.

Methodological approaches to estimate human population exposure to chemical substances

The public awareness that chemical substances are present ubiquitously in the environment, can be assumed through the diet and can exhibit various health effects, is very high in Europe and Italy. National and international institutions are called to provide figures on the magnitude, frequency, and duration of the population exposure to chemicals, including both natural or anthropogenic substances, voluntarily added to consumers’ good or accidentally entering the production chains. This thesis focuses broadly on how human population exposure to chemicals can be estimated, with particular attention to the methodological approaches and specific focus on dietary exposure assessment and biomonitoring. From the results obtained in the different studies collected in this thesis, it has been pointed out that when selecting the approach to use for the estimate of the exposure to chemicals, several different aspects must be taken into account: the nature of the chemical substance, the population of interest, clarify if the objective is to assess chronic or acute exposure, and finally, take into account the quality and quantity of data available in order to specify and quantify the uncertainty of the estimate.

Portfolio Approaches in Constraint Programming

Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of —possibly on-average slower— algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance. In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core. We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.