New catalytic processes for the synthesis of adipic acid

The aim of my Ph.D. research was to study the new synthetic ways for the production of adipic acid. Three different pathways were studied: i) oxidation of cyclohexanone with molecular oxygen using Keggin – heteropolycompounds as the catalyst, ii) Baeyer – Villiger oxidation of cyclohexanone with hydrogen peroxide in the presence of two different heterogeneous catalysts, titanium silicalite and silica grafted decatungstate, iii) two step synthesis of adipic acid starting from cyclohexene via 1,2-cyclohexanediol. The first step was catalyzed by H2WO4 in the presence of the phase transfer catalyst, the oxidant was hydrogen peroxide. The second step, oxidation of 1,2 – cyclohexanediol was performed in the presence of oxygen and the heterogeneous catalyst – ruthenium on alumina. The results of my research showed that: i) Oxidation of cyclohexanone with molecular oxygen using Keggin heteropolycompounds is possible, anyway the conversion of ketone is low and the selectivity to adipic acid is lowered by the consecutive reaction to from lower diacids. Moreover it was found out, that there are two mechanisms involved: redox type and radicalic chain-reaction autoxidation. The presence of the different mechanism is influenced by the reaction condition. ii) It is possible to perform thermally activated oxidation of cyclohexanone and obtain non negligible amount of the products (caprolactone and adipic acid). Performing the catalyzed reaction it was demonstrated that the choice of the reaction condition and of the catalyst plays a crucial role in the product selectivity, explaining the discrepancies between the literature and our research. iii) Interesting results can be obtained performing the two step oxidation of cyclohexene via 1,2-cyclohexanediol. In the presence of phase transfer catalyst it is possible to obtain high selectivity to alcohol with stoichiometric amount of oxidant. In the second step of the synthesis, the conversion of alcohol is rather low with modest selectivity to adipic acid

Finite element models of coseismic deformation due to the 2009 L'Aquila (Italy) and 2008 Wenchuan(China) earthquakes

The topic of my Ph.D. thesis is the finite element modeling of coseismic deformation imaged by DInSAR and GPS data. I developed a method to calculate synthetic Green functions with finite element models (FEMs) and then use linear inversion methods to determine the slip distribution on the fault plane. The method is applied to the 2009 L’Aquila Earthquake (Italy) and to the 2008 Wenchuan earthquake (China). I focus on the influence of rheological features of the earth's crust by implementing seismic tomographic data and the influence of topography by implementing Digital Elevation Models (DEM) layers on the FEMs. Results for the L’Aquila earthquake highlight the non-negligible influence of the medium structure: homogeneous and heterogeneous models show discrepancies up to 20% in the fault slip distribution values. Furthermore, in the heterogeneous models a new area of slip appears above the hypocenter. Regarding the 2008 Wenchuan earthquake, the very steep topographic relief of Longmen Shan Range is implemented in my FE model. A large number of DEM layers corresponding to East China is used to achieve the complete coverage of the FE model. My objective was to explore the influence of the topography on the retrieved coseismic slip distribution. The inversion results reveals significant differences between the flat and topographic model. Thus, the flat models frequently adopted are inappropriate to represent the earth surface topographic features and especially in the case of the 2008 Wenchuan earthquake.

Identificazione dei fenotipi clinici dell’insufficienza renale cronica che predicono la progressione della malattia. Un’applicazione della tecnica degli alberi decisionali

The primary aim of this dissertation to identify subgroups of patients with chronic kidney disease (CKD) who have a differential risk of progression of illness and the secondary aim is compare 2 equations to estimate the glomerular filtration rate (GFR). To this purpose, the PIRP (Prevention of Progressive Kidney Disease) registry was linked with the dialysis and mortality registries. The outcome of interest is the mean annual variation of GFR, estimated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. A decision tree model was used to subtype CKD patients, based on the non-parametric procedure CHAID (Chi-squared Automatic Interaction Detector). The independent variables of the model include gender, age, diabetes, hypertension, cardiac diseases, body mass index, baseline serum creatinine, haemoglobin, proteinuria, LDL cholesterol, tryglycerides, serum phoshates, glycemia, parathyroid hormone and uricemia. The decision tree model classified patients into 10 terminal nodes using 6 variables (gender, age, proteinuria, diabetes, serum phosphates and ischemic cardiac disease) that predict a differential progression of kidney disease. Specifically, age <=53 year, male gender, proteinuria, diabetes and serum phosphates >3.70 mg/dl predict a faster decrease of GFR, while ischemic cardiac disease predicts a slower decrease. The comparison between GFR estimates obtained using MDRD4 and CKD-EPI equations shows a high percentage agreement (>90%), with modest discrepancies for high and low age and serum creatinine levels. The study results underscore the need for a tight follow-up schedule in patients with age <53, and of patients aged 54 to 67 with diabetes, to try to slow down the progression of the disease. The result also emphasize the effective management of patients aged>67, in whom the estimated decrease in glomerular filtration rate corresponds with the physiological decrease observed in the absence of kidney disease, except for the subgroup of patients with proteinuria, in whom the GFR decline is more pronounced.

Detection and Modeling of Boundary Layer Height

This thesis tackles the problem of the automated detection of the atmospheric boundary layer (BL) height, h, from aerosol lidar/ceilometer observations. A new method, the Bayesian Selective Method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in an statistically optimal way different sources of information. Firstly atmospheric stratification boundaries are located from discontinuities in the ceilometer back-scattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneus physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice. The BSM algorithm has been tested for four months of continuous ceilometer measurements collected during the BASE:ALFA project and is shown to realistically diagnose the BL depth evolution in many different weather conditions. Then the BASE:ALFA dataset is used to investigate the boundary layer structure in stable conditions. Functions from the Obukhov similarity theory are used as regression curves to fit observed velocity and temperature profiles in the lower half of the stable boundary layer. Surface fluxes of heat and momentum are best-fitting parameters in this exercise and are compared with what measured by a sonic anemometer. The comparison shows remarkable discrepancies, more evident in cases for which the bulk Richardson number turns out to be quite large. This analysis supports earlier results, that surface turbulent fluxes are not the appropriate scaling parameters for profiles of mean quantities in very stable conditions. One of the practical consequences is that boundary layer height diagnostic formulations which mainly rely on surface fluxes are in disagreement to what obtained by inspecting co-located radiosounding profiles.

MicroRNAs expression analysis in high grade gliomas

Several biomarkers had been proposed as useful parameters to better define the prognosis or to delineate new target therapy strategies for glioblastoma (GBM) patients. MicroRNAs could represent interesting molecules, for their role in tumorigenesis and cancer progression and for their specific tissue expression. Although many studies have tried to identify a specific microRNAs signature for glioblastoma, by now an exhaustive GBM microRNAs profile is far to be well defined. In this work we set up a real-time qPCR, based on LNA primers, to investigate the expression of 19 microRNAs in brain tumors, focusing our attention on GBMs. MiRNAs expression in 30 GBM paired FFPE-Fresh/Frozen samples was firstly analyzed. The good correlation obtained comparing miRNAs results confirmed the feasibility of performing miRNAs analysis starting from FFPE tissues. This leads to many advantages, as a good disposal of archival tumor and normal brain specimens and the possibility to verify the percentage of tumor cells in the analyzed sample. In the second part we compared 3 non-neoplastic brain references to use as control in miRNAs analysis. Normal adjacent the tumor, epileptic specimens and a commercial total RNA were analyzed for miRNAs expression and results showed that different non-neoplastic controls could lead to important discrepancies in GBM miRNAs profiles. Analyzing 50 FFPE GBMs using all 3 non-neoplastic references, we defined a putative GBM miRNAs signature: mir-10b, miR-21 and miR-27a resulted upregulated, while miR-7, miR-9, miR-26a, miR-31, miR-101, miR-137, miR-222 and miR-330 were downregulated. Comparing miRNAs expression among GBM group and gliomas of grade I, II and III, we obtained 3 miRNAs (miR-10b, mir-34a and miR-101) showing a different regulation status between high grade and low grade gliomas. Intriguingly, miR-10b was upregulated in high grade and significantly downregulated in low grade gliomas, suggesting that could be a candidate for a GBM target therapy.