Rainfall spatial predictions: a two-part model and its assessment

Spatial prediction of hourly rainfall via radar calibration is addressed. The change of support problem (COSP), arising when the spatial supports of different data sources do not coincide, is faced in a non-Gaussian setting; in fact, hourly rainfall in Emilia-Romagna region, in Italy, is characterized by abundance of zero values and right-skeweness of the distribution of positive amounts. Rain gauge direct measurements on sparsely distributed locations and hourly cumulated radar grids are provided by the ARPA-SIMC Emilia-Romagna. We propose a three-stage Bayesian hierarchical model for radar calibration, exploiting rain gauges as reference measure. Rain probability and amounts are modeled via linear relationships with radar in the log scale; spatial correlated Gaussian effects capture the residual information. We employ a probit link for rainfall probability and Gamma distribution for rainfall positive amounts; the two steps are joined via a two-part semicontinuous model. Three model specifications differently addressing COSP are presented; in particular, a stochastic weighting of all radar pixels, driven by a latent Gaussian process defined on the grid, is employed. Estimation is performed via MCMC procedures implemented in C, linked to R software. Communication and evaluation of probabilistic, point and interval predictions is investigated. A non-randomized PIT histogram is proposed for correctly assessing calibration and coverage of two-part semicontinuous models. Predictions obtained with the different model specifications are evaluated via graphical tools (Reliability Plot, Sharpness Histogram, PIT Histogram, Brier Score Plot and Quantile Decomposition Plot), proper scoring rules (Brier Score, Continuous Rank Probability Score) and consistent scoring functions (Root Mean Square Error and Mean Absolute Error addressing the predictive mean and median, respectively). Calibration is reached and the inclusion of neighbouring information slightly improves predictions. All specifications outperform a benchmark model with incorrelated effects, confirming the relevance of spatial correlation for modeling rainfall probability and accumulation.

Immunohistochemical and Molecular Prognostic/Predictive Markers in Neoplastic Diseases

Traditional morphological examinations are not anymore sufficient for a complete evaluation of tumoral tissue and the use of neoplastic markers is of utmost importance. Neoplastic markers can be classified in: diagnostic, prognostic and predictive markers. Three markers were analyzed. 1) Insulin-like growth factor binding protein 2 (IGFBP2) was immunohistochemically examined in prostatic tissues: 40 radical prostatectomies from hormonally untreated patients with their preoperative biopsies, 10 radical prostatectomies from patients under complete androgen ablation before surgery and 10 simple prostatectomies from patients with bladder outlet obstruction. Results were compared with α-methylacyl-CoA racemase (AMACR). IGFBP2 was expressed in the cytoplasm of untreated adenocarcinomas and, to a lesser extent, in HG-PIN; the expression was markedly lower in patients after complete androgen ablation. AMACR was similarly expressed in both adenocarcinoma and HG-PIN, the level being similar in both lesions; the expression was slightly lower in patients after complete androgen ablation. IGFBP2 may be used a diagnostic marker of prostatic adenocarcinomas. 2) Heparan surface proteoglycan immunohistochemical expression was examined in 150 oral squamous cell carcinomas. Follow up information was available in 93 patients (range: 6-34 months, mean: 19±7). After surgery, chemotherapy was performed in 8 patients and radiotherapy in 61 patients. Multivariate and univariate overall survival analyses showed that high expression of syndecan-1 (SYN-1) was associated with a poor prognosis. In patients treated with radiotherapy, such association was higher. SYN-1 is a prognostic marker in oral squamous cell carcinomas; it may also represent a predictive factor for responsiveness to radiotherapy. 3) EGFR was studied in 33 pulmonary adenocarcinomas with traditional DNA sequencing methods and with two mutation-specific antibodies. Overall, the two antibodies had 61.1% sensitivity and 100% specificity in detecting EGFR mutations. EGFR mutation-specific antibodies may represent a predictive marker to identify patients candidate to tyrosine kinase inhibitors therapy.

The vulnerable carotid plaque. Identification of endothelial markers predictive of plaque weakness, rupture predisposition and neoangiogenesis

Background. Neoangiogenesis is crucial in plaque progression and instability. Previous data from our group demonstrated that intra-plaque neovessels show both a Nestin+/WT+ and a Nestin+/WT1- phenotype, the latter being correlated with complications and plaque instability. Aims. The aims of the present thesis are: (i) to confirm our previous results on Nestin/WT1 phenotype in a larger series of carotid atheromatous plaques, (ii) to evaluate the relationship between the Nestin+/WT1- neoangiogenesis phenotype and plaque morphology, (iii) to evaluate the relationship between the immunohistochemical and histopathological characteristics and the clinical instability of the plaques. Materials and Methods. Seventy-three patients (53 males, 20 females, mean age 71 years) were consecutively enrolled. Symptoms, brain CT scan, 14 histological variables, including intraplaque hemorrhage and diffuse calcifications, were collected. Immunohistochemistry for CD34, Nestin and WT1 was performed. RT-PCR was performed to evaluate Nestin and WT1 mRNA (including 5 healthy arteries as controls). Results. Diffusely calcified plaques (13 out of 73) were found predominantly in females (P=0.017), with a significantly lower incidence of symptoms (TIA/stroke) and brain focal lesions (P=0.019 and P=0.013 respectively) than not-calcified plaques, but with the same incidence of intraplaque complications (P=0.156). Accordingly, both calcified and not calcified plaques showed similar mean densities of positivity for CD34, Nestin and WT1. The density of Nestin and WT1 correlated with the occurrence of intra-plaque hemorrhage in all cases, while the density of CD34 correlated only in not-calcified plaques. Conclusions. We confirmed that the Nestin+/WT1- phenotype characterizes the neovessels of instable plaques, regardless the real amount of CD34-positive neoangiogenesis. The calcified plaques show the same incidence of histological complications, albeit they do not influence symptomatology and plaque vulnerability. Female patients show a much higher incidence of not-complicated or calcified plaques, receiving de facto a sort of protection compared to male patients.

Development of predictive energy management strategies for hybrid electric vehicles supported by connectivity

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of the global warming. In this context, the transportation sector plays a vital role, since it is responsible for a large part of carbon dioxide production. In order to address these issues, the present thesis deals with the development of advanced control strategies for the energy efficiency optimization of plug-in hybrid electric vehicles (PHEVs), supported by the prediction of future working conditions of the powertrain. In particular, a Dynamic Programming algorithm has been developed for the combined optimization of vehicle energy and battery thermal management. At this aim, the battery temperature and the battery cooling circuit control signal have been considered as an additional state and control variables, respectively. Moreover, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to handle zero-emission zones, where engine propulsion is not allowed. Navigation data represent an essential element in the achievement of these tasks. With this aim, a novel simulation and testing environment has been developed during the PhD research activity, as an effective tool to retrieve routing information from map service providers via vehicle-to-everything connectivity. Comparisons between the developed and the reference strategies are made, as well, in order to assess their impact on the vehicle energy consumption. All the activities presented in this doctoral dissertation have been carried out at the Green Mobility Research Lab} (GMRL), a research center resulting from the partnership between the University of Bologna and FEV Italia s.r.l., which represents the industrial partner of the research project.

The predictive role of the human gut microbiota in covid-19: implications for disease severity and post-covid syndrome

The COVID-19 pandemic, sparked by the SARS-CoV-2 virus, stirred global comparisons to historical pandemics. Initially presenting a high mortality rate, it later stabilized globally at around 0.5-3%. Patients manifest a spectrum of symptoms, necessitating efficient triaging for appropriate treatment strategies, ranging from symptomatic relief to antivirals or monoclonal antibodies. Beyond traditional approaches, emerging research suggests a potential link between COVID-19 severity and alterations in gut microbiota composition, impacting inflammatory responses. However, most studies focus on severe hospitalized cases without standardized criteria for severity. Addressing this gap, the first study in this thesis spans diverse COVID-19 severity levels, utilizing 16S rRNA amplicon sequencing on fecal samples from 315 subjects. The findings highlight significant microbiota differences correlated with severity. Machine learning classifiers, including a multi-layer convoluted neural network, demonstrated the potential of microbiota compositional data to predict patient severity, achieving an 84.2% mean balanced accuracy starting one week post-symptom onset. These preliminary results underscore the gut microbiota's potential as a biomarker in clinical decision-making for COVID-19. The second study delves into mild COVID-19 cases, exploring their implications for ‘long COVID’ or Post-Acute COVID-19 Syndrome (PACS). Employing longitudinal analysis, the study unveils dynamic shifts in microbial composition during the acute phase, akin to severe cases. Innovative techniques, including network approaches and spline-based longitudinal analysis, were deployed to assess microbiota dynamics and potential associations with PACS. The research suggests that even in mild cases, similar mechanisms to hospitalized patients are established regarding changes in intestinal microbiota during the acute phase of the infection. These findings lay the foundation for potential microbiota-targeted therapies to mitigate inflammation, potentially preventing long COVID symptoms in the broader population. In essence, these studies offer valuable insights into the intricate relationships between COVID-19 severity, gut microbiota, and the potential for innovative clinical applications.