Determinants of cesarean delivery, a population-based study in the Emilia Romagna Region

Cesarean Delivery (CD) rates are rising in many parts of the world. In order to define strategies to reduce them, it is important to explore the role of clinical and organizational factors. This thesis has the objective to describe the contemporary CD practice and study clinical and organizational variables as determinants of CD in all women who gave birth between 2005 and June 2010 in the Emilia Romagna region (Italy). All hospital discharge abstracts of women who delivered between 2005 and mid 2010 in the region were selected and linked with birth certificates. In addition to descriptive statistics, in order to study the role of clinical and organizational variables (teaching or non-teaching hospital, birth volumes, time and day of delivery) multilevel Poisson regression models and a classification tree were used. A substantial inter-hospital variability in CD rate was found, and this was only partially explained by the considered variables. The most important risk factors of CD were: previous CD (RR 4,95; 95%CI: 4,85-5,05), cord prolapse (RR 3,51; 95% CI:2,96-4,16), and malposition/malpresentation (RR 2,72; 95%CI: 2,66-2,77). Delivery between 7 pm and 7 am and during non working days protect against CD in all subgroups including those with a small number of elective CDs while delivery at a teaching hospital and birth volumes were not statistically significant risk factors. The classification tree shows that previous CD and malposition/malpresentation are the most important variables discriminating between high and low risk of CD. These results indicate that other not considered factors might explain CD variability and do not provide clear evidence that small hospitals have a poor performance in terms of CD rate. Some strategies to reduce CD could be found by focusing on the differences in delivery practice between day and night and between working and no-working day deliveries.

Terapia chirurgica dell'acalasia scompensata: prevalenza e fattori di rischio per lo sviluppo del carcinoma esofageo, risultati clinico-funzionali a lungo termine della chirurgia conservativa e dell'esofagectomia

La chirurgia conservativa o l’esofagectomia, possono essere indicate per il trattamento della disfagia nell’acalasia scompensata. L’esofagectomia è inoltre finalizzata alla prevenzione dello sviluppo del carcinoma esofageo. Gli obiettivi erano: a) definire prevalenza e fattori di rischio per il carcinoma epidermoidale; b) confrontare i risultati clinici e funzionali di Heller-Dor con pull-down della giunzione esofagogastrica (PD-HD) ed esofagectomia. I dati in analisi, ricavati da un database istituito nel 1973 e finalizzato alla ricerca prospettica, sono stati: a) le caratteristiche cliniche, radiologiche ed endoscopiche di 573 pazienti acalasici; b) il risultato oggettivo e la qualità della vita, definita mediante questionario SF-36, dopo intervento di PD-HD (29 pazienti) e dopo esofagectomia per acalasia scompensata o carcinoma (20 pazienti). Risultati: a) sono stati diagnosticati 17 carcinomi epidermoidali ed un carcinosarcoma (3.14%). Fattori di rischio sono risultati essere: il diametro esofageo (p<0.001), il ristagno esofageo (p<0.01) e la durata dei sintomi dell’acalasia (p<0.01). Secondo l’albero di classificazione, soltanto i pazienti con esito insufficiente del trattamento ai controlli clinico-strumentali ed acalasia sigmoidea presentavano un rischio di sviluppare il carcinoma squamocellulare del 52.9%. b) Non sono state riscontrate differenze statisticamente significative tra i pazienti sottoposti ad intervento conservativo e quelli trattati con esofagectomia per quanto concerne l’esito dell’intervento valutato mediante parametri oggettivi (p=0.515). L’analisi della qualità della vita non ha evidenziato differenze statisticamente significative per quanto concerne i domini GH, RP, PF e BP. Punteggi significativamente più elevati nei domini RE (p=0.012), VT (p<0.001), MH (p=0.001) e SF (p=0.014) sono stati calcolati per PD-HD rispetto alle esofagectomie. In conclusione, PD-HD determina una miglior qualità della vita, ed è pertanto la procedura di scelta per i pazienti con basso rischio di cancro. A coloro che abbiano già raggiunto i parametri di rischio, si offrirà l’esofagectomia o l'opzione conservativa seguita da protocolli di follow-up.

New biomaterials for packaging: a green approach to biopolymers and biocomposites

The impellent global environmental issues related to plastic materials can be addressed by following two different approaches: i) the development of synthetic strategies towards novel bio-based polymers, deriving from biomasses and thus identifiable as CO2-neutral materials, and ii) the development of new plastic materials, such as biocomposites, which are bio-based and biodegradable and therefore able to counteract the accumulation of plastic waste. In this framework, this dissertation presents extensive research efforts have been devoted to the synthesis and characterization of polyesters based on various bio-based monomers, including ω-pentadecalactone, vanillic acid, 2,5-furan dicarboxylic acid, and 5-hydroxymethylfurfural. With the aim of achieving high molecular weight polyesters, different synthetic strategies have been used as melt polycondensation, enzymatic polymerization, ring-opening polymerization and chain extension reaction. In particular, poly(ethylene vanillate) (PEV), poly(ω-pentadecalactone) (PPDL), poly(ethylene vanillate-co-pentadecalactone) (P(EV-co-PDL)), poly(2-hydroxymethyl 5-furancarboxylate) (PHMF), poly(ethylene 2,5-furandicarboxylate) (PEF) with different amount of diethylene glycol (DEG) unit amount, poly(propylene 2,5-furandicarboxylate) (PPF), poly(hexamethylene 2,5-furandicarboxylate), (PHF) have been prepared and extensively characterized. To improve the lacks of poly(hydroxybutyrate-co-valerate) (PHBV), its minimal formulations with natural additives and its blending with medium chain length PHAs (mcl-PHAs) have been tested. Additionally, this dissertation presents new biocomposites based on polylactic acid (PLA), poly(butylene succinate) (PBS), and PHBV, which are polymers both bio-based and biodegradable. To maintain their biodegradability only bio-fillers have been taken into account as reinforcing agents. Moreover, the commitment to sustainability has further limited the selection and led to the exclusive use of agricultural waste as fillers. Detailly, biocomposites have been obtained and discussed by using the following materials: PLA and agro-wastes like tree pruning, potato peels, and hay leftovers; PBS and exhausted non-compliant coffee green beans; PHBV and industrial starch extraction residues.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Effect of training systems and pruning methods on fruit quality in apple

The introduction of dwarfed rootstocks in apple crop has led to a new concept of intensive planting systems with the aim of producing early high yield and with returns of the initial high investment. Although yield is an important aspect to the grower, the consumer has become demanding regards fruit quality and is generally attracted by appearance. To fulfil the consumer’s expectations the grower may need to choose a proper training system along with an ideal pruning technique, which ensure a good light distribution in different parts of the canopy and a marketable fruit quality in terms of size and skin colour. Although these aspects are important, these fruits might not reach the proper ripening stage within the canopy because they are often heterogeneous. To describe the variability present in a tree, a software (PlantToon®), was used to recreate the tree architecture in 3D in the two training systems. The ripening stage of each of the fruits was determined using a non-destructive device (DA-Meter), thus allowing to estimate the fruit ripening variability. This study deals with some of the main parameters that can influence fruit quality and ripening stage within the canopy and orchard management techniques that can ameliorate a ripening fruit homogeneity. Significant differences in fruit quality were found within the canopies due to their position, flowering time and bud wood age. Bi-axis appeared to be suitable for high density planting, even though the fruit quality traits resulted often similar to those obtained with a Slender Spindle, suggesting similar fruit light availability within the canopies. Crop load confirmed to be an important factor that influenced fruit quality as much as the interesting innovative pruning method “Click”, in intensive planting systems.