Effects of artificial defences and flooding on coastal habitats and assemblages

Since large stretches of European coasts are already retreating and projected scenarios are worsening, many artificial structures, such as breakwaters and seawalls, are built as tool against coastal erosion. However artificial structures produce widespread changes that alter the coastal zones and affect the biological communities. My doctoral thesis analyses the consequences of different options for coastal protection, namely hard engineering ‘artificial defences’ (i.e. impact of human-made structures) and ‘no-defence’ (i.e. impact of seawater inundation). I investigated two new aspects of the potential impact of coastal defences. The first was the effect of artificial hard substrates on the fish communities structure. In particular I was interested to test if the differences among breakwaters and natural rocky reef would change depending on the nature of the surrounding habitat of the artificial structure (prevalent sandy rather than rocky). The second was the effect on the native natural sandy habitats of the organic detritus derived from hard-bottom species (green algae and mussels) detached from breakwaters. Furthermore, I investigated the ecological implication of the “no-defend” option, which allow the inundation of coastal habitats. The focus of this study was the potential effect of seawater intrusion on the degradation process of marine, salt-marsh and terrestrial detritus, including changes on the breakdown rates and the associated macrofauna. The PhD research was conducted in three areas along European coasts: North Adriatic sea, Sicilian coast and South-West England where different habitats (coastal, estuarine), biological communities (soft-bottom macro-benthos; rocky-coastal fishes; estuarine macro-invertebrates) and processes (organic enrichment; assemblage structure; leaf-litter breakdown) were analyzed. The research was carried out through manipulative and descriptive field-experiments in which specific hypothesis were tested by univariate and multivariate analyses.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.