Evaluation of Silver European EEL (anguilla anguilla) for the implementation of an Effective EEL Management plan in Mediterranean Coastal Lagoons

This thesis presents SEELF (Sustainable EEL fishery) Index, a methodology for evaluation of European eel (Anguilla anguilla) for the implementation of an effective Eel Management Plan, as defined by EU Regulation No.1100/2007. SEELF uses internal and external indices, age and blood parameters, and selects suitable specimen for restocking; it is also a reliable tool for eel stock management. In fact, SEELF Index, was developed in two versions: SEELF A, to be used in field operations (catch&release, eel status monitoring) and SEELF B to be used for quality control (food production) and research (eel status monitoring). Health status was evaluated also by biomarker analysis (ChE), and data were compared with age of eel. Age determination was performed with otolith reading and fish scale reading and a calibration between the two methods was possible. The study area was the Comacchio lagoon, a brackish coastal lagoon in Italy, well known as an example of suitable environment for eel fishery, where the capability to use the local natural resources has long been a key factor for a successful fishery management. Comacchio lagoon is proposed as an area where an effective EMP can be performed, in agreement with the main features (management of basins, reduction of mortality due to predators,etc.) highlighted for designation of European Restocking Area (ERA). The ERA is a new concept, proposed as a pillar of a new strategy on eel management and conservation. Furthermore, the features of ERAs can be useful in the framework of European Scale Eel Management Plan (ESEMP), proposed as a European scale implementation of EMP, providing a more effectiveness of conservation measures for eel management.

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.