Analyzing convergence of Agricultural and Rural Areas in the Enlarged EU: The case of Hungary

The fall of the Berlin Wall opened the way for a reform path – the transition process – which accompanied ten former Socialist countries in Central and South Eastern Europe to knock at the EU doors. By the way, at the time of the EU membership several economic and structural weaknesses remained. A tendency towards convergence between the new Member States (NMS) and the EU average income level emerged, together with a spread of inequality at the sub-regional level, mainly driven by the backwardness of the agricultural and rural areas. Several progresses were made in evaluating the policies for rural areas, but a shared definition of rurality is still missing. Numerous indicators were calculated for assessing the effectiveness of the Common Agricultural Policy and Rural Development Policy. Previous analysis on the Central and Eastern European countries found that the characteristics of the most backward areas were insufficiently addressed by the policies enacted; the low data availability and accountability at a sub-regional level, and the deficiencies in institutional planning and implementation represented an obstacle for targeting policies and payments. The next pages aim at providing a basis for understanding the connections between the peculiarities of the transition process, the current development performance of NMS and the EU role, with particular attention to the agricultural and rural areas. Applying a mixed methodological approach (multivariate statistics, non-parametric methods, spatial econometrics), this study contributes to the identification of rural areas and to the analysis of the changes occurred during the EU membership in Hungary, assessing the effect of CAP introduction and its contribution to the convergence of the Hungarian agricultural and rural. The author believes that more targeted – and therefore efficient – policies for agricultural and rural areas require a deeper knowledge of their structural and dynamic characteristics.

Computational Modelling of Cardiac Electrophysiology: from Cell to Bedside

Heart diseases are the leading cause of death worldwide, both for men and women. However, the ionic mechanisms underlying many cardiac arrhythmias and genetic disorders are not completely understood, thus leading to a limited efficacy of the current available therapies and leaving many open questions for cardiac electrophysiologists. On the other hand, experimental data availability is still a great issue in this field: most of the experiments are performed in vitro and/or using animal models (e.g. rabbit, dog and mouse), even when the final aim is to better understand the electrical behaviour of in vivo human heart either in physiological or pathological conditions. Computational modelling constitutes a primary tool in cardiac electrophysiology: in silico simulations, based on the available experimental data, may help to understand the electrical properties of the heart and the ionic mechanisms underlying a specific phenomenon. Once validated, mathematical models can be used for making predictions and testing hypotheses, thus suggesting potential therapeutic targets. This PhD thesis aims to apply computational cardiac modelling of human single cell action potential (AP) to three clinical scenarios, in order to gain new insights into the ionic mechanisms involved in the electrophysiological changes observed in vitro and/or in vivo. The first context is blood electrolyte variations, which may occur in patients due to different pathologies and/or therapies. In particular, we focused on extracellular Ca2+ and its effect on the AP duration (APD). The second context is haemodialysis (HD) therapy: in addition to blood electrolyte variations, patients undergo a lot of other different changes during HD, e.g. heart rate, cell volume, pH, and sympatho-vagal balance. The third context is human hypertrophic cardiomyopathy (HCM), a genetic disorder characterised by an increased arrhythmic risk, and still lacking a specific pharmacological treatment.

Distribution and state of fishery resources in the Northern and Central Adriatic Sea using trawl surveys data

The Ecosystem Approach to Fisheries represents the most recent research line in the international context, showing interest both towards the whole community and toward the identification and protection of all the “critical habitats” in which marine resources complete their life cycles. Using data coming from trawl surveys performed in the Northern and Central Adriatic from 1996 to 2010, this study provides the first attempt to appraise the status of the whole demersal community. It took into account not only fishery target species but also by-catch and discharge species by the use of a suite of biological indicators both at population and multi-specific level, allowing to have a global picture of the status of the demersal system. This study underlined the decline of extremely important species for the Adriatic fishery in recent years; adverse impact on catches is expected for these species in the coming years, since also minimum values of recruits recently were recorded. Both the excessive exploitation and environmental factors affected availability of resources. Moreover both distribution and nursery areas of the most important resources were pinpointed by means of geostatistical methods. The geospatial analysis also confirmed the presence of relevant recruitment areas in the North and Central Adriatic for several commercial species, as reported in the literature. The morphological and oceanographic features, the relevant rivers inflow together with the mosaic pattern of biocenoses with different food availability affected the location of the observed relevant nursery areas.

Organic fertilization of peach trees: implication on nitrogen availability, root growth and carbon distribution within plant

In the last years, sustainable horticulture has been increasing; however, to be successful this practice needs an efficient soil fertility management to maintain a high productivity and fruit quality standards. For this purpose composted organic materials from agri-food industry and municipal solid waste has been used as a source to replace chemical fertilizers and increase soil organic matter. To better understand the influence of compost application on soil fertility and plant growth, we carried out a study comparing organic and mineral nitrogen (N) fertilization in micro propagated plants, potted trees and commercial peach orchard with these aims: 1. evaluation of tree development, CO2 fixation and carbon partition to the different organs of two-years-old potted peach trees. 2. Determination of soil N concentration and nitrate-N effect on plant growth and root oxidative stress of micro propagated plant after increasing rates of N applications. 3. Assessment of soil chemical and biological fertility, tree growth and yield and fruit quality in a commercial orchard. The addition of compost at high rate was effective in increasing CO2 fixation, promoting root growth, shoot and fruit biomass. Furthermore, organic fertilizers influenced C partitioning, favoring C accumulation in roots, wood and fruits. The higher CO2 fixation was the result of a larger tree leaf area, rather than an increase in leaf photosynthetic efficiency, showing a stimulation of plant growth by application of compost. High concentrations of compost increased total soil N concentration, but were not effective in increasing nitrate-N soil concentration; in contrast mineral-N applications increased linearly soil nitrate-N, even at the lowest rate tested. Soil nitrate-N concentration influenced positively plant growth at low rate (60- 80 mg kg-1), whereas at high concentrations showed negative effects. In this trial, the decrease of root growth, as a response to excessive nitrate-N soil concentration, was not anticipated by root oxidative stress. Continuous annual applications of compost for 10 years enhanced soil organic matter content and total soil N concentration. Additionally, high rate of compost application (10 t ha-1 year-1) enhanced microbial biomass. On the other hand, different fertilizers management did not modify tree yield, but influenced fruit size and precocity index. The present data support the idea that organic fertilizers can be used successfully as a substitute of mineral fertilizers in fruit tree nutrient management, since they promote an increase of soil chemical and biological fertility, prevent excessive nitrate-N soil concentration, promote plant growth and potentially C sequestration into the soil.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.