Il contributo della psicopatologia ansiosa materna prenatale sul temperamento infantile e la relazione precoce madre-bambino

La sintomatologia ansiosa materna nel periodo prenatale risulta influire negativamente non sullo stato materno ma anche sul successivo sviluppo infantile, Tuttavia, sono limitati gli studi che hanno considerato lo specifico contributo dei disturbi d’ansia nel periodo prenatale. L’obiettivo generale dello studio è quello di indagare nel primo periodo post partum la relazione tra psicopatologia ansiosa materna e: temperamento e sviluppo neonatale, qualità del caregiving materno e dei pattern interattivi madre-bambino. 138 donne sono state intervistate utilizzando SCID-I (First et al., 1997) durante il terzo trimestre di gravidanza. 31 donne (22,5%) presentano disturbo d’ansia nel periodo prenatale. A 1 mese post partum il comportamento del neonato è stato valutato mediante NBAS (Brazelton, Nugent, 1995), mentre le madri hanno compilato MBAS (Brazelton, Nugent, 1995). A 3 mesi postpartum, una sequenza interattiva madre-bambino è stata videoregistrata e codificata utilizzando GRS (Murray et al., 1996). La procedura dello Stranger Episode (Murray et al., 2007) è stata utilizzata per osservare i pattern interattivi materni e infantili nell’interazione con una persona estranea. I neonati di madri con disturbo d’ansia manifestano alle NBAS minori capacità a livello di organizzazione di stati comportamentali, minori capacità attentive e di autoregolazione. Le madri ansiose si percepiscono significativamente meno sicure nell’occuparsi di loro, valutando i propri figli maggiormente instabili e irregolari. Nell’interazione face to face, esse mostrano comportamenti significativamente meno sensibilI, risultando meno coinvolte attivamente con il proprio bambino. Durante lo Stranger Episode, le madri con fobia sociale presentano maggiori livelli di ansia e incoraggiando in modo significativamente inferiore l’interazione del bambino con l’estraneo. I risultati sottolineano l’importanza di valutare in epoca prenatale la psicopatologia ansiosa materna. Le evidenze confermano la rilevanza che può assumere un modello multifattoriale di rischio in cui i disturbi d’ansia prenatali e la qualità del caregiving materno possono agire in modo sinergico nell’influire sugli esiti infantili.

Analysis and mathematical modeling of wave-structure interaction

The aim of this thesis, included within the THESEUS project, is the development of a mathematical model 2DV two-phase, based on the existing code IH-2VOF developed by the University of Cantabria, able to represent together the overtopping phenomenon and the sediment transport. Several numerical simulations were carried out in order to analyze the flow characteristics on a dike crest. The results show that the seaward/landward slope does not affect the evolution of the flow depth and velocity over the dike crest whereas the most important parameter is the relative submergence. Wave heights decrease and flow velocities increase while waves travel over the crest. In particular, by increasing the submergence, the wave height decay and the increase of the velocity are less marked. Besides, an appropriate curve able to fit the variation of the wave height/velocity over the dike crest were found. Both for the wave height and for the wave velocity different fitting coefficients were determined on the basis of the submergence and of the significant wave height. An equation describing the trend of the dimensionless coefficient c_h for the wave height was derived. These conclusions could be taken into consideration for the design criteria and the upgrade of the structures. In the second part of the thesis, new equations for the representation of the sediment transport in the IH-2VOF model were introduced in order to represent beach erosion while waves run-up and overtop the sea banks during storms. The new model allows to calculate sediment fluxes in the water column together with the sediment concentration. Moreover it is possible to model the bed profile evolution. Different tests were performed under low-intensity regular waves with an homogeneous layer of sand on the bottom of a channel in order to analyze the erosion-deposition patterns and verify the model results.

The Social Life of Pots. The reconstruction of ceramic production and distribution patterns of pottery assemblages in the Northern Levant during the Iron Age

The aim of the project is the creation of a new model for the analysis of the political and social structures of the Northern Levant during the Iron Age, through the study of the production and circulation of ceramics in urban and rural centers. The project includes an innovative approach compared to a traditional contextual and analytical study of ceramic material. The geographical area under consideration represents an ideal context for understanding these dynamics, as a place of interaction between culturally different but constantly communicating areas (Eastern Mediterranean, Syria, Upper Mesopotamia). They corresponds to present-day southeastern Turkey and northern Syria, with the Mediterranean coast and the Euphrates River as limits to the west and east, respectively. The chronological interval taken into consideration by the study extends from the twelfth century BC. to the seventh century BC, corresponding to a phase of political fragmentation of the region into small-medium state entities and their subsequent conquest by the Neo-Assyrian empire starting from the end of the ninth century BC.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.