Characterization and exploiment of Microbial Strains to be used in Meat Processing and Fermentation

The principal aim of this research project has been the evaluation of the specific role of yeasts in ripening processes of dry-cured meat products, i.e. speck and in salami produced by adding Lactobacilli starter cultures, i.e. L. sakei, L. casei, L. fermentum, L. rhamnosus, L.sakei + S.xylosus. In particular the contribution of the predominant yeasts to the hydrolytic patterns of meat proteins has been studied both in model system and in real products. In fact, although several papers have been published on the microbial, enzymatic, aromatic and chemical characterization of dry-cured meat e.g. ham over ripening, the specific role of yeasts has been often underestimated. Therefore this research work has been focused on the following aspects: 1. Characterization of the yeasts and lactic acid bacteria in samples of speck produced by different farms and analyzed during the various production and ripening phases 2. Characterization of the superficial or internal yeasts population in salami produced with or without the use of lactobacilli as starter cultures 3. Molecular characterization of different strains of yeasts and detection of the dominant biotypes able to survive despite environmental stress factors (such as smoke, salt) 4. Study of the proteolytic profiles of speck and salami during the ripening process and comparison with the proteolytic profiles produced in meat model systems by a relevant number of yeasts isolated from speck and salami 5. Study of the proteolytic profiles of Lactobacilli starter cultures in meat model systems 6. Comparative statistical analysis of the proteolytic profiles to find possible relationships between specific bands and peptides and specific microorganisms 7. Evaluation of the aromatic characteristics of speck and salami to assess relationships among the metabolites released by the starter cultures or the dominant microflora

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Reverse Engineering tools: development and experimentation of innovative methods for physical and geometrical data integration and post-processing

In recent years, the use of Reverse Engineering systems has got a considerable interest for a wide number of applications. Therefore, many research activities are focused on accuracy and precision of the acquired data and post processing phase improvements. In this context, this PhD Thesis deals with the definition of two novel methods for data post processing and data fusion between physical and geometrical information. In particular a technique has been defined for error definition in 3D points’ coordinates acquired by an optical triangulation laser scanner, with the aim to identify adequate correction arrays to apply under different acquisition parameters and operative conditions. Systematic error in data acquired is thus compensated, in order to increase accuracy value. Moreover, the definition of a 3D thermogram is examined. Object geometrical information and its thermal properties, coming from a thermographic inspection, are combined in order to have a temperature value for each recognizable point. Data acquired by an optical triangulation laser scanner are also used to normalize temperature values and make thermal data independent from thermal-camera point of view.

Application of chromatographic and spectroscopic techniques in the evaluation of the lipid fraction of animal products

Lipolysis and oxidation of lipids in foods are the major biochemical and chemical processes that cause food quality deterioration, leading to the characteristic, unpalatable odour and flavour called rancidity. In addition to unpalatability, rancidity may give rise to toxic levels of certain compounds like aldehydes, hydroperoxides, epoxides and cholesterol oxidation products. In this PhD study chromatographic and spectroscopic techniques were employed to determine the degree of rancidity in different animal products and its relationship with technological parameters like feeding fat sources, packaging, processing and storage conditions. To achieve this goal capillary gas chromatography (CGC) was employed not only to determine the fatty acids profile but also, after solid phase extraction, the amount of free fatty acids (FFA), diglycerides (DG), sterols (cholesterol and phytosterols) and cholesterol oxidation products (COPs). To determine hydroperoxides, primary products of oxidation and quantify secondary products UV/VIS absorbance spectroscopy was applied. Most of the foods analysed in this study were meat products. In actual fact, lipid oxidation is a major deterioration reaction in meat and meat products and results in adverse changes in the colour, flavour and texture of meat. The development of rancidity has long recognized as a serious problem during meat handling, storage and processing. On a dairy product, a vegetal cream, a study of lipid fraction and development of rancidity during storage was carried out to evaluate its shelf-life and some nutritional features life saturated/unsaturated fatty acids ratio and phytosterols content. Then, according to the interest that has been growing around functional food in the last years, a new electrophoretic method was optimized and compared with HPLC to check the quality of a beehive product like royal jelly. This manuscript reports the main results obtained in the five activities briefly summarized as follows: 1) comparison between HPLC and a new electrophoretic method in the evaluation of authenticity of royal jelly; 2) study of the lipid fraction of a vegetal cream under different storage conditions; 3) study of lipid oxidation in minced beef during storage under a modified atmosphere packaging, before and after cooking; 4) evaluation of the influence of dietary fat and processing on the lipid fraction of chicken patties; 5) study of the lipid fraction of typical Italian and Spanish pork dry sausages and cured hams.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Empirical and fundamental mechanical tests in the evaluation of dough and bread rheological properties

Bread dough and particularly wheat dough, due to its viscoelastic behaviour, is probably the most dynamic and complicated rheological system and its characteristics are very important since they highly affect final products’ textural and sensorial properties. The study of dough rheology has been a very challenging task for many researchers since it can provide numerous information about dough formulation, structure and processing. This explains why dough rheology has been a matter of investigation for several decades. In this research rheological assessment of doughs and breads was performed by using empirical and fundamental methods at both small and large deformation, in order to characterize different types of doughs and final products such as bread. In order to study the structural aspects of food products, image analysis techniques was used for the integration of the information coming from empirical and fundamental rheological measurements. Evaluation of dough properties was carried out by texture profile analysis (TPA), dough stickiness (Chen and Hoseney cell) and uniaxial extensibility determination (Kieffer test) by using a Texture Analyser; small deformation rheological measurements, were performed on a controlled stress–strain rheometer; moreover the structure of different doughs was observed by using the image analysis; while bread characteristics were studied by using texture profile analysis (TPA) and image analysis. The objective of this research was to understand if the different rheological measurements were able to characterize and differentiate the different samples analysed. This in order to investigate the effect of different formulation and processing conditions on dough and final product from a structural point of view. For this aim the following different materials were performed and analysed: - frozen dough realized without yeast; - frozen dough and bread made with frozen dough; - doughs obtained by using different fermentation method; - doughs made by Kamut® flour; - dough and bread realized with the addition of ginger powder; - final products coming from different bakeries. The influence of sub-zero storage time on non-fermented and fermented dough viscoelastic performance and on final product (bread) was evaluated by using small deformation and large deformation methods. In general, the longer the sub-zero storage time the lower the positive viscoelastic attributes. The effect of fermentation time and of different type of fermentation (straight-dough method; sponge-and-dough procedure and poolish method) on rheological properties of doughs were investigated using empirical and fundamental analysis and image analysis was used to integrate this information throughout the evaluation of the dough’s structure. The results of fundamental rheological test showed that the incorporation of sourdough (poolish method) provoked changes that were different from those seen in the others type of fermentation. The affirmative action of some ingredients (extra-virgin olive oil and a liposomic lecithin emulsifier) to improve rheological characteristics of Kamut® dough has been confirmed also when subjected to low temperatures (24 hours and 48 hours at 4°C). Small deformation oscillatory measurements and large deformation mechanical tests performed provided useful information on the rheological properties of samples realized by using different amounts of ginger powder, showing that the sample with the highest amount of ginger powder (6%) had worse rheological characteristics compared to the other samples. Moisture content, specific volume, texture and crumb grain characteristics are the major quality attributes of bread products. The different sample analyzed, “Coppia Ferrarese”, “Pane Comune Romagnolo” and “Filone Terra di San Marino”, showed a decrease of crumb moisture and an increase in hardness over the storage time. Parameters such as cohesiveness and springiness, evaluated by TPA that are indicator of quality of fresh bread, decreased during the storage. By using empirical rheological tests we found several differences among the samples, due to the different ingredients used in formulation and the different process adopted to prepare the sample, but since these products are handmade, the differences could be account as a surplus value. In conclusion small deformation (in fundamental units) and large deformation methods showed a significant role in monitoring the influence of different ingredients used in formulation, different processing and storage conditions on dough viscoelastic performance and on final product. Finally the knowledge of formulation, processing and storage conditions together with the evaluation of structural and rheological characteristics is fundamental for the study of complex matrices like bakery products, where numerous variable can influence their final quality (e.g. raw material, bread-making procedure, time and temperature of the fermentation and baking).

Food lipids: their effects on quality and oxidative stability of animal tissues and emulsions

Lipolysis and oxidation of lipids in foods are the major biochemical and chemical processes that cause food quality deterioration, leading to the characteristic, unpalatable odour and flavour called rancidity. In addition to unpalatability, rancidity may give rise to toxic levels of certain compounds like aldehydes, hydroperoxides, epoxides and cholesterol oxidation products. In this PhD study chromatographic and spectroscopic techniques were employed to determine the degree of lipid oxidation in different animal products and its relationship with technological parameters like feeding fat sources, packaging, processing and storage conditions. To achieve this goal capillary gas chromatography (CGC) was employed not only to determine the fatty acids profile but also, after solid phase extraction, the amount of sterols (cholesterol and phytosterols) and cholesterol oxidation products (COPs). To determine hydroperoxides, primary products of oxidation and quantify secondary products UV/VIS absorbance spectroscopy was applied. Beef and pork meat in this study were analysed. In actual fact, lipid oxidation is a major deterioration reaction in meat, meat products and results in adverse changes in the colour, flavour, texture of meat and develops different compounds which should be a risk to human health as oxysterols. On beef and pork meat, a study of lipid fraction during storage was carried out to evaluate its shelf-life and some nutritional features life saturated/unsaturated fatty acids ratio and sterols content, in according to the interest that has been growing around functional food in the last years. The last part of this research was focused on the study of lipid oxidation in emulsions. In oil-in-water emulsions antioxidant activity of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) was evaluated. The rates of lipid oxidation of 1.0% stripped soybean oil-in-water emulsions with DOPC were followed by monitoring lipid hydroperoxide and hexanal as indicators of primary and secondary oxidation products and the droplet surface charge or zeta potential (ζ) of the emulsions with varying concentrations of DOPC were tested. This manuscript reports the main results obtained in the three activities briefly summarized as follows: 1. study on effects of feeding composition on the photoxidative stability of lipids from beef meat, evaluated during storage under commercial retail conditions; 2. evaluation of effects of diets and storage conditions on the oxidative stability of pork meat lipids; 3. study on oxidative behavior of DOPC in stripped soybean oil-in-water emulsions stabilized by nonionic surfactant.

Advances in understanding of osmotic dehydration and vacuum impregnation of fruits

Osmotic Dehydration and Vacuum Impregnation are interesting operations in the food industry with applications in minimal fruit processing and/or freezing, allowing to develop new products with specific innovative characteristics. Osmotic dehydration is widely used for the partial removal of water from cellular tissue by immersion in hypertonic (osmotic) solution. The driving force for the diffusion of water from the tissue is provided by the differences in water chemical potential between the external solution and the internal liquid phase of the cells. Vacuum Impregnation of porous products immersed in a liquid phase consist of reduction of pressure in a solid-liquid system (vacuum step) followed by the restoration of atmospheric pressure (atmospheric step). During the vacuum step the internal gas in the product pores is expanded and partially flows out while during the atmospheric step, there is a compression of residual gas and the external liquid flows into the pores (Fito, 1994). This process is also a very useful unit operation in food engineering as it allows to introduce specific solutes in the tissue which can play different functions (antioxidants, pH regulators, preservatives, cryoprotectants etc.). The present study attempts to enhance our understanding and knowledge of fruit as living organism, interacting dynamically with the environment, and to explore metabolic, structural, physico-chemical changes during fruit processing. The use of innovative approaches and/or technologies such as SAFES (Systematic Approach to Food Engineering System), LF-NMR (Low Frequency Nuclear Magnetic Resonance), GASMAS (Gas in Scattering Media Absorption Spectroscopy) are very promising to deeply study these phenomena. SAFES methodology was applied in order to study irreversibility of the structural changes of kiwifruit during short time of osmotic treatment. The results showed that the deformed tissue can recover its initial state 300 min after osmotic dehydration at 25 °C. The LF-NMR resulted very useful in water status and compartmentalization study, permitting to separate observation of three different water population presented in vacuole, cytoplasm plus extracellular space and cell wall. GASMAS techniques was able to study the pressure equilibration after Vacuum Impregnation showing that after restoration of atmospheric pressure in the solid-liquid system, there was a reminding internal low pressure in the apple tissue that slowly increases until reaching the atmospheric pressure, in a time scale that depends on the vacuum applied during the vacuum step. The physiological response of apple tissue on Vacuum Impregnation process was studied indicating the possibility of vesicular transport within the cells. Finally, the possibility to extend the freezing tolerance of strawberry fruits impregnated with cryoprotectants was proven.

Procedures of quality control and data analysis of multi-site ground-based observations for the absolute flux calibration of Gaia

The Gaia space mission is a major project for the European astronomical community. As challenging as it is, the processing and analysis of the huge data-flow incoming from Gaia is the subject of thorough study and preparatory work by the DPAC (Data Processing and Analysis Consortium), in charge of all aspects of the Gaia data reduction. This PhD Thesis was carried out in the framework of the DPAC, within the team based in Bologna. The task of the Bologna team is to define the calibration model and to build a grid of spectro-photometric standard stars (SPSS) suitable for the absolute flux calibration of the Gaia G-band photometry and the BP/RP spectrophotometry. Such a flux calibration can be performed by repeatedly observing each SPSS during the life-time of the Gaia mission and by comparing the observed Gaia spectra to the spectra obtained by our ground-based observations. Due to both the different observing sites involved and the huge amount of frames expected (≃100000), it is essential to maintain the maximum homogeneity in data quality, acquisition and treatment, and a particular care has to be used to test the capabilities of each telescope/instrument combination (through the “instrument familiarization plan”), to devise methods to keep under control, and eventually to correct for, the typical instrumental effects that can affect the high precision required for the Gaia SPSS grid (a few % with respect to Vega). I contributed to the ground-based survey of Gaia SPSS in many respects: with the observations, the instrument familiarization plan, the data reduction and analysis activities (both photometry and spectroscopy), and to the maintenance of the data archives. However, the field I was personally responsible for was photometry and in particular relative photometry for the production of short-term light curves. In this context I defined and tested a semi-automated pipeline which allows for the pre-reduction of imaging SPSS data and the production of aperture photometry catalogues ready to be used for further analysis. A series of semi-automated quality control criteria are included in the pipeline at various levels, from pre-reduction, to aperture photometry, to light curves production and analysis.

Residual visual processing following real or virtual lesions to primary visual pathways

Lesions to the primary geniculo-striate visual pathway cause blindness in the contralesional visual field. Nevertheless, previous studies have suggested that patients with visual field defects may still be able to implicitly process the affective valence of unseen emotional stimuli (affective blindsight) through alternative visual pathways bypassing the striate cortex. These alternative pathways may also allow exploitation of multisensory (audio-visual) integration mechanisms, such that auditory stimulation can enhance visual detection of stimuli which would otherwise be undetected when presented alone (crossmodal blindsight). The present dissertation investigated implicit emotional processing and multisensory integration when conscious visual processing is prevented by real or virtual lesions to the geniculo-striate pathway, in order to further clarify both the nature of these residual processes and the functional aspects of the underlying neural pathways. The present experimental evidence demonstrates that alternative subcortical visual pathways allow implicit processing of the emotional content of facial expressions in the absence of cortical processing. However, this residual ability is limited to fearful expressions. This finding suggests the existence of a subcortical system specialised in detecting danger signals based on coarse visual cues, therefore allowing the early recruitment of flight-or-fight behavioural responses even before conscious and detailed recognition of potential threats can take place. Moreover, the present dissertation extends the knowledge about crossmodal blindsight phenomena by showing that, unlike with visual detection, sound cannot crossmodally enhance visual orientation discrimination in the absence of functional striate cortex. This finding demonstrates, on the one hand, that the striate cortex plays a causative role in crossmodally enhancing visual orientation sensitivity and, on the other hand, that subcortical visual pathways bypassing the striate cortex, despite affording audio-visual integration processes leading to the improvement of simple visual abilities such as detection, cannot mediate multisensory enhancement of more complex visual functions, such as orientation discrimination.

Chemistry of aerosol particles and fog droplets during fall-winter season in the Po Valley

Air quality represents a key issue in the so-called pollution “hot spots”: environments in which anthropogenic sources are concentrated and dispersion of pollutants is limited. One of these environments, the Po Valley, normally experiences exceedances of PM10 and PM2.5 concentration limits, especially in winter when the ventilation of the lower layers of the atmosphere is reduced. This thesis provides a highlight of the chemical properties of particulate matter and fog droplets in the Po Valley during the cold season, when fog occurrence is very frequent. Fog-particles interactions were investigated with the aim to determine their impact on the regional air quality. Size-segregated aerosol samples were collected in Bologna, urban site, and San Pietro Capofiume (SPC), rural site, during two campaigns (November 2011; February 2013) in the frame of Supersito project. The comparison between particles size-distribution and chemical composition in both sites showed the relevant contribution of the regional background and secondary processes in determining the Po Valley aerosol concentration. Occurrence of fog in November 2011 campaign in SPC allowed to investigate the role of fog formation and fog chemistry in the formation, processing and deposition of PM10. Nucleation scavenging was investigated with relation to the size and the chemical composition of particles. We found that PM1 concentration is reduced up to 60% because of fog scavenging. Furthermore, aqueous-phase secondary aerosol formation mechanisms were investigated through time-resolved measurements. In SPC fog samples have been systematically collected and analysed since the nineties; a 20 years long database has been assembled. This thesis reports for the first time the results of this long time series of measurements, showing a decrease of sulphate and nitrate concentration and an increase of pH that reached values close to neutrality. A detailed discussion about the occurred changes in fog water composition over two decades is presented.

Flow Field–Flow Fractionation for size analysis and characterization of nanoparticles for applications in Life Sciences

Nanotechnologies are rapidly expanding because of the opportunities that the new materials offer in many areas such as the manufacturing industry, food production, processing and preservation, and in the pharmaceutical and cosmetic industry. Size distribution of the nanoparticles determines their properties and is a fundamental parameter that needs to be monitored from the small-scale synthesis up to the bulk production and quality control of nanotech products on the market. A consequence of the increasing number of applications of nanomaterial is that the EU regulatory authorities are introducing the obligation for companies that make use of nanomaterials to acquire analytical platforms for the assessment of the size parameters of the nanomaterials. In this work, Asymmetrical Flow Field-Flow Fractionation (AF4) and Hollow Fiber F4 (HF5), hyphenated with Multiangle Light Scattering (MALS) are presented as tools for a deep functional characterization of nanoparticles. In particular, it is demonstrated the applicability of AF4-MALS for the characterization of liposomes in a wide series of mediums. Afterwards the technique is used to explore the functional features of a liposomal drug vector in terms of its biological and physical interaction with blood serum components: a comprehensive approach to understand the behavior of lipid vesicles in terms of drug release and fusion/interaction with other biological species is described, together with weaknesses and strength of the method. Afterwards the size characterization, size stability, and conjugation of azidothymidine drug molecules with a new generation of metastable drug vectors, the Metal Organic Frameworks, is discussed. Lastly, it is shown the applicability of HF5-ICP-MS for the rapid screening of samples of relevant nanorisk: rather than a deep and comprehensive characterization it this time shown a quick and smart methodology that within few steps provides qualitative information on the content of metallic nanoparticles in tattoo ink samples.