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Relaxation dynamics in borate glass formers probed by photon correlation at the microscopic and macroscopic length scale
Get PDFX-ray photon correlation is used to probe the dynamics of the strong glass former boron trioxide and of a series of alkali borate glasses, (M2O)x(B2O3)1-x where M is the alkali modifier (M=Li, Na and K). The decay times τ of the obtained correlation functions in B2O3 are consistent with visible light scattering results and independent of the incoming beam intensity in the undercooled liquid phase; are instead temperature independent and show a definite dependence on the X-ray beam intensity in the glass. We are therefore witnessing an atomic dynamics induced by the X-ray beam.
Furthermore, we clearly demonstrate that the value of τ is related to absorption by investigating a series of alkali borate glass with the same molar ratio and as a function of the alkali modifier. Finally, we highlight the role played by the structure in the X-ray induced dynamics by studying a series of lithium borate glasses with different molar ratios, and by investigating the wave vector dependence.
Despite the observed dynamics is clearly intensity dependent, we obtain very interesting information on glasses not available with other experimental techniques
Characterization of the hnRNP RALY in RNA transcription and metabolism
No full textThe heterogeneous nuclear ribonucleoproteins (hnRNPs) form a large family of RNA-binding proteins (RBPs) that exert numerous functions in RNA metabolism. For example, soluble hnRNPs bind to RNAs to mediate their maturation, processing, and shuttling from the nuclear compartment to the cytoplasm. Additionally, hnRNPs might interact with chromatin to regulate the transcription and the post-transcriptional modification of nascent transcripts.
RALY is a member of the hnRNP family that binds poly-U rich elements within several RNAs and regulates the expression of specific transcripts. RALY is upregulated in different types of cancer and its downregulation has been shown to impair cell proliferation. In my PhD project, I characterized RALY to interact with transcriptionally active chromatin in a transcription-dependent manner and to cause a global decrease of RNA Polymerase II (RNAPII)-mediated transcription when downregulated, without affecting RNAPII elongation rate. Through microarray analysis of RALY-downregulated HeLa cells, I detected an altered expression of numerous genes involved in transcription promotion and cell cycle regulation, including the E2F transcription factors family. Due to its relevant role in regulating the cell cycle, I focused on the proliferation-promoting factor E2F1. I demonstrated that the stability of E2F1 mRNA is reduced in cells lacking RALY expression, with a resulting reduction of E2F1 protein levels. As a consequence of RALY knock-out, HeLa cells present a slower cell proliferation compared to control cells. Finally, by crossing the list of RALY targets with the list of genes affected by RALY downregulation, I propose a positive role of RALY in regulating the fate of specific transcripts.
Taken together, my results highlight the importance of RALY expression for transcription and cell proliferation
I won't let you "behind". The impact of migration on sending households
Get PDFA boost in the scale and complexity of international migration flows have occurred in the last decades. Movements of large numbers of people may produce welfare gains to families and communities left behind. This thesis analyses the implications of migration on well-being of sending societies, adopting a household-level perspective and addressing two specific issues: the impact of remittances on health consumption decisions of relatives left behind, and the role of migration as risk management strategy in response to natural shock exposure. The effect of international remittances on household healthcare
consumption is tested using data from the “Peruvian National Survey of Households”. Remittances positively impact on healthcare consumption shares and this propensity is independent of the occurrence of a health shock, confirming the importance of migrant transfers for human capital accumulation. In the second part, I identify whether and under which circumstances migration represents a coping strategy to deal with sudden onset climatic shocks, examining the case of Hurricane Mitch in Nicaragua. The findings obtained show that shock severity does not act as push factor for international migration as a whole. Only individuals belonging to agricultural households experiencing high exposure to the natural disaster increase their later likelihood to move abroad. Remittances turn out to be an efficient insurance tool to recover after natural shocks. Income flows from international migrants support household welfare preservation over the two years following the disaster, reducing the risk of being trapped into poverty
Rivers Hydromorphological Characterization from High Resolution Remotely Sensed Data
Get PDFRemote sensing techniques could enable remarkable advances in characterizing rivers hydromorphology by providing spatially and temporally explicit information. Remote mapping of hydromorphology can play a decisive role in a wide range of river science and management applications including habitat modeling and river restoration. High resolution satellite imagery (HRSI) has recently emerged as potentially powerful means of mapping riverine environments. This research aims to develop advanced methodologies for processing HRSI to map and quantify a set of key hydromorphological attributes including: (1) river boundaries, (2) bathymetry and (3) riverbed types and compositions. Boundary pixels of rivers are subject to spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super resolution mapping (SRM) are focused as two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for NDWI (OBA-NDWI) is proposed to identify the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as a predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) and interpolation-based algorithms are applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2=90%, RMSE=7% for WorldView-2 (WV-2) image and R2=87%, RMSE=9% for Geoeye image). The spectral bands of WV-2 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas about 10% with respect to conventional hard classification. This research introduces multiple optimal depth predictors analysis (MODPA) that combines previously developed depth predictors along with other measures such as the intensity components of HSI color space. To avoid over-fitting of the linear model, statistically optimal predictors are selected based on one of partial least square (PLS), stepwise and principal component (PC) regressions. The primary focus of this study is on shallow and clearly flowing streams where substrate variability could have pronounced effect on depth retrievals. Spectroscopic experiments are performed in controlled condition of a hydraulic laboratory to examine the robustness of bathymetry models with respect to changes in bottom types. Further, simulations from radiative transfer modeling are used to extend the analysis by isolating the effect of inherent optical properties (IOPs) and also by investigating the performance of bathymetry models in optically complex and also deeper streams. Bathymetry of Sarca, a shallow river in Italian Alps, is also mapped using a WorldView-2 (WV-2) image where the atmospheric compensation (AComp) product is evaluated for the first time. Results indicate the robustness of multiple-predictor models particularly MODPA rather than single-predictor models such as optimal band ratio analysis (OBRA) with respect to heterogeneity of bottom types, IOPs and atmospheric effects. This study suggests extra predictors when the multiple regression is assisted with an optimal predictors selection process (e.g. MODPA). The extra predictors enhance the accuracy of depth retrievals particularly in optically complex waters and also for low spectral resolution imagery (e.g. GeoEye). Further, enhanced spectral resolution of WV-2 compared to GeoEye improves the bathymetry retrievals. MODPA based on PLS regression provided improvements on the order of 0.05 R2 and 0.7 cm RMSE compared to multiple Lyzenga and 0.18 R2 and 2 cm RMSE compared to OBRA using AComp reflectances of WV-2 for Sarca River with a maximum 0.8 m depth. In addition, a theoretical approach namely hydraulically assisted bathymetry (HAB) is assessed and further modified for calibration of bathymetry models that provided comparable results with the empirical calibration approach.
Substrate mapping in fluvial systems has not received as much attention as that in nearshore optically shallow waters of inland and coastal areas. The research to date has been primarily based on surface spectral reflectance data without accounting for water column attenuations. This study aims at retrieving the bottom reflectances in shallow rivers and then examining the effectiveness of inferred bottom spectra in mapping of substrate types. Bathymetry and diffuse attenuation coefficient (kd) are derived from above-water reflectances for which some in-situ/known depths are required. Following the retrievals of depth and kd, bottom reflectances are estimated based on a water column correction method. Moreover, the efficacy of vegetation indices (VIs) is examined for making distinction among the densities of submerged aquatic vegetation (SAV) using either above-water or retrievals of bottom reflectances. This research benefits, for the first time, from three different approaches including controlled spectroscopic measurements in a hydraulic lab, simulations from radiative transfer modeling and an 8-band WordView-3 (WV-3) image. The results indicate the significant enhancements of streambed mapping using inferred bottom reflectances than using above-water spectra. This is evident, for instance, on clustering of three bottom types using simulated spectra with 20% enhancement of overall accuracy. Deep-water correction demonstrated to have most of an impact on retrievals of bottom reflectances only in NIR bands when the water column is relatively thick (> 0.5 m) and/or when the water is turbid. The red-edge (RE) band of WV-3/WV-2 improves remarkably the detection of SAV densities based on the VIs either using above-water or retrieved bottom spectra. Further, the simulated spectra suggest that enhanced spectral resolution of 8-band WV-3 leads to improvements in streambed mapping compared to traditional 4-band imagery. This study demonstrated the feasibility of retrieving bottom reflectances and mapping SAV densities from space in a shallow river using the WV-3 image (user and producer accuracies of 67% and 60% in average for three levels of SAV densities). Moreover, the feasibility of mapping grain size classes is assessed using spectral information based on laboratory experiments coupled with simulations. The changes in grain sizes affect the magnitude of reflectances while the shape of spectra remains almost identical. This characteristic feature demonstrated high potentials for mapping grain size classes by retrieving the bottom reflectances.
In summary, HRSI provided promising results and effective means of mapping the selected hydromorphological attributes of shallow rivers in spatially continuous and in large extents
Homogenization and analysis of hydrological time series
No full textIn hydrological studies, it is very important to properly analyze the relationship among the different components of the water cycle, due to the complex feedback mechanisms typical of this system. The analysis of available time series is hence a fundamental step, which has to be performed before any modeling activity. Moreover, time series analysis can shed light over the spatial and temporal dynamics of correlated hydrological and climatological processes. In this work, we focus on three tools applied for time series analysis: homogeneity tests, wavelet analysis and copula analysis. Homogeneity tests allow to identify a first important kind of variability in the time series, which is not due to climate nor seasonal variability. Testing for inhomogeneities is therefore an important step that should be always performed on a time series before using it for any application. The homogenization of snow depth data, in particular, is a challenging task. Up to now, it has been performed analyzing available metadata, which often present contradictions and are rarely complete. In this work, we present a procedure to test the homogeneity of snow depth time series based on the Standard Normal Homogeneity Test (SNHT). The performance of the SNHT for the detection of inhomogeneities in snow depth data is further investigated with a comparison experiment, in which a dataset of snow depth time series relative to Austrian stations has been analyzed with both the SNHT and the HOMOP algorithm. The intercomparison study indicates that the two algorithms show comparable performance.
The wavelet transform analysis allows to obtain a different kind of information about the variability of a time series. In fact, it determines the different frequency content of a signal in different time intervals. Moreover, the wavelet coherence analysis allows to identify periods where two time series are correlated and their phase shift. We apply the wavelet transform to a dataset of snow depth time series of stations distributed in the Adige catchment and on a dataset of 16 discharge time series located in the Adige and in the Inn catchments. The same datasets are used to perform a wavelet coherence analysis considering the Mediterranean Oscillation Index (MOI) and the North Atlantic Oscillation Index (NAOI). This analysis highlights a difference in the behavior of the snow time series collected below and above 1650 m a.s.l.. We also observe a difference between low and high elevation sites in the amount of mean seasonal snow depth and snow cover duration. More interestingly, snow time series collected at different elevations respond differently to temperature and more in general to climate changes. The wavelet analysis allows us also to distinguish between gauging stations belonging to different catchments, while the wavelet coherence analysis revealed non-stationary correlations with the MOI and NAOI, indicating a very complex relation between the measured quantities and climatic indexes. Finally the application of copulas allows modeling the marginal of each variable and their dependence structure independently. We apply this technique to two relevant cases. First we study snow related variables in relation with temperature, the NAOI and the MOI, which we already investigated with the wavelet coherence analysis. Then we model flood events registered at two stations of the Inn river: Wasserburg and Passau. This last analysis is performed with the goal of predicting future flood events and derive construction parameters for retention basins. We test three different combinations of variables (direct peak discharge-direct volume, direct peak discharge-direct volume-rising time-base flow, direct peak discharge-direct volume-rising time-moving threshold) describing the flood events and compare the results. The consistency in the results indicates that the proposed methodology is robust and reliable. This study shows the importance of approaching the analysis to hydrological time series from several points of view: quality of the data, variability of the time series and relation between different variables. Moreover, it shows that integrating the use of various time series analysis methods can greatly improve our understanding of the system behavior
Brain functional connectivity and its aberrations in mouse models of autism
Get PDFFunctional Magnetic Resonance Imaging (fMRI) has consistently highlighted aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological under- pinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to impaired connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity.
In this work, we first describe the intrinsic organization of the mouse brain at the macroscale as seen through resting-state fMRI (rsfMRI). The analysis of a large rsfMRI dataset revealed the presence of six distinct functional modules related to known brainwide functional partitions, including a homologue of the human default-mode network (DMN). Consistent with human studies, interconnected functional hubs were identified in several sub-regions of the DMN, in the thalamus, and in small foci within integrative cortical structures such as the insular and temporal association cortices.
We then study the effects of mutations in contactin associated protein-like 2 (Cntnap2), a neurexin-related cell-adhesion protein, on functional connectivity. Homozygous mutations in this gene are strongly linked to autism and epilepsy in humans, and using rsfMRI, we showed that homozygous mice lacking Cntnap2 exhibit aberrant functional connectivity in prefrontal and midline functional hubs, an effect that was associated with reduced social investigation, a core “autism trait” in mice. Notably, viral tracing revealed reduced frequency of prefrontal-projecting neural clusters in the cingulate cortex of Cntnap2−/− mutants, suggesting a possible contribution of defective mesoscale axonal wiring to the observed functional impairments. Macroscale cortico-cortical white-matter organization appeared to be otherwise preserved in these animals. These findings revealed a key contribution of ASD-associated gene CNTNAP2 in modulating macroscale functional connectivity, and suggest that homozygous loss-of-function mutations in this gene may predispose to neurodevelopmental disorders and autism through a selective dysregulation of connectivity in integrative prefrontal areas.
Finally, we discuss the role mouse models could play in generating and testing mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism and recent progress towards this goal
A flexible approach to the estimation of water budgets and its connection to the travel time theory
Get PDFThe increasing impacts of climate changes on water related sectors are leading the scientists’ attentions to the development of comprehensive models, allowing better descriptions of the water and solute transport processes. "Getting the right answers for the right reasons", in terms of hydrological response, is one of the main goals of most of the recent literature. Semi-distributed hydrological models, based on the partition of
basins in hydrological response units (HRUs) to be connected, eventually, to describe a whole catchment, proved to be robust in the reproduction of observed catchment dynamics. ’Embedded reservoirs’ are often used for each HRU, to allow a consistent representation of the processes. In this work, a new semi-distributed model for runoff and evapotranspiration is presented: five different reservoirs are inter-connected in order
to capture the dynamics of snow, canopy, surface flow, root-zone and groundwater compartments.
The knowledge of the mass of water and solute stored and
released through different outputs (e.g. discharge,
evapotranspiration) allows the analysis of the hydrological travel times and solute transport in catchments. The latter have been studied extensively, with some recent benchmark contributions in the last decade. However, the literature remains obscured by
different terminologies and notations, as well as model
assumptions are not fully explained. The thesis presents a detailed description of a new theoretical approach that reworks the theory from the point of view of the hydrological storages and fluxes involved.Major aspects of the new theory are the ’age-ranked’ definition of the hydrological variables, the explicit treatment of evaporative fluxes and of their influence on the transport, the analysis of the outflows partitioning coefficients and the explicit formulation of the ’age-ranked’ equations for solutes.Moreover, the work presents concepts in a new systematic and clarified way, helping the application of the theory. To give substance to the theory, a small catchment in the prealpine area was chosen as an example and the results illustrated. The new semi-distributed model for runoff and evapotranspiration and the travel time theory were implemented and integrated in the semi-distributed hydrological system JGrass-NewAge. Thanks to the environmentalmodeling framework OMS3, each part of the hydrological cycle is implemented as a component that can be selected, adopted, and connected at run-time to obtain a user-customized hydrologicalmodel. The system is flexible, expandable and applicable in a variety of modeling solutions. In
this work, the model code underwent to an extensive revision: new components were added (coupled storages water budget, travel times components); old components were enhanced (Kriging, shortwave, longwave, evapotranspiration, rain-snow separation, SWE and melting components); documentation was standardized and deployed.
Since the Thesis regards in wide sense the building of a
collaborative system, a discussion of some general purpose tools that were implemented or improved for supporting the present research is also presented. They include the description and the verification of a software component dealing with the long-wave
radiation budget and another component dealing with an
implementation of some Kriging procedure
Social Context and Decisions : Essays in experimental economics
No full textHumans are social animals who evolved to live in societies. They are "encultured" actors as their preferences, perceptions and values are shaped by the social
context to which they are exposed. Part of economic failures is due to suboptimal social contexts which determine individuals' decisions. These social contexts can be better
designed by organizations and governments. The ultimate goal of this research is to emphasize that social context can be detrimental for individual decisions, providing empirically-based behavioral insights for policy makers who wish to implement regulatory policies on corruption, gender gap and injustice. Behavioral and Experimental Economics provides a clean tool to keep the internal validity necessary to disentangle complex behavioral aspects that cannot be easily
observed in the field, such as those related to the influence of social environment. This Doctoral Thesis is a collection of three laboratory experimental essays about
the interplay between suboptimal social contexts and decisions. The first Chapter investigates the role of group identity in unethical decisions motivated by unfairness.
The second Chapter provides evidence of gender stereotype in perceptions of others'risk attitudes. The third Chapter shows that small contextual changes can promote the
diffusion of corruption while others inhibit it
General relativistic magnetohydrodynamic simulations of binary neutron star mergers
No full textIn this thesis I present results of my fully general relativistic magnetohydrodynamic (GRMHD) binary neutron star merger (BNS) simulations, conducted by using the numerical code "Whisky" under various conditions, such as, different Equation of State (EOS) for neutron matter (APR4, Ideal fluid and H4 EOSs), masses (with equal/unequal masses
for two neutron stars), different magnetic field configurations (both fields of two neutron stars aligned with the inspiral axis, one aligned and one anti-aligned, and both anti-aligned) to investigate the effect of these parameters on the dynamics of the simulations and possibility of forming relativistic jets, which is thought to be one of the necessary conditions for the central engine of short gamma-ray bursts (SGRBs)
Adaptation Methods for Statistical Machine Translation In Business Scenarios
No full textAdaptation methods for phrase-based statistical Machine Translation (MT) have been explored in the literature under different paradigms, such as domain adaptation and topic adaptation, and most of the times in rather ideal experimental set-ups. We address this subject in three real-life industrial use cases, in which MT has to quickly adapt in accordance with specific operating conditions.
In particular, we explore domain adaptation when no in-domain parallel data are available, which is a typical use case of MT service providers. Then, we investigate topic adaptation for the translation of short highly ambiguous item titles in an e-commerce setting.
Finally, we consider the Computer Assisted Translation (CAT) scenario, in which MT interacts with a human translator by providing them with translation drafts and by adapting from their post-editions. In this scenario, we investigate online adaptation from human post-editions, respectively, in a single-user setting and in a multi-user setting, in which multiple translators are working on different parts of the same document. In addition, for the single-user case we also discuss the optimisation of the hyper-parameters of the employed online adaptation method