Functional characterization of the RNA binding protein RALY

Of 25000 genes encoded from genome, more than 90% are subject to alternative splicing or other post-transcriptional modifications. All these events produce a high number of different proteins that form the basis for the high variety of cells. The RNAbinding proteins (RBPs) play crucial roles in this variability by regulating many steps of biological processes regarding RNA metabolism. The heterogeneous nuclear ribonucleoproteins (hnRNPs) belong to big family of RBPs involved in many aspects of RNA metabolism including RNA stability, intracellular transport and translation. More recently, RALY, a RNA-binding protein associated with the lethal yellow mutation in mouse, has been identified as new member of the hnRNP family even if, its biological function remains still elusive. My PhD project aimed to characterize human RALY and to assess its function in mammalian cells. Initially I dentified the expression pattern of this protein into the cell and I characterized the functional nuclear localization sequence that localizes RALY protein into the nuclear compartment. In order to better understand the role of RALY in the cells, I identified the proteins component of RALY-containing complexes using a new assay named iBioPQ (in vivo-Biotinylation-Pulldown-Quant assay). I also performed polyribosome profiling assay to check the resence of RALY in translating mRNAs. Moreover, a microarray assay was performed in order to identify potential mRNAs whose metabolism appears dependent on RALY expression. Taken together, the results that I obtained suggest that RALY is involved in mRNA metabolism. Unfortunately more studies remain to do before shedding some light on the biological role of RALY in mammal

Two-fluid Hydrodynamics of a quasi-1D unitary Fermi gas

This thesis is devoted to the study of the hydrodynamic behavior of the unitary Fermi gas trapped by a highly elongated harmonic potential. Propagation of sound is one of the most exciting features exhibited by interacting many-body systems. It provides crucial information on the dynamic behavior of the system as well as on key thermodynamic quantities. The propagation of sound is particularly interesting in superfluids where two-fluid hydrodynamic theory predicts the occurrence of two different sounds: first sound, where the normal and superfluid component oscillate in phase, and second sound, where the two components oscillate with opposite phase. In the thesis, we investigate the propagation of sound waves of the unitary Fermi gas in a cylindrical geometry by solving the equations of two-fluid hydrodynamics in the `1D' scenario at finite temperature. The relevant thermodynamic functions entering the hydrodynamic equations are discussed in the superfluid and normal regimes in terms of universal scaling functions. Both the first sound and second sound solutions are calculated as a function of temperature and the role of the superfluid density is explicitly pointed out. The density fluctuations in the second sound wave are found to be large enough to be measured as a consequence of the finite thermal expansion coefficient of the gas, which is the strategy used in a recent experiment carried out at Innsbruck where second sound was detected in the unitary Fermi gas. We also provide an investigation of the temperature dependence of the collective oscillations of first sound nature exhibited by a highly elongated harmonically trapped Fermi gas at unitarity, including the region below the critical temperature for superfluidity. Differently from the lowest axial breathing mode, the hydrodynamic frequencies of the higher-nodal excitations show a temperature dependence, which is calculated starting from Landau two-fluid theory and using the available experimental knowledge of the equation of state

Advanced models of supervised structural clustering

The strength and power of structured prediction approaches in machine learning originates from a proper recognition and exploitation of inherent structural dependencies within complex objects, which structural models are trained to output. Among the complex tasks that benefited from structured prediction approaches, clustering is of a special interest. Structured output models based on representing clusters by latent graph structures made the task of supervised clustering tractable. While in practice these models proved effective in solving the complex NLP task of coreference resolution, in this thesis, we aim at exploring their capacity to be extended to other tasks and domains, as well as the methods for performing such adaptation and for improvement in general, which, as a result, go beyond clustering and are commonly applicable in structured prediction. Studying the extensibility of the structural approaches for supervised clustering, we apply them to two different domains in two different ways. First, in the networking domain, we do clustering of network traffic by adapting the model, taking into account the continuity of incoming data. Our experiments demonstrate that the structural clustering approach is not only effective in such a scenario, but also, if changing the perspective, provides a novel potentially useful tool for detecting anomalies. The other part of our work is dedicated to assessing the amenability of the structural clustering model to joint learning with another structural model, for ranking. Our preliminary analysis in the context of the task of answer-passage reranking in question answering reveals a potential benefit of incorporating auxiliary clustering structures. Due to the intrinsic complexity of the clustering task and, respectively, its evaluation scenarios, it gave us grounds for studying the possibility and the effect from optimizing task-specific complex measures in structured prediction algorithms. It is common for structured prediction approaches to optimize surrogate loss functions, rather than the actual task-specific ones, in or- der to facilitate inference and preserve efficiency. In this thesis, we, first, study when surrogate losses are sufficient and, second, make a step towards enabling direct optimization of complex structural loss functions. We propose to learn an approximation of a complex loss by a regressor from data. We formulate a general structural framework for learning with a learned loss, which, applied to a particular case of a clustering problem – coreference resolution, i) enables the optimization of a coreference metric, by itself, having high computational complexity, and ii) delivers an improvement over the standard structural models optimizing simple surrogate objectives. We foresee this idea being helpful in many structured prediction applications, also as a means of adaptation to specific evaluation scenarios, and especially when a good loss approximation is found by a regressor from an induced feature space allowing good factorization over the underlying structure

Privacy Preserving Enforcement of Sensitive Policies in Outsourced and Distributed Environments

The enforcement of sensitive policies in untrusted environments is still an open challenge for policy-based systems. On the one hand, taking any appropriate security decision requires access to these policies. On the other hand, if such access is allowed in an untrusted environment then confidential information might be leaked by the policies. The key challenge is how to enforce sensitive policies and protect content in untrusted environments. In the context of untrusted environments, we mainly distinguish between outsourced and distributed environments. The most attractive paradigms concerning outsourced and distributed environments are cloud computing and opportunistic networks, respectively. In this dissertation, we present the design, technical and implementation details of our proposed policy-based access control mechanisms for untrusted environments. First of all, we provide full confidentiality of access policies in outsourced environments, where service providers do not learn private information about policies. We support expressive policies and take into account contextual information. The system entities do not share any encryption keys. For complex user management, we offer the full-fledged Role-Based Access Control (RBAC) policies. In opportunistic networks, we protect content by specifying expressive policies. In our proposed approach, brokers match subscriptions against policies associated with content without compromising privacy of subscribers. As a result, unauthorised brokers neither gain access to content nor learn policies and authorised nodes gain access only if they satisfy policies specified by publishers. Our proposed system provides scalable key management in which loosely-coupled publishers and subscribers communicate without any prior contact. Finally, we have developed a prototype of the system that runs on real smartphones and analysed its performance.Comment: Ph.D. Dissertation. http://eprints-phd.biblio.unitn.it/1124

A Service Robot for Navigation Assistance and Physical Rehabilitation of Seniors

The population of the advanced countries is ageing, with the direct consequence that an increasing number of people will have to live with sensitive, cognitive and physical disabilities. People with impaired physical ability are not confident to move alone, especially in crowded environment and for long journeys, highly reducing the quality of their life. We propose a new generation of robotic walking assistants whose mechanical and electronic components are conceived to optimize the collaboration between the robot and its users. We will apply these general ideas to investigate the interaction between older adults and a robotic walker, named FriWalk, exploiting it either as a navigational or as a rehabilitation aid. For the use of the FriWalk as a navigation assistance, the system guides the user securing high levels of safety, a perfect compliance with the social rules and non-intrusive interaction between human and machine. To this purpose, we developed several guidance systems ranging from completely passive strategies to active solutions exploiting either the rear or the front motors mounted on the robot. The common strategy at the basis of all the algorithms is that the responsibility of the locomotion belongs always to the user, both to increase the mobility of elder users and to enhance their perception of control over the robot. This way the robot intervenes only whenever it is strictly necessary not to mitigate the user safety. Moreover, the robotic walker has been endowed with a tablet and graphical user interface (GUI) which provides the user with the visual indications about the path to follow. Since the FriWalk was developed to suit the needs of users with different deficits, we conducted extensive human-robot interaction (HRI) experiments with elders, complemented with direct interviews of the participants. As concerns the use of the FriWalk as a rehabilitation aid, force sensing to estimate the torques applied by the user and change the user perceived inertia can be exploited by doctors to let the user feel the device heavier or lighter. Moreover, thanks to a new generation of sensors, the device can be exploited in a clinical context to track the performance of the users' rehabilitation exercises, in order to assist nurses and doctors during the hospitalization of older adults

Essays on Farm Household Decision-Making: Evidence from Vietnam

This thesis contains three studies which provide theoretical analysis and empirical evidence on the decision-making of farm households under shocks and imperfect markets in Vietnam. The first study attempts to investigate the effects of the 2007-08 global food crisis on the investment, saving and consumption decisions of household producers by using the panel data of the Vietnam Household Living Standard Survey (VHLSS), covering 2006 and 2008. The results show that the high food prices had a positive effect on only fixed asset investments in the period of the crisis. When the price shocks are incorporated in the financial conditions, the findings reveal that the effects of household incomes, loans obtained and land sizes matter. The second study uses the Vietnam Access to Resources Household Survey (VARHS) of 2010 to assess the determinants of chemical fertiliser adoption for rice cultivation, and effects on productivity and household welfare. The analysis implements both nonparametric (propensity score matching) and parametric (instrumental variables) approaches. The findings show determinants affecting decision of adoption differ from those affecting decision of adoption intensity. The results show unsurprisingly positive impact on outcomes, but focus on advantage of using parametric approach to estimate these impacts. The third study employs a sub-sample from the 2008 VHLSS that is restricted to rural areas and to children from 10 to 14 years old to explore the relationship between farmland and the employment of children on their family’s farm. The hypothesis is tested in three models (the Tobit, Heckit and double-hurdle models), in which the dependent variables are examined for two stages of decision-making, including the probability of participation and the extent of participation. Empirical evidence supports the hypothesis that child labour increases in land-rich households and decreases in land-poor households

A flexible approach to the estimation of water budgets and its connection to the travel time theory.

The 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-disitrbuted 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 rainfall-runoff model and the travel time theory were implemented and integrated in the semi-distributed hydrological system JGrass-NewAge. Thanks to the environmental modelling 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 hydrological model. The system is flexible, expandable and applicable in a variety of modelling 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

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201

Authority-Sharing Control of Assistive Robotic Walkers

A recognized consequence of population aging is a reduced level of mobility, which undermines the life quality of several senior citizens. A promising solution is represented by assisitive robotic walkers, combining the benefits of standard walkers (improved stability and physical support) with sensing and computing ability to guarantee cognitive support. In this context, classical robot control strategies designed for fully autonomous systems (such as fully autonomous vehicles, where the user is excluded from the loop) are clearly not suitable, since the user’s residual abilities must be exploited and practiced. Conversely, to guarantee safety even in the presence of user’s cognitive deficits, the responsibility of controlling the vehicle motion cannot be entirely left to the assisted person. The authority-sharing paradigm, where the control authority, i.e., the capability of controlling the vehicle motion, is shared between the human user and the control system, is a promising solution to this problem. This research develops control strategies for assistive robotic walkers based on authority-sharing: this way, we ensure that the walker provides the user only the help he/she needs for safe navigation. For instance, if the user requires just physical support to reach the restrooms, the robot acts as a standard rollator; however, if the user’s cognitive abilities are limited (e.g., the user does not remember where the restrooms are, or he/she does not recognize obstacles on the path), the robot also drives the user towards the proper corridors, by planning and following a safe path to the restrooms. The authority is allocated on the basis of an error metric, quantifying the distance between the current vehicle heading and the desired movement direction to perform the task. If the user is safely performing the task, he/she is endowed with control authority, so that his/her residual abilities are exploited. Conversely, if the user is not capable of safely solving the task (for instance, he/is going to collide with an obstacle), the robot intervenes by partially or totally taking the control authority to help the user and ensure his/her safety (for instance, avoiding the collision). We provide detailed control design and theoretical and simulative analyses of the proposed strategies. Moreover, extensive experimental validation shows that authority-sharing is a successful approach to guide a senior citizen, providing both comfort and safety. The most promising solutions include the use of haptic systems to suggest the user a proper behavior, and the modification of the perceived physical interaction of the user with the robot to gradually share the control authority using a variable stiffness vehicle handling

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation