Multimodal (EEG-fMRI) functional connectivity study of levodopa effect in Parkinson’s disease

Aim: To assess if the intake of levodopa in patients with Parkinson’s Disease (PD) changes cerebral connectivity, as revealed by simultaneous recording of hemodynamic (functional MRI, or fMRI) and electric (electroencephalogram, EEG) signals. Particularly, we hypothesize that the strongest changes in FC will involve the motor network, which is the most impaired in PD. Methods: Eight patients with diagnosis of PD “probable”, therapy with levodopa exclusively, normal cognitive and affective status, were included. Exclusion criteria were: moderate-severe rest tremor, levodopa induced dyskinesia, evidence of gray or white matter abnormalities on structural MRI. Scalp EEG (64 channels) were acquired inside the scanner (1.5 Tesla) before and after the intake of levodopa. fMRI functional connectivity was computed from four regions of interest: right and left supplementary motor area (SMA) and right and left precentral gyrus (primary motor cortex). Weighted partial directed coherence (w-PDC) was computed in the inverse space after the removal of EEG gradient and cardioballistic artifacts. Results and discussion: fMRI group analysis shows that the intake of levodopa increases hemodynamic functional connectivity among the SMAs / primary motor cortex and: sensory-motor network itself, attention network and default mode network. w-PDC analysis shows that EEG connectivity among regions of the motor network has the tendency to decrease after the intake the levodopa; furthermore, regions belonging to the DMN have the tendency to increase their outflow toward the rest of the brain. These findings, even if in a small sample of patients, suggest that other resting state physiological functional networks, beyond the motor one, are affected in patients with PD. The behavioral and cognitive tasks corresponding to the affected networks could benefit from the intake of levodopa.

Progetto di reti Sensori Wireless e tecniche di Fusione Sensoriale

Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques.

Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.

Middleware principles and design for the integration of ubiquitos mobile services

Nowadays, in Ubiquitous computing scenarios users more and more require to exploit online contents and services by means of any device at hand, no matter their physical location, and by personalizing and tailoring content and service access to their own requirements. The coordinated provisioning of content tailored to user context and preferences, and the support for mobile multimodal and multichannel interactions are of paramount importance in providing users with a truly effective Ubiquitous support. However, so far the intrinsic heterogeneity and the lack of an integrated approach led to several either too vertical, or practically unusable proposals, thus resulting in poor and non-versatile support platforms for Ubiquitous computing. This work investigates and promotes design principles to help cope with these ever-changing and inherently dynamic scenarios. By following the outlined principles, we have designed and implemented a middleware support platform to support the provisioning of Ubiquitous mobile services and contents. To prove the viability of our approach, we have realized and stressed on top of our support platform a number of different, extremely complex and heterogeneous content and service provisioning scenarios. The encouraging results obtained are pushing our research work further, in order to provide a dynamic platform that is able to not only dynamically support novel Ubiquitous applicative scenarios by tailoring extremely diverse services and contents to heterogeneous user needs, but is also able to reconfigure and adapt itself in order to provide a truly optimized and tailored support for Ubiquitous service provisioning.

Uniquitous internet middleware: architecture design and prototype evaluation

Technology advances in recent years have dramatically changed the way users exploit contents and services available on the Internet, by enforcing pervasive and mobile computing scenarios and enabling access to networked resources almost from everywhere, at anytime, and independently of the device in use. In addition, people increasingly require to customize their experience, by exploiting specific device capabilities and limitations, inherent features of the communication channel in use, and interaction paradigms that significantly differ from the traditional request/response one. So-called Ubiquitous Internet scenario calls for solutions that address many different challenges, such as device mobility, session management, content adaptation, context-awareness and the provisioning of multimodal interfaces. Moreover, new service opportunities demand simple and effective ways to integrate existing resources into new and value added applications, that can also undergo run-time modifications, according to ever-changing execution conditions. Despite service-oriented architectural models are gaining momentum to tame the increasing complexity of composing and orchestrating distributed and heterogeneous functionalities, existing solutions generally lack a unified approach and only provide support for specific Ubiquitous Internet aspects. Moreover, they usually target rather static scenarios and scarcely support the dynamic nature of pervasive access to Internet resources, that can make existing compositions soon become obsolete or inadequate, hence in need of reconfiguration. This thesis proposes a novel middleware approach to comprehensively deal with Ubiquitous Internet facets and assist in establishing innovative application scenarios. We claim that a truly viable ubiquity support infrastructure must neatly decouple distributed resources to integrate and push any kind of content-related logic outside its core layers, by keeping only management and coordination responsibilities. Furthermore, we promote an innovative, open, and dynamic resource composition model that allows to easily describe and enforce complex scenario requirements, and to suitably react to changes in the execution conditions.

Studio di ausili tecnologici compensativi di supporto all'iter di apprendimento matematico secondario e universitario degli studenti con dislessia

Data coming out from various researches carried out over the last years in Italy on the problem of school dispersion in secondary school show that difficulty in studying mathematics is one of the most frequent reasons of discomfort reported by students. Nevertheless, it is definitely unrealistic to think we can do without such knowledge in today society: mathematics is largely taught in secondary school and it is not confined within technical-scientific courses only. It is reasonable to say that, although students may choose academic courses that are, apparently, far away from mathematics, all students will have to come to terms, sooner or later in their life, with this subject. Among the reasons of discomfort given by the study of mathematics, some mention the very nature of this subject and in particular the complex symbolic language through which it is expressed. In fact, mathematics is a multimodal system composed by oral and written verbal texts, symbol expressions, such as formulae and equations, figures and graphs. For this, the study of mathematics represents a real challenge to those who suffer from dyslexia: this is a constitutional condition limiting people performances in relation to the activities of reading and writing and, in particular, to the study of mathematical contents. Here the difficulties in working with verbal and symbolic codes entail, in turn, difficulties in the comprehension of texts from which to deduce operations that, once combined together, would lead to the problem final solution. Information technologies may support this learning disorder effectively. However, these tools have some implementation limits, restricting their use in the study of scientific subjects. Vocal synthesis word processors are currently used to compensate difficulties in reading within the area of classical studies, but they are not used within the area of mathematics. This is because the vocal synthesis (or we should say the screen reader supporting it) is not able to interpret all that is not textual, such as symbols, images and graphs. The DISMATH software, which is the subject of this project, would allow dyslexic users to read technical-scientific documents with the help of a vocal synthesis, to understand the spatial structure of formulae and matrixes, to write documents with a technical-scientific content in a format that is compatible with main scientific editors. The system uses LaTex, a text mathematic language, as mediation system. It is set up as LaTex editor, whose graphic interface, in line with main commercial products, offers some additional specific functions with the capability to support the needs of users who are not able to manage verbal and symbolic codes on their own. LaTex is translated in real time into a standard symbolic language and it is read by vocal synthesis in natural language, in order to increase, through the bimodal representation, the ability to process information. The understanding of the mathematic formula through its reading is made possible by the deconstruction of the formula itself and its “tree” representation, so allowing to identify the logical elements composing it. Users, even without knowing LaTex language, are able to write whatever scientific document they need: in fact the symbolic elements are recalled by proper menus and automatically translated by the software managing the correct syntax. The final aim of the project, therefore, is to implement an editor enabling dyslexic people (but not only them) to manage mathematic formulae effectively, through the integration of different software tools, so allowing a better teacher/learner interaction too.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Reaching to a target in the 3-D space

Reaching and grasping an object is an action that can be performed in light, under visual guidance, as well as in darkness, under proprioceptive control only. Area V6A is a visuomotor area involved in the control of reaching movements. V6A, besides neurons activated by the execution of reaching movements, shows passive somatosensory and visual responses. This suggests fro V6A a multimodal capability of integrating sensory and motor-related information, We wanted to know whether this integration occurrs in reaching movements and in the present study we tested whether the visual feedback influenced the reaching activity of V6A neurons. In order to better address this question, we wanted to interpret the neural data in the light of the kinematic of reaching performance. We used an experimental paradigm that could examine V6A responses in two different visual backgrounds, light and dark. In these conditions, the monkey performed an istructed-delay reaching task moving the hand towards different target positions located in the peripersonal space. During the execution of reaching task, the visual feedback is processed in a variety of patterns of modulation, sometimes not expected. In fact, having already demonstrated in V6A reach-related discharges in absence of visual feedback, we expected two types of neural modulation: 1) the addition of light in the environment enhanced reach-related discharges recorded in the dark; 2) the light left the neural response unmodified. Unexpectedly, the results show a complex pattern of modulation that argues against a simple additive interaction between visual and motor-related signals.

Role of nitric oxide and endocannabinoids in synaptic plasticity in the perirhinal cortex and in visual recognition memory

Introduction and aims of the research Nitric oxide (NO) and endocannabinoids (eCBs) are major retrograde messengers, involved in synaptic plasticity (long-term potentiation, LTP, and long-term depression, LTD) in many brain areas (including hippocampus and neocortex), as well as in learning and memory processes. NO is synthesized by NO synthase (NOS) in response to increased cytosolic Ca2+ and mainly exerts its functions through soluble guanylate cyclase (sGC) and cGMP production. The main target of cGMP is the cGMP-dependent protein kinase (PKG). Activity-dependent release of eCBs in the CNS leads to the activation of the Gαi/o-coupled cannabinoid receptor 1 (CB1) at both glutamatergic and inhibitory synapses. The perirhinal cortex (Prh) is a multimodal associative cortex of the temporal lobe, critically involved in visual recognition memory. LTD is proposed to be the cellular correlate underlying this form of memory. Cholinergic neurotransmission has been shown to play a critical role in both visual recognition memory and LTD in Prh. Moreover, visual recognition memory is one of the main cognitive functions impaired in the early stages of Alzheimer’s disease. The main aim of my research was to investigate the role of NO and ECBs in synaptic plasticity in rat Prh and in visual recognition memory. Part of this research was dedicated to the study of synaptic transmission and plasticity in a murine model (Tg2576) of Alzheimer’s disease. Methods Field potential recordings. Extracellular field potential recordings were carried out in horizontal Prh slices from Sprague-Dawley or Dark Agouti juvenile (p21-35) rats. LTD was induced with a single train of 3000 pulses delivered at 5 Hz (10 min), or via bath application of carbachol (Cch; 50 μM) for 10 min. LTP was induced by theta-burst stimulation (TBS). In addition, input/output curves and 5Hz-LTD were carried out in Prh slices from 3 month-old Tg2576 mice and littermate controls. Behavioural experiments. The spontaneous novel object exploration task was performed in intra-Prh bilaterally cannulated adult Dark Agouti rats. Drugs or vehicle (saline) were directly infused into the Prh 15 min before training to verify the role of nNOS and CB1 in visual recognition memory acquisition. Object recognition memory was tested at 20 min and 24h after the end of the training phase. Results Electrophysiological experiments in Prh slices from juvenile rats showed that 5Hz-LTD is due to the activation of the NOS/sGC/PKG pathway, whereas Cch-LTD relies on NOS/sGC but not PKG activation. By contrast, NO does not appear to be involved in LTP in this preparation. Furthermore, I found that eCBs are involved in LTP induction, but not in basal synaptic transmission, 5Hz-LTD and Cch-LTD. Behavioural experiments demonstrated that the blockade of nNOS impairs rat visual recognition memory tested at 24 hours, but not at 20 min; however, the blockade of CB1 did not affect visual recognition memory acquisition tested at both time points specified. In three month-old Tg2576 mice, deficits in basal synaptic transmission and 5Hz-LTD were observed compared to littermate controls. Conclusions The results obtained in Prh slices from juvenile rats indicate that NO and CB1 play a role in the induction of LTD and LTP, respectively. These results are confirmed by the observation that nNOS, but not CB1, is involved in visual recognition memory acquisition. The preliminary results obtained in the murine model of Alzheimer’s disease indicate that deficits in synaptic transmission and plasticity occur very early in Prh; further investigations are required to characterize the molecular mechanisms underlying these deficits.

Interventi normativi, interni ed uniformi, dettati dalle nuove esigenze e dai nuovi quesiti posti dalla modernizzazione dei traffici, con particolare riferimento al trasporto containerizzato e multimodale

Come si evince dal titolo della tesi, la ricerca effettuata dal presente candidato nel corso del dottorato di ricerca ha avuto ad oggetto lo studio della materia relativa ai trasporti nell’ambito di diversi sistemi giuridici europei, con particolare attenzione ai risvolti di carattere pratico che l’interpretazione delle normative uniformi poteva produrre nell’ambito delle diverse giurisdizioni nonché alle diverse impostazioni e alla variegata gamma di soluzioni interpretative che nell’ambito di problemi simili possono essere adottate a seconda che una stessa questione venga discussa in un ordinamento piuttosto che in un altro. Dall’avvento del trasporto marittimo di containers alla necessità di disciplinare l’intera materia attraverso una normativa multimodale il passo è estremamente breve, posto che, proprio in considerazione delle caratteristiche proprie del trasporto containerizzato, gli aventi diritto al carico sono principalmente interessati al completamente del trasferimento door-to-door inteso nella sua globalità, piuttosto che al buon esito del trasporto sulla singola tratta marittima. Il progetto di revisione delle Regole dell’Aja-Visby adottato dall’United Nations Commission on International Trade Law (Uncitral) e il Comité Maritime International (CMI) costituisce per definizione un progetto limitato ad un trasporto multimodale comprendente necessariamente una tratta marittima, ma rappresenta comunque un interessante banco di prova per valutare la funzionalità di strumenti di recente impiego, come ad esempio i cosiddetti e-documents, concetto peraltro già inserito nel progetto UNCITRAL, anche se con scarsi elementi di reale novità rispetto alla tradizionale disciplina relativa ai documenti cartacei. Proprio la parte relativa ai documenti e titoli di viaggio merita particolare attenzione soprattutto in riferimento alle problematiche connesse al traffico containerizzato, con particolare riferimento al concetto di transhipment, e alla conseguente necessità che la polizza garantisca al legittimo portatore una copertura completa sull’intero viaggio della merce, oltre a dargli la possibilità di individuare agevolmente la propria controparte contrattuale, e cioè il vettore.

The Perception of American Adolescent Culture Through the Dubbing and Fansubbing of a Selection of US Teen Series from 1990 to 2013

The present dissertation aims at analyzing the construction of American adolescent culture through teen-targeted television series and the shift in perception that occurs as a consequence of the translation process. In light of the recent changes in television production and consumption modes, largely caused by new technologies, this project explores the evolution of Italian audiences, focusing on fansubbing (freely distributed amateur subtitles made by fans for fan consumption) and social viewing (the re-aggregation of television consumption based on social networks and dedicated platforms, rather than on physical presence). These phenomena are symptoms of a sort of ‘viewership 2.0’ and of a new type of active viewing, which calls for a revision of traditional AVT strategies. Using a framework that combines television studies, new media studies, and fandom studies with an approach to AVT based on Descriptive Translation Studies (Toury 1995), this dissertation analyzes the non-Anglophone audience’s growing need to participation in the global dialogue and appropriation process based on US scheduling and informed by the new paradigm of convergence culture, transmedia storytelling, and affective economics (Jenkins 2006 and 2007), as well as the constraints intrinsic to multimodal translation and the different types of linguistic and cultural adaptation performed through dubbing (which tends to be more domesticating; Venuti 1995) and fansubbing (typically more foreignizing). The study analyzes a selection of episodes from six of the most popular teen television series between 1990 and 2013, which has been divided into three ages based on the different modes of television consumption: top-down, pre-Internet consumption (Beverly Hills, 90210, 1990 – 2000), emergence of audience participation (Buffy the Vampire Slayer, 1997 – 2003; Dawson’s Creek, 1998 – 2003), age of convergence and Viewership 2.0 (Gossip Girl, 2007 – 2012; Glee, 2009 – present; The Big Bang Theory, 2007 - present).