What ethologically based models have taught us about the neural systems underlying fear and anxiety

Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.

BDNF gene polymorphism, cognition and symptom severity in a Brazilian population-based sample of first-episode psychosis subjects

OBJECTIVE: To investigate the influence of brain-derived neurotrophic factor (BDNF) gene variations on cognitive performance and clinical symptomatology in first-episode psychosis (FEP). METHODS: We performed BDNF val66met variant genotyping, cognitive testing (verbal fluency and digit spans) and assessments of symptom severity (as assessed with the PANSS) in a population-based sample of FEP patients (77 with schizophreniform psychosis and 53 with affective psychoses) and 191 neighboring healthy controls. RESULTS: There was no difference in the proportion of Met allele carriers between FEP patients and controls, and no significant influence of BDNF genotype on cognitive test scores in either of the psychosis groups. A decreased severity of negative symptoms was found in FEP subjects that carried a Met allele, and this finding reached significance for the subgroup with affective psychoses (p < 0.01, ANOVA). CONCLUSIONS: These results suggest that, in FEP, the BDNF gene Val66Met polymorphism does not exert a pervasive influence on cognitive functioning but may modulate the severity of negative symptoms.

Niemann-Pick disease type C symptomatology: an expert-based clinical description

Niemann-Pick disease type C (NP-C) is a rare, progressive, irreversible disease leading to disabling neurological manifestations and premature death. The estimated disease incidence is 1:120,000 live births, but this likely represents an underestimate, as the disease may be under-diagnosed due to its highly heterogeneous presentation. NP-C is characterised by visceral, neurological and psychiatric manifestations that are not specific to the disease and that can be found in other conditions. The aim of this review is to provide non-specialists with an expert-based, detailed description of NP-C signs and symptoms, including how they present in patients and how they can be assessed. Early disease detection should rely on seeking a combination of signs and symptoms, rather than isolated findings. Examples of combinations which are strongly suggestive of NP-C include: splenomegaly and vertical supranuclear gaze palsy (VSGP); splenomegaly and clumsiness; splenomegaly and schizophrenia-like psychosis; psychotic symptoms and cognitive decline; and ataxia with dystonia, dysarthria/dysphagia and cognitive decline. VSGP is a hallmark of NP-C and becomes highly specific of the disease when it occurs in combination with other manifestations (e.g. splenomegaly, ataxia). In young infants (<2 years), abnormal saccades may first manifest as slowing and shortening of upward saccades, long before gaze palsy onset. While visceral manifestations tend to predominate during the perinatal and infantile period (2 months–6 years of age), neurological and psychiatric involvement is more prominent during the juvenile/adult period (>6 years of age). Psychosis in NP-C is atypical and variably responsive to treatment. Progressive cognitive decline, which always occurs in patients with NP-C, manifests as memory and executive impairment in juvenile/adult patients. Disease prognosis mainly correlates with the age at onset of the neurological signs, with early-onset forms progressing faster. Therefore, a detailed and descriptive picture of NP-C signs and symptoms may help improve disease detection and early diagnosis, so that therapy with miglustat (Zavesca®), the only available treatment approved to date, can be started as soon as neurological symptoms appear, in order to slow disease progression.

In loving memory: Escritura última y memoria en los cementerios ingleses de Canarias

[ES] El estudio de las inscripciones que se conservan en los tres cementerios ingleses de Canarias, así como de los propios monumentos funerarios en los que estas se inscriben, permite un acercamiento al ars moriendi, pero también a los mecanismos de autorrepresentación de los miembros de la comunidad británica radicada en Canarias cuyos restos reposan en estas necrópolis. El presente estudio pretende analizar estas escrituras últimas en el contexto de una historia social de la cultura escrita en Canarias que cuenta, como elemento de diferenciación con respecto a otras regiones españolas, con una importante presencia británica, evidenciada en la mayor concentración de cementerios protestantes de todo el país.

Active fibre-reinforced composites with embedded shape memory alloys

This dissertation concerns active fibre-reinforced composites with embedded shape memory alloy wires. The structural application of active materials allows to develop adaptive structures which actively respond to changes in the environment, such as morphing structures, self-healing structures and power harvesting devices. In particular, shape memory alloy actuators integrated within a composite actively control the structural shape or stiffness, thus influencing the composite static and dynamic properties. Envisaged applications include, among others, the prevention of thermal buckling of the outer skin of air vehicles, shape changes in panels for improved aerodynamic characteristics and the deployment of large space structures. The study and design of active composites is a complex and multidisciplinary topic, requiring in-depth understanding of both the coupled behaviour of active materials and the interaction between the different composite constituents. Both fibre-reinforced composites and shape memory alloys are extremely active research topics, whose modelling and experimental characterisation still present a number of open problems. Thus, while this dissertation focuses on active composites, some of the research results presented here can be usefully applied to traditional fibre-reinforced composites or other shape memory alloy applications. The dissertation is composed of four chapters. In the first chapter, active fibre-reinforced composites are introduced by giving an overview of the most common choices available for the reinforcement, matrix and production process, together with a brief introduction and classification of active materials. The second chapter presents a number of original contributions regarding the modelling of fibre-reinforced composites. Different two-dimensional laminate theories are derived from a parent three-dimensional theory, introducing a procedure for the a posteriori reconstruction of transverse stresses along the laminate thickness. Accurate through the thickness stresses are crucial for the composite modelling as they are responsible for some common failure mechanisms. A new finite element based on the First-order Shear Deformation Theory and a hybrid stress approach is proposed for the numerical solution of the two-dimensional laminate problem. The element is simple and computationally efficient. The transverse stresses through the laminate thickness are reconstructed starting from a general finite element solution. A two stages procedure is devised, based on Recovery by Compatibility in Patches and three-dimensional equilibrium. Finally, the determination of the elastic parameters of laminated structures via numerical-experimental Bayesian techniques is investigated. Two different estimators are analysed and compared, leading to the definition of an alternative procedure to improve convergence of the estimation process. The third chapter focuses on shape memory alloys, describing their properties and applications. A number of constitutive models proposed in the literature, both one-dimensional and three-dimensional, are critically discussed and compared, underlining their potential and limitations, which are mainly related to the definition of the phase diagram and the choice of internal variables. Some new experimental results on shape memory alloy material characterisation are also presented. These experimental observations display some features of the shape memory alloy behaviour which are generally not included in the current models, thus some ideas are proposed for the development of a new constitutive model. The fourth chapter, finally, focuses on active composite plates with embedded shape memory alloy wires. A number of di®erent approaches can be used to predict the behaviour of such structures, each model presenting different advantages and drawbacks related to complexity and versatility. A simple model able to describe both shape and stiffness control configurations within the same context is proposed and implemented. The model is then validated considering the shape control configuration, which is the most sensitive to model parameters. The experimental work is divided in two parts. In the first part, an active composite is built by gluing prestrained shape memory alloy wires on a carbon fibre laminate strip. This structure is relatively simple to build, however it is useful in order to experimentally demonstrate the feasibility of the concept proposed in the first part of the chapter. In the second part, the making of a fibre-reinforced composite with embedded shape memory alloy wires is investigated, considering different possible choices of materials and manufacturing processes. Although a number of technological issues still need to be faced, the experimental results allow to demonstrate the mechanism of shape control via embedded shape memory alloy wires, while showing a good agreement with the proposed model predictions.

Memory hierarchy and data communication in heterogeneous reconfigurable SoCs

The miniaturization race in the hardware industry aiming at continuous increasing of transistor density on a die does not bring respective application performance improvements any more. One of the most promising alternatives is to exploit a heterogeneous nature of common applications in hardware. Supported by reconfigurable computation, which has already proved its efficiency in accelerating data intensive applications, this concept promises a breakthrough in contemporary technology development. Memory organization in such heterogeneous reconfigurable architectures becomes very critical. Two primary aspects introduce a sophisticated trade-off. On the one hand, a memory subsystem should provide well organized distributed data structure and guarantee the required data bandwidth. On the other hand, it should hide the heterogeneous hardware structure from the end-user, in order to support feasible high-level programmability of the system. This thesis work explores the heterogeneous reconfigurable hardware architectures and presents possible solutions to cope the problem of memory organization and data structure. By the example of the MORPHEUS heterogeneous platform, the discussion follows the complete design cycle, starting from decision making and justification, until hardware realization. Particular emphasis is made on the methods to support high system performance, meet application requirements, and provide a user-friendly programmer interface. As a result, the research introduces a complete heterogeneous platform enhanced with a hierarchical memory organization, which copes with its task by means of separating computation from communication, providing reconfigurable engines with computation and configuration data, and unification of heterogeneous computational devices using local storage buffers. It is distinguished from the related solutions by distributed data-flow organization, specifically engineered mechanisms to operate with data on local domains, particular communication infrastructure based on Network-on-Chip, and thorough methods to prevent computation and communication stalls. In addition, a novel advanced technique to accelerate memory access was developed and implemented.

ATG based combination therapy - a novel clinically relevant approach to promote regulation and induce long-term allograft survival

Regulatory T cells (Treg) actively regulate alloimmune responses and promote transplantation tolerance. Polyclonal anti-thymocyte globulin (ATG), a widely used induction therapy in clinical organ transplantation, depletes peripheral T cells. However, resistance to tolerance induction is seen with certain T cell depleting strategies and is attributed to alterations in the balance of naïve, memory and regulatory T cells. Here we report a novel reagent, murine ATG (mATG), depletes T cells but preferentially spares CD25+ natural Tregs which limit skewing of T cell repertoire toward T-effector-memory (Tem) phenotype among the recovering T cells. T-cell depletion with mATG combined with CTLA4Ig and Sirolimus synergize to prolong graft survival by tipping the Treg/Tem balance further in favor of Tregs by preserving Tregs, facilitating generation of new Tregs by a conversion mechanism and limiting Tem expansion in response to alloantigen and homeostatic proliferation. These results provide the rationale for translating such novel combination therapies to promote tolerance in primate and human organ transplantation.

Advanced Numerical Simulation of Silicon-Based Solar Cells

Photovoltaic (PV) conversion is the direct production of electrical energy from sun without involving the emission of polluting substances. In order to be competitive with other energy sources, cost of the PV technology must be reduced ensuring adequate conversion efficiencies. These goals have motivated the interest of researchers in investigating advanced designs of crystalline silicon solar (c-Si) cells. Since lowering the cost of PV devices involves the reduction of the volume of semiconductor, an effective light trapping strategy aimed at increasing the photon absorption is required. Modeling of solar cells by electro-optical numerical simulation is helpful to predict the performance of future generations devices exhibiting advanced light-trapping schemes and to provide new and more specific guidelines to industry. The approaches to optical simulation commonly adopted for c-Si solar cells may lead to inaccurate results in case of thin film and nano-stuctured solar cells. On the other hand, rigorous solvers of Maxwell equations are really cpu- and memory-intensive. Recently, in optical simulation of solar cells, the RCWA method has gained relevance, providing a good trade-off between accuracy and computational resources requirement. This thesis is a contribution to the numerical simulation of advanced silicon solar cells by means of a state-of-the-art numerical 2-D/3-D device simulator, that has been successfully applied to the simulation of selective emitter and the rear point contact solar cells, for which the multi-dimensionality of the transport model is required in order to properly account for all physical competing mechanisms. In the second part of the thesis, the optical problems is discussed. Two novel and computationally efficient RCWA implementations for 2-D simulation domains as well as a third RCWA for 3-D structures based on an eigenvalues calculation approach have been presented. The proposed simulators have been validated in terms of accuracy, numerical convergence, computation time and correctness of results.

Analysis of charge-transport properties in GST materials for next generation phase-change memory devices

The quest for universal memory is driving the rapid development of memories with superior all-round capabilities in non-volatility, high speed, high endurance and low power. The memory subsystem accounts for a significant cost and power budget of a computer system. Current DRAM-based main memory systems are starting to hit the power and cost limit. To resolve this issue the industry is improving existing technologies such as Flash and exploring new ones. Among those new technologies is the Phase Change Memory (PCM), which overcomes some of the shortcomings of the Flash such as durability and scalability. This alternative non-volatile memory technology, which uses resistance contrast in phase-change materials, offers more density relative to DRAM, and can help to increase main memory capacity of future systems while remaining within the cost and power constraints. Chalcogenide materials can suitably be exploited for manufacturing phase-change memory devices. Charge transport in amorphous chalcogenide-GST used for memory devices is modeled using two contributions: hopping of trapped electrons and motion of band electrons in extended states. Crystalline GST exhibits an almost Ohmic I(V) curve. In contrast amorphous GST shows a high resistance at low biases while, above a threshold voltage, a transition takes place from a highly resistive to a conductive state, characterized by a negative differential-resistance behavior. A clear and complete understanding of the threshold behavior of the amorphous phase is fundamental for exploiting such materials in the fabrication of innovative nonvolatile memories. The type of feedback that produces the snapback phenomenon is described as a filamentation in energy that is controlled by electron–electron interactions between trapped electrons and band electrons. The model thus derived is implemented within a state-of-the-art simulator. An analytical version of the model is also derived and is useful for discussing the snapback behavior and the scaling properties of the device.

Multicomponent nanodevices based on molecular and nanocrystal moieties

Nanoscience is an emerging and fast-growing field of science with the aim of manipulating nanometric objects with dimension below 100 nm. Top down approach is currently used to build these type of architectures (e.g microchips). The miniaturization process cannot proceed indefinitely due to physical and technical limitations. Those limits are focusing the interest on the bottom-up approach and construction of nano-objects starting from “nano-bricks” like atoms, molecules or nanocrystals. Unlike atoms, molecules can be “fully programmable” and represent the best choice to build up nanostructures. In the past twenty years many examples of functional nano-devices able to perform simple actions have been reported. Nanocrystals which are often considered simply nanostructured materials, can be active part in the development of those nano-devices, in combination with functional molecules. The object of this dissertation is the photophysical and photochemical investigation of nano-objects bearing molecules and semiconductor nanocrystals (QDs) as components. The first part focuses on the characterization of a bistable rotaxane. This study, in collaboration with the group of Prof. J.F. Stoddart (Northwestern University, Evanston, Illinois, USA) who made the synthesis of the compounds, shows the ability of this artificial machine to operate as bistable molecular-level memory under kinetic control. The second part concerns the study of the surface properties of luminescent semiconductor nanocrystals (QDs) and in particular the effect of acid and base on the spectroscopical properties of those nanoparticles. In this section is also reported the work carried out in the laboratory of Prof H. Mattoussi (Florida State University, Tallahassee, Florida, USA), where I developed a novel method for the surface decoration of QDs with lipoic acid-based ligands involving the photoreduction of the di-thiolane moiety.

Gpu-based many-core architecture emulation: A double level approach

The efficient emulation of a many-core architecture is a challenging task, each core could be emulated through a dedicated thread and such threads would be interleaved on an either single-core or a multi-core processor. The high number of context switches will results in an unacceptable performance. To support this kind of application, the GPU computational power is exploited in order to schedule the emulation threads on the GPU cores. This presents a non trivial divergence issue, since GPU computational power is offered through SIMD processing elements, that are forced to synchronously execute the same instruction on different memory portions. Thus, a new emulation technique is introduced in order to overcome this limitation: instead of providing a routine for each ISA opcode, the emulator mimics the behavior of the Micro Architecture level, here instructions are date that a unique routine takes as input. Our new technique has been implemented and compared with the classic emulation approach, in order to investigate the chance of a hybrid solution.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.

Shape memory and elastoplastic materials: from constitutive and numerical to fatigue modeling

Shape memory materials (SMMs) represent an important class of smart materials that have the ability to return from a deformed state to their original shape. Thanks to such a property, SMMs are utilized in a wide range of innovative applications. The increasing number of applications and the consequent involvement of industrial players in the field have motivated researchers to formulate constitutive models able to catch the complex behavior of these materials and to develop robust computational tools for design purposes. Such a research field is still under progress, especially in the prediction of shape memory polymer (SMP) behavior and of important effects characterizing shape memory alloy (SMA) applications. Moreover, the frequent use of shape memory and metallic materials in biomedical devices, particularly in cardiovascular stents, implanted in the human body and experiencing millions of in-vivo cycles by the blood pressure, clearly indicates the need for a deeper understanding of fatigue/fracture failure in microsize components. The development of reliable stent designs against fatigue is still an open subject in scientific literature. Motivated by the described framework, the thesis focuses on several research issues involving the advanced constitutive, numerical and fatigue modeling of elastoplastic and shape memory materials. Starting from the constitutive modeling, the thesis proposes to develop refined phenomenological models for reliable SMA and SMP behavior descriptions. Then, concerning the numerical modeling, the thesis proposes to implement the models into numerical software by developing implicit/explicit time-integration algorithms, to guarantee robust computational tools for practical purposes. The described modeling activities are completed by experimental investigations on SMA actuator springs and polyethylene polymers. Finally, regarding the fatigue modeling, the thesis proposes the introduction of a general computational approach for the fatigue-life assessment of a classical stent design, in order to exploit computer-based simulations to prevent failures and modify design, without testing numerous devices.

Microstructure of epitaxial thin films of the ferromagnetic shape memory alloy Ni 2 MnGa

Gegenstand dieser Arbeit ist die Präparation und die ausführliche Charakterisierung epitaktischer Dünnschicht-Proben der Heusler Verbindung Ni2MnGa. Diese intermetallische Verbindung zeigt einen magnetischen Formgedächtnis-Effekt (MFG), der sowohl im Bezug auf mögliche Anwendungen, als auch im Kontext der Grundlagenforschung äußerst interessant ist. In Einkristallen nahe der Stöchiometrie Ni2MnGa wurden riesige magnetfeldinduzierte Dehnungen von bis zu 10 % nachgewiesen. Der zugrundeliegende Mechanismus basiert auf einer Umverteilung von kristallographischen Zwillings-Varianten, die eine tetragonale oder orthorhombische Symmetrie besitzen. Unter dem Einfluss des Magnetfeldes bewegen sich die Zwillingsgrenzen durch den Kristall, was eine makroskopische Formänderung mit sich bringt. Die somit erzeugten reversiblen Längenänderungen können mit hoher Frequenz geschaltet werden, was Ni2MnGa zu einem vielversprechenden Aktuatorwerkstoff macht. rnDa der Effekt auf einem intrinsischen Prozess beruht, eignen sich Bauteile aus MFG Legierungen zur Integration in Mikrosystemen (z.B. im Bereich der Mikrofluidik). rnrnBislang konnten große magnetfeldinduzierte Dehnungen nur für Einkristalle und Polykristalle mit hoher Porosität („foams") nachgewiesen werden. Um den Effekt für Anwendungen nutzbar zu machen, werden allerdings Konzepte zur Miniaturisierung benötigt. Eine Möglichkeit bieten epitaktische dünne Filme, die im Rahmen dieser Arbeit hergestellt und untersucht werden sollen. Im Fokus stehen dabei die Optimierung der Herstellungsparameter, sowie die Präparation von freitragenden Schichten. Zudem werden verschiedene Konzepte zur Herstellung freistehender Mikrostrukturen erprobt. Mittels Röntgendiffraktometrie konnte die komplizierte Kristallstruktur für verschiedene Wachstumsrichtungen verstanden und die genaue Verteilung der Zwillingsvarianten aufgedeckt werden. In Verbindung mit Mikroskopie-Methoden konnte so die Zwillingsstruktur auf verschiedenen Längenskalen geklärt werden. Die Ergebnisse erklären das Ausbleiben des MFG Effekts in den Proben mit (100) Orientierung. Andererseits wurde für Schichten mit (110) Wachstum eine vielversprechende Mikrostruktur entdeckt, die einen guten Ausgangspunkt für weitere Untersuchungen bietet.rnDurch die spezielle Geometrie der Proben war es möglich, Spektroskopie-Experimente in Transmission durchzuführen. Die Ergebnisse stellen den ersten experimentellen Nachweis der Änderungen in der elektronischen Struktur einer metallischen Verbindung während des martensitischen Phasenübergangs dar. Durch Messen des magnetischen Zirkulardichroismus in der Röntgenabsorption konnten quantitative Aussagen über die magnetischen Momente von Ni und Mn getroffen werden. Die Methode erlaubt überdies die Beiträge von Spin- und Bahn-Moment separat zu bestimmen. Durch winkelabhängige Messungen gelang es, die mikroskopische Ursache der magnetischen Anisotropie aufzuklären. Diese Ergebnisse tragen wesentlich zum Verständnis der komplexen magnetischen und strukturellen Eigenschaften von Ni2MnGa bei.rn