Parallel Architectures for Many-Core Systems-On-Chip in Deep Sub-Micron Technology

Despite the several issues faced in the past, the evolutionary trend of silicon has kept its constant pace. Today an ever increasing number of cores is integrated onto the same die. Unfortunately, the extraordinary performance achievable by the many-core paradigm is limited by several factors. Memory bandwidth limitation, combined with inefficient synchronization mechanisms, can severely overcome the potential computation capabilities. Moreover, the huge HW/SW design space requires accurate and flexible tools to perform architectural explorations and validation of design choices. In this thesis we focus on the aforementioned aspects: a flexible and accurate Virtual Platform has been developed, targeting a reference many-core architecture. Such tool has been used to perform architectural explorations, focusing on instruction caching architecture and hybrid HW/SW synchronization mechanism. Beside architectural implications, another issue of embedded systems is considered: energy efficiency. Near Threshold Computing is a key research area in the Ultra-Low-Power domain, as it promises a tenfold improvement in energy efficiency compared to super-threshold operation and it mitigates thermal bottlenecks. The physical implications of modern deep sub-micron technology are severely limiting performance and reliability of modern designs. Reliability becomes a major obstacle when operating in NTC, especially memory operation becomes unreliable and can compromise system correctness. In the present work a novel hybrid memory architecture is devised to overcome reliability issues and at the same time improve energy efficiency by means of aggressive voltage scaling when allowed by workload requirements. Variability is another great drawback of near-threshold operation. The greatly increased sensitivity to threshold voltage variations in today a major concern for electronic devices. We introduce a variation-tolerant extension of the baseline many-core architecture. By means of micro-architectural knobs and a lightweight runtime control unit, the baseline architecture becomes dynamically tolerant to variations.

Personalization of Electronic Nicotine Delivery Systems Toxicological impact in in-vivo model

Electronic nicotine delivery systems (ENDS) use has recently grown. E-cig generates carcinogenic chemical compounds and reactive oxygen species (ROS). Carbonyls and ROS are formed when the liquid comes into contact with the heating element. In this study the chemical and biological effects of coil resistance applied on the same device were investigated. A preliminary in-vivo study the new heat-not-burn devices (IQOS®) has been conducted to evaluate the effect of the device on antioxidant biomarkers. The amount of formaldehyde, acetaldehyde, acrolein was measured by GC-MS analysis. The two e-liquids used for carbonyls detection differed only for the presence of nicotine. The nicotine-free liquid was then used for the detection of ROS in the aerosol. The impact of the non-nicotine vapor on cell viability in H1299 human lung carcinoma cells, as well as the biological effects in a rat model of e-cig aerosol exposure, were also evaluated. After the exposure of Sprague Dawley rats to e-cig and IQOS® aerosol, the effect of 28-day treatment was examined on enzymatic and non-enzymatic antioxidant response, lung inflammation, blood homeostasis and tissue damage by using scanning electron microscope (SEM) technique. The results show a significant correlation between the low resistance and the generation of higher concentrations of the selected carbonyls and ROS in aerosols. Cell viability was reduced with an inverse relation to coil resistance. The experimental model highlighted an impairment of the pulmonary antioxidant and detoxifying machinery. Frames from SEM show disorganization of alveolar and bronchial epithelium. IQOS® exposed animals shows a significant production of ROS related to the unbalance of antioxidant defense and alteration of macromolecule integrity. This research demonstrates how several toxicological aspects can potentially occur in e-cig consumers who use low resistance device coupled with nicotine-free liquid. ENDS may expose users to hazardous compounds, which, may promote chronic pathologies and degenerative diseases.

Clustering of variables around latent components: an application in consumer science

The present work proposes a method based on CLV (Clustering around Latent Variables) for identifying groups of consumers in L-shape data. This kind of datastructure is very common in consumer studies where a panel of consumers is asked to assess the global liking of a certain number of products and then, preference scores are arranged in a two-way table Y. External information on both products (physicalchemical description or sensory attributes) and consumers (socio-demographic background, purchase behaviours or consumption habits) may be available in a row descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this method is to automatically provide a consumer segmentation where all the three matrices play an active role in the classification, getting homogeneous groups from all points of view: preference, products and consumer characteristics. The proposed clustering method is illustrated on data from preference studies on food products: juices based on berry fruits and traditional cheeses from Trentino. The hedonic ratings given by the consumer panel on the products under study were explained with respect to the product chemical compounds, sensory evaluation and consumer socio-demographic information, purchase behaviour and consumption habits.

Biomaterials for Clinical Application in Endodontics

Endodontic therapy consists in the management of several tissues such as pulp tissue, periodontal tissue, periapical bone and dentine. These tissues are often contaminated by blood, periapical exudates and biological fluids. An ideal orthograde or retrograde filling material should be non toxic, noncarcinogenic, nongenotoxic, biocompatible with the host tissues, insoluble in tissue fluids, and dimensionally stable. Calcium-silicate MTA based cements own many of these ideal characteristics, but the long setting time, the non-easy handling and the lack of mechanical properties at early times are few drawbacks which may complicate the clinical application. The aim of this study was to investigate the chemical, physical and biological properties of calcium-silicate MTA cements in order to improve the mechanical properties and the handling keeping the biological characteristics unchanged. Chemical and physical properties such as setting time, solubility, water-uptake, ion release, sealing ability were investigated according the ISO and ADA specifications. The bioactivity (ability to produce apatite nano-sferulities) of MTA cements were evaluated using ESEM/EDX, micro-Raman and ATR/FTIR spettroscopy.

Excitonic processes in multifunctional organic semiconductors for application in organic-light emitting transistor

In this thesis, I report on a comprehensive study about the photo-physical properties both in solution and in solid-state of a new thiophene based material (2,2’-(2,2’-bithiophene-5,5’-diyl)bis(5-butyl-5H-thieno[2,3-c]pyrrole-4,6)-dione (T4DIM) which shows an ambipolar semiconducting behavior together with electroluminescence in single-layer OLET device architecture[14

In vitro characterisation and expansion of human regulatory T cells for their in vivo application in the induction of tolerance in haematopoietic stem cell and solid organ transplantation

Solid organ transplantation (SOT) is considered the treatment of choice for many end-stage organ diseases. Thus far, short term results are excellent, with patient survival rates greater than 90% one year post-surgery, but there are several problems with the long term acceptance and use of immunosuppressive drugs. Hematopoietic Stem Cells Transplantation (HSCT) concerns the infusion of haematopoietic stem cells to re-establish acquired and congenital disorders of the hematopoietic system. The main side effect is the Graft versus Host Disease (GvHD) where donor T cells can cause pathology involving the damage of host tissues. Patients undergoing acute or chronic GvHD receive immunosuppressive regimen that is responsible for several side effects. The use of immunosuppressive drugs in the setting of SOT and GvHD has markedly reduced the incidence of acute rejection and the tissue damage in GvHD however, the numerous adverse side effects observed boost the development of alternative strategies to improve the long-term outcome. To this effect, the use of CD4+CD25+FOXP3+ regulatory T cells (Treg) as a cellular therapy is an attractive approach for autoimmunity disease, GvHD and limiting immune responses to allograft after transplantation. Treg have a pivotal role in maintaining peripheral immunological tolerance, by preventing autoimmunity and chronic inflammation. Results of my thesis provide the characterization and cell processing of Tregs from healthy controls and patients in waiting list for liver transplantation, followed by the development of an efficient expansion-protocol and the investigation of the impact of the main immunosuppressive drugs on viability, proliferative capacity and function of expanded cells after expansion. The conclusion is that ex vivo expansion is necessary to infuse a high Treg dose and although many other factors in vivo can contribute to the success of Treg therapy, the infusion of Tregs during the administration of the highest dose of immunosuppressants should be carefully considered.

Photoredox Catalysis and Nucleophilic Organometallic Reagents – Application in Organic Synthesis

The main purpose of my PhD was the combination of the principles of transition metal catalysis with photoredox catalysis. We focused our attention on the development of novel dual catalytic protocols for the functionalization of carbonyl compounds through the generation of transient nucleophilic organometallic species. Specifically, we focused on the development of new methodologies combining photoredox catalysis with titanium and nickel in low oxidation state. Firstly, a Barbier-type allylation of aromatic and aliphatic aldehydes –catalytic in titanium– in the presence of a blue photon-absorbing dye was developed. Parallelly, we were pleased to observe that the developed methodology could also be extended to the propargylation of aldehydes under analogous conditions. After an extensive re–optimization of all the reaction parameters, we developed an enantioselective and diastereoselective pinacol coupling of aromatic aldehydes promoted by non-toxic, cheap and easy to synthetize titanium complexes. The key feature, that allows the complete (dia)stereocontrol played by titanium, is the employment of a red-absorbing organic dye. The tailored (photo)redox properties of the red-absorbing organic dye [nPr–DMQA+][BF4–] promote the selective reduction of Ti(IV) to Ti(III). Moreover, even if the major contribution in dual photoredox and nickel catalysis is devoted to the realization of cross-coupling-type reactions, we wanted to evaluate different possible scenarios. Our focus was on the possibility of exploiting intermediates arising from the oxidative addition of nickel complexes as transient nucleophilic species. The first topic considered regarded the possibility to perform allylation of aldehydes by dual photoredox and nickel catalysis. In the first instance, a non–stereocontrolled version of the reaction was presented. Finally, after a long series of drastic modification of the reaction conditions, a highly enantioselective variant of the protocol was also reported. All the reported methodologies are supported by careful photophysical analysis and, in some cases, computational modelling.

Functional engineering of hybrid heterostructures for application in electronic, optical and optoelectronic nanostructured devices

Interfacing materials with different intrinsic chemical-physical characteristics allows for the generation of a new system with multifunctional features. Here, this original concept is implemented for tailoring the functional properties of bi-dimensional black phosphorus (2D bP or phosphorene) and organic light-emitting transistors (OLETs). Phosphorene is highly reactive under atmospheric conditions and its small-area/lab-scale deposition techniques have hampered the introduction of this material in real-world applications so far. The protection of 2D bP against the oxygen by means of functionalization with alkane molecules and pyrene derivatives, showed long-term stability with respect to the bare 2D bP by avoiding remarkable oxidation up to 6 months, paving the way towards ultra-sensitive oxygen chemo-sensors. A new approach of deposition-precipitation heterogeneous reaction was developed to decorate 2D bP with Au nanoparticles (NP)s, obtaining a “stabilizer-free” that may broaden the possible applications of the 2D bP/Au NPs interface in catalysis and biodiagnostics. Finally, 2D bP was deposited by electrospray technique, obtaining oxidized-phosphorous flakes as wide as hundreds of µm2 and providing for the first time a phosphorous-based bidimensional system responsive to electromechanical stimuli. The second part of the thesis focuses on the study of organic heterostructures in ambipolar OLET devices, intriguing optoelectronic devices that couple the micro-scaled light-emission with electrical switching. Initially, an ambipolar single-layer OLET based on a multifunctional organic semiconductor, is presented. The bias-depending light-emission shifted within the transistor channel, as expected in well-balanced ambipolar OLETs. However, the emitted optical power of the single layer-based device was unsatisfactory. To improve optoelectronic performance of the device, a multilayer organic architecture based on hole-transporting semiconductor, emissive donor-acceptor blend and electron-transporting semiconductor was optimized. We showed that the introduction of a suitable electron-injecting layer at the interface between the electron-transporting and light-emission layers may enable a ≈ 2× improvement of efficiency at reduced applied bias.

Study and characterization of polymorphs of BTBT derivatives and their application in organic electronics

The perquisites of organic semiconductors (OSCs) in the field of organic electronics have attracted much attention due to the advantages like cost-effectiveness, solution processibility, etc. A key property in OSCs is charge carrier mobility, which depends on molecular packing, as even the slightest changes in the packing of OSC can significantly impact the mobility. Organic molecules are constructed by weak interactions, which makes the OSCs prone to adopt multiple packing arrangements, thus giving rise to polymorphism. Therefore, polymorph screening in bulk and thin films is crucial for material development. This thesis aims to present a systematic study of polymorphism of [1]benzothieno[3,2-b]benzothiophene (BTBT) derivatives functionalized with different side chains. The role of peripheral side chains has been studied since they can promote different packing arrangements. The bulk polymorph screening of OSCs was approached with conventional solution mediated recrystallization experiments like evaporation, slurry maturation, anti-solvent precipitation, etc. Each of the polymorphs were inspected for their relative stability and the kinetics of transformation was evaluated. Polymorphism in thin films was also investigated for selected OSCs. Non-equilibrium methods like, thermal gradient and solution shearing were employed to examine the nucleation, crystal growth and morphology in controlled crystallization conditions. After careful analysis of crystal phases in bulk and thin films, OFETs have been fabricated by optimizing the manufacturing conditions and the hole mobility values were extracted. The charge transport property of the OSCs tested for OFETs was supported by the ionization potential and transfer integrals calculation. An attempt to correlate the solid-state structure to electronic properties was carried out. For some of the molecules, mechanical properties have been also investigated, as the response to mechanical stress is highly susceptible to packing arrangements and the intermolecular interaction energy contributions. Additionally, collaborative research was carried out by solving and analysing the crystal structures of six oligorylene molecules.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.

Integrating structural variant calling, annotation and prioritization into whole genome analysis workflows: a practical application in the molecular diagnosis of neurodevelopmental disorders

Background: WGS is increasingly used as a first-line diagnostic test for patients with rare genetic diseases such as neurodevelopmental disorders (NDD). Clinical applications require a robust infrastructure to support processing, storage and analysis of WGS data. The identification and interpretation of SVs from WGS data also needs to be improved. Finally, there is a need for a prioritization system that enables downstream clinical analysis and facilitates data interpretation. Here, we present the results of a clinical application of WGS in a cohort of patients with NDD. Methods: We developed highly portable workflows for processing WGS data, including alignment, quality control, and variant calling of SNVs and SVs. A benchmark analysis of state-of-the-art SV detection tools was performed to select the most accurate combination for SV calling. A gene-based prioritization system was also implemented to support variant interpretation. Results: Using a benchmark analysis, we selected the most accurate combination of tools to improve SV detection from WGS data and build a dedicated pipeline. Our workflows were used to process WGS data from 77 NDD patient-parent families. The prioritization system supported downstream analysis and enabled molecular diagnosis in 32% of patients, 25% of which were SVs and suggested a potential diagnosis in 20% of patients, requiring further investigation to achieve diagnostic certainty. Conclusion: Our data suggest that the integration of SNVs and SVs is a main factor that increases diagnostic yield by WGS and show that the adoption of a dedicated pipeline improves the process of variant detection and interpretation.