Profiling of circulating microRNAs in body fluids from Autism Spectrum Disorder patients

Autism Spectrum Disorder is the name for a heterogeneous group of neurodevelopmental conditions, clinically defined by: defects in social interaction and communication; fixed interests and repetitive behaviors. Molecular basis of ASD is heterogeneous and only partially known. ASD-associated variants have been characterized in hundreds of genes and separate transcriptome studies have identified points of convergence among these loci, proving that common biological processes play a role in this disorder. However, no common ASD-associated variants with large effect size, that would be appropriate for its molecular diagnosis, have been identified to date, and therefore, diagnosis just relies on clinical assessment and confirmation. Many factors, including disorders comorbid with ASD, like Tourette Syndrome, complicate ASD behavior-based diagnosis and make it vulnerable to bias. Extracellular microRNAs have attracted researchers for their potential as non-invasive tools for diagnosis, prognosis, and treatment evaluation of human diseases and disorders. Circulating miRNAs can be detected in all mammalian body fluids, from serum to saliva. Stability and general consistency of levels among individuals, along with the existence of specific expression signatures in association with both physiological and pathological conditions, make circulating miRNAs appropriate biomarkers. To investigate ASD etiology and to identify potential biomarkers to support its diagnosis, we used TLDA technology to profile serum miRNAs from ASD, TS, and TS+ASD patients and NCs (unaffected controls). Through validation assays, we demonstrated that miR-140-3p is upregulated in ASD vs: NC, TS, and TS+ASD. We found that delta Ct values for miR-140-3p and YGTSS scores are positively correlated. Our network functional analysis showed that nodes controlled by miR-140-3p, especially CD38 and NRIP1, are involved in processes convergingly dysregulated in ASD, such as synaptic plasticity, immune response, and chromatin binding. Through biomarker analysis, we proved that serum miR-140-3p can discriminate among ASD and NC, ASD and TS, and ASD and TS+ASD, showing that it could be useful to strengthen the behavior-based diagnosis of either ASD or TS+ASD, which can be challenging in some clinical cases. Among all body fluids, saliva represents the most accessible and complete source of different types of molecules that could reflect genetic, epigenetic, environmental, metabolic, emotional, and behavioral alterations in ASD. Therefore, we also used NanoString nCounter technology to profile supernatant saliva circulating miRNAs from ASD patients and NCs. Through validation assays, we demonstrated that both miR-29a-3p and miR-141-3p are upregulated in ASD saliva compared to NC one. We observed that delta Ct values for both miRNAs are correlated with neuropsychiatric scores evaluating ASD defects in social interaction and verbal communication. Target genes of these miRNAs represent main components and regulators of pathways and processes known to be dysregulated in ASD. Through biomarker performance evaluation, we proved that saliva miR-29a-3p and miR-141-3p when used in combination could be useful and non-invasive tools for discriminating ASD patients. In particular, these miRNAs could be used as supportive means for the recognition of ASD verbal and social defects. Overall, our findings suggest that profiling of circulating miRNAs in body fluids can represent an easy and innovative approach to address important biomedical issues, such as the need for biomarkers and the necessity to investigate neurodevelopmental disorders through more accessible patient biopsies. In fact, through the characterization of miRNAs in ASD serum and saliva, we identified three miRNAs that could facilitate ASD clinical assessment and that are worth being further investigated for their potential role in neurodevelopment

A-priori estimates for some classes of elliptic problems

L'obiettivo di questa tesi è di studiare alcuni aspetti di un potente strumento ampiamente utilizzato in analisi matematica, che è rappresentato dalle stime a priori. Infatti, le stime a priori hanno un ruolo chiave nella teoria delle equazioni differenziali a derivate parziali e nel calcolo delle variazioni, perché sono intimamente legate all'esistenza di soluzione per un dato problema. Nella tesi vengono presentati tre lavori scritti durante il periodo del dottorato, in ciascuno dei quali vengono utilizzate le stime a priori. Il primo lavoro, scritto in collaborazione con il Prof. S. Mosconi, riguarda l'esistenza di soluzione per la seguente equazione differenziale ordinaria del quarto ordine (equazione di Swift-Hohenberg), $u''''+ qu''+ F'(u)= 0$, dove $q$ è un parametro reale e $F$ è una funzione $C^2$, coerciva e quasi-convessa. Il secondo lavoro, scritto in collaborazione con il prof. P. Winkert, riguarda stime a priori per un problema ellittico in cui gli operatori hanno crescita critica, sia nel dominio che sulla frontiera. Il terzo lavoro, scritto in collaborazione con i Prof. S.A. Marano e A. Moussaoui, riguarda l'esistenza di soluzione per un sistema ellittico definito in tutto lo spazio $\R^N$, in cui le nonlinearità contengono termini singolari, cioè che possono tendere a $+\infty$ quando la variabile tende a zero

Libertà Fondamentali e Diritto di Proprietà nella Russia Post-Sovietica

Il presente elaborato intende tracciare un affresco dei peculiari sviluppi del costituzionalismo russo attraverso la comparazione tra il modello delle Costituzioni sovietiche e quello, formalmente diverso, della Costituzione post-sovietica del 1993. L angolo prospettico per svolgere siffatta comparazione è quello della disciplina delle c.d. libertà economiche o, come suole dirsi, della costituzione economica russa nel suo evolversi storico. In particolare, si è ritenuto opportuno centrare il focus dell indagine sul diritto di proprietà e sul peculiare regime per esso previsto nell ordinamento della Russia sovietica e post-sovietica. Necessariamente, lo studio delle libertà economiche (e dei diritti ad esse correlati) si traduce, in chiave giuridico-costituzionale, in una riflessione sul rapporto tra modello economico e democrazia. Da qui, l esigenza di osservare lo stato (e lo stadio) della neo-democrazia russa dall analisi della costituzione economica , formalmente stabilita dalla Costituzione del 1993, che altrettanto formalmente sembra ispirarsi ai modelli delle liberal-democrazie occidentali, nonché di (tentare di) comprendere se l ispirazione a un preciso modello economico (e democratico) si sia tradotto, nella Russia post sovietica, nella concreta ed effettiva adesione a siffatto modello

Codimension two ACM varieties in P1xP1xP1 and regularity of bicyclic graphs and their powers

In this PhD thesis, we discuss several different results about some homological invariants (e.g., graded Betti numbers, Hilbert function, regularity) of some special varieties. In particular, we focus on the codimension two ACM varieties in P1×P1×P1 (called varieties of lines), and the edge ideals of bicyclic graphs. We study the Hilbert function of Ferrers varieties of lines, a special case of ACM variety of lines, and we describe the trigraded minimal free resolution of the defining ideal of a variety of lines arising from a complete intersection of points. We also compute the Castelnuovo-Mumford regularity of the defining ideal of grids of lines and complete intersections of lines in P1×P1×P1. Then we study the regularity of another special variety, i.e., the edge ideal of a bicyclic graph and its powers. Specifically, we compute the regularity of the edge ideal of a dumbbell graph, and then we give a combinatorial characterization of the regularity of the edge ideal of an arbitrary bicyclic graph in terms of its induced matching number. Finally we study the regularity of powers of edge ideals of some specific bicyclic graphs, i.e., dumbbell graphs with path having at most two vertices

Aggregation, Spatio-Temporal Structures and Well-Posedness in Chemotaxis Models of Inflammatory Diseases

Inflammation is the body's immune response to outside threats and traumas, aiming to prevent the insurgence of diseases. Although it is a protective mechanism, a derangement of the inflammatory response can lead to severe and debilitating diseases, such as Multiple Sclerosis. For this reason, understanding the mechanisms driving an inflammatory response has become one of the biggest challenge in immunology. The subject of this Thesis is the study of mathematical models aiming to explore the mechanisms of the inflammatory response and the resulting clinical patterns. Our aim to prove that the proposed models, within biologically relevant ranges of the parameter values, are able to reproduce different pathological scenarios observed in patients

Electrochemical sensors for environmental and clinical analyses

Nowadays, the concepts of smart cities, smart houses and Homo Deus (potential next stage in human evolution) are taking more and more attention. Sensors have a key role in this context: recording different parameters it will be possible to design cities able to manage efficiently all the resources or to warn people if a particular kind of disease, such as cancer or diabetes, is taking place. Current way of detecting these parameters or molecules are often laborious and expensive and cannot be used as in situ and real time. Electrochemical sensors, especially nano-sized sensors, are perfect candidates to address these challenges. Indeed, these sensors do not require special instrumentations to work but just an usual battery, so that this technology is cheap and suitable for in situ action. Furthermore, the electrical signal can be recorded over time and can be acquired and managed in remote. The main challenge of this technology is to achieve a Limit Of Detection (LOD) low enough to make the use of these sensors competitive with other analytical techniques, such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Enzyme Linked ImmunoSorbent Assay (ELISA). These goals can be achieved by nano-sized materials because they enhance mass transport and electron transfer rate. In addition, to once found the right sensing material, it is possible to select the electrochemical detection technique giving the best performances in dependence on the analyte that has to be detected. During my Ph.D I studied different electrodes to detect, electrochemically, 3 analytes: i) heavy metals, ii) proteins, and iii) H2O2. Heavy metals are the main source of water pollution. They have been extensively used in various applications due to their specific properties. The main problem using these chemicals is that they are non-biodegradable and thus they can accumulate in the human body through the food chain. Among heavy metals, one of the most dangerous is mercury because just the exposure to some ppb (µg/l) can cause several problems to human body. Lead, cadmium, zinc, arsenic and copper are considered toxic and dangerous as well, therefore, their monitoring is really important. H2O2 is a widely used chemical, employed as bleaching agent in textile and paper industry, for medical and pharmaceutical applications and to remove organic compounds from waste water and contaminated soil. Furthermore, H2O2 has a key role in the human body as well. For instance, its detection can be useful because can give indications about the glucose concentration and so it could be useful for diabetic patients. Furthermore, it is a biomarker of oxidative stress that is a pathological condition due to breakdown of the antioxidant defense system.. Detection of proteins was also investigated during my Ph.D. In order to detect these bio-compounds it is mandatory to use some bio recognition elements (such as antibodies, DNA or aptamers) so that the sensors are usually named biosensors. During my studies, I developed a biosensors towards Human ImmunoGlobulin G (H-IgG) and ParaThyroid Hormone Like Hormone (PTHLH). H-IgG is a protein always present in the human fluids (blood, urine, sweat) and its detection has not any particular relevance. It can be used as a model because is a cheap protein that has the bio-chemical properties of many other proteins. Instead, PTHLH is overproduced owing to different kind of cancer, consequently it can be used as a biomarker. This kind of application is really of great value because PTHLH starts to be produced at the beginning of the disease, so that its detection is useful for early diagnosis. Summarizing, the main goals of this Ph.D work are: 1. The development of new and innovative ways to fabricate electrodes with high surface area; 2. To find new, cheap, robust electrochemical sensors for detecting H2O2, heavy metals, and proteins; 3. Validate these sensors using real sample

Deeply Incorporating Human Capabilities into Machine Learning Models for Fine-Grained Visual Categorization

Artificial intelligence and machine learning have long attempted to emulate human visual system. With the recent advances in deep neural networks, which take inspiration from the architecture of the primate visual hierarchy, human-level visual abilities are now coming within reach of artificial systems. However, the existing computational models are designed with engineering goals, loosely emulating computations and connections of biological neurons, especially in terms of intermediate visual representations. In this thesis we aim at investigating how human skills can be integrated into computational models in order to perform fine-grained image categorization, a task which requires the application of specific perceptive and cognitive abilities to be solved. In particular, our goal is to develop systems which, either implicitly or explicitly, combine human reasoning processes with deep classification models. Our claims is that by the emulation of the process carried out by humans while performing a recognition task it is possible to yield improved classification performance. To this end, we first attempt to replicate human visual attention by modeling a saliency detection system able to emulate the integration of the top-down (task-controlled, classification-driven) and bottom-up (sensory information) processes; thus, the generated saliency maps are able to represent implicitly the way humans perceive and focus their attention while performing recognition, and, therefore, a useful supervision for the automatic classification system. We then investigate if and to what extent the learned saliency maps can support visual classification in nontrivial cases. To achieve this, we propose SalClassNet, a CNN framework consisting of two networks jointly trained: a) the first one computing top-down saliency maps from input images, and b) the second one exploiting the computed saliency maps for visual classification. Gaze shifts change in relation to a task is not the only process when performing classification in specific domains, but humans also leverage a-priori specialized knowledge to perform recognition. For example, distinguishing between different dog breeds or fruit varieties requires skills that not all human possess but only domain experts. Of course, one may argue that the typical learning-by-example approach can be applied by asking domain experts to collect enough annotations from which machine learning methods can derive the features necessary for the classification. Nevertheless, this is a really costly process and often infeasible. Thus, the second part of this thesis aim at explicitly modeling and exploiting domain-specific knowledge to perform recognition. To this end, we introduce and demonstrate that computational ontologies can explicitly encode human knowledge and that it can be used to support multiple tasks from data annotation to classification. In particular, we propose an ontology-based annotation tool, able to reduce significantly the efforts to collect highly-specialized labels and demonstrate its effectiveness building the VegImage dataset, a collection of about 4,000 images belonging to 24 fruit varieties, annotated with over 65,000 bounding boxes and enriched with a large knowledge base consisting of more than 1,000,000 OWL triples. We then exploit this ontology-structured knowledge by combining a semantic-classifier, which performs inference based on the information encoded in the domain ontology, with a visual convolutional neural network, showing that the integration of semantics into automatic classification models can represents the key to solve a complex task such as the fine-grained recognition of fruit varieties, a task which requires the contribution of domain expert to be completely solved. Performance evaluation of the proposed approaches provides a basis to assess the validity of our claim along with the scientific soundness of developed models

Orbit dynamics studies of injection, acceleration and extraction of high-intensity beams for the upgrade of the INFN-LNS Superconducting Cyclotron

The nuclear research carried out at the LNS laboratory in Catania is mainly allowed by the ion beams delivered by two ion accelerators, a 15 MV Tandem and a k800 Superconducting Cyclotron (the so-called CS). These accelerators deliver to the INFN-LNS scientific community a large variety of stable ion beams with energies ranging from a few MeV/amu to 80 MeV/amu. NUMEN, a nuclear physics project born recently at INFN-LNS, proposes the use of the heavy ion induced double charge exchange reactions as a tool to access quantitative information relevant for nuclear matrix elements for neutrinoless double beta decay. The pilot experiment carried out by the NUMEN team at LNS in Catania has already demonstrated that beam power of the order of 1-10 kW of Carbon, Oxigen and Neon with energies in the range 15-70 MeV/amu are mandatory for the NUMEN reaction study. An additional requirement is that the beam energy resolution should not overcome 1/1000 FWHM. Currently, the maximum CS beam power does not exceed 100 W, so a substantial upgrade of the CS is needed to fulfil the NUMEN requirements. In the frame of the CS upgrade, this thesis is devoted to the simulations of beam dynamics in the LNS cyclotron, with the aim to overcome the current CS limitations and to propose innovative solutions for achieving the beam characteristics in terms of beam power and energy resolution required by the NUMEN project. In this thesis, one of the main topic is the stripping extraction from the CS. The study has allowed to individuate: i) the stripper foil position for each ion to be extracted by stripping, ii) the transverse dimension and direction of the new extraction channel in the CS to be used for all the ions to be extracted by stripping and iii) the features of the magnetic channels to be installed inside the new extraction channel. The second subject of this thesis is the beam injection and acceleration up to the extraction in the LNS cyclotron. This study has been possible thanks to the development of the beam tracking model of the INFN-LNS Superconducting Cyclotron, performed in collaboration with the Ion Beam Applications company. This work has shown that the total transmission efficiency from the CS bore injection up to the extraction system, simulating also a process of energy selection outside the CS according to the NUMEN requirement, is only around the 2.7%, a low value compared to the expected value of 15%. The energy selection process is the main cause of the low total efficiency. We demonstrated that the major contribution to the beam energy spread at the extraction in the LNS cyclotron is due to the large emittance circulating in the LNS cyclotron. The energy gain per turn contributes only partially to the energy spread at the extraction but, in any case, it sets an inferior limit on the minimum energy spread obtainable in the CS cyclotron. This value stays around 0.2%, about the twice of the NUMEN requirement. This thesis allows also to establish a roadmap of the goals and milestones to be achieved in next months/years. According to the simulation results, the goal of reduction of the beam energy spread at the extraction can be achieved only paying attention on the ion beam production and transport to the CS, since these processes determine the emittance in the horizontal and vertical phase spaces of the beam entering the CS central region. Also a good quality of the accelerated beam will be necessary since an initial beam offset in the central region implies a further increase of the beam energy spread at the extraction. This work has also shown that an increase of the injection efficiency is possible by applying higher dee voltages than the nominal one and modifying slightly the existing central region design. These changes have allowed to increase the injection efficiency up to a factor of about 1.7

K*(892)± resonance with the ALICE detector at LHC

K*(892)± resonance with the ALICE detector at LHC Author: Kunal Garg PhD Cycle XXXI, University of Catania It has been established that ultra-relativistic heavy-ion collisions produce a hot and dense QCD system which behaves like a perfect fluid. The study of the Quark Gluon Plasma created in these collisions is important to understand the cosmic evolution of our Universe. The study of strange hadronic resonances in pp collisions contributes to the study of strangeness production in small systems. Usually, measurements in pp collisions constitute a reference for the study in larger colliding systems and provide constraints for tuning QCD-inspired event generators and then to test specific aspects of QCD in the non-perturbative sector. However recent observations at the LHC have shown striking similarities between Pb-Pb collisions and high-multiplicity p-Pb and pp collisions. In the elementary collisions a large variation of the characteristics of the event and of the strange particle production rate has been observed as a function of the charged particle multiplicity density. In particular it has been observed as particle production depends only from the event multiplicity and it is independent of the system size and collision energy. This thesis reports about first measurement of K^{*}(892)^{\pm} in pp collisions at \sqrt{s} = 13 TeV in inelastic pp collisions and in different charged particle multiplicity classes. In particular the transverse momentum (p_{T}) spectrum, the integrated yield, the mean p_{T} and the ratio to stable hadrons as pions and kaons have been measured. Moreover the K^{*}(892)^{0} p_{T} spectrum in inelastic pp collisions at the same energy has been also measured. Similar results have been obtained for charged and neutral K^{*}. The K*(892)± p_{T} spectrum has been compared to the predictions of some event generators as PYTHIA6, PYTHIA8 and EPOS-LHC. Furthermore, the comparison of the p_{T} spectrum with the one obtained at different energies has shown a hardening of the spectra with increasing energy of the collisions. Increase of the K*(892)± yield and mean p_{T} when growing the event multiplicity, confirms the independence of the particle yields from the collision system or energy. From the distribution of the K^{*}/K ratio as a function of the charged particle multiplicity, a hint of suppression of the K* production has been observed in high multiplicity pp collisions. This in an analogy to the K^{*}/K results in heavy-ion collisions, is consistent with the presence of re-scattering effects in an hadronic phase in high multiplicity pp collisions

Metal-TiO2 nanocomposites towards efficient solar-driven photocatalysis

Water, together with energy and food, has been addressed as one of the main urgent problem of humanity. The reduction of fresh clean water sources will definitely lead to huge issues in the next future, especially in developing countries. The conventional wastewater treatments suffer some limitations related to the effectiveness in decontamination (mechanical filtration), in the heavy use of chemicals (chlorination), or in elevate operational costs and energy requirements (desalination and reverse osmosis). In this sense, new materials such as nanocomposites may overcome these issues taking advantage of the peculiar properties of materials at nanoscale. Research on novel nanotechnologies must bring advances in order to contrast and prevent water scarcity and pollution. In order to be effective, these nanotechnologies should run at low operational cost, even in places unequipped by strong infrastructures and in concert with conventional cheap methodologies. Among the alternative water purification methods, TiO2-based photocatalysis has attracted great attention due to material stability, abundance, non-toxicity and high decontamination efficiency. In this material, electron-hole pairs, generated by light absorption, separate from each other and migrate to catalytically active sites at the surface of the photocatalyst. Photogenerated carriers are able to induce the water splitting reaction and, consequently, to decompose organic pollutants. The main deficiency of this material, related to its large band gap, is that only the UV fraction of the solar spectrum is effective to this purpose. Several approaches have been proposed to overpass this issue and, among them, the use of metal-TiO2 nanocomposites with proper nanostructurarion seems very promising for water purification strategies. Aim of this work is to investigate the possibility to develop efficient solar-driven TiO2 photocatalyst taking advantage of metallic nanostructures to efficiently couple the incident light to the photoactive semiconductor. Two approaches have been followed: TiO2 nanoparticles obtained via pulsed laser ablation in liquid and optical engineering of multilayered metal-TiO2 thin films. The first approach maximizes the exposed surface, thus enhancing the photocatalytic efficiency. However, in this case nanomaterials is dispersed in the surrounding environment, and in order to avoid this drawback we have investigated, as second approach, the use of metal-TiO2 thin films