Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Kinematics and statics of cable-driven parallel robots by interval-analysis-based methods

In the past two decades the work of a growing portion of researchers in robotics focused on a particular group of machines, belonging to the family of parallel manipulators: the cable robots. Although these robots share several theoretical elements with the better known parallel robots, they still present completely (or partly) unsolved issues. In particular, the study of their kinematic, already a difficult subject for conventional parallel manipulators, is further complicated by the non-linear nature of cables, which can exert only efforts of pure traction. The work presented in this thesis therefore focuses on the study of the kinematics of these robots and on the development of numerical techniques able to address some of the problems related to it. Most of the work is focused on the development of an interval-analysis based procedure for the solution of the direct geometric problem of a generic cable manipulator. This technique, as well as allowing for a rapid solution of the problem, also guarantees the results obtained against rounding and elimination errors and can take into account any uncertainties in the model of the problem. The developed code has been tested with the help of a small manipulator whose realization is described in this dissertation together with the auxiliary work done during its design and simulation phases.

mHealth-based Methods for Neuromotor Assessment and Rehabilitation

Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, etc. These technologies are prevalent in various fields: from wellness/sports/fitness to the healthcare domain. The spread of this phenomenon led the World-Health-Organization to define the term 'mHealth' as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices". Furthermore, mHealth solutions are suitable to perform real-time wearable Biofeedback (BF) systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. During the COVID-19 pandemic, this transformation of the healthcare system has been dramatically accelerated by new clinical demands, including the need to prevent hospital surges and to assure continuity of clinical care services, allowing pervasive healthcare. Never as of today, we can say that the integration of mHealth technologies will be the basis of this new era of clinical practice. In this scenario, this PhD thesis's primary goal is to investigate new and innovative mHealth solutions for the Assessment and Rehabilitation of different neuromotor functions and diseases. For the clinical assessment, there is the need to overcome the limitations of subjective clinical scales. Creating new pervasive and self-administrable mHealth solutions, this thesis investigates the possibility of employing innovative systems for objective clinical evaluation. For rehabilitation, we explored the clinical feasibility and effectiveness of mHealth systems. In particular, we developed innovative mHealth solutions with BF capability to allow tailored rehabilitation. The main goal that a mHealth-system should have is improving the person's quality of life, increasing or maintaining his autonomy and independence. To this end, inclusive design principles might be crucial, next to the technical and technological ones, to improve mHealth-systems usability.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Permutation-based stochastic ordering using pairwise comparisons

The topic of this work concerns nonparametric permutation-based methods aiming to find a ranking (stochastic ordering) of a given set of groups (populations), gathering together information from multiple variables under more than one experimental designs. The problem of ranking populations arises in several fields of science from the need of comparing G>2 given groups or treatments when the main goal is to find an order while taking into account several aspects. As it can be imagined, this problem is not only of theoretical interest but it also has a recognised relevance in several fields, such as industrial experiments or behavioural sciences, and this is reflected by the vast literature on the topic, although sometimes the problem is associated with different keywords such as: "stochastic ordering", "ranking", "construction of composite indices" etc., or even "ranking probabilities" outside of the strictly-speaking statistical literature. The properties of the proposed method are empirically evaluated by means of an extensive simulation study, where several aspects of interest are let to vary within a reasonable practical range. These aspects comprise: sample size, number of variables, number of groups, and distribution of noise/error. The flexibility of the approach lies mainly in the several available choices for the test-statistic and in the different types of experimental design that can be analysed. This render the method able to be tailored to the specific problem and the to nature of the data at hand. To perform the analyses an R package called SOUP (Stochastic Ordering Using Permutations) has been written and it is available on CRAN.

Assessment of the population structure and temporal changes in spatial dynamics and genetic characteristics of the Atlantic bluefin tuna under a fishery independent framework.

As a large and long-lived species with high economic value, restricted spawning areas and short spawning periods, the Atlantic bluefin tuna (BFT; Thunnus thynnus) is particularly susceptible to over-exploitation. Although BFT have been targeted by fisheries in the Mediterranean Sea for thousands of years, it has only been in these last decades that the exploitation rate has reached far beyond sustainable levels. An understanding of the population structure, spatial dynamics, exploitation rates and the environmental variables that affect BFT is crucial for the conservation of the species. The aims of this PhD project were 1) to assess the accuracy of larval identification methods, 2) determine the genetic structure of modern BFT populations, 3) assess the self-recruitment rate in the Gulf of Mexico and Mediterranean spawning areas, 4) estimate the immigration rate of BFT to feeding aggregations from the various spawning areas, and 5) develop tools capable of investigating the temporal stability of population structuring in the Mediterranean Sea. Several weaknesses in modern morphology-based taxonomy including demographic decline of expert taxonomists, flawed identification keys, reluctance of the taxonomic community to embrace advances in digital communications and a general scarcity of modern user-friendly materials are reviewed. Barcoding of scombrid larvae revealed important differences in the accuracy of the taxonomic identifications carried out by different ichthyoplanktologists following morphology-based methods. Using a Genotyping-by-Sequencing a panel of 95 SNPs was developed and used to characterize the population structuring of BFT and composition of adult feeding aggregations. Using novel molecular techniques, DNA was extracted from bluefin tuna vertebrae excavated from late iron age, ancient roman settlements Byzantine-era Constantinople and a 20th century collection. A second panel of 96 SNPs was developed to genotype historical and modern samples in order to elucidate changes in population structuring and allele frequencies of loci associated with selective traits.

Impatto di rifaximina sul microbiota intestinale: selezione di bifidobatteri antibiotico resistenti

The ideal approach for the long term treatment of intestinal disorders, such as inflammatory bowel disease (IBD), is represented by a safe and well tolerated therapy able to reduce mucosal inflammation and maintain homeostasis of the intestinal microbiota. A combined therapy with antimicrobial agents, to reduce antigenic load, and immunomodulators, to ameliorate the dysregulated responses, followed by probiotic supplementation has been proposed. Because of the complementary mechanisms of action of antibiotics and probiotics, a combined therapeutic approach would give advantages in terms of enlargement of the antimicrobial spectrum, due to the barrier effect of probiotic bacteria, and limitation of some side effects of traditional chemiotherapy (i.e. indiscriminate decrease of aggressive and protective intestinal bacteria, altered absorption of nutrient elements, allergic and inflammatory reactions). Rifaximin (4-deoxy-4’-methylpyrido[1’,2’-1,2]imidazo[5,4-c]rifamycin SV) is a product of synthesis experiments designed to modify the parent compound, rifamycin, in order to achieve low gastrointestinal absorption while retaining good antibacterial activity. Both experimental and clinical pharmacology clearly show that this compound is a non systemic antibiotic with a broad spectrum of antibacterial action, covering Gram-positive and Gram-negative organisms, both aerobes and anaerobes. Being virtually non absorbed, its bioavailability within the gastrointestinal tract is rather high with intraluminal and faecal drug concentrations that largely exceed the MIC values observed in vitro against a wide range of pathogenic microorganisms. The gastrointestinal tract represents therefore the primary therapeutic target and gastrointestinal infections the main indication. The little value of rifaximin outside the enteric area minimizes both antimicrobial resistance and systemic adverse events. Fermented dairy products enriched with probiotic bacteria have developed into one of the most successful categories of functional foods. Probiotics are defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” (FAO/WHO, 2002), and mainly include Lactobacillus and Bifidobacterium species. Probiotic bacteria exert a direct effect on the intestinal microbiota of the host and contribute to organoleptic, rheological and nutritional properties of food. Administration of pharmaceutical probiotic formula has been associated with therapeutic effects in treatment of diarrhoea, constipation, flatulence, enteropathogens colonization, gastroenteritis, hypercholesterolemia, IBD, such as ulcerative colitis (UC), Crohn’s disease, pouchitis and irritable bowel syndrome. Prerequisites for probiotics are to be effective and safe. The characteristics of an effective probiotic for gastrointestinal tract disorders are tolerance to upper gastrointestinal environment (resistance to digestion by enteric or pancreatic enzymes, gastric acid and bile), adhesion on intestinal surface to lengthen the retention time, ability to prevent the adherence, establishment and/or replication of pathogens, production of antimicrobial substances, degradation of toxic catabolites by bacterial detoxifying enzymatic activities, and modulation of the host immune responses. This study was carried out using a validated three-stage fermentative continuous system and it is aimed to investigate the effect of rifaximin on the colonic microbial flora of a healthy individual, in terms of bacterial composition and production of fermentative metabolic end products. Moreover, this is the first study that investigates in vitro the impact of the simultaneous administration of the antibiotic rifaximin and the probiotic B. lactis BI07 on the intestinal microbiota. Bacterial groups of interest were evaluated using culture-based methods and molecular culture-independent techniques (FISH, PCR-DGGE). Metabolic outputs in terms of SCFA profiles were determined by HPLC analysis. Collected data demonstrated that rifaximin as well as antibiotic and probiotic treatment did not change drastically the intestinal microflora, whereas bacteria belonging to Bifidobacterium and Lactobacillus significantly increase over the course of the treatment, suggesting a spontaneous upsurge of rifaximin resistance. These results are in agreement with a previous study, in which it has been demonstrated that rifaximin administration in patients with UC, affects the host with minor variations of the intestinal microflora, and that the microbiota is restored over a wash-out period. In particular, several Bifidobacterium rifaximin resistant mutants could be isolated during the antibiotic treatment, but they disappeared after the antibiotic suspension. Furthermore, bacteria belonging to Atopobium spp. and E. rectale/Clostridium cluster XIVa increased significantly after rifaximin and probiotic treatment. Atopobium genus and E. rectale/Clostridium cluster XIVa are saccharolytic, butyrate-producing bacteria, and for these characteristics they are widely considered health-promoting microorganisms. The absence of major variations in the intestinal microflora of a healthy individual and the significant increase in probiotic and health-promoting bacteria concentrations support the rationale of the administration of rifaximin as efficacious and non-dysbiosis promoting therapy and suggest the efficacy of an antibiotic/probiotic combined treatment in several gut pathologies, such as IBD. To assess the use of an antibiotic/probiotic combination for clinical management of intestinal disorders, genetic, proteomic and physiologic approaches were employed to elucidate molecular mechanisms determining rifaximin resistance in Bifidobacterium, and the expected interactions occurring in the gut between these bacteria and the drug. The ability of an antimicrobial agent to select resistance is a relevant factor that affects its usefulness and may diminish its useful life. Rifaximin resistance phenotype was easily acquired by all bifidobacteria analyzed [type strains of the most representative intestinal bifidobacterial species (B. infantis, B. breve, B. longum, B. adolescentis and B. bifidum) and three bifidobacteria included in a pharmaceutical probiotic preparation (B. lactis BI07, B. breve BBSF and B. longum BL04)] and persisted for more than 400 bacterial generations in the absence of selective pressure. Exclusion of any reversion phenomenon suggested two hypotheses: (i) stable and immobile genetic elements encode resistance; (ii) the drug moiety does not act as an inducer of the resistance phenotype, but enables selection of resistant mutants. Since point mutations in rpoB have been indicated as representing the principal factor determining rifampicin resistance in E. coli and M. tuberculosis, whether a similar mechanism also occurs in Bifidobacterium was verified. The analysis of a 129 bp rpoB core region of several wild-type and resistant bifidobacteria revealed five different types of miss-sense mutations in codons 513, 516, 522 and 529. Position 529 was a novel mutation site, not previously described, and position 522 appeared interesting for both the double point substitutions and the heterogeneous profile of nucleotide changes. The sequence heterogeneity of codon 522 in Bifidobacterium leads to hypothesize an indirect role of its encoded amino acid in the binding with the rifaximin moiety. These results demonstrated the chromosomal nature of rifaximin resistance in Bifidobacterium, minimizing risk factors for horizontal transmission of resistance elements between intestinal microbial species. Further proteomic and physiologic investigations were carried out using B. lactis BI07, component of a pharmaceutical probiotic preparation, as a model strain. The choice of this strain was determined based on the following elements: (i) B. lactis BI07 is able to survive and persist in the gut; (ii) a proteomic overview of this strain has been recently reported. The involvement of metabolic changes associated with rifaximin resistance was investigated by proteomic analysis performed with two-dimensional electrophoresis and mass spectrometry. Comparative proteomic mapping of BI07-wt and BI07-res revealed that most differences in protein expression patterns were genetically encoded rather than induced by antibiotic exposure. In particular, rifaximin resistance phenotype was characterized by increased expression levels of stress proteins. Overexpression of stress proteins was expected, as they represent a common non specific response by bacteria when stimulated by different shock conditions, including exposure to toxic agents like heavy metals, oxidants, acids, bile salts and antibiotics. Also, positive transcription regulators were found to be overexpressed in BI07-res, suggesting that bacteria could activate compensatory mechanisms to assist the transcription process in the presence of RNA polymerase inhibitors. Other differences in expression profiles were related to proteins involved in central metabolism; these modifications suggest metabolic disadvantages of resistant mutants in comparison with sensitive bifidobacteria in the gut environment, without selective pressure, explaining their disappearance from faeces of patients with UC after interruption of antibiotic treatment. The differences observed between BI07-wt e BI07-res proteomic patterns, as well as the high frequency of silent mutations reported for resistant mutants of Bifidobacterium could be the consequences of an increased mutation rate, mechanism which may lead to persistence of resistant bacteria in the population. However, the in vivo disappearance of resistant mutants in absence of selective pressure, allows excluding the upsurge of compensatory mutations without loss of resistance. Furthermore, the proteomic characterization of the resistant phenotype suggests that rifaximin resistance is associated with a reduced bacterial fitness in B. lactis BI07-res, supporting the hypothesis of a biological cost of antibiotic resistance in Bifidobacterium. The hypothesis of rifaximin inactivation by bacterial enzymatic activities was verified by using liquid chromatography coupled with tandem mass spectrometry. Neither chemical modifications nor degradation derivatives of the rifaximin moiety were detected. The exclusion of a biodegradation pattern for the drug was further supported by the quantitative recovery in BI07-res culture fractions of the total rifaximin amount (100 μg/ml) added to the culture medium. To confirm the main role of the mutation on the β chain of RNA polymerase in rifaximin resistance acquisition, transcription activity of crude enzymatic extracts of BI07-res cells was evaluated. Although the inhibition effects of rifaximin on in vitro transcription were definitely higher for BI07-wt than for BI07-res, a partial resistance of the mutated RNA polymerase at rifaximin concentrations > 10 μg/ml was supposed, on the basis of the calculated differences in inhibition percentages between BI07-wt and BI07-res. By considering the resistance of entire BI07-res cells to rifaximin concentrations > 100 μg/ml, supplementary resistance mechanisms may take place in vivo. A barrier for the rifaximin uptake in BI07-res cells was suggested in this study, on the basis of the major portion of the antibiotic found to be bound to the cellular pellet respect to the portion recovered in the cellular lysate. Related to this finding, a resistance mechanism involving changes of membrane permeability was supposed. A previous study supports this hypothesis, demonstrating the involvement of surface properties and permeability in natural resistance to rifampicin in mycobacteria, isolated from cases of human infection, which possessed a rifampicin-susceptible RNA polymerase. To understand the mechanism of membrane barrier, variations in percentage of saturated and unsaturated FAs and their methylation products in BI07-wt and BI07-res membranes were investigated. While saturated FAs confer rigidity to membrane and resistance to stress agents, such as antibiotics, a high level of lipid unsaturation is associated with high fluidity and susceptibility to stresses. Thus, the higher percentage of saturated FAs during the stationary phase of BI07-res could represent a defence mechanism of mutant cells to prevent the antibiotic uptake. Furthermore, the increase of CFAs such as dihydrosterculic acid during the stationary phase of BI07-res suggests that this CFA could be more suitable than its isomer lactobacillic acid to interact with and prevent the penetration of exogenous molecules including rifaximin. Finally, the impact of rifaximin on immune regulatory functions of the gut was evaluated. It has been suggested a potential anti-inflammatory effect of rifaximin, with reduced secretion of IFN-γ in a rodent model of colitis. Analogously, it has been reported a significant decrease in IL-8, MCP-1, MCP-3 e IL-10 levels in patients affected by pouchitis, treated with a combined therapy of rifaximin and ciprofloxacin. Since rifaximin enables in vivo and in vitro selection of Bifidobacterium resistant mutants with high frequency, the immunomodulation activities of rifaximin associated with a B. lactis resistant mutant were also taken into account. Data obtained from PBMC stimulation experiments suggest the following conclusions: (i) rifaximin does not exert any effect on production of IL-1β, IL-6 and IL-10, whereas it weakly stimulates production of TNF-α; (ii) B. lactis appears as a good inducer of IL-1β, IL-6 and TNF-α; (iii) combination of BI07-res and rifaximin exhibits a lower stimulation effect than BI07-res alone, especially for IL-6. These results confirm the potential anti-inflammatory effect of rifaximin, and are in agreement with several studies that report a transient pro-inflammatory response associated with probiotic administration. The understanding of the molecular factors determining rifaximin resistance in the genus Bifidobacterium assumes an applicative significance at pharmaceutical and medical level, as it represents the scientific basis to justify the simultaneous use of the antibiotic rifaximin and probiotic bifidobacteria in the clinical treatment of intestinal disorders.

Solutions to common issues in widefield microscopy: vignetting, mosaicing and depth of focus

In this thesis we have developed solutions to common issues regarding widefield microscopes, facing the problem of the intensity inhomogeneity of an image and dealing with two strong limitations: the impossibility of acquiring either high detailed images representative of whole samples or deep 3D objects. First, we cope with the problem of the non-uniform distribution of the light signal inside a single image, named vignetting. In particular we proposed, for both light and fluorescent microscopy, non-parametric multi-image based methods, where the vignetting function is estimated directly from the sample without requiring any prior information. After getting flat-field corrected images, we studied how to fix the problem related to the limitation of the field of view of the camera, so to be able to acquire large areas at high magnification. To this purpose, we developed mosaicing techniques capable to work on-line. Starting from a set of overlapping images manually acquired, we validated a fast registration approach to accurately stitch together the images. Finally, we worked to virtually extend the field of view of the camera in the third dimension, with the purpose of reconstructing a single image completely in focus, stemming from objects having a relevant depth or being displaced in different focus planes. After studying the existing approaches for extending the depth of focus of the microscope, we proposed a general method that does not require any prior information. In order to compare the outcome of existing methods, different standard metrics are commonly used in literature. However, no metric is available to compare different methods in real cases. First, we validated a metric able to rank the methods as the Universal Quality Index does, but without needing any reference ground truth. Second, we proved that the approach we developed performs better in both synthetic and real cases.

Secondary Microseisms Characterization and Green’s Function Extraction at the Larderello-Travale Geothermal Field (Italy)

Over the past ten years, the cross-correlation of long-time series of ambient seismic noise (ASN) has been widely adopted to extract the surface-wave part of the Green’s Functions (GF). This stochastic procedure relies on the assumption that ASN wave-field is diffuse and stationary. At frequencies <1Hz, the ASN is mainly composed by surface-waves, whose origin is attributed to the sea-wave climate. Consequently, marked directional properties may be observed, which call for accurate investigation about location and temporal evolution of the ASN-sources before attempting any GF retrieval. Within this general context, this thesis is aimed at a thorough investigation about feasibility and robustness of the noise-based methods toward the imaging of complex geological structures at the local (∼10-50km) scale. The study focused on the analysis of an extended (11 months) seismological data set collected at the Larderello-Travale geothermal field (Italy), an area for which the underground geological structures are well-constrained thanks to decades of geothermal exploration. Focusing on the secondary microseism band (SM;f>0.1Hz), I first investigate the spectral features and the kinematic properties of the noise wavefield using beamforming analysis, highlighting a marked variability with time and frequency. For the 0.1-0.3Hz frequency band and during Spring- Summer-time, the SMs waves propagate with high apparent velocities and from well-defined directions, likely associated with ocean-storms in the south- ern hemisphere. Conversely, at frequencies >0.3Hz the distribution of back- azimuths is more scattered, thus indicating that this frequency-band is the most appropriate for the application of stochastic techniques. For this latter frequency interval, I tested two correlation-based methods, acting in the time (NCF) and frequency (modified-SPAC) domains, respectively yielding esti- mates of the group- and phase-velocity dispersions. Velocity data provided by the two methods are markedly discordant; comparison with independent geological and geophysical constraints suggests that NCF results are more robust and reliable.

Development of new analytical procedures aimet at the characterization of artistic samples

In this thesis, new advances in the development of spectroscopic based methods for the characterization of heritage materials have been achieved. As concern FTIR spectroscopy new approaches aimed at exploiting near and far IR region for the characterization of inorganic or organic materials have been tested. Paint cross-section have been analysed by FTIR spectroscopy in the NIR range and an “ad hoc” chemometric approach has been developed for the elaboration of hyperspectral maps. Moreover, a new method for the characterization of calcite based on the use of grinding curves has been set up both in MIR and in FAR region. Indeed, calcite is a material widely applied in cultural heritage, and this spectroscopic approach is an efficient and rapid tool to distinguish between different calcite samples. Different enhanced vibrational techniques for the characterisation of dyed fibres have been tested. First a SEIRA (Surface Enhanced Infra-Red Absorption) protocol has been optimised allowing the analysis of colorant micro-extracts thanks to the enhancement produced by the addition of gold nanoparticles. These preliminary studies permitted to identify a new enhanced FTIR method, named ATR/RAIRS, which allowed to reach lower detection limits. Regarding Raman microscopy, the research followed two lines, which have in common the aim of avoiding the use of colloidal solutions. AgI based supports obtained after deposition on a gold-coated glass slides have been developed and tested spotting colorant solutions. A SERS spectrum can be obtained thanks to the photoreduction, which the laser may induce on the silver salt. Moreover, these supports can be used for the TLC separation of a mixture of colorants and the analyses by means of both Raman/SERS and ATR-RAIRS can be successfully reached. Finally, a photoreduction method for the “on fiber” analysis of colorant without the need of any extraction have been optimised.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

Applied genomics to monitor the genetic diversity and integrity of honey bee (Apis mellifera) populations and define sustainable strategies to benefit the beekeeping sector

Honey bees are considered keystone species in ecosystem, the effect of harmful pesticides for the honey bees, the action of extreme climatic waves and their consequence on honey bees health can cause the loss of many colonies which could contribute to the reduction of the effective population size and incentive the use of non-autochthonous queens to replace dead colonies. Over the last decades, the use of non-ligustica bee subspecies in Italy has increased and together with the mentioned phenomena exposed native honey bees to hybridization, laeding to a dramatic loss of genetic erosion and admixture. Healthy genetic diversity within honey bee populations is critical to provide tolerance and resistance to current and future threatening. Nowadays it is urgent to design strategies for the conservation of local subspecies and their valorisation on a productive scale. In this Thesis we applied genomics tool for the analysis of the genetic diversity and the genomic integrity of honey bee populations in Italy are described. In this work mtDNA based methods are presented using honey bee DNA or honey eDNA as source of information of the genetic diversity of A. mellifera at different level. Taken together, the results derived from these studies should enlarge the knowledge of the genetic diversity and integrity of the honey bee populations in Italy, filling the gap of information necessary to design efficient conservation programmes. Furthermore, the methods presented in these works will provide a tool for the honey authentication to sustain and valorise beekeeping products and sector against frauds.