Analytical challenges in the fields of Nanobioscience and Nanobiotecnology: integrated methods for separation and characterization

Nano(bio)science and nano(bio)technology play a growing and tremendous interest both on academic and industrial aspects. They are undergoing rapid developments on many fronts such as genomics, proteomics, system biology, and medical applications. However, the lack of characterization tools for nano(bio)systems is currently considered as a major limiting factor to the final establishment of nano(bio)technologies. Flow Field-Flow Fractionation (FlFFF) is a separation technique that is definitely emerging in the bioanalytical field, and the number of applications on nano(bio)analytes such as high molar-mass proteins and protein complexes, sub-cellular units, viruses, and functionalized nanoparticles is constantly increasing. This can be ascribed to the intrinsic advantages of FlFFF for the separation of nano(bio)analytes. FlFFF is ideally suited to separate particles over a broad size range (1 nm-1 μm) according to their hydrodynamic radius (rh). The fractionation is carried out in an empty channel by a flow stream of a mobile phase of any composition. For these reasons, fractionation is developed without surface interaction of the analyte with packing or gel media, and there is no stationary phase able to induce mechanical or shear stress on nanosized analytes, which are for these reasons kept in their native state. Characterization of nano(bio)analytes is made possible after fractionation by interfacing the FlFFF system with detection techniques for morphological, optical or mass characterization. For instance, FlFFF coupling with multi-angle light scattering (MALS) detection allows for absolute molecular weight and size determination, and mass spectrometry has made FlFFF enter the field of proteomics. Potentialities of FlFFF couplings with multi-detection systems are discussed in the first section of this dissertation. The second and the third sections are dedicated to new methods that have been developed for the analysis and characterization of different samples of interest in the fields of diagnostics, pharmaceutics, and nanomedicine. The second section focuses on biological samples such as protein complexes and protein aggregates. In particular it focuses on FlFFF methods developed to give new insights into: a) chemical composition and morphological features of blood serum lipoprotein classes, b) time-dependent aggregation pattern of the amyloid protein Aβ1-42, and c) aggregation state of antibody therapeutics in their formulation buffers. The third section is dedicated to the analysis and characterization of structured nanoparticles designed for nanomedicine applications. The discussed results indicate that FlFFF with on-line MALS and fluorescence detection (FD) may become the unparallel methodology for the analysis and characterization of new, structured, fluorescent nanomaterials.

Emerging contaminants in agricultural ecosystems: impact of selected pharmaceutical on water and soil ecology and pratical implications

Pharmaceuticals are useful tools to prevent and treat human and animal diseases. Following administration, a significant fraction of pharmaceuticals is excreted unaltered into faeces and urine and may enter the aquatic ecosystem and agricultural soil through irrigation with recycled water, constituting a significant source of emerging contaminants into the environment. Understanding major factors influencing their environmental fate is consequently needed to value the risk, reduce contamination, and set up bioremediation technologies. The antiviral drug Tamiflu (oseltamivir carboxylate, OC) has received recent attention due to the potential use as a first line defence against H5N1 and H1N1 influenza viruses. Research has shown that OC is not removed during conventional wastewater treatments, thus having the potential to enter surface water bodies. A series of laboratory experiments investigated the fate and the removal of OC in surface water systems in Italy and Japan and in a municipal wastewater treatment plant. A preliminary laboratory study investigated the persistence of the active antiviral drug in water samples from an irrigation canal in northern Italy (Canale Emiliano Romagnolo). After an initial rapid decrease, OC concentration slowly decreased during the remaining incubation period. Approximately 65% of the initial OC amount remained in water at the end of the 36-day incubation period. A negligible amount of OC was lost both from sterilized water and from sterilized water/sediment samples, suggesting a significant role of microbial degradation. Stimulating microbial processes by the addition of sediments resulted in reduced OC persistence. Presence of OC (1.5 μg mL-1) did not significantly affect the metabolic potential of the water microbial population, that was estimated by glyphosate and metolachlor mineralization. In contrast, OC caused an initial transient decrease in the size of the indigenous microbial population of water samples. A second laboratory study focused on basic processes governing the environmental fate of OC in surface water from two contrasting aquatic ecosystems of northern Italy, the River Po and the Venice Lagoon. Results of this study confirmed the potential of OC to persist in surface water. However, the addition of 5% of sediments resulted in rapid OC degradation. The estimated half-life of OC in water/sediment of the River Po was 15 days. After three weeks of incubation at 20 °C, more than 8% of 14C-OC evolved as 14CO2 from water/sediment samples of the River Po and Venice Lagoon. OC was moderately retained onto coarse sediments from the two sites. In water/sediment samples of the River Po and Venice Lagoon treated with 14C-OC, more than 30% of the 14C-residues remained water-extractable after three weeks of incubation. The low affinity of OC to sediments suggests that the presence of sediments would not reduce its bioavailability to microbial degradation. Another series of laboratory experiments investigated the fate and the removal of OC in two surface water ecosystems of Japan and in the municipal wastewater treatment plant of the city of Bologna, in Northern Italy. The persistence of OC in surface water ranged from non-detectable degradation to a half-life of 53 days. After 40 days, less than 3% of radiolabeled OC evolved as 14CO2. The presence of sediments (5%) led to a significant increase of OC degradation and of mineralization rates. A more intense mineralization was observed in samples of the wastewater treatment plant when applying a long incubation period (40 days). More precisely, 76% and 37% of the initial radioactivity applied as 14C-OC was recovered as 14CO2 from samples of the biological tank and effluent water, respectively. Two bacterial strains growing on OC as sole carbon source were isolated and used for its removal from synthetic medium and environmental samples, including surface water and wastewater. Inoculation of water and wastewater samples with the two OC-degrading strains showed that mineralization of OC was significantly higher in both inoculated water and wastewater, than in uninoculated controls. Denaturing gradient gel electrophoresis and quantitative PCR analysis showed that OC would not affect the microbial population of surface water and wastewater. The capacity of the ligninolytic fungus Phanerochaete chrysosporium to degrade a wide variety of environmentally persistent xenobiotics has been largely reported in literature. In a series of laboratory experiments, the efficiency of a formulation using P. chrysosporium was evaluated for the removal of selected pharmaceuticals from wastewater samples. Addition of the fungus to samples of the wastewater treatment plant of Bologna significantly increased (P < 0.05) the removal of OC and three antibiotics, erythromycin, sulfamethoxazole, and ciprofloxacin. Similar effects were also observed in effluent water. OC was the most persistent of the four pharmaceuticals. After 30 days of incubation, approximately two times more OC was removed in bioremediated samples than in controls. The highest removal efficiency of the formulation was observed with the antibiotic ciprofloxacin. The studies included environmental aspects of soil contamination with two emerging veterinary contaminants, such as doramectin and oxibendazole, wich are common parasitic treatments in cattle farms.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Contaminants of emerging concern in drinking water: assessment of the effectiveness of removal by drinking water treatments and possible risks to human health

Contaminants of emerging concern are increasingly detected in the water cycle, with endocrine-disrupting chemicals (EDCs) receiving attention due to their potential to cause adverse health effects even at low concentrations. Although the EU has recently introduced some EDCs into drinking water legislation, most drinking water treatment plants (DWTPs) are not designed to remove EDCs, making their detection and removal in DWTPs an important challenge. The aim of this doctoral project was to investigate hormones and phenolic compounds as suspected EDCs in drinking waters across the Romagna area (Italy). The main objectives were to assess the occurrence of considered contaminants in source and drinking water from three DWTPs, characterize the effectiveness of removal by different water treatment processes, and evaluate the potential biological impact on drinking water and human health. Specifically, a complementary approach of target chemical analysis and effect-based methods was adopted to explore drinking water quality, treatment efficacy, and biological potential. This study found that nonylphenol (NP) was prevalent in all samples, followed by BPA. Sporadic contamination of hormones was found only in source waters. Although the measured EDC concentrations in drinking water did not exceed threshold guideline values, the potential role of DWTPs as an additional source of EDC contamination should be considered. Significant increases in BPA and NP levels were observed during water treatment steps, which were also reflected in estrogenic and mutagenic responses in water samples after the ultrafiltration. This highlights the need to monitor water quality during various treatment processes to improve the efficiency of DWTPs. Biological assessments on finished water did not reveal any bioactivity, except for few treated water samples that exhibited estrogenic responses. Overall, the data emphasize the high quality of produced drinking water and the value of applying integrated chemical analysis and in vitro bioassays for water quality assessment.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

An Innovation System Perspective on Adaptation Strategies to Climate Variability and Water Management in India

Government policies play a critical role in influencing market conditions, institutions and overall agricultural productivity. The thesis therefore looks into the history of agriculture development in India. Taking a political economy perspective, the historical account looks at significant institutional and technological innovations carried out in pre- independent and post independent India. It further focuses on the Green Revolution in Asia, as forty years after; the agricultural community still faces the task of addressing recurrent issue of food security amidst emerging challenges, such as climate change. It examines the Green Revolution that took place in India during the late 1960s and 70s in a historical perspective, identifying two factors of institutional change and political leadership. Climate change in agriculture development has become a major concern to farmers, researchers and policy makers alike. However, there is little knowledge on the farmers’ perception to climate change and to the extent they coincide with actual climatic data. Using a qualitative approach,it looks into the perceptions of the farmers in four villages in the states of Maharashtra and Andhra Pradesh. While exploring the adaptation strategies, the chapter looks into the dynamics of who can afford a particular technology and who cannot and what leads to a particular adaptation decision thus determining the adaptive capacity in water management. The final section looks into the devolution of authority for natural resource management to local user groups through the Water Users’ Associations as an important approach to overcome the long-standing challenges of centralized state bureaucracies in India. It addresses the knowledge gap of why some local user groups are able to overcome governance challenges such as elite capture, while others-that work under the design principles developed by Elinor Ostrom. It draws conclusions on how local leadership, can be promoted to facilitate participatory irrigation management.

Contribution of rare damaging variants in Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a heterogeneous and highly heritable neurodevelopmental disorder with a complex genetic architecture, consisting of a combination of common low-risk and more penetrant rare variants. This PhD project aimed to explore the contribution of rare variants in ASD susceptibility through NGS approaches in a cohort of 106 ASD families including 125 ASD individuals. Firstly, I explored the contribution of inherited rare variants towards the ASD phenotype in a girl with a maternally inherited pathogenic NRXN1 deletion. Whole exome sequencing of the trio family identified an increased burden of deleterious variants in the proband that could modulate the CNV penetrance and determine the disease development. In the second part of the project, I investigated the role of rare variants emerging from whole genome sequencing in ASD aetiology. To properly manage and analyse sequencing data, a robust and efficient variant filtering and prioritization pipeline was developed, and by its application a stringent set of rare recessive-acting and ultra-rare variants was obtained. As a first follow-up, I performed a preliminary analysis on de novo variants, identifying the most likely deleterious variants and highlighting candidate genes for further analyses. In the third part of the project, considering the well-established involvement of calcium signalling in the molecular bases of ASD, I investigated the role of rare variants in voltage-gated calcium channels genes, that mainly regulate intracellular calcium concentration, and whose alterations have been correlated with enhanced ASD risk. Specifically, I functionally tested the effect of rare damaging variants identified in CACNA1H, showing that CACNA1H variation may be involved in ASD development by additively combining with other high risk variants. This project highlights the challenges in the analysis and interpretation of variants from NGS analysis in ASD, and underlines the importance of a comprehensive assessment of the genomic landscape of ASD individuals.

Semantic-based middleware solutions to support context-aware service provisioning in pervasive environments

The dynamicity and heterogeneity that characterize pervasive environments raise new challenges in the design of mobile middleware. Pervasive environments are characterized by a significant degree of heterogeneity, variability, and dynamicity that conventional middleware solutions are not able to adequately manage. Originally designed for use in a relatively static context, such middleware systems tend to hide low-level details to provide applications with a transparent view on the underlying execution platform. In mobile environments, however, the context is extremely dynamic and cannot be managed by a priori assumptions. Novel middleware should therefore support mobile computing applications in the task of adapting their behavior to frequent changes in the execution context, that is, it should become context-aware. In particular, this thesis has identified the following key requirements for novel context-aware middleware that existing solutions do not fulfil yet. (i) Middleware solutions should support interoperability between possibly unknown entities by providing expressive representation models that allow to describe interacting entities, their operating conditions and the surrounding world, i.e., their context, according to an unambiguous semantics. (ii) Middleware solutions should support distributed applications in the task of reconfiguring and adapting their behavior/results to ongoing context changes. (iii) Context-aware middleware support should be deployed on heterogeneous devices under variable operating conditions, such as different user needs, application requirements, available connectivity and device computational capabilities, as well as changing environmental conditions. Our main claim is that the adoption of semantic metadata to represent context information and context-dependent adaptation strategies allows to build context-aware middleware suitable for all dynamically available portable devices. Semantic metadata provide powerful knowledge representation means to model even complex context information, and allow to perform automated reasoning to infer additional and/or more complex knowledge from available context data. In addition, we suggest that, by adopting proper configuration and deployment strategies, semantic support features can be provided to differentiated users and devices according to their specific needs and current context. This thesis has investigated novel design guidelines and implementation options for semantic-based context-aware middleware solutions targeted to pervasive environments. These guidelines have been applied to different application areas within pervasive computing that would particularly benefit from the exploitation of context. Common to all applications is the key role of context in enabling mobile users to personalize applications based on their needs and current situation. The main contributions of this thesis are (i) the definition of a metadata model to represent and reason about context, (ii) the definition of a model for the design and development of context-aware middleware based on semantic metadata, (iii) the design of three novel middleware architectures and the development of a prototypal implementation for each of these architectures, and (iv) the proposal of a viable approach to portability issues raised by the adoption of semantic support services in pervasive applications.

Analysis of optimal control problems for the incompressible MHD equations and implementation in a finite element multiphysics code

This thesis deals with the study of optimal control problems for the incompressible Magnetohydrodynamics (MHD) equations. Particular attention to these problems arises from several applications in science and engineering, such as fission nuclear reactors with liquid metal coolant and aluminum casting in metallurgy. In such applications it is of great interest to achieve the control on the fluid state variables through the action of the magnetic Lorentz force. In this thesis we investigate a class of boundary optimal control problems, in which the flow is controlled through the boundary conditions of the magnetic field. Due to their complexity, these problems present various challenges in the definition of an adequate solution approach, both from a theoretical and from a computational point of view. In this thesis we propose a new boundary control approach, based on lifting functions of the boundary conditions, which yields both theoretical and numerical advantages. With the introduction of lifting functions, boundary control problems can be formulated as extended distributed problems. We consider a systematic mathematical formulation of these problems in terms of the minimization of a cost functional constrained by the MHD equations. The existence of a solution to the flow equations and to the optimal control problem are shown. The Lagrange multiplier technique is used to derive an optimality system from which candidate solutions for the control problem can be obtained. In order to achieve the numerical solution of this system, a finite element approximation is considered for the discretization together with an appropriate gradient-type algorithm. A finite element object-oriented library has been developed to obtain a parallel and multigrid computational implementation of the optimality system based on a multiphysics approach. Numerical results of two- and three-dimensional computations show that a possible minimum for the control problem can be computed in a robust and accurate manner.