Development Aid to Water Management in Mali: The Actors, ‘Global’ Paradigms, and ‘Local’ Translations

Development aid involves a complex network of numerous and extremely heterogeneous actors. Nevertheless, all actors seem to speak the same ‘development jargon’ and to display a congruence that extends from the donor over the professional consultant to the village chief. And although the ideas about what counts as ‘good’ and ‘bad’ aid have constantly changed over time —with new paradigms and policies sprouting every few years— the apparent congruence between actors more or less remains unchanged. How can this be explained? Is it a strategy of all actors to get into the pocket of the donor, or are the social dynamics in development aid more complex? When a new development paradigm appears, where does it come from and how does it gain support? Is this support really homogeneous? To answer the questions, a multi-sited ethnography was conducted in the sector of water-related development aid, with a focus on 3 paradigms that are currently hegemonic in this sector: Integrated Water Resources Management, Capacity Building, and Adaptation to Climate Change. The sites of inquiry were: the headquarters of a multilateral organization, the headquarters of a development NGO, and the Inner Niger Delta in Mali. The research shows that paradigm shifts do not happen overnight but that new paradigms have long lines of descent. Moreover, they require a lot of work from actors in order to become hegemonic; the actors need to create a tight network of support. Each actor, however, interprets the paradigms in a slightly different way, depending on the position in the network. They implant their own interests in their interpretation of the paradigm (the actors ‘translate’ their interests), regardless of whether they constitute the donor, a mediator, or the aid recipient. These translations are necessary to cement and reproduce the network.

Ravenna e i piu' significativi centri dell'Impero. Il mosaico parietale tra V e VI secolo: Revisione critica

Tra il V ed il VI secolo, la città di Ravenna, per tre volte capitale, emerge fra i più significativi centri dell’impero, fungendo da cerniera tra Oriente e Occidente, soprattutto grazie ai mosaici parietali degli edifici di culto, perfettamente inseriti in una koinè culturale e artistica che ha come comune denominatore il Mar Mediterraneo, nel contesto di parallele vicende storiche e politiche. Rispetto ai ben noti e splendidi mosaici ravennati, che insieme costituiscono senza dubbio un unicum nel panorama artistico dell’età tardoantica e altomedievale, nelle decorazioni musive parietali dei coevi edifici di culto dei diversi centri dell’impero d’Occidente e d’Oriente, e in particolare in quelli localizzati nelle aree costiere, si possono cogliere divergenze, ma anche simmetrie dal punto di vista iconografico, iconologico e stilistico. Sulla base della letteratura scientifica e attraverso un poliedrico esame delle superfici musive parietali, basato su una metodologia interdisciplinare, si è cercato di chiarire l’articolato quadro di relazioni culturali, ideologiche ed artistiche che hanno interessato e interessano tuttora Ravenna e i vari centri della tarda antichità, insistendo sulla pluralità, sulla complessità e sulla confluenza di diverse esperienze artistiche sui mosaici di Ravenna. A tale scopo, i dati archeologici e artistici sono stati integrati con quelli storici, agiografici ed epigrafici, con opportuni collegamenti all’architettura, alla scultura, alle arti decorative e alle miniature, a testimonianza dell’unità di intenti di differenti media artistici, orientati, pur nella diversità, verso le medesime finalità dogmatiche, politiche e celebrative. Si tratta dunque di uno studio di revisione e di sintesi sui mosaici parietali mediterranei di V e VI secolo, allo scopo di aggiungere un nuovo tassello alla già pur vasta letteratura dedicata all’argomento.

Development of new chemical entities to target neuroinflammatory pathways.

Neuroinflammatory pathways are main culprits of neurodegenerative diseases' onset and progression, including Alzheimer’s disease (AD). On this basis, several anti-inflammatory drugs were repurposed in clinical trials. However, they have failed, probably because neuroinflammation is a complex network, still not fully understood. From these evidences, this thesis focused on the design and synthesis of new chemical entities as potential neuroinflammatory drugs or chemical probes. Projects 1 and 2 aimed to multi-target-directed ligand (MTDL) development to target neuroinflammation in AD. Polypharmacology by MTDLs is considered one of the most promising strategies to face the multifactorial nature of neurodegenerative diseases. Particularly, Project 1 took inspiration from a cromolyn-ibuprofen drug combination polypharmacological approach, which was recently investigated in AD clinical trials. Based on that, two cromolyn-(S)-ibuprofen codrug series were designed and synthesized. Parent drugs were combined via linking or fusing strategies in 1:2 or 1:1 ratio, by means of hydrolyzable bonds. Project 2 started from a still ongoing AD clinical trial on investigational drug neflamapimod. It is a selective inhibitor of p38α-MAPK, a kinase strictly involved in neuroinflammatory pathways. On the other side, rasagiline, an anti-Parkinson drug, was also repurposed as AD treatment. Indeed, rasagiline’s propargylamine fragment demonstrated to be responsible not only for the MAO-B selective inhibition, but also for the neuroprotective activity. Thus, to synergistically combine these two effects into single-molecules, a small set of neflamapimod-rasagiline hybrids was developed. In the end BMX, a poorly investigated kinase, which seems to be involved in pro-inflammatory mediator production, was explored for the development of new chemical probes. High-quality chemical probes are a powerful tool in target validation and starting points for the development of new drug candidates. Thus, Project 3 focused on the design and synthesis of two series of optimized BMX covalent inhibitors as selective chemical probes.

Aquivion-based spray freeze-dried composite catalysts for the cascade conversion of furfural to γ-valerolactone

Furfural is one of the most promising biomass derived platform molecules. It is to this day produced in volumes above 300 ktons per year from the hydrolysis and dehydration of hemicellulose, one of the main components of lignocellulosic biomass. While the majority of the yearly production is destined to selective reduction to furfuryl alcohol for the production of furan resins, these molecules hold great potential for the production of more valuable chemicals, fuels, fuel additives and solvents. Among these products are alkyl levulinates and γ-valerolactone. To convert furfural to these target products, a cascade process involving Lewis acidity-catalysed reduction steps and Brønsted acidity-catalysed steps. In order to develop catalysts capable of promoting the one-pot domino reaction from furfural to γ-valerolactone, the two kinds of acidity must both be present. To this end, in this work, the spray freeze-drying technique is employed to combine the high activity and strong Brønsted acidity of Aquivion with the structural properties and Lewis acidity of different supporting metal oxide, forming composite catalysts. The flexibility of the spray freeze-drying technique and the modulable composition of the catalysts allowed a thorough study of the complex network of equilibria underlying the cascade reaction, while achieving high selectivities towards the final product.

Design, synthesis and biological evaluation of dual inhibitors of GSK-3B and Fyn kinases for the study of inflammatory pathways in neurodegenerative diseases

Neuroinflammation represents a key hallmark of neurodegenerative diseases and is the result of a complex network of signaling cascades within microglial cells. A positive feedback loop exists between inflammation, microglia activation and protein misfolding processes, that, together with oxidative stress and excitotoxicity, lead to neuronal degeneration. Therefore, targeting this vicious cycle can be beneficial for mitigating neurodegeneration and cognitive decline in central nervous system disorders. At molecular level, GSK-3B and Fyn kinases play a crucial role in microglia activation and their deregulation has been associated to many neurodegenerative diseases. Thus, we envisioned their combined targeting as an effective approach to disrupt this toxic loop. Specifically in this project, a hit compound, based on a 7-azaindole-3-aminothiazole structure, was first identified in a virtual screening campaign, and displayed a weak dual inhibitory activity on GSK-3B and Fyn, unbalanced towards the former. Then, in a commitment to uncover the structural features required for modulating the activity on the two targets, we systematically manipulated this compound by inserting various substitution patterns in different positions. The most potent compounds obtained were advanced to deeper investigations to test their ability of tackling the inflammatory burden also in cellular systems and to unveil their binding modes within the catalytic pocket. The new class of molecules synthesized emerged as a valuable tool to deepen our understanding of the complex network governing the inflammatory events in neurodegenerative disorders.

Design and Performance Evaluation of Network-on-Chip Communication Protocols and Architectures

The scale down of transistor technology allows microelectronics manufacturers such as Intel and IBM to build always more sophisticated systems on a single microchip. The classical interconnection solutions based on shared buses or direct connections between the modules of the chip are becoming obsolete as they struggle to sustain the increasing tight bandwidth and latency constraints that these systems demand. The most promising solution for the future chip interconnects are the Networks on Chip (NoC). NoCs are network composed by routers and channels used to inter- connect the different components installed on the single microchip. Examples of advanced processors based on NoC interconnects are the IBM Cell processor, composed by eight CPUs that is installed on the Sony Playstation III and the Intel Teraflops pro ject composed by 80 independent (simple) microprocessors. On chip integration is becoming popular not only in the Chip Multi Processor (CMP) research area but also in the wider and more heterogeneous world of Systems on Chip (SoC). SoC comprehend all the electronic devices that surround us such as cell-phones, smart-phones, house embedded systems, automotive systems, set-top boxes etc... SoC manufacturers such as ST Microelectronics , Samsung, Philips and also Universities such as Bologna University, M.I.T., Berkeley and more are all proposing proprietary frameworks based on NoC interconnects. These frameworks help engineers in the switch of design methodology and speed up the development of new NoC-based systems on chip. In this Thesis we propose an introduction of CMP and SoC interconnection networks. Then focusing on SoC systems we propose: • a detailed analysis based on simulation of the Spidergon NoC, a ST Microelectronics solution for SoC interconnects. The Spidergon NoC differs from many classical solutions inherited from the parallel computing world. Here we propose a detailed analysis of this NoC topology and routing algorithms. Furthermore we propose aEqualized a new routing algorithm designed to optimize the use of the resources of the network while also increasing its performance; • a methodology flow based on modified publicly available tools that combined can be used to design, model and analyze any kind of System on Chip; • a detailed analysis of a ST Microelectronics-proprietary transport-level protocol that the author of this Thesis helped developing; • a simulation-based comprehensive comparison of different network interface designs proposed by the author and the researchers at AST lab, in order to integrate shared-memory and message-passing based components on a single System on Chip; • a powerful and flexible solution to address the time closure exception issue in the design of synchronous Networks on Chip. Our solution is based on relay stations repeaters and allows to reduce the power and area demands of NoC interconnects while also reducing its buffer needs; • a solution to simplify the design of the NoC by also increasing their performance and reducing their power and area consumption. We propose to replace complex and slow virtual channel-based routers with multiple and flexible small Multi Plane ones. This solution allows us to reduce the area and power dissipation of any NoC while also increasing its performance especially when the resources are reduced. This Thesis has been written in collaboration with the Advanced System Technology laboratory in Grenoble France, and the Computer Science Department at Columbia University in the city of New York.

Microscopic Modeling on complex networks

The field of complex systems is a growing body of knowledge, It can be applied to countless different topics, from physics to computer science, biology, information theory and sociology. The main focus of this work is the use of microscopic models to study the behavior of urban mobility, which characteristics make it a paradigmatic example of complexity. In particular, simulations are used to investigate phase changes in a finite size open Manhattan-like urban road network under different traffic conditions, in search for the parameters to identify phase transitions, equilibrium and non-equilibrium conditions . It is shown how the flow-density macroscopic fundamental diagram of the simulation shows,like real traffic, hysteresis behavior in the transition from the congested phase to the free flow phase, and how the different regimes can be identified studying the statistics of road occupancy.

Multidimensional analysis of complex networks

Complex Networks analysis turn out to be a very promising field of research, testified by many research projects and works that span different fields. Those analysis have been usually focused on characterize a single aspect of the system and a study that considers many informative axes along with a network evolve is lacking. We propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions, space and time. To achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network as a whole. By focusing on space dimension, we characterized spatial alteration in terms of abstraction levels. We proposed a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We verified that statistical indicators depend strongly on the granularity with which a system is described and on the class of networks. We keep fixed the space axes and we isolated the dynamics behind networks evolution process. We detected new instincts that trigger social networks utilization and spread the adoption of novel communities. We formalized this enhanced social network evolution by adopting special nodes (called sirens) that, thanks to their ability to attract new links, were able to construct efficient connection patterns. We simulated the dynamics of the system by considering three well-known growth models. Applying this framework to real and synthetic networks, we showed that the sirens, even when used for a limited time span, effectively shrink the time needed to get a network in mature state. In order to provide a concrete context of our findings, we formalized the cost of setting up such enhancement and provided the best combinations of system's parameters, such as number of sirens, time span of utilization and attractiveness.

Plasticity and neuromodulation of the extended recurrent visual network

The extended visual network, which includes occipital, temporal and parietal posterior cortices, is a system characterized by an intrinsic connectivity consisting of bidirectional projections. This network is composed of feedforward and feedback projections, some hierarchically arranged and others bypassing intermediate areas, allowing direct communication across early and late stages of processing. Notably, the early visual cortex (EVC) receives considerably more feedback and lateral inputs than feedforward thalamic afferents, placing it at the receiving end of a complex cortical processing cascade, rather than just being the entrance stage of cortical processing of retinal input. The critical role of back-projections to visual cortices has been related to perceptual awareness, amplification of neural activity in lower order areas and improvement of stimulus processing. Recently, significant results have shown behavioural evidence suggesting the importance of reentrant projections in the human visual system, and demonstrated the feasibility of inducing their reversible modulation through a transcranial magnetic stimulation (TMS) paradigm named cortico-cortical paired associative stimulation (ccPAS). Here, a novel research line for the study of recurrent connectivity and its plasticity in the perceptual domain was put forward. In the present thesis, we used ccPAS with the aim of empowering the synaptic efficacy, and thus the connectivity, between the nodes of the visuocognitive system to evaluate the impact on behaviour. We focused on driving plasticity in specific networks entailing the elaboration of relevant social features of human faces (Chapters I & II), alongside the investigation of targeted pathways of sensory decisions (Chapter III). This allowed us to characterize perceptual outcomes which endorse the prominent role of the EVC in visual awareness, fulfilled by the activity of back-projections originating from distributed functional nodes.

Network assimilation methodology and sectorization of water distribution systems by graph mining and optimization algorithm

Water Distribution Networks (WDNs) play a vital importance rule in communities, ensuring well-being band supporting economic growth and productivity. The need for greater investment requires design choices will impact on the efficiency of management in the coming decades. This thesis proposes an algorithmic approach to address two related problems:(i) identify the fundamental asset of large WDNs in terms of main infrastructure;(ii) sectorize large WDNs into isolated sectors in order to respect the minimum service to be guaranteed to users. Two methodologies have been developed to meet these objectives and subsequently they were integrated to guarantee an overall process which allows to optimize the sectorized configuration of WDN taking into account the needs to integrated in a global vision the two problems (i) and (ii). With regards to the problem (i), the methodology developed introduces the concept of primary network to give an answer with a dual approach, of connecting main nodes of WDN in terms of hydraulic infrastructures (reservoirs, tanks, pumps stations) and identifying hypothetical paths with the minimal energy losses. This primary network thus identified can be used as an initial basis to design the sectors. The sectorization problem (ii) has been faced using optimization techniques by the development of a new dedicated Tabu Search algorithm able to deal with real case studies of WDNs. For this reason, three new large WDNs models have been developed in order to test the capabilities of the algorithm on different and complex real cases. The developed methodology also allows to automatically identify the deficient parts of the primary network and dynamically includes new edges in order to support a sectorized configuration of the WDN. The application of the overall algorithm to the new real case studies and to others from literature has given applicable solutions even in specific complex situations.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.