5603 research outputs found
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Transport and Natural Attenuation of Emerging Contaminants in Groundwater
No full textEmerging Contaminants (ECs) encompass a wide array of substances, including microplastics, pharmaceuticals and personal care products (PPCPs), pesticides, per- and polyfluoroalkyl
substances (PFAS), and hydrocarbon additives such as MTBE and ETBE. These compounds, often
originating from human activities, are of particular concern due to their persistence in the
environment and potential adverse effects on human health and ecosystems. The interaction
between surface water and groundwater becomes increasingly critical when polluted surface
waters contribute to aquifer recharge through lateral transfer or percolation processes. In this
context, PPCPs present a notable challenge. Once released into wastewater, they are poorly
removed during treatment, and their presence in groundwater can be exacerbated by sewer pipe
failures that allow direct discharge of contaminants into the surrounding environment. The integration of data from different phases of the project has led to significant progress in understanding the dynamics influencing groundwater contamination by emerging contaminants. The interaction between surface and groundwater, particularly in the context of surface waters vulnerable to anthropogenic pollution, proves to be a crucial factor, as these waters can influence groundwater through lateral transfer and/or infiltration processes. In this framework, both microplastics and personal care products (PCPs) present significant challenges, as wastewater treatment plants have proven ineffective in removing them. Moreover, the domestic use of these
compounds exacerbates the contamination risk in case of sewer pipe failures, leading to direct release into the soil. Both the discharge of wastewater into surface water bodies and sewer pipe failures are therefore determining factors in the risk of groundwater contamination. The study of the biodegradability of these substances remains an evolving field of research, requiring further studies and in-depth exploration. The conclusions of this work underscore the urgency of addressing, from multiple fronts, the challenges posed by emerging contaminants, emphasizing the increasing need to develop analytical techniques that lower the detection limits for these substances, as well as effective methodologies for their removal from wastewater, in order to implement more effective monitoring and management strategies for the protection of groundwater
«Remaneat in hereditate mea et aliam alteri non acrescat». Entità, protezione e trasmissione patrimoniale dei Mandelli di Milano (secc. XIV-XVI)
No full textIl patrimonio (materiale e immateriale, mobile e immobile) è un elemento di grande rilevanza nello studio delle famiglie nobili rinascimentali. In tale ambito, i Mandelli di Milano (in particolare il ramo di Caorso) rappresentano un interessante caso di studio in virtù di una ricca e varia documentazione d’archivio che consente di saggiare la cultura materiale del casato e le sue strategie di trasmissione patrimoniale.
Pharmacological Profiling of Novel PP-Inhibitors Derived from Cholenic Acid for Targeting Eph-Ephrin Signaling in Glioblastoma
No full textThere are mounting evidence that the Eph-ephrin system is involved in several pathological processes, especially in cancer. Altered expression of this system has been observed in many tumours such as breast, colon, liver, prostate, and glioblastoma. Eph receptors and their ligands have been related with malignant progression, tumour angiogenesis, metastasis, propagation and maintenance of tumour stem cell. Looking at this evidence, it is reasonable the idea to target this system as a new or alternative therapeutic strategy in oncology. Different approaches to interfere with Eph/ephrin signalling have been explored over the years and some of them have reached clinical trials, showing contradictory results. Since 2009, our research group has focused its attention on the development of small molecules able to hamper the Eph-ephrin binding by targeting the extracellular ligand binding domain of Eph receptor. As noted in the introduction, UniPR1331, an Eph receptors pan-inhibitor, has demonstrated activity in disrupting the EphA2-ephrin-A1 interaction and has shown efficacy in reducing tumour growth in an in vivo model103. However, it is important to remember that the Eph-ephrin system is a redundant system, in which almost every ephrin can bind to multiple Eph kinases67. Thus, the silencing of one single ephrin-or Eph kinase-could easily be compensated by other members of the family. Thereby, this redundancy makes it difficult to assess to what extent the two subfamilies, A and B, each contribute to the various processes that support tumor growth. Thus, my research might be the starting point for a more extended investigation that will contribute to explaining the role of these two subfamilies in detail in different tumorigenic processes. Indeed, the treatment with selective molecules active on either A or B subfamily could clarify the role played by the two subfamilies in modulating the different tumorigenic processes of glioblastoma.
Based on that and thanks to the computational analyses developed by Professor A. Lodola's research group, the synthesis of UniPR1447 was achieved. UniPR1447 is a beta-homologue of UniPR1331. UniPR1447 (1), specifically, is the L-β-homotryptophan conjugate of 3-β-hydroxy-Δ5-cholenic acid. This compound interacts with the EphA2 receptor by accommodating its rigid core within a hydrophobic channel, with its carboxylic group forming a salt bridge with Arg103 and its indole ring positioned in an accessory pocket adjacent to Met73. The presence of this accessory pocket is a distinctive feature of the ligand-binding domain (LBD) of EphA2, absent in EphB2.
Exploiting this unique characteristic, a N-sulfonylphenyl substituent was introduced at the indole nitrogen atom of UniPR1447(1). This modification led to the synthesis of compounds UniPR1449 (2). These compounds exhibited significant affinity for the EphA2 receptor while demonstrating no interaction with different EphB receptors at concentrations up to 30 μM. The introduction of the bulky N-sulfonylphenyl substituent conferred a degree of selectivity for EphA2, underscoring the potential for developing EphA2-specific antagonists.UniPR1331 derivates were pharmacologically characterized with the aim to identify potent Eph binders, interfering with the Eph/ephrin system and endowed by anti-tumoral properties. As first step, all new synthetized molecules have been tested in displacement studies in order to select only the most potent. Then, these selected compounds were investigated for their ability to inhibit the EphA2 activation, cytotoxic effects and through functional assays performed on cells. Finally, compounds were tested in pharmacokinetic studies
ECG-Based Characterization of Heart Diseases by leveraging AI techniques
No full textThis research analyses three distinct approaches to improve the diagnosis and classification of cardiovascular conditions through the utilisation of machine learning techniques applied to electrocardiographic (ECG) data. The first study proposes a predictive model based on a two-dimensional convolutional neural network (CNN-2D) and Gradient-weighted Class Activation Mapping (Grad-CAM) to analyse ECG images, offering an interpretable solution for detecting myocardial infarctions and arrhythmias. The second study develops a CNN to distinguish between arrhythmias and other types of disturbances, utilising data from 1,052 patients and achieving an overall accuracy of 90%, with promising results but challenges related to signal noise and device variability. Finally, the third study explores three distinct approaches, integrating CNN, heart rate variability (HRV) feature extraction, and classification models such as Random Forest; this last approach proves to be the most effective, reaching an accuracy of 92.01%, demonstrating potential for supporting clinical decision-making
A simple organism to address big questions: how Saccharomyces cerevisiae can support mitochondrial medicine
No full textDuring my Ph.D. I focused on several aspects of mitochondrial functions. In particular, many variants
were identified by different collaborators through NGS techniques in nine different genes encoding for
proteins involved in six mitochondrial macro-pathways. I studied and characterized these variants
exploiting the yeast Saccharomyces cerevisiae as a model system to verify their pathogenicity (validate
them) and/or to deepen our knowledge of the involved pathways. Both heterologous and homologous
complementation approaches were used, depending on the gene, to have the most suitable disease
model. The effect of rationally selected molecules/formulations was also tested on specific disease
models (COQ7, SURF1, and PITRM1 models) to identify novel pharmacological therapies since no
effective treatment exists for mitochondrial diseases (MDs). Overall, the data obtained using the yeast
Saccharomyces cerevisiae allowed to support mitochondrial medicine highlighting how such a
“simple” model organism, used beside bioinformatic tools (sequencing, omics analyses and protein
modeling), can be useful and allow a key step forward in the study of mitochondrial disorders (MDs) and
their treatment
Modeling transient vegetation effects in slope stability analysis for rainfall-induced shallow landslides: CRITERIA-1D and CRITERIA-3D
No full textInstability of natural slopes is a worldwide problem, with an increasing trend in the occurrence of landslides and the associated aggravation of socioeconomic losses. With regard to shallow landslides, phenomena exacerbation is due to extreme rainfall events, which are becoming more frequent because of the global temperature arise. Several attempts to model landslides' occurrence over the years have been done, involving probabilistic, deterministic or hybrid approaches. Statistical-based models need to be trained upon past landslide events for detecting new future occurrences. However, many shallow landslides were recently observed to occur as new activation, and thus their triggering conditions are mainly experienced for the first time. Physically-based models have the potentiality of quantifying the different processes involved during the landslide formation. Therefore, they are particularly suitable for addressing consequences of extreme climatic events. When involving transient hydrological phenomena, these models can reach high levels of accuracy, although they are forced to make simplifications in order to be practically applied. The interest in modeling vegetation effects on both unsaturated soil mechanics and hydrology is relatively recent. Yet, only few models consider the transient reinforcement effects exerted by plants on slopes, as the most common approach in slope stability models is to consider them as static components in the analyses. The aim of this thesis is to explore the effectiveness of modeling transient stability effects induced by vegetation growth and evapotranspiration activity in physically-based deterministic models for shallow landslides. The proposed models, CRITERIA-1D and CRITERIA-3D, are agro-hydrological models developed by the Regional Agency for the Environmental Protection and Energy of Emilia-Romagna region (Arpae-SIMC). In both models a simplified slope stability analysis, based on the infinite slope assumption and on the Factor of Safety computation, has been added. Results show that the proposed methodology is effective in reproducing real case studies at different application scales, and that neglecting vegetation effects in transient hydrology computation can lead to unsatisfactory results
Analogies, Metaphors, Allegories: Categorical architectures of general intelligence
No full textThis thesis presents a bird eye view of Artificial General Intelligence (AGI) and Hypercomputing through the lens of Category Theory and Topos Theory. The work discusses various frameworks for AGI development, focusing on explainability and alignment with human values. It examines the use of Hyper Dimensional Computing (HDC) and Vector Symbolic Architectures (VSA) as tools for bridging Symbolic and Connectionist approaches, aiming to unify diverse paradigms in AI.
The thesis also delves into the cognitive structures necessary for creating self aware, interpretable AGI systems capable of making ethical decisions in dynamic environments. Category theory and Topos Theory in particular are the background mathematical theories in which the present contributions find a natural language to be discussed, since they provide the foundations for modeling cognitive architectures and describe their inner processes. The present work explores how to generalise the paradigm of Manifold Learning by leveraging concepts from Category Theory, such as (co)limits, enrichment and allegories, envisioning autonomous artificial agents capable of reasoning about geometric patterns in abstract spaces.
Besides contributing to the theoretical foundations for AGI, the present work addresses future challenges in aligning AI development with ethical considerations, proposing models that integrate explainability at their core. Finally, we propose an implementation of an Episodic Memory SubModule (EMSM) within the context of Retrieval Augmented Generation (RAG) architectures, exploring its role in enhancing contextual understanding and memory retention in AI systems.This thesis presents a bird eye view of Artificial General Intelligence (AGI) and Hypercomputing through the lens of Category Theory and Topos Theory. The work discusses various frameworks for AGI development, focusing on explainability and alignment with human values. It examines the use of Hyper Dimensional Computing (HDC) and Vector Symbolic Architectures (VSA) as tools for bridging Symbolic and Connectionist approaches, aiming to unify diverse paradigms in AI.
The thesis also delves into the cognitive structures necessary for creating self aware, interpretable AGI systems capable of making ethical decisions in dynamic environments. Category theory and Topos Theory in particular are the background mathematical theories in which the present contributions find a natural language to be discussed, since they provide the foundations for modeling cognitive architectures and describe their inner processes. The present work explores how to generalise the paradigm of Manifold Learning by leveraging concepts from Category Theory, such as (co)limits, enrichment and allegories, envisioning autonomous artificial agents capable of reasoning about geometric patterns in abstract spaces.
Besides contributing to the theoretical foundations for AGI, the present work addresses future challenges in aligning AI development with ethical considerations, proposing models that integrate explainability at their core. Finally, we propose an implementation of an Episodic Memory SubModule (EMSM) within the context of Retrieval Augmented Generation (RAG) architectures, exploring its role in enhancing contextual understanding and memory retention in AI systems
Forecasting landslide behavior with Deep Learning algorithms for Early Warning purposes
No full textDevelopment of a Deep Learning algorithm for landslide forecasting. It uses past displacements and other optional information to predict the next displacements. Many versions of this algorithm are tested, changing input data, underlying Neural Network, loss function, and other characteristics.
Development of a Deep Learning algorithm for tree species recognition from drone images. This is achieved with models pre-trained on ImageNet, and subsequently fine-tuned on task-specific images. The output produced can be used as additional input for the landslide forecasting algorithm.
Development of various auxiliary functions like custom loss, custom metrics, automatic hyperparameter space search, data cleaning and processing, data augmentation…
Test of both algorithms on real world data with presentation and discussion of the results
Archeologia delle acque a Parma e territorio nel contesto dei centri romani a continuità di vita lungo la via Emilia: gestione di lungo periodo delle risorse idriche, modellazione del paesaggio, applicazioni digitali e water cultural heritage.
No full textIl tema della regimentazione, dello sfruttamento e della gestione della risorsa idrica in rapporto alla costruzione del paesaggio antico è analizzato nel contesto della regio VIII e, nello specifico, sul caso di Parma. Per questo contesto, attraverso l'analisi in ambiente digitale di dati archeologici inediti, è stato possibile proporre una lettura del ruolo urbanistico, monumentale funzionale che i paesaggi delle acque urbane hanno nello sviluppo della città antica. L'individuazione, inedita, dello sviluppo dell'infrastruttura idrica antica e degli eventi alluvionali ha portato all'individuazione a fenomeni di resilienza che hanno caratterizzato la formazione del water heritage del territorio
Biotechnology for Sustainability: Nanobiotechnological Approaches to Improve Plant Resilience
No full textAgriculture is severely suffering due to the escalating incidence of abiotic stresses, exacerbated by global warming, which has been leading to reduced agricultural yields and extensive economic losses. Despite its longstanding presence over the centuries, salinity pollution is a very current issue that has not been solved and still endangers agricultural productivity worldwide, threatening food supply and human well-being. Nano-biotechnology offers promising and eco-friendly solutions that can support the transition to a more sustainable and resilient agri-food system. Microbial-based biostimulants can be based on the use of plant growth-promoting rhizobacteria (PGPR) as single species or as microbial consortia (MCs). In either case, traceability procedures are required to improve their performance in field trials. To assess the shelf life of PGPR inoculated into the soil is important to determine which elements contribute to their effectiveness and their interactions with plants and indigenous soil microbiota. The present work developed a real-time PCR (qtPCR) method to specifically detect and quantify bacteria when added as a microbial consortium to agricultural soils. It has been proved that three species of the microbial consortium (B. ambifaria, B. amyloliquefaciens and R. aquatilis) administered to wheat plants with biochar and the mycorrhizal fungus Rhizophagus intraradices successfully colonized the wheat rhizosphere. The results also showed biochar had a positive effect on PGPR growth. The research work also focused on investigating the interaction between microbial-based products and nanomaterials in counteracting abiotic stresses. Silica and chitosan-tripolyphosphate nanoparticles were tested in association with a commercial microbial-based product on soybean (Glycine max L.) and cherry tomato (Solanum lycopersicum L.) plants grown in the greenhouse. The results proved that the coupled nano-biotechnological approaches have been effective in enhancing plant health and development under salt stress conditions, by restoring photosynthetic activity, favoring nutrients uptake and limiting sodium accumulation into the roots