Flow Field–Flow Fractionation for size analysis and characterization of nanoparticles for applications in Life Sciences

Nanotechnologies are rapidly expanding because of the opportunities that the new materials offer in many areas such as the manufacturing industry, food production, processing and preservation, and in the pharmaceutical and cosmetic industry. Size distribution of the nanoparticles determines their properties and is a fundamental parameter that needs to be monitored from the small-scale synthesis up to the bulk production and quality control of nanotech products on the market. A consequence of the increasing number of applications of nanomaterial is that the EU regulatory authorities are introducing the obligation for companies that make use of nanomaterials to acquire analytical platforms for the assessment of the size parameters of the nanomaterials. In this work, Asymmetrical Flow Field-Flow Fractionation (AF4) and Hollow Fiber F4 (HF5), hyphenated with Multiangle Light Scattering (MALS) are presented as tools for a deep functional characterization of nanoparticles. In particular, it is demonstrated the applicability of AF4-MALS for the characterization of liposomes in a wide series of mediums. Afterwards the technique is used to explore the functional features of a liposomal drug vector in terms of its biological and physical interaction with blood serum components: a comprehensive approach to understand the behavior of lipid vesicles in terms of drug release and fusion/interaction with other biological species is described, together with weaknesses and strength of the method. Afterwards the size characterization, size stability, and conjugation of azidothymidine drug molecules with a new generation of metastable drug vectors, the Metal Organic Frameworks, is discussed. Lastly, it is shown the applicability of HF5-ICP-MS for the rapid screening of samples of relevant nanorisk: rather than a deep and comprehensive characterization it this time shown a quick and smart methodology that within few steps provides qualitative information on the content of metallic nanoparticles in tattoo ink samples.

Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications

Ill-conditioned inverse problems frequently arise in life sciences, particularly in the context of image deblurring and medical image reconstruction. These problems have been addressed through iterative variational algorithms, which regularize the reconstruction by adding prior knowledge about the problem's solution. Despite the theoretical reliability of these methods, their practical utility is constrained by the time required to converge. Recently, the advent of neural networks allowed the development of reconstruction algorithms that can compute highly accurate solutions with minimal time demands. Regrettably, it is well-known that neural networks are sensitive to unexpected noise, and the quality of their reconstructions quickly deteriorates when the input is slightly perturbed. Modern efforts to address this challenge have led to the creation of massive neural network architectures, but this approach is unsustainable from both ecological and economic standpoints. The recently introduced GreenAI paradigm argues that developing sustainable neural network models is essential for practical applications. In this thesis, we aim to bridge the gap between theory and practice by introducing a novel framework that combines the reliability of model-based iterative algorithms with the speed and accuracy of end-to-end neural networks. Additionally, we demonstrate that our framework yields results comparable to state-of-the-art methods while using relatively small, sustainable models. In the first part of this thesis, we discuss the proposed framework from a theoretical perspective. We provide an extension of classical regularization theory, applicable in scenarios where neural networks are employed to solve inverse problems, and we show there exists a trade-off between accuracy and stability. Furthermore, we demonstrate the effectiveness of our methods in common life science-related scenarios. In the second part of the thesis, we initiate an exploration extending the proposed method into the probabilistic domain. We analyze some properties of deep generative models, revealing their potential applicability in addressing ill-posed inverse problems.

Analisi critica di modelli previsionali per le frane in Emilia Romagna

Questa tesi di dottorato è inserita nell’ambito della convenzione tra ARPA_SIMC (che è l’Ente finanziatore), l’Agenzia Regionale di Protezione Civile ed il Dipartimento di Scienze della Terra e Geologico - Ambientali dell’Ateneo di Bologna. L’obiettivo principale è la determinazione di possibili soglie pluviometriche di innesco per i fenomeni franosi in Emilia Romagna che possano essere utilizzate come strumento di supporto previsionale in sala operativa di Protezione Civile. In un contesto geologico così complesso, un approccio empirico tradizionale non è sufficiente per discriminare in modo univoco tra eventi meteo innescanti e non, ed in generale la distribuzione dei dati appare troppo dispersa per poter tracciare una soglia statisticamente significativa. È stato quindi deciso di applicare il rigoroso approccio statistico Bayesiano, innovativo poiché calcola la probabilità di frana dato un certo evento di pioggia (P(A|B)) , considerando non solo le precipitazioni innescanti frane (quindi la probabilità condizionata di avere un certo evento di precipitazione data l’occorrenza di frana, P(B|A)), ma anche le precipitazioni non innescanti (quindi la probabilità a priori di un evento di pioggia, P(A)). L’approccio Bayesiano è stato applicato all’intervallo temporale compreso tra il 1939 ed il 2009. Le isolinee di probabilità ottenute minimizzano i falsi allarmi e sono facilmente implementabili in un sistema di allertamento regionale, ma possono presentare limiti previsionali per fenomeni non rappresentati nel dataset storico o che avvengono in condizioni anomale. Ne sono esempio le frane superficiali con evoluzione in debris flows, estremamente rare negli ultimi 70 anni, ma con frequenza recentemente in aumento. Si è cercato di affrontare questo problema testando la variabilità previsionale di alcuni modelli fisicamente basati appositamente sviluppati a questo scopo, tra cui X – SLIP (Montrasio et al., 1998), SHALSTAB (SHALlow STABility model, Montgomery & Dietrich, 1994), Iverson (2000), TRIGRS 1.0 (Baum et al., 2002), TRIGRS 2.0 (Baum et al., 2008).