Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarkers for neurological or neurodevelopmental disorders. Lastly, CNNs were developed to decode single-neuron activity, providing a better compromise between performance and model complexity.

Preharvest factors affecting quality and antioxidant levels on Abate Fétel pears: study of superficial scald with a multivariate statistical approach and neural networks

There are only a few insights concerning the influence that agronomic and management variability may have on superficial scald (SS) in pears. Abate Fétel pears were picked during three seasons (2018, 2019 and 2020) from thirty commercial orchards in the Emilia Romagna region, Italy. Using a multivariate statistical approach, high heterogeneity between farms for SS development after cold storage with regular atmosphere was demonstrated. Indeed, some factors seem to affect SS in all growing seasons: high yields, soil texture, improper irrigation and Nitrogen management, use of plant growth regulators, late harvest, precipitations, Calcium and cow manure, presence of nets, orchard age, training system and rootstock. Afterwards, we explored the spatio/temporal variability of fruit attributes in two pear orchards. Environmental and physiological spatial variables were recorded by a portable RTK GPS. High spatial variability of the SS index was observed. Through a geostatistical approach, some characteristics, including soil electrical conductivity and fruit size, have been shown to be negatively correlated with SS. Moreover, regression tree analyses were applied suggesting the presence of threshold values of antioxidant capacity, total phenolic content, and acidity against SS. High pulp firmness and IAD values before storage, denoting a more immature fruit, appeared to be correlated with low SS. Finally, a convolution neural networks (CNN) was tested to detect SS and the starch pattern index (SPI) in pears for portable device applications. Preliminary statistics showed that the model for SS had low accuracy but good precision, and the CNN for SPI denoted good performances compared to the Ctifl and Laimburg scales. The major conclusion is that Abate Fétel pears can potentially be stored in different cold rooms, according to their origin and quality features, ensuring the best fruit quality for the final consumers. These results might lead to a substantial improvement in the Italian pear industry.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Influence of genetic variants and post transcriptional factors on imatinib transporters as determinants of the pharmacological response

Introduction – Although imatinib (IM) is a recognized gold standard in chronic myeloid leukemia (CML) therapy, resistance has emerged in a significant proportion of patients. Aim – The aim of this study was: (1) to investigate the role of genetic variants in genes encoding for IM transporters, as candidate of IM responsiveness and (2) to test the influence of miRNAs on IM response, focusing on efflux transporters. Methods – As a first step, a panel of polymorphisms (SNPs) was genotyped in a subgroup population of 189 patients enrolled in the Tyrosine Kinase Inhibitor Optimization and Selectivity (TOPS) trial. The association with cytogenetic response and molecular response (MR) was assessed for each SNP. As a second step, an in vitro IM-resistant model (K-562 CML cell line) was established. miRNAs profiles were analyzed using Taqman arrays and in silico search was performed for miRNAs deregulated after IM treatment. mRNA and protein expression were quantified using TaqMan realtime PCR and Western blotting, respectively. Results – (1) Among Caucasian patients, ABCB1 rs60023214 significantly correlated with complete MR (P = 0.005). Concerning SNPs combination in IM uptake transporters, the associations with treatment outcomes were statistically significant for both major and complete MR (P = 0.005 and P = 0.01, respectively). (2) ABCB1 protein was not expressed under any conditions of treatment, differently from ABCG2. Two deregulated miRNAs, namely miR-212 and miR-328, were identified to be inversely correlated with ABCG2 (r2= 0.57; p=0.03 and r2=0.47; p=0.06, respectively). Experiments of loss and gain of function confirmed the functional influence of these miRNAs on ABCG2. Conclusion – The multiple candidate gene approach identified single and combination of SNPs that can be proposed as predictor of IM response. The in vitro study suggested that IM resistance could be mediated by miRNA-dependent mechanism. Further studies are needed to validate these preliminary findings.

Genetic engineering and preclinical evaluation of oncolytic herpes simplex viruses retargeted to cancer-specific receptors

Oncolytic virotherapy exploits the ability of viruses to infect and kill cells. It is suitable as treatment for tumors that are not accessible by surgery and/or respond poorly to the current therapeutic approach. HSV is a promising oncolytic agent. It has a large genome size able to accommodate large transgenes and some attenuated oncolytic HSVs (oHSV) are already in clinical trials phase I and II. The aim of this thesis was the generation of HSV-1 retargeted to tumor-specific receptors and detargeted from HSV natural receptors, HVEM and Nectin-1. The retargeting was achieved by inserting a specific single chain antibody (scFv) for the tumor receptor selected inside the HSV glycoprotein gD. In this research three tumor receptors were considered: epidermal growth factor receptor 2 (HER2) overexpressed in 25-30% of breast and ovarian cancers and gliomas, prostate specific membrane antigen (PSMA) expressed in prostate carcinomas and in neovascolature of solid tumors; and epidermal growth factor receptor variant III (EGFRvIII). In vivo studies on HER2 retargeted viruses R-LM113 and R-LM249 have demonstrated their high safety profile. For R-LM249 the antitumor efficacy has been highlighted by target-specific inhibition of the growth of human tumors in models of HER2-positive breast and ovarian cancer in nude mice. In a murine model of HER2-positive glioma in nude mice, R-LM113 was able to significantly increase the survival time of treated mice compared to control. Up to now, PSMA and EGFRvIII viruses (R-LM593 and R-LM613) are only characterized in vitro, confirming the specific retargeting to selected targets. This strategy has proved to be generally applicable to a broad spectrum of receptors for which a single chain antibody is available.

Networks, Uncertainty, Applications and a Crusade for Optimality

In this thesis we address a collection of Network Design problems which are strongly motivated by applications from Telecommunications, Logistics and Bioinformatics. In most cases we justify the need of taking into account uncertainty in some of the problem parameters, and different Robust optimization models are used to hedge against it. Mixed integer linear programming formulations along with sophisticated algorithmic frameworks are designed, implemented and rigorously assessed for the majority of the studied problems. The obtained results yield the following observations: (i) relevant real problems can be effectively represented as (discrete) optimization problems within the framework of network design; (ii) uncertainty can be appropriately incorporated into the decision process if a suitable robust optimization model is considered; (iii) optimal, or nearly optimal, solutions can be obtained for large instances if a tailored algorithm, that exploits the structure of the problem, is designed; (iv) a systematic and rigorous experimental analysis allows to understand both, the characteristics of the obtained (robust) solutions and the behavior of the proposed algorithm.