Spatial and temporal characterisation of ground deformation recorded by geodetic techniques

A critical point in the analysis of ground displacements time series is the development of data driven methods that allow the different sources that generate the observed displacements to be discerned and characterised. A widely used multivariate statistical technique is the Principal Component Analysis (PCA), which allows reducing the dimensionality of the data space maintaining most of the variance of the dataset explained. Anyway, PCA does not perform well in finding the solution to the so-called Blind Source Separation (BSS) problem, i.e. in recovering and separating the original sources that generated the observed data. This is mainly due to the assumptions on which PCA relies: it looks for a new Euclidean space where the projected data are uncorrelated. The Independent Component Analysis (ICA) is a popular technique adopted to approach this problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this problem, I use a variational bayesian ICA (vbICA) method, which models the probability density function (pdf) of each source signal using a mix of Gaussian distributions. This technique allows for more flexibility in the description of the pdf of the sources, giving a more reliable estimate of them. Here I present the application of the vbICA technique to GPS position time series. First, I use vbICA on synthetic data that simulate a seismic cycle (interseismic + coseismic + postseismic + seasonal + noise) and a volcanic source, and I study the ability of the algorithm to recover the original (known) sources of deformation. Secondly, I apply vbICA to different tectonically active scenarios, such as the 2009 L'Aquila (central Italy) earthquake, the 2012 Emilia (northern Italy) seismic sequence, and the 2006 Guerrero (Mexico) Slow Slip Event (SSE).

Effetto antinfiammatorio del trattamento emodialitico in pazienti con malattia renale cronica allo stadio terminale e COVID-19: confronto fra due filtri dializzatori (ATA vs PMMA)

Background I filtri dializzatori ad alto flusso potrebbero mitigare la “tempesta citochinica" nell'infezione da Sars-COV-2, ma il loro impatto nei pazienti in dialisi cronica non è accertato. Lo scopo delle studio è valutare l’effetto del filtro in triacetato asimmetrico di cellulosa (ATA) e in polimetilmetacrilato (PMMA) sui marcatori infiammatori in pazienti in dialisi cronica affetti da SARS-CoV-2. Metodi Si tratta di uno studio prospettico osservazionale su pazienti in trattamento emodialitico cronicp con COVID-19 arruolati da marzo 2020 a Maggio 2021.Le variabili cliniche, la conta leucocitaria, la IL-6, la proteina C-reattiva (PCR), la procalcitonina (PCT) e la ferritina sono state determinate al basale. I valori ematici di PCR, PCT, e IL-6 sono stati determinati pre e post-dialisi per ogni seduta effettuata (i valori ottenuti sono stati corretti per ’emoconcentrazione). I pazienti sono stati trattati con emodiafiltrazione online con un filtro ad alto flusso in PMMA o ATA. L’end-point primario è stato valutare l’effetto dei due filtri sulle molecole infiammatorie, in particolare sulla reduction ratio (RR) della IL-6. Risultati Dei 74 pazienti arruolati, 48 sono trati trattati con filtro ATA e 26 con filtro PMMA (420 vs 191 sedute dialitiche). La RR percentuale mediana della IL-6 è risultata maggiore nel gruppo ATA (17,08% IQR -9,0 - 40.0 vs 2,95% IQR -34,63 – 27,32. Anche le RR percentuale di PCR e PCT sono state maggiori nel gruppo ATA. La regressione logistica multipla avente come variabile dipendente una IL-6RR maggiore del 25%, ha mostrato che ATA determinava una maggiore probabilità di raggiungere l’outcome dopo correzione per i parametri infiammatori pre-dialisi (OR 1,721 95% CI 1,176 – 2,538 p=0,0056). Al contrario una PCR elevata riduceva la probabilità di ottenere una IL-6RR significativa (OR 0,9101 95% CI 0,868 – 0,949, p<0.0001). Conclusioni Nella nostra popolazione il filtro ATA ha mostrato un migliore profilo antiinfiammatorio.

Intervista camminata e altri metodi qualitativi per monitorare l'efficacia dei progetti di Educazione Avventura con adolescenti a rischio

INTRODUZIONE - La presente ricerca è incentrata sul monitoraggio dell’efficacia dei progetti di Educazione Avventura con adolescenti difficili, in particolare del progetto “Lunghi cammini educativi”. A partire da un’analisi della letteratura sull’educazione esperienziale nature-based e in particolare sull’Adventure Education con adolescenti difficili, è stata progettata una rilevazione empirica attraverso cui sperimentare un metodo di monitoraggio finalizzato a cogliere la dimensione processuale (che nella ricerca nell’ambito resta spesso inesplorata, poiché sono maggiormente diffusi i metodi di monitoraggio cosiddetti “black-box”), utilizzando un sistema integrato di diverse tecniche di rilevazione. Le due principali domande che hanno guidato la ricerca sono state: 1.Quali processi educativi significativi si innescano e possono essere osservati durante l’esperienza? 2.Il metodo dell’intervista camminata, integrato ad altri metodi, è utile per individuare e monitorare questi processi? METODO - Collocandosi all’interno di un framework metodologico qualitativo (influenzato da riflessioni post-qualitative, paradigma delle mobilità e sguardo fenomenologico), la ricerca prende la forma di uno studio di caso singolo con due unità di analisi, e prevede la triangolazione di diversi metodi di raccolta dei dati: analisi documentale; osservazione partecipante nei cammini e nelle riunioni di équipe; interviste (prima, durante, dopo il cammino) con differenti tecniche: camminata, “image-elicited”, tradizionale, online. RISULTATI - L’analisi tematica abduttiva delle interviste e delle osservazioni conferma quanto già evidenziato dalla letteratura circa la centralità della dilatazione del campo d’esperienza e del lavoro su alcune life skills (in particolare, competenze personali e growth mindset). Emergono anche alcuni key findings inattesi: il notevole “peso” dello stile educativo dell’accompagnatore; la “scoperta” del ruolo della quotidianità all’interno dell’esperienza straordinaria; la necessità di consapevolezza riguardo al potenziale educativo dell’ambiente (naturale e/o antropizzato), per una maggiore intenzionalità nelle scelte strategiche di cammino. L’intervista camminata, nonostante alcuni limiti, si conferma come metodo effettivamente utile a cogliere la dimensione processuale, e coerente con il contesto indagato.

Development of original analytical methods for the therapeutic drug monitoring of CNS druges: Antipsychotics, Antidepressants and Anxiolytics-hypnotics

Great strides have been made in the last few years in the pharmacological treatment of neuropsychiatric disorders, with the introduction into the therapy of several new and more efficient agents, which have improved the quality of life of many patients. Despite these advances, a large percentage of patients is still considered “non-responder” to the therapy, not drawing any benefits from it. Moreover, these patients have a peculiar therapeutic profile, due to the very frequent application of polypharmacy, attempting to obtain satisfactory remission of the multiple aspects of psychiatric syndromes. Therapy is heavily individualised and switching from one therapeutic agent to another is quite frequent. One of the main problems of this situation is the possibility of unwanted or unexpected pharmacological interactions, which can occur both during polypharmacy and during switching. Simultaneous administration of psychiatric drugs can easily lead to interactions if one of the administered compounds influences the metabolism of the others. Impaired CYP450 function due to inhibition of the enzyme is frequent. Other metabolic pathways, such as glucuronidation, can also be influenced. The Therapeutic Drug Monitoring (TDM) of psychotropic drugs is an important tool for treatment personalisation and optimisation. It deals with the determination of parent drugs and metabolites plasma levels, in order to monitor them over time and to compare these findings with clinical data. This allows establishing chemical-clinical correlations (such as those between administered dose and therapeutic and side effects), which are essential to obtain the maximum therapeutic efficacy, while minimising side and toxic effects. It is evident the importance of developing sensitive and selective analytical methods for the determination of the administered drugs and their main metabolites, in order to obtain reliable data that can correctly support clinical decisions. During the three years of Ph.D. program, some analytical methods based on HPLC have been developed, validated and successfully applied to the TDM of psychiatric patients undergoing treatment with drugs belonging to following classes: antipsychotics, antidepressants and anxiolytic-hypnotics. The biological matrices which have been processed were: blood, plasma, serum, saliva, urine, hair and rat brain. Among antipsychotics, both atypical and classical agents have been considered, such as haloperidol, chlorpromazine, clotiapine, loxapine, risperidone (and 9-hydroxyrisperidone), clozapine (as well as N-desmethylclozapine and clozapine N-oxide) and quetiapine. While the need for an accurate TDM of schizophrenic patients is being increasingly recognized by psychiatrists, only in the last few years the same attention is being paid to the TDM of depressed patients. This is leading to the acknowledgment that depression pharmacotherapy can greatly benefit from the accurate application of TDM. For this reason, the research activity has also been focused on first and second-generation antidepressant agents, like triciclic antidepressants, trazodone and m-chlorophenylpiperazine (m-cpp), paroxetine and its three main metabolites, venlafaxine and its active metabolite, and the most recent antidepressant introduced into the market, duloxetine. Among anxiolytics-hypnotics, benzodiazepines are very often involved in the pharmacotherapy of depression for the relief of anxious components; for this reason, it is useful to monitor these drugs, especially in cases of polypharmacy. The results obtained during these three years of Ph.D. program are reliable and the developed HPLC methods are suitable for the qualitative and quantitative determination of CNS drugs in biological fluids for TDM purposes.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction.

In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.