Efficacia e complicanze durante la curva di apprendimento della Thulep: risultati a breve e medio termine delle prime 48 procedure

MATERIALI E METODI: Tra il 2012 e il 2013, abbiamo analizzato in uno studio prospettico i dati di 48 pazienti sottoposti a ThuLEP con approccio autodidatta. I pazienti sono stati rivalutati a 3, 6, 12 e 24 mesi con la valutazione del PSA, il residuo post-minzionale (RPM), l'uroflussometria (Qmax), l'ecografia transrettale e questionari validati (IPSS: international prostate symptom score e QoL: quality of life) RISULTATI: Il volume medio della prostata è di 63 ± 5,3 ml. Il tempo operatorio medio è stato di 127,58 ± 28.50 minuti. Il peso medio del tessuto asportato è stato di 30,40 ± 13,90 gr. A 6 mesi dopo l'intervento l'RPM medio è diminuito da 165,13 ± 80,15 ml a 7,78 ± 29.19 ml, mentre il Qmax medio è aumentato da 5.75 ± 1.67ml / s a 18.1 ± 5.27 ml / s. I valori medi dei IPSS e QoL hanno dimostrato un progressivo miglioramento: da 19.15 (IQR: 2-31) e 4 (IQR: 1-6) nel preoperatorio a 6.04 (IQR: 1-20) e 1.13 (IQR: 1-4), rispettivamente. Durante la curva di apprendimento si è assistito ad un progressivo aumento del peso del tessuto enucleato e ad una progressiva riduzione del tempo di ospedalizzazione e di cateterismo. Tra le principali complicanze ricordiamo un tasso di incontinenza transitoria del 12,5% a 3 mesi e del 2.1% a 12 mesi. CONCLUSIONI: ThuLEP rappresenta una tecnica chirurgica efficace, sicura e riproducibile indipendentemente dalle dimensioni della prostata. I nostri dati suggeriscono che la ThuLEP offre un miglioramento significativo dei parametri funzionali comparabili con le tecniche tradizionali, nonostante una lunga curva di apprendimento.

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

Appartenenze, identità e ruoli di genere negli adolescenti di origine immigrata. Uno studio qualitativo nel territorio di Monza e della Brianza

Starting from the contexts on which the researches about migrant minors and adolescents have been concentrated so far, school, free time, friends, family, society integration, this work puts attention on gender dimension, supporting the ideas that socialization is a life-long process, that gender and gender roles are a cultural construction and the subject has multiple identities. The research aim to understand if being male or female, related with ethnic and cultural origin, influences the identity construction, the gender belonging and roles, the behaviours, in a different way, in interaction with the different everyday contexts. The research points out how being male or female affects: - daily choices, expectations and behaviours inside peer group, family and school; - future expectations as adult inside family, work and society; - idea about the adolescence and the self-decription as adolescent, female, male and immigrant. The analysis highlights that the gender belonging, as the ethnic and cultural belonging, doesn’t drive behaviours, attitudes, expectations totally to tradition or totally to “western way”, in the different everyday contexts. There is rather a combination of these ways, choosing the one or the other way in the different contexts according to be in a position in which there are more or less contacts with the society they live in. Differently, the self perception as adolescent and as individual is relatively independent from gender and ethniccultural belonging, over which prevail the idea of “ peer normality”. Above all, it is important to put in evidence that they are experiencing a very high level of complexity and change as adolescent and migrant or migrant’ son. Personal, cultural and social transitions can explain a large part of variability and our difficulty to construct high defined classifications.

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators.

Development of unsupervised learning methods with applications to life sciences data

Machine Learning makes computers capable of performing tasks typically requiring human intelligence. A domain where it is having a considerable impact is the life sciences, allowing to devise new biological analysis protocols, develop patients’ treatments efficiently and faster, and reduce healthcare costs. This Thesis work presents new Machine Learning methods and pipelines for the life sciences focusing on the unsupervised field. At a methodological level, two methods are presented. The first is an “Ab Initio Local Principal Path” and it is a revised and improved version of a pre-existing algorithm in the manifold learning realm. The second contribution is an improvement over the Import Vector Domain Description (one-class learning) through the Kullback-Leibler divergence. It hybridizes kernel methods to Deep Learning obtaining a scalable solution, an improved probabilistic model, and state-of-the-art performances. Both methods are tested through several experiments, with a central focus on their relevance in life sciences. Results show that they improve the performances achieved by their previous versions. At the applicative level, two pipelines are presented. The first one is for the analysis of RNA-Seq datasets, both transcriptomic and single-cell data, and is aimed at identifying genes that may be involved in biological processes (e.g., the transition of tissues from normal to cancer). In this project, an R package is released on CRAN to make the pipeline accessible to the bioinformatic Community through high-level APIs. The second pipeline is in the drug discovery domain and is useful for identifying druggable pockets, namely regions of a protein with a high probability of accepting a small molecule (a drug). Both these pipelines achieve remarkable results. Lastly, a detour application is developed to identify the strengths/limitations of the “Principal Path” algorithm by analyzing Convolutional Neural Networks induced vector spaces. This application is conducted in the music and visual arts domains.

Rich media content adaptation in e-learning systems

The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become a key factor in enabling people with different access needs to enjoy quality learning experiences and services. Another e-learning challenge is represented by m-learning (which stands for mobile learning), which is emerging as a consequence of mobile terminals diffusion and provides the opportunity to browse didactical materials everywhere, outside places that are traditionally devoted to education. Both such situations share the need to access materials in limited conditions and collide with the growing use of rich media in didactical contents, which are designed to be enjoyed without any restriction. Nowadays, Web-based teaching makes great use of multimedia technologies, ranging from Flash animations to prerecorded video-lectures. Rich media in e-learning can offer significant potential in enhancing the learning environment, through helping to increase access to education, enhance the learning experience and support multiple learning styles. Moreover, they can often be used to improve the structure of Web-based courses. These highly variegated and structured contents may significantly improve the quality and the effectiveness of educational activities for learners. For example, rich media contents allow us to describe complex concepts and process flows. Audio and video elements may be utilized to add a “human touch” to distance-learning courses. Finally, real lectures may be recorded and distributed to integrate or enrich on line materials. A confirmation of the advantages of these approaches can be seen in the exponential growth of video-lecture availability on the net, due to the ease of recording and delivering activities which take place in a traditional classroom. Furthermore, the wide use of assistive technologies for learners with disabilities injects new life into e-learning systems. E-learning allows distance and flexible educational activities, thus helping disabled learners to access resources which would otherwise present significant barriers for them. For instance, students with visual impairments have difficulties in reading traditional visual materials, deaf learners have trouble in following traditional (spoken) lectures, people with motion disabilities have problems in attending on-site programs. As already mentioned, the use of wireless technologies and pervasive computing may really enhance the educational learner experience by offering mobile e-learning services that can be accessed by handheld devices. This new paradigm of educational content distribution maximizes the benefits for learners since it enables users to overcome constraints imposed by the surrounding environment. While certainly helpful for users without disabilities, we believe that the use of newmobile technologies may also become a fundamental tool for impaired learners, since it frees them from sitting in front of a PC. In this way, educational activities can be enjoyed by all the users, without hindrance, thus increasing the social inclusion of non-typical learners. While the provision of fully accessible and portable video-lectures may be extremely useful for students, it is widely recognized that structuring and managing rich media contents for mobile learning services are complex and expensive tasks. Indeed, major difficulties originate from the basic need to provide a textual equivalent for each media resource composing a rich media Learning Object (LO). Moreover, tests need to be carried out to establish whether a given LO is fully accessible to all kinds of learners. Unfortunately, both these tasks are truly time-consuming processes, depending on the type of contents the teacher is writing and on the authoring tool he/she is using. Due to these difficulties, online LOs are often distributed as partially accessible or totally inaccessible content. Bearing this in mind, this thesis aims to discuss the key issues of a system we have developed to deliver accessible, customized or nomadic learning experiences to learners with different access needs and skills. To reduce the risk of excluding users with particular access capabilities, our system exploits Learning Objects (LOs) which are dynamically adapted and transcoded based on the specific needs of non-typical users and on the barriers that they can encounter in the environment. The basic idea is to dynamically adapt contents, by selecting them from a set of media resources packaged in SCORM-compliant LOs and stored in a self-adapting format. The system schedules and orchestrates a set of transcoding processes based on specific learner needs, so as to produce a customized LO that can be fully enjoyed by any (impaired or mobile) student.

The secondary market for life insurance policies in the United States market evolution and product valuation

In this work we discuss the secondary market for life insurance policies in the United States of America. First, we give an overview of the life settlement market: how it came into existence, its growth prospects and the ethical issues it arises. Secondly, we discuss the characteristics of the different life insurance products present in the market and describe how life settlements are originated. Life settlement transactions tend to be long and complex transactions that require the involvement of a number of parties. Also, a direct investment into life insurance policies is fraught with a number of practical issues and entails risks that are not directly related to longevity. This may reduce the efficiency of a direct investment in physical policies. For these reasons, a synthetic longevity market has evolved. The number of parties involved in a synthetic longevity transaction is typically smaller and the broker-dealer transferring the longevity exposure will be retaining most or all of the risks a physical investment entails. Finally, we describe the main methods used in the market to evaluate life settlement investments and the role of life expectancy providers.

Asymmetric aminocatalysis: a modern strategy for molecules, challenges and life

The studies conducted during my Phd thesis were focused on two different directions: 1. In one case we tried to face some long standing problems of the asymmetric aminocatalysis as the activation of encumbered carbonyl compounds and the control of the diastereoisomeric ratio in the diastero- and enantioselective construction of all carbon substituted quaternary stereocenters adjacent a tertiary one. In this section (Challenges) was described the asymmetric aziridination of ,-unsaturated ketones, the activation of ,-unsaturated -branched aldehydes and the Michael addition of oxindoles to enals and enones. For the activation via iminium ion formation of sterically demanding substrates, as ,-unsaturated ketones and ,-unsaturated -branched aldehydes, we exploited a chiral primary amine in order to overcome the problem of the iminium ion formation between the catalyst and encumbered carbonylic componds. For the control of diastereoisomeric ratio in the diastero- and enantioselective construction of all carbon substituted quaternary stereocenters adjacent a tertiary one we envisaged that a suitable strategy was the Michael addition to 3 substituted oxindoles to enals activated via LUMO-lowering catalysis. In this synthetic protocol we designed a new bifunctional catalyst with an amine moiety for activate the aldehyde and a tioureidic fragment for direct the approach of the oxindole. This part of the thesis (Challenges) could be considered pure basic research, where the solution of the synthetic problem was the goal itself of the research. 2. In the other hand (Molecules) we applied our knowledge about the carbonylic compounds activation and about cascade reaction to the synthesis of three new classes of spirooxindole in enantiopure form. The construction of libraries of these bioactive compounds represented a scientific bridge between medicinal chemistry or biology and the asymmetric catalysis.

Applicazione della Metodologia LCA(Life Cycle Assesment): La sostenibilità ambientale di Impianti a Biomassa della regione Emilia Romagna

Lo studio che la candidata ha elaborato nel progetto del Dottorato di ricerca si inserisce nel complesso percorso di soluzione del problema energetico che coinvolge necessariamente diverse variabili: economiche, tecniche, politiche e sociali L’obiettivo è di esprimere una valutazione in merito alla concreta “convenienza” dello sfruttamento delle risorse rinnovabili. Il percorso scelto è stato quello di analizzare alcuni impianti di sfruttamento, studiare il loro impatto sull’ambiente ed infine metterli a confronto. Questo ha consentito di trovare elementi oggettivi da poter valutare. In particolare la candidata ha approfondito il tema dello sfruttamento delle risorse “biomasse” analizzando nel dettaglio alcuni impianti in essere nel Territorio della Regione Emilia-Romagna: impianti a micro filiera, filiera corta e filiera lunga. Con la collaborazione di Arpa Emilia-Romagna, Centro CISA e dell’Associazione Prof. Ciancabilla, è stata fatta una scelta degli impianti da analizzare: a micro filiera: impianto a cippato di Castel d’Aiano, a filiera corta: impianto a biogas da biomassa agricola “Mengoli” di Castenaso, a filiera lunga: impianto a biomasse solide “Tampieri Energie” di Faenza. Per quanto riguarda la metodologia di studio utilizzata è stato effettuato uno studio di Life Cycle Assesment (LCA) considerando il ciclo di vita degli impianti. Tramite l’utilizzo del software “SimaPro 6.0” si sono ottenuti i risultati relativi alle categorie di impatto degli impianti considerando i metodi “Eco Indicator 99” ed “Edip Umip 96”. Il confronto fra i risultati dell’analisi dei diversi impianti non ha portato a conclusioni di carattere generale, ma ad approfondite valutazioni specifiche per ogni impianto analizzato, considerata la molteplicità delle variabili di ogni realtà, sia per quanto riguarda la dimensione/scala (microfiliera, filiera corta e filiera lunga) che per quanto riguarda le biomasse utilizzate.

L'utilizzo dei casi di studio per favorire l'apprendimento di metodologie e strumenti per lo sviluppo organizzativo o di percorsi di cura nelle aziende sanitarie

La formazione, in ambito sanitario, è considerata una grande leva di orientamento dei comportamenti, ma la metodologia tradizionale di formazione frontale non è la più efficace, in particolare nella formazione continua o “long-life education”. L’obiettivo primario della tesi è verificare se l’utilizzo della metodologia dello “studio di caso”, di norma utilizzata nella ricerca empirica, può favorire, nel personale sanitario, l’apprendimento di metodi e strumenti di tipo organizzativo-gestionale, partendo dalla descrizione di processi, decisioni, risultati conseguiti in contesti reali. Sono stati progettati e realizzati 4 studi di caso con metodologia descrittiva, tre nell’Azienda USL di Piacenza e uno nell’Azienda USL di Bologna, con oggetti di studio differenti: la continuità di cura in una coorte di pazienti con stroke e l’utilizzo di strumenti di monitoraggio delle condizioni di autonomia; l’adozione di un approccio “patient-centred” nella presa in carico domiciliare di una persona con BPCO e il suo caregiver; la percezione che caregiver e Medici di Medicina Generale o altri professionisti hanno della rete aziendale Demenze e Alzheimer; la ricaduta della formazione di Pediatri di Libera Scelta sull’attività clinica. I casi di studio sono stati corredati da note di indirizzo per i docenti e sono stati sottoposti a quattro referee per la valutazione dei contenuti e della metodologia. Il secondo caso è stato somministrato a 130 professionisti sanitari all’interno di percorso di valutazione delle competenze e dei potenziali realizzato nell’AUSL di Bologna. I referee hanno commentato i casi e gli strumenti di lettura organizzativa, sottolineando la fruibilità, approvando la metodologia utilizzata, la coniugazione tra ambiti clinico-assistenziali e organizzativi, e le teaching note. Alla fine di ogni caso è presente la valutazione di ogni referee.