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Study Protocol of a Pilot Trial Evaluating the Efficacy of an Integrated Therapeutic Intervention Based on Role-Playing Games (RPGs) in Adolescents and Young Adults with Anxiety, Depression and Emotional Dysregulation Disorders
No full textAdolescence and early adulthood are critical developmental periods marked by an increasing vulnerability to emotional dysregulation and social difficulties, highlighting the need for engaging psychosocial interventions. This protocol presents a pilot study on the efficacy, feasibility, and acceptability of a structured group intervention based on Role-Playing Games (RPGs), designed to promote and support psychological well-being in transitional-aged youths. The study plans to recruit 54 participants (aged 15–24) who will take part in a 12 weekly, 2 h RPG-based intervention facilitated by trained clinicians. These clinicians will guide patients through narrative role-playing and a guided mentalization-based therapy through the gaming experience. All participants will be assessed at pre-, mid- and post-intervention, as well as during the 1- and 6-month follow-up, in the following dimensions: (a) mood, (b) anxiety, (c) emotional regulation, (d) alexithymia, and (e) coping skills. The following assessment tools will be administered: Hospital Anxiety and Depression Scale (HADS), Difficulties in Emotion Regulation Scale (DERS), Toronto Alexithymia Scale (TAS-20), and Coping Orientation to Problems Experienced (Brief-COPE). We expect the trial pilot will demonstrate good feasibility, greater participant engagement and treatment adherence, and improvements in all emotional and affective dimensions. This study seeks to establish foundational data to inform larger randomized controlled trials, with a follow-up, positioning RPG-based group interventions as potentially accessible, engaging, and convenient tools within youth mental health services
Exploiting Knowledge Graph Communities to Fine-Tune Large Language Models
No full textSince the introduction of GPT-2, Large Language Models (LLMs) have proven to be able to handle various tasks with impressive performance. However, they sometimes generate incorrect output or even hallucinations. To overcome this problem, many researchers have investigated the possibility of integrating external factual knowledge, such as that encoded in Knowledge Graphs (KGs), into LLMs. Although there are many approaches in the existing literature that integrate KGs and LLMs in different ways, few of them use KGs to fine-tune LLMs, and none of them systematically use KG substructures. In this paper, we propose CoFine (Community-Based Fine-Tuner), an approach to fine-tune an LLM using the communities of a KG. CoFine works as follows: it first divides the KG into communities, each of which contains a homogeneous portion of the knowledge expressed by the KG. It then uses these communities to fine-tune the LLM. This way of proceeding allows LLM fine-tuning to focus on specific homogeneous information contained in the KG expressed by each community. CoFine allows the LLM to achieve a very high accuracy in knowledge completion tasks. This is evidenced by comparisons between CoFine and a baseline LLM fine-tuning approach, which showed that our approach achieves better results for all metrics considered with several KG
Numerical modeling of hydrogen-based technologies for the energy system decarbonisation
No full textL’idrogeno è sempre più riconosciuto come elemento chiave per i futuri sistemi energetici a basse emissioni di carbonio, in particolare quando prodotto tramite elettrolisi alimentata da fonti rinnovabili. Nonostante la vasta letteratura dedicata ai modelli per le singole tecnologie elettrochimiche, mancano ancora framework modellistici trasversali, validati sperimentalmente ed applicabili a più tecnologie. Questa tesi ha l’obiettivo di sviluppare una piattaforma di modellazione unificata per la produzione e l’utilizzo di idrogeno verde, integrando campagne sperimentali, modelli empirici e semi-empirici, ottimizzazione numerica e analisi a livello di sistema per le tecnologie alcalina, PEM, AEM, ossido solido e carbonati fusi. L’obiettivo di questa tesi è progettare, calibrare e validare modelli a bassa complessità ma ad alta accuratezza, adatti ad applicazioni reali. Per i sistemi alcalini, i test effettuati tra 25–40 °C e 4,5 bar hanno permesso di calibrare un modello semi-empirico compatto in grado di descrivere gli effetti combinati di temperatura, pressione e concentrazione dell’elettrolita, ottenendo valori dell’RMSE fino a 0,042 V. Per la tecnologia PEM, il modello di Ulleberg è stato adattato per la prima volta a questi sistemi, producendo una nuova formulazione a 10 parametri con RMSE calcolati tra 0,044–0,058 V, seguito poi da un modello a cinque parametri per le membrane di Nafion in grado di catturare gli effetti simultanei di temperatura, umidità relativa e spessore. Per l’elettrolisi AEM, un modello semi-empirico riparametrizzato con soli cinque coefficienti ha raggiunto un RMSE di 0,03 V e un errore medio dell’1,68%, superando diversi modelli presenti in letteratura. Per le celle reversibili a ossidi solidi, il lavoro fornisce la prima analisi sistematica dell’influenza della concentrazione di vapore al fuel electrode sull’affidabilità del modello, evidenziando i limiti delle misure EIS in condizioni di alta umidità. Per le celle reversibili a carbonati fusi, è stato sviluppato uno dei primi modelli di processo in Aspen HYSYS, in grado di riprodurre il funzionamento reversibile MCFC/MCEC con valori di MAPE compresi tra 0,1–0,5%. Infine, i modelli validati sono stati integrati in una microrete residenziale a idrogeno comprendente impianto fotovoltaico, elettrolizzatore PEM, sistema di accumulo di idrogeno e cella a combustibile PEM. I risultati mostrano l’autosufficienza elettrica annuale, con 7.095 Nm3 di idrogeno prodotti, 6.902 Nm3 consumati e copertura del 22% del fabbisogno termico tramite cogenerazione. Nel complesso, questa tesi fornisce una nuova rete di strumenti modellistici validati sperimentalmente ed efficienti dal punto di vista computazionale, idonei all’integrazione in sistemi digitali e framework di ottimizzazione per la progettazione e il controllo dei sistemi energetici a idrogeno di nuova generazione. Hydrogen is increasingly recognised as a key vector for future low-carbon energy systems, especially when produced via renewable-powered electrolysis. Despite numerous modeling studies on individual electrochemical technologies, compre- hensive and experimentally validated cross-technology frameworks remain scarce. This thesis develops a unified modeling platform for green hydrogen production and utilisation, integrating experimental campaigns, empirical and semi-empirical models, numerical optimisation, and system-level analysis across alkaline, PEM, AEM, solid-oxide, and molten-carbonate technologies. The objective is to design, calibrate, and validate low-complexity yet accurate models suitable for real-world applications. For alkaline electrolysers, experiments at 25–40°C and 4.5 bar enabled the calibration of a compact semi-empirical model capturing the combined effects of temperature, pressure, and electrolyte concentration, achieving RMSE down to 0.042 V. For PEM electrolysers, the Ulleberg’s model was generalised to PEM technology for the first time, yielding a 10-parameter formulation with RMSE values of 0.044–0.058 V, complemented by a five-parameter Nafion membrane model capturing temperature, humidity, and thickness effects. For AEM electrolysis, a re-parameterised semi-empirical model with only five coefficients achieved an RMSE of 0.03 V and a mean error of 1.68%, outperforming several literature models. For reversible solid oxide cells, the study provides the first systematic investigation of fuel-side steam content on the model’s reliability, revealing EIS limitations under high-humidity conditions. For molten carbonate reversible cells, one of the first Aspen-based process models was developed, reproducing MCFC/MCEC operation with MAPE values of 0.1–0.5%. Finally, these validated models were integrated into a residential hydrogen microgrid combining PV, PEM electrolyser, hydrogen storage and PEM fuel cell. Results show full yearly electrical self-sufficiency, 7,095 Nm3 of hydrogen produced, 6,902 Nm3 consumed, and 22% thermal coverage through cogeneration. Overall, this thesis provides original, experimentally grounded, and computationally efficient models suitable for digital-twin applications and optimisation of next-generation hydrogen systems.
Correlating Mechanical Properties, Residual Stresses, and Magnetic Response in LPBF-Manufactured Duplex Stainless Steel Graded Lattice Structure
No full textLe strutture reticolari graduate (GLS) realizzate tramite fusione laser a letto di polvere (LPBF) offrono la possibilità di creare componenti leggeri, strutturalmente efficienti e multifunzionali. Gli acciai inossidabili duplex (DSS), grazie alla loro microstruttura bilanciata ferrite–austenite e alla resistenza alla corrosione, sono particolarmente adatti ai settori marino, petrolchimico e della filtrazione, dove è necessaria la coesistenza di requisiti strutturali e magnetici. Tuttavia, la lavorazione LPBF di GLS in DSS rappresenta ancora una grande sfida a causa della limitata comprensione della formazione delle tensioni residue, dell’evoluzione delle fasi, del comportamento meccanico e della risposta magnetica, soprattutto nel caso di trattamenti termici post-processo applicati a strutture architettate.
In questo lavoro viene utilizzato un approccio combinato computazionale–sperimentale per studiare GLS in DSS fabbricati tramite LPBF, considerando aspetti quali producibilità, microstruttura, trattamento termico, stima delle tensioni residue, prestazioni meccaniche e risposta magnetica preliminare con relativo comportamento dei domini. Siemens NX è stato impiegato per simulare il processo e individuare la localizzazione delle tensioni e le regioni soggette a tensioni residue di trazione e compressione all’interno dell’architettura reticolare. Trattamenti termici a diverse temperature sono stati utilizzati per valutarne l’impatto sulla trasformazione microstrutturale, sul bilanciamento ferrite–austenite, sulla diminuzione della densità di dislocazioni e sui fenomeni di recupero.
Le indagini meccaniche e microstrutturali, comprendenti microscopia ottica, SEM e XRD, che illustrano l’evoluzione microstrutturale durante il processo di stampa e dopo il trattamento termico, insieme a microdurezza e nanoindentazione, hanno dimostrato che le GLS realizzate tramite LPBF presentano elevati valori di micro- e nanodurezza, pari a circa 7,7 GPa per la ferrite e 7 GPa per l’austenite. Ciò è dovuto principalmente all’elevata densità di dislocazioni, conseguenza della rapidissima solidificazione. I trattamenti termici riducono la durezza a causa dell’omogeneizzazione microstrutturale e della ricristallizzazione.
È stato sviluppato un metodo di stima delle tensioni residue basato sulla nanoindentazione, stabilendo una correlazione tra il modulo elastico ridotto e le tensioni residue localizzate. I risultati mostrano un buon accordo con le tensioni residue stimate tramite NX, validando così il metodo anche per geometrie reticolari complesse. La risposta magnetica è stata studiata mediante magnetometria a campione vibrante (VSM) e microscopia a forza magnetica (MFM). Le diminuzioni osservate nella magnetizzazione di saturazione e nella coercitività dopo il trattamento sono state attribuite a variazioni nella frazione di ferrite e al raffinamento della microstruttura. Le immagini MFM hanno mostrato che i domini magnetici sono molto sensibili alle tensioni: le regioni in trazione presentavano domini più fini, paralleli e allineati, mentre le regioni in compressione mostravano configurazioni più grossolane e ramificate. Il calcolo della densità delle pareti di dominio ha evidenziato che l’effetto magnetoelastico è direttamente correlato ai gradienti di tensione.
Nel complesso, questo lavoro rappresenta un passo verso una comprensione più completa delle relazioni tra processo LPBF, struttura e proprietà nei GLS in DSS, e propone una tecnica pratica per la mappatura delle tensioni residue tramite nanoindentazione e caratterizzazione dei domini magnetici. I risultati aprono la strada alla progettazione di architetture reticolari in DSS per applicazioni multifunzionali che combinano efficienza strutturale, resistenza alla corrosione e responsività magnetica.Graded lattice structures (GLS) fabricated via laser powder bed fusion (LPBF) provide the possibility of creating lightweight, structurally efficient, and multifunctional components. Duplex stainless steels (DSS), with their microstructure of balanced ferrite–austenite and corrosion resistance, are very suitable in the marine, petrochemical, and filtration industries, where the co-existence of structural and magnetic requirements is necessary. However, LPBF processing of DSS GLS is still a great challenge due to the lack of understanding of residual stress formation, phase evolution, mechanical behavior, and magnetic response, especially in the case of post-processing heat treatments of architected structures. A combined computational–experimental approach is used in this work to study LPBF-fabricated DSS GLS related to the aspects of manufacturability, microstructure, thermal processing, residual stress estimation, mechanical performance, and magnetic preliminary response and domain behavior. Siemens NX was used for process simulations to stress localization and the regions of tensile and compressive residual stresses in the GLS architecture. Heat treatments at different temperatures were used to examine their impact on microstructural transformation, ferrite–austenite balance, decrease of dislocation density, and recovery phenomena. The mechanical and microstructural investigations comprising optical microscopy, SEM, and XRD, which illustrate microstructural evolution during the printing process and after thermal treatment, microhardness, and nanoindentation have demonstrated that GLS fabricated by LPBF show high micro- and nanohardness, which is in the range of 7.7 GPa for ferrite and 7 GPa for austenite. This is mainly due to the high dislocation density, which is a result of very rapid solidification. The thermal treatments lower the hardness due to microstructural homogenization and recrystallization. A method for estimating residual stress based on nanoindentation was conducted by establishing a correlation between the reduced elastic modulus and localized residual stresses. The results display a good agreement with the NX estimated residual stresses; thus, the method demonstrates validation for complex lattice geometries. Magnetic response was studied using vibrating sample magnetometry (VSM) and magnetic force microscopy (MFM). The observed declines in saturation magnetization and coercivity after the treatment were attributed to changes in ferrite fraction and refinement of the microstructure. MFM images demonstrated that magnetic domains are very sensitive to stress and, therefore, tensile regions showed domains that were refined, parallel, and aligned, whereas compressive regions showed coarser, branched configurations. The domain-wall density calculation showed that the magnetoelastic effect has a direct relationship with the stress gradients. This work, in general, is a step towards a comprehensive understanding of the LPBF processing–structure–property relationships in DSS GLS and presents a practical technique for residual stress mapping through nanoindentation and magnetic domain characterization. The results open the way for the design of DSS lattice architectures for multifunctional applications that combine structural efficiency, corrosion resistance, and magnetic responsiveness
From underutilized to innovative application: Exploring consumer acceptance of acidified and fermented sea fennel preserves in Italy
No full textHalophytes, salt-tolerant plants that grow naturally or are partially cultivated in coastal ecosystems—especially in the Mediterranean basin—hold significant potential to enhance dietary diversity and promote sustainable food production. Among them, sea fennel ( Crithmum maritimum L. ), a relatively underutilized marine vegetable, is gaining recognition in the scientific community for its nutritional value and functional food properties. However, to date, very few studies have explored consumers’ preferences and overall acceptance of this plant. Furthermore, its limited consumption and low consumer awareness hinder the development and diffusion of its potential culinary applications. Therefore, this study aims to explore the acceptance of sea fennel among Italian consumers, particularly by comparing acidified and fermented sea fennel preserves. The findings reveal a clear preference for fermented sea fennel, indicating strong consumer acceptance of the traditional fermentation process when applied to this underutilized plant. However, no significant differences were observed across sociodemographic characteristics. Although the study is based on a local convenience sample, these preliminary insights offer valuable guidance for future research and product development. Promoting fermented sea fennel products could help expand their commercial value and gastronomic appeal. Future research should focus on developing strategies to increase consumer familiarity with fermented sea fennel, emphasizing its health benefits and versatile culinary applications
Multi-stakeholder governance for social sustainability: a multi-level analysis of institutional, ethical and relational governance in Italian Social Enterprises.Three studies on the governance tensions of hybrid organisations
No full textSocial enterprises (SEs) have gained increasing prominence over the past three decades as hybrid organisations operating at the intersection of market mechanisms, civic engagement, and public welfare. Their dual identity—combining a social mission with market-based economic activity—creates inherent governance tensions, as SEs must navigate divergent stakeholder expectations, competing institutional logics, and persistent ethical dilemmas. Despite the growth of SE research, existing studies remain fragmented and offer limited insight into how governance systems function across different organisational levels to manage these socio-economic tensions. This dissertation seeks to advance conceptual and empirical understanding of governance in SEs by examining both the internal mechanisms through which decision-making authority, accountability, and ethical reasoning are structured, and the relational processes that shape collaboration in cross-sector partnerships. Focusing on Italian social cooperatives as a paradigmatic form of SE, this dissertation adopts a multi-method, three-study design. Study One provides a structured literature review that maps how SE governance has been theorised and highlights the need for stronger ethical and normative foundations. Study Two addresses this gap by introducing principlism as a conceptual lens for interpreting governance dilemmas, thereby offering a framework to systematise ethical reasoning in multi-stakeholder contexts. Study Three complements these contributions with an empirical case study exploring governance practices in partnerships between a social cooperative and for-profit firms, illustrating how internal and inter-organisational governance mechanisms interact in practice. Taken together, the three studies contribute to a more integrated and ethically attuned understanding of governance in hybrid organisations, offering theoretical and practical insights into how SEs sustain their social mission while engaging in complex market and collaborative environments.
A Tensor PCA to Derive a Spectrum Domain LSTM Architecture for Mild Cognitive Impairment Detection by EEG
No full textThe Electroencephalogram (EEG) is a cost-effective and highly sensitive biomarker for accurate and timely diagnosis of Mild Cognitive Impairment (MCI) and essential for halting the progression of dementia. Due to high dimensionality, non-stationarity and nonlinearity of the EEG signal, as well as the influence of a large number of background waveforms and artifacts, the automatic detection of MCI with this technique is a challenging problem. Among various Deep Learning (DL) techniques that have been proposed during the latest years to address MCI detection from EEG signals, architectures based on Long Short-Term Memory (LSTM) networks, are the best suited to classify sequential data like EEG. However, in order to obtain the best performance with such networks is of paramount importance to reduce the dimensionality of data. The aim of this chapter is to derive an architecture called Spectrum-Domain LSTM (SD-LSTM), in which a tensor PCA transforms pre-processed data in high-dimensional time-domain to data in low-dimensional spectrum-domain. The core of the proposed approach is the development of a closed-form tensor PCA that is able to overcome the limitations of techniques commonly used for dimensionality reduction of tensor data, thus enabling the implementation of a high-performance low-complexity SD-LSTM architecture for EEG signal classification. Several experimental results showing the performance achieved with the SD-LSTM architecture prove the relevance of the proposed approach in solving typical MCI detection problems by EEG signal
SINGLE-CELL RNA ANALYSIS APPLIED TO PANCREATIC CANCER ENABLES THE IDENTIFICATION OF CELL POPULATIONS, MUTATIONS AND CELL-SPECIFIC TRANSCRIPTOMIC SIGNATURES
No full textIl carcinoma duttale pancreatico (PDAC) è tra i tumori più letali, con un tasso di sopravvivenza globale a cinque anni pari al 13%. La sua aggressività è determinata dalla marcata eterogeneità inter- e intra-tumorale, che complica la diagnosi, limita l’efficacia terapeutica e ostacola lo sviluppo di biomarcatori affidabili. Una comprensione più approfondita dei meccanismi molecolari che guidano questa eterogeneità è fondamentale per migliorare la stratificazione dei pazienti e identificare bersagli prognostici e terapeutici. Questa tesi affronta tali sfide attraverso un’analisi integrata di dati genomici, trascrittomici e a singola cellula per caratterizzare l’eterogeneità del PDAC e identificare alterazioni funzionali nei compartimenti tumorali e stromali.
Il primo capitolo analizza le variazioni del numero di copie (CNV), riconosciute come importanti determinanti dell’instabilità genomica e dell’evoluzione tumorale. Le CNV influenzano il dosaggio genico, alterano l’architettura regolatoria e favoriscono oncogenesi e resistenza terapeutica. Una revisione sistematica della letteratura ne descrive l’origine, l’impatto biologico e la rilevanza clinica nel PDAC, evidenziando sia eventi somatici sia germinali. L’analisi mostra come le CNV contribuiscano all’eterogeneità tumorale e al valore prognostico, sottolineando la necessità di tecniche di rilevazione più sensibili, in particolare approcci single-cell, e di studi su coorti più ampie.
Il secondo capitolo valuta quattro strumenti computazionali (sciCNV, InferCNV, CopyKAT e SCEVAN) per l’identificazione delle cellule tumorali da dati scRNA-seq mediante inferenza di CNV. Utilizzando dataset provenienti da tumori PDAC, tessuto adiacente e pancreas sano, è emersa una forte variabilità nelle prestazioni: InferCNV ha mostrato la sensibilità più alta (0,72) e SCEVAN la specificità più elevata (0,75). Tuttavia, la concordanza tra strumenti era limitata (<30%) e frequenti erano i falsi positivi. Ciò dimostra che l’identificazione tumorale basata esclusivamente sulle CNV non è affidabile e richiede conferma tramite biomarcatori noti del PDAC.
Il terzo capitolo affronta la mancanza di un archivio centralizzato di firme trascrittomiche del PDAC con valore clinico. Una revisione sistematica di 399 pubblicazioni ha identificato 732 firme geniche, singole o multigeniche, legate a progressione, immunomodulazione e risposta terapeutica. Queste firme sono state integrate in PanSCOPE, un database consultabile che permette di esplorare firme, parametri clinici associati e confronti con dataset bulk o single-cell. PanSCOPE supporta la scoperta di biomarcatori, la stratificazione dei pazienti e l’identificazione di sottopopolazioni tumorali rilevanti.
Il quarto capitolo utilizza dati scRNA-seq per caratterizzare le varianti a singolo nucleotide (SNV) in sei pazienti con PDAC, collegando le mutazioni ai loro specifici contesti cellulari. Sono state identificate circa 4.000 SNV, tra cui 77 arricchite nel comparto tumorale e 114 arricchite nel comparto non tumorale. La prioritizzazione funzionale, ottenuta mediante diversi strumenti predittivi, ha evidenziato varianti potenzialmente in grado di influenzare la struttura proteica, lo splicing dell’RNA o la regolazione post-trascrizionale. I risultati rivelano pattern specifici per tipo cellulare, diversità subclonale e adattamenti associati al microambiente.
Nel complesso, questa tesi fornisce un approccio integrato per lo studio dell’eterogeneità del PDAC, combinando l’analisi delle CNVs, il confronto di strumenti computazionali, risorse trascrittomiche curate e analisi single-cell delle mutazioni. I metodi e le risorse sviluppati, in particolare PanSCOPE, costituiscono un supporto utile per l’identificazione di biomarcatori, la stratificazione dei pazienti e lo sviluppo di strategie di oncologia di precisione.Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers, with a five-year overall survival rate of 13%. Its aggressiveness is driven by marked inter- and intra-tumoral heterogeneity, which complicates diagnosis, limits therapeutic efficacy, and hinders biomarker development. A deeper understanding of the molecular processes shaping this heterogeneity is essential for improving patient stratification and identifying clinically relevant targets. This thesis addresses these challenges through an integrated analysis of genomic, transcriptomic, and single-cell data to characterize PDAC heterogeneity and uncover functional alterations across tumor and stromal compartments.
The first chapter examines copy number variations (CNVs), recognized contributors to genomic instability and tumor evolution. CNVs influence gene dosage, disrupt regulatory architecture, and promote oncogenesis and therapy resistance. A comprehensive literature review outlines their origins, biological impact, and clinical significance in PDAC, highlighting both somatic and germline events. The analysis emphasizes how CNVs contribute to tumor diversity, affect prognosis, and may guide patient stratification. It also identifies gaps in current knowledge and the need for more sensitive detection technologies, particularly single-cell approaches, and larger cohorts to fully define CNV-driven mechanisms in PDAC.
The second chapter benchmarks four computational tools (sciCNV, InferCNV, CopyKAT, and SCEVAN) for identifying tumor cells from single-cell RNA sequencing (scRNA-seq) data based on CNV inference. Using scRNA-seq datasets from PDAC tumors, adjacent tissue, and healthy pancreas, substantial variability in performance was observed: InferCNV showed the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). However, overlap between tools was limited (<30%), and false positives were frequent. These results show that CNV-based tumor cell calling is unreliable when used alone and must be complemented with known PDAC biomarkers. The findings also highlight the need for more robust computational strategies for tumor cell identification.
The third chapter addresses the lack of a centralized, clinically informative repository of PDAC transcriptomic signatures. A systematic review of 399 publications identified 732 single-gene and multi-gene signatures linked to tumor progression, immune modulation, and therapy response. These signatures were integrated into PanSCOPE, a searchable database enabling exploration of gene or signature-level information, associated clinical parameters, and compatibility with bulk or single-cell transcriptomic datasets. PanSCOPE supports biomarker discovery, patient stratification, and the study of tumor subpopulations that may influence disease trajectory or treatment outcomes.
The fourth chapter uses scRNA-seq to characterize single-nucleotide variants (SNVs) in six PDAC patients, linking mutations to specific cellular contexts. Approximately 4,000 SNVs were detected, including 77 tumor-enriched and 114 non-tumor-enriched variants. Functional prioritization using multiple predictive tools identified variants potentially affecting protein structure, RNA splicing, or post-transcriptional regulation. The results reveal cell-type-specific mutational patterns, subclonal diversity, and microenvironment-associated adaptations.
Overall, this thesis provides a multi-layered framework for understanding PDAC heterogeneity, integrating CNV profiling, computational benchmarking, curated transcriptomic resources, and single-cell mutational analysis. The methods and tools developed, particularly PanSCOPE, offer valuable resources for biomarker discovery, patient stratification, and precision oncology
A strong version of the Hilbert Nullstellensatz for slice regular polynomials in several quaternionic variables
No full textIn this paper we prove a strong version of the Hilbert Nullstellensatz in the ring H[q1,...,qn] of slice regular polynomials in several quaternionic variables. Our proof deeply depends on a detailed analysis of the common zeros of slice regular polynomials which belong to an ideal in H[q1,...,qn]. This study motivates the introduction of a new notion of algebraic set in the quaternionic setting, which allows us to define a Zariski-type topology on Hn