Unraveling Novel Genetic Determinants of Thiopurine Response Via TWAS

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Thiopurines such as 6-mercaptopurine (6MP) are essential in ALL maintenance therapy. However, dose-limiting toxicities can significantly disrupt treatment. While genetic variants in TPMT and NUDT15 are known to affect thiopurine response, many patients with normal function genotypes in these genes still experience adverse effects, suggesting that additional genes might be involved. We analyzed 663 pediatric ALL patients enrolled in the AALL03N1 trial to identify novel genetic determinants of 6MP sensitivity, focusing on individuals with normal function TPMT and NUDT15 genotypes. A transcriptome-wide association study (TWAS) was conducted to focus on expression quantitative trait loci (eQTLs). Findings were validated in two independent cohorts: St. Jude Total Therapy XV (n = 390) and XVI (n = 552). TWAS identified 31 genes associated with 6MP dose intensity (q-value < 0.90). Of these, the imputed GNAQ expression was positively correlated with 6MP dose intensity and passed multiple testing thresholds in the validation cohorts. The rs60561071 variant, the eQTL in the GNAQ TWAS model, was associated with reduced gene expression and lower 6MP dose intensity. This study identifies GNAQ as a novel gene associated with thiopurine tolerance in ALL patients lacking known risk alleles in TPMT and NUDT15. Moreover, this research highlighted the innovative use of TWAS, providing deeper insights into the molecular mechanisms that explain drug response variability

Weak Lefschetz property of equigenerated complete intersections: Applications

In this paper, we prove that any Artinian complete intersection homogeneous ideal I in K[x0,··· , xn] generated by n + 1 forms of degree d ≥ 2 satisfies the weak Lefschetz property (WLP) in degree t < d + ⌈d/n ⌉. As a consequence, we get that the Jacobian ideal of a smooth 3-fold of degree d ≥ 6 in P4 satisfies the weak Lefschetz property in degree d, answering a recent question of Beauville [Hyperplane sections of cubic threefolds, Proc. Amer. Math. Soc. 153 (2025), no. 12, 5167–5170]

Upper Semicontinuous Representations of Semiorders as Interval Orders

We characterize the upper semicontinuous representability of a semiorder $\prec$ as an interval order (namely, by a pair $(u,v)$ of upper semicontinuous real-valued functions) on a topological space with a countable basis of open sets, where one of the representing functions is a one-way utility for the characteristic weak order $\prec^0$ associated to the semiorder. Such a description generalizes the {\em upper semicontinuous threshold representation}. To this aim, we introduce a suitable upper semicontinuity condition concerning a semiorder, namely {\em strict upper semicontinuity}. We further characterize the mere existence of an upper semicontinuous one-way utility for this characteristic weak order, with a view to the identification of maximal elements on compact metric spaces

Stated car choices in Norway and Italy: a comparison based on the integrated choice and latent variable model

The study investigates whether the large difference in battery electric vehicle (BEV) uptake between Norway and Italy could be explained by differences in car buyers’ preference structures, either in terms of their evaluation of the vehicles’ characteristics or in terms of their perceptions\attitudes towards BEVs. Based on stated preference data collected in the two countries, we find that car drivers evaluate vehicle attributes very similarly. Norwegians value BEV driving range slightly more and are more sensitive to fuel\electricity costs. Ceteris paribus, Italian respondents, in contrast to Norwegian ones, still prefer petrol cars to BEVs. The results of the integrated choice and latent variable (ICLV) model indicate that respondents’ perceptions\attitudes influence car choice in both countries. In Norway, BEVs are preferred by those who view them as economically, environmentally, technically, and morally superior. In Italy, the evidence is similar but for the environmental aspects, which are not decisive for BEV choice. Such perceptions\attitudes are correlated with age, sex, and BEV density

Appello

Una disamina completa della disciplina dell'appello penale, svolta in chiave diacronica, tenendo conto delle frequenti e significative riforme legislative e dei rilevanti contributi giurisprudenziali in materi

From Pipeline Optimization To Problem-oriented Automl: Advancing Clustering Automation

Automated Machine Learning (AutoML) aims to lower the entry barrier of machine learning by automating the design of pipelines, including the selection of techniques, algorithms and their parameters. While substantial progress has been made in supervised learning, unsupervised learning remains challenging due to the absence of universal goals such as accuracy. In this context, meta-learning plays a crucial role by leveraging prior knowledge to recommend algorithms or configurations based on dataset characteristics. Yet clustering is inherently subjective: success often depends on user goals. Since Au- toML’s mission is to place the user at the center, this thesis explores how AutoML and meta-learning can be unified to automatically provide users with problem-oriented clus- tering pipelines. We first investigate pipeline synthesis by extending evolutionary optimisation meth- ods from supervised learning to clustering. Benchmarking across diverse datasets shows that optimising for individual clustering validity indices or their ensembles is insufficient. These results motivate the use of meta-objectives and surrogate models to flexibly guide search in alignment with user intent. Next, we study what is required to build robust meta-spaces and meta-objectives. Through a systematic review of AutoClustering literature, we propose a taxonomy of datasets and meta-features, analyse their influence, and show how meta-models can be simplified without substantial performance loss. Finally, we integrate these insights into the Problem-oriented AutoML in Clustering (PoAC) framework, which aligns meta-features, objectives, and optimisation strategies with problem-specific requirements, enabling adaptive, algorithm-agnostic clustering au- tomation.Automated Machine Learning (AutoML) aims to lower the entry barrier of machine learning by automating the design of pipelines, including the selection of techniques, algorithms and their parameters. While substantial progress has been made in supervised learning, unsupervised learning remains challenging due to the absence of universal goals such as accuracy. In this context, meta-learning plays a crucial role by leveraging prior knowledge to recommend algorithms or configurations based on dataset characteristics. Yet clustering is inherently subjective: success often depends on user goals. Since Au- toML’s mission is to place the user at the center, this thesis explores how AutoML and meta-learning can be unified to automatically provide users with problem-oriented clus- tering pipelines. We first investigate pipeline synthesis by extending evolutionary optimisation meth- ods from supervised learning to clustering. Benchmarking across diverse datasets shows that optimising for individual clustering validity indices or their ensembles is insufficient. These results motivate the use of meta-objectives and surrogate models to flexibly guide search in alignment with user intent. Next, we study what is required to build robust meta-spaces and meta-objectives. Through a systematic review of AutoClustering literature, we propose a taxonomy of datasets and meta-features, analyse their influence, and show how meta-models can be simplified without substantial performance loss. Finally, we integrate these insights into the Problem-oriented AutoML in Clustering (PoAC) framework, which aligns meta-features, objectives, and optimisation strategies with problem-specific requirements, enabling adaptive, algorithm-agnostic clustering au- tomation

Enhancing risk-based engineering design: a hybrid fuzzy failure analysis with empirical validation

Introduction: Precise risk-based design is essential for accurately identifying and assessing threats, improving reliability, and ensuring the overall safety of safetycritical systems. Failure Mode and Effect Analysis (FMEA) is a widely employed technique for the evaluation of risk of components, systems, services, and processes. To address subjectivity and ambiguity in decision-makers’ judgments in traditional FMEA, several methodological improvements have been proposed; however, a state-of-the-art review shows that several research avenues are still open in this domain. Reducing the variation in priority ranking within failure analysis remains a mostly underexplored area. This significant gap serves as the main motivation for investigating whether the synergy between different aggregation methods and normalization techniques, when combined with a fuzzy reference-based approach, can effectively decrease the distinct rankings. Methodology: This study proposes an improved FMEA methodology that combines the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), Fuzzy Elimination Et Choix Traduisant la REalité (Fuzzy ELECTRE III), and Entropy methods to derive a logical ranking of FMEA failure modes, thereby enhancing the effectiveness of FMEA. The proposed approach employs linguistic variables to set S, O, and D weights, FMEA using the Entropy and Fuzzy AHP methods, integrates these weights using Fuzzy ELECTRE III, and finally analyzes the priority of the options. To validate the practical applicability of the proposed framework, a real-world case study on a safety-critical machine component, the clutch system, which is a suitable case for risk-based engineering design, is conducted. Results and discussion: The results are compared with those obtained by the integration of TOPSIS and VIKOR with FMEA, showing that the proposed method provides fewer priority rankings while delivering more effective results. Such clustering provides a more realistic representation of risk, acknowledging that minor distinctions between failure modes are often statistically insignificant. This ensures that resources are not diverted to minor issues at the expense of catastrophic but rare failure modes

Necessity causality in mental health research: Applying necessary condition analysis in clinical psychology and psychiatry

Understanding the causal mechanisms underlying the development of mental disorders and their symptoms is essential for advancing effective prevention and treatment strategies. However, research in this field has predominantly relied on sufficiency logic within a probabilistic framework, coupled with traditional statistical methods (i.e., multiple linear regression, Structural Equation Modelling, etc.) where risk factors are associated with an increased likelihood of developing a disorder. While valuable, this approach also carries inherent assumptions and limitations. Additionally, the crucial concept of causal necessity has been largely overlooked. By integrating necessity logic within a deterministic framework—where the absence of a necessary risk factor prevents the development of a disorder in nearly everyone— we propose a novel and promising approach, exemplified by Necessary Condition Analysis (NCA). In this paper, we outline the theoretical foundations of NCA and illustrate its potential for advancing mental health research, with a specific application to the Interpersonal Theory of Suicide. We also discuss how NCA can address critical challenges in mental health science, refine existing methodologies, and open new pathways for enhancing both research and clinical practice

Revealing acid-induced enamel damage: Deep UV Raman and multivariate analysis of hydroxyapatite loss

Deep ultraviolet (DUV) Raman spectroscopy was employed to investigate molecular alterations in human enamel after orthophosphoric acid etching. This etching process served as an in vitro model for hydroxyapatite loss and surface conditioning. By leveraging resonance enhancement and effective fluorescence suppression, the technique enabled the acquisition of high-quality spectra directly from intact tooth surfaces. Spectral analysis revealed a consistent reduction in the phosphate band (∼950 cm-1) and a relative increase in the protein-associated amide band (∼1620 cm-1) in treated regions, resulting in a decreased mineral-to-organic ratio (I950/I1620). These changes are indicative of localized hydroxyapatite demineralization and exposure of the organic matrix. The study underscores the importance of intra-tooth comparisons to minimize inter-sample variability and accurately characterize localized chemical changes. Partial Least Squares Discriminant Analysis (PLS-DA) was employed as chemometric tool to highlight the most discriminant spectral regions and guide band selection for ratio metrics, paving the way for a future formal validation of predictive performance. These findings support DUV Raman spectroscopy as a non-destructive, label-free approach for sensitive assessment of early structural changes in dental enamel, with potential implications for diagnostic and restorative dentistry

Sustainable multi-objective optimization problem for environmental impact and electricity production analysis of dams

In this paper, we develop a multi-objective optimization framework that employs a variant of the Multi-Objective Particle Swarm Optimizer (MOPSO) to balance the competing objectives represented by the total electricity generation and the reduction of carbon emissions, due to the construction of dams. This challenge highlights two conflicting aspects in the context of climate change and sustainability: on the one hand, hydropower represents a renewable energy source; on the other hand, the construction of dams leads to large greenhouse gas emissions. We analyse a dataset of 509 dams in the Amazon basin, categorized by geographical and technical features, to assess the impact of site selection. We further inspect the key features of dams that compose the best configurations to maximize energy output while minimizing emissions. The results show that, in such configurations, the most efficient dams are located in the upland zones of Peru, while inefficient dams are located on Brazilian territory, whose geographic conformation allows the construction of only downland dams that have a greater environmental impact due to a larger reservoir to satisfy a determined energy need. Finally, it seems that some existing dams are completely inefficient from the optimization viewpoint