Barcelona Graduate School of Mathematics

Schizophrenia and autism share neurobiological mechanisms and overlapping clinical features, often resulting in the emergence of autistic traits in early stages of psychosis. The PANSS Autism Severity Score (PAUSS) provides a rapid measure of autistic features within the standard PANSS assessment. We aimed to determine the prevalence of autistic features in first-episode psychosis (FEP), characterise their clinical, cognitive, and functional profile, and examine their impact on 2-year outcomes. A total of 328 FEP patients were included from the PEPs multicentre cohort, followed for 2 years. Autistic features were rated using PAUSS (cut-off ≥ 30), yielding autistic (n = 38) and non-autistic (n = 290) groups. Sociodemographic, clinical, cognitive, and functional variables were analysed. Longitudinal analyses examined symptomatic remission rates and trajectories of psychopathology and functioning using logistic regression and mixed-model ANOVA. The autistic group represented 11.6% of the sample. At baseline, they exhibited lower birth weight, greater medication side effects, higher general psychopathology and depressive severity, and poorer global functioning. Cognitively, they showed significant deficits in working memory, social cognition, and cognitive reserve compared to the non-autistic group. Over 2 years, this group was 3.6 times less likely to achieve symptomatic remission and consistently exhibited higher symptom severity and lower functioning across all follow-ups. Autistic features in FEP identify a subgroup with a possible distinct profile of neurodevelopmental markers, greater cognitive and functional impairments, and poorer clinical outcomes. Early identification may guide more personalised interventions, although further research is needed to refine PAUSS specificity and develop targeted, tailored treatments.

Purpose The decommissioning of flexible pipelines in the offshore oil and gas industry presents both environmental challenges and opportunities for resource recovery. This study proposes a Sustainable Pipeline Upcycling Framework that integrates circular economy principles and additive manufacturing (AM) techniques to transform decommissioned flexible pipes into valuable industrial components. Design/methodology/approach The research follows a multi-phase methodological approach, including Focus Group Interviews, Fuzzy Delphi, and Fuzzy DEMATEL, to systematically identify, prioritize, and map causal relationships among barriers and drivers influencing framework adoption. Findings Key barriers identified include limitations in AM scalability, challenges in material qualification, and high initial investment requirements. On the other hand, enabling drivers such as advancements in 3D printing, sustainability policies, and cost reductions through on-demand production were found to exert strong causal influence over these barriers. Originality/value The findings offer strategic insights for managers and policymakers seeking to implement sustainable decommissioning practices while enhancing operational efficiency and environmental performance in both emerging and developed economies.

To compare laboratory production time, clinical adjustment time, and patient-reported comfort of three occlusal splint fabrication techniques (heat-cured acrylic, vacuum-adapted acrylic, and CAD-CAM 3D-printed splints) in a pilot feasibility study. Three participants each received three splints, one fabricated with each technique. Laboratory production time, chairside adjustment time, and comfort (VAS) were recorded. Vacuum-adapted splints required the shortest laboratory production time (mean = 92 min, SD = 25.35). Heat-cured splints required longer processing (mean = 114 min, SD = 6.08). The CAD-CAM splints showed the longest total workflow duration (mean = 133 min, SD = 6.08), although they required less manual technician work. Intraoral adjustment times were similar between heat-cured and vacuum-adapted splints (means = 28 min and 26.66 min, respectively). None of the CAD-CAM splints seated fully at delivery, preventing proper adjustment. Vacuum-adapted splints received the highest comfort scores. Within the limitations of this pilot study with three participants, vacuum-adapted and heat-cured splints showed clinically acceptable performance and comparable adjustment times. CAD-CAM splints reduced manual workload but suffered from significant seating and fit issues, indicating the need for workflow refinement before clinical implementation. Even as splint fabrication is moving towards a more digital workflow, the old methods, especially vacuum-adapted splints, continue to deliver timely and comfortable results to patients. Further studies with more participants need to be done so that there can be a clear digital splint fabrication workflow.

Abstract We characterize the Carleson measures $$\mu $$ μ on the unit disk for which the image of the Hardy space $$H^p$$ H p under the corresponding embedding operator is closed in $$L^p(\mu )$$ L p ( μ ) . In fact, a more general result involving (p, q)-Carleson measures is obtained. A similar problem is solved in the setting of Bergman spaces.

<bold>Background:</bold> The treatment landscape of SSc-ILD has changed over the past decades. <bold>Objective:</bold> Evaluate current treatment regimens and identify factors associated with these strategies. <bold>Methods:</bold> SSc-ILD patients from the EUSTAR database starting immunesuppresive and/or anti-fibrotic therapies ≥2017 were included. Treatment regimens, including mono and combination therapies, switch and discontinuation and factors associated with treatment decisions, were analyzed using multivariable logistic regression. <bold>Results:</bold> Among 277 patients, 159 (57.4%) were started on treatment: 377 regimens with mycophenolate mofetil, 186 with methotrexate, 257 with rituximab and 122 with tocilizumab were identified, Figure 1 summarized combinations. 14% of regimens were switched and 24% were discontinued. Factors associated with combination therapies included younger age (OR 0.97, p<0.001), lower %pDLCO (OR 0.98, p=0.008), and diffuse SSc (OR 1.77, p=0.009). Switching therapies was associated with diffuse SSc (OR 1.90, p=0.038) and arthritis (OR 2.97, p=0.012); no factors were identified for discontinuation. <bold>Conclusion:</bold> Currently, the majority of SSc-ILD patients are treated with an immunosuppressive agent, often in combination regimes. Multiorgan manifestation significantly impact treatment choices. Figure 1. Treatment regimens in SSc-ILD: A mycophenolate mofetil-; B methotrexate-; C rituximab-,D tocilizumab -based regimens. <fig><object-id>erj;66/suppl_69/PA4060/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>

Abstract The structure and dynamics of important biological quasi-two-dimensional systems, ranging from cytoskeletal gels to tissues, are controlled by nematic order, flow, defects and activity. Continuum hydrodynamic descriptions combined with numerical simulations have been used to understand such complex systems. The development of thermodynamically consistent theories and numerical methods to model active nemato-hydrodynamics is eased by mathematical formalisms enabling systematic derivations and structured-preserving algorithms. Alternative to classical nonequilibrium thermodynamics and bracket formalisms, here we develop a theoretical and computational framework for active nematics based on Onsager’s variational formalism to irreversible thermodynamics, according to which the dynamics result from the minimization of a Rayleighian functional capturing the competition between free-energy release, dissipation and activity. We show that two standard incompressible models of active nemato-hydrodynamics can be framed in the variational formalism, and develop a new compressible model for density-dependent active nemato-hydrodynamics relevant to model actomyosin gels. We show that the variational principle enables a direct and transparent derivation not only of the governing equations, but also of the finite element numerical scheme. We exercise this model in two representative examples of active nemato-hydrodynamics relevant to the actin cytoskeleton during wound healing and to the dynamics of confined colonies of elongated cells.

This study performs an empirical analysis on the relationship between article publication delay and its future citations. We use a sample from top accounting journals, employ multivariate regression analysis, and results reveal an overall negative relationship between article publication delay and future citations. Additionally, we identify a U-shaped relationship; negativity is associated with the lower 87.5% of the distribution range of publication delay, while positivity is observed for the upper 12.5%. According to our findings, enhancing article citation through the review procedure appears unfeasible. Therefore, the review process should prioritize the detection and rejection of low-quality manuscripts. It should also adopt a more flexible, less demanding approach and expedite the acceptance of the remaining manuscripts. The current practice of subjecting submitted manuscripts to stringent and detailed requirements in accounting journals results in prolonged reviews, causing significant publication delays and subsequently lower citations, likely stemming from a loss of relevance or quality.

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request.Graphic abstractSupplementary InformationThe online version contains supplementary material available at 10.1007/s11517-025-03355-5.