AUTOMATIZING DEGRADATION MAPPING OF ANCIENT STELAE BY DUAL-BAND IMAGING AND MACHINE LEARNING-BASED CLASSIFICATION

Degradation patterns are the visible consequence of the impacts of environmental factors and biological agents on stone heritage. Accurately documenting them is a key requisite when studying exposed stone antiquities to interpret weathering causes, identify conservation needs, and plan cleaning interventions. However, a significant gap can be identified in practical automatized procedures for mapping patterns on stone antiquities, such as ancient stelae. This work evaluates a workflow that uses visible and near-infrared imaging, combined with machine learning-based digital image segmentation tools, to classify degradation patterns on marble stelae correctly and cost-effectively. For this work, different classification methods are considered. Results are analyzed using error matrixes and reference degradation maps. The application cases include stelae displayed in the courtyard of the Archaeological Museum of Eretria (Euboea, Greece). The proposed methodology aims at being easily adapted to facilitate the conservators’ work

FOXN1 forms higher-order nuclear condensates displaced by mutations causing immunodeficiency

The transcription factor FOXN1 is a master regulator of thymic epithelial cell (TEC) development and function. Here, we demonstrate that FOXN1 expression is differentially regulated during organogenesis and participates in multimolecular nuclear condensates essential for the factor’s transcriptional activity. FOXN1’s C-terminal sequence regulates the diffusion velocity within these aggregates and modulates the binding to proximal gene regulatory regions. These dynamics are altered in a patient with a mutant FOXN1 that is modified in its C-terminal sequence. This mutant is transcriptionally inactive and acts as a dominant negative factor displacing wild-type FOXN1 from condensates and causing athymia and severe lymphopenia in heterozygotes. Expression of the mutated mouse ortholog selectively impairs mouse TEC differentiation, revealing a gene dose dependency for individual TEC subtypes. We have therefore identified the cause for a primary immunodeficiency disease and determined the mechanism by which this FOXN1 gain-of-function mutant mediates its dominant negative effect

The additional value of patient-reported health status in predicting 1-year mortality after invasive coronary procedures: A report from the Euro Heart Survey on Coronary Revascularisation

Objective: Self-perceived health status may be helpful in identifying patients at high risk for adverse outcomes. The Euro Heart Survey on Coronary Revascularization (EHS-CR) provided an opportunity to explore whether impaired health status was a predictor of 1-year mortality in patients with coronary artery disease (CAD) undergoing angiographic procedures. Methods: Data from the EHS-CR that included 5619 patients from 31 member countries of the European Society of Cardiology were used. Inclusion criteria for the current study were completion of a self-report measure of health status, the EuroQol Questionnaire (EQ-5D) at discharge and information on 1-year follow-up, resulting in a study population of 3786 patients. Results: The 1-year mortality was 3.2% (n = 120). Survivors reported fewer problems on the five dimensions of the EQ-5D as compared with non-survivors. A broad range of potential confounders were adjusted for, which reached a p<0.10 in the unadjusted analyses. In the adjusted analyses, problems with self-care (OR 3.45; 95% CI 2.14 to 5.59) and a low rating (≤ 60) on health status (OR 2.41; 95% CI 1.47 to 3.94) were the most powerful independent predictors of mortality, among the 22 clinical variables included in the analysis. Furthermore, patients who reported no problems on all five dimensions had significantly lower 1-year mortality rates (OR 0.47; 95% CI 0.28 to 0.81). Conclusions: This analysis shows that impaired health status is associated with a 2-3-fold increased risk of all-cause mortality in patients with CAD, independent of other conventional risk factors. These results highlight the importance of including patients' subjective experience of their own health status in the evaluation strategy to optimise risk stratification and management in clinical practice