Cátia Vaz

Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI-based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and AI-based estimation methods. It is being developed together with domain experts, and it builds on upper and established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. The AIdentifyAGE ontology is a fundamental step to enhance consistency, transparency, and explainability, establishing a robust foundation for ontology-driven decision support systems in medico-legal and judicial contexts.

Background: The development of a single therapeutic compound can exceed 1.8 billion USD and take more than a decade, underscoring the urgent need to accelerate drug discovery. Computational methods have become indispensable; however, traditional approaches, such as docking simulations, face limitations because they depend on protein and ligand structures that may be unavailable, incomplete, or of low accuracy. Even recent breakthroughs, such as AlphaFold, do not consistently provide models precise enough to identify ligand-binding sites or drug–target interactions. Results: We present ViralBindPredict, a deep learning framework that predicts viral protein–ligand binding sites directly from sequence. We also introduce the first curated large-scale benchmark of viral protein–ligand interactions, comprising >10,000 viral chains and ≈13,000 interactions processed using a 4.5 Å heavy-atom contact threshold. ViralBindPredict combines Mordred ligand descriptors with contextual protein embeddings from ESM2 or ProtTrans, enabling structure-free learning of binding preferences. Leakage-controlled data splits were applied to prevent overlap across protein sequence clusters and ligand scaffolds (Cluster90%, NoRed90%→Cluster90%, Cluster40%, NoRed90%→Cluster40%). Across most regimes, multilayer perceptrons, especially with ESM-2 embeddings, outperformed LightGBM baselines, maintaining strong precision–recall for unseen ligands but showing larger drops for unseen proteins, indicating that the protein context dominates generalization. Conclusions: ViralBindPredict introduces the first leakage-controlled benchmark for viral protein–ligand interactions and demonstrates accurate ligand-binding residue prediction directly from protein sequence. Together, these advances establish ViralBindPredict as a robust and extensible workflow for sequence-based antiviral discovery, supporting rapid target prioritization, compound repurposing, and de novo drug design, even in the absence of structural data.

Background: Chronological age estimation based on biological markers plays a crucial role in forensic, legal, and clinical contexts. Among available methods, dental age assessment (DAA) using orthopantomograms (OPGs) is considered one of the most accurate approaches. However, conventional DAA methods rely on expert interpretation, introducing subjectivity and limiting reproducibility. In recent years, artificial intelligence (AI) has emerged as a promising solution to overcome these limitations. This systematic review with meta-analysis aimed to evaluate the performance of AI-based models for dental age assessment using OPGs. Methods: The review protocol followed PRISMA guidelines and was registered in PROSPERO. A total of 27 studies, published between 2020 and 2025, were included, encompassing a wide range of machine learning and deep learning approaches, predominantly convolutional neural networks (CNNs). Results: For chronological age estimation, the pooled root mean squared error (RMSE) was 1.76 years, indicating that AI models can predict age with an average deviation of less than two years from true chronological age. The pooled coefficient of determination (R²) was 0.92, reflecting strong agreement and high explanatory power between predicted and actual ages. In classification tasks related to legal age thresholds, AI models demonstrated high discriminatory performance, with an overall area under the curve (AUC) exceeding 0.90 and a high diagnostic odds ratio (DOR). Hierarchical summary receiver operator characteristic analyses (HSROC) revealed high pooled sensitivity and specificity, with a slightly stronger ability to correctly classify individuals below the legal threshold. Network meta-analyses consistently ranked human expert assessments lower than AI-based models, especially CNN architectures such as EfficientNet, ResNet, and VGG16, although differences were not statistically significant. Conclusions: Despite substantial methodological heterogeneity across studies, the findings suggest that AI-based models for DAA using OPGs have reached a level of accuracy and robustness suitable for forensic and legal applications. AI systems should be regarded as complementary tools, enhancing objectivity, reproducibility, and consistency in age estimation, being good decision-support tools in legal and forensic settings.

Document type: Proceedings Book

This survey investigates how ontologies, semantic log processing, and Large Language Models (LLMs) enhance cybersecurity. Ontologies structure domain knowledge, enabling interoperability, data integration, and advanced threat analysis. Security logs, though critical, are often unstructured and complex. To address this, automated construction of Knowledge Graphs (KGs) from raw logs is emerging as a key strategy for organizing and reasoning over security data. LLMs enrich this process by providing contextual understanding and extracting insights from unstructured content. This work aligns with European Union (EU) efforts such as NIS 2 and the Cybersecurity Taxonomy, highlighting challenges and opportunities in intelligent ontology-driven cyber defense.

There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce phylogenetic trees, with resulting trees not being unique. Thus, methods for comparing phylogenies that are capable of revealing where two phylogenetic trees agree or differ are required. An approach is then proposed to compute a similarity or dissimilarity measure between trees, with the Robinson–Foulds distance being one of the most used, and which can be computed in linear time and space. Nevertheless, given the large and increasing volume of phylogenetic data, phylogenetic trees are becoming very large with hundreds of thousands of leaves. In this context, space requirements become an issue both while computing tree distances and while storing trees. We propose then an efficient implementation of the Robinson–Foulds distance over tree succinct representations. Our implementation also generalizes the Robinson–Foulds distances to labelled phylogenetic trees, i.e., trees containing labels on all nodes, instead of only on leaves. Experimental results show that we are able to still achieve linear time while requiring less space. Our implementation in C++ is available as an open-source tool.

There are several tools available to infer phylogenetic trees, which depict the evolutionary relationships among biological entities such as viral and bacterial strains in infectious outbreaks, or cancerous cells in tumor progression trees. These tools rely on several inference methods available to produce phylogenetic trees, with resulting trees not being unique. Thus, methods for comparing phylogenies that are capable of revealing where two phylogenetic trees agree or differ are required. An approach is then to compute a similarity or dissimilarity measure between trees, with the Robinson- Foulds distance being one of the most used, and which can be computed in linear time and space. Nevertheless, given the large and increasing volume of phylogenetic data, phylogenetic trees are becoming very large with hundreds of thousands of leafs. In this context, space requirements become an issue both while computing tree distances and while storing trees. We propose then an efficient implementation of the Robinson-Foulds distance over trees succinct representations. Our implementation generalizes also the Robinson-Foulds distances to labelled phylogenetic trees, i.e., trees containing labels on all nodes, instead of only on leaves. Experimental results show that we are able to still achieve linear time while requiring less space. Our implementation is available as an open-source tool at https://github.com/pedroparedesbranco/TreeDiff.