Jorge Goncalves

Background: The Peer Assessment Rating (PAR) index quantifies malocclusion severity and is widely used to determine orthodontic treatment outcome. Manual scoring of estimated PAR components on intraoral photographs is time-consuming and subject to substantial inter-examiner variability, and automation is further complicated by the need to integrate five disparate clinical views. This study developed and evaluated a deep-learning framework for automated classification of orthodontic treatment need based on estimated PAR from routine intraoral photographs. Methods: A total of 2965 photographs from 593 patient visits at a single centre were scored by a board-certified orthodontist using the weighted PAR index and dichotomised at PAR > 10. The model was trained and evaluated as a binary treatment-need classifier on this dichotomised label, not as a regressor of the continuous PAR score or its individual components. A self-supervised foundation model isolated the dental region; the resulting views were embedded and aggregated by an attention-based multiple instance learning layer to yield a visit-level prediction. A held-out set of 80 visits was rescored independently by a second orthodontist and again by the first after 6 weeks. Agreement was quantified with Cohen’s κ; 95% confidence intervals (CIs) and paired differences (Δκ) were estimated by bootstrap (2000 resamples). The final model was compared against input-, early-, and late-fusion baselines. Results: The model achieved an AUROC of 0.82. Agreement with the training annotator (an optimistic reference) was κ = 0.55 (95% CI 0.36–0.72) and with the independent rater (the conservative reference) κ = 0.41 (0.22–0.60), compared with κ = 0.52 between the two orthodontists and κ = 0.70 within the same rater across sessions. Agreement with the independent rater did not differ significantly from human inter-rater agreement. Conclusions: Automated classification of treatment need based on photograph-estimated PAR reached moderate agreement with orthodontists. The model reproduces an expert’s photograph-based estimate and is not a clinically validated assessment of treatment need.

Background: Metapopulation models, which consider epidemic spread across interconnected regions, can provide more accurate epidemic predictions compared to isolated models for the corresponding regions. Still, their added complexity and data requirements raise questions about their tangible benefits over simpler, localized models. Methods: We develop and validate two networked compartmental metapopulation models for predicting influenza-like illness across Europe: a detailed network-based model, including international travel dynamics, and a simpler mean-field model, aggregating average regional data. The network is constructed using public mobility data and complemented with population densities at border regions. Incidence data of influenza-like illnesses from 28 countries are integrated using an Extended Kalman filter. Results: We show that networked epidemic models effectively capture epidemic dynamics across regions and epidemic phases. The models enable accurate forecasts, missing data imputation, and actionable insights: network models outperform isolated models in forecasting epidemic progression, particularly during critical periods such as wave onsets and peaks, and maintain reliability in scenarios with missing data. Conclusions: The findings unveil and quantify the advantages of metapopulation models for epidemic forecasting in interconnected regions, and pave the way to the integration of mobility and epidemic surveillance to improve the monitoring and prediction of spreading diseases.

In 2023 the European Centre for Disease Prevention and Control (ECDC) launched RespiCast, the first European Respiratory Diseases Forecasting Hub, to provide probabilistic forecasts for influenza-like illness (ILI) and acute respiratory infection (ARI) incidence across 26 European countries. During the 2023/24 and 2024/25 winter seasons, RespiCast collected one- to four-week-ahead forecasts from multiple models contributed by different international teams and combined them into an ensemble. Our analysis shows that, when evaluated using the weighted interval score (WIS) and the absolute error (AE), the ensemble consistently outperformed the baseline model (defined as a persistence model that projects the last observed value forward) as well as individual models across most countries and forecasting rounds for both ILI and ARI incidence in the two seasons. Analysis of ensemble coverage (defined as the proportion of times observed values fall within the specified prediction intervals) indicated that forecast prediction intervals were reliable, although a general overconfidence trend (i.e., prediction intervals that are too narrow) was observed, particularly in specific countries. The relative performance of the ensemble declined in certain weeks, likely due to reduced participation from modelling teams, epidemic dynamics, higher data noise, and reporting delays. Forecast scores varied across countries, with some exhibiting consistently higher relative errors than others. Overall, the findings highlight the strengths of ensemble approaches in improving the accuracy and reliability of epidemiological forecasts while identifying areas for improvement, such as managing overconfidence and addressing variability in performance across countries and over time.

Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these regulatory networks have primarily focused on single-omic studies, overlooking inter-layer regulatory relationships. To address these limitations, we developed MINIE, a computational method that integrates multi-omic data from bulk metabolomics and single-cell transcriptomics through a Bayesian regression approach that explicitly models the timescale separation between molecular layers. We validate the method on both simulated datasets and experimental Parkinson’s disease data. MINIE exhibits accurate and robust predictive performance across and within omic layers, including curated multi-omic networks and the lac operon. Benchmarking demonstrated significant improvements over state-of-the-art methods while ranking among the top performers in comprehensive single-cell network inference analysis. The integration of regulatory dynamics across molecular layers and temporal scales provides a powerful tool for comprehensive multi-omic network inference.

Motivation: Modelling gene expression is a central problem in systems biology. Single-cell technologies have revolutionized the field by enabling sequencing at the resolution of individual cells. This results in a much richer data compared to what is obtained by bulk technologies, offering new possibilities and challenges for gene regulatory network inference. Results: In this work, we introduce GRIT (gene regulation inference by transport)—a method to fit a differential equation model and to infer gene regulatory networks from single-cell data using the theory of optimal transport. The idea consists in tracking the evolution of the cell distribution over time and finding the system whose temporal marginals minimize the transport cost with the observations. GRIT is finally used to identify genes and pathways affected by two Parkinson’s disease associated mutations. Availability and implementation: Matlab implementation of the method and code for data generation are at gitlab.com/uniluxembourg/lcsb/systems-control/grit together with a user guide. A snapshot of the code used for the results of this article is at doi: 10.5281/zenodo.15582432.

Understanding the complex mechanisms of gene regulatory networks (GRNs) has emerged as a transformative approach in agricultural research. By deciphering the regulatory mechanisms underlying key traits, GRN studies offer opportunities to enhance crop resilience to environmental challenges, improve yield and ensure sustainable food production. In this Review, we highlight the importance of GRN research in agriculture and explore how cutting-edge biotechnology, interdisciplinary approaches and computational modeling techniques are addressing the challenges in the field. We discuss how integrating diverse datasets at different resolutions empowers us to unravel the complex genetic networks governing crop responses to climate change, pests and diseases. By harnessing the power of GRNs, we have the potential to transform crop improvement strategies, develop stress-tolerant varieties and ensure global food security. We provide insights into the current opportunities and challenges of GRN research in agriculture, bridging the gap between scientific advancements and the pressing need for sustainable agricultural practices.