Institute for Scientific Interchange

High Mobility Group Box 1 (HMGB1) and S100 proteins are major ligands of Receptor for Advanced Glycation End-products (RAGE) and have causal roles in endometriosis lesions. Yet the AGE-RAGE pathway that unifies Advanced Glycation End-products (AGEs) with these ligands has not been assessed in endometriosis. In diabetes, atherosclerosis, and chronic kidney disease, AGE-RAGE links insulin resistance and oxidative stress to inflammation, fibrosis, and organ harm. Endometriosis shares key drivers of AGE accumulation, including insulin resistance, oxidative stress, and chronic inflammation. Endometriosis is also linked to higher vascular risk and arterial stiffness. We asked whether AGE-RAGE could bridge metabolic stress to pelvic lesions and systemic risk. We did a focused review of mechanisms and an evidence map of studies on AGEs, RAGE, or known RAGE ligands in endometriosis. We grouped findings as most consistent with a driver, amplifier, consequence, or parallel role. We included 29 studies across human samples, cell systems, and animal models. Few studies measured AGE adducts directly. Most work tracked RAGE ligands (mainly HMGB1 and S100 proteins) and downstream immune and angiogenic programs. Across models, this pattern fits best with a self-reinforcing loop after lesions form. RAGE expression often aligned with lesion remodeling, especially fibrosis. Blood and skin readouts of AGE burden were mixed and varied by cohort and sample type. A central gap is receptor proof. Many models point to shared Toll-like receptor 4 (TLR4)/ nuclear factor kappa B (NF-κB) signaling, but few test RAGE dependence. Overall, current evidence supports AGE-RAGE as a disease-amplifying loop involved in chronic inflammation and fibrosis rather than an initiating trigger. Its effects likely vary by stage and site. Priorities now include direct lesion AGE measurement, paired systemic-pelvic sampling over time, receptor-level studies, and trials testing diet or drug interventions against clear endpoints. Outcomes could include fibrosis, angiogenesis, immune state, pain, and oocyte and follicle function.

Understanding how plants perceive and respond to environmental and developmental cues requires tools capable of monitoring molecular signals in vivo, across whole tissues, and in real time. Genetically encoded fluorescent indicators, coupled with fluorescence microscopy, have transformed plant biology, but their application remains largely confined to small model organisms and specialized microscopy instrumentation. Here, we present MAcro Plant Projection Imaging (MAPPI), an open-source, low-cost, and modular fluorescence imaging platform for soil-grown plants beyond the model organism or seedling stage. MAPPI enables wide field-of-view, dual-projection imaging of fluorescent reporters, supporting real-time visualization of systemic signals under near-physiological conditions. We validate MAPPI by tracking calcium and l-glutamate dynamics in adult Nicotiana benthamiana plants, revealing developmentally regulated long-distance calcium waves triggered by wounding, burning, or submergence, including bidirectional shoot-to-root and root-to-shoot signaling. By democratizing access to whole-plant functional imaging, MAPPI provides a scalable tool for dissecting signal propagation, stress adaptation, and systemic communication in both model and nonmodel species.

Close-proximity interactions are considered a key risk factor for respiratory virus transmission, but their importance relative to shared space and air quality remains unclear. We conducted a six-week longitudinal study in a Swiss secondary school (67 students, aged 14-15). We detected 87 infections in saliva samples and recorded absences to identify plausible transmissions, excluding implausible ones through genomic analysis. Time in close proximity (within 1.5 metres) was measured using wearable sensors and air quality via CO2 monitors. Students spent 21.2 minutes per day in close proximity (interquartile range 7.8-44.2) and 5.3 hours in shared classrooms (IQR 3.8-6.2), during which air quality was suboptimal for 1.9 hours (IQR 1.2-3.0). Using pairwise survival models, we found that transmission was more likely within than between classes. Close proximity was modestly associated with higher transmission risk overall (rate ratio 1.16 per doubling daily time, 95%-CI 1.01-1.33), while time in shared classrooms (RR 3.17, 95%-CI 1.96-5.17)and suboptimal air quality (RR 1.90 95%-CI 1.23-2.94) also predicted within-class risk. Prolonged exposure in shared, poorly ventilated spaces, which potentially includes several infectious sources, drives respiratory virus transmission more than close contact.

We present Epydemix, an open-source Python package for the development and calibration of stochastic compartmental epidemic models. The framework supports flexible model structures that incorporate demographic information, age-stratified contact matrices, and dynamic public health interventions. A key feature of Epydemix is its integration of Approximate Bayesian Computation (ABC) techniques to perform parameter inference and model calibration through comparison between observed and simulated data. The package offers a range of ABC methods such as simple rejection sampling, simulation-budget-constrained rejection, and Sequential Monte Carlo (ABC-SMC). Epydemix is modular, and supports ABC-based calibration both for models defined within the package and for those developed externally. To demonstrate the computational framework capabilities, we discuss usage examples that include (i) simulating an intervention-driven model with time-varying parameters, and (ii) benchmarking calibration performance using synthetic epidemic data. We further illustrate the use of the package in a retrospective case study that includes scenario projections under alternative intervention assumptions. By lowering the barrier for the implementation of computational and inference approaches, Epydemix makes epidemic modeling more accessible to a wider range of users, from academic researchers to public health professionals.

By 2050, nearly 70% of the global population will live in cities (UN, 2018), increasing the demand for urban green spaces. Urban areas are facing increasing risks from climate change, including heatwaves, flooding, wildfires, and growing social inequality, which challenges urban planning and design. Urban forests form the backbone of green infrastructure supporting resilient, equitable, and sustainable cities. Importantly, their cost-effective benefits advance sustainable development, climate action, and biodiversity conservation. Urban forests include all woody and understorey vegetation within and around dense settlements, from cultivated trees in streets, parks, and gardens to self-sustaining stands in remnant and peri-urban woodlands (FAO, 2016). As essential nature-based solutions (Cohen-Shacham et al., 2016), urban forests provide multiple ecosystem services. They help cool urban temperatures, reduce air pollution, enhance soil infiltration, slow stormwater runoff, buffer extreme weather, and support human health (Livesley et al., 2016). They contribute significantly to climate adaptation and moderately to mitigation by reducing the energy demand for cooling (McPhearson et al., 2023). Urban forests also enhance biodiversity by providing habitats and climate refugia at multiple scales (Alvey, 2006). Trade-offs in urban forest benefits, costs, and the impacts of policy interventions, such as those related to measurement, outcomes, or implementation, remain complex (Vogt et al., 2015), but the loss of canopy reduces air quality, biodiversity, and resilience to floods, droughts, pests, and extreme heat (Nowak, 2018). Canopy loss impairs recreation and impacts physical and mental health (Carrus et al., 2015). Because urban forests are inherently dynamic systems, the death or removal of large and mature trees should be anticipated through proactive planning for their replacement, including careful consideration of which species are selected and why. Unequal access drives social-environmental injustice and health inequities, which can be addressed through greenspace expansion, equitable distribution, and better management (Esperon-Rodriguez et al., 2025). Urban forests are increasingly at risk due to significant stewardship gaps. Despite the existence of international management standards, ongoing tree losses result from pests introduced through trade, climate and pollution stress, inadequate legal protections, rapid urban densification, and insufficient maintenance (Esperon-Rodriguez et al., 2022; Paap et al., 2017; Vogt et al., 2015). Planting alone cannot offset accelerated mature tree losses or replace the vital functions these trees provide over their shortened lifespans in urban environments. Closing the stewardship gap demands urgent investment, robust funding, and stronger policy to sustain diverse and resilient urban forests. Urban forests are among the most effective, equitable nature-based solutions available. When protected and resourced, they cool neighborhoods, manage stormwater, store carbon, support biodiversity, and improve health, especially in underserved communities. Yet mature tree loss outpaces replacement amid increasing climate and biological stresses. We urge COP30 policymakers to treat urban forests as essential and critical city infrastructure: safeguard mature trees, set and finance SMART canopy, diversity and access targets, mainstream urban forests in climate and biodiversity plans, and fund long-term operations, monitoring, nursery capacity, and biosecurity. Implementing this visionary action delivers cooler, healthier, more biodiverse, and more equitable cities now and for future generations. MER and MGT led the initiative and drafted the letter. All authors provided feedback, edited, and agreed with the content of the letter. All authors, except MER and MGT, are listed alphabetically. MER received funding from Western Sydney University's Research Theme Program. KDP was supported by the Research Foundation Flanders (FWO, grant 12A0L25N). RMM was supported by a Discovery Early Career Researcher Award (project DE200100649), funded by the Australian Research Council of the Australian Government. JCS received funding from the Danish National Research Foundation (grant DNRF173) and EARTHKEEPER (Global South Biodiversity Leadership Initiative). CS's contribution was funded by the National Research Foundation of South Africa (grant no 84379). We declare no conflict of interest. There are no data associated with the article.

Non-dyadic higher-order interactions affect collective behavior in various networked dynamical systems. Here, we discuss the properties of a novel Ising model with higher-order interactions and characterize its phase transitions between the ordered and the disordered phase. By a mean-field treatment, we show that the transition is continuous when only three-body interactions are considered, but becomes abrupt when interactions of higher orders are introduced. Using a Georges-Yedidia expansion to go beyond a naïve mean-field approximation, we reveal a quantitative shift in the critical point of the phase transition, which does not affect the universality class of the model. Finally, we compare our results with traditional p-spin models with many-body interactions. Our work unveils new collective phenomena on complex interacting systems, revealing the importance of investigating higher-order systems beyond three-body interactions.

Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN-based node classification task. A counterfactual evidence is a pair of nodes such that, regardless they exhibit great similarity both in the features and in their neighborhood subgraph structures, they are classified differently by the GNN. We develop effective and efficient search algorithms and a novel indexing solution that leverages both node features and structural information to identify counterfactual evidences, and generalizes beyond any specific GNN. Through various downstream applications, we demonstrate the potential of counterfactual evidences to enhance fairness and accuracy of GNNs.

The summer of 2025 is approaching and the need to finalize our plans for the next few years is urgent. The next Padua Days of Muscle and Mobility Medicine (PDM3) will be held from 3 to 6 March 2026 at the Hotel Petrarca, Euganean Thermae (Padova, Italy). A preliminary flyer for 2026PDM3 is almost ready with organizers, session schedule and keynote speakers. A new Website, named Padua Muscle Days, will be soon released with all the needed information for How to Join, Registration, Accommodation, Payments and the Template for the 2026 PDM3. We hope that readers will circulate its link (www.paduamuscledays.com) among Colleagues and Friends. No doubt that 2026 PDM3 will be a great gift for all Participants. The other novelty scheduled for June 2025 is the 2024 Impact Factor that Clarivate will publish. As authors who publish in other journals, but even more as reviewers, readers could contribute to making bigger a small gift. In any case, it is a gift that has been waited for decades. Four Guest Editors have done their best to make the 35(2) 2025 European Journal of Translational Myology and Mobility Medicine (Ejtm) a very special ssue. Indeed, it is mainly dedicated to two Thematic Sections. The first, with more works, is named Advances in Musculoskeletal and Neuromuscular Rehabilitation – Maria Chiara Maccarone & Stefano Masiero, Eds. The second is called: Generative AI for Translational Mobility Medicine, Giorgio Fanò-Illic & Riccardo Forni, Eds., and aims to attract works that use Digital Methods for Translational Mobility Medicine. We invite the members of the Ejtm editorial board and the authors of highly cited articles to follow these examples and suggest further thematic sections for the Ejtm of the next decade.