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Cytosolic Ca2+ is a highly dynamic, tightly regulated and broadly conserved cellular signal. Ca2+ dynamics have been studied widely in cellular monocultures, yet organs in vivo comprise heterogeneous populations of stem and differentiated cells. Here, we examine Ca2+ dynamics in the adult Drosophila intestine, a self-renewing epithelial organ in which stem cells continuously produce daughters that differentiate into either enteroendocrine cells or enterocytes. Live imaging of whole organs ex vivo reveals that stem-cell daughters adopt strikingly distinct patterns of Ca2+ oscillations after differentiation: enteroendocrine cells exhibit single-cell Ca2+ oscillations, whereas enterocytes exhibit rhythmic, long-range Ca2+ waves. These multicellular waves do not propagate through immature progenitors (stem cells and enteroblasts), of which the oscillation frequency is approximately half that of enteroendocrine cells. Organ-scale inhibition of gap junctions eliminates Ca2+ oscillations in all cell types - even, intriguingly, in progenitor and enteroendocrine cells that are surrounded only by enterocytes. Our findings establish that cells adopt fate-specific modes of Ca2+ dynamics as they terminally differentiate and reveal that the oscillatory dynamics of different cell types in a single, coherent epithelium are paced independently.

Among the land use–land cover products, tree cover maps are essential tools for assessing forest functionality and ecosystem services, and implementing sustainable forest management. By combining open-source and ancillary high-resolution cartographic datasets, this study aims to map trees and forests in the Latium region in central Italy, highlighting their spatial configuration, function, and forest typology. The main findings show that trees cover 44.2% of the regional land area. Forests cover 508,056 ha, forming the core matrix of the Latium mountain landscape, providing significant ecological and socio-economic value for forest management and the regional wood supply chain. Although trees outside the forest represent only 3.1% of regional tree cover, they play a crucial role in enhancing ecological connectivity and landscape resilience. Approximately 2% of the tree and forest cover occurs in urban areas, contributing significantly to climate regulation and air quality in densely populated environments. The dominant forest types in Lazio include Turkey oak, temperate broadleaf, beech, downy oak, and Holm oak, which together account for 58.6% of the total tree cover. The accuracy tests confirm the feasibility of using open-source data for reliable, cost-effective forest mapping. Regular updates of these maps can support continuous monitoring and promote sustainable forest management practices. [ABSTRACT FROM AUTHOR]

Background: The ultra-rare nature of melanoma in children, adolescents, and young adults poses significant challenges to the development and implementation of effective prevention and diagnostic strategies. This article delves into the ELSIs surrounding these strategies, placing particular emphasis on the transformative potential of AI-driven tools and applications. Methods: Using an exploratory sequential mixed methods approach, this study integrated a PICO-guided literature review and qualitative insights from two focus groups. The review included 26 peer-reviewed articles published in English from January 2019 to January 2024, addressing ELSIs in melanoma, rare diseases, and AI in dermatology. Focus groups included a March 2024 session in Berlin with 15 stakeholders (patients, caregivers, advocates, healthcare professionals) and a November 2024 online session with 5 interdisciplinary experts. Results: Six key priorities for healthcare policies emerged: addressing cultural factors, such as the glorification of tanned skin; enhancing professional training for accurate diagnosis; balancing the risks of overdiagnosis and underdiagnosis; promoting patient autonomy through transparent communication; reducing inequalities to ensure equitable access to care; and making ethical and legal use of AI in healthcare. Conclusion: These priorities provide a comprehensive framework for advancing the prevention and diagnosis of melanoma in children, adolescents, and young adults, leveraging AI technologies while prioritizing equitable and patient-centered healthcare delivery. [ABSTRACT FROM AUTHOR]

Accurate measurement of forest growing stock is a prerequisite for implementing climate-smart forestry strategies. This study deals with the use of airborne laser scanning data to assess carbon stock at the tree level. It aims to demonstrate that the combined use of two unsupervised techniques will improve the accuracy of estimation supporting sustainable forest management. Based on the heterogeneity of tree height and point cloud density, we classified 31 forest stands into four complexity categories. The point cloud of each stand was further divided into three horizontal layers, improving the accuracy of tree detection at tree level for which we calculated volume and carbon stock. The average accuracy of tree detection was 0.48. The accuracy was higher for forest stands with lower tree density and higher frequency of large trees, as well as a dense point cloud (0.65). The prediction of carbon stock was higher with a bias ranging from -0.3% to 1.5% and a root mean square error ranging from 0.14% to 1.48%. Key words: tree detection, airborne laser scanning (ALS), forest structure, carbon stock, climate-smart forestry. La mesure precise du capital forestier en croissance est un prerequis a l'implantation de strategies forestieres intelligentes face au climat. Cette etude porte sur l'utilisation de donnees de balayage laser aeroporte pour evaluer le stock de carbone a l'echelle de l'arbre. Elle vise a demontrer que l'utilisation combinee de deux techniques non supervisees va ameliorer la precision de l'estimation sur laquelle s'appuie un amenagement forestier durable. Sur la base de l'heterogeneite de la hauteur des arbres et de la densite du nuage de points, nous avons classe 31 peuplements forestiers dans quatre categories de complexite. Le nuage de points de chaque peuplement a par la suite ete divise en trois couches horizontales, ce qui ameliore la precision de la detection de chacun des arbres pour lesquels nous avons calcule le volume et le stock de carbone. La precision moyenne de la detection des arbres etait de 0,48. La precision etait plus elevee pour les peuplements forestiers qui avaient une plus faible densite et une frequence plus elevee de gros arbres, ainsi qu'un nuage de points dense (0,65). La prediction du stock de carbone etait plus elevee avec un biais allant de -0,3 a 1,5 % et un ecart moyen quadratique (EMQ) entre 0,14 et 1,48 %. [Traduit par la Redaction] Mots-cles: detection des arbres, balayage laser aeroporte (BLA), structure de la foret, stock de carbone, foresterie intelligente face au climat., 1. Introduction In Europe, forests cover about 35% of the total land area (SoEF 2020) and play a significant role in climate change mitigation thanks to their capacity to remove [...]

Among the most significant impacts related to the spread of settlements and the densification of urban areas, the reduction in the availability of public green spaces plays a central role in the definition of livable cities, in terms of the environment and social cohesion, interaction, and equality. In the framework of target 11.7 of the Sustainable Development Goals (SDG) 11, the United Nations has established the objective of ensuring universal, safe, and inclusive access to public spaces by 2030, for women, children, the elderly, and people with disabilities. This study proposes the evaluation of this objective for the urban area of the 14 Italian metropolitan cities, as defined by EUROSTAT and adopted by the United Nations and the Nature Restoration Law (NRL). A methodology based on open-source data and network analysis tools is tested for the provision of an unprecedented mapping of the availability and accessibility to green urban public spaces, which shows that less than 30% of metropolitan city residents have access to a green space within 300 m on foot, according to OpenStreetMap data (less than one in five for the Urban Atlas data). Furthermore, a critical analysis on the geometric and semantic definition of green urban public spaces adopted by the main European and international tools is carried out, which underlines the strategic role of crowdsourcing but also the need for mapping rules that make the data more consistent with the monitoring objectives set at the institutional level. [ABSTRACT FROM AUTHOR]

A critical challenge for urban forests is the arrival of Toumeyella parvicornis (or pine tortoise scale) in Italy, as this species damages stone pine (Pinus pinea L.), an emblematic Mediterranean species. The aim of this study is to evaluate the effectiveness of remote-sensing data for monitoring pest invasions in the urban area of Rome, using PlanetScope images with a 1-day revisit time and 3 m spatial resolution, making them ideal for detecting outbreaks in complex urban areas. First, we constructed a reference dataset, georeferencing 238 healthy trees in Tenuta San Rossore (Tuscany) and more than 2000 damaged trees in Rome's green areas. In any case, this dataset of healthy trees—obtained from forest areas—was expected to exhibit higher photosynthetic activity compared to urban green areas. Second, more than 30,000 PlanetScope images were analyzed to test the effectiveness of the Renormalized Difference Vegetation Index in detecting this specific forest disturbance. Finally, different thresholds were examined, allowing for the identification of an optimal threshold to discriminate healthy trees from damaged trees. The index results showed a marked drop during the summer in the infested areas, compared to the healthy areas. The identified threshold provided 99% accuracy in detecting infested trees. The approach applied in this study demonstrated that PlanetScope imagery proved effective in detecting T. parvicornis, leading to promising results. [ABSTRACT FROM AUTHOR]

Accurate evapotranspiration (ET) estimation is crucial for sustainable water management in the diverse and water-scarce Mediterranean region. This study compares three prominent models (Simulator of Terrestrial Ecohydrological Processes and Systems (STEPS), Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE), and Two-Source Energy Balance (TSEB)) with established global datasets (Moderate Resolution Imaging Spectroradiometer 8-day global terrestrial product (MOD16A2), Global Land Evaporation Amsterdam Model (GLEAM), and TerraClimate) at multiple spatial and temporal scales and validates model outcomes with eddy covariance based ground measurements. Insufficient ground-based observations limit comprehensive model validation in the eastern Mediterranean part (Turkey and Balkans). The results reveal significant discrepancies among models and datasets, highlighting the challenges of capturing ET variability in this complex region. Differences are attributed to variations in ecosystem type, energy balance calculations, and water availability constraints. Ground validation shows that STEPS performs well in some French and Italian forests and crops sites but struggles with seasonal ET patterns in some locations. SCOPE mostly overestimates ET due to detailed radiation flux calculations and lacks accurate water limitation representation. TSEB faces challenges in capturing ET variations across different ecosystems at a coarser 10 km resolution. No single model and global dataset accurately represent ET across the entire region. Model performance varies by region and ecosystem. As GLEAM and TSEB excel in semi-arid Savannahs, STEPS and SCOPE are better in grasslands, croplands, and forests in few locations (5 out of 18 sites) which indicates these models need calibration for other locations and ecosystem types. Thus, a region-specific model calibration and validation, sensitive to extremely humid and arid conditions can improve ET estimation across the diverse Mediterranean region. [ABSTRACT FROM AUTHOR]

Integrating the conservation of biodiversity into silvicultural practices is one of the main challenges facing forest owners in the promotion of an emergent approach to sustainable forest management across European forests. The Mediterranean forests are among the richest biodiverse forest ecosystems due to their environmental heterogeneity, but climate change is threatening their integrity, with critical implications for the availability of ecosystem services. Forests managed for commercial purposes are under increasing pressure to provide timber while supporting biodiversity. This study aims to analyse the relationship between tree-related microhabitats (TreMs), which are structures occurring on living or standing dead trees that constitute an essential substrate for several living species, and beetles, an important indicator species group for forest biodiversity monitoring. Four generalized linear models were fitted to assess the influence of environmental factors, forest structure, and biodiversity-related parameters on the abundance and richness of beetles, mainly on saproxylic beetles. The study highlights significant associations between the abundance and richness of individual groups and types of TreMs and the Italian Red List of saproxylic species. Therefore, TreMs can serve as a tool to map, monitor, and enhance forest biodiversity in managed forests. [ABSTRACT FROM AUTHOR]

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA's Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). [ABSTRACT FROM AUTHOR]