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Abstract The inherent scalability, low production cost and mechanical flexibility of laser-induced graphene (LIG) combined with its high electrical conductivity, hierarchical porosity and large surface area are appealing characteristics for many applications. Still, other materials can be combined with LIG to provide added functionalities and enhanced performance. This work exploits the most adequate electrodeposition parameters to produce LIG/ZnO nanocomposites. Low-temperature pulsed electrodeposition allowed the conformal and controlled deposition of ZnO rods deep inside the LIG pores whilst maintaining its inherent porosity, which constitute fundamental advances regarding other methods for LIG/ZnO composite production. Compared to bare LIG, the composites more than doubled electrode capacitance up to 1.41 mF cm−2 in 1 M KCl, while maintaining long-term cycle stability, low ohmic losses and swift electron transfer. The composites also display a luminescence band peaked at the orange/red spectral region, with the main excitation maxima at ~ 3.33 eV matching the expected for the ZnO bandgap at room temperature. A pronounced sub-bandgap tail of states with an onset absorption near 3.07 eV indicates a high amount of defect states, namely surface-related defects. This work shows that these environmentally sustainable multifunctional nanocomposites are valid alternatives for supercapacitors, electrochemical/optical biosensors and photocatalytic/photoelectrochemical devices.

Early detection of invasive species is crucial to prevent biological invasions. To increase the success of detection efforts, it is often essential to know when key phenological stages of invasive species are reached. This includes knowing, for example, when invasive insect species are in their adult phase, invasive plants are flowering or invasive mammals have finished their hibernation. Unfortunately, this kind of information is often unavailable or is provided at very coarse temporal and spatial resolutions. On the other hand, opportunistic records of the location and timing of observations of these stages are increasingly available from biodiversity data repositories. Here, we demonstrate how to apply these data for predicting the timing of phenological stages of invasive species. The predictions are made across Europe, at a daily temporal resolution, including in near real time and for multiple days ahead. We apply this to phenological stages of relevance for the detection of four well-known invasive species: the freshwater jellyfish, the geranium bronze butterfly, the floating primrose-willow and the garden lupine. Our approach uses machine-learning and statistical-based algorithms to identify the set of temporal environmental conditions (e.g. temperature values and trends, precipitation, snow depth and wind speed) associated with the observation of each phenological stage, while accounting for spatial and temporal biases in recording effort. Correlation between predictions from models and the actual timing of observations often exceeded values of 0.9. However, some inter-taxa variation occurred, with models using direct predictors of phenological drivers and trained on thousands of observation records outperforming those relying on indirect predictors and only a few hundred training records. The analysis of daily predictions also allowed mapping European-wide regions with similar phenological dynamics (i.e. ‘phenoregions’). Our results underscore the significant potential of opportunistic biodiversity observation data in developing models capable of predicting and forecasting species phenological stages across broad spatial extents. By enhancing our current ability to anticipate the phenological stages of invasive species, this approach has the potential to significantly improve decision-making in invasion surveillance and monitoring activities.

Abstract Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

The satellite monitoring of vegetation moisture content (VMC) and soil moisture content (SMC) in Southern European Atlantic mountains remains poorly understood but is a fundamental tool to better manage landscape moisture dynamics under climate change. In the Atlantic humid mountains of Portugal, we investigated an empirical model incorporating satellite (Sentinel-1 radar, S1; Sentinel-2 optical, S2) and ancillary predictors (topography and vegetation cover type) to monitor VMC (%) and SMC (%). Predictors derived from the S1 (VV, HH and VV/HH) and S2 (NDVI and NDMI) are compared to field measurements of VMC (n = 48) and SMC (n = 48) obtained during the early, mid and end of summer. Linear regression modelling was applied to uncover the feasibility of a landscape model for VMC and SMC, the role of vegetation type models (i.e. native forest, grasslands and shrubland) to enhance predictive capacity and the seasonal variation in the relationships between satellite predictors and VMC and SMC. Results revealed a significant but weak relationship between VMC and predictors at landscape level (R2 = 0.30, RMSEcv = 69.9 %) with S2_NDMI and vegetation cover type being the only significant predictors. The relationship improves in vegetation type models for grasslands (R2 = 0.35, RMSEcv = 95.0 % with S2_NDVI) and shrublands conditions (R2 = 0.52, RMSEcv = 45.3 %). A model incorporating S2_NDVI and S1_VV explained 52 % of the variation in VMC in shrublands. The relationship between SMC and satellite predictors at the landscape level was also weak, with only the S2_NDMI and vegetation cover type exhibiting a significant relationship (R2 = 0.28, RMSEcv = 18.9 %). Vegetation type models found significant associations with SMC only in shrublands (R2 = 0.31, RMSEcv = 9.03 %) based on the S2_NDMI and S1_VV/VH ratio. The seasonal analysis revealed however that predictors associated to VMC and SMC may vary over the summer. The relationships with VMC were stronger in the early summer (R2 = 0.31, RMSEcv = 90.1 %; based on S2_NDMI) and mid (R2 = 0.37, RMSEcv = 70.8 %; based on S2_NDVI), butnon-significant in the end of summer. Similar pattern was found for SMC, where the link with predictors decreases from the early summer (R2 = 0.33, RMSEcv = 16.0 %; based on S1_VH) and mid summer (R2 = 0.30, RMSEcv = 17.8 %; based on S2_NDMI) to the end of summer (non-significant). Overall, the hypothesis of a universal landscape model for VMC and SMC was not fully supported. Vegetation type models showed promise, particularly for VMC in shrubland conditions. Sentinel optical and radar data were the most significant predictors in all models, despite the inclusion of ancillary predictors. S2_NDVI, S2_NDMI, S1_VV and S1_VV/VH ratio were the most relevant predictors for VMC and, to a lesser extent, SMC. Future research should quantify misregistration effects using plot vs. moving window values for the satellite predictors, consider meteorological control factors, and enhance sampling to overcome a main limitation of our study, small sample size.

Abstract Invasion dynamics are determined, among other aspects, by the spatial behaviour of invasive populations. The invasive toad Duttaphrynus melanostictus is spreading inland from the eastern coast of Madagascar, causing considerable ecological impacts. Understanding the basic factors determining the spread dynamics can inform management strategies and provide insights into spatial evolutionary processes. We radio-tracked 91 adult toads in three localities along the invasion gradient to determine whether spatial sorting of dispersive phenotypes is occurring, and investigate intrinsic and extrinsic determinants of spatial behaviour. Overall, toads in our study appeared as habitat generalists, and their sheltering behaviour was tied to water proximity, with toads changing shelter more frequently closer to waterbodies. Toads showed low displacement rates (mean = 4.12 m/day) and quite a philopatric behaviour but were able to perform daily movements of over 50 m. We did not detect any spatial sorting of dispersal-relevant traits nor sex- or size-biased dispersal. Our results suggest that toads are more likely to expand their range during the wet season, and that the range expansion is probably dominated by short-distance dispersal at this stage of the invasion, although a future increase in invasion speed is expected, due to the capacity for long-distance movements of this species.

A single population of interferon-regulatory factor 8 (Irf8)-dependent conventional dendritic cell (cDC type1) is considered to be responsible for both immunogenic and tolerogenic responses depending on the surrounding cytokine milieu. Here, we challenge this concept of an omnipotent single Irf8-dependent cDC1 cluster through analysis of pulmonary cDCs at single cell resolution. We report existence of a pulmonary cDC1 cluster lacking Xcr1 with an immunogenic signature that clearly differs from the Xcr1 positive cDC1 cluster. The Irf8+Batf3+Xcr1- cluster expresses high levels of pro-inflammatory genes associated with antigen presentation, migration and co-stimulation such as Ccr7, Cd74, MHC-II, Ccl5, Il12b and Relb while, the Xcr1+ cDC1 cluster expresses genes corresponding to immune tolerance mechanisms like Clec9a, Pbx1, Cadm1, Btla and Clec12a. In concordance with their pro-inflammatory gene expression profile, the ratio of Xcr1- cDC1s but not Xcr1+cDC1 is increased in the lungs of allergen-treated mice compared to the control group, in which both cDC1 clusters are present in comparable ratios. The existence of two distinct Xcr1+ and Xcr1- cDC1 clusters is furthermore supported by velocity analysis showing markedly different temporal patterns of Xcr1- and Xcr1+cDC1s. In summary, we present evidence for the existence of two different cDC1 clusters with distinct immunogenic profiles in vivo. Our findings have important implications for DC-targeting immunomodulatory therapies.

Covid-19 pandemic affected virtually every sector of life, due to the need to avoid agglomerations and maintaining social isolation. In education, teaching-learning processes have changed drastically and now are in a state of fast transformation. Schools all around the world were closed and teachers and students had to migrate to online environments. In this paper, we present a whole design process of conceiving an educational mobile application that explores the students' houses as a source of knowledge through gamification and augmented reality. Field studies such as online surveys and user diaries were employed and resulted into the creation of eleven personas representing teachers, parents, and students. After a brainstorming session and the definition of the basic requirements, a low-fidelity prototype was created and evaluated by ten students aged 6 to 11 years old from both public and private schools. The results of the evaluation pointed to the potential of the app in keeping children motivated and interested in learning while at home. We highlight the challenges and lessons learned in doing user research involving kids during a pandemic.

Latent tuberculosis infection (LTBI) represents a subclinical, asymptomatic mycobacterial state affecting approximately 25% of the global population. The substantial prevalence of LTBI, combined with the risk of progressing to active tuberculosis, underscores its central role in the increasing incidence of tuberculosis (TB). Accurate identification and timely treatment are vital to contain and reduce the spread of the disease, forming a critical component of the global strategy known as “End TB.” This review aims to examine and highlight the most recent scientific evidence related to new diagnostic approaches and emerging therapeutic treatments for LTBI. While prevalent diagnostic methods include the tuberculin skin test (TST) and interferon gamma release assay (IGRA), WHO’s approval of two specific IGRAs for Mycobacterium tuberculosis (MTB) marked a significant advancement. However, the need for a specific test with global application viability has propelled research into diagnostic tests based on molecular diagnostics, pulmonary immunity, epigenetics, metabolomics, and a current focus on next-generation MTB antigen-based skin test (TBST). It is within these emerging methods that the potential for accurate distinction between LTBI and active TB has been demonstrated. Therapeutically, in addition to traditional first-line therapies, anti-LTBI drugs, anti-resistant TB drugs, and innovative candidates in preclinical and clinical stages are being explored. Although the advancements are promising, it is crucial to recognize that further research and clinical evidence are needed to solidify the effectiveness and safety of these new approaches, in addition to ensuring access to new drugs and diagnostic methods across all health centers. The fight against TB is evolving with the development of more precise diagnostic tools that differentiate the various stages of the infection and with more effective and targeted treatments. Once consolidated, current advancements have the potential to transform the prevention and treatment landscape of TB, reinforcing the global mission to eradicate this disease.

Changing ecosystem conditions and diverse socio-economical events have contributed to an ingrained presence of non-native tree species (NNTs) in the natural and cultural European landscapes. Recent research endeavors have focused on different aspects of NNTs such as legislation, benefits, and risks for forestry, emphasizing that large knowledge gaps remain. As an attempt to fulfill part of these gaps, within the PEN-CAFoRR COST Action (CA19128) network, we established an open-access questionnaire that allows both academic experts and practitioners to provide information regarding NNTs from 20 European countries. Then, we integrated the data originating from the questionnaire, related to the country-based assessment of both peer-reviewed and grey literature, with information from available datasets (EUFORGEN and EU-Forest), which gave the main structure to the study and led to a mixed approach review. Finally, our study provided important insights into the current state of knowledge regarding NNTs. In particular, we highlighted NNTs that have shown to be less commonly addressed in research, raising caution about those characterized by an invasive behavior and used for specific purposes (e.g., wood production, soil recultivation, afforestation, and reforestation). NNTs were especially explored in the context of resilient and adaptive forest management. Moreover, we emphasized the assisted and natural northward migration of NNTs as another underscored pressing issue, which needs to be addressed by joint efforts, especially in the context of the hybridization potential. This study represents an additional effort toward the knowledge enhancement of the NNTs situation in Europe, aiming for a continuously active common source deriving from interprofessional collaboration.

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.

This paper discusses the problem of room usage optimization for university timetables: given a timetable, we want to optimize the room occupation by determining the events allocated to each room, while ensuring that the rooms have enough capacity to “seat” all people participating in those events. This paper contributes with two approaches to the problem of optimizing the timetable scheduled for each room. The first approach consists of a two-stage Integer Linear Programming (ILP) which applies a lexicographic optimization wherein the goal of the first stage is to maximize the number of students seated and that of the second stage is to optimize the room occupation. This is provably optimal, in both optimization criteria. However, it is computationally demanding, requiring significant computation time for large problems. To address this issue, we propose a second approach, consisting of a greedy algorithm. The algorithm assigns lectures to rooms greedily, according to a specific cost function that seeks to maximize the number of students seated. We show that the proposed cost function guarantees that the greedy algorithm performs within 63% of the total number of students. We apply both algorithms in a case study involving real data from Instituto Superior Técnico (IST), the engineering school from Universidade de Lisboa. Our results confirm that the greedy algorithm is two orders of magnitude faster than ILP when considering large data sets. Comparing the performance of the two methods we observe that the performance of the greedy algorithm, when compared to the ILP-based approach, is within 2% for the number of seated students and 34% for the room occupation. The GRASP algorithm is a good extension of the greedy algorithm, which is able to improve in 12% the quality of the solution (in terms of compactness) without adding significant CPU time. Overall, the two proposed approaches provide significant gains for both optimization criteria when compared to the current hand-made solutions. Keywords: University timetabling, Room usage optimization, Integer linear programming, Greedy algorithms

Cellular responses are governed by regulatory networks subject to external signals from surrounding cells and to other micro-environmental cues. The logical (Boolean or multi-valued) framework proved well suited to study such processes at the cellular level, by specifying qualitative models of involved signalling pathways and gene regulatory networks. Here, we describe and illustrate the main features of EpiLog, a computational tool that implements an extension of the logical framework to the tissue level. EpiLog defines a collection of hexagonal cells over a 2D grid, which embodies a mono-layer epithelium. Basically, it defines a cellular automaton in which cell behaviours are driven by associated logical models subject to external signals. EpiLog is freely available on the web at http://epilog-tool.org. It is implemented in Java (version ≥1.7 required) and the source code is provided at https://github.com/epilog-tool/epilog under a GNU General Public License v3.0.