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Sets of presence records used to model species’ distributions typically consist of observations collected opportunistically rather than systematically. As a result, sampling probability is geographically uneven, which may confound the model's characterization of the species’ distribution. Modelers frequently address sampling bias by manipulating training data: either subsampling presence data or creating a similar spatial bias in non‐presence background data. We tested a new method, which we call ‘background thickening’, in the latter category. Background thickening entails concentrating background locations around presence locations in proportion to presence location density. We compared background thickening to two established sampling bias correction methods – target group background selection and presence thinning – using simulated data and data from a case study. In the case study, background thickening and presence thinning performed similarly well, both producing better model discrimination than target group background selection, and better model calibration than models without correction. In the simulation, background thickening performed better than presence thinning when the number of simulated presence locations was low, and vice versa. We discuss drawbacks to target group background selection, why background thickening and presence thinning are conservative but robust sampling bias correction methods, and why background thickening is better than presence thinning for small sample sizes. Particularly, background thickening is advantageous for treating sampling bias when data are scarce because it avoids discarding presence records.

The increasing volume of presence‐only (PO) data generated by citizen science initiatives has greatly expanded biodiversity databases, but the statistical use of these data in species distribution models (SDMs) remains limited by strong sampling biases and the absence of reliable absence information. Existing approaches based on Poisson point processes, such as Maxent, provide powerful tools, yet rely on predefined features that restrict their flexibility and scalability. We introduce DeepMaxent, a new SDM framework that leverages neural networks to learn a shared, data‐driven feature extractor across multiple species while remaining grounded in the maximum entropy principle of Maxent, enabling efficient learning even on very large datasets with thousands of species. DeepMaxent uses a normalized Poisson likelihood, which models the probability of choosing each site given a species, to estimate species‐specific suitability surfaces directly from PO observations. In other words, the model predicts suitable locations for each species rather than predicting which species occurs at a given site. We evaluate DeepMaxent on two contrasting datasets: the National Centre for Ecological Analysis and Synthesis (NCEAS) benchmark, containing six small case studies designed to evaluate the impact of spatial sampling biases, and the much larger GeoPlant, dataset covering the whole of Europe. Using PO data for calibration and independent presence–absence data for validation, DeepMaxent consistently outperforms Maxent and leading deep learning‐based SDMs. Compared with Maxent, it achieves an area under the ROC curve of 0.768 versus 0.760 on NCEAS, 0.860 versus 0.823 on GeoPlant and enables the use of high‐dimensional data modalities, such as satellite images, for which Maxent is unsuitable. DeepMaxent combines the normalized Poisson formulation of Maxent with the learnable features, shared among species, of deep learning approaches. This results in better performance than either Maxent or previous deep learning methods, and lower compute requirements than single‐species SDMs, while the formulation makes the method compatible with the integration of survey data to further improve sampling bias correction.

Ecological niche models, crucial for estimating the potential distribution of species under global change, can face reduced accuracy when the timing of occurrence data does not align with the environmental data. One solution is to ensure a close temporal match between the environment and the observation date. While this approach is typically recommended for highly mobile species, a few findings support its use for species with limited mobility, whose distributions may be responding to climate change via local population changes. Additionally, it remains unclear what specific temporal resolution could improve model performance. This study assesses the effectiveness of temporal matching for a species with low mobility, the Mexican small‐eared shrew (Cryptotis mexicanus), by evaluating different temporal resolutions (one‐, five‐, and ten‐year averaged environmental data) against the standard method (30‐year). Occurrences between 1981 and 2010 were used for model training and cross‐validation, while those outside this range were used for independent evaluation. To address the temporal bias in occurrence data, dates were assigned to all background points through geographic interpolation of observation dates of species that can be captured similarly to the shrew. Based on the omission rate of the independent evaluation occurrences, the approaches that matched environmental data performed better than the standard 30‐year average approach, while the rest of validation metrics (for any temporal resolution) were not different. Visual inspection indicated that the geographic predictions resulting from time‐matched approaches were as realistic as the one from the standard 30‐year approach. The improved prediction of temporally independent occurrence data (not used in model training) with time‐matched approaches underscores the practical advantage of this methodology for low‐mobility species, enhancing model performance and geographic predictions, which may also improve forecasts for future environmental conditions. Additionally, this approach identifies a potential time lag between climatic changes and population responses in this species. Studies can select the optimal temporal resolution by exploring several or using available information about population responses to climate change.

The presence-only data stored in natural history collections is the most important source of information available regarding the distribution of organisms. These data and profile techniques can be used to generate species distribution models (SDMs), but pseudo-absences must be generated to use group discriminative techniques. In this study, we evaluated whether the SDMs generated with pseudo-absences are reliable and also if there are differences in the results obtained with profile and group discriminative techniques. Ecuador, South America. The SDMs were generated with a training data set for each of the five species of Anthurium and six different methods: two profile techniques (BIOCLIM and Gower's distance index), three group discriminative techniques [logistic multiple regression (LMR), multivariate adaptative regression splines (MARS) and M axent] and a mixed modelling approach genetic algorithm for rule-set production (GARP), which employs a combination of profile and group discriminative techniques and generates its own pseudo-absences. For LMR, MARS and M axent, three types of absences were generated: (1) random pseudo-absences in equal number to presences and excluding a buffer area around presences (except for M axent, which assumes that this background sample includes presences), (2) a large number (10,000) of random pseudo-absences, also excluding a buffer area around each presence and (3) 'target-group absences' (TGA), consisting of sites where other species of the group have been collected by the specialist, but not the species being modelled. To compare the predictive performance of the SDMs, the area under the curve statistic was calculated using an independent testing data set for each species. MARS, M axent and LMR produce better results than the profile techniques. The models created with TGA are generally more accurate than those generated with pseudo-absences. The advantages and disadvantages of different options for using pseudo-absences and TGA with profile and group discriminative modelling techniques are explained and recommendations are made for the future.

Unmanned aerial vehicle (UAV) cluster combat is a typical example of an intelligent cluster application, and it is characterized by its large scale, low cost, retrievability, and intra-cluster autonomous coordination. An unmanned reconnaissance mission for a target group (URMFTG) is a significant pattern in UAV cluster combat. This paper discusses the collaborative path planning problem of unmanned aerial vehicle formations (UAVFs) and refueling tankers in a URMFTG with threat areas and fuel constraints. The purpose of collaborative path planning is to ensure that the UAVFs (with fuel constraints) can complete the reconnaissance mission for the target group with the assistance of refueling tankers, which is one of the most important constraints in the collaborative path planning. In this paper, a collaborative path-planning model is designed to analyze the relationship between the planning path of the UAVFs and the tankers, and a threat avoidance strategy is designed considering the threat area. This paper proposes a two-stage solution algorithm. It creates a UAVFs path-planning algorithm based on the fast search genetic algorithm (FSGA) and a refueling tanker path-planning algorithm based on the improved non-dominated sorting genetic algorithm II (NSGA-II). Based on simulation experiments, the solution method proposed in this paper can provide a better path-planning scheme for a URMFTG. That is, it decreases the rate of the UAVF’s distance growth from 3.1% to 2.2% for the path planning of UAVFs and provides a better Pareto solution set for the path planning of refueling tankers.

Predicting species geographic distributions is key to managing invasive species, conserving biodiversity, and understanding species' environmental requirements. Species distribution models (SDMs) commonly focus on climatic predictors, but other environmental factors can also be essential, particularly for species with specialized habitats defined by hydrologic, topographic, or edaphic conditions (e.g., riparian, wetland, alpine, coastal, serpentine). Here, we demonstrate a novel approach for capturing strong effects of both hydrologic and climatic predictors in SDMs for riparian plants, by merging analyses targeted at environmental drivers within riparian ecosystems and across the western USA (3.8 × 106 km2). We developed presence‐background SDMs from five algorithms for three invasive riparian trees (Tamarix ramossisima/chinensis [saltcedar], Elaeagnus angustifolia [Russian olive], and Ulmus pumila [Siberian elm]) and three native Populus spp. (cottonwoods). We used separate background datasets to develop models with different spatial scales of inference: (1) spatially filtered random points to represent available habitat across the study area and (2) target‐group points from Salix (willow) occurrences to represent available riparian habitat. Random‐background models captured hydrologic drivers of riparian tree distributions relative to the largely upland western USA, whereas Salix‐background models captured climatic drivers within the context of riparian ecosystems. Combining predictions from the two backgrounds identified hydrologically suitable habitats within climatically suitable regions, resulting in fewer false “absences” than either background alone, improving predictions over previous SDMs, and providing more complete information to guide management decisions. Surprisingly, the predicted habitat for U. pumila, a newly recognized riparian invader, was as or more extensive than Populus deltoides/fremontii, T. ramossisima/chinensis, and E. angustifolia, the most common riparian tree complexes in the western USA. Watersheds constituting 20% of U. pumila predicted habitat contained no occurrence records, indicating high risk of future and unrecognized invasions. Combining models from random and ecosystem‐specific target‐group backgrounds may improve SDMs for species from many specialized habitats, providing a method to link predicted distributions to localized geographic features while capturing broad‐scale climatic requirements.

Background Health reporting shall provide up-to-date health-related data to inform policy-makers, researchers and the public. To this end, health reporting formats should be tailored to the needs and competencies of the target groups and provide comparable and high-quality information. Within the Joint Action on Health Information ‘InfAct’, we aimed at gaining an overview of health reporting practices in the EU Member States and associated countries, and developed quality criteria for the preparation of public health reports. The results are intended to facilitate making health information adequately available while reducing inequalities in health reporting across the EU. Methods A web-based desk research was conducted among EU Member States and associated countries to generate an overview of different formats of national health reporting and their respective target groups. To identify possible quality criteria for public health reports, an exploratory literature review was performed and earlier projects were analysed. The final set of criteria was developed in exchange with experts from the InfAct consortium. Results The web-based desk research showed that public health reports are the most frequently used format across countries (94%), most often addressed to scientists and researchers (51%), politicians and decision-makers (41%). However, across all reporting formats, the general public is the most frequently addressed target group. With regards to quality criteria for public health reports, the literature review has yielded few results. Therefore, two earlier projects served as main sources: the ‘Evaluation of National and Regional Public Health Reports’ and the guideline ‘Good Practice in Health Reporting‘from Germany. In collaboration with experts, quality criteria were identified and grouped into eight categories, ranging from topic selection to presentation of results, and compiled in a checklist for easy reference. Conclusion Health reporting practices in the EU are heterogeneous across Member States. The assembled quality criteria are intended to facilitate the preparation, dissemination and access to better comparable high-quality public health reports as a basis for evidence-based decision-making. A comprehensive conceptual and integrative approach that incorporates the policy perspective would be useful to investigate which dissemination strategies are the most suitable for specific requirements of the targeted groups.

Background Many patients suffering from peripheral arterial disease (PAD) are dependent on bypass surgery. However, in some patients no suitable replacements (i.e. autologous or prosthetic bypass grafts) are available. Advances have been made to develop autologous tissue engineered vascular grafts (TEVG) using endothelial colony forming cells (ECFC) obtained by peripheral blood draw in large animal trials. Clinical translation of this technique, however, still requires additional data for usability of isolated ECFC from high cardiovascular risk patients. Bovine carotid arteries (BCA) were decellularized using a combined SDS (sodium dodecyl sulfate) -free mechanical-osmotic-enzymatic-detergent approach to show the feasibility of xenogenous vessel decellularization. Decellularized BCA chips were seeded with human ECFC, isolated from a high cardiovascular risk patient group, suffering from diabetes, hypertension and/or chronic renal failure. ECFC were cultured alone or in coculture with rat or human mesenchymal stromal cells (rMSC/hMSC). Decellularized BCA chips were evaluated for biochemical, histological and mechanical properties. Successful isolation of ECFC and recellularization capabilities were analyzed by histology. Results Decellularized BCA showed retained extracellular matrix (ECM) composition and mechanical properties upon cell removal. Isolation of ECFC from the intended target group was successfully performed (80% isolation efficiency). Isolated cells showed a typical ECFC-phenotype. Upon recellularization, co-seeding of patient-isolated ECFC with rMSC/hMSC and further incubation was successful for 14 ( n  = 9) and 23 ( n  = 5) days. Reendothelialization (rMSC) and partial reendothelialization (hMSC) was achieved. Seeded cells were CD31 and vWF positive, however, human cells were detectable for up to 14 days in xenogenic cell-culture only. Seeding of ECFC without rMSC was not successful. Conclusion Using our refined decellularization process we generated easily obtainable TEVG with retained ECM- and mechanical quality, serving as a platform to develop small-diameter (< 6 mm) TEVG. ECFC isolation from the cardiovascular risk target group is possible and sufficient. Survival of diabetic ECFC appears to be highly dependent on perivascular support by rMSC/hMSC under static conditions. ECFC survival was limited to 14 days post seeding.

Aim Accounting for sampling bias is the greatest challenge facing presence‐only and presence‐background species distribution models; no matter what type of model is chosen, using biased data will mask the true relationship between occurrences and environmental predictors. To address this issue, we review four established bias correction techniques, using empirical occurrences with known sampling effort, and virtual species with known distributions. Innovation Occurrence data come from a national recording scheme of hoverflies (Syrphidae) in Great Britain, spanning 1983–2002. Target‐group backgrounds, distance‐restricted backgrounds, travel time to cities and human population density were used to account for sampling bias in 58 species of hoverfly. Distributions generated by bias correction techniques were compared in geographical space to the distribution produced accounting for known sampling effort, using Schoener's distance, centroid shifts and range size changes. To validate our results, we performed the same comparisons using 50 randomly generated virtual species. We used sampling effort from the hoverfly recording scheme to structure our biased sampling regime, emulating complex real‐life sampling bias. Main conclusions Models made without any correction typically produced distributions that mapped sampling effort rather than the underlying habitat suitability. Target‐group backgrounds performed the best at emulating sampling effort and unbiased virtual occurrences, but also showed signs of overcompensation in places. Other methods performed better than no‐correction, but often differences were difficult to visually detect. In line with previous studies, when sampling effort is unknown, target‐group backgrounds provide a useful tool for reducing the effect of sampling bias. Models should be visually inspected for biological realism to identify any areas of potential overcompensation. Given the disparity between corrected and un‐corrected models, sampling bias constitutes a major source of error in species distribution modelling, and more research is needed to confidently address the issue.

Stigma and discrimination in relation to mental illnesses have been described as having worse consequences than the conditions themselves. Most medical literature in this area of research has been descriptive and has focused on attitudes towards people with mental illness rather than on interventions to reduce stigma. In this narrative Review, we summarise what is known globally from published systematic reviews and primary data on effective interventions intended to reduce mental-illness-related stigma or discrimination. The main findings emerging from this narrative overview are that: (1) at the population level there is a fairly consistent pattern of short-term benefits for positive attitude change, and some lesser evidence for knowledge improvement; (2) for people with mental illness, some group-level anti-stigma inventions show promise and merit further assessment; (3) for specific target groups, such as students, social-contact-based interventions usually achieve short-term (but less clearly long-term) attitudinal improvements, and less often produce knowledge gains; (4) this is a heterogeneous field of study with few strong study designs with large sample sizes; (5) research from low-income and middle-income countries is conspicuous by its relative absence; (6) caution needs to be exercised in not overgeneralising lessons from one target group to another; (7) there is a clear need for studies with longer-term follow-up to assess whether initial gains are sustained or attenuated, and whether booster doses of the intervention are needed to maintain progress; (8) few studies in any part of the world have focused on either the service user's perspective of stigma and discrimination or on the behaviour domain of behavioural change, either by people with or without mental illness in the complex processes of stigmatisation. We found that social contact is the most effective type of intervention to improve stigma-related knowledge and attitudes in the short term. However, the evidence for longer-term benefit of such social contact to reduce stigma is weak. In view of the magnitude of challenges that result from mental health stigma and discrimination, a concerted effort is needed to fund methodologically strong research that will provide robust evidence to support decisions on investment in interventions to reduce stigma.