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Bio-based building materials are susceptible to degradation by decay fungi when exposed to favourable conditions for fungal growth. The risk of decay depends on both the environmental conditions and material resistance. Although durability data is available for solid wood, standard tests mainly focus on mass loss assessment after a fixed degradation period. In-depth knowledge on time-dependent decay patterns is lacking, yet could prove relevant for optimisations of the structural design of engineered wood products to increase their service life. Here, a novel X-ray CT scanning set-up and analysis pipeline for non-destructive assessment of the influence of material structure on temporal decay patterns under laboratory conditions is presented. Wood specimens that were severely degraded showed a pattern in which the density decreased substantially after 2 weeks, due to wood mass being metabolised into mycelium, carbon dioxide and water. The influence of various anatomical features, such as the vessel orientation and the number of growth rings, on wood degradation patterns was demonstrated. This approach is a starting point for further in-depth research on the influence of material structure on decay. The method is especially interesting to assess the efficacy of structural modification strategies that are expected to inhibit decay or enhance service life.

While microbiome alterations are increasingly proposed as a rapid mechanism to buffer organisms under changing environmental conditions, studies of these processes in the marine realm are lagging far behind their terrestrial counterparts. Here, we used a controlled laboratory experiment to examine whether the thermal tolerance of the brown seaweed Dictyota dichotoma, a common species in European coastal ecosystems, could be enhanced by the repeated addition of bacteria from its natural environment. Juvenile algae from three genotypes were subjected for two weeks to a temperature gradient, spanning almost the entire thermal range that can be tolerated by the species (11-30 degrees C). At the start of the experiment and again in the middle of the experiment, the algae were inoculated with bacteria from their natural environment or left untouched as a control. Relative growth rate was measured over the two-week period, and we assessed bacterial community composition prior to and at the end of the experiment. Since the growth of D. dichotoma over the full thermal gradient was not affected by supplementing bacteria, our results indicate no scope for bacterial-mediated stress alleviation. The minimal changes in the bacterial communities linked to bacterial addition, particularly at temperatures above the thermal optimum (22-23 degrees C), suggest the existence of a barrier to bacterial recruitment. These findings indicate that ecological bacterial rescue is unlikely to play a role in mitigating the effects of ocean warming on this brown seaweed.

We present a novel method to study two-dimensional rotation-symmetric number-conserving multi-state cellular automata with the von Neumann neighborhood with radius one. This method enables a succinct and easy enumeration in all cases examined so far in literature, i.e., cellular automata with at most five states. Moreover, it allows to find all such cellular automata with six and seven states, while so far, even enumerating six-state rules was beyond the reach of computing machines. Such enumeration allows us to revisit some unresolved questions in the field. Furthermore, we give some rough estimates of the asymptotic growth of the number of such cellular automata with n states, as n tends to infinity. The results are obtained for finite square grids with periodic boundary conditions, but they are also valid in the case of the infinite square grid.

Cryptic species are morphologically identical but genetically distinct, and are prominent across numerous phyla. The coexistence of such closely related species on local scales would seem to run counter to traditional coexistence and competition theory; it has been hypothesized as a consequence of differences in their resource use or tolerances to environmental conditions. We developed an individual-based model of a community of three cryptic Litoditis marina (nematode) species, to understand how individual-level interspecific and intraspecific interactions might explain the coexistence of these closely related species. The model incorporates individuals' reproduction, competition, dispersal and resource use. Data characterizing the cryptic species (growth rates, dispersal ability, competitive interactions and responses to changing environmental conditions) were obtained from laboratory experiments involving both mono- and multispecific nematode cultures, and are used to parameterize the model. Simulation studies are used to investigate which individual-level mechanisms of dispersal and interaction lead to the characteristic population-level patterns observed experimentally. Our results highlight the key role of intraspecific competition in mediating dispersal and therefore co-occurrence of the cryptic species. The differences in dispersal also influence the response of the cryptic species to competition, a combination of factors that provides an explanation for their co-occurrence. These results provide insights into how changes in individual-level processes can be amplified to affect population-level co-occurrence.

The effect of fungicidal components in wood has been known for ages, yet there is no method to assess the impact of such components on the durability of a wood species, as compared to other material characteristics that influence decay. In this paper, the importance of fungicidal effects on the natural durability of 10 wood species is assessed in relation to other decay-influencing factors with a new test, the so-called ‘paste test’. By comparing results from this test with the ‘mini-block test’, on both heartwood and leached sapwood, insight is gained into the significance of fungicidal components on the one hand and other material characteristics on the other hand. The durability of species such as Prunus avium was attributed mainly to fungicidal components. For species such as Pterocarpus soyauxii, durability seemed to be an effect of both fungicidal components and moisture-regulating components, while the latter seemed to be of main importance in regulating the decay of Aucoumea klaineana and Entandrophragma cylindricum. Wood-anatomical features, such as the parenchyma content (in case of brown rot fungi) and the vessel-fiber ratio, possibly affect degradation as well. This work shows that fungicidal components are not always of major importance for the durability of a wood species. The authors hereby emphasize the importance of moisture-regulating components and wood anatomy on the durability of wood.

Predicting kiln-drying schedules for a given wood species is not yet possible, despite many research efforts. This is mainly due to insufficiently considering important spatio-temporal variables such as moisture and temperature gradients, and wood anatomical features during drying in the predictive modelling framework. To understand the influence of aforementioned variables, an experimental measurement set-up was developed consisting of heat-resistant load cells mounted in an oven to record the weight of the wood specimens dried at 100 °C. A camera was mounted outside the oven to acquire images of the end grain through the transparent door of the oven, as such enabling to monitor the occurrence of cracks. Furthermore, custom-made electrodes were inserted as well at different depths in the specimen to measure the electrical resistance, which relates to the moisture content. Specimens were also equipped with thermistors to record the temperature in the specimens at different depths from the surface. Heat-resistant wires from load cells, electrodes and thermistors were connected with data loggers outside the oven. Three specimens of basralocus (Dicorynia guianensis) were selected, measuring 50 mm wide, 50 mm thick and 120 mm long. The results show that it is possible to real-time monitor average moisture loss, local moisture content gradient represented by the electrical resistance gradient and wood temperature gradient as a function of time. These temporally resolved gradients can be related to the occurrence of cracks. This methodology enables to unveil interrelationships between the measured variables and changes of the wood anatomical structure, of utmost importance for the fine-tuning of kiln-drying schedules.

An understanding of the oceanic response to tropical cyclones is of importance for studies on climate change, ecological variability and environmental protection. Hurricane Irma (2017, Atlantic Ocean) broke many records, including the fact that it was the first category 5 hurricane making landfall in Cuba since 1924. In this study, we assess the oceanic response of the waters of the Cuban Exclusive Economic Zone (EEZ) and the eastern Gulf of Mexico (GoM) to the passage of this hurricane. Overall, Irma led to a weak sea surface cooling in the EEZ, which was associated with the thermal structure of its waters and the fact that it was affected by the left-side quadrants of this hurricane. This cooling was driven by mixing and upwelling processes. In contrast, the chlorophyll-a (chl-a) concentration increase was comparable with climatological records, suggesting that horizontal advection of coastal waters and entrainment of chl-a rich waters from remote regions of the GoM influenced the post-storm chl-a concentration. Moreover, Irma increased the chl-a concentration in the northeastern GoM and stimulated the offshore transport of these chl-a-rich waters to the interior GoM. A high chl-a plume (HCP) extended southward across the eastern GoM during the first post-storm week of Irma, and these waters reached the northwestern Cuban coast following the Loop Current. An intensification of the geostrophic currents of an anticyclonic eddy at the upper front of the Loop Current, the formation of an anticyclonic-cyclonic eddy pair in the northeastern GoM and wind-driven advection governed the extension of this HCP.

In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed.

Classification is one of the most important problems in the theory of CA. For instance, assessing how CA design choices impact the generated dynamics requires an overview of the types of dynamics that can occur. Nevertheless, an overview and critical comparison that includes works from the last five years is currently lacking. This paper provides such a structured overview, where firstly classifications based on the CA’s rule table are considered, and secondly classifications based on the space–time pattern generated by a CA. The review indicates that most currently available classification schemes are limited to elementary cellular automata and highlights the existing dichotomy in CA research: theoretical research focusing on analytical results from topological dynamics and the theory of computation on the one hand, experimental research focusing on the statistical properties of the simulated space–time patterns on the other hand.

Many disciplines rely on testing combinations of compounds, materials, proteins, or bacterial species to drive scientific discovery. It is time-consuming and expensive to determine experimentally, via trial-and-error or random selection approaches, which of the many possible combinations will lead to desirable outcomes. Hence, there is a pressing need for more rational and efficient experimental design approaches to reduce experimental effort. In this work, we demonstrate the potential of machine learning methods for the in silico selection of promising co-culture combinations in the application of bioaugmentation. We use the example of pollutant removal in drinking water treatment plants, which can be achieved using co-cultures of a specialized pollutant degrader with combinations of bacterial isolates. To reduce the experimental effort needed to discover high-performing combinations, we propose a data-driven experimental design. Based on a dataset of mineralization performance for all pairs of 13 bacterial species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompertz mineralization parameters of the co-cultures of two and three species, based on the single-strain parameters. We subsequently used this model in a Bayesian optimization scheme to suggest potentially high-performing combinations of bacteria. We achieved good performance with this approach, both for predicting mineralization parameters and for selecting effective co-cultures, despite the limited dataset. As a novel application of Bayesian optimization in bioremediation, this experimental design approach has promising applications for highlighting co-culture combinations for in vitro testing in various settings, to lessen the experimental burden and perform more targeted screenings.

Individual-based modelling is an increasingly popular framework for modelling biological systems. Many of these models represent space as a lattice, thus imposing unrealistic limitations on the movement of the modelled individuals. We adapt an existing model of three competing species by using a lattice-free approach, thereby improving the realism of the spatial dynamics. We retrieve the same qualitative dynamics as the lattice-based approach. However, by facilitating a higher spatial heterogeneity and allowing for small spatial refuges to form and persist, the maintenance of coexistence is promoted, in correspondence with experimental results. We also implement a directed movement mechanism allowing individuals of different species to pursue or flee from each other. Simulations show that the effects on coexistence depend on the level of aggregation in the community: a high level of aggregation is advantageous for maintaining coexistence, whereas a low level of aggregation is disadvantageous. This agrees with experimental results, where pursuing and escaping behaviour has been observed to be advantageous only in certain circumstances.

The extinction of ecosystems and the mechanisms that support or limit species coexistence have long been studied by scientists. It has been shown that competition and cyclic dominance among species promote species coexistence, such as in the classic Rock-Paper-Scissors (RPS) game. However, individuals’ mobility and the underlying topology that defines the neighbourhood relations between individuals also play an important role in maintaining biodiversity. Typically, square grids are used for simulating such interactions. However, these constrain the individuals’ spatial degrees of freedom. In this work, we investigate the effect of the underlying topology on the RPS dynamics. For that purpose, we considered networks with varying node degree distributions and generated according to different theoretical models. We analyzed the time to the first extinction and the patchiness of the in silico ecosystem over time. In general, we observed a distinct large effect of the network topology on the RPS dynamics. Moreover, leaving regular networks aside, the probability of extinction is very high for some network models due to their inherent long-range connections. On the other hand, spatial arrangements characterized by nearest neighbors interactions have fewer long-range correlations, which is essential for biodiversity.

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we pres ent a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wild land fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence.

Objectives The COVID-19 pandemic has had a major impact on our society, with drastic policy restrictions being implemented to contain the spread of the severe acute respiratory syndrome coronavirus 2. This study aimed to provide an overview of the available evidence on the cost-effectiveness of various coronavirus disease 2019 policy measures. Methods A systematic literature search was conducted in PubMed, Embase, and Web of Science. Health economic evaluations considering both costs and outcomes were included. Their quality was comprehensively assessed using the Consensus Health Economic Criteria checklist. Next, the quality of the epidemiological models was evaluated. Results A total of 3688 articles were identified (March 2021), of which 23 were included. The studies were heterogeneous with regard to methodological quality, contextual factors, strategies’ content, adopted perspective, applied models, and outcomes used. Overall, testing/screening, social distancing, personal protective equipment, quarantine/isolation, and hygienic measures were found to be cost-effective. Furthermore, the most optimal choice and combination of strategies depended on the reproduction number and context. With a rising reproduction number, extending the testing strategy and early implementation of combined multiple restriction measures are most efficient. Conclusions The quality assessment highlighted numerous flaws and limitations in the study approaches; hence, their results should be interpreted with caution because the specific context (country, target group, etc) is a key driver for cost-effectiveness. Finally, including a societal perspective in future evaluations is key because this pandemic has an indirect impact on the onset and treatment of other conditions and on our global economy.

Bio-based materials are gaining importance in the building industry, as the focus on sustainability and life-cycle-assessment has increased substantially over the last decade. Wood and wood-engineered products as well as insulation materials made from cellulose, wood, flax, hemp, etc. are increasingly used. These materials are made from renewable resources and with considerably lower energy consumption than various other building materials, such as insulation polymers, steel and concrete. As steel can corrode and concrete can rot, so can bio-based building materials degrade over time when exposed to those conditions that favour decay. Since fungi cause not only aesthetical degradation, but can also severely compromise the structural integrity of a building component this is critical for any service life approach. Consequently, a proper understanding of the fungal susceptibility of bio-based materials is needed, both for optimal application of bio-based materials as for the design of new materials. Based on a combination of tests we try to unravel the role of the material’s chemical components, structure and moisture dynamics on its fungal susceptibility, as well as the interaction between those material characteristics. In a first test set-up, the ‘paste test’, the material’s structure is removed and fungal growth is assessed over time in 2D, with only the material’s chemical components playing a role. In the second test set-up, the ‘X-ray CT test’, fungal development is assessed non-destructively in 3D with X-ray CT, giving an indication of moisture production and distribution over time, in relation to the material’s structure. By comparing the results, we have a better idea of how much each material characteristic influences fungal susceptibility. This knowledge can then be used for optimising fungal testing of bio-based materials, ensuring optimal application and providing the building industry with the confidence they need to pave the way to a more sustainable future.

As a response to a rapidly rising number of SARS-CoV-2 infections, the Belgian government imposed strict social contact restrictions on March 13th, 2020. After nearly two months, the curve was succesfully flattened and social restrictions were gradually released. Unfortunately, pharmaceutical interventions are not yet available so it is expected that preventing COVID-19 outbreaks will depend mostly on the successful implementation of non-pharmaceutical interventions, hence the need for well-informed models. In this study, we built a deterministic, continuous-time, age-stratified-SEIRD model with detailed hospital dynamics. Because the hospitalization data for Belgium have not been made public yet by the Belgian scientific institute of public health, we computed the hospitalization parameters based on data from 370 patients in two Ghent (Belgium) hospitals. The basic reproduction number during March 2020 was estimated as R0 = 2.83 and the model fits the hospitalization and ICU admission incidence under lockdown measures well. In spite of the release of social restrictions, hospitalizations have been steadily declining. We recomputed the basic reproduction number under lockdown release and found that it had to be as low as R0 = 0.73 to explain the endemic trend under the observed community mobility data. We further found that although the basic reproduction number in the population older than 70 years was smaller than one, this group compromises nearly half of the expected hospitalizations. This indicates that protection of the elderly may be the most efficient way to reduce strain on the public health care system in case of another SARS-CoV-2 outbreak.

In recent decades, large wildfires have inflicted considerable damage on valuable Natura 2000 regions in Belgium. Despite these events and the general perception that global change will exacerbate wildfire prevalence, this has not been studied yet in the Belgian context. Therefore, the national government initiated the national action plan on wildfires in order to evaluate the wildfire risk, on the one hand, and the materials, procedures, and training of fire services, on the other hand. This study focuses on the spatial distribution of the ignition probability, a component of the wildfire risk framework. In a first stage, we compile a historical wildfire database using (i) newspaper articles between 1994 and 2016 and (ii) a list of wildfire interventions between 2010 and 2013, provided by the government. In a second stage, we use a straightforward method relying on Bayes' rule and a limited number of covariates to calculate the ignition probability. It appears that most wildfire-prone areas in Belgium are located in heathland where military exercises are held. The provinces that have the largest relative areas with a high or very high wildfire risk are Limburg and Antwerp. Our study also revealed that most wildfire ignitions in Belgium are caused by humans (both arson and negligence) and that natural causes such as lightning are rather scarce. Wildfire prevention can be improved by (i) excluding military activity in fire-prone areas during the fire season, (ii) improving collaboration with foreign emergency services, (iii) concentrating the dedicated resources in the areas that display the highest ignition probabilities, (iv) improving fire detection methods, and (v) raising more awareness among the public ispartof: Natural Hazards And Earth System Sciences vol:20 issue:2 pages:363-376 status: published

In this paper, the classical density classification problem is considered in the context of affine continuous cellular automata. It has been shown earlier that there exists no general solution to this problem that is valid for any number of cells. Here, we consider this problem in the case of a fixed number of cells. Necessary conditions for solving the problem are formulated. Based on this knowledge, a specific class of affine continuous cellular automata is evaluated experimentally for 23 cells. A rich solution set is analysed and visualised.