An Updated Algorithm for the Generation of Neutral Landscapes by Spectral Synthesis

Background: Patterns that arise from an ecological process can be driven as much from the landscape over which the process is run as it is by some intrinsic properties of the process itself. The disentanglement of these effects is aided if it possible to run models of the process over artificial landscapes with controllable spatial properties. A number of different methods for the generation of so-called ‘neutral landscapes’ have been developed to provide just such a tool. Of these methods, a particular class that simulate fractional Brownian motion have shown particular promise. The existing methods of simulating fractional Brownian motion suffer from a number of problems however: they are often not easily generalisable to an arbitrary number of dimensions and produce outputs that can exhibit some undesirable artefacts. Methodology: We describe here an updated algorithm for the generation of neutral landscapes by fractional Brownian motion that do not display such undesirable properties. Using Monte Carlo simulation we assess the anisotropic properties of landscapes generated using the new algorithm described in this paper and compare it against a popular benchmark algorithm. Conclusion/Significance: The results show that the existing algorithm creates landscapes with values strongly correlated in the diagonal direction and that the new algorithm presented here corrects this artefact. A number of extensions of the algorithm described here are also highlighted: we describe how the algorithm can be employed to generate landscapes that display different properties in different dimensions and how they can be combined with an environmental gradient to produce landscapes that combine environmental variation at the local and macro scales

Tearing Out the Income Tax by the (Grass)Roots

Landscapes are increasingly fragmented, and conservation programs have started to look at network approaches for maintaining populations at a larger scale. We present an agent-based model of predator–prey dynamics where the agents (i.e. the individuals of either the predator or prey population) are able to move between different patches in a landscaped network. We then analyze population level and coexistence probability given node-centrality measures that characterize specific patches. We show that both predator and prey species benefit from living in globally well-connected patches (i.e. with high closeness centrality). However, the maximum number of prey species is reached, on average, at lower closeness centrality levels than for predator species. Hence, prey species benefit from constraints imposed on species movement in fragmented landscapes since they can reproduce with a lesser risk of predation, and their need for using anti-predatory strategies decreases.authorCount :

Computer-aided prediction of short-term tumor growth in sporadic vestibular schwannomas using both structural and dynamic-contrast enhanced MR imaging

Introduction:Recent studies have demonstrated that microvascular parameters derived from dynamic-contrast enhanced (DCE) MR imaging significantly correlate with tumor growth in vestibularschwannomas (VS). Other studies provide evidence that the use of artificial intelligence (AI)on structural MR data provides similar predictive value for tumor growth.Methods: This prospective study investigates the combination of structural and DCE imaging data for AI to predict short-term tumor growth in VS. A total of 110 newly diagnosed unilateral sporadic VS patients underwent both T2-weighted and DCE MR imaging. Established pipelines were used to estimate the values of DCE-derived parameters Ktrans, ve, and vp. Subsequently, tumors were delineated and only voxel values within the delineation were considered for the AI model development. Radiomic features were extracted from both the structural images and DCE-derived parameter maps. A classifier was trained on the radiomic features to predict tumor growth.Results: Growth was observed in 69 (63%) of the 110 patients during follow-up. A support vector machine (SVM) model was trained on Ktrans and ve radiomic features using five-fold-crossvalidation. This model resulted in an accuracy of 82.5%, sensitivity of 81.2%, specificity of 82.9%, and area-under-the-curve of 0.85. The predictive value of structural MR imaging features is currently under investigation, as well as the use of more complex AI models. It is hypothesized that the addition of structural features and increase in model complexity will improve the model's predictive power. Conclusion: Preliminary results have shown that DCE-derived parameter values exhibit a high predictive value for tumor growth prediction in sporadic VS. Other radiomic features and model types will be analyzed in order to investigate whether they improve the current AI model. These results will be presented during the conference.<br/

Computer-aided prediction of short-term tumor growth in sporadic vestibular schwannomas using both structural and dynamic-contrast enhanced MR imaging

Introduction:Recent studies have demonstrated that microvascular parameters derived from dynamic-contrast enhanced (DCE) MR imaging significantly correlate with tumor growth in vestibularschwannomas (VS). Other studies provide evidence that the use of artificial intelligence (AI)on structural MR data provides similar predictive value for tumor growth.Methods: This prospective study investigates the combination of structural and DCE imaging data for AI to predict short-term tumor growth in VS. A total of 110 newly diagnosed unilateral sporadic VS patients underwent both T2-weighted and DCE MR imaging. Established pipelines were used to estimate the values of DCE-derived parameters Ktrans, ve, and vp. Subsequently, tumors were delineated and only voxel values within the delineation were considered for the AI model development. Radiomic features were extracted from both the structural images and DCE-derived parameter maps. A classifier was trained on the radiomic features to predict tumor growth.Results: Growth was observed in 69 (63%) of the 110 patients during follow-up. A support vector machine (SVM) model was trained on Ktrans and ve radiomic features using five-fold-crossvalidation. This model resulted in an accuracy of 82.5%, sensitivity of 81.2%, specificity of 82.9%, and area-under-the-curve of 0.85. The predictive value of structural MR imaging features is currently under investigation, as well as the use of more complex AI models. It is hypothesized that the addition of structural features and increase in model complexity will improve the model's predictive power. Conclusion: Preliminary results have shown that DCE-derived parameter values exhibit a high predictive value for tumor growth prediction in sporadic VS. Other radiomic features and model types will be analyzed in order to investigate whether they improve the current AI model. These results will be presented during the conference.<br/

Linked randomised controlled trials of face-to-face and electronic brief intervention methods to prevent alcohol related harm in young people aged 14–17 years presenting to Emergency Departments (SIPS junior)

Background: Alcohol is a major global threat to public health. Although the main burden of chronic alcohol-related disease is in adults, its foundations often lie in adolescence. Alcohol consumption and related harm increase steeply from the age of 12 until 20 years. Several trials focusing upon young people have reported significant positive effects of brief interventions on a range of alcohol consumption outcomes. A recent review of reviews also suggests that electronic brief interventions (eBIs) using internet and smartphone technologies may markedly reduce alcohol consumption compared with minimal or no intervention controls. Interventions that target non-drinking youth are known to delay the onset of drinking behaviours. Web based alcohol interventions for adolescents also demonstrate significantly greater reductions in consumption and harm among ‘high-risk’ drinkers; however changes in risk status at follow-up for non-drinkers or low-risk drinkers have not been assessed in controlled trials of brief alcohol interventions

The Dispersal Ecology of Rhodesian Sleeping Sickness Following Its Introduction to a New Area

Tsetse-transmitted human and animal trypanosomiasis are constraints to both human and animal health in sub-Saharan Africa, and although these diseases have been known for over a century, there is little recent evidence demonstrating how the parasites circulate in natural hosts and ecosystems. The spread of Rhodesian sleeping sickness (caused by Trypanosoma brucei rhodesiense) within Uganda over the past 15 years has been linked to the movement of infected, untreated livestock (the predominant reservoir) from endemic areas. However, despite an understanding of the environmental dependencies of sleeping sickness, little research has focused on the environmental factors controlling transmission establishment or the spatially heterogeneous dispersal of disease following a new introduction. In the current study, an annually stratified case-control study of Rhodesian sleeping sickness cases from Serere District, Uganda was used to allow the temporal assessment of correlations between the spatial distribution of sleeping sickness and landscape factors. Significant relationships were detected between Rhodesian sleeping sickness and selected factors, including elevation and the proportion of land which was “seasonally flooding grassland” or “woodlands and dense savannah.” Temporal trends in these relationships were detected, illustrating the dispersal of Rhodesian sleeping sickness into more ‘suitable’ areas over time, with diminishing dependence on the point of introduction in concurrence with an increasing dependence on environmental and landscape factors. These results provide a novel insight into the ecology of Rhodesian sleeping sickness dispersal and may contribute towards the implementation of evidence-based control measures to prevent its further spread

Immigration Rates in Fragmented Landscapes – Empirical Evidence for the Importance of Habitat Amount for Species Persistence

BACKGROUND: The total amount of native vegetation is an important property of fragmented landscapes and is known to exert a strong influence on population and metapopulation dynamics. As the relationship between habitat loss and local patch and gap characteristics is strongly non-linear, theoretical models predict that immigration rates should decrease dramatically at low levels of remaining native vegetation cover, leading to patch-area effects and the existence of species extinction thresholds across fragmented landscapes with different proportions of remaining native vegetation. Although empirical patterns of species distribution and richness give support to these models, direct measurements of immigration rates across fragmented landscapes are still lacking. METHODOLOGY/PRINCIPAL FINDINGS: Using the Brazilian Atlantic forest marsupial Gray Slender Mouse Opossum (Marmosops incanus) as a model species and estimating demographic parameters of populations in patches situated in three landscapes differing in the total amount of remaining forest, we tested the hypotheses that patch-area effects on population density are apparent only at intermediate levels of forest cover, and that immigration rates into forest patches are defined primarily by landscape context surrounding patches. As expected, we observed a positive patch-area effect on M. incanus density only within the landscape with intermediate forest cover. Density was independent of patch size in the most forested landscape and the species was absent from the most deforested landscape. Specifically, the mean estimated numbers of immigrants into small patches were lower in the landscape with intermediate forest cover compared to the most forested landscape. CONCLUSIONS/SIGNIFICANCE: Our results reveal the crucial importance of the total amount of remaining native vegetation for species persistence in fragmented landscapes, and specifically as to the role of variable immigration rates in providing the underlying mechanism that drives both patch-area effects and species extinction thresholds

Experimental Beetle Metapopulations Respond Positively to Dynamic Landscapes and Reduced Connectivity

Interactive effects of multiple environmental factors on metapopulation dynamics have received scant attention. We designed a laboratory study to test hypotheses regarding interactive effects of factors affecting the metapopulation dynamics of red flour beetle, Tribolium castaneum. Within a four-patch landscape we modified resource level (constant and diminishing), patch connectivity (high and low) and patch configuration (static and dynamic) to conduct a 23 factorial experiment, consisting of 8 metapopulations, each with 3 replicates. For comparison, two control populations consisting of isolated and static subpopulations were provided with resources at constant or diminishing levels. Longitudinal data from 22 tri-weekly counts of beetle abundance were analyzed using Bayesian Poisson generalized linear mixed models to estimate additive and interactive effects of factors affecting abundance. Constant resource levels, low connectivity and dynamic patches yielded greater levels of adult beetle abundance. For a given resource level, frequency of colonization exceeded extinction in landscapes with dynamic patches when connectivity was low, thereby promoting greater patch occupancy. Negative density dependence of pupae on adults occurred and was stronger in landscapes with low connectivity and constant resources; these metapopulations also demonstrated greatest stability. Metapopulations in control landscapes went extinct quickly, denoting lower persistence than comparable landscapes with low connectivity. When landscape carrying capacity was constant, habitat destruction coupled with low connectivity created asynchronous local dynamics and refugia within which cannibalism of pupae was reduced. Increasing connectivity may be counter-productive and habitat destruction/recreation may be beneficial to species in some contexts

Correlation of exhaled breath temperature with bronchial blood flow in asthma

In asthma elevated rates of exhaled breath temperature changes (Δe°T) and bronchial blood flow (Q(aw)) may be due to increased vascularity of the airway mucosa as a result of inflammation. We investigated the relationship of Δe°T with Q(aw )and airway inflammation as assessed by exhaled nitric oxide (NO). We also studied the anti-inflammatory and vasoactive effects of inhaled corticosteroid and β(2)-agonist. Δe°T was confirmed to be elevated (7.27 ± 0.6 Δ°C/s) in 19 asthmatic subjects (mean age ± SEM, 40 ± 6 yr; 6 male, FEV(1 )74 ± 6 % predicted) compared to 16 normal volunteers (4.23 ± 0.41 Δ°C/s, p < 0.01) (30 ± 2 yr) and was significantly increased after salbutamol inhalation in normal subjects (7.8 ± 0.6 Δ°C/ s, p < 0.05) but not in asthmatic patients. Q(aw), measured using an acetylene dilution method was also elevated in patients with asthma compared to normal subjects (49.47 ± 2.06 and 31.56 ± 1.6 μl/ml/min p < 0.01) and correlated with exhaled NO (r = 0.57, p < 0.05) and Δe°T (r = 0.525, p < 0.05). In asthma patients, Q(aw )was reduced 30 minutes after the inhalation of budesonide 400 μg (21.0 ± 2.3 μl/ml/min, p < 0.05) but was not affected by salbutamol. Δe°T correlates with Q(aw )and exhaled NO in asthmatic patients and therefore may reflect airway inflammation, as confirmed by the rapid response to steroids

Organic Farming Improves Pollination Success in Strawberries

Pollination of insect pollinated crops has been found to be correlated to pollinator abundance and diversity. Since organic farming has the potential to mitigate negative effects of agricultural intensification on biodiversity, it may also benefit crop pollination, but direct evidence of this is scant. We evaluated the effect of organic farming on pollination of strawberry plants focusing on (1) if pollination success was higher on organic farms compared to conventional farms, and (2) if there was a time lag from conversion to organic farming until an effect was manifested. We found that pollination success and the proportion of fully pollinated berries were higher on organic compared to conventional farms and this difference was already evident 2–4 years after conversion to organic farming. Our results suggest that conversion to organic farming may rapidly increase pollination success and hence benefit the ecosystem service of crop pollination regarding both yield quantity and quality