Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.

BACKGROUND Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). METHODS Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. RESULTS On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. CONCLUSIONS Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability

Spatial complementarity and the coexistence of species

Coexistence of apparently similar species remains an enduring paradox in ecology. Spatial structure has been predicted to enable coexistence even when population-level models predict competitive exclusion if it causes each species to limit its own population more than that of its competitor. Nevertheless, existing hypotheses conflict with regard to whether clustering favours or precludes coexistence. The spatial segregation hypothesis predicts that in clustered populations the frequency of intra-specific interactions will be increased, causing each species to be self-limiting. Alternatively, individuals of the same species might compete over greater distances, known as heteromyopia, breaking down clusters and opening space for a second species to invade. In this study we create an individual-based model in homogeneous two-dimensional space for two putative sessile species differing only in their demographic rates and the range and strength of their competitive interactions. We fully characterise the parameter space within which coexistence occurs beyond population-level predictions, thereby revealing a region of coexistence generated by a previously-unrecognised process which we term the triadic mechanism. Here coexistence occurs due to the ability of a second generation of offspring of the rarer species to escape competition from their ancestors. We diagnose the conditions under which each of three spatial coexistence mechanisms operates and their characteristic spatial signatures. Deriving insights from a novel metric — ecological pressure — we demonstrate that coexistence is not solely determined by features of the numerically-dominant species. This results in a common framework for predicting, given any pair of species and knowledge of the relevant parameters, whether they will coexist, the mechanism by which they will do so, and the resultant spatial pattern of the community. Spatial coexistence arises from complementary combinations of traits in each species rather than solely through self-limitation

Longitudinal trends of future climate change and oil palm growth: empirical evidence for tropical Africa

Palms are highly significant tropical plants. Oil palms produce palm oil, the basic commodity of a highly important industry. Climate change from greenhouse gasses is likely to decrease the ability of palms to survive, irrespective of them providing ecosystem services to communities. Little information about species survival in tropical regions under climate change is available and data on species migration under climate change is important. Palms are particularly significant in Africa: a palm oil industry already exists with Nigeria being the largest producer. Previous work using CLIMEX modelling indicated that Africa will have reduced suitable climate for oil palm in Africa. The current paper employs this modelling to assess how suitable climate for growing oil palm changed in Africa from current time to 2100. An increasing trend in suitable climate from west to east was observed indicating that refuges could be obtained along the African tropical belt. Most countries had reduced suitable climates but others had increased, with Uganda being particularly high. There may be a case for developing future oil palm plantations towards the east of Africa. The information may be usefully applied to other palms. However, it is crucial that any developments will fully adhere to environmental regulations. Future climate change will have severe consequences to oil palm cultivation but there may be scope for eastwards mitigation in Africa.info:eu-repo/semantics/publishedVersio

Measured greenhouse gas budgets challenge emission savings from palm-oil biodiesel

Special thanks to our field assistants in Indonesia (Basri, Bayu and Darwis) and to Frank Tiedemann, Edgar Tunsch, Dietmar Fellert and Malte Puhan for technical assistance. We thank PTPN VI and the owner of the plantation at Pompa Air for allowing us to conduct our research at their plantation. We would also like to thank the Spanish national project GEISpain (CGL2014-52838-C2-1-R) and the DAAD (scholarship from the programme ‘Research Stays for University Academics and Scientist 2018, ref. no. 91687130)' for partly financing A. Meijide during the preparation of this paper.The potential of palm-oil biofuels to reduce greenhouse gas (GHG) emissions compared with fossil fuels is increasingly questioned. So far, no measurement-based GHG budgets were available, and plantation age was ignored in Life Cycle Analyses (LCA). Here, we conduct LCA based on measured CO2, CH4 and N2O fluxes in young and mature Indonesian oil palm plantations. CO2 dominates the on-site GHG budgets. The young plantation is a carbon source (1012 ± 51 gC m−2 yr−1), the mature plantation a sink (−754 ± 38 gC m−2 yr−1). LCA considering the measured fluxes shows higher GHG emissions for palm-oil biodiesel than traditional LCA assuming carbon neutrality. Plantation rotation-cycle extension and earlier-yielding varieties potentially decrease GHG emissions. Due to the high emissions associated with forest conversion to oil palm, our results indicate that only biodiesel from second rotation-cycle plantations or plantations established on degraded land has the potential for pronounced GHG emission savings.This study was financed by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)— Project-ID 192626868—in the framework of the collaborative German-Indonesian research project CRC990 (subprojects A03, A04 and A05).Spanish national project GEISpain (CGL2014-52838-C2-1-R) and the DAAD (scholarship from the programme ‘Research Stays for University Academics and Scientist 2018, ref. no. 91687130

Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting ‘functional connectivity’, namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict

Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy

The nexus of population growth and changing diets has increased the demands placed on agriculture to supply food for human consumption, animal feed and fuel. Rising incomes lead to dietary changes, from staple crops, towards commodities with greater land requirements, e.g. meat and dairy products. Despite yield improvements partially offsetting increases in demand, agricultural land has still been expanding, causing potential harm to ecosystems, e.g. through deforestation. We use country-level panel data (1961-2011) to allocate the land areas used to produce food for human consumption, waste and biofuels, and to attribute the food production area changes to diet, population and yields drivers. The results show that the production of animal products dominates agricultural land use and land use change over the 50-year period, accounting for 65% of land use change. The rate of extensification of animal production was found to have reduced more recently, principally due to the smaller effect of population growth. The area used for bioenergy was shown to be relatively small, but formed a substantial contribution (36%) to net agricultural expansion in the most recent period. Nevertheless, in comparison to dietary shifts in animal products, bioenergy accounted for less than a tenth of the increase in demand for agricultural land. Population expansion has been the largest driver for agricultural land use change, but dietary changes are a significant and growing driver. China was a notable exception, where dietary transitions dominate food consumption changes, due to rapidly rising incomes. This suggests that future dietary changes will become the principal driver for land use change, pointing to the potential need for demand-side measures to regulate agricultural expansion. (C) 2015 Elsevier Ltd. All rights reserved