Structured Prediction Problem Archive

Structured prediction problems are one of the fundamental tools in machinelearning. In order to facilitate algorithm development for their numericalsolution, we collect in one place a large number of datasets in easy to readformats for a diverse set of problem classes. We provide archival links todatasets, description of the considered problems and problem formats, and ashort summary of problem characteristics including size, number of instancesetc. For reference we also give a non-exhaustive selection of algorithmsproposed in the literature for their solution. We hope that this centralrepository will make benchmarking and comparison to established works easier.We welcome submission of interesting new datasets and algorithms for inclusionin our archive.<br

Combating the effects of climatic change on forests by mitigation strategies

<p>Abstract</p> <p>Background</p> <p>Forests occur across diverse biomes, each of which shows a specific composition of plant communities associated with the particular climate regimes. Predicted future climate change will have impacts on the vulnerability and productivity of forests; in some regions higher temperatures will extend the growing season and thus improve forest productivity, while changed annual precipitation patterns may show disadvantageous effects in areas, where water availability is restricted. While adaptation of forests to predicted future climate scenarios has been intensively studied, less attention was paid to mitigation strategies such as the introduction of tree species well adapted to changing environmental conditions.</p> <p>Results</p> <p>We simulated the development of managed forest ecosystems in Germany for the time period between 2000 and 2100 under different forest management regimes and climate change scenarios. The management regimes reflect different rotation periods, harvesting intensities and species selection for reforestations. The climate change scenarios were taken from the IPCC's Special Report on Emission Scenarios (SRES). We used the scenarios A1B (rapid and successful economic development) and B1 (high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development). Our results indicate that the effects of different climate change scenarios on the future productivity and species composition of German forests are minor compared to the effects of forest management.</p> <p>Conclusions</p> <p>The inherent natural adaptive capacity of forest ecosystems to changing environmental conditions is limited by the long life time of trees. Planting of adapted species and forest management will reduce the impact of predicted future climate change on forests.</p

Dynamics of initial carbon allocation after drought release in mature Norway spruce—Increased belowground allocation of current photoassimilates covers only half of the carbon used for fine‐root growth

After drought events, tree recovery depends on sufficient carbon (C) allocation to the sink organs. The present study aimed to elucidate dynamics of tree-level C sink activity and allocation of recent photoassimilates (C$_{new}$) and stored C in c. 70-year-old Norway spruce (Picea abies) trees during a 4-week period after drought release. We conducted a continuous, whole-tree $^{13}$C labeling in parallel with controlled watering after 5 years of experimental summer drought. The fate of C$_{new}$ to growth and CO$_{2}$ efflux was tracked along branches, stems, coarse- and fine roots, ectomycorrhizae and root exudates to soil CO$_{2}$ efflux after drought release. Compared with control trees, drought recovering trees showed an overall 6% lower C sink activity and 19% less allocation of C$_{new}$ to aboveground sinks, indicating a low priority for aboveground sinks during recovery. In contrast, fine-root growth in recovering trees was seven times greater than that of controls. However, only half of the C used for new fine-root growth was comprised of C$_{new}$ while the other half was supplied by stored C. For drought recovery of mature spruce trees, in addition to C$_{new}$, stored C appears to be critical for the regeneration of the fine-root system and the associated water uptake capacity

Accuracy, realism and general applicability of European forest models

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models\u27 performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe\u27s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests

Accuracy, realism and general applicability of European forest models

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.Peer reviewe

Treatment of Bladder Cancer with Bi-213-anti-EGFR Immunoconjugates in a Nude Mouse Model

Therapy of non-muscle-invasive bladder cancer (carcinoma in situ) comprises transurethral resection of the tumor and subsequent instillation of the chemotherapeutic drug mitomycin C in order to eradicate remaining tumor cells. Yet 15 – 40% of treated patients relapse within 5 years. Therefore, new therapeutic strategies to combat tumor recurrence are needed. Alpha-particle emitting radionuclides efficiently kill single tumor cells or small tumor cell clusters. Because the epidermal growth factor receptor (EGFR) is overexpressed on bladder cancer cells conjugates composed of the alpha-emitter Bi-213 and the anti-EGFR antibody matuzumab should provide a powerful drug to eliminate disseminated bladder cancer cells. Therefore, the aims of our study were (i) to analyse the cytotoxic effects of Bi-213-anti-EGFR radioimmunoconjugates at the cellular level, (ii) to evaluate therapeutic efficacy of intravesically applied Bi-213-anti-EGFR-Mab in a nude mouse model with intravesical human bladder cancer xenografts, (iii) to compare Bi-213-anti-EGFR-Mab efficacy with chemotherapy using mitomycin C and (iv) to demonstrate that radioimmunotherapy does not damage the bladder wall.JRC.E.5-Nuclear chemistr