Data science for environmental health

Ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our ecosystems as well as their ecosystem functions. The relationships between drivers, stress and ecosystem functions in ecosystems are complex, multi- faceted and often non-linear and yet environmental managers, decision makers and politicians need to be able to make rapid decisions that are data-driven and based on short- and long-term monitoring information, complex modeling and analysis approaches. A huge number of long-standing and standardized ecosystem health approaches like the essential variables already exist and are increasingly integrating remote-sensing based monitoring approaches [1-2]. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. This presentation therefore discusses the requirements for using Data Science as a bridge between complex and multidimensional Big Data for environmental health. It became apparent that no existing monitoring approach, technique, model or platform is sufficient on its own to monitor, model, forecast or assess vegetation health and its resilience. In order to advance the development of a multi-source ecosystem health monitoring network, we argue that in order to gain a better understanding of ecosystem health in our complex world it would be conducive to implement the concepts of Data Science with the components: (i) digitalization, (ii) standardization with metadata management adhering to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, (iii) Semantic Web, (iv) proof, trust and uncertainties, (v) complex tools for Data Science analysis and (vi) easy tools for scientists, data managers and stakeholders for decision-making support [3-4]. REFERENCES: 1.Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J.M., Heurich, M., Jung, A., Klenke, R., Neumann, C., Pause, M., Rocchini, D., Schaepman, M.E.; Schmidtlein, S., Schulz, K., Selsam, P., Settele, J., Skidmore, A.K., Cord, A.F., 2016. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecol. Indic. 70, 317–339. doi:10.1016/j.ecolind.2016.06.022. 2.Lausch, A., Erasmi, S., Douglas, J., King, Magdon, P., Heurich, M., 2016. Understanding forest health with remote sensing - Part I - A review of spectral traits, processes and remote sensing characteristics. Remote Sens. 8, 1029; doi:10.3390/rs8121029. 3.Lausch, A.; Bastian O.; Klotz, S.; Leitão, P. J.; Jung, A.; Rocchini, D.; Schaepman, M.E.; Skidmore, A.K.; Tischendorf, L.; Knapp, S. 2018. Understanding and assessing vegetation health by in-situ species and remote sensing approaches. Methods Ecol. Evol. 00, 1–11. doi:10.1111/2041-210X.13025. 4.Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., Paasche, H., Paulheim, H., Pause, P., Schweitzer, C., Schmulius, C., Settele, J., Skidmore, A.K.,, Wegmann, M., Zacharias, S., Kirsten, T.; Schaepman, M.E., 2018. Understanding forest health with remote sensing -Part III - Requirements for a scalable multi-source forest health monitoring network based on Data Science approaches. (Remote Sens., in review)

A Toll-Like Receptor 2 Pathway Regulates the Ppargc1a/b Metabolic Co-Activators in Mice with Staphylococcal aureus Sepsis

Activation of the host antibacterial defenses by the toll-like receptors (TLR) also selectively activates energy-sensing and metabolic pathways, but the mechanisms are poorly understood. This includes the metabolic and mitochondrial biogenesis master co-activators, Ppargc1a (PGC-1α) and Ppargc1b (PGC-1β) in Staphylococcus aureus (S. aureus) sepsis. The expression of these genes in the liver is markedly attenuated inTLR2−/− mice and markedly accentuated in TLR4−/− mice compared with wild type (WT) mice. We sought to explain this difference by using specific TLR-pathway knockout mice to test the hypothesis that these co-activator genes are directly regulated through TLR2 signaling. By comparing their responses to S. aureus with WT mice, we found that MyD88-deficient and MAL-deficient mice expressed hepatic Ppargc1a and Ppargc1b normally, but that neither gene was activated in TRAM-deficient mice. Ppargc1a/b activation did not require NF-kβ, but did require an interferon response factor (IRF), because neither gene was activated in IRF-3/7 double-knockout mice in sepsis, but both were activated normally in Unc93b1-deficient (3d) mice. Nuclear IRF-7 levels in TLR2−/− and TLR4−/− mice decreased and increased respectively post-inoculation and IRF-7 DNA-binding at the Ppargc1a promoter was demonstrated by chromatin immunoprecipitation. Also, a TLR2-TLR4-TRAM native hepatic protein complex was detected by immunoprecipitation within 6 h of S. aureus inoculation that could support MyD88-independent signaling to Ppargc1a/b. Overall, these findings disclose a novel MyD88-independent pathway in S. aureus sepsis that links TLR2 and TLR4 signaling in innate immunity to Ppargc1a/b gene regulation in a critical metabolic organ, the liver, by means of TRAM, TRIF, and IRF-7

Selective targeting of the αC and DFG-out pocket in p38 MAPK

The p38 MAPK cascade is a key signaling pathway linked to a multitude of physiological functions and of central importance in inflammatory and autoimmune diseases. Although studied extensively, little is known about how conformation-specific inhibitors alter signaling outcomes. Here, we have explored the highly dynamic back pocket of p38 MAPK with allosteric urea fragments. However, screening against known off-targets showed that these fragments maintained the selectivity issues of their parent compound BIRB-796, while combination with the hinge-binding motif of VPC-00628 greatly enhanced inhibitor selectivity. Further efforts focused therefore on the exploration of the αC-out pocket of p38 MAPK, yielding compound 137 as a highly selective type-II inhibitor. Even though 137 is structurally related to a recent p38 type-II chemical probe, SR-318, the data presented here provide valuable insights into back-pocket interactions that are not addressed in SR-318 and it provides an alternative chemical tool with good cellular activity targeting also the p38 back pocket

Search for supersymmetry in events with one lepton and multiple jets in proton-proton collisions at root s=13 TeV

Peer reviewe