@article{GawlikWehnerMendeetal.2010,
  author    = {Gawlik, Micha and Wehner, Ingeborg and Mende, Meinhard and Jung, Sven and Pfuhlmann, Bruno and Knapp, Michael and Stoeber, Gerald},
  title     = {The DAOA/G30 locus and affective disorders: haplotype based association study in a polydiagnostic approach},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-67963},
  year      = {2010},
  abstract  = {Background: The DAOA/G30 (D-amino acid oxidase activator) gene complex at chromosomal region 13q32-33 is one of the most intriguing susceptibility loci for the major psychiatric disorders, although there is no consensus about the specific risk alleles or haplotypes across studies. Methods: In a case-control sample of German descent (affective psychosis: n = 248; controls: n = 188) we examined seven single nucleotide polymorphisms (SNPs) around DAOA/G30 (rs3916966, rs1935058, rs2391191, rs1935062, rs947267, rs3918342, and rs9558575) for genetic association in a polydiagnostic approach (ICD 10; Leonhard's classification). Results: No single marker showed evidence of overall association with affective disorder neither in ICD10 nor Leonhard's classification. Haplotype analysis revealed no association with recurrent unipolar depression or bipolar disorder according to ICD10, within Leonhard's classification manic-depression was associated with a 3-locus haplotype (rs2391191, rs1935062, and rs3916966; P = 0.022) and monopolar depression with a 5-locus combination at the DAOA/G30 core region (P = 0.036). Conclusion: Our data revealed potential evidence for partially overlapping risk haplotypes at the DAOA/G30 locus in Leonhard's affective psychoses, but do not support a common genetic contribution of the DAOA/G30 gene complex to the pathogenesis of affective disorders.},
  subject      = {Psychisch Kranker},
  language  = {en}
}
@article{LauschBorgBumbergeretal.2018,
  author    = {Lausch, Angela and Borg, Erik and Bumberger, Jan and Dietrich, Peter and Heurich, Marco and Huth, Andreas and Jung, Andr{\´a}s and Klenke, Reinhard and Knapp, Sonja and Mollenhauer, Hannes and Paasche, Hendrik and Paulheim, Heiko and Pause, Marion and Schweitzer, Christian and Schmulius, Christiane and Settele, Josef and Skidmore, Andrew K. and Wegmann, Martin and Zacharias, Steffen and Kirsten, Toralf and Schaepman, Michael E.},
  title     = {Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches},
  series = {Remote Sensing},
  volume    = {10},
  journal   = {Remote Sensing},
  number    = {7},
  issn      = {2072-4292},
  doi       = {10.3390/rs10071120},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197691},
  pages     = {1120},
  year      = {2018},
  abstract  = {Forest 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 forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest 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 forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. 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. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest 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 after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.},
  language  = {en}
}