@article{PaethPaxianSeinetal.2017,
  author    = {Paeth, Heiko and Paxian, Andreas and Sein, Dimitry V. and Jacob, Daniela and Panitz, Hans-J{\"u}rgen and Warscher, Michael and Fink, Andreas H. and Kunstmann, Harald and Breil, Marcus and Engel, Thomas and Krause, Andreas and Toedter, Julian and Ahrens, Bodo},
  title     = {Decadal and multi-year predictability of the West African monsoon and the role of dynamical downscaling},
  series = {Meteorologische Zeitschrift},
  volume    = {26},
  journal   = {Meteorologische Zeitschrift},
  number    = {4},
  doi       = {10.1127/metz/2017/0811},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172018},
  pages     = {363-377},
  year      = {2017},
  abstract  = {West African summer monsoon precipitation is characterized by distinct decadal variability. Due to its welldocumented link to oceanic boundary conditions in various ocean basins it represents a paradigm for decadal predictability. In this study, we reappraise this hypothesis for several sub-regions of sub-Saharan West Africa using the new German contribution to the coupled model intercomparison project phase 5 (CMIP5) near-term prediction system. In addition, we assume that dynamical downscaling of the global decadal predictions leads to an enhanced predictive skill because enhanced resolution improves the atmospheric response to oceanic forcing and landsurface feedbacks. Based on three regional climate models, a heterogeneous picture is drawn: none of the regional climate models outperforms the global decadal predictions or all other regional climate models in every region nor decade. However, for every test case at least one regional climate model was identified which outperforms the global predictions. The highest predictive skill is found in the western and central Sahel Zone with correlation coefficients and mean-square skill scores exceeding 0.9 and 0.8, respectively.},
  language  = {en}
}
@article{MolochnikovRabeyDobronevskyetal.2012,
  author    = {Molochnikov, Leonid and Rabey, Jose M. and Dobronevsky, Evgenya and Bonuccelli, Ubaldo and Ceravolo, Roberto and Frosini, Daniela and Gr{\"u}nblatt, Edna and Riederer, Peter and Jacob, Christian and Aharon-Peretz, Judith and Bashenko, Yulia and Youdim, Moussa B. H. and Mandel, Silvia A.},
  title     = {A molecular signature in blood identifies early Parkinson's disease},
  series = {Molecular Neurodegeneration},
  volume    = {7},
  journal   = {Molecular Neurodegeneration},
  number    = {26},
  doi       = {10.1186/1750-1326-7-26},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-134508},
  year      = {2012},
  abstract  = {Background: The search for biomarkers in Parkinson's disease (PD) is crucial to identify the disease early and monitor the effectiveness of neuroprotective therapies. We aim to assess whether a gene signature could be detected in blood from early/mild PD patients that could support the diagnosis of early PD, focusing on genes found particularly altered in the substantia nigra of sporadic PD. Results: The transcriptional expression of seven selected genes was examined in blood samples from 62 early stage PD patients and 64 healthy age-matched controls. Stepwise multivariate logistic regression analysis identified five genes as optimal predictors of PD: p19 S-phase kinase-associated protein 1A (odds ratio [OR] 0.73; 95\% confidence interval [CI] 0.60-0.90), huntingtin interacting protein-2 (OR 1.32; CI 1.08-1.61), aldehyde dehydrogenase family 1 subfamily A1 (OR 0.86; 95\% CI 0.75-0.99), 19 S proteasomal protein PSMC4 (OR 0.73; 95\% CI 0.60-0.89) and heat shock 70-kDa protein 8 (OR 1.39; 95\% CI 1.14-1.70). At a 0.5 cut-off the gene panel yielded a sensitivity and specificity in detecting PD of 90.3 and 89.1 respectively and the area under the receiving operating curve (ROC AUC) was 0.96. The performance of the five-gene classifier on the de novo PD individuals alone composing the early PD cohort (n = 38), resulted in a similar ROC with an AUC of 0.95, indicating the stability of the model and also, that patient medication had no significant effect on the predictive probability (PP) of the classifier for PD risk. The predictive ability of the model was validated in an independent cohort of 30 patients at advanced stage of PD, classifying correctly all cases as PD (100\% sensitivity). Notably, the nominal average value of the PP for PD (0.95 (SD = 0.09)) in this cohort was higher than that of the early PD group (0.83 (SD = 0.22)), suggesting a potential for the model to assess disease severity. Lastly, the gene panel fully discriminated between PD and Alzheimer's disease (n = 29). Conclusions: The findings provide evidence on the ability of a five-gene panel to diagnose early/mild PD, with a possible diagnostic value for detection of asymptomatic PD before overt expression of the disorder.},
  language  = {en}
}