TY - JOUR A1 - Nitze, Ingmar A1 - Grosse, Guido A1 - Jones, Benjamin M. A1 - Arp, Christopher D. A1 - Ulrich, Mathias A1 - Fedorov, Alexander A1 - Veremeeva, Alexandra T1 - Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions JF - Remote sensing N2 - Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region. KW - lake dynamics KW - lake change KW - permafrost region KW - Landsat KW - Alaska KW - Siberia KW - thermokarst KW - trend analysis KW - machine-learning Y1 - 2017 U6 - https://doi.org/10.3390/rs9070640 SN - 2072-4292 VL - 9 PB - MDPI CY - Basel ER - TY - THES A1 - Stößel, Daniel T1 - Biomarker Discovery in Multiple Sclerosis and Parkinson’s disease T1 - Biomarkerentwicklung in Multiple Sklerose und der Parkinson-Krankheit BT - novel insights into metabolic disease mechanisms N2 - Neuroinflammatory and neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS) often result in a severe impairment of the patient´s quality of life. Effective therapies for the treatment are currently not available, which results in a high socio-economic burden. Due to the heterogeneity of the disease subtypes, stratification is particularly difficult in the early phase of the disease and is mainly based on clinical parameters such as neurophysiological tests and central nervous imaging. Due to good accessibility and stability, blood and cerebrospinal fluid metabolite markers could serve as surrogates for neurodegenerative processes. This can lead to an improved mechanistic understanding of these diseases and further be used as "treatment response" biomarkers in preclinical and clinical development programs. Therefore, plasma and CSF metabolite profiles will be identified that allow differentiation of PD from healthy controls, association of PD with dementia (PDD) and differentiation of PD subtypes such as akinetic rigid and tremor dominant PD patients. In addition, plasma metabolites for the diagnosis of primary progressive MS (PPMS) should be investigated and tested for their specificity to relapsing-remitting MS (RRMS) and their development during PPMS progression. By applying untargeted high-resolution metabolomics of PD patient samples and in using random forest and partial least square machine learning algorithms, this study identified 20 plasma metabolites and 14 CSF metabolite biomarkers. These differentiate against healthy individuals with an AUC of 0.8 and 0.9 in PD, respectively. We also identify ten PDD specific serum metabolites, which differentiate against healthy individuals and PD patients without dementia with an AUC of 1.0, respectively. Furthermore, 23 akinetic-rigid specific plasma markers were identified, which differentiate against tremor-dominant PD patients with an AUC of 0.94 and against healthy individuals with an AUC of 0.98. These findings also suggest more severe disease pathology in the akinetic-rigid PD than in tremor dominant PD. In the analysis of MS patient samples a partial least square analysis yielded predictive models for the classification of PPMS and resulted in 20 PPMS specific metabolites. In another MS study unknown changes in human metabolism were identified after administration of the multiple sclerosis drug dimethylfumarate, which is used for the treatment of RRMS. These results allow to describe and understand the hitherto completely unknown mechanism of action of this new drug and to use these findings for the further development of new drugs and targets against RRMS. In conclusion, these results have the potential for improved diagnosis of these diseases and improvement of mechanistic understandings, as multiple deregulated pathways were identified. Moreover, novel Dimethylfumarate targets can be used to aid drug development and treatment efficiency. Overall, metabolite profiling in combination with machine learning identified as a promising approach for biomarker discovery and mode of action elucidation. N2 - Neuroinflammatorische and neurodegenerative Erkrankungen wie Parkinson (PD) und Multiple Sklerose (MS) gehen oft mit einer starken Beeinträchtigung der Lebensqualität einher. Effektive Therapien für die Behandlung sind derzeit nicht verfügbar, was nicht zuletzt eine hohe sozioökonomische Last zur Folge hat. Aufgrund der Heterogenität der Krankheitsbilder ist eine Stratifizierung gerade in der Frühphase der Erkrankung schwierig und basiert hauptsächlich auf klinischen Parametern wie bspw. neurophysiologischen Tests und bildgebenden Verfahren. Aufgrund ihrer guten Zugänglichkeit und Stabilität könnten bestimmte Blut- und Liquor-Metabolitenmarker als Surrogat für neurodegenerative Prozesse dienen, zu einem verbesserten mechanistischen Verständnis dieser Krankheiten führen und nicht zuletzt als “treatment response“ Biomarker in präklinischen und klinischen Entwicklungsprogrammen herangezogen werden. In dieser Arbeit sollten deshalb Plasma- und CSF-Metabolitprofile identifiziert werden, die eine Differenzierung von PD zu gesunden Kontrollen, Assoziierung zu PD mit Demenz (PDD) sowie eine Abgrenzung zu unterschiedlichen PD-Subtypen wie akinetisch-rigiden sowie tremor-dominanten PD-Patienten ermöglichen. Weiterhin wurden in dieser Arbeit Plasmametabolite zur Diagnose von primär-progressiver MS (PPMS) erforscht und auf ihre Spezifität gegenüber schubförmig remittierender MS (RRMS) und PD geprüft sowie deren Verlauf während der PPMS Progression getestet. Hierbei konnten durch “untargeted Metabolomics“ in Kombination mit statistischen Modellen mehrere Plasma- und CSF-Metabolite in PD-Patienten/Erkrankten ermittelt werden, die mit Hilfe von statistischen Diagnosemodellen eine Differenzierung zu gesunden Personen ermöglichen. Darüber hinaus wurden in dieser Arbeit PDD-spezifische Serummetabolite identifiziert, die wiederum genutzt werden können, um diesen PD-Typen von gesunden Individuen und PD-Patienten ohne Demenz abzugrenzen. Des Weiteren konnten bei akinetisch-rigiden PD-Patienten spezifische Metabolite entdeckt werden, die im Vergleich zu tremor-dominanten PD-Patienten eine stärkere metabolische Krankheitssymptomatik suggerieren. Im Zusammenhang mit PPMS wurden in dieser Arbeit spezifische Plasma-Metabolite entdeckt, die zur Diagnose gegen RRMS, PD und gesunden Kontrollen genutzt werden können. Interessanterweise zeigte dabei ein spezifisches Lipid geringere Werte im PPMS Krankheitsverlauf, wodurch sich dieses als möglicher Marker zur Progressionsdiagnostik dieser Krankheit qualifiziert. Abschließend konnten in dieser Arbeit im humanen Stoffwechsel bisher unbekannte Angriffspunkte des Medikaments Dimethylfumarat, das zur Behandlung von RRMS verwendet wird, ermittelt werden. Durch diese Ergebnisse kann der bis jetzt gänzlich unbekannte Wirkungsmechanismus dieses neuen Medikaments besser beschrieben und verstanden, sowie zur Weiterentwicklung neuer Medikamente gegen RRMS genutzt werden. KW - metabolomics KW - biomarker KW - multiple sclerosis KW - Parkinson's disease KW - neurodegeneration KW - neuroinflammation KW - machine-learning KW - Parkinson-Krankheit KW - Biomarker KW - Maschinelles-Lernen KW - Metabolomics KW - Multiple-Sklerose Y1 - 2018 ER -