Thermal and Mechanical Design and Simulation for the first high precision Quantum Optics Experiment on a Sounding Rocket

The MAIUS-1 payload is a high precision quantum optics experiment about to fly on a VSB-30 sounding rocket with the scientific objective to demonstrate the feasibility of creating the first Bose-Einstein condensates and performing atom interferometry in Space. To achieve these goals the experiment is using various sensitive instruments imposing strong requirements on the thermal and mechanical design. In the introduction this thesis gives a short overview and characterization of available microgravity platforms in Europe. Moreover a detailed characterization of the environment aboard the VSB-30 sounding rocket is presented based on flight data from former MASER and TEXUS missions. In the main chapters the mechanical and thermal design of the MAIUS-1 scientific payload is described in detail. This includes various technical solutions as for example a low-cost vibration isolation, a sealing for RADAX hull segments of pressurized payloads or umbilicals to provide water cooling until lift-off. In addition the test methods and results for the different payload components is presented. The design and test of the ultra-high vacuum system with a nominal pressure of 1E-10 hPa is described in a dedicated chapter. This includes theoretical background on outgassing of technical surfaces and calculation of the conductance of a vacuum system. Different pumping and sealing techniques are introduced. Furthermore the results of intensive testing of Conflat (CF) and Indium sealings under vibrational and static loads are presented as well as test results for the entire pumping system. The thermal control system of the MAIUS-1 scientific payload has been designed using multiple MATLAB codes in combination with ANSYS to estimate the heat flux into the rocket hull by aerodynamic heating during ascent as well as the heat transfer from the heated rocket hull to the system housing walls by natural convection. These codes and their theoretical background are presented herein as well. The thesis closes with recommendations and possible improvements for future space-born quantum optics experiments

A general approach for discriminative de-novo motif discovery from highthroughput data

De novo motif discovery has been an important challenge of bioinformatics for the past two decades. Since the emergence of high-throughput techniques like ChIP-seq, ChIP-exo and protein-binding microarrays (PBMs), the focus of de novo motif discovery has shifted to runtime and accuracy on large data sets. For this purpose, specialized algorithms have been designed for discovering motifs in ChIP-seq or PBM data. However, none of the existing approaches work perfectly for all three high-throughput techniques. In this article, we propose Dimont, a general approach for fast and accurate de novo motif discovery from high-throughput data. We demonstrate that Dimont yields a higher number of correct motifs from ChIP-seq data than any of the specialized approaches and achieves a higher accuracy for predicting PBM intensities from probe sequence than any of the approaches specifically designed for that purpose. Dimont also reports the expected motifs for several ChIP-exo data sets. Investigating differences between in vitro and in vivo binding, we find that for most transcription factors, the motifs discovered by Dimont are in good accordance between techniques, but we also find notable exceptions. We also observe that modeling intra-motif dependencies may increase accuracy, which indicates that more complex motif models are a worthwhile field of research

MotifAdjuster: a tool for computational reassessment of transcription factor binding site annotations

MotifAdjuster helps to detect errors in binding site annotations

Organic carbon stored in a thermokarst affected landscape on Baldwin Peninsula, Alaska

As Arctic warming continues and permafrost degrades, more organic carbon (OC) will be decomposed in high northern latitudes. Still, uncertainties remain in the quality and quantity of OC stored in permafrost. This study presents OC data from permafrost deposits on the Baldwin Peninsula, West-Alaska. We analyzed cryostratigraphical, biogeochemical and biomarker parameters of yedoma- and drained thermokarst lake basin (DTLB) deposits as well as thermokarst lake sediments to identify the size and quality of OC pools in ice-rich permafrost. Here we show that two thirds of soil OC in this region are stored in frozen DTLB deposits and that the lake sediments have the highest volumetric OC content. The n-alkane distribution shows, however, that OC stored in yedoma is of higher quality than that stored in DTLB deposits. These findings highlight the importance of molecular OC analysis for determining the potential future greenhouse gas emissions from thawing permafrost

Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia

Ice rich Yedoma-dominated landscapes store considerable amounts of organic carbon (C) and nitrogen (N) and are vulnerable to degradation under climate warming. We investigate the C and N pools in two thermokarst-affected Yedoma landscapes – on Sobo-Sise Island and on Bykovsky Peninsula in the North of East Siberia. Soil cores up to three meters depth were collected along geomorphic gradients and analysed for organic C and N contents. A high vertical sampling density in the profiles allowed the calculation of C and N stocks for short soil column intervals and enhanced understanding of within-core parameter variability. Profile-level C and N stocks were scaled to the landscape level based on landform classifications from five-meter resolution, multispectral RapidEye satellite imagery. Mean landscape C and N storage in the first meter of soil for Sobo-Sise Island is estimated to be 20.2 kg C m−2 and 1.8 kg N m−2 and for Bykovsky Peninsula 25.9 kg C m−2 and 2.2 kg N m−2. Radiocarbon dating demonstrates the Holocene age of thermokarst basin deposits but also suggests the presence of thick Holocene aged cover layers which can reach up to two meters on top of intact Yedoma landforms. Reconstructed sedimentation rates of 0.10 mm yr−1–0.57 mm yr−1 suggest sustained mineral soil accumulation across all investigated landforms. Both Yedoma and thermokarst landforms are characterized by limited accumulation of organic soil layers (peat). We further estimate that an active layer deepening by about 100 cm will increase organic C availability in a seasonally thawed state in the two study areas by ~ 5.8 Tg (13.2 kg C m−2). Our study demonstrates the importance of increasing the number of C and N storage inventories in ice-rich Yedoma and thermokarst environments in order to account for high variability of permafrost and thermokarst environments in pan-permafrost soil C and N pool estimates

Organic carbon in permafrost

With ongoing climate change, the Arctic will continue to warm approximately twice as fast as the lower latitudes. As large parts of the Arctic are affected by permafrost, large-scale degradation processes such as thermokarst and thermal erosion are expected. Ice-rich permafrost, such as yedoma permafrost, covers large areas in Alaska and Siberia. These deposits reach thickness up to 50 m and include large ice-wedges. Therefore, warming can trigger especially rapid and deep thaw processes, which can mobilize organic carbon even well below 1 m soil depth. Undisturbed yedoma deposits are characterized by relatively high quality organic carbon stored and are presumably highly susceptible for future degradation. To improve the estimates of the rate and amount of organic carbon that can be released from permafrost thaw with warming, the quantity and quality of the organic carbon needs to be identified

High methane production in drained lake basin wetlands in northern Alaska

Wetlands in drained lake basins are important elements of the Arctic carbon budget. They may store large amounts of carbon while also producing substantial amounts of greenhouse gasses. After lake drainage the former lake bottom is colonized by pioneer graminoids, succeeded by mosssedge-dwarf shrub vegetation, producing a typical peat sequence. However, post-drainage organic matter dynamics are not well studied. We hypothesize that vegetation composition reflects both succession and surface wetness, which in turn determine soil organic matter content and methane production. We propose that vegetation types detected by remote sensing-based landcover classification may be used to extrapolate methane production and organic matter composition across drained lake basin landscapes. We investigated (i) plots along a temporal drainage gradient, surveying vegetation, surface sediment, and pond water. We then used (ii) landcover classification of main eco-hydrological classes to (iii) upscale from plot to basin scale. We found that vegetation and organic matter changed markedly between recently drained basins and older age classes. Overall, vegetation composition differed more between eco-hydrological classes than between age classes. Surface sediments had very high water contents (>80 %), suggesting largely anaerobic conditions favouring methane production. Methane concentrations were indeed relatively constant throughout, and particularly high in sediments beneath few centimetres of water (“wet patches”, up to 200 μmol/L) and in pond water (up to 22 μmol/L). Landcover classification yielded seven classes including five classes we also identified using statistical clustering of vegetation data plus a water class and a bare ground class. We found that 67 % of basin areas were occupied by wet patches with especially high methane production. Our study shows that remote sensing-based landcover classifications are useful for quantifying wet-vs-moist patches and high-vs-moderate methane production in Arctic drained lake basins. The study highlights the potential for future upscaling of methane emissions from these abundant wetland environments