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- ItemDetection of Protein Glycosylation Using Tip-Enhanced Raman Scattering(Columbus, Ohio : American Chemical Society, 2016) Cowcher, David P.; Deckert-Gaudig, Tanja; Brewster, Victoria L.; Ashton, Lorna; Deckert, Volker; Goodacre, RoystonThe correct glycosylation of biopharmaceutical glycoproteins and their formulations is essential for them to have the desired therapeutic effect on the patient. It has recently been shown that Raman spectroscopy can be used to quantify the proportion of glycosylated protein from mixtures of native and glycosylated forms of bovine pancreatic ribonuclease (RNase). Here we show the first steps toward not only the detection of glycosylation status but the characterization of glycans themselves from just a few protein molecules at a time using tip-enhanced Raman scattering (TERS). While this technique generates complex data that are very dependent on the protein orientation, with the careful development of combined data preprocessing, univariate and multivariate analysis techniques, we have shown that we can distinguish between the native and glycosylated forms of RNase. Many glycoproteins contain populations of subtly different glycoforms; therefore, with stricter orientation control, we believe this has the potential to lead to further glycan characterization using TERS, which would have use in biopharmaceutical synthesis and formulation research.
- ItemMeasurements of gaseous H2SO4 by AP-ID-CIMS during CAREBeijing 2008 Campaign(München : European Geopyhsical Union, 2011) Zheng, J.; Hu, M.; Zhang, R.; Yue, D.; Wang, Z.; Guo, S.; Li, X.; Bohn, B.; Shao, M.; He, L.; Huang, X.; Wiedensohler, A.; Zhu, T.As part of the 2008 Campaign of Air Quality Research in Beijing and Surrounding Regions (CAREBeijing 2008), measurements of gaseous sulfuric acid (H2SO4) have been conducted at an urban site in Beijing, China from 7 July to 25 September 2008 using atmospheric pressure ion drift – chemical ionization mass spectrometry (AP-ID-CIMS). This represents the first gaseous H2SO4 measurements in China. Diurnal profile of sulfuric acid is strongly dependent on the actinic flux, reaching a daily maximum around noontime and with an hourly average concentration of 5 × 106 molecules cm−3. Simulation of sulfuric acid on the basis of the measured sulfur dioxide concentration, photolysis rates of ozone and nitrogen dioxide, and aerosol surface areas captures the trend of the measured H2SO4 diurnal variation within the uncertainties, indicating that photochemical production and condensation onto preexisting particle surface dominate the observed diurnal H2SO4 profile. The frequency of the peak H2SO4 concentration exceeding 5 × 106 molecules cm−3 increases by 16 % during the period of the summer Olympic Games (8–24 August 2008), because of the implementation of air quality control regulations. Using a multivariate statistical method, the critical nucleus during nucleation events is inferred, containing two H2SO4 molecules (R2 = 0.85). The calculated condensation rate of H2SO4 can only account for 10–25 % of PM1 sulfate formation, indicating that either much stronger sulfate production exists at the SO2 source region or other sulfate production mechanisms are responsible for the sulfate production.
- ItemSurface modification of mineral dust particles by sulphuric acid processing: Implications for ice nucleation abilities(München : European Geopyhsical Union, 2011) Reitz, P.; Spindler, C.; Mentel, T.F.; Poulain, L.; Wex, H.; Mildenberger, K.; Niedermeier, D.; Hartmann, S.; Clauss, T.; Stratmann, F.; Sullivan, R.C.; DeMott, P.J.; Petters, M.D.; Sierau, B.; Schneider, J.The ability of coated mineral dust particles to act as ice nuclei (IN) was investigated at LACIS (Leipzig Aerosol Cloud Interaction Simulator) during the FROST1- and FROST2-campaigns (Freezing of dust). Sulphuric acid was condensed on the particles which afterwards were optionally humidified, treated with ammonia vapour and/or heat. By means of aerosol mass spectrometry we found evidence that processing of mineral dust particles with sulphuric acid leads to surface modifications of the particles. These surface modifications are most likely responsible for the observed reduction of the IN activation of the particles. The observed particle mass spectra suggest that different treatments lead to different chemical reactions on the particle surface. Possible chemical reaction pathways and products are suggested and the implications on the IN efficiency of the treated dust particles are discussed.
- ItemEarth system data cubes unravel global multivariate dynamics(Göttingen : Copernicus Publ., 2020) Mahecha, Miguel D.; Gans, Fabian; Brandt, Gunnar; Christiansen, Rune; Cornell, Sarah E.; Fomferra, Normann; Kraemer, Guido; Peters, Jonas; Bodesheim, Paul; Camps-Valls, Gustau; Donges, Jonathan F.; Dorigo, Wouter; Estupinan-Suarez, Lina M.; Gutierrez-Velez, Victor H.; Gutwin, Martin; Jung, Martin; Londoño, Maria C.; Miralles, Diego G.; Papastefanou, Phillip; Reichstein, MarkusUnderstanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- ItemThe effect of univariate bias adjustment on multivariate hazard estimates(Göttingen : Copernicus Publ., 2019) Zscheischler, Jakob; Fischer, Erich M.; Lange, StefanBias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure. © 2019 Copernicus GmbH. All rights reserved.
- ItemOrder patterns networks (orpan) - A method to estimate time-evolving functional connectivity from multivariate time series(Lausanne : Frontiers Research Foundation, 2012) Schinkel, S.; Zamora-López, G.; Dimigen, O.; Sommer, W.; Kurths, J.Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.
- ItemMeteorological and trace gas factors affecting the number concentration of atmospheric Aitken (DP Combining double low line 50 nm) particles in the continental boundary layer: Parameterization using a multivariate mixed effects model(München : European Geopyhsical Union, 2011) Mikkonen, S.; Korhonen, H.; Romakkaniemi, S.; Smith, J.N.; Joutsensaari, J.; Lehtinen, K.E.J.; Hamed, A.; Breider, T.J.; Birmili, W.; Spindler, G.; Plass-Duelmer, C.; Facchini, M.C.; Laaksonen, A.Measurements of aerosol size distribution and different gas and meteorological parameters, made in three polluted sites in Central and Southern Europe: Po Valley, Italy, Melpitz and Hohenpeissenberg in Germany, were analysed for this study to examine which of the meteorological and trace gas variables affect the number concentration of Aitken (Dp= 50 nm) particles. The aim of our study was to predict the number concentration of 50 nm particles by a combination of in-situ meteorological and gas phase parameters. The statistical model needs to describe, amongst others, the factors affecting the growth of newly formed aerosol particles (below 10 nm) to 50 nm size, but also sources of direct particle emissions in that size range. As the analysis method we used multivariate nonlinear mixed effects model. Hourly averages of gas and meteorological parameters measured at the stations were used as predictor variables; the best predictive model was attained with a combination of relative humidity, new particle formation event probability, temperature, condensation sink and concentrations of SO2, NO2 and ozone. The seasonal variation was also taken into account in the mixed model structure. Model simulations with the Global Model of Aerosol Processes (GLOMAP) indicate that the parameterization can be used as a part of a larger atmospheric model to predict the concentration of climatically active particles. As an additional benefit, the introduced model framework is, in theory, applicable for any kind of measured aerosol parameter.
- ItemSimultaneous statistical inference for epigenetic data(San Francisco, California, US : PLOS, 2015) Schildknecht, Konstantin; Olek, Sven; Dickhaus, ThorstenEpigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.
- ItemOn multivariate chi-square distributions and their applications in testing multiple hypotheses(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2014) Dickhaus, Thorsten; Royen, ThomasWe are considered with three different types of multivariate chi-square distributions. Their members play important roles as limiting distributions of vectors of test statistics in several applications of multiple hypotheses testing. We explain these applications and provide formulas for computing multiplicity-adjusted p-values under the respective global hypothesis.
- ItemMultivariate non-parametric Euclidean distance model for hourly disaggregation of daily climate data(Wien [u.a.] : Springer, 2021) Görner, Christina; Franke, Johannes; Kronenberg, Rico; Hellmuth, Olaf; Bernhofer, ChristianThe algorithm for and results of a newly developed multivariate non-parametric model, the Euclidean distance model (EDM), for the hourly disaggregation of daily climate data are presented here. The EDM is a resampling method based on the assumption that the day to be disaggregated has already occurred once in the past. The Euclidean distance (ED) serves as a measure of similarity to select the most similar day from historical records. EDM is designed to disaggregate daily means/sums of several climate elements at once, here temperature (T), precipitation (P), sunshine duration (SD), relative humidity (rH), and wind speed (WS), while conserving physical consistency over all disaggregated elements. Since weather conditions and hence the diurnal cycles of climate elements depend on the weather pattern, a selection approach including objective weather patterns (OWP) was developed. The OWP serve as an additional criterion to filter the most similar day. For a case study, EDM was applied to the daily climate data of the stations Dresden and Fichtelberg (Saxony, Germany). The EDM results agree well with the observed data, maintaining their statistics. Hourly results fit better for climate elements with homogenous diurnal cycles, e.g., T with very high correlations of up to 0.99. In contrast, the hourly results of the SD and the WS provide correlations up to 0.79. EDM tends to overestimate heavy precipitation rates, e.g., by up to 15% for Dresden and 26% for Fichtelberg, potentially due to, e.g., the smaller data pool for such events, and the equal-weighted impact of P in the ED calculation. The OWPs lead to somewhat improved results for all climate elements in terms of similar climate conditions of the basic stations. Finally, the performance of EDM is compared with the disaggregation tool MELODIST (Förster et al. 2015). Both tools deliver comparable and well corresponding results. All analyses of the generated hourly data show that EDM is a very robust and flexible model that can be applied to any climate station. Since EDM can disaggregate daily data of climate projections, future research should address whether the model is capable to respect and (re)produce future climate trends. Further, possible improvements by including the flow direction and future OWPs should be investigated, also with regard to reduce the overestimation of heavy rainfall rates.