Refine
Has Fulltext
- yes (148)
Year of publication
- 2021 (148) (remove)
Document Type
- Postprint (148) (remove)
Is part of the Bibliography
- yes (148)
Keywords
- embodied cognition (4)
- adolescents (3)
- diffusion (3)
- exercise (3)
- Arabidopsis thaliana (2)
- Bombina bombina (2)
- Environmental sciences (2)
- German (2)
- Kinematics (2)
- LC-MS/MS (2)
Institute
- Institut für Biochemie und Biologie (24)
- Strukturbereich Kognitionswissenschaften (17)
- Institut für Geowissenschaften (16)
- Department Psychologie (13)
- Department Sport- und Gesundheitswissenschaften (12)
- Institut für Ernährungswissenschaft (10)
- Institut für Chemie (8)
- Institut für Umweltwissenschaften und Geographie (8)
- Institut für Physik und Astronomie (7)
- Hasso-Plattner-Institut für Digital Engineering GmbH (5)
- Fakultät für Gesundheitswissenschaften (4)
- Humanwissenschaftliche Fakultät (4)
- Department Erziehungswissenschaft (3)
- Strukturbereich Bildungswissenschaften (3)
- Department Linguistik (2)
- Department für Inklusionspädagogik (2)
- Extern (2)
- Fachgruppe Betriebswirtschaftslehre (2)
- Hochschulambulanz (2)
- Institut für Philosophie (2)
- Sozialwissenschaften (2)
- Wirtschaftswissenschaften (2)
- Department Grundschulpädagogik (1)
- Digital Engineering Fakultät (1)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (1)
- Institut für Anglistik und Amerikanistik (1)
- Universitätsbibliothek (1)
Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.
The co-occurrence of warm spells and droughts can lead to detrimental socio-economic and ecological impacts, largely surpassing the impacts of either warm spells or droughts alone. We quantify changes in the number of compound warm spells and droughts from 1979 to 2018 in the Mediterranean Basin using the ERA5 data set. We analyse two types of compound events: 1) warm season compound events, which are extreme in absolute terms in the warm season from May to October and 2) year-round deseasonalised compound events, which are extreme in relative terms respective to the time of the year. The number of compound events increases significantly and especially warm spells are increasing strongly – with an annual growth rates of 3.9 (3.5) % for warm season (deseasonalised) compound events and 4.6 (4.4) % for warm spells –, whereas for droughts the change is more ambiguous depending on the applied definition. Therefore, the rise in the number of compound events is primarily driven by temperature changes and not the lack of precipitation. The months July and August show the highest increases in warm season compound events, whereas the highest increases of deseasonalised compound events occur in spring and early summer. This increase in deseasonalised compound events can potentially have a significant impact on the functioning of Mediterranean ecosystems as this is the peak phase of ecosystem productivity and a vital phenophase.
We use ultrafast x-ray diffraction to investigate the effect of expansive phononic and contractive magnetic stress driving the picosecond strain response of a metallic perovskite SrRuO3 thin film upon femtosecond laser excitation. We exemplify how the anisotropic bulk equilibrium thermal expansion can be used to predict the response of the thin film to ultrafast deposition of energy. It is key to consider that the laterally homogeneous laser excitation changes the strain response compared to the near-equilibrium thermal expansion because the balanced in-plane stresses suppress the Poisson stress on the picosecond timescale. We find a very large negative Grüneisen constant describing the large contractive stress imposed by a small amount of energy in the spin system. The temperature and fluence dependence of the strain response for a double-pulse excitation scheme demonstrates the saturation of the magnetic stress in the high-fluence regime.
The particle noch (‘still’) can have an additive reading similar to auch (‘also’). We argue that both particles indicate that a previously partially answered QUD is re-opened to add a further answer. The particles differ in that the QUD, in the case of auch, can be re-opened with respect to the same topic situation, whereas noch indicates that the QUD is re-opened with respect to a new topic situation. This account predicts a difference in the accommodation behavior of the two particles. We present an experiment whose results are in line with this prediction.
A tale of shifting relations
(2021)
Understanding the dynamics between the East Asian summer (EASM) and winter monsoon (EAWM) is needed to predict their variability under future global warming scenarios. Here, we investigate the relationship between EASM and EAWM as well as the mechanisms driving their variability during the last 10,000 years by stacking marine and terrestrial (non-speleothem) proxy records from the East Asian realm. This provides a regional and proxy independent signal for both monsoonal systems. The respective signal was subsequently analysed using a linear regression model. We find that the phase relationship between EASM and EAWM is not time-constant and significantly depends on orbital configuration changes. In addition, changes in the Atlantic Meridional Overturning circulation, Arctic sea-ice coverage, El Niño-Southern Oscillation and Sun Spot numbers contributed to millennial scale changes in the EASM and EAWM during the Holocene. We also argue that the bulk signal of monsoonal activity captured by the stacked non-speleothem proxy records supports the previously argued bias of speleothem climatic archives to moisture source changes and/or seasonality.
Reciprocal space slicing
(2021)
An experimental technique that allows faster assessment of out-of-plane strain dynamics of thin film heterostructures via x-ray diffraction is presented. In contrast to conventional high-speed reciprocal space-mapping setups, our approach reduces the measurement time drastically due to a fixed measurement geometry with a position-sensitive detector. This means that neither the incident (ω) nor the exit (2θ) diffraction angle is scanned during the strain assessment via x-ray diffraction. Shifts of diffraction peaks on the fixed x-ray area detector originate from an out-of-plane strain within the sample. Quantitative strain assessment requires the determination of a factor relating the observed shift to the change in the reciprocal lattice vector. The factor depends only on the widths of the peak along certain directions in reciprocal space, the diffraction angle of the studied reflection, and the resolution of the instrumental setup. We provide a full theoretical explanation and exemplify the concept with picosecond strain dynamics of a thin layer of NbO2.
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
The suitability of a newly developed cell-based functional assay was tested for the detection of the activity of a range of neurotoxins and neuroactive pharmaceuticals which act by stimulation or inhibition of calcium-dependent neurotransmitter release. In this functional assay, a reporter enzyme is released concomitantly with the neurotransmitter from neurosecretory vesicles. The current study showed that the release of a luciferase from a differentiated human neuroblastoma-based reporter cell line (SIMA-hPOMC1-26-GLuc cells) can be stimulated by a carbachol-mediated activation of the Gq-coupled muscarinic-acetylcholine receptor and by the Ca2+-channel forming spider toxin α-latrotoxin. Carbachol-stimulated luciferase release was completely inhibited by the muscarinic acetylcholine receptor antagonist atropine and α-latrotoxin-mediated release by the Ca2+-chelator EGTA, demonstrating the specificity of luciferase-release stimulation. SIMA-hPOMC1-26-GLuc cells express mainly L- and N-type and to a lesser extent T-type VGCC on the mRNA and protein level. In accordance with the expression profile a depolarization-stimulated luciferase release by a high K+-buffer was effectively and dose-dependently inhibited by L-type VGCC inhibitors and to a lesser extent by N-type and T-type inhibitors. P/Q- and R-type inhibitors did not affect the K+-stimulated luciferase release. In summary, the newly established cell-based assay may represent a versatile tool to analyze the biological efficiency of a range of neurotoxins and neuroactive pharmaceuticals which mediate their activity by the modulation of calcium-dependent neurotransmitter release.
Stereoselective [4+2] Cycloaddition of Singlet Oxygen to Naphthalenes Controlled by Carbohydrates
(2021)
Stereoselective reactions of singlet oxygen are of current interest. Since enantioselective photooxygenations have not been realized efficiently, auxiliary control is an attractive alternative. However, the obtained peroxides are often too labile for isolation or further transformations into enantiomerically pure products. Herein, we describe the oxidation of naphthalenes by singlet oxygen, where the face selectivity is controlled by carbohydrates for the first time. The synthesis of the precursors is easily achieved starting from naphthoquinone and a protected glucose derivative in only two steps. Photooxygenations proceed smoothly at low temperature, and we detected the corresponding endoperoxides as sole products by NMR. They are labile and can thermally react back to the parent naphthalenes and singlet oxygen. However, we could isolate and characterize two enantiomerically pure peroxides, which are sufficiently stable at room temperature. An interesting influence of substituents on the stereoselectivities of the photooxygenations has been found, ranging from 51:49 to up to 91:9 dr (diastereomeric ratio). We explain this by a hindered rotation of the carbohydrate substituents, substantiated by a combination of NOESY measurements and theoretical calculations. Finally, we could transfer the chiral information from a pure endoperoxide to an epoxide, which was isolated after cleavage of the sugar chiral auxiliary in enantiomerically pure form.
Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories
(2021)
Extensive time-series encoding the position of particles such as viruses, vesicles, or individualproteins are routinely garnered insingle-particle tracking experiments or supercomputing studies.They contain vital clues on how viruses spread or drugs may be delivered in biological cells.Similar time-series are being recorded of stock values in financial markets and of climate data.Such time-series are most typically evaluated in terms of time-averaged mean-squareddisplacements (TAMSDs), which remain random variables for finite measurement times. Theirstatistical properties are different for differentphysical stochastic processes, thus allowing us toextract valuable information on the stochastic process itself. To exploit the full potential of thestatistical information encoded in measured time-series we here propose an easy-to-implementand computationally inexpensive new methodology, based on deviations of the TAMSD from itsensemble average counterpart. Specifically, we use the upper bound of these deviations forBrownian motion (BM) to check the applicability of this approach to simulated and real data sets.By comparing the probability of deviations fordifferent data sets, we demonstrate how thetheoretical bound for BM reveals additional information about observed stochastic processes. Weapply the large-deviation method to data sets of tracer beads tracked in aqueous solution, tracerbeads measured in mucin hydrogels, and of geographic surface temperature anomalies. Ouranalysis shows how the large-deviation properties can be efficiently used as a simple yet effectiveroutine test to reject the BM hypothesis and unveil relevant information on statistical propertiessuch as ergodicity breaking and short-time correlations.