Universität Potsdam
  • Potsdam, Brandenburg, Germany
Recent publications
The Burma Terrane has yielded some of the earliest pieces of evidence for monsoonal rainfall in the Bay of Bengal. However, Burmese ecosystems and their potential monsoonal imprint remain poorly studied. This study focuses on the late Eocene Yaw Formation (23° N) in central Myanmar, which was located near the equator (c. 5° N) during the Eocene. We quantitatively assessed the past vegetation, climate, and depositional environments with sporomorph diagrams, bioclimatic analysis, and sequence biostratigraphy. We calculated the palynological diversity and drew inferences with rarefaction analysis by comparing with four other middle to late Eocene tropical palynofloras. Palynological results highlight a high floristic diversity for the palynoflora throughout the section formed by six pollen zones characterized by different vegetation. They indicate that lowland evergreen forests and swamps dominated in the Eocene Burmese deltaic plains while terra firma areas were occupied by seasonal evergreen, seasonally dry, and deciduous forests. This vegetation pattern is typical to what is found around the Bay of Bengal today and supports a monsoon-like climate at the time of the Yaw Formation. Bioclimatic analysis further suggests that in the late Eocene, the Yaw Formation was more seasonal, drier, and cooler compared to modern-day climate at similar near-equatorial latitude. More seasonal and drier conditions can be explained by a well-marked seasonal migration of the Intertropical Convergence Zone (ITCZ), driver of proto-monsoonal rainfall. Cooler temperatures in the late Eocene of central Myanmar may be due to the lack of adequate modern analogues for the Eocene monsoonal climate, while those found at other three Eocene Asian paleobotanical sites (India and South China) may be caused by the effect of canopy evapotranspirational cooling. Our data suggest that paleoenvironmental change including two transgressive-regressive depositional sequences is controlled by global sea level change, which may be driven by climate change and tectonics. The high diversity of the Yaw Formation palynoflora, despite well-marked seasonality, is explained by its cross-roads location for plant dispersals between India and Asia.
Background Prolonged periods of sedentary behaviour, for instance, engendered by home confinement in Shenzhen city, has led to negative mental health consequences, especially in adolescents. Previous research suggests, in general, that sedentary behavior can increase negative emotions. However, the specific mechanism driving the relationship between sedentary behavior and negative emotions is still relatively unclear. Social support and sleep quality might partly explain the effect of sedentary behavior on negative emotions. Thus, the current study aimed to examine the associations between sedentary behavior and negative emotions, and to investigate if social support and sleep quality mediate such a relationship. Method During home confinement due to the COVID-19 Omicron variant outbreak, 1179 middle and high school students in Shenzhen were invited to voluntarily complete an e-questionnaire, including the 21-item Depression Anxiety Stress Scale (DASS-21), the short form of the International Physical Activity Questionnaire (IPAQ-SF), the Social Support Rating Scale (SSRS) and the Pittsburgh Sleep Quality Index (PSQI). Data from 1065 participants were included in the analysis. Results We observed significant sex-related and demografic-related differences in emotional (e.g., anxiety, stress and social support) and other outcome variables (e.g., sitting duration and PSQI score). Furthermore, sedentary behavior, social support, and sleep quality were associated with negative emotions (p < .01), even after controlling for sex, age, only-child case, body mass index, and metabolic equivalent level. In addition, social support and sleep quality partially mediated the association between sedentary behavior and negative emotions. Conclusion The findings of the current study suggest that social support and sleep quality partially mediate the relationship between sedentary behavior and negative emotions in middle and high school students during home confinement in Shenzhen city.
Background/Objective Emerging adulthood (EA, age range between 18 to 29 years) is an important developmental stage that is characterized by marked social and psychological changes. Currently, its developmental features are quantified by the Inventory of the Dimensions of Emerging Adulthood (IDEA) but a validated Chinese version of this questionnaire (IDEA-C) is lacking. Thus, this research, which consists of two consecutive studies, aimed to investigate the psychometric properties of the translated IDEA in a Chinese sample of emerging adults. Method Firstly, a forward-backward translation of the IDEA-C scale was conducted. Item analysis and exploratory factor analysis were performed in Sample 1a (n = 2438), followed by structural validity test in Sample 1b (n = 2461). Concurrent validity and internal consistency were evaluated in Sample 1(n = 4899). Finally, test-retest reliability was tested in Sample 2 (n = 185). Then, the second study aimed to test the factor structure proposed by study 1 in the non-student sample (n = 2200) by confirmatory factor analysis. In addition, the second study also investigated whether the attainment of college education influenced the EA experience of non-student emerging adults in China. And the association was examined between the socioeconomic status of emerging adults and the subscales of IDEA. Results In the college sample, the IDEA-C scale presented a four-factor structure different from the original five-factor structure (χ2(190)=1116.84, p < 0.001; CFI = 0.97; TLI = 0.96; SRMR = 0.039; RMSEA = 0.050 [90%CI=0.047-0.052]). In addition, IDEA-C exhibited good internal consistency reliability (Cronbach's alpha >0.77), test-retest reliability (r>0.49, p < 0.01) and concurrent validity. And the CFA in non-student sample also showed an adequate fit indices (χ2(158) =710.10, p < 0.001, TLI=0.93, CFI=0.94, SRMR=0.038, RMSEA=0.04 [90%CI=0.037-0.040]) and an adequate internal consistency (Cronbach's alpha >0.64) and test-retest reliability (r>0.43, p < 0.01). Conclusion The results of the present study confirmed that the Chinese version of the IDEA is found to be valid for measuring psychological characteristics of EA in Chinese-speaking samples of emerging adults.
Decision Trees (DT) describe a type of machine learning method that has been widely used in the geosciences to automatically extract patterns from complex and high dimensional data. However, like any data-based method, the application of DT is hindered by data limitations, such as significant biases, leading to potentially physically unrealistic results. We develop interactive DT (iDT) that put humans in the loop to integrate the power of experts' scientific knowledge with the power of the algorithms to automatically learn patterns from large datasets. We created an open-source Python toolbox that implements the iDT framework. Users can interactively create new composite variables, change the variable and threshold to split, prune and group variables based on their physical meaning. We demonstrate with three case studies how iDT overcomes problems with current DT thus achieving higher interpretability and robustness of the result.
Companies contribute to a large extent to greenhouse gas emission. To mitigate this, measures for reducing these emissions can be applied. There is, however, neither a systematized general overview of existing measures nor an estimation of their application and their effectiveness to reduce greenhouse gas emissions. This study strives to close this gap by reviewing research on the reduction of corporate greenhouse gas emissions and synthesizing emission reduction measures in a taxonomy. Furthermore, the application of these measures and their perceived effectiveness is empirically assessed using a survey among companies that are involved in emission reduction activities. On this basis, a cluster analysis is conducted to identify measure types and to unveil application patterns. 27 different measures and 65 respective implementation examples are identified and structured within nine categories: energy, product, process, technology, 6R and waste management, office and mobility, management, reporting and disclosure, and compensation measures. The empirical analysis shows that there exist measures with a high efficiency to reduce emission, which are rarely applied in companies. On the other side, a large share of applied measures is not perceived as highly effective. Companies can use these results to structure their emission reduction activities and identify best practices.
Soil erosion affects 20% of croplands worldwide. However, understanding the effect of soil erosion on N2O emissions, which is one of the most potent greenhouse gases, is still limited. This limitation is likely because the small-scale differences in soil properties and fertility induced by erosion (i.e. ranges of erosion states) have barely been considered in studies quantifying N2O emissions from croplands. There are, however, indications that the erosion state itself strongly impacts N2O emission, similar to the N fertilizer form. Therefore, our investigations aimed to further explore these indications. We measured N2O fluxes for three years and at five sites within an erosion affected field experiment. N2O emissions were quantified using a manual chamber system. Three sites were established on a summit position (Albic Luvisol; non-eroded) but differed in N fertilizer forms (organic biogas fermented residues, calcium ammonium nitrate and a mixture of both fertilizers). Two additional sites were established on an extremely eroded soil (Calcaric Regosol) and wet depositional soil in a depression (Endogleyic Colluvic Regosol) to measure the effect of soil erosion states on N2O emissions. Both additional sites were fertilized with calcium ammonium nitrate only. In case of the non-eroded soil (summit), organic fertilization resulted in the highest cumulative N2O emission (6.2 ± 0.21 kg N2O-N/ha y⁻¹) compared to mixed (5.5 ± 0.18 kg N2O-N/ha y⁻¹) and mineral (1.9 ± 0.17 kg N2O-N/ha y⁻¹) fertilization. These high emissions were probably caused by soluble C and N substrates from organic fertilizer, resulting in microbial activities favoring high N2O emissions. Regarding the erosion status, we observed the highest N2O emissions in the depositional soil (2.8 ± 0.21 kg N2O-N/ha y⁻¹), followed by the non-eroded (1.9 ± 0.17 kg N2O-N/ha y⁻¹) and the extremely eroded soil (0.6 ± 0.03 kg N2O-N/ha y⁻¹). These differences in N2O emissions were mainly due to the site-specific, erosion induced differences in soil properties such as soil texture, soil organic C and total N contents and stocks, water-filled pore space and soil pH. These results indicate that soil erosion state may indeed be of similar importance, as N fertilizer form, for the magnitude of N2O emissions from croplands.
  • Jan Philipp DapprichJan Philipp Dapprich
  • William Paul CockshottWilliam Paul Cockshott
In this paper, we show how socialist planning can be based on input-output data. We argue that the information required for this can be obtained by a central planning agency and thus dismiss Hayek’s information argument against socialism. We further show how economic planning can be made responsive to consumer demand through a feedback control mechanism. Output targets of products would be adjusted in response to observed consumer demand or based on predictions about future demand. Planners can use machine learning to make more accurate forecasts. The valuation of goods plays an important role in the feedback control mechanism. The values of goods can either be measured by the labour time necessary for their production (labour values) or through shadow prices based on linear programming.
Dictyostelium amoebae perform a semi-closed mitosis, in which the nuclear envelope is fenestrated at the insertion sites of the mitotic centrosomes and around the central spindle during karyokinesis. During late telophase the centrosome relocates to the cytoplasmic side of the nucleus, the central spindle disassembles and the nuclear fenestrae become closed. Our data indicate that Dictyostelium spastin (DdSpastin) is a microtubule-binding and severing type I membrane protein that plays a role in this process. Its mitotic localization is in agreement with a requirement for the removal of microtubules that would hinder closure of the fenestrae. Furthermore, DdSpastin interacts with the HeH/ LEM-family protein Src1 in BioID analyses as well as the inner nuclear membrane protein Sun1, and shows subcellular co-localizations with Src1, Sun1, the ESCRT component CHMP7 and the IST1-like protein filactin, suggesting that the principal pathway of mitotic nuclear envelope remodeling is conserved between animals and Dictyostelium amoebae.
  • Kristina NormanKristina Norman
  • Catrin HerpichCatrin Herpich
  • Ursula Müller-WerdanUrsula Müller-Werdan
Age-related changes in body composition reflect an increased risk for disease as well as disability. Bioimpedance analysis is a safe and inexpensive bed side method to measure body composition, but the calculation of body compartments with BIA is hampered in older adults. Phase angle, a raw parameter derived from bioimpedance analysis, is free from calculation-inherent errors. It declines with age and disease and is highly predictive of a variety of clinical outcomes as well as mortality. This review summarizes the current evidence linking the phase angle to geriatric syndromes such as malnutrition, sarcopenia and frailty and also investigates whether the phase angle reacts to interventions. Since the majority of studies show an association between the phase angle and these geriatric syndromes, a low phase angle is not suitable to exclusively indicate a specific condition. It does not inform on the underlying cause and as such, a low phase angle mainly indicates increased risk. Phase angle decline over time is reflected by deterioration of e.g. frailty status. It reacts to physical training and detraining, but studies investigating whether these induced changes are also associated with improved outcome are missing.
Today, firms pursuing a pioneering strategy are often engaged in supply-chain relationships to benefit from external resources and to improve their innovation. However, this effort can be impeded by power asymmetries in such relationships and especially by the execution of coercive power by their partner firm. Contracts could potentially reduce this risk of opportunistic behavior. Our survey study on 778 small to medium-sized enterprises in the European packaging and medical equipment industries examines how coercive power of the partner and the contractual arrangement between firms moderate the pioneering strategy's innovation outcomes in the short and long run. Our results confirm the negative effect of coercive power on innovation performance in both the short and long term. However, the compensating effect of rather complete contracts differs temporally. Whereas contract completeness protects against higher dependency at the beginning of the collaboration, their effect diminishes over time. In contrast, rather incomplete contracts enhance innovation performance in the long term, possibly complemented with trust.
Background The association between bivariate variables may not necessarily be homogeneous throughout the whole range of the variables. We present a new technique to describe inhomogeneity in the association of bivariate variables. Methods We consider the correlation of two normally distributed random variables. The 45° diagonal through the origin of coordinates represents the line on which all points would lie if the two variables completely agreed. If the two variables do not completely agree, the points will scatter on both sides of the diagonal and form a cloud. In case of a high association between the variables, the band width of this cloud will be narrow, in case of a low association, the band width will be wide. The band width directly relates to the magnitude of the correlation coefficient. We then determine the Euclidean distances between the diagonal and each point of the bivariate correlation, and rotate the coordinate system clockwise by 45°. The standard deviation of all Euclidean distances, named “global standard deviation” , reflects the band width of all points along the former diagonal. Calculating moving averages of the standard deviation along the former diagonal results in “locally structured standard deviations” and reflect patterns of “locally structured correlations (LSC)”. LSC highlight inhomogeneity of bivariate correlations. We exemplify this technique by analyzing the association between body mass index (BMI) and hip circumference (HC) in 6313 healthy East German adults aged 18 to 70 years. Results The correlation between BMI and HC in healthy adults is not homogeneous. LSC is able to identify regions where the predictive power of the bivariate correlation between BMI and HC increases or decreases, and highlights in our example that slim people have a higher association between BMI and HC than obese people. Conclusion Locally structured correlations (LSC) identify regions of higher or lower than average correlation between two normally distributed variables.
Carl Bergmann was an astute naturalist and physiologist. His ideas about animal size and shape were important advances in the pre-Darwinian nineteenth century. Bergmann’s rule claims that that in cold climates, large body mass increases the ratio of volume-to-surface area and provides for maximum metabolic heat retention in mammals and birds. Conversely, in warmer temperatures, smaller body mass increases surface area relative to volume and allows for greater heat loss. For humans, we now know that body size and shape are regulated more by social-economic-political-emotional (SEPE) factors as well as nutrition-infection interactions. Temperature has virtually no effect. Bergmann’s rule is a “just-so” story and should be relegated to teaching and scholarship about the history of science. That “rule” is no longer acceptable science and has nothing to tell us about physiological anthropology.
Background Intermittent hypoxia applied at rest or in combination with exercise promotes multiple beneficial adaptations with regard to performance and health in humans. It was hypothesized that replacing normoxia by moderate hyperoxia can increase the adaptive response to the intermittent hypoxic stimulus. Objective Our objective was to systematically review the current state of the literature on the effects of chronic intermittent hypoxia–hyperoxia (IHH) on performance- and health-related outcomes in humans. Methods PubMed, Web of Science™, Scopus, and Cochrane Library databases were searched in accordance with PRISMA guidelines (January 2000 to September 2021) using the following inclusion criteria: (1) original research articles involving humans, (2) investigation of the chronic effect of IHH, (3) inclusion of a control group being not exposed to IHH, and (4) articles published in peer-reviewed journals written in English. Results Of 1085 articles initially found, eight studies were included. IHH was solely performed at rest in different populations including geriatric patients ( n = 1), older patients with cardiovascular ( n = 3) and metabolic disease ( n = 2) or cognitive impairment ( n = 1), and young athletes with overtraining syndrome ( n = 1). The included studies confirmed the beneficial effects of chronic exposure to IHH, showing improvements in exercise tolerance, peak oxygen uptake, and global cognitive functions, as well as lowered blood glucose levels. A trend was discernible that chronic exposure to IHH can trigger a reduction in systolic and diastolic blood pressure. The evidence of whether IHH exerts beneficial effects on blood lipid levels and haematological parameters is currently inconclusive. A meta-analysis was not possible because the reviewed studies had a considerable heterogeneity concerning the investigated populations and outcome parameters. Conclusion Based on the published literature, it can be suggested that chronic exposure to IHH might be a promising non-pharmacological intervention strategy for improving peak oxygen consumption, exercise tolerance, and cognitive performance as well as reducing blood glucose levels, and systolic and diastolic blood pressure in older patients with cardiovascular and metabolic diseases or cognitive impairment. However, further randomized controlled trials with adequate sample sizes are needed to confirm and extend the evidence. This systematic review was registered on the international prospective register of systematic reviews (PROSPERO-ID: CRD42021281248) ( https://www.crd.york.ac.uk/prospero/ ).
Soft actuator performance can be tuned by chemistry or mechanical manipulation, but this adjustability is limited especially in view of their growing technological relevance. Inspired from textile engineering, we designed and fabricated fiber mesh actuators and introduced new features like anisotropic behavior and soft-tissue like elastic deformability. Design criteria for the meshes are the formation of fiber bundles, the angle between fiber bundles in different stacked layers and covalent crosslinks forming within and between fibers at their interfacial contact areas. Through crosslinking the interfiber bond strength increased from a bond transmitting neither axial nor rotational loads (pin joint) to a bond strength capable of both (welded joint). For non-linear elastic stiffening, stacked fiber bundles with four embracing fibers were created forming microstructural rhombus shapes. Loading the rhombus diagonally allowed generation of “soft tissue”-like mechanics. By adjustment of stacking angles, the point of strong increase in stress is tuned. While the highest stresses are observed in aligned and crosslinked fiber mats along the direction of the fiber, the strongest shape-memory actuation behavior is found in randomly oriented fiber mats. Fiber mesh actuators controlled by temperature are of high significance as soft robot skins and as for active patches supporting tissue regeneration.
In this article, we address the measurement of individualized instruction in the context of regular classroom instruction. Our study assessed instructional practices geared towards individualization in German third grade reading lessons by combining self-report data from 621 students, from their teachers (n = 57), and live observations. We then investigated the reliability of these different approaches to measuring individualization as well as the agreement between them. All three approaches yielded reliable indicators of individualized practices, but not all of them corresponded with each other. We found considerable agreement between students and observers, but neither agreed with teachers' self-reports. Upon closer examination, we found that students’ ratings only correlated with teacher ratings that were provided close to the timepoint of interest. This correlation increased when teacher measures were corrected for response tendencies. We conclude with some recommendations for future studies that aim to measure individualized instruction in the classroom.
In this study, we contribute to the scholarly conversation on firm-level business model changes following a neoconfigurational approach. By exploring configurations of business model changes over time, we add the direction of business model changes—namely business model convergence or divergence—as a vital avenue to the business model innovation literature. We identify necessary business model convergence and divergence recipes in a sample of N = 217 strategic dyadic alliances. Firstly, technological proximity emerges as a single pre-condition to both converging and diverging business models. Secondly, business models between competitors either converge through complementarities or tend not to change relative to each other. Thirdly, equity participation enables business model divergence through co-specialization. We conclude with a discussion of business model trajectories and future research directions.
Information regarding the spatial distribution of soil water content is key in many disciplines and applications including soil and atmospheric sciences, hydrology, and agricultural engineering. Thus, within the past decades various experimental methods and strategies have been developed to map spatial variations in soil moisture distribution and to monitor temporal changes. Our study examines the combination of electrical resistivity mapping and point observations of soil moisture to infer the spatial and the temporal variability of soil moisture. Over a period of around two years, we performed field measurements on six days to collect repeated electrical resistivity mapping data for a nine-hectare test site south-east of Berlin, Germany. Permanently installed TDR probes, temporary TDR measurements within permanently installed tubes, and gravimetric measurements using soil samples provided soil moisture data at various selected points. In addition, soil analysis and classification results are available for 132 regularly distributed positions up to depths of 1.2 m. We compare and link three-dimensional resistivity models obtained via data inversion to soil composition and soil moisture as provided by our point data. Both the soil samples and the resistivity models indicate a two-layer medium characterized by a sandy top layer with varying thickness and a loamy bottom soil. For all six field campaigns, we observe similar resistivity patterns reflecting the temporally stable influence of soil texture. While the overall patterns are stable, the range of resistivity values changes with soil moisture. Finally, to estimate spatial models of soil moisture, we link our soil moisture and resistivity data using empirical petrophysical models relying on a second order polynomial function. We observe a mean prediction error for soil moisture of +/- 0.034 m³ • m⁻³ using all observation points while we notice that point-specific models further reduce the error. Thus, we conclude that our experimental and data analysis strategies represent a reliable approach to establish site-specific models and to estimate three-dimensional moisture distribution including temporal variations.
Spatial predictions of biomass production and biodiversity at regional scale in grasslands are critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can predict these grassland characteristics with varying accuracy. However, such studies frequently fail to cover a sufficiently broad range of environmental conditions, and their prediction models are often case-specific. To address this gap, we have modelled above-ground biomass and species richness in 150 spatially independent grassland plots of three geographical regions in Germany. These regions follow a North-South climate gradient and differ in soil types, topography, elevation, climatic conditions, historical contexts, and management intensities. The predictors tested in this study are Sentinel-1 backscatter, Sentinel-2 time series of surface reflectance along with derived vegetation indices and Rao's Q, and a set of topoedaphic variables. We compared the performance of a feed-forward deep neural network (DNN) with a random forest (RF) regression algorithm. The DNN achieved the best estimations of biomass (r² = 0.45) when trained with Sentinel-2 surface reflectance only. Moreover, the DNN showed a higher generalizability than RF during spatial cross-validations (i.e., calibrating and validating in different regions, r² = 0.38 vs. 0.26). Species richness predictions by both algorithms improved when the full time series of Sentinel-2 surface reflectance values were used (highest r² = 0.42 achieved by the DNN), but both performed poorly during spatial cross-validations. Overall, the DNN-based models were more robust than RF models, showed a lower bias and lower systematic error, and required fewer inputs. Explainability analysis indicated that red-edge and near infrared information from May and October was the most relevant to predict species richness. This study presents an important step forward in generating robust spatially explicit predictions of grassland attributes and biodiversity variables across large areas, environmental gradients, and phenological stages.
Background Sodium glucose cotransporter 2 (SGLT2) inhibitors originally developed for the treatment of type 2 diabetes are clinically very effective drugs halting chronic kidney disease progression. The underlying mechanisms are, however, not fully understood. Methods We generated single-cell transcriptomes of kidneys from rats with 5/6 nephrectomy before and after SGLT2 inhibitors treatment by single-cell RNA sequencing. Findings Empagliflozin treatment decreased BUN, creatinine and urinary albumin excretion compared to placebo by 39.8%, 34.1%, and 55%, respectively (p < 0.01 in all cases). Renal interstitial fibrosis and glomerulosclerosis was likewise decreased by 51% and 66.8%; respectively (p < 0.05 in all cases). 14 distinct kidney cell clusters could be identified by scRNA-seq. The polarization of M2 macrophages from state 1 (CD206⁻CD68⁻ M2 macrophages) to state 5 (CD206⁺CD68⁺ M2 macrophages) was the main pro-fibrotic process, as CD206⁺CD68⁺ M2 macrophages highly expressed fibrosis-promoting genes and can convert into fibrocytes. Empagliflozin remarkably inhibited the expression of fibrosis-promoting (IFG1 and TREM2) and polarization-associated genes (GPNMB, LGALS3, PRDX5, and CTSB) in CD206⁺CD68⁺ M2 macrophages and attenuated inflammatory signals from CD8⁺ effector T cells. The inhibitory effect of empagliflozin on CD206⁺CD68⁺ M2 macrophages polarization was mainly achieved by affecting mitophagy and mTOR pathways. Interpretation We propose that the beneficial effects of empagliflozin on kidney function and morphology in 5/6 nephrectomyiced rats with established CKD are at least partially due to an inhibition of CD206⁺CD68⁺ M2 macrophage polarization by targeting mTOR and mitophagy pathways and attenuating inflammatory signals from CD8⁺ effector T cells. Fundings A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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Dominik Kröner
  • Institute of Chemistry
Zoran Stamenkovic
  • Institute of Computer Science
Andre Kleinridders
  • Institute of Nutritional Sciences
Nathalie Dehne
  • Health Science
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