500 Naturwissenschaften und Mathematik
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Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
The GRACE-FO satellites launched in May 2018 are able to quantify the water mass deficit in Central Europe during the two consecutive summer droughts of 2018 and 2019. Relative to the long-term climatology, the water mass deficits were-112 +/- 10.5 Gt in 2018 and-145 +/- 12 Gt in 2019. These deficits are 73% and 94% of the mean amplitude of seasonal water storage variations, which is so severe that a recovery cannot be expected within 1 year. The water deficits in 2018 and 2019 are the largest in the whole GRACE and GRACE-FO time span. Globally, the data do not show an offset between the two missions, which proves the successful continuation of GRACE by GRACE-FO and thus the reliability of the observed extreme events in Central Europe. This allows for a joint assessment of the four Central European droughts in 2003, 2015, 2018, and 2019 in terms of total water storage deficits.
Natural ponds are perceived as spatially and temporally highly variable ecosystems. This perception is in contrast to the often-applied sampling design with high spatial but low temporal replication. Based on a data set covering a period of six years and 20 permanently to periodically inundated ponds, we investigated whether this widely applied sampling design is sufficient to identify differences between single ponds or single years with regard to water quality and macrophyte community composition as measures of ecosystem integrity.
In our study, the factor "pond", which describes differences between individual ponds, explained 56 % and 63 %, respectively, of the variance in water quality and macrophyte composition. In contrast, the factor "year" that refers to changes between individual years, contributed less to understand the observed variability in water quality and macrophyte composition (10 % and 7 % respectively, of the variance explained). The low explanation of variance for "year" and the low year-to-year correlation for the single water quality parameter or macrophyte coverage values, respectively, indicated high but non-consistent temporal variability affecting individual pond patterns.
In general, the results largely supported the ability of the widely applied sampling strategy with about one sampling date per year to capture differences in water quality and macrophyte community composition between ponds. Hence, future research can be rest upon sampling designs that give more weight to the number of ponds than the number of years in dependence on the research question and the available resources. Nonetheless, pond research would miss a substantial amount of information (7 to 10 % of the variance explained), when the sampling would generally be restricted to one year. Moreover, we expect that the importance of multiple-year sampling will likely increase in periods and regions of higher hydrological variability compared to the average hydrological conditions encountered in the studied period.
Scope:
Nutrition is a critical determinant of a functional immune system. The aim of this study is to investigate the molecular mechanisms by which immune cells are influenced by zinc and sodium.
Methods and Results:
Mixed lymphocyte cultures and Jurkat cells are generated and incubated with zinc, sodium, or a combination of both for further tests. Zinc induces the number of regulatory T cells (Treg) and decreases T helper 17 cells (Th17), and sodium has the opposite effect. The transforming growth factor beta receptor signaling pathway is also enhanced by zinc and reduced by sodium as indicated by contrary phosphoSmad 2/3 induction. Antagonistic effects can also be seen on zinc transporter and metallothionein-1 (MT-1) mRNA expression: zinc declines Zip10 mRNA expression while sodium induces it, whereas MT-1 mRNA expression is induced by zinc while it is reduced by sodium.
Conclusion:
This data indicate that zinc and sodium display opposite effects regarding Treg and Th17 induction in MLC, respectively, resulting in a contrary effect on the immune system. Additionally, it reveals a direct interaction of zinc and sodium in the priming of T cell subpopulations and shows that Zip10 and MT-1 play a significant role in those differentiation pathways.
Climate change entails an intensification of extreme weather events that can potentially trigger socioeconomic and energy system disruptions. As we approach 1 degrees C of global warming we should start learning from historical extremes and explicitly incorporate such events in integrated climate-economy and energy systems models.
The ability to reflect is considered an essential element of Education for Sustainable Development (ESD) and a key competence for learners and educators in ESD (UNECE Strategy for ESD, 2012). In contrast to its high importance, little is known about how reflective thinking can be identified, influenced or increased in the classroom. Therefore, the objective of this study is to address this need by developing an empirical multi-stage model designed to help educators diagnose different levels of reflective thinking and to identify factors that influence students’ reflective thinking about sustainability. Based on a 4–8-week project with grade 10 and 11 students studying sustainability, reflective thinking performance using weblogs as reflective journals was analysed. In addition, qualitative semi-structured interviews were conducted with the teachers to comprehend the learning environment and the personal value they assigned to ESD in their geography class. To determine the levels of reflective thinking achieved by the students, the study built on the work of Dewey (1933) and pre-existing multi-stage models of reflective thinking (Bain, Ballantyne, & Packer, 1999; Chen, Wei, Wu, & Uden, 2009). Using a qualitative, iterative data analysis, the study adapted the stage models to be applicable in ESD and found great differences in the students’ reflection levels. Furthermore, the study identified eight factors that influence students’ reflective thinking about sustainability. The outcomes of this study may be valuable for educators in high school and higher education, who seek to diagnose their students’ reflective thinking performance and facilitate reflection about sustainability.
Physical activity and exercise are effective approaches in prevention and therapy of multiple diseases. Although the specific characteristics of lengthening contractions have the potential to be beneficial in many clinical conditions, eccentric training is not commonly used in clinical populations with metabolic, orthopaedic, or neurologic conditions. The purpose of this pilot study is to investigate the feasibility, functional benefits, and systemic responses of an eccentric exercise program focused on the trunk and lower extremities in people with low back pain (LBP) and multiple sclerosis (MS). A six-week eccentric training program with three weekly sessions is performed by people with LBP and MS. The program consists of ten exercises addressing strength of the trunk and lower extremities. The study follows a four-group design (N = 12 per group) in two study centers (Israel and Germany): three groups perform the eccentric training program: A) control group (healthy, asymptomatic); B) people with LBP; C) people with MS; group D (people with MS) receives standard care physiotherapy. Baseline measurements are conducted before first training, post-measurement takes place after the last session both comprise blood sampling, self-reported questionnaires, mobility, balance, and strength testing. The feasibility of the eccentric training program will be evaluated using quantitative and qualitative measures related to the study process, compliance and adherence, safety, and overall program assessment. For preliminary assessment of potential intervention effects, surrogate parameters related to mobility, postural control, muscle strength and systemic effects are assessed. The presented study will add knowledge regarding safety, feasibility, and initial effects of eccentric training in people with orthopaedic and neurological conditions. The simple exercises, that are easily modifiable in complexity and intensity, are likely beneficial to other populations. Thus, multiple applications and implementation pathways for the herein presented training program are conceivable.
Physical activity and exercise are effective approaches in prevention and therapy of multiple diseases. Although the specific characteristics of lengthening contractions have the potential to be beneficial in many clinical conditions, eccentric training is not commonly used in clinical populations with metabolic, orthopaedic, or neurologic conditions. The purpose of this pilot study is to investigate the feasibility, functional benefits, and systemic responses of an eccentric exercise program focused on the trunk and lower extremities in people with low back pain (LBP) and multiple sclerosis (MS). A six-week eccentric training program with three weekly sessions is performed by people with LBP and MS. The program consists of ten exercises addressing strength of the trunk and lower extremities. The study follows a four-group design (N = 12 per group) in two study centers (Israel and Germany): three groups perform the eccentric training program: A) control group (healthy, asymptomatic); B) people with LBP; C) people with MS; group D (people with MS) receives standard care physiotherapy. Baseline measurements are conducted before first training, post-measurement takes place after the last session both comprise blood sampling, self-reported questionnaires, mobility, balance, and strength testing. The feasibility of the eccentric training program will be evaluated using quantitative and qualitative measures related to the study process, compliance and adherence, safety, and overall program assessment. For preliminary assessment of potential intervention effects, surrogate parameters related to mobility, postural control, muscle strength and systemic effects are assessed. The presented study will add knowledge regarding safety, feasibility, and initial effects of eccentric training in people with orthopaedic and neurological conditions. The simple exercises, that are easily modifiable in complexity and intensity, are likely beneficial to other populations. Thus, multiple applications and implementation pathways for the herein presented training program are conceivable.
Sprache
Englisch
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusionmodel and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a wellcalibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous-diffusion data to include an uncertainty estimate in addition to the predicted output. To avoid the Black-Box-Problem a Bayesian-Deep-Learning technique named Stochastic-Weight-Averaging-Gaussian is used to train models for both the classification of the diffusionmodel and the regression of the anomalous diffusion exponent of single-particle-trajectories. Evaluating their performance, we find that these models can achieve a wellcalibrated error estimate while maintaining high prediction accuracies. In the analysis of the output uncertainty predictions we relate these to properties of the underlying diffusion models, thus providing insights into the learning process of the machine and the relevance of the output.