Refine
Year of publication
Document Type
- Article (21267) (remove)
Language
- English (21267) (remove)
Keywords
- climate change (95)
- Germany (73)
- stars: massive (57)
- diffusion (47)
- morphology (47)
- stars: early-type (47)
- gamma rays: general (46)
- German (45)
- stars: winds, outflows (45)
- Climate change (43)
Institute
- Institut für Physik und Astronomie (4063)
- Institut für Biochemie und Biologie (3383)
- Institut für Geowissenschaften (2601)
- Institut für Chemie (2231)
- Department Psychologie (1126)
- Institut für Mathematik (958)
- Department Linguistik (763)
- Institut für Ernährungswissenschaft (726)
- Institut für Informatik und Computational Science (572)
- Institut für Umweltwissenschaften und Geographie (571)
Author summary <br /> The use of orally inhaled drugs for treating lung diseases is appealing since they have the potential for lung selectivity, i.e. high exposure at the site of action -the lung- without excessive side effects. However, the degree of lung selectivity depends on a large number of factors, including physiochemical properties of drug molecules, patient disease state, and inhalation devices. To predict the impact of these factors on drug exposure and thereby to understand the characteristics of an optimal drug for inhalation, we develop a predictive mathematical framework (a "pharmacokinetic model"). In contrast to previous approaches, our model allows combining knowledge from different sources appropriately and its predictions were able to adequately predict different sets of clinical data. Finally, we compare the impact of different factors and find that the most important factors are the size of the inhaled particles, the affinity of the drug to the lung tissue, as well as the rate of drug dissolution in the lung. In contrast to the common belief, the solubility of a drug in the lining fluids is not found to be relevant. These findings are important to understand how inhaled drugs should be designed to achieve best treatment results in patients. <br /> The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics.
Species richness has been shown to increase biomass production of plant communities. Such overyielding occurs when a community performs better than its component monocultures due to the complementarity or dominance effect and is mostly detected in substrate-bound plant communities (terrestrial plants or submerged macrophytes) where resource use complementarity can be enhanced due to differences in rooting architecture and depth. Here, we investigated whether these findings arc generalizeable for free-floating phytoplankton with little potential for spatial differences in resource use. We performed aquatic microcosm experiments with eight phytoplankton species belonging to four functional groups to determine the manner in which species and community biovolume varies in relation to the number of functional groups and hypothesized that an increasing number of functional groups within a community promotes overyielding. Unexpectedly, we did not detect overyielding in any algal community. Instead. total community biovolume tended to decrease with all increasing, number of functional groups. This underyielding was mainly caused by the negative dominance effect that originated from a trade-off between growth rate and filial biovolume. In monoculture, slow-groing species built up higher biovolumes that fast-growing ones, whereas in mixture a fast-growing but low-productive species monopolized most of the nutrients and prevented competing species from developing high biovolumes expected from monocultures. Our results indicated that the Magnitude of the community biovolume was largely determined by the identify of one species. Functional diversity and resource use complementarity were of minor Importance among free-floating phytoplankton, possibly reflecting the lack of spatially heterogeneous resource distribution. As a consequence, biodiversity-ecosystem functioning relationships may not be easily generalizeable from substrate-bound plant to phytoplankton communities and vice versa.
A matter of concern
(2021)
Neurons are post-mitotic cells in the brain and their integrity is of central importance to avoid neurodegeneration. Yet, the inability of self-replenishment of post-mitotic cells results in the need to withstand challenges from numerous stressors during life. Neurons are exposed to oxidative stress due to high oxygen consumption during metabolic activity in the brain. Accordingly, DNA damage can occur and accumulate, resulting in genome instability. In this context, imbalances in brain trace element homeostasis are a matter of concern, especially regarding iron, copper, manganese, zinc, and selenium. Although trace elements are essential for brain physiology, excess and deficient conditions are considered to impair neuronal maintenance. Besides increasing oxidative stress, DNA damage response and repair of oxidative DNA damage are affected by trace elements. Hence, a balanced trace element homeostasis is of particular importance to safeguard neuronal genome integrity and prevent neuronal loss. This review summarises the current state of knowledge on the impact of deficient, as well as excessive iron, copper, manganese, zinc, and selenium levels on neuronal genome stability
Structural kinetic modeling (SKM) enables the analysis of dynamical properties of metabolic networks solely based on topological information and experimental data. Current SKM-based experiments are hampered by the time-intensive process of assigning model parameters and choosing appropriate sampling intervals for MonteCarlo experiments. We introduce a toolbox for the automatic and efficient construction and evaluation of structural kinetic models (SK models). Quantitative and qualitative analyses of network stability properties are performed in an automated manner. We illustrate the model building and analysis process in detailed example scripts that provide toolbox implementations of previously published literature models.
The Pleistocene archeological record in East Africa has revealed unusual accumulations of Acheulean handaxes at prehistoric sites. In particular, there has been intensive debate concerning whether the artifact accumulation at the Middle Pleistocene Olorgesailie (Southern Kenya Rift) and Kariandusi (Central Kenya Rift) sites were a result of fluvial reworking or of in situ deposition by hominids. We used a two-step approach to test the hypothesis of fluvial reworking. Firstly, the behavior of handaxes in water currents was investigated in a current flume and the flow threshold required to reorientate the handaxes was determined. The results of these experiments suggested that, in relatively high energy and non-steady flow conditions, handaxes will reorientate themselves perpendicular to the current direction. Secondly, an automated image analysis routine was developed and applied to archeological plans from three Acheulean sites, two at Olorgesailie and one at Kariandusi, in order to determine the orientations of the handaxes. A Rayleigh test was then applied to the orientation data to test for a preferred orientation. The results revealed that the handaxes at the Upper Kariandusi Site and the Olorgesailie Main Site Mid Trench had a preferential orientation, suggesting reworking by a paleocurrent. The handaxes from the Olorgesailie Main Site H/6A, however, appeared to be randomly oriented and in situ deposition by the producers therefore remains a possibility.
Author summary <br /> Switching between local and global attention is a general strategy in human information processing. We investigate whether this strategy is a viable approach to model sequences of fixations generated by a human observer in a free viewing task with natural scenes. Variants of the basic model are used to predict the experimental data based on Bayesian inference. Results indicate a high predictive power for both aggregated data and individual differences across observers. The combination of a novel model with state-of-the-art Bayesian methods lends support to our two-state model using local and global internal attention states for controlling eye movements. <br /> Understanding the decision process underlying gaze control is an important question in cognitive neuroscience with applications in diverse fields ranging from psychology to computer vision. The decision for choosing an upcoming saccade target can be framed as a selection process between two states: Should the observer further inspect the information near the current gaze position (local attention) or continue with exploration of other patches of the given scene (global attention)? Here we propose and investigate a mathematical model motivated by switching between these two attentional states during scene viewing. The model is derived from a minimal set of assumptions that generates realistic eye movement behavior. We implemented a Bayesian approach for model parameter inference based on the model's likelihood function. In order to simplify the inference, we applied data augmentation methods that allowed the use of conjugate priors and the construction of an efficient Gibbs sampler. This approach turned out to be numerically efficient and permitted fitting interindividual differences in saccade statistics. Thus, the main contribution of our modeling approach is two-fold; first, we propose a new model for saccade generation in scene viewing. Second, we demonstrate the use of novel methods from Bayesian inference in the field of scan path modeling.
Industry 4.0 is transforming how businesses innovate and, as a result, companies are spearheading the movement towards 'Digital Transformation'. While some scholars advocate the use of design thinking to identify new innovative behaviours, cognition experts emphasise the importance of top managers in supporting employees to develop these behaviours. However, there is a dearth of research in this domain and companies are struggling to implement the required behaviours. To address this gap, this study aims to identify and prioritise behavioural strategies conducive to design thinking to inform the creation of a managerial mental model. We identify 20 behavioural strategies from 45 interviewees with practitioners and educators and combine them with the concepts of 'paradigm-mindset-mental model' from cognition theory. The paper contributes to the body of knowledge by identifying and prioritising specific behavioural strategies to form a novel set of survival conditions aligned to the new industrial paradigm of Industry 4.0.
We apply and evaluate a recent machine learning method for the automatic classification of seismic waveforms. The method relies on Dynamic Bayesian Networks (DBN) and supervised learning to improve the detection capabilities at 3C seismic stations. A time-frequency decomposition provides the basis for the required signal characteristics we need in order to derive the features defining typical "signal" and "noise" patterns. Each pattern class is modeled by a DBN, specifying the interrelationships of the derived features in the time-frequency plane. Subsequently, the models are trained using previously labeled segments of seismic data. The DBN models can now be compared against in order to determine the likelihood of new incoming seismic waveform segments to be either signal or noise. As the noise characteristics of seismic stations varies smoothly in time (seasonal variation as well as anthropogenic influence), we accommodate in our approach for a continuous adaptation of the DBN model that is associated with the noise class. Given the difficulty for obtaining a golden standard for real data (ground truth) the proof of concept and evaluation is shown by conducting experiments based on 3C seismic data from the International Monitoring Stations, BOSA and LPAZ.
B2 1215+30 is a BL-Lac-type blazar that was first detected at TeV energies by the MAGIC atmospheric Cherenkov telescopes and subsequently confirmed by the Very Energetic Radiation Imaging Telescope Array System (VERITAS) observatory with data collected between 2009 and 2012. In 2014 February 08, VERITAS detected a large-amplitude flare from B2. 1215+30 during routine monitoring observations of the blazar 1ES. 1218+304, located in the same field of view. The TeV flux reached 2.4 times the Crab Nebula flux with a variability timescale of <3.6 hr. Multiwavelength observations with Fermi-LAT, Swift, and the Tuorla Observatory revealed a correlated high GeV flux state and no significant optical counterpart to the flare, with a spectral energy distribution where the gamma-ray luminosity exceeds the synchrotron luminosity. When interpreted in the framework of a onezone leptonic model, the observed emission implies a high degree of beaming, with Doppler factor delta > 10, and an electron population with spectral index p < 2.3.
Ancient evaporite deposits are geological archives of depositional environments characterized by a long‐term negative precipitation balance and bear evidence for global ocean element mass balance calculations. Here, Cretaceous selenite pseudomorphs from western Anatolia (‘Rosetta Marble’) — characterized by their exceptional morphological preservation — and their ‘marine’ geochemical signatures are described and interpreted in a process‐oriented context. These rocks recorded Late Cretaceous high‐pressure/low‐temperature, subduction‐related metamorphism with peak conditions of 1·0 to 1·2 GPa and 300 to 400°C. Metre‐scale, rock‐forming radiating rods, now present as fibrous calcite marble, clearly point to selenitic gypsum as the precursor mineral. Stratigraphic successions are recorded along a reconstructed proximal to distal transect. The cyclical alternation of selenite beds and radiolarian ribbon‐bedded cherts in the distal portions are interpreted as a two type of seawater system. During arid intervals, shallow marine brines cascaded downward into basinal settings and induced precipitation. During more humid times, upwelling‐induced radiolarian blooms caused the deposition of radiolarite facies. Interestingly, there is no comparable depositional setting known from the Cenozoic world. Meta‐selenite geochemical data (δ13C, δ18O and 87Sr/86Sr) plot within the range of reconstructed middle Cretaceous seawater signatures. Possible sources for the 13C‐enriched (mean 2·2‰) values include methanogenesis, gas hydrates and cold seep fluid exhalation. Spatially resolved component‐specific analysis of a rock slab displays isotopic variances between meta‐selenite crystals (mean δ13C 2·2‰) and host matrix (mean δ13C 1·3‰). The Cretaceous evaporite‐pseudomorphs of Anatolia represent a basin wide event coeval with the Aptian evaporites of the Proto‐Atlantic and the pseudomorphs share many attributes, including lateral distribution of 600 km and stratigraphic thickness of 1·5 to 2·0 km, with the evaporites formed during the younger Messinian salinity crisis. The Rosetta Marble of Anatolia may represent the best‐preserved selenite pseudomorphs worldwide and have a clear potential to act as a template for the study of meta‐selenite in deep time.
Over the last two decades, the multi-dimensional notion of job performance has been fully brought to life. The differentiation between core task performance and various aspects of discretionary work behaviour is flow commonly applied. A multitude of empirical studies, enhancing our knowledge of the antecedents and consequences of the different performance aspects, have recently been summarised through various meta-analyses. We use this as all occasion for taking stock in order to identify new areas of theorising and empirical research. Focusing in particular oil proactive performance aspects, the present paper identifies three themes that could inspire new research and model development. We suggest taking a new approach to the treatment of time in order to account for the dynamic nature of performance oil the one hand, and to consider life-span changes oil the other, developing comprehensive models oil proactivity-enhancing interventions, and more strongly incorporating a cross-cultural perspective.
Aging in speech production is a multidimensional process. Biological, cognitive, social, and communicative factors can change over time, stay relatively stable, or may even compensate for each other. In this longitudinal work, we focus on stability and change at the laryngeal and supralaryngeal levels in the discourse particle euh produced by 10 older French-speaking females at two times, 10 years apart. Recognizing the multiple discourse roles of euh, we divided out occurrences according to utterance position. We quantified the frequency of euh, and evaluated acoustic changes in formants, fundamental frequency, and voice quality across time and utterance position. Results showed that euh frequency was stable with age. The only acoustic measure that revealed an age effect was harmonics-to-noise ratio, showing less noise at older ages. Other measures mostly varied with utterance position, sometimes in interaction with age. Some voice quality changes could reflect laryngeal adjustments that provide for airflow conservation utterance-finally. The data suggest that aging effects may be evident in some prosodic positions (e.g., utterance-final position), but not others (utterance-initial position). Thus, it is essential to consider the interactions among these factors in future work and not assume that vocal aging is evident throughout the signal.
Previous studies have indicated a higher risk of disordered eating in certain types of elite sports such as aesthetic sports (e.g., rhythmical gymnastics, figure skating). But even though some studies on risk factors for disordered eating in sports exist, most research on this topic is based on cross-sectional data with limitations on causal inferences. We examined sports-related risk factors for disordered eating in a 1-year longitudinal study with two assessment points. The participants were 65 adolescent athletes from aesthetic sports (mean age 14.0 +/- 2.2years) who completed measures of disordered eating, social pressure from the sports environment, sports-related body dissatisfaction, desire to be leaner to improve sports performance, and emotional distress resulting from missed exercise sessions. All variables were relatively stable in the mean. Individual changes in the desire to be leaner to improve sports performance were associated with individual changes in disordered eating. Furthermore, a cross-lagged partial correlation analysis showed that the desire to be leaner to improve sports performance was predictive of disordered eating and not vice versa. The results of our study indicate that athletes are more at risk for disordered eating if they believe it is possible to enhance their sports performance through weight regulation.
We introduce a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing graph repairs from which a user may select a graph repair based on non-formalized further requirements. This incremental approach features delta preservation as it allows to restrict the generation of graph repairs to delta-preserving graph repairs, which do not revert the additions and deletions of the most recent consistency-violating graph update. We specify consistency of graphs using the logic of nested graph conditions, which is equivalent to first-order logic on graphs. Technically, the incremental approach encodes if and how the graph under repair satisfies a graph condition using the novel data structure of satisfaction trees, which are adapted incrementally according to the graph updates applied. In addition to the incremental approach, we also present two state-based graph repair algorithms, which restore consistency of a graph independent of the most recent graph update and which generate additional graph repairs using a global perspective on the graph under repair. We evaluate the developed algorithms using our prototypical implementation in the tool AutoGraph and illustrate our incremental approach using a case study from the graph database domain.
Ensemble Kalman filter techniques are widely used to assimilate observations into dynamical models. The phase- space dimension is typically much larger than the number of ensemble members, which leads to inaccurate results in the computed covariance matrices. These inaccuracies can lead, among other things, to spurious long-range correlations, which can be eliminated by Schur-product-based localization techniques. In this article, we propose a new technique for implementing such localization techniques within the class of ensemble transform/square-root Kalman filters. Our approach relies on a continuous embedding of the Kalman filter update for the ensemble members, i.e. we state an ordinary differential equation (ODE) with solutions that, over a unit time interval, are equivalent to the Kalman filter update. The ODE formulation forms a gradient system with the observations as a cost functional. Besides localization, the new ODE ensemble formulation should also find useful application in the context of nonlinear observation operators and observations that arrive continuously in time.
A Local Dimension of Integration Policies? A Comparative Study of Berlin, Malmo, and Rotterdam
(2015)
This study examines three theses on local integration policies by a qualitative comparative case study of integration policies in three cities in three different countries (Berlin, Malmo, and Rotterdam). We found little evidence of a congruent local dimension of integration policies. Local policies resemble their national policy frameworks fairly well in terms of policy approaches and domains. Our multi-level perspective shows that this is not the result of top-down hierarchical governance, but rather of a multilevel dynamic of two-way interaction. Local policy legacies and local politics matter and national policies are also influenced by local approaches of integration.
A novel quantum method to deal with typical system-bath dynamical problems is introduced. Subsystem discrete variable representation and bath coherent-state sets are used to write down a multiconfigurational expansion of the wave function of the whole system. With the help of the Dirac-Frenkel variational principle, simple equations of motion-a kind of Schrodinger-Langevin equation for the subsystem coupled to (pseudo) classical equations for the bath-are derived. True dissipative dynamics at all times is obtained by coupling the bath to a secondary, classical Ohmic bath, which is modeled by adding a friction coefficient in the derived pseudoclassical bath equations. The resulting equations are then solved for a number of model problems, ranging from tunneling to vibrational relaxation dynamics. Comparison of the results with those of exact, multiconfiguration time-dependent Hartree calculations in systems with up to 80 bath oscillators shows that the proposed method can be very accurate and might be of help in studying realistic problems with very large baths. To this end, its linear scaling behavior with respect to the number of bath degrees of freedom is shown in practice with model calculations using tens of thousands of bath oscillators.
A Little Piece of the Shire
(2014)