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Much research on language control in bilinguals has relied on the interpretation of the costs of switching between two languages. Of the two types of costs that are linked to language control, switching costs are assumed to be transient in nature and modulated by trial-specific manipulations (e.g., by preparation time), while mixing costs are supposed to be more stable and less affected by trial-specific manipulations. The present study investigated the effect of preparation time on switching and mixing costs, revealing that both types of costs can be influenced by trial-specific manipulations.
Rhythm perception is assumed to be guided by a domain-general auditory principle, the Iambic/Trochaic Law, stating that sounds varying in intensity are grouped as strong-weak, and sounds varying in duration are grouped as weak-strong. Recently, Bhatara et al. (2013) showed that rhythmic grouping is influenced by native language experience, French listeners having weaker grouping preferences than German listeners. This study explores whether L2 knowledge and musical experience also affect rhythmic grouping. In a grouping task, French late learners of German listened to sequences of coarticulated syllables varying in either intensity or duration. Data on their language and musical experience were obtained by a questionnaire. Mixed-effect model comparisons showed influences of musical experience as well as L2 input quality and quantity on grouping preferences. These results imply that adult French listeners' sensitivity to rhythm can be enhanced through L2 and musical experience.
Background: Dementia is a psychiatric condition the development of which is associated with numerous aspects of life. Our aim was to estimate dementia risk factors in German primary care patients.
Methods: The case-control study included primary care patients (70-90 years) with first diagnosis of dementia (all-cause) during the index period (01/2010-12/2014) (Disease Analyzer, Germany), and controls without dementia matched (1:1) to cases on the basis of age, sex, type of health insurance, and physician. Practice visit records were used to verify that there had been 10 years of continuous follow-up prior to the index date. Multivariate logistic regression models were fitted with dementia as a dependent variable and the potential predictors.
Results: The mean age for the 11,956 cases and the 11,956 controls was 80.4 (SD: 5.3) years. 39.0% of them were male and 1.9% had private health insurance. In the multivariate regression model, the following variables were linked to a significant extent with an increased risk of dementia: diabetes (OR: 1.17; 95% CI: 1.10-1.24), lipid metabolism (1.07; 1.00-1.14), stroke incl. TIA (1.68; 1.57-1.80), Parkinson's disease (PD) (1.89; 1.64-2.19), intracranial injury (1.30; 1.00-1.70), coronary heart disease (1.06; 1.00-1.13), mild cognitive impairment (MCI) (2.12; 1.82-2.48), mental and behavioral disorders due to alcohol use (1.96; 1.50-2.57). The use of statins (OR: 0.94; 0.90-0.99), proton-pump inhibitors (PPI) (0.93; 0.90-0.97), and antihypertensive drugs (0.96, 0.94-0.99) were associated with a decreased risk of developing dementia.
Conclusions: Risk factors for dementia found in this study are consistent with the literature. Nevertheless, the associations between statin, PPI and antihypertensive drug use, and decreased risk of dementia need further investigations.
An egalitarian approach to the fair representation of voters specifies three main institutional requirements: proportional representation, legislative majority rule and a parliamentary system of government. This approach faces two challenges: the under-determination of the resulting democratic process and the idea of a trade-off between equal voter representation and government accountability. Linking conceptual with comparative analysis, the article argues that we can distinguish three ideal-typical varieties of the egalitarian vision of democracy, based on the stages at which majorities are formed. These varieties do not put different relative normative weight onto equality and accountability, but have different conceptions of both values and their reconciliation. The view that accountability is necessarily linked to clarity of responsibility', widespread in the comparative literature, is questioned - as is the idea of a general trade-off between representation and accountability. Depending on the vision of democracy, the two values need not be in conflict.
Background: Given the well-established association between perceived stress and quality of life (QoL) in dementia patients and their partners, our goal was to identify whether relationship quality and dyadic coping would operate as mediators between perceived stress and QoL.
Methods: 82 dyads of dementia patients and their spousal caregivers were included in a cross-sectional assessment from a prospective study. QoL was assessed with the Quality of Life in Alzheimer's Disease scale (QoL-AD) for dementia patients and the WHO Quality of Life-BREF for spousal caregivers. Perceived stress was measured with the Perceived Stress Scale (PSS-14). Both partners were assessed with the Dyadic Coping Inventory (DCI). Analyses of correlation as well as regression models including mediator analyses were performed.
Results: We found negative correlations between stress and QoL in both partners (QoL-AD: r = -0.62; p < 0.001; WHO-QOL Overall: r = -0.27; p = 0.02). Spousal caregivers had a significantly lower DCI total score than dementia patients (p < 0.001). Dyadic coping was a significant mediator of the relationship between stress and QoL in spousal caregivers (z = 0.28; p = 0.02), but not in dementia patients. Likewise, relationship quality significantly mediated the relationship between stress and QoL in caregivers only (z = -2.41; p = 0.02).
Conclusions: This study identified dyadic coping as a mediator on the relationship between stress and QoL in (caregiving) partners of dementia patients. In patients, however, we found a direct negative effect of stress on QoL. The findings suggest the importance of stress reducing and dyadic interventions for dementia patients and their partners, respectively.
Observed recent and expected future increases in frequency and intensity of climatic extremes in central Europe may pose critical challenges for domestic tree species. Continuous dendrometer recordings provide a valuable source of information on tree stem radius variations, offering the possibility to study a tree's response to environmental influences at a high temporal resolution. In this study, we analyze stem radius variations (SRV) of three domestic tree species (beech, oak, and pine) from 2012 to 2014. We use the novel statistical approach of event coincidence analysis (ECA) to investigate the simultaneous occurrence of extreme daily weather conditions and extreme SRVs, where extremes are defined with respect to the common values at a given phase of the annual growth period. Besides defining extreme events based on individual meteorological variables, we additionally introduce conditional and joint ECA as new multivariate extensions of the original methodology and apply them for testing 105 different combinations of variables regarding their impact on SRV extremes. Our results reveal a strong susceptibility of all three species to the extremes of several meteorological variables. Yet, the inter-species differences regarding their response to the meteorological extremes are comparatively low. The obtained results provide a thorough extension of previous correlation-based studies by emphasizing on the timings of climatic extremes only. We suggest that the employed methodological approach should be further promoted in forest research regarding the investigation of tree responses to changing environmental conditions.
Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2's of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage, spectral coverage, and repetition rate. Efficient algorithms are needed to process, or possibly reprocess, those big amounts of data. Techniques based on top-of-atmosphere reflectance spectra for single-pixels without exploitation of external data or spatial context offer the largest potential for parallel data processing and highly optimized processing throughput. Such algorithms can be seen as a baseline for possible trade-offs in processing performance when the application of more sophisticated methods is discussed. We present several ready-to-use classification algorithms which are all based on a publicly available database of manually classified Sentinel-2A images. These algorithms are based on commonly used and newly developed machine learning techniques which drastically reduce the amount of time needed to update the algorithms when new images are added to the database. Several ready-to-use decision trees are presented which allow to correctly label about 91% of the spectra within a validation dataset. While decision trees are simple to implement and easy to understand, they offer only limited classification skill. It improves to 98% when the presented algorithm based on the classical Bayesian method is applied. This method has only recently been used for this task and shows excellent performance concerning classification skill and processing performance. A comparison of the presented algorithms with other commonly used techniques such as random forests, stochastic gradient descent, or support vector machines is also given. Especially random forests and support vector machines show similar classification skill as the classical Bayesian method.
The spider mite Tetranychus urticae Koch and the aphid Myzus persicae (Sulzer) both infest a number of economically significant crops, including tomato (Solanurn lycopersicum). Although used for decades to control pests, the impact of green lacewing larvae Chrysoperla carnea (Stephens) on plant biochemistry was not investigated. Here, we used profiling methods and targeted analyses to explore the impact of the predator and herbivore(s)-predator interactions on tomato biochemistry. Each pest and pest -predator combination induced a characteristic metabolite signature in the leaf and the fruit thus, the plant exhibited a systemic response. The treatments had a stronger impact on non-volatile metabolites including abscisic acid and amino acids in the leaves in comparison with the fruits. In contrast, the various biotic factors had a greater impact on the carotenoids in the fruits. We identified volatiles such as myrcene and alpha-terpinene which were induced by pest -predator interactions but not by single species, and we demonstrated the involvement of the phytohormone abscisic acid in tritrophic interactions for the first time. More importantly, C. carnea larvae alone impacted the plant metabolome, but the predator did not appear to elicit particular defense pathways on its own. Since the presence of both C. carnea larvae and pest individuals elicited volatiles which were shown to contribute to plant defense, C. carnea larvae could therefore contribute to the reduction of pest infestation, not only by its preying activity, but also by priming responses to generalist herbivores such as T urticae and M. persicae. On the other hand, the use of C. carnea larvae alone did not impact carotenoids thus, was not prejudicial to the fruit quality. The present piece of research highlights the specific impact of predator and tritrophic interactions with green lacewing larvae, spider mites, and aphids on different components of the tomato primary and secondary metabolism for the first time, and provides cues for further in-depth studies aiming to integrate entomological approaches and plant biochemistry.
The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/ semi-arid regions, GLDAS based storage estimations perform better than WGHM.
In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics "up" and "down" states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons. In particular, we observe that the crucial role both in interruption and in resumption of global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is more influenced by their presynaptic environment in the network than by their formal types, assigned in accordance with their response to constant current.