983 resultados para Hymns, Czech.


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Dietary interventions with flavan-3-ols have shown beneficial effects on vascular function. The translation of these findings into the context of the health of the general public requires detailed information on habitual dietary intake. However, only limited data are currently available for European populations. Therefore, in the present study, we assessed the habitual intake of flavan-3-ol monomers, proanthocyanidins (PA) and theaflavins in the European Union (EU) and determined their main food sources using the EFSA (European Food Safety Authority) Comprehensive European Food Consumption Database. Data for adults aged 18–64 years were available from fourteen European countries, and intake was determined using the FLAVIOLA Flavanol Food Composition Database, developed for the present study and based on the latest US Department of Agriculture and Phenol-Explorer databases. The mean habitual intake of flavan-3-ol monomers, theaflavins and PA ranged from 181 mg/d (Czech Republic) to 793 mg/d (Ireland). The highest intakes of flavan-3-ol monomers and theaflavins were observed in Ireland (191/505 mg/d) and the lowest intakes in Spain (24/9 mg/d). In contrast, the daily intake of PA was highest in Spain (175 mg/d) and lowest in The Netherlands (96 mg/d). Main sources were tea (62 %), pome fruits (11 %), berries (3 %) and cocoa products (3 %). Tea was the major single contributor to monomer intake (75 %), followed by pome fruits (6 %). Pome fruits were also the main source of PA (28 %). The present study provides important data on the population-based intake of flavanols in the EU and demonstrates that dietary intake amounts for flavan-3-ol monomers, PA and theaflavins vary significantly across European countries. The average habitual intake of flavan-3-ols is considerably below the amounts used in most dietary intervention studies.

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A statistical–dynamical downscaling (SDD) approach for the regionalization of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily mean sea level pressure fields with the central point being located over Germany. Seventy-seven weather classes based on the associated CWT and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamically downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different data sets, the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes, results of SDD are compared to wind observations and to simulated Eout of purely dynamical downscaling (DD) methods. For the present climate, SDD is able to simulate realistic PDFs of 10-m wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD-simulated Eout. In terms of decadal hindcasts, results of SDD are similar to DD-simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout time series of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the Earth System Model of the Max Planck Institute (MPI-ESM) decadal prediction system. Long-term climate change projections in Special Report on Emission Scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to the results of other studies using DD methods, with increasing Eout over northern Europe and a negative trend over southern Europe. Despite some biases, it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.

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Soil biodiversity plays a key role in regulating the processes that underpin the delivery of ecosystem goods and services in terrestrial ecosystems. Agricultural intensification is known to change the diversity of individual groups of soil biota, but less is known about how intensification affects biodiversity of the soil food web as a whole, and whether or not these effects may be generalized across regions. We examined biodiversity in soil food webs from grasslands, extensive, and intensive rotations in four agricultural regions across Europe: in Sweden, the UK, the Czech Republic and Greece. Effects of land-use intensity were quantified based on structure and diversity among functional groups in the soil food web, as well as on community-weighted mean body mass of soil fauna. We also elucidate land-use intensity effects on diversity of taxonomic units within taxonomic groups of soil fauna. We found that between regions soil food web diversity measures were variable, but that increasing land-use intensity caused highly consistent responses. In particular, land-use intensification reduced the complexity in the soil food webs, as well as the community-weighted mean body mass of soil fauna. In all regions across Europe, species richness of earthworms, Collembolans, and oribatid mites was negatively affected by increased land-use intensity. The taxonomic distinctness, which is a measure of taxonomic relatedness of species in a community that is independent of species richness, was also reduced by land-use intensification. We conclude that intensive agriculture reduces soil biodiversity, making soil food webs less diverse and composed of smaller bodied organisms. Land-use intensification results in fewer functional groups of soil biota with fewer and taxonomically more closely related species. We discuss how these changes in soil biodiversity due to land-use intensification may threaten the functioning of soil in agricultural production systems.

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The roles of some cake ingredients – oil, a leavening agent, and inulin – in the structure and physicochemical properties of batter and cakes were studied in four different formulations. Oil played an important role in the batter stability, due to its contribution to increasing batter viscosity and occluding air during mixing. The addition of the leavening agent was crucial to the final height and sponginess of the cakes. When inulin was used as a fat replacer, the absence of oil caused a decrease in the stability of the batter, where larger air bubbles were occluded. Inulin dispersed uniformly in the batter could create a competition for water with the flour components: gluten was not properly hydrated and some starch granules were not fully incorporated into the matrix. Thus, the development of a continuous network was disrupted and the cake was shorter and softer; it contained interconnected air cells in the crumb, and was easily crumbled. The structure studies were decisive to understand the physicochemical properties.

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Data from four experimental research projects are presented which have in common that unexpected results caused a change in direction of the research. A plant growth accelerator caused the appearance of white black bean aphids, a synthetic pyrethroid suspected of enhancing aphid reproduction proved to enhance plant growth, a chance conversation with a colleague initiated a search for fungal DNA in aphids, and the accidental invasion of aphid cultures by a parasitoid reversed the aphid population ranking of two Brussels sprout cultivars. This last result led to a whole series of studies on the plant odour preferences of emerging parasitoids which in turn revealed the unexpected phenomenon that chemical cues to the maternal host plant are left with the eggs at oviposition. It is pointed out that, too often, researchers fail to follow up unexpected results because they resist accepting flaws in their hypotheses; also that current application criteria for research funding make it hard to accommodate unexpected findings.

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At the Paris Peace Conferences of 1918-1919, new states aspiring to be nation-states were created for 60 million people, but at the same time 25 million people found themselves as ethnic minorities. This change of the old order in Europe had a considerable impact on one such group, more than 3 million Bohemian German-speakers, later referred to as Sudeten Germans. After the demise of the Habsburg Empire In 1918, they became part of the new state of Czechoslovakia. In 1938, the Munich Agreement – prelude to the Second World War – integrated them into Hitler’s Reich; in 1945-1946 they were expelled from the reconstituted state of Czechoslovakia. At the centre of this War Child case study are German children from the Northern Bohemian town and district, formerly known as Gablonz an der Neisse, famous for exquisite glass art, now Jablonec nad Nisou in the Czech Republic. After their expulsion they found new homes in the post-war Federal Republic of Germany. In addition, testimonies have been drawn upon of some Czech eyewitnesses from the same area, who provided their perspective from the other side, as it were. It turned out to be an insightful case study of the fate of these communities, previously studied mainly within the context of the national struggle between Germans and Czechs. The inter-disciplinary research methodology adopted here combines history and sociological research to demonstrate the effect of larger political and social developments on human lives, not shying away from addressing sensitive political and historical issues, as far as these are relevant within the context of the study. The expellees started new lives in what became Neugablonz in post-war Bavaria where they successfully re-established the industries they had had to leave behind in 1945-1946. Part 1 of the study sheds light on the complex Czech-German relationship of this important Central European region, addressing issues of democracy, ethnicity, race, nationalism, geopolitics, economics, human geography and ethnography. It also charts the developments leading to the expulsion of the Sudeten Germans from Czechoslovakia after 1945. What is important in this War Child study is how the expellees remember their history while living as children in Sudetenland and later. The testimony data gained indicate that certain stereotypes often repeated within the context of Sudeten issues such as the confrontational nature of inter-ethnic relations are not reflected in the testimonies of the respondents from Gablonz. In Part 2 the War Child Study explores the memories of the former Sudeten war children using sociological research methods. It focuses on how they remember life in their Bohemian homeland and coped with the life-long effects of displacement after their expulsion. The study maps how they turned adversity into success by showing a remarkable degree of resilience and ingenuity in the face of testing circumstances due to the abrupt break in their lives. The thesis examines the reasons for the relatively positive outcome to respondents’ lives and what transferable lessons can be deduced from the results of this study.

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Tick-borne encephalitis virus (TBEV) is the most important arboviral agent causing disease of the central nervous system in central Europe. In this study, 61 TBEV E gene sequences derived from 48 isolates from the Czech Republic, and four isolates and nine TBEV strains detected in ticks from Germany, covering more than half a century from 1954 to 2009, were sequenced and subjected to phylogenetic and Bayesian phylodynamic analysis to determine the phylogeography of TBEV in central Europe. The general Eurasian continental east-to-west pattern of the spread of TBEV was confirmed at the regional level but is interlaced with spreading that arises because of local geography and anthropogenic influence. This spread is reflected by the disease pattern in the Czech Republic that has been observed since 1991. The overall evolutionary rate was estimated to be approximately 8x10(-4) substitutions per nucleotide per year. The analysis of the TBEV E genes of 11 strains isolated at one natural focus in Zd`ar Kaplice proved for the first time that TBEV is indeed subject to local evolution.

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Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.