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Microbial communities play an important role in cheese ripening and determine the flavor and taste of different cheese types to a large extent. However, under adverse conditions human pathogens may colonize cheese samples during ripening and may thus cause severe outbreaks of diarrhoea and other diseases. Therefore in the present study we investigated the bacterial community structure of three raw ewe's milk cheese types, which are produced without the application of starter cultures during ripening from two production sites based on fingerprinting in combination with next generation sequencing of 16S rRNA gene amplicons. Overall a surprisingly high diversity was found in the analyzed samples and overall up to 213 OTU97 could be assigned. 20 of the major OTUs were present in all samples and include mostly lactic acid bacteria (LAB), mainly Lactococcus, and Enterococcus species. Abundance and diversity of these genera differed to a large extent between the 3 investigated cheese types and in response to the ripening process. Also a large number of non LAB genera could be identified based on phylogenetic alignments including mainly Enterobacteriaceae and Staphylococcacae. Some species belonging to these two families could be clearly assigned to species which are known as potential human pathogens like Staphylococcus saprophyticus or Salmonella spp. However, during cheese ripening their abundance was reduced. The bacterial genera, namely Lactobacillus, Streptococcus, Leuconostoc, Bifidobacterium, Brevibacterium, Corynebacterium, Clostridium, Staphylococcus, Thermoanerobacterium, E. coli, Hafnia, Pseudomonas, Janthinobacterium, Petrotoga, Kosmotoga, Megasphaera, Macrococcus, Mannheimia, Aerococcus, Vagococcus, Weissella and Pediococcus were identified at a relative low level and only in selected samples. Overall the microbial composition of the used milk and the management of the production units determined the bacterial community composition for all cheese types to a large extend, also at the late time points of cheese ripening.

Legumes can be considered as pioneer plants during ecosystem development, as they form a symbiosis with different nitrogen fixing rhizobia species, which enable the plants to grow on soils with low available nitrogen content. In this study we compared the abundance and diversity of nitrogen fixing microbes based on the functional marker gene nifH, which codes for a subunit of the Fe-protein of the dinitrogenase reductase, in nodules of different size classes of Trifolium arvense (L.). Additionally, carbon and nitrogen contents of the bulk soil and plant material were measured. Plants were harvested from different sites, reflecting 2 (2a) and 5 (5a) yr of ecosystem development, of an opencast lignite mining area in the south of Cottbus, Lower Lusatia (Germany) where the artificial catchment "Chicken Creek" was constructed to study the development of terrestrial ecosystems. Plants from the 5a site revealed higher amounts of carbon and nitrogen, although nifH gene abundances in the nodules and carbon and nitrogen contents between the two soils did not differ significantly. Analysis of the nifH clone libraries showed a significant effect of the nodule size on the community composition of nitrogen fixing microbes. Medium sized nodules (2–5 mm) contained a uniform community composed of Rhizobium leguminosarum bv. trifolii, whereas the small nodules (Rhizobium nifH gene sequences. Regarding the impact of the soil age on the community composition a clear distinction between the small and the medium nodules can be made. While clone libraries from the medium nodules were pretty similar at both soil ages, soil age had a significant effect on the community compositions of the small nodules, where the proportion of R. leguminosarum bv. trifolii increased with soil age.

The stable oxygen isotopic signature delta O-18 of water has been proved to be a useful marker in hydrological lake studies. However, it is applied only sporadically to determine the extent of mixis and stratification, which is vital for all hydrological and limnological studies. We present a study of the changes in delta O-18, temperature and Schmidt stability in the water column of three adjacent lakes of very different mixis types (polymictic to meromictic) over almost one year (May 2008 to April 2009). The response of delta O-18 to important hydrologic processes (entrainment, stratification, circulation, groundwater inflow) and weather influences (cooling period, ice cover and melt water inflow) is discussed. The lakes are part of the Osterseen chain of lakes south of Munich, Germany. Although hydrologically connected, these lakes show various mixis types (polymictic to meromictic) due to large differences in size, groundwater inflow and water renewal time. Polymixis and the strong subsurface inflow of groundwater in Lake Waschsee (25.6 x 10(3) m(3)) were indicated by the same trends in the delta O-18 signature throughout all water layers and by the mean overall signature (-9.94 parts per thousand) being very close to delta O-18 of local groundwater (-10.01 parts per thousand +/- 0.06). delta O-18 signatures of the larger dimictic Lake Fohnsee (2298.3 x 10(3) m(3)) revealed a highly significant trend towards lower values of delta O-18 in its hypolimnion, indicating inflow of groundwater. A cooling period during the summer stratification characterised by high wind speeds resulted in a considerable drop of lake surface temperatures and Schmidt stability (up to 25%) in lakes Fohnsee and Eishaussee and was followed by a deepening of the mixed upper water layer and entrainment of hypolimnetic water layers. This was clearly shown by a signal change in deeper water layers formerly constant in delta O-18. The permanent meromixis present in Lake Eishaussee (297.0 x 10(3) m(3)) could also be confirmed by isotopic signatures, as bottom water layers remained significantly isolated in delta O-18 from the remaining water column over the whole study period. We summarize that the oxygen isotopic signature of water is an easy to interpret, excellent indicator of important hydrologic processes in a lake and can readily be integrated into routine sampling. The present findings will further contribute to the analysis of hydrological data as well as to the interpretation of paleoclimatic reconstructions using proxies of lake water delta O-18.

This is the proceeding from the fifth workshop in Order Theory in Environ-mental Science. In this workshop series the concept of Partial Order Theory is development in relation to application and the use is tested based on specific problems. The Partial Order Theory will have a potential use in cases where more than one criterion is included in a prioritisation problem both in relation to decision support and in relation to data-mining and interpretation. Espe-cially the problems where a high degree of complexity results in considerable uncertainty are good candidates for application of Partial Ord

Although ductal carcinoma in situ (DCIS) of the breast is accepted as a potential precursor lesion for invasive ductal cancer (IDC), the critical genetic events associated with the tumor progression remain unknown. Since some extensive DCIS may show a small focus of IDC, these cases seem to be particularly suitable to investigate the primary abnormalities that determine the progression from in situ to early invasive cancer. We combined laser-microdissection with degenerative oligonucleotide-primed PCR (DOP-PCR) and comparative genomic hybridization (CGH) to detect copy number changes in 7 cases of extensive (>4 cm) DCIS with 1 small adjacent invasive focus. In 3 of the cases, single lymph node metastases (LN) were already present and were also investigated. Analysis of DCIS, IDC and LN components in the same patients revealed several consistent chromosomal changes present at all 3 sites: 1q, 7q, 8q, 16, 17, 19, 20q, 21q and 22q, the most frequent losses on 4q, 11q and 13q. DNA gain on 3p and 12q were more frequently found in IDC than in DCIS, suggesting the presence of proto-oncogenes activated during the progression to invasive cancer on these regions. Using paired analysis, resemblence of alterations found in DCIS and IDC could be quantified (odds ratio 7.0, p< or = 0.01). Gains on 6p, 10q, 14q and 15q and losses on 9p were identified in DCIS and IDC but not in LN, which may, therefore, represent early events in the carcinogenic process. Additional losses were found in the LNs on 2q, 3q, 5q, 6q, 12q and 16q. CGH results on chromosome 1 and 20 were confirmed by FISH and on chromosomal region 9p by microsatellite analyses. Our findings strongly underline the precursor status of high-grade DCIS, in which most of the chromosomal changes identified in IDC are already present. However, although the early stages of breast cancer, i.e., DCIS and the small foci of IDC were mainly characterized by DNA gains, the progression to metastatic tumor (LN) must have involved additional DNA losses on several regions.

Background In microarray data analysis, the comparison of gene-expression profiles with respect to different conditions and the selection of biologically interesting genes are crucial tasks. Multivariate statistical methods have been applied to analyze these large datasets. Less work has been published concerning the assessment of the reliability of gene-selection procedures. Here we describe a method to assess reliability in multivariate microarray data analysis using permutation-validated principal components analysis (PCA). The approach is designed for microarray data with a group structure. Results We used PCA to detect the major sources of variance underlying the hybridization conditions followed by gene selection based on PCA-derived and permutation-based test statistics. We validated our method by applying it to well characterized yeast cell-cycle data and to two datasets from our laboratory. We could describe the major sources of variance, select informative genes and visualize the relationship of genes and arrays. We observed differences in the level of the explained variance and the interpretability of the selected genes. Conclusions Combining data visualization and permutation-based gene selection, permutation-validated PCA enables one to illustrate gene-expression variance between several conditions and to select genes by taking into account the relationship of between-group to within-group variance of genes. The method can be used to extract the leading sources of variance from microarray data, to visualize relationships between genes and hybridizations and to select informative genes in a statistically reliable manner. This selection accounts for the level of reproducibility of replicates or group structure as well as gene-specific scatter. Visualization of the data can support a straightforward biological interpretation.

Track structures of high-LET particles can be simulated by various linear approaches. The distribution of distances seems to be an important parameter in understanding the type of interactions which occur and the biological effects which these excitations and ionizations will create; therefore, the distance distributions of these simulated track structures were calculated. Three presentations show that their exact appearance depends on the scaling parameter: the number of classes. In one approach the theoretical density of the distances was calculated by the techniques of convolution and by forming mixed distributions which confirm the findings of the simulation.