Oscypek is a traditional Polish scalded-smoked cheese, with a protected-designation-of-origin (PDO) status, manufactured from raw sheep's milk without starter cultures in the Tatra Mountains region of Poland. This study was undertaken in order to gain insight into the microbiota that develops and evolves during the manufacture and ripening stages of Oscypek. To this end, we made use of both culturing and the culture-independent methods of PCR followed by denaturing gradient gel electrophoresis (PCR-DGGE) and pyrosequencing of 16S rRNA gene amplicons. The culture-dependent technique and PCR-DGGE fingerprinting detected the predominant microorganisms in traditional Oscypek, whereas the next-generation sequencing technique (454 pyrosequencing) revealed greater bacterial diversity. Besides members of the most abundant bacterial genera in dairy products, e.g., Lactococcus, Lactobacillus, Leuconostoc, Streptococcus, and Enterococcus, identified by all three methods, other, subdominant bacteria belonging to the families Bifidobacteriaceae and Moraxellaceae (mostly Enhydrobacter), as well as various minor bacteria, were identified by pyrosequencing. The presence of bifidobacterial sequences in a cheese system is reported for the first time. In addition to bacteria, a great diversity of yeast species was demonstrated in Oscypek by the PCR-DGGE method. Culturing methods enabled the determination of a number of viable microorganisms from different microbial groups and their isolation for potential future applications in specific cheese starter cultures.

Lactic acid bacteria (LAB) are fermentative bacteria naturally dwelling in or intentionally added to nutrient -rich environments where carbohydrates and proteins are usually abundant. The effi cient use of nutrients and the concomitant production of lactic acid during growth endow LAB with remarkable selective advantages in the diverse ecological niches they inhabit. Besides lactic acid, LAB metabolism produces a variety of compounds, such as diacetyl, acetoin and 2 -3 -butanediol from the utilization of citrate, and a vast array of volatile compounds and bioactive peptides from the catabolism of amino acids. The enzymatic reactions of LAB metabolism further modify the organoleptic, rheological, and nutritive properties of the raw materials, giving rise to fi nal fermented products. Last decade witnessed an impressive amount of data on several aspects of LAB physiology and genetics. The latest knowledge was gathered through sequencing and analysis of LAB genomes, and the subsequent use of post -genomic techniques, such as proteomics, comparative genome hybridization, transcriptomics, and metabolomics. Manipulation of the metabolic pathways of LAB to improve their effi ciency in various industrial applications (as starters, adjunct cultures, and probiotics) was undertaken soon after the development of early engineering tools. The availability of complete genome sequences of different LAB species and strains has expanded our ability to further study LAB metabolism from a global perspective, strengthening a full exploitation of LAB ' s metabolic potential.

The tomato microarray TOM1 offers the possibility to monitor the levels of several thousand transcripts in parallel. The microelements represented on this tomato microarray have been putatively assigned to unigenes, and organised in functional classes using the MapMan ontology (Thimm et al., 2004. Plant J. 37: 914-939). This ontology was initially developed for use with the Arabidopsis ATH1 array, has a low level of redundancy, and can be combined with the MapMan software to provide a biologically structured overview of changes of transcripts, metabolites and enzyme activities. Use of this application is illustrated using three case studies with published or novel TOM1 array data sets for Solanaceous species. Comparison of previously reported data on transcript levels in potato leaves in the middle of the day and the middle of the night identified coordinated changes in the levels of transcripts of genes involved in various metabolic pathways and cellular events. Comparison with diurnal changes of gene expression in Arabidopsis revealed common features, illustrating how MapMan can be used to compare responses in different organisms. Comparison of transcript levels in new experiments performed on the leaves of the cultivated tomato S. lycopersicum and the wild relative S. pennellii revealed a general decrease of levels of transcripts of genes involved in terpene and, phenylpropanoid metabolism as well as chorismate biosynthesis in the crop compared to the wild relative. This matches the recently reported decrease of the levels of secondary metabolites in the latter. In the third case study, new expression array data for two genotypes deficient in TCA cycle enzymes is analysed to show that these genotypes have elevated levels of transcripts associated with photosynthesis. This in part explains the previously documented enhanced rates of photosynthesis in these genotypes. Since the Solanaceous MapMan is intended to be a community resource it will be regularly updated on improvements in tomato gene annotation and transcript profiling resources.

Thaumatin represents a unique class of the sweet-tasting plant proteins. Transgenic cucumber (Cucumis sativus L.) plants with stable integrated constructs consisting of the cauliflower mosaic virus 35S promoter and thaumatin II cDNA were produced. Transformed cucumber plants were obtained using Agrobacterium tumefaciens, with one, two or five integration sites in diploid cucumber and with inheritance confirmed by a 3:1 Mendelian ratio and normal morphologies and viable seeds. Inter-and intra-transformant variabilities in the expression of the thaumatin II gene were observed. The variability was independent of integrated copy number of the T-DNA. Variation in thaumatin II protein accumulation levels in the ripe fruits and the lack of correlation between protein and mRNA levels were observed, suggesting that thaumatin may be controlled at the levels of both transcription and translation. Transgenic fruits accumulating thaumatin II protein exhibited sweet phenotype and positive correlation between thaumatin accumulation levels and sweet taste intensity was noticed. Thaumatin II belongs to the pathogenesis-related (PR) proteins family. Some of the T2 progeny plants expressing thaumatin II protein did not exhibit tolerance for pathogenic fungus Pseudoperonospora cubensis. These results, together with previously reported results, suggest no relationship between transgenic protein levels and the increased tolerance phenotype.

The purpose of this study was to examine physiological and physical determinants of ice-hockey performance in order to assess their impact on the result during a selection for ice hockey. A total of 42 ice hockey players took part in the selection camp. At the end of the camp 20 best players were selected by team of expert coaches to the ice hockey team and created group G1, while the second group (G2) consisted of not selected players (non-successful group Evaluation of goodness of fit of the model to the data was based on the Hosmer Lemeshow test. Ice hockey players selected to the team were taller 181.95±4.02 cm, had lower% body fat 13.17±3.17%, a shorter time to peak power 2.47±0.35 s, higher relative peak power 21.34±2.41 W·kg−1 and higher relative total work 305.18±28.41 J·kg−1. The results of the aerobic capacity test showed significant differences only in case of two variables. Ice hockey players in the G1 had higher VO2max 4.07±0.31 l·min−1 values than players in the G2 as well as ice hockey players in G1 showed a higher level of relative VO2max 51.75±2.99 ml·min−1·kg−1 than athletes in G2. Ice hockey players selected to the team (G1) performed better in the 30 m Forwards Sprint 4.28±0.31 s; 6x9 Turns 12.19±0.75 s; 6x9 stops 12.79±0.49 s and Endurance test (6x30 m stops) 32.01±0.80 s than players in G2. The logistic regression model showed that the best predictors of success in the recruitment process of top level ice hockey players were time to peak power, relative peak power, VO2max and 30 m sprint forwards on ice. On the basis of the constructed predictive logistic regression model it will be possible to determine the probability of success of the athletes during following the selection processes to the team.