Deoxynivalenol affects the composition of the basement membrane proteins and influences en route the migration of CD16+ cells into the intestinal epithelium

The numerous pores in the basement membrane (BM) of the intestinal villi are essential for the communication of enterocytes with cells in the lamina propria, an important mechanism for the induction of intestinal immune responses. The intestinal epithelial barrier is affected by the mycotoxin deoxynivalenol (DON) from both the apical (luminal) and basolateral (serosal) side. The pig is the most susceptible species to the anorectic and immune-modulating effects of DON, which is most prevalent in crops. We analysed in pigs the effect of DON-contaminated feed on the composition and perforation of the BM and the presence of CD16⁺ cells or their dendrites in the epithelium. In addition to in vivo experiments, in vitro studies were carried out. Using microarray analyses, the effects of DON on IPEC-J2 cells were studied with the focus on the BM. Our in vivo results showed in the control pigs: (1) a significant increased pore number (p?≤?0.001) in the jejunum in comparison to ileum, (2) no difference in the pore size, and (3) comparable frequency of intraepithelial CD16⁺ cells/dendrites in the jejunum and ileum. There was a marked trend that DON feeding increases: (1) the pore number in jejunum, and (2) the number of CD16⁺ cells/dendrites in the epithelium (Tukey–Kramer; p?=?0.055 and p?=?0.067, respectively). The in vivo results were extended with microarray analyses of epithelial cell (IPEC-J2 cells). The down-regulation of genes like syndecan, fibulin 6 and BM-40 was observed. These proteins are important factors in the BM composition and in formation of pores. Our results provide evidence that already low basolateral concentrations of DON (50 ng/mL) influence the production of the BM protein laminin by epithelial cells. Thus, DON affects the composition of the BM.     

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn''t generally perform well, highlighting the need for some means of feature selection.     

L<Subscript>2</Subscript>-norm multiple kernel learning and its application to biomedical data fusion

Background  

This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L _∞, L ₁, and L ₂ MKL. In particular, L ₂ MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L _∞ MKL method. In real biomedical applications, L ₂ MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.     

Effect of tamoxifen and 2-methoxyestradiol alone and in combination on human breast cancer cell proliferation

Endocrine therapy is widely accepted for the treatment of hormone receptor-positive breast cancer. However, in many cases eventually resistance will develop and tumor regrows. Combination therapy may be one way to resolve this problem. In the present study we investigated the effect of a combination of the widely used antiestrogen tamoxifen with the endogenous estradiol metabolite 2-methoxyestradiol (2-ME) on the proliferation of human estrogen receptor-positive and receptor-negative breast cancer cells.The receptor-positive cell line MCF-7 and the receptor-negative cell line BM were treated with 4-hydroxytamoxifen (4-OHTam) and 2-methoxyestradiol in the concentration range of 0.8-25 microM alone and equimolar combinations for 4 days. The proliferation of the cells was determined using the ATP-chemosensitivity test.4-Hydroxytamoxifen inhibited proliferation of MCF-7 and BM cells with IC(50) values of 31 and 10 microM, the corresponding figures for 2-methoxyestradiol were 52 and 8 microM. The combination showed IC(50) values of 6 microM and 4 microM.These results indicate that a combination of tamoxifen with 2-methoxyestradiol showed an additive inhibitory effect concerning the proliferation of estrogen receptor-positive and receptor-negative breast cancer cell lines. Thus a combination of these substances may allow ameliorating of adverse events of tamoxifen by reducing its concentrations and probably also drug resistance and should be tested in clinical trials.     

Comparative analysis of DNA polymorphisms and phylogenetic relationships among Syzygium cumini Skeels based on phenotypic characters and RAPD technique

The Indian black berry (Syzygium cumini Skeels) has a great nutraceutical and medicinal properties. As in other fruit crops, the fruit characteristics are important attributes for differentiation were also determined for different accessions of S. cumini. The fruit weight, length, breadth, length: breadth ratio, pulp weight, pulp content, seed weight and pulp: seed ratio significantly varied in different accessions. Molecular characterization was carried out using PCR based RAPD technique. Out of 80 RAPD primers, only 18 primers produced stable polymorphisms that were used to examine the phylogenetic relationship. A sum of 207 loci were generated out of which 201 loci found polymorphic. The average genetic dissimilarity was 97 per cent among jamun accessions. The phylogenetic relationship was also determined by principal coordinates analysis (PCoA) that explained 46.95 per cent cumulative variance. The two-dimensional PCoA analysis showed grouping of the different accessions that were plotted into four sub-plots, representing clustering of accessions. The UPGMA (r = 0.967) and NJ (r = 0.987) dendrogram constructed based on the dissimilarity matrix revealed a good degree of fit with the cophenetic correlation value. The dendrogram grouped the accessions into three main clusters according to their eco-geographical regions which given useful insight into their phylogenetic relationships.     

不同耕作措施对冬小麦-夏玉米复种连作系统土壤有机碳和水分利用效率的影响

在连续8年田间定位试验的基础上,分析了关中平原冬小麦 夏玉米复种连作系统2008—2009年连续两个生长季期间不同耕作措施(结合秸秆还田和不还田)对土壤有机碳和水分利用率的影响.结果表明: 相对于传统耕作,保护性耕作有利于土壤有机碳、水分利用效率和作物产量的提高,其中在“深松+秸秆还田”耕作模式下的增幅最高,土壤有机碳含量在0～30 cm土层增幅达到19.5%,水分利用效率和作物产量提高了16.9%和20.5%,而免耕模式则有效提高了0～10 cm土层有机碳含量.在该地区土壤和气候条件下,深松结合秸秆粉碎还田是最理想的耕作模式,最有利于土壤有机碳累积,并提高水分利用效率和作物产量.