Experiences with designing and managing organic rotation trials

This report was presented at the UK Organic Research 2002 Conference. Practical problems encountered in two long-term organic rotation trials at Aberdeen and Elgin are discussed. Compromises have had to be made in designing and managing the trials: how to include livestock and measure output, plot size, marking and fencing, discards and paths, replication, rotation length, randomisation of crop sequence, site uniformity, manoeuvrability of machines, soil compaction and exposure to pest damage

Untersuchungen zu Liegenischen für Milchziegen

The legislative rules of keeping ruminants on organic farms do not differentiate between the requirements of the different species for the housing systems. Thus, the types of locomotion typical for goats (e. g. climbing, jumping) have not to take in consideration in stables designed for goat keeping. Some farmers offer their animals elevated resting platforms (niches), but data concerning the preferences of the goats and the labour management of such areas are not available. A herd of non-lactating goats (breed: Bunte Deutsche Edelziege) was parted in six experimental groups with ten animals in each group. Over a period of seven days the animals were kept separated in a part of the stable furnished with resting niches. Twelve niches were arranged on three levels. Half of them contained a layer of deep-litter. The working time needed for cleaning of the niches was measured during each change of the groups. The cleanliness of the niches was documented by photography. Pictures were evaluated by means of an image processing system. Animal behaviour was registered by video-observation. The results showed a relationship between the labour time needed for cleaning of the niches and the preference of niches by the goats. Due to the necessary filling up with straw, the niches with the bedding of deep litter required the same amount of labour time as the niches without bedding, which needed more time for cleaning. The image processing system might be used to evaluate cleanliness of resting areas designed for other species, too

Testing for equal correlation matrices with application to paired gene expression data

We present a novel method for testing the hypothesis of equality of two correlation matrices using paired high-dimensional datasets. We consider test statistics based on the average of squares, maximum and sum of exceedances of Fisher transform sample correlations and we derive approximate null distributions using asymptotic and non-parametric distributions. Theoretical results on the power of the tests are presented and backed up by a range of simulation experiments. We apply the methodology to a case study of colorectal tumour gene expression data with the aim of discovering biological pathway lists of genes that present significantly different correlation matrices on healthy and tumour samples. We find strong evidence for a large part of the pathway lists correlation matrices to change among the two medical conditions.Comment: 31 pages, 3 figure

Radiation-induced caveolin-1 associated EGFR internalization is linked with nuclear EGFR transport and activation of DNA-PK

<p>Abstract</p> <p>Background</p> <p>To elucidate the role of src kinase in caveolin-1 driven internalization and nuclear transport of EGFR linked to regulation of DNA-repair in irradiated cells.</p> <p>Results</p> <p>Ionizing radiation resulted in src kinase stabilization, activation and subsequent src mediated caveolin-1 Y14- and EGFR Y845-phosphorylations. Both phosphorylations were radiation specific and could not be observed after treatment with EGF. Inhibition of EGFR by the antibody Erbitux resulted in a strong accumulation of caveolin/EGFR complexes within the cytoplasm, which could not be further increased by irradiation. Radiation-induced caveolin-1- and EGFR-phosphorylations were associated with nuclear EGFR transport and activation of DNA-PK, as detected by phosphorylation at T2609. Blockage of src activity by the specific inhibitor PP2, decreased nuclear transport of EGFR and inhibited caveolin-1- and DNA-PK-phosphorylation. Knockdown of src by specific siRNA blocked EGFR phosphorylation at Y845, phosphorylation of caveolin-1 at Y14 and abolished EGFR transport into the nucleus and phosphorylation of DNA-PK. Consequently, both knockdown of src by specific siRNA and also inhibition of src activity by PP2 resulted in an enhanced residual DNA-damage as quantified 24 h after irradiation and increased radiosensitivity.</p> <p>Conclusion</p> <p>Src kinase activation following irradiation triggered caveolin-1 dependent EGFR internalization into caveolae. Subsequently EGFR shuttled into the nucleus. As a consequence, inhibition of internalization and nuclear transport of EGFR blocked radiation-induced phosphorylation of DNA-PK and hampered repair of radiation-induced double strand breaks.</p

The TLR4 D299G and T399I SNPs Are Constitutively Active to Up-Regulate Expression of Trif-Dependent Genes

Peer reviewedPublisher PD

Diverse LEF/TCF Expression in Human Colorectal Cancer Correlates with Altered Wnt-Regulated Transcriptome in a Meta-Analysis of Patient Biopsies

Funding: CM acknowledges funding from the Scottish Government: Rural & Environment Science & Analytical Services. (RESAS). SH is a Royal Society/Leverhulme Trust Senior Research Fellow (SRF\R1\191017) and acknowledges research funding from the Biotechnology and Biological Sciences Research Council (BB/S018190/1, BB/M001695/1). Author Contributions: C.-D.M. and S.H. had conceived and supervised this project; C.-D.M. curated the data and carried out some analysis; S.M.L.G., carried out most of the analysis; F.A. wrote an original draft together with Stefan Hoppler. All authors have read and agreed to the published version of the manuscript. Supplementary Materials: The following are available online at www.mdpi.com/2073-4425/11/5/538/s1: Figure S1: Principal Component Analysis of selected studies, Figure S2: Principal Component Analysis of de-selected study, Table S1: Transcriptomics Data (Correlation Coefficients) Table S1A: Transcript correlation between eight selected genes (TCF7, LEF1, TCF7L1, TCF7L2, AXIN2, DKK1, FZD7, LGR5); Table S1B: The TCF7-correlated transcriptome; Table S1C: The LEF1correlated transcriptome; Table S1D: The TCF7L1-correlated transcriptome; Table S1E: The TCF7L2-correlated transcriptome; Table S1F: The AXIN2-correlated transcriptome; Table S1G: The DKK1-correlated transcriptome; Table S1H: The FZD7-correlated transcriptome; Table S1I: The LGR5-correlated transcriptome; Table S1J: Differences in LEF/TCF-correlated transcriptomes; Table S1K: Differences between AXIN2- and LEF/TCF-correlated transcriptomes, Table S2: Correlated Transcriptome in normal and tumor tissue, Table S3: Comparison of LEF/TCF-correlated transcriptomes, Table S4: Differences between AXIN1- and LEF/TCF-correlated transcriptomes.Peer reviewedPublisher PD

Joint Estimation of Sparse Networks with application to Paired Gene Expression data

We consider a method to jointly estimate sparse precision matrices and their underlying graph structures using dependent high-dimensional datasets. We present a penalized maximum likelihood estimator which encourages both sparsity and similarity in the estimated precision matrices where tuning parameters are automatically selected by controlling the expected number of false positive edges. We also incorporate an extra step to remove edges which represent an overestimation of triangular motifs. We conduct a simulation study to show that the proposed methodology presents consistent results for different combinations of sample size and dimension. Then, we apply the suggested approaches to a high-dimensional real case study of gene expression data with samples in two medical conditions, healthy and colon cancer tissues, to estimate a common network of genes as well as the differentially connected genes that are important to the disease. We find denser graph structures for healthy samples than for tumor samples, with groups of genes interacting together in the shape of clusters.Comment: 34 pages, 10 figures, 7 table

An evaluation of two-channel ChIP-on-chip and DNA methylation microarray normalization strategies

<p>Abstract</p> <p>Background</p> <p>The combination of chromatin immunoprecipitation with two-channel microarray technology enables genome-wide mapping of binding sites of DNA-interacting proteins (ChIP-on-chip) or sites with methylated CpG di-nucleotides (DNA methylation microarray). These powerful tools are the gateway to understanding gene transcription regulation. Since the goals of such studies, the sample preparation procedures, the microarray content and study design are all different from transcriptomics microarrays, the data pre-processing strategies traditionally applied to transcriptomics microarrays may not be appropriate. Particularly, the main challenge of the normalization of "regulation microarrays" is (i) to make the data of individual microarrays quantitatively comparable and (ii) to keep the signals of the enriched probes, representing DNA sequences from the precipitate, as distinguishable as possible from the signals of the un-enriched probes, representing DNA sequences largely absent from the precipitate.</p> <p>Results</p> <p>We compare several widely used normalization approaches (VSN, LOWESS, quantile, T-quantile, Tukey's biweight scaling, Peng's method) applied to a selection of regulation microarray datasets, ranging from DNA methylation to transcription factor binding and histone modification studies. Through comparison of the data distributions of control probes and gene promoter probes before and after normalization, and assessment of the power to identify known enriched genomic regions after normalization, we demonstrate that there are clear differences in performance between normalization procedures.</p> <p>Conclusion</p> <p>T-quantile normalization applied separately on the channels and Tukey's biweight scaling outperform other methods in terms of the conservation of enriched and un-enriched signal separation, as well as in identification of genomic regions known to be enriched. T-quantile normalization is preferable as it additionally improves comparability between microarrays. In contrast, popular normalization approaches like quantile, LOWESS, Peng's method and VSN normalization alter the data distributions of regulation microarrays to such an extent that using these approaches will impact the reliability of the downstream analysis substantially.</p