Boundedness of dyadic paraproducts on matrix weighted LpL^p

In this paper, we show that dyadic paraproducts $\pi_b$ with $b$ in dyadic BMO are bounded on matrix weighted $L^p(W)$ if $W$ is a matrix $\text{A}_p$ weight.Comment: This paper has been withdrawn, and is vastly generalized by https://arxiv.org/abs/1507.0403

Magnetic Properties of a-Si films doped with rare-earth elements

Amorphous silicon films doped with Y, La, Gd, Er, and Lu rare-earth elements (a-Si:RE) have been prepared by co-sputtering and studied by means of electron spin resonance (ESR), dc-magnetization, ion beam analysis, optical transmission, and Raman spectroscopy. For comparison the magnetic properties of laser-crystallized and hydrogenated a-Si:RE films were also studied. It was found that the rare-earth species are incorporated in the a-Si:RE films in the RE3+ form and that the RE-doping depletes the neutral dangling bonds (D0) density. The reduction of D0 density is significantly larger for the magnetic REs (Gd3+ and Er3+) than for the non-magnetic ones (Y3+, La3+, Lu3+). These results are interpreted in terms of a strong exchange-like interaction, J RE-DB SRE SDB, between the spin of the magnetic REs and that of the D0. All our Gd-doped Si films showed basically the same broad ESR Gd3+ resonance (DHpp ~ 850 Oe) at g ~ 2.01, suggesting the formation of a rather stable RE-Si complex in these films.Comment: 15 pages, 7 figure

The expression and signalling patterns of CD180 toll like receptor in Chronic Lymphocytic Leukaemia (CLL)

Chronic lymphocytic leukaemia (CLL) is characterised by a progressive accumulation of mature CD5+CD20+CD23+ lymphocytes. Despite the remarkable progress in our understanding of the immunobiology of CLL, the aetiology of the disease remains unknown. The consensus is that CLL cells are driven by (auto)antigen(s) through the B cell receptor (BCR) and are regulated by a variety of signals received from the microenvironment, including toll-like receptors (TLR).Our group has previously shown that engagement of the CD180 orphan TLR expressed by approximately 60% of CLL cells, can re-wire the sIgM-mediated signalling from a pro-survival pathway, involving phosphatidylinositol-4,5-bisphosphate3-kinase (PI3K) and protein kinase B (AKT) to the potentially pro-apoptotic pathway through mitogen-activated protein kinase (p38MAPK). However, little is known about the function of the other BCR - sIgD in CLL and its possible interaction with CD180. Here we studied intracellular signalling and apoptosis of CLL cells following sole or sequential ligation of CD180 and sIgD. Our data indicated that following sequential ligation of CD180 and sIgD, CLL samples demonstrated enhanced p38MAPK phosphorylation leading to increased apoptosis of CLL cells indicating synergistic relationship between CD180 and sIgD. To better understand the prognostic importance of CD180 expression we sought to determine whether CD180 and other prognostic markers such as CD38 and ZAP70 displayed any correlation with the known cytogenetic aberrations:TP53 and DLEU1. Our results suggested that CLL cells with DLEU1 deletion are characterised by the negative expression of both, CD180 and CD38, and this might have a significance for CLL prognosis. To explain this correlation, we hypothesised that interaction of CLL cells with their microenvironment through TLRs leads to the expansion of leukaemic clones, in vivo, in lymph nodes. Our results indicated that CD180 is heterogeneously expressed in the paraffin tissue sections of the lymph nodes of CLL patients and its expression positively correlates with the expression of Ki-67. Our data demonstrated, that although CD180 expression and signaling might have negative prognostic importance in CLL due to the enhanced proliferation of leukaemic cells, its interaction with sIgD would re-direct leukaemic cells towards apoptosis thus opening new opportunities for the disease immunotherapy

Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version

Although computationally aligning sequence is a crucial step in the vast majority of comparative genomics studies our understanding of alignment biases still needs to be improved. To infer true structural or homologous regions computational alignments need further evaluation. It has been shown that the accuracy of aligned positions can drop substantially in particular around gaps. Here we focus on re-evaluation of score-based alignments with affine gap penalty costs. We exploit their relationships with pair hidden Markov models and develop efficient algorithms by which to identify gaps which are significant in terms of length and multiplicity. We evaluate our statistics with respect to the well-established structural alignments from SABmark and find that indel reliability substantially increases with their significance in particular in worst-case twilight zone alignments. This points out that our statistics can reliably complement other methods which mostly focus on the reliability of match positions.Comment: 17 pages, 7 figure

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer