Anisotropic Charge Modulation in Ladder Planes of Sr_14-xCa_xCu_24O_41

The charge response of the ladders in Sr_14-xCa_xCu_24O_41 is characterized by dc resistivity, low frequency dielectric and optical spectroscopy in all three crystallographic directions. The collective charge-density wave screened mode is observed in the direction of the rungs for x=0, 3 and 6, in addition to the mode along the legs. For x=8 and 9, the charge-density-wave response along the rungs fully vanishes, while the one along the legs persists. The transport perpendicular to the planes is always dominated by hopping.Comment: 4 pages, 3 figures, submitted to PRB R

Effects of interladder couplings in the trellis lattice

Strongly correlated models on coupled ladders in the presence of frustration, in particular the trellis lattice, are studied by numerical techniques. For the undoped case, the possibility of incommensurate peaks in the magnetic structure factor at low temperatures is suggested. In the doped case, our main conclusion for the trellis lattice is that by increasing the interladder coupling, the balance between the magnetic energy in the ladders and the kinetic energy in the zig-zag chains is altered leading eventually to the destruction of the hole pairs initially formed and localized in the ladders.Comment: final version, to appear in Physical Review

Identification of Phosphoglycerate Kinase 1 (PGK1) as a reference gene for quantitative gene expression measurements in human blood RNA

<p>Abstract</p> <p>Background</p> <p>Blood is a convenient sample and increasingly used for quantitative gene expression measurements with a variety of diseases including chronic fatigue syndrome (CFS). Quantitative gene expression measurements require normalization of target genes to reference genes that are stable and independent from variables being tested in the experiment. Because there are no genes that are useful for all situations, reference gene selection is an essential step to any quantitative reverse transcription-PCR protocol. Many publications have described appropriate genes for a wide variety of tissues and experimental conditions, however, reference genes that may be suitable for the analysis of CFS, or human blood RNA derived from whole blood as well as isolated peripheral blood mononuclear cells (PBMCs), have not been described.</p> <p>Findings</p> <p>Literature review and analyses of our unpublished microarray data were used to narrow down the pool of candidate reference genes to six. We assayed whole blood RNA from Tempus tubes and cell preparation tube (CPT)-collected PBMC RNA from 46 subjects, and used the geNorm and NormFinder algorithms to select the most stable reference genes. <it>Phosphoglycerate kinase 1 (PGK1) </it>was one of the optimal normalization genes for both whole blood and PBMC RNA, however, additional genes differed for the two sample types; <it>Ribosomal protein large, P0 (RPLP0</it>) for PBMC RNA and <it>Peptidylprolyl isomerase B </it>(<it>PPIB) </it>for whole blood RNA. We also show that the use of a single reference gene is sufficient for normalization when the most stable candidates are used.</p> <p>Conclusions</p> <p>We have identified <it>PGK1 </it>as a stable reference gene for use with whole blood RNA and RNA derived from PBMC. When stable genes are selected it is possible to use a single gene for normalization rather than two or three. Optimal normalization will improve the ability of results from PBMC RNA to be compared with those from whole blood RNA and potentially allows comparison of gene expression results from blood RNA collected and processed by different methods with the intention of biomarker discovery. Results of this study should facilitate large-scale molecular epidemiologic studies using blood RNA as the target of quantitative gene expression measurements.</p

Far infrared Fourier transform spectrometer breadboard activities for the FORUM mission, ESA's 9th Earth Explorer

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Stepwise Release of Biologically Active HMGB1 during HSV-2 Infection

BACKGROUND: High mobility group box 1 protein (HMGB1) is a major endogenous danger signal that triggers inflammation and immunity during septic and aseptic stresses. HMGB1 recently emerged as a key soluble factor in the pathogenesis of various infectious diseases, but nothing is known of its behaviour during herpesvirus infection. We therefore investigated the dynamics and biological effects of HMGB1 during HSV-2 infection of epithelial HEC-1 cells. METHODOLOGY/PRINCIPAL FINDINGS: Despite a transcriptional shutdown of HMGB1 gene expression during infection, the intracellular pool of HMGB1 protein remained unaffected, indicating its remarkable stability. However, the dynamics of HMGB1 was deeply modified in infected cells. Whereas viral multiplication was concomitant with apoptosis and HMGB1 retention on chromatin, a subsequent release of HMGB1 was observed in response to HSV-2 mediated necrosis. Importantly, extracellular HMGB1 was biologically active. Indeed, HMGB1-containing supernatants from HSV-2 infected cells induced the migration of fibroblasts from murine or human origin, and reactivated HIV-1 from latently infected T lymphocytes. These effects were specifically linked to HMGB1 since they were blocked by glycyrrhizin or by a neutralizing anti-HMGB1 antibody, and were mediated through TLR2 and the receptor for Advanced Glycation End-products (RAGE). Finally, we show that genital HSV-2 active infections also promote HMGB1 release in vivo, strengthening the clinical relevance of our experimental data. CONCLUSIONS: These observations target HMGB1 as an important actor during HSV-2 genital infection, notably in the setting of HSV-HIV co-infection

Right drug, right patient, right time: aspiration or future promise for biologics in rheumatoid arthritis?

Individualising biologic disease-modifying anti-rheumatic drugs (bDMARDs) to maximise outcomes and deliver safe and cost-effective care is a key goal in the management of rheumatoid arthritis (RA). Investigation to identify predictive tools of bDMARD response is a highly active and prolific area of research. In addition to clinical phenotyping, cellular and molecular characterisation of synovial tissue and blood in patients with RA, using different technologies, can facilitate predictive testing. This narrative review will summarise the literature for the available bDMARD classes and focus on where progress has been made. We will also look ahead and consider the increasing use of ‘omics’ technologies, the potential they hold as well as the challenges, and what is needed in the future to fully realise our ambition of personalised bDMARD treatment

Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood

Background Gene expression studies require appropriate normalization methods. One such method uses stably expressed reference genes. Since suitable reference genes appear to be unique for each tissue, we have identified an optimal set of the most stably expressed genes in human blood that can be used for normalization. Methods Whole-genome Affymetrix Human 2.0 Plus arrays were examined from 526 samples of males and females ages 2 to 78, including control subjects and patients with Tourette syndrome, stroke, migraine, muscular dystrophy, and autism. The top 100 most stably expressed genes with a broad range of expression levels were identified. To validate the best candidate genes, we performed quantitative RT-PCR on a subset of 10 genes (TRAP1, DECR1, FPGS, FARP1, MAPRE2, PEX16, GINS2, CRY2, CSNK1G2 and A4GALT), 4 commonly employed reference genes (GAPDH, ACTB, B2M and HMBS) and PPIB, previously reported to be stably expressed in blood. Expression stability and ranking analysis were performed using GeNorm and NormFinder algorithms. Results Reference genes were ranked based on their expression stability and the minimum number of genes needed for nomalization as calculated using GeNorm showed that the fewest, most stably expressed genes needed for acurate normalization in RNA expression studies of human whole blood is a combination of TRAP1, FPGS, DECR1 and PPIB. We confirmed the ranking of the best candidate control genes by using an alternative algorithm (NormFinder). Conclusion The reference genes identified in this study are stably expressed in whole blood of humans of both genders with multiple disease conditions and ages 2 to 78. Importantly, they also have different functions within cells and thus should be expressed independently of each other. These genes should be useful as normalization genes for microarray and RT-PCR whole blood studies of human physiology, metabolism and disease.Boryana S Stamova, Michelle Apperson, Wynn L Walker, Yingfang Tian, Huichun Xu, Peter Adamczy, Xinhua Zhan, Da-Zhi Liu, Bradley P Ander, Isaac H Liao, Jeffrey P Gregg, Renee J Turner, Glen Jickling, Lisa Lit and Frank R Shar

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses