Raman study of the anharmonicity in YBa2_2Cu3_3Ox_x

A systematic Raman study in the visible carried out on the YBa2Cu316,18Ox (x=6-7) compounds, with isotopic substitution of 18O for 16O, has detected a doping dependent deviation from harmonic behavior for the frequency shift of the in-phase mode, a smaller amount of anharmonicity for the apex mode, and almost no effect for the out-of-phase B1g-symmetry phonon. It appears that the amount of anharmonicity depends strongly on the oxygen concentration; it diminishes close to the tetragonal to orthorhombic structural phase transition and close to optimal doping, while it reaches its maximum value for the ortho-II and a tetragonal phase. The almost zero anharmonicity at optimal doping persists even at 77K. The data in the overdoped oxygen concentration, where a softening of the in-phase phonon frequency occurs, indicate that the anharmonicity is not enhanced by the sudden increase in the CuO2 buckling. The results fully agree with recent studies of the ortho-II phase but they do not comply with a static double-well potential of the apical oxygen atom at optimal doping.Comment: Dedicated to Prof. K. A. M\"uller on the Occasion of his 90th Birthda

ALMS1-Deficient Fibroblasts Over-Express Extra-Cellular Matrix Components, Display Cell Cycle Delay and Are Resistant to Apoptosis

Alström Syndrome (ALMS) is a rare genetic disorder (483 living cases), characterized by many clinical manifestations, including blindness, obesity, type 2 diabetes and cardiomyopathy. ALMS is caused by mutations in the ALMS1 gene, encoding for a large protein with implicated roles in ciliary function, cellular quiescence and intracellular transport. Patients with ALMS have extensive fibrosis in nearly all tissues resulting in a progressive organ failure which is often the ultimate cause of death. To focus on the role of ALMS1 mutations in the generation and maintenance of this pathological fibrosis, we performed gene expression analysis, ultrastructural characterization and functional assays in 4 dermal fibroblast cultures from ALMS patients. Using a genome-wide gene expression analysis we found alterations in genes belonging to specific categories (cell cycle, extracellular matrix (ECM) and fibrosis, cellular architecture/motility and apoptosis). ALMS fibroblasts display cytoskeleton abnormalities and migration impairment, up-regulate the expression and production of collagens and despite the increase in the cell cycle length are more resistant to apoptosis. Therefore ALMS1-deficient fibroblasts showed a constitutively activated myofibroblast phenotype even if they do not derive from a fibrotic lesion. Our results support a genetic basis for the fibrosis observed in ALMS and show that both an excessive ECM production and a failure to eliminate myofibroblasts are key mechanisms. Furthermore, our findings suggest new roles for ALMS1 in both intra- and extra-cellular events which are essential not only for the normal cellular function but also for cell-cell and ECM-cell interactions

Whole genome analysis of linezolid resistance in Streptococcus pneumoniae reveals resistance and compensatory mutations

<p>Abstract</p> <p>Background</p> <p>Several mutations were present in the genome of <it>Streptococcus pneumoniae </it>linezolid-resistant strains but the role of several of these mutations had not been experimentally tested. To analyze the role of these mutations, we reconstituted resistance by serial whole genome transformation of a novel resistant isolate into two strains with sensitive background. We sequenced the parent mutant and two independent transformants exhibiting similar minimum inhibitory concentration to linezolid.</p> <p>Results</p> <p>Comparative genomic analyses revealed that transformants acquired G2576T transversions in every gene copy of 23S rRNA and that the number of altered copies correlated with the level of linezolid resistance and cross-resistance to florfenicol and chloramphenicol. One of the transformants also acquired a mutation present in the parent mutant leading to the overexpression of an ABC transporter (spr1021). The acquisition of these mutations conferred a fitness cost however, which was further enhanced by the acquisition of a mutation in a RNA methyltransferase implicated in resistance. Interestingly, the fitness of the transformants could be restored in part by the acquisition of altered copies of the L3 and L16 ribosomal proteins and by mutations leading to the overexpression of the spr1887 ABC transporter that were present in the original linezolid-resistant mutant.</p> <p>Conclusions</p> <p>Our results demonstrate the usefulness of whole genome approaches at detecting major determinants of resistance as well as compensatory mutations that alleviate the fitness cost associated with resistance.</p

A Robust Statistical Method for Association-Based eQTL Analysis

Background: It has been well established that theoretical kernel for recently surging genome-wide association study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several confounding factors of which population stratification is the most prominent. Whilst many methods have been proposed to correct for the influence either through predicting the structure parameters or correcting inflation in the test statistic due to the stratification, these may not be feasible or may impose further statistical problems in practical implementation. Methodology: We propose here a novel statistical method to control spurious LD in GWAS from population structure by incorporating a control marker into testing for significance of genetic association of a polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of structure prediction which may be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Utility and statistical properties of the new method were tested through an intensive computer simulation study and an association-based genome-wide mapping of expression quantitative trait loci in genetically divergent human populations. Results/Conclusions: The analyses show that the new method confers an improved statistical power for detecting genuin

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

The Bacterial Preparation OK432 Induces IL-12p70 Secretion in Human Dendritic Cells in a TLR3 Dependent Manner

Dendritic cells (DC) used in therapeutic cancer immunotherapy have to be able to stimulate T cells resulting in an immune response that can efficiently target the cancer cells. One of the critical hurdles has been the lack of IL-12p70 production when maturating the DC, which is rectified by using the bacterial preparation OK432 (trade name Picibanil) to mature the cells. In order to identify the mechanism behind OK432 stimulation of DC, we investigated the contribution of different TLR to examine their involvement in IL-12p70 production. By combining different inhibitors of TLR signaling, we demonstrate here that TLR3 is responsible for the IL-12p70 production of DC induced by OK432. Moreover, our data suggest that the ligand triggering IL-12p70 secretion upon TLR3 stimulation is sensitive to proteinase and partly also RNAse treatment. The fact that a bacterial compound like OK432 can activate the TLR3 pathway in human DC is a novel finding. OK432 demonstrates a critical ability to induce IL-12p70 production, which is of great relevance in DC based cancer immunotherapy

Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers

<p>Abstract</p> <p>Methods</p> <p>We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.</p> <p>Results</p> <p>Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.</p> <p>Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.</p> <p>Conclusion</p> <p>The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.</p

Blood Signature of Pre-Heart Failure: A Microarrays Study

International audienceBACKGROUND: The preclinical stage of systolic heart failure (HF), known as asymptomatic left ventricular dysfunction (ALVD), is diagnosed only by echocardiography, frequent in the general population and leads to a high risk of developing severe HF. Large scale screening for ALVD is a difficult task and represents a major unmet clinical challenge that requires the determination of ALVD biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: 294 individuals were screened by echocardiography. We identified 9 ALVD cases out of 128 subjects with cardiovascular risk factors. White blood cell gene expression profiling was performed using pangenomic microarrays. Data were analyzed using principal component analysis (PCA) and Significant Analysis of Microarrays (SAM). To build an ALVD classifier model, we used the nearest centroid classification method (NCCM) with the ClaNC software package. Classification performance was determined using the leave-one-out cross-validation method. Blood transcriptome analysis provided a specific molecular signature for ALVD which defined a model based on 7 genes capable of discriminating ALVD cases. Analysis of an ALVD patients validation group demonstrated that these genes are accurate diagnostic predictors for ALVD with 87% accuracy and 100% precision. Furthermore, Receiver Operating Characteristic curves of expression levels confirmed that 6 out of 7 genes discriminate for left ventricular dysfunction classification. CONCLUSIONS/SIGNIFICANCE: These targets could serve to enhance the ability to efficiently detect ALVD by general care practitioners to facilitate preemptive initiation of medical treatment preventing the development of HF

Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential

BACKGROUND: The acceptance of microarray technology in regulatory decision-making is being challenged by the existence of various platforms and data analysis methods. A recent report (E. Marshall, Science, 306, 630–631, 2004), by extensively citing the study of Tan et al. (Nucleic Acids Res., 31, 5676–5684, 2003), portrays a disturbingly negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. RESULTS: We reanalyzed Tan's dataset and found that the intra-platform consistency was low, indicating a problem in experimental procedures from which the dataset was generated. Furthermore, by using three gene selection methods (i.e., p-value ranking, fold-change ranking, and Significance Analysis of Microarrays (SAM)) on the same dataset we found that p-value ranking (the method emphasized by Tan et al.) results in much lower cross-platform concordance compared to fold-change ranking or SAM. Therefore, the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms, as suggested by Tan et al. and Marshall. CONCLUSION: Our results illustrate the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms and the proficiency of individual laboratories as well as the merits of various data analysis procedures. Thus, we are progressively coordinating the MAQC project, a community-wide effort for microarray quality control