Development of antigen-specific ELISA for circulating autoantibodies to extracellular matrix protein 1 in lichen sclerosus

Lichen sclerosus is a common, acquired chronic inflammatory skin disease of unknown etiology, although circulating autoantibodies to the glycoprotein extracellular matrix protein 1 (ECM1) have been detected in most patients’ sera. We have examined the nature of ECM1 epitopes in lichen sclerosus sera, developed an ELISA system for serologic diagnosis, and assessed clinicopathological correlation between ELISA titer and disease. Epitope-mapping studies revealed that lichen sclerosus sera most frequently recognized the distal second tandem repeat domain and carboxyl-terminus of ECM1. We analyzed serum autoantibody reactivity against this immunodominant epitope in 413 individuals (95 subjects with lichen sclerosus, 161 normal control subjects, and 157 subjects with other autoimmune basement membrane or sclerosing diseases). The ELISA assay was highly sensitive; 76 of 95 lichen sclerosus patients (80.0%) exhibited IgG reactivity. It was also highly specific (93.7%) in discriminating between lichen sclerosus and other disease/control sera. Higher anti-ECM1 titers also correlated with more longstanding and refractory disease and cases complicated by squamous cell carcinoma. Furthermore, passive transfer of affinity-purified patient IgG reproduced some histologic and immunopathologic features of lichen sclerosus skin. This new ELISA is valuable for the accurate detection and quantification of anti-ECM1 autoantibodies. Moreover, the values may have clinical significance in patients with lichen sclerosus

Realization of the farad from the dc quantum Hall effect with digitally-assisted impedance bridges

A new traceability chain for the derivation of the farad from dc quantum Hall effect has been implemented at INRIM. Main components of the chain are two new coaxial transformer bridges: a resistance ratio bridge, and a quadrature bridge, both operating at 1541 Hz. The bridges are energized and controlled with a polyphase direct-digital-synthesizer, which permits to achieve both main and auxiliary equilibria in an automated way; the bridges and do not include any variable inductive divider or variable impedance box. The relative uncertainty in the realization of the farad, at the level of 1000 pF, is estimated to be 64E-9. A first verification of the realization is given by a comparison with the maintained national capacitance standard, where an agreement between measurements within their relative combined uncertainty of 420E-9 is obtained.Comment: 15 pages, 11 figures, 3 table

Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

In the United States, estimation of flood frequency quantiles at ungauged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e., flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, canonical kriging, CK (or physiographical-space-based interpolation, PSBI), and topological kriging, TK (or top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross-validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10, 50, 100 and 500 yr floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatments of spatial correlation when using regression-based or spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS

Spatial distribution of PAH concentrations and stable isotope signatures (δ13C, δ15N) in mosses from three European areas – Characterization by multivariate analysis

Polycyclic aromatic hydrocarbon (PAH) concentrations and N, C stable isotope signatures were determined in mosses Hypnum cupressiforme Hedw. from 61 sites of 3 European regions: Île-de-France (France); Navarra (Spain); the Swiss Plateau and Basel area (Switzerland). Total PAH concentrations of 100-700 ng g-1, as well as δ13C values of -32 to -29‰ and δ15N values of -11 to -3‰ were measured. Pearson correlation tests revealed opposite trends between high molecular weight PAH (4-6 aromatic rings) content and δ13C values. Partial Least Square regressions explained the very significant correlations (r > 0.91, p < 0.001) between high molecular weight PAH concentrations by local urban land use (<10 km) and environmental factors such as elevation and pluviometry. Finally, specific correlations between heavy metal and PAH concentrations were attributed to industrial emissions in Switzerland and road traffic emissions in Spain

Modes of Foreign Entry under Asymmetric Information about Potential Technology Spillovers

This paper studies the effect of technology spillovers on the entry decision of a multinational enterprise into a foreign market. Two alternative entry modes for a foreign direct investment are considered: Greenfield investment versus acquisition. We find that with quantity competition a spillover makes acquisitions less attractive, while with price competition acquisitions become more attractive. Asymmetric information about potential spillovers always reduces the number of acquisitions independently of whether the host country or the entrant has private information. Interestingly, we find that asymmetric information always hurts the entrant, while it sometimes is in favor of the host country

Mutation Bias is the Driving Force of Codon Usage in the Gallus gallus genome

Synonymous codons are used with different frequencies both among species and among genes within the same genome and are controlled by neutral processes (such as mutation and drift) as well as by selection. Up to now, a systematic examination of the codon usage for the chicken genome has not been performed. Here, we carried out a whole genome analysis of the chicken genome by the use of the relative synonymous codon usage (RSCU) method and identified 11 putative optimal codons, all of them ending with uracil (U), which is significantly departing from the pattern observed in other eukaryotes. Optimal codons in the chicken genome are most likely the ones corresponding to highly expressed transfer RNA (tRNAs) or tRNA gene copy numbers in the cell. Codon bias, measured as the frequency of optimal codons (Fop), is negatively correlated with the G + C content, recombination rate, but positively correlated with gene expression, protein length, gene length and intron length. The positive correlation between codon bias and protein, gene and intron length is quite different from other multi-cellular organism, as this trend has been only found in unicellular organisms. Our data displayed that regional G + C content explains a large proportion of the variance of codon bias in chicken. Stepwise selection model analyses indicate that G + C content of coding sequence is the most important factor for codon bias. It appears that variation in the G + C content of CDSs accounts for over 60% of the variation of codon bias. This study suggests that both mutation bias and selection contribute to codon bias. However, mutation bias is the driving force of the codon usage in the Gallus gallus genome. Our data also provide evidence that the negative correlation between codon bias and recombination rates in G. gallus is determined mostly by recombination-dependent mutational patterns

Development And Evaluation Of A Multimodal Marker Of Major Depressive Disorder

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques—penalized logistic regression, random forest, and support vector machine (SVM)—were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses—two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results