Using a sulfur-bearing silane to improve rubber formulations for potential use in industrial rubber articles

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Adhesion Science and Technology on 13/08/2012, available online: http://dx.doi.org/10.1080/01694243.The availability of the coupling agent bis (3-triethoxysilylpropyl)-tetrasulfide (TESPT) has provided an opportunity for enhancing the reinforcing capabilities of precipitated amorphous white silica in rubber. Styrene-butadiene rubber, synthetic polyisoprene rubber (IR), acrylonitrile-butadiene rubber, and natural rubber (NR) containing the same loading of a precipitated silica filler were prepared. The silica surface was pretreated with TESPT, which is a sulfur-bearing bifunctional organosilane to chemically bond silica to the rubber. The rubber compounds were subsequently cured by reacting the tetrasulfane groups of TESPT with double bonds in the rubber chains and the cure was optimized by adding sulfenamide accelerator and zinc oxide. The IR and NR needed more accelerators for curing. Surprisingly, there was no obvious correlation between the internal double bond content and the accelerator requirement for the optimum cure of the rubbers. Using the TESPT pretreated silanized silica was a very efficient method for cross-linking and reinforcing the rubbers. It reduced the use of the chemical curatives significantly while maintaining excellent mechanical properties of the cured rubbers. Moreover, it improved health and safety at work-place, reduced cost, and minimized damage to the environment because less chemical curatives were used. Therefore, TESPT was classified as "green silane" for use in rubber formulations

Polygenic risk and hazard scores for Alzheimer's disease prediction

OBJECTIVE: Genome‐wide association studies (GWAS) have identified over 30 susceptibility loci associated with Alzheimer's disease (AD). Using AD GWAS data from the International Genomics of Alzheimer's Project (IGAP), Polygenic Risk Score (PRS) was successfully applied to predict life time risk of AD development. A recently introduced Polygenic Hazard Score (PHS) is able to quantify individuals with age‐specific genetic risk for AD. The aim of this study was to quantify the age‐specific genetic risk for AD with PRS and compare the results generated by PRS with those from PHS. // METHODS: Quantification of individual differences in age‐specific genetic risk for AD identified by the PRS, was performed with Cox Regression on 9903 (2626 cases and 7277 controls) individuals from the Genetic and Environmental Risk in Alzheimer's Disease consortium (GERAD). Polygenic Hazard Scores were generated for the same individuals. The age‐specific genetic risk for AD identified by the PRS was compared with that generated by the PHS. This was repeated using varying SNPs P‐value thresholds for disease association. // RESULTS: Polygenic Risk Score significantly predicted the risk associated with age at AD onset when SNPs were preselected for association to AD at P ≤ 0.001. The strongest effect (B = 0.28, SE = 0.04, P = 2.5 × 10−12) was observed for PRS based upon genome‐wide significant SNPs (P ≤ 5 × 10−8). The strength of association was weaker with less stringent SNP selection thresholds. // INTERPRETATION: Both PRS and PHS can be used to predict an age‐specific risk for developing AD. The PHS approach uses SNP effect sizes derived with the Cox Proportional Hazard Regression model. When SNPs were selected based upon AD GWAS case/control P ≤ 10−3, we found no advantage of using SNP effects sizes calculated with the Cox Proportional Hazard Regression model in our study. When SNPs are selected for association with AD risk at P > 10−3, the age‐specific risk prediction results are not significant for either PRS or PHS. However PHS could be more advantageous than PRS of age specific AD risk predictions when SNPs are prioritized for association with AD age at onset (i.e., powerful Cox Regression GWAS study)

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

Search for rare decays of D0 mesons into two muons

A search for the very rare D 0 → μ + μ − decay is performed using data collected by the LHCb experiment in proton-proton collisions at √ s = 7 , 8, and 13 TeV, corresponding to an integrated luminosity of 9     fb − 1 . The search is optimized for D 0 mesons from D * + → D 0 π + decays but is also sensitive to D 0 mesons from other sources. No evidence for an excess of events over the expected background is observed. An upper limit on the branching fraction of this decay is set at B ( D 0 → μ + μ − ) < 3.1 × 10 − 9 at a 90% C.L. This represents the world’s most stringent limit, constraining models of physics beyond the standard model

Measurement of the ratios of branching fractions R(D*) and R(D0)

The ratios of branching fractions R ( D ∗ ) ≡ B ( ¯ B → D ∗ τ − ¯ ν τ ) / B ( ¯ B → D ∗ μ − ¯ ν μ ) and R ( D 0 ) ≡ B ( B − → D 0 τ − ¯ ν τ ) / B ( B − → D 0 μ − ¯ ν μ ) are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0     fb − 1 of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode τ − → μ − ν τ ¯ ν μ . The measured values are R ( D ∗ ) = 0.281 ± 0.018 ± 0.024 and R ( D 0 ) = 0.441 ± 0.060 ± 0.066 , where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is ρ = − 0.43 . The results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the standard model

Test of lepton universality in b→sℓ+ℓ− decays

The first simultaneous test of muon-electron universality using B + → K + ℓ + ℓ − and B 0 → K * 0 ℓ + ℓ − decays is performed, in two ranges of the dilepton invariant-mass squared, q 2 . The analysis uses beauty mesons produced in proton-proton collisions collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9     fb − 1 . Each of the four lepton universality measurements reported is either the first in the given q 2 interval or supersedes previous LHCb measurements. The results are compatible with the predictions of the Standard Model

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms

BACKGROUND: Beer quality is mainly defined by its colour, foamability and foam stability, which are influenced by the chemical composition of the product such as proteins, carbohydrates, pH and alcohol. Traditional methods to assess specific chemical compounds are usually time-consuming and costly. This study used rapid methods to evaluate 15 foam and colour-related parameters using a robotic pourer (RoboBEER) and chemical fingerprinting using near infrared spectroscopy (NIR) from six replicates of 21 beers from three types of fermentation. Results from NIR were used to create partial least squares regression (PLS) and artificial neural networks (ANN) models to predict four chemometrics such as pH, alcohol, Brix and maximum volume of foam. RESULTS: The ANN method was able to create more accurate models (R2 = 0.95) compared to PLS. Principal components analysis using RoboBEER parameters and NIR overtones related to protein explained 67% of total data variability. Additionally, a sub-space discriminant model using the absorbance values from NIR wavelengths resulted in the successful classification of 85% of beers according to fermentation type. CONCLUSION: The method proposed showed to be a rapid system based on NIR spectroscopy and RoboBEER outputs of foamability that can be used to infer the quality, production method and chemical parameters of beer with minimal laboratory equipment. © 2017 Society of Chemical Industry