Predictors of self and parental vaccination decisions in England during the 2009 H1N1 pandemic: Analysis of the Flu Watch pandemic cohort data

During the 2009 H1N1 pandemic, UK uptake of the pandemic influenza vaccine was very low. Furthermore, attitudes governing UK vaccination uptake during a pandemic are poorly characterised. To the best of our knowledge, there is no published research explicitly considering predictors of both adult self-vaccination and decisions regarding whether or not to vaccinate one’s children among the UK population during the H1N1 pandemic. We therefore aimed to identify predictors of both self-vaccination decisions and parental vaccination decisions using data collected during the H1N1 pandemic as part of the Flu Watch cohort study. Data were analysed separately for 798 adults and 85 children: exploratory factor analysis facilitated reduction of 16 items on attitudes to pandemic vaccine into a smaller number of factors. Single variable analyses with vaccine uptake as the outcome were used to identify variables that were predictive of vaccination in children and adults. Potential predictors were: attitudinal factors created by data reduction, age group, sex, region, deprivation, ethnicity, chronic condition, vocation, healthcare-related occupation and previous influenza vaccination. Consistent with previous literature concerning adult self-vaccination decisions, we found that vaccine efficacy/safety and perceived risk of pandemic influenza were significant predictors of both self-vaccination decisions and parental vaccination decisions. This study provides the first systematic attempt to understand both the predictors of self and parental vaccination uptake among the UK general population during the H1N1 pandemic. Our findings indicate that concerns about vaccine safety, and vaccine effectiveness may be a barrier to increased uptake for both self and parental vaccination

Integral transform solution of random coupled parabolic partial differential models

[EN] Random coupled parabolic partial differential models are solved numerically using random cosine Fourier transform together with non-Gaussian random numerical integration that captures the highly oscillatory behaviour of the involved integrands. Sufficient condition of spectral type imposed on the random matrices of the system is given so that the approximated stochastic process solution and its statistical moments are numerically convergent. Numerical experiments illustrate the results.Spanish Ministerio de Economia, Industria y Competitividad (MINECO); Agencia Estatal de Investigacion (AEI); Fondo Europeo de Desarrollo Regional (FEDER UE), Grant/Award Number: MTM2017-89664-PCasabán Bartual, MC.; Company Rossi, R.; Egorova, VN.; Jódar Sánchez, LA. (2020). Integral transform solution of random coupled parabolic partial differential models. Mathematical Methods in the Applied Sciences. 43(14):8223-8236. https://doi.org/10.1002/mma.6492S822382364314Bäck, J., Nobile, F., Tamellini, L., & Tempone, R. (2010). Stochastic Spectral Galerkin and Collocation Methods for PDEs with Random Coefficients: A Numerical Comparison. Spectral and High Order Methods for Partial Differential Equations, 43-62. doi:10.1007/978-3-642-15337-2_3Bachmayr, M., Cohen, A., & Migliorati, G. (2016). Sparse polynomial approximation of parametric elliptic PDEs. Part I: affine coefficients. ESAIM: Mathematical Modelling and Numerical Analysis, 51(1), 321-339. doi:10.1051/m2an/2016045Ernst, O. G., Sprungk, B., & Tamellini, L. (2018). Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables (with Application to Elliptic PDEs). SIAM Journal on Numerical Analysis, 56(2), 877-905. doi:10.1137/17m1123079Sheng, D., & Axelsson, K. (1995). Uncoupling of coupled flows in soil—a finite element method. International Journal for Numerical and Analytical Methods in Geomechanics, 19(8), 537-553. doi:10.1002/nag.1610190804Mitchell, J. K. (1991). Conduction phenomena: from theory to geotechnical practice. Géotechnique, 41(3), 299-340. doi:10.1680/geot.1991.41.3.299Das, P. K. (1991). Optical Signal Processing. doi:10.1007/978-3-642-74962-9Ashkenazy, Y. (2017). Energy transfer of surface wind-induced currents to the deep ocean via resonance with the Coriolis force. Journal of Marine Systems, 167, 93-104. doi:10.1016/j.jmarsys.2016.11.019Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764Galiano, G. (2012). On a cross-diffusion population model deduced from mutation and splitting of a single species. Computers & Mathematics with Applications, 64(6), 1927-1936. doi:10.1016/j.camwa.2012.03.045Casabán, M. C., Company, R., & Jódar, L. (2019). Numerical solutions of random mean square Fisher‐KPP models with advection. Mathematical Methods in the Applied Sciences, 43(14), 8015-8031. doi:10.1002/mma.5942Casabán, M. C., Company, R., & Jódar, L. (2019). Numerical Integral Transform Methods for Random Hyperbolic Models with a Finite Degree of Randomness. Mathematics, 7(9), 853. doi:10.3390/math7090853Shampine, L. F. (2008). Vectorized adaptive quadrature in MATLAB. Journal of Computational and Applied Mathematics, 211(2), 131-140. doi:10.1016/j.cam.2006.11.021Iserles, A. (2004). On the numerical quadrature of highly-oscillating integrals I: Fourier transforms. IMA Journal of Numerical Analysis, 24(3), 365-391. doi:10.1093/imanum/24.3.365Ma, J., & Liu, H. (2018). On the Convolution Quadrature Rule for Integral Transforms with Oscillatory Bessel Kernels. Symmetry, 10(7), 239. doi:10.3390/sym10070239Jódar, L., & Goberna, D. (1996). Exact and analytic numerical solution of coupled diffusion problems in a semi-infinite medium. Computers & Mathematics with Applications, 31(9), 17-24. doi:10.1016/0898-1221(96)00038-7Jódar, L., & Goberna, D. (1998). A matrix D’Alembert formula for coupled wave initial value problems. Computers & Mathematics with Applications, 35(9), 1-15. doi:10.1016/s0898-1221(98)00052-2Ostrowski, A. M. (1959). A QUANTITATIVE FORMULATION OF SYLVESTER’S LAW OF INERTIA. Proceedings of the National Academy of Sciences, 45(5), 740-744. doi:10.1073/pnas.45.5.740Ashkenazy, Y., Gildor, H., & Bel, G. (2015). The effect of stochastic wind on the infinite depth Ekman layer model. EPL (Europhysics Letters), 111(3), 39001. doi:10.1209/0295-5075/111/3900

PGRMC1 phosphorylation affects cell shape, motility, glycolysis, mitochondrial form and function, and tumor growth.

BackgroundProgesterone Receptor Membrane Component 1 (PGRMC1) is expressed in many cancer cells, where it is associated with detrimental patient outcomes. It contains phosphorylated tyrosines which evolutionarily preceded deuterostome gastrulation and tissue differentiation mechanisms.ResultsWe demonstrate that manipulating PGRMC1 phosphorylation status in MIA PaCa-2 (MP) cells imposes broad pleiotropic effects. Relative to parental cells over-expressing hemagglutinin-tagged wild-type (WT) PGRMC1-HA, cells expressing a PGRMC1-HA-S57A/S181A double mutant (DM) exhibited reduced levels of proteins involved in energy metabolism and mitochondrial function, and altered glucose metabolism suggesting modulation of the Warburg effect. This was associated with increased PI3K/AKT activity, altered cell shape, actin cytoskeleton, motility, and mitochondrial properties. An S57A/Y180F/S181A triple mutant (TM) indicated the involvement of Y180 in PI3K/AKT activation. Mutation of Y180F strongly attenuated subcutaneous xenograft tumor growth in NOD-SCID gamma mice. Elsewhere we demonstrate altered metabolism, mutation incidence, and epigenetic status in these cells.ConclusionsAltogether, these results indicate that mutational manipulation of PGRMC1 phosphorylation status exerts broad pleiotropic effects relevant to cancer and other cell biology

Correlations of behavioral deficits with brain pathology assessed through longitudinal MRI and histopathology in the R6/1 mouse model of huntington's disease

Huntington's disease (HD) is caused by the expansion of a CAG repeat in the huntingtin (HTT) gene. The R6 mouse models of HD express a mutant version of exon 1 HTT and typically develop motor and cognitive impairments, a widespread huntingtin (HTT) aggregate pathology and brain atrophy. Unlike the more commonly used R6/2 mouse line, R6/1 mice have fewer CAG repeats and, subsequently, a less rapid pathological decline. Compared to the R6/2 line, fewer descriptions of the progressive pathologies exhibited by R6/1 mice exist. The association between the molecular and cellular neuropathology with brain atrophy, and with the development of behavioral phenotypes remains poorly understood in many models of HD. In attempt to link these factors in the R6/1 mouse line, we have performed detailed assessments of behavior and of regional brain abnormalities determined through longitudinal, in vivo magnetic resonance imaging (MRI), as well as an end-stage, ex vivo MRI study and histological assessment. We found progressive decline in both motor and non-motor related behavioral tasks in R6/1 mice, first evident at 11 weeks of age. Regional brain volumes were generally unaffected at 9 weeks, but by 17 weeks there was significant grey matter atrophy. This age-related brain volume loss was validated using a more precise, semi-automated Tensor Based morphometry assessment. As well as these clear progressive phenotypes, mutant HTT (mHTT) protein, the hallmark of HD molecular pathology, was widely distributed throughout the R6/1 brain and was accompanied by neuronal loss. Despite these seemingly concomitant, robust pathological phenotypes, there appeared to be little correlation between the three main outcome measures: behavioral performance, MRI-detected brain atrophy and histopathology. In conclusion, R6/1 mice exhibit many features of HD, but the underlying mechanisms driving these clear behavioral disturbances and the brain volume loss, still remain unclear. © 2013 Rattray et al

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction

<b>Background</b> The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.<p></p> <b>Results</b> We developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasets.<p></p> <b>Conclusions</b> The k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

Buses, cars, bicycles and walkers the influence of the type of human transport on the flight responses of waterbirds

One way to manage disturbance to waterbirds in natural areas where humans require access is to promote the occurrence of stimuli for which birds tolerate closer approaches, and so cause fewer responses. We conducted 730 experimental approaches to 39 species of waterbird, using five stimulus types (single walker, three walkers, bicycle, car and bus) selected to mimic different human management options available for a controlled access, Ramsar-listed wetland. Across species, where differences existed (56% of 25 cases), motor vehicles always evoked shorter flight-initiation distances (FID) than humans on foot. The influence of stimulus type on FID varied across four species for which enough data were available for complete cross-stimulus analysis. All four varied FID in relation to stimuli, differing in 4 to 7 of 10 possible comparisons. Where differences occurred, the effect size was generally modest, suggesting that managing stimulus type (e.g. by requiring people to use vehicles) may have species-specific, modest benefits, at least for the waterbirds we studied. However, different stimulus types have different capacities to reduce the frequency of disturbance (i.e. by carrying more people) and vary in their capacity to travel around important habita

Haplotype analysis of TP53 polymorphisms, Arg72Pro and Ins16, in BRCA1 and BRCA2 mutation carriers of French Canadian descent

Research articl

Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice

The millions of common DNA variations that occur in the human population, or among inbred strains of mice and rats, perturb the expression (transcript levels) of a large fraction of the genes expressed in a particular tissue. The hundreds or thousands of common cis-acting variations that occur in the population may in turn affect the expression of thousands of other genes by affecting transcription factors, signaling molecules, RNA processing, and other processes that act in trans. The levels of transcripts are conveniently quantitated using expression arrays, and the cis- and trans-acting loci can be mapped using quantitative trait locus (QTL) analysis, in the same manner as loci for physiologic or clinical traits. Thousands of such expression QTL (eQTL) have been mapped in various crosses in mice, as well as other experimental organisms, and less detailed maps have been produced in studies of cells from human pedigrees. Such an integrative genetics approach (sometimes referred to as “genetical genomics”) is proving useful for identifying genes and pathways that contribute to complex clinical traits. The coincidence of clinical trait QTL and eQTL can help in the prioritization of positional candidate genes. More importantly, mathematical modeling of correlations between levels of transcripts and clinical traits in genetic crosses can allow prediction of causal interactions and the identification of “key driver” genes. An important objective of such studies will be to model biological networks in physiologic processes. When combined with high-density single nucleotide polymorphism (SNP) mapping, it should be feasible to identify genes that contribute to transcript levels using association analysis in outbred populations. In this review we discuss the basic concepts and applications of this integrative genomic approach to cardiovascular and metabolic diseases