Description: A new method for finding solutions to ordinary differential equation boundary value problems is introduced, in which Sobolev gradient steepest descent is used to determine eigenfunctions and eigenvalues simultaneously in an iterative scheme. The technique is then applied to the 1-D Love wave problem. The algorithm has several advantages when computing dispersion curves. It avoids the problem of mode skipping, and can handle arbitrary Earth structure profiles in depth. For a given frequency range, computation times scale approximately as the square root of the number of frequencies, and the computation of dispersion curves can be implemented in a fully parallel manner over the modes involved. The steepest descent solutions are within a fraction of a per cent of the analytic solutions for the first 25 modes for a two-layer model. Since all corresponding eigenfunctions are computed along with the dispersion curves, the impact on group and phase velocity of the displacement behaviour with depth is thoroughly examined. The dispersion curves are used to compute synthetic Love wave seismograms that include many higher order modes. An example includes addition of attenuation to a model with a low-velocity zone, with values as low as Q = 20. Finally, a confirming comparison is made with a layer matrix method on the upper 700 km of a whole Earth model.

Description: Background The mitogen-activated protein–kinase pathway consisting of the kinases RAF, MEK, and ERK is central to cell proliferation and survival and is deregulated in more than 90% of melanomas. MEK inhibitors are currently trialled in the clinic, but despite efficient target inhibition, cytostatic rather than cytotoxic activity limits their efficacy. Methods We assessed the cytotoxicity to MEK inhibitors (PD184352 and selumetinib) in melanoma cells by toluidine-blue staining, caspase 3 cleavage, and melanoma-sphere growth. Western blotting and quantitative real-time polymerase chain reaction were applied to determine SMAD-specific E3 ubiquitin protein ligase 2 (SMURF2), PAX3, and MITF expression. Human melanoma samples (n = 77) from various stages were analyzed for SMURF2 and PAX3 expression. RNA interference was performed to target SMURF2 during MEK inhibition in vivo in melanoma xenografts in mice and zebrafish. All statistical tests were two-sided. Results Activation of transforming growth factor β (TGF-β) signalling sensitized melanoma cells to the cytotoxic effects of MEK inhibition. Melanoma cells resistant to the cytotoxic effects of MEK inhibitors counteracted TGF-β signalling through overexpression of the E3 ubiquitin ligase SMURF2, which resulted in increased expression of the transcription factors PAX3 and MITF. High MITF expression protected melanoma cells against MEK inhibitor cytotoxicity. Depleting SMURF2 reduced MITF expression and substantially lowered the threshold for MEK inhibitor–induced apoptosis. Moreover, SMURF2 depletion sensitized melanoma cells to the cytotoxic effects of selumetinib, leading to cell death at concentrations approximately 100-fold lower than the concentration required to induce cell death in SMURF2-expressing cells. Mice treated with selumetinib alone at a dosage of 10mg/kg body weight once daily produced no response, but in combination with SMURF2 depletion, selumetinib suppressed tumor growth by 97.9% (95% confidence interval = 38.65% to 155.50%, P = .005). Conclusions Targeting SMURF2 may be a novel therapeutic approach for increasing the antitumor efficacy of MEK inhibitors.

Description: Fever predicts clinical outcomes in sepsis, trauma and during cardiovascular stress, yet the genetic determinants are poorly understood. We used an integrative genomics approach to identify novel genomic determinants of the febrile response to experimental endotoxemia. We highlight multiple integrated lines of evidence establishing the clinical relevance of this novel fever locus. Through genome-wide association study (GWAS) of evoked endotoxemia (lipopolysaccharide (LPS) 1 ng/kg IV) in healthy subjects of European ancestry we discovered a locus on chr7p11.2 significantly associated with the peak febrile response to LPS (top single nucleotide polymorphism (SNP) rs7805622, P = 2.4 x 10 –12 ), as well as with temperature fluctuation over time. We replicated this association in a smaller independent LPS study (rs7805622, P = 0.03). In clinical translation, this locus was also associated with temperature and mortality in critically ill patients with trauma or severe sepsis. The top GWAS SNPs are not located within protein-coding genes, but have significant cis -expression quantitative trait loci (eQTL) associations with expression of a cluster of genes ~400 kb upstream, several of which ( SUMF2 , CCT6A , GBAS ) are regulated by LPS in vivo in blood cells. LPS- and cold-treatment of adipose stromal cells in vitro suggest genotype-specific modulation of eQTL candidate genes ( PSPH ). Several eQTL genes were up-regulated in brown and white adipose following cold exposure in mice, highlighting a potential role in thermogenesis. Thus, through genomic interrogation of experimental endotoxemia, we identified and replicated a novel fever locus on chr7p11.2 that modulates clinical responses in trauma and sepsis, and highlight integrated in vivo and in vitro evidence for possible novel cis candidate genes conserved across human and mouse.

Description: We present stellar atmosphere and evolution models of main-sequence stars in two stellar populations of the Galactic globular cluster NGC 6752. These populations represent the two extremes of light-element abundance variations in the cluster. NGC 6752 is a benchmark cluster in the study of multiple stellar populations because of the rich array of spectroscopic abundances and panchromatic Hubble Space Telescope photometry. The spectroscopic abundances are used to compute stellar atmosphere and evolution models. The synthetic spectra for the two populations show significant differences in the ultraviolet and, for the coolest temperatures, in the near-infrared. The stellar evolution models exhibit insignificant differences in the Hertzsprung-Russell (H–R) diagram except on the lower main sequence. The appearance of multiple sequences in the colour–magnitude diagrams (CMDs) of NGC 6752 is almost exclusively due to spectral effects caused by the abundance variations. The models reproduce the observed splitting and/or broadening of sequences in a range of CMDs. The ultraviolet CMDs are sensitive to variations in carbon, nitrogen, and oxygen but the models are not reliable enough to directly estimate abundance variations from photometry. On the other hand, the widening of the lower main sequence in the near-infrared CMD, driven by oxygen variation via the water molecule, is well described by the models and can be used to estimate the range of oxygen present in a cluster from photometry. We confirm that it is possible to use multiband photometry to estimate helium variations among the different populations, with the caveat that the estimated amount of helium enhancement is model dependent.

Description: Correctly estimating isoform-specific gene expression is important for understanding complicated biological mechanisms and for mapping disease susceptibility genes. However, estimating isoform-specific gene expression is challenging because various biases present in RNA-Seq (RNA sequencing) data complicate the analysis, and if not appropriately corrected, can affect isoform expression estimation and downstream analysis. In this article, we present PennSeq, a statistical method that allows each isoform to have its own non-uniform read distribution. Instead of making parametric assumptions, we give adequate weight to the underlying data by the use of a non-parametric approach. Our rationale is that regardless what factors lead to non-uniformity, whether it is due to hexamer priming bias, local sequence bias, positional bias, RNA degradation, mapping bias or other unknown reasons, the probability that a fragment is sampled from a particular region will be reflected in the aligned data. This empirical approach thus maximally reflects the true underlying non-uniform read distribution. We evaluate the performance of PennSeq using both simulated data with known ground truth, and using two real Illumina RNA-Seq data sets including one with quantitative real time polymerase chain reaction measurements. Our results indicate superior performance of PennSeq over existing methods, particularly for isoforms demonstrating severe non-uniformity. PennSeq is freely available for download at http://sourceforge.net/projects/pennseq .