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Glioblastoma is the most aggressive primary brain tumor in adults and due to the invasive nature cannot be completely removed. The WNT inhibitory factor 1 (WIF1), a secreted inhibitor of WNTs, is systematically downregulated in glioblastoma and acts as strong tumor suppressor. The aim of this study was the dissection of WIF1-associated tumor-suppressing effects mediated by canonical and non-canonical WNT signaling. We found that WIF1 besides inhibiting the canonical WNT pathway selectively downregulates the WNT/calcium pathway associated with significant reduction of p38-MAPK (p38-mitogen-activated protein kinase) phosphorylation. Knockdown of WNT5A, the only WNT ligand overexpressed in glioblastoma, phenocopied this inhibitory effect. WIF1 expression inhibited cell migration in vitro and in an orthotopic brain tumor model, in accordance with the known regulatory function of the WNT/Ca 2+ pathway on migration and invasion. In search of a mediator for this function differential gene expression profiles of WIF1-expressing cells were performed. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long non-coding RNA and key positive regulator of invasion, emerged as the top downregulated gene. Indeed, knockdown of MALAT1 reduced migration in glioblastoma cells, without effect on proliferation. Hence, loss of WIF1 enhances the migratory potential of glioblastoma through WNT5A that activates the WNT/Ca 2+ pathway and MALAT1. These data suggest the involvement of canonical and non-canonical WNT pathways in glioblastoma promoting key features associated with this deadly disease, proliferation on one hand and invasion on the other. Successful targeting will require a dual strategy affecting both canonical and non-canonical WNT pathways.

Purpose Although glioblastoma (GBM) is the most common primary brain malignancy, few tools exist to pre-operatively risk-stratify patients by overall survival (OS) or common genetic alterations. We developed an MRI-based radiomics model to identify patients with EGFR amplification, MGMT methylation, GBM subtype, and OS greater than 12 months. Methods We retrospectively identified 235 patients with pathologically confirmed GBMs from the Cancer Genome Atlas (88; TCGA) and MD Anderson Cancer Center (147; MDACC). After two neuroradiologists segmented MRI tumor volumes, we extracted first-order and second-order radiomic features (gray-level co-occurrence matrices). We used the Maximum Relevance Minimum Redundancy technique to identify the 100 most relevant features and validated models using leave-one-out-cross-validation and validation on external datasets (i.e., TCGA). Our results were reported as the area under the curve (AUC). Results The MDACC patient cohort had significantly higher OS (22 months) than the TCGA dataset (14 months). On both LOOCV and external validation, our radiomics models were able to identify EGFR amplification (all AUCs > 0.83), MGMT methylation (all AUCs > 0.85), GBM subtype (all AUCs > 0.92), and OS (AUC > 0.91 on LOOCV and 0.71 for TCGA validation). Conclusions Our robust radiomics pipeline has the potential to pre-operatively discriminate common genetic alterations and identify patients with favorable survival.

A simulative investigation of noise effects in wavelength modulation spectroscopy (WMS) and direct absorption diode laser absorption spectroscopy is presented. Special attention is paid to the impact of quantization noise of the analog-to-digital conversion (ADC) of the photodetector signal in the two detection schemes with the goal of estimating the necessary ADC resolution for each technique. With laser relative intensity noise (RIN), photodetector shot noise and thermal amplifier noise included, the strategies used for noise reduction in direct and wavelength modulation spectroscopy are compared by simulating two respective systems. Results show that because of the combined effects of dithering by RIN and signal averaging, the resolutions required for the direct absorption setup are only slightly higher than for the WMS setup. Only for small contributions of RIN an increase in resolution will significantly improve signal quality in the direct scheme.

Future radio surveys will generate catalogs of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources (most radio surveys have a median redshift greater than 1) and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multiwavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all-sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best in the presence of high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately, using specific templates and priors. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine-learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts.

We prove two identities of Hall–Littlewood polynomials, which appeared recently in Betea and Wheeler (2014). We also conjecture, and in some cases prove, new identities which relate infinite sums of symmetric polynomials and partition functions associated with symmetry classes of alternating sign matrices. These identities generalize those already found in Betea and Wheeler (2014), via the introduction of additional parameters. The left-hand side of each of our identities is a simple refinement of a relevant Cauchy or Littlewood identity. The right-hand side of each identity is (one of the two factors present in) the partition function of the six-vertex model on a relevant domain.

Background: Epidermal growth factor receptor (EGFR) signalling is frequently altered during glioblastoma de novo pathogenesis. An important downstream modulator of this signal cascade is SHP2 (Src homology domain-containing phosphatase 2). Methods: We examined the The Cancer Genome Atlas (TCGA) database for SHP2 mutations. We also examined the expression of a further 191 phosphatases in the TCGA database and used principal component and comparative marker analysis available from the Broad Institute to recapitulate the TCGA-defined subgroups and identify the specific phosphatases defining each subgroup. We identified five siRNAs from two independent commercial sources that were reported by the vendor to be pre-optimised in their specificity of SHP2 silencing. The specificity and physiological effects of these siRNAs were tested using an in vitro glioma model. Results: TCGA data demonstrate SHP2 to be mutated in 2% of the glioblastoma multiforme's studied. Both mutations identified in this study are likely to be activating mutations. We found that the four subgroups of GBM as defined by TCGA differ significantly with regard to the expression level of specific phosphatases as revealed by comparative marker analysis. Surprisingly, the four subgroups can be defined solely on the basis of phosphatase expression level by principal component analysis. This result suggests that critical phosphatases are responsible for the modulation of specific molecular pathways within each subgroup. Src homology domain-containing phosphatase 2 constitutes one of the 12 phosphatases that define the classical subgroup . We confirmed the biological significance by siRNA knockdown of SHP2. All five siRNAs tested reduced SHP2 expression by 70–100% and reduced glioblastoma cell line growth by up to 80%. Profiling the established molecular targets of SHP2 (ERK1/2 and STAT3) confirmed specificity of these siRNAs. The loss of cell viability induced by SHP2 silencing could not be explained by a significant increase in apoptosis alone as demonstrated by terminal deoxyribonucleotidyl transferase-mediated nick-end labelling and propidium iodide staining. Src homology domain-containing phosphatase 2 silencing, however, did induce an increase in β -galactosidase staining. Propidium iodide staining also showed that SHP2 silencing increases the population of glioblastoma cells in the G1 phase of the cell cycle and reduces the population of such cells in the G2/M- and S-phase. Conclusion: Src homology domain-containing phosphatase 2 promotes the growth of glioblastoma cells by suppression of cellular senescence, a phenomenon not described previously. Selective inhibitors of SHP2 are commercially available and may be considered as a strategy for glioblastoma therapy.

Laser diode line widths and line shapes are experimentally investigated in dependence on the diode current and on back reflections from an optical system. Four distributed-feedback (DFB)-type diode lasers and two vertical-cavity surface-emitting lasers (VCSELs) have been tested within the same optical setup and using the same fitting methods. System back reflection ratios of light reflected back to the laser have been varied between −1 dB and −45 dB and were below −60 dB when all reflections were blocked. The background of this investigation is the evaluation of different laser types with respect to their suitability for sensor applications in which optical back reflections may occur, for example tunable diode-laser spectroscopy (TDLS). While DFB-type lasers showed almost pure Lorentzian line shapes and line widths of a few MHz, the tested VCSELs had a strong Gaussian contribution to the line shape, indicating stronger 1/ f noise, which was also observed in the relative intensity noise of these particular lasers. System reflection ratios above −25 dB had strong effects on the line width in both DFB diode lasers and VCSELs, while some influences have been observed at even lower reflection ratios for DFB diode lasers. As much smaller reflection ratios are typically required in TDLS systems to avoid etalon-like fringes and self-mixing interference effects, we conclude that the influence on the line width is not the most important reason to minimize back reflections in practical TDLS systems or to choose one type of diode laser over the other.

We show how equivariant volumes of tensor product quiver varieties of type A are given by matrix elements of vertex operators of centrally extended doubles of Yangians and how these elements satisfy the rational level-one quantum Knizhnik–Zamolodchikov equation in some cases.

We present multiple-residue integral formulas for partial sums in the basis of link patterns of the polynomial solution of the level-1 \\(U_q (sl_2 )\\) quantum Knizhnik-Zamolodchikov equation at arbitrary values of the quantum parameter q. These formulas allow rewriting and generalizing a recent conjecture of Di Francesco connecting these sums to generating polynomials for weighted totally symmetric self-complementary plane partitions. We reduce the corresponding conjectures to a single integral identity, yet to be proved.

We define matrix models that converge to the generating functions of a wide variety of loop models with fugacity taken in sets with an accumulation point. The latter can also be seen as moments of a non-commutative law on a subfactor planar algebra. We apply this construction to compute the generating functions of the Potts model on a random planar map.