Brain Metastases from Lung Cancer Show Increased Expression of DVL1, DVL3 and Beta-Catenin and Down-Regulation of E-Cadherin

The susceptibility of brain to secondary formation from lung cancer primaries is a well-known phenomenon. In contrast, the molecular basis for invasion and metastasis to the brain is largely unknown. In the present study, 31 brain metastases that originated from primary lung carcinomas were analyzed regarding over expression of Dishevelled-1 (DVL1), Dishevelled-3 (DVL3), E-cadherin (CDH1) and beta-catenin (CTNNB1). Protein expressions and localizations were analyzed by immunohistochemistry. Genetic alterations of E-cadherin were tested by polymerase chain reaction (PCR)/loss of heterozygosity (LOH). Heteroduplex was used to investigate mutations in beta-catenin. DVL1 and DVL3 showed over expression in brain metastasis in 87.1% and 90.3% of samples respectively. Nuclear staining was observed in 54.8% of cases for DVL1 and 53.3% for DVL3. The main effector of the Wnt signaling, beta-catenin, was up-regulated in 56%, and transferred to the nucleus in 36% of metastases. When DVL1 and DVL3 were up-regulated the number of cases with nuclear beta-catenin significantly increased (p = 0.0001). Down-regulation of E-cadherin was observed in 80% of samples. Genetic analysis showed 36% of samples with LOH of the CDH1. In comparison to other lung cancer pathologies, the diagnoses adenocarcinoma and small cell lung cancer (SCLC) were significantly associated to CDH1 LOH (p = 0.001). Microsatellite instability was detected in one metastasis from adenocarcinoma. Exon 3 of beta-catenin was not targeted. Altered expression of Dishevelled-1, Dishevelled-3, E-cadherin and beta-catenin were present in brain metastases which indicates that Wnt signaling is important and may contribute to better understanding of genetic profile conditioning lung cancer metastasis to the brain.     

Simple route for the preparation of graphene/poly(styrene‐b‐butadiene‐b‐styrene) nanocomposite films with enhanced electrical conductivity and hydrophobicity

This paper reports a simple route for the preparation of graphene/poly(styrene‐b‐butadiene‐b‐styrene) (SBS) nanocomposite films employing a vacuum filtration method. Graphene is exfoliated well by an electrochemical procedure and homogeneously dispersed in the polymer matrix. The prepared nanocomposite films were characterized by XRD, Fourier transform IR (FTIR) spectroscopy, X‐ray photoelectron spectroscopy (XPS), Raman spectroscopy, AFM and SEM. Morphological studies showed that graphene formed a smooth coating over the surface of SBS. The increase in graphene concentration induces the wrinkling of graphene sheets at the composite surface which causes a further increase in surface roughness. The FTIR, Raman and XPS spectra of graphene/SBS nanocomposite films indicate the strong interactions between graphene and the polymer matrix. According to the XRD patterns, introducing SBS into graphene did not modify the graphene structure additionally, i.e. the crystal lattice parameters do not depend on SBS content in graphene/SBS nanocomposite films. The graphene/SBS nanocomposite films also exhibited better hydrophobicity due to the increased surface roughness and lower sheet resistivity (reduced 10 times) compared to exfoliated graphene. © 2018 Society of Chemical Industry     

Color reproduction on varnished cardboard packaging by using lower ink coverages due to the gray component replacement image processing

Even in these challenging times, cardboard packaging industry is increasing its revenue with stable annual increase prediction, but customers are increasing demands on the packaging with respect to environmental protection, attractiveness, and branding. This article aims to determine the influence of image processing in terms of gray component replacement and overprint varnishing on the color reproduction on a cardboard packaging material. A test chart with tertiary colors was defined and modified by different gray component replacement levels. It was printed in accordance with ISO norm on coated and uncoated cardboard and finished by overprinting water based, offset, and UV varnish. The prepared samples were evaluated by measuring surface coverage, tone value increase (dot gain) of primary colors and calculating ΔE₀₀ of defined tertiary colors. The results showed that gray component replacement application reduces ink consumption. Higher gray component replacement applied results in lower ink surface coverage, but the decrease is also dependent on the image content. The TVI (dot gain) is significantly influenced only by applying UV varnish, resulting with higher TVI. The color differences of the tertiary colors showed that increased level of the gray component replacement applied did not cause significant color differences, even decreased color difference in some cases. Additionally, applying gray component replacement decreased color differences in “reddish” part of the gamut. This research proved that lowering the ink consumption due to the gray component replacement will not cause significant differences in the color reproduction. Furthermore, varnishing with commercial offset or water-based varnish will not cause higher deviations in color reproduction, but use of the UV varnish would.     

Adaptative time constants improve the prediction capability of recurrent neural networks

Classical statistical techniques for prediction reach their limitations in applications with nonlinearities in the data set; nevertheless, neural models can counteract these limitations. In this paper, we present a recurrent neural model where we associate an adaptative time constant to each neuron-like unit and a learning algorithm to train these dynamic recurrent networks. We test the network by training it to predict the Mackey-Glass chaotic signal. To evaluate the quality of the prediction, we computed the power spectra of the two signals and computed the associated fractional error. Results show that the introduction of adaptative time constants associated to each neuron of a recurrent network improves the quality of the prediction and the dynamical features of a neural model. The performance of such dynamic recurrent neural networks outperform time-delay neural networks.     

Fuel Assemblies for Investigating Accidents Using Large-Scale Stands in Application to VVÉR-1000 Reactors

The results of development work performed on and the application of fuel-assembly models with fuel-element simulators are presented. The technical solutions which make it possible to move the simulators under heat-cycling conditions, improve the temperature regimes of the simulators, and provide simulator operating conditions close to those in a reactor are presented.     

A stabilization mechanism for the perovskite La2/3TiO3 compound with Fe2O3: A structural and electrical investigation

We have stabilized the perovskite La_2/3TiO₃ by adding LaFeO₃ and shown that in general the stabilization mechanism for the (1 − x)La_2/3TiO_3–xLaFeO₃ mixture involves the formation of a solid solution for compositions with x ≥ 0.04. The crystal structure of the solid solution transforms from orthorhombic to tetragonal at x = 0.2, becomes cubic in the range 0.3 < x < 0.8, and transforms again into orthorhombic (typical for pure LaFeO₃) for values greater than 0.8. Detailed impedance-spectroscopy measurements for various compositions and conditions showed that the limiting step in the conduction mechanism was conduction across the grain boundaries. In the concentration range 0.04 < x < 0.25 the room temperature conductivity increases up to 0.0017 S cm⁻¹, after which it decreases again. Part of the initial increase is probably due to the formation of free electrons in accordance with (Fe_Ti)′ → (Fe_Ti)^x + n′. Other defect-formation mechanisms are also discussed, but are ruled out for a variety of reasons. Another interesting phenomenon that also affected the average conductivity was identified, i.e., the variation of the average particle size with composition.