Membrane-bound β-catenin degradation is enhanced by ETS2-mediated Siah1 induction in Helicobacter pylori-infected gastric cancer cells.

β-catenin has two different cellular functions: intercellular adhesion and transcriptional activity. The E3 ubiquitin ligase Siah1 causes ubiquitin-mediated degradation of the cytosolic β-catenin and therefore, impairs nuclear translocation and oncogenic function of β-catenin. However, the effect of Siah1 on the cell membrane bound β-catenin has not been studied. In this study, we identified that the carcinogenic bacterium H. pylori increased ETS2 transcription factor-mediated Siah1 protein expression in gastric cancer cells (GCCs) MKN45, AGS and Kato III. Siah1 protein level was also noticeably higher in gastric adenocarcinoma biopsy samples as compared to non-cancerous gastric epithelia. Siah1 knockdown significantly decreased invasiveness and migration of H. pylori-infected GCCs. Although, Siah1 could not increase degradation of the cytosolic β-catenin and its nuclear translocation, it enhanced degradation of the membrane-bound β-catenin in the infected GCCs. This loss of membrane-bound pool of β-catenin was not associated with the proteasomal degradation of E-cadherin. Thus, this work delineated the role of Siah1 in increasing invasiveness of H. pylori-infected GCCs

Many faces of low mass neutralino dark matter in the unconstrained MSSM, LHC data and new signals

If all strongly interacting sparticles (the squarks and the gluinos) in an unconstrained minimal supersymmetric standard model (MSSM) are heavier than the corresponding mass lower limits in the minimal supergravity (mSUGRA) model, obtained by the current LHC experiments, then the existing data allow a variety of electroweak (EW) sectors with light sparticles yielding dark matter (DM) relic density allowed by the WMAP data. Some of the sparticles may lie just above the existing lower bounds from LEP and lead to many novel DM producing mechanisms not common in mSUGRA. This is illustrated by revisiting the above squark-gluino mass limits obtained by the ATLAS Collaboration, with an unconstrained EW sector with masses not correlated with the strong sector. Using their selection criteria and the corresponding cross section limits, we find at the generator level using Pythia, that the changes in the mass limits, if any, are by at most 10-12% in most scenarios. In some cases, however, the relaxation of the gluino mass limits are larger ($\approx 20$). If a subset of the strongly interacting sparticles in an unconstrained MSSM are within the reach of the LHC, then signals sensitive to the EW sector may be obtained. This is illustrated by simulating the $blj$\etslash, $l= e and \mu$, and $b\tau j$\etslash signals in i) the light stop scenario and ii) the light stop-gluino scenario with various light EW sectors allowed by the WMAP data. Some of the more general models may be realized with non-universal scalar and gaugino masses.Comment: 27 pages, 1 figure, references added, minor changes in text, to appear in JHE

Economic growth, law, and corruption: evidence from India

Is corruption influenced by economic growth? Are legal institutions such as the ‘Right to Information Act (RTI) 2005’ in India effective in curbing corruption? Using a panel dataset covering 20 Indian states for the years 2005 and 2008 we estimate the effects of growth and law on corruption. Accounting for endogeneity, omitted fixed factors, and other nationwide changes we find that economic growth reduces overall corruption as well as corruption in banking, land administration, education, electricity, and hospitals. Growth reduces bribes but has little impact on corruption perception. In contrast the RTI Act reduces both corruption experience and corruption perceptio

Viscoelastic confinement induces periodic flow reversals in active nematics

We use linear stability analysis and hybrid lattice Boltzmann simulations to study the dynamical behavior of an active nematic confined in a channel made of viscoelastic material. We find that the quiescent, ordered active nematic is unstable above a critical activity. The transition is to a steady flow state for high elasticity of the channel surroundings. However, below a threshold elastic modulus, the system produces spontaneous oscillations with periodic flow reversals. We provide a phase diagram that highlights the region where time-periodic oscillations are observed and explain how they are produced by the interplay of activity and viscoelasticity. Our results suggest experiments to study the role of viscoelastic confinement in the spatiotemporal organization and control of active matter

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

Dynamical R-parity Breaking at the LHC

In a class of extensions of the minimal supersymmetric standard model with (B-L)/left-right symmetry that explains the neutrino masses, breaking R-parity symmetry is an essential and dynamical requirement for successful gauge symmetry breaking. Two consequences of these models are: (i) a new kind of R-parity breaking interaction that protects proton stability but adds new contributions to neutrinoless double beta decay and (ii) an upper bound on the extra gauge and parity symmetry breaking scale which is within the large hadron collider (LHC) energy range. We point out that an important prediction of such theories is a potentially large mixing between the right-handed charged lepton ($e^c$) and the superpartner of the right-handed gauge boson ($\widetilde W_R^+$), which leads to a brand new class of R-parity violating interactions of type $\widetilde{\mu^c}^\dagger\nu_\mu^c e^c$ and \widetilde{d^c}^\dagger\u^c e^c. We analyze the relevant constraints on the sparticle mass spectrum and the LHC signatures for the case with smuon/stau NLSP and gravitino LSP. We note the "smoking gun" signals for such models to be lepton flavor/number violating processes: $pp\to \mu^\pm\mu^\pm e^+e^-jj$ (or $\tau^\pm\tau^\pm e^+e^-jj$) and $pp\to\mu^\pm e^\pm b \bar{b} jj$ (or $\tau^\pm e^\pm b \bar{b} jj$) without significant missing energy. The predicted multi-lepton final states and the flavor structure make the model be distinguishable even in the early running of the LHC.Comment: 30 pages, 13 figures, 6 tables, reference adde

Hot embossing for fabrication of a microfluidic 3D cell culture

Clinically relevant studies of cell function in vitro require a physiologically-representative microenvironment possessing aspects such as a 3D extracellular matrix (ECM) and controlled biochemical and biophysical parameters. A polydimethylsiloxane (PDMS) microfluidic system with a 3D collagen gel has previously served for analysis of factors inducing different responses of cells in a 3D microenvironment under controlled biochemical and biophysical parameters. In the present study, applying the known commercially-viable manufacturing methods to a cyclic olefin copolymer (COC) material resulted in a microfluidic device with enhanced 3D gel capabilities, controlled surface properties, and improved potential to serve high-volume applications. Hot embossing and roller lamination molded and sealed the microfluidic device. A combination of oxygen plasma and thermal treatments enhanced the sealing, ensured proper placement of the 3D gel, and created controlled and stable surface properties within the device. Culture of cells in the new device indicated no adverse effects of the COC material or processing as compared to previous PDMS devices. The results demonstrate a methodology to transition microfludic devices for 3D cell culture from scientific research to high-volume applications with broad clinical impact.National Cancer Institute (U.S.) (award R21CA140096)Charles Stark Draper Laboratory (IR&D Grant

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Background Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly. Methods With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor’s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented. Results The main findings are that (1) expert elicitation as predominant assessment method does not satisfy all requirements, (2) Bayesian model averaging for input variable modelling meets the requirements and allows evaluating a vast amount of potentially relevant influences on input variables and (3) posterior model probabilities of input variable models can be translated in uncertainty associated with the input variable. Conclusions An uncertainty assessment of energy scenarios is relevant if policy measures are (partially) based on modelling exercises. Potential implications of these findings include that energy scenarios could be associated with uncertainty that is presently neither assessed explicitly nor communicated adequately

Structure and function of a broad-specificity chitin deacetylase from <i>Aspergillus nidulans </i>FGSC A4

publishedVersio

Investigation of robust stability for fractional-order LTI systems with multilinear structure of ellipsoidal parametric uncertainty

The contribution focuses on the investigation of robust stability for fractional-order linear time-invariant (LTI) systems with the multilinear structure of ellipsoidal parametric uncertainty, i.e., the analyzed family of fractional-order polynomials has the multilinear uncertainty structure and an ellipsoid-shaped uncertainty bounding set. The robust stability test is based on the numerical calculation and subsequent plot of the value sets, and the application of the zero exclusion condition. Unlike the previously published works, this contribution shows that, contrary to the case of a two-dimensional ellipse of parameters, the internal points of a three-dimensional ellipsoid of parameters cannot create the boundary of the value set in the complex plane even under more complicated uncertainty structures, such as the multilinear one. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG