Expression levels of vascular endothelial growth factors A and C in patients with peptic ulcers and gastric cancer

Purpose: Vascular endothelial growth factor (VEGF) is one of the most important growth factors for metastatic tumors. To clarify the role of VEGF-A and C in patients with peptic ulcer disease (PUD) or gastric cancer (GC), we evaluated the expression levels of these two molecules. We also analyzed the effect of Helicobacter pylori infection on VEGF-A and C expression levels

Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society

Multi-model ensemble hydrologic prediction using Bayesian model averaging

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights. © 2006 Elsevier Ltd. All rights reserved

Development of a Self-assembly Technique for Drug-Delivering Hydroxyapatite Coatings for Ti-Based Implants

PhD 2010 QMTo facilitate the long term osteointegration of Ti implants of various forms, methods aiming to facilitate hydroxyapatite deposition and enhance its adhesion to the Ti surfaces have to be developed. This work investigates the novel route of Ti surface functionalization with self-assembled monolayers (SAMs) in order to facilitate hydroxyapatite deposition and strengthen its bonding with the Ti surface and further equip the surface with localized antibiotic delivery to combat post-implantation infections. The main findings demonstrate that the formation of SAMs on non-model Ti substrates is challenging, since it requires the simultaneous control of many factors to achieve a densely packed well-organized SAM on a large surface area. By pre-treating the substrate with techniques such as electropolishing, the initial surface contamination can be kept at minimum while the hydroxylated surface remains smooth for the formation of well-oriented SAMs. Hence, after electropolishing, the Ti surface could be functionalized with molecules carrying reactive or neutral groups to facilitate hydroxyapatite deposition and/or antibiotic immobilization. Such a surface functionalization is found to facilitate hydroxyapatite deposition. The hydroxyapatite formed on SAM-modified Ti surfaces is made of small crystals of 6 nm and a 12 μm thick hydroxyapatite film, which can grow in 1 month. The SAM modified surfaces are covered with hydroxyapatite spheres in less than 7 days, while no spheres are observed on the unmodified Ti surface under similar conditions. Enhanced hydroxyapatite deposition rates on SAM-modified surfaces are explained by a decrease of nucleation barrier for hydroxyapatite. Additionally, preliminary investigations demonstrate the possibility of further functionalizing the Ti surface to allow the immobilization of antibiotic (Ciprofloxacin here) simultaneously with hydroxyapatite growth. The release of Ciprofloxacin was found to occur after 1 day and continue up to 20 days. The combination of these two functionalities on the Ti surfaces could find applications in load-bearing implants

Modeling and Propagation of Noisy Waveforms in Static Timing Analysis

A technique based on the sensitivity of the output to input waveform is presented for accurate propagation of delay information through a gate for the purpose of static timing analysis (STA) in the presence of noise. Conventional STA tools represent a waveform by its arrival time and slope. However, this is not an accurate way of modeling the waveform for the purpose of noise analysis. The key contribution of our work is the development of a method that allows efficient propagation of equivalent waveforms throughout the circuit. Experimental results demonstrate higher accuracy of the proposed sensitivity-based gate delay propagation technique, SGDP, compared to the best of existing approaches. SGDP is compatible with the current level of gate characterization in conventional ASIC cell libraries, and as a result, it can be easily incorporated into commercial STA tools to improve their accuracy.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

Punching shear of concrete flat slabs reinforced with fibre reinforced polymer bars

Fibre reinforcement polymers (FRP) are non-corrodible materials used instead of conventional steel and have been approved to be an effective way to overcome corrosion problems. FRP, in most cases, can have a higher tensile strength, but a lower tensile modulus of elasticity compared to that of conventional steel bars. This study aimed to examine flat slab specimens reinforced with glass fibre reinforced polymer (GFRP) and steel bar materials for punching shear behaviour. Six full-scale two-way slab specimens were constructed and tested under concentric load up to failure. One of the main objectives is to study the effect of reinforcement spacing with the same reinforcement ratio on the punching shear strength. In addition, two other parameters were considered, namely, slab depth, and compressive strength of concrete. The punching shear provisions of two code of practises CSA S806 (Canadian Standards 2012) and JSCE (JSCE et al. 1997) reasonably predicted the load capacity of GFRP reinforced concrete flat slab, whereas, ACI 440 (ACI Committee 440 2015) showed very conservative load capacity prediction. On the other hand, a dynamic explicit solver in nonlinear finite element (FE) modelling is used to analyse a connection of column to concrete flat slabs reinforced with GFRP bars in terms of ultimate punching load. All FE modelling was performed in 3D with the appropriate adoption of element size and mesh. The numerical and experimental results were compared in order to evaluate the developed FE, aiming to predict the behaviour of punching shear in the concrete flat slab. In addition, a parametric study was created to explore the behaviour of GFRP reinforced concrete flat slab with three parameters, namely, concrete strength, shear load perimeter to effective depth ratio, and, flexural reinforcement ratio. It was concluded that the developed models could accurately capture the behaviour of GFRP reinforced concrete flat slabs subjected to a concentrated load. Artificial Neural Networks (ANN) is used in this research to predict punching shear strength, and the results were shown to match more closely with the experimental results. A parametric study was performed to investigate the effects of five parameters on punching shear capacity of GFRP reinforced concrete flat slab. The parametric investigation revealed that the effective depth has the most substantial impact on the load carrying capacity of the punching shear followed by reinforcement ratio, column perimeter, the compressive strength of the concrete, and, the elastic modulus of the reinforcement