Properties and applications of β‐glycosidase from Bacteroides thetaiotaomicron that specifically hydrolyses isoflavone glycosides

To modify the glycan part of glycosides, the gene encoding β‐glycosidase was cloned from Bacteroides thetaiotaomicron VPI‐5482. The cloned gene, bt_1780, was expressed in Escherichia coli MC1061 and the expressed enzyme was purified using Ni‐NTA affinity chromatography. The purified enzyme, BTBG, showed optimal activity at 50 °C and pH 5.5. Interestingly, this enzyme did not have any hydrolysing activity on ordinary β‐linkage–containing substrates such as xylobiose, lactose and cello‐oligosaccharide, but specifically hydrolysed isoflavone glycosides such as daidzin, genistin and glycitin. Compared to a commercial beta glucosidase, BTBG selectively hydrolysed isoflavone glycosides in soybean extract mixture solution. These results suggest that BTBG may be a specialized enzyme for the hydrolysis of glycosides and that the substrate specificity of BTBG is applicable for the bioconversion of isoflavone glycosides in the food industry.     

Effects of extended dislocations on charge distribution in GaN epilayer

We have investigated the effect of extended dislocations (0.5-3 μm) on charge distribution in GaN epilayer grown by metalorganic chemical vapor deposition on (0001) sapphire using atomic force microscopy (AFM) and scanning surface potential microscopy (SSPM). It has been observed for the surface at the extended dislocations present in undoped GaN film to be negatively charged showing 0.04-0.2 V higher potential relative to regions that contain no dislocations. In addition to the higher potential at the dislocation core, the surrounding surfaces, including the edge of the dislocations, are also negatively charged in a symmetric way around the dislocations revealing crater-shaped higher potential regions (∼0.04 V) relative to surrounding dislocation-free area. The experimental results show that the protrusion-type of dislocation is also negatively charged and its potential is dependent on the size of dislocation.     

Attenuating Effects of Dieckol on Hypertensive Nephropathy in Spontaneously Hypertensive Rats

Hypertension induces renal fibrosis or tubular interstitial fibrosis, which eventually results in end-stage renal disease. Epithelial-to-mesenchymal transition (EMT) is one of the underlying mechanisms of renal fibrosis. Though previous studies showed that Ecklonia cava extracts (ECE) and dieckol (DK) had inhibitory action on angiotensin (Ang) I-converting enzyme, which converts Ang I to Ang II. It is known that Ang II is involved in renal fibrosis; however, it was not evaluated whether ECE or DK attenuated hypertensive nephropathy by decreasing EMT. In this study, the effect of ECE and DK on decreasing Ang II and its down signal pathway of angiotensin type 1 receptor (AT1R)/TGFβ/SMAD, which is related with the EMT and restoring renal function in spontaneously hypertensive rats (SHRs), was investigated. Either ECE or DK significantly decreased the serum level of Ang II in the SHRs. Moreover, the renal expression of AT1R/TGFβ/SMAD was decreased by the administration of either ECE or DK. The mesenchymal cell markers in the kidney of SHRs was significantly decreased by ECE or DK. The fibrotic tissue of the kidney of SHRs was also significantly decreased by ECE or DK. The ratio of urine albumin/creatinine of SHRs was significantly decreased by ECE or DK. Overall, the results of this study indicate that ECE and DK decreased the serum levels of Ang II and expression of AT1R/TGFβ/SMAD, and then decreased the EMT and renal fibrosis in SHRs. Furthermore, the decrease in EMT and renal fibrosis could lead to the restoration of renal function. It seems that ECE or DK could be beneficial for decreasing hypertensive nephropathy by decreasing EMT and renal fibrosis.     

Synergistic Flame Retardant Effects of Carbon Nanotube-Based Multilayer Nanocoatings

In an effort to develop highly functionalized flame retardant materials, hybrid nanocoatings are prepared by alternately depositing a positively charged polyaniline (PANi) and negatively charged montmorillonite (MMT) using the layer-by-layer (LbL) assembly technique. Carbon nanotubes (CNTs) are employed in polymer nanocomposites as effective reinforcement, where nanotubes are stabilized in MMT aqueous solution. The 3D structure and high density of CNTs deposited in the PANi/CNTs-MMT multilayers produce thicker and heavier coatings in comparison to the LbL assemblies without CNTs. Vertical and horizontal flame testing show that the incorporation of CNTs improves fire resistance. Additionally, cone calorimetry reveals that stacking two nanomaterials (MMT and CNTs) in a single coating shows a significant reduction in peak heat release rate (up to 51%), total smoke release (up to 47%), and total heat release (up to 37%) for the polyurethane foam. The enhancement of flame retardancy is attributed to a synergistic effect; MMT serves as a physical barrier that retards the diffusion of heat and gas. The addition of CNTs strengthens the thermal stability and high char yield. These results, coupled with the simplicity with which the LbL deposition is applied, present a viable alternative to halogen-free flame retardant nanocoatings to natural and synthetic fibers.     

Correlation of vapor-liquid equilibria for binary mixtures with free energy-based equation of state mixing rules: Carbon dioxide with alcohols, hydrocarbons, and several other compounds

The correlation of vapor-liquid equilibrium data for high-pressure carbon dioxide systems is of interest in a number of industrial applications, including supercritical extraction. Here, we consider the correlation of data for 12 binary systems of carbon dioxide separately with alcohols, with hydrocarbons, and with acetone, benzene, and water. The Wong-Sandler (W-S) and modified Huron — Vidal first order (MHV1) free energy-based equation of state mixing rules (the W-S and MHV1 models) were used in the calculations. Both combined equation of state+free energy models generally resulted in good correlations of the experimental data over wide ranges of temperature and pressure with temperature — independent parameters. However, for the carbon dioxide+water system, the W-S model produced an 11% average absolute deviation in pressure, while no parameter that resulted in an AAD in pressure of less than 20% could be found for the MHV1 model.     

Transaction Management for Flash Media Databases in Portable Computing Environments

Flash memory is becoming a major database storage in building embedded systems or portable devices because of its non-volatile, shock-resistant, power-economic nature, and fast access time for read operations. Flash memory, however, should be erased before it can be rewritten and the erase and write operations are very slow as compared to main memory. Due to this drawback, traditional database management schemes are not easy to apply directly to flash memory database for portable devices. Therefore, we improve the traditional schemes and propose a new scheme called flash two phase locking (F2PL) scheme for efficient transaction processing in a flash memory database environment. F2PL achieves high transaction performance by exploiting the notion of the alternative version coordination which allows previous version reads and efficiently handles slow write/erase operations in lock management processes. We also propose a simulation model to show the performance of F2PL. Based on the results of the performance evaluation, we conclude that F2PL scheme outperforms the traditional schemes.     

Human tracking and silhouette extraction for human–robot interaction systems

In this study, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with a pan-tilt stereo camera, so that it can assist in gesture and gait recognition in the field of Human–Robot Interaction (HRI). The proposed system consists of three modules: detection, tracking and silhouette extraction. These modules are robust to camera movements, and they work interactively in near real-time. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift-based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap was estimated in advance and then effectively incorporated into the graph-cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data, and it was shown to detect and track multiple people very well and also produce high-quality silhouettes.

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

Single-mode GaAs/AIGaAs W waveguides with a low propagation loss

We have designed a low-loss single-mode GaAs/AIGaAs optical waveguide with a symmetric five-layer heterostructure at the 1.31-μm wavelength by use of an effective-index method. Waveguides with a W-shaped refractive-index profile have been grown by use of a metallo-organic chemical vapor deposition technique and fabricated with a chemical wet-etching method. Propagation loss has been measured by use of the Fabry-Perot resonance method and a sequential-cleaving experiment. The measured loss is as low as 0.19 dB/cm for waveguides with 2.3-μm thickness and 4.3-μm width, which is comparable to the lowest-loss semiconductor waveguides yet reported. These waveguides could be used to make low-loss modulators for guided-wave devices.