Reclamation of Marine Chitinous Materials for Chitosanase Production via Microbial Conversion by Paenibacillus macerans

[[abstract]]: Chitinous materials from marine byproducts elicit great interest among biotechnologists for their potential biomedical or agricultural applications. In this study, four kinds of marine chitinous materials (squid pens, shrimp heads, demineralized shrimp shells, and demineralized crab shells) were used to screen the best source for producing chitosanase by Paenibacillus macerans TKU029. Among them, the chitosanase activity was found to be highest in the culture using the medium containing squid pens as the sole carbon/nitrogen (C/N) source. A chitosanase which showed molecular weights at 63 kDa was isolated from P. macerans cultured on a squid pens medium. The purified TKU029 chitosanase exhibited optimum activity at 60 ◦C and pH 7, and was stable at temperatures under 50 ◦C and pH 3-8. An analysis by MALDI-TOF MS revealed that the chitosan oligosaccharides (COS) obtained from the hydrolysis of water-soluble chitosan by TKU029 crude enzyme showed various degrees of polymerization (DP), varying from 3–6. The obtained COS enhanced the growth of four lactic acid bacteria strains but exhibited no effect on the growth of E. coli. By specialized growth enhancing effects, the COS produced from hydrolyzing water soluble chitosan with TKU029 chitinolytic enzymes could have potential for use in medicine or nutraceuticals.[[sponsorship]]MOST[[notice]]補正完

EEG-based driver fatigue detection using hybrid deep generic model

© 2016 IEEE. Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG

Improving EEG-based driver fatigue classification using sparse-deep belief networks

© 2017 Chai, Ling, San, Naik, Nguyen, Tran, Craig and Nguyen. This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively

Bioactivity-guided purification of novel herbal antioxidant and anti-NO compounds from Euonymus laxiflorus Champ

[[abstract]]Euonymus laxiflorus Champ., a medicinal herb collected in Vietnam, has been reported to show several potent bioactivities, including anti-NO, enzyme inhibition, hypoglycemic and antidiabetic effects. Recently, the antioxidant activity of Euonymus laxiflorus Champ. trunk bark (ELCTB) has also been reported. However, the active antioxidant and anti-NO constituents existing in ELCTB have not been reported in the literature. The objective of this study was to purify the active antioxidants from ELCTB and investigate the anti-NO effect of the major constituents. Twenty-two phenolics isolated from ELCTB, including 12 compounds newly isolated in this study (1–12) and 10 constituents obtained from our previous work, were evaluated for their antioxidant activity. Of these, 12 compounds (4–6, 9, 13–15, 18–22) showed a potent antioxidant capacity (FRS50 = 7.8–58.11 µg/mL), in comparison to α-tocopherol (FRS50 = 23 µg/mL). In the anti-NO activity tests, Walterolactone A (1a) and B (1b) β-D-glucopyranoside (13) demonstrated the most effective and comparable activity to that of quercetin with max inhibition and IC50 values of 100%, 1.3 µg/mL, and 100%, 1.21 µg/mL, respectively. The crude extract and its major compounds showed no cytotoxicity on normal cells. Notably, three constituents (9, 11, and 12) were identified as new compounds, another three constituents, including 1, 7, and 8, were found to be new natural products, constituents 9 and 13 were determined to be new antioxidants, and compound 13 was reported to have novel potent anti-NO activity for the first time. The results of this study contribute to the enrichment of new natural products and compounds, as well as the novel biological activity of constituents isolated from Euonymus laxiflorus Champ. The current study also indicates ELCTB as a rich natural source of active phenolics. It is suggested that ELCTB could be developed as a health food with promising antioxidant and anti-NO effects, as well as other beneficial biological activities.[[sponsorship]]科技部[[notice]]補正完

A nutrient method for cutivation of macroalgae Ulva papenfussii

Macroalgae species of the genus Ulva are widely distributed in the wild. Many species of this genus has been used as food as an attractive material for the study of materials, fuels, food etc.. In this paper, we are focusing on nutrient method for cultivation of Ulva papenfussii and A nutrient source for cultivation of U. papenfussii was also investigated with the perspective of utilizing the produced biomass for feed. U. papenfussii is fragmented into 1 × 1 cm size, then it keep in Ulva extract of 0.1 g/l concentrate for 7 days. Then continue to keep fragments in the following conditions: 20 ml/l of PES medium, 700 μmol photon/m2/s of light, 25oC of temperature, 3% of salinity, 28 days of time. Under this condition the productivity U. papenfussii was 17.8 g/l of weight and its growth rate was 4.3–6.5% day. Nutritional cultivation is successful for U. papenfussii speceies, which is of great importance to study the potential of producing seaweed varieties like blades for commercial application of seaweed species.

Production of Sucrolytic Enzyme by Bacillus licheniformis by the Bioconversion of Pomelo Albedo as a Carbon Source

[[abstract]]Recently, there has been increasing use of agro-byproducts in microbial fermentation to produce a variety of value-added products. In this study, among various kinds of agro-byproducts, pomelo albedo powder (PAP) was found to be the most effective carbon source for the production of sucrose hydrolyzing enzyme by Bacillus licheniformis TKU004. The optimal medium for sucrolytic enzyme production contained 2% PAP, 0.75% NH4NO3 , 0.05% MgSO4 , and 0.05% NaH2PO4 and the optimal culture conditions were pH 6.7, 35 ◦C, 150 rpm, and 24 h. Accordingly, the highest sucrolytic activity was 1.87 U/mL, 4.79-fold higher than that from standard conditions using sucrose as the carbon source. The purified sucrolytic enzyme (sleTKU004) is a 53 kDa monomeric protein and belongs to the glycoside hydrolase family 68. The optimum temperature and pH of sleTKU004 were 50 ◦C, and pH = 6, respectively. SleTKU004 could hydrolyze sucrose, raffinose, and stachyose by attacking the glycoside linkage between glucose and fructose molecules of the sucrose unit. The Km and Vmax of sleTKU004 were 1.16 M and 5.99 µmol/min, respectively. Finally, sleTKU004 showed strong sucrose tolerance and presented the highest hydrolytic activity at the sucrose concentration of 1.2 M–1.5 M.[[sponsorship]]科技部[[notice]]補正完

Excitonic optical transitions characterized by Raman excitation profiles in single-walled carbon nanotubes

We examine the excitonic nature of the E33 optical transition of the individual free-standing index-identified (23, 7) single-walled carbon nanotube by means of the measurements of its radial-breathing-mode and G-mode Raman excitation profiles. We confirm that it is impossible to determine unambiguously the nature of its E33 optical transition (excitonic vs band to band) based only on the excitation profiles. Nevertheless, by combining Raman scattering, Rayleigh scattering, and optical absorption measurements on strictly the same individual (23, 7) single-walled carbon nanotube, we show that the absorption, Rayleigh spectra, and Raman excitation profiles of the longitudinal and transverse G modes are best fitted by considering the nature of the E33 transition as excitonic. The fit of the three sets of data gives close values of the transition energy E33 and damping parameter G33. This comparison shows that the fit of the Raman excitation profiles provides with good accuracy the energy and damping parameter of the excitonic optical transitions in single-walled carbon nanotubes

Design and Stability Analysis of a Super-Twisting Controller for a PS-FBC based Fuel Cell Module

Proton‐exchange membrane fuel cells have been established as a really promising technology, specially due to their high efficiency and scalability features, additionally to their low pollution emissions. In a typical topology, fuel cell module (FCM) is usually integrated into a hybrid power system, where the FCM is designed to satisfy the main power requirements and reduce the current ripple at the fuel cell output. In this framework, the aim of this paper is to analyze and design a sliding mode control (SMC) for a FCM based on an isolated phase‐shifted full bridge converter. This particular topology provides a high conversion ratio and attains a reduction of switching losses, which allow its application in low and medium power systems. From the control viewpoint, the proposed module represents a challenge due to the highly nonlinear behavior and wide operation range of the FCM, together with system parameter uncertainties and perturbations. To solve these issues, a second‐order sliding mode super‐twisting algorithm (STA) is proposed. As its main advantage, the STA reduces significantly the control chattering while preserving several features of conventional SMCs, such as robustness and finite time convergence. In order to analyze the zero dynamics stability, a Lyapunov study is proposed, taking advantage of its particular Liérnad‐type system structure. Finally, the designed algorithm is thoroughly analyzed and validated by computer simulation on a commercial 10‐kW FCM and compared to first‐order SMC.Fil: Anderson Azzano, Jorge Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Moré, Jerónimo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; ArgentinaFil: Puleston, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales. Universidad Nacional de La Plata. Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales; Argentin

Observation of whispering gallery modes in InGaN/GaN multi-quantum well microdisks with Ag plasmonic nanoparticles on Si pedestals

In this study, plasmonic freestanding InGaN/GaN multi-quantum well (MQW) microdisks were fabricated on Si (111) pedestals using wet chemical undercut etching, followed by decorating of Ag nanoparticles on microdisks to improve whispering gallery mode (WGM) resonance emission. The enhancement resulted from the plasmonic coupling effect between excitons in MQWs and localized surface plasmons of Ag. The radial resonance of WGMs from optically pumped microdisk cavities were observed in the photoluminescence spectra at a threshold optical pumping power density of 4.7 kW/cm2 with a WGM mode spacing of Δλ = 1.3 nm