Alcohol oxidation reactions are widely used for the preparation of aldehydes and ketones. The electrolysis of alcohols to carbonyl compounds have been underutilized owing to low efficiency. Herein, ...we report an electrochemical oxidation of various alcohols in a continuous-flow reactor without external oxidants, base or mediators. The robust electrochemical oxidation is performed for a variety of alcohols with good functional group tolerance, high efficiency and atom economy, whereas mechanistic studies support the benzylic radical intermediate formation and hydrogen evolution. The electrochemical oxidation proves viable on diols with excellent levels of selectivity for the benzylic position.
The expanding energy demand, surging oil prices, depleting oil reserves, environmental pollution and climate change problems associated with the utilization of fossil fuels have revived interest to ...find out clean alternative fuels. Methanol is one of the most competitive alternative fuels due to its liquid nature, high oxygen contents, and high octane number and could produce from renewable sources. In this review, recent engine experimental and computational studies concerned with methanol fumigation on diesel engine were summarized. Technical and safety issue such as physical and chemical effect, environmental and health risk associated with the use of this technology were discussed. Modeling and simulation, engine performance and emissions, and recent advanced concept for methanol fumigated diesel engine were then reviewed respectively. At the beginning, the chemical and physical effect by the addition of methanol on the diesel fuels combustion were analyzed. The results showed that the fumigation of methanol could significantly prohibit the formation of PAHs. Then, for engine experiments, the effect of methanol fumigation on performance, combustion and emission characteristics of DMDF diesel engines were analyzed. It is examined that the fumigation of methanol fuel could reduce diesel engine emissions without adverse impacts on the performance of diesel engines. Further, new engine concepts such as RCCI operated with methanol fumigated diesel engine has also been summarized. Finally, this article puts forward some suggestions for the researches of diesel methanol dual fuel engine in the future.
Purpose
Recent evidence has demonstrated that the gut microbiota plays a critical role in the treatment of obesity and other metabolic dysfunctions. Ginger (
Zingiber officinale
Roscoe), one of the ...most commonly used spices and dietary supplements, has been shown to exert beneficial effects against obesity and related disorders. However, to date, the mechanisms linking these effects to the gut microbiota remain unclear. This study aims to investigate the relationship between the gut microbiota and the metabolic adaptations resulting from ginger supplementation in mice.
Methods
Four groups of mice were fed a normal chow diet (NCD) or a high-fat diet (HFD) with or without ginger supplementation for 16 weeks. Lipid profiles, proinflammatory cytokines, glucose tolerance, microbiota composition and short-chain fatty acid (SCFA) concentrations were analyzed at the end of the experiment. In addition, microbiota-depleted mice were transplanted with the fecal microbiota of mice fed a HFD or mice fed a HFD along with ginger supplementation. Glucose tolerance and microbiota composition were assessed after a 8-week fecal microbiota transplantation (FMT).
Results
We observed marked decreases in body weight, liver steatosis, and low-grade inflammation as well as amelioration of insulin resistance in the HFD-fed mice treated with ginger. Furthermore, ginger supplementation modulated the gut microbiota composition and increased species belonging to the
Bifidobacterium
genus and SCFA-producing bacteria (
Alloprevotella
and
Allobaculum
), along with increases in fecal SCFA concentrations. The FMT experiment showed anti-obesity and microbiota-modulating effects similar to those observed in the oral ginger-feeding experiment.
Conclusions
This study suggests that modulation of the gut microbiota as a result of ginger supplementation has a therapeutic effect on obesity in mice.
A smart home network will support various smart devices and applications, e.g., home automation devices, E-health devices, regular computing devices, and so on. Most devices in a smart home access ...the Internet through a home gateway (HGW). In this paper, we propose a software-defined-network (SDN)-HGW framework to better manage distributed smart home networks and support the SDN controller of the core network. The SDN controller enables efficient network quality-of-service management based on real-time traffic monitoring and resource allocation of the core network. However, it cannot provide network management in distributed smart homes. Our proposed SDN-HGW extends the control to the access network, i.e., a smart home network, for better end-to-end network management. Specifically, the proposed SDN-HGW can achieve distributed application awareness by classifying data traffic in a smart home network. Most existing traffic classification solutions, e.g., deep packet inspection, cannot provide real-time application awareness for encrypted data traffic. To tackle those issues, we develop encrypted data classifiers (denoted as DataNets) based on three deep learning schemes, i.e., multilayer perceptron, stacked autoencoder, and convolutional neural networks, using an open data set that has over 200 000 encrypted data samples from 15 applications. A data preprocessing scheme is proposed to process raw data packets and the tested data set so that DataNet can be created. The experimental results show that the developed DataNets can be applied to enable distributed application-aware SDN-HGW in future smart home networks.
Glycyrrhizin (GL), the principal sweet-tasting bioactive ingredient of licorice (root of
Glycyrrhiza glabra
), shows poor oral absorption and gut microbial transformation of GL to glycyrrhetinic acid ...(GA) plays a major role for its multiple pharmacological effects. Co-administration of GL-hydrolyzing bacteria appears to be a feasible strategy to enhance GA exposure. This study reported a gut bacterial strain
Staphylococcus pasteuri
3I10 which exhibited moderate
p
-nitrophenyl-β-D-glucuronide (PNPG)-hydrolyzing activity but low GL deglucuronidation activity in its crude lysate. The
gus
gene encoding
S. pasteuri
3I10 β-glucuronidase was successfully cloned and overexpressed in
Escherichia coli
BL21(DE3). The purified β-glucuronidase (SpasGUS) was 71 kDa and showed optimal pH and temperature at 6.0 and 50 °C, respectively. Comparing to
E. coli
β-glucuronidase (EcoGUS), SpasGUS displayed lower velocity and affinity to PNPG hydrolysis (
V
max
16.1 ± 0.9 vs 140.0 ± 4.1 μmolmin
−1
mg
−1
;
K
m
469.4 ± 73.4 vs 268.0 ± 25.8 μM), but could selectively convert GL to GA at much higher efficiency (
V
max
0.41 ± 0.011 vs 0.005 ± 0.002 μmolmin
−1
mg
−1
;
K
m
116.9 ± 15.4 vs 53.4 ± 34.8 μM). Molecular docking studies suggested SpasGUS formed hydrogen bond interactions with the glucuronic acids at Asn414, Glu415 and Leu450, and Val159, Tyr475, Ala368, and Phe367 provided a hydrophobic environment for enhanced activity. Two special substrate interaction loops near the binding pocket of SpasGUS (loop 1 β-glucuronidase) may account for the selective and efficient bioconversion of GL to GA, predicting that loop 1 β-glucuronidases show high possibility in processing GL than mini-loop 1 and loop 2 β-glucuronidases. These findings support potential applications of SpasGUS in cleaving GL to facilitate GA production in vivo or in pharmaceutical industry.
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•PVC-Li1.6Mn1.6O4 precursor membranes were prepared by the solvent exchange method.•The lithium ion-sieve membrane has high selectivity for Li+ with coexisting ions.•It exhibits high ...stability after eight repeated adsorption–desorption cycles.•The thickness of membrane effects on adsorption capacity and mechanical strength.
A series of PVC-Li1.6Mn1.6O4 precursor membranes were prepared by the solvent exchange method using spinel-type lithium manganese oxide powder (Li1.6Mn1.6O4) as the precursor, poly(vinyl chloride) (PVC) as the binder, and N,N-dimethyl acetamide (DMAc) as solvent. The Li+ of the precursor membrane was extracted by treated with HCl solution to obtain PVC-H1.6Mn1.6O4 lithium ion-sieve membrane adsorbent. The preparation conditions were investigated by changing the concentration of PVC and Li1.6Mn1.6O4, and the thickness of liquid film. The structure, morphology and adsorption properties of PVC-H1.6Mn1.6O4 lithium ion-sieve membrane were carried out by scanning electron microscope and atomic absorption spectrophotometer. The adsorption capacity depended on the preparation conditions. The membrane prepared with concentration of 10wt% PVC and 15wt% Li1.6Mn1.6O4 in DMAc and liquid film thickness of 0.30mm is optimum for the adsorption of Li+ from aqueous solution. The thickness of membrane prepared under above conditions is about 0.1mm. Repeated adsorption–desorption test indicates that the PVC-H1.6Mn1.6O4 lithium ion-sieve membrane can be effectively regenerated with HCl solution and reused for Li+ adsorption without significant loss in the adsorption capacity. Li+ adsorption experiments confirm that the PVC-H1.6Mn1.6O4 lithium ion-sieve membrane possesses high selectivity for Li+ in the presence of Na+, K+, Ca2+ and Mg2+. According to the coefficients, the isothermal data correlated with the Langmuir model better than the Freundlich model, and the adsorption process follows a pseudo-second-order kinetic model.
Epidemiological studies about the effect of essential metal mixture on fasting plasma glucose (FPG) levels among elderly people are sparse. The object of this study was to examine the associations of ...single essential metals and essential metal mixture with FPG levels in Chinese community-dwelling elderly people.
The study recruited 2348 community-dwelling elderly people in total. Inductively coupled plasma-mass spectrometry was adopted to detect the levels of vanadium (V), selenium (Se), magnesium (Mg), cobalt (Co), calcium (Ca), and molybdenum (Mo) in urine. The relationships between single essential metals and essential metal mixture and FPG levels were evaluated by linear regression and Bayesian kernel machine regression (BKMR) models, respectively.
In multiple-metal linear regression models, urine V and Mg were negatively related to the FPG levels (β = − 0.016, 95 % CI: − 0.030 to − 0.003 for V; β = − 0.021, 95 % CI: − 0.033 to − 0.009 for Mg), and urine Se was positively related to the FPG levels (β = 0.024, 95 % CI: 0.014–0.034). In BKMR model, the significant relationships of Se and Mg with the FPG levels were also found. The essential metal mixture was negatively associated with FPG levels in a dose-response pattern, and Mg had the maximum posterior inclusion probability (PIP) value (PIP = 1.0000), followed by Se (PIP = 0.9968). Besides, Co showed a significant association with decreased FPG levels in older adults without hyperlipemia and in women.
Both Mg and Se were associated with FPG levels, individually and as a mixture. The essential metal mixture displayed a linear dose-response relationship with reduced FPG levels, with Mg having the largest contribution to FPG levels, followed by Se. Further prospective investigations are necessary to validate these exploratory findings.
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•Reduced urine Mg and elevated Se were associated with elevated FPG levels.•The mixture of six essential metals was negatively associated with FPG levels.•BKMR model was used to flexibly fit the combined effect of essential metal mixture.•Supplementing essential metals was benefit to maintain FPG levels in older adults.
This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and ...results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development of word vector in deep learning, we demonstrate the concept of “stock vector.” The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long short-term memory neural network (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, to vectorize the data, in a bid to forecast the stock via long short-term memory neural network. The experimental results show that the deep LSTM with embedded layer is better. Specifically, the accuracy of two models is 57.2 and 56.9%, respectively, for the Shanghai A-shares composite index. Furthermore, they are 52.4 and 52.5%, respectively, for individual stocks. We demonstrate research contributions in IMMT for neural network-based financial analysis.
As an organic optical fiber with a diameter comparable to or less than the wavelength of light, polymer nanofibers have been attracting increasing attention as a platform for manipulating light at ...the nanoscale. A variety of applications for polymer optical nanofibers, including waveguides, light sources and sensors, have been reported in recent years. In this article, the recent progress in the field of polymer optical nanofibers is reviewed in terms of their fabrication, characterization and applications. In particular, we focus on functionalized polymer nanofibers doped with functional materials, such as dye molecules, noble metal nanoparticles, quantum dots and rare earth ions, which greatly expand their capabilities of generating, propagating, converting and modulating light at the nanoscale.
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