Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite

From the standpoints of both basic research and biotechnology, there is considerable interest in reaching a clearer understanding of the diversity of biological mechanisms employed during lignocellulose degradation. Globally, termites are an extremely successful group of wood-degrading organisms and are therefore important both for their roles in carbon turnover in the environment and as potential sources of biochemical catalysts for efforts aimed at converting wood into biofuels. Only recently have data supported any direct role for the symbiotic bacteria in the gut of the termite in cellulose and xylan hydrolysis. Here we use a metagenomic analysis of the bacterial community resident in the hindgut paunch of a wood-feeding 'higher' Nasutitermes species (which do not contain cellulose-fermenting protozoa) to show the presence of a large, diverse set of bacterial genes for cellulose and xylan hydrolysis. Many of these genes were expressed in vivo or had cellulase activity in vitro, and further analyses implicate spirochete and fibrobacter species in gut lignocellulose degradation. New insights into other important symbiotic functions including H2 metabolism, CO2-reductive acetogenesis and N2 fixation are also provided by this first system-wide gene analysis of a microbial community specialized towards plant lignocellulose degradation. Our results underscore how complex even a 1-microl environment can be.     

Sequential interactions with Sec23 control the direction of vesicle traffic

How the directionality of vesicle traffic is achieved remains an important unanswered question in cell biology. The Sec23p/Sec24p coat complex sorts the fusion machinery (SNAREs) into vesicles as they bud from the endoplasmic reticulum (ER). Vesicle tethering to the Golgi begins when the tethering factor TRAPPI binds to Sec23p. Where the coat is released and how this event relates to membrane fusion is unknown. Here we use a yeast transport assay to demonstrate that an ER-derived vesicle retains its coat until it reaches the Golgi. A Golgi-associated kinase, Hrr25p (CK1δ orthologue), then phosphorylates the Sec23p/Sec24p complex. Coat phosphorylation and dephosphorylation are needed for vesicle fusion and budding, respectively. Additionally, we show that Sec23p interacts in a sequential manner with different binding partners, including TRAPPI and Hrr25p, to ensure the directionality of ER-Golgi traffic and prevent the back-fusion of a COPII vesicle with the ER. These events are conserved in mammalian cells.     

高性能高应变InGaAs量子阱脊型波导半导体激光器

介绍了利用分子束外延系统生长高应变InGaAs量子阱半导体激光器材料, 采用脉冲阳极氧化技术制作脊型波导半导体激光器. 4 μm条宽脊型波导半导体激光器在室温下单面连续输出功率达到50 mW. 腔长600 μm时, 器件阈值电流密度为300 A/cm2. 在100 mA电流下, 激光器的峰值波长为1.19 μm, 激光器的最大斜率效率为0.45 W/A. 在20℃至100℃温度下, 激光器的特征温度为129 K.     

Modeling the effects of ore properties on water recovery in the thickening process

A better understanding of solid-liquid separation would assist in improving the thickening performance and perhaps water recovery as well. The present work aimed to develop an empirical model to study the effects of ore properties on the thickening process based on pilot tests using a column. A hydro-cyclone was used to prepare the required samples for the experiments. The model significantly predicted the experimental underflow solid content using a regression equation at a given solid flux and bed level for different samples, indicating that ore properties are the effective parameters in the thickening process. This work confirmed that the water recovery would be increased about 5% by separating the feed into two parts, overflow and underflow, and introducing two different thickeners into them separately. This is duo to the fact that thickeners are limited by permeability and compressibility in operating conditions.     

INTELLIGENT SECURITY SYSTEMS ENGINEERING FOR MODELING FIRE CRITICAL INCIDENTS： TOWARDS SUSTAINABLE SECURITY

An intelligent security systems engineering approach is used to analyze fire and explosive critical incidents, a growing concern in urban communities. A feed-forward back-propagation neural network models the damages arising from these critical incidents. The overall goal is to promote fire safety and sustainable security. The intelligent security systems engineering prediction model uses a fully connected multilayer neural network and considers a number of factors related to the fire or explosive incident including the type of property affected, the time of day, and the ignition source. The network was trained on a large number of critical incident records reported in Toronto, Canada between 2000 and 2006. Our intelligent security systems engineering approach can help emergency responders by improving critical incident analysis, sustainable security, and fire risk management.     

Evaluation and modification of inclusion characteristics in HK40 heatresistant cast steel

Specifications of inclusions such as the type, morphology, number, composition, size, and place of inclusions in HK40 heat-resistant casting steel were studied by optical microscopy and scanning electron microscopy equipped with an energy-dispersive spectroscopy system. The effects of calcium silicide (CaSi) addition on these characteristics were evaluated at two pouring temperatures of 1420℃ and 1470℃. It was found that most of the appeared inclusions were in the type of chromium oxide. CaSi addition had a significant effect on the morphological modification, size and distribution of inclusions as well as changing the composition to oxide-sulfide compounds.     

A self-similar local neuro-fuzzy model for short-term demand forecasting

This paper proposes a selfsimilar local neurofuzzy （SSLNF） model with mutual informati onbased input selection algorithm for the shortterm electricity demand forecasting. The proposed self similar model is composed of a number of local models, each being a local linear neurofuzzy （LLNF） model, and their associated validity functions and can be interpreted itself as an LLNF model. The proposed model is trained by a nested local liner model tree （NLOLIMOT） learning algorithm which partitions the input space into axisorthogonal subdomains and then fits an LLNF model and its associated validity function on each subdomain. Furthermore, the proposed approach allows different input spaces for rule premises （validity functions） and consequents （local models）. This appealing property is employed to assign the candidate input variables （i.e., previous load and temperature） which influence shortterm electricity demand in linear and nonlinear ways to local models and validity functions, respectively. Numerical results from shortterm load forecasting in the New England in 2002 demonstrated the accuracy of the SSLNF model for the STLF applications.     

Influence of alkaline treatment and fiber loading on the physical and mechanical properties of kenaf/polypropylene composites for variety of applications

Due to current trend and increasing interest towards natural based fiber products, Kenaf (Hibiscus cannabinus) fibers have been used for the developments of many products. Therefore, Kenaf fiber-reinforced composites have been widely used in engineering and industrial applications. The present work deals with the fabricating and characterization of untreated and treated kenaf/polypropylene (PP)-reinforced composites. Composites of PP reinforced with treated and untreated kenaf fibers were fabricated using the injection molding technique. Different fiber loadings of 10, 20, 30, 40, 50 wt% treated and untreated kenaf composites were also prepared. X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and thermo gravimetric analysis (TGA) were performed on the treated, untreated kenaf fibers and kenaf/PP composites. Moreover, the alkaline-treated kenaf composites exhibit better physical, morphological, and mechanical properties because of the compatibility of kenaf with PP. However, variations in tensile and flexural properties depend on treatment and kenaf fiber contents. The percentage increase in the mechanical properties of the treated kenaf/PP composites relative to that of PP was also measured. In addition, 40 wt% kenaf fiber loading resulted in higher mechanical properties. By contrast, kenaf/PP composite with 50% fiber loading was not successfully prepared because of improper mixing and the burning of kenaf fibers in the PP matrix. To conclude, 40% kenaf/PP composites with superior physical and mechanical properties may be used in variety of applications such as automotive, sports, construction, animal bedding, and mass production industries.