C-terminal His-tagging results in substrate specificity changes of the thioesterase I from Escherichia coli

The biochemical properties of Escherichia coli thioesterase I, His-tagged (HT) on the C-terminal, were systematically analyzed and compared with that without the His-tag (WT). These two types of enzymes exhibit similar optimal temperature and pH dependence, but subtle differences were detected. Kinetic studies revealed that the k _car/JK _m values of the HT enzyme for the substrates palmitoyl-CoA and p-nitrophenyl dodecanoate were 36- and 10-fold lower than those of the WT, respectively. In contrast, HT had a fivefold increased catalytic efficiency for p-nitrophenyl acetate, and up to fourfold increases toward phenylalanine- and tyrosine-derived ester substrates, l-NBPNPE (N-carbobenzoxy-l-phenylalanine p-nitrophenyl ester) and l-NBTNPE (N-carbobenzoxyl-l-tyrosine p-nitrophenyl ester), respectively. For l-NBPNPE and l-NBTNPE, the increases were attributed to the higher k _cat values with little changes in K _m, whereas the increase for p-nitrophenyl acetate was mainly attributed to the lower K _m value. It is concluded that addition of six hydrophilic histidine residues on the C-terminus resulted in a change in substrate specificity of E. coli thioesterase I toward more hydrophilic substrates.     

Characteristics of the Transformation of Retained Austenite in Tempered Austempered Ductile Iron

Controlling the amount of retained austenite is a concern in austempered ductile iron formation. Retained austenite has a strong influence on austempered ductile iron properties, such as hardness and wear resistance. In this research, the characteristics of the transformation of retained austenite were investigated as a function of the number of tempering cycles. The hardness of the austempered ductile iron samples was measured, and the specific amount of retained austenite was analyzed by x-ray diffraction (XRD). Wear tests were conducted on a ball-on-flat sliding fixture. The tempering process was found to have no effect on the hardness of the austempered ductile iron samples. This may be due to retained austenite being partially converted into brittle quenched martensite during the tempering process. However, tougher tempered martensite was also formed from existing martensite. The two effects seemed to offset each other, and no significant differences occurred in overall hardness. XRD analysis showed that under the same austempering temperature and holding time, the amount of retained austenite decreased with additional tempering cycles. Also, with the same holding time and tempering cycles, less retained austenite was contained in the matrix at higher austempering temperatures. This was due to more high carbon content austenite and needle-like ferrite being present in the austempered ductile iron matrix. In addition, tempered austempered ductile iron exhibited significantly higher wear resistance as compared to traditionally treated ductile iron.     

Location of steel reinforcement in concrete using ground penetrating radar and neural networks

Ground-penetrating radar is becoming increasingly popular for use as a non-destructive assessment method for investigating reinforced concrete structures. The amount of data collected however can be very large and take a significant level of subjective experience to interpret. This study focuses upon the use of a neural network approach to automate and facilitate the post-processing of ground penetrating radar results. The radar data is reduced to a simplified data set by using an edge detection routine. Signal reflections from reinforcing bars displaying a hyperbolic image format are detected using a multi-layer perceptron (MLP) network with a single hidden layer containing 8 nodes to recognise a simplified hyperbolic shape. Training and testing of the network was carried out making use of an emulsion analogue tank, simulating the properties of concrete, and using real concrete specimens. The results showed that the use of a MLP neural network approach could be quite effective in automating the identification and location of embedded steel reinforcing bars from a radar investigation. Accurate estimation of depth, or cover, requires a reliable knowledge of the dielectric properties of the concrete, and recent work using a specially-developed wideband horn antenna for direct determination of in situ properties is also outlined.     

Pyrolysis GC-FTIR studies of a LOVA propellant formulation series

Abstract

Pyrolysis - gas chromatography - Fourier transform infrared (P-GC-FTIR) spectroscopy has been used to examine the pyrolysis product distributions of a LOVA propellant formulation series. The propellants in the series were composed of oxidizer (HMX or RDX), polymeric binder (GAP, HTPB, BAMO/AMMO, or BAMO/THF), and plasticizer (BTTN or TMETN). Trends in product distribution as a function of formulation, as well as a correlation between pyrolysis products and performance data, were identified. In general, pyrolysis product distributions were found to be most strongly affected by the presence and type of plasticizer.     

Differential hydrolysis of alk(en)yl cysteine sulphoxides by alliinase in onion macerates: flavour implications

Intact cells of Allium spp have no odour, but when cells are disrupted, the enzyme alliinase (EC 4.4.1.4) hydrolyses the S-alk(en)yl sulphoxides (ACSOs) to produce pyruvate, ammonia and the many volatile sulphur compounds associated with flavour and odour. In onions, there are three main sulphoxides: methyl, propyl and 1-propenyl, which gives rise to onion's tear-producing effect. The relative proportions of these are a factor in determining the subsequent flavour. γ-Glutamyl peptides, which are biosynthetic intermediates to ACSOs, are also present in significant amounts, but their contribution to flavour is not known. There is little work on the reaction of alliinase in vivo. The hydrolysis of ACSOs by alliinase was studied in rapidly macerated bulbs at intervals between 5 s and 2 h. ACSO and γ-glutamyl peptide levels were measured by HPLC, and pyruvate levels were measured spectrophotometrically. Although the hydrolysis of propenyl cysteine sulphoxide (PrenCSO) was immediate and almost 100% between 5 and 20 s after bulb maceration, the hydrolysis of propyl cysteine sulphoxide (PCSO) and methyl cysteine sulphoxide (MCSO) was incomplete. After 5 s maceration, about 50% PCSO and MCSO remained and thereafter no further hydrolysis occurred. The levels of ACSOs and the extent to which they were hydrolysed were dependent on the cultivar and sulphur environment in which it was grown. The addition of pyridoxal phosphate cofactor enhanced the extent of MCSO and PCSO hydrolysis. Additional purified alliinase per se had no effect. Substantial hydrolysis of γ-glutamyl peptides also occurred after maceration, and this was unexpected. Levels of pyruvate product were between 15 and 25% of the expected amount from ACSO hydrolysis assuming a stoichiometric relationship. The incomplete hydrolysis of MCSO and PCSO, enhancement of activity by additional pyridoxal phosphate and non-stoichiometric production of pyruvate are evidenced that reaction inhibition of alliinase may be occurring in onion macerates. © 1998 Society of Chemical Industry.     

Integrated biocatalytic process for trehalose production and separation from rice hydrolysate using a bioreactor system

Rice starch can be hydrolyzed into maltose for trehalose bioconversion by enzymatic methods. In this study, we have successfully established an efficient production system for our recombinant PTTS in large scale. Three bio-treatments were developed to simplify the separation and purification of trehalose from complex rice saccharified liquid. The trehalose conversion rate of 64.63 ± 4.05% at 30 °C can be reached using rice hydrolysate as the substrate in a 5 l fermentor system. By 1% of raw material koji fermentation, the highest concentration of bioethanol (3.61 ± 0.07%) was obtained at 30 °C for 36 h. After 12 h of reaction time, the gluconic acid (24.47 ± 0.33 mM) was successfully produced by glucose oxidase (40 U/g rice) using residual glucose as a substrate. After the batch/continuous ionic exchange process, the trehalose can be successfully separated, crystallized and identified as 92.6 ± 0.02% purity and 94.2% of the recovery yield, respectively.     

Fluorimetric Nerve Gas Sensing Based on Pyrene Imines Incorporated into Films and Sub‐Micrometer Fibers

The chemical sensing of nerve gas agents has become an increasingly important goal due to the 1995 terrorist attack in a Tokyo subway as well as national security concerns in regard to world affairs. Chemical detection needs to be sensitive and selective while being facile, portable, and timely. In this paper, a sensing approach using a pyrene imine molecule is presented that is fluorimetric in response. The detection of a chloro‐Sarin surrogate is measured at 5 ppmv in less than 1 second and is highly selective towards halogenated organophosphates. The pyrene imine molecule is incorporated into polystyrene films as well as micrometer and sub‐micrometer fibers. Using both a direct drawing approach and electrospinning, micrometer and nanofibers can be easily manufactured. Applications for functional sensing micrometer and nanofibers are envisioned for optical devices and photonics in addition to solution and airflow sensing devices.     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.