The role of fluconazole in the regulation of fatty acid and unsaponifiable matter biosynthesis in Schizochytrium sp. MYA 1381.

BACKGROUND(#br)Schizochytrium has been widely used in industry for synthesizing polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA). However, unclear biosynthesis pathway of PUFAs inhibits further production of the Schizochytrium. Unsaponifiable matter (UM) from mevalonate pathway is crucial to cell growth and intracellular metabolism in all higher eukaryotes and microalgae. Therefore, regulation of UM biosynthesis in Schizochytrium may have important effects on fatty acids synthesis. Moreover, it is well known that UMs, such as squalene and β-carotene, are of great commercial value. Thus, regulating UM biosynthesis may also allow for an increased valuation of Schizochytrium.(#br)RESULTS(#br)To investigate the correlation of UM biosynthesis with fatty acids accumulation in Schizochytrium, fluconazole was used to block the sterols pathway. The addition of 60 mg/L fluconazole at 48 h increased the total lipids (TLs) at 96 h by 16% without affecting cell growth, which was accompanied by remarkable changes in UMs and NADPH. Cholesterol content was reduced by 8%, and the squalene content improved by 45% at 72 h, which demonstrated fluconazole’s role in inhibiting squalene flow to cholesterol. As another typical UM with antioxidant capacity, the β-carotene production was increased by 53% at 96 h. The increase of squalene and β-carotene could boost intracellular oxidation resistance to protect fatty acids from oxidation. The NADPH was found to be 33% higher than that of the control at 96 h, which meant that the cells had more reducing power for fatty acid synthesis. Metabolic analysis further confirmed that regulation of sterols was closely related to glucose absorption, pigment biosynthesis and fatty acid production in Schizochytrium.(#br)CONCLUSION(#br)This work first reported the effect of UM biosynthesis on fatty acid accumulation in Schizochytrium. The UM was found to affect fatty acid biosynthesis by changing cell membrane function, intracellular antioxidation and reducing power. We believe that this work provides valuable insights in improving PUFA and other valuable matters in microalgae

The impacts of carbon tax and complementary policies on Chinese economy

Under the pressure of global warming, it is imperative for Chinese government to impose effective policy instruments to promote domestic energy saving and carbon emissions reduction. As one of the most important incentive-based policy instruments, carbon tax has sparked a lively controversy in China. This paper explores the impact of carbon tax on Chinese economy, as well as the cushion effects of the complementary policies, by constructing a dynamic recursive general equilibrium model. The model can describe the new equilibrium for each sequential independent period (e.g. one year) after carbon tax and the complementary policies are imposed, and thus describe the long-term impacts of the policies. The simulation results show that carbon tax is an effective policy tool because it can reduce carbon emissions with a little negative impact on economic growth; reducing indirect tax in the meantime of imposing carbon tax will help to reduce the negative impact of the tax on production and competitiveness; in addition, giving households subsidy in the meantime will help to stimulate household consumptions. Therefore, complementary policies used together with carbon tax will help to cushion the negative impacts of carbon tax on the economy. The dynamic CGE analysis shows the impact of carbon tax policy on the GDP is relatively small, but the reduction of carbon emission is relatively large.Carbon tax China Complementary policy

Trichoderma reesei Cellulase Complex in Hydrolysis of Agricultural Waste of Grapefruit Peel and Orange Peel

Previous attempts have already been performed for the production of sugars and, later, bioproducts from orange peel using different Trichoderma reesei commercial cocktails in combination with other kinds of enzymes. In this study, the feasibility of simple pretreatments combined with enzymatic treatments was compared between grapefruit inner peel (GFIP), orange inner peel (OIP), grapefruit whole peel (GFWP), and orange whole peel (OWP). The four biomaterials were characterized with respect to the contents of cellulose and hemi-cellulose, elemental analysis, and Fourier transform infrared (FTIR) spectrometry. The 3,5-dinitrosalicylic acid assay demonstrated that GFIP and OIP produced 31.7% and 34.9% more reducing sugar than GFWP and OWP, respectively. Further investigation of the bioprocess showed the optimal conditions include the following: (i) a solid to liquid ratio of 4%, (ii) enzymatic activity of 0.075 U/mL, and (iii) reaction at 55 °C and a pH of 5.0. Moreover, the major products after cellulolytic hydrolysis were fructose, glucose, and cellobiose. This study provides an alternative and effective approach to extend the utilization of agricultural waste in the fields of food and energy

Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation

Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides

A Hybrid Early Warning Method for the Landslide Acceleration Process Based on Automated Monitoring Data

The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lishanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides

A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier

An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm

Preparation of MIP microspheres by precipitation polymerization with 1-phenyl-1-propanol as template

Conference Name:2nd International Conference on Advances in Materials and Manufacturing Processes, ICAMMP 2011. Conference Address: Guilin, China. Time:December 16, 2011 - December 18, 2011.University of Wollongong; Northeastern University; University of Science and Technology Beijing; Hebei Polytechnic UniversityMolecular imprinting polymer microspheres were prepared by precipitation polymerization with methacrylic acid as functional monomer and 1-phenyl-1-propanol as template. The effects of synthesis conditions, including the kind of solvent, polymerization temperature, the concentration of template and initiator, rotational speed and pre-polymerization time, on the characteristics of the molecularly imprinted polymer microspheres were studied. The rebinding experiments showed that the molecularly imprinted polymer microspheres synthesized by precipitation polymerization at 60掳C, with acetonitrile as solvent and the concentrations of both template and initiator of 0.01mol L-1, have good specific recognition and higher affinity capacity

Prediction Interval Estimation of Landslide Displacement Using Bootstrap, Variational Mode Decomposition, and Long and Short-Term Time-Series Network

Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model that combines Variational Mode Decomposition (VMD) and the Long and Short-Term Time-Series Network (LSTNet). The bootstrap algorithm is then used to estimate the Prediction Intervals (PIs) to quantify the uncertainty of the proposed model. First, the cumulative displacements are decomposed into trend displacement, periodic displacement, and random displacement using the VMD with the minimum sample entropy constraint. The feature factors are also decomposed into high-frequency components and low-frequency components. Second, this study uses an improved polynomial function fitting method combining the time window and threshold to predict trend displacement and uses feature factors obtained by grey relational analysis to train the LSTNet networks and predict periodic and random displacements. Finally, the predicted trend, periodic, and random displacement are summed to the predicted cumulative displacement, while the bootstrap algorithm is used to evaluate the PIs of the proposed model at different confidence levels. The proposed model was verified and evaluated by the case of the Baishuihe landslide in the Three Gorges reservoir area of China. The case results show that the proposed model has better point prediction accuracy than the three baseline models of LSSVR, BP, and LSTM, and the reliability and quality of the PIs constructed at 90%, 95%, and 99% confidence levels are also better than those of the baseline models

Tundish Cover Flux Thickness Measurement Method and Instrumentation Based on Computer Vision in Continuous Casting Tundish

Thickness of tundish cover flux (TCF) plays an important role in continuous casting (CC) steelmaking process. Traditional measurement method of TCF thickness is single/double wire methods, which have several problems such as personal security, easily affected by operators, and poor repeatability. To solve all these problems, in this paper, we specifically designed and built an instrumentation and presented a novel method to measure the TCF thickness. The instrumentation was composed of a measurement bar, a mechanical device, a high-definition industrial camera, a Siemens S7-200 programmable logic controller (PLC), and a computer. Our measurement method was based on the computer vision algorithms, including image denoising method, monocular range measurement method, scale invariant feature transform (SIFT), and image gray gradient detection method. Using the present instrumentation and method, images in the CC tundish can be collected by camera and transferred to computer to do imaging processing. Experiments showed that our instrumentation and method worked well at scene of steel plants, can accurately measure the thickness of TCF, and overcome the disadvantages of traditional measurement methods, or even replace the traditional ones