Detection of the recombinant proteins in single transgenic microbial cell using laser tweezers and Raman spectroscopy

Laser tweezers Raman spectroscopy (LTRS) has been used for the rapid detection of recombinant somatolactin protein produced in single Escherichia coli bacteria and Pichia pastoris yeast cell in the current study. A cDNA sequence encoding mature peptide of zebrafish somatolactin beta was inserted into two different expression vectors and transfected into E. coli or P. pastoris yeast cells. We measured Raman spectra of single E. coli cells at different culture times following the induction with isopropyl beta-d-1-thiogalactopyranoside, from which the amount of the generated somatolactin proteins was obtained by the projection of the entire cell's spectrum onto the spectrum of the pure somatolactin proteins or the dot product between these two spectral vectors. We found that the intensity of the somatolactin beta protein-associated spectra from single E. coli cells increased as the function of the culture time, which correlates with the accumulation of recombinant proteins inside the cells. This spectral observation was supported by evidence obtained by conventional methods of sodium dodecyl sulfate-polyacrylamide gel electrophoresis and Western blotting analyses. The increased intensities of recombinant protein-associated Raman bands were also observed in another expression system, P. pastoris yeast cells. These findings demonstrate that the LTRS is a useful method for rapid sensing of recombination production in single host microorganism in vivo.     

Cu-ZnO@Al2O3 hybrid nanoparticle with enhanced activity for catalytic CO2 conversion to methanol

A facile approach is demonstrated for the fabrication of Cu-ZnO-based hybrid nanostructures for the catalytic CO₂ conversion to methanol. The method combines colloid stabilization of Al₂O₃ nanoparticles (as support material) and controlled co-precipitation of Cu (active metal) and ZnO (promoter) onto the Al₂O₃ nanoparticles. Complementary approaches, including X-ray diffractometry, Brunauer-Emmett-Teller surface area analysis, N₂O pulse chemisorption, CO₂-based temperature-programmed desorption, transmission electron microscopy coupled with energy dispersive spectroscopy, inductively coupled plasma optical emission spectrometry and thermal gravimetric analysis are employed for the characterization of catalyst materials. The results show a successful synthesis of ultrafine Cu-ZnO nanocrystallites deposited on the Al₂O₃ nanoparticle clusters (Cu-ZnO@Al₂O₃). Hybridization with Al₂O₃ nanoparticles enhanced metal dispersion and number of basic sites of the Cu-ZnO-based nanocatalyst. Aminosilane-based surface functionalization on the Al₂O₃ nanoparticle increased metal surface area in the hybrid nanostructure. The CO₂ conversion catalyzed by the synthesized Cu-ZnO@Al₂O₃ was shown to be proportional to active surface area of the hybrid nanostructure. An optimum selectivity of the synthesized catalyst was identified (≈47–49%) when the mass fraction of Al₂O₃ was (35–36) %, in correspondence to the highest moderate basicity of the synthesized hybrid nanostructures. The highest yield of methanol achieved 12989 ± 2007 µmolg^?1 h^?1 by the developed Cu-ZnO@Al₂O₃. Our work demonstrates a prototype study of fabricating high-performance hybrid nanocatalyst with the support of mechanistic understanding in material synthesis for the synergistic catalysis of CO₂ hydrogenation to methanol.     

Real-time image segmentation based on a parallel and pipelined watershed algorithm

The watershed transformation is a popular image segmentation technique for gray scale images. This paper describes a real-time image segmentation based on a parallel and pipelined watershed algorithm which is designed for hardware implementation. In our algorithm: (1) pixels in a given image are repeatedly scanned from top-left to bottom-right, and then from bottom-right to top-left, in order to achieve high performance on a pipelined circuit by simplifying memory access sequences, (2) all steps in the algorithm are executed at the same time in the pipelined circuit, (3) the amount of data that are scanned is gradually reduced as the calculation progresses by memorizing which data are modified in the previous scan, and (4) N pixels can be processed in parallel. In our current implementation on an off-the-shelf field-programmable gate array board, up to four pixels can be processed in parallel. The performance for 512 × 512 pixel images is fast enough to be the first step in real-time applications.

Time forward observer based adaptive controller for a teleoperation system

This paper presents a design of a teleoperation system using time forward observer-based adaptive controller. The controller is robust to the time-variant delays and the environmental uncertainties while assuring the stability and the transparent performance. A novel theoretical framework and algorithms for this teleoperation system have been built up with neural network-based multiple model control and time forward state observer. Conditions for stability and transparency performance are also investigated.     

Tracking setpoint robust model predictive control for input saturated and softened state constraints

This paper starts with a brief review of robust model predictive control (RMPC) schemes for uncertain systems using linear matrix inequalities (LMIs) subject to input saturated and softened state constraints. However when RMPC has both input and state constraints, difficulties will arise due to the inability to satisfy the state constraints. In this paper, we develop two new tracking setpoint RMPC schemes with common Lyapunov function and with zero terminal equality subject to input saturated and softened state constraints. A brief comparative simulation of the two new RMPC schemes is implemented via examples to demonstrate the ability of the new RMPC schemes.     

A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam)

Determining the shear strength of soil is an important task in the design phase of construction project. This study puts forward an artificial intelligence (AI) solution to estimate this parameter of soil. The proposed approach is a hybrid AI model that integrates the least squares support vector machine (LSSVM) and the cuckoo search optimization (CSO). A dataset of 332 soil samples collected from the Trung Luong National Expressway Project in Viet Nam have been used for constructing and validating the AI model. The sample depth, sand percentage, loam percentage, clay percentage, moisture content, wet density of soil, specific gravity, liquid limit, plastic limit, plastic index, and liquid index are used as input variables to predict the output variable of shear strength. In the hybrid AI framework, LSSVM is employed to generalize the functional mapping that estimates the shear strength from the information provided by the aforementioned input variables. Since the model establishment of LSSVM requires a proper setting of the regularization and the kernel function parameters, the CSO algorithm is utilized to automatically determine these parameters. Experimental results show that the prediction accuracy of the hybrid method of LSSVM and CSO (RMSE = 0.082, MAPE = 14.841, and R² = 0.885) is better than those of the benchmark approaches including the standard LSSVM, the artificial neural network, and the regression tree. Therefore, the proposed method is a promising alternative for assisting construction engineers in the task of soil shear strength estimation.

     

A model to predict the effect of contact angle on the bubble departure diameter during heterogeneous boiling

Over the past eighty years, bubble release during heterogeneous boiling has been the subject of numerous investigations. However, current understanding of factors that influence this process is still incomplete. In this paper, a model is developed to describe the effect of contact angle on the bubble departure from an upward-facing horizontal surface. Based on the concept of macro- and micro-contact angles, this model gives an explicit theoretical relation between the bubble departure diameter and the contact angle. Agreement with previous experimental data confirms the predictive ability of the present model.     

Improving variable-fidelity modelling by exploring global design space and radial basis function networks for aerofoil design

The global variable-fidelity modelling (GVFM) method presented in this article extends the original variable-complexity modelling (VCM) algorithm that uses a low-fidelity and scaling function to approximate a high-fidelity function for efficiently solving design-optimization problems. GVFM uses the design of experiments to sample values of high- and low-fidelity functions to explore global design space and to initialize a scaling function using the radial basis function (RBF) network. This approach makes it possible to remove high-fidelity-gradient evaluation from the process, which makes GVFM more efficient than VCM for high-dimensional design problems. The proposed algorithm converges with 65% fewer high-fidelity function calls for a one-dimensional problem than VCM and approximately 80% fewer for a two-dimensional numerical problem. The GVFM method is applied for the design optimization of transonic and subsonic aerofoils. Both aerofoil design problems show design improvement with a reasonable number of high- and low-fidelity function evaluations.