Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential ¹²C-/¹³C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N-Methyl-D-aspartic and tyrosine were identified. The changes of these biomarkers were likely due to the disruption of the endocrine system of silkworm by DDT. This work illustrates that the method of CIL LC-MS is useful to generate quantitative submetabolome profiles from a small volume of silkworm hemolymph with much higher coverage than conventional LC-MS methods, thereby facilitating the discovery of potential metabolite biomarkers related to EDC or other chemical exposure.     

Complex-valued Zhang neural network for online complex-valued time-varying matrix inversion

In this paper, a new complex-valued recurrent neural network (CVRNN) called complex-valued Zhang neural network (CVZNN) is proposed and simulated to solve the complex-valued time-varying matrix-inversion problems. Such a CVZNN model is designed based on a matrix-valued error function in the complex domain, and utilizes the complex-valued first-order time-derivative information of the complex-valued time-varying matrix for online inversion. Superior to the conventional complex-valued gradient-based neural network (CVGNN) and its related methods, the state matrix of the resultant CVZNN model can globally exponentially converge to the theoretical inverse of the complex-valued time-varying matrix in an error-free manner. Moreover, by exploiting the design parameter γ>1, superior convergence can be achieved for the CVZNN model to solve such complex-valued time-varying matrix inversion problems, as compared with the situation without design parameter γ involved (i.e., the situation with γ=1). Computer-simulation results substantiate the theoretical analysis and further demonstrate the efficacy of such a CVZNN model for online complex-valued time-varying matrix inversion.     

Different Zhang functions leading to different Zhang-dynamics models illustrated via time-varying reciprocal solving

Along with neural dynamics (based on analog solvers) widely arising in scientific computation and optimization fields in recent decades which attracts extensive interest and investigation of researchers, a novel type of neural dynamics, called Zhang dynamics (ZD), has been formally proposed by Zhang et al. for the online solution of time-varying problems. By following Zhang et al.’s neural-dynamics design method, the ZD model, which is based on an indefinite Zhang function (ZF), can guarantee the exponential convergence performance for the online time-varying problems solving. In this paper, different indefinite Zhang functions, which can lead to different ZD models, are proposed and developed as the error-monitoring functions for the time-varying reciprocal problem solving. Additionally, for the goal of developing the floating-point processors or coprocessors for the future generation of computers, the MATLAB Simulink modeling and simulative verifications of such different ZD models are further presented for online time-varying reciprocal solving. The modeling results substantiate the efficacy of such different ZD models for time-varying reciprocal solving.     

Continuous and discrete Zhang dynamics for real-time varying nonlinear optimization

Online solution of time-varying nonlinear optimization problems is considered an important issue in the fields of scientific and engineering research. In this study, the continuous-time derivative (CTD) model and two gradient dynamics (GD) models are developed for real-time varying nonlinear optimization (RTVNO). A continuous-time Zhang dynamics (CTZD) model is then generalized and investigated for RTVNO to remedy the weaknesses of CTD and GD models. For possible digital hardware realization, a discrete-time Zhang dynamics (DTZD) model, which can be further reduced to Newton-Raphson iteration (NRI), is also proposed and developed. Theoretical analyses indicate that the residual error of the CTZD model has an exponential convergence, and that the maximum steady-state residual error (MSSRE) of the DTZD model has an O(τ²) pattern with τ denoting the sampling gap. Simulation and numerical results further illustrate the efficacy and advantages of the proposed CTZD and DTZD models for RTVNO.     

Equivalence of velocity-level and acceleration-level redundancy-resolution of manipulators

The equivalence of velocity-level and acceleration-level redundancy resolution of robot manipulators is investigated in this Letter. Theoretical analysis based on gradient-descent method and computer simulations based on PUMA560 robot manipulator both demonstrate the equivalence of redundancy-resolution schemes at different levels.     

Using GD to conquer the singularity problem of conventional controller for output tracking of nonlinear system of a class

Recently, gradient dynamics (GD) has been shown as a powerful tool for problems solving. Based on the GD method, a novel controller is proposed and investigated for output tracking of nonlinear system of a class. Such a GD controller can not only achieve the tracking-control task successfully as a conventional controller does, but also conquer the singularity problem, which is a difficult problem in conventional tracking-controller design and rarely studied in previous researches. Simulative results substantiate the efficacy and superiority of the proposed novel GD controller for conquering the singularity problem in output tracking of nonlinear system of a class.     

Global exponential convergence and stability of gradient-based neural network for online matrix inversion

Wang proposed a gradient-based neural network (GNN) to solve online matrix-inverses. Global asymptotical convergence was shown for such a neural network when applied to inverting nonsingular matrices. As compared to the previously-presented asymptotical convergence, this paper investigates more desirable properties of the gradient-based neural network; e.g., global exponential convergence for nonsingular matrix inversion, and global stability even for the singular-matrix case. Illustrative simulation results further demonstrate the theoretical analysis of gradient-based neural network for online matrix inversion.     

A passive pump-assisted microfluidic assay for quantifying endothelial wound healing in response to fluid shear stress

There as an urgent need to quantify the endothelial wound-healing process in response to fluid shear stress to improve the biological and clinical understanding of healing mechanisms, which is of great importance for preventing healing impairment, chronic wounds, and postoperative in-stent restenosis. However, current experimental platforms not only require expensive, cumbersome, and powered pumping devices (to, e.g., generate cell scratches and load shear stress stimulation) but also lack quantitative controls for quantitative analysis. In this paper, a passive pump-assisted microfluidic assay is developed to quantify endothelial wound healing in response to fluid shear stress. Our assay consists of passive constant-flow pumps based on the siphon principle and a three-inlet microfluidic chip for cell wound-healing experiments. We also propose a method for quantitatively adjusting cell scratch size by controlling trypsin flow. Both numerical simulations and fluorescein experiments validate the effectiveness of this method. Moreover, we use the designed microfluidic assay to successfully generate cell scratches, load a 12-h shear stress of 5 dyn/cm² to the cells, and observe wound healing. The results indicate that the healing of a cell scratch is significantly accelerated under the stimulation of shear stress. In conclusion, our passive pump-assisted microfluidic assay shows versatility, applicability, and the potential for quantifying endothelial wound healing in response to fluid shear stress.     

The Structure and Catalytic Properties of MoVTeNbO Catalysts Modified by Adding Cr,Fe, Ce and W

Cr, Fe, Ce and W doped MoVTeNbO M2 phase catalysts were synthesized and used in the selective oxidation of propylene to acrylic acid (AA). Results show that the introduction of Cr, Fe, Ce and W substantially affects the physicochemical properties and catalytic performance of MoVTeNbO-based catalysts. Un-doped catalyst consists of M2 phase and TeO₂, while Cr, Fe, Ce and W-doped catalysts are mainly composed of M2 and MoO₃. It is indicated that doping of Cr, Fe, Ce and W can restrain the formation of TeO₂, but favour the formation of MoO₃. Un-doped, Cr and W-doped catalysts display irregular-shaped particles morphology, while Fe and Ce-doped catalysts display nanosheets morphology. In addition, the valence of superficial elements of catalysts changed greatly with the doping elements. For catalytic performance, in addition to Cr, the propylene conversion of the catalyst decreases obviously with doping of other elements, probably due to the drastically reduced specific surface area with doping of Fe, Ce and W. The existence of Cr and Ce can increase the selectivity to AA at all test temperatures (360–440 ℃), while Fe and W-doped catalysts only show higher selectivity than the un-doped one at high temperature of 420 and 440 ℃. It is illustrated that the catalysts with redox ability at relatively low temperature is more favorable for the selectivity to AA. Among them, Cr-doped catalyst shows the highest selectivity (85.3%) and yield (63.5%) of AA at test temperature of 380 ℃, which are 15.3 and 7.5% higher than that of un-doped catalyst, respectively.

Graphic Abstract

The M2 phase MoVTeNbO catalysts doped with Cr, Fe, Ce and W have been synthesized. It is demonstrated that the addition of Cr improves the stability of Te⁴⁺, and Cr-doped M2 phase shows excellent catalytic performance in the selective oxidation of propylene to acrylic acid.