Gold Nanoparticle Assembly Microfluidic Reactor for Efficient On-line Proteolysis

A microchip reactor coated with a gold nanoparticle network entrapping trypsin was designed for the efficient on-line proteolysis of low level proteins and complex extracts originating from mouse macrophages. The nanostructured surface coating was assembled via a layer-bylayer electrostatic binding of poly(diallyldimethylammonium chloride) and gold nanoparticles. The assembly process was monitored by UV-visible spectroscopy, atomic force microscopy, and quartz crystal microbalance. The controlled adsorption of trypsin was theoretically studied on the basis of the Langmuir isotherm model, and the fitted !max and K values were estimated to be 1.2 X 10-7 mol/m2 and 4.1 X 105 M-1, respectively. An enzymatic kinetics assay confirmed that trypsin, which was entrapped in the biocompatible gold nanoparticle network with a high loading capacity, preserved its bioactivity. The maximum proteolytic rate of the adsorbed trypsin was 400 mM/(min.µg). Trace amounts of proteins down to femtomole per analysis were digested using the microchip reactor, and the resulting tryptic products were identified by MALDI-TOF MS/MS. The protein mixtures extracted from the mouse macrophages were efficiently identified by online digestion and LC-ESI-MS/MS analysis

Platooning of Connected Vehicles with Directed Graph: H∞H_\infty Robustness Analysis and Synthesis

This paper revisits the robustness analysis and distributed ${ H}_\infty$ controller design for the platooning of connected vehicles. Recently, the relevant result subjected to the undirected topology has been studied, in the light of the symmetry of Laplace matrix. It is well known that the same problem is more challenging for the \emph{directed} topology, since the Laplace matrix ceases to be symmetric. In this paper, the problem is solved by introducing more weighting parameters and setting suitable values for them. Then we show that the introduced weighting parameters lead to a positive effect on robustness, and solve the problem of feedback high gain. Finally, two numerical simulations and a practical simulation based on Next Generation Simulation (NGSIM) dataset are used to illustrate the effectiveness of our method.Comment: 8 pages, 9 figures, 18 reference

Output Regulation for a Class of Parameter Uncertain Systems

The problem of robust output regulation is studied for a class of parameter uncertain systems under unity output feedback control. The objective is tracking of the desired reference trajectory in the presence of the disturbance, both generated by a common exosystem. Because of the anti-stability of the exosystem and potentially unbounded reference trajectory, the output tracking error is employed as the measurement signal and used as the input to the feedback controller. The method of p-copy of the internal model is utilized to augment the plant dynamics. Assuming that the output regulation condition is satisfied for all the parameter uncertainties, it is shown that the problem of robust output regulation is equivalent to the problem of robust output stabilization. Furthermore for quadratically bounded parameter uncertainties, an application of the notion of the quadratic stability leads to H∞ based robust control, and the maximum allowable uncertainty bound can be computed, below which the robust output regulation can be achieved

Подход с использованием весовой функции для нейросетевого нелинейного анализа временных рядов спутникового дистанционного зондирования ливней

Лишэн Сюй, Джили Дин, Сяобо Дэн. Подход с использованием весовой функции для нейросетевого нелинейного анализа временных рядов спутникового дистанционного зондирования ливнейOne of frequently used neural networks, i.e., a radial-based function network (RBFN) with Gaussian activation functions is employed to study the nonlinear time series by carrying out the characterization experiments for a GMS- 5 satellite 11 µm IR observations of rainstorm process. The proposed methodology mainly uses RBFN to approximate the nonlinear time series signal first: then the characteristics of its weighting functions changed with time are analyzed. The difficulty due to the effects of high noise on the signal processing using neural networks is addressed. Thus, finally a more integrated method combining the neural network analysis with wavelet packet decomposition is introduced. The preliminary results show that the proposed approach for nonlinear time series analysis is efficient and promising

Extraction and Purification of Nicotine from Tobacco Rhizomes by Supercritical CO₂

Currently, in the ongoing development of the tobacco industry, a large amount of tobacco rhizomes is discarded as waste. These wastes are usually disposed of through incineration or burial. However, these tobacco wastes still have some economic value. High-purity nicotine has a promising market outlook as the primary raw material for electronic cigarette liquid. Nicotine is not only found in tobacco leaves but also in the rhizomes of tobacco plants. This study presents a method for treating tobacco waste and extracting high-purity nicotine from it. After mixing the raw material powder and entrainer in specific ratios, as much of the nicotine in tobacco roots can be extracted as possible using supercritical carbon dioxide extraction. The effects of temperature, the ratio of the entrainer, and the volume fraction of ethanol in the entrainer on the nicotine yield in supercritical fluid extraction (SFE) at 25 MPa for 120 min were discussed. By using 90% ethanol (a raw material mass-to-volume ratio of 1:5) as the entrainer, we obtained the highest nicotine yield of 0.49% at 65 °C. Meanwhile, the purity of the crude extract was 61.71%, and after purification, it increased to 97.57%. In this way, we can not only obtain nicotine with market value but also further reduce the harm to the environment caused by tobacco waste disposal

Long-term monitoring of ultratrace nucleic acids using tetrahedral nanostructure-based NgAgo on wearable microneedles

Abstract Real-time and continuous monitoring of nucleic acid biomarkers with wearable devices holds potential for personal health management, especially in the context of pandemic surveillance or intensive care unit disease. However, achieving high sensitivity and long-term stability remains challenging. Here, we report a tetrahedral nanostructure-based Natronobacterium gregoryi Argonaute (NgAgo) for long-term stable monitoring of ultratrace unamplified nucleic acids (cell-free DNAs and RNAs) in vivo for sepsis on wearable device. This integrated wireless wearable consists of a flexible circuit board, a microneedle biosensor, and a stretchable epidermis patch with enrichment capability. We comprehensively investigate the recognition mechanism of nucleic acids by NgAgo/guide DNA and signal transformation within the Debye distance. In vivo experiments demonstrate the suitability for real-time monitoring of cell-free DNA and RNA with a sensitivity of 0.3 fM up to 14 days. These results provide a strategy for highly sensitive molecular recognition in vivo and for on-body detection of nucleic acid

Sensitive Detection of Haloperidol and Hydroxyzine at Multi-Walled Carbon Nanotubes-Modified Glassy Carbon Electrodes

Haloperidol (i.e. HPD) and hydroxyzine (i.e. HXY), two effective and important tranquilizers with low redox activity, were found to generate an irreversible anodic peak at about +0.86 V (vs. SCE) or two anodic peaks at about +0.83 and +0.91 V in 0.05 M NaH2PO4-Na2HPO4 (pH=7.0) buffer solution with a multi-walled carbon nanotubes-modified glassy carbon electrode (i.e. MWNTs/GC), respectively. Their sensitive and quantitative measurement based on the first two anodic peaks was established under the optimum conditions. The anodic peak current was linear to HPD and HXY concentration from 1×10-7 to 2.5 ×10-5 M and 5×10-8 to 2.5 ×10-5 M, the detection limits obtained were 8×10-9 and 5×10-9 M, separately. The modified electrode exhibited some excellent characteristics including easy regeneration, high stability, good reproducibility and selectivity. The method proposed was successfully applied to the detection of HPD and HXY in drug tablets and proved to be reliable compared with ultraviolet spectrophotometry. The modified electrode was characterized by electrochemical methods

TARBP2 promotes human breast cancer cell metastasis by degrading AKAP12 transcript

Objective To study the role and mechanism of trans-activation response RNA-binding protein 2 (TARBP2) in post-transcriptional regulation of the expression of AKAP12, a metastasis suppressor gene, in breast cancer cells. Methods The expression of TARBP2 in breast tumor tissues and potential relationship between TARBP2 expression and tumor metastasis were analyzed by bioinformatics methods. Bioinformatics methods were used to analyze the correlation between TARBP2 and AKAP12 expression in different breast cancer datasets. MDA-MB-231 cells were transfected with GFP-tagged TARBP2 plasmids by Lipofectamine 2000 and screening with G418. Lung metastasis in tumor-bearing nude mice was detected by HE staining. RT-qPCR was used to test the expression of AKAP12 and its half-life. Luciferase assay was carried out to determine whether the transcript decay was mediated through targeting the 3′UTR. EMSA assay was performed to detect the physically binding of TARBP2 with stem-loop structure of AKAP12. Results The high expression of TARBP2 in human breast tumor tissue was closely related to lymph node metastasis. TARBP2 over-expression in MDA-MB-231 cells significantly promoted tumor metastasis in vitro(P＜0.05). TARBP2 inhibited the expression of AKAP12(P＜0.01) and reduced its mRNA half-life(P＜0.05). TARBP2 significantly suppressed luciferase activity of AKAP12 3′UTR reporter(P＜0.05) and bound to the stem-loop structure. There was a significant negative correlation between TARBP2 and AKAP12 expression in human breast cancer samples(P＜0.001). Conclusions TARBP2-mediated inhibition of AKAP12 gene expression might be one of the mechanisms of TARBP2 promoting breast cancer metastasis