Sensitive Detection of RNase A Activity and Collaborative Drug Screening Based on rGO and Fluorescence Probe.

In addition to being an important object in theoretical and experimental studies in enzymology, RNase A also plays an important role in the development of many kinds of diseases by regulating various physiological or pathological processes, including cell growth, proliferation, differentiation, and invasion. Thus, it can be used as a useful biomarker for disease theranostics. Here, a simple, sensitive, and low-cost assay for RNase A was constructed by combining a fluorogenic substrate with reduced graphene oxide (rGO). The method with detection limit of 0.05 ng/mL was first applied for RNase A targeted drug screening, and 14 natural compounds were identified as activators of this enzyme. Then, it was applied to detect the effect of drug treatment and Hepatitis B virus (HBV) infection on RNase A activity. The results indicated that RNase A level in tumor cells was upregulated by G-10 and Chikusetsusaponin V in a concentration-dependent manner, while the average level of RNase A in the HBV infection group was significantly inhibited compared with that in the control group. Furthermore, the concentration-dependent inhibitory effect of heavy metal ions on RNase A was observed using the method and the results indicated that Ba2+, Co2+, Pb2+, As3+, and Cu2+ inhibited RNase A activity with IC50 values of 93.7 µM (Ba2+), 90.9 µM (Co2+), 110.6 µM (Pb2+), 171.5 µM (As3+), and 165.1 µM (Cu2+), respectively. In summary, considering the benefits of rapidity and high sensitivity, the method is practicable for RNase A assay in biosamples and natural compounds screening in vitro and in vivo.

Hydrogen doping control method for gasoline engine acceleration transient air-fuel ratio.

One of the primary contributors to automobile exhaust pollution is the significant deviation between the actual and theoretical air-fuel ratios during transient conditions, leading to a decrease in the conversion efficiency of three-way catalytic converters. Therefore, it becomes imperative to enhance fuel economy, reduce pollutant emissions, and improve the accuracy of transient control over air-fuel ratio (AFR) in order to mitigate automobile exhaust pollution. In this study, we propose a Linear Active Disturbance Rejection Control (LADRC) Hydrogen Doping Compensation Controller (HDC) to achieve precise control over the acceleration transient AFR of gasoline engines. By analyzing the dynamic effects of oil film and its impact on AFR, we establish a dynamic effect model for oil film and utilize hydrogen's exceptional auxiliary combustion characteristics as compensation for fuel loss. Comparative experimental results demonstrate that our proposed algorithm can rapidly regulate the AFR close to its ideal value under three different transient conditions while exhibiting superior anti-interference capability and effectively enhancing fuel economy.

Novel fractional-order decentralized control for nonlinear fractional-order composite systems with time delays.

A novel decentralized non-integer order controller applied on nonlinear fractional-order composite system(NFOCS) is proposed. In addition, some novel results for the asymptotic stabilization are shown with fractional parameter α∈0,1. First, we derive certain novel results useful for the Mittag-Leffler function. Then, we design a new decentralized fractional-order controller for the NFOCS according to the novel results applied to Mittag-Leffler function. Next, this novel asymptotic stabilization condition has been proposed. Compared with other controllers our controller has wider control gain range and weaker requirements. Moreover, we solve the asymptotic stabilization problem of the NFOCS with time delays via the novel controller. In the end, four general examples are performed to show the progressiveness of the new fractional-order decentralized controller.

Energy-Saving Optimization and Control of Autonomous Electric Vehicles With Considering Multiconstraints.

The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle's powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.

Discriminative dictionary learning algorithm with pairwise local constraints for histopathological image classification.

Histopathological image contains rich pathological information that is valued for the aided diagnosis of many diseases such as cancer. An important issue in histopathological image classification is how to learn a high-quality discriminative dictionary due to diverse tissue pattern, a variety of texture, and different morphologies structure. In this paper, we propose a discriminative dictionary learning algorithm with pairwise local constraints (PLCDDL) for histopathological image classification. Inspired by the one-to-one mapping between dictionary atom and profile, we learn a pair of discriminative graph Laplacian matrices that are less sensitive to noise or outliers to capture the locality and discriminating information of data manifold by utilizing the local geometry information of category-specific dictionaries rather than input data. Furthermore, graph-based pairwise local constraints are designed and incorporated into the original dictionary learning model to effectively encode the locality consistency with intra-class samples and the locality inconsistency with inter-class samples. Specifically, we learn the discriminative localities for representations by jointly optimizing both the intra-class locality and inter-class locality, which can significantly improve the discriminability and robustness of dictionary. Extensive experiments on the challenging datasets verify that the proposed PLCDDL algorithm can achieve a better classification accuracy and powerful robustness compared with the state-of-the-art dictionary learning methods. Graphical abstract The proposed PLCDDL algorithm. 1) A pair of graph Laplacian matrices are first learned based on the class-specific dictionaries. 2) Graph-based pairwise local constraints are designed to transfer the locality for coding coefficients. 3) Class-specific dictionaries can be further updated.