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Background: Artemisiae capillariae (Yinchen, YC) is a well-known herbal medicine used to treat drug-induced liver diseases, while the bioactive phytochemicals and pharmacological targets of YC remain unclear. Objective: The study aimed to probe the key active components in YC and determine the potential molecular mechanisms of YC protect against DILI Method: In this study, we first delved into the active chemicals and targets of YC, identified potential anti-AILI targets for YC, mapped the components-targets network, performed protein-protein interaction (PPI) analysis, gene ontology (GO) enrichment, and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway analyses of the action targets. This led to figure out the liver protective mechanism of YC against AILI. Analyzing the molecular docking of key targets, binding domain of ingredients and targets reveals the effective interaction, and the binding energy explains the efficiency and stability of the interactions. Results: Network analysis identified 53 components in YC; by systematic screening 13 compounds were selected, which were associated with 123 AILI-related genes. The core ingredients were quercetin, capillarisin and Skrofulein, and the identified crucial genes were AKT1, TNF, and IL6. The GO and KEGG pathway enrichment analysis results indicated that the anti-AILI targets of YC mainly take a part in the regulation of oxidative stress and immune, with related signaling pathways including PI3K/AKT and IL17. Furthermore, the binding pockets of YC bioactive ingredients and key targets were revealed, and the binding ability was proved by molecular docking analysis. Conclusion: This study has revealed the potential bioactive molecules and mechanism of YC in AILI and provided a possible strategy for the identification of active phytochemicals against drug-induced liver injury.

This brief proposes a low voltage ultra-low power 10-bit 100-MS/s successive approximation register (SAR) analog-to-digital converter (ADC). To overcome the performance degradation due to the signal-related current fluctuation under lower supply voltage, an adaptive-biasing comparator with current self-compensation (CSC) technique is proposed to obtain a constant working current, reducing the minimum supply voltage from 0.7V in conventional ADCs to 0.55V. Since full-scale range shrinks with the supply voltage, voltage undershoot in power rail caused by capacitor digital-to-analog converter (C-DAC) deteriorates effective-number-of-bits (ENOB). Conventional solutions involve enhanced regulators and large decoupling capacitors, which cost remarkable power dissipation and large chip area. This brief presents a pulse-injection undershoot compensation technique which reduces the DAC-related supply fluctuation from a typical 15mV to 1.8mV, and improves the ENOB by 0.4 bit. The prototype was fabricated in TSMC 28nm HPC CMOS technology. The proposed SAR ADC achieves an SNDR of 54.7dB and an SFDR of 68dB with the power consumption of 0.16mW under 0.55V supply voltage, a figure of merit (FoM) of 3.6-fJ/conversion-step is achieved. The chip area of the ADC core is 130μm×225μm.

In the field of image classification, the Convolutional Neural Networks (CNNs) are effective. Most of the work focuses on improving and innovating CNN’s network structure. However, using labeled data more effectively for training has also been an essential part of CNN’s research. Combining image disturbance and consistency regularization theory, this paper proposes a model training method (PairTraining) that takes image pairs as input and dynamically modify the training difficulty according to the accuracy of the model in the training set. According to the accuracy of the model in the training set, the training process will be divided into three stages: the qualitative stage, the fine learning stage and the strengthening learning stage. Contrastive learning images are formed using a progressively enhanced image disturbance strategy at different training stages. The input image and contrast learning image are combined into image pairs for model training. The experiments are tested on four public datasets using eleven CNN models. These models have different degrees of improvement in accuracy on the four datasets. PairTraining can adapt to a variety of CNN models for image classification training. This method can better improve the effectiveness of training and improve the degree of generalization of classification models after training. The classification model obtained by PairTraining has better performance in practical application.

The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard.

The inverse Q filtering method with a stabilization factor solves the noise amplification problem caused by the exponential growth of the amplitude compensation operator in traditional inverse Q filtering to a certain extent, but it is not ideal for reducing noise and amplitude compensation of deep seismic records. The inverse Q filtering method with a variable stabilization factor is superior to the inverse Q filtering method with a constant stable factor in reducing noise, but the amplitude compensation for the deep seismic record is still not ideal, especially when the quality factor Q value is small. In addition, because its compensation function changing with time and frequency is fixed, its amplitude compensation cannot be flexibly carried out according to the characteristic of seismic records. We have developed a novel inverse Q filtering method with a new variable stabilization factor under the constraint of signal-to-noise ratio (S/N). The S/N of seismic data is taken as prior information, and the stabilization factor is a function of the S/N and the quality factor. The amplitude compensation operator changes dynamically with time, frequency, S/N, and the quality factor, which can accurately compensate for the effective amplitude while avoiding enhancing the noise energy. Theoretical synthetic seismic data and real seismic data using the new method find significant improvements in amplitude compensation and suppression of noise energy. The new method has the strongest comprehensive ability of amplitude compensation and noise suppression. The resolution and S/N of seismic data processed by the new method have been improved.

Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.

Background: There is increasing evidence that supplementation of S-adenosylmethionine (SAM) can improve cognitive function in animals and humans, although the outcomes are not always inconsistent. Objective: We conducted a systematic review and meta-analysis to evaluate the correlation between SAM supplementation and improved cognitive function. Methods: We searched articles in the PubMed, Cochrane Library, Embase, Web of Science, and Clinical Trials databases from January 1, 2002 to January 1, 2022. Risk of bias was assessed using the Cochrane risk of bias 2.0 (human studies) and the Systematic Review Center for Laboratory Animal Experimentation risk of bias (animal studies) tools; and evidence quality was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation. STATA software was employed to performed meta-analysis, evaluating the standardized mean difference with 95% confidence intervals using random-effects models. Results: Out of the 2,375 studies screened, 30 studies met the inclusion criteria. Meta-analyses of animal (p = 0.213) and human (p = 0.047) studies showed that there were no significant differences between the SAM supplementation and control groups. The results of the subgroup analyses showed that the animals aged ≤8 weeks (p = 0.027) and the intervention duration >8 weeks (p = 0.009) were significantly different compared to the controls. Additionally, the Morris water maze test (p = 0.005) used to assess the cognitive level of the animals revealed that SAM could enhance spatial learning and memory in animals. Conclusion: SAM supplementation showed no significant improvement in cognition. Therefore, further studies are needed to assess the effectiveness of SAM supplementation.