Image stack of Phymolepis cuifengshanensis

Image stack of Phymolepis cuifengshanensis (IVPP V4425.2) for virtual restoration

3D model of the head and trunk shields of Phymolepis cuifengshanensis

3D model of the head and trunk shields of Phymolepis cuifengshanensis (IVPP V4425.2)

Residual current real measurements for different types.

Residual current real measurements for different types.</p

BPNN detection and prediction.

(a) Actual and detected values (b) Actual and predicted values.</p

S2 Data -

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.</div

Ti₃C₂ MXene Nanosheet-Based Dual-Enzyme Cascade Reaction to Facilitate Dual-Stimulation-Mediated Breast Cancer Therapy

Starvation therapy mediated by glucose oxidase is a widely used therapeutic approach for tumor treatment, but it is limited by the simultaneous drawbacks of weak therapeutic efficacy, nonspecificity, and systemic toxicity. Thus, combination therapy was used to complement the widely used therapeutic strategy for anticancer therapy. On the basis of starvation therapy, we designed a catalytic model of nanosheets with biological cascade enzymes synergizing with anticancer drugs. In short, two cascade enzymes (glucose oxidase and horseradish peroxidase) are covalently immobilized on Ti3C2 MXene nanosheet and a cascade enzyme nanoreactor is formed by electrostatically adsorbing positive charged DOX. Finally, the outer layer is coated with hyaluronic acid. By combining glucose oxidase-mediated starvation therapy, photothermal therapy, and chemotherapy, we have achieved the therapeutic effect of “killing three birds with one stone” by combining the dual stimulation response of endogenous and exogenous sources to the tumor site. This method not only achieves the targeting of cancer cells but also improves the systemic toxicity and reduced efficacy of biological enzymes and realizes synergistic cancer therapy with enhanced cascade reactions. It opens up a new path for the research of nanomedicine

BPNN topology diagram.

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.</div

BPNN plant electrocution current detection.

(a) Actual and detected values (b) Actual and predicted values.</p

Fig 6 -

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution.</div

Image_2_Analysis of serum antioxidant capacity and gut microbiota in calves at different growth stages in Tibet.png

IntroductionThe hypoxic environment at high altitudes poses a major physiological challenge to animals, especially young animals, as it disturbs the redox state and induces intestinal dysbiosis. Information about its effects on Holstein calves is limited.MethodsHere, serum biochemical indices and next-generation sequencing were used to explore serum antioxidant capacity, fecal fermentation performance, and fecal microbiota in Holstein calves aged 1, 2, 3, 4, 5, and 6 months in Tibet.Results and DiscussionSerum antioxidant capacity changed with age, with the catalase and malondialdehyde levels significantly decreasing (p  0.05) in total volatile fatty acid levels were noted between the groups. In all groups, Firmicutes, Bacteroidetes, and Actinobacteria were the three most dominant phyla in the gut. Gut microbial alpha diversity significantly increased (p < 0.05) with age. Principal coordinate analysis plot based on Bray–Curtis dissimilarity revealed significant differences (p = 0.001) among the groups. Furthermore, the relative abundance of various genera changed dynamically with age, and the serum antioxidant capacity was associated with certain gut bacteria. The study provides novel insights for feeding Holstein calves in high-altitude regions.</p