Adaptive Sliding Mode Control Based on Uncertainty and Disturbance Estimator

This paper presents an original adaptive sliding mode control strategy for a class of nonlinear systems on the basis of uncertainty and disturbance estimator. The nonlinear systems can be with parametric uncertainties as well as unmatched uncertainties and external disturbances. The novel adaptive sliding mode control has several advantages over traditional sliding mode control method. Firstly, discontinuous sign function does not exist in the proposed adaptive sliding mode controller, and it is not replaced by saturation function or similar approximation functions as well. Therefore, chattering is avoided in essence, and the chattering avoidance is not at the cost of reducing the robustness of the closed-loop systems. Secondly, the uncertainties do not need to satisfy matching condition and the bounds of uncertainties are not required to be unknown. Thirdly, it is proved that the closed-loop systems have robustness to parameter uncertainties as well as unmatched model uncertainties and external disturbances. The robust stability is analyzed from a second-order linear time invariant system to a nonlinear system gradually. Simulation on a pendulum system with motor dynamics verifies the effectiveness of the proposed method

Adaptive Sliding Mode Control Based on Uncertainty and Disturbance Estimator

This paper presents an original adaptive sliding mode control strategy for a class of nonlinear systems on the basis of uncertainty and disturbance estimator. The nonlinear systems can be with parametric uncertainties as well as unmatched uncertainties and external disturbances. The novel adaptive sliding mode control has several advantages over traditional sliding mode control method. Firstly, discontinuous sign function does not exist in the proposed adaptive sliding mode controller, and it is not replaced by saturation function or similar approximation functions as well. Therefore, chattering is avoided in essence, and the chattering avoidance is not at the cost of reducing the robustness of the closed-loop systems. Secondly, the uncertainties do not need to satisfy matching condition and the bounds of uncertainties are not required to be unknown. Thirdly, it is proved that the closed-loop systems have robustness to parameter uncertainties as well as unmatched model uncertainties and external disturbances. The robust stability is analyzed from a second-order linear time invariant system to a nonlinear system gradually. Simulation on a pendulum system with motor dynamics verifies the effectiveness of the proposed method

Transient Inhibition of mTORC1 Signaling Ameliorates Irradiation-Induced Liver Damage

Recurrent liver cancer after surgery is often treated with radiotherapy, which induces liver damage. It has been documented that activation of the TGF-β and NF-κB signaling pathways plays important roles in irradiation-induced liver pathologies. However, the significance of mTOR signaling remains undefined after irradiation exposure. In the present study, we investigated the effects of inhibiting mTORC1 signaling on irradiated livers. Male C57BL/6J mice were acutely exposed to 8.0 Gy of X-ray total body irradiation and subsequently treated with rapamycin. The effects of rapamycin treatment on irradiated livers were examined at days 1, 3, and 7 after exposure. The results showed that 8.0 Gy of irradiation resulted in hepatocyte edema, hemorrhage, and sinusoidal congestion along with a decrease of ALB expression. Exposure of mice to irradiation significantly activated the mTORC1 signaling pathway determined by pS6 and p-mTOR expression via western blot and immunostaining. Transient inhibition of mTORC1 signaling by rapamycin treatment consistently accelerated liver recovery from irradiation, which was evidenced by decreasing sinusoidal congestion and increasing ALB expression after irradiation. The protective role of rapamycin on irradiated livers might be mediated by decreasing cellular apoptosis and increasing autophagy. These data suggest that transient inhibition of mTORC1 signaling by rapamycin protects livers against irradiation-induced damage

Tripterygium glycosides sensitizes cisplatin chemotherapeutic potency by modulating gut microbiota in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is a fatal gynecological malignancy with limited therapeutic options. Previous research has demonstrated that Tripterygium glycosides (GTW) can enhance effectiveness of cisplatin (DDP) chemotherapy against EOC. However, the underlying mechanism of GTW alleviating EOC still remains unclear. In this article, an ID8 cell-derived xenograft mouse model was established to evaluate the anti-tumor efficacy of GTW combined with DDP. Consistent with previous findings, the results suggested that GTW combined with DDP can exhibit a stronger tumor suppressive effect than DDP alone. Additionally, GTW was found can further exert gastrointestinal protection against DDP by reducing pathological damage on colon tissue. Secondly, to verify whether gut microbiota play an instrumental role in GTW’s anticancer effect, we treated mice models with antibiotic to eliminate gut microbiota. And our experimental results indicated that all drug groups showed a weaker tumor suppressive effect and more severe gastrointestinal damage post antibiotic supplement. At genus level, the relative abundance of Lactobacillus was dramatically diminished by the antibiotic treatment, while combined treatment of GTW and DDP can significantly restore the level. Moreover, we performed Lactobacillus acidophilus transplantation and healthy mice fecal microbiota transplantation experiments to further investigate the link between the anticancer effect of GTW and gut microbiota. Our results suggested that both cisplatin-sensitizing and intestinal barrier-protecting effects of GTW can be recovered to a different extent. In conclusion, our results indicated that GTW is a promising chemosensitization and intestinal barrier repair drug for EOC, and the potential mechanism may corelate with the restoration of the compromised intestinal microbial balance

A Modified Hyaluronic Acid–Based Dissolving Microneedle Loaded With Daphnetin Improved the Treatment of Psoriasis

Psoriasis is a common chronic immune-inflammatory disease. Challenges exist in the present treatment of psoriasis, such as difficulties in transdermal drug administration and severe side effects. We hope to achieve a better therapeutic outcome for psoriasis treatment. By using modified soluble microneedles (MNs) loaded with daphnetin, the psoriasis symptoms of mice, the abnormal proliferation of keratinocytes, and the secretion of inflammatory factors were significantly reduced. In vitro, daphnetin is proven to inhibit the NF-κB signaling pathway and to inhibit the proliferation of HaCaT cells and the release of inflammatory factors, especially CCL20. This research showed that the modified microneedle loaded with daphnetin optimized transdermal drug delivery and relieved the symptoms of psoriasis more effectively. The novel route of Daph administration provides a future research direction for the treatment of psoriasis

TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations