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Ischemia–reperfusion (I/R) injury, a prevalent pathological condition in medical practice, presents significant treatment challenges. Hydrogen sulfide (H2S), acknowledged as the third gas signaling molecule, profoundly impacts various physiological and pathophysiological processes. Extensive research has demonstrated that H2S can mitigate I/R damage across multiple organs and tissues. This review investigates the protective effects of H2S in preventing I/R damage in the heart, brain, liver, kidney, intestines, lungs, stomach, spinal cord, testes, eyes, and other tissues. H2S provides protection against I/R damage by alleviating inflammation and endoplasmic reticulum stress; inhibiting apoptosis, oxidative stress, and mitochondrial autophagy and dysfunction; and regulating microRNAs. Significant advancements in understanding the mechanisms by which H2S reduces I/R damage have led to the development and synthesis of H2S-releasing agents such as diallyl trisulfide-loaded mesoporous silica nanoparticles (DATS-MSN), AP39, zofenopril, and ATB-344, offering a new therapeutic avenue for I/R injury. [ABSTRACT FROM AUTHOR]

This paper proposes a novel federated recommendation framework that incorporates differential privacy to safeguard user privacy without compromising on the accuracy of recommendations. Unlike conventional recommendation systems that centralize user data, leading to potential privacy breaches, our framework ensures that user data remain on local devices. It leverages a federated learning approach, where a global model is trained across multiple devices without exchanging raw data. To enhance privacy protection, we integrate a specially designed differential privacy algorithm that adds carefully calibrated noise to the aggregated data updates. This mechanism ensures that the global model cannot be exploited to infer individual user information. We evaluate our framework on two real-world datasets, one from the e-commerce sector and another from the multimedia content recommendation domain. The results exhibit that our framework achieves competitive recommendation accuracy compared to traditional centralized approaches, with minimal loss in precision and recall metrics, while significantly enhancing user privacy. Our work stands as a testament to the feasibility of creating recommendation systems that do not have to choose between privacy and performance, paving the way for more ethical AI applications in sensitive domains. [ABSTRACT FROM AUTHOR]

Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users' behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption. [ABSTRACT FROM AUTHOR]

The theory of "lung governing skin and hair" has always occupied an important position in the clinical practice of traditional Chinese medicine. However, in western medicine, the close correlation between the lungs and the skin in health and disease has not been established. The difference between these two medical views has triggered an urgent need for the scientific interpretation and clinical application of the theory of "lung governing skin and hair" in western medicine. Therefore, the purpose of this paper is to discuss the application of "lung governing skin and hair" in chinese medicine and the correlation between the lung and the skin in western medicine from the connotation and history of " lung governing skin and hair" in Chinese medicine. In addition, we will discuss the common pathologic biomarkers of lung and skin diseases in western medicine and the co-morbidities between lung and skin, with the aim to provide new ideas for the clinical diagnosis and treatment of lung and skin diseases and modern research, as well as providing a direction for the integration of the theory of "lung governing skin and hair" into the clinical practice of western medicine. [ABSTRACT FROM AUTHOR]

Developing efficient electrocatalysts for CO2 reduction to syngas with tunable H2/CO ratios and high total faradaic efficiency is challenging. Herein, we report an effective catalyst composed of in situ reconstructed AgZn3 nanoparticles and Zn nanoplates for syngas synthesis, showing nearly 100% Faraday efficiency to syngas with a tunable H2/CO ratio from 2 : 1 to 1 : 2. Moreover, the in situ electrochemical measurements coupled with theoretical calculations disclose that the Zn site in AgZn3 nanoparticles and the hollow site between Ag and Zn in AgZn3 are the possible active sites for CO and H2 generation, respectively. This work has guiding significance for designing dual site catalysts for CO2 electroreduction to tunable syngas. [ABSTRACT FROM AUTHOR]

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel–rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel–rail coupled faults will improve the safety of rail vehicles' operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel–rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time–frequency analysis method is proposed in this paper based on the PDMF adopting Rényi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Rényi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles' variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method. [ABSTRACT FROM AUTHOR]

A series of N, N '-bis([1,2,4]triazole[4,3- b ][1,2,4,5]tetrazine-6-yl)alkylamine derivatives is designed, synthesized and evaluated for their inhibition activities against three tumor cell lines and c-Met kinase activity in vitro. These compounds are fully characterized by 1H NMR, 13C NMR, MS, IR and elemental analysis. Antitumor experiments indicate that some of these compounds exhibit significant inhibition activities against A549, Bewo and MCF-7 cancer cell lines. Among them, the IC50 values of 4a indicate better antitumor activities against the A549 (1.21 μM), Bewo (0.68 μM) and MCF-7 (3.74 μM) cell lines than the positive agent cisplatin (9.97 μM for A549, 10.46 μM for Bewo, and 15.03 μM for MCF-7), respectively. [ABSTRACT FROM AUTHOR]