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Our previous studies showed that silent mating-type information regulation 2 homologue-1 (SIRT1, a deacetylase) upregulation could attenuate sepsis-induced acute kidney injury (SAKI). Upregulated SIRT1 can deacetylate certain autophagy-related proteins (Beclin1, Atg5, Atg7 and LC3) in vitro. However, it remains unclear whether the beneficial effect of SIRT1 is related to autophagy induction and the underlying mechanism of this effect is also unknown. In the present study, caecal ligation and puncture (CLP)-induced mice, and an LPS-challenged HK-2 cell line were established to mimic a SAKI animal model and a SAKI cell model, respectively. Our results demonstrated that SIRT1 activation promoted autophagy and attenuated SAKI. SIRT1 deacetylated only Beclin1 but not the other autophagy-related proteins in SAKI. SIRT1-induced autophagy and its protective effect against SAKI were mediated by the deacetylation of Beclin1 at K430 and K437. Moreover, two SIRT1 activators, resveratrol and polydatin, attenuated SAKI in CLP-induced septic mice. Our study was the first to demonstrate the important role of SIRT1-induced Beclin1 deacetylation in autophagy and its protective effect against SAKI. These findings suggest that pharmacologic induction of autophagy via SIRT1-mediated Beclin1 deacetylation may be a promising therapeutic approach for future SAKI treatment.

Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA). In this model, UAVs are endowed with the capability to dynamically offload tasks among one another, thereby fostering a more equitable load distribution across the UAV swarm. Furthermore, the integration of NOMA is strategically employed to alleviating the inherent queuing delays in the communication infrastructure. Considering delay and energy consumption constraints, we formulate a task offloading strategy optimization problem with the objective of minimizing the overall system delay. To solve this problem, we design a delay-optimized offloading strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. By jointly optimizing task offloading decisions and UAV flight trajectories, the system delay is significantly reduced. Simulation results show that, compared to traditional approaches, the proposed algorithm achieves a delay reduction of 20.2%, 9.8%, 17.0%, 12.7%, 15.0%, and 11.6% under different scenarios, including varying task volumes, the number of IoT devices, UAV flight speed, flight time, IoT device computing capacity, and UAV computing capability. These results demonstrate the effectiveness of the proposed solution and offloading decisions in reducing the overall system delay.

This study established a dynamic impact simulation system for a coral limestone cement composite subjected to bidirectional stress confinement conditions by using a coupled method of continuous medium FDM (a coupled continuum-discontinuum approach integrating finite difference continuum modeling (FDM) and the discrete element method (DEM) granular analysis), and verified its accuracy through indoor experiments. The study first conducted dynamic mechanical performance tests on reef limestone concrete using an SHPB experimental device, exploring the effects of the strain-rate governed high-rate response, energy evolution, and failure modes. Subsequently, an FDM-DEM coupled model was used to simulate the impact-induced behavior of concrete at multiaxial stress conditions and constraint conditions, analyzing the strain-rate dependent performance of concrete exposed to biaxial monotonic loading. Test outcomes indicate that the increase in strain rate significantly enhanced the dynamic peak stress, and the collapse behavior shifted from type I to type II. As static loading in the σ2 direction increased, the dynamic peak stress in the σ1 direction decreased, while the dynamic peak stress in the σ2 direction increased, indicating that the constraint stress in the σ2 direction had an inhibitory effect on the sample’s failure. Through the time-history monitoring and analysis of cracks, it was found that the internal crack growth rate accelerated as the stress increased, while the crack growth tended to stabilize when the stress decreased. Additionally, this study explored the effect of stress constraints on the fragmentation patterns, revealing changes in the failure modes and crack distributions of the sample under different stress states, providing a theoretical basis and technical support for island and reef construction and engineering protection.

To ensure the safe operation of railway tunnels and prevent methane disasters in auxiliary tunnels, this paper focuses on the post-construction closure of an auxiliary tunnel (cross tunnel) in a railway tunnel with methane presence. Computational Fluid Dynamics (CFD) simulations were employed to investigate methane migration and accumulation patterns under different sealing conditions in railway auxiliary tunnels. The optimal auxiliary tunnel end-face closure method was identified. Subsequently, the influences of factors such as tunnel length and methane concentration on the explosion characteristics were analyzed under the optimal closed process conditions. The results show that after methane escapes from the coal seam, it initially accumulates at the tunnel’s roof and then diffuses downward due to the concentration gradient. When the lower end face of the auxiliary tunnel is opened and the upper end face is sealed, the degree of methane enrichment in the tunnel is the lowest and the enrichment speed is the slowest. Under partial methane conditions, the explosion pressure propagated and released more easily within the tunnel, leading to higher peak pressure. As the length of the tunnel increases, the peak pressure of the explosion increases, and the explosion power becomes greater. The overpressure of the explosion shock wave follows a nonlinear relationship with distance and is inversely proportional to the square root of the distance. The findings provide theoretical guidance for the prevention and control of methane-related accidents and disasters.

When auxiliary tunnels pass through gas-bearing strata, there is a risk of gas explosion, but there is less analysis on the impact of gas explosions on operating railways after the auxiliary tunnels are sealed post-construction. To address the shortcomings of existing research, this paper establishes a numerical model of intersecting tunnels that closely resembles actual conditions. It first studies the law of gas explosion in the closed tunnel, obtaining the overpressure curve of the gas explosion. Based on this, the evolution law of plastic expansion of blocking walls of different thicknesses and at different positions under explosive impact is derived, and the safe thickness of the blocking wall is fitted. The study shows that: in the sealed auxiliary tunnel after construction, the overpressure of the gas explosion initially tends to be stable, then increases rapidly, and finally tends to be stable, with the peak value being up to 0.79 MPa. The duration of the explosion increases with the increase of the tunnel length, and the explosion pressure decreases as the tunnel length increases; under the condition of the highest explosion pressure, the maximum deformation of the blocking wall after the gas explosion is 36.7 mm, the shear strain at the center position of the wall surface is the largest, and the range of tensile shear damage is larger, which is a severely damaged area. The final determination establishes that the minimum safe thickness of the wall is 8.33 m.

Accurate and detailed mapping of coastal wetlands is essential for effective wetland resource management. However, due to periodic tidal inundation, frequent cloud cover, and spectral similarity of land cover types, reliable coastal wetland classification methods remain limited. To address these issues, we developed an integrated pixel- and object-based hierarchical classification strategy based on multi-source remote sensing data to achieve fine-grained coastal wetland classification on Google Earth Engine. With the random forest classifier, pixel-level classification was performed to classify rough wetland and non-wetland types, followed by object-based classification to differentiate artificial and natural attributes of water bodies. In this process, multi-dimensional features including water level, phenology, variation, topography, geography, and geometry were extracted from Sentinel-1/2 time-series images, topographic data and shoreline data, which can fully capture the variability and dynamics of coastal wetlands. Feature combinations were then optimized through Recursive Feature Elimination and Jeffries–Matusita analysis to ensure the model’s ability to distinguish complex wetland types while improving efficiency. The classification strategy was applied to typical coastal wetlands in central Jiangsu in 2020 and finally generated a 10 m wetland map including 7 wetland types and 3 non-wetland types, with an overall accuracy of 92.50% and a Kappa coefficient of 0.915. Comparative analysis with existing datasets confirmed the reliability of this strategy, particularly in extracting intertidal mudflats, salt marshes, and artificial wetlands. This study can provide a robust framework for fine-grained wetland mapping and support the inventory and conservation of coastal wetland resources.

At present, diabetes mellitus (DM) has been one of the most endangering healthy diseases. Current therapies contain controlling high blood sugar, reducing risk factors like obesity, hypertension, and so on; however, DM patients inevitably and eventually progress into different types of diabetes complications, resulting in poor quality of life. Unfortunately, the clear etiology and pathogenesis of diabetes complications have not been elucidated owing to intricate whole‐body systems. The immune system was responsible to regulate homeostasis by triggering or resolving inflammatory response, indicating it may be necessary to diabetes complications. In fact, previous studies have been shown inflammation plays multifunctional roles in the pathogenesis of diabetes complications and is attracting attention to be the meaningful therapeutic strategy. To this end, this review systematically concluded the current studies over the relationships of susceptible diabetes complications (e.g., diabetic cardiomyopathy, diabetic retinopathy, diabetic peripheral neuropathy, and diabetic nephropathy) and inflammation, ranging from immune cell response, cytokines interaction to pathomechanism of organ injury. Besides, we also summarized various therapeutic strategies to improve diabetes complications by target inflammation from special remedies to conventional lifestyle changes. This review will offer a panoramic insight into the mechanisms of diabetes complications from an inflammatory perspective and also discuss contemporary clinical interventions. Inflammatory response and its related signaling pathways in diabetes complications. In the pathogenesis of diabetes complications, inflammatory factors, inflammasomes, immune cells, adhesion molecules, chemokine, and chemokine receptor are involved in the progression of diabetes complications through a series of inflammatory reactions. Related signaling pathways, including NF‐κB, Toll‐like receptors, MAPK, JAK/STAT, PI3K/Akt, mediate a series of inflammatory responses of diabetes complications.

Large-scale single-cell projects generate rapidly growing datasets, but downstream analysis is often confounded by data sources, requiring data integration methods to do correction. Existing data integration methods typically require data centralization, raising privacy and security concerns. Here, we introduce Federated Harmony, a novel method combining properties of federated learning with Harmony algorithm to integrate decentralized omics data. This approach preserves privacy by avoiding raw data sharing while maintaining integration performance comparable to Harmony. Experiments on various types of single-cell data showcase superior results, highlighting a novel data integration approach for distributed multi-omics data without compromising data privacy or analytical performance.

Influenza A virus infection causes a series of diseases, but the factors associated with disease severity are not fully understood. Disruption of the endothelial glycocalyx contributes to acute lung injury in sepsis, but has not been well studied in H1N1 influenza. We aim to determine whether the plasma glycocalyx components levels are predictive of disease severity in H1N1 influenza. This prospective observational study included 53 patients with influenza A (H1N1) during the influenza season, and 30 healthy controls in our hospital. Patients were grouped by severity and survival. We collected clinical data and blood samples at admission. Inflammatory factors (tumor necrosis factor-α, interleukin-6, interleukin-10) and endothelial glycocalyx components (syndecan-1, hyaluronan, heparan sulfate) were measured. The plasma levels of syndecan-1, hyaluronan, and heparan sulfate were significantly higher in patients with severe influenza A (H1N1) than in mild cases. Syndecan-1 and hyaluronan were positively correlated with disease severity, which was indicated by the APACHE II and SOFA scores and lactate levels, and negatively correlated with albumin levels. At a cutoff point ≥ 173.9 ng/mL, syndecan-1 had a 81.3% sensitivity and 70.3% specificity for predicting of 28-day mortality. Kaplan – Meier analysis demonstrated a strong association between syndecan-1 levels and 28-day mortality (log-rank 11.04, P  = 0.001). Elevated plasma levels of syndecan-1 has a potential role in systemic organ dysfunction and may be indicative of disease severity in patients with influenza A (H1N1).

Background and aimsMacrophage innate immune response plays an important role in tumorigenesis. However, the role and mechanism of macrophage STING signaling in modulating tumor microenvironment to suppress tumor growth at secondary sites remains largely unclear.MethodsSTING expression was assessed in liver samples from patients with colorectal cancer (CRC) liver metastasis. Global or myeloid stimulator of interferon gene (STING)-deficient mice, myeloid NOD-like receptor protein 3 (NLRP3)-deficient mice, and wild-type (WT) mice were subjected to a mouse model of CRC liver metastasis by intrasplenic injection of murine colon carcinoma cells (MC38). Liver non-parenchymal cells including macrophages and natural killer (NK) cells were isolated for flow cytometry analysis. Bone marrow-derived macrophages pretreated with MC38 were co-cultured with splenic NK cells for in vitro studies.ResultsSignificant activation of STING signaling were detected in adjacent and tumor tissues and intrahepatic macrophages. Global or myeloid STING-deficient mice had exacerbated CRC liver metastasis and shorten survival, with decreased intrahepatic infiltration and impaired antitumor function of NK cells. Depletion of NK cells in WT animals increased their metastatic burden, while no significant effects were observed in myeloid STING-deficient mice. STING activation contributed to the secretion of interleukin (IL)-18 and IL-1β by macrophages, which optimized antitumor activity of NK cells by promoting the expression of 4-1BBL in macrophages and 4-1BB in NK cells, respectively. Moreover, MC38 treatment activated macrophage NLRP3 signaling, which was inhibited by STING depletion. Myeloid NLRP3 deficiency increased tumor burden and suppressed activation of NK cells. NLRP3 activation by its agonist effectively suppressed CRC liver metastasis in myeloid SITNG-deficient mice.ConclusionsWe demonstrated that STING signaling promoted NLRP3-mediated IL-18 and IL-1β production of macrophages to optimize the antitumor function of NK cells via the co-stimulation signaling of 4-1BBL/4-1BB.