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ABSTRACT Phagocytosis of Mycobacterium tuberculosis (Mtb) followed by its integration into the matured lysosome is critical in the host defense against tuberculosis. How Mtb escapes this immune attack remains elusive. In this study, we unveiled a novel regulatory mechanism by which SIRT7 regulates cytoskeletal remodeling by modulating RAC1 activation. We discovered that SIRT7 expression was significantly reduced in CD14+ monocytes of TB patients. Mtb infection diminished SIRT7 expression by macrophages at both the mRNA and protein levels. SIRT7 deficiency impaired actin cytoskeleton-dependent macrophage phagocytosis, LC3II expression, and bactericidal activity. In a murine tuberculosis model, SIRT7 deficiency detrimentally impacted host resistance to Mtb, while Sirt7 overexpression significantly increased the host defense against Mtb, as determined by bacterial burden and inflammatory–histopathological damage in the lung. Mechanistically, we demonstrated that SIRT7 limits Mtb infection by directly interacting with and activating RAC1, through which cytoskeletal remodeling is modulated. Therefore, we concluded that SIRT7, in its role regulating cytoskeletal remodeling through RAC1, is critical for host responses during Mtb infection and proposes a potential target for tuberculosis treatment.IMPORTANCETuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global health issue. Critical to macrophages’ defense against Mtb is phagocytosis, governed by the actin cytoskeleton. Previous research has revealed that Mtb manipulates and disrupts the host’s actin network, though the specific mechanisms have been elusive. Our study identifies a pivotal role for SIRT7 in this context: Mtb infection leads to reduced SIRT7 expression, which, in turn, diminishes RAC1 activation and consequently impairs actin-dependent phagocytosis. The significance of our research is that SIRT7 directly engages with and activates Rac Family Small GTPase 1 (RAC1), thus promoting effective phagocytosis and the elimination of Mtb. This insight into the dynamic between host and pathogen in TB not only broadens our understanding but also opens new avenues for therapeutic development.

Abstract Senescent cell clearance is emerging as a promising strategy for treating age-related diseases. Senolytics are small molecules that promote the clearance of senescent cells; however, senolytics are uncommon and their underlying mechanisms remain largely unknown. Here, we investigated whether genomic instability is a potential target for senolytic. We screened small-molecule kinase inhibitors involved in the DNA damage response (DDR) in Zmpste24 −/− mouse embryonic fibroblasts, a progeroid model characterized with impaired DDR and DNA repair. 4,5,6,7-tetrabromo-2-azabenzamidazole (TBB), which specifically inhibits casein kinase 2 (CK2), was selected and discovered to preferentially trigger apoptosis in Zmpste24 −/− cells. Mechanistically, inhibition of CK2 abolished the phosphorylation of heterochromatin protein 1α (HP1α), which retarded the dynamic HP1α dissociation from repressive histone mark H3K9me3 and its relocalization with γH2AX to DNA damage sites, suggesting that disrupting heterochromatin remodeling in the initiation of DDR accelerates apoptosis in senescent cells. Furthermore, feeding Zmpste24-deficient mice with TBB alleviated progeroid features and extended their lifespan. Our study identified TBB as a new class senolytic compound that can reduce age-related symptoms and prolong lifespan in progeroid mice.

Highlights Digital holography can realize the in situ observation of electrode/electrolyte interface and provide dynamic evolution information of the liquid phase of electrode, which is both efficient and effective in investing the interficial electrochemical mechanism and screening electrolyte additives. Thioacetamide electrolyte additive effectively enhances the electrochemical performance of Zn anode by regulating the interficial ion flux to induce dendrite-free Zn deposition and constructing adsorption molecule layers to inhibit side reactions.

Tailoring the morphologies and optical properties of the 2D and hierarchical nanostructures self-assembled by the π-conjugated molecules is both interesting and challenging. Herein, a series of 2D ribbon-like nanostructures with single or multiple H-aggregated perylene bisimides (PBI) monolayer and hierarchical nanostructures (including straw-like, dumbbell-shaped, and rod-like nanostructures) are fabricated by solution self-assembly of three chiral alanine-decorated PBI. The influence of the solvent’s dissolving capacity, the chirality of alanine, and the preparation methods on the morphologies and optical properties of the nanostructures were extensively studied. It was observed that the hierarchical nanostructures are formed by the reorganization of the 2D ribbon-like nanostructures. The size of the 2D ribbon-like nanostructures and the amount of the hierarchical nanostructures increase with the decrease in the solvent’s dissolving capacity. The small chiral alanine moiety is unable to induce chirality in the nanostructures, owing to its low steric hindrance and the dominant strong π-π stacking interaction of the PBI skeleton. A weaker π-π stacking interaction and better H-aggregated arrangement of the PBI skeleton could reduce the low-wavelength fluorescence intensity. The process of heating, cooling, and aging promotes the formation of H-aggregation in the PBI skeleton. The region of spectral overlap of the PBI solutions increases with the decrease in the dissolving capacity of the solvent and the steric hindrance of the chiral alanine. This study supplies a view to tailor the morphologies and optical properties of the nanostructures, which could be used as sensors and photocatalysts.

Summary: Triacylglyceride (TAG) synthesis in the small intestine determines the absorption of dietary fat, but the underlying mechanisms remain to be further studied. Here, we report that the RNA-binding protein HuR (ELAVL1) promotes TAG synthesis in the small intestine. HuR associates with the 3′ UTR of Dgat2 mRNA and intron 1 of Mgat2 pre-mRNA. Association of HuR with Dgat2 3′ UTR stabilizes Dgat2 mRNA, while association of HuR with intron 1 of Mgat2 pre-mRNA promotes the processing of Mgat2 pre-mRNA. Intestinal epithelium-specific HuR knockout reduces the expression of DGAT2 and MGAT2, thereby reducing the dietary fat absorption through TAG synthesis and mitigating high-fat-diet (HFD)-induced non-alcoholic fatty liver disease (NAFLD) and obesity. Our findings highlight a critical role of HuR in promoting dietary fat absorption.

Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading conditions, this study proposes a hybrid learning model for the intelligent prediction of the unloading strength of rocks using simple parameters in rock unloading tests. The XGBoost technique was used to construct a model, and the PSO-XGBoost hybrid model was developed by employing particle swarm optimization (PSO) to refine the XGBoost parameters for better prediction. In order to verify the validity and accuracy of the proposed hybrid model, 134 rock sample sets containing various common rock types in rock excavation were collected from international and Chinese publications for the purpose of modeling, and the rock unloading strength prediction results were compared with those obtained by the Random Forest (RF) model, the Support Vector Machine (SVM) model, the XGBoost (XGBoost) model, and the Grid Search Method-based XGBoost (GS-XGBoost) model. Meanwhile, five statistical indicators, including the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), were calculated to check the acceptability of these models from a quantitative perspective. A review of the comparison results revealed that the proposed PSO-XGBoost hybrid model provides a better performance than the others in predicting rock unloading strength. Finally, the importance of the effect of each input feature on the generalization performance of the hybrid model was assessed. The insights garnered from this research offer a substantial reference for tunnel excavation design and other representative projects.

Developing a flow rate model for the screw feeder and optimizing discharge performance are crucial for achieving automated intelligent precision feeding. This study constructs a mass flow rate model for screw conveyors, considering the coupled structural parameters of the hopper and screw conveyor. The model is developed using single-factor tests and central composite design (CCD) response surface tests and is validated through actual discharge tests. Results indicate that the discharge rate in the hopper–screw conveyor system is primarily influenced by the screw conveyor itself. Among the structural parameters, the hopper inclination angle and the hopper discharge opening length significantly affect the filling coefficient. Validation tests show an average error of 6.8% between the predicted and simulated mass flow rates and 5.0% with the actual mass flow rate, demonstrating the model’s high precision and accuracy.

One popular method of obtaining the acoustic parameters of seabed sediments is by measuring the sound velocity of sediment samples in a laboratory. However, the effects of environmental variation and physical perturbation on acoustic properties are typically neglected in the application of such measured acoustic parameters. In this study, sediment samples were collected from the Huanghai and Bohai seas to measure sound velocity both on the deck and in the laboratory using a digital sonic measuring system. Additionally, sediment compositions, physical and mechanical properties and microstructures were determined. Sound velocity comparisons between on-deck and laboratory measurements indicate that laboratory-measured velocities are generally higher than those measured on the deck, with differences ranging from 1.45 m/s to 130.05 m/s, due to the water loss and densification of sedimentary particles in laboratory settings. The discrepancy of sound velocities measured in the laboratory and on the deck also differ based on the types of sediments sampled. Overall, the discrepancy increases with measurement frequency, increasing average grain diameter and sand content, as well as with decreasing clay content.

Based on data on the shear wave speed and physical properties of the shallow sediment samples collected in the northwest South China Sea, the hyperparameter selection and contribution of the characteristic factors of the machine learning model for predicting the shear wave speed of seafloor sediments were studied using the eXtreme Gradient Boosting (XGBoost) algorithm. An XGBoost model for predicting the shear wave speed of seafloor sediments was established based on four physical parameters of the sediments: porosity (n), water content (w), density (ρ), and average grain size (MZ). The result reveals that: (1) The shear wave speed has a good correlation with n, w, ρ, and MZ, and their Pearson correlation coefficients are all above 0.75, indicating that they can be used as the suitable characteristic parameters for predicting the shear wave speed based on the XGBoost model; (2) When the number of weak learners (n_estimators) is 115 and the maximum depth of the tree (max_depth) is 6, the XGBoost model has a very high goodness of fit (R2) of the validation data of 0.914, the very low mean absolute error (MAE) and mean absolute percentage error (MAPE) of the predicted shear wave speed are 3.366 m/s and 9.90%, respectively; (3) Compared with grain-shearing (GS) model and single- and dual-parameter regression equation prediction models, the XGBoost model for the shear wave speed of seafloor sediments has higher fitting goodness and lower prediction error.

The highly conserved Notch signaling pathway affects embryonic development, neurogenesis, homeostasis, tissue repair, immunity, and numerous other essential processes. Although previous studies have demonstrated the location and function of the core components of Notch signaling in various animal phyla, a more comprehensive summary of the Notch core components in lower organisms is still required. In this review, we objectively summarize the molecular features of the Notch signaling pathway constituents, their current expression profiles, and their functions in invertebrates, with emphasis on their effects on neurogenesis and regeneration. We also analyze the evolution and other facets of Notch signaling and hope that the contents of this review will be useful to interested researchers.

Graphene and its derivatives have been confirmed to be among the best fillers for rubber due to their excellent properties, such as high mechanical strength, improved interface interaction, and strain-induced crystallization capabilities. Graphene rubber materials can be widely used in tires, shoes, high-barrier conductive seals, electromagnetic shielding seals, shock absorbers, etc. In order to reduce the graphene loading and endow more desirable functions to rubber materials, graphene-based hybrid fillers are extensively employed, which can effectively enhance the performance of rubber composites. This review briefly summarizes the recent research on rubber composites with graphene-based hybrid fillers consisting of carbon black, silica, carbon nanotubes, metal oxide, and one-dimensional nanowires. The preparation methods, performance improvements, and applications of different graphene-based hybrid fillers/rubber composites have been investigated. This study also focuses on methods that can ensure the effectiveness of graphene hybrid fillers in reinforcing rubber composites. Furthermore, the enhanced mechanism of graphene- and graphene derivative-based hybrid fillers in rubber composites is investigated to provide a foundation for future studies.

This study focuses on the inversion of sub-bottom profile (SBP) data in the northern South China Sea using an empirical relationship derived from sediment acoustic data. The sub-bottom profile is primarily utilized for various marine applications, such as geological mapping and resource exploration. In this research, we present a study conducted in the northern slope canyon of the South China Sea. Firstly, we obtained the seabed reflection coefficient from sub-bottom profiles obtained by the autonomous underwater vehicle (AUV) detection system. Secondly, we utilized the acoustic empirical relationship in the northern South China Sea to establish relationship equations between the seabed reflection coefficient and the porosity, density, and average particle size of the sediment at a main frequency of 4 kHz (the AUV shallow profile main frequency). Then, using these equations, we were able to invert the physical parameters such as porosity, density, and average particle size of the seabed surface sediments. Finally, the inverted results are compared and analyzed by using the sediment samples test data. The overall deviation rate of the inverted physical parameters is within the range of ±10% when compared. The inverted results closely match the measured values, accurately reflecting the dynamic changes in the physical properties of seabed surface sediments. Notably, the average grain size is a direct indicator of the sediment particles size with smaller particles found in deeper water. The variation characteristics of sediment physical parameters align well with the variation of sediment types in the canyon, which is consistent with changes in the water depth, topography, and hydrodynamic conditions of the area. This further demonstrates the reliability of the inversion results.