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BackgroundUnderstanding the regional differences in child care is critical as the gendered division of child care in the family remains unequal between husbands and wives in China.ObjectiveThe study aims to assess how child care time is divided differently between husband and wife within the families in urban and rural sectors, and how these divisions are associated with factors such as one’s own or spouse’s employment status, educational achievement, and earnings.MethodWe analyzed data from the China Health and Nutrition Survey (2004, 2006, 2009, and 2011), using the relative resources theory, “doing gender” perceptive, as well as the gender attitudes model to explain gender differentials in child care among urban and rural families.ResultsThe gender difference in child care continues to persist but with a variation between urban and rural sectors. In addition to the wife’s own employment status, the husband’s employment status as well as income has played important roles in influencing the child care division inside the household.ConclusionsThe relative resources theory explains the pattern of the gendered division of child care in rural sectors but cannot account for the patterns in urban sectors. Instead, patterns in urban women’s child care time were more consistent with a “doing gender” perspective and urban men’s child care time were consistent with an egalitarian gender attitudes model.

Background Little research describes the mechanisms underlying depressive symptoms and personal relative deprivation in Chinese populations. Methods In this study, the respondents were ( N  = 914) residents of Beijing (17–59 years old) and robust multiple linear regressions were used to assess the main relationship between relative deprivation and depressive symptoms and social support as a potential moderator for that relationship. Results Individuals who reported higher personal relative deprivation had greater depressive symptoms than those who reported lower personal relative deprivation. Perceived social support buffered the relationship between depressive symptoms and personal relative deprivation. Conclusions The findings of this current study demonstrate the importance of relative deprivation for psychological strain and income in explaining how socioeconomic indices correlate with depressive symptoms. They also demonstrate the need to acknowledge the interaction of perceived social support and personal relative deprivation for influencing depression.

In order to improve the adaptability and robustness of gliding guidance under complex environments and multiple constraints, this study proposes an intelligent gliding guidance strategy based on the optimal guidance, predictor-corrector technique, and deep reinforcement learning (DRL). Longitudinal optimal guidance was introduced to satisfy the altitude and velocity inclination constraints, and lateral maneuvering was used to control the terminal velocity magnitude and position. The maneuvering amplitude was calculated by the analytical prediction of the terminal velocity, and the direction was learned and determined by the deep Q-learning network (DQN). In the direction decision model construction, the state and action spaces were designed based on the flight status and maneuvering direction, and a reward function was proposed using the terminal predicted state and terminal constraints. For DQN training, initial data samples were generated based on the heading-error corridor, and the experience replay pool was managed according to the terminal guidance error. The simulation results show that the intelligent gliding guidance strategy can satisfy various terminal constraints with high precision and ensure adaptability and robustness under large deviations.

In advanced integrated circuits, signal transmission delay arising from interconnect resistance is a main problem hindering the development of electronic devices, while the conventional several-nanometer-thick TaN/Ta barrier with high resistivity causes a surge in interconnect resistance due to the size effect. To address this issue, it is crucial to develop Cu barrier materials. Here, we design an integrated ultra-thin Cu diffusion barrier (~1.4 nm) consisting of single-atom Ru-supported reduced graphene oxide (Ru SA-rGO) and self-assembled monolayer (SAM) derived from (3-aminopropyl)triethoxysilane, which combines the dual functions of liner and barrier. The supporting of Ru requires N-doping as a bridge. Remarkably, the mean time-to-failure of devices with Ru SA-rGO/SAM is approximately 24 times longer than barrier-free devices. Ru atoms can both physically block Cu diffusion by filling rGO vacancies and chemically capture Cu through enhanced adsorption. Our work provides insight into diffusion barrier development in advanced Cu interconnects. This work designs a ~ 1.4 nm structure that integrates the dual functions of barrier and liner for advanced Cu interconnects. It blocks Cu diffusion up to 600 °C and extends the mean time-to-failure by 24 times compared to barrier-free devices.

Based on ab initio software packages using nonorthogonal localized orbitals, we develop a general scheme of calculating response functions. We test the performance of this method by calculating nonlinear optical responses of materials, like the shift current conductivity of monolayer WS2, and achieve good agreement with previous calculations. This method bears many similarities to Wannier interpolation, which requires a challenging optimization of Wannier functions due to the conflicting requirements of orthogonality and localization. Although computationally heavier compared to Wannier interpolation, our procedure avoids the construction of Wannier functions and thus enables automated high throughput calculations of linear and nonlinear responses related to electrical, magnetic and optical material properties.

With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology. While existing machine learning models have yielded superior performances in many occasions, most of them model and process molecular systems in terms of homogeneous graph, which severely limits the expressive power for representing diverse interactions. In practice, graph data with multiple node and edge types is ubiquitous and more appropriate for molecular systems. Thus, we propose the heterogeneous relational message passing network (HermNet), an end-to-end heterogeneous graph neural networks, to efficiently express multiple interactions in a single model with ab initio accuracy. HermNet performs impressively against many top-performing models on both molecular and extended systems. Specifically, HermNet outperforms other tested models in nearly 75%, 83% and 69% of tasks on revised Molecular Dynamics 17 (rMD17), Quantum Machines 9 (QM9) and extended systems datasets, respectively. In addition, molecular dynamics simulations and material property calculations are performed with HermNet to demonstrate its performance. Finally, we elucidate how the design of HermNet is compatible with quantum mechanics from the perspective of the density functional theory. Besides, HermNet is a universal framework, whose sub-networks could be replaced by other advanced models.

Brain tumors are the leading cause of cancer-related death in children. Tazemetostat is an FDA-approved enhancer of zeste homolog (EZH2) inhibitor. To determine its role in difficult-to-treat pediatric brain tumors, we examined EZH2 levels in a panel of 22 PDOX models and confirmed EZH2 mRNA over-expression in 9 GBM (34.6 ± 12.7-fold) and 11 medulloblastoma models (6.2 ± 1.7 in group 3, 6.0 ± 2.4 in group 4) accompanied by elevated H3K27me3 expression. Therapeutic efficacy was evaluated in 4 models (1 GBM, 2 medulloblastomas and 1 ATRT) via systematically administered tazemetostat (250 and 400 mg/kg, gavaged, twice daily) alone and in combination with cisplatin (5 mg/kg, i.p., twice) and/or radiation (2 Gy/day × 5 days). Compared with the untreated controls, tazemetostat significantly (Pcorrected < 0.05) prolonged survival times in IC-L1115ATRT (101% at 400 mg/kg) and IC-2305GBM (32% at 250 mg/kg, 45% at 400 mg/kg) in a dose-dependent manner. The addition of tazemetostat with radiation was evaluated in 3 models, with only one [IC-1078MB (group 4)] showing a substantial, though not statistically significant, prolongation in survival compared to radiation treatment alone. Combining tazemetostat (250 mg/kg) with cisplatin was not superior to cisplatin alone in any model. Analysis of in vivo drug resistance detected predominance of EZH2-negative cells in the remnant PDOX tumors accompanied by decreased H3K27me2 and H3K27me3 expressions. These data supported the use of tazemetostat in a subset of pediatric brain tumors and suggests that EZH2-negative tumor cells may have caused therapy resistance and should be prioritized for the search of new therapeutic targets. This study confirms the preservation of EZH2 overexpression in 22 patient-derived orthotopic xenograft models of pediatric brain tumors. The authors demonstrate the activity of an FDA-approved EZH2 inhibitor, tazemetostat, alone and in combination with radiation in a subset of the models, and identifies EZH2-negative cells as potential cause of therapy resistance.

In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape.

The protective effects of ripe Pu‐erh tea extract (PTE) against alcohol‐induced gastric injury were investigated through in vitro analysis, animal treatments, and nontargeted metabolomic analysis. In this study, in vitro analysis revealed that catechins were altered during digestive phases, whereas gallocatechin and gallic acid persisted until the intestinal period. In vitro antioxidant capacity of PTE could be maintained until the stage of gastric digestion. In acute alcohol‐exposed mice, PTE prevented alcohol‐induced gastric injury, which was characterized by significant reduction of gastric ulcer index. The gastric barrier integrity (PGE2, Muc2, Occludin and ZO‐1) was restored in PTE‐treated mice. The alcohol‐induced decreasing trends of antioxidant enzymatic activities (SOD and GSH) were significantly reversed by PTE, which was associated with the activation of oxidative stress pathways (Keap1/Nrf2/HO‐1). Besides, PTE reduced the levels of pro‐inflammatory cytokines (IL‐6, IL‐1β, and TNF‐α), likely owing to the inhibition of the NF‐κB pathway. Furthermore, nontargeted metabolomics identified elevated levels of anti‐inflammatory bile acids (e.g., hyodeoxycholic acid) in PTE‐treated mice, suggesting ethanol‐induced inflammation might be ameliorated by PTE through modulation of bile acid metabolism. Overall, PTE could be a functional beverage for treating acute alcohol consumption‐induced gastric injury and metabolomic disorders. PTE ameliorates alcohol‐induced gastric injury by regulating oxidative stress and inflammatory responses. Activation of the Keap1/Nrf2/HO‐1 pathway and suppression of NF‐κB pathway may represent the molecular mechanisms by which PTE mediates antioxidative and anti‐inflammatory effects.

Aqueous zinc‐ion batteries (AZIBs) represent a promising next‐generation energy storage solution. However, AZIBs suffer from severe dendrite growth caused by rampant Zn2⁺ 2D diffusion and sluggish desolvation kinetics, thus exhibiting extremely short cycle life under high‐rate conditions. Here in, a novel additive DL‐O‐Methylserine (MeSer) is reported, which effectively optimizes Zn2⁺ diffusion behavior and facilitates the desolvation process. Experimental and computational results reveal that MeSer− adsorption on the electrode surface compresses the electric double layer (EDL), thereby reducing repulsive forces within it. The decrease in repulsion further enhances Zn2⁺ 3D diffusion leading to uniform deposition. Furthermore, MeSer− interacts with Zn2⁺ located in solvation sheath, reducing desolvation energy barriers and improving rate capability. Consequently, Zn||Zn symmetric cells with MeSer exhibits superior cycling stability of 2320 h under 5 mA cm−2 and 5 mA h cm−2 and can endure extreme high‐current conditions (20 mA cm−2, 20 mA h cm−2) for up to 600 h, such performance exceeds most of the previously documented results. The Zn||V2O5 full cells maintained 86% capacity retention after 3500 cycles at 5 A g−1. This work demonstrates the remarkable effectiveness of a simple EDL regulation strategy in enhancing AZIB performance. Here, the ionic additive MeSer compresses the EDL, reducing its thickness and internal repulsion, thereby enhancing 3D diffusion to achieve smooth Zn deposition. Meanwhile, MeSer− promote Zn2⁺ desolvation within the EDL, improving rate performance. Consequently, the symmetric cell operates for 600 h at 20 mA cm−2 and 20 mA h cm−2, and the full cell stably cycles over 3500 cycles at 5 A g−1.