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The large-scale ammonia synthesis using the Haber-Bosch process is crucial in modern society and the reaction is known to be facile over Ru-based catalysts. Herein, first-principles kinetic Monte Carlo (kMC) simulations are utilized to explore the reaction kinetics on Ru nanoparticles (NPs), extending the current knowledge that is mainly based on calculations of single crystal surfaces. It is only by accounting for the effects of kinetic couplings between different sites and inherent strain in the NPs that experimental turnover frequencies (TOFs) can be reproduced. The enhanced activity of inherently strained NPs is attributed to the co-existence of sites with both tensile and compressive strain, which simultaneously promotes N 2 dissociation and NH x (x = 0, 1 and 2) hydrogenation. We propose that kinetic couplings on Ru NPs with tailored strain-patterns offer a strategy to break the limitations of linear scaling relations in the design of ammonia synthesis catalysts. Large-scale ammonia synthesis is crucial in modern society. Here, the authors present first principles based kinetic Monte Carlo simulations that reveal how the ammonia synthesis reaction can be enhanced over Ru nanoparticles.

Understanding the externalities of transportation networks in the process of the agglomeration and diffusion of production factors has theoretical and practical significance for the coordinated development of China's economic growth in urban agglomerations. Therefore, the social network analysis method is introduced in this paper with the case of the Pan Pearl River Delta Urban Agglomeration to analyze the characteristics of the traffic connection network of the production factor flow within this urban agglomeration, and subsequently, an econometric panel model is adopted to quantitatively analyze the effect of the connection network on the economic growth of the urban agglomeration. The results show that (1) the traffic connection of the Pan Pearl River Delta Urban Agglomeration has network characteristics typical of a “small world”. Although the connections between cities are gradually strengthening, the regional differences are obvious, showing a core–edge pattern of eastern agglomeration and western sparseness. (2) Among the network nodes, Guangzhou, Shenzhen and other cities have obvious agglomeration and diffusion effects, stabilizing economic growth while driving the development of surrounding cities. The \"polarization effect\" in Chongqing and Chengdu has significantly increased, and the accumulation of factors mainly meets their own economic development but has not yet spread. (3) The Pan Pearl River Delta Urban Agglomeration's transportation network influences the region’s economic growth through the structural effect, as it strengthens the economic ties between cities, and through the action of resource factors, as the network represents the aggregation and diffusion path of factor flow. (4) Due to the different traffic connections and industrial structures across the Pan Pearl River Delta Urban Agglomeration, the factor flow of each suburban agglomeration has a differentiated impact on the regional economic growth under the traffic connection network. Therefore, to realize the coordinated economic development of the Pan Pearl River Delta Urban Agglomeration, it is necessary to \"adjust measures to local conditions\" and formulate accurate and precise policies.

Engineering the reaction interface to preferentially attract reactants to inner Helmholtz plane is highly desirable for kinetic advancement of most electro-catalysis processes, including hydrogen evolution reaction (HER). This, however, has rarely been achieved due to the inherent complexity for precise surface manipulation down to molecule level. Here, we build a MoS 2 di-anionic surface with controlled molecular substitution of S sites by –OH. We confirm the –OH group endows the interface with reactant dragging functionality, through forming strong non-covalent hydrogen bonding to the reactants (hydronium ions or water). The well-conditioned surface, in conjunction with activated sulfur atoms (by heteroatom metal doping) as active sites, giving rise to up-to-date the lowest over potential and highest intrinsic activity among all the MoS 2 based catalysts. The di-anion surface created in this study, with atomic mixing of active sites and reactant dragging functionalities, represents a effective di-functional interface for boosted kinetic performance. H 2 energy as an alternative to fossil fuels requires cost-effective catalysts with fast kinetics for splitting water. Here, authors design MoS 2 materials with di-anionic surfaces to improve the electrocatalytic H 2 evolution activities.

Students’ motivation for using mobile phones is mainly entertainment, followed by a sense of achievement and finally social interaction. Mobile phone dependency is a real problem for students in higher vocational institutions and accounts for up to 90% of the population. Students’ mobile phone dependence is influenced by their motivation to use, and the result is that the individual is physically and mentally impaired, which has some negative impact on their social functioning. In tests of students’ mobile phone dependence, the results are out of control > avoidance > wariness > inefficiency. Three ways of intervention are recommended: by actively guiding students in the scientific use of mobile phones, by strengthening control over classroom order management and by establishing a harmonious teacher-student connection through mobile phones.

Over half of mitochondrial proteins are imported from the cytosol via the pre-sequence pathway, controlled by the TOM complex in the outer membrane and the TIM23 complex in the inner membrane. The mechanisms through which proteins are translocated via the TOM and TIM23 complexes remain unclear. Here we report the assembly of the active TOM–TIM23 supercomplex of Saccharomyces cerevisiae with translocating polypeptide substrates. Electron cryo-microscopy analyses reveal that the polypeptide substrates pass the TOM complex through the center of a Tom40 subunit, interacting with a glutamine-rich region. Structural and biochemical analyses show that the TIM23 complex contains a heterotrimer of the subunits Tim23, Tim17 and Mgr2. The polypeptide substrates are shielded from lipids by Mgr2 and Tim17, which creates a translocation pathway characterized by a negatively charged entrance and a central hydrophobic region. These findings reveal an unexpected pre-sequence pathway through the TOM–TIM23 supercomplex spanning the double membranes of mitochondria. Here the authors report structural and biochemical analyses of the mitochondrial TOM–TIM23 supercomplex, providing insights into how the substrates are transported through the outer and inner membranes.

Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model based on machine learning (ML) approaches for MASLD among the adult population. The national cross-sectional study collected data from the National Health and Nutrition Examination Survey from 2017 to 2020, consisting of 13,436 participants, who were randomly split into 70% training, 20% internal validation, and 10% external validation cohorts. MASLD was defined based on transient elastography and cardiometabolic risk factors. With 50 medical characteristics easily obtained, six ML algorithms were used to develop prediction models. Several evaluation parameters were used to compare the predictive performance, including the area under the receiver-operating-characteristic curve (AUC) and precision-recall (P-R) curve. The recursive feature elimination method was applied to select the optimal feature subset. The Shapley Additive exPlanations method offered global and local explanations for the model. The random forest (RF) model performed best in discriminative ability among 6 ML models, and the optimal 10-feature RF model was finally chosen. The final model could accurately predict MASLD in internal and external validation cohorts (AUC: 0.928, 0.918; area under P-R curve: 0.876, 0.863, respectively). The final model performed better than each of the traditional risk indicators for MASLD. An explainable 10-feature prediction model with excellent discrimination and calibration performance was successfully developed and validated for MASLD based on clinical data easily extracted using an RF algorithm.

Background The use of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) brings remarkable benefits for the survival of patients with advanced NSCLC harboring EGFR mutations. Unfortunately, acquired resistance seems to be inevitable and limits the application of EGFR-TKIs in clinical practice. This study reported a common molecular mechanism sustaining resistance and potential treatment options to overcome EGFR-TKIs resistance. Methods EGFR-TKIs resistant NSCLC cells were established and confirmed by MTT assay. Cholesterol content was detected and the promotional function of cholesterol on NSCLC growth was determined in vivo. Then, we identified ERRα expression as the downstream factor of cholesterol-mediated drug resistance. To dissect the regulatory mechanism, we conducted experiments, including immunofluorescence, co-immunoprecipitation, luciferase reporter assay and chromatin immunoprecipitation assay. Results Long-term exposure to EGFR-TKIs generate drug resistance with the characteristic of cholesterol accumulation in lipid rafts, which promotes EGFR and Src to interact and lead EGFR/Src/Erk signaling reactivation-mediated SP1 nuclear translocation and ERRα re-expression. Further investigation identifies ERRα as a target gene of SP1. Functionally, re-expression of ERRα sustains cell proliferation by regulating ROS detoxification process. Lovastatin, a drug used to decrease cholesterol level, and XCT790, an inverse agonist of ERRα, overcome gefitinib and osimertinib resistance both in vitro and in vivo. Conclusions Our study indicates that cholesterol/EGFR/Src/Erk/SP1 axis-induced ERRα re-expression promotes survival of gefitinib and osimertinib-resistant cancer cells. Besides, we demonstrate the potential of lowing cholesterol and downregulation of ERRα as effective adjuvant treatment of NSCLC.

Objective To explore the use of genomic screening for congenital adrenal hyperplasia (CAH) based on long-read sequencing (LRS), aiming to provide an effective method for LRS-based screening (LRSBCS). Methods All newborns underwent traditional CAH screening via the collection of dried blood spots. We conducted a retrospective clinical study of 73 cases, including 12 confirmed cases of CAH, 18 cases with false-positive biochemical screening results, and 43 healthy newborns as control. Full-length CAH-related genes, including CYP21A2 , CYP11B1 , CYP17A1 , HSD3B2 , and STAR were amplified and sequenced on a Sequel II platform (Pacific Biosciences). Results Among the 235,999 newborns, 12 were confirmed to have CAH, based on biochemical and/or genetic testing. The positive-predictive values of the initial and positive recall results were 0.60% (12/1958) and 3.68% (12/326), respectively. The 12 children with CAH were accurately diagnosed using LRSBCS. For LRS, the Bayesian-estimated sensitivity is 96.2% (95% CrI: 80.3%–99.9%) and the specificity is 99.2% (95% CrI: 98.0%–99.9%). Eleven pathogenic variants of CYP21A2 were detected, including eight SNVs/indels and three deletions. The most frequent variants were c.293–13 C > G (7/11) and c.518T > A (7/11). Furthermore, LRSBCS can directly report the characteristics of gene variants (cis or trans mutations) and effectively distinguish between functional genes and pseudogenes. Conclusions LRSBCS represents a novel molecular screening approach tailored specifically for CAH, demonstrating preliminary feasibility in clinical settings. Clinical trial number Not applicable.

The α-tubulin subtype, Tubulin α-1b chain (TUBA1B), has been shown to influence immune cell infiltration, cancer growth, and survival across various malignancies. However, a comprehensive study has not yet been undertaken examining the immunological and predictive effects of TUBA1B in a pan-carcinoma context. Using data from TCGA, GEO, and other databases, we analyzed TUBA1B expression across various carcinoma types using transcriptional profiling, prognostic implications, genetic and epigenetic alterations, methylation patterns, and immunological significance. To validate our findings, we conducted Western blot analysis to assess TUBA1B protein levels in matched breast cancer tissue samples and performed CCK-8 proliferation assay, flow cytometry, transwell invasion, and migration assays to comprehensively examine the functional impact of TUBA1B on breast cancer cells. Our pan-cancer analysis found TUBA1B upregulation across most tumor types, with varying expression patterns in distinct immune and molecular subtypes. High TUBA1B expression was an independent risk factor and associated with poor prognoses in several cancers, including BRCA, KICH, LGG, LUAD, and MESO. TUBA1B also demonstrates moderate to high diagnostic accuracy in most tumor types. Increased m6A methylation levels were observed in the TUBA1B gene, while its promoter region displayed low methylation levels. TUBA1B's expression impacted some cancers by elevating tumor mutation burden, microsatellite instability, neoantigen formation, immune cell infiltration, and the modulation of immune checkpoints. Functional enrichment analysis highlights TUBA1B’s involvement in important cellular processes such as the cell cycle, p53 signaling, cell senescence, programmed cell death, and the regulation of immune-related pathways. Moreover, our study reveals higher TUBA1B protein expression in breast cancer tissues compared to adjacent tissues. In vitro experiments confirm that TUBA1B deletion reduces breast cancer cell proliferation, invasion, and migration while increasing apoptosis. In conclusion, our study suggests that TUBA1B could potentially serve as a diagnostic marker for predicting cancer immunological profiles and survival outcomes and shed light on the expression and role of TUBA1B in breast cancer, providing a solid foundation for considering it as a promising therapeutic target for breast cancer patient treatment.

The strong coupling between lateral and longitudinal dynamics in autonomous vehicles presents a significant challenge for trajectory tracking control, especially under high-dynamic and complex conditions. To address this, this paper proposes a real-time optimal control method driven by a Reservoir Computing (RC)-based vehicle inverse dynamics model. The approach first involves training an RC network on a comprehensive vehicle dynamics dataset, covering multiple operating conditions, to learn the inverse mapping from accelerations to control commands. Second, an online correction mechanism incorporating Proportional-Derivative (PD) feedback is designed to dynamically adjust the desired acceleration inputs based on trajectory tracking errors. Finally, these corrected accelerations are fed into the trained RC network to rapidly compute high-precision control commands, completing the closed-loop tracking. Comprehensive simulations on double-lane-change, figure-eight, and Rössler chaotic trajectories demonstrate that the proposed method achieves high-precision tracking with remarkable computational efficiency and excellent robustness against control disturbances and sensor noise. Notably, moderate sensor noise exhibits trajectory-dependent performance enhancement, with system failure boundaries under combined disturbances clearly characterized.