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In aqueous aluminum‐ion batteries (AAIBs), the insertion/extraction chemistry of Al3+ often leads to poor kinetics, whereas the rapid diffusion kinetics of hydronium ions (H3O+) may offer the solution. However, the presence of considerable Al3+ in the electrolyte hinders the insertion reaction of H3O+. Herein, we report how oxygen‐deficient α‐MoO3 nanosheets unlock selective H3O+ insertion in a mild aluminum‐ion electrolyte. The abundant oxygen defects impede the insertion of Al3+ due to excessively strong adsorption, while allowing H3O+ to be inserted/diffused through the Grotthuss proton conduction mechanism. This research advances our understanding of the mechanism behind selective H3O+ insertion in mild electrolytes. [ABSTRACT FROM AUTHOR]

In aqueous aluminum‐ion batteries (AAIBs), the insertion/extraction chemistry of Al3+ often leads to poor kinetics, whereas the rapid diffusion kinetics of hydronium ions (H3O+) may offer the solution. However, the presence of considerable Al3+ in the electrolyte hinders the insertion reaction of H3O+. Herein, we report how oxygen‐deficient α‐MoO3 nanosheets unlock selective H3O+ insertion in a mild aluminum‐ion electrolyte. The abundant oxygen defects impede the insertion of Al3+ due to excessively strong adsorption, while allowing H3O+ to be inserted/diffused through the Grotthuss proton conduction mechanism. This research advances our understanding of the mechanism behind selective H3O+ insertion in mild electrolytes. [ABSTRACT FROM AUTHOR]

Explosion flow fields are characterized by shock waves with varying intensity and position (i.e., explosive loads), which are the primary causes of structural damage. Accurate and rapid prediction of explosive loads is crucial for structural blast-resistant design and daily security management. While existing empirical models and numerical simulation methods can capture the propagation characteristics of explosive shock waves, high-precision simulation requires a massive computational workload, which is insufficient to meet the fast computational demands of various explosive scenarios. To address this contradiction, this study constructed a sparse reconstruction model for two-dimensional explosion fields based on machine learning algorithms. The model utilizes sparse observational data to establish a mapping relationship to the distribution of the entire flow field. The model is built by a physics-informed graph neural network (PIGN). The graph neural network is employed to associate node features, while the physical network is utilized to control model convergence, aiming to enhance model performance. Using the constructed dataset, the PIGN model was tested. Performance and generalization capabilities of the model were assessed by comparing its results with numerical simulation. This evaluation analyzed the relative error distribution and error statistical results of the reconstructed flow field. The results indicate that the PIGN model can effectively reconstruct explosion fields, with an average error in the reconstructed flow field below 4%. Furthermore, when the number of probe points reaches 10, the average error of the flow field reconstructed by the model is close to 6%. This model not only provides a highly reliable distribution of explosion overpressure and pressure-time variations but also, with a well-trained model, accomplishes flow field reconstruction within 1 ms. It offers a novel approach for achieving rapid and reasonable prediction of explosion fields or two-dimensional compressible flow fields. [ABSTRACT FROM AUTHOR]

Background: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies. Methods: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies. Results: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue. Conclusion: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies. [ABSTRACT FROM AUTHOR]

Groundwater has been a relatively safe water source and associated with a series of global pressing issues. It can, however, be vulnerable to pathogen contamination and exhibit risks to public health. While groundwater has often been considered a relatively closed ecosystem, it is important to understand the transport of pathogens into and within groundwater and groundwater-dependent ecosystems in the context of their physiological characteristics, soil/sediment texture, hydrogeological and climatic conditions. The rapid progress in monitoring and modeling tools in recent years has facilitated the detection of pathogens and the prediction of their migration in groundwater, which aids the surveillance and remediation of health-related risks. This review aims to provide an updated understanding about groundwater pathogens, their impacts on human health, surveillance, risk assessment, and remediation strategies in the context of the global environment and climate changes. The challenges and perspectives in protecting groundwater biosafety are also discussed. [ABSTRACT FROM AUTHOR]

In this research paper, we investigate a generalization of Vincze's type functional equations involving several (up to four) unknown functions in connection with the maximum functional equation as max { ψ (x y) , ψ (x y - 1) } = ψ (x) η (y) + ψ (y) , max { ψ (x y) , ψ (x y - 1) } = ψ (x) η (y) + χ (y) , max { ψ (x y) , ψ (x y - 1) } = ϕ (x) η (y) , max { ψ (x y) , ψ (x y - 1) } = ϕ (x) η (y) + χ (y) , where G is an arbitrary group, x , y ∈ G , and ψ , η , χ , ϕ : G → R are unknown functions. [ABSTRACT FROM AUTHOR]

This research advances the workplace thriving literature by offering a multilevel view regarding the impact of positive affective resources on employee and team thriving. We conducted our study with 285 employees from 62 teams to examine a multilevel model involving the relationship between high‐activated positive affect (HAPA) and thriving at individual and team levels. Results demonstrated that team HAPA triggered team cohesion, which in turn enhanced team thriving, and that individual HAPA promoted individual thriving. While task interdependence did not moderate the effects of team HAPA on team cohesion or, in turn, on team thriving, cross‐level moderation showed that task interdependence strengthened the relationship between individual HAPA and individual thriving. These findings extend the knowledge regarding the relationship between positive affect and thriving by confirming the role of affect activation, identifying a team‐level mechanism, and clarifying a boundary condition. [ABSTRACT FROM AUTHOR]

Using high temperature CO 2 sorbent for carbon capture is an effective approach to reduce carbon footprint. Copper slag, as an abundant metallurgical waste containing substantial amount of Si and Fe resources, was being explored as cheap raw material for the preparation of lithium-based sorbent. The results shows that the lithiation is initiated at 450–600 °C, where fayalite and magnetite in slag are firstly oxidized by released water vapor from the decomposition of LiOH, then further react with Li 2 O to form Li 4 SiO 4 and LiFeO 2. As the temperature rises, the conversion of LiFeO 2 to Li 5 FeO 4 and incorporation of Fe3+, Al3+ into Li 4 SiO 4 lattice occur in parallel. The resulting sorbent exhibited excellent CO 2 chemisorption ability with a capture capacity of 420 mg/g material at 725 °C. Particularly, even in the gas stream containing 5 vol% CO 2 , it showed a CO 2 uptake capacity of 383 mg/g material. In the CO 2 cycling tests, the sorbent suffered a decay in sorption capacity, which was ascribed to the poor regeneration ability of Li 5 FeO 4. Comparatively, it possesses a relatively high capacity of 241 mg/g material in 20 vol% CO 2 after 20 cycles, which is higher than those of previously reported Li-silicate sorbents derived from wastes. [ABSTRACT FROM AUTHOR]

Background: The relation of social deprivation with single cardiometabolic disease (CMD) was widely investigated, whereas the association with cardiometabolic multi-morbidity (CMM), defined as experiencing more than two CMDs during the lifetime, is poorly understood. Methods: We analyzed 345,417 UK Biobank participants without any CMDs at recruitment to study the relation between social deprivation and four CMDs including type II diabetes (T2D), coronary artery disease (CAD), stroke and hypertension. Social deprivation was measured by Townsend deprivation index (TDI), and CMM was defined as occurrence of two or more of the above four diseases. Multivariable Cox models were performed to estimate hazard ratios (HRs) per one standard deviation (SD) change and in quartile (Q1-Q4, with Q1 as reference), as well as 95% confidence intervals (95% CIs). Results: During the follow up, 68,338 participants developed at least one CMD (median follow up of 13.2 years), 16,225 further developed CMM (median follow up of 13.4 years), and 18,876 ultimately died from all causes (median follow up of 13.4 years). Compared to Q1 of TDI (lowest deprivation), the multivariable adjusted HR (95%CIs) of Q4 (highest deprivation) among participants free of any CMDs was 1.23 (1.20 ~ 1.26) for developing one CMD, 1.42 (1.35 ~ 1.48) for developing CMM, and 1.34 (1.27 ~ 1.41) for all-cause mortality. Among participants with one CMD, the adjusted HR (95%CIs) of Q4 was 1.30 (1.27 ~ 1.33) for developing CMM and 1.34 (1.27 ~ 1.41) for all-cause mortality, with HR (95%CIs) = 1.11 (1.06 ~ 1.16) for T2D patients, 1.07 (1.03 ~ 1.11) for CAD patients, 1.07 (1.00 ~ 1.15) for stroke patients, and 1.24 (1.21 ~ 1.28) for hypertension patients. Among participants with CMM, TDI was also related to the risk of all-cause mortality (HR of Q4 = 1.35, 95%CIs 1.28 ~ 1.43). Conclusions: We revealed that people living with high deprived conditions would suffer from higher hazard of CMD, CMM and all-cause mortality. [ABSTRACT FROM AUTHOR]

Simple Summary: Phosphorus is an essential nutrient for plants. It is usually available in the form of inorganic phosphate in the plant environment. Yet, most environments contain extremely low amounts of phosphate, causing a major nutritional deficiency for plant life. During their evolution, plants have acquired a number of adaptations that help them to survive chronic phosphate deficit. Many of these adaptations are documented in the scientific literature and show changes in gene expression and modifications in the levels of enzymes and metabolites involved in plant carbon and respiratory metabolism. This research uses potato cells cultivated in vitro to measure major metabolic carbon fluxes (rates at which molecules are processed, consumed or degraded) in response to two phosphate regimes (normal and deficiency). Our results show a remarkable stability of several metabolic fluxes in cells regardless of their phosphate regime. This is the case for the rate at which the carbon source is taken up from the medium. Two important metabolic fluxes used to fuel cell respiration are also not affected by phosphate. In all the experiments, cell age is the main factor affecting carbon metabolic fluxes. These findings lead us to conclude that potato cells maintain stable carbon fluxes during phosphate deficiency. Nutritional phosphate deficiency is a major limitation to plant growth. Here, we monitored fluxes in pathways supporting respiratory metabolism in potato (Solanum tuberosum) cell cultures growing in control or limiting phosphate conditions. Sugar uptake was quantified using [U-14C]sucrose as precursor. Carbohydrate degradation through glycolysis and respiratory pathways was estimated using the catabolism of [U-14C]sucrose to 14CO2. Anaplerotic carbon flux was assessed by labeling with NaH14CO3. The data showed that these metabolic fluxes displayed distinct patterns over culture time. However, phosphate depletion had relatively little impact on the various fluxes. Sucrose uptake was higher during the first six days of culture, followed by a decline, which was steeper in Pi-sufficient cells. Anaplerotic pathway flux was more important at day three and decreased thereafter. In contrast, the flux between sucrose and CO2 was at a maximum in the mid-log phase of the culture, with a peak at Day 6. Metabolization of [U-14C]sucrose into neutral, basic and acidic fractions was also unaffected by phosphate nutrition. Hence, the well-documented changes in central metabolism enzymes activities in response to Pi deficiency do not drastically modify metabolic fluxes, but rather result in the maintenance of the carbon fluxes that support respiration. [ABSTRACT FROM AUTHOR]

This paper introduces a novel surrogate model for two-dimensional adaptive steady-state thermal convection fields based on deep learning technology. The proposed model aims to overcome limitations in traditional frameworks caused by network types, such as the requirement for extensive training data, accuracy loss due to pixelated preprocessing of original data, and inability to predict information near the boundaries with precision. We propose a new framework that consists primarily of a physical-informed neural network (PINN) and a graph convolutional neural network (GCN). The GCN serves as the prediction module and predicts thermal convection in the two-dimensional computational domain by considering the mutual influence between unstructured nodes and their neighbors. On the other hand, the PINN acts as the physical constraint module of the framework by embedding the control equation of thermal convection into the loss function of the neural network, ensuring that the inference and prediction results of the GCN comply with the constraints of the control equation. The advantages of this framework lie in two aspects. First, the computation mechanism of the GCN is more in line with the actual evolution of temperature fields. Second, the PINN enhances the cognitive ability of the surrogate model toward the convection field information. It accurately describes the changes of temperature gradient information at the boundary position and reduces the model's demand for training data. To validate the advantages of the proposed model, we gradually analyzed the model's geometric adaptability and predictive accuracy from the single cylinder case to the double cylinder case. We also investigated the impact of the number of sampling points on model training and compared the model's prediction results with those of a purely data-driven model. The results show that the proposed model exhibits good geometric adaptability and stability. With only 20 training data, the mean error of the proposed model in predicting the velocity and temperature field is less than 1% and 0.6% for the single cylinder, and less than 2% and 1% for the double cylinder case, while the mean error of the purely data-driven GCN model in predicting the velocity and temperature field is 9.4% and 6.4% for the double cylinder case. These findings demonstrate the effectiveness of the proposed physics-informed graph convolutional neural network, allowing for more accurate prediction of fluid flow and heat convection using surrogate model. [ABSTRACT FROM AUTHOR]

This article introduces the research community to the power of machine learning over traditional approaches when analyzing longitudinal data. Although traditional approaches work well with small to medium datasets, machine learning models are more appropriate as the available data becomes larger and more complex. Additionally, machine learning methods are ideal for analyzing longitudinal data because they do not make any assumptions about the distribution of the dependent and independent variables or the homogeneity of the underlying population. They can also analyze cases with partial information. In this article, we use the Household, Income, and Labour Dynamics in Australia (HILDA) survey to illustrate the benefits of machine learning. Using a machine learning algorithm, we analyze the relationship between job‐related variables and neuroticism across 13 years of the HILDA survey. We suggest that the results produced by machine learning can be used to generate generalizable rules from the data to augment our theoretical understanding of the domain. With a technical guide, this article offers critical information and best‐practice recommendations that can assist social science researchers in conducting machine learning analysis with longitudinal data. [ABSTRACT FROM AUTHOR]

This study offers a new perspective on how organisational factors influence migrant workers' cultural intelligence (CQ) by examining a moderated mediation model of the mechanism underlying the relationship between perceived supervisor support and CQ. We tested our model using a survey on a sample of 462 migrants. We found that employees' social exchange and subjective career success mediated the relationship between their perceptions of supervisor support and CQ. Furthermore, perceived organisational support moderated the social exchange–CQ relationship, and this relationship was stronger among workers with perceived high organisational support than for those with perceptions of low support. We also found that the indirect effect of employees' perceived supervisor support on CQ via social exchange was stronger for those with perceived high organisational support than for those with perceptions of low support. This study contributes to a better understanding of factors that foster migrant workers' abilities to navigate diverse workplaces. [ABSTRACT FROM AUTHOR]

Drawing on job demands-resources theory, we propose that perceived red tape, as a hindrance job demand, triggers attitudinal and behavioral precursors of turnover in employees (turnover intentions and job search behaviors) by reducing their work engagement. In addition, we hypothesize that career adaptability, as a personal resource, buffers the detrimental effects of perceived red tape. In Study 1, three-wave data collected from employees (N = 202) working in Tanzanian public sector organizations supports the finding that work engagement mediates the effect of red tape on turnover intentions. Study 2 confirms this mediation, using data (N = 405) collected at three time points from a Chinese private organization, further verifying the mediating role of work engagement in the effect of red tape on job search behaviors. Supporting the moderating role of career adaptability, Study 2 also found that career adaptability attenuated the influence of red tape on work engagement and, subsequently, on turnover intentions and job search behaviors. Our article theoretically and empirically contributes to the understanding of how and when perceived red tape in organizations leads employees to consider leaving and prepare to leave. [ABSTRACT FROM AUTHOR]

Objective: As one of the most common endocrine disorders in women of reproductive age, polycystic ovary syndrome (PCOS) is highly heterogeneous with varied clinical features and diverse gestational complications among individuals. The patients with PCOS have 2-fold higher risk of preterm labor which is associated with substantial infant morbidity and mortality and great socioeconomic cost. The study was designated to identify molecular subtypes and the related hub genes to facilitate the susceptibility assessment of preterm labor in women with PCOS. Methods: Four mRNA datasets (GSE84958, GSE5090, GSE43264 and GSE98421) were obtained from Gene Expression Omnibus database. Twenty-eight candidate genes related to preterm labor or labor were yielded from the researches and our unpublished data. Then, we utilized unsupervised clustering to identify molecular subtypes in PCOS based on the expression of above candidate genes. Key modules were generated with weighted gene co-expression network analysis R package, and their hub genes were generated with CytoHubba. The probable biological function and mechanism were explored through Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. In addition, STRING and Cytoscape software were used to identify the protein-protein interaction (PPI) network, and the molecular complex detection (MCODE) was used to identify the hub genes. Then the overlapping hub genes were predicted. Results: Two molecular subtypes were found in women with PCOS based on the expression similarity of preterm labor or labor-related genes, in which two modules were highlighted. The key modules and PPI network have five overlapping five hub genes, two of which, GTF2F2 and MYO6 gene, were further confirmed by the comparison between clustering subgroups according to the expression of hub genes. Conclusions: Distinct PCOS molecular subtypes were identified with preterm labor or labor-related genes, which might uncover the potential mechanism underlying heterogeneity of clinical pregnancy complications in women with PCOS. [ABSTRACT FROM AUTHOR]

This paper presents a novel reduced-order model for internal and external flow field estimations based on a sparse convolution neural network. Since traditional convolution neural network requires "rectangular" matrixes as input, the convolutional operation is computationally inefficient when applied to these problems with input matrix having sparse information. In our approach, we innovatively introduce a sparse convolution neural network (SCNN) to collect spatial information on geometries that are inherently sparse, e.g., the flow in thin pipelines in a much larger domain or the pipelines with random branches. Different from the traditional convolution neural network (CNN) model, the SCNN only collects features from areas with flow information for both the input matrix and each convolutional layer, which not only reduces the consumption of computational resources but also significantly increases network attention to flow area. The model learns the mapping relationship between geometries and the physical field of fluid flow, and the spatial positions of geometry are represented using the nearest wall signed distance function. The proposed SCNN model has the promising adaptability to arbitrary geometry and less computational resource cost compared to the traditional CNN model: the mean error of the SCNN is less than 5.4% (while the CNN is 7.1%) for the internal flow and less than 6.5% (while the CNN is 8.1%) for the external flow. Moreover, the SCNN has 72% less GPU resource usage and 52% less random access memory cost than the CNN for internal flow. For the first time, our framework introduces the sparse convolution network to flow field prediction, and the SCNN shows outstanding performance on prediction accuracy and computational resource saving for the flow problems with a sparse input information. [ABSTRACT FROM AUTHOR]

BACKGROUND: Nutritional risk is common among patients admitted to the emergency department and is associated with adverse clinical outcomes. Despite its large population, few comprehensive studies have been conducted in China concerning the nutritional status of patients admitted to emergency department observation units (EDOUs). METHODS: Patients admitted to EDOUs of 90 tertiary hospitals in China between June 2020 and December 2020 were enrolled. Demographic information, laboratory parameters, nutritional support therapies, and 28-day mortality were recorded. Risk factors for mortality were examined using multi-variate-adjusted logistic regression analysis. Receiver operating characteristic (ROC) curves for each predictor of mortality were plotted, and the area under the ROC (AUROC) curves was compared. RESULTS: A total of 2,005 eligible patients were finally enrolled. At the 28-day follow-up, 1,911 patients survived, and 94 died. The group with a Nutritional Risk Screening 2002 (NRS 2002) score of 3--4 points was the largest (52.01%). The number of patients receiving oral nutritional supplements, enteral nutrition (EN), parenteral nutrition (PN), and the combination of EN and PN was 425, 314, 853, and 413, respectively. Among the total, 77.55% of patients had nutritional risk (NRS 2002 ≥3). The proportion of patients with high nutritional risk (NRS2002≥5) in the age group >80 years was significantly higher than that in the age group 66--80 years (29.00% vs. 23.93%, P=0.032), but not significantly higher than that in the age group 18--65 years (29.00% vs. 26.54%, P=0.449). Logistic regression analysis revealed that heart failure (odds ratio [OR] 1.856, 95% confidence interval [CI] 1.087-3.167, P=0.023), consciousness (OR 2.967, 95% CI 1.894-4.648, P<0.001), Acute Physiology and Chronic Health Evaluation II (APACHE II) score (OR 1.037, 95% CI 1.017-1.058, P<0.001), NRS 2002 score (OR 1.286, 95% CI 1.115-1.483, P=0.001), and Mini Nutritional Assessment-Short Form score (OR 0.946, 95% CI 0.898-0.997, P=0.039) were all independent risk factors for 28-day mortality. APACHE II and NRS 2002 scores were superior to other predictors according to the comparison of AUROC. CONCLUSIONS: Nutritional risk is prevalent among older patients in EDOUs in China. APACHE II and NRS 2002 scores are important risk factors for mortality in patients admitted to the EDOU. Timely and appropriate nutritional screening and support measures are critical to reduce patients' length of hospital stay and mortality. [ABSTRACT FROM AUTHOR]

Individuals often need to be proactive in order to successfully navigate their career development journeys. To what extent one is vocationally proactive has critical implications for his or her attitudes, behaviors, and other outcomes in career and work‐related settings. However, research in career proactivity has been accumulating from divergent perspectives, resulting in a substantially fragmented literature that has not been comprehensively, objectively synthesized to guide the field to move forward. To advance the domain of career proactivity, this paper synthesizes theoretical and empirical literatures using two major bibliometric analyses. We first analyze the intellectual basis of the career proactivity literature by performing document citation analysis. We then review the developmental trends of main conceptual themes in career proactivity literature using a temporal co‐word analysis. Informed by these bibliometric findings, we propose a roadmap for future research highlighting the need to clear up concepts, account for context, develop new meso‐level theories, and bridge the domains of organizational behavior and vocational development. [ABSTRACT FROM AUTHOR]

In the article, the authors discuss the diverse conceptual and theoretical perspectives in advancing career proactivity research. Other topics include the research themes in vocational and organizational psychology, the conceptualization of career proactivity, the behavioral nature of career proactivity, and the conduct of bibliographic analyses in research.

This study develops a geometry adaptive, physical field predictor for the combined forced and natural convection flow of a nanofluid in horizontal single or double-inner cylinder annular pipes with various inner cylinder sizes and placements based on deep learning. The predictor is built with a convolutional-deconvolutional structure, where the input is the annulus cross-section geometry and the output is the temperature and the Nusselt number for the nanofluid-filled annulus. Profiting from the proven ability of dealing with pixel-like data, the convolutional neural network (CNN)-based predictor enables an accurate end-to-end mapping from the geometry input and the desired nanofluid physical field. Taking the computational fluid dynamics (CFD) calculation as the basis of our approach, the obtained results show that the average accuracy of the predicted temperature field and the coefficient of determination R2 are more than 99.9% and 0.998 accurate for single-inner cylinder nanofluid-filled annulus; while for the more complex case of double-inner cylinder, the results are still very close, higher than 99.8% and 0.99, respectively. Furthermore, the predictor takes only 0.038 s for each nanofluid field prediction, four orders of magnitude faster than the numerical simulation. The high accuracy and the fast speed estimation of the proposed predictor show the great potential of this approach to perform efficient inner cylinder configuration design and optimization for nanofluid-filled annulus. [ABSTRACT FROM AUTHOR]

In the interdisciplinary field of data-driven models and computational fluid mechanics, the reduced-order model for flow field prediction is mainly constructed by a convolutional neural network (CNN) in recent years. However, the standard CNN is only applicable to data with Euclidean spatial structure, while data with non-Euclidean properties can only be convolved after pixelization, which usually leads to decreased data accuracy. In this work, a novel data-driven framework based on graph convolution network (GCN) is proposed to allow the convolution operator to predict fluid dynamics on non-uniform structured or unstructured mesh data. This is achieved by the fact that the graph data inherit the spatial characteristics of the mesh and by the message passing mechanism of GCN. The conversion method from the form of mesh data to graph data and the operation mechanism of GCN are clarified. Moreover, additional relevance features and weight loss function of the dataset are also investigated to improve the model performance. The model learns an end-to-end mapping between the mesh spatial features and the physical flow field. Through our studies of various cases of internal flow, it is shown that the proposed GCN-based model offers excellent adaptability to non-uniformly distributed mesh data, while also achieving a high accuracy and three-order speedup compared with numerical simulation. Our framework generalizes the graph convolution network to flow field prediction and opens the door to further extending GCN to most existing data-driven architectures of fluid dynamics in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the discovery of the WarBurg effect, the veil of the tumorigenic role of lactic acid has been gradually revealed. Recently, it was proposed that lactic acid that is produced by tumor cells was secreted into the extracellular space to create immunosuppressive tumor microenvironment (TME) in a variety of ways. However, the intersection genes and the association with immunotherapy are unclear. At present, we identified six lactate-metabolism-associated genes, which were thought to enable tumor progression, that were related to LUAD immunotherapy and we constructed an LAR-score risk model. Background: The role of lactate acid in tumor progression was well proved. Recently, it was found that lactate acid accumulation induced an immunosuppressive microenvironment. However, these results were based on a single gene and it was unclear that lactate acid genes were associated with immunotherapy and able to predict overall survival. Methods: Genes and survival data were acquired from TCGA, GEO and GENECARDS. PCA and TSNE were used to distinguish sample types according to lactate metabolism-associated gene expression. A Wilcox-test examined the expression differences between normal and tumor samples. The distribution in chromatin and mutant levels were displayed by Circo and MAfTools. The lactate metabolism-associated gene were divided into categories by consistent clustering and visualized by Cytoscape. Immune cell infiltration was evaluated by CIBERSORT and LM22 matrix. Enrichment analysis was performed by GSVA. We used the ConsensusClusterPlus package for consistent cluster analysis. A prognostic model was constructed by Univariate Cox regression and Lasso regression analysis. Clinical specimens were detected their expression of genes in model by IHC. Results: Most lactate metabolism-associated gene were significantly differently expressed between normal and tumor samples. There was a strong correlation between the expression of lactate metabolism-associated gene and the abundance of immune cells. We divided them into two clusters (lactate.cluster A,B) with significantly different survival. The two clusters showed a difference in signal, immune cells, immune signatures, chemokines, and clinical features. We identified 162 differential genes from the two clusters, by which the samples were divided into three categories (gene.cluster A,B,C). They also showed a difference in OS and immune infiltration. Finally, a risk score model that was composed of six genes was constructed. There was significant difference in the survival between the high and low risk groups. ROC curves of 1, 3, 5, and 10 years verified the model had good predictive efficiency. Gene expression were correlated with ORR and PFS in patients who received anti-PD-1/L1. Conclusion: The lactate metabolism-associated genes in LUAD were significantly associated with OS and immune signatures. The risk scoring model that was constructed by us was able to well identify and predict OS and were related with anti-PD-1/L1 therapy outcome. [ABSTRACT FROM AUTHOR]

A selective shift in energy storage mechanisms is demonstrated by Wei Zhang, Weitao Zheng et al. in their Communication (e202405592). By meticulously engineering the oxygen vacancies within the α‐MoO3 structure, the selective insertion of H3O+ ions (blue droplets) in aqueous aluminum‐ion batteries is enabled...By Chengxiang Huang; Zhou Jiang; Fuxi Liu; Wenwen Li; Qing Liang; Zhenzhen Zhao; Xin Ge; Kexin Song; Lirong Zheng; Xinyan Zhou; Sifan Qiao; Wei Zhang and Weitao ZhengReported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

A selective shift in energy storage mechanisms is demonstrated by Wei Zhang, Weitao Zheng et al. in their Communication (e202405592). By meticulously engineering the oxygen vacancies within the α‐MoO3 structure, the selective insertion of H3O+ ions (blue droplets) in aqueous aluminum‐ion batteries is enabled...By Chengxiang Huang; Zhou Jiang; Fuxi Liu; Wenwen Li; Qing Liang; Zhenzhen Zhao; Xin Ge; Kexin Song; Lirong Zheng; Xinyan Zhou; Sifan Qiao; Wei Zhang and Weitao ZhengReported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

A physics-informed graph convolutional network (PINN-GCN) data-driven model was proposed to solve steady-state fluid flows with various geometries. A GCN guarantees a data-driven model that adapts to irregularly structured or unstructured mesh data. In addition, the mean squared residuals of the governing equations in partial differential forms and boundary conditions are introduced into the loss function of the network model, the Physics-Informed Neural Network (PINN) increases the knowledge of the physical rules of the model, enhances the model accuracy even with sparse training data. With nine training cases containing approximately 2000 spatial point data individually, PINN-GCN could accurately predict the physical flow field over a two-dimensional object of variable size, achieving above 98.6% for mean accuracy. With five training data cases, the model could predict flow over a cylinder with variable position, achieving above 98.2% for mean accuracy. Moreover, the PINN-GCN framework can produce high-resolution predictions, even with low-resolution field training data. Furthermore, the PINN-GCN prediction speed was approximately 1000 times faster than that of the numerical simulations. Thus, the proposed model can rapidly predict high-resolution flow fields using a physics-informed, grid- and geometry-adaptive, data-driven model. • The proposed PINN-GCN model obtains the target PDEs solutions in the irregular domains with unstructured grids and without the pixelated preprocessing. • The proposed model can be trained on sparse data (low-resolution) and then predictions can be made on dense data (high-resolution). • The trained model can achieve direct predictions for domains with unseen and new geometries, which fully reflects its scalability. [ABSTRACT FROM AUTHOR]

The compressibility effect when an oblique shock wave impinges on a turbulent boundary layer was analyzed with a direct numerical simulation using Helmholtz decomposition. The turbulent intensity near the impinging region is significantly enhanced by the interaction of the shock wave and boundary layer. In particular, the interaction behavior can enhance the dilatational components of the Reynolds stress and velocity fluctuations. This also enhances the negative correlation between the dilatational components of streamwise and normal velocity fluctuations. The dilatation fluctuation in the impinging region increases significantly. Moreover, in the impinging region, the dilatational components of the production term and transport term contribute to the production and transport terms of the kinetic energy equation mainly in the vicinity of the interface. This simulation shows that the interaction behavior of an oblique shock wave and turbulent boundary layer can enhance flow compressibility significantly. In the interaction region, the turbulent intensity of the flow field is stronger than those upstream and downstream. This study provides a theoretical basis for the improvement of other simulation methods and turbulence modeling for the interaction of an oblique shock wave and turbulent boundary layer. [ABSTRACT FROM AUTHOR]

Background: Targeted delivery of chemotherapeutic drugs to tumour cells is a major challenge for cancer chemotherapy. Recent studies show that tumour cell-derived microparticles can be used as vectors to package chemotherapeutic drugs, and selectively deliver drugs to tumour cells. Nevertheless, since the particle size range of microparticles is relatively wide, the sizes may exhibit different pharmacokinetic characteristics in the body, which will have a great impact on the application of drug-loaded microparticles. Here in this report, we compare the characteristics, distribution in vivo and antitumour efficacy of small microparticles (SMPs, ≤ 200 nm) and large microparticles (LMPs, > 200 nm) which loaded with methotrexate, in order to screen out more suitable carrier sizes. Results: In vivo and in vitro studies have proved that the drug-loaded vesicles of SMPs (mainly 100–200 nm) are more reasonable, and the drug content and maintenance in tumour tissues. The time is significantly higher than that of LMPs (mainly 400–500 nm). At the same time, we found that SMPs can be better taken up and processed by DC cells to promote the proliferation of specific T cells. SMPs show obvious advantages in both drug delivery and immune activation, which is verified by the comparison of the efficacy of SMPs and LMPs in the treatment of solid tumours in mice. Conclusions: The present data demonstrate that the SMPs had a higher cumulative concentration in tumour tissue, and the tumour suppressive effect was also significantly better than that of LMPs. It provides important process parameters for the drug-loaded vesicle delivery system. Future works will aim to expand production scale and improve the separation and purification process of the microparticles. Although the research and application of drug-loaded vesicles derived from tumour cells is still in its infancy, it has broad prospects for tumour therapy. [ABSTRACT FROM AUTHOR]

Hypoxia‐inducible factors (hifs) are involved in infectious diseases inflammatory reactions, and immune regulation. Common carp, a representative allotetraploid species that has undergone genome‐wide replication events, has important research value. In this study, common carp were infected twice with Aeromonas hydrophila. Liver tissues of common carp were collected at 4 h, 12 h, 24 h, 48 h, 3 days, 7 days post‐first infection and 4 h, 12 h, 24 h post‐second infection. The mRNA levels of hif genes were determined at different time points. The hif2a‐2, hif3a‐2, hif3b‐1 and hif3b‐2 expression levels in the infected group were upregulated when compared with those in the control group, whereas the expression levels of other genes were downregulated after the second infection. This indicates that the effect of A. hydrophila infection on gene expression pattern is dependent on the host, pathogen, infected tissue and gene. Pressure analysis of the hif gene family revealed that the non‐synonymous substitution to synonymous substitution ratio of 12 hif genes was <1, which indicated that they were in a state of purification and selection. Combined with the differences between copy genes, the polyclonal antibodies against Hif1b‐1 and Hif1b‐2 were successfully prepared in this study. Western blot analysis showed that the protein expression of Hif1b‐1 and Hif1b‐2 reached to the highest level 48 h after the first infection. After the second A. hydrophila infection, the protein expression levels of Hif1b‐1 and Hif1b‐2 reached the highest levels at 4 and 48 h, respectively. This may indicate that the Hif1b‐1 and Hif1b‐2 genes in common carp play an important role in the immune mechanism at the protein level. The findings of this study will lay the foundation for future studies on the immune regulatory function of common carp hif genes, which may aid in devising novel therapeutic strategies for common carp diseases, such as A. hydrophila infection. [ABSTRACT FROM AUTHOR]

Pharmaceutical intervention of aging requires targeting multiple pathways, thus there is rationale to test combinations of drugs targeting different but overlapping processes. In order to determine if combining drugs shown to extend lifespan and healthy aging in mice would have greater impact than any individual drug, a cocktail diet containing 14 ppm rapamycin, 1000 ppm acarbose, and 1000 ppm phenylbutyrate was fed to 20-month-old C57BL/6 and HET3 4-way cross mice of both sexes for three months. Mice treated with the cocktail showed a sex and strain-dependent phenotype consistent with healthy aging including decreased body fat, improved cognition, increased strength and endurance, and decreased age-related pathology compared to mice treated with individual drugs or control. The severity of age-related lesions in heart, lungs, liver, and kidney was consistently decreased in mice treated with the cocktail compared to mice treated with individual drugs or control, suggesting an interactive advantage of the three drugs. This study shows that a combination of three drugs, each previously shown to enhance lifespan and health span in mice, is able to delay aging phenotypes in middle-aged mice more effectively than any individual drug in the cocktail over a 3-month treatment period. [ABSTRACT FROM AUTHOR]

The valance of Mo is critical for FeMo cofactor in ambient ammonia synthesis. However, the valence effect of Mo has not been well studied in heterogeneous nanoparticle catalysts for electrochemical nitrogen reduction reaction (NRR) due to the dissolution of Mo as MoO42− in alkaline electrolytes. Here, a MoO2+x catalyst enriched with surface Mo6+ is reported. The Mo6+ is stabilized by a native oxide layer to prevent corrosion and its speciation is identified as (MoO3)n clusters. This native layer with Mo6+ suppresses the hydrogen evolution significantly and promotes the activation of nitrogen as supported by both experimental characterization and theoretical calculation. The as‐prepared MoO2+x catalyst shows a high ammonia yield of 3.95 µg mgcat−1h−1 with a high Faradaic efficiency of 22.1% at −0.2 V versus reversible hydrogen electrode, which is much better than the MoO2 catalyst with Mo6+ etched away. The accuracy of experimental results for NRR is confirmed by various control experiments and quantitative isotope labeling. [ABSTRACT FROM AUTHOR]

The microstructure and mechanical properties of marine 10Ni5CrMoV steel are systematically investigated at tempering temperatures ranging from 630 to 670 °C. With increasing tempering temperature, several key parameters exhibit notable changes: the volume fraction of reversed austenite decreases from 17.36% to 13.39%. The equivalent grain size increases from 0.91 to 1.09 μm. The equivalent diameter of precipitates increases from 59 to 79 nm. The dislocation density decreases from 0.82 × 10−15 to 0.51 × 10−15 m−2. As the tempering temperature increases, the pinning effect of precipitates on dislocations and martensite laths decreases. This leads to reductions in both dislocation strengthening and precipitation strengthening mechanisms. Consequently, the yield strength of the specimen decreases from 876 to 683 MPa. The increase in the ductile‐brittle transition temperature of the specimens from −116 to −95 °C can be attributed to several factors. First, the increase in precipitate size reduces the critical nucleation stress of cleavage cracks and promotes the nucleation of such cracks. Additionally, the decrease in dislocation density mobility leads to a reduction in the dimple nucleation rate, thereby diminishing the role of dimples in hindering crack propagation. [ABSTRACT FROM AUTHOR]

Chrysoeriol is an active ingredient derived from the Chinese medicinal herb (CMH) "Lonicerae japonicae flos" in the dried flower bud or bloomed flower of Lonicera japonica Thunberg. Dermatoses are the most common diseases in humans, including eczema, acne, psoriasis, moles, and fungal infections, which are temporary or permanent and may be painless or painful. Topical corticosteroids are widely used in Western medicine, but there are some side effects when it is continuously and regularly utilized in a large dosage. Chrysoeriol is a natural active ingredient, nontoxic, and without any adverse reactions in the treatment of dermatological conditions. Methods: Nine electronic databases were searched, including WanFang Data, PubMed, Science Direct, Scopus, Web of Science, Springer Link, SciFinder, and China National Knowledge Infrastructure (CNKI), without regard to language constraints. The pharmacological activities of chrysoeriol from Lonicerae japonicae flos to fight against skin diseases were explained and evaluated through the literature review of either in vitro or in vivo studies. Results: Chrysoeriol decreased the mRNA levels of proinflammatory cytokines IL-6, IL-1β, and TNF-α. These were transcriptionally regulated by NF-κB and STAT3 to combat skin inflammation. It also showed promising actions in treating many skin ailments including wound healing, depigmentation, photoprotection, and antiaging. Conclusion: The cutaneous route is the best delivery approach to chrysoeriol across the skin barrier. However, toxicity, dosage, and safety assessments of chrysoeriol in a formulation or nanochrysoeriol on the human epidermis for application in skin diseases must be further investigated. [ABSTRACT FROM AUTHOR]

A spatially developing supersonic flat plate boundary layer flow at M ∞ = 2.25 is analyzed using the constrained large-eddy simulation (CLES) method. The Reynolds number based on the momentum thickness of the inlet boundary layer is R e θ = 3429. The mean and statistical quantities, including mean velocity, mean temperature, and total temperature, are obtained and compared among the present approach, large eddy simulation (LES) with the dynamic Smagorinsky model, detached eddy simulation (DES), and naturally developed direct numerical simulation (ND-DNS). As a result, CLES can predict these mean quantities and statistics more accurately than LES and DES, and the results are in good agreement with the ND-DNS data. This demonstrates that CLES is an effective method for spatially developing supersonic flat-plate boundary layer flows. [ABSTRACT FROM AUTHOR]

We develop a deep neural network-based reduced-order model (ROM) for rapid prediction of the steady-state velocity field with arbitrary geometry and various boundary conditions. The input matrix of the network is composed of the nearest wall signed distance function (NWSDF), which contains more physical information than the signed distance function (SDF) and binary map; the boundary conditions are represented by specifically designed values and fused with NWSDF. The network architecture comprises convolutional and transpose-convolutional layers, and convolutional layers are employed to encode and extract the physical information from NWSDF. The highly encoded information is decoded by transpose-convolutional layers to estimate the velocity fields. Furthermore, we introduce a pooling layer to innovatively emphasize/preserve information of boundary conditions, which are gradually flooded by other features during the convolutional operation. The network model is trained using several simple geometries and tested with more complex cases. The proposed network model shows excellent adaptability to arbitrary complex geometry and variable boundary conditions. The average prediction error of the network model on the testing dataset is less than 6%, and the prediction speed is two orders faster than that of the numerical simulation. In contrast to the current model, the average error of the network model with the input matrix of the binary map, traditional SDF, and model without pooling layers is around 12%, 11%, and 11%, respectively. The outstanding performance of the proposed network model indicates the potential of the deep neural network-based ROM for real-time control and rapid optimization, while encouraging further investigation to achieve practical application. [ABSTRACT FROM AUTHOR]

In this paper, we establish the two-weight norm inequalities for fractional maximal functions and fractional integral operators related to Schrödinger differential operator on weighted Morrey spaces related to certain nonnegative potentials belonging to the reverse Hölder class. [ABSTRACT FROM AUTHOR]

• We test when and how emotional intelligence (EI) affects safety performance (SP). • We use two-wave survey data collected from airplane pilots to test our hypotheses. • Training inadequacy strengthens the effect of EI on situational awareness (SA). • SA is more likely to mediate the EI–SP link as training inadequacy worsens. Introduction: Safety outcomes in the workplace require individual employees to perform (behave) safely in everyday duties. While the literature suggests that emotional management capabilities or traits can be positively related to individual performance in certain conditions, it is not clear how they can influence safety-related performance in high-risk work contexts. Drawing upon trait activation theory, this paper aims to examine when emotional intelligence (EI) benefits employees' safety performance. We propose that when employees receive inadequate safety training, EI is more likely to trigger their situational awareness and consequently promote their safety performance. Method: We collected time-lagged data from 133 full-time airplane pilots working in commercial aviation industry. Hierarchical moderated regression analysis was conducted to test the moderating effect of safety training inadequacy on the EI–situational awareness relationship. The moderated mediation model, which involves conditional indirect effects of EI on safety performance via situational awareness across different levels of safety training inadequacy, was tested using the PROCESS-based bootstrap confidence interval. Results: Safety training inadequacy negatively moderated the relationship between EI and situational awareness, such that EI was significantly related to situational awareness only when safety training inadequacy was more salient. The more inadequate safety training was, the greater the indirect effect of EI on safety performance via situational awareness was. Conclusions: Inadequate safety training, as a negative situational cue, can activate individuals' EI to drive their safety-related cognitions (e.g., situational awareness) and behaviors. Effective safety training may be able to complement employees' low EI in shaping their situational awareness and safety behaviors. Practical Applications : Aviation managers should monitor the adequacy and effectiveness of safety training; this could make pilots' situational awareness and safety performance depend less on personal attributes (e.g., EI), which organizations are less able to control. When training capacity is temporarily limited, priority might be given to those with low EI. [ABSTRACT FROM AUTHOR]

Grassland resources are an important part of land resources. Moreover, it has the functions of regulating the climate, windproof and sand fixation, conserving water sources, maintaining water and soil, raising livestock, providing food, purifying the air, and beautifying the environment in terrestrial ecosystems. Grassland resource evaluation is of great significance to the sustainable development of grassland resources. Therefore, this paper improves the BP neural network, uses the comprehensive index method to calculate the weights in the analytic hierarchy process, and constructs a water resources carrying capacity research and analysis system based on the entropy weight extension decision theory. Meanwhile, this paper analyzes different levels of resource and environmental carrying capacity to achieve the purpose of comprehensive evaluation of resource and environmental carrying capacity. In addition, based on the theory of sustainable development, under the guidance of the principle of index system construction, this paper studies the actual situation of grassland resources and the availability and operability of data, and combines with the opinions given by experts to form an evaluation index system of grassland resources and environmental carrying capacity. Finally, through the actual case study analysis, it is concluded that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has gradually emerged as a novel and effective method for intelligent control of conjugate heat transfer. Through proper training, DRL agent usually can find a better control strategy than the one optimized manually. For numerous numerical simulation-based investigations, the promising results have only been obtained on 2-dimensional models due to the heavy burden of data acquisition. This paper proposes a novel strategy of transferring the control policy learned in 2-dimensional environment into a new 3-dimensional environment. PDD-DQN (Prioritized Dueling Double Deep Q-Networks) algorithm is used to recognize the underlying relationship between the forced fluid flow and heat transfer performance to produce a control strategy. The DRL controller is first trained on a simple 2-dimensional cavity with one heat source, and the DRL controller is able to reduce the maximum temperature to 315 K which is 2 K lower than the manually optimized control strategy; then the control strategy is transferred to 3-dimensional models where the maximum temperature is further reduced to 308 K; and compared to training the DRL agent directly on 3-dimensional model, the policy-transfer strategy requires only 10% computational expenditure. The proposed strategy is then applied to a more complex testbed with multiple heat sources for further investigating its ability; in both 2D and 3D environment, the DRL controller can cool the maximum temperature to be 307 K which is 8 K lower than the manually optimized control policy; and the computational cost drop to be 3%. The generalization ability of the trained DRL agent in inter-dimensional geometries is confirmed. For the cases with huge grid size, the policy-transfer strategy can economize exponential computational cost, which is of great significance for applying DRL methods to practical thermal control problems. [ABSTRACT FROM AUTHOR]

• The fluid dynamic and energy equations are embedded into the neural network. • The proposed model can accurately solve natural convection with various geometries. • The model can accurately predict the physical information at the boundaries. • The model training only requires a small amount of dataset. This paper presents a novel deep learning-based surrogate model for steady-state natural convection problem with variable geometry. Traditional deep learning based surrogate models are more or less limited by the requirement for large amounts of training data, loss of accuracy due to pixelization of the original data, the low accurate prediction near the boundaries and low geometry adaptive capability. To overcome the above challenges, the proposed natural convection prediction framework is mainly composed of a physics-informed neural network (PINN) and a graph convolutional neural network (GCN), called natural convection prediction model based on physics-informed graph convolutional network (NCV-PIGN). The GCN serves as the prediction module, inferring and predicting natural convection phenomena by considering the interactions between unstructured nodes and their neighbor; the PINN incorporates the governing equations of natural convection into the loss function of the neural network, allowing the predictions from GCN to satisfy the constraints imposed by the physical laws. The advantages of this framework are twofold: the operation principles of the GCN better align with the development of the temperature field in real situations, and the embedding of physical information strengthens the model's understanding of the flow field, accurately describing the variations of temperature gradients at the boundary positions while reducing the model's reliance on training data. Finally, to validate the superiority of the NCV-PIGN, we analyze its geometric adaptability and accuracy of prediction using single and dual heat source cases. We compare the model's prediction results at different sampling point quantities and contrast them with those of purely data-driven models. The results demonstrate that the excellent geometric adaptability and prediction capability of the proposed model can be achieved with only 20 training data and once the fully trained the model can solve natural convection problems within 3 ms. The max and mean relative errors in predicting the temperature field are less than 2% and 0.4% for both single and dual heat source cases. Compared to the pure data-driven model, the proposed model has reduced the maximum error by 65.5% and the mean error by 72%. These results validate the effectiveness of the developed NCV-PIGN model, enabling better performance of the deep learning-based surrogate models for natural convection problems. [ABSTRACT FROM AUTHOR]

The effects of air pollutants on cardiometabolic diseases (CMDs) have been widely explored, whereas their influences on cardiometabolic multi-morbidity (CMM) were not clear. We employed the UK Biobank cohort (N = 317,160) to study the association between six air pollutants (PM 2.5 , PM 10 , PM 2.5-10 , PM 2.5abs , NO 2 , and NO x) and four CMDs including type II diabetes (T2D), coronary artery disease (CAD), stroke and hypertension. CMM was defined as occurrence of two or more of the four diseases. Multi-state Cox models were performed to estimate hazard ratio (HR) and its 95% confidence interval (95%CI). During a median follow-up of 12.8 years, 52,211 participants developed only one CMD, 15,446 further developed CMM, and 16,861 ultimately died. It was demonstrated that per interquartile range increase (IQR) increases in PM 2.5 , PM 10 , PM 2.5-10 , PM 2.5abs , NO 2 , and NO x would increase 12% (9%–15%), 4% (1%–7%), 3% (1%–6%), 7% (4%–10%), 11% (8%–15%) and 10% (7%–13%) higher risk of developing one CMD from health baseline; 7% (2%–12%), 8% (3%–13%), 6% (2%–11%), 10% (5%–15%), 13% (7%–18%) and 10% (5%–15%) greater risk of occurring CMM from one CMD baseline; and 11% (−2%∼26%), 22% (7%–38%), 17% (3%–32%), 31% (16%–49%), 33% (17%–51%) and 32% (17%–50%) larger risk of causing death from CMM baseline, respectively. We revealed that people living in areas with high air pollution suffered from higher hazard of CMD, CMM and all-cause mortality; our findings implied keeping clean air was an effective approach to prevent or mitigate initiation, progression, and death from healthy to CMDs and from CMDs to CMM. • A total of 317,160 UK Biobank participants enrolled from 2006 to 2010 were included and followed-up until July 19, 2022, with a median follow-up of 12.8 years. • Air pollution presented significantly long-term adverse effects on the incidence, progress, and prognosis of CMM. • The highest hazard ratio was observed for T2D patients to develop CMM induced by air pollution, and NO 2 presented the highest hazard ratio to cause CMM among people with one CMD and lead to death among people with CMM. [ABSTRACT FROM AUTHOR]

Herein, the Ce‐containing inclusions in8Cr4Mo4V‐bearing steel are taken as the research objective to develop anumerical model of inclusions motions behavior. The model is validated bycomparing it with experimental data. During the vacuum arc remelting process, inclusions move along the molten pool's surface toward the edge under theaction of resultant force, and finally become trapped within the mushy zone. Notably, the majority of inclusions are concentrated near the ingot's edge, andtheir numbers decrease as the radial distance decreases. Furthermore, thenumber of Ce‐Mg‐O‐S inclusions exceeds that of Ce‐O‐S inclusions near the ingot's center. This discrepancy arises due to lower density of Ce‐Mg‐O‐Sinclusions, which exhibits a stronger tendency to migrate toward the moltenpool's center. Additionally, as the inclusion diameter increases, buoyancy forceacting on the inclusions increases, resulting in a higher number of inclusionsnear the ingot's center. When considering the effect of gas cooling, theinclusion distribution becomes more pronounced at the ingot's edge. This isattributed to the enhanced cooling capacity of the mold resulting in the decreasing of molten pool depth and consequently a stronger ability of themushy zone to trap inclusions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a neural network based reduced-order model for predicting the unsteady flow field over single/multiple cylinders. The neural network model constructs a mapping function between the temporal evolution of the pressure signal on the cylinder surface and the surrounding velocity field, where Convolutional Neural Network (CNN) layers are used as the encoder and deconvolutional neural network layers are used as the decoder. Compared with the network model with a fully connected (FC) decoder, the model with the deconvolution connected (DC) decoder is good for capturing and reconstructing the spatial relationships of low-rank feature spaces, such as edge intersections, parallelism, and symmetry, while the fluid flow, which is described by Navier–Stokes equations containing convection and diffusion terms, displays outstanding features of locality. In this article, the performance of the network models with the FC decoder and the DC decoder is evaluated by studying the problem of flow over a single cylinder first, and then the complexity of the flow structure of the studied problems is enhanced by increasing the number of cylinders and the Reynolds number. The results indicate that both the CNN-FC decoder model and CNN-DC decoder model achieve fast and accurate prediction on the velocity field, and the CNN-DC decoder model gives more robust and precise performance for all studied problems. [ABSTRACT FROM AUTHOR]

In this article, we propose an unsteady data-driven reduced order model (ROM) (surrogate model) for predicting the velocity field around an airfoil. The network model applies a convolutional neural network (CNN) as the encoder and a deconvolutional neural network (DCNN) as the decoder. The model constructs a mapping function between temporal evolution of the pressure signal on the airfoil surface and the surrounding velocity field. For improving the model performance, the input matrix is designed to further incorporate the information of the Reynolds number, the geometry of the airfoil, and the angle of attack. The DCNN works as the decoder for better reconstructing the spatial and temporal information of the features extracted by the CNN encoder. The training and testing datasets of flow fields under different conditions are obtained by solving the Navier–Stokes equations using the computational fluid dynamics method. After model training, the neural network based ROM shows accurate and dramatically fast predictions on the flow field of the testing dataset with extended angles of attack and Reynolds numbers. According to the current study, the neural network-based ROM has exhibited attractive potentials on ROM of the unsteady fluid dynamic problem, and the model can potentially serve on investigating flow control or optimization problems in the future. [ABSTRACT FROM AUTHOR]

BACKGROUND: To evaluate the accuracy of National Early Warning Score (NEWS) in predicting clinical outcomes (28-day mortality, intensive care unit [ICU] admission, and mechanical ventilation use) for septic patients with community-acquired pneumonia (CAP) compared with other commonly used severity scores (CURB65, Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick SOFA [qSOFA], and Mortality in Emergency Department Sepsis [MEDS]) and admission lactate level. METHODS: Adult patients diagnosed with CAP admitted between January 2017 and May 2019 with admission SOFA =2 from baseline were enrolled. Demographic characteristics were collected. The primary outcome was the 28-day mortality after admission, and the secondary outcome included ICU admission and mechanical ventilation use. Outcome prediction value of parameters above was compared using receiver operating characteristics (ROC) curves. Cox regression analyses were carried out to determine the risk factors for the 28-day mortality. Kaplan-Meier survival curves were plotted and compared using optimal cut-off values of qSOFA and NEWS. RESULTS: Among the 340 enrolled patients, 90 patients were dead after a 28-day follow-up, 62 patients were admitted to ICU, and 84 patients underwent mechanical ventilation. Among single predictors, NEWS achieved the largest area under the receiver operating characteristic (AUROC) curve in predicting the 28-day mortality (0.861), ICU admission (0.895), and use of mechanical ventilation (0.873). NEWS+lactate, similar to MEDS+lactate, outperformed other combinations of severity score and admission lactate in predicting the 28-day mortality (AUROC 0.866) and ICU admission (AUROC 0.905), while NEWS+lactate did not outperform other combinations in predicting mechanical ventilation (AUROC 0.886). Admission lactate only improved the predicting performance of CURB65 and qSOFA in predicting the 28-day mortality and ICU admission. CONCLUSIONS: NEWS could be a valuable predictor in septic patients with CAP in emergency departments. Admission lactate did not predict well the outcomes or improve the severity scores. A qSOFA ≥2 and a NEWS =9 were strongly associated with the 28-day mortality, ICU admission, and mechanical ventilation of septic patients with CAP in the emergency departments. [ABSTRACT FROM AUTHOR]

Emerging evidence has demonstrated that long noncoding RNAs (lncRNAs) play critical roles in the epigenetic and transcriptional regulation of mammalian circadian systems. Circadian rhythmicity regulates many aspects of our immune system, and perturbation of the circadian clock can augment the inflammatory response. However, knowledge of the precise functions of lncRNAs in the regulation of immune functions within the circadian system is relatively limited. In this study, differentially expressed lncRNAs induced by Clock knockdown were screened via mRNA/lncRNA microarray and bioinformatic prediction analysis. We identified a Clock -regulated lncRNA, AK028245, which was correlated with the activation of the immune response. The expression levels of AK028245 were decreased in the spleen of immunosuppressed mice and elevated in immune-activated mice treated with lipopolysaccharide (LPS). Further, Clock knockdown decreased the expression of OTUD7B and A20, 2 early immune response factors acting on the NF-κB signaling pathway. Interestingly, inhibition of AK028245 increased their expression, mitigating the effects of Clock knockdown. In addition, inhibition of AK028245 downregulated the expression of tumor necrosis factor–α and interleukin-6 in the late stages of LPS stimulation and the expression of interferon-γ and Cxcl12 in the peak stages. We conclude that this newly identified lncRNA plays a role in the crosstalk between Clock and immune response regulators, likely resulting in a proinflammatory response targeting OTUD7B and A20. The lncRNA AK028245 has revealed a new mechanism of the immune response and provided new targets for the treatment of immune disorders. [ABSTRACT FROM AUTHOR]

Solute carrier 15 family (Slc15) are membrane proteins that utilize the proton gradient and negative membrane protential for the transmembrane transporter of di-/tripeptide and peptide-mimetic molecules, in addition, they also play important roles in immunoreaction. In this study, 10 Slc15 genes were identified in the common carp genome database. Comparative genomics analysis showed considerable expansion of the Slc15 genes and verified the four-round whole genome duplication (WGD) event in common carp. Phylogenetic analysis revealed all Slc15 genes of common carp were clustered into orthologous groups indicating the highly conservative during evolution. Besides, the tissues and temporal expression examined by RT-PCR and qRT-PCR showed that most of the Slc15 genes had a narrow tissue distribution and exhibited tissue-specific expression patterns. Expression divergences were observed between these copies proving function divergence after the WGD. Then, we investigated the dietary supplementation effects of three Lactococcus lactis strains on the expression of Slc15 genes in common carp infected by A. hydrophila to find an effective way to treat aquatic diseases. Almost all of the Slc15 genes had an increased expression trend in the early post-challenge stage, and reached the highest expression level at 12h post-challenge. Then, the expression level showed a bluff descent at the last two stages and the expression level reached the lowest at 48 h post-challenge. Slc15 genes expression is actively up-regulated when stimulated by inflammatory factors, which can "amplify" immune signals, and improve the body's defense against foreign invasion in the early stage of the inflammatory response. So activation of the Slc15 genes may be an effective way for infectious disease treatment. As expected, three strains improved the expression of Slc15 genes variously compared with the control/infection groups. The strain 3 of L. lactis had a better induction of Slc15 genes compared with strain 1 and strain 2. It might be applied as a potential activation of Slc15 genes for disease treatment and adding befitting L. lactis may be a good way to protect aquatilia from bacillosis. • This work presented the first identification of Slc15 gene family in common carp. • Comparative genomics and phylogenetic analysis showed considerable expansion of Slc15 gene family and approved the four round whole genome duplication (WGD) event in common carp. • It applied as a potential activation of Slc15 genes for disease treatment and adding befitting L. lactis maybe a good way to protect aquatilia from bacillosis. [ABSTRACT FROM AUTHOR]

A metal passivator is often added to transformer oil to inhibit oil sulfur corrosion. However, the addition of passivator can result in gaseous anomaly in the oil and need to be added regularly. In this paper, the copper winding of transformer is treated by grain boundary engineering technology, and the corrosion resistance of the winding before and after treatment is compared according to IEC 62535 standard. The experimental results show that the copper windings treated by grain boundary engineering technology have good corrosion resistance. The original copper winding began to corrode when dibenzyl disulphide (DBDS) concentration achieved 50 mg/kg. Even DBDS concentration was up to 2000 mg/kg, the treated windings still had a good anticorrosion effect. Further, corrosion resistance mechanisms were explored, it is found that the corrosion resistance of the treated winding is better than that of addition passivator. The statistical analysis of the internal grain boundary of the copper windings shows that the twin grain boundary of treated windings increases obviously, and the increased twin grain boundary breaks the connectivity of the grain boundary, thus improving the corrosion resistance of the windings. [ABSTRACT FROM AUTHOR]

Purpose: To construct a competing endogenous RNA (ceRNA) topology network of RNA-seq data and micro RNA-seq (miRNA-seq) data to identify key prognostic long non-coding RNA (lncRNAs) in luminal breast cancer, and validate the results by human luminal breast cancer samples. Materials and Methods: The RNA-seq data and miRNA-seq data of luminal A breast cancer in the The Cancer Genome Atlas (TCGA) database were downloaded and compared with those in the miRcode database to obtain lncRNA–miRNA relationship pairs. Final target genes were predicted by all three databases (miRDB, miRTarBase, and TargetScan), thereby obtaining the miRNA-messenger RNA (miRNA-mRNA) relationship pairs and a ceRNA topology network was constructed, then mRNA enrichment analysis, ceRNA topological and stability analysis, univariate and multivariate Cox regression analysis were performed. Overall survival (OS) was evaluated and the key prognostic RNAs were identified. The expression difference between normal and tumor, as well as the correlation of high expression in tumor with pathological parameters (Ki-67, Grade, tumor diameter) were validated by human breast cancer specimens. Results: A ceRNA topology network was constructed and six lncRNAs were finally identified (The higher expression of PART1, IGF2.AS, WT1.AS, OIP5.AS1, and SLC25A5.AS1 was associated with poor prognosis while AL035706.1 was adverse) and the poor prognostic ones were higher expressed in tumor tissue and correlated with a higher Ki-67 (> 10%), tumor grades (II, III) and tumor diameters (> 1.5 cm). Using six lncRNAs, we constructed a prognostic model, which performed well for the classification of prognosis in the module. Conclusion: We identified and verified six biomarkers (OS-predicting) in luminal breast cancer, which significantly enriched the prediction and potential targets of this subtype. [ABSTRACT FROM AUTHOR]

Cartilage-derived pluripotent cells reside in hyaline cartilage and fibrocartilage. These cells have the potential for multidirectional differentiation; can undergo adipogenesis, osteogenesis, and chondrogenesis; and have been classified as mesenchymal stem cells (MSCs) conforming to the minimal criteria of the International Society for Cellular Therapy. Cartilage tissue is prone to injury and is difficult to repair. As cartilage-derived pluripotent cells are the closest cell source to cartilage tissue, they are expected to have the strongest ability to differentiate into cartilage compared to other MSCs. This review focuses on the organizational distribution, expression, and function of cartilage-derived pluripotent cells in joint development and repair to help explore the therapeutic potential of in situ cartilage-derived pluripotent cells for joint cartilage repair. [ABSTRACT FROM AUTHOR]