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With the development of knowledge graph in the field of artificial intelligence,there is an increasing demand to integrate knowledge graph from different sources to obtain a big knowledge graph with wider coverage.Ontology is the superstructure that can guide the construction of knowledge graph.To solve the problem of ontology alignment in knowledge graph fusion,this paper proposes an ontology alignment method based on self-attention model to combine multidimensional similarities.Firstly,two concepts from two ontologies are multi-dimensional measured by string-based,semantic-based and structure-based similarities.Then,self-attention model is used to combine above similarity calculations to judge whether the two concepts are similar or not and align them.Experiments on public datasets show that,compared with existing ontology alignment methods,the proposed method can obtain better alignment results by aggregating multi-dimensional similarity features.

Plasma levels of oncofetal chondroitin sulfate (ofCS)‐modified CD44 have emerged as a promising biomarker for multi‐cancer detection. Here, we explored its potential to predict the survival of patients with lung cancer. A prospective observational cohort was conducted involving 274 newly diagnosed patients with lung cancer at the Sun Yat‐sen University Cancer Center from 2013 to 2015. The plasma levels of ofCS‐modified CD44 were measured, and Cox regression analysis was performed to assess the association between plasma‐modified CD44 levels and overall survival (OS) as well as other prognostic outcomes. Prognostic nomograms were constructed based on plasma ofCS‐modified CD44 levels to predict survival outcomes for patients with lung cancer. Patients with high expression ofCS‐modified CD44 exhibited significantly worse outcomes in terms of OS (HR = 1.61, 95%CI = 1.13–2.29, p = 0.009) and progression‐free survival (PFS). These findings were consistent across various analyses. The concordance index of the prognostic nomogram for predicting OS in both the training set and validation set were 0.723 and 0.737, respectively. Additionally, time‐dependent receiver operating characteristic (ROC) curves showed that the nomogram could serve as a useful tool for predicting OS in patients with lung cancer. Plasma ofCS‐modified CD44 may serve as an independent prognosis marker for patients with lung cancer. Further validation of its predictive value could enhance prognostic assessment and guide personalized treatment strategies for patients with lung cancer. [ABSTRACT FROM AUTHOR]

As a key physical process, water vapor condensation has attracted significant attention because of its potential in engineering applications. The non-condensable gas in the surrounding vapor has a significant influence on condensation heat transfer. Considering as a crucial aspect, this work developed a transient multiphysics coupled solver to investigate droplet condensation in a moist air environment (considering dry air as the non-condensable gas). The current solver couples the time-dependent vapor-liquid phase-change heat transfer, mass transport of water vapor, and two-phase fluid flow. In contrast to the classical thermal resistance theory model, this solver can capture the dynamic and strong coupling characteristics during condensation comprehensively. The results demonstrate that for small-scale droplets, vapor condensation is driven by the coupled internal conduction-dominated heat transfer and external vapor diffusion. As the droplet grows and the contact angle increases, internal convection driven by the Marangoni effect becomes increasingly important. The enhanced fluid mixing inside the droplet can affect both the internal heat transfer and the external vapor diffusion. Because of the significant diffusion resistance, the droplet growth rates in a moist air environment are reduced up to 1-2 orders of magnitude compared with the case of pure steam. For large-scale droplets, the internal convection can increase the droplet growth rate up to 18.7%. Furthermore, the contact angle, the subcooling temperature, and the relative humidity have significant influences on droplet condensation in a moist air environment. This work not only promotes the mechanistic understanding of condensation heat transfer in a moist air ambient but also provides a flexible solver for vapor-liquid phase change problems. [ABSTRACT FROM AUTHOR]

Summary: Gliadin is an important protein present in cereals. Owing to its solubility in alcohol, gliadin contributes significantly to the field of food industry. However, at present, it is challenging to study the structural characteristics of gliadin at the molecular level because the exact 3‐D structure cannot be obtained using experimental methods. In this study, an α‐gliadin model was constructed by combining homology modelling, ab initio modelling, and threading methods to study the structural properties of gliadin in ethanol aqueous solution. Our results indicated that glutamine is a key element of α‐gliadin. Also, α‐gliadin exhibited relatively low hydrophilicity and high lipid solubility. Molecular dynamics simulations suggested that there are hydrogen bonds and van der Waals forces existed between α‐gliadin and ethanol molecules. This study has explored the structure of wheat gliadin and its conformation in different ethanol solutions and provides a deeper understanding towards the deacetylation mechanism and potential applications of α‐gliadin in the food industry. [ABSTRACT FROM AUTHOR]

Owing to the unique features of electronic ink displays, including the bistability, paper‐like appearance, and sunlight visibility, electronic ink displays have been applied in many Internet of Things (IoT) fields. We reviewed mechanisms that have been proposed to be essential for electro‐optical behavior of electronic ink displays. This review might facilitate beginners to start their research in electronic ink studies. [ABSTRACT FROM AUTHOR]

Background: Nasopharyngeal carcinoma (NPC) is closely associated with genetic factors and Epstein-Barr virus infection, showing strong familial aggregation. Individuals with a family history suffer elevated NPC risk, requiring effective genetic counseling for risk stratification and individualized prevention., Methods: We performed whole-exome sequencing on 502 familial NPC patients and 404 unaffected relatives and controls. We systematically evaluated the established cancer predisposition genes and investigated novel NPC susceptibility genes, making comparisons with 21 other familial cancers in the UK biobank (N = 5218)., Results: Rare pathogenic mutations in the established cancer predisposition genes were observed in familial NPC patients, including ERCC2 (1.39%), TP63 (1.00%), MUTYH (0.80%), and BRCA1 (0.80%). Additionally, 6 novel susceptibility genes were identified. RAD54L, involved in the DNA repair pathway together with ERCC2, MUTYH, and BRCA1, showed the highest frequency (4.18%) in familial NPC. Enrichment analysis found mutations in TP63 were enriched in familial NPC, and RAD54L and EML2 were enriched in both NPC and other Epstein-Barr virus-associated cancers. Besides rare variants, common variants reported in the studies of sporadic NPC were also associated with familial NPC risk. Individuals in the top quantile of common variant-derived genetic risk score while carrying rare variants exhibited increased NPC risk (odds ratio = 13.47, 95% confidence interval = 6.33 to 28.68, P = 1.48 × 10-11); men in this risk group showed a cumulative lifetime risk of 24.19%, much higher than those in the bottom common variant-derived genetic risk score quantile and without rare variants (2.04%)., Conclusions: This study expands the catalog of NPC susceptibility genes and provides the potential for risk stratification of individuals with an NPC family history., (© The Author(s) 2022. Published by Oxford University Press.)

Epstein-Barr virus (EBV) infection is associated with multiple malignancies, including pulmonary lymphoepithelioma-like carcinoma (pLELC), a particular subtype of primary lung cancer. However, the genomic characteristics of EBV related to pLELC remain unclear. Here, we obtained the whole-genome data set of EBV isolated from 78 pLELC patients and 37 healthy controls using EBV-captured sequencing. Compared with the reference genome (NC&#95;007605), a total of 3,995 variations were detected across pLELC-derived EBV sequences, with the mutational hot spots located in latent genes. Combined with 180 published EBV sequences derived from healthy people in Southern China, we performed a genome-wide association study and identified 32 variations significantly related to pLELC ( P <  2.56 × 10 -05 , Bonferroni correction), with the top signal of single nucleotide polymorphism (SNP) coordinate T7327C (OR = 1.22, P =  2.39 × 10 -15 ) locating in the origin of plasmid replication (OriP). The results of population structure analysis of EBV isolates in East Asian showed the EBV strains derived from pLELC were more similar to those from nasopharyngeal carcinoma (NPC) than other EBV-associated diseases. In addition, typical latency type-II infection were recognized for EBV of pLELC at both transcription and methylation levels. Taken together, we defined the global view of EBV genomic profiles in pLELC patients for the first time, providing new insights to deepening our understanding of this rare EBV-associated primary lung carcinoma. IMPORTANCE Pulmonary lymphoepithelioma-like carcinoma (pLELC) is a rare, distinctive subtype of primary lung cancer closely associated with Epstein-Barr virus (EBV) infection. Here, we gave the first overview of pLELC-derived EBV at the level of genome, methylation and transcription. We obtained the EBV sequences data set from 78 primary pLELC patients, and revealed the sequences diversity across EBV genome and detected variability in known immune epitopes. Genome-wide association analysis combining 217 healthy controls identifies significant variations related to the risk of pLELC. Meanwhile, we characterized the integration landscapes of EBV at the genome-wide level. These results provided new insight for understanding EBV's role in pLELC tumorigenesis.

Previous studies have demonstrated that sevoflurane postconditioning can provide neuroprotection after hypoxic-ischemic injury and improve learning and memory function in developing rodent brains. The classical Rice-Vannucci model was used to induce hypoxic-ischemic injury, and newborn (postnatal day 7) rats were treated with 2.4% sevoflurane for 30 minutes after hypoxic-ischemic injury. Our results showed that sevoflurane postconditioning significantly improved the learning and memory function of rats, decreased astrogliosis and glial scar formation, increased numbers of dendritic spines, and protected the histomorphology of the hippocampus. Mechanistically, sevoflurane postconditioning decreased expression of von Hippel-Lindau of hypoxia-inducible factor-1α and increased expression of DJ-1. Injection of 1.52 μg of the hypoxia-inducible factor-1α inhibitor YC-1 (Lificiguat) into the left lateral ventricle 30 minutes before hypoxic-ischemic injury reversed the neuroprotection induced by sevoflurane. This finding suggests that sevoflurane can effectively alleviate astrogliosis in the hippocampus and reduce learning and memory impairments caused by glial scar formation after hypoxic-ischemic injury. The underlying mechanism may be related to upregulated DJ-1 expression, reduced ubiquitination of hypoxia-inducible factor-1α, and stabilized hypoxia-inducible factor-1α expression. This study was approved by the Laboratory Animal Care Committee of China Medical University, China (approval No. 2016PS337K) on November 9, 2016., Competing Interests: None

The phenomenon of jump is one of the importantly external forms of hydrological variabi-lity under environmental changes, representing the adaption of hydrological nonlinear systems to the influence of external disturbances. Presently, the related studies mainly focus on the methods for identifying the jump positions and jump times in hydrological time series. In contrast, few studies have focused on the quantitative description and classification of jump degree in hydrological time series, which make it difficult to understand the environmental changes and evaluate its potential impacts. Here, we proposed a theatrically reliable and easy-to-apply method for the classification of jump degree in hydrological time series, using the correlation coefficient as a basic index. The statistical tests verified the accuracy, reasonability, and applicability of this method. The relationship between the correlation coefficient and the jump degree of series were described using mathematical equation by derivation. After that, several thresholds of correlation coefficients under different statistical significance levels were chosen, based on which the jump degree could be classified into five levels: no, weak, moderate, strong and very strong. Finally, our method was applied to five diffe-rent observed hydrological time series, with diverse geographic and hydrological conditions in China. The results of the classification of jump degrees in those series were closely accorded with their physically hydrological mechanisms, indicating the practicability of our method.

A stochastic hydrological process is influenced by both stochastic and deterministic factors. A hydrological time series contains not only pure random components reflecting its inheri-tance characteristics, but also deterministic components reflecting variability characteristics, such as jump, trend, period, and stochastic dependence. As a result, the stochastic hydrological process presents complicated evolution phenomena and rules. To better understand these complicated phenomena and rules, this study described the inheritance and variability characteristics of an inconsistent hydrological series from two aspects: stochastic process simulation and time series analysis. In addition, several frequency analysis approaches for inconsistent time series were compared to reveal the main problems in inconsistency study. Then, we proposed a new concept of hydrological genes origined from biological genes to describe the inconsistent hydrolocal processes. The hydrologi-cal genes were constructed using moments methods, such as general moments, weight function moments, probability weight moments and L-moments. Meanwhile, the five components, including jump, trend, periodic, dependence and pure random components, of a stochastic hydrological process were defined as five hydrological bases. With this method, the inheritance and variability of inconsistent hydrological time series were synthetically considered and the inheritance, variability and evolution principles were fully described. Our study would contribute to reveal the inheritance, variability and evolution principles in probability distribution of hydrological elements.

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.