Enhancing cross-evidence reasoning graph for document-level relation extraction.

The objective of document-level relation extraction (RE) is to identify the semantic connections that exist between named entities present within a document. However, most entities are distributed among different sentences, there is a need for inter-entity relation prediction across sentences. Existing research has focused on framing sentences throughout documents to predict relationships between entities. However, not all sentences play a substantial role in relation extraction, which inevitably introduces noisy information. Based on this phenomenon, we believe that we can extract evidence sentences in advance and use these evidence sentences to construct graphs to mine semantic information between entities. Thus, we present a document-level RE model that leverages an Enhancing Cross-evidence Reasoning Graph (ECRG) for improved performance. Specifically, we design an evidence extraction rule based on center-sentence to pre-extract higher-quality evidence. Then, this evidence is constructed into evidence graphs to mine the connections between mentions within the same evidence. In addition, we construct entity-level graphs by aggregating mentions from the same entities within the evidence graphs, aiming to capture distant interactions between entities. Experiments result on both DocRED and RE-DocRED datasets demonstrate that our model improves entity RE performance compared to existing work.

Global and regional trends in prevalence of untreated caries in permanent teeth: Age-period-cohort analysis from 1990 to 2019 and projections until 2049.

OBJECTIVES: This study aims to update the relevant epidemiological information of untreated caries in permanent teeth. METHODS: Data were derived from the Global Burden of Disease (GBD) study 2019. We described temporal trends in age-standardized prevalence rate (ASPR) of untreated caries in permanent teeth by gender and region from 1990 to 2019. Age-period-cohort (APC) model was utilized to analyze age, period and cohort effects on prevalence, and we used the Bayesian age-period-cohort (BAPC) model to make projections of prevalence between 2020 and 2049. RESULTS: The global ASPR of untreated caries in permanent teeth presented a decreasing trend from 1990 to 2019 (26593.58/105 vs. 25625.53/105), with females exceeding males annually. Negative correlation was observed between ASPR and Socio-demographic Index (SDI) levels. APC analyses showed that net drift was -0.16 % globally and generally below 0 across all SDI regions. The overall global peak in prevalence occurred in the 20-24 years group (36319.99/105), and there was a decrease trend in the overall global period rate ratio (RR). Compared to younger birth cohorts, prior birth cohorts had higher prevalence risks globally and across all SDI regions. Significant upward trends was predicted in the global ASPR of untreated caries in permanent teeth for both genders from 2020 to 2049. CONCLUSIONS: Age-period-cohort effects exerted a significant impact on the prevalence of untreated caries in permanent teeth during the study period. CLINICAL SIGNIFICANCE: The ASPR of untreated caries in permanent teeth may increase in the next 30 years by projections. And the disease burden of untreated caries in permanent teeth may be affected by population ageing. It is essential to implement targeted prevention and control policies to disadvantaged groups and attempt to reduce caries inequalities.

Global, regional, and national burden of oral cancer and its attributable risk factors from 1990 to 2019.

BACKGROUND: This study aims to provide a theoretical basis for the prevention of oral cancer by analyzing the epidemiological trends of oral cancer. MATERIALS AND METHODS: The data on oral cancer from 1990 to 2019 were extracted from the Global Burden of Disease 2019 database. The incidence, mortality, disability-adjusted life years (DALYs), and age-standardized rate as well as attributable risk factors of oral cancer were used for the analysis. Estimated annual percentage change (EAPC) was calculated to describe the changes in age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized DALYs rate (ASDR). RESULTS: The global ASIR of oral cancer showed an increasing trend from 1990 to 2019. ASIR in high SDI regions showed a decreasing trend during the studied period, with high SDI regions having the lowest ASMR in 2019. In 2019, the highest ASIR, ASMR, and ASDR were detected in South Asia. At the national level, Pakistan had the highest ASMR and ASDR in 2019. The increasing disease burden was observed in younger populations aged below 45 during the studied period. Smoking and alcohol use still exerted profound impacts on the oral cancer burden, with South Asia having the greatest increase in the percentage of deaths due to oral cancer attributable to chewing tobacco from 1990 to 2019. CONCLUSION: In conclusion, there is a large variability in the temporal and spatial burden of oral cancer, and it is essential for priority countries to take targeted intervention policies and measures to reduce the disease burden of oral cancer. In addition, the oral cancer burden caused by attributable risk factors should also receive close attention.

Factors that impacted mobile-payment adoption in China during the COVID-19 pandemic.

The Unified Theory of Acceptance and Use of Technology (UTAUT) is a potential paradigm for explaining technology adoption and can be applied to a wide range of scenarios. During the COVID-19 (C-19) outbreak in China, mobile-payment platforms (Mpayment) were used extensively in everyday life because they allowed people to avoid direct and indirect connections during transactions, adhere to social-distancing guidelines, and support social-economic stabilization. By exploring the technological and psychological variables that influenced user Mpayment-adoption intentions during the C-19 pandemic, this study broadens the literature on technology adoption in emergency circumstances and expands the UTAUT. A total of 593 complete samples were collected online, with SPSS used for data analysis. The empirical findings reveal that performance expectancy, trust, perceived security, and social influence all had a significant influence on Mpayment acceptance during the C-19 outbreak, with social distancing having the greatest impact, followed by fear of C-19. Interestingly, perceived-effort expectancy had a negative influence on payment acceptance. These findings suggest that future studies should apply the expanded model to different countries and areas to investigate the impact of the C-19 pandemic on Mpayment acceptance.