Knowledge Adaptive Multi-Way Matching Network for Biomedical Named Entity Recognition via Machine Reading Comprehension.

Rapid and effective utilization of biomedical literature is paramount to combat diseases like COVID19. Biomedical named entity recognition (BioNER) is a fundamental task in text mining that can help physicians accelerate knowledge discovery to curb the spread of the COVID-19 epidemic. Recent approaches have shown that casting entity extraction as the machine reading comprehension task can significantly improve model performance. However, two major drawbacks impede higher success in identifying entities (1) ignoring the use of domain knowledge to capture the context beyond sentences and (2) lacking the ability to deeper understand the intent of questions. In this paper, to remedy this, we introduce and explore external domain knowledge which cannot be implicitly learned in text sequence. Previous works have focused more on text sequence and explored little of the domain knowledge. To better incorporate domain knowledge, a multi-way matching reader mechanism is devised to model representations of interaction between sequence, question and knowledge retrieved from Unified Medical Language System (UMLS). Benefiting from these, our model can better understand the intent of questions in complex contexts. Experimental results indicate that incorporating domain knowledge can help to obtain competitive results across 10 BioNER datasets, achieving absolute improvement of up to 2.02% in the f1 score.

COVID-19 vaccine acceptance among pregnant, breastfeeding, and nonpregnant reproductive-aged women.

BACKGROUND: Although mass vaccination against COVID-19 may prove to be the most efficacious end to this deadly pandemic, there remain concern and indecision among the public toward vaccination. Because pregnant and reproductive-aged women account for a large proportion of the population with particular concerns regarding vaccination against COVID-19, this survey aimed at investigating their current attitudes and beliefs within our own institution. OBJECTIVE: This study aimed to understand vaccine acceptability among pregnant, nonpregnant, and breastfeeding respondents and elucidate factors associated with COVID-19 vaccine acceptance. STUDY DESIGN: We administered an anonymous online survey to all women (including patients, providers, and staff) at our institution assessing rates of acceptance of COVID-19 vaccination. Respondents were contacted in 1 of 3 ways: by email, advertisement flyers, and distribution of quick response codes at virtual town halls regarding the COVID-19 vaccine. Based on their responses, respondents were divided into 3 mutually exclusive groups: (1) nonpregnant respondents, (2) pregnant respondents, and (3) breastfeeding respondents. The primary outcome was acceptance of vaccination. Prevalence ratios were calculated to ascertain the independent effects of multiple patient-level factors on vaccine acceptability. RESULTS: The survey was administered from January 7, 2021, to January 29, 2021, with 1012 respondents of whom 466 (46.9%) identified as non-Hispanic White, 108 (10.9%) as non-Hispanic Black, 286 (28.8%) as Hispanic, and 82 (8.2%) as non-Hispanic Asian. The median age was 36 years (interquartile range, 25-47 years). Of all the respondents, 656 respondents (64.8%) were nonpregnant, 216 (21.3%) were pregnant, and 122 (12.1%) were breastfeeding. There was no difference in chronic comorbidities when evaluated as a composite variable (Table 1). A total of 390 respondents (39.2%) reported working in healthcare. Nonpregnant respondents were most likely to accept vaccination (457 respondents, 76.2%; P<.001) with breastfeeding respondents the second most likely (55.2%). Pregnant respondents had the lowest rate of vaccine acceptance (44.3%; P<.001). Prevalence ratios revealed all non-White races except for non-Hispanic Asian respondents, and Spanish-speaking respondents were less likely to accept vaccination (Table 3). Working in healthcare was not found to be associated with vaccine acceptance among our cohort. CONCLUSION: In this survey study of only women at a single institution, pregnant respondents of non-White or non-Asian races were more likely to decline vaccination than nonpregnant and breastfeeding respondents. Working in healthcare was not associated with vaccine acceptance.

Evident PM2.5 drops in the east of China due to the COVID-19 quarantine measures in February

The top-level emergency response to the COVID-19 pandemic involved exhaustive quarantine measures in China. The impacts of the COVID-19 quarantine on the decline in fine particulate matter (PM2.5) were quantitatively assessed based on numerical simulations and observations in February. Relative to both February 2017 and the climate mean, anomalous southerlies and moister air occurred in the east of China in February 2020, which caused considerable PM2.5 anomalies. Thus, it is a must to disentangle the contributions of stable meteorology from the effects of the COVID-19 lockdown. The contributions of routine emission reductions were also quantitatively extrapolated. The top-level emergency response substantially alleviated the level of haze pollution in the east of China. Although climate variability elevated the PM2.5 by 29 % (relative to 2020 observations), a 59 % decline related to the COVID-19 pandemic and a 20 % decline from the expected pollution regulation dramatically exceeded the former in North China. The COVID-19 quarantine measures decreased the PM2.5 in the Yangtze River Delta by 72 %. In Hubei Province where most pneumonia cases were confirmed, the impact of total emission reduction (72 %) evidently exceeded the rising percentage of PM2.5 driven by meteorology (13 %).