Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Clinical characteristics of liver injury in SARS-CoV-2 Omicron variant- and Omicron subvariant-infected patients

Introduction and Objectives: Liver injury in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant- and Omicron subvariant-infected patients is unknown at present, and the aim of this study is to summarize liver injury in these patients. Patients and Methods: In this study, 460 SARS-CoV-2-infected patients were enrolled. Five severe or critical patients were excluded, and 34 patients were also excluded because liver injury was not considered to be related to SARS-CoV-2 infection. Liver injury was compared between Omicron and non-Omicron variants- and between Omicron subvariant-infected patients; additionally, the clinical data related to liver injury were also analyzed. Results: Among the 421 patients enrolled for analysis, liver injury was detected in 76 (18.1%) patients, including 46 Omicron and 30 non-Omicron variant-infected patients. The ratios did not differ between Omicron and non-Omicron variant-, Omicron BA.1, BA.2 and BA.5 subvariant-infected patients (P>0.05). The majority of abnormal parameters of liver function tests were mildly elevated (1-3 × ULN), the most frequently elevated parameter of liver function test was γ-glutamyl transpeptidase (GGT, 9.5%, 40/421), and patients with cholangiocyte or biliary duct injury markers were higher than with hepatocellular injury markers. Multivariate analysis showed that age (>40 years old, OR=1.898, 95% CI=1.058–3.402, P=0.032), sex (male gender, OR=2.031, 95% CI=1.211–3.408, P=0.007), serum amyloid A (SAA) level (>10 mg/ml, OR=3.595, 95% CI=1.840–7.026, P<0.001) and vaccination status (No, OR=2.131, 95% CI=1.089–4.173, P=0.027) were independent factors related to liver injury. Conclusions: Liver injury does not differ between Omicron and non-Omicron variants or between Omicron subvariant-infected patients. The elevations of cholangiocyte or biliary duct injury biomarkers are dominant in SARS-CoV-2-infected patients

Detecting Rule of Simplicity from Photos

ABSTRACT Simplicity refers to one of the most important photography composition rules. Simplicity states that simplifying the image background can draw viewers&apos; attention to the subject of interest in a photograph and help them better comprehend and appreciate it. Understanding whether a photo respects photography rules or not facilitates photo quality assessment. In this paper, we present a method to automatically detect whether a photo is composed according to the rule of simplicity. We design features according to the definition, implementation and effect of the rule. First, we make use of saliency analysis to infer the subject of interest in a photo and measure its compactness. Second, we segment an image into background and foreground and measure the homogeneity within the background as another feature. Third, when looking at an image created with the rule of simplicity, different viewers tend to agree on what the subject of interest is in this photo. We accordingly measure the consistency among various saliency detection results as a feature. We experiment with these features in a range of machine learning methods. Our experiments show that our methods, together with these features, provide an encouraging result in detecting the rule of simplicity in a photo

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press