Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen from the effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration.

PubMed® is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguous, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID®, and identifying fine-grained affiliation data from MapAffil. Through the integration of these credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving an F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities.

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PurposeThis study evaluated the differences in the facial morphological characteristics of female patients exhibiting skeletal class II deformity with and without temporomandibular joint osteoarthrosis.MethodsEighty-three female patients with skeletal class II deformity were included in this study; these patients were classified into three groups on the basis of the condylar features shown in cone-beam computed tomography scans: normal group, indeterminate for osteoarthrosis group, and osteoarthrosis group. The cephalometric differences among the three groups were evaluated through one-way ANOVA.ResultsOf the 83 patients, 52.4% were diagnosed with osteoarthrosis, as indicated by the changes in the condylar osseous component. The cephalometric measurements that represented skeletal characteristics, including mandibular position relative to the cranial base, mandibular plane angle (MP-SN), posterior facial height (S-Go), and facial height ratio, were significantly different among the three groups (p < 0.05). The patients in the osteoarthrosis group yielded the smallest S-Go, the highest MP-SN, and the most retruded mandible.ConclusionsTemporomandibular joint osteoarthrosis is commonly observed in female patients with skeletal class II deformity. The morphological characteristics of the facial skeleton in patients with bilateral condylar osteoarthrosis may be altered.