OBJECTIVE: This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem.
METHODS: Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation.
RESULTS: Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance.
CONCLUSIONS: We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.
METHODS: Osteochondral samples at different stages of cartilage degradation were collected from 16 femoral heads with OA. Osteochondral samples with normal cartilage were collected from seven femoral heads with osteoporosis. Microcomputed tomography was used for the investigation of subchondral bone microarchitecture and mineral densities. Immunohistochemistry was used to study the expression and distribution of MMP13 and ADAMTS4 in cartilage.
RESULTS: The microarchitecture and mineral properties of the subchondral plate and trabecular bone in OA varied with the severity of the degradation of the overlying cartilage. Chondrocytes expressing MMP13 and ADAMTS4 are mainly located in the upper zone(s) of cartilage regardless of the histopathological grades. The zonal expression of these enzymes in OA (i.e., the percentage of positive cells in the superficial, middle, and deep zones), rather than their overall expression (the percentage of positive cells in the full thickness of the cartilage), exhibited significant variation in relation to the severity of cartilage degradation. The associations between the subchondral bone properties and zonal and overall expression of these enzymes in the cartilage were generally weak or nonsignificant.
CONCLUSIONS: Phenotypic changes in chondrocytes and remodelling of subchondral bone proceed at different rates throughout the process of cartilage degradation. Biological influences are more important for cartilage degradation at early stages, while biomechanical damage to the compromised tissue may outrun the phenotypic change of chondrocytes and is critical in the advanced stages.
METHODS AND RESULTS: Wild-type flies (Oregon-R) were crossed with glass multimer reporter-GAL4 (GMR-GAL4) to produce GMR-OreR (Control), while UAS-Aβ42 (#33769) were crossed with GMR-GAL4 to produce transgenic Drosophila line that expressed Aβ42 (GMR-Aβ42). Feed containing seven different LAB strains (Lactobacillus paracasei 0291, Lactobacillus helveticus 1515, Lactobacillus reuteri 30242, L. reuteri 8513d, Lactobacillus fermentum 8312, Lactobacillus casei Y, Lactobacillus sakei Probio65) were given to GMR-Aβ42 respectively, while feed without LAB strains were given to control and transgenic GMR-Aβ42.nf Drosophila lines. The morphology of the eyes was viewed with scanning electron microscopy (SEM). The changes in gut microbiota profiles associated with LAB were analysed using 16s high throughput sequencing. Malformation of eye structures in transgenic GMR-Aβ42 Drosophila were reversed upon the administration of LAB strains, with more prevalent effects from L. sakei Probio65 and L. paracasei 0291. The GMR-Aβ42.nf group showed dominance of Wolbachia in the gut, a genus that was almost absent in the normal control group (P
METHODS: A random group of 1404 persons from universities, factories, companies, and elderly centers in Changchun completed a structured questionnaire. This study centered on life satisfaction indicators, which included the current whole life, income, family relationships, peer relationships, relationships with the neighbors, living environment, personal health, family health, spare time, and housework share. Other collected data included the Body Mass Index, blood pressure, self-rated health, Breslow's seven health practices, medical treatment within the past 6 months, physical examinations, General Health Questionnaire (GHQ)-12 Scale, social activities, networking relationships with persons around the community, social support, and sociodemographic variables. Associations between life satisfaction, demographics, and health-related variables were analyzed through a multiway ANOVA.
RESULTS: The living environment and income of Chinese persons were related to their low life satisfaction. The multiway ANOVA showed that the independent relationship of self-rated health, regular physical examinations, GHQ-12 Scale, trust in the community, communication with the neighbors, education, and age related with life satisfaction accounting for 20.3% of the variance. Education and age showed interactive effects on life satisfaction.
CONCLUSION: This study identified seven factors that influenced the life satisfaction of persons in mainland China. Life satisfaction can be enhanced through interventions to improve self-rated health, regular physical examinations, mental health, trust in the community, communication with the neighbors, education, and improvement in the health service.