PMC:7143846 / 455-833 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"20","span":{"begin":302,"end":309},"obj":"Disease"},{"id":"21","span":{"begin":311,"end":321},"obj":"Disease"}],"attributes":[{"id":"A20","pred":"tao:has_database_id","subj":"20","obj":"MESH:D001007"},{"id":"A21","pred":"tao:has_database_id","subj":"21","obj":"MESH:D000275"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"sts) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indica"}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T5","span":{"begin":302,"end":321},"obj":"Disease"},{"id":"T6","span":{"begin":302,"end":309},"obj":"Disease"},{"id":"T8","span":{"begin":311,"end":321},"obj":"Disease"}],"attributes":[{"id":"A5","pred":"mondo_id","subj":"T5","obj":"http://purl.obolibrary.org/obo/MONDO_0041086"},{"id":"A6","pred":"mondo_id","subj":"T6","obj":"http://purl.obolibrary.org/obo/MONDO_0005618"},{"id":"A7","pred":"mondo_id","subj":"T6","obj":"http://purl.obolibrary.org/obo/MONDO_0011918"},{"id":"A8","pred":"mondo_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/MONDO_0002050"}],"text":"sts) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indica"}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T7","span":{"begin":104,"end":110},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"}],"text":"sts) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indica"}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":302,"end":309},"obj":"Phenotype"},{"id":"T2","span":{"begin":311,"end":321},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0000739"},{"id":"A2","pred":"hp_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/HP_0000716"}],"text":"sts) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indica"}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T1","span":{"begin":206,"end":214},"obj":"http://purl.obolibrary.org/obo/GO_0007612"}],"text":"sts) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indica"}

{"project":"LitCovid-sentences","denotations":[{"id":"T6","span":{"begin":54,"end":233},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"sts) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indica"}