PMC:7589389 / 33788-34512 JSONTXT

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T15","span":{"begin":12,"end":15},"obj":"Body_part"},{"id":"T16","span":{"begin":20,"end":23},"obj":"Body_part"},{"id":"T17","span":{"begin":536,"end":539},"obj":"Body_part"},{"id":"T18","span":{"begin":544,"end":547},"obj":"Body_part"}],"attributes":[{"id":"A15","pred":"fma_id","subj":"T15","obj":"http://purl.org/sig/ont/fma/fma84129"},{"id":"A16","pred":"fma_id","subj":"T16","obj":"http://purl.org/sig/ont/fma/fma84130"},{"id":"A17","pred":"fma_id","subj":"T17","obj":"http://purl.org/sig/ont/fma/fma84129"},{"id":"A18","pred":"fma_id","subj":"T18","obj":"http://purl.org/sig/ont/fma/fma84130"}],"text":"To test the H2a and H2b, we generate the regression model as follows:CAR = β0 + β1CIPHT + β2CIPHT × Conditioning_VAR + β3Conditioning_VAR + β4PRO_CASE + β5SIZE + β6ROA + β7CURR + β8R\u0026D + β9LOSS + β10LEV + β11OPCF + β12TURN + β13CEO_AGE+ β14CEO_COM + β15CEO_TEN + β16CEO_DUA + Week FE + Industry FE + Province FE + ε(3) where Conditioning_VAR is a conditioning variable that moderates the association between continued increasing public health threats and accumulative abnormal return. All other variables are above-mentioned. To test H2a and H2b, Conditioning_VAR is in terms of the provincial information accessibility and provincial economic growth, respectively. We explain the detail proxies in the following sections."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T169","span":{"begin":3,"end":7},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T170","span":{"begin":181,"end":184},"obj":"http://purl.obolibrary.org/obo/CLO_0008693"},{"id":"T171","span":{"begin":181,"end":184},"obj":"http://purl.obolibrary.org/obo/CLO_0008770"},{"id":"T172","span":{"begin":347,"end":348},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T173","span":{"begin":531,"end":535},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"}],"text":"To test the H2a and H2b, we generate the regression model as follows:CAR = β0 + β1CIPHT + β2CIPHT × Conditioning_VAR + β3Conditioning_VAR + β4PRO_CASE + β5SIZE + β6ROA + β7CURR + β8R\u0026D + β9LOSS + β10LEV + β11OPCF + β12TURN + β13CEO_AGE+ β14CEO_COM + β15CEO_TEN + β16CEO_DUA + Week FE + Industry FE + Province FE + ε(3) where Conditioning_VAR is a conditioning variable that moderates the association between continued increasing public health threats and accumulative abnormal return. All other variables are above-mentioned. To test H2a and H2b, Conditioning_VAR is in terms of the provincial information accessibility and provincial economic growth, respectively. We explain the detail proxies in the following sections."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T18","span":{"begin":282,"end":284},"obj":"Chemical"},{"id":"T19","span":{"begin":296,"end":298},"obj":"Chemical"},{"id":"T20","span":{"begin":310,"end":312},"obj":"Chemical"}],"attributes":[{"id":"A18","pred":"chebi_id","subj":"T18","obj":"http://purl.obolibrary.org/obo/CHEBI_74712"},{"id":"A19","pred":"chebi_id","subj":"T19","obj":"http://purl.obolibrary.org/obo/CHEBI_74712"},{"id":"A20","pred":"chebi_id","subj":"T20","obj":"http://purl.obolibrary.org/obo/CHEBI_74712"}],"text":"To test the H2a and H2b, we generate the regression model as follows:CAR = β0 + β1CIPHT + β2CIPHT × Conditioning_VAR + β3Conditioning_VAR + β4PRO_CASE + β5SIZE + β6ROA + β7CURR + β8R\u0026D + β9LOSS + β10LEV + β11OPCF + β12TURN + β13CEO_AGE+ β14CEO_COM + β15CEO_TEN + β16CEO_DUA + Week FE + Industry FE + Province FE + ε(3) where Conditioning_VAR is a conditioning variable that moderates the association between continued increasing public health threats and accumulative abnormal return. All other variables are above-mentioned. To test H2a and H2b, Conditioning_VAR is in terms of the provincial information accessibility and provincial economic growth, respectively. We explain the detail proxies in the following sections."}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T11","span":{"begin":646,"end":652},"obj":"http://purl.obolibrary.org/obo/GO_0040007"}],"text":"To test the H2a and H2b, we generate the regression model as follows:CAR = β0 + β1CIPHT + β2CIPHT × Conditioning_VAR + β3Conditioning_VAR + β4PRO_CASE + β5SIZE + β6ROA + β7CURR + β8R\u0026D + β9LOSS + β10LEV + β11OPCF + β12TURN + β13CEO_AGE+ β14CEO_COM + β15CEO_TEN + β16CEO_DUA + Week FE + Industry FE + Province FE + ε(3) where Conditioning_VAR is a conditioning variable that moderates the association between continued increasing public health threats and accumulative abnormal return. All other variables are above-mentioned. To test H2a and H2b, Conditioning_VAR is in terms of the provincial information accessibility and provincial economic growth, respectively. We explain the detail proxies in the following sections."}

{"project":"LitCovid-sentences","denotations":[{"id":"T246","span":{"begin":0,"end":486},"obj":"Sentence"},{"id":"T247","span":{"begin":487,"end":527},"obj":"Sentence"},{"id":"T248","span":{"begin":528,"end":667},"obj":"Sentence"},{"id":"T249","span":{"begin":668,"end":724},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"To test the H2a and H2b, we generate the regression model as follows:CAR = β0 + β1CIPHT + β2CIPHT × Conditioning_VAR + β3Conditioning_VAR + β4PRO_CASE + β5SIZE + β6ROA + β7CURR + β8R\u0026D + β9LOSS + β10LEV + β11OPCF + β12TURN + β13CEO_AGE+ β14CEO_COM + β15CEO_TEN + β16CEO_DUA + Week FE + Industry FE + Province FE + ε(3) where Conditioning_VAR is a conditioning variable that moderates the association between continued increasing public health threats and accumulative abnormal return. All other variables are above-mentioned. To test H2a and H2b, Conditioning_VAR is in terms of the provincial information accessibility and provincial economic growth, respectively. We explain the detail proxies in the following sections."}

{"project":"LitCovid-PubTator","denotations":[{"id":"218","span":{"begin":20,"end":23},"obj":"Gene"},{"id":"219","span":{"begin":544,"end":547},"obj":"Gene"},{"id":"220","span":{"begin":12,"end":15},"obj":"Gene"},{"id":"221","span":{"begin":536,"end":539},"obj":"Gene"}],"attributes":[{"id":"A218","pred":"tao:has_database_id","subj":"218","obj":"Gene:8349"},{"id":"A219","pred":"tao:has_database_id","subj":"219","obj":"Gene:8349"},{"id":"A220","pred":"tao:has_database_id","subj":"220","obj":"Gene:113457"},{"id":"A221","pred":"tao:has_database_id","subj":"221","obj":"Gene:113457"}],"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":"To test the H2a and H2b, we generate the regression model as follows:CAR = β0 + β1CIPHT + β2CIPHT × Conditioning_VAR + β3Conditioning_VAR + β4PRO_CASE + β5SIZE + β6ROA + β7CURR + β8R\u0026D + β9LOSS + β10LEV + β11OPCF + β12TURN + β13CEO_AGE+ β14CEO_COM + β15CEO_TEN + β16CEO_DUA + Week FE + Industry FE + Province FE + ε(3) where Conditioning_VAR is a conditioning variable that moderates the association between continued increasing public health threats and accumulative abnormal return. All other variables are above-mentioned. To test H2a and H2b, Conditioning_VAR is in terms of the provincial information accessibility and provincial economic growth, respectively. We explain the detail proxies in the following sections."}