PMC:7589389 / 23998-24720
Annnotations
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T60 | 571-579 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T92 | 46-50 | http://purl.obolibrary.org/obo/UBERON_0000473 | denotes | test |
| T93 | 100-105 | http://purl.obolibrary.org/obo/UBERON_0000473 | denotes | tests |
| T94 | 137-141 | http://purl.obolibrary.org/obo/UBERON_0000473 | denotes | test |
| T95 | 258-261 | http://purl.obolibrary.org/obo/CLO_0008693 | denotes | R&D |
| T96 | 258-261 | http://purl.obolibrary.org/obo/CLO_0008770 | denotes | R&D |
| T97 | 399-402 | http://purl.obolibrary.org/obo/CLO_0002199 | denotes | CAR |
| T98 | 460-467 | http://purl.obolibrary.org/obo/CLO_0009955 | denotes | CAR [−1 |
| T99 | 476-479 | http://purl.obolibrary.org/obo/CLO_0002199 | denotes | CAR |
| T100 | 482-486 | http://purl.obolibrary.org/obo/CLO_0050507 | denotes | 2, 2 |
| T101 | 690-691 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
LitCovid-PD-CHEBI
| Id | Subject | Object | Predicate | Lexical cue | chebi_id |
|---|---|---|---|---|---|
| T8 | 354-356 | Chemical | denotes | FE | http://purl.obolibrary.org/obo/CHEBI_74712 |
| T9 | 368-370 | Chemical | denotes | FE | http://purl.obolibrary.org/obo/CHEBI_74712 |
| T10 | 382-384 | Chemical | denotes | FE | http://purl.obolibrary.org/obo/CHEBI_74712 |
| T11 | 502-511 | Chemical | denotes | indicator | http://purl.obolibrary.org/obo/CHEBI_47867 |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T177 | 0-57 | Sentence | denotes | We describe the regression model for the main test of H1. |
| T178 | 58-133 | Sentence | denotes | The regression models for cross-sectional tests are described in Section 5. |
| T179 | 134-665 | Sentence | denotes | To test H1, we apply the multiple regression model as follows:CAR = β0 + β1CIPHT + β2PRO_CASE + β3SIZE + β4ROA + β5CURR + β6R&D + β7LOSS+ β8LEV + β9OPCF + β10TURN + β11CEO_AGE+ β12CEO_COM + β13CEO_TEN+ β14CEO_DUA + Week FE + Industry FE + Province FE + ε(2) where CAR refers to our two types of accumulative abnormal return (CAR [−1, 1] and CAR [−2, 2]), CIPHT is an indicator variable that equals one if there have been provincial new COVID-19 cases for at least six consecutive days including the current day and zero otherwise. |
| T180 | 666-722 | Sentence | denotes | Based on H1, we suppose a negative coefficient of CIPHT. |
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 165 | 571-579 | Disease | denotes | COVID-19 | MESH:C000657245 |