> top > docs > PMC:7333997 > annotations

PMC:7333997 JSONTXT

Annnotations TAB JSON ListView MergeView

LitCovid_Glycan-Motif-Structure

Id Subject Object Predicate Lexical cue
T1 11048-11052 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T2 11048-11052 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T3 11092-11096 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T4 11092-11096 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T5 11190-11194 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T6 11190-11194 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T7 11471-11475 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T8 11471-11475 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T9 11525-11529 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T10 11525-11529 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T11 11616-11620 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T12 11616-11620 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T13 12612-12616 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T14 12612-12616 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T15 18045-18049 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T16 18045-18049 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T17 18154-18158 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T18 18154-18158 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T19 18344-18348 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T20 18344-18348 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T21 18525-18529 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T22 18525-18529 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T23 18639-18643 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T24 18639-18643 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T25 18768-18772 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T26 18768-18772 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T27 19350-19354 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T28 19350-19354 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T29 19642-19646 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T30 19642-19646 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T31 20345-20349 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T32 20345-20349 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T33 21054-21058 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T34 21054-21058 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T35 21094-21098 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T36 21094-21098 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T37 21127-21131 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T38 21127-21131 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T39 21189-21193 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T40 21189-21193 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T41 21939-21943 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T42 21939-21943 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T43 26548-26552 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T44 26548-26552 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T45 27881-27885 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T46 27881-27885 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T47 30458-30462 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T48 30458-30462 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T49 30515-30519 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T50 30515-30519 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T51 30532-30536 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T52 30532-30536 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3
T53 30554-30558 https://glytoucan.org/Structures/Glycans/G56516VH denotes g/m3
T54 30554-30558 https://glytoucan.org/Structures/Glycans/G91237TK denotes g/m3

LitCovid-PMC-OGER-BB

Id Subject Object Predicate Lexical cue
T1 23-31 SP_7 denotes COVID-19
T2 237-248 NCBITaxon:11118 denotes coronavirus
T3 340-349 SP_7 denotes SARS-CoV2
T4 374-382 SP_7 denotes COVID-19
T5 639-644 NCBITaxon:10239 denotes virus
T6 1352-1356 UBERON:0002048 denotes lung
T7 1364-1370 GO:0016265 denotes deaths
T8 1384-1390 GO:0016265 denotes deaths
T9 1412-1417 UBERON:0000948 denotes heart
T10 1426-1432 GO:0016265 denotes deaths
T11 1651-1654 CHEBI:8102;CHEBI:8102 denotes PM2
T12 1679-1685 CHEBI:8102;CHEBI:8102 denotes matter
T13 2055-2063 SP_7 denotes COVID-19
T15 2499-2507 SP_7 denotes COVID-19
T16 2527-2530 CHEBI:8102;CHEBI:8102 denotes PM2
T17 2977-2985 GO:0051866 denotes adaptive
T18 3553-3558 NCBITaxon:10239 denotes virus
T19 3789-3794 NCBITaxon:10239 denotes virus
T20 3852-3859 NCBITaxon:10239 denotes viruses
T21 4351-4357 NCBITaxon:9606 denotes people
T22 4415-4425 CHEBI:33893;CHEBI:33893 denotes pollutants
T23 4459-4466 CHEBI:32168;CHEBI:32168 denotes sulphur
T24 4467-4473 CHEBI:25741;CHEBI:25741 denotes oxides
T25 4481-4496 CHEBI:35196;CHEBI:35196 denotes nitrogen oxides
T26 4504-4519 CHEBI:17245;CHEBI:17245 denotes carbon monoxide
T27 4529-4543 CHEBI:16526;CHEBI:16526 denotes carbon dioxide
T28 4545-4548 CHEBI:16526;CHEBI:16526 denotes CO2
T29 4768-4774 GO:0016265 denotes deaths
T30 4874-4880 NCBITaxon:9606 denotes people
T31 4881-4884 GO:0016265 denotes die
T32 4912-4915 CHEBI:8102;CHEBI:8102 denotes PM2
T33 4959-4977 UBERON:0001004 denotes respiratory system
T34 5003-5007 UBERON:0002048 denotes lung
T35 5056-5060 UBERON:0002048 denotes lung
T36 5089-5098 UBERON:0002048 denotes pulmonary
T37 5108-5113 UBERON:0000948 denotes heart
T38 5126-5137 UBERON:0001004 denotes respiratory
T39 5702-5709 NCBITaxon:10239 denotes viruses
T40 5900-5905 NCBITaxon:10239 denotes virus
T41 5913-5922 SP_7 denotes SARS-CoV2
T42 5940-5948 SP_7 denotes COVID-19
T43 6033-6039 NCBITaxon:9606 denotes people
T44 6097-6102 NCBITaxon:10239 denotes virus
T45 6765-6768 CHEBI:16526;CHEBI:16526 denotes CO2
T46 6944-6950 GO:0016265 denotes deaths
T47 6956-6964 SP_7 denotes COVID-19
T48 7002-7008 GO:0016265 denotes deaths
T49 7025-7041 CHEBI:33101;CHEBI:33101 denotes nitrogen dioxide
T50 7042-7045 CHEBI:17997;CHEBI:17997 denotes NO2
T51 7474-7485 NCBITaxon:11118 denotes coronavirus
T52 7590-7598 GO:0007612 denotes learning
T53 8355-8366 NCBITaxon:11118 denotes coronavirus
T54 8409-8415 GO:0016265 denotes deaths
T55 8609-8620 CHEBI:25212;CHEBI:25212 denotes particulate
T56 8621-8631 CHEBI:33893;CHEBI:33893 denotes pollutants
T57 8662-8673 NCBITaxon:11118 denotes coronavirus
T58 9147-9150 CHEBI:8102;CHEBI:8102 denotes PM2
T59 9702-9710 SP_7 denotes COVID-19
T60 10932-10940 SP_7 denotes COVID-19
T61 11746-11754 SP_7 denotes COVID-19
T62 15375-15383 SP_7 denotes COVID-19
T63 16902-16905 CHEBI:8102;CHEBI:8102 denotes PM2
T64 17981-17984 CHEBI:8102;CHEBI:8102 denotes PM2
T65 17997-18006 CHEBI:33893;CHEBI:33893 denotes pollutant
T66 19479-19482 CHEBI:8102;CHEBI:8102 denotes PM2
T67 20428-20431 CHEBI:8102;CHEBI:8102 denotes PM2
T68 20446-20452 CHEBI:8102;CHEBI:8102 denotes matter
T69 21325-21328 CHEBI:8102;CHEBI:8102 denotes PM2
T70 21513-21519 UBERON:0002020 denotes matter
T71 22075-22079 CHEBI:33290;CHEBI:33290 denotes food
T72 22085-22088 CHEBI:8102;CHEBI:8102 denotes PM2
T73 22680-22683 CHEBI:8102;CHEBI:8102 denotes PM2
T74 22787-22794 GO:0065007 denotes control
T75 22896-22899 CHEBI:8102;CHEBI:8102 denotes PM2
T76 23083-23085 CL:0002322 denotes ES
T77 23105-23107 CL:0002322 denotes ES
T78 23218-23225 GO:0065007 denotes control
T79 23283-23285 CL:0002322 denotes ES
T80 23480-23482 CL:0002322 denotes ES
T81 23547-23549 CL:0002322 denotes ES
T82 23592-23594 CL:0002322 denotes ES
T83 23667-23669 CL:0002322 denotes ES
T84 23713-23715 CL:0002322 denotes ES
T85 23731-23733 CL:0002322 denotes ES
T86 24122-24124 CL:0002322 denotes ES
T87 24140-24142 CL:0002322 denotes ES
T88 24245-24247 CL:0002322 denotes ES
T89 24264-24266 CL:0002322 denotes ES
T90 24758-24760 CL:0002322 denotes ES
T91 24778-24780 CL:0002322 denotes ES
T92 25536-25538 CL:0002322 denotes ES
T93 25863-25867 UBERON:0000104 denotes life
T94 26223-26230 GO:0065007 denotes monitor
T95 26352-26356 UBERON:0000104 denotes life
T96 27844-27847 CHEBI:8102;CHEBI:8102 denotes PM2
T97 27899-27909 CHEBI:15854;CHEBI:15854 denotes quarantine
T98 28405-28408 CHEBI:8102;CHEBI:8102 denotes PM2
T99 30312-30315 CHEBI:8102;CHEBI:8102 denotes PM2
T100 31201-31203 CL:0002322 denotes ES
T86723 23-31 SP_7 denotes COVID-19
T76174 237-248 NCBITaxon:11118 denotes coronavirus
T24145 340-349 SP_7 denotes SARS-CoV2
T67406 374-382 SP_7 denotes COVID-19
T47626 639-644 NCBITaxon:10239 denotes virus
T71604 1352-1356 UBERON:0002048 denotes lung
T74592 1364-1370 GO:0016265 denotes deaths
T33726 1384-1390 GO:0016265 denotes deaths
T14468 1412-1417 UBERON:0000948 denotes heart
T18748 1426-1432 GO:0016265 denotes deaths
T97518 1651-1654 CHEBI:8102;CHEBI:8102 denotes PM2
T12933 1679-1685 CHEBI:8102;CHEBI:8102 denotes matter
T11492 2055-2063 SP_7 denotes COVID-19
T14 2499-2507 SP_7 denotes COVID-19
T10699 29775-29783 GO:0051866 denotes adaptive
T55010 22503-22506 CHEBI:8102;CHEBI:8102 denotes ent
T70895 22610-22618 GO:0065007 denotes in capi
T71376 22718-22721 CHEBI:8102;CHEBI:8102 denotes e i
T21234 22905-22907 CL:0002322 denotes .1
T4018 22928-22930 CL:0002322 denotes s
T89422 23040-23047 GO:0065007 denotes itants
T30581 23105-23107 CL:0002322 denotes ES
T57333 23303-23305 CL:0002322 denotes er
T88886 23370-23372 CL:0002322 denotes bl
T83449 23415-23417 CL:0002322 denotes el
T41730 23491-23493 CL:0002322 denotes ti
T40092 23536-23538 CL:0002322 denotes 12
T88635 23554-23556 CL:0002322 denotes .
T19457 23994-23996 CL:0002322 denotes P
T11760 24012-24014 CL:0002322 denotes e,
T9560 24117-24119 CL:0002322 denotes 0
T80611 24136-24138 CL:0002322 denotes P
T42912 24629-24631 CL:0002322 denotes t
T33214 24649-24651 CL:0002322 denotes a.
T35384 25409-25411 CL:0002322 denotes bo
T57601 25736-25740 UBERON:0000104 denotes nt r
T27039 26096-26103 GO:0065007 denotes greater
T23617 26225-26229 UBERON:0000104 denotes nito
T49927 27763-27766 CHEBI:8102;CHEBI:8102 denotes (1
T66344 27819-27829 CHEBI:15854;CHEBI:15854 denotes a, to Skop
T83987 28324-28327 CHEBI:8102;CHEBI:8102 denotes ute
T84902 30312-30315 CHEBI:8102;CHEBI:8102 denotes PM2
T24115 31201-31203 CL:0002322 denotes ES

LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 1352-1356 Body_part denotes lung http://purl.org/sig/ont/fma/fma7195
T2 1412-1417 Body_part denotes heart http://purl.org/sig/ont/fma/fma7088
T3 3168-3172 Body_part denotes face http://purl.org/sig/ont/fma/fma24728
T4 4959-4977 Body_part denotes respiratory system http://purl.org/sig/ont/fma/fma7158
T5 5003-5007 Body_part denotes lung http://purl.org/sig/ont/fma/fma7195
T6 5056-5060 Body_part denotes lung http://purl.org/sig/ont/fma/fma7195
T7 5108-5113 Body_part denotes heart http://purl.org/sig/ont/fma/fma7088
T8 5242-5246 Body_part denotes hand http://purl.org/sig/ont/fma/fma9712
T9 6284-6288 Body_part denotes hand http://purl.org/sig/ont/fma/fma9712
T10 12213-12217 Body_part denotes hand http://purl.org/sig/ont/fma/fma9712
T11 12589-12596 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T12 16452-16456 Body_part denotes hand http://purl.org/sig/ont/fma/fma9712
T13 18288-18295 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T14 18469-18476 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T15 18837-18844 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T16 18994-19001 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T17 20473-20480 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T18 20637-20644 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T19 21699-21706 Body_part denotes Capital http://purl.org/sig/ont/fma/fma23727
T20 27012-27015 Body_part denotes map http://purl.org/sig/ont/fma/fma67847

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 1352-1356 Body_part denotes lung http://purl.obolibrary.org/obo/UBERON_0002048
T2 1412-1417 Body_part denotes heart http://purl.obolibrary.org/obo/UBERON_0000948
T3 3168-3172 Body_part denotes face http://purl.obolibrary.org/obo/UBERON_0001456
T4 4959-4977 Body_part denotes respiratory system http://purl.obolibrary.org/obo/UBERON_0001004
T5 5003-5007 Body_part denotes lung http://purl.obolibrary.org/obo/UBERON_0002048
T6 5056-5060 Body_part denotes lung http://purl.obolibrary.org/obo/UBERON_0002048
T7 5108-5113 Body_part denotes heart http://purl.obolibrary.org/obo/UBERON_0000948
T8 5242-5246 Body_part denotes hand http://purl.obolibrary.org/obo/UBERON_0002398
T9 6284-6288 Body_part denotes hand http://purl.obolibrary.org/obo/UBERON_0002398
T10 12213-12217 Body_part denotes hand http://purl.obolibrary.org/obo/UBERON_0002398
T11 16452-16456 Body_part denotes hand http://purl.obolibrary.org/obo/UBERON_0002398

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 23-31 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 340-344 Disease denotes SARS http://purl.obolibrary.org/obo/MONDO_0005091
T3 374-382 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 1352-1363 Disease denotes lung cancer http://purl.obolibrary.org/obo/MONDO_0008903
T5 1357-1363 Disease denotes cancer http://purl.obolibrary.org/obo/MONDO_0004992
T6 1379-1383 Disease denotes COPD http://purl.obolibrary.org/obo/MONDO_0005002
T7 1403-1425 Disease denotes ischemic heart disease http://purl.obolibrary.org/obo/MONDO_0024644
T8 1412-1425 Disease denotes heart disease http://purl.obolibrary.org/obo/MONDO_0005267
T9 2055-2063 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T10 2499-2507 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T11 3963-3972 Disease denotes influenza http://purl.obolibrary.org/obo/MONDO_0005812
T12 5048-5054 Disease denotes stroke http://purl.obolibrary.org/obo/MONDO_0005098|http://purl.obolibrary.org/obo/MONDO_0011057
T14 5056-5067 Disease denotes lung cancer http://purl.obolibrary.org/obo/MONDO_0008903
T15 5061-5067 Disease denotes cancer http://purl.obolibrary.org/obo/MONDO_0004992
T16 5069-5106 Disease denotes chronic obstructive pulmonary disease http://purl.obolibrary.org/obo/MONDO_0005002
T17 5089-5106 Disease denotes pulmonary disease http://purl.obolibrary.org/obo/MONDO_0005275
T18 5108-5121 Disease denotes heart disease http://purl.obolibrary.org/obo/MONDO_0005267
T19 5126-5148 Disease denotes respiratory infections http://purl.obolibrary.org/obo/MONDO_0024355
T20 5157-5166 Disease denotes pneumonia http://purl.obolibrary.org/obo/MONDO_0005249
T21 5913-5917 Disease denotes SARS http://purl.obolibrary.org/obo/MONDO_0005091
T22 5940-5948 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T23 6956-6964 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T24 9702-9710 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T25 10932-10940 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T26 11746-11754 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T27 14275-14278 Disease denotes EFE http://purl.obolibrary.org/obo/MONDO_0009169
T28 15375-15383 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 186-198 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T2 221-222 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T3 261-262 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 401-403 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T5 431-432 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T6 514-522 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes humanity
T7 523-526 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T8 527-532 http://purl.obolibrary.org/obo/UBERON_0001456 denotes faced
T9 639-644 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T10 651-654 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T11 1014-1015 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T12 1352-1356 http://purl.obolibrary.org/obo/UBERON_0002048 denotes lung
T13 1352-1356 http://www.ebi.ac.uk/efo/EFO_0000934 denotes lung
T14 1412-1417 http://purl.obolibrary.org/obo/UBERON_0000948 denotes heart
T15 1412-1417 http://purl.obolibrary.org/obo/UBERON_0007100 denotes heart
T16 1412-1417 http://purl.obolibrary.org/obo/UBERON_0015228 denotes heart
T17 1412-1417 http://www.ebi.ac.uk/efo/EFO_0000815 denotes heart
T18 2097-2098 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T19 2123-2125 http://purl.obolibrary.org/obo/CLO_0001000 denotes 35
T20 2512-2513 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T21 2546-2547 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T22 2628-2629 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T23 3168-3172 http://purl.obolibrary.org/obo/UBERON_0001456 denotes face
T24 3404-3405 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T25 3500-3501 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T26 3551-3552 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T27 3553-3558 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T28 3789-3794 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T29 3852-3859 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T30 3899-3900 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T31 4262-4265 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T32 4656-4668 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T33 4819-4831 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T34 4833-4837 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T35 4959-4977 http://purl.obolibrary.org/obo/UBERON_0001004 denotes respiratory system
T36 5003-5007 http://purl.obolibrary.org/obo/UBERON_0002048 denotes lung
T37 5003-5007 http://www.ebi.ac.uk/efo/EFO_0000934 denotes lung
T38 5056-5060 http://purl.obolibrary.org/obo/UBERON_0002048 denotes lung
T39 5056-5060 http://www.ebi.ac.uk/efo/EFO_0000934 denotes lung
T40 5108-5113 http://purl.obolibrary.org/obo/UBERON_0000948 denotes heart
T41 5108-5113 http://purl.obolibrary.org/obo/UBERON_0007100 denotes heart
T42 5108-5113 http://purl.obolibrary.org/obo/UBERON_0015228 denotes heart
T43 5108-5113 http://www.ebi.ac.uk/efo/EFO_0000815 denotes heart
T44 5248-5249 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T45 5271-5275 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T46 5632-5636 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T47 5654-5658 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T48 5702-5709 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes viruses
T49 5855-5867 http://purl.obolibrary.org/obo/OBI_0000245 denotes organization
T50 5880-5881 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T51 5900-5905 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T52 5949-5952 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T53 5968-5976 http://purl.obolibrary.org/obo/CLO_0001658 denotes activity
T54 5980-5981 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T55 5997-6000 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T56 6097-6102 http://purl.obolibrary.org/obo/NCBITaxon_10239 denotes virus
T57 6301-6304 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T58 6398-6401 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T59 6576-6579 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T60 7199-7202 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T61 7411-7412 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T62 7557-7558 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T63 7588-7589 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T64 7843-7845 http://purl.obolibrary.org/obo/CLO_0001000 denotes 35
T65 8055-8056 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T66 8273-8276 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T67 8283-8284 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T68 8428-8429 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T69 8639-8640 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T70 9103-9104 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T71 9260-9261 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T72 10440-10446 http://purl.obolibrary.org/obo/CLO_0001929 denotes Berlin
T73 10619-10622 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T74 11225-11229 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T75 11480-11484 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T76 11542-11548 http://purl.obolibrary.org/obo/CLO_0001929 denotes Berlin
T77 11625-11629 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T78 12018-12019 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T79 12092-12093 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T80 12409-12410 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T81 12492-12493 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T82 13361-13364 http://purl.obolibrary.org/obo/CLO_0002742 denotes del
T83 13378-13380 http://purl.obolibrary.org/obo/CLO_0001302 denotes 34
T84 13482-13484 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T85 14033-14039 http://purl.obolibrary.org/obo/NCBITaxon_9005 denotes Turkey
T86 14355-14357 http://purl.obolibrary.org/obo/CLO_0050509 denotes 27
T87 14601-14603 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T88 14940-14946 http://purl.obolibrary.org/obo/CLO_0001929 denotes Berlin
T89 15209-15210 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T90 15280-15281 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T91 15469-15479 http://purl.obolibrary.org/obo/CLO_0001658 denotes activities
T92 15720-15721 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T93 16488-16490 http://purl.obolibrary.org/obo/CLO_0053799 denotes 45
T94 16882-16883 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T95 17370-17373 http://purl.obolibrary.org/obo/PR_000001343 denotes aim
T96 17476-17477 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T97 17493-17495 http://purl.obolibrary.org/obo/CLO_0050509 denotes 27
T98 17517-17518 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T99 17642-17643 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T100 17692-17694 http://purl.obolibrary.org/obo/CLO_0053733 denotes 11
T101 17784-17785 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T102 18020-18021 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T103 18079-18080 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T104 18129-18130 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T105 18160-18163 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T106 18255-18256 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T107 18444-18445 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T108 18599-18602 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T109 18603-18604 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T110 18803-18805 http://purl.obolibrary.org/obo/CLO_0050510 denotes 18
T111 19452-19453 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T112 19638-19640 http://purl.obolibrary.org/obo/CLO_0001382 denotes 48
T113 19651-19652 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T114 19687-19689 http://purl.obolibrary.org/obo/CLO_0001000 denotes 35
T115 19857-19858 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T116 20190-20191 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T117 20297-20298 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T118 20944-20945 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T119 21319-21320 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T120 21456-21460 http://purl.obolibrary.org/obo/CLO_0001550 denotes a 10
T121 21542-21543 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T122 21624-21627 http://purl.obolibrary.org/obo/CLO_0001562 denotes a 2
T123 21624-21627 http://purl.obolibrary.org/obo/CLO_0001563 denotes a 2
T124 21886-21887 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T125 21914-21915 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T126 22120-22122 http://purl.obolibrary.org/obo/CLO_0001000 denotes 35
T127 22134-22135 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T128 22441-22453 http://purl.obolibrary.org/obo/OBI_0000245 denotes Organization
T129 22455-22459 http://purl.obolibrary.org/obo/CLO_0001185 denotes 2018
T130 22487-22488 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T131 23083-23085 http://purl.obolibrary.org/obo/CLO_0053755 denotes ES
T132 23102-23107 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T133 23145-23146 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T134 23280-23285 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T135 23353-23354 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T136 23477-23482 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T137 23525-23526 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T138 23544-23549 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T139 23589-23594 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T140 23664-23669 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T141 23710-23715 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T142 23728-23733 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T143 24028-24029 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T144 24092-24093 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T145 24119-24124 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T146 24137-24142 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T147 24242-24247 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T148 24261-24266 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T149 24393-24394 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T150 24561-24564 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T151 24734-24735 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T152 24755-24760 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T153 24775-24780 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T154 24975-24976 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T155 25333-25334 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T156 25399-25400 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T157 25497-25500 http://purl.obolibrary.org/obo/CLO_0051582 denotes has
T158 25501-25502 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T159 25533-25538 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T160 26177-26178 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T161 26290-26295 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/Es
T162 26583-26584 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T163 26828-26830 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T164 26906-26907 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T165 27003-27004 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T166 27310-27311 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T167 27341-27342 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T168 27709-27710 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T169 28097-28098 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T170 28220-28221 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T171 28273-28274 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T172 28433-28435 http://purl.obolibrary.org/obo/CLO_0001000 denotes 35
T173 28544-28545 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T174 28576-28577 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T175 28590-28591 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T176 28906-28907 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T177 29250-29251 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T178 29317-29318 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T179 29406-29407 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T180 29415-29416 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T181 29592-29602 http://purl.obolibrary.org/obo/CLO_0001658 denotes activities
T182 30090-30091 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T183 30298-30299 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T184 30424-30425 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T185 30489-30490 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T186 30652-30653 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T187 30698-30699 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T188 30728-30729 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T189 30743-30745 http://purl.obolibrary.org/obo/CLO_0050507 denotes 22
T190 30802-30803 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T191 30843-30844 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T192 31198-31203 http://purl.obolibrary.org/obo/CLO_0008479 denotes Pp/ES
T193 31829-31832 http://purl.obolibrary.org/obo/CLO_0051582 denotes has

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 549-551 Chemical denotes II http://purl.obolibrary.org/obo/CHEBI_74067
T2 4454-4456 Chemical denotes PM http://purl.obolibrary.org/obo/CHEBI_141444|http://purl.obolibrary.org/obo/CHEBI_16410|http://purl.obolibrary.org/obo/CHEBI_53551
T5 4459-4466 Chemical denotes sulphur http://purl.obolibrary.org/obo/CHEBI_17909|http://purl.obolibrary.org/obo/CHEBI_26833
T7 4467-4473 Chemical denotes oxides http://purl.obolibrary.org/obo/CHEBI_25741
T8 4481-4496 Chemical denotes nitrogen oxides http://purl.obolibrary.org/obo/CHEBI_35196
T9 4481-4489 Chemical denotes nitrogen http://purl.obolibrary.org/obo/CHEBI_25555
T10 4490-4496 Chemical denotes oxides http://purl.obolibrary.org/obo/CHEBI_25741
T11 4498-4501 Chemical denotes NOx http://purl.obolibrary.org/obo/CHEBI_35196
T12 4504-4519 Chemical denotes carbon monoxide http://purl.obolibrary.org/obo/CHEBI_17245
T13 4504-4510 Chemical denotes carbon http://purl.obolibrary.org/obo/CHEBI_27594|http://purl.obolibrary.org/obo/CHEBI_33415
T15 4521-4523 Chemical denotes CO http://purl.obolibrary.org/obo/CHEBI_17245
T16 4529-4543 Chemical denotes carbon dioxide http://purl.obolibrary.org/obo/CHEBI_16526
T17 4529-4535 Chemical denotes carbon http://purl.obolibrary.org/obo/CHEBI_27594|http://purl.obolibrary.org/obo/CHEBI_33415
T19 4545-4548 Chemical denotes CO2 http://purl.obolibrary.org/obo/CHEBI_16526
T20 5192-5194 Chemical denotes Gu http://purl.obolibrary.org/obo/CHEBI_42820
T21 5745-5747 Chemical denotes Lu http://purl.obolibrary.org/obo/CHEBI_33382
T22 6765-6768 Chemical denotes CO2 http://purl.obolibrary.org/obo/CHEBI_16526
T23 7025-7041 Chemical denotes nitrogen dioxide http://purl.obolibrary.org/obo/CHEBI_33101
T24 7025-7033 Chemical denotes nitrogen http://purl.obolibrary.org/obo/CHEBI_25555
T25 7042-7045 Chemical denotes NO2 http://purl.obolibrary.org/obo/CHEBI_16301|http://purl.obolibrary.org/obo/CHEBI_33101
T27 13039-13042 Chemical denotes Abu http://purl.obolibrary.org/obo/CHEBI_35621
T28 13278-13280 Chemical denotes La http://purl.obolibrary.org/obo/CHEBI_33336
T29 13632-13634 Chemical denotes La http://purl.obolibrary.org/obo/CHEBI_33336
T30 13765-13767 Chemical denotes La http://purl.obolibrary.org/obo/CHEBI_33336
T31 13937-13939 Chemical denotes La http://purl.obolibrary.org/obo/CHEBI_33336
T32 14446-14448 Chemical denotes DW http://purl.obolibrary.org/obo/CHEBI_73831
T33 15506-15512 Chemical denotes carbon http://purl.obolibrary.org/obo/CHEBI_27594|http://purl.obolibrary.org/obo/CHEBI_33415
T35 23056-23058 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T36 23083-23085 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T37 23102-23104 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T38 23105-23107 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T39 23280-23282 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T40 23283-23285 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T41 23477-23479 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T42 23480-23482 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T43 23544-23546 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T44 23547-23549 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T45 23589-23591 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T46 23592-23594 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T47 23664-23666 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T48 23667-23669 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T49 23710-23712 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T50 23713-23715 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T51 23728-23730 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T52 23731-23733 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T53 24119-24121 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T54 24122-24124 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T55 24137-24139 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T56 24140-24142 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T57 24242-24244 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T58 24245-24247 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T59 24261-24263 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T60 24264-24266 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T61 24755-24757 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T62 24758-24760 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T63 24775-24777 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T64 24778-24780 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T65 25335-25338 Chemical denotes GDP http://purl.obolibrary.org/obo/CHEBI_17552|http://purl.obolibrary.org/obo/CHEBI_58189
T67 25533-25535 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T68 25536-25538 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509
T69 26290-26292 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T70 26293-26295 Chemical denotes Es http://purl.obolibrary.org/obo/CHEBI_33393
T71 31069-31080 Chemical denotes fossil fuel http://purl.obolibrary.org/obo/CHEBI_35230
T72 31076-31080 Chemical denotes fuel http://purl.obolibrary.org/obo/CHEBI_33292
T73 31198-31200 Chemical denotes Pp http://purl.obolibrary.org/obo/CHEBI_26294
T74 31201-31203 Chemical denotes ES http://purl.obolibrary.org/obo/CHEBI_73509

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
1 23-31 Disease denotes COVID-19 MESH:C000657245
9 237-248 Species denotes coronavirus Tax:11118
10 340-349 Species denotes SARS-CoV2 Tax:2697049
11 374-382 Disease denotes COVID-19 MESH:C000657245
12 1352-1370 Disease denotes lung cancer deaths MESH:D008175
13 1379-1390 Disease denotes COPD deaths MESH:D029424
14 1403-1425 Disease denotes ischemic heart disease MESH:D003324
15 1426-1432 Disease denotes deaths MESH:D003643
17 2055-2063 Disease denotes COVID-19 MESH:C000657245
19 2499-2507 Disease denotes COVID-19 MESH:C000657245
21 3658-3662 Species denotes H1N1 Tax:114727
37 4351-4357 Species denotes people Tax:9606
38 4874-4880 Species denotes people Tax:9606
39 4459-4473 Chemical denotes sulphur oxides
40 4481-4489 Chemical denotes nitrogen MESH:D009584
41 4504-4519 Chemical denotes carbon monoxide MESH:D002248
42 4521-4523 Chemical denotes CO MESH:D002248
43 4529-4543 Chemical denotes carbon dioxide MESH:D002245
44 4545-4548 Chemical denotes CO2 MESH:D002245
45 4768-4774 Disease denotes deaths MESH:D003643
46 5048-5054 Disease denotes stroke MESH:D020521
47 5056-5067 Disease denotes lung cancer MESH:D008175
48 5069-5106 Disease denotes chronic obstructive pulmonary disease MESH:D029424
49 5108-5121 Disease denotes heart disease MESH:D006331
50 5126-5148 Disease denotes respiratory infections MESH:D012141
51 5157-5166 Disease denotes pneumonia MESH:D011014
57 5913-5922 Species denotes SARS-CoV2 Tax:2697049
58 6033-6039 Species denotes people Tax:9606
59 6290-6293 Chemical denotes oil MESH:D009821
60 6388-6391 Chemical denotes oil MESH:D009821
61 5940-5948 Disease denotes COVID-19 MESH:C000657245
73 7474-7485 Species denotes coronavirus Tax:11118
74 8355-8366 Species denotes coronavirus Tax:11118
75 8662-8673 Species denotes coronavirus Tax:11118
76 6765-6768 Chemical denotes CO2 MESH:D002245
77 7025-7033 Chemical denotes nitrogen MESH:D009584
78 6944-6950 Disease denotes deaths MESH:D003643
79 6956-6964 Disease denotes COVID-19 MESH:C000657245
80 7002-7008 Disease denotes deaths MESH:D003643
81 8331-8340 Disease denotes mortality MESH:D003643
82 8409-8415 Disease denotes deaths MESH:D003643
83 8674-8683 Disease denotes mortality MESH:D003643
86 9676-9685 Disease denotes paralysis MESH:D010243
87 9702-9710 Disease denotes COVID-19 MESH:C000657245
89 10932-10940 Disease denotes COVID-19 MESH:C000657245
92 14033-14039 Species denotes Turkey Tax:9103
93 13421-13428 Disease denotes Colombo
95 11746-11754 Disease denotes COVID-19 MESH:C000657245
98 15506-15512 Chemical denotes carbon MESH:D002244
99 15375-15383 Disease denotes COVID-19 MESH:C000657245
101 18886-18893 Disease denotes Colombo
104 18713-18719 Chemical denotes Astana
105 18538-18545 Disease denotes Colombo
107 22085-22090 Chemical denotes PM2.5
110 23105-23107 Chemical denotes ES
111 23283-23285 Chemical denotes ES
113 24272-24279 Disease denotes Colombo
115 25536-25538 Chemical denotes ES
117 26293-26295 Chemical denotes Es MESH:D004540
119 26648-26651 Chemical denotes PM2
121 28405-28410 Chemical denotes PM2.5
123 30999-31006 Disease denotes Colombo

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 1639-1647 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior
T2 1882-1890 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior
T3 2780-2788 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior
T4 5437-5443 http://purl.obolibrary.org/obo/GO_0040007 denotes growth
T5 6239-6248 http://purl.obolibrary.org/obo/GO_0006810 denotes transport
T6 7590-7598 http://purl.obolibrary.org/obo/GO_0007612 denotes learning
T7 15125-15133 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior
T8 27551-27559 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior
T9 29380-29388 http://purl.obolibrary.org/obo/GO_0007610 denotes behavior
T10 31103-31114 http://purl.obolibrary.org/obo/GO_0065007 denotes regulations
T11 31503-31512 http://purl.obolibrary.org/obo/GO_0006810 denotes transport

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-100 Sentence denotes Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world☆
T2 102-110 Sentence denotes Abstract
T3 111-307 Sentence denotes On December 31, 2019, the Chinese authorities reported to the World Health Organization (WHO) the outbreak of a new strain of coronavirus that causes a serious disease in the city of Wuhan, China.
T4 308-391 Sentence denotes This outbreak was classified as SARS-CoV2 and is the cause of the COVID-19 disease.
T5 392-645 Sentence denotes On March 11, 2020, the WHO declares it a Pandemic and today it is considered the greatest challenge in global health that humanity has faced since World War II and it is estimated that between 40 and 60% of the population worldwide will catch the virus.
T6 646-775 Sentence denotes This has caused enormous challenges in countries around the world in social, economic, environmental and obviously health issues.
T7 776-941 Sentence denotes These challenges are mainly due to the effects of the established quarantines in almost all capitals and major cities around the world, from Asia, Europe to America.
T8 942-1296 Sentence denotes However, these lockdown which began worldwide from January 23, have had a significant impact on the environment and on the air quality of cities as recently reported by NASA (National Aeronautics and Space Administration) and ESA (European Space Agency), with reductions according to them of up to 30% in some of the epicenters such as the case of Wuhan.
T9 1297-1602 Sentence denotes Knowing that air pollution causes approximately 29% of lung cancer deaths, 43% of COPD deaths, and 25% of ischemic heart disease deaths, it is important to know the effects of quarantines in cities regarding air quality to take measures that favor populations and urban ecosystems when the emergency ends.
T10 1603-1812 Sentence denotes Therefore, this paper describes the behavior of PM2.5 emissions particulate matter from the 50 most polluted capital cities in the world according to the WHO, measured before-after the start of the quarantine.
T11 1813-1945 Sentence denotes Likewise, the impact at the local and global level of this emissions behavior, which averaged 12% of PM2.5 decrease in these cities.
T12 1947-1965 Sentence denotes Graphical abstract
T13 1967-1977 Sentence denotes Highlights
T14 1978-2064 Sentence denotes • 12% reduction in the 50 most contaminated capitals during the lockdown by COVID-19.
T15 2065-2135 Sentence denotes • Dhaka, Kampala and Delhi had a PM2.5 reduction of 14%, 35% and 40%.
T16 2136-2223 Sentence denotes • The greatest PM2.5 reduction within the data collected is Bogotá, Colombia with 57%.
T17 2224-2293 Sentence denotes • Kubait City, presents the second largest reduction of PM2.5 (42%).
T18 2294-2380 Sentence denotes • The greatest PM2.5 reductions occurred in the capitals of America, Asia and Africa.
T19 2382-2395 Sentence denotes Main finding:
T20 2396-2569 Sentence denotes 12% of PM2.5 reduction in the 50 most contaminated capitals cities of the world during the lockdown by COVID-19 and a the greatest PM2.5 reduction in a capital city was 57%.
T21 2571-2586 Sentence denotes 1 Introduction
T22 2587-3021 Sentence denotes Currently, socio-ecological systems have a great impact on companies, cities and territories; the sustainability and technology associated with smart cities are merged to better understand the behavior of this type of systems, and the data provides cities and territories with the information necessary for sufficient monitoring and evaluation leading to coherent environmental policies in adaptive environments (Waylen et al., 2019).
T23 3022-3229 Sentence denotes It is necessary to formulate new socio-ecological models that allow describing the coevolution of the economy, the environment and society in the face of the dynamics of wealth and population (Ursino, 2019).
T24 3230-3371 Sentence denotes However, few models efficiently predict the entry of random variables into these complex processes, which validate their evolution over time.
T25 3372-3559 Sentence denotes One of the clearest examples of a chaotic variable is climate, however, there are other variables that can quickly intervene in a socio-ecological system and wreak havoc, such as a virus.
T26 3560-3795 Sentence denotes For example, in Brisbane, Australia ecological factors appear to have played an important role in H1N1 transmission cycles (Hu et al., 2012), with temperature and precipitation being substantial variables in the evolution of the virus.
T27 3796-4008 Sentence denotes There are some other works of socioeconomic studies and viruses such as (Mamelund et al., 2019), where a study is carried out between the socioeconomic levels and the influenza-related pandemics of 1918 and 2009.
T28 4009-4215 Sentence denotes The foregoing demonstrates the importance of correlating the factors that can substantially alter the socio-ecological systems in which we live and to be able to study their evolution and impact on society.
T29 4216-4398 Sentence denotes One of the key factors in recent years, which has been the subject of several scientific studies, is the impact of poor air quality on people’s health and its consequences over time.
T30 4399-4796 Sentence denotes The emission of pollutants such as particulate matter (PM), sulphur oxides (SOx), nitrogen oxides (NOx), carbon monoxide (CO) and carbon dioxide (CO2), are the pollutants that are generated in greater quantity and according to estimates of the World Health Organization (WHO) 2016 produce annually in cities and rural areas around the world about 4.2 million premature deaths (Cohen et al., 2017).
T31 4797-5228 Sentence denotes The WHO (World Health Organization, 2018) estimates that about seven million people die each year from exposure to PM2.5 particles, which enter directly into the respiratory system and are deposited in the lung region causing serious diseases such as stroke, lung cancer, chronic obstructive pulmonary disease, heart disease and respiratory infections such as pneumonia. (Cachon et al., 2014), (Gu et al., 2017), (Ng et al., 2019).
T32 5229-5660 Sentence denotes On the other hand, a World Bank report of 2018 shows in graphs the most relevant socio-economic and socio-ecological aspects that impact the world, where global warming, poor air quality and urban population growth among others, leave chilling figures, as 91% of the world population lives in places with poor air quality, places like cities that increased by 55% their urban residents between 1960 and 2018 (The World Bank, 2018).
T33 5661-5762 Sentence denotes In December 2019, one of the most deadly viruses in the last 100 years is reported (Lu et al., 2020).
T34 5763-5891 Sentence denotes China reports this new pathogen to the WHO on December 31, and only three months later this organization declares it a pandemic.
T35 5892-6077 Sentence denotes The new virus called SARS-CoV2 and the cause of COVID-19 has stopped global activity in a few months and has taken the lives of thousands of people in different cities around the world.
T36 6078-6270 Sentence denotes The impact of this virus on the socio-economic level is causing markets to tremble, world stock markets to collapse, all flights to be cancelled and borders and transport systems to be closed.
T37 6271-6432 Sentence denotes On the other hand, oil demand has dropped and producers are running out of places to store all the excess barrels of oil as it has fallen below $0 US per barrel.
T38 6433-6555 Sentence denotes However, this pandemic also caused air quality to improve in many of the world’s cities, reducing environmental pollution.
T39 6556-6741 Sentence denotes This global closure has made it possible to obtain interesting environmental data for analysis and several scientific investigations related precisely to these socio-ecological changes.
T40 6742-6858 Sentence denotes In China, for example, CO2 emissions were reduced by 25% and by 6% worldwide according to (Hanaoka and Masui, 2020).
T41 6859-7056 Sentence denotes In (Dutheil et al., 2020), an initial comparative analysis was made of the number of deaths from COVID-19 and the number of annual air quality deaths with respect to nitrogen dioxide NO2 emissions.
T42 7057-7298 Sentence denotes This analysis was based on data obtained by satellite (NASA, 2020) showing the advantages that the isolation of the population in their homes has had due to the emergency by the shutdown of industries and vehicle mobility (Tan et al., 2009).
T43 7299-7486 Sentence denotes The same information from NASA, plus information taken from ESA, was used in (Muhammad et al., 2020) to perform a compilation of satellite environmental data before and after coronavirus.
T44 7487-7664 Sentence denotes The figures in this paper show the temporary environmental benefit as a major positive impact and as a learning model for governments to enable new socio-environmental policies.
T45 7665-7729 Sentence denotes This last analysis was done for Europe, China and North America.
T46 7730-8003 Sentence denotes Likewise, in (Mollalo et al., 2020) models are made of the type of spatial dependence and weighted regression of 35 variables from the environmental to the socioeconomic ones related to the incidence of the disease in the first 90 days of the outbreak in the United States.
T47 8004-8247 Sentence denotes These results, according to Mollalo, will serve as a basis for future geographic modeling of any disease, as well as for policy with targeted, science-based interventions that can be extrapolated to other cities and countries around the world.
T48 8248-8367 Sentence denotes Finally, Ogen’s research has found a direct relationship between contamination and mortality caused by the coronavirus.
T49 8368-8558 Sentence denotes The study concludes that 78% of the 4443 deaths recorded on a single day in Europe (19 march) occurred in five specific, highly contaminated areas: four regions of northern Italy and Madrid.
T50 8559-8754 Sentence denotes These results indicate that long-term exposure to particulate pollutants may be a major contributor to coronavirus mortality, not only in these regions, but in the rest of the world (Ogen, 2020).
T51 8755-8989 Sentence denotes All these analyses described above are necessary to evaluate the socio-ecological and socio-economic changes in all the cities of the world and try to show the positive impacts in order to obtain some benefits from this global crisis.
T52 8990-9226 Sentence denotes Ultimately, this paper uses data from the weather stations of the 50 most polluted cities in the world and makes a comparison of air quality with respect to PM2.5 particulate matter before and during the quarantine of each capital city.
T53 9228-9242 Sentence denotes 2 Methodology
T54 9243-9399 Sentence denotes In the year 2019 a study was presented on Meteosim online platform where they made an analysis on the most polluting capitals in the world (Meteosim, 2019).
T55 9400-9597 Sentence denotes This research indicates that one of the most dangerous pollutants is fine particulate matter with diameters ≤2.5 μm (PM2.5), so the analysis of the fifty most contaminated capitals was carried out.
T56 9598-9807 Sentence denotes Based on the above information and according to the automotive and industrial paralysis in the world by COVID-19, for this research they were collected the PM2.5 data from an online platform (WAQ Index, 2020).
T57 9808-10036 Sentence denotes This tool, called World Air Quality Index (WAQI) is used for obtained the information historical of Air Quality Data in especial for this paper of PM2.5 particulate matter of each capital before quarantine and during quarantine.
T58 10037-10164 Sentence denotes For each capital city, the information was corroborated with the territorial entity of data administration at the public level.
T59 10165-10286 Sentence denotes For example, Bogotá, Colombia was verified on the IBOCA platform of the Secretary of the Environment (Environment, 2019).
T60 10287-10435 Sentence denotes For Delhi, India was verified on the Real Time Ambient Air Quality Data Platform of the Department of Environment (Department of Environment, 2020).
T61 10436-10542 Sentence denotes For Berlin, Germany was verify with the Umwelt Bundesamt platform of the German Environment Agency (2020).
T62 10543-10567 Sentence denotes And so on for each city.
T63 10568-10657 Sentence denotes In this context, the research process in the paper has been divided into five main steps.
T64 10658-10660 Sentence denotes 1.
T65 10661-10715 Sentence denotes Identification of the 50 most polluted capital cities.
T66 10716-10718 Sentence denotes 2.
T67 10719-10756 Sentence denotes Review of the quarantine information.
T68 10757-10759 Sentence denotes 3.
T69 10760-10815 Sentence denotes Data collection of population and the weather stations.
T70 10816-10818 Sentence denotes 4.
T71 10819-10841 Sentence denotes PM2.5 Data extraction.
T72 10842-10844 Sentence denotes 5.
T73 10845-10867 Sentence denotes Graphics and analysis.
T74 10869-10940 Sentence denotes 3 World’s most contaminated capital cities and quarantined by COVID-19
T75 10941-11230 Sentence denotes One of the most important guidelines carried out by the WHO in matters of air quality is not to exceed 10 μg/m3 of annual average concentration or 25 μg/m3 of 24-h concentrations, for that reason Meteosim (2019), presents the cities that exceed 10 μg/m3 of annual average in the year 2018.
T76 11231-11331 Sentence denotes Table 1 shows the start of the quarantine or the alarm status of the world’s most polluted capitals.
T77 11332-11530 Sentence denotes As shown in the table, firstly Delhi (India) is the capital most polluted by fine particulate matter, reaching an annual average of 113,5 μg/m3 for 2018, secondly Dhaka (Bangladesh) with 97,1 μg/m3.
T78 11531-11630 Sentence denotes Lisbon and Berlin are least polluted capitals, presenting an annual average of 11,7 μg/m3 for 2018.
T79 11631-11755 Sentence denotes Regarding the aforementioned ranking, each capital was taken and the lockdown date was sought due to the effect of COVID-19.
T80 11756-11802 Sentence denotes 12% of the capitals do not apply any lockdown.
T81 11803-11900 Sentence denotes The first countries to start blocking mobility to colleges, universities and apply telework were:
T82 11901-11920 Sentence denotes Mongolia and China.
T83 11921-11987 Sentence denotes In March 9th lockdowns began to be applied in the other countries.
T84 11988-12199 Sentence denotes Not all countries carried out a lockdown, there were some exceptions such as Kazakhstan (Astana), where a state of emergency is declared on March 15th, as well as in Romania on 24 March and Indonesia on April 2.
T85 12200-12386 Sentence denotes On the other hand, other countries took partial or more drastic lockdown measures, for example, in Slovakia (Bratislava) it is allowed to walk or exercise outdoors with the mask protect.
T86 12387-12515 Sentence denotes Mexico City, declared a voluntary quarantine and Bangkok (Thailand) and Belgrade (Serbia), have declared a curfew since April 4.
T87 12516-12570 Sentence denotes Table 1 Location-start to quarantine (Meteosim, 2019).
T88 12571-12651 Sentence denotes Country Location (Capital cities) PM2.5 μg/m3 (Annual) Date (Quarantine) Studies
T89 12652-12691 Sentence denotes India Delhi 113,5 25/03/20 Soler (2020)
T90 12692-12743 Sentence denotes Bangladesh Dhaka 97,1 16/03/20 Global Voices (2020)
T91 12744-12795 Sentence denotes Afghanistan Kabul 61,8 28/03/20 Europapress (2020a)
T92 12796-12826 Sentence denotes Bahrain (Barein) Manama 59,8 ∗
T93 12827-12881 Sentence denotes Mongolia Ulaanbaatar 58,5 25/01/20 Anandsaikhan (2020)
T94 12882-12931 Sentence denotes Kuwait Kubait City 56 9/03/20 Europapress (2020b)
T95 12932-12985 Sentence denotes Nepal Kathmandu 54,4 24/03/20 The Jakarta Post (2020)
T96 12986-13034 Sentence denotes China Bejing 50,9 29/01/20 BBC News Mundo (2020)
T97 13035-13055 Sentence denotes UAE Abu Dhabi 48,8 ∗
T98 13056-13110 Sentence denotes Indonesia Jakarta 45,3 2/04/20 The Jakarta Post (2020)
T99 13111-13154 Sentence denotes Uganda Kampala 40,8 1/04/20 Museveni (2020)
T100 13155-13205 Sentence denotes Vietnam Hanoi 40,8 19/03/20 TheStraitsTimes (2020)
T101 13206-13232 Sentence denotes Pakistan Islammabad 38,6 ∗
T102 13233-13299 Sentence denotes Bosnia & Hersegovina sarajevo, 38,4 17/03/20 La Vanguardia (2020a)
T103 13300-13350 Sentence denotes Uzbekistan Tashkent 34,3 24/03/20 Pikulicka (2020)
T104 13351-13410 Sentence denotes Macedonia del norte Skopje 34 10/04/20 BalkanInsight (2020)
T105 13411-13460 Sentence denotes Sri Lanka Colombo 32 12/03/20 Europapress (2020c)
T106 13461-13511 Sentence denotes Kosovo Pristina 30,4 11/04/20 Gazetaexpress (2020)
T107 13512-13559 Sentence denotes Kazahstan Astana 29,8 15/03/20 Gussarova (2020)
T108 13560-13602 Sentence denotes Chile Santiago 29,4 26/03/20 Perfil (2020)
T109 13603-13653 Sentence denotes Bulgaria Sofia 28,2 20/03/20 La Vanguardia (2020b)
T110 13654-13691 Sentence denotes Peru Lima 28 19/03/20 (AS Peru, 2020)
T111 13692-13734 Sentence denotes Iran Tehran 26,1 25/03/20 CNN Mundo (2020)
T112 13735-13786 Sentence denotes Thailand Bangkok 25,2 4/04/20 La Vanguardia (2020c)
T113 13787-13827 Sentence denotes Poland Warsaw 24,2 13/03/20 Solis (2020)
T114 13828-13878 Sentence denotes Serbia Belgrade 23,9 10/04/20 BalkanInsight (2020)
T115 13879-13903 Sentence denotes South Korea Seoul 23,3 ∗
T116 13904-13957 Sentence denotes Romania Bucharest 20,3 24/03/20 (La vanguardia, 2020)
T117 13958-13983 Sentence denotes Cambodia PhnomPenh 20,1 ∗
T118 13984-14032 Sentence denotes Mexico Mexico city 19,7 23/03/20 Mancilla (2020)
T119 14033-14077 Sentence denotes Turkey Ankara 19,6 4/04/20 GardaWorld (2020)
T120 14078-14125 Sentence denotes Isarel Tel Aviv 19,5 19/03/20 Itón Gadol (2020)
T121 14126-14177 Sentence denotes Lithuania Vilnius 18,2 14/03/20 Europapress (2020d)
T122 14178-14221 Sentence denotes Nicosia Cyprus 17,4 21/03/20 (GOV.UK, 2020)
T123 14222-14285 Sentence denotes Czech Republic Prague 17,4 16/03/20 Sociedad Agencia EFE (2020)
T124 14286-14332 Sentence denotes Slovakia Bratislavia 17,2 16/03/20 Tort (2020)
T125 14333-14376 Sentence denotes Hungary Budapest 16,5 27/03/20 Dunai (2020)
T126 14377-14416 Sentence denotes France Paris 15,6 17/03/20 Solis (2020)
T127 14417-14455 Sentence denotes Austria Vienna 15,2 15/03/20 DW (2020)
T128 14456-14476 Sentence denotes Taiwan Taipei 14,9 ∗
T129 14477-14524 Sentence denotes Singapore Singapore 14,8 8/04/20 Naurana (2020)
T130 14525-14578 Sentence denotes Philippines Manila 14,3 17/03/20 (Europapress, 2020e)
T131 14579-14628 Sentence denotes Belgium Brussels 14,1 18/03/20 Theguardian (2020)
T132 14629-14691 Sentence denotes Colombia Bogota 13,9 25/03/20 (Eltiempo.comEltiempo.com, 2020)
T133 14692-14751 Sentence denotes Ukraine Kyiv 13,8 17/03/20 (Kyivpost.comKyivpost.com, 2020)
T134 14752-14814 Sentence denotes Japan Tokyo 13,1 25/03/20 (Japantimes.comJapantimes.com, 2020)
T135 14815-14866 Sentence denotes Switzerland Bern 12,8 16/03/20 (Swissinfo.ch, 2020)
T136 14867-14931 Sentence denotes United Kingdom London 12 23/03/20 (nbcnews.comnbcnews.com, 2020)
T137 14932-14981 Sentence denotes Germany Berlin 11,7 13/03/20 (Lanacion.com, 2020)
T138 14982-15026 Sentence denotes Portugal Lisbon 11,7 20/03/20 Sampson (2020)
T139 15027-15042 Sentence denotes ∗No quarantine.
T140 15044-15088 Sentence denotes 4 PM2.5 assessment before–during quarantine
T141 15089-15384 Sentence denotes This research seeks to evaluate the behavior of the main most polluting cities in the world from the comparison between a typical week, before quarantine (BQut), (considered in this document a week measured before entering confinement), and an atypical week, during quarantine (Qut) by COVID-19.
T142 15385-15523 Sentence denotes This week (Qut) is considered for analysis, due to the restriction of most economic activities that involve reducing the carbon footprint.
T143 15524-15666 Sentence denotes As an example, we have restrictive measures regarding citizen mobility and, therefore, vehicle mobility and industrial production are reduced.
T144 15667-15781 Sentence denotes These dates were selected according to Table 1, with a study range of one month before and after the start of Qut.
T145 15782-15983 Sentence denotes The PM2.5 values are obtained from the online platform (WAQ Index, 2020) and official weather stations of each city, the analyzed information depends on obtaining or recording data from the study date.
T146 15984-16130 Sentence denotes Cases are presented where meteorological stations that record PM2.5 are not obtained but only PM10, therefore, these cities could not be analyzed.
T147 16131-16283 Sentence denotes Likewise, there are days of the weeks that present alterations due to atmospheric conditions in each city, therefore, no data was taken from these days.
T148 16284-16394 Sentence denotes For 50 analized capital cities, 20% do not record PM2.5 data, corresponding to capitals from the countries of:
T149 16395-16438 Sentence denotes Germany, Philippines, Romania and Bulgaria.
T150 16439-16666 Sentence denotes On the other hand, for capitals with PM2.5 data, 45% of capitals correspond to the continent of Asia, followed by 42% to the continent of Europe, 10% to the continent of America and the remaining 25% to the continent of Africa.
T151 16667-16804 Sentence denotes The air pollution level, health implications and cautionary statement according to the PM2.5 conditions can be seen in (WAQ Index, 2020).
T152 16805-16950 Sentence denotes Likewise, in the analyzed countries presented below, decreases, increases or a constant level of PM2.5 emissions are observed during confinement.
T153 16951-17138 Sentence denotes This can be attributed to the confinement level in each country, at the beginning of the quarantine, or to the electricity generation increase depending on the generation technology used.
T154 17139-17388 Sentence denotes It is important to clarify that not all countries indicate reductions in their PM2.5 particles emitted during the analyzed week (Qut); each capital reflects special conditions that must be analyzed individually and this is not the aim of this paper.
T155 17390-17399 Sentence denotes 4.1 Asia
T156 17400-17472 Sentence denotes From Fig. 1, Fig. 2 , the most polluting capitals of Asia are presented.
T157 17473-17696 Sentence denotes At a general level, 27% of the capitals had a tendency to decrease the PM2.5 emission during Qut, however, the cities Kathmandu, Hanoi, Jakarta, Singapore and Tokyo had a tendency to increase the PM2.5 concentration by 11%.
T158 17697-17884 Sentence denotes It should be noted that Tokyo, with the exception of the other cities, did not present a mandatory quarantine but as an option of the Government, they requested non-mandatory teleworking.
T159 17885-18050 Sentence denotes For the analyzed Asian capitals, Dhaka in typical times (BQut), is the capital with the highest PM2.5 particles pollutant, registering a weekly average of 183 μg/m3.
T160 18051-18116 Sentence denotes This capital city, presents a reduction of 24% in the Qut period.
T161 18117-18287 Sentence denotes Delhi, with a weekly average of 140 μg/m3, has the highest reduction in particles polluting compared to other Asian countries, presenting a 40% reduction during Qut week.
T162 18288-18421 Sentence denotes Capital cities with an average concentration of 121.91 μg/m3 (Kabul, Ullabantar and Kuwait City) show average reductions of 33% BQut.
T163 18422-18468 Sentence denotes However, Bejing shows a lower reduction of 8%.
T164 18469-18591 Sentence denotes Capital cities with an average concentration of 106.83 μg/m3 (Kabul, Colombo and Tashkent) show average reductions of 28%.
T165 18592-18644 Sentence denotes Tehran has a typical week concentration of 90 μg/m3.
T166 18645-18703 Sentence denotes However, its reduction during the quarantine week was 39%.
T167 18704-18829 Sentence denotes Finally, Astana, with an average weekly concentration of 61.25 μg/m3, reduced its concentration by 18% during the Qut season.
T168 18830-18986 Sentence denotes Fig. 1 Capital cities Ankara, Astana, Bangkok, Beijing, Colombo, Delhi, Dhaka, Kabul and Hanoi, PM2.5 levels before and during quarantine (WAQ Index, 2020).
T169 18987-19163 Sentence denotes Fig. 2 Capital cities Jakarta, Kathmandu, Kubait city, Ulaanbaatar, Tashkent, Tehran, Tel Aviv, Tokio and Singapore PM2.5 levels before and during quarantine (WAQ Index, 2020).
T170 19165-19176 Sentence denotes 4.2 Europe
T171 19177-19258 Sentence denotes From Fig. 3, Fig. 4 it can be seen the seventeen (17) European capitals analyzed.
T172 19259-19394 Sentence denotes In general, European capital cities in typical weeks record PM2.5 concentrations below 80 μg/m3, with an AQI between Good and Moderate.
T173 19395-19520 Sentence denotes 50% of the studied European capitals, during the Qut had a tendency to decrease the PM2.5 concentration at an average of 23%.
T174 19521-19587 Sentence denotes However, the other 50% show an increase in the confinement season.
T175 19588-19691 Sentence denotes Budapest, with an annual average concentration of 48 μg/m3, is a city that presents an increase of 35%.
T176 19692-19892 Sentence denotes This capital does not present total confinement, they are allowed to exercise, go to work, with measures that allow them to circulate more widely, going from having a good to moderate AQI during BQut.
T177 19893-19977 Sentence denotes Bratislava, does not apply quarantine, leaving citizens to walk freely and exercise.
T178 19978-20113 Sentence denotes However, the date presented in Table 1, there are distance restrictions, suspension of classes and closure of sectors such as hostelry.
T179 20114-20219 Sentence denotes This European capital presents an increase in the Qut week, which went from a good to moderate AQI level.
T180 20220-20350 Sentence denotes Paris, London, Vienna, Brussels and Prague, are capitals that generally have a good AQI level, with an average record of 31 μg/m3.
T181 20351-20465 Sentence denotes However, in the confinement season, an increase in the concentrations of the PM2.5 particulate matter is observed.
T182 20466-20629 Sentence denotes Fig. 3 Capital cities Belgrade, Bern, Bratislavia, Brussels, Budapest, Cyprus, Kyiv, Lisbon and London PM2.5 levels before and during quarantine (WAQ Index, 2020).
T183 20630-20782 Sentence denotes Fig. 4 Capital cities Paris, Prague, Pristina, Sarajevo, Skopje, Vienna, Vilnius and Warsaw PM2.5 levels before and during quarantine (WAQ Index, 2020).
T184 20784-20807 Sentence denotes 4.3 America and Africa
T185 20808-20890 Sentence denotes Regarding the American Continent, four (4) capitals are analyzed in this research.
T186 20891-21059 Sentence denotes These capitals during typical periods, BQut, present a moderate AQI level, where Bogota is the capital with the highest PM2.5 concentration with an average of 98 μg/m3.
T187 21060-21132 Sentence denotes Followed by, Mexico City with 74 μg/m3, Santiago de Chile with 68 μg/m3.
T188 21133-21194 Sentence denotes Finally, Lima registers an average concentration of 58 μg/m3.
T189 21195-21341 Sentence denotes As well as, the period in confinement in Bogota, presents the greatest decrease in the cities analyzed during the Qut, with a 57% PM2.5 reduction.
T190 21342-21520 Sentence denotes Santiago de Chile, does not present quarantine in the entire city but in seven specific communes, which registers a 10% reduction in the concentration of this particulate matter.
T191 21521-21691 Sentence denotes Mexico City presents a voluntary quarantine by the population without government restrictions, however a 2% reduction is observed during the study week in Qut. (Fig. 5 ).
T192 21692-21840 Sentence denotes Fig. 5 Capital cities Bogotá, Lima, Mexico City, Santiago de Chile and Kampala (Africa) PM2.5 levels before and during quarantine (WAQ Index, 2020).
T193 21841-21971 Sentence denotes Kampala, as the only registered capital with a high AQI level, registers a weekly average of 146 μg/m3 during the study week BQut.
T194 21972-22155 Sentence denotes During the quarantine season and with the prohibition of vehicles and the closure of all stores except food, the PM2.5 concentration was reduced by 35%, going to a moderate AQI level.
T195 22157-22183 Sentence denotes 5 Results and discussions
T196 22184-22291 Sentence denotes The state of air quality is based on the environmental monitoring stations that are available in each city.
T197 22292-22405 Sentence denotes These stations determine the hourly concentration of air pollutant particles, including PM2.5 and PM10 particles.
T198 22406-22521 Sentence denotes According to the WHO (World Health Organization, 2018), air pollution represents a major environmental health risk.
T199 22522-22733 Sentence denotes For this reason, it is so important to have air quality monitored in cities, essentially in capital cities, and in this case, it is more important to monitor PM2.5 due to the risks explained in the introduction.
T200 22734-22902 Sentence denotes Therefore, it is essential to know how environmental control of capitals is, for this, it is essential to know the number of meteorological stations that measure PM2.5.
T201 22904-22981 Sentence denotes 5.1 Air quality stations vs. population of the most polluted capitals cities
T202 22982-23125 Sentence denotes Fig. 6 presents the relationship between quantity of inhabitants per one (Pp) environmental station (ES) is presented. (Pp/ES) in each capital.
T203 23126-23187 Sentence denotes It is reflected by a range of colors applied to each capital.
T204 23188-23399 Sentence denotes The main cities where greater control of air quality is observed, measured according to the Pp/ES ratio, are registered in quartile 2 (Q2) and quartile 3 (Q3), with a dark and light blue color, the which are: i.
T205 23400-23487 Sentence denotes Tel Aviv, Brussels and Pristina, these capitals present an average of 61,926 Pp/ES. ii.
T206 23488-23555 Sentence denotes Bratislavia, Bern, Sarajevo, present a ratio of 126,317 Pp/ES. iii.
T207 23556-23599 Sentence denotes Vienna, and Ulaanbaatar, 170,930 Pp/ES. iv.
T208 23600-23670 Sentence denotes Budapest, Lisbon and Prague present an average ratio of 244,696 Pp/ES.
T209 23671-23790 Sentence denotes Capitals that register between 300,000 Pp/ES and 500,000 Pp/ES, are registered between quartile (Q1) and quartile (Q3).
T210 23791-23901 Sentence denotes In Q1, the capitals London and Bangkok are observed, which present populations and stations above the average.
T211 23902-23946 Sentence denotes Fig. 6 Population vs Environmental stations.
T212 23947-24073 Sentence denotes In Q3 are the capital cities Skopje, Kathmandu, Paris and Belgrade, who register a population and stations lower than average.
T213 24074-24219 Sentence denotes The capitals with a registry between 500,000 Pp/ES and 700,000 Pp/ES are Bogota, Tehran, Tokyo, Vilnues, Kyiv, Warsaw, Kubait City, and Santiago.
T214 24220-24312 Sentence denotes Finally, from 700,000 Pp/ES to 1,000,000 Pp/ES, are Colombo, Ankara, Mexico City and Cyprus.
T215 24313-24368 Sentence denotes These capitals cities are identified between Q1 and Q3.
T216 24369-24492 Sentence denotes In Q4 the capitals with a low number of stations measuring PM2.5 can be seen, due to the number of inhabitants of each one.
T217 24493-24555 Sentence denotes Above 3,000,000 Jakarta, Hanoi, Lima and Dhaka are identified.
T218 24556-24651 Sentence denotes Lima has several weather stations, however, there is only one station that PM2.5 register data.
T219 24652-24794 Sentence denotes The cities with the highest pollution, Delhi (India) and Beijing (China), present a ratio of 1,766,333 Pp/ES and 1,227,059 Pp/ES respectively.
T220 24795-25116 Sentence denotes Although meteorological stations are installed based on population, it is observed that countries such as Singapore, Hanoi, Kakarta and Dhaka with populations over 5,000,000, have a number of below-average meteorological stations, which makes monitoring difficult and not It allows to have data of the real contamination.
T221 25117-25262 Sentence denotes Likewise, it should be considered that the analysis of any study is oriented to an economic system that restricts the conditions of each country.
T222 25263-25460 Sentence denotes In this case, when analyzed by continents and population, Europe with a GDP higher than the South American or Asian economies, presents a greater boost in reducing emissions of polluting particles.
T223 25461-25572 Sentence denotes This, indicating that the continent has a good AQI index and an optimal Pp/ES ratio for environmental analysis.
T224 25573-25723 Sentence denotes Their concern for the environment and compliance with the Kyoto protocol and the commitments to COP21 are reflected in their environmental indicators.
T225 25724-25882 Sentence denotes This continent recognizes that air quality influences labor productivity, investments in healthcare expenses and improvement in quality of life, among others.
T226 25883-26127 Sentence denotes In contrast, the Asian continent, with an unfavorable view compared to other continents, it is observed that the Asian economies are the main contributors to environmental pollution, which is why it attributes to greater inequality and poverty.
T227 26128-26302 Sentence denotes This allows linking to this study (Fig. 6) where a low update is observed in technologies that monitor the quality air in Asian cities, this reflected in the low Pp/Es ratio.
T228 26303-26435 Sentence denotes This indicates the low commitment to improve the life quality and the low incentives to reduce the cost attributed by air pollution.
T229 26436-26553 Sentence denotes In general, the most densified in population cities reflect PM2.5 contamination high with rates higher than 50 μg/m3.
T230 26555-26575 Sentence denotes 5.2 Global analysis
T231 26576-26700 Sentence denotes Making a global balance of the analyzed countries, the variation of the PM2.5 concentration had an average reduction of 12%.
T232 26701-26966 Sentence denotes The highest reduction occurs in the African continent with one (1) country analyzed (33%), followed by the American continent (22%) and the Asian continent (16%); finally, the European continent, in which a favorable reduction result is not generally observed (5%).
T233 26967-27034 Sentence denotes Fig. 7 shows the PM2.5 variation in a global map of capital cities.
T234 27035-27111 Sentence denotes The absolute value of the variation can be identified by size of the circle.
T235 27112-27356 Sentence denotes In red we find the cities with increases in their PM2.5 measurement and in pink the cities with PM2.5 reduction, the city with the greatest PM2.5 reduction within the data collected is Bogota, with a reduction of 57% compared to a typical week.
T236 27357-27399 Sentence denotes Fig. 7 PM2.5 Reduction in quarantine week.
T237 27400-27514 Sentence denotes Fig. 8 shows in detail the PM2.5 quantitative variation of the analyzed cities, as well as its mean concentration.
T238 27515-27643 Sentence denotes The gray color represents the PM2.5 behavior under typical conditions, and the light blue color represents the confinement mode.
T239 27644-27725 Sentence denotes Dhaka, the most polluted capital of this particulate matter, had a 14% reduction.
T240 27726-27963 Sentence denotes It is observed as the first seventeen (17) most contaminated capitals in the world, from Dhaka, to Skopje, exceed the PM2.5 average concentrations (75.78 μg/m3) from before quarantine, maintaining an AQI level from moderate to unhealthy.
T241 27964-28114 Sentence denotes Likewise, during typical times twelve (12) countries present an AQI level below the quarantine average (Bern - Kyiv) almost all with a good AQI level.
T242 28115-28290 Sentence denotes During the quarantine, sixteen (16) capitals exceed the PM2.5 registered average (66.92%), equivalent to a moderate AQI level, and nine (9) cities registered a good AQI level.
T243 28291-28458 Sentence denotes Specifically, the three most polluted capital cities that are Dhaka, followed by Kampala and Delhi, reduced their PM2.5 concentration by 14%, 35% and 40% respectively.
T244 28459-28607 Sentence denotes The capital city with the highest PM2.5 reduction during quarantine was Bogotá, with a percentage of 57%, going from a moderate to a good AQI level.
T245 28608-28671 Sentence denotes Kubait City, presents the second largest PM2.5 reduction (42%).
T246 28672-28748 Sentence denotes Finally, with reductions over 40%, there are the cities of Delhi and Tehran.
T247 28749-28824 Sentence denotes Fig. 8 Weekly average without quarantine vs weekly average with quarantine.
T248 28825-29087 Sentence denotes In Europe, the continent with the best environmental conditions in normal times, a very high increase in PM2.5 is observed in the cities of Prague, Vienna and Bratislava, cities very close to each other; apparently, an isolated effect produced by winds or fires.
T249 29089-29103 Sentence denotes 6 Conclusions
T250 29104-29375 Sentence denotes Based on the data collection of the concentration of the most harmful particles for health (PM2.5), in the different capital cities of the world, a comparative analysis of the concentration was carried out during a typical time of normal mobility and during the lockdown.
T251 29376-29540 Sentence denotes The behavior patterns show as a result a decrease in their concentration during the confinement season, favorably restoring the air quality of most cities analyzed.
T252 29541-29817 Sentence denotes If these data are subsequently correlated with the activities stopped during the confinement of each city, one can think of public policies that promote new socio-ecological models, as well as coherent environmental policies in these adaptive environments that are the cities.
T253 29818-29959 Sentence denotes In general, the results showed that automobile demobilization and factory shutdowns play an important role in reducing pollution in capitals.
T254 29960-30218 Sentence denotes For the fifty countries analyzed, Bogotá (Colombia)(Insider, 2019), as one of the cities with the most traffic in the world, with a 65% concentration of traffic during the day, presents the PM2.5 greatest reduction. during the period of confinement with 57%.
T255 30219-30339 Sentence denotes Likewise, Delhi (India), the most polluted capital city in the world, presents a decrease in PM2.5 contamination of 40%.
T256 30340-30389 Sentence denotes Some specific conclusions of the research are: i.
T257 30390-30564 Sentence denotes During lockdown, Europe maintains a Good AQI level of less than 50 μg/m3, followed by America with a Moderate AQI level (57 μg/m3), Asia (82 μg/m3) and Africa (95 μg/m3). ii.
T258 30565-30752 Sentence denotes America presented the highest distinction in the PM2.5 decrease air pollution. between a typical period of conventional mobility and a period of confinement, with a reduction of 22%. iii.
T259 30753-30928 Sentence denotes The capitals that in typical days (BQut) present a moderate AQI level and which presented a PM2.5 decrease between 60% and 20% during the period of confinement (Qut), such as:
T260 30929-31135 Sentence denotes Bogotá, Kubait City, Delhi, Tehran, Taskhkent, Ulaanbaatar, kabul and Colombo; They must present alternatives to reduce the displacement of fossil fuel vehicles and stricter regulations for their factories.
T261 31136-31340 Sentence denotes This study considered the relationship between population and Pp/ES air quality stations, where the number of stations per inhabitant is obviously higher in developed countries than in emerging countries.
T262 31341-31607 Sentence denotes Therefore, it is important to consider that the lack of access to data on air pollution, especially in emerging countries and where the use of public and private transport systems is high, causes errors or alterations in real information on the state of air quality.
T263 31609-31642 Sentence denotes Declaration of competing interest
T264 31643-31813 Sentence denotes The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
T265 31815-31886 Sentence denotes ☆ This paper has been recommended for acceptance by Pavlos Kassomenos.

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T1 1352-1363 Phenotype denotes lung cancer http://purl.obolibrary.org/obo/HP_0100526
T2 1379-1383 Phenotype denotes COPD http://purl.obolibrary.org/obo/HP_0006510
T3 5048-5054 Phenotype denotes stroke http://purl.obolibrary.org/obo/HP_0001297
T4 5056-5067 Phenotype denotes lung cancer http://purl.obolibrary.org/obo/HP_0100526
T5 5069-5106 Phenotype denotes chronic obstructive pulmonary disease http://purl.obolibrary.org/obo/HP_0006510
T6 5126-5148 Phenotype denotes respiratory infections http://purl.obolibrary.org/obo/HP_0011947
T7 5157-5166 Phenotype denotes pneumonia http://purl.obolibrary.org/obo/HP_0002090
T8 9676-9685 Phenotype denotes paralysis http://purl.obolibrary.org/obo/HP_0003470

MyTest

Id Subject Object Predicate Lexical cue
32650158-30690371-28447745 3015-3019 30690371 denotes 2019
32650158-30841411-28447746 3223-3227 30841411 denotes 2019
32650158-22572115-28447747 3695-3699 22572115 denotes 2012
32650158-28408086-28447748 4790-4794 28408086 denotes 2017
32650158-24333687-28447749 5184-5188 24333687 denotes 2014
32650158-28431315-28447750 5203-5207 28431315 denotes 2017
32650158-31185348-28447751 5222-5226 31185348 denotes 2019
32650158-31950516-28447752 5756-5760 31950516 denotes 2020
32650158-32334164-28447753 7394-7398 32334164 denotes 2020
32650158-32335404-28447754 7760-7764 32335404 denotes 2020
32650158-32302812-28447755 8748-8752 32302812 denotes 2020

2_test

Id Subject Object Predicate Lexical cue
32650158-30690371-28447745 3015-3019 30690371 denotes 2019
32650158-30841411-28447746 3223-3227 30841411 denotes 2019
32650158-22572115-28447747 3695-3699 22572115 denotes 2012
32650158-28408086-28447748 4790-4794 28408086 denotes 2017
32650158-24333687-28447749 5184-5188 24333687 denotes 2014
32650158-28431315-28447750 5203-5207 28431315 denotes 2017
32650158-31185348-28447751 5222-5226 31185348 denotes 2019
32650158-31950516-28447752 5756-5760 31950516 denotes 2020
32650158-32334164-28447753 7394-7398 32334164 denotes 2020
32650158-32335404-28447754 7760-7764 32335404 denotes 2020
32650158-32302812-28447755 8748-8752 32302812 denotes 2020