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GoldHamster

Id Subject Object Predicate Lexical cue
T1 383-390 D004194 denotes disease
T2 383-390 D004194 denotes disease
T3 508-515 D004194 denotes disease
T4 508-515 D004194 denotes disease
T5 563-569 PR:Q54JW9 denotes impact
T6 563-569 PR:Q642J4 denotes impact
T7 1029-1036 UBERON:0011631 denotes Tabular
T8 1029-1036 UBERON:2000663 denotes Tabular
T9 1064-1068 PR:Q9VCA8 denotes mask
T10 1476-1483 UBERON:0011631 denotes Tabular
T11 1476-1483 UBERON:2000663 denotes Tabular
T12 1691-1697 PR:Q54JW9 denotes impact
T13 1691-1697 PR:Q642J4 denotes impact
T14 1770-1777 UBERON:0011631 denotes Tabular
T15 1770-1777 UBERON:2000663 denotes Tabular
T16 1953-1960 D004194 denotes disease
T17 1953-1960 D004194 denotes disease

PubMed_ArguminSci

Id Subject Object Predicate Lexical cue
T1 192-391 DRI_Approach denotes Disease monitoring and surveillance play a crucial role in control and eradication programs, as it is important to track implemented strategies in order to reduce and/or eliminate a specific disease.
T2 392-654 DRI_Approach denotes The objectives of this study were to assess the performance of different statistical monitoring methods for endemic disease control program scenarios, and to explore what impact of variation (noise) in the data had on the performance of these monitoring methods.
T3 655-728 DRI_Approach denotes We simulated 16 different scenarios of changes in weekly sero-prevalence.
T4 729-854 DRI_Outcome denotes The changes included different combinations of increases, decreases and constant sero-prevalence levels (referred as events).
T5 855-1174 DRI_Approach denotes Two space-state models were used to model the time series, and different statistical monitoring methods (such as univariate process control algorithms-Shewart Control Chart, Tabular Cumulative Sums, and the V-mask- and monitoring of the trend component-based on 99% confidence intervals and the trend sign) were tested.
T6 1175-1315 DRI_Approach denotes Performance was evaluated based on the number of iterations in which an alarm was raised for a given week after the changes were introduced.
T7 1316-1500 DRI_Outcome denotes Results revealed that the Shewhart Control Chart was better at detecting increases over decreases in sero-prevalence, whereas the opposite was observed for the Tabular Cumulative Sums.
T8 1501-1627 DRI_Outcome denotes The trend-based methods detected the first event well, but performance was poorer when adapting to several consecutive events.
T9 1628-1838 DRI_Outcome denotes The V-Mask method seemed to perform most consistently, and the impact of noise in the baseline was greater for the Shewhart Control Chart and Tabular Cumulative Sums than for the V-Mask and trend-based methods.
T10 1839-1977 DRI_Outcome denotes The performance of the different statistical monitoring methods varied when monitoring increases and decreases in disease sero-prevalence.
T11 1978-2151 DRI_Background denotes Combining two of more methods might improve the potential scope of surveillance systems, allowing them to fulfill different objectives due to their complementary advantages.

Goldhamster2_Cellosaurus

Id Subject Object Predicate Lexical cue
T1 0-1 CVCL_6479|Finite_cell_line|Mus musculus denotes A
T2 112-116 CVCL_0047|Telomerase_immortalized_cell_line|Homo sapiens denotes time
T3 233-234 CVCL_6479|Finite_cell_line|Mus musculus denotes a
T4 372-373 CVCL_6479|Finite_cell_line|Mus musculus denotes a
T5 655-657 CVCL_5M23|Cancer_cell_line|Mesocricetus auratus denotes We
T6 694-701 CVCL_0238|Cancer_cell_line|Homo sapiens denotes changes
T7 733-740 CVCL_0238|Cancer_cell_line|Homo sapiens denotes changes
T8 901-905 CVCL_0047|Telomerase_immortalized_cell_line|Homo sapiens denotes time
T9 1244-1246 CVCL_8754|Cancer_cell_line|Homo sapiens denotes an
T10 1244-1246 CVCL_H241|Cancer_cell_line|Homo sapiens denotes an
T11 1268-1269 CVCL_6479|Finite_cell_line|Mus musculus denotes a
T12 1291-1298 CVCL_0238|Cancer_cell_line|Homo sapiens denotes changes