Fig 4 - by Takafumi Kabuto (14797727)

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Fig 6 - by Takafumi Kabuto (14797727)

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Fig 5 - by Takafumi Kabuto (14797727)

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Determination of bladder blood flow in LETO and OLETF rats. by Takafumi Kabuto (14797727)

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Measurements of the amount of ATP released by bladder distention and VNUT expression in the bladder. by Takafumi Kabuto (14797727)

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Measurement of micturition behavior in the rats. by Takafumi Kabuto (14797727)

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Estimated marginal mean values representative of the average time on ball and the relative frequency of ball touches and releases per minute of match duration, performed by profess... by Emily Escreet (20552634)

Mean percent survival of newly emerged worker bees (n = 180/group) in the laboratory inoculated with 1 × 10⁷ CFU/bee <i>M. plutonius</i> 2019BC1 and/or one (1X) or three... by Midhun Sebastian Jose (19336846)

Clade body sizes using log transformed mean adult size to represent each turritellid and tegulid species’ size, and typical size to represent the size of each species belonging to... by Brendan M. Anderson (11234356)

Root mean square fluctuations of the wild-type (green line) and mutant (H5Q) (purple line) proteins of AKR1C3 (complexed with the ligand NADPH). by Md. Akeruzzaman Shaon (21511929)

Flowchart outlining the protocol adopted in this systematic review. by Yasmine Makhlouf (17409866)

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Characteristics of JIA patients. by Yasmine Makhlouf (17409866)

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List of excluded articles. by Yasmine Makhlouf (17409866)

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Growth assessment parameters before and after biological treatment in the different studies. by Yasmine Makhlouf (17409866)

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Sound stress exposure prolonged the period of decreased withdrawal threshold after complete Freund’s adjuvant (CFA) injection. by Satoka Kasai (3861115)

“…(B) 50% withdrawal threshold in CFA-treated mice exposed to sound stress. They showed a significant decrease in 50% withdrawal threshold on day 7 after CFA injection, and CFA-treated mice exposed to sound stress showed a significant decrease in 50% withdrawal threshold during days 7–21 after CFA injection (CFA, on day 7, ****<i>P</i> < 0.001 vs day 0, on day 10, ***<i>P</i> < 0.001 vs day0; CFA + stress, day 7, 10, 14, and 21, ****<i>P</i> < 0.0001 vs day 0, Dunnett’s test). …”

Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample scenario description. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”