HFD changes lysosomal activity. by Tong Yue (6033305)

Trends in ESR per survey round by age group. by Alex Daama (18351052)

“…ESR prevalence was higher among participants who reported to have more than one sexual partner than participants with one partner, aPR = 1.59, 95% CI: [1.16,2.20]. In the fourth survey from 2018–2020, ESR prevalence was significantly higher among participants with primary education than participants with post-primary, aPR = 2.38, 95% CI: [1.31, 4.30]. …”

Recognition of <i>F. novicida</i> strains by lysophagosomes and lysosomes in THP-1 cells. by Dhandy Koesoemo Wardhana (21956781)

Recognition of <i>F. novicida</i> strains by lysophagosomes and lysosomes in HeLa–FcγRII cells. by Dhandy Koesoemo Wardhana (21956781)

HFD impairs autophagic activity in fly brains. by Tong Yue (6033305)

Recognition of <i>F. novicida</i> strains by the mitochondria and Golgi complex in THP-1 cells. by Dhandy Koesoemo Wardhana (21956781)

Recognition of <i>F. novicida</i> strains by the mitochondria and Golgi complex in HeLa–FcγRII cells. by Dhandy Koesoemo Wardhana (21956781)

Expression levels of lysosome-related genes in HFD-fed flies and control group. by Tong Yue (6033305)

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. …”

Marginal means – 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. …”

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. …”

Methodological flowchart. 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. …”

Preference for the EIA vs. ETA 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. …”

Overexpression of miR-184 disrupts the blood-brain barrier and larval locomotion. by Sravya Paluri (5475260)

Intracellular growth of the wild-type strain and <i>FTN_0096</i> mutant in THP-1 cells. by Dhandy Koesoemo Wardhana (21956781)

Intracellular growth and cytotoxicity of the wild-type strain and <i>FTN_0096</i> mutant in HeLa–FcγRII cells. by Dhandy Koesoemo Wardhana (21956781)

Screening of the Transposon Mutant Library. by Dhandy Koesoemo Wardhana (21956781)