Changes in body weight and glycolipid metabolism in different groups. by Shujuan Hu (22656045)

Subjects:

The expression of irisin, NLRP3 and inflammation-related factors in C2C12 cells. by Shujuan Hu (22656045)

Subjects:

Changes in the body color of cherry shrimp (<i>N</i>. <i>davidi</i> var. red) across different feed treatment groups (Control, AX100, AX100 + BP, AX200, AX200 + BP) over time. by Wei-Wei Hou (3405947)

Subjects:

Effects of aerobic exercise on the expression levels of NLRP3 and its related factors in the skeletal muscle of mice. by Shujuan Hu (22656045)

Subjects:

Transient suppression of <i>atg5</i> in neurons does not impair ITM. by Tong Yue (6033305)

HFD changes lysosomal activity. by Tong Yue (6033305)

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)

Experimental design of this study. by Renya Kawakami (20469088)

“…In fact, naive old males exhibited significantly higher paternity success compared with old males who had previously mated. …”

All relevant data of this study. by Renya Kawakami (20469088)

“…In fact, naive old males exhibited significantly higher paternity success compared with old males who had previously mated. …”

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. …”