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)

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

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)

ASP protects the AC16 cells from DOX-induced cardiotoxicity via Nrf2 activation. by Xueyang Bai (20550422)

“…<p>(A) Representative images of protein expression detected by Western blot of p-PI3K,p-AKT, AKT, and the Nrf2 downstream signaling pathways in AC16 cells; (B, C) ML385 does not affect PI3K/AKT (n = 3); (D-F) The statistical results show that ML385 significantly represses NRF2 activation, leading to a decrease in its downstream gene expression (n = 3); (G, H) Lipid ROS levels (n = 3); (I, J) Quantitative q-PCR analysis of relative ANP and BNP mRNA expression (n = 3); (K, L) Representative images (Scale bar =  100 μm) and statistical analysis of JC-1 (n = 150); One-way ANOVA (Tukey post-test), means ±  SD. …”

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. …”

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. …”

Baseline characteristics. by Seung Min Lee (1644409)

“…The distance of the FD decreased (<i>P</i> < 0.001) and FVs increased (<i>P</i> < 0.001, both). …”

Data file used in this study. by Seung Min Lee (1644409)

“…The distance of the FD decreased (<i>P</i> < 0.001) and FVs increased (<i>P</i> < 0.001, both). …”

Changes in foveal location after surgery. by Seung Min Lee (1644409)

“…The distance of the FD decreased (<i>P</i> < 0.001) and FVs increased (<i>P</i> < 0.001, both). …”

Changes in the genotypes of <i>NLRP1</i>, <i>NLRP3</i>, and <i>NLRP4</i>. by Hao Deng (409186)

Subjects: