(A) FC and VIP bubble plots of the Model.vs.SHR; (B) Hierarchical clustering results of differential metabolites in the Model.vs.SHR (only the top 50 metabolites with the highest V... by Xiao Zhang (152326)

Virucidal activity of LA, SCFA, and succinic acid combinations associated with an optimal vaginal microbiota or BV, representing a non-optimal vaginal microbiota. by Muriel Aldunate (22411177)

Stereo Microscopy Samples (a) ABS (800 rpm,10 mm/min) (b) ABS (1200 rpm,10 mm/min) (c) ABS (1200 rpm,20 mm/min) (d) PC (800 rpm,40 mm/min) (e) PC (1200 rpm,20 mm/min).... by Shaheer Ahmed Khan (17797647)

IFN-γ and several PARP genes are upregulated in infected olfactory bulbs of JHMV infected mice. by Catherine M. Kerr (21492574)

Mediation effect of muscle strength on the associations of obesity with incidence of knee OA. by Laijun Yan (19797455)

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)

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)

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)

The mRNA (A) and protein (B) levels of <i>NlPHB2</i> in viruliferous and nonviruliferous <i>N. lugens</i> determined by RT-qPCR and Western blot assays at 6 dpvf. by Lianshun Zheng (22086585)

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. …”