Table_1_The Dual Role of AQP4 in Cytotoxic and Vasogenic Edema Following Spinal Cord Contusion and Its Possible Association With Energy Metabolism via COX5A.XLSX by Yuan Huang (79836)

“…However, the neuronal area is larger than in the sham group in the 7 days (P < 0.05) with the total water content of spinal cord decrease. Meanwhile, AQP4 migrates from VECs to neuronal cytomembrane, which indicates that AQP4 plays a crucial role in aggravating the formation and development of cytotoxic edema in the middle stages of SCC. …”

Image_4_The Dual Role of AQP4 in Cytotoxic and Vasogenic Edema Following Spinal Cord Contusion and Its Possible Association With Energy Metabolism via COX5A.JPEG by Yuan Huang (79836)

“…However, the neuronal area is larger than in the sham group in the 7 days (P < 0.05) with the total water content of spinal cord decrease. Meanwhile, AQP4 migrates from VECs to neuronal cytomembrane, which indicates that AQP4 plays a crucial role in aggravating the formation and development of cytotoxic edema in the middle stages of SCC. …”

A histidine-kinase <i>cheA</i> gene of <i>Pseudomonas pseudoalcaligens</i> KF707 not only has a key role in chemotaxis but also affects biofilm formation and cell metabolism by V. Tremaroli (472692)

“…Notably, ≥95% decrease in the number of cells attached to the polystyrene surface was observed in <i>cheA</i> mutants compared to the KF707 wild-type biofilm phenotype. …”

Effect of doxycycline on MMP-2 in the infarct border zone six weeks after MI. by Kimberly Spaulding (797016)

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. …”

Characterization, Source Apportionment and Health Risk Assessment of PM2.5 for a Rural Classroom in the Amazon: A Case Study by Karenn S. Fernandes (10352226)

“…<div><p>Classrooms are microenvironments in which children and teenagers may be exposed to fine particulate matter (PM2.5). Iranduba is a rural city in the Amazon region close to many brick kilns and road with high traffic levels. …”

Do children and adolescents who consume ultra-processed foods have a worse lipid profile? A systematic review by Jéssica Batista Beserra (5222036)

“…After screening, 14 studies were included, of which nine demonstrated that ultra-processed food consumption was related to increased LDL-c, total cholesterol, triglycerides and a reduction in HDL-c. Three studies found no relationship and two demonstrated that the increased intake of ready-to-eat cereals was related to the decrease in total cholesterol and LDL-c. …”

OPJ: Origin data for Fig 9a. by Nacer Badi (14046883)

“…<div><p>In hot dry regions, photovoltaic modules are exposed to excessive temperatures, which leads to a drop in performance and the risk of overheating. …”

OPJ: Origin data for Fig 7a. by Nacer Badi (14046883)

“…<div><p>In hot dry regions, photovoltaic modules are exposed to excessive temperatures, which leads to a drop in performance and the risk of overheating. …”

OPJ: Origin data for Fig 3a. by Nacer Badi (14046883)

“…<div><p>In hot dry regions, photovoltaic modules are exposed to excessive temperatures, which leads to a drop in performance and the risk of overheating. …”

A Regulatory Network for Coordinated Flower Maturation by Paul H. Reeves (185822)

“…The Arabidopsis transcription factors AUXIN RESPONSE FACTOR 6 (ARF6) and ARF8 regulate this complex process by promoting petal expansion, stamen filament elongation, anther dehiscence, and gynoecium maturation, thereby ensuring that pollen released from the anthers is deposited on the stigma of a receptive gynoecium. ARF6 and ARF8 induce jasmonate production, which in turn triggers expression of <em>MYB21</em> and <em>MYB24</em>, encoding R2R3 MYB transcription factors that promote petal and stamen growth. …”

The core PTO is embedded in a larger TTFL. by Ximing Qin (247294)

“…If on the other hand, new synthesis of unphosphorylated KaiC happens at a phase when hexamers are predominantly hyper-phosphorylated, this leads to an overall decrease in the KaiC phosphorylation status, thereby altering the phase of the KaiABC oscillator (phase shift) and/or reducing its amplitude.…”

OPJ: Origin data for Fig 6a. by Nacer Badi (14046883)

“…<div><p>In hot dry regions, photovoltaic modules are exposed to excessive temperatures, which leads to a drop in performance and the risk of overheating. …”