Classification of inactive compounds. by MohammedBashar Al-Qazzan (22439236)

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Active compounds identified as glyoxalase-I inhibitors. by MohammedBashar Al-Qazzan (22439236)

Subjects:

The inhibitory activity of the most active compounds against Glo-I enzyme with less than 50 µM. Their supportive corresponding docking values on two different protocols are display... by MohammedBashar Al-Qazzan (22439236)

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3D and 2D pose of the docked Tolcapone diagram displaying a variety of interactions with enzyme’s active region. by MohammedBashar Al-Qazzan (22439236)

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Average ratio of the concentrations obtained with qPCR, before and after DNase treatment, and with DAO before DNase treatment of the ISP phage stocks. by Saar Van Overfelt (20378458)

Average concentration of the ISP phage stocks obtained with qPCR, before and after DNase treatment, and with the DAO method before the DNase treatment. by Saar Van Overfelt (20378458)

Assembly error angle of a single spline. by Zhe Hu (787283)

“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”

IFA significantly attenuated capsule-mediated resistance to the binding of C3b and C5b-9 to bacterial surface. by Tingting Wang (123983)

Different hematological parameters at day 45 of trial of male Japanese quail exposed to acetochlor @ 0, 20, 30 and 40 mg/kg BW reared in groups A, B, C, and D respectively via crop... by Naveed Arshad (19712985)

Different hematological parameters at day 30 of trial of male Japanese quail exposed to acetochlor @ 0, 20, 30 and 40 mg/kg BW reared in groups A, B, C, and D respectively via crop... by Naveed Arshad (19712985)

Different hematological parameters at day 15 of trial of male Japanese quail exposed to acetochlor @ 0, 20, 30 and 40 mg/kg BW reared in groups A, B, C, and D respectively via crop... by Naveed Arshad (19712985)

Relative weight of lungs, testes, heart, and brain of male Japanese quail exposed to acetochlor @ 0, 20, 30 and 40 mg/kg BW reared in groups A, B, C, and D respectively via crop tu... by Naveed Arshad (19712985)

Manhattan plot for genome-wide association study (GWAS) in a European population with depression. by Xiaomei He (795333)

“…<p>A: The x-axis represents the chromosomal position, and the y-axis represents the significance on a –log10 scale. …”

Data Sheet 1_Host identity, more than elevation, shapes bee microbiomes along a tropical elevation gradient.pdf by Andrea Pinos (22268176)

“…Our findings indicate a general decrease in bacterial diversity and a high turnover of microbial taxa along the elevation gradient, with notable differences among host tribes. …”

Rarefaction curves of quality reads per sample at (A) phylum and (B) genus level operational taxonomic units (OTUs), in patient and control samples. by Duangnapa Roongpiboonsopit (12829198)

Minimal data set. by Xiaoxia Liu (294088)

“…Specifically, with a positive Soret coefficient, the growth rate of spherical crystals in a binary dilute alloy melt decreases as the coefficient increases, while the solute concentration near the interface increases. …”

Benchmark regression results. by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”

Heterogeneity test. by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”

S1 File - by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”

The robustness test. by Pengyu Yang (2668450)

“…The findings reveal that: (1) At this stage, digital transformation in listed companies effectively reduces their carbon intensity, but the relationship between the two is not linear; instead, it exhibits a U-shaped trajectory, initially decreasing then increasing. (2) Analysis of mechanism indicates that costs associated with environmental governance and innovations in green technology serve as critical pathways through which corporate digital transformation influences carbon intensity. (3) The analysis of driving effect suggests that the digital transformation significantly curtails the carbon emission intensity of both upstream and downstream enterprises as well as those within the same industry and geographical region, through industrial linkage and the cohort effect. (4) Heterogeneity analysis elucidates that the digital transformation of enterprises in regions with stronger government environmental regulations has a markedly more pronounced effect on reducing the carbon emission intensity. …”