Fig 5 - by Zeenat A. Shyr (12107048)

Fig 1 - by Sue E. Moore (2932644)

Distribution of infaunal crustacean abundance (#/m²) at all stations where macrofauna were sampled during the period 2010–2019 in the northern Bering and eastern Chukchi... by Sue E. Moore (2932644)

Fig 3 - by Sue E. Moore (2932644)

Fig 3 - by Zeenat A. Shyr (12107048)

Additional behavioral patterns in the 5€, 100€, and 1000€ treatments. by Pablo Brañas-Garza (336425)

Gray whale combined July-August-September sighting rates (SR) in the Hope Basin area in the southern Chukchi Sea, 2009–2019. by Sue E. Moore (2932644)

Interannual variability in gray whale monthly distribution and histogram of sighting rates (SR) for Wainwright and Peard Bay analytical areas in the northeastern Chukchi Sea, 2009–... by Sue E. Moore (2932644)

Interannual variability in gray whale monthly distribution and Sighting Rates (SR) for the Hope Basin area in the southern Chukchi Sea, 2014–2019. by Sue E. Moore (2932644)

Schematic representation of workflow. by Muhammad Bilal Azmi (14519913)

“…The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). …”

Gray whale distribution from sightings made during marine mammal watches on DBO cruises, and the broad scale Aerial Surveys of Arctic Marine Mammals (ASAMM) program, 2009–2019. by Sue E. Moore (2932644)

Top 5 templates of CNN1 isoform 2 by PHYRE2 tool. by Muhammad Bilal Azmi (14519913)

“…The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). …”

Distribution of study areas. by Jie Zhang (64655)

“…(main findings) The results show the following: (1) the primary term of government innovation preferences has a positive effect on the ecological resilience of resource-based cities, and the secondary term has a negative effect, with a decreasing marginal effect. …”

Descriptive statistics of variables. by Jie Zhang (64655)

“…(main findings) The results show the following: (1) the primary term of government innovation preferences has a positive effect on the ecological resilience of resource-based cities, and the secondary term has a negative effect, with a decreasing marginal effect. …”

Code. by Jie Zhang (64655)

“…(main findings) The results show the following: (1) the primary term of government innovation preferences has a positive effect on the ecological resilience of resource-based cities, and the secondary term has a negative effect, with a decreasing marginal effect. …”

Schematic of the conclusions. by Jie Zhang (64655)

“…(main findings) The results show the following: (1) the primary term of government innovation preferences has a positive effect on the ecological resilience of resource-based cities, and the secondary term has a negative effect, with a decreasing marginal effect. …”

Robustness test. by Jie Zhang (64655)

“…(main findings) The results show the following: (1) the primary term of government innovation preferences has a positive effect on the ecological resilience of resource-based cities, and the secondary term has a negative effect, with a decreasing marginal effect. …”

Data. by Jie Zhang (64655)

“…(main findings) The results show the following: (1) the primary term of government innovation preferences has a positive effect on the ecological resilience of resource-based cities, and the secondary term has a negative effect, with a decreasing marginal effect. …”

Fig 2 - by Zeenat A. Shyr (12107048)

The effect of CA4P treatment on cytokine levels in C3H mammary carcinomas in CDF1 mice. by Anja Bille Bohn (641992)