<i>clec-4(ok2050)</i> and <i>clec-4(ya1)</i> decrease <i>C</i>. <i>elegans</i> feeding leading to a reduced bacterial load. by Barbara Pees (3313560)

LCoE in 2030 to 2050 assuming 10% p.a. WACC by ESC and exporter. by Johannes Hampp (14593021)

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021 by Stein Emil, Vollset

“…The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. …”
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Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021 by Stein Emil, Vollset

“…The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. …”
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Ecology Group 4 Wk3 Dataset 3 by Michael Angelini (796301)

York.woodlot.oct20-2016.csv by Brenda Espin (3120879)

Table 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

Table 4_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

Image 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.tif by Ning An (618997)

Table 3_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

Table 2_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

YorkU.forest.Oct25-2016.csv by Andrew Laruffa (3116073)

yorku.forest.October25.2016.csv by Mubashira Islam (3115869)

“…Using the sweeping net, which had a length of 90 centimeters and a diameter of 30 centimeters (area of 708 centimeters square) the abundance of invertebrates were observed and recorded.The student walked down the 50 meter transect line 10 times, for a total of 10 replications for the sweeping net experiment. …”

JIL-1–dependent H3S10phK14ac is enriched on the compensated X chromosome together with JIL-1. by Catherine Regnard (227884)

“…(D) Decrease of H3S10phK14ac ChIP signal at the 3′ end of the genes <i>phl</i>, <i>CG12467</i>, <i>Suv4.20</i>, <i>CG14804</i> and <i>RpII215</i> after JIL-1 RNAi as compared to a control GST RNAi. …”

Data_Sheet_1_Dynamical Projections of the Mean and Extreme Wave Climate in the Bohai Sea, Yellow Sea and East China Sea.docx by Delei Li (3370511)

Data_Sheet_1_Dynamical Projections of the Mean and Extreme Wave Climate in the Bohai Sea, Yellow Sea and East China Sea.docx by Delei Li (3370511)

Expected change in the mass-specific fecundity (in percentage) for a female of a given mass at a given latitude under predicted climate change scenarios. by Mariana Álvarez-Noriega (14275263)

R scripts. by Ehsan Ghafouri (21688451)

Bioclimatic variables used in the study. by Ehsan Ghafouri (21688451)

Response Curves of environmental variables. by Ehsan Ghafouri (21688451)