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- 2019
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Tidskriftsartikel (refereegranskat)
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- Fu, Zhongjie, et al.
(författare)
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Photoreceptor glucose metabolism determines normal retinal vascular growth
- 2018
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Ingår i: EMBO Molecular Medicine. - : EMBO. - 1757-4676 .- 1757-4684. ; 10:1, s. 76-90
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Tidskriftsartikel (refereegranskat)abstract
- The neural cells and factors determining normal vascular growth are not well defined even though vision-threatening neovessel growth, a major cause of blindness in retinopathy of prematurity (ROP) (and diabetic retinopathy), is driven by delayed normal vascular growth. We here examined whether hyperglycemia and low adiponectin (APN) levels delayed normal retinal vascularization, driven primarily by dysregulated photoreceptor metabolism. In premature infants, low APN levels correlated with hyperglycemia and delayed retinal vascular formation. Experimentally in a neonatal mouse model of postnatal hyperglycemia modeling early ROP, hyperglycemia caused photoreceptor dysfunction and delayed neurovascular maturation associated with changes in the APN pathway; recombinant mouse APN or APN receptor agonist AdipoRon treatment normalized vascular growth. APN deficiency decreased retinal mitochondrial metabolic enzyme levels particularly in photoreceptors, suppressed retinal vascular development, and decreased photoreceptor platelet-derived growth factor (Pdgfb). APN pathway activation reversed these effects. Blockade of mitochondrial respiration abolished AdipoRon-induced Pdgfb increase in photoreceptors. Photoreceptor knockdown of Pdgfb delayed retinal vascular formation. Stimulation of the APN pathway might prevent hyperglycemia-associated retinal abnormalities and suppress phase I ROP in premature infants.
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- Ren, Zhihui, et al.
(författare)
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Temporal Scaling Characteristics of Sub-Daily Precipitation in Qinghai-Tibet Plateau
- 2024
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Ingår i: EARTHS FUTURE. - 2328-4277. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- The Qinghai-Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub-daily precipitation observations in this region has hindered our understanding of rainstorm-related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub-daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub-daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub-daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1-km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm-related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub-daily precipitation and assessing its uncertainty in ungauged regions. As one of famous hotspots for natural disaster studies on Earth, the Qinghai-Tibet Plateau (QTP) is highly vulnerable to destructive rainstorm hazard and related natural disasters, causing significant damage to property, infrastructure, agriculture, and resulting in extensive loss of life. Short-duration heavy precipitation at sub-daily scales is an important trigger for flash flood, debris flows and other disasters in QTP. However, it is a poorly gauged high mountain region, observed data for sub-daily precipitation is extremely limited. Although there have been several satellite products and reanalysis data for sub-daily precipitation in QTP, their quality has large bias and uncertainty compared to observations. It leaves a large data gap of sub-daily precipitation, hindering the studies of rainstorm-related natural disasters in the region. In this work, we develop a new strategy to quantify the temporal scaling characteristics of sub-daily precipitation, as a basis of temporal downscaling. Then we use the new strategy to generate a parameters data set, to fill the data gap of sub-daily precipitation in QTP. The parameters data set generated provides an effective way to estimate sub-daily precipitation and its uncertainty, which can effectively serve for the rainstorm-related natural disasters study in QTP. A high-resolution gridded parameters data set is generated to estimate sub-daily precipitation and its uncertainty in QTP The temporal scaling characteristics of sub-daily precipitation in QTP is well described by a logarithmic function Spatial heterogeneity in the temporal scaling characteristics of sub-daily precipitation in QTP is closely related to geographical conditions
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| 5. |
- Sang, Yan Fang, et al.
(författare)
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What Caused the Decline of Water Level of Yamzho Yumco During 1975–2012 in the Southern Tibetan Plateau?
- 2020
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Ingår i: Journal of Geophysical Research: Atmospheres. - 2169-897X .- 2169-8996. ; 125:6
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Tidskriftsartikel (refereegranskat)abstract
- ©2020. American Geophysical Union. All Rights Reserved. Variability of water balance in closed lakes in the Tibetan Plateau (TP) can be a useful indicator of climate change. While most of lakes over the TP have been expanding, especially in the inner TP, the Yamzho Yumco in the Southern TP faced a significant water level decline in 1975–2012. This study focused on attribution of the water level changes to various factors with the help of a physically based and a statistical model for the water balance of the lake, along with observed precipitation and lake surface evaporation data. Results showed that climatic conditions dominated the changes in water level until 1997, and human activity through the construction and operation of a hydropower station started to play a strong role after 1998. The lake level has gone up and down due to changes in precipitation-generated recharge and evaporation over the study period. The water level decline in 1975–1997 was mainly due to low level of the recharge from precipitation and land runoff. However, the drastic decline of water level in 2006–2012 was far from the reach of the climatic conditions. It was indeed caused by the intensified influence of the building and operation of a nearby hydropower station. It was also found that the water level change during the whole period (1975–2012) followed the aridity index for the basin, especially before 1989. This provides an effective way to predict future changes of lake level, if reliable projections for future climate change and human activity are available.
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