| 1. |
- Jia, Xiaotong, et al.
(författare)
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V. cholerae MakA is a cholesterol-binding pore-forming toxin that induces non-canonical autophagy
- 2022
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Ingår i: Journal of Cell Biology. - : Rockefeller University Press. - 0021-9525 .- 1540-8140. ; 221:12
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Tidskriftsartikel (refereegranskat)abstract
- Pore-forming toxins (PFTs) are important virulence factors produced by many pathogenic bacteria. Here, we show that the Vibrio cholerae toxin MakA is a novel cholesterol-binding PFT that induces non-canonical autophagy in a pH-dependent manner. MakA specifically binds to cholesterol on the membrane at pH < 7. Cholesterol-binding leads to oligomerization of MakA on the membrane and pore formation at pH 5.5. Unlike other cholesterol-dependent cytolysins (CDCs) which bind cholesterol through a conserved cholesterol-binding motif (Thr-Leu pair), MakA contains an Ile-Ile pair that is essential for MakA-cholesterol interaction. Following internalization, endosomal acidification triggers MakA pore-assembly followed by ESCRT-mediated membrane repair and V-ATPase-dependent unconventional LC3 lipidation on the damaged endolysosomal membranes. These findings characterize a new cholesterol-binding toxin that forms pores in a pH-dependent manner and reveals the molecular mechanism of host autophagy manipulation.
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| 2. |
- Yao, Yunjun, et al.
(författare)
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Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations
- 2016
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Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 223, s. 151-167
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Tidskriftsartikel (refereegranskat)abstract
- The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the global hydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison with eddy covariance (EC) observations from 240 globally distributed sites from 2000 to 2009. In addition, we improved global terrestrial LE estimates for different land cover types by synthesis of seven best CMIP5 models and EC observations based on a Bayesian model averaging (BMA) method. The comparison results showed substantial differences in monthly LE among all GCMs. The model CESM1-CAM5 has the best performance with the highest predictive skill and a Taylor skill score (S) from 0.51-0.75 for different land cover types. The cross-validation results illustrate that the BMA method has improved the accuracy of the CMIP5 GCM's LE simulation with a decrease in the averaged root-mean-square error (RMSE) by more than 3 W/m2 when compared to the simple model averaging (SMA) method and individual GCMs. We found an increasing trend in the BMA-based global terrestrial LE (slope of 0.018 W/m2 yr-1, p <0.05) during the period 1970-2005. This variation may be attributed directly to the inter-annual variations in air temperature (Ta), surface incident solar radiation (Rs) and precipitation (P). However, our study highlights a large difference from previous studies in a continuous increasing trend after 1998, which may be caused by the combined effects of the variations of Rs, Ta, and P on LE for different models on these time scales. This study provides corrected-modeling evidence for an accelerated global water cycle with climate change.
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| 3. |
- Yao, Yunjun, et al.
(författare)
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Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method
- 2017
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Ingår i: Journal of Hydrology. - : Elsevier BV. - 0022-1694. ; 553, s. 508-526
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of high-resolution terrestrial evapotranspiration (ET) from Landsat data is important in many climatic, hydrologic, and agricultural applications, as it can help bridging the gap between existing coarse-resolution ET products and point-based field measurements. However, there is large uncertainty among existing ET products from Landsat that limit their application. This study presents a simple Taylor skill fusion (STS) method that merges five Landsat-based ET products and directly measured ET from eddy covariance (EC) to improve the global estimation of terrestrial ET. The STS method uses a weighted average of the individual ET products and weights are determined by their Taylor skill scores (S). The validation with site-scale measurements at 206 EC flux towers showed large differences and uncertainties among the five ET products. The merged ET product exhibited the best performance with a decrease in the averaged root-mean-square error (RMSE) by 2–5 W/m2 when compared to the individual products. To evaluate the reliability of the STS method at the regional scale, the weights of the STS method for these five ET products were determined using EC ground-measurements. An example of regional ET mapping demonstrates that the STS-merged ET can effectively integrate the individual Landsat ET products. Our proposed method provides an improved high-resolution ET product for identifying agricultural crop water consumption and providing a diagnostic assessment for global land surface models.
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