Browsing by Author "Chang, Qing"

Now showing 1 - 8 of 8
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    Assessing variability of optimum air temperature for photosynthesis across site-years, sites and biomes and their effects on photosynthesis estimation
    (Elsevier, 2021) Chang, Qing; Xiao, Xiangming; Doughty, Russell; Wu, Xiaocui; Jiao, Wenzhe; Qin, Yuanwei; Earth and Environmental Sciences, School of Science
    Gross primary productivity (GPP) of vegetation is affected by air temperature. Biogeochemical models use the optimum air temperature (Topt) parameter, which comes from biome-specific look-up tables (Topt−b−LT). Many studies have shown that plants have the capacity to adapt to changes in environmental conditions over time, which suggests that the static Topt−b−LT parameters in the biogeochemical models may poorly represent actual Topt and induce uncertainty in GPP estimates. Here, we estimated biome-specific, site-year-specific, and site-specific optimum air temperature using GPP data from eddy covariance (EC) flux tower sites (GPPEC) (Topt−b−EC, Topt−sy−EC, Topt−s−EC), the Enhanced Vegetation Index (EVI) from MODIS images (Topt−b−EVI, Topt−sy−EVI, Topt−s−EVI), and mean daytime air temperature (TDT). We evaluated the consistency among the four Topt parameters (Topt−b, Topt−sy, Topt−s and Topt−b−LT), and assessed how they affect satellite-based GPP estimates. We find that Topt parameters from MODIS EVI agree well with those from GPPEC, which indicates that EVI can be used as a variable to estimate Topt at individual pixels over large spatial domains. Topt−b, Topt−sy, and Topt−s differed significantly from Topt−b−LT. GPP estimates using Topt−b and Topt−sy were more consistent with GPPEC than when using Topt−b−LT for all the land cover types. Our use of Topt−sy substantially improved 8-day and annual GPP estimates across biomess (from 1% to 34%), especially for cropland, grassland, and open shrubland. Our simple calculation shows that global GPP estimates differ by up to 10 Pg C/yr when using our suggested Topt−sy−EVI instead of using the static Topt−b−LT. Our new approach on estimating Topt has the potential to improve estimates of GPP from satellite-based models, which could lead to better understanding of carbon-climate interactions.
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    Comprehensive quantification of the responses of ecosystem production and respiration to drought time scale, intensity and timing in humid environments: A FLUXNET synthesis
    (Wiley, 2022-05) Jiao, Wenzhe; Wang, Lixin; Wang, Honglang; Lanning, Matthew; Chang, Qing; Novick, Kimberly A.; Earth Sciences, School of Science
    Drought is one of the most important natural hazards impacting ecosystem carbon cycles. However, it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. First, drought impacts can have different time dimensions such as simultaneous, cumulative, and lagged impacts on ecosystem carbon balance. Second, drought is not only a multiscale (e.g., temporal and spatial) but also a multidimensional (e.g., intensity, time-scale, and timing) phenomenon, and ecosystem production and respiration may respond to each drought dimension differently. In this study, we conducted a comprehensive drought impact assessment on ecosystem productivity and respiration in humid regions by including different drought dimensions using global FLUXNET observations. Short-term drought (e.g., 1-month drought) generally did not induce a decrease in plant productivity even under high severity drought. However, ecosystem production and respiration significantly decreased as drought intensity increased for droughts longer than one month in duration. Drought timing was important, and ecosystem productivity was most vulnerable when drought occurred during or shortly after the peak vegetation growth. We found that lagged drought impacts more significantly affected ecosystem carbon uptake than simultaneous drought, and that ecosystem respiration was less sensitive to drought time scale than ecosystem production. Overall, our results indicated that temporally-standardized meteorological drought indices can be used to reflect plant productivity decline, but drought timing, antecedent, and cumulative drought conditions need to be considered together.
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    An In Vivo Screen Identifies PYGO2 as a Driver for Metastatic Prostate Cancer
    (American Association for Cancer Research, 2018-07-15) Lu, Xin; Pan, Xiaolu; Wu, Chang-Jiun; Zhao, Di; Feng, Shan; Zang, Yong; Lee, Rumi; Khadka, Sunada; Amin, Samirkumar B.; Jin, Eun-Jung; Shang, Xiaoying; Deng, Pingna; Luo, Yanting; Morgenlander, William R.; Weinrich, Jacqueline; Lu, Xuemin; Jiang, Shan; Chang, Qing; Navone, Nora M.; Troncoso, Patricia; DePinho, Ronald A.; Wang, Y. Alan; Biostatistics, IU School of Medicine
    Advanced prostate cancer displays conspicuous chromosomal instability and rampant copy number aberrations, yet the identity of functional drivers resident in many amplicons remain elusive. Here, we implemented a functional genomics approach to identify new oncogenes involved in prostate cancer progression. Through integrated analyses of focal amplicons in large prostate cancer genomic and transcriptomic datasets as well as genes upregulated in metastasis, 276 putative oncogenes were enlisted into an in vivo gain-of-function tumorigenesis screen. Among the top positive hits, we conducted an in-depth functional analysis on Pygopus family PHD finger 2 (PYGO2), located in the amplicon at 1q21.3. PYGO2 overexpression enhances primary tumor growth and local invasion to draining lymph nodes. Conversely, PYGO2 depletion inhibits prostate cancer cell invasion in vitro and progression of primary tumor and metastasis in vivo In clinical samples, PYGO2 upregulation associated with higher Gleason score and metastasis to lymph nodes and bone. Silencing PYGO2 expression in patient-derived xenograft models impairs tumor progression. Finally, PYGO2 is necessary to enhance the transcriptional activation in response to ligand-induced Wnt/β-catenin signaling. Together, our results indicate that PYGO2 functions as a driver oncogene in the 1q21.3 amplicon and may serve as a potential prognostic biomarker and therapeutic target for metastatic prostate cancer.Significance: Amplification/overexpression of PYGO2 may serve as a biomarker for prostate cancer progression and metastasis. Cancer Res; 78(14); 3823-33. ©2018 AACR.
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    A new multi-sensor integrated index for drought monitoring
    (Elsevier, 2019-04) Jiao, Wenzhe; Wang, Lixin; Chang, Qing; Novick, Kimberly A.; Tian, Chao; Earth Sciences, School of Science
    Drought is one of the most expensive but least understood natural disasters. Remote sensing based integrated drought indices have the potential to describe drought conditions comprehensively, and multi-criteria combination analysis is increasingly used to support drought assessment. However, conventional multi-criteria combination methods and most existing integrated drought indices fail to adequately represent spatial variability. An index that can be widely used for drought monitoring across all climate regions would be of great value for ecosystem management. To this end, we proposed a framework for generating a new integrated drought index applicable across diverse climate regions. In this new framework, a local ordered weighted averaging (OWA) model was used to combine the Temperature Condition Index (TCI) from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Vegetation Condition Index (VCI) developed using the Vegetation Index based on Universal Pattern Decomposition method (VIUPD), the Soil Moisture Condition Index (SMCI) derived from the Advanced Microwave Scanning Radiometer–Earth Observation System (AMSR-E), and the Precipitation Condition Index (PCI) derived from the Tropical Rainfall Measuring Mission (TRMM). This new index, which we call the “Geographically Independent Integrated Drought Index (GIIDI),” was validated in diverse climate divisions across the continental United States. Results showed that GIIDI was better correlated with in-situ PDSI, Z-index, SPI-1, SPI-3 and SPEI-6 (overall r-value = 0.701, 0.794, 0.811, 0.733, 0.628; RMSE = 1.979, 0.810, 0.729, 1.049 and 1.071, respectively) when compared to the Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Scaled Drought Condition Index (SDCI), PCI, TCI, SMCI, and VCI. GIIDI also performed well in most climate divisions for both short-term and long-term drought monitoring. Because of the superior performance of GIIDI across diverse temporal and spatial scales, GIIDI has considerable potential for improving our ability to monitor drought across a range of biomes and climates.
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    A new station-enabled multi-sensor integrated index for drought monitoring
    (Elsevier, 2019-07) Jiao, Wenzhe; Wang, Lixin; Novick, Kimberly A.; Chang, Qing; Earth Sciences, School of Science
    Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available.
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    Observed increasing water constraint on vegetation growth over the last three decades
    (Springer Nature, 2021-06-18) Jiao, Wenzhe; Wang, Lixin; Smith, William K.; Chang, Qing; Wang, Honglang; D’Odorico, Paolo; Earth and Environmental Sciences, School of Science
    Despite the growing interest in predicting global and regional trends in vegetation productivity in response to a changing climate, changes in water constraint on vegetation productivity (i.e., water limitations on vegetation growth) remain poorly understood. Here we conduct a comprehensive evaluation of changes in water constraint on vegetation growth in the extratropical Northern Hemisphere between 1982 and 2015. We document a significant increase in vegetation water constraint over this period. Remarkably divergent trends were found with vegetation water deficit areas significantly expanding, and water surplus areas significantly shrinking. The increase in water constraints associated with water deficit was also consistent with a decreasing response time to water scarcity, suggesting a stronger susceptibility of vegetation to drought. We also observed shortened water surplus period for water surplus areas, suggesting a shortened exposure to water surplus associated with humid conditions. These observed changes were found to be attributable to trends in temperature, solar radiation, precipitation, and atmospheric CO2. Our findings highlight the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate.
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    Opposing roles of TGFβ and BMP signaling in prostate cancer development
    (Cold Spring Harbor Laboratory Press, 2017-12-01) Lu, Xin; Jin, Eun-Jung; Cheng, Xi; Feng, Shan; Shang, Xiaoying; Deng, Pingna; Jiang, Shan; Chang, Qing; Rahmy, Sharif; Chaudhary, Seema; Lu, Xuemin; Zhao, Ren; Wang, Y. Alan; DePinho, Ronald A.; Medicine, School of Medicine
    SMAD4 constrains progression of Pten-null prostate cancer and serves as a common downstream node of transforming growth factor β (TGFβ) and bone morphogenetic protein (BMP) pathways. Here, we dissected the roles of TGFβ receptor II (TGFBR2) and BMP receptor II (BMPR2) using a Pten-null prostate cancer model. These studies demonstrated that the molecular actions of TGFBR2 result in both SMAD4-dependent constraint of proliferation and SMAD4-independent activation of apoptosis. In contrast, BMPR2 deletion extended survival relative to Pten deletion alone, establishing its promoting role in BMP6-driven prostate cancer progression. These analyses reveal the complexity of TGFβ-BMP signaling and illuminate potential therapeutic targets for prostate cancer.
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    The sensitivity of satellite solar‐induced chlorophyll fluorescence (SIF) to meteorological drought
    (Wiley, 2019) Jiao, Wenzhe; Chang, Qing; Wang, Lixin; Earth Sciences, School of Science
    Solar‐induced chlorophyll fluorescence (SIF) could provide information on plant physiological response to water stress (e.g., drought). There are growing interests to study the effect of drought on SIF. However, to what extent SIF responds to drought and how the responses vary under different precipitation, temperature and potential evapotranspiration conditions are not clear. In this regard, we evaluated the relationship between satellite‐based SIF product and four commonly used meteorological drought indices (Standardized Precipitation‐Evapotranspiration Index, SPEI; Standardized Precipitation Index, SPI; Temperature Condition Index, TCI; and Palmer Drought Severity Index, PDSI) under diverse climate regions in the continental United States. The four drought indices were used because they estimate meteorological drought conditions from either single or combined meteorological factors such as precipitation, temperature, and potential evapotranspiration, representing different perspectives of drought. The relationship between SIF and meteorological drought varied spatially and differed for different ecosystem types. The high sensitivity occurred in dry areas characterized by a high mean annual growing season temperature and low vegetation productivity. Through random forest regression analyses, we found that temperature, gross primary production, precipitation, and land cover are the major factors affecting the relationships between SIF and meteorological drought indices. Taken together, satellite SIF is highly sensitive to meteorological drought but the high sensitivity is constrained to dry regions.