Browsing by Author "Gao, Feng"

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    Clustering Response-Stressor Relationships in Ecological Studies
    Gao, Feng (Virginia Tech, 2007-06-20)
    This research is motivated by an issue frequently encountered in water quality monitoring and ecological assessment. One concern for researchers and watershed resource managers is how the biological community in a watershed is affected by human activities. The conventional single model approach based on regression and logistic regression usually fails to adequately model the relationship between biological responses and environmental stressors since the study samples are collected over a large spatial region and the response-stressor relationships are usually weak in this situation. In this dissertation, we propose two alternative modeling approaches to partition the whole region of study into disjoint subregions and model the response-stressor relationships within subregions simultaneously. In our examples, these modeling approaches found stronger relationships within subregions and should help the resource managers improve impairment assessment and decision making. The first approach is an adjusted Bayesian classification and regression tree (ABCART). It is based on the Bayesian classification and regression tree approach (BCART) and is modified to accommodate spatial partitions in ecological studies. The second approach is a Voronoi diagram based partition approach. This approach uses the Voronoi diagram technique to randomly partition the whole region into subregions with predetermined minimum sample size. The optimal partition/cluster is selected by Monte Carlo simulation. We propose several model selection criteria for optimal partitioning and modeling according to the nature of the study and extend it to multivariate analysis to find the underlying structure of response-stressor relationships. We also propose a multivariate hotspot detection approach (MHDM) to find the region where the response-stressor relationship is the strongest according to an R-square-like criterion. Several sets of ecological data are studied in this dissertation to illustrate the implementation of the above partition modeling approaches. The findings from these studies are consistent with other studies.
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    Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
    Menden, Michael P.; Wang, Dennis; Mason, Mike J.; Szalai, Bence; Bulusu, Krishna C.; Guan, Yuanfang; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaslavskiy, Mikhail; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric K. Y.; Garnett, Mathew J.; Di Veroli, Giovanni Y.; Fawell, Stephen; Stolovitzky, Gustavo; Guinney, Justin; Dry, Jonathan R.; Saez-Rodriguez, Julio; Abante, Jordi; Abecassis, Barbara Schmitz; Aben, Nanne; Aghamirzaie, Delasa; Aittokallio, Tero; Akhtari, Farida S.; Al-lazikani, Bissan; Alam, Tanvir; Allam, Amin; Allen, Chad; de Almeida, Mariana Pelicano; Altarawy, Doaa; Alves, Vinicius; Amadoz, Alicia; Anchang, Benedict; Antolin, Albert A.; Ash, Jeremy R.; Romeo Aznar, Victoria; Ba-alawi, Wail; Bagheri, Moeen; Bajic, Vladimir; Ball, Gordon; Ballester, Pedro J.; Baptista, Delora; Bare, Christopher; Bateson, Mathilde; Bender, Andreas; Bertrand, Denis; Wijayawardena, Bhagya; Boroevich, Keith A.; Bosdriesz, Evert; Bougouffa, Salim; Bounova, Gergana; Brouwer, Thomas; Bryant, Barbara; Calaza, Manuel; Calderone, Alberto; Calza, Stefano; Capuzzi, Stephen; Carbonell-Caballero, Jose; Carlin, Daniel; Carter, Hannah; Castagnoli, Luisa; Celebi, Remzi; Cesareni, Gianni; Chang, Hyeokyoon; Chen, Guocai; Chen, Haoran; Chen, Huiyuan; Cheng, Lijun; Chernomoretz, Ariel; Chicco, Davide; Cho, Kwang-Hyun; Cho, Sunghwan; Choi, Daeseon; Choi, Jaejoon; Choi, Kwanghun; Choi, Minsoo; De Cock, Martine; Coker, Elizabeth; Cortes-Ciriano, Isidro; Cserzo, Miklos; Cubuk, Cankut; Curtis, Christina; Van Daele, Dries; Dang, Cuong C.; Dijkstra, Tjeerd; Dopazo, Joaquin; Draghici, Sorin; Drosou, Anastasios; Dumontier, Michel; Ehrhart, Friederike; Eid, Fatma-Elzahraa; ElHefnawi, Mahmoud; Elmarakeby, Haitham A.; van Engelen, Bo; Engin, Hatice Billur; de Esch, Iwan; Evelo, Chris; Falcao, Andre O.; Farag, Sherif; Fernandez-Lozano, Carlos; Fisch, Kathleen; Flobak, Asmund; Fornari, Chiara; Foroushani, Amir B. K.; Fotso, Donatien Chedom; Fourches, Denis; Friend, Stephen; Frigessi, Arnoldo; Gao, Feng; Gao, Xiaoting; Gerold, Jeffrey M.; Gestraud, Pierre; Ghosh, Samik; Gillberg, Jussi; Godoy-Lorite, Antonia; Godynyuk, Lizzy; Godzik, Adam; Goldenberg, Anna; Gomez-Cabrero, David; Gonen, Mehmet; de Graaf, Chris; Gray, Harry; Grechkin, Maxim; Guimera, Roger; Guney, Emre; Haibe-Kains, Benjamin; Han, Younghyun; Hase, Takeshi; He, Di; He, Liye; Heath, Lenwood S.; Hellton, Kristoffer H.; Helmer-Citterich, Manuela; Hidalgo, Marta R.; Hidru, Daniel; Hill, Steven M.; Hochreiter, Sepp; Hong, Seungpyo; Hovig, Eivind; Hsueh, Ya-Chih; Hu, Zhiyuan; Huang, Justin K.; Huang, R. Stephanie; Hunyady, Laszlo; Hwang, Jinseub; Hwang, Tae Hyun; Hwang, Woochang; Hwang, Yongdeuk; Isayev, Olexandr; Walk, Oliver Bear Don't; Jack, John; Jahandideh, Samad; Ji, Jiadong; Jo, Yousang; Kamola, Piotr J.; Kanev, Georgi K.; Karacosta, Loukia; Karimi, Mostafa; Kaski, Samuel; Kazanov, Marat; Khamis, Abdullah M.; Khan, Suleiman Ali; Kiani, Narsis A.; Kim, Allen; Kim, Jinhan; Kim, Juntae; Kim, Kiseong; Kim, Kyung; Kim, Sunkyu; Kim, Yongsoo; Kim, Yunseong; Kirk, Paul D. W.; Kitano, Hiroaki; Klambauer, Gunter; Knowles, David; Ko, Melissa; Kohn-Luque, Alvaro; Kooistra, Albert J.; Kuenemann, Melaine A.; Kuiper, Martin; Kurz, Christoph; Kwon, Mijin; van Laarhoven, Twan; Laegreid, Astrid; Lederer, Simone; Lee, Heewon; Lee, Jeon; Lee, Yun Woo; Leppaho, Eemeli; Lewis, Richard; Li, Jing; Li, Lang; Liley, James; Lim, Weng Khong; Lin, Chieh; Liu, Yiyi; Lopez, Yosvany; Low, Joshua; Lysenko, Artem; Machado, Daniel; Madhukar, Neel; De Maeyer, Dries; Malpartida, Ana Belen; Mamitsuka, Hiroshi; Marabita, Francesco; Marchal, Kathleen; Marttinen, Pekka; Mason, Daniel; Mazaheri, Alireza; Mehmood, Arfa; Mehreen, Ali; Michaut, Magali; Miller, Ryan A.; Mitsopoulos, Costas; Modos, Dezso; Van Moerbeke, Marijke; Moo, Keagan; Motsinger-Reif, Alison; Movva, Rajiv; Muraru, Sebastian; Muratov, Eugene; Mushthofa, Mushthofa; Nagarajan, Niranjan; Nakken, Sigve; Nath, Aritro; Neuvial, Pierre; Newton, Richard; Ning, Zheng; De Niz, Carlos; Oliva, Baldo; Olsen, Catharina; Palmeri, Antonio; Panesar, Bhawan; Papadopoulos, Stavros; Park, Jaesub; Park, Seonyeong; Park, Sungjoon; Pawitan, Yudi; Peluso, Daniele; Pendyala, Sriram; Peng, Jian; Perfetto, Livia; Pirro, Stefano; Plevritis, Sylvia; Politi, Regina; Poon, Hoifung; Porta, Eduard; Prellner, Isak; Preuer, Kristina; Angel Pujana, Miguel; Ramnarine, Ricardo; Reid, John E.; Reyal, Fabien; Richardson, Sylvia; Ricketts, Camir; Rieswijk, Linda; Rocha, Miguel; Rodriguez-Gonzalvez, Carmen; Roell, Kyle; Rotroff, Daniel; de Ruiter, Julian R.; Rukawa, Ploy; Sadacca, Benjamin; Safikhani, Zhaleh; Safitri, Fita; Sales-Pardo, Marta; Sauer, Sebastian; Schlichting, Moritz; Seoane, Jose A.; Serra, Jordi; Shang, Ming-Mei; Sharma, Alok; Sharma, Hari; Shen, Yang; Shiga, Motoki; Shin, Moonshik; Shkedy, Ziv; Shopsowitz, Kevin; Sinai, Sam; Skola, Dylan; Smirnov, Petr; Soerensen, Izel Fourie; Soerensen, Peter; Song, Je-Hoon; Song, Sang Ok; Soufan, Othman; Spitzmueller, Andreas; Steipe, Boris; Suphavilai, Chayaporn; Tamayo, Sergio Pulido; Tamborero, David; Tang, Jing; Tanoli, Zia-ur-Rehman; Tarres-Deulofeu, Marc; Tegner, Jesper; Thommesen, Liv; Tonekaboni, Seyed Ali Madani; Tran, Hong T.; De Troyer, Ewoud; Truong, Amy; Tsunoda, Tatsuhiko; Turu, Gabor; Tzeng, Guang-Yo; Verbeke, Lieven; Videla, Santiago; Vis, Daniel; Voronkov, Andrey; Votis, Konstantinos; Wang, Ashley; Wang, Hong-Qiang Horace; Wang, Po-Wei; Wang, Sheng; Wang, Wei; Wang, Xiaochen; Wang, Xin; Wennerberg, Krister; Wernisch, Lorenz; Wessels, Lodewyk; van Westen, Gerard J. P.; Westerman, Bart A.; White, Simon Richard; Willighagen, Egon; Wurdinger, Tom; Xie, Lei; Xie, Shuilian; Xu, Hua; Yadav, Bhagwan; Yau, Christopher; Yeerna, Huwate; Yin, Jia Wei; Yu, Michael; Yu, MinHwan; Yun, So Jeong; Zakharov, Alexey; Zamichos, Alexandros; Zanin, Massimiliano; Zeng, Li; Zenil, Hector; Zhang, Frederick; Zhang, Pengyue; Zhang, Wei; Zhao, Hongyu; Zhao, Lan; Zheng, Wenjin; Zoufir, Azedine; Zucknick, Manuela (Springer Nature, 2019-06-17)
    The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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    Corporate Social Performance and Managerial Labor Market
    Dai, Xin; Gao, Feng; Lisic, Ling; Zhang, Ivy (2021-10-13)
    This paper examines the impact of a firm’s social performance on the CEO’s employment prospects. We find that CEOs are more (less) likely to leave office when there is a significant recent decline (improvement) in social performance. We then track departing CEOs’ subsequent employment records and find that the social performance of their previous employers improves their labor market outcomes. These CEOs are more likely to find a new executive position, move up to a larger public firm, and receive higher compensation from the new public firm. Using a Cox proportional hazard model, we find that the strong social performance of the previous employer helps CEOs find their next executive positions sooner. Overall, our results suggest that corporate social performance enhances CEOs’ labor market potentials.
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    Current status of Landsat program, science, and applications
    Wulder, Michael A.; Loveland, Thomas R.; Roy, David P.; Crawford, Christopher J.; Masek, Jeffrey G.; Woodcock, Curtis E.; Allen, Richard G.; Anderson, Martha C.; Belward, Alan S.; Cohen, Warren B.; Dwyer, John; Erb, Angela; Gao, Feng; Griffiths, Patrick; Helder, Dennis; Hermosilla, Txomin; Hipple, James D.; Hostert, Patrick; Hughes, M. Joseph; Huntington, Justin; Johnson, David M.; Kennedy, Robert E.; Kilic, Ayse; Li, Zhan; Lymburner, Leo; McCorkel, Joel; Pahlevan, Nima; Scambos, Theodore A.; Schaaf, Crystal; Schott, John R.; Sheng, Yongwei; Storey, James; Vermote, Eric; Vogelmann, James E.; White, Joanne C.; Wynne, Randolph H.; Zhu, Zhe (Elsevier Inc., 2019-03-11)
    Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat-1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and follow-up with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop—justifying and encouraging current and future programmatic support for Landsat.
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    Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion
    Yang, Yun; Anderson, Martha C.; Gao, Feng; Hain, Christopher R.; Semmens, Kathryn A.; Kustas, William P.; Noormets, Asko; Wynne, Randolph H.; Thomas, Valerie A.; Sun, Ge (2017-02-17)
    As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and biological processes and is directly affected by water and land management, land use change and climate variability. Satellite remote sensing provides an effective means for diagnosing ET patterns over heterogeneous landscapes; however, limitations on the spatial and temporal resolution of satellite data, combined with the effects of cloud contamination, constrain the amount of detail that a single satellite can provide. In this study, we describe an application of a multi-sensor ET data fusion system over a mixed forested/agricultural landscape in North Carolina, USA, during the growing season of 2013. The fusion system ingests ET estimates from the Two-Source Energy Balance Model (TSEB) applied to thermal infrared remote sensing retrievals of land surface temperature from multiple satellite platforms: hourly geostationary satellite data at 4 km resolution, daily 1 km imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) and biweekly Landsat thermal data sharpened to 30 m. These multiple ET data streams are combined using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to estimate daily ET at 30m resolution to investigate seasonal water use behavior at the level of individual forest stands and land cover patches. A new method, also exploiting the STARFM algorithm, is used to fill gaps in the Land-sat ET retrievals due to cloud cover and/or the scan-line corrector (SLC) failure on Landsat 7. The retrieved daily ET time series agree well with observations at two AmeriFlux eddy covariance flux tower sites in a managed pine plantation within the modeling domain: US-NC2 located in a mid-rotation (20-year-old) loblolly pine stand and US-NC3 located in a recently clear-cut and replanted field site. Root mean square errors (RMSEs) for NC2 and NC3 were 0.99 and 1.02 mm day(-1), respectively, with mean absolute errors of approximately 29% at the daily time step, 12% at the monthly time step and 0.7% over the full study period at the two flux tower sites. Analyses of water use patterns over the plantation indicate increasing seasonal ET with stand age for young to mid-rotation stands up to 20 years, but little dependence on age for older stands. An accounting of consumptive water use by major land cover classes representative of the modeling domain is presented, as well as relative partitioning of ET between evaporation (E) and transpiration (T) components obtained with the TSEB. The study provides new insights about the effects of management and land use change on water yield over forested landscapes.
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    Investigating impacts of drought and disturbance on evapotranspiration over a forested landscape in North Carolina, USA using high spatiotemporal resolution remotely sensed data
    Yang, Yun; Anderson, Martha C.; Gao, Feng; Hain, Christopher R.; Noormets, Asko; Sun, Ge; Wynne, Randolph H.; Thomas, Valerie A.; Sun, Liang (2020-03-01)
    Forest ecosystem services such as clean water, wildlife habitat, and timber supplies are increasingly threatened by drought and disturbances (e.g., harvesting, fires and conversion to other uses), which can have great impacts on stand development and water balance. Improved understanding of the hydrologic response of forested systems to drought and disturbance at spatiotemporal resolutions commensurate with these impacts is important for effective forest management. Evapotranspiration (ET) is a key hydrologic variable in assessing forest functioning and health, but it remains a challenge to accurately quantify ET at landscape scales with the spatial and temporal detail required for effective decision-making. In this study, we apply a multi-sensor satellite data fusion approach to study the response of forest ET to drought and disturbance over a 7-year period. This approach combines Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) ET product time series retrieved using a surface energy balance model to generate a multi-year ET datacube at 30-m resolution and daily timesteps. The study area (similar to 900 km(2)) contains natural and managed forest as well as croplands in the humid lower coastal plains in North Carolina, USA, and the simulation period from 2006 to 2012 includes both normal and severe drought conditions. The model results were evaluated at two AmeriFlux sites (US-NC2 and US-NC1) dominated by a mature and a recently clearcut pine plantation, respectively, and showed good agreement with observed fluxes, with 813% relative errors at monthly timesteps. Changes in water use patterns in response to drought and disturbance as well as forest stand aging were assessed using the remotely sensed time series describing total evapotranspiration, the transpiration (T) component of ET, and a moisture stress metric given by the actual-to-reference ET ratio (f(RET)). Analyses demonstrate differential response to drought by land cover type and stand age, with larger impacts on total ET observed in young pine stands than in mature stands which have substantially deeper rooting systems. Transpiration flux shows a clear ascending trend with the growth of young pine plantations, while stand thinning within the plantation leads to decreases in both remotely sensed leaf area index and T, as expected. Time series maps of f(RET) anomalies at 30-m resolution capture signals of drought, disturbance and the subsequent recovery after clearcut at the stand scale and may be an effective indicator for water use change detection and monitoring in forested landscapes.
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    Tetrahydrocurcumin protects against nonalcoholic fatty liver disease by improving lipid metabolism and redox homeostasis
    Gao, Feng; Chen, Manyu; Yu, Jianfeng; Xu, Lu; Yu, Lisha; Jiang, Honglin; Gu, Zhiliang (Elsevier, 2022-02)
    Nonalcoholic fatty liver disease (NAFLD) is the most common liver metabolic disease in the world. In this study we investigated the effect of tetrahydrocurcumin (THC), a potent antioxidant, on NAFLD. We found that THC significantly reduced the body weight, serum and liver lipids, serum malondialdehyde (MDA) and fasting blood glucose (FBG) of male NAFLD mice induced by feeding a high-fat diet (HFD) for 8 weeks. A RNA-seq analysis revealed that THC also affected the expression of many genes involved in lipid metabolism in the liver of NAFLD mice, including increasing the mRNA expression of CYP51 and FOXQ1. Moreover, THC significantly reduced sodium oleate-induced lipid accumulation, activated the NRF2 pathway, and up-regulated the mRNA expression of FGF21 in HepG2 cells. Overall, our study showed that THC can improve the antioxidant capacity of liver, which suggests that dietary THC may be used to treat NAFLD.