Browsing by Author "Shan, Liang"

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    Assessment of drought tolerance of 49 switchgrass (Panicum virgatum) genotypes using physiological and morphological parameters
    Liu, Yiming; Zhang, Xunzhong; Tran, Hong T.; Shan, Liang; Kim, Jeongwoon; Childs, Kevin L.; Ervin, Erik H.; Frazier, Taylor P.; Zhao, Bingyu Y. (2015-09-22)
    Background Switchgrass (Panicum virgatum L.) is a warm-season C4 grass that is a target lignocellulosic biofuel species. In many regions, drought stress is one of the major limiting factors for switchgrass growth. The objective of this study was to evaluate the drought tolerance of 49 switchgrass genotypes. The relative drought stress tolerance was determined based on a set of parameters including plant height, leaf length, leaf width, leaf sheath length, leaf relative water content (RWC), electrolyte leakage (EL), photosynthetic rate (Pn), stomatal conductance (g s), transpiration rate (Tr), intercellular CO2 concentration (Ci), and water use efficiency (WUE). Results SRAP marker analysis determined that the selected 49 switchgrass genotypes represent a diverse genetic pool of switchgrass germplasm. Principal component analysis (PCA) and drought stress indexes (DSI) of each physiological parameter showed significant differences in the drought stress tolerance among the 49 genotypes. Heatmap and PCA data revealed that physiological parameters are more sensitive than morphological parameters in distinguishing the control and drought treatments. Metabolite profiling data found that under drought stress, the five best drought-tolerant genotypes tended to have higher levels of abscisic acid (ABA), spermine, trehalose, and fructose in comparison to the five most drought-sensitive genotypes. Conclusion Based on PCA ranking value, the genotypes TEM-SEC, TEM-LoDorm, BN-13645-64, Alamo, BN-10860-61, BN-12323-69, TEM-SLC, T-2086, T-2100, T-2101, Caddo, and Blackwell-1 had relatively higher ranking values, indicating that they are more tolerant to drought. In contrast, the genotypes Grif Nebraska 28, Grenville-2, Central Iowa Germplasm, Cave-in-Rock, Dacotah, and Nebraska 28 were found to be relatively sensitive to drought stress. By analyzing physiological response parameters and different metabolic profiles, the methods utilized in this study identified drought-tolerant and drought-sensitive switchgrass genotypes. These results provide a foundation for future research directed at understanding the molecular mechanisms underlying switchgrass tolerance to drought.
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    Joint Gaussian Graphical Model for multi-class and multi-level data
    Shan, Liang (Virginia Tech, 2016-07-01)
    Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables, which are called lower level variables. In this dissertation, we made several contributions to the area of joint estimation of Gaussian graphical models across heterogeneous classes: the first is to propose a joint estimation method for estimating Gaussian graphical models across unbalanced multi-classes, whereas the second considers multilevel variable information during the joint estimation procedure and simultaneously estimates higher level network and lower level network. For the first project, we consider the problem of jointly estimating Gaussian graphical models across unbalanced multi-class. Most existing methods require equal or similar sample size among classes. However, many real applications do not have similar sample sizes. Hence, in this dissertation, we propose the joint adaptive graphical lasso, a weighted L1 penalized approach, for unbalanced multi-class problems. Our joint adaptive graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. We also introduce regularization into the adaptive term so that the unbalancedness of data is taken into account. Simulation studies show that our approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. We demonstrate the advantage of our approach using liver cancer data set. For the second one, we propose a method to jointly estimate the multilevel Gaussian graphical models across multiple classes. Currently, methods are still limited to investigate a single level conditional dependency structure when there exists the multilevel structure among variables. Due to the fact that higher level variables may work together to accomplish certain tasks, simultaneously exploring conditional dependency structures among higher level variables and among lower level variables are of our main interest. Given multilevel data from heterogeneous classes, our method assures that common structures in terms of the multilevel conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing a higher level variable factor within a class, and then common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantage of our approach using breast cancer patient data.
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    Revenue Risk Management for P3 Highway Projects: Implementation of Revenue Guarantees in the U.S. Market
    Shan, Liang (Virginia Tech, 2010-06-02)
    The Public-Private Partnership (P3 or PPP) model has been proposed as an alternative delivery system to address funding shortage problems associated with large-scale projects. Appropriately allocating and managing risks among project participants is critically important for a P3 project's success. This thesis focuses on one of the tools to manage revenue risk, the revenue guarantee, where a guarantor compensates a concessionaire with a predetermined amount of revenue in the event of a revenue shortfall. It is a form of real option—specifically a put option if a premium is paid for the downside protection or a collar option if potential upside revenue is traded for the protection. Previous research has explored the purpose and valuation of revenue guarantee options. This study focuses on the feasibility of utilizing a guarantee in US P3 highway projects through preparatory study and field investigation. In the preparatory phase, the work examines existing revenue risk management methods and how revenue guarantee options supplement them while also proposing an implementation framework. Additionally, it discusses a new option type,a collar option, including its concept, benefits, applicability, and valuation. In the field investigation phase, the preparatory work is synthesized into interview protocols that are used to seek market perspectives on revenue risks and revenue guarantee feasibility. Twenty people representing government officials, concessionaires, financial advisors and lending institutions were interviewed. The interview results indicated that a revenue guarantee shows promise as a viable tool, and the government should be willing to provide one. The decision to utilize a revenue guarantee depends on funding method selection, a public agency's institutional capacity, and the effectiveness of alternative risk mitigation approaches. Suggestions for implementation, such as applicable projects and a guarantee triggering criterion, are also provided.