Browsing by Author "Li, Jie"

Now showing 1 - 8 of 8
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    Assessing predictive performance and transferability of species distribution models for freshwater fish in the United States
    Huang, Jian (Virginia Tech, 2015-05-27)
    Rigorous modeling of the spatial species distributions is critical in biogeography, conservation, resource management, and assessment of climate change. The goal of chapter 2 of this dissertation was to evaluate the potential of using historical samples to develop high-resolution species distribution models (SDMs) of stream fishes of the United States. I explored the spatial transferability and temporal transferability of stream–fish distribution models in chapter 3 and chapter 4 respectively. Chapter 2 showed that the discrimination power of SDMs for 76 non-game fish species depended on data quality, species' rarity, statistical modeling technique, and incorporation of spatial autocorrelation. The area under the Receiver-Operating-Characteristic curve (AUC) in the cross validation tended to be higher in the logistic regression and boosted regression trees (BRT) than the presence-only MaxEnt models. AUC in the cross validation was also higher for species with large geographic ranges and small local populations. Species prevalence affected discrimination power in the model training but not in the validation. In chapter 3, spatial transferability of SDMs was low for over 70% of the 21 species examined. Only 24% of logistic regression, 12% of BRT, and 16% of MaxEnt had AUC > 0.6 in the spatial transfers. Friedman's rank sum test showed that there was no significant difference in the performance of the three modeling techniques. Spatial transferability could be improved by using spatial logistic regression under Lasso regularization in the training of SDMs and by matching the range and location of predictor variables between training and transfer regions. In chapter 4, testing of temporal SDM transfer on independent samples resulted in discrimination power of the moderate to good range, with AUC > 0.6 for 80% of species in all three types of models. Most cool water species had good temporal transferability. However, biases and misspecified spread occurred frequently in the temporal model transfers. To reduce under- or over-estimation bias, I suggest rescaling the predicted probability of species presence to ordinal ranks. To mitigate inappropriate spread of predictions in the climate change scenarios, I recommended to use large training datasets with good coverage of environmental gradients, and fine-tune predictor variables with regularization and cross validation.
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    Assessing Urban Landscape Variables’ Contributions to Microclimates
    Parece, Tammy E.; Li, Jie; Campbell, James B. Jr.; Carroll, David F. (Hindawi, 2015-12-24)
    The well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurban microclimate variability presents an opportunity to assess spatial dimensions of urban environments and identify locations that heat or cool faster than other locales. Our study employs mobile weather units and fixed weather stations to collect air temperatures across Roanoke, Virginia, USA, on selected dates over a two-year interval. Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. Our results estimated temperatures with small mean square errors (ranging from 0.03 to 0.14); landscape metrics explained between 60 and 91% of temperature variations (higher when the previous day’s average temperatures were included as a variable). For all days, similar spatial patterns appeared for cooler and warmer areas in mornings, with distinctive patterns as landscapes warmed during the day and over successive days. Our results revealed that the most potent landscape variables vary according to season and time of day. Our analysis contributes new dimensions and new levels of spatial and temporal detail to urban microclimate research.
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    Comment on "A numerical study of periodic disturbances on two-layer Couette flow" [Phys. Fluids 10, 3056 (1998)]
    Renardy, Yuriko Y.; Li, Jie (AIP Publishing, 1999-10)
    The flow of fluids with different viscosities, subjected to an interfacial perturbation, can lead to fingering and migration...
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    Extraembryonic Endoderm (XEN) Cells Capable of Contributing to Embryonic Chimeras Established from Pig Embryos
    Park, Chi-Hun; Jeoung, Young-Hee; Uh, Kyungjun; Park, Ki-Eun; Bridge, Jessica; Powell, Anne M.; Li, Jie; Pence, Laramie; Zhang, Luhui; Liu, Tianbin; Sun, Hai-Xi; Gu, Ying; Shen, Yue; Wu, Jun; Belmonte, Juan-Carlos Izpisua; Telugu, Bhanu P. (2021-01-12)
    Most of our current knowledge regarding early lineage specification and embryo-derived stem cells comes from studies in rodent models. However, key gaps remain in our understanding of these developmental processes from nonrodent species. Here, we report the detailed characterization of pig extraembryonic endoderm (pXEN) cells, which can be reliably and reproducibly generated from primitive endoderm (PrE) of blastocyst. Highly expandable pXEN cells express canonical PrE markers and transcriptionally resemble rodent XENs. The pXEN cells contribute both to extraembryonic tissues including visceral yolk sac as well as embryonic gut when injected into host blastocysts, and generate live offspring when used as a nuclear donor in somatic cell nuclear transfer (SCNT). The pXEN cell lines provide a novel model for studying lineage segregation, as well as a source for genome editing in livestock.
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    Impression of DC: Research for Basic Element of Architecture in Three Dimension
    Li, Jie (Virginia Tech, 2021-04-14)
    For finishing this thesis, I want to rethink the meaning of architecture after studying architecture in these years. I reviewed buildings in the world. In the process of reviewing, I analyzed these buildings by the method which called finite element method. The finite element method is usually used for analyzing the complex system of structure. In this system, The line is the basic element for a one dimension. The plane is the basic element for a two dimension. By following this logic, the cube is the basic element for the three dimension. After some researches for cube in architecture and geometry, I found there were a lot of examples for using cube to design. And for cube itself, there were many properties. Then, I decided to use cube to design the building in my thesis. And in the process for designing, I understood the meaning of architecture and design better than before. For finishing this design, I related Washington, D.C. with my design. So I designed a multi-function museum for this city to help people to understand how this city was built in the history. As a landmark building, this building, "Impressions of DC," is a result of this thesis study and exploration of the cube.
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    Modeling the Impact of Projected Land Cover on Lyme Disease Emergence
    Surendrababu, Jayashree (Virginia Tech, 2014-06-06)
    Lyme disease is a common tick borne disease in the US. Lyme disease emerged from the Northeast and in the past decade, Virginia has been witnessing a rapidly increasing trend in incidence. This thesis uses land cover projection data as a basis to look at the potential future trend of Lyme disease incidence in Virginia for the IPCC (Intergovernmental Panel on Climate change) scenarios of A1B and A2, which indicate a global and regional focus respectively. This study is a continuation of previous work done by an NSF funded research team at Virginia Tech, in exploring the variables affecting Lyme disease in Virginia. A Poisson point process is implemented in this thesis with land cover parameters (implemented land, water bodies, and edge metrics) and demographic parameters (population percentage and per capita income) as the spatial covariates. Lyme disease incidence data obtained from the Virginia Department of Health was used for model validation. The overall model was implemented using Python and its associated libraries while ArcGIS software was used for preliminary covariate analysis and data visualization. This thesis generates risk maps for A1B and A2 scenarios for each decade from 2010 through 2060. Spatial occurrence of disease incidence has been generated by the Poisson point process and the risk level of each county in Virginia has been calculated based on the incidence count predicted for it. Population and area at risk under each scenario for each decade was calculated. Results show that in A1B scenario 22.1% and 42.9% of the total population of Virginia are under high risk and in the A2 scenario, 21% and 33% of the total population of Virginia are under high risk of Lyme disease in 2010 and 2060 respectively. In terms of the area, A1B scenario has 28% under high risk in 2010 and 66% of the total area under high risk in 2060, while A2 scenarGIS, Lyme disease, Land cover projections, IPCC scenariosio has 22.4% under high risk of Lyme disease in 2010 62.7% of the total area in Virginia is under high risk in 2060.
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    A numerical study of periodic disturbances on two-layer Couette flaw
    Li, Jie; Renardy, Yuriko Y.; Renardy, Michael J. (AIP Publishing, 1998-12)
    The flow of two viscous liquids is investigated numerically with a volume of fluid scheme. The scheme incorporates a semi-implicit Stokes solver to enable computations at low Reynolds numbers, and a second-order velocity interpolation. The code is validated against linear theory for the stability of two-layer Couette flow, and weakly nonlinear theory for a Hopf bifurcation. Examples of long-time wave saturation are shown. The formation of fingers for relatively small initial amplitudes as well as larger amplitudes are presented in two and three dimensions as initial-value problems. Fluids of different viscosity and density are considered, with an emphasis on the effect of the viscosity difference. Results at low Reynolds numbers show elongated fingers in two dimensions that break in three dimensions to form drops, while different topological changes take place at higher Reynolds numbers. (C) 1998 American Institute of Physics. [S1070-6631(98)00612-6].
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    Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data
    Duan, Yuanyuan (Virginia Tech, 2014-03-04)
    This dissertation aims to develop methods for statistical predictions based on various types of data from different areas. We focus on applications from reliability and spatial epidemiology. Chapter 1 gives a general introduction of statistical predictions. Chapters 2 and 3 investigate the photodegradation of an organic coating, which is mainly caused by ultraviolet (UV) radiation but also affected by environmental factors, including temperature and humidity. In Chapter 2, we identify a physically motivated nonlinear mixed-effects model, including the effects of environmental variables, to describe the degradation path. Unit-to-unit variabilities are modeled as random effects. The maximum likelihood approach is used to estimate parameters based on the accelerated test data from laboratory. The developed model is then extended to allow for time-varying covariates and is used to predict outdoor degradation where the explanatory variables are time-varying. Chapter 3 introduces a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with random effects to describe the degradation paths and a vector time series model to describe the covariate process. Shape restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters of these models are estimated by using the maximum likelihood method. Algorithms for computing the estimated lifetime distribution are also described. The proposed methods are applied to predict the photodegradation path of an organic coating in a complicated dynamic environment. Chapter 4 investigates the Lyme disease emergency in Virginia at census tract level. Based on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011, we analyze the spatial patterns of the disease using statistical smoothing techniques. We also use the space and space-time scan statistics to reveal the presence of clusters in the spatial and spatial/temporal distribution of Lyme disease. Chapter 5 builds a predictive model for Lyme disease based on historical data and environmental/demographical information of each census tract. We propose a Divide-Recombine method to take advantage of parallel computing. We compare prediction results through simulation studies, which show our method can provide comparable fitting and predicting accuracy but can achieve much more computational efficiency. We also apply the proposed method to analyze Virginia Lyme disease spatio-temporal data. Our method makes large-scale spatio-temporal predictions possible. Chapter 6 gives a general review on the contributions of this dissertation, and discusses directions for future research.