Matching Items (159)
Description
The growth rate hypothesis (GRH) proposes that higher growth rate (the rate of change in biomass per unit biomass, μ) is associated with higher P concentration and lower C∶P and N∶P ratios. However, the applicability of the GRH to vascular plants is not well-studied and few studies have been done on belowground biomass. Here we showed that, for aboveground, belowground and total biomass of three study species, μ was positively correlated with N∶C under N limitation and positively correlated with P∶C under P limitation. However, the N∶P ratio was a unimodal function of μ, increasing for small values of μ, reaching a maximum, and then decreasing. The range of variations in μ was positively correlated with variation in C∶N∶P stoichiometry. Furthermore, μ and C∶N∶P ranges for aboveground biomass were negatively correlated with those for belowground. Our results confirm the well-known association of growth rate with tissue concentration of the limiting nutrient and provide empirical support for recent theoretical formulations.
ContributorsYu, Qiang (Author) / Wu, Honghui (Author) / He, Nianpeng (Author) / Lu, Xiaotao (Author) / Wang, Zhiping (Author) / Elser, James (Author) / Wu, Jianguo (Author) / Han, Xingguo (Author) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor)
Created2012-03-13
Grasshoppers Regulate N: P Stoichiometric Homeostasis by Changing Phosphorus Contents in Their Frass
Description
Nitrogen (N) and phosphorus (P) are important limiting nutrients for plant production and consumer performance in a variety of ecosystems. As a result, the N:P stoichiometry of herbivores has received increased attention in ecology. However, the mechanisms by which herbivores maintain N:P stoichiometric homeostasis are poorly understood. Here, using a field manipulation experiment we show that the grasshopper Oedaleus asiaticus maintains strong N:P stoichiometric homeostasis regardless of whether grasshoppers were reared at low or high density. Grasshoppers maintained homeostasis by increasing P excretion when eating plants with higher P contents. However, while grasshoppers also maintained constant body N contents, we found no changes in N excretion in response to changing plant N content over the range measured. These results suggest that O. asiaticus maintains P homeostasis primarily by changing P absorption and excretion rates, but that other mechanisms may be more important for regulating N homeostasis. Our findings improve our understanding of consumer-driven P recycling and may help in understanding the factors affecting plant-herbivore interactions and ecosystem processes in grasslands.
ContributorsZhang, Zijia (Author) / Elser, James (Author) / Cease, Arianne (Author) / Zhang, Ximei (Author) / Yu, Qiang (Author) / Han, Xingguo (Author) / Zhang, Guangming (Author) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor)
Created2014-08-04
Description
A large fraction of the world grasslands and savannas are undergoing a rapid shift from herbaceous to woody-plant dominance. This land-cover change is expected to lead to a loss in livestock production (LP), but the impacts of woody-plant encroachment on this crucial ecosystem service have not been assessed. We evaluate how tree cover (TC) has affected LP at large spatial scales in rangelands of contrasting social–economic characteristics in the United States and Argentina. Our models indicate that in areas of high productivity, a 1% increase in TC results in a reduction in LP ranging from 0.6 to 1.6 reproductive cows (Rc) per km[superscript 2]. Mean LP in the United States is 27 Rc per km[superscript 2], so a 1% increase in TC results in a 2.5% decrease in mean LP. This effect is large considering that woody-plant cover has been described as increasing at 0.5% to 2% per y. On the contrary, in areas of low productivity, increased TC had a positive effect on LP. Our results also show that ecological factors account for a larger fraction of LP variability in Argentinean than in US rangelands. Differences in the relative importance of ecological versus nonecological drivers of LP in Argentina and the United States suggest that the valuation of ecosystem services between these two rangelands might be different. Current management strategies in Argentina are likely designed to maximize LP for various reasons we are unable to explore in this effort, whereas land managers in the United States may be optimizing multiple ecosystem services, including conservation or recreation, alongside LP.
ContributorsAnadon, Jose Daniel (Author) / Sala, Osvaldo (Author) / Turner II, B. L. (Author) / Bennett, Elena M. (Author) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2014-09-02
Description
We studied the microbial community structure of pilot two-stage membrane biofilm reactors (MBfRs) designed to reduce nitrate (NO[subscript 3]–) and perchlorate (ClO[subscript 4]–) in contaminated groundwater. The groundwater also contained oxygen (O[subscript 2]) and sulfate (SO[2 over 4]–), which became important electron sinks that affected the NO[subscript 3]– and ClO[subscript 4]– removal rates. Using pyrosequencing, we elucidated how important phylotypes of each “primary” microbial group, i.e., denitrifying bacteria (DB), perchlorate-reducing bacteria (PRB), and sulfate-reducing bacteria (SRB), responded to changes in electron-acceptor loading. UniFrac, principal coordinate analysis (PCoA), and diversity analyses documented that the microbial community of biofilms sampled when the MBfRs had a high acceptor loading were phylogenetically distant from and less diverse than the microbial community of biofilm samples with lower acceptor loadings. Diminished acceptor loading led to SO[2 over 4]– reduction in the lag MBfR, which allowed Desulfovibrionales (an SRB) and Thiothrichales (sulfur-oxidizers) to thrive through S cycling. As a result of this cooperative relationship, they competed effectively with DB/PRB phylotypes such as Xanthomonadales and Rhodobacterales. Thus, pyrosequencing illustrated that while DB, PRB, and SRB responded predictably to changes in acceptor loading, a decrease in total acceptor loading led to important shifts within the “primary” groups, the onset of other members (e.g., Thiothrichales), and overall greater diversity.
ContributorsOntiveros-Valencia, Aura (Author) / Tang, Youneng (Author) / Zhao, Heping (Author) / Friese, David (Author) / Overstreet, Ryan (Author) / Smith, Jennifer (Author) / Evans, Patrick (Author) / Rittmann, Bruce (Author) / Krajmalnik-Brown, Rosa (Author) / Biodesign Institute (Contributor) / Swette Center for Environmental Biotechnology (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor)
Created2014-07-01
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The City of Phoenix Street Transportation Department partnered with the Rob and Melani Walton Sustainability Solutions Service at Arizona State University (ASU) and researchers from various ASU schools to evaluate the effectiveness, performance, and community perception of the new pavement coating. The data collection and analysis occurred across multiple neighborhoods and at varying times across days and/or months over the course of one year (July 15, 2020–July 14, 2021), allowing the team to study the impacts of the surface treatment under various weather conditions.
ContributorsMiddel, Ariane (Author) / Vanos, Jennifer K. (Author) / Hondula, David M. (Author) / Kaloush, Kamil (Author) / Sailor, David (Author) / Medina, Jose R. (Jose Roberto) (Author) / Schneider, Florian (Author) / Ortiz, Johny Cordova (Author) / Campbell, Bill (Author) / Epel, Erin (Contributor) / Rice, Brendan (Contributor) / Garcia, Ruth (Contributor) / Phoenix (Ariz.). Street Transportation Department (Contributor) / City of Phoenix (Funder) / Industrial Development Authority of the County of Maricopa (Funder) / Julie Ann Wrigley Global Institute of Sustainability (Funder) / Arizona State University. Healthy Urban Environments Program (Contributor)
Created2021-09
Description
This dissertation constructs a new computational processing framework to robustly and precisely quantify retinotopic maps based on their angle distortion properties. More generally, this framework solves the problem of how to robustly and precisely quantify (angle) distortions of noisy or incomplete (boundary enclosed) 2-dimensional surface to surface mappings. This framework builds upon the Beltrami Coefficient (BC) description of quasiconformal mappings that directly quantifies local mapping (circles to ellipses) distortions between diffeomorphisms of boundary enclosed plane domains homeomorphic to the unit disk. A new map called the Beltrami Coefficient Map (BCM) was constructed to describe distortions in retinotopic maps. The BCM can be used to fully reconstruct the original target surface (retinal visual field) of retinotopic maps. This dissertation also compared retinotopic maps in the visual processing cascade, which is a series of connected retinotopic maps responsible for visual data processing of physical images captured by the eyes. By comparing the BCM results from a large Human Connectome project (HCP) retinotopic dataset (N=181), a new computational quasiconformal mapping description of the transformed retinal image as it passes through the cascade is proposed, which is not present in any current literature. The description applied on HCP data provided direct visible and quantifiable geometric properties of the cascade in a way that has not been observed before. Because retinotopic maps are generated from in vivo noisy functional magnetic resonance imaging (fMRI), quantifying them comes with a certain degree of uncertainty. To quantify the uncertainties in the quantification results, it is necessary to generate statistical models of retinotopic maps from their BCMs and raw fMRI signals. Considering that estimating retinotopic maps from real noisy fMRI time series data using the population receptive field (pRF) model is a time consuming process, a convolutional neural network (CNN) was constructed and trained to predict pRF model parameters from real noisy fMRI data
ContributorsTa, Duyan Nguyen (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Hansford, Dianne (Committee member) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022