Matching Items (127)
Description
Though it is a widespread adaptation in humans and many other animals, parental care comes in a variety of forms and its subtle physiological costs, benefits, and tradeoffs related to offspring are often unknown. Thus, I studied the hydric, respiratory, thermal, and fitness dynamics of maternal egg-brooding behavior in Children's pythons (Antaresia childreni). I demonstrated that tight coiling detrimentally creates a hypoxic developmental environment that is alleviated by periodic postural adjustments. Alternatively, maternal postural adjustments detrimentally elevate rates of egg water loss relative to tight coiling. Despite ventilating postural adjustments, the developmental environment becomes increasingly hypoxic near the end of incubation, which reduces embryonic metabolism. I further demonstrated that brooding-induced hypoxia detrimentally affects offspring size, performance, locomotion, and behavior. Thus, parental care in A. childreni comes at a cost to offspring due to intra-offspring tradeoffs (i.e., those that reflect competing offspring needs, such as water balance and respiration). Next, I showed that, despite being unable to intrinsically produce body heat, A. childreni adjust egg-brooding behavior in response to shifts in nest temperature, which enhances egg temperature (e.g., reduced tight coiling during nest warming facilitated beneficial heat transfer to eggs). Last, I demonstrated that A. childreni adaptively adjust their egg-brooding behaviors due to an interaction between nest temperature and humidity. Specifically, females' behavioral response to nest warming was eliminated during low nest humidity. In combination with other studies, these results show that female pythons sense environmental temperature and humidity and utilize this information at multiple time points (i.e., during gravidity [egg bearing], at oviposition [egg laying], and during egg brooding) to enhance the developmental environment of their offspring. This research demonstrates that maternal behaviors that are simple and subtle, yet easily quantifiable, can balance several critical developmental variables (i.e., thermoregulation, water balance, and respiration).
ContributorsStahlschmidt, Zachary R (Author) / DeNardo, Dale F (Thesis advisor) / Harrison, Jon (Committee member) / McGraw, Kevin (Committee member) / Rutowski, Ronald (Committee member) / Walsberg, Glenn (Committee member) / Arizona State University (Publisher)
Created2011
Description
Studies of animal contests often focus solely on a single static measurement of fighting ability, such as the size or the strength of the individual. However, recent studies have highlighted the importance of individual variation in the dynamic behaviors used during a fight, such as, assessment strategies, decision making, and fine motor control, as being strong predictors of the outcome of aggression. Here, I combined morphological and behavioral data to discover how these features interact during aggressing interactions in male virile crayfish, Faxonius virilis. I predicted that individual variation in behavioral skill for decision making (i.e., number of strikes thrown), would determine the outcome of contest success in addition to morphological measurements (e.g. body size, relative claw size). To evaluate this prediction, I filmed staged territorial interactions between male F. virilis and later analyzed trial behaviors (e.g. strike, pinches, and bout time) and aggressive outcomes. I found very little support for skill to predict win/loss outcome in trials. Instead, I found that larger crayfish engaged in aggression for longer compared to smaller crayfish, but that larger crayfish did not engage in a greater number of claw strikes or pinches when controlling for encounter duration. Future studies should continue to investigate the role of skill, by using finer-scale techniques such as 3D tracking software, which could track advanced measurements (e.g. speed, angle, and movement efficiency). Such studies would provide a more comprehensive understanding of the relative influence of fighting skill technique on territorial contests.
ContributorsNguyen, Phillip Huy (Author) / Angilletta, Michael (Thesis director) / McGraw, Kevin (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Functional magnetic resonance imaging (fMRI) has been widely used to measure the retinotopic organization of early visual cortex in the human brain. Previous studies have identified multiple visual field maps (VFMs) based on statistical analysis of fMRI signals, but the resulting geometry has not been fully characterized with mathematical models. This thesis explores using concepts from computational conformal geometry to create a custom software framework for examining and generating quantitative mathematical models for characterizing the geometry of early visual areas in the human brain. The software framework includes a graphical user interface built on top of a selected core conformal flattening algorithm and various software tools compiled specifically for processing and examining retinotopic data. Three conformal flattening algorithms were implemented and evaluated for speed and how well they preserve the conformal metric. All three algorithms performed well in preserving the conformal metric but the speed and stability of the algorithms varied. The software framework performed correctly on actual retinotopic data collected using the standard travelling-wave experiment. Preliminary analysis of the Beltrami coefficient for the early data set shows that selected regions of V1 that contain reasonably smooth eccentricity and polar angle gradients do show significant local conformality, warranting further investigation of this approach for analysis of early and higher visual cortex.
ContributorsTa, Duyan (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Wonka, Peter (Committee member) / Arizona State University (Publisher)
Created2013
Description
In blindness research, the corpus callosum (CC) is the most frequently studied sub-cortical structure, due to its important involvement in visual processing. While most callosal analyses from brain structural magnetic resonance images (MRI) are limited to the 2D mid-sagittal slice, we propose a novel framework to capture a complete set of 3D morphological differences in the corpus callosum between two groups of subjects. The CCs are segmented from whole brain T1-weighted MRI and modeled as 3D tetrahedral meshes. The callosal surface is divided into superior and inferior patches on which we compute a volumetric harmonic field by solving the Laplace's equation with Dirichlet boundary conditions. We adopt a refined tetrahedral mesh to compute the Laplacian operator, so our computation can achieve sub-voxel accuracy. Thickness is estimated by tracing the streamlines in the harmonic field. We combine areal changes found using surface tensor-based morphometry and thickness information into a vector at each vertex to be used as a metric for the statistical analysis. Group differences are assessed on this combined measure through Hotelling's T2 test. The method is applied to statistically compare three groups consisting of: congenitally blind (CB), late blind (LB; onset > 8 years old) and sighted (SC) subjects. Our results reveal significant differences in several regions of the CC between both blind groups and the sighted groups; and to a lesser extent between the LB and CB groups. These results demonstrate the crucial role of visual deprivation during the developmental period in reshaping the structural architecture of the CC.
ContributorsXu, Liang (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Over 2 billion people are using online social network services, such as Facebook, Twitter, Google+, LinkedIn, and Pinterest. Users update their status, post their photos, share their information, and chat with others in these social network sites every day; however, not everyone shares the same amount of information. This thesis explores methods of linking publicly available data sources as a means of extrapolating missing information of Facebook. An application named "Visual Friends Income Map" has been created on Facebook to collect social network data and explore geodemographic properties to link publicly available data, such as the US census data. Multiple predictors are implemented to link data sets and extrapolate missing information from Facebook with accurate predictions. The location based predictor matches Facebook users' locations with census data at the city level for income and demographic predictions. Age and relationship based predictors are created to improve the accuracy of the proposed location based predictor utilizing social network link information. In the case where a user does not share any location information on their Facebook profile, a kernel density estimation location predictor is created. This predictor utilizes publicly available telephone record information of all people with the same surname of this user in the US to create a likelihood distribution of the user's location. This is combined with the user's IP level information in order to narrow the probability estimation down to a local regional constraint.
ContributorsMao, Jingxian (Author) / Maciejewski, Ross (Thesis advisor) / Farin, Gerald (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Human-induced rapid environmental change (HIREC) influences nearly all of Earth's ecosystems through processes such as urbanization. Previous studies have found that urbanization influences biodiversity patterns, often yielding an increase in the abundance of a few urban-adapted taxa at the expense of native species diversity. The western black widow spider, Latrodectus hesperus, is a medically-important pest species that often forms dense urban subpopulations (i.e., infestations) relative to the low-density subpopulations found throughout undisturbed, desert habitat. Here, I employ field and laboratory studies to examine the population ecology and stoichiometry of this urban pest to increase our understanding of the mechanisms underlying its success. The population ecology of ten black widow subpopulations spread across metropolitan Phoenix, AZ was examined during the peak breeding season (June-August). This study revealed that arthropod prey abundance, female mass and population density of females showed significant spatial variation across the ten subpopulations. Additionally, prey abundance and foraging success, measured as the number of carcasses found in webs, were a strong determinant of female mass and population density within each subpopulation. To test the mechanisms that drive black widow infestations, I used ecological stoichiometry to examine the nutrient (nitrogen and phosphorus) composition of spiders and arthropod prey from urban habitat, desert habitat and a laboratory diet regime. These studies revealed that (1) spiders are more nutrient rich than cricket prey in the field, (2) spider subpopulations exhibit significant spatial variation in their nitrogen composition, (3) nutrient composition of urban spider subpopulations does not differ significantly from Sonoran desert subpopulations, (4) laboratory-reared spiders fed a diet of only laboratory-reared crickets are more nitrogen and phosphorus limited than field-captured spiders, and (5) cannibalism by laboratory-reared spiders alleviated phosphorus limitation, but not nitrogen limitation, when compared to field-captured spiders. This work highlights the need to examine the population ecology of species relationships, such as predator-prey dynamics, to fully understand the fecundity and population growth of urban pest species. Moreover, the integration of population ecology and stoichiometry illustrates the need to address mechanisms like nutrient limitation that may explain why urban pest populations thrive and native species diversity suffers following HIREC.
ContributorsTrubl, Patricia (Author) / Johnson, James C. (Thesis advisor) / Rutowski, Ronald (Thesis advisor) / McGraw, Kevin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
This document presents a new implementation of the Smoothed Particles Hydrodynamics algorithm using DirectX 11 and DirectCompute. The main goal of this document is to present to the reader an alternative solution to the largely studied and researched problem of fluid simulation. Most other solutions have been implemented using the NVIDIA CUDA framework; however, the proposed solution in this document uses the Microsoft general-purpose computing on graphics processing units API. The implementation allows for the simulation of a large number of particles in a real-time scenario. The solution presented here uses the Smoothed Particles Hydrodynamics algorithm to calculate the forces within the fluid; this algorithm provides a Lagrangian approach for discretizes the Navier-Stockes equations into a set of particles. Our solution uses the DirectCompute compute shaders to evaluate each particle using the multithreading and multi-core capabilities of the GPU increasing the overall performance. The solution then describes a method for extracting the fluid surface using the Marching Cubes method and the programmable interfaces exposed by the DirectX pipeline. Particularly, this document presents a method for using the Geometry Shader Stage to generate the triangle mesh as defined by the Marching Cubes method. The implementation results show the ability to simulate over 64K particles at a rate of 900 and 400 frames per second, not including the surface reconstruction steps and including the Marching Cubes steps respectively.
ContributorsFigueroa, Gustavo (Author) / Farin, Gerald (Thesis advisor) / Maciejewski, Ross (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2012