Matching Items (122)
Filtering by
- Language: English
Description
Learning student names has been promoted as an inclusive classroom practice, but it is unknown whether students value having their names known by an instructor. We explored this question in the context of a high-enrollment active-learning undergraduate biology course. Using surveys and semistructured interviews, we investigated whether students perceived that instructors know their names, the importance of instructors knowing their names, and how instructors learned their names. We found that, while only 20% of students perceived their names were known in previous high-enrollment biology classes, 78% of students perceived that an instructor of this course knew their names. However, instructors only knew 53% of names, indicating that instructors do not have to know student names in order for students to perceive that their names are known. Using grounded theory, we identified nine reasons why students feel that having their names known is important. When we asked students how they perceived instructors learned their names, the most common response was instructor use of name tents during in-class discussion. These findings suggest that students can benefit from perceiving that instructors know their names and name tents could be a relatively easy way for students to think that instructors know their names. Academic self-concept is one's perception of his or her ability in an academic domain compared to other students. As college biology classrooms transition from lecturing to active learning, students interact more with each other and are likely comparing themselves more to students in the class. Student characteristics, such as gender and race/ethnicity, can impact the level of academic self-concept, however this has been unexplored in the context of undergraduate biology. In this study, we explored whether student characteristics can affect academic self-concept in the context of a college physiology course. Using a survey, students self-reported how smart they perceived themselves in the context of physiology compared to the whole class and compared to the student they worked most closely with in class. Using logistic regression, we found that males and native English speakers had significantly higher academic self-concept compared to the whole class compared with females and non-native English speakers, respectively. We also found that males and non-transfer students had significantly higher academic self-concept compared to the student they worked most closely with in class compared with females and transfer students, respectively. Using grounded theory, we identified ten distinct factors that influenced how students determined whether they are more or less smart than their groupmate. Finally, we found that students were more likely to report participating less than their groupmate if they had a lower academic self-concept. These findings suggest that student characteristics can influence students' academic self-concept, which in turn may influence their participation in small group discussion.
ContributorsKrieg, Anna Florence (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
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
the project led by Professor Emma Frow, researching of stem cell clinics focused on stem cell applications, adherence to FDA guidelines, and characterization of information available and physician credentials. Regenerative medicine clinics commonly offered stem cell therapy, but introduced platelet rich plasma (PRP) and prolotherapy as regenerative therapies.
PRP and Prolotherapy are individual treatments that were even suggested and used in combination with stem cell therapies. Prolotherapy predates PRP as a chemical irritant therapy originally used to sclerose tissues. Prolotherapy is meant to stimulate platelet derived growth factors release to improve tissue healing response. Prolotherapy shows negligible efficacy improvements over corticosteroids, but may have underlying side effects from being an irritant. PRP is a more modern therapy for improved healing. Speculations state initial use was in an open heart surgery to improve healing post-surgery. PRP is created via centrifugation of patient blood to isolate growth factors by removing serum and other biological components to increase platelet concentration. PRP is comparable to corticosteroid injections in efficacy, but as an autologous application, there are no side effects making it more advantageous. Growth factors induce healing response and reduce inflammation. Growth factors stimulate cell growth, proliferation, differentiation, and stimulate cellular response mechanism such as angiogenesis and mitogenesis. The growth factor stimulation of PRP and prolotherapy both assist stem cell proliferation. Additional research is needed to determine differential capacity to ensure multipotent stem cells regenerate the correct cell type from the increased differential capacity offered by growth factor recruitment. The application of combination therapy for stem cells is unsubstantiated and applications violate FDA ‘minimal manipulation’ guidelines.
PRP and Prolotherapy are individual treatments that were even suggested and used in combination with stem cell therapies. Prolotherapy predates PRP as a chemical irritant therapy originally used to sclerose tissues. Prolotherapy is meant to stimulate platelet derived growth factors release to improve tissue healing response. Prolotherapy shows negligible efficacy improvements over corticosteroids, but may have underlying side effects from being an irritant. PRP is a more modern therapy for improved healing. Speculations state initial use was in an open heart surgery to improve healing post-surgery. PRP is created via centrifugation of patient blood to isolate growth factors by removing serum and other biological components to increase platelet concentration. PRP is comparable to corticosteroid injections in efficacy, but as an autologous application, there are no side effects making it more advantageous. Growth factors induce healing response and reduce inflammation. Growth factors stimulate cell growth, proliferation, differentiation, and stimulate cellular response mechanism such as angiogenesis and mitogenesis. The growth factor stimulation of PRP and prolotherapy both assist stem cell proliferation. Additional research is needed to determine differential capacity to ensure multipotent stem cells regenerate the correct cell type from the increased differential capacity offered by growth factor recruitment. The application of combination therapy for stem cells is unsubstantiated and applications violate FDA ‘minimal manipulation’ guidelines.
ContributorsKrum, Logan (Author) / Frow, Emma (Thesis director) / Brafman, David (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Current research into live-cell dynamics, particularly those relating to chromatin structure and remodeling, are limited. The tools that are used to detect state changes in chromatin, such as Chromatin Immunoprecipitation and qPCR, require that the cell be killed off. This limits the ability of researchers to pinpoint changes in live cells over a longer period of time. As such, there is a need for a live-cell sensor that can detect chromatin state changes. The Chromometer is a transgenic chromatin state sensor designed to better understand human cell fate and the chromatin changes that occur. HOXD11.12, a DNA sequence that attracts repressive Polycomb group (PCG) proteins, was placed upstream of a core promoter-driven fluorescent reporter (AmCyan fluorescent protein, CFP) to link chromatin repression to a CFP signal. The transgene was stably inserted at an ectopic site in U2-OS (osteosarcoma) cells. Expression of CFP should reflect the epigenetic state at the HOXD locus, where several genes are regulated by Polycomb to control cell differentiation. U2-OS cells were transfected with the transgene and grown under selective pressure. Twelve colonies were identified as having integrated parts from the transgene into their genomes. PCR testing verified 2 cell lines that contain the complete transgene. Flow cytometry indicated mono-modal and bimodal populations in all transgenic cell colonies. Further research must be done to determine the effectiveness of this device as a sensor for live cell state change detection.
ContributorsBarclay, David (Co-author) / Simper, Jan (Co-author) / Haynes, Karmella (Thesis director) / Brafman, David (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Course-Based Undergraduate Research Experiences, or CUREs have become an increasingly popular way to integrate research opportunities into the undergraduate biology curriculum. Unlike traditional cookbook labs which provide students with a set experimental design and known outcome, CUREs offer students the opportunity to participate in novel and interesting research that is of interest to the greater biology community. While CUREs have been championed as a way to provide more students with the opportunity to experience, it is unclear whether students benefit differently from participating in different CURE with different structural elements. In this study we focused in on one proposed element of a CURE, collaboration, to determine whether student's perception of this concept change over the course of a CURE and whether it differs among students enrolled in different CUREs. We analyzed pre and post open-ended surveys asking the question "Why might collaboration be important in science?" in two CUREs with different structures of collaboration. We also compared CURE student responses to the responses of senior honors thesis students who had been conducting authentic research. Five themes emerged in response to students' conceptions of collaboration. Comparing two CURE courses, we found that students' conceptions of collaboration were varied within each individual CURE, as well as what students were leaving with compared to the other CURE course. Looking at how student responses compared between 5 different themes, including "Different Perspectives", "Validate/Verify Results", "Compare Results", "Requires Different Expertise", and "Compare results", students appeared to be thinking about collaboration in distinct different ways by lack of continuity in the amount of students discussing each of these among the classes. In addition, we found that student responses in each of the CURE courses were not significantly different for any of the themes except "Different Expertise" compared to the graduating seniors. However, due to the small (n) that the graduating seniors group had, 22, compared to each of the CURE classes composing of 155 and 98 students, this comparison must be taken in a preliminary manner. Overall, students thought differently about collaboration between different CUREs. Still, a gap filling what it means to "collaborate", and whether the structures of CUREs are effective to portray collaboration are still necessary to fully elaborate on this paper's findings.
ContributorsWassef, Cyril Alexander (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
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
Cardiovascular disease (CVD) remains the leading cause of mortality, resulting in 1 out of 4 deaths in the United States at the alarming rate of 1 death every 36 seconds, despite great efforts in ongoing research. In vitro research to study CVDs has had limited success, due to lack of biomimicry and structural complexity of 2D models. As such, there is a critical need to develop a 3D, biomimetic human cardiac tissue within precisely engineered in vitro platforms. This PhD dissertation involved development of an innovative anisotropic 3D human stem cell-derived cardiac tissue on-a-chip model (i.e., heart on-a-chip), with an enhanced maturation tissue state, as demonstrated through extensive biological assessments. To demonstrate the potential of the platform to study cardiac-specific diseases, the developed heart on-a-chip was used to model myocardial infarction (MI) due to exposure to hypoxia. The successful induction of MI on-a-chip (heart attack-on-a-chip) was evidenced through fibrotic tissue response, contractile dysregulation, and transcriptomic regulation of key pathways.This dissertation also described incorporation of CRISPR/Cas9 gene-editing to create a human induced pluripotent stem cell line (hiPSC) with a mutation in KCNH2, the gene implicated in Long QT Syndrome Type 2 (LQTS2). This novel stem cell line, combined with the developed heart on-a-chip technology, led to creation of a 3D human cardiac on-chip tissue model of LQTS2 disease.. Extensive mechanistic biological and electrophysiological characterizations were performed to elucidate the mechanism of R531W mutation in KCNH2, significantly adding to existing knowledge about LQTS2. In summary, this thesis described creation of a LQTS2 cardiac on-a-chip model, incorporated with gene-edited hiPSC-cardiomyocytes and hiPSC-cardiac fibroblasts, to study mechanisms of LQTS2.
Overall, this dissertation provides broad impact for fundamental studies toward cardiac biological studies as well as drug screening applications. Specifically, the developed heart on-a-chip from this dissertation provides a unique alternative platform to animal testing and 2D studies that recapitulates the human myocardium, with capabilities to model critical CVDs to study disease mechanisms, and/or ultimately lead to development of future therapeutic strategies.
ContributorsVeldhuizen, Jaimeson (Author) / Nikkhah, Mehdi (Thesis advisor) / Brafman, David (Committee member) / Ebrahimkhani, Mo (Committee member) / Migrino, Raymond Q (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2021
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