ASU Regents' Professors Open Access Works
The title “Regents’ Professor” is the highest faculty honor awarded at Arizona State University. It is conferred on ASU faculty who have made pioneering contributions in their areas of expertise, who have achieved a sustained level of distinction, and who enjoy national and international recognition for these accomplishments. This collection contains primarily open access works by ASU Regents' Professors.
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- Creators: Ji, Shuiwang
- Creators: Vega Lopez, Sonia
Description
Women with breast cancer often experience weight gain during and after treatment, significantly increasing risk for recurrence as well as all-cause mortality. Based on a growing body of evidence, meditative movement practices may be effective for weight management. First, we describe the effects of stress on factors associated with weight gain for breast cancer survivors. Then, a model is proposed that utilizes existing evidence to suggest how meditative movement supports behavioral, psychological, and neurohormonal changes that may explain weight loss. Application of the model suggests how a novel “mindful-body-wisdom” approach may work to help reduce weight for this at-risk group.
ContributorsLarkey, Linda (Author) / Vega Lopez, Sonia (Author) / Keller, Colleen (Author) / McClain, Darya (Author) / Ainsworth, Barbara (Author) / Ohri-Vachaspati, Punam (Author) / Smith, Lisa (Author) / Jeong, Mihyun (Author) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-12-24
Description
Despite their low cost and high nutrient density, the contribution of eggs to nutrient intake and dietary quality among Mexican-American postpartum women has not been evaluated. Nutrient intake and dietary quality, as assessed by the Healthy Eating Index 2010 (HEI-2010), were measured in habitually sedentary overweight/obese (body mass index (BMI) = 29.7 ± 3.5 kg/m[superscript 2]) Mexican-American postpartum women (28 ± 6 years) and compared between egg consumers (n = 82; any egg intake reported in at least one of three 24-h dietary recalls) and non-consumers (n = 57). Egg consumers had greater intake of energy (+808 kJ (193 kcal) or 14%; p = 0.033), protein (+9 g or 17%; p = 0.031), total fat (+9 g or 19%; p = 0.039), monounsaturated fat (+4 g or 24%; p = 0.020), and several micronutrients than non-consumers. Regarding HEI-2010 scores, egg consumers had a greater total protein foods score than non-consumers (4.7 ± 0.7 vs. 4.3 ± 1.0; p = 0.004), and trends for greater total fruit (2.4 ± 1.8 vs. 1.9 ± 1.7; p = 0.070) and the total composite HEI-2010 score (56.4 ± 12.6 vs. 52.3 ± 14.4; p = 0.082). Findings suggest that egg intake could contribute to greater nutrient intake and improved dietary quality among postpartum Mexican-American women. Because of greater energy intake among egg consumers, recommendations for overweight/obese individuals should include avoiding excessive energy intake and incorporating eggs to a nutrient-dense, fiber-rich dietary pattern.
ContributorsVega Lopez, Sonia (Author) / Pignotti, Giselle (Author) / Todd, Michael (Author) / Keller, Colleen (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor) / College of Public Service and Community Solutions (Contributor) / Southwest Interdisciplinary Research Center (Contributor) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor)
Created2015-10-02
Description
Background:
Drosophila gene expression pattern images document the spatiotemporal dynamics of gene expression during embryogenesis. A comparative analysis of these images could provide a fundamentally important way for studying the regulatory networks governing development. To facilitate pattern comparison and searching, groups of images in the Berkeley Drosophila Genome Project (BDGP) high-throughput study were annotated with a variable number of anatomical terms manually using a controlled vocabulary. Considering that the number of available images is rapidly increasing, it is imperative to design computational methods to automate this task.
Results:
We present a computational method to annotate gene expression pattern images automatically. The proposed method uses the bag-of-words scheme to utilize the existing information on pattern annotation and annotates images using a model that exploits correlations among terms. The proposed method can annotate images individually or in groups (e.g., according to the developmental stage). In addition, the proposed method can integrate information from different two-dimensional views of embryos. Results on embryonic patterns from BDGP data demonstrate that our method significantly outperforms other methods.
Conclusion:
The proposed bag-of-words scheme is effective in representing a set of annotations assigned to a group of images, and the model employed to annotate images successfully captures the correlations among different controlled vocabulary terms. The integration of existing annotation information from multiple embryonic views improves annotation performance.
ContributorsJi, Shuiwang (Author) / Li, Ying-Xin (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / School of Electrical, Computer and Energy Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2009-04-21
Description
Background
Weight gain during the childbearing years and failure to lose pregnancy weight after birth contribute to the development of obesity in postpartum Latinas.
Methods
Madres para la Salud [Mothers for Health] was a 12-month, randomized controlled trial exploring a social support intervention with moderate-intensity physical activity (PA) seeking to effect changes in body fat, fat tissue inflammation, and depression symptoms in sedentary postpartum Latinas. This report describes the efficacy of the Madres intervention.
Results
The results show that while social support increased during the active intervention delivery, it declined to pre-intervention levels by the end of the intervention. There were significant achievements in aerobic and total steps across the 12 months of the intervention, and declines in body adiposity assessed with bioelectric impedance.
Conclusions
Social support from family and friends mediated increases in aerobic PA resulting in decrease in percent body fat.
Weight gain during the childbearing years and failure to lose pregnancy weight after birth contribute to the development of obesity in postpartum Latinas.
Methods
Madres para la Salud [Mothers for Health] was a 12-month, randomized controlled trial exploring a social support intervention with moderate-intensity physical activity (PA) seeking to effect changes in body fat, fat tissue inflammation, and depression symptoms in sedentary postpartum Latinas. This report describes the efficacy of the Madres intervention.
Results
The results show that while social support increased during the active intervention delivery, it declined to pre-intervention levels by the end of the intervention. There were significant achievements in aerobic and total steps across the 12 months of the intervention, and declines in body adiposity assessed with bioelectric impedance.
Conclusions
Social support from family and friends mediated increases in aerobic PA resulting in decrease in percent body fat.
ContributorsKeller, Colleen (Author) / Ainsworth, Barbara (Author) / Records, Kathryn (Author) / Todd, Michael (Author) / Belyea, Michael (Author) / Vega Lopez, Sonia (Author) / Permana, Paska (Author) / Coonrod, Dean (Author) / Williams, Allison (Author) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor) / College of Public Service and Community Solutions (Contributor) / Southwest Interdisciplinary Research Center (Contributor)
Created2014-09-19
Description
Background
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.
ContributorsSun, Qian (Author) / Muckatira, Sherin (Author) / Yuan, Lei (Author) / Ji, Shuiwang (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / Ira A. Fulton Schools of Engineering (Contributor)
Created2013-12-03
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Background
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
ContributorsZhang, Wenlu (Author) / Feng, Daming (Author) / Li, Rongjian (Author) / Chernikov, Andrey (Author) / Chrisochoides, Nikos (Author) / Osgood, Christopher (Author) / Konikoff, Charlotte (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ji, Shuiwang (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-12-28
Description
Background
Mexican Americans are the largest minority group in the US and suffer disproportionate rates of diseases related to the lack of physical activity (PA). Since many of these Mexican Americans are Spanish-speaking, it is important to validate a Spanish language physical activity assessment tool that can be used in epidemiology as well as clinical practice. This study explored the utility of two Spanish translated physical activity questionnaires, the Stanford Brief Activity Survey (SBAS) and the Rapid Assessment of Physical Activity (RAPA), for use among Spanish-speaking Mexican Americans.
Methods
Thirty-four participants (13 M, 21 F; 37.6 ± 9.5 y) completed each of the two PA surveys twice, one week apart. During that week 31 participants also wore an ActiGraph GT1M accelerometer for 7 days to objectively measure PA. Minutes of moderate and vigorous PA (MVPA) were determined from the accelerometer data using Freedson and Matthews cut points.
Results
Validity, determined by Spearman correlation coefficients between questionnaire scores and minutes of ActiGraph measured MVPA were 0.38 and 0.45 for the SBAS and RAPA, respectively. Test-retest reliability was 0.61 for the SBAS and 0.65 for the RAPA. Sensitivity and specificity were 0.60 and 0.47 for the SBAS, and 0.73 and 0.75 for the RAPA. Participants who were classified as meeting the 2008 National Physical Activity Guidelines by the RAPA engaged in significantly (p < 0.05) more minutes of MVPA than those who were not, while there were no significant differences in minutes of MVPA classified by the SBAS.
Conclusions
The SBAS and the RAPA are both reasonably valid measures for quickly assessing PA and determining compliance to the PA guidelines in Spanish-speaking Mexican Americans. Although the two questionnaires had comparable reliability, the RAPA was better able to distinguish between those who met and did not meet National PA Guidelines.
Mexican Americans are the largest minority group in the US and suffer disproportionate rates of diseases related to the lack of physical activity (PA). Since many of these Mexican Americans are Spanish-speaking, it is important to validate a Spanish language physical activity assessment tool that can be used in epidemiology as well as clinical practice. This study explored the utility of two Spanish translated physical activity questionnaires, the Stanford Brief Activity Survey (SBAS) and the Rapid Assessment of Physical Activity (RAPA), for use among Spanish-speaking Mexican Americans.
Methods
Thirty-four participants (13 M, 21 F; 37.6 ± 9.5 y) completed each of the two PA surveys twice, one week apart. During that week 31 participants also wore an ActiGraph GT1M accelerometer for 7 days to objectively measure PA. Minutes of moderate and vigorous PA (MVPA) were determined from the accelerometer data using Freedson and Matthews cut points.
Results
Validity, determined by Spearman correlation coefficients between questionnaire scores and minutes of ActiGraph measured MVPA were 0.38 and 0.45 for the SBAS and RAPA, respectively. Test-retest reliability was 0.61 for the SBAS and 0.65 for the RAPA. Sensitivity and specificity were 0.60 and 0.47 for the SBAS, and 0.73 and 0.75 for the RAPA. Participants who were classified as meeting the 2008 National Physical Activity Guidelines by the RAPA engaged in significantly (p < 0.05) more minutes of MVPA than those who were not, while there were no significant differences in minutes of MVPA classified by the SBAS.
Conclusions
The SBAS and the RAPA are both reasonably valid measures for quickly assessing PA and determining compliance to the PA guidelines in Spanish-speaking Mexican Americans. Although the two questionnaires had comparable reliability, the RAPA was better able to distinguish between those who met and did not meet National PA Guidelines.
ContributorsVega Lopez, Sonia (Author) / Chavez, Adrian (Author) / Farr, Kristin (Author) / Ainsworth, Barbara (Author) / College of Health Solutions (Contributor) / School of Nutrition and Health Promotion (Contributor)
Created2014-01-13