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Description
Intermittent social defeat stress induces cross-sensitization to psychostimulants and escalation of drug self-administration. These behaviors could result from the stress-induced neuroadaptation in the mesocorticolimbic dopamine circuit. Brain-derived neurotrophic factor (BDNF) in the ventral tegmental area (VTA) is persistently elevated after social defeat stress, and may contribute to the stress-induced neuroadaptation in the mesocorticolimbic dopamine circuit. BDNF modulates synaptic plasticity, and facilitates stress- and drug-induced neuroadaptations in the mesocorticolimbic system. The present research examined the role of mesolimbic BDNF signaling in social defeat stress-induced cross-sensitization to psychostimulants and the escalation of cocaine self-administration in rats. We measured drug taking behavior with the acquisition, progressive ratio, and binge paradigms during self-administration. With BDNF overexpression in the ventral tegmental area (VTA), single social defeat stress-induced cross-sensitization to amphetamine (AMPH) was significantly potentiated. VTA-BDNF overexpression also facilitates acquisition of cocaine self-administration, and a positive correlation between the level of VTA BDNF and drug intake during 12 hour binge was observed. We also found significant increase of DeltaFosB expression in the nucleus accumbens (NAc), the projection area of the VTA, in rats received intra-VTA BDNF overexpression. We therefore examined whether BDNF signaling in the NAc is important for social defeat stress-induced cross-sensitization by knockdown of the receptor of BDNF (neurotrophin tyrosine kinase receptor type 2, TrkB) there. NAc TrkB knockdown prevented social defeat stress-induced cross-sensitization to psychostimulant. Also social defeat stress-induced increase of DeltaFosB in the NAc was prevented by TrkB knockdown. Several other factors up-regulated by stress, such as the GluA1 subunit of Alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor and BDNF in the VTA were also prevented. We conclude that BDNF signaling in the VTA increases social defeat stress-induced vulnerability to psychostimulants, manifested as potentiated cross-sensitization/sensitization to AMPH and escalation of cocaine self-administration. Also BDNF signaling in the NAc is necessary for the stress-induced neuroadaptation and behavioral sensitization to psychostimulants. Therefore, TrkB in the NAc could be a therapeutic target to prevent stress-induced vulnerability to drugs of abuse in the future. DeltaFosB in the NAc shell could be a neural substrate underlying persistent cross-sensitization and augmented cocaine self-administration induced by social defeat stress.
ContributorsWang, Junshi (Author) / Hammer, Ronald (Thesis advisor) / Feuerstein, Burt (Committee member) / Nikulina, Ella (Committee member) / Neisewander, Janet (Committee member) / Arizona State University (Publisher)
Created2013
Description
In the past decade, research has demonstrated the relationship between higher levels of self-compassion and lower levels of negative psychological outcomes. More recently, the concept of self-compassion has been explored within the context of various health behaviors. Very few studies have investigated the potential relationship between self-compassion and eating behaviors. Based on literature and the established relationship between negative self-evaluation and abnormal eating behaviors/eating disorders, the current study sought to examine correlations between self-compassion, eating behaviors, and stress in first time college freshmen. The study population consisted of 1478 participants; ages 18-22 years; females = 936 (63%), males = 541 (37%). Participants self-reported measures of the Perceived Stress Scale (PSS), the Three Factor Eating Questionnaire (TFEQ), and the Self Compassion Scale (SCS). PSS score, the overall score and individual subscale scores of SCS, and the three subscale scores of the TFEQ (restraint, disinhibiton, hunger) were examined with Pearson correlations. Results of this study indicate significant (p = < .05) differences between males and females in PSS and all three negative SCS subscales. There was a strong and consistent correlation between the eating behavior of disinhibition and all three negative constructs of self-compassion (self-judgment, r = .29; isolation, r = .23; over-identification, r = .28) in females. The eating behavior of restraint was similarly correlated with SCS self-judgment in females (r = .26). More research is needed to understand differences in stress, self-compassion, and eating behaviors between males and females and to better comprehend the weak associations between eating behaviors and the positive psychological constructs of self-compassion (self-kindness, common humanity, and mindfulness) for males and females. Additionally, future research should focus on the three subscales of disinhibition as they relate to the negative constructs of self-compassion. The preliminary results of this study suggest it would be beneficial, particularly to female college freshmen, to more fully understand the dynamics of the relationship between eating behaviors and self-compassion; this knowledge may help to better structure appropriate coping strategies for the prevention of disordered eating behaviors.
ContributorsJames, Darith (Author) / Sebren, Ann (Thesis advisor) / Swan, Pamela D. (Committee member) / Der Ananian, Cheryl (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
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
One of the main challenges in planetary robotics is to traverse the shortest path through a set of waypoints. The shortest distance between any two waypoints is a direct linear traversal. Often times, there are physical restrictions that prevent a rover form traversing straight to a waypoint. Thus, knowledge of the terrain is needed prior to traversal. The Digital Terrain Model (DTM) provides information about the terrain along with waypoints for the rover to traverse. However, traversing a set of waypoints linearly is burdensome, as the rovers would constantly need to modify their orientation as they successively approach waypoints. Although there are various solutions to this problem, this research paper proposes the smooth traversability of the rover using splines as a quick and easy implementation to traverse a set of waypoints. In addition, a rover was used to compare the smoothness of the linear traversal along with the spline interpolations. The data collected illustrated that spline traversals had a less rate of change in the velocity over time, indicating that the rover performed smoother than with linear paths.
ContributorsKamasamudram, Anurag (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Objectives Through a cross-sectional observational study, this thesis evaluates the relationship between food insecurity and weight status, eating behaviors, the home food environment, meal planning and preparation, and perceived stress as it relates to predominantly Hispanic/Latino parents in Phoenix, Arizona. The purpose of this study was to address gaps in the literature by examining differences in "healthy" and "unhealthy" eating behaviors, foods available in the home, how time and low energy impact meal preparation, and the level of stress between food security groups. Methods Parents, 18 years or older, were recruited during two pre-scheduled health fairs, from English as a second language classes, or from the Women, Infants, and Children's clinic at a local community center, Golden Gate Community Center, in Phoenix, Arizona. An interview, electronic, or paper survey were offered in either Spanish or English to collect data on the variables described above. In addition to the survey, height and weight were collected for all participants to determine BMI and weight status. One hundred and sixty participants were recruited. Multivariate linear and logistic regression models, adjusting for weight status, education, race/ethnicity, income level, and years residing in the U.S., were used to assess the relationship between food security status and weight status, eating behaviors, the home food environment, meal planning and preparation, and perceived stress. Results Results concluded that food insecurity was more prevalent among parents reporting lower income levels compared to higher income levels (p=0.017). In adjusted models, higher perceived cost of fruits (p=0.004) and higher perceived level of stress (p=0.001) were associated with food insecurity. Given that the sample population was predominately women, a post-hoc analysis was completed on women only. In addition to the two significant results noted in the adjusted analyses, the women-only analysis revealed that food insecure mothers reported lower amounts of vegetables served with meals (p=0.019) and higher use of fast-food when tired or running late (p=0.043), compared to food secure mothers. Conclusion Additional studies are needed to further assess differences in stress levels between food insecure parents and food insecure parents, with special consideration for directionality and its relationship to weight status.
ContributorsVillanova, Christina (Author) / Bruening, Meg (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Vega-Lopez, Sonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
As the complexity of robotic systems and applications grows rapidly, development of high-performance, easy to use, and fully integrated development environments for those systems is inevitable. Model-Based Design (MBD) of dynamic systems using engineering software such as Simulink® from MathWorks®, SciCos from Metalau team and SystemModeler® from Wolfram® is quite popular nowadays. They provide tools for modeling, simulation, verification and in some cases automatic code generation for desktop applications, embedded systems and robots. For real-world implementation of models on the actual hardware, those models should be converted into compilable machine code either manually or automatically. Due to the complexity of robotic systems, manual code translation from model to code is not a feasible optimal solution so we need to move towards automated code generation for such systems. MathWorks® offers code generation facilities called Coder® products for this purpose. However in order to fully exploit the power of model-based design and code generation tools for robotic applications, we need to enhance those software systems by adding and modifying toolboxes, files and other artifacts as well as developing guidelines and procedures. In this thesis, an effort has been made to propose a guideline as well as a Simulink® library, StateFlow® interface API and a C/C++ interface API to complete this toolchain for NAO humanoid robots. Thus the model of the hierarchical control architecture can be easily and properly converted to code and built for implementation.
ContributorsRaji Kermani, Ramtin (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Our research focuses on finding answers through decentralized search, for complex, imprecise queries (such as "Which is the best hair salon nearby?") in situations where there is a spatiotemporal constraint (say answer needs to be found within 15 minutes) associated with the query. In general, human networks are good in answering imprecise queries. We try to use the social network of a person to answer his query. Our research aims at designing a framework that exploits the user's social network in order to maximize the answers for a given query. Exploiting an user's social network has several challenges. The major challenge is that the user's immediate social circle may not possess the answer for the given query, and hence the framework designed needs to carry out the query diffusion process across the network. The next challenge involves in finding the right set of seeds to pass the query to in the user's social circle. One other challenge is to incentivize people in the social network to respond to the query and thereby maximize the quality and quantity of replies. Our proposed framework is a mobile application where an individual can either respond to the query or forward it to his friends. We simulated the query diffusion process in three types of graphs: Small World, Random and Preferential Attachment. Given a type of network and a particular query, we carried out the query diffusion by selecting seeds based on attributes of the seed. The main attributes are Topic relevance, Replying or Forwarding probability and Time to Respond. We found that there is a considerable increase in the number of replies attained, even without saturating the user's network, if we adopt an optimal seed selection process. We found the output of the optimal algorithm to be satisfactory as the number of replies received at the interrogator's end was close to three times the number of neighbors an interrogator has. We addressed the challenge of incentivizing people to respond by associating a particular amount of points for each query asked, and awarding the same to people involved in answering the query. Thus, we aim to design a mobile application based on our proposed framework so that it helps in maximizing the replies for the interrogator's query by diffusing the query across his/her social network.
ContributorsSwaminathan, Neelakantan (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013