CHANGES IN EXPRESSION OF AKT PATHWAY PROTEINS FOLLOWING TREATMENT WITH rG3 IN VITRO

To assess changes in AKT pathway signaling, a recombinant protein of the G3 domain of rat laminin-5 (rG3) that specifically binds the alpha subunit of integrins α6β1 and α6β4 expressed on cancer cells (e.g., MDA-MB-231) was produced. This recombinant protein is believed to interrupt the intracellular signaling events of the AKT pathway, causing a decrease in proliferation and survival of cells after treatment. Viability assays confirmed an apoptotic effect of rG3 on cells in a dose-dependent manner. However, data from gene expression studies of Caspase-9, GRB10, and CDKNIB proved non-conclusive that rG3 is acting upon gene expression, leading to the further investigation of the AKT pathway and proteins involved in this signaling cascade. P53 and phosphorylation of AKT, NFkB/p65, and IKKαβ were evaluated after treatment with rG3 at 0, 3, 6, 9 and 12 hours. Results show significant differences in protein expression for these proteins in cells treated with rG3 compared to untreated cells. Significantly higher levels of AKT and phosphorylated AKT were seen in untreated cells, indicating the inhibitory effect the rG3 protein has on this pathway. Both IKKαβ and the phosphorylated IKKβ catalytic subunit were expressed at a significantly higher level in untreated cells, as were the levels of phosphorylated nuclear NFkB. These results also indicate an inhibition of downstream proteins of the AKT cell survival pathway with rG3 treatment. Cytosolic NFkB, however, was expressed at significantly higher level in cells treated with rG3 when compared with untreated cells because the majority of this protein in actively proliferating untreated cells is in the phosphorylated form. The greatest change was seen in expression of the pro-apoptotic protein, p53. In treated cells, this protein was expressed at greatly higher levels than in the untreated cells, especially at 9 hours after treatment, indicating the large impact rG3 treatment has on the AKT pathway and proves to significantly reduce cell viability through specific signaling events

UNDERSTANDING THE OPIOID EPIDEMIC IN RURAL OHIO: A MIXED-METHODS ANALYSIS OF MORAL VALUES, STIGMA, AND MEDICATION FOR OPIOID USE DISORDER (MOUD)

Background: The opioid crisis continues to take a devastating toll in rural Appalachia. While evidence-based treatment options for opioid use, including medication for opioid use disorder (MOUD) are available, drug-related stigma remains a barrier to service uptake. Moral and religious underpinnings of this stigma deserve more exploration, as they may shape stigma related to drug use and treatment but remain understudied. Developing successful stigma reduction interventions and scaling up treatment for opioid use requires first understanding more about the relationship between morality, stigma, and treatment attitudes and uptake in rural Appalachia. Methods: I conducted a mixed-methods dissertation using data from six counties in rural Appalachian Ohio. First, I used structural equation modeling (SEM) techniques to test the validity of measures of moral intuitions in a population of PWUD (n = 319) and to test a mediation model of the effects of moral intuitions on internalized drug-related stigma and MOUD uptake. In the second study, I analyzed data from qualitative interviews with 45 PWUD and non-PWUD community stakeholders, to explore moral and religious views related to addiction and treatment held by PWUD and non-PWUD stakeholders. Results: Quantitative measures of moral values did not function as expected in a sample of PWUD and should be adjusted to ensure validity. Full mediation models revealed no significant direct or indirect effect of moral values on internalized drug-related stigma and MOUD uptake; however, stigma was positively associated with MOUD uptake, potentiallyreflecting exposure to stigma when accessing MOUD and/or differential levels of acceptability surrounding different modalities of MOUD. Religious practice emerged as a significant predictor of stigma among PWUD. Qualitative results supported the importance of different types of religiosity in influencing attitudes toward addiction and treatment. Conclusions: This dissertation explored the connection between morality, stigma, and evidence-based treatment attitudes and uptake in a rural Appalachian context. Findings revealed the importance of religiosity on stigma and attitudes among PWUD and non-PWUD community members and highlighted the need for better measures of morality among this population. Study results suggest new avenues for stigma reduction interventions and community partnerships to address opioid use in rural Appalachia.Doctor of Philosoph

Evaluating classification accuracy for modern learning approaches

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149333/1/sim8103_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149333/2/sim8103.pd

Surface blemish detection from passive imagery using learned fuzzy set concepts

An image analysis method for real-time surface blemish detection using passive imagery and fuzzy set concepts is described. The method develops an internal knowledge representation for surface blemish characteristics on the basis of experience, thus facilitating autonomous learning based upon positive and negative exemplars. The method incorporates fuzzy set concepts in the learning subsystem and image segmentation algorithms, thereby mimicking human visual perception. This enables a generic solution for color image segmentation. This method has been applied in the development of ARIES (Autonomous Robotic Inspection Experimental System), designed to inspect DOE warehouse waste storage drums for rust. In this project, the ARIES vision system is used to acquire drum surface images under controlled conditions and subsequently perform visual inspection leading to the classification of the drum as acceptable or suspect

Image processing algorithm design and implementation for real-time autonomous inspection of mixed waste

The ARIES {number_sign}1 (Autonomous Robotic Inspection Experimental System) vision system is used to acquire drum surface images under controlled conditions and subsequently perform autonomous visual inspection leading to a classification as `acceptable` or `suspect`. Specific topics described include vision system design methodology, algorithmic structure,hardware processing structure, and image acquisition hardware. Most of these capabilities were demonstrated at the ARIES Phase II Demo held on Nov. 30, 1995. Finally, Phase III efforts are briefly addressed

An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

When the Social Meets the Semantic: Social Semantic Web or Web 2.5

The social trend is progressively becoming the key feature of current Web understanding (Web 2.0). This trend appears irrepressible as millions of users, directly or indirectly connected through social networks, are able to share and exchange any kind of content, information, feeling or experience. Social interactions radically changed the user approach. Furthermore, the socialization of content around social objects provides new unexplored commercial marketplaces and business opportunities. On the other hand, the progressive evolution of the web towards the Semantic Web (or Web 3.0) provides a formal representation of knowledge based on the meaning of data. When the social meets semantics, the social intelligence can be formed in the context of a semantic environment in which user and community profiles as well as any kind of interaction is semantically represented (Semantic Social Web). This paper first provides a conceptual analysis of the second and third version of the Web model. That discussion is aimed at the definition of a middle concept (Web 2.5) resulting in the convergence and integration of key features from the current and next generation Web. The Semantic Social Web (Web 2.5) has a clear theoretical meaning, understood as the bridge between the overused Web 2.0 and the not yet mature Semantic Web (Web 3.0).Pileggi, SF.; Fernández Llatas, C.; Traver Salcedo, V. (2012). When the Social Meets the Semantic: Social Semantic Web or Web 2.5. Future Internet. 4(3):852-854. doi:10.3390/fi4030852S85285443Chi, E. H. (2008). The Social Web: Research and Opportunities. Computer, 41(9), 88-91. doi:10.1109/mc.2008.401Bulterman, D. C. A. (2001). SMIL 2.0 part 1: overview, concepts, and structure. IEEE Multimedia, 8(4), 82-88. doi:10.1109/93.959106Boll, S. (2007). MultiTube--Where Web 2.0 and Multimedia Could Meet. IEEE Multimedia, 14(1), 9-13. doi:10.1109/mmul.2007.17Fraternali, P., Rossi, G., & Sánchez-Figueroa, F. (2010). Rich Internet Applications. IEEE Internet Computing, 14(3), 9-12. doi:10.1109/mic.2010.76Lassila, O., & Hendler, J. (2007). Embracing «Web 3.0». IEEE Internet Computing, 11(3), 90-93. doi:10.1109/mic.2007.52Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud Computing: Distributed Internet Computing for IT and Scientific Research. IEEE Internet Computing, 13(5), 10-13. doi:10.1109/mic.2009.103Mangione-Smith, W. H. (1998). Mobile computing and smart spaces. IEEE Concurrency, 6(4), 5-7. doi:10.1109/4434.736391Greaves, M. (2007). Semantic Web 2.0. IEEE Intelligent Systems, 22(2), 94-96. doi:10.1109/mis.2007.40Bojars, U., Breslin, J. G., Peristeras, V., Tummarello, G., & Decker, S. (2008). Interlinking the Social Web with Semantics. IEEE Intelligent Systems, 23(3), 29-40. doi:10.1109/mis.2008.50Definition of Web 2.0http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.htmlZhang, D., Guo, B., & Yu, Z. (2011). The Emergence of Social and Community Intelligence. Computer, 44(7), 21-28. doi:10.1109/mc.2011.65Pentlan, A. (2005). Socially aware, computation and communication. Computer, 38(3), 33-40. doi:10.1109/mc.2005.104Staab, S., Domingos, P., Mika, P., Golbeck, J., Li Ding, Finin, T., … Vallacher, R. R. (2005). Social Networks Applied. IEEE Intelligent Systems, 20(1), 80-93. doi:10.1109/mis.2005.16The Semantic Webhttp://www.scientificamerican.com/article.cfm?id=the-semantic-webDecker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., … Horrocks, I. (2000). The Semantic Web: the roles of XML and RDF. IEEE Internet Computing, 4(5), 63-73. doi:10.1109/4236.877487OWL Web Ontology Language Overviewhttp://www.w3.org/TR/owl-features/Vetere, G., & Lenzerini, M. (2005). Models for semantic interoperability in service-oriented architectures. IBM Systems Journal, 44(4), 887-903. doi:10.1147/sj.444.0887Fensel, D., & Musen, M. A. (2001). The semantic web: a brain for humankind. IEEE Intelligent Systems, 16(2), 24-25. doi:10.1109/mis.2001.920595Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The Semantic Web Revisited. IEEE Intelligent Systems, 21(3), 96-101. doi:10.1109/mis.2006.62Dodds, P. S., & Danforth, C. M. (2009). Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents. Journal of Happiness Studies, 11(4), 441-456. doi:10.1007/s10902-009-9150-9Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173. doi:10.1002/asi.21662Blogmeterhttp://www.blogmeter.it/Christakis, N. A., & Fowler, J. H. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE, 5(9), e12948. doi:10.1371/journal.pone.0012948Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149Bernal, P. A. (2010). Web 2.5: The Symbiotic Web. International Review of Law, Computers & Technology, 24(1), 25-37. doi:10.1080/13600860903570145Mikroyannidis, A. (2007). Toward a Social Semantic Web. Computer, 40(11), 113-115. doi:10.1109/mc.2007.405Jung, J. J. (2012). Computational reputation model based on selecting consensus choices: An empirical study on semantic wiki platform. Expert Systems with Applications, 39(10), 9002-9007. doi:10.1016/j.eswa.2012.02.03

Eliciting Domain Knowledge in Handwritten Digit Recognition

Abstract. Pattern recognition methods for complex structured objects such as handwritten characters often have to deal with vast search spaces. Developed techniques, despite significant advancement in the last decade, still face some performance barriers. We believe that additional knowl-edge about the structure of patterns, elicited from humans perceptions, will help improve the recognition’s performance, especially when it comes to classify irregular, outlier cases. We propose a framework for the trans-fer of such knowledge from human experts and show how to incorporate it into the learning process of a recognition system using methods based on rough mereology. We also demonstrate how this knowledge acquisi-tion can be conducted in an interactive manner, with a large dataset of handwritten digits as an example.

“I Was Raised in Addiction”: Constructions of the Self and the Other in Discourses of Addiction and Recovery

The aim of this article is to address how conceptualizations of addiction shape the lived experiences of people who use drugs (PWUDs) during the current opioid epidemic. Using a discourse analytic approach, we examine interview transcripts from 27 PWUDs in rural Appalachian Ohio. We investigate the ways in which participants talk about their substance use, what these linguistic choices reveal about their conceptions of self and other PWUDs, and how participants’ discursive caches might be constrained by or defined within broader social discourses. We highlight three subject positions enacted by participants during the interviews: addict as victim of circumstance, addict as good Samaritan, and addict as motivated for change. We argue participants leverage these positions to contrast themselves with a reified addict-other whose identity carries socially ascribed characteristics of being blameworthy, immoral, callous, and complicit. We implicate these processes in the perpetuation of intragroup stigma and discuss implications for intervention