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Eastern Michigan University is a comprehensive, co-educational public university located in Ypsilanti, Michigan. Ypsilanti is 35 miles west of Detroit and eight miles east of Ann Arbor. The university was founded in 1849 as Michigan State Normal School. Today, the university is governed by an eight-member Board of Regents, who are appointed by the Governor of Michigan for eight-year terms. The school belongs to the Mid-American Conference and is accredited by the Higher Learning Commission of the North Central Association of Colleges and Schools. Since 1991 EMU athletics has gone by the name "Eagles". Then in 1994, "Swoop" was officially adopted by the university as the school's mascot. Currently, EMU comprises seven colleges and schools: College of Arts and science, College of Business, College of Education, College of Health and Human Services, College of Technology, an Honors College, and a Graduate School. The university's site is composed of an academic and athletic campus spread across 800 acres , with over 120 buildings. EMU has a total enrollment of more than 23,000 students. Wikipedia.

Texter J.,Eastern Michigan University
Macromolecular Rapid Communications | Year: 2012

Stimuli responsiveness in polymer design is providing basis for diversely new and advanced materials that exhibit switchable porosity in membranes and coatings, switchable particle formation and thermodynamically stable nanoparticle dispersions, polymers that provide directed mechanical stress in response to intensive fields, and switchable compatibility of nanomaterials in changing environments. The incorporation of ionic liquid monomers has resulted in many new polymers based on the imidazolium group. These polymers exhibit all of the above-articulated material properties. Some insight into how these anion responsive polymers function has become empirically available. Much opportunity remains for extending our understanding as well as for designing more refined stimuli-responsive materials. The anion or solvent stimuli responsiveness of imidazolium-based copolymers arises from dramatic solubility variations among particular anion-imidazolium ion pairs. These ion pairs can be tuned from solvophilic to solvophobic through anion exchange, and reversible poration, condensation, and stabilization are examples of concomitant properties. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Texter J.,Eastern Michigan University
Current Opinion in Colloid and Interface Science | Year: 2014

Aqueous dispersions of graphene are of interest to afford environmentally safe handing of graphene for coating, composite, and other material applications. The dispersion of graphene in water and some other solvents using surfactants, polymers, and other dispersants is reviewed and results show that nearly completely exfoliated graphene may be obtained at concentrations from 0.001 to 5% by weight in water. The molecular features promoting good dispersion are reviewed. A critical review of optical extinction shows that the visible absorption coefficients of graphene have been reported over the ranges of 12 to 66cm2/mg at various wavelengths. The practice of energetically activating graphene in various solvents with various stabilizers followed by centrifugation to isolate the "good" dispersion components is fine for producing samples amenable to TEM analysis and quantification, but cannot be expected to drive value added production of products on the kg or higher scale. Such approaches lack practical application and often involve 90-99% wasted graphene. However, alternative approaches omitting centrifugation are yielding dispersions 0.5 to 5% by weight graphene, with higher yields likely in the near future. These dispersions yield effective extinctions of about 49cm2/mg, in conformity with macroscopic optical analysis of single and few layer graphene. © 2014 Elsevier Ltd.

Texter J.,Eastern Michigan University
Current Opinion in Colloid and Interface Science | Year: 2015

Various uses of graphene oxide and graphene as Pickering stabilizers in emulsification, emulsion polymerization, and suspension polymerization applications are discussed. The use of such stabilizers in composites, graphite dispersions, foams, aerogels, and porous materials is reviewed. Other advanced material applications of these Pickering stabilizers are presented for select applications, including electro-rheological fluids, opto-rheological fluids, particles for supercapacitors, phase change materials, catalysis, and stabilizers. © 2015 Elsevier Ltd.

Han H.,Eastern Michigan University
BMC Bioinformatics | Year: 2010

Background: As a novel cancer diagnostic paradigm, mass spectroscopic serum proteomic pattern diagnostics was reported superior to the conventional serologic cancer biomarkers. However, its clinical use is not fully validated yet. An important factor to prevent this young technology to become a mainstream cancer diagnostic paradigm is that robustly identifying cancer molecular patterns from high-dimensional protein expression data is still a challenge in machine learning and oncology research. As a well-established dimension reduction technique, PCA is widely integrated in pattern recognition analysis to discover cancer molecular patterns. However, its global feature selection mechanism prevents it from capturing local features. This may lead to difficulty in achieving high-performance proteomic pattern discovery, because only features interpreting global data behavior are used to train a learning machine.Methods: In this study, we develop a nonnegative principal component analysis algorithm and present a nonnegative principal component analysis based support vector machine algorithm with sparse coding to conduct a high-performance proteomic pattern classification. Moreover, we also propose a nonnegative principal component analysis based filter-wrapper biomarker capturing algorithm for mass spectral serum profiles.Results: We demonstrate the superiority of the proposed algorithm by comparison with six peer algorithms on four benchmark datasets. Moreover, we illustrate that nonnegative principal component analysis can be effectively used to capture meaningful biomarkers.Conclusion: Our analysis suggests that nonnegative principal component analysis effectively conduct local feature selection for mass spectral profiles and contribute to improving sensitivities and specificities in the following classification, and meaningful biomarker discovery. © 2010 Han; licensee BioMed Central Ltd.

Han X.,Eastern Michigan University
IEEE/ACM Transactions on Computational Biology and Bioinformatics | Year: 2010

As a well-established feature selection algorithm, principal component analysis (PCA) is often combined with the state-of-the-art classification algorithms to identify cancer molecular patterns in microarray data. However, the algorithm's global feature selection mechanism prevents it from effectively capturing the latent data structures in the high-dimensional data. In this study, we investigate the benefit of adding nonnegative constraints on PCA and develop a nonnegative principal component analysis algorithm (NPCA) to overcome the global nature of PCA. A novel classification algorithm NPCA-SVM is proposed for microarray data pattern discovery. We report strong classification results from the NPCA-SVM algorithm on five benchmark microarray data sets by direct comparison with other related algorithms. We have also proved mathematically and interpreted biologically that microarray data will inevitably encounter overfitting for an SVM/PCA-SVM learning machine under a Gaussian kernel. In addition, we demonstrate that nonnegative principal component analysis can be used to capture meaningful biomarkers effectively. © 2006 IEEE.

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