19 research outputs found

    Integrated Biomarker Profiling of Smokers with Periodontitis

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    Background In the context of precision medicine, understanding patient‐specific variation is an important step in developing targeted and patient‐tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. Aim The goal of this study was to discern biological response profiles of periodontitis and non‐periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). Material & Methods The investigation uses a novel ensemble classification approach (SVA‐SVM) to differentiate disease groups and patient‐specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. Results Sensitivity of SVA‐SVM is shown to be considerably higher than several traditional independent classifier systems. Patient‐specific networks generated from SVA‐SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High‐confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. Conclusions Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient‐specific risk profiles and tailored interventions

    Cross-Talk Between Clinical and Host-Response Parameters of Periodontitis in Smokers

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    Background and Objective Periodontal diseases are a major public health concern leading to tooth loss and have also been shown to be associated with several chronic systemic diseases. Smoking is a major risk factor for the development of numerous systemic diseases, as well as periodontitis. While it is clear that smokers have a significantly enhanced risk for developing periodontitis leading to tooth loss, the population varies regarding susceptibility to disease associated with smoking. This investigation focused on identifying differences in four broad sets of variables, consisting of: (i) host‐response molecules; (ii) periodontal clinical parameters; (iii) antibody responses to periodontal pathogens and oral commensal bacteria; and (iv) other variables of interest, in a population of smokers with (n = 171) and without (n = 117) periodontitis. Material and Methods Bayesian network structured learning (BNSL) techniques were used to investigate potential associations and cross‐talk between the four broad sets of variables. Results BNSL revealed two broad communities with markedly different topology between the populations of smokers, with and without periodontitis. Confidence of the edges in the resulting network also showed marked variations within and between the periodontitis and nonperiodontitis groups. Conclusion The results presented validated known associations and discovered new ones with minimal precedence that may warrant further investigation and novel hypothesis generation. Cross‐talk between the clinical variables and antibody profiles of bacteria were especially pronounced in the case of periodontitis and were mediated by the antibody response profile to Porphyromonas gingivalis

    Perturbation theory for eigenvalues and resonances of Schrodinger hamiltonians

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    Suppose that e2[epsilon]|x|V [set membership, variant] ReLP(R3) for some p > 2 and for g [set membership, variant] R, H(g) = - [Delta] + g V, H(g) = -[Delta] + gV. The main result, Theorem 3, uses Puiseaux expansions of the eigenvalues and resonances of H(g) to study the behavior of eigenvalues [lambda](g) as they are absorbed by the continuous spectrum, that is [lambda](g) [NE pointing arrow]6 0 as g [searr]5 g0 > 0. We find a series expansion in powers of (g - g0)1/2, [lambda](g) = [summation operator]n = 2[infinity] an(g - g0)n/2 whose values for g g0 correspond to resonances near the origin. These resonances can be viewed as the traces left by the just absorbed eigenvalues.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23314/1/0000253.pd

    The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

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    The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

    Especiação e seus mecanismos: histórico conceitual e avanços recentes

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    Heterogeneity of Human Serum Antibody Responses to P. Gingivalis in Periodontitis: Effects of Age, Race/Ethnicity, and Sex

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    Aging humans display an increased prevalence and severity of periodontitis, although the mechanisms underlying these findings remain poorly understood. This report examined antigenic diversity of P. gingivalis related to disease presence and patient demographics. Serum IgG antibody to P. gingivalis strains ATCC33277, FDC381, W50 (ATCC53978), W83, A7A1-28 (ATCC53977) and A7436 was measured in 426 participants [periodontally healthy (n = 61), gingivitis (N = 66) or various levels of periodontitis (N = 299)]. We hypothesized that antigenic diversity in P. gingivalis could contribute to a lack of “immunity” in the chronic infections of periodontal disease. Across the strains, the antibody levels in the oldest age group were lower than in the youngest groups, and severe periodontitis patients did not show higher antibody with aging. While 80 % of the periodontitis patients in any age group showed an elevated response to at least one of the P. gingivalis strains, the patterns of individual responses in the older group were also substantially different than the other age groups. Significantly greater numbers of older patients showed strain-specific antibody profiles to only 1 strain. The findings support that P. gingivalis may demonstrate antigenic diversity/drift within patients and could be one factor to help explain the inefficiency/ineffectiveness of the adaptive immune response in managing the infection

    Distribution of the salivary biomarkers.

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    <p>Histograms representing the distribution of molecular expression profiles of (IL-1ß, IL-6, MMP-8, MIP-1α) across the gingivitis (n = 40, top row) and periodontitis (n = 40, bottom row) subjects. The mean and standard deviation of (IL-1ß, IL-6, MMP-8, MIP-1α) across gingivitis were (29.6±49.5, 3.9±5.9, 208.2±194.2, 10.9±14.5) whereas those across periodontitis were (157.6±217, 12.1±10.2, 397.9±302.1, 24.4±29.8).</p

    Heatmap Visualization.

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    <p>Heatmap visualization of the consensus map (τ) representing the consensus between the ensemble sets of the gingivitis (1
40) and periodontitis (41
80) samples. Significant overlap is represented by bright color and absence of overlap by dark color. Heatmap for the four different classification techniques (SVA-LDA, SVA-QDA, SVA-NB, SVA-SVM) are enclosed in the subplots (a-d) respectively. In each subplot, there are three distinct regions (<b>G x G, P x P, G x P</b>) corresponding to overlap within the gingivitis samples (triangle), within the periodontitis samples (triangle) and between the gingivitis and periodontitis samples (square). The misclassified gingivitis (4, 15, 17, 18, 23, 25, 28, 38) and periodontitis (41, 47, 51, 62, 64, 67, 70, 72, 78) samples are accompanied by pronounced dark streaks in each of the subplots.</p

    Working Principle of the Selective Voting Approach.

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    <p>An example of traditional binary classification using all features and a single classifier is shown in <b>(a)</b>. SVA approach that selectively votes on samples using an ensemble of classifiers and pairs of features is shown in <b>(b)</b>. Three molecular markers (M1, M2, M3) and four samples (S1, S2, S3, S4) with clinical labels (G, P, P, G) corresponding to gingivitis (G) and periodontitis (P) are considered. Sample S4 is set aside as the test sample with (S1, S2, S3) as the training samples in the classification process. The single classifier approach implicitly assumes the markers to be identical and fixed across the samples and votes the test sample as either G or P. In contrast, SVA enables the pairs of features to selectively vote on the samples. For the above example we have <sup>3</sup>C<sub>2</sub> = 3 potential classifiers {(M1, M2), (M1, M3), (M2, M3)}. While {(M1, M2), (M2, M3)} vote on the test sample as gingivitis represented by σ<sup>G</sup> {(M1, M3)} vote on the test sample as periodontitis represented by σ<sup>P</sup>. Based on majority votes (2 votes as periodontitis and 1 vote as gingivitis) the SVA class label of the test sample is deemed as periodontitis. More importantly, the test sample with clinical label gingivitis is classified as periodontitis by SVA. It is important to note that if we were to repeat the above process by setting aside the other gingivitis sample (S1) as test sample, the ensemble sets and votes need not necessarily be the same. This critical selective voting aspect of SVA enables personalization of the ensemble sets across samples within and between the classes (i.e. disease groups). Also, in contrast to the single classifier that deems the given test sample as G or P, SVA determines the proclivity of the test sample to G and P given by the normalized vote counts (1/3, 2/3).</p
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