66 research outputs found

    The creation of a multi-ethnic housing cooperative a social intervention

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    The present study documents the development of the Housing Working Group—a group which investigated strategies to improve the housing situation of new Canadians in Kitchener-Waterloo—and the subsequent formation of the founding board of directors of a multi-ethnic housing cooperative. It emphasizes the importance of new Canadian involvement in the intervention and the need to view their empowerment as a long-term goal, attainable only after a series of small, measurable successes. The research revealed two dilemmas in the practice of community development. First, though the empowerment of powerless people requires their active participation in self-help projects, the interventionist cannot force them to participate. Secondly, the awareness that inadequate housing is part of a wider problem had to be balanced with the need to keep the project focused on one issue (housing) in order to maintain the involvement of new Canadians. The research also pointed to the need for at least two interventionists in order to avoid burnout and to maintain a balanced view of the intervention. In terms of the more specific issue of creating non-profit housing, it was found that community groups involved in such projects must assert control over their proposals rather than allowing a community resource organization (which provides technical assistance in the development of housing projects) to take control for them. Ontario’s current non-profit housing program was praised for its involvement of community groups in the development of low-cost housing, but was found to suffer from too much red tape and a lack of program flexibility. It was suggested that groups should consider whether they are contributing to the problem of inadequate affordable housing by participating in a program which requires high numbers of subsidized units within projects and fails to enable access to centrally located land. Further research, including an evaluation of the completed intervention, is needed

    BioSimulator.jl: Stochastic simulation in Julia

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    Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ\tau-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.Comment: 27 pages, 5 figures, 3 table

    Stem Cell Niche Dynamics: From Homeostasis to Carcinogenesis

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    The stem cell microenvironment is involved in regulating the fate of the stem cell with respect to self-renewal, quiescence, and differentiation. Mathematical models are helpful in understanding how key pathways regulate the dynamics of stem cell maintenance and homeostasis. This tight regulation and maintenance of stem cell number is thought to break down during carcinogenesis. As a result, the stem cell niche has become a novel target of cancer therapeutics. Developing a quantitative understanding of the regulatory pathways that guide stem cell behavior will be vital to understanding how these systems change under conditions of stress, inflammation, and cancer initiation. Predictions from mathematical modeling can be used as a clinical tool to guide therapy design. We present a survey of mathematical models used to study stem cell population dynamics and stem cell niche regulation, both in the hematopoietic system and other tissues. Highlighting the quantitative aspects of stem cell biology, we describe compelling questions that can be addressed with modeling. Finally, we discuss experimental systems, most notably Drosophila, that can best be used to validate mathematical predictions

    DNA methylation age is elevated in breast tissue of healthy women

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    BACKGROUND: Limited evidence suggests that female breast tissue ages faster than other parts of the body according to an epigenetic biomarker of aging known as the "epigenetic clock." However, it is unknown whether breast tissue samples from healthy women show a similar accelerated aging effect relative to other tissues, and what could drive this acceleration. The goal of this study is to validate our initial finding of advanced DNA methylation (DNAm) age in breast tissue, by directly comparing it to that of peripheral blood tissue from the same individuals, and to do a preliminary assessment of hormonal factors that could explain the difference. METHODS: We utilized n = 80 breast and 80 matching blood tissue samples collected from 40 healthy female participants of the Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center who donated these samples at two time points spaced at least a year apart. DNA methylation levels (Illumina 450K platform) were used to estimate the DNAm age. RESULTS: DNAm age was highly correlated with chronological age in both peripheral blood (r = 0.94, p < 0.0001) and breast tissues (r = 0.86, p < 0.0001). A measure of epigenetic age acceleration (age-adjusted DNAm Age) was substantially increased in breast relative to peripheral blood tissue (p = 1.6 × 10-11). The difference between DNAm age of breast and blood decreased with advancing chronologic age (r = -0.53, p = 4.4 × 10-4). CONCLUSIONS: Our data clearly demonstrate that female breast tissue has a higher epigenetic age than blood collected from the same subject. We also observe that the degree of elevation in breast diminishes with advancing age. Future larger studies will be needed to examine associations between epigenetic age acceleration and cumulative hormone exposure

    Sex steroid metabolism polymorphisms and mammographic density in pre- and early peri-menopausal women

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    Abstract Introduction We examined the association between mammographic density and single-nucleotide polymorphisms (SNPs) in genes encoding CYP1A1, CYP1B1, aromatase, 17&#946;-HSD, ESR1, and ESR2 in pre- and early perimenopausal white, African-American, Chinese, and Japanese women. Methods The Study of Women's Health Across the Nation is a longitudinal community-based cohort study. We analyzed data from 451 pre- and early perimenopausal participants of the ancillary SWAN Mammographic Density study for whom we had complete information regarding mammographic density, genotypes, and covariates. With multivariate linear regression, we examined the relation between percentage mammographic breast density (outcome) and each SNP (primary predictor), adjusting for age, race/ethnicity, parity, cigarette smoking, and body mass index (BMI). Results After multivariate adjustment, the CYP1B1 rs162555 CC genotype was associated with a 9.4% higher mammographic density than the TC/TT genotype (P = 0.04). The CYP19A1 rs936306 TT genotype was associated with 6.2% lower mammographic density than the TC/CC genotype (P = 0.02). The positive association between CYP1A1 rs2606345 and mammographic density was significantly stronger among participants with BMI greater than 30 kg/m2 than among those with BMI less than 25 kg/m2 (Pinteraction = 0.05). Among white participants, the ESR1 rs2234693 CC genotype was associated with a 7.0% higher mammographic density than the CT/TT genotype (P = 0.01). Conclusions SNPs in certain genes encoding sex steroid metabolism enzymes and ESRs were associated with mammographic density. Because the encoded enzymes and ESR1 are expressed in breast tissue, these SNPs may influence breast cancer risk by altering mammographic density.http://deepblue.lib.umich.edu/bitstream/2027.42/78273/1/bcr2340.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78273/2/bcr2340.pdfPeer Reviewe

    The Effects of Lifetime Estrogen Exposure on Breast Epigenetic Age

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    DNA methylation age is elevated in breast tissue of healthy women.

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