‘Greenness’ in the Eye of the Beholder: Comparing Perceptions of Sustainability and Well-being Between Artificial and Natural Turfgrass

Turfgrass lawns are a central component in urban green space and provide a variety of ecosystem services. Traditionally, natural turfgrass lawns can have substantial input requirements (e.g., water, herbicides), which if not managed properly can have harmful ecological effects. A proposed solution that has already been adopted in many cities are artificial turfgrass lawns which do not require some of the traditional inputs of natural lawns. However, understanding perceptions of the sustainability and well-being benefits between these two surfaces are unknown. We surveyed adults in the United States in order to understand perceptions of sustainability and well-being between artificial and natural turfgrass lawns. The survey utilized a pre-post design which presented participants with photos and information about each surface type with questions related to sustainability and well-being before and after the information was presented. Overall, participants perceived natural turfgrass lawns as more sustainable and better for human health and well-being than artificial turfgrass lawns. More work needs to be done to understand the specific reasons behind such perceptions and if perceptions change when in direct contact with the two lawn surfaces

Playing the odds in clinical decision making: lessons from berry aneurysms undetected by magnetic resonance angiography

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Ionization of clusters in intense laser pulses through collective electron dynamics

The motion of electrons and ions in medium-sized rare gas clusters (1000 atoms) exposed to intense laser pulses is studied microscopically by means of classical molecular dynamics using a hierarchical tree code. Pulse parameters for optimum ionization are found to be wavelength dependent. This resonant behavior is traced back to a collective electron oscillation inside the charged cluster. It is shown that this dynamics can be well described by a driven and damped harmonic oscillator allowing for a clear discrimination against other energy absorption mechanisms.Comment: 4 pages (4 figures

Using Functional Annotation for the Empirical Determination of Bayes Factors for Genome-Wide Association Study Analysis

A genome wide association study (GWAS) typically results in a few highly significant ‘hits’ and a much larger set of suggestive signals (‘near-hits’). The latter group are expected to be a mixture of true and false associations. One promising strategy to help separate these is to use functional annotations for prioritisation of variants for follow-up. A key task is to determine which annotations might prove most valuable. We address this question by examining the functional annotations of previously published GWAS hits. We explore three annotation categories: non-synonymous SNPs (nsSNPs), promoter SNPs and cis expression quantitative trait loci (eQTLs) in open chromatin regions. We demonstrate that GWAS hit SNPs are enriched for these three functional categories, and that it would be appropriate to provide a higher weighting for such SNPs when performing Bayesian association analyses. For GWAS studies, our analyses suggest the use of a Bayes Factor of about 4 for cis eQTL SNPs within regions of open chromatin, 3 for nsSNPs and 2 for promoter SNPs

Exotic Meson Decay Widths using Lattice QCD

A decay width calculation for a hybrid exotic meson h, with JPC=1-+, is presented for the channel h->pi+a1. This quenched lattice QCD simulation employs Luescher's finite box method. Operators coupling to the h and pi+a1 states are used at various levels of smearing and fuzzing, and at four quark masses. Eigenvalues of the corresponding correlation matrices yield energy spectra that determine scattering phase shifts for a discrete set of relative pi+a1 momenta. Although the phase shift data is sparse, fits to a Breit-Wigner model are attempted, resulting in a decay width of about 60 MeV when averaged over two lattice sizes.Comment: 9 pages, 8 figures, RevTex4, minor change to Fig.

A bulky glycocalyx fosters metastasis formation by promoting G1 cell cycle progression.

Metastasis depends upon cancer cell growth and survival within the metastatic niche. Tumors which remodel their glycocalyces, by overexpressing bulky glycoproteins like mucins, exhibit a higher predisposition to metastasize, but the role of mucins in oncogenesis remains poorly understood. Here we report that a bulky glycocalyx promotes the expansion of disseminated tumor cells in vivo by fostering integrin adhesion assembly to permit G1 cell cycle progression. We engineered tumor cells to display glycocalyces of various thicknesses by coating them with synthetic mucin-mimetic glycopolymers. Cells adorned with longer glycopolymers showed increased metastatic potential, enhanced cell cycle progression, and greater levels of integrin-FAK mechanosignaling and Akt signaling in a syngeneic mouse model of metastasis. These effects were mirrored by expression of the ectodomain of cancer-associated mucin MUC1. These findings functionally link mucinous proteins with tumor aggression, and offer a new view of the cancer glycocalyx as a major driver of disease progression

HBsAg-vectored DNA vaccines elicit concomitant protective responses to multiple CTL epitopes relevant in human disease.

Vaccines capable of controlling neoplastic and infectious diseases which depend on the cellular immune response for their resolution, have proven difficult to develop. We, and others, have previously demonstrated that the potent immunogenicity of hepatitis B surface antigen (HBsAg), the already- licensed human vaccine for hepatitis B infection, may be exploited to deliver foreign antigens for cytotoxic T-lymphocyte (CTL) induction. In this study we demonstrate that recombinant (r) HBsAg DNA delivering a CTL polyepitope appended at the C' terminus elicits concomitant responses to multiple epitopes restricted through a diversity of MHC class I haplotypes, which are relevant in a number of human diseases. We show that the rHBsAg DNA vaccine elicits concomitant protection against neoplastic and infectious disease. These studies vindicate the use of HBsAg as a powerful vector to deliver CTL responses to foreign antigens, and have implications for a multi-disease vaccine applicable to the HLA-polymorphic human population

A Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals ("hits") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data

Glueball Spectroscopy in a Relativistic Many-Body Approach to Hadron Structure

A comprehensive, relativistic many-body approach to hadron structure is advanced based on the Coulomb gauge QCD Hamiltonian. Our method incorporates standard many-body techniques which render the approximations amenable to systematic improvement. Using BCS variational methods, dynamic chiral symmetry breaking naturally emerges and both quarks and gluons acquire constituent masses. Gluonia are studied both in the valence and in the collective, random phase approximations. Using representative values for the strong coupling constant and string tension, calculated quenched glueball masses are found to be in remarkable agreement with lattice gauge theory.Comment: 12 pages, 1 uuencoded ps figure, RevTe