Drag coefficients for partially inflated flat circular parachutes

Free-body tests of flat circular parachutes and determination of aerodynamic drag coefficients during partial inflatio

Theory of continuum percolation I. General formalism

The theoretical basis of continuum percolation has changed greatly since its beginning as little more than an analogy with lattice systems. Nevertheless, there is yet no comprehensive theory of this field. A basis for such a theory is provided here with the introduction of the Potts fluid, a system of interacting $s$-state spins which are free to move in the continuum. In the $s \to 1$ limit, the Potts magnetization, susceptibility and correlation functions are directly related to the percolation probability, the mean cluster size and the pair-connectedness, respectively. Through the Hamiltonian formulation of the Potts fluid, the standard methods of statistical mechanics can therefore be used in the continuum percolation problem.Comment: 26 pages, Late

Fractional diffusion in periodic potentials

Fractional, anomalous diffusion in space-periodic potentials is investigated. The analytical solution for the effective, fractional diffusion coefficient in an arbitrary periodic potential is obtained in closed form in terms of two quadratures. This theoretical result is corroborated by numerical simulations for different shapes of the periodic potential. Normal and fractional spreading processes are contrasted via their time evolution of the corresponding probability densities in state space. While there are distinct differences occurring at small evolution times, a re-scaling of time yields a mutual matching between the long-time behaviors of normal and fractional diffusion

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

Statin and metformin use and outcomes in patients with castration-resistant prostate cancer treated with enzalutamide: A meta-analysis of AFFIRM, PREVAIL and PROSPER

Castration-resistant prostate cancer; Metformin; Overall survivalCàncer de pròstata resistent a la castració; Metformina; Supervivència globalCáncer de próstata resistente a la castración; Metformina; Supervivencia globalBackground: Statins and metformin are commonly prescribed for patients, including those with prostate cancer. Preclinical and epidemiologic studies of each agent have suggested anti-cancer properties. Methods: Patient data from three randomised, double-blind, placebo-controlled, phase III studies evaluating enzalutamide (AFFIRM, PREVAIL and PROSPER) in patients with castration-resistant prostate cancer were included in this analysis. This post hoc, retrospective study examined the association of statin and metformin on radiographic progression-free survival (rPFS), metastasis-free survival (MFS), toxicity and overall survival (OS). After adjusting for available clinical prognostic variables, multivariate analyses were performed on pooled data from AFFIRM and PREVAIL, all three trials pooled, and each trial individually, to assess differential efficacy in these end-points associated with the baseline use of these medications. Results: In the multivariate analysis of the individual trials, OS and rPFS/MFS were not significantly influenced by statin or metformin use in AFFIRM or PROSPER. However, in PREVAIL, OS was significantly influenced by statin (hazard ratio [HR] 0.72; 95% confidence interval [CI] 0.59-0.89) and rPFS was significantly influenced by metformin (HR, 0.48; 95% CI 0.34-0.70). In pooled analyses, improved OS was significantly associated with statin use but not metformin use for AFFIRM+PREVAIL trials (HR 0.83; 95% CI 0.72-0.96) and AFFIRM+PREVAIL+PROSPER (HR 0.75; 95% CI 0.66-0.85). Conclusions: The association between statin or metformin use and rPFS, MFS and OS was inconsistent across three trials. Analyses of all three trials pooled and AFFIRM+PREVAIL pooled revealed that statin but not metformin use was significantly associated with a reduced risk of death in enzalutamide-treated patients. Additional prospective, controlled studies are warranted.This study was sponsored by Pfizer Inc. (New York, NY, USA) and Astellas Pharma, Inc. (Northbrook, IL, USA), the co-developers of enzalutamide. Medical writing and editorial support funded by the sponsors were provided by Stephanie Vadasz, PhD, and Dena McWain of Ashfield MedComms (an Ashfield Health company), Lauren Rainer, BSc, and Julie B. Stimmel, PhD, of Onyx (a Prime Global agency)

Scaling relation for determining the critical threshold for continuum percolation of overlapping discs of two sizes

We study continuum percolation of overlapping circular discs of two sizes. We propose a phenomenological scaling equation for the increase in the effective size of the larger discs due to the presence of the smaller discs. The critical percolation threshold as a function of the ratio of sizes of discs, for different values of the relative areal densities of two discs, can be described in terms of a scaling function of only one variable. The recent accurate Monte Carlo estimates of critical threshold by Quintanilla and Ziff [Phys. Rev. E, 76 051115 (2007)] are in very good agreement with the proposed scaling relation.Comment: 4 pages, 3 figure