Separation quality of a geometric ratchet

We consider an experimentally relevant model of a geometric ratchet in which particles undergo drift and diffusive motion in a two-dimensional periodic array of obstacles, and which is used for the continuous separation of particles subject to different forces. The macroscopic drift velocity and diffusion tensor are calculated by a Monte-Carlo simulation and by a master-equation approach, using the correponding microscopic quantities and the shape of the obstacles as input. We define a measure of separation quality and investigate its dependence on the applied force and the shape of the obstacles

Granular packings of elongated faceted particles deposited under gravity

We report experimental and theoretical results of the effect that particle shape has on the packing properties of granular materials. We have systematically measured the particle angular distribution, the cluster size distribution and the stress profiles of ensembles of faceted elongated particles deposited in a bidimensional box. Stress transmission through this granular system has been numerically simulated using a two-dimensional model of irregular particles. For grains of maximum symmetry (squares), the stress propagation localizes and forms chain-like forces analogous to those observed for granular materials composed of spheres. For thick layers of grains, a pressure saturation is observed for deposit depths beyond a characteristic length. This scenario correlates with packing morphology and can be understood in terms of stochastic models of aggregation and random multiplicative processes. As grains elongate and lose their symmetry, stress propagation is strongly affected. Lateral force transmission becomes less favored than vertical transfer, and hence, an increase in the pressure develops with depth, hindering force saturation

Rectification and Phase Locking for Particles on Two Dimensional Periodic Substrates

We show that a novel rectification phenomena is possible for overdamped particles interacting with a 2D periodic substrate and driven with a longitudinal DC drive and a circular AC drive. As a function of DC amplitude, the longitudinal velocity increases in a series of quantized steps with transverse rectification occuring near these transitions. We present a simple model that captures the quantization and rectification behaviors.Comment: 4 pages, 4 postscript figure

Superconducting Fluxon Pumps and Lenses

We study stochastic transport of fluxons in superconductors by alternating current (AC) rectification. Our simulated system provides a fluxon pump, "lens", or fluxon "rectifier" because the applied electrical AC is transformed into a net DC motion of fluxons. Thermal fluctuations and the asymmetry of the ratchet channel walls induce this "diode" effect, which can have important applications in devices, like SQUID magnetometers, and for fluxon optics, including convex and concave fluxon lenses. Certain features are unique to this novel two-dimensional (2D) geometric pump, and different from the previously studied 1D ratchets.Comment: Phys. Rev. Lett. 83, in press (1999); 4 pages, 5 .gif figures; figures also available at http://www-personal.engin.umich.edu/~nori/ratche

Breaking of general rotational symmetries by multi-dimensional classical ratchets

We demonstrate that a particle driven by a set of spatially uncorrelated, independent colored noise forces in a bounded, multidimensional potential exhibits rotations that are independent of the initial conditions. We calculate the particle currents in terms of the noise statistics and the potential asymmetries by deriving an n-dimensional Fokker-Planck equation in the small correlation time limit. We analyze a variety of flow patterns for various potential structures, generating various combinations of laminar and rotational flows.Comment: Accepted, Physical Review

GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence—An overview in the context of health decision-making

Objectives: The objective of the study is to present the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) conceptual approach to the assessment of certainty of evidence from modeling studies (i.e., certainty associated with model outputs). / Study Design and Setting: Expert consultations and an international multidisciplinary workshop informed development of a conceptual approach to assessing the certainty of evidence from models within the context of systematic reviews, health technology assessments, and health care decisions. The discussions also clarified selected concepts and terminology used in the GRADE approach and by the modeling community. Feedback from experts in a broad range of modeling and health care disciplines addressed the content validity of the approach. / Results: Workshop participants agreed that the domains determining the certainty of evidence previously identified in the GRADE approach (risk of bias, indirectness, inconsistency, imprecision, reporting bias, magnitude of an effect, dose–response relation, and the direction of residual confounding) also apply when assessing the certainty of evidence from models. The assessment depends on the nature of model inputs and the model itself and on whether one is evaluating evidence from a single model or multiple models. We propose a framework for selecting the best available evidence from models: 1) developing de novo, a model specific to the situation of interest, 2) identifying an existing model, the outputs of which provide the highest certainty evidence for the situation of interest, either “off-the-shelf” or after adaptation, and 3) using outputs from multiple models. We also present a summary of preferred terminology to facilitate communication among modeling and health care disciplines. / Conclusion: This conceptual GRADE approach provides a framework for using evidence from models in health decision-making and the assessment of certainty of evidence from a model or models. The GRADE Working Group and the modeling community are currently developing the detailed methods and related guidance for assessing specific domains determining the certainty of evidence from models across health care–related disciplines (e.g., therapeutic decision-making, toxicology, environmental health, and health economics)

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency’s ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some–but not all–cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals