Long-Range Interaction between Heterogeneously Charged Membranes

Despite their neutrality, surfaces or membranes with equal amounts of positive and negative charge can exhibit long-range electrostatic interactions if the surface charge is heterogeneous; this can happen when the surface charges form finite-size domain structures. These domains can be formed in lipid membranes where the balance of the different ranges of strong but short-ranged hydrophobic interactions and longer-ranged electrostatic repulsion result in a finite, stable domain size. If the domain size is large enough, oppositely charged domains in two opposing surfaces or membranes can be strongly correlated by the elecrostatic interactions; these correlations give rise to an attractive interaction of the two membranes or surfaces over separations on the order of the domain size. We use numerical simulations to demonstrate the existence of strong attractions at separations of tens of nanometers. Large line tensions result in larger domains but also increase the charge density within the domain. This promotes correlations and, as a result, increases the intermembrane attraction. On the other hand, increasing the salt concentration increases both the domain size and degree of domain anticorrelation, but the interactions are ultimately reduced due to increased screening. The result is a decrease in the net attraction as salt concentration is increased

Paths to accuracy for radiation parameterizations in atmospheric models

Radiative transfer is sufficiently well understood that its parameterization in atmospheric models is primarily an effort to balance computational cost and accuracy. The most common approach is to compute radiative transfer with the highest practical spectral accuracy but infrequently in time and/or space, though errors introduced by this approximation are difficult to quantify. An alternative is to perform spectrally sparse calculations frequently in time using randomly chosen spectral quadrature points. Here we show that purely random quadrature points, though effective in some large-eddy simulations, are not a good choice for models in which the land surface responds to radiative fluxes because surface temperature perturbations can be large enough, and persistent long enough, to affect model evolution. These errors may be mitigated by choosing teams of spectral points designed to limit the maximum surface flux error; teams, rather than individual quadrature points, are then chosen randomly. The approach is implemented in the ECHAM6 global model and the results are examined using “perfect-model” experiments on time scales ranging from a day to a month. In this application the approach introduces errors commensurate with the infrequent calculation of broadband calculations for the same computational cost. But because teams need not increase with size, and indeed may become better and more balanced with increased spectral density, improvements in radiative transfer may not need to be traded off against spatiotemporal sampling

Counterion density profiles at charged flexible membranes

Counterion distributions at charged soft membranes are studied using perturbative analytical and simulation methods in both weak coupling (mean-field or Poisson-Boltzmann) and strong coupling limits. The softer the membrane, the more smeared out the counterion density profile becomes and counterions pentrate through the mean-membrane surface location, in agreement with anomalous scattering results. Membrane-charge repulsion leads to a short-scale roughening of the membrane.Comment: 4 pages, 4 figure

The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6

The phrasing of the first of three questions motivating CMIP6 – “How does the Earth system respond to forcing?” – suggests that forcing is always well-known, yet the radiative forcing to which this question refers has historically been uncertain in coordinated experiments even as understanding of how best to infer radiative forcing has evolved. The Radiative Forcing Model Intercomparison Project (RFMIP) endorsed by CMIP6 seeks to provide a foundation for answering the question through three related activities: (i) accurate characterization of the effective radiative forcing relative to a near-preindustrial baseline and careful diagnosis of the components of this forcing; (ii) assessment of the absolute accuracy of clear-sky radiative transfer parameterizations against reference models on the global scales relevant for climate modeling; and (iii) identification of robust model responses to tightly specified aerosol radiative forcing from 1850 to present. Complete characterization of effective radiative forcing can be accomplished with 180 years (Tier 1) of atmosphere-only simulation using a sea-surface temperature and sea ice concentration climatology derived from the host model's preindustrial control simulation. Assessment of parameterization error requires trivial amounts of computation but the development of small amounts of infrastructure: new, spectrally detailed diagnostic output requested as two snapshots at present-day and preindustrial conditions, and results from the model's radiation code applied to specified atmospheric conditions. The search for robust responses to aerosol changes relies on the CMIP6 specification of anthropogenic aerosol properties; models using this specification can contribute to RFMIP with no additional simulation, while those using a full aerosol model are requested to perform at least one and up to four 165-year coupled ocean–atmosphere simulations at Tier 1

On the transitions in marine boundary layer cloudiness

Satellite observations and meteorological reanalysis are used to examine the transition from unbroken sheets of stratocumulus to fields of scattered cumulus, and the processes controlling them, in four subtropical oceans. A Lagrangian analysis suggests that both the transition, defined as the temporal evolution in cloudiness, and the processes driving the transition, are quite similar among the subtropical oceans. The increase in sea surface temperature and the associated decrease in lower tropospheric stability appear to play a far more important role in cloud evolution than other factors including changes in large scale divergence and upper tropospheric humidity. During the summer months, the transitions in marine boundary layer cloudiness appear so systematically that their characteristics obtained by documenting the flow of thousands of individual air masses are well reproduced by the mean (or climatological) fields of the different data sets. This highlights interesting opportunities for future observational and modeling studies of these transitions

Systematic Error Detection in Laboratory Medicine

Measurements in laboratory medicine have a degree of uncertainty; this uncertainty is often called “error” and refers to imprecisions and inaccuracies in measurement. This measurement error refers to the difference between the true value of the measured sample and the measured value. One of the types of error is systematic error, also called bias, because these errors errors are reproducible and skew the results consistently in the same direction. A common approach to identify systematic error is to use control samples with a method comparison approach. An alternative is use of statistical methods that analyze actual patient values either as an “Average of Normals” or a “Moving Patient Averages.” Fundamental questions should be decided before a quality control method is used: how are weights assigned to the results? Is preference given to more recent samples or to the older samples? How sensitive should the model be? In this chapter, we will expand the fundamental notion of systematic error and explain why it is difficult to identify and measure and current statistical methods that are used to detect systematic error or bias

Methodological Implications of Nonlinear Dynamical Systems Models for Whole Systems of Complementary and Alternative Medicine

This paper focuses on the worldview hypotheses and research design approaches from nonlinear dynamical complex systems (NDS) science that can inform future studies of whole systems of complementary and alternative medicine (WS-CAM), e.g., Ayurveda, traditional Chinese medicine, and homeopathy. The worldview hypotheses that underlie NDS and WS-CAM (contextual, organismic, interactive-integrative - Pepper, 1942) overlap with each other, but differ fundamentally from those of biomedicine (formistic, mechanistic). Differing views on the nature of causality itself lead to different types of study designs. Biomedical efficacy studies assume a simple direct mechanistic cause-effect relationship between a specific intervention and a specific bodily outcome, an assumption less relevant to WS-CAM outcomes. WS-CAM practitioners do not necessarily treat a symptom directly. Rather, they intervene to modulate an intrinsic central imbalance of the person as a system and to create a more favorable environmental context for the emergence of health, e. g., with dietary changes compatible with the constitutional type. The rebalancing of the system thereby fosters the emergence of indirect, diffuse, complex effects throughout the person and the person\u27s interactions with his/her environment. NDS theory-driven study designs thus have the potential for greater external and model validity than biomedically driven efficacy studies (e. g., clinical trials) for evaluating the indirect effects of WS-CAM practices. Potential applications of NDS analytic techniques to WS-CAM include characterizing different constitutional types and documenting the evolution and dynamics of whole-person healing and well-being over time. Furthermore, NDS provides models and methods for examining interactions across organizational scales, from genomic/proteomic/metabolomic networks to individuals and social groups

Exact Lyapunov Exponent for Infinite Products of Random Matrices

In this work, we give a rigorous explicit formula for the Lyapunov exponent for some binary infinite products of random $2\times 2$ real matrices. All these products are constructed using only two types of matrices, $A$ and $B$, which are chosen according to a stochastic process. The matrix $A$ is singular, namely its determinant is zero. This formula is derived by using a particular decomposition for the matrix $B$, which allows us to write the Lyapunov exponent as a sum of convergent series. Finally, we show with an example that the Lyapunov exponent is a discontinuous function of the given parameter.Comment: 1 pages, CPT-93/P.2974,late