Two-lane traffic-flow model with an exact steady-state solution

We propose a stochastic cellular-automaton model for two-lane traffic flow based on the misanthrope process in one dimension. The misanthrope process is a stochastic process allowing for an exact steady-state solution; hence we have an exact flow-density diagram for two lane traffic. In addition, we introduce two parameters that indicate respectively driver's driving-lane preference and passing-lane priority. Due to the additional parameters, the model shows a deviation of the density ratio for driving-lane use and a biased lane-efficiency in flow. Then, a mean-field approach explicitly describes the asymmetric flow by the hop rates, the driving-lane preference, and the passing-lane priority. Meanwhile, the simulation results are in good agreement with an observational data, and we thus estimate these parameters. We conclude that the proposed model successfully produces two-lane traffic flow particularly with the driving-lane preference and the passing-lane priority.Comment: 8 pages, 3 figure

Exact solution of the zero-range process: fundamental diagram of the corresponding exclusion process

In this paper, we propose a general way of computing expectation values in the zero-range process, using an exact form of the partition function. As an example, we provide the fundamental diagram (the flux-density plot) of the asymmetric exclusion process corresponding to the zero-range process.We express the partition function for the steady state by the Lauricella hypergeometric function, and thereby have two exact fundamental diagrams each for the parallel and random sequential update rules. Meanwhile, from the viewpoint of equilibrium statistical mechanics, we work within the canonical ensemble but the result obtained is certainly in agreement with previous works done in the grand canonical ensemble.Comment: 12 pages, 2 figure

Ultra-discrete Optimal Velocity Model: a Cellular-Automaton Model for Traffic Flow and Linear Instability of High-Flux Traffic

In this paper, we propose the ultra-discrete optimal velocity model, a cellular-automaton model for traffic flow, by applying the ultra-discrete method for the optimal velocity model. The optimal velocity model, defined by a differential equation, is one of the most important models; in particular, it successfully reproduces the instability of high-flux traffic. It is often pointed out that there is a close relation between the optimal velocity model and the mKdV equation, a soliton equation. Meanwhile, the ultra-discrete method enables one to reduce soliton equations to cellular automata which inherit the solitonic nature, such as an infinite number of conservation laws, and soliton solutions. We find that the theory of soliton equations is available for generic differential equations, and the simulation results reveal that the model obtained reproduces both absolutely unstable and convectively unstable flows as well as the optimal velocity model.Comment: 9 pages, 6 figure

Exact solution and asymptotic behaviour of the asymmetric simple exclusion process on a ring

In this paper, we study an exact solution of the asymmetric simple exclusion process on a periodic lattice of finite sites with two typical updates, i.e., random and parallel. Then, we find that the explicit formulas for the partition function and the average velocity are expressed by the Gauss hypergeometric function. In order to obtain these results, we effectively exploit the recursion formula for the partition function for the zero-range process. The zero-range process corresponds to the asymmetric simple exclusion process if one chooses the relevant hop rates of particles, and the recursion gives the partition function, in principle, for any finite system size. Moreover, we reveal the asymptotic behaviour of the average velocity in the thermodynamic limit, expanding the formula as a series in system size.Comment: 10 page

IL-7 promotes long-term in vitro survival of unique long-lived memory subset generated from mucosal effector memory CD4(+) T cells in chronic colitis mice

Colitogenic memory CD4(+) T cells are important in the pathogenesis of inflammatory bowel disease (IBD). Although memory stem cells with high survival and self-renewal capacity were recently identified in both mice and humans, it is unclear whether a similar subset is present in chronic colitis mice. We sought to identify and purify a long-lived subset of colitogenic memory CD4(+) T cells, which may be targets for treatment of IBD. A long-lived subset of colitogenic memory CD4(+) T cells was purified using a long-term culture system. The characteristics of these cells were assessed. Interleukin (IL)-7 promoted the in vitro survival for >8 weeks of lamina propria (LP) CD4(+) T cells from colitic SOD mice previously injected with CD4(+)CD45RB(high) T cells. These cells were in a quiescent state and divided a maximum of 5 times in 4 weeks. LP CD4(+) T cells expressed higher levels of Bcl-2, integrin-alpha 4 beta 7, CXCR3 and CD25 after than before culture, as well as secreting high concentrations of IL-2 and low concentrations of IFN-gamma and IL-17 in response to intestinal bacterial antigens. LP CD4(+) T cells from colitic mice cultured with IL-7 for 8 weeks induced more severe colitis than LP CD4(+) T cells cultured for 4 weeks. We developed a novel culture system to purify a long-lived, highly pathogenic memory subset from activated LP CD4(+) T cells. IL-7 promoted long-term in vitro survival of this subset in a quiescent state. This subset will be a novel, effective target for the treatment of IBD

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10−4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.</p