Turing patterns on networks

Turing patterns formed by activator-inhibitor systems on networks are considered. The linear stability analysis shows that the Turing instability generally occurs when the inhibitor diffuses sufficiently faster than the activator. Numerical simulations, using a prey-predator model on a scale-free random network, demonstrate that the final, asymptotically reached Turing patterns can be largely different from the critical modes at the onset of instability, and multistability and hysteresis are typically observed. An approximate mean-field theory of nonlinear Turing patterns on the networks is constructed.Comment: 4 pages, 4 figure

Free-Shape Polygonal Object Localization

Polygonal objects are prevalent in man-made scenes. Early approaches to detecting them relied mainly on geometry while subsequent ones also incorporated appearance-based cues. It has recently been shown that this could be done fast by searching for cycles in graphs of line-fragments, provided that the cycle scoring function can be expressed as additive terms attached to individual fragments. In this paper, we propose an approach that eliminates this restriction. Given a weighted line-fragment graph, we use its cyclomatic number to partition the graph into managebly-sized sub-graphs that preserve nodes and edges with a high weight and are most likely to contain object contours. Object contours are then detected as maximally scoring elementary circuits enumerated in each sub-graph. Our approach can be used with any cycle scoring function and multiple candidates that share line fragments can be found. This is unlike in other approaches that rely on a greedy approach to finding candidates. We demonstrate that our approach significantly outperforms the state-of-the-art for the detection of building rooftops in aerial images and polygonal object categories from ImageNet

A conserved sequence in calmodulin regulated spectrin-associated protein 1 links its interaction with spectrin and calmodulin to neurite outgrowth

Calmodulin regulated spectrin-associated protein 1 (CAMSAP1) is a vertebrate microtubule-binding protein, and a representative of a family of cytoskeletal proteins that arose with animals. We reported previously that the central region of the protein, which contains no recognized functional domain, inhibited neurite outgrowth when over-expressed in PC12 cells [Baines et al., Mol. Biol. Evol. 26 (2009), p. 2005]. The CKK domain (DUF1781) binds microtubules and defines the CAMSAP/ssp4 family of animal proteins (Baines et al. 2009). In the central region, three short well-conserved regions are characteristic of CAMSAP-family members. One of these, CAMSAP-conserved region 1 (CC1), bound to both ?II?1-spectrin and Ca2+/calmodulin in vitro. The binding of Ca2+/calmodulin inhibited spectrin binding. Transient expression of CC1 in PC12 cells inhibited neurite outgrowth. siRNA knockdown of CAMSAP1 inhibited neurite outgrowth in PC12 cells or primary cerebellar granule cells: this could be rescued in PC12 cells by wild-type CAMSAP1-enhanced green fluorescent protein, but not by a CC1 mutant. We conclude that CC1 represents a functional region of CAMSAP1, which links spectrin-binding to neurite outgrowth

T2 mapping of the sacroiliac joints in patients with axial spondyloarthritis

Purpose: To test whether T2 mapping of the sacro-iliac joints (SIJs) might help identifying patients with spondyloarthritis. Method: This study included 20 biologic-naive patients with axial spondyloarthritis (10 females; mean age: 38 ± 9years; range, 19–47) and 27 controls (16 males; mean age = 39 ± 13years; range = 28–71) who prospectively underwent SIJs MRI at 1.5 T, including a multislice multiecho spin-echo sequence. Standard MRIs were reviewed to assess the SIJs according to the Assessment of SpondyloArthritis International Society (ASAS) criteria and SPondyloArthritis Research Consortium of Canada (SPARCC) MRI index. T2 maps obtained from multiecho sequences were used to draw regions of interests in the cartilaginous part of the SIJs. Disease activity was assessed using BASDAI questionnaire. Bland-Altman method, ROC curve analysis, Chi square, Mann-Whitney U, Pearson's and Spearman's correlation coefficient were used for data analysis. Results: According to ASAS criteria, MRI was positive for sacroiliitis in 5/20 patients (25 %). Inter-observer reproducibility of T2 values was 87 % (coefficient of repeatability = 7.0; bias = 0.49; p < .001). Mean T2 values of patients (58.5 ± 4.4 ms, range: 52.6–68.2 ms) were significantly higher (p < .001) than those of controls (44.1 ± 6.6 ms, range: 33.6–67.2 ms). A T2 value of 52.51 ms yielded 100 % sensitivity and 91.7 % specificity to differentiate patients from controls. No statistically significant association/correlation was found between T2 values and BASDAI (r=˗.026, p = .827), disease duration (r = .024, p = .871), SPARCC (r=-.004, p = .981), ASAS criteria (p = .476), HLA-B27-positivity (p = .139), age (r=-.2.53, p = .891), and gender (p = .404). Conclusions: T2 relaxation times of the SIJs were significantly higher in patients than in healthy controls, making this tool potentially helpful to early identify patients with spondyloarthritis