Dynamical strategies for obstacle avoidance during Dictyostelium discoideum aggregation: a Multi-agent system model

Chemotaxis, the movement of an organism in response to chemical stimuli, is a typical feature of many microbiological systems. In particular, the social amoeba \textit{Disctyostelium discoideum} is widely used as a model organism, but it is not still clear how it behaves in heterogeneous environments. A few models focusing on mechanical features have already addressed the question; however, we suggest that phenomenological models focusing on the population dynamics may provide new meaningful data. Consequently, by means of a specific Multi-agent system model, we study the dynamical features emerging from complex social interactions among individuals belonging to amoeba colonies.\\ After defining an appropriate metric to quantitatively estimate the gathering process, we find that: a) obstacles play the role of local topological perturbation, as they alter the flux of chemical signals; b) physical obstacles (blocking the cellular motion and the chemical flux) and purely chemical obstacles (only interfering with chemical flux) elicit similar dynamical behaviors; c) a minimal program for robustly gathering simulated cells does not involve mechanisms for obstacle sensing and avoidance; d) fluctuations of the dynamics concur in preventing multiple stable clusters. Comparing those findings with previous results, we speculate about the fact that chemotactic cells can avoid obstacles by simply following the altered chemical gradient. Social interactions are sufficient to guarantee the aggregation of the whole colony past numerous obstacles

Data-Adaptive Wavelets and Multi-Scale Singular Spectrum Analysis

Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series of length $N$ whose intermittency can give rise to the divergence of their variance. SSA relies on the construction of the lag-covariance matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W = M Dt, with Dt the time step. The proposed multi-scale SSA is a local SSA analysis within a moving window of width M <= W <= N. Multi-scale SSA varies W, while keeping a fixed W/M ratio, and uses the eigenvectors of the corresponding lag-covariance matrix C_M as a data-adaptive wavelets; successive eigenvectors of C_M correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency domain, by a suitable localization of the signal's covariance matrix. We present several examples of application to synthetic signals with fractal or power-law behavior which mimic selected features of certain climatic and geophysical time series. A real application is to the Southern Oscillation index (SOI) monthly values for 1933-1996. Our methodology highlights an abrupt periodicity shift in the SOI near 1960. This abrupt shift between 4 and 3 years supports the Devil's staircase scenario for the El Nino/Southern Oscillation phenomenon.Comment: 24 pages, 19 figure

Post-Baccalaureate Wage Growth within Four Years of Graduation: The Effects of College Quality and College Major

This paper examines the impact of college quality and academic major on the earnings of a nationally representative sample of baccalaureate recipients. We extend previous work in this area by analyzing the magnitude of change in the influence of these factors at two points in the early career of these graduates. Our results demonstrate that, despite significant variation, graduates from higher quality colleges enjoy a greater rate of growth in earnings during their early career. We also show that growth in earnings varies significantly by the graduates’ major field of study. Wage growth for women and racial minorities is also examined