Control of Italian ryegrass and Alexandergrass in corn using different corn sowing date, pre- and post-emergent herbicides.

Glyphosate-resistant (GR) Italian ryegrass (LOLMU) and Alexandergrass (URPLA) are troublesome weeds in corn cropping systems in Southern Brazil. The emergence pattern of those weeds is not uniform and may change according to the season?s environmental characteristics. Also, herbicide resistance has been diminishing the success of the weed control programs. The objectives of this study were to evaluate the influence of corn-sowing date on LOLMU and URPLA densities and their control provided by pre- and post-emergent herbicides. Field trials were conducted in two crop seasons in Southern Brazil consisting of three corn sowing date (August, September, and October) and the application of atrazine + S-metolachlor (residual) in corn pre-emergence in different post-emergence weed control programs with glyphosate, ammonium-glufosinate, nicosulfuron, and atrazine. The results indicated that the sowing date had a significant influence on LOLMU and URPLA densities. Corn sown in the earliest period was exposed to a higher LOLMU density, whereas corn sown in the latest period had a higher density of URPLA. Also, the application of residual herbicide at corn pre-emergence reduced both weed species densities and decreased the pressure for the control of glyphosate-resistant LOLMU for the post-emergence herbicides. The use of residual herbicides in corn pre-emergence is an efficient strategy to be considered in the LOLMU and URPLA control programs, followed by post-emergence application of glyphosate, ammonium-glufosinate, atrazine, and nicosulfuron

Spreading Patterns of the Influenza A (H1N1) Pandemic

We investigate the dynamics of the 2009 influenza A (H1N1/S-OIV) pandemic by analyzing data obtained from World Health Organization containing the total number of laboratory-confirmed cases of infections - by country - in a period of 69 days, from 26 April to 3 July, 2009. Specifically, we find evidence of exponential growth in the total number of confirmed cases and linear growth in the number of countries with confirmed cases. We also find that, i) at early stages, the cumulative distribution of cases among countries exhibits linear behavior on log-log scale, being well approximated by a power law decay; ii) for larger times, the cumulative distribution presents a systematic curvature on log-log scale, indicating a gradual change to lognormal behavior. Finally, we compare these empirical findings with the predictions of a simple stochastic model. Our results could help to select more realistic models of the dynamics of influenza-type pandemics

Evolution of scaling emergence in large-scale spatial epidemic spreading

Background: Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which is still hardly been clarified. Methodology/Principal Findings: In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States(U.S.) domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. Conclusions/Significance: The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.Comment: 24pages, 7figures, accepted by PLoS ON