Data_Sheet_1_Limited predictability of body length in a fish population.docx

Recent theoretical studies have identified chaotic dynamics in eco-evolutionary models. Yet, empirical evidence for eco-evolutionary chaos in natural ecosystems is lacking. In this study, we combine analyses of empirical data and an eco-evolutionary model to uncover chaotic dynamics of body length in a fish population (northeast Arctic cod: Gadus morhua). Consistent with chaotic attractors, the largest Lyapunov exponent (LE) of empirical data is positive, and approximately matches the LE of the model calculation, thus suggesting the potential for chaotic dynamics in this fish population. We also find that the autocorrelation function (ACF) of both empirical data and eco-evolutionary model shows a similar lag of approximately 7 years. Our combined analyses of natural time series and mathematical models suggest that chaotic dynamics of a phenotypic trait may be driven by trait evolution. This finding supports a growing theory that eco-evolutionary feedbacks can produce chaotic dynamics.</p

Data_Sheet_2_Limited predictability of body length in a fish population.docx

Recent theoretical studies have identified chaotic dynamics in eco-evolutionary models. Yet, empirical evidence for eco-evolutionary chaos in natural ecosystems is lacking. In this study, we combine analyses of empirical data and an eco-evolutionary model to uncover chaotic dynamics of body length in a fish population (northeast Arctic cod: Gadus morhua). Consistent with chaotic attractors, the largest Lyapunov exponent (LE) of empirical data is positive, and approximately matches the LE of the model calculation, thus suggesting the potential for chaotic dynamics in this fish population. We also find that the autocorrelation function (ACF) of both empirical data and eco-evolutionary model shows a similar lag of approximately 7 years. Our combined analyses of natural time series and mathematical models suggest that chaotic dynamics of a phenotypic trait may be driven by trait evolution. This finding supports a growing theory that eco-evolutionary feedbacks can produce chaotic dynamics.</p

Nondestructive Intervention to Multi-Agent Systems through an Intelligent Agent

<div><p>For a given multi-agent system where the local interaction rule of the existing agents can not be re-designed, one way to intervene the collective behavior of the system is to add one or a few special agents into the group which are still treated as normal agents by the existing ones. We study how to lead a Vicsek-like flocking model to reach synchronization by adding special agents. A popular method is to add some simple leaders (fixed-headings agents). However, we add one intelligent agent, called ‘shill’, which uses online feedback information of the group to decide the shill's moving direction at each step. A novel strategy for the shill to coordinate the group is proposed. It is strictly proved that a shill with this strategy and a limited speed can synchronize every agent in the group. The computer simulations show the effectiveness of this strategy in different scenarios, including different group sizes, shill speed, and with or without noise. Compared to the method of adding some fixed-heading leaders, our method can guarantee synchronization for any initial configuration in the deterministic scenario and improve the <i>synchronization level</i> significantly in low density groups, or model with noise. This suggests the advantage and power of feedback information in intervention of collective behavior.</p></div

Examples of the moving route from location of agent 1 () to location of agent 2 () for the shill (starting from location ).

<p>The big dash-line square indicates the current group area (note that to show ideas of the shill route, for convenience, the group area shown here is supposed to be static. In fact, the actual consistent moving strategy considers the cases that normal agents are moving when the shill moves, i.e., the group area keeps changing, which is much more complicated.). Two moving routes for the shill are shown: <b>(a) a simple U-turn route</b>: first it goes forward to a location which is much far away from the whole group(dash line part (1)), then it makes a big U-turn (dash line parts of (2)–(3)–(4)) far away outside the group area and gets back to the left side of the whole group, finally goes forward and affects agent (dash line part (5)). Its heading is set to be zero in parts (1) and (5), while it can have different headings during (2)–(3)–(4) because there are no neighboring agents. <b>(b) A more efficient route</b> for the shill moving from agent to agent . Radius of the small dash line circle centered at the shill represents the neighborhood size which is the same as the radius of normal agents. The shill tries to find a shorter route which maintains a ‘safe’ distance (larger than ) away from any normal agent when its heading is not zero. It is much more efficient than the simple U-turn route.</p

ERP responses evoked by the names.

<p>A. Grand averaged waveforms of the two conditions at twelve representative electrodes. Waveforms are time-locked to the onset of the names. Negative is plotted upward. B. Topographies of the congruity effect (Incongruent vs. Congruent) of the critical names in two time windows. The electrodes that showed significant effects over 75% of the selected time windows were marked by *.</p

1‑Trifluoromethylisoquinolines from α‑Benzylated Tosylmethyl Isocyanide Derivatives in a Modular Approach

The preparation of various 1-trifluoromethylisoquinolines from α-benzylated tosylmethyl isocyanide derivatives and the commercial Togni reagent using a radical cascade is reported. The starting isocyanides are readily prepared in a modular sequence from commercial tosylmethyl isocyanide via sequential double α-alkylation, and the radical reaction proceeds under mild conditions with high efficiency without any transition-metal catalyst via electron catalysis. This valuable protocol has been successfully applied to the total synthesis of CF₃-mansouramycin B

Synchronization level(mean ) for respectively in the following cases: self-organized without any intervention; one intelligent shill with is added into the group; and fixed-heading simple shills are added into the group respectively.

<p>Synchronization level(mean ) for respectively in the following cases: self-organized without any intervention; one intelligent shill with is added into the group; and fixed-heading simple shills are added into the group respectively.</p

Examples of four sets of discourses.

<p> Note: The examples were originally in Chinese. The English translations are given in brackets below the original Chinese materials. The critical phrases that created violations are in boldface and italicized. The critical names are underlined.</p

Distribution of observed gender-specific overall crossover frequency in 22 autosomes

<p><b>Copyright information:</b></p><p>Taken from "A variance component analysis on recombination rate in the COGA pedigrees"</p><p></p><p>BMC Genetics 2005;6(Suppl 1):S58-S58.</p><p>Published online 30 Dec 2005</p><p>PMCID:PMC1866725.</p><p></p

Mean value (over 500 realizations) of during time evolution for and under 3 scenarios: (1)self-organized; (2)one intelligent agent with is added into the group; (3) ( = ) fixed-heading shills are added into the group.

<p>Mean value (over 500 realizations) of during time evolution for and under 3 scenarios: (1)self-organized; (2)one intelligent agent with is added into the group; (3) ( = ) fixed-heading shills are added into the group.</p