Accumulated chilling was estimated by applying three different models to the hourly autumn-winter temperature records from Santiago (33°34 S lat; 625 m.a.s.l.) and Vicuña (30°02´ S lat; 643 m.a.s.l.) for the years 2005 and 2006. The model of chilling hours, currently used in Chile as an agroclimatic indicator, was of limited use for effectively contrasting a subtropical condition (Vicuña) with a temperate area such as Santiago. The application of the Utah model gave negative values from March to May, and even up to June in Vicuña, since in this model the chilling effect is "negated" by warmer temperatures. However, a modified version of the Utah model named Positive Chilling Units (PCU), in which negative values are omitted, showed differences in the accumulated chilling between both regions, although these differences were of small magnitude and were noted only from July onwards. The Dynamic Model, which considers that chilling is irreversibly accumulated as quantum or Chill Portions (CP), showed that chilling in Santiago doubled that of Vicuña, and that these differences in location were already expressed at the beginning of autumn, confirming, thus, the suitability of the model for subtropical conditions. In this work the advantages of the dynamic model over other models are discussed.

Cycles are abundant in most kinds of networks, especially in biological ones. Here, we investigate their role in the evolution of a chemical reaction system from one self-sustaining composition of molecular species to another and their influence on the stability of these compositions. While it is accepted that, from a topological standpoint, they enhance network robustness, the consequence of cycles to the dynamics are not well understood. In a former study, we developed a necessary criterion for the existence of a fixed point, which is purely based on topological properties of the network. The structures of interest we identified were a generalization of closed autocatalytic sets, called chemical organizations. Here, we show that the existence of these chemical organizations and therefore steady states is linked to the existence of cycles. Importantly, we provide a criterion for a qualitative transition, namely a transition from one self-sustaining set of molecular species to another via the introduction of a cycle. Because results purely based on topology do not yield sufficient conditions for dynamic properties, e.g. stability, other tools must be employed, such as analysis via ordinary differential equations. Hence, we study a special case, namely a particular type of reflexive autocatalytic network. Applications for this can be found in nature, and we give a detailed account of the mitotic spindle assembly and spindle position checkpoints. From our analysis, we conclude that the positive feedback provided by these networks' cycles ensures the existence of a stable positive fixed point. Additionally, we use a genome-scale network model of the Escherichia coli sugar metabolism to illustrate our findings. In summary, our results suggest that the qualitative evolution of chemical systems requires the addition and elimination of cycles.

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Most neurons of the mammalian brain display intrinsic resonance with frequency selectivity (f R ) for inputs within the theta-range (4-10 Hz). Variations in network oscillation along this range depend on the animal behavior; however, whether neurons can dynamically tune their f R has not been addressed. Using slice electrophysiology, dynamic clamping and computer modeling we studied three types of cortical neurons, finding that the input resistance (R in ) inversely sets f R into the theta range, following a power law. We demonstrate that physiological changes in R in modulate f R and response phase, serving as a mechanism that instantaneously tunes oscillatory responses. Moreover, these modulations are translated into spiking regimes, modifying spike frequency and timing. Since synaptic inputs reduce R in , this modulation provides a mean for adjusting the frequency and timing of firing of individual neurons in interplay with the network fluctuations. This might be a widespread property among resonant neurons.peer-reviewed)

No abstract

Conan is a C++ library created for the accurate and efficient modelling, inference and analysis of complex networks. It implements the generation and modification of graphs according to several published models, as well as the unexpensive computation of global and local network properties. Other features include network inference and community detection. Furthermore, Conan provides a Python interface to facilitate the use of the library and its integration in currently existing applications.Conan is available at http://github.com/rhz/conan/.