BackgroundIn plant roots, auxin is critical for patterning and morphogenesis. It regulates cell elongation and division, the development and maintenance of root apical meristems, and other processes. In Arabidopsis, auxin distribution along the central root axis has several maxima: in the root tip, in the basal meristem and at the shoot/root junction. The distal maximum in the root tip maintains the stem cell niche. Proximal maxima may trigger lateral or adventitious root initiation.ResultsWe propose a reflected flow mechanism for the formation of the auxin maximum in the root apical meristem. The mechanism is based on auxin's known activation and inhibition of expressed PIN family auxin carriers at low and high auxin levels, respectively. Simulations showed that these regulatory interactions are sufficient for self-organization of the auxin distribution pattern along the central root axis under varying conditions. The mathematical model was extended with rules for discontinuous cell dynamics so that cell divisions were also governed by auxin, and by another morphogen Division Factor which combines the actions of cytokinin and ethylene on cell division in the root. The positional information specified by the gradients of these two morphogens is able to explain root patterning along the central root axis.ConclusionWe present here a plausible mechanism for auxin patterning along the developing root, that may provide for self-organization of the distal auxin maximum when the reverse fountain has not yet been formed or has been disrupted. In addition, the proximal maxima are formed under the reflected flow mechanism in response to periods of increasing auxin flow from the growing shoot. These events may predetermine lateral root initiation in a rhyzotactic pattern. Another outcome of the reflected flow mechanism - the predominance of lateral or adventitious roots in different plant species - may be based on the different efficiencies with which auxin inhibits its own transport in different species, thereby distinguishing two main types of plant root architecture: taproot vs. fibrous.

We define a class of probabilistic models in terms of an operator algebra of stochastic processes, and a representation for this class in terms of stochastic parameterized grammars. A syntactic specification of a grammar is formally mapped to semantics given in terms of a ring of operators, so that composition of grammars corresponds to operator addition or multiplication. The operators are generators for the time-evolution of stochastic processes. The dynamical evolution occurs in continuous time but is related to a corresponding discrete-time dynamics. An expansion of the exponential of such time-evolution operators can be used to derive a variety of simulation algorithms. Within this modeling framework one can express data clustering models, logic programs, ordinary and stochastic differential equations, branching processes, graph grammars, and stochastic chemical reaction kinetics. The mathematical formulation connects these apparently distant fields to one another and to mathematical methods from quantum field theory and operator algebra. Such broad expressiveness makes the framework particularly suitable for applications in machine learning and multiscale scientific modeling.

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First responders have been observed to be at an increased risk of cardio-vascular diseases compared to the general population with a high percentage of cardiac events occurring during mission execution. Continuous physiological monitoring during missions can be effective in reducing the number of fatalities. Real-time physiological data such as ECG can be collected using sensors worn on the body. This sensor data can be processed on the body itself or can be communicated over an ad hoc wireless network to the incident command center or base station located near by. First responder missions often take place inside building structures where network connectivity is intermittent. Intermittent connectivity can lead to loss of critical physiological data or delay in that information reaching the base station. Hence, some amount of local processing is needed in order to limit the data that is communicated. In this paper, we introduce a novel Hidden Markov Model based myocardial infarction detection approach. The fidelity of this approach can be adapted based on the processing power available. We present a peer-to-peer networking protocol for communication over disrupted networks. A low fidelity classifier is used to perform local processing and assign priorities to the data based on its criticality. It is complemented by a disruptionaware epidemic forwarding protocol for transferring first responder's physiological data to the base station. With prioritized epidemic forwarding and buffer eviction policy, our protocol increases packet delivery ratio and reduces networking delay when end-to-end route disruption occurs. Finally, we report the effect of network disruption on myocardial infarction detection rate and latency of detection and the improvements achieved by our protocol.