Reliability and variability of neuronal activity are both thought to be important for the proper function of neuronal networks. The crustacean pyloric rhythm (~1 Hz) is driven by a group of pacemaker neurons (AB/PD) which inhibit and burst out of phase with all follower pyloric neurons. The only known chemical synaptic feedback to the pacemakers is an inhibitory synapse from the follower LP neuron. Although this synapse has been extensively studied, its role in the generation and coordination of the pyloric rhythm is unknown. We examine the hypothesis that this synapse acts to stabilize the oscillation by reducing the variability in cycle period on a cycle-by-cycle basis. Our experimental data show that functionally removing the LP to PD synapse by hyperpolarizing the LP neuron increases the pyloric period variability. The increase in pyloric rhythm stability in the presence of the LP-to-PD synapse is demonstrated by a decrease in the amplitude of the phase response curve of the PD neuron. These experimental results are explained by a reduced mathematical model. Phase plane analysis on this model demonstrates that the effect of the periodic inhibition is to produce asymptotic stability in the oscillation phase which leads to a reduction of variability of the oscillation cycle period.

Memory impairment is often associated with disrupted regulation of gene induction. For example, deficits in cAMP response elementbinding protein (CREB) binding protein (CBP; an essential cofactor for activation of transcription by CREB) impair long-term synaptic plasticity and memory. Previously, we showed that small interfering RNA (siRNA)-induced knockdown of CBP in individual sensory neurons significantly reduced levels of CBP and impaired 5-HT-induced long-term facilitation (LTF) in sensorimotor cocultures from Aplysia. Moreover, computational simulations of the biochemical cascades underlying LTF successfully predicted training protocols that restored LTF following CBP knockdown. We examined whether simulations could also predict a training protocol that restores LTF impaired by siRNA-induced knockdown of the transcription factor CREB1. Simulations based on a previously described model predicted rescue protocols that were specific to CREB1 knockdown. Empirical studies demonstrated that one of these rescue protocols partially restored impaired LTF. In addition, the effectiveness of the rescue protocol was enhanced by pretreatment with rolipram, a selective cAMP phosphodiesterase inhibitor. These results provide further evidence that computational methods can help rescue disruptions in signaling cascades underlying memory formation. Moreover, the study demonstrates that the effectiveness of computationally designed training protocols can be enhanced with complementary pharmacological approaches.

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Magnetoacoustic tomography with magnetic induction (MAT-MI) was recently introduced as a noninvasive electrical conductivity imaging approach with high spatial resolution close to ultrasound imaging. In the present study, we test the feasibility of the MAT-MI method for breast tumor imaging using numerical modeling and computer simulation. Using the finite element method, we have built three dimensional numerical breast models with varieties of embedded tumors for this simulation study. In order to obtain an accurate and stable forward solution that does not have numerical errors caused by singular MAT-MI acoustic sources at conductivity boundaries, we first derive an integral forward method for calculating MAT-MI acoustic sources over the entire imaging volume. An inverse algorithm for reconstructing the MAT-MI acoustic source is also derived with spherical measurement aperture, which simulates a practical setup for breast imaging. With the numerical breast models, we have conducted computer simulations under different imaging parameter setups and all the results suggest that breast tumors that have large conductivity contrast to its surrounding tissues as reported in literature may be readily detected in the reconstructed MAT-MI images. In addition, our simulations also suggest that the sensitivity of imaging breast tumors using the presented MAT-MI setup depends more on the tumor location and the conductivity contrast between the tumor and its surrounding tissues than on the tumor size.

Aplysia sensorimotor synapses provide a useful model system for analyzing molecular processes that contribute to heterosynaptic plasticity. For example, previous studies demonstrated that multiple kinase cascades contribute to serotonin (5-HT)-induced short-term synaptic facilitation (STF), including protein kinase A (PKA) and protein kinase C (PKC). Moreover, the contribution of each kinase is believed to depend on the state of the synapse (e.g., depressed or nondepressed) and the time after application of 5-HT. Here, a previously unappreciated role for PKC-dependent processes was revealed to underlie the maintenance of STF at relatively nondepressed synapses. This PKC dependence was revealed when the synapse was stimulated repeatedly after application of 5-HT. The contributions of the PKA and PKC pathways were examined by blocking adenylyl cyclase-coupled 5-HT receptors with methiothepin and by blocking PKC with chelerythrine. STF was assessed 20 s after 5-HT application. The effects of PKC were consistent with enhanced mobilization of transmitter, as assessed by application of hypertonic sucrose solutions to measure the readily releasable pool of vesicles and recovery of the readily releasable pool after depletion. A computational model of transmitter release demonstrated that a PKC-dependent mobilization process was sufficient to explain the maintenance of STF at nondepressed synapses and the facilitation of depressed synapses.

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To investigate time-variant and nonlinear characteristics in industrial processes, a soft sensor modelling method based on time difference moving-window recursive partial least square (PLS) and adaptive model updating is proposed. In this method, time difference values of input and output variables are used as training samples to construct the model, which can reduce the effects of the nonlinear characteristic on modelling accuracy and retain the advantages of recursive PLS algorithm. To solve the high updating frequency of the model, a confidence value is introduced, which can be updated adaptively according to the results of the model performance assessment. Once the confidence value is updated, the model can be updated. The proposed method has been used to predict the 4-CBA (i.e. carboxy-benz-aldehyde) content in the PTA (i.e. purified terephthalic acid) oxidation reaction process. The results show that the proposed soft sensor modelling method can reduce computation effectively, improve prediction accuracy by making use of process information and reflects the process characteristics accurately.