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Itch can be induced by activation of small-diameter DRG neurons, which express abundant intracellular fibroblast growth factor 13 (FGF13). Although FGF13 is revealed to be essential for heat nociception, its role in mediating itch remains to be investigated. Here, we reported that loss of FGF13 in mouse DRG neurons impaired the histamine-induced scratching behavior. Calcium imaging showed that the percentage of histamine-responsive DRG neurons was largely decreased in FGF13-deficient mice; and consistently, electrophysiological recording exhibited that histamine failed to evoke action potential firing in most DRG neurons from these mice. Given that the reduced histamine-evoked neuronal response was caused by knockdown of FGF13 but not by FGF13A deficiency, FGF13B was supposed to mediate this process. Furthermore, overexpression of histamine Type 1 receptor H1R, but not H2R, H3R, nor H4R, increased the percentage of histamine-responsive DRG neurons, and the scratching behavior in FGF13-deficient mice was highly reduced by selective activation of H1R, suggesting that H1R is mainly required for FGF13-mediated neuronal response and scratching behavior induced by histamine. However, overexpression of H1R failed to rescue the histamine-evoked neuronal response in FGF13-deficient mice. Histamine enhanced the FGF13 interaction with NaV1.7. Disruption of this interaction by a membrane-permeable competitive peptide, GST-Flag-NaV1.7CT-TAT, reduced the percentage of histamine-responsive DRG neurons, and impaired the histamine-induced scratching, indicating that the FGF13/NaV1.7 interaction is a key molecular determinant in the histamine-induced itch sensation. Therefore, our study reveals a novel role of FGF13 in mediating itch sensation via the interaction of NaV1.7 in the peripheral nervous system. [ABSTRACT FROM AUTHOR]

Pancreatitis-associated proteins (PAPs) display multiple functions in visceral diseases. Previous studies showed that the expression level of PAP-I was low in the DRG of naive rats but was de novo expressed after peripheral nerve injury. However, its role in neuropathic pain remains unknown. We found that PAP-I expression was continuously upregulated in the DRG neurons from rat spared nerve injury models, and transported toward the spinal dorsal horn to act as a proinflammatory factor. Intrathecal delivery of PAP-I enhanced sensory hyperalgesia, whereas PAP-I deficiency by either gene knockout or antibody application alleviated tactile allodynia at the maintenance phase after spared nerve injury. Furthermore, PAP-I functioned by activating the spinal microglia via C-C chemokine receptor Type 2 that participated in neuropathic pain. Inhibition of either microglial activation or C-C chemokine receptor Type 2 abolished the PAP-I-induced hyperalgesia. Thus, PAP-I mediates the neuron-microglial crosstalk after peripheral nerve injury and contributes to the maintenance of neuropathic pain. [ABSTRACT FROM AUTHOR]

Background: Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity. Results: We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time. Conclusions: CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs. [ABSTRACT FROM AUTHOR]

The article examines the noisy chaotic neural networks with variable thresholds (NCNN-VT) approach in solving the frequency assignment problem in satellite communications. According to the study, the NCNN-VT approach obtains better solutions compared with gradual neural network on the benchmark examples. Compared to simulated annealing, the NCNN-VT finds optimal or suboptimal solutions with less computation costs. It also achieves better solutions with fewer iterations compared to transiently chaotic neural networks and noisy chaotic neural networks.

A new model of cellular neural networks (CNNs) with transient chaos is proposed by adding negative self-feedbacks into CNNs after transforming the dynamic equation to discrete time via Euler's method. The simulation on the single neuron model shows stable fix points, bifurcation and chaos. Hence, this new CNN model has richer and more flexible dynamics, and therefore may possess better capabilities of solving various problems, compared to the conventional CNN with only stable dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TOMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network. [ABSTRACT FROM AUTHOR]