Objective. The extraction and identification of single-unit activities in intracortically recorded electric signals have a key role in basic neuroscience, but also in applied fields, like in the development of high-accuracy brain–computer interfaces. The purpose of this paper is to present our current results on the detection, classification and prediction of neural activities based on multichannel action potential recordings. Approach. Throughout our investigations, a deep learning approach utilizing convolutional neural networks and a combination of recurrent and convolutional neural networks was applied, with the latter used in case of spike detection and the former used for cases of sorting and predicting spiking activities. Main results. In our experience, the algorithms applied prove to be useful in accomplishing the tasks mentioned above: our detector could reach an average recall of 69%, while we achieved an average accuracy of 89% in classifying activities produced by more than 20 distinct neurons. Significance. Our findings support the concept of creating real-time, high-accuracy action potential based BCIs in the future, providing a flexible and robust algorithmic background for further development.
Objective. The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting. Approach. In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion. Main results. The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F1 scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points). Significance. ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.
Helminthic infections, which are particularly common in the developing world, are associated with the accumulation of mucosal mast cells (MMCs) in the epithelial layer of the gut. Although intestinal parasite infection models argue that IL-18 plays a role in MMC differentiation and function, the direct effect of IL-18 on MMCs is still not well understood. To clarify the role of IL-18 in mast cell biology, we analyzed gene expression changes in MMCs in vitro. DNA microarray technology uncovered a group of chemokines regulated by IL-18, among which Ccl1 (I-309, TCA-3) showed the highest up-regulation. Ccl1 induction was only transient in mast cells and was characteristic for both immature and mature MMCs, but not for connective tissue-type mast cells. IL-18 exerts its Ccl1-inducing effect in MMCs primarily via the activation of NFkappaB. Moreover, IL-18 was effective both in the absence and the presence of IgE-antigen complex. The Ccl1 receptor (CCR8) is known to be expressed by T(h)2 cells and is involved in their recruitment. Our present findings suggest that IL-18 may contribute to mast cell-influenced Th2 responses by inducing Ccl1 production.
We previously showed that transgenic enhancement of histamine production in B16-F10 melanomas strongly supports tumor growth in C57BL/6 mice. In the present study, gene expression profiles of transgenic mouse melanomas, secreting different amounts of histamine, were compared by whole genome microarrays. Array results were validated by real-time PCR, and genes showing histamine-affected behavior were further analyzed by immunohistochemistry. Regulation of histamine-coupled genes was investigated by checking the presence and functional integrity of all four known histamine receptors in experimental melanomas and by administering histamine H1 receptor (H1R) and H2 receptor (H2R) antagonists to tumor-bearing mice. Finally, an attempt was made to integrate histamine-affected genes in known gene regulatory circuits by in silico pathway analysis. Our results show that histamine enhances melanoma growth via H1R rather than through H2R. We show that H1R activation suppresses RNA-level expression of the tumor suppressor insulin-like growth factor II receptor (IGF-IIR) and the antiangiogenic matrix protein fibulin-5 (FBLN5), decreases their intracellular protein levels, and also reduces their availability in the plasma membrane and extracellular matrix, respectively. Pathway analysis suggests that because plasma membrane-bound IGF-IIR is required to activate matrixbound, latent transforming growth factor-B1, a factor suggested to sustain FBLN5 expression, the data can be integrated in a known antineoplastic regulatory pathway that is suppressed by H1R. On the other hand, we show that engagement of H2R also reduces intracellular protein pools of IGF-IIR and FBLN5, but being a downstream acting posttranslational effect with minimal consequences on exported IGF-IIR and FBLN5 protein levels, H2R is rather irrelevant compared with H1R in melanoma. [
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