Feasibility of a novel neurofeedback system: a parallel randomized single-blinded pilot study.

Neurocognitive assessment tools have been proposed to optimize, maintain, and improve perceptual-cognitive performance. Here, we investigated the feasibility and efficacy of a novel neurofeedback system, neuroMoon (nM), on cognitive abilities compared with one of the most popular perceptual-cognitive training (PCT) tools both in sports and rehabilitation called NeuroTracker (NT). Thirty-one young athletes performed a comprehensive battery of cognitive tests from the Vienna Test System before and after a 12-session computer-based cognitive training program using nM (n = 11, age 22.6 ± 3.8 years), nM sham (CON, n = 10, age 20.3 ± 1.2 years) or NT (n = 10, age 20.5 ± 1.7 years) device. A series of repeated-measures ANOVA was performed to detect changes in cognitive abilities in response to the training. Participants had faster median reaction time in both the color-naming and word-reading conditions of the Stroop test (all p < 0.005), regardless of group. Regarding the task switching test, statistical analysis indicated faster working time and mean reaction time of the incongruent stimuli, repetition task, and shifting task (all p < 0.005), nevertheless, these changes were also regardless of group. In addition, we found fewer omitted (pre: 17.5 ± 8.3, post: 6.4 ± 1.5, d = 1.311) and more correct (pre: 261.6 ± 36.1, post: 278.6 ± 38.7, d = - 1.020) post-intervention answers in the determination test, regardless of group. Finally, participants in each group performed the digit span backward test with larger post (6.42 ± 1.54) vs. pre (5.55 ± 1.43) scores following the PCT (d = - 0.801). Overall, PCT with nM as compared with NT induced similar results in cognitive abilities suggesting its potential to be used to achieve and maintain better mental performance. However, considering that the sham stimulation also induced similar improvements in cognitive abilities, future studies should clearly determine the cognitive measures that could benefit from NF training.

PlatypOUs-A Mobile Robot Platform and Demonstration Tool Supporting STEM Education.

Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile robot platform-named PlatypOUs-which can be remotely controlled via an electromyography (EMG) appliance using the MindRove brain-computer interface (BCI) headset as a sensor for the purpose of signal acquisition. The gathered bio-signals are classified by a Support Vector Machine (SVM) whose results are translated into motion commands for the mobile platform. Along with the physical mobile robot platform, a virtual environment was implemented using Gazebo (an open-source 3D robotic simulator) inside the Robot Operating System (ROS) framework, which has the same capabilities as the real-world device. This can be used for development and test purposes. The main goal of the PlatypOUs project is to create a tool for STEM education and extracurricular activities, particularly laboratory practices and demonstrations. With the physical robot, the aim is to improve awareness of STEM outside and beyond the scope of regular education programmes. It implies several disciplines, including system design, control engineering, mobile robotics and machine learning with several application aspects in each. Using the PlatypOUs platform and the simulator provides students and self-learners with a firsthand exercise, and teaches them to deal with complex engineering problems in a professional, yet intriguing way.

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution.

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 F1scores 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.

Spike detection and sorting with deep learning.

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.

IL-18 induces a marked gene expression profile change and increased Ccl1 (I-309) production in mouse mucosal mast cell homologs.

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.

Histamine suppresses fibulin-5 and insulin-like growth factor-II receptor expression in melanoma.

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 matrix-bound, latent transforming growth factor-beta1, 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.

Fine-needle aspiration cytology of medullary breast carcinoma: report of two cases and review of the literature with emphasis on differential diagnosis.

Medullary carcinoma is a rare variant of breast carcinoma with a relatively good clinical prognosis as strictly defined. Characteristic features on fine-needle aspiration cytology (FNAC) allow medullary carcinoma to be considered in the cytologic differential diagnosis. We present two FNAC cases with such features, including high cellularity with clusters and single intact malignant cells, bizarre stripped nuclei with prominent nucleoli in a lymphoplasmacytic background, and illustrate one case in liquid-based preparation. Surgical excision revealed that one patient had medullary carcinoma, while the second patient had high-grade infiltrating ductal carcinoma. Breast FNAC samples with syncytial fragments, bizarre nuclei with prominent nucleoli, and a chronic inflammatory infiltrate should raise the possibility of medullary carcinoma. However, the differential diagnosis also includes high-grade ductal carcinoma, lymphoma, or metastasis to breast or intramammary lymph nodes; thus, histopathologic analysis is required for definitive diagnosis.

Epithelioid malignant peripheral nerve sheath tumor in the infraorbital nerve.

Epithelioid malignant peripheral nerve sheath tumors are rarely found in cranial nerves. This report describes one such tumor originating from the infraorbital nerve in a 55-year-old man who presented with diplopia and right cheek numbness. Imaging showed a mass along the floor of the right orbit, which was excised. Histology suggested melanoma or carcinoma. Immunohistochemical staining was positive for S-100 and negative for HMB-45 and cytokeratin. Four other cases from the literature involving cranial nerves are discussed.