Exploring inter-trial coherence for inner speech classification in EEG-based brain-computer interface.

Objective. In recent years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM).Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

Repression by the H-NS/YmoA histone-like protein complex enables IscR dependent regulation of the Yersinia T3SS.

The type III secretion system (T3SS) is an appendage used by many bacterial pathogens, such as pathogenic Yersinia, to subvert host defenses. However, because the T3SS is energetically costly and immunogenic, it must be tightly regulated in response to environmental cues to enable survival in the host. Here we show that expression of the Yersinia Ysc T3SS master regulator, LcrF, is orchestrated by the opposing activities of the repressive H-NS/YmoA histone-like protein complex and induction by the iron and oxygen-regulated IscR transcription factor. While deletion of iscR or ymoA has been shown to decrease and increase LcrF expression and type III secretion, respectively, the role of H-NS in this system has not been definitively established because hns is an essential gene in Yersinia. Using CRISPRi knockdown of hns, we show that hns depletion causes derepression of lcrF. Furthermore, we find that while YmoA is dispensable for H-NS binding to the lcrF promoter, YmoA binding to H-NS is important for H-NS repressive activity. We bioinformatically identified three H-NS binding regions within the lcrF promoter and demonstrate binding of H-NS to these sites in vivo using chromatin immunoprecipitation. Using promoter truncation and binding site mutation analysis, we show that two of these H-NS binding regions are important for H-NS/YmoA-mediated repression of the lcrF promoter. Surprisingly, we find that IscR is dispensable for lcrF transcription in the absence of H-NS/YmoA. Indeed, IscR-dependent regulation of LcrF and type III secretion in response to changes in oxygen, such as those Yersinia is predicted to experience during host infection, only occurs in the presence of an H-NS/YmoA complex. These data suggest that, in the presence of host tissue cues that drive sufficient IscR expression, IscR can act as a roadblock to H-NS/YmoA-dependent repression of RNA polymerase at the lcrF promoter to turn on T3SS expression.

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.

Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning.

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub, airports, or for evacuation of disaster zones. Many methodologies have been explored to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have become more popular in this kind of scenarios, given their robustness and scalability. In this paper, we implement an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be capable of solving the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we showcase some initial experimentation in a pair of robots with different mechanical characteristics. Further implementations of this work include communicating the robots for other tasks, such as teaching assistance, remote classes, and other innovations in higher education.

Genome Scale Analysis Reveals IscR Directly and Indirectly Regulates Virulence Factor Genes in Pathogenic Yersinia.

The iron-sulfur cluster coordinating transcription factor IscR is important for the virulence of Yersinia pseudotuberculosis and a number of other bacterial pathogens. However, the IscR regulon has not yet been defined in any organism. To determine the Yersinia IscR regulon and identify IscR-dependent functions important for virulence, we employed chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) of Y. pseudotuberculosis expressing or lacking iscR following iron starvation conditions, such as those encountered during infection. We found that IscR binds to the promoters of genes involved in iron homeostasis, reactive oxygen species metabolism, and cell envelope remodeling and regulates expression of these genes in response to iron depletion. Consistent with our previous work, we also found that IscR binds in vivo to the promoter of the Ysc type III secretion system (T3SS) master regulator LcrF, leading to regulation of T3SS genes. Interestingly, comparative genomic analysis suggested over 93% of IscR binding sites were conserved between Y. pseudotuberculosis and the related plague agent Yersinia pestis. Surprisingly, we found that the IscR positively regulated sufABCDSE Fe-S cluster biogenesis pathway was required for T3SS activity. These data suggest that IscR regulates the T3SS in Yersinia through maturation of an Fe-S cluster protein critical for type III secretion, in addition to its known role in activating T3SS genes through LcrF. Altogether, our study shows that iron starvation triggers IscR to coregulate multiple, distinct pathways relevant to promoting bacterial survival during infection. IMPORTANCE How bacteria adapt to the changing environment within the host is critical for their ability to survive and cause disease. For example, the mammalian host severely restricts iron availability to limit bacterial growth, referred to as nutritional immunity. Here, we show that pathogenic Yersinia use the iron-sulfur (Fe-S) cluster regulator IscR, a factor critical for pathogenesis, to sense iron availability and regulate multiple pathways known or predicted to contribute to virulence. Under low iron conditions that mimic those Yersinia encounter during infection, IscR levels increase, leading to modulation of genes involved in iron metabolism, stress resistance, cell envelope remodeling, and subversion of host defenses. These data suggest that IscR senses nutritional immunity to coordinate processes important for bacterial survival within the mammalian host.

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator.

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

Iron availability and oxygen tension regulate the Yersinia Ysc type III secretion system to enable disseminated infection.

The enteropathogen Yersinia pseudotuberculosis and the related plague agent Y. pestis require the Ysc type III secretion system (T3SS) to subvert phagocyte defense mechanisms and cause disease. Yet type III secretion (T3S) in Yersinia induces growth arrest and innate immune recognition, necessitating tight regulation of the T3SS. Here we show that Y. pseudotuberculosis T3SS expression is kept low under anaerobic, iron-rich conditions, such as those found in the intestinal lumen where the Yersinia T3SS is not required for growth. In contrast, the Yersinia T3SS is expressed under aerobic or anaerobic, iron-poor conditions, such as those encountered by Yersinia once they cross the epithelial barrier and encounter phagocytic cells. We further show that the [2Fe-2S] containing transcription factor, IscR, mediates this oxygen and iron regulation of the T3SS by controlling transcription of the T3SS master regulator LcrF. IscR binds directly to the lcrF promoter and, importantly, a mutation that prevents this binding leads to decreased disseminated infection of Y. pseudotuberculosis but does not perturb intestinal colonization. Similar to E. coli, Y. pseudotuberculosis uses the Fe-S cluster occupancy of IscR as a readout of oxygen and iron conditions that impact cellular Fe-S cluster homeostasis. We propose that Y. pseudotuberculosis has coopted this system to sense entry into deeper tissues and induce T3S where it is required for virulence. The IscR binding site in the lcrF promoter is completely conserved between Y. pseudotuberculosis and Y. pestis. Deletion of iscR in Y. pestis leads to drastic disruption of T3S, suggesting that IscR control of the T3SS evolved before Y. pestis split from Y. pseudotuberculosis.

Genome-wide identification of genes directly regulated by ChvI and a consensus sequence for ChvI binding in Sinorhizobium meliloti.

ExoS/ChvI two-component signaling in the nitrogen-fixing α-proteobacterium Sinorhizobium meliloti is required for symbiosis and regulates exopolysaccharide production, motility, cell envelope integrity and nutrient utilization in free-living bacteria. However, identification of many ExoS/ChvI direct transcriptional target genes has remained elusive. Here, we performed chromatin immunoprecipitation followed by microarray analysis (chIP-chip) to globally identify DNA regions bound by ChvI protein in S. meliloti. We then performed qRT-PCR with chvI mutant strains to test ChvI-dependent expression of genes downstream of the ChvI-bound DNA regions. We identified 64 direct target genes of ChvI, including exoY, rem and chvI itself. We also identified ChvI direct target candidates, like exoR, that are likely controlled by additional regulators. Analysis of upstream sequences from the 64 ChvI direct target genes identified a 15 bp-long consensus sequence. Using electrophoretic mobility shift assays and transcriptional fusions with exoY, SMb21440, SMc00084, SMc01580, chvI, and ropB1, we demonstrated this consensus sequence is important for ChvI binding to DNA and transcription of ChvI direct target genes. Thus, we have comprehensively identified ChvI regulon genes and a 'ChvI box' bound by ChvI. Many ChvI direct target genes may influence the cell envelope, consistent with the critical role of ExoS/ChvI in growth and microbe-host interactions.

Control of hmu Heme Uptake Genes in Yersinia pseudotuberculosis in Response to Iron Sources.

Despite the mammalian host actively sequestering iron to limit pathogenicity, heme (or hemin when oxidized) and hemoproteins serve as important sources of iron for many bloodborne pathogens. The HmuRSTUV hemin uptake system allows Yersinia species to uptake and utilize hemin and hemoproteins as iron sources. HmuR is a TonB-dependent outer membrane receptor for hemin and hemoproteins. HmuTUV comprise a inner membrane ABC transporter that transports hemin and hemoproteins from the periplasmic space into the bacterial cytoplasm, where it is degraded by HmuS. Here we show that hmuSTUV but not hmuR are expressed under iron replete conditions, whereas hmuR as well as hmuSTUV are expressed under iron limiting conditions, suggesting complex transcriptional control. Indeed, expression of hmuSTUV in the presence of inorganic iron, but not in the presence of hemin, requires the global regulator IscR acting from a promoter in the intergenic region between hmuR and hmuS. This effect of IscR appears to be direct by binding a site mapped by DNaseI footprinting. In contrast, expression of hmuR under iron limiting conditions requires derepression of the ferric uptake regulator Fur acting from the hmuR promoter, as Fur binding upstream of hmuR was demonstrated biochemically. Differential expression by both Fur and IscR would facilitate maximal hemin uptake and utilization when iron and heme availability is low while maintaining the capacity for periplasmic removal and cytosolic detoxification of heme under a wider variety of conditions. We also demonstrate that a Y. pseudotuberculosis ΔiscR mutant has a survival defect when incubated in whole blood, in which iron is sequestered by heme-containing proteins. Surprisingly, this phenotype was independent of the Hmu system, the type III secretion system, complement, and the ability of Yersinia to replicate intracellularly. These results suggest that IscR regulates multiple virulence factors important for Yersinia survival and growth in mammalian tissues and reveal a surprising complexity of heme uptake expression and function under differing conditions of iron.