Quantifying Yersinia pseudotuberculosis Type III Secretion System Activity Following Iron Starvation and Anaerobic Growth.

A key virulence mechanism for many Gram-negative pathogens is the type III secretion system (T3SS), a needle-like appendage that translocates cytotoxic or immunomodulatory effector proteins into host cells. The T3SS is a target for antimicrobial discovery campaigns since it is accessible extracellularly and largely absent from non-pathogenic bacteria. Recent studies demonstrated that the T3SS of Yersinia and Salmonella are regulated by factors responsive to iron and oxygen, which are important niche-specific signals encountered during mammalian infection. Described here is a method for iron starvation of Yersinia pseudotuberculosis, with subsequent optional supplementation of inorganic iron. To assess the impact of oxygen availability, this iron starvation process is demonstrated under both aerobic and anaerobic conditions. Finally, incubating the cultures at the mammalian host temperature of 37 °C induces T3SS expression and allows quantification of Yersinia T3SS activity by visualizing effector proteins released into the supernatant. The steps detailed here offer an advantage over the use of iron chelators in the absence of iron starvation, which is insufficient for inducing robust iron starvation, presumably due to efficient Yersinia iron uptake and scavenging systems. Likewise, acid-washing laboratory glassware is detailed to ensure the removal of residual iron, which is essential for inducing robust iron starvation. Additionally, using a chelating agent is described to remove residual iron from media, and culturing the bacteria for several generations in the absence of iron to deplete bacterial iron stores. By incorporating standard protocols of trichloroacetic acid-induced protein precipitation, SDS-PAGE, and silver staining, this procedure demonstrates accessible ways to measure T3SS activity. While this procedure is optimized for Y. pseudotuberculosis, it offers a framework for studies in pathogens with similar robust iron uptake systems. In the age of antibiotic resistance, these methods can be expanded to assess the efficacy of antimicrobial compounds targeting the T3SS under host-relevant conditions.

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.