The cytosolic lncRNA dutA affects STATa signaling and developmental commitment in Dictyostelium.

STATa is a pivotal transcription factor for Dictyostelium development. dutA is the most abundant RNA transcribed by RNA polymerase II in Dictyostelium, and its functional interplay with STATa has been suggested. This study demonstrates that dutA RNA molecules are distributed as spot-like structures in the cytoplasm, and that its cell type-specific expression changes dramatically during development. dutA RNA was exclusively detectable in the prespore region of slugs and then predominantly localized in prestalk cells, including the organizer region, at the Mexican hat stage before most dutA transcripts, excluding those in prestalk O cells, disappeared as culmination proceeded. dutA RNA was not translated into small peptides from any potential open reading frame, which confirmed that it is a cytoplasmic lncRNA. Ectopic expression of dutA RNA in the organizer region of slugs caused a prolonged slug migration period. In addition, buffered suspension-cultured cells of the strain displayed reduced STATa nuclear translocation and phosphorylation on Tyr702. Analysis of gene expression in various dutA mutants revealed changes in the levels of several STATa-regulated genes, such as the transcription factors mybC and gtaG, which might affect the phenotype. dutA RNA may regulate several mRNA species, thereby playing an indirect role in STATa activation.

Data-Adaptive Spatiotemporal ERP Cleaning for Single-Trial BCI Implementation.

This paper presents a data-adaptive approach to enhance the discriminative information of event-related potential (ERP) for the implementation of a brain-computer interface (BCI). The use of single-trial ERP in a real-time BCI application is challenging, due to its inherent noise contamination. Usually, multiple-trial ERPs are averaged to derive discriminative features of different classes by reducing their noise effects. Time-domain filtering is implemented here using an array wavelet transform. Sometimes, several channels can carry the signals, which are irrelevant to actual EPR information against the respective stimuli. A spatial filtering method based on clustering is introduced, to suppress such channels if any. Hence, the single-trial ERP is filtered in both the spatial and temporal domains to improve its discriminative features. The spatial-temporal discriminate analysis is employed to derive the features leading to the performance of target and non-target classification by using linear discriminant analysis. The proposed method is validated using a data set recorded from our experiments. The experimental results show that the performance of the proposed method is superior to that of the recently developed algorithms for single-trial ERP classification.

Complex sparse spatial filter for decoding mixed frequency and phase coded steady-state visually evoked potentials.

BACKGROUND: Mixed frequency and phase coding (FPC) can achieve the significant increase of the number of commands in steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI). However, the inconsistent phases of the SSVEP over channels in a trial and the existence of non-contributing channels due to noise effects can decrease accurate detection of stimulus frequency. NEW METHOD: We propose a novel command detection method based on a complex sparse spatial filter (CSSF) by solving ℓ1- and ℓ2,1-regularization problems for a mixed-coded SSVEP-BCI. In particular, ℓ2,1-regularization (aka group sparsification) can lead to the rejection of electrodes that are not contributing to the SSVEP detection. RESULTS: A calibration data based canonical correlation analysis (CCA) and CSSF with ℓ1- and ℓ2,1-regularization cases were demonstrated for a 16-target stimuli with eleven subjects. The results of statistical test suggest that the proposed method with ℓ1- and ℓ2,1-regularization significantly achieved the highest ITR. COMPARISON WITH EXISTING METHODS: The proposed approaches do not need any reference signals, automatically select prominent channels, and reduce the computational cost compared to the other mixed frequency-phase coding (FPC)-based BCIs. CONCLUSIONS: The experimental results suggested that the proposed method can be usable implementing BCI effectively with reduce visual fatigue.

SSVEP-based brain-computer interfaces using FSK-modulated visual stimuli.

A brain-computer interface (BCI) based on steady-state visual-evoked potentials (SSVEP) has two difficulties: limitation of the number of commands and uneven probabilities of command execution. To address these problems, the present paper proposes a paradigm of BCI using frequency-modulated visual stimuli. The commands are translated into code words consisting of binary digits, to which visual stimuli with distinct frequencies are assigned. Frequencies of SSVEP are recognized to detect bits, and a command to be executed is determined from the sequence of detected bits. Experimental results show that the proposed paradigm achieves a reliable BCI with higher accuracies and balanced command executing probabilities.