Influence of Modifications Related to Safe Operating Area Demands on Operation of a Specialized Medium/High-Voltage Unity-Gain Buffer

This paper presents safety-related modifications to the improved high-voltage unity-gain buffer and their impact on the operation quality of this circuit. The analyzed buffer architecture combines the virtues of source and gate followers. It provides high input impedance to the gate follower and voltage gain precision to the source follower while retaining a very simple structure and an extremely short signal path. These properties enable its various applications, e.g., as an interconnection of voltage and current mode function blocks in signal paths of medium- and high-voltage integrated circuits. The scrutinized buffer consists of MOS devices with different maximum interterminal voltages, which results in the necessity of enhancing its architecture with a set of safety devices to ensure non-destructive power-up, normal operation, and power-down phases of the buffer operation. The consequences of the implemented safety changes vs. the influence of the physical implementation process on the buffer operation capabilities are presented in comparison to its ancestral source and gate followers. The results show that the analyzed buffer retains the best signal processing quality among the compared buffer structures after the complete physical implementation process.

An approach of re-organizing input dataset to enhance the quality of emotion recognition using the bio-signals dataset of MIT

The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.

The Use of Supervised Learning Models in Studying Agonistic Behavior and Communication in Weakly Electric Fish

Despite considerable advances, studying electrocommunication of weakly electric fish, particularly in pulse-type species, is challenging as very short signal epochs at variable intervals from a few hertz up to more than 100 Hz need to be assigned to individuals. In this study, we show that supervised learning approaches offer a promising tool to automate or semiautomate the workflow, and thereby allowing the analysis of much longer episodes of behavior in a reasonable amount of time. We provide a detailed workflow mainly based on open resource software. We demonstrate the usefulness by applying the approach to the analysis of dyadic interactions of Gnathonemus petersii. Coupling of the proposed methods with a boundary element modeling approach, we are thereby able to model the information gained and provided during agonistic encounters. The data indicate that the passive electrosensory input, in particular, provides sufficient information to localize a contender during the pre-contest phase, fish did not use or rely on the theoretically also available sensory information of the contest outcome-determining size difference between contenders before engaging in agonistic behavior.

Auditory Attention Detection via Cross-Modal Attention

Humans show a remarkable perceptual ability to select the speech stream of interest among multiple competing speakers. Previous studies demonstrated that auditory attention detection (AAD) can infer which speaker is attended by analyzing a listener's electroencephalography (EEG) activities. However, previous AAD approaches perform poorly on short signal segments, more advanced decoding strategies are needed to realize robust real-time AAD. In this study, we propose a novel approach, i.e., cross-modal attention-based AAD (CMAA), to exploit the discriminative features and the correlation between audio and EEG signals. With this mechanism, we hope to dynamically adapt the interactions and fuse cross-modal information by directly attending to audio and EEG features, thereby detecting the auditory attention activities manifested in brain signals. We also validate the CMAA model through data visualization and comprehensive experiments on a publicly available database. Experiments show that the CMAA achieves accuracy values of 82.8, 86.4, and 87.6% for 1-, 2-, and 5-s decision windows under anechoic conditions, respectively; for a 2-s decision window, it achieves an average of 84.1% under real-world reverberant conditions. The proposed CMAA network not only achieves better performance than the conventional linear model, but also outperforms the state-of-the-art non-linear approaches. These results and data visualization suggest that the CMAA model can dynamically adapt the interactions and fuse cross-modal information by directly attending to audio and EEG features in order to improve the AAD performance.

Is So Called “Split Alpha” in EEG Spectral Analysis a Result of Methodological and Interpretation Errors?

This paper attempts to explain some methodological issues regarding EEG signal analysis which might lead to misinterpretation and therefore to unsubstantiated conclusions. The so called “split-alpha,” a “new phenomenon” in EEG spectral analysis described lately in few papers is such a case. We have shown that spectrum feature presented as a “split alpha” can be the result of applying improper means of analysis of the spectrum of the EEG signal that did not take into account the significant properties of the applied Fast Fourier Transform (FFT) method. Analysis of the shortcomings of the FFT method applied to EEG signal such as limited duration of analyzed signal, dependence of frequency resolution on time window duration, influence of window duration and shape, overlapping and spectral leakage was performed. Our analyses of EEG data as well as simulations indicate that double alpha spectra called as “split alpha” can appear, as spurious peaks, for short signal window when the EEG signal being studied shows multiple frequencies and frequency bands. These peaks have no relation to any frequencies of the signal and are an effect of spectrum leakage. Our paper is intended to explain the reasons underlying a spectrum pattern called as a “split alpha” and give some practical indications for using spectral analysis of EEG signal that might be useful for readers and allow to avoid EEG spectrum misinterpretation in further studies and publications as well as in clinical practice.

Diagnostics of perceptive and emotional components of psychological readiness for selfregulation of professional activity

Psychological readiness is a complex phenomenon that includes a variety of motivational and regulatory components, a system of cognitive patterns of future activities and working conditions, predictive assessments, as well as managing your own emotional reactions. In the professional field of «Man-Technique», the subject of labor, managing a complex technical system, must have a high level of stress tolerance and self-regulation, which gives particular importance to the problem of professional diagnosis and selection. Subjective criteria can catch the «subtle» emotional experiences, the nuances of cognitive-affective processes that simultaneously occur in the psyche of the individual. Objective criteria — often require a rather expensive research procedure. In this regard, diagnostic techniques that combine efficiency and short duration with validity criteria are most in demand. To test the assumptions of their effectiveness, a study was conducted of psychological readiness for professional activity among novice drivers, as well as among cadets-pilots of civil aviation who begin training flight training. Samples «Falling words», «Manifest words» study the perceptual mechanisms underlying the subject’s interpretation of the situation as potentially stressful, diagnosing perceptive alertness / protection. A professional who has a high willingness to interpret the received signals as stressful will recognize these words faster, which will be reflected in the objective criterion — a short signal recognition time. A comparison of the data with the results of the coping tests revealed that for novice drivers, perceptual vigilance prevails over perceptual protection. More experienced drivers often discharge suppressed emotions (usually hostility, anger), directing them to objects that are less dangerous or more accessible than those that caused negative emotions and feelings. The psychological readiness for training flights among cadets needs an additional study of perceptual and emotional components that will be used in self-regulation of resistance to emotional and psychological stress associated with upcoming professional activities.

Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns

ObjectiveNoise reduction algorithms in current hearing devices lack information about the sound source a user attends to when multiple sources are present. To resolve this issue, they can be complemented with auditory attention decoding (AAD) algorithms, which decode the attention using electroencephalography (EEG) sensors. State-of-the-art AAD algorithms employ a stimulus reconstruction approach, in which the envelope of the attended source is reconstructed from the EEG and correlated with the envelopes of the individual sources. This approach, however, performs poorly on short signal segments, while longer segments yield impractically long detection delays when the user switches attention.MethodsWe propose decoding the directional focus of attention using filterbank common spatial pattern filters (FB-CSP) as an alternative AAD paradigm, which does not require access to the clean source envelopes.ResultsThe proposed FB-CSP approach outperforms both the stimulus reconstruction approach on short signal segments, as well as a convolutional neural network approach on the same task. We achieve a high accuracy (80% for 1 s windows and 70% for quasi-instantaneous decisions), which is sufficient to reach minimal expected switch durations below 4 s. We also demonstrate that the decoder can adapt to unlabeled data from an unseen subject and works with only a subset of EEG channels located around the ear to emulate a wearable EEG setup.ConclusionThe proposed FB-CSP method provides fast and accurate decoding of the directional focus of auditory attention.SignificanceThe high accuracy on very short data segments is a major step forward towards practical neuro-steered hearing devices.

Viroid pathogenesis: a critical appraisal of the role of RNA silencing in triggering the initial molecular lesion

ABSTRACT The initial molecular lesions through which viroids, satellite RNAs and viruses trigger signal cascades resulting in plant diseases are hotly debated. Since viroids are circular non-protein-coding RNAs of ∼250–430 nucleotides, they appear very convenient to address this issue. Viroids are targeted by their host RNA silencing defense, generating viroid-derived small RNAs (vd-sRNAs) that are presumed to direct Argonaute (AGO) proteins to inactivate messenger RNAs, thus initiating disease. Here, we review the existing evidence. Viroid-induced symptoms reveal a distinction. Those attributed to vd-sRNAs from potato spindle tuber viroid and members of the family Pospiviroidae (replicating in the nucleus) are late, non-specific and systemic. In contrast, those attributed to vd-sRNAs from peach latent mosaic viroid (PLMVd) and other members of the family Avsunviroidae (replicating in plastids) are early, specific and local. Remarkably, leaf sectors expressing different PLMVd-induced chloroses accumulate viroid variants with specific pathogenic determinants. Some vd-sRNAs containing such determinant guide AGO1-mediated cleavage of mRNAs that code for proteins regulating chloroplast biogenesis/development. Therefore, the initial lesions and the expected phenotypes are connected by short signal cascades, hence supporting a cause-effect relationship. Intriguingly, one virus satellite RNA initiates disease through a similar mechanism, whereas in the Pospiviroidae and in plant viruses the situation remains uncertain.

Enhancer architecture sensitizes cell specific responses to Notch gene dose via a bind and discard mechanism

Notch pathway haploinsufficiency can cause severe developmental syndromes with highly variable penetrance. Currently, we have a limited mechanistic understanding of phenotype variability due to gene dosage. Here, we unexpectedly found that inserting an enhancer containing pioneer transcription factor sites coupled to Notch dimer sites can induce a subset of Notch haploinsufficiency phenotypes in Drosophila with wild type Notch gene dose. Using Drosophila genetics, we show that this enhancer induces Notch phenotypes in a Cdk8-dependent, transcription-independent manner. We further combined mathematical modeling with quantitative trait and expression analysis to build a model that describes how changes in Notch signal production versus degradation differentially impact cellular outcomes that require long versus short signal duration. Altogether, these findings support a ‘bind and discard’ mechanism in which enhancers with specific binding sites promote rapid Cdk8-dependent Notch turnover, and thereby reduce Notch-dependent transcription at other loci and sensitize tissues to gene dose based upon signal duration.

Anion channel SLAH3 is a regulatory target of chitin receptor-associated kinase PBL27 in microbial stomatal closure

In plants, antimicrobial immune responses involve the cellular release of anions and are responsible for the closure of stomatal pores. Detection of microbe-associated molecular patterns (MAMPs) by pattern recognition receptors (PRRs) induces currents mediated via slow-type (S-type) anion channels by a yet not understood mechanism. Here, we show that stomatal closure to fungal chitin is conferred by the major PRRs for chitin recognition, LYK5 and CERK1, the receptor-like cytoplasmic kinase PBL27, and the SLAH3 anion channel. PBL27 has the capacity to phosphorylate SLAH3, of which S127 and S189 are required to activate SLAH3. Full activation of the channel entails CERK1, depending on PBL27. Importantly, both S127 and S189 residues of SLAH3 are required for chitin-induced stomatal closure and anti-fungal immunity at the whole leaf level. Our results demonstrate a short signal transduction module from MAMP recognition to anion channel activation, and independent of ABA-induced SLAH3 activation.