Transcranial alternating current stimulation ameliorates emotional attention through neural oscillations modulation.

Background: Numerous clinical reports have suggested that psychopathy like schizophrenia, anxiety and depression is accompanied by early attentional abnormalities in emotional processing. Recently, the efficacy of transcranial alternating current stimulation (tACS) in changing emotional functioning has been repeatedly observed and demonstrated a causal relationship between endogenous oscillations and emotional processing. Aims: Up to now, tACS effects on emotional attention have not yet been tested. To assess such ability, we delivered active-tACS at individual alpha frequency (IAF), 10 Hz or sham-tACS for 7 consecutive days in the bilaterally dorsolateral prefrontal cortex (dlPFC) to totally 79 healthy participants. Results: IAF-tACS group showed significant alpha entrainment at-rest, especially in open state around stimulation area and showed an obvious advantage compared to 10 Hz-tACS. Event-related potential revealed a significant larger P200 amplitude after active-tACS and IAF group showed wider range of emotions than 10 Hz-tACS, indicating the attentional improvement in facial emotion processing. A notable positive correlation between alpha power and P200 amplitude provided an electrophysiological interpretation regarding the role of tACS in emotional attention modulation instead of somatosensory effects. Conclusion: These results support a seminal outcome for the effect of IAF-tACS on emotional attention modulation, demonstrating a feasible and individual-specific therapy for neuropsychiatric disorders related to emotion processing, especially regarding oscillatory disturbances.

Flight trajectory prediction enabled by time-frequency wavelet transform.

Accurate flight trajectory prediction is a crucial and challenging task in air traffic control, especially for maneuver operations. Modern data-driven methods are typically formulated as a time series forecasting task and fail to retain high accuracy. Meantime, as the primary modeling method for time series forecasting, frequency-domain analysis is underutilized in the flight trajectory prediction task. In this work, an innovative wavelet transform-based framework is proposed to perform time-frequency analysis of flight patterns to support trajectory forecasting. An encoder-decoder neural architecture is developed to estimate wavelet components, focusing on the effective modeling of global flight trends and local motion details. A real-world dataset is constructed to validate the proposed approach, and the experimental results demonstrate that the proposed framework exhibits higher accuracy than other comparative baselines, obtaining improved prediction performance in terms of four measurements, especially in the climb and descent phase with maneuver control. Most importantly, the time-frequency analysis is confirmed to be effective to achieve the flight trajectory prediction task.

[Tracking analysis of viral nucleic acid Ct value in patients with re-positive SARS-CoV-2 infection].

OBJECTIVE: To track analysis of viral nucleic acid test results in patients with re-positive SARS-CoV-2 infection, and provide clinical reference for nucleic acid test of re-positive cases. METHODS: A retrospective study was conducted. The multiple nucleic acid results of 96 cases with SARS-CoV-2 infection tested by medical laboratory of Shenzhen Luohu Hospital Group from January to September in 2022 were analyzed. The test dates and cycle threshold (Ct) values of detectable positive virus nucleic acid in the 96 cases were summarized and analyzed. RESULTS: A total of 96 patients with SARS-CoV-2 infection were retested re-sampled for nucleic acid testing at least 12 days after the initial positive screening. Among them, 54 cases (56.25%) had Ct value of < 35 for nucleocapsid protein gene (N) and/or open reading frame 1ab gene (ORF 1ab), 42 cases (43.75%) had Ct value ≥ 35. In the re-sampling of infected patients, N gene titers were 25.08 to 39.98 Ct cycles, and ORF 1ab gene titers were 23.16 to 39.56 Ct cycles. Compared with the positive results of the initial screening, the Ct values of N gene and/or ORF 1ab gene positive were increased in 90 cases (93.75%). Among them, the patients with the longest duration of nucleic acid positive could still be positive for double targets (the Ct value of N gene was 38.60, and the Ct value of ORF 1ab gene was 38.11) at an interval of 178 days after the initial positive screening. CONCLUSIONS: Patients infected with SARS-CoV-2 can be sustained or repeatedly tested positive for nucleic acid for a long period of time, and most of them had Ct values < 35. But whether it is infectious needs to be comprehensively evaluated by combining epidemiology, variant type, samples with the alive virus, and clinical symptoms and signs.

Performance comparison of two nucleic acid amplification systems for SARS-CoV-2 detection: A multi-center study.

BACKGROUND: Many rapid nucleic acid testing systems have emerged to halt the development and spread of COVID-19. However, so far relatively few studies have compared the diagnostic performance between these testing systems and conventional detection systems. Here, we performed a retrospective analysis to evaluate the clinical detection performance between SARS-CoV-2 rapid and conventional nucleic acid detection system. METHODS: Clinical detection results of 63,352 oropharyngeal swabs by both systems were finally enrolled in this analysis. Sensitivity (SE), specificity (SP), and positive and negative predictive value (PPV, NPV) of both systems were calculated to evaluate their diagnostic accuracy. Concordance between these two systems were assessed by overall, positive, negative percent agreement (OPA, PPA, NPA) and κ value. Sensitivity of SARS-CoV-2 rapid nucleic acid detection system (Daan Gene) was further analyzed with respect to the viral load of clinical specimens. RESULTS: Sensitivity of Daan Gene was slightly lower than that of conventional detection system (0.86 vs. 0.979), but their specificity was equivalent. Daan Gene had ≥98.0% PPV and NPV for SARS-CoV-2. Moreover, Daan Gene demonstrated an excellent test agreement with conventional detection system (κ = 0.893, p = 0.000). Daan Gene was 99.31% sensitivity for specimens with high viral load (Ct < 35) and 50% for low viral load (Ct ≥ 35). CONCLUSIONS: While showing an analytical sensitivity slightly below than that of conventional detection system, rapid nucleic acid detection system may be a diagnostic alternative to rapidly identify SARS-CoV-2-infected individuals with high viral loads and a powerful complement to current detection methods.

Transcranial Direct Current Stimulation Over the Left Dorsolateral Prefrontal Cortex Reduced Attention Bias Toward Negative Facial Expression: A Pilot Study in Healthy Subjects.

Research in the cognitive neuroscience field has shown that individuals with a stronger attention bias for negative information had higher depression risk, which may be the underlying pathogenesis of depression. This dysfunction of affect-biased attention also represents a decline in emotion regulation ability. Clinical studies have suggested that transcranial direct current stimulation (tDCS) treatment can improve the symptoms of depression, yet the neural mechanism behind this improvement is still veiled. This study aims to investigate the effects of tDCS on affect-biased attention. A sample of healthy participants received 20 min active (n = 22) or sham tDCS (n = 19) over the left dorsolateral prefrontal cortex (DLPFC) for 7 consecutive days. Electroencephalographic (EEG) signals were recorded while performing the rest task and emotional oddball task. The oddball task required response to pictures of the target (positive or negative) emotional facial stimuli and neglecting distracter (negative or positive) or standard (neutral) stimuli. Welch power spectrum estimation algorithm was applied to calculate frontal alpha asymmetry (FAA) in the rest task, and the overlapping averaging method was used to extract event-related potentials (ERP) components in the oddball task. Compared to sham tDCS, active tDCS caused an obvious increment in FAA in connection with emotion regulation (p < 0.05). Also, participants in the active tDCS group show greater P3 amplitudes following positive targets (p < 0.05) and greater N2 amplitudes following negative distracters (p < 0.05), reflecting emotion-related attention biases. These results offer valuable insights into the relationship between affect-biased attention and the effects of tDCS, which may be of assistance in exploring the neuropathological mechanism of depression and anxiety and new treatment strategies for tDCS.

A Unified Framework for Multilingual Speech Recognition in Air Traffic Control Systems.

This work focuses on robust speech recognition in air traffic control (ATC) by designing a novel processing paradigm to integrate multilingual speech recognition into a single framework using three cascaded modules: an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The AM converts ATC speech into phoneme-based text sequences that the PM then translates into a word-based sequence, which is the ultimate goal of this research. The LM corrects both phoneme- and word-based errors in the decoding results. The AM, including the convolutional neural network (CNN) and recurrent neural network (RNN), considers the spatial and temporal dependences of the speech features and is trained by the connectionist temporal classification loss. To cope with radio transmission noise and diversity among speakers, a multiscale CNN architecture is proposed to fit the diverse data distributions and improve the performance. Phoneme-to-word translation is addressed via a proposed machine translation PM with an encoder-decoder architecture. RNN-based LMs are trained to consider the code-switching specificity of the ATC speech by building dependences with common words. We validate the proposed approach using large amounts of real Chinese and English ATC recordings and achieve a 3.95% label error rate on Chinese characters and English words, outperforming other popular approaches. The decoding efficiency is also comparable to that of the end-to-end model, and its generalizability is validated on several open corpora, making it suitable for real-time approaches to further support ATC applications, such as ATC prediction and safety checking.

Enhanced Auditory Steady-State Response Using an Optimized Chirp Stimulus-Evoked Paradigm.

Objectives: It has been reported recently that gamma measures of the electroencephalogram (EEG) might provide information about the candidate biomarker of mental diseases like schizophrenia, Alzheimer's disease, affective disorder and so on, but as we know it is a difficult issue to induce visual and tactile evoked responses at high frequencies. Although a high-frequency response evoked by auditory senses is achievable, the quality of the recording response is not ideal, such as relatively low signal-to-noise ratio (SNR). Recently, auditory steady-state responses (ASSRs) play an essential role in the field of basic auditory studies and clinical uses. However, how to improve the quality of ASSRs is still a challenge which researchers have been working on. This study aims at designing a more comfortable and suitable evoked paradigm and then enhancing the quality of the ASSRs in healthy subjects so as to further apply it in clinical practice. Methods: Chirp and click stimuli with 40 Hz and 60 Hz were employed to evoke the gamma-ASSR respectively, and the sound adjusted to 45 dB sound pressure level (SPL). Twenty healthy subjects with normal-hearing participated, and 64-channel EEGs were simultaneously recorded during the experiment. Event-related spectral perturbation (ERSP) and SNR of the ASSRs were measured and analyzed to verify the feasibility and adaptability of the proposed evoked paradigm. Results: The results showed that the evoked paradigm proposed in this study could enhance ASSRs with strong feasibility and adaptability. 1) ASSR waves in time domain indicated that 40 Hz stimuli could significantly induce larger peak-to-peak values of ASSRs compared to 60 Hz stimuli (p < 0.01**); ERSP showed that obvious ASSRs were obtained at each lead for both 40 Hz and 60 Hz, as well as the click and chirp stimuli. 2) The SNR of the ASSRs were ⁻3.23 ± 1.68, ⁻2.44 ± 2.90, ⁻4.66 ± 2.09, and ⁻3.53 ± 3.49 respectively for 40 Hz click, 40 Hz chirp, 60 Hz click and 60 Hz chirp, indicating the chirp stimuli could induce significantly better ASSR than the click, and 40 Hz ASSRs had the higher SNR than 60 Hz (p < 0.01**). Limitation: In this study, sample size was small and the age span was not large enough. Conclusions: This study verified the feasibility and adaptability of the proposed evoked paradigm to improve the quality of the gamma-ASSR, which is significant in clinical application. The results suggested that 40 Hz ASSR evoked by chirp stimuli had the best performance and was expected to be used in clinical practice, especially in the field of mental diseases such as schizophrenia, Alzheimer's disease, and affective disorder.

Effect of Emotion States on the Updating Function of Working Memory.

In recent years, the interaction between emotion and working memory has been investigated with mixed results. Yet, few neurophysiological studies systematically investigated the effect of emotion states on the updating function of working memory that is a core part of executive functioning. Executive functioning has also been identified as cognitive function, which plays an important role in everyday life and even be a predictor of higher-level cognitive function. Here, we studied the effect of emotions on the updating function of working memory. 16 subjects were recruited to participate in verbal working memory task, and 14 in spatial working memory task. Pictures from International Affective Picture System(IAPS) were employed to evoke positive, negative and neutral states, and 4 digits or 4 spatial positions instructions created verbal or spatial working memory tasks. Combining event-related potentials(ERPs) and repeated measures ANOVA methods, the results showed that the updating time significantly increased in the positive state, and P3 amplitude was significantly lower than that of the neutral state in verbal working memory task; while in spatial working memory task, the updating time significantly increased in the negative state, but there was no significant difference in ERP components. These findings suggest that the updating function of verbal working memory may be affected by the positive state, while that of the spatial working memory may be affected by the negative state.

Incorporation of Multiple-Days Information to Improve the Generalization of EEG-Based Emotion Recognition Over Time.

Current studies have got a series of satisfying accuracies in EEG-based emotion classification, but most of the classifiers used in previous studies are totally time-limited. To produce generalizable results, the emotion classifier should be stable over days, in which the day-to-day variations of EEG should be appropriately handled. To improve the generalization of EEG-based emotion recognition over time by learning multiple-days information which embraces the day-to-day variations, in this paper, 17 subjects were recruited to view several video clips to experience different emotion states, and each subject was required to perform five sessions in 5 days distributed over 1 month. Support vector machine was built to perform a classification, in which the training samples may come from 1, 2, 3, or 4 days' sessions but have a same number, termed learning 1-days information (L1DI), learning 2-days information (L2DI), learning 3-days information (L3DI), and learning 4-days information (L4DI) conditions, respectively. The results revealed that the EEG variability could impair the performance of emotion classifier dramatically, and learning more days' information to construct a classifier could significantly improve the generalization of EEG-based emotion recognition over time. Mean accuracies were 62.78, 67.92, 70.75, and 72.50% at L1DI, L2DI, L3DI, and L4DI conditions, respectively. Features at L4DI condition were ranked by modified RFE, and features providing better contribution were applied to obtain the performances of all conditions, results showed that the performance of SVMs trained and tested with the feature subset were all improved for L1DI, L2DI (*p < 0.05), L3DI (**p < 0.01), and L4DI (*p < 0.05) conditions. It could be a substantial step forward in the development of emotion recognition from EEG signals because it may enable a classifier trained on one time to handle another.