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Sustained attention is one of the basic abilities of humans to maintain concentration on relevant information while ignoring irrelevant information over extended periods. The purpose of the review is to provide insight into how to integrate neural mechanisms of sustained attention with computational models to facilitate research and application. Although many studies have assessed attention, the evaluation of humans’ sustained attention is not sufficiently comprehensive. Hence, this study provides a current review on both neural mechanisms and computational models of visual sustained attention. We first review models, measurements, and neural mechanisms of sustained attention and propose plausible neural pathways for visual sustained attention. Next, we analyze and compare the different computational models of sustained attention that the previous reviews have not systematically summarized. We then provide computational models for automatically detecting vigilance states and evaluation of sustained attention. Finally, we outline possible future trends in the research field of sustained attention.

In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion. However, when dealing with such signals, traditional automatic microseismic waveform identification algorithms have difficulty in accurately identifying weak waveforms and are prone to misjudging background noise. This study innovatively introduces the deep - learning convolutional neural network (CNN), integrating the concepts and methods of computational communication to analyze microseismic signals. 8,341 pieces of background noise data and 5,860 pieces of microseismic data are carefully selected from the data of coal - seam borehole hydraulic fracturing. After adding noise at 12 levels and performing translation with 10 different degrees of displacement, 101,123 pieces of background noise and 102,546 effective waveforms are obtained. Subsequently, by applying the information - propagation dynamics model of computational communication, microseismic signals are regarded as information carriers. A signal - propagation network is constructed, and features such as network degree distribution are extracted. These features, combined with traditional time - domain and frequency - domain features, are converted into time - domain and Fourier images and then input into a two - dimensional CNN model. Experiments show that the time - domain CNN model achieves a precision rate of 100% and a recall rate of 68% in microseismic event identification, significantly outperforming traditional methods such as AIC, STA/LTA, and the Fourier CNN model. Furthermore, the time-frequency fusion CNN model—integrating time-domain waveforms, Fourier frequency-domain features, and time-frequency characteristics (e.g., short-time Fourier transform)—achieves an identical precision rate of 100% and a higher recall rate of 72%, outperforming the single-domain time-domain CNN model. The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.

COVID-19 patients exhibit differential disease severity after SARS-CoV-2 infection. It is currently unknown as to the correlation between the magnitude of neutralizing antibody (NAb) responses and the disease severity in COVID-19 patients. In a cohort of 59 recovered patients with disease severity including severe, moderate, mild, and asymptomatic, we observed the positive correlation between serum neutralizing capacity and disease severity, in particular, the highest NAb capacity in sera from the patients with severe disease, while a lack of ability of asymptomatic patients to mount competent NAbs. Furthermore, the compositions of NAb subtypes were also different between recovered patients with severe symptoms and with mild-to-moderate symptoms. These results reveal the tremendous heterogeneity of SARS-CoV-2-specific NAb responses and their correlations to disease severity, highlighting the needs of future vaccination in COVID-19 patients recovered from asymptomatic or mild illness.

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.

Influenza A virus (IAV) infection is a complicated process. After IAVs spread to the lung, extensive pro-inflammatory cytokines and chemokines are released, which largely determine the outcome of infection. Using a single-cell RNA sequencing (scRNA-seq) assay, we systematically and sequentially analyzed the transcriptome of more than 16,000 immune cells in the pulmonary tissue of infected mice, and demonstrated that two waves of pro-inflammatory factors were released. A group of IAV-infected PD-L1+ neutrophils were the major contributor to the first wave at an earlier stage (day 1-3 post infection). Notably, at a later stage (day 7 post infection) when IAV was hardly detected in the immune cells, a group of platelet factor 4-positive (Pf4+)-macrophages generated another wave of pro-inflammatory factors, which were probably the precursors of alveolar macrophages (AMs). Furthermore, single-cell signaling map identified inter-lineage crosstalk between different clusters and helped better understand the signature of PD-L1+ neutrophils and Pf4+-macrophages. Our data characteristically clarified the infiltrated immune cells and their production of pro-inflammatory factors during the immunopathogenesis development, and deciphered the important mechanisms underlying IAV-driven inflammatory reactions in the lung.

•Our study utilized stereo-electroencephalogram to investigate the dynamic interaction between attentional control and habituation processes in the prefrontal cortex, providing insights into how these fundamental mechanisms synergize during cognitive tasks.•Two distinct neural pathways within the subregions of prefrontal cortex are identified – a bottom-up dominant pathway orchestrated by the IFG, facilitating habitual responses, and a top-down dominant pathway modulated by the OFC, enabling attentional control to override ingrained habits.•Through an information model, we unveil unique patterns of information flow within the subregions of prefrontal cortex, particularly during color-word congruent and incongruent conditions, contributing to a comprehensive understanding of attentional regulation mechanisms. Attentional control, guided by top-down processes, enables selective focus on pertinent information, while habituation, influenced by bottom-up factors and prior experiences, shapes cognitive responses by emphasizing stimulus relevance. These two fundamental processes collaborate to regulate cognitive behavior, with the prefrontal cortex and its subregions playing a pivotal role. Nevertheless, the intricate neural mechanisms underlying the interaction between attentional control and habituation are still a subject of ongoing exploration. To our knowledge, there is a dearth of comprehensive studies on the functional connectivity between subsystems within the prefrontal cortex during attentional control processes in both primates and humans. Utilizing stereo-electroencephalogram (SEEG) recordings during the Stroop task, we observed top-down dominance effects and corresponding connectivity patterns among the orbitofrontal cortex (OFC), the middle frontal gyrus (MFG), and the inferior frontal gyrus (IFG) during heightened attentional control. These findings highlighting the involvement of OFC in habituation through top-down attention. Our study unveils unique connectivity profiles, shedding light on the neural interplay between top-down and bottom-up attentional control processes, shaping goal-directed attention.

EEG-based emotion recognition uses high-level information from neural activities to predict emotional responses in subjects. However, this information is sparsely distributed in frequency, time, and spatial domains and varied across subjects. To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). To capture high-level information distributed in time, spatial, and frequency domains, TSGCN considers both neural oscillation changes in different time windows and topological structures between different brain regions. Specifically, a Minimum Category Confusion (MCC) loss is used in TSGCN to reduce the inconsistencies between subjective ratings and predefined labels. In addition, to improve the generalization of TSGCN on cross-subject variation, we propose Deep and Shallow feature Dynamic Adversarial Learning (DSDAL) to calculate the distance between the source domain and the target domain. Extensive experiments were conducted on public datasets to demonstrate that TSGCN outperforms state-of-the-art methods in EEG-based emotion recognition. Ablation studies show that the mixed neural networks and our proposed methods in TSGCN significantly contribute to its high performance and robustness. Detailed investigations further provide the effectiveness of TSGCN in addressing the challenges in emotion recognition.

COVID-19 has caused a severe global pandemic. Until now, there has been no simple and reliable system available in a lower-biosafety-grade laboratory for SARS-CoV-2 virologic research and inhibitor screening. The etiologic agent of COVID-19 is highly contagious and has caused a severe global pandemic. Until now, there has been no simple and reliable system available in a lower-biosafety-grade laboratory for SARS-CoV-2 virologic research and inhibitor screening. In this study, we reported a replicon system which consists of four plasmids expressing the required segments of SARS-CoV-2. Our study revealed that the features for viral RNA synthesis and responses to antivirus drugs of the replicon are similar to those of wild-type viruses. Further analysis indicated that ORF6 provided potent in trans stimulation of the viral replication. Some viral variations, such as 5′UTR-C241T and ORF8-(T28144C) L84S mutation, also exhibit their different impact upon viral replication. Besides, the screening of clinically used drugs identified that several tyrosine kinase inhibitors and DNA-Top II inhibitors potently inhibit the replicon, as well as authentic SARS-CoV-2 viruses. Collectively, this replicon system provides a biosafety-worry-free platform for studying SARS-CoV-2 virology, monitoring the functional impact of viral mutations, and developing viral inhibitors. IMPORTANCE COVID-19 has caused a severe global pandemic. Until now, there has been no simple and reliable system available in a lower-biosafety-grade laboratory for SARS-CoV-2 virologic research and inhibitor screening. We reported a replicon system which consists of four ordinary plasmids expressing the required segments of SARS-CoV-2. Using the replicon system, we developed three application scenarios: (i) to identify the effects of viral proteins on virus replication, (ii) to identify the effects of mutations on viral replication during viral epidemics, and (iii) to perform high-throughput screening of antiviral drugs. Collectively, this replicon system would be useful for virologists to study SARS-CoV-2 virology, for epidemiologists to monitor virus mutations, and for industry to develop antiviral drugs.

Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects. Most neuronal reconstruction software can automatically trace single neuronal morphologies but tracing multiple, densely interwoven neurons is much more challenging. Here the authors develop G-Cut, a computational approach for accurate segmentation of densely interconnected neuron clusters.

In this study, the corrosion behavior of AlMn lightweight weathering steel (LWS) in the simulated marine atmosphere was investigated by means of the dry/wet corrosion cycle test. The results showed that Al was present as FeAl 2 O 4 and enriched in the inner layer, which significantly optimizes the rust layer in terms of compactness, elemental distribution, phase constitution, and electrochemical properties. The Mn oxides promoted the formation of FeAl 2 O 4 and enhanced the anti–rupture ability of the LWS’s rust layer.