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Abstract This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system’s regularity like multiscale permutation entropy (MPE), and metrics that reflect a system’s dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.

ProposeTo review published Physical Activity (PA) on the Mental Health of Children and Adolescents aged 5 to 18 years during COVID-19 pandemic-induced school closures.MethodsFrom the database creation to April 2022, 10 databases are retrieved, with 4427 records filtered, 14 included in this research. The research takes Agency for Healthcare Research and Quality (AHRQ) evaluation standards.ResultsThe thesis selects 14 studies from 6 countries, involving 400009 children and adolescents. These studies happened during the lockdown of COVID-19 (from December 2019 to April 2021). During the lockdown of COVID-19, schools were closed, which was considered part of a more extensive lockdown. Schools were closed for 1 to 4 weeks. There were 10 high quality studies (71.4%) and 4 medium quality studies (28.6%). 4 studies report that the pandemic reduces the time of PA but increases the time of watching screen and sitting. 10 studies (71.4%) identify that PA is positive for the mental health, because it helps reduce mental symptoms to a certain extent, especially anxiety, depression, and emotional disorders. 5 studies show that PA may not improve the mental health of children and adolescents under 12 during the pandemic. 4 studies indicate that the influence of PA on mental health of children and adolescents is determined by the amount of activity, including the extent, intensity, frequency, and duration, etc.ConclusionsIn this narrative synthesis of reports from the class suspension period, reports that PA has a improve on the mental health of children and adolescents to a certain extent. it is found that PA may be helpful in reducing mental health symptoms of children and adolescents who are influenced by class suspension because of the COVID-19 pandemic. Therefore, stakeholders of the mental health of children and adolescents around the world should recommend PA because it is a practicable and beneficial way for long-term mental support.

Brain machine interfaces (BMI) connect brains directly to the outside world, bypassing natural neural systems and actuators. Neuronal-activity-to-motion transformation algorithms allow applications such as control of prosthetics or computer cursors. These algorithms lie within a spectrum between bio-mimetic control and bio-feedback control. The bio-mimetic approach relies on increasingly complex algorithms to decode neural activity by mimicking the natural neural system and actuator relationship while focusing on machine learning: the supervised fitting of decoder parameters. On the other hand, the bio-feedback approach uses simple algorithms and relies primarily on user learning, which may take some time, but can facilitate control of novel, non-biological appendages. An increasing amount of work has focused on the arguably more successful bio-mimetic approach. However, as chronic recordings have become more accessible and utilization of novel appendages such as computer cursors have become more universal, users can more easily spend time learning in a bio-feedback control paradigm. We believe a simple approach which leverages user learning and few assumptions will provide users with good control ability. To test the feasibility of this idea, we implemented a simple firing-rate-to-motion correspondence rule, assigned groups of neurons to virtual "directional keys" for control of a 2D cursor. Though not strictly required, to facilitate initial control, we selected neurons with similar preferred directions for each group. The groups of neurons were kept the same across multiple recording sessions to allow learning. Two Rhesus monkeys used this BMI to perform a center-out cursor movement task. After about a week of training, monkeys performed the task better and neuronal signal patterns changed on a group basis, indicating learning. While our experiments did not compare this bio-feedback BMI to bio-mimetic BMIs, the results demonstrate the feasibility of our control paradigm and paves the way for further research in multi-dimensional bio-feedback BMIs.

Background: Modic changes (MCs), vertebral end plate and bone marrow damage observed by magnetic resonance imaging, are an independent risk factor for low back pain. The compositions of and interaction between microbiota and metabolites in the lumbar cartilaginous end plates (LCEPs) of patients with MCs have not been identified. Methods: Patients with lumbar disc degeneration who were undergoing lumbar spinal fusion surgery were recruited between April 2020 and April 2021. LCEPs were collected for 16S rRNA sequencing and liquid chromatography-mass spectrometry (LC/MS)-based targeted metabolomic profiling. Of the 54 patients recruited, 24 had no MCs and 30 had changes classified as Modic type 2 or 3. The primary goal was to identify specific genera of microbiota associated with MCs, and secondary goals included investigating differences in metabolites between patients with and without MCs and exploring the correlation between these metabolites and microorganisms. Results: Investigation of the microbiota community structure revealed that both alpha diversity and beta diversity were significantly different between patients with and without MCs, and the abundances of 26 genera were significantly different between these 2 groups. Metabolomic analysis revealed that 26 metabolites were significantly different between the 2 groups. The unsaturated fatty acid pathway was found to be the main pathway related to MCs. Multiomic correlation analysis suggested that Caulobacteraceae (unclassified) and Mycobacterium, Clostridium, Blautia, and Bifidobacterium at the genus level were linked to dysregulation of fatty acid metabolism, contributing to the pathogenesis of MCs. Conclusions: Our study represents a foundational effort to examine the landscape of the microbiota and metabolites in patients with MCs, informing future studies on the pathogenesis of and targeted therapy for MCs. [ABSTRACT FROM AUTHOR]

Herein, we report a novel rapidly formed hydrogel membrane that is crosslinked by polyetherimide and polyethyleneimine within 10 s. The polyetherimide as the backbone provided rigidity to the hydrogel membrane, and the polyethyleneimine as the crosslinker provided hydrophilicity. The mechanical strength and electrical performance of the hydrogel membranes can be finely regulated by adjusting the ratio of these two polymers. The hydrogel membranes exhibited high proton conductivity (78-124 mS cm-1) from 20 8C to 160 8C (RH = 100%). [ABSTRACT FROM AUTHOR]