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Highlights We revealed a universal self-adaptive structural reconstruction from Cu2O to Cu@CuxO composites, ending with feeding gas-dependent microstructures and catalytic performances. We uncovered a CO2-induced passivation behavior by identifying a reduction-resistant but catalytic active Cu(I)-rich amorphous layer. We designed and fabricated hollow Cu2O nanospheres, demonstrating durable electrolysis at a partial current density of −200 mA cm−2 in producing C2H4 with an FE of up to 61% at −0.6 VRHE.

IntroductionAccurate classification of single-trial electroencephalogram (EEG) is crucial for EEG-based target image recognition in rapid serial visual presentation (RSVP) tasks. P300 is an important component of a single-trial EEG for RSVP tasks. However, single-trial EEG are usually characterized by low signal-to-noise ratio and limited sample sizes.MethodsGiven these challenges, it is necessary to optimize existing convolutional neural networks (CNNs) to improve the performance of P300 classification. The proposed CNN model called PSAEEGNet, integrates standard convolutional layers, pyramid squeeze attention (PSA) modules, and deep convolutional layers. This approach arises the extraction of temporal and spatial features of the P300 to a finer granularity level.ResultsCompared with several existing single-trial EEG classification methods for RSVP tasks, the proposed model shows significantly improved performance. The mean true positive rate for PSAEEGNet is 0.7949, and the mean area under the receiver operating characteristic curve (AUC) is 0.9341 (p < 0.05).DiscussionThese results suggest that the proposed model effectively extracts features from both temporal and spatial dimensions of P300, leading to a more accurate classification of single-trial EEG during RSVP tasks. Therefore, this model has the potential to significantly enhance the performance of target recognition systems based on EEG, contributing to the advancement and practical implementation of target recognition in this field.

IntroductionAcupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy.MethodsThis study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU.ResultsThe YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5–0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%.DiscussionWith its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection.

Propane dehydrogenation (PDH) is a crucial approach for propylene production. However, commonly used CrOx–based catalysts have issues including easy sintering at elevated reaction temperatures and relying on high acidity supports. In this work, we develop a strategy, to strongly anchor and isolate active sites against their commonly observed aggregation during reactions, by taking advantage of the net trap effect in chromate intercalated Zn–Cr layered hydroxides as precursors. Furthermore, the intercalated chromate overcomes the collapse of traditional layered hydroxides during their transformation to metal oxide, thus exposing more available active sites. A joint fine modulation including crystal structure, surface acidity, specific surface area, and active sites dispersion is performed on the final mixed metal oxides for propane dehydrogenation. As a result, Zn1Cr2–CrO42−–MMO delivers attractive propane conversion (~27%) and propylene selectivity (>90%) as compared to other non–noble–metal–based catalysts.

Interspecies coaggregation promotes transcriptional changes in oral bacteria, affecting bacterial pathogenicity. Streptococcus gordonii (S. gordonii) and Fusobacterium nucleatum (F. nucleatum) are common oral inhabitants. The present study investigated the transcriptional profiling of S. gordonii and F. nucleatum subsp. polymorphum in response to the dual-species coaggregation using RNA-seq. Macrophages were infected with both species to explore the influence of bacterial coaggregation on both species’ abilities to survive within macrophages and induce inflammatory responses. Results indicated that, after the 30-min dual-species coaggregation, 116 genes were significantly up-regulated, and 151 genes were significantly down-regulated in S. gordonii; 97 genes were significantly down-regulated, and 114 genes were significantly up-regulated in F. nucleatum subsp. polymorphum. Multiple S. gordonii genes were involved in the biosynthesis and export of cell-wall proteins and carbohydrate metabolism. F. nucleatum subsp. polymorphum genes were mostly associated with translation and protein export. The coaggregation led to decreased expression levels of genes associated with lipopolysaccharide and peptidoglycan biosynthesis. Coaggregation between S. gordonii and F. nucleatum subsp. polymorphum significantly promoted both species’ intracellular survival within macrophages and attenuated the production of pro-inflammatory cytokines IL-6 and IL-1β. Physical interactions between these two species promoted a symbiotic lifestyle and repressed macrophage’s killing and pro-inflammatory responses.