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Emotion recognition plays an important part in human-computer interaction (HCI). Currently, the main challenge in electroencephalogram (EEG)-based emotion recognition is the non-stationarity of EEG signals, which causes performance of the trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network (TDANN) to construct a transfer model for EEG-based emotion recognition. Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network. These features are then passed through TDANN for two-level domain confusion. The first level uses the maximum mean discrepancy (MMD) to reduce the distribution discrepancy of deep features between source domain and target domain, and the second uses the domain adversarial neural network (DANN) to force the deep features closer to their corresponding class centers. We evaluated the domain-transfer performance of the model on both our self-built data set and the public data set SEED. In the cross-day transfer experiment, the ability to accurately discriminate joy from other emotions was high: sadness (84%), anger (87.04%), and fear (85.32%) on the self-built data set. The accuracy reached 74.93% on the SEED data set. In the cross-subject transfer experiment, the ability to accurately discriminate joy from other emotions was equally high: sadness (83.79%), anger (84.13%), and fear (81.72%) on the self-built data set. The average accuracy reached 87.9% on the SEED data set, which was higher than WGAN-DA. The experimental results demonstrate that the proposed TDANN can effectively handle the domain transfer problem in EEG-based emotion recognition.

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High-frequency electroencephalography (EEG) signals play an important role in research on human emotions. However, the different network patterns under different emotional states in the high gamma band (50–80 Hz) remain unclear. In this paper, we investigate different emotional states using functional network analysis on various frequency bands. We constructed multiple functional networks on different frequency bands and performed functional network analysis and time–frequency analysis on these frequency bands to determine the significant features that represent different emotional states. Furthermore, we verified the effectiveness of these features by using them in emotion recognition. Our experimental results revealed that the network connections in the high gamma band with significant differences among the positive, neutral, and negative emotional states were much denser than the network connections in the other frequency bands. The connections mainly occurred in the left prefrontal, left temporal, parietal, and occipital regions. Moreover, long-distance connections with significant differences among the emotional states were observed in the high frequency bands, particularly in the high gamma band. Additionally, high gamma band fusion features derived from the global efficiency, network connections, and differential entropies achieved the highest classification accuracies for both our dataset and the public dataset. These results are consistent with literature and provide further evidence that high gamma band EEG signals are more sensitive and effective than the EEG signals in other frequency bands in studying human affective perception.

In this study, we outlined the green synthesis of Zinc oxide nanoparticles (ZnO NPs) using the plant-mediated method. Employing the nitrate derivative of Zinc and the extract from the native medicinal plant, Ottonia anisum, the nanoparticles were effectively produced. After obtaining a yellow-colored paste, it was meticulously dried, gathered, and set aside for subsequent examination. The UV-visible spectrometry analysis indicated an absorption peak at 320 nm, which is indicative of ZnO NPs. Characterization techniques, such as XRD and HR-TEM, confirmed the existence of agglomerated ZnO NPs with an average diameter of 40 nm. Through EDS analysis, distinct energy signals for both Zinc and Oxygen were observed, confirming their composition. Furthermore, FT-IR spectroscopy highlighted an absorption peak for Zn-O bonding in the range of 400 to 600 cm-1. Further, we employed three distinct pain models in mice to evaluate the influence of ZnO NPs on the nociceptive threshold. Our findings revealed that, when orally administered, ZnO NPs at concentrations ranging from 5-20 mg/kg exerted a dose-dependent analgesic effect in both the hot-plate and the acetic acid-induced writhing tests. Moreover, when ZnO NPs were administered at doses between 2.5-10 mg/kg, there was a notable reduction in pain responses during both the initial and subsequent phases of the formalin test, but no change in PGE2 production within the mice's hind paw was found. On the other hand, acute lung injury studies revealed that the administration of ZnO NPs orally 90 minutes prior to HCl instillation decreased the neutrophil infiltration into the lungs in a dose- responsive manner. This reduction in pulmonary inflammation was paralleled by a significant decrease in lung edema, as evidenced by the reduced total protein content in the BALF. Additionally, the ZnO NPs appeared to recalibrate the lung's redox equilibrium following HCl exposure, which was determined through measurements of ROS, malondialdehyde, glutathione, and catalase activity. All these results further indicated the potential of biofabricated ZnO NPs for future applications in analgesics and acute lung injury treatments. [ABSTRACT FROM AUTHOR]

Abstract To explore the impact of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids in nulliparous women on subsequent pregnancy and delivery. A retrospective analysis was conducted of nulliparous women who received HIFU treatment at Chongqing Medical University, Chongqing,China, from January 1, 2010, to January 1, 2015. A total of 189 cases were enrolled, the median follow-up time was three years. Among them,there were 131 cases pregnancy with a total of 133 times,the pregnancy rate was 69.3% (131/189),and the spontaneous conception rate was 95.4% (125/131). Of 131 pregnant women, 19 were on-going pregnancy, terminated pregnancy 114 times,which include 93 times successfully delivery with a 76.3% (87/114) full-term birth rate,and the cesarean section rate was 72.0% (67/93). Of 94 newborns,the average birth weight was (3.3 ± 0.4)kg (range:1.5–4.8 kg), and a pair of them were identical twins. The incidence of complications during pregnancy and delivery were 10.8% (10/93) and 7.5% (7/93),respectively,except one woman failed on-going pregnancy and one woman suffered hysterectomy due to the complications,others all successful pregnant and delivered. Multiple-factor regression analysis found that age and infertility history were the important factors that may affect pregnancy after HIFU (P

Background: Gallbladder cancer (GBC) is an aggressive and highly lethal biliary tract malignancy, with extremely poor prognosis. In the present study, we analyzed the potential involvement of MYBL2, a member of the Myb transcription factor family, in the carcinogenesis of human GBC. Methods: MYBL2 expression levels were measured in GBC and cholecystitis tissue specimens using quantitative real-time PCR (qRT-PCR) and immunohistochemical (IHC) assays. The effects of MYBL2 on cell proliferation and DNA synthesis were evaluated using Cell Counting Kit-8 assay (CCK-8), colony formation, and 5-ethynyl-2'-deoxyuridine (EdU) retention assay, flow cytometry analysis, western blot, and a xenograft model of GBC cells in nude mice. Results: MYBL2 expression was increased in GBC tissues and associated with histological differentiation, tumour invasion, clinical stage and unfavourable overall survival in GBC patients. The downregulation of MYBL2 expression resulted in the inhibition of GBC cell proliferation, and DNA replication in vitro, and the growth of xenografted tumours in nude mice. Conversely, MYBL2 overexpression resulted in the opposite effects. Conclusions: MYBL2 overexpression promotes GBC cell proliferation through the regulation of the cell cycle at the S and G2/M phase transitions. Thus, MYBL2 could serve as a potential prognostic and therapeutic biomarker in GBC patients.

The Chinese formula Pien Tze Huang (PZH) has been used to treat hepatocellular carcinoma (HCC) and showed positive clinical effects. However, the antitumor mechanism of PZH in HCC remains unclear. In this study, HCC xenograft Balb/c mice were treated with PZH; then, proteomics detection and Ingenuity Pathway Analysis (IPA) were used to analyze the differentiated phosphorylated proteins in tumor tissues. The results indicated that PZH could inhibit tumor weight by 50.76%. Eighty-four upregulated and 11 downregulated phosphorylated proteins were identified in PZH-treated mice. Twenty signaling pathways were associated with inflammation (including the IL-6 and TNFR1/2 pathways), cancer growth (including the p53 and FAK pathways), and the cell cycle (including the G2/M and G1/S checkpoint regulation pathways). Moreover, TNF-α, IL-6, and several typical differentially expressed phosphorylated proteins (such as p-CCNB1, p-FOXO3, and p-STAT3) in tumor tissues, tumor cell viability, and cell cycle arrest assay in vitro further verify the results of IPA. These results revealed that PZH achieved antitumor activity in HCC; the underlying mechanisms of which were mainly through regulating the inflammation-associated cytokine secretion, cancer growth pathways, and induction of G2/M arrest. These data provided the potential molecular basis for PZH to act as a therapeutic drug or a supplement to chemotherapy drugs for human HCC in the future.

The incomplete version of the Macdonald function has various appellations in literature and earns a well-deserved reputation of being a computational challenge. This paper ties together the previously disjoint literature and presents the basic properties of the incomplete Macdonald function, such as recurrence and differential relations and series and asymptotic expansions. This paper also shows that the incomplete Macdonald function, as a simple closed-form expression, is a particular solution to a parabolic partial differential equation, which arises naturally in a wide variety of transient and diffusion-related phenomena.

Traditional models for semantic segmentation in point clouds primarily focus on smaller scales. However, in real-world applications, point clouds often exhibit larger scales, leading to heavy computational and memory requirements. The key to handling large-scale point clouds lies in leveraging randomsampling, which offers higher computational efficiency and lower memory consumption compared to other sampling methods. Nevertheless, the use of random sampling can potentially result in the loss of crucial points during the encoding stage. To address these issues, this paper proposes cross-fusion self-attention network (CFSA-Net), a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds. At the core of this network is the incorporation of randomsampling alongside a local feature extractionmodule based on cross-fusion self-attention (CFSA). This module effectively integrates long-range contextual dependencies between points by employing hierarchical position encoding (HPC). Furthermore, it enhances the interaction between each point’s coordinates and feature information through cross-fusion self-attention pooling, enabling the acquisition of more comprehensive geometric information. Finally, a residual optimization (RO) structure is introduced to extend the receptive field of individual points by stacking hierarchical position encoding and cross-fusion self-attention pooling, thereby reducing the impact of information loss caused by random sampling. Experimental results on the Stanford Large-Scale 3D Indoor Spaces (S3DIS), Semantic3D, and SemanticKITTI datasets demonstrate the superiority of this algorithm over advanced approaches such as RandLA-Net and KPConv. These findings underscore the excellent performance of CFSA-Net in large-scale 3D semantic segmentation. [ABSTRACT FROM AUTHOR]

Porous microstructure pressure sensors that are highly sensitive, reliable, low-cost, and environment-friendly have aroused wide attention in intelligent biomedical diagnostics, human–machine interactions, and soft robots. Here, an all-tissue-based piezoresistive pressure sensor with ultrahigh sensitivity and reliability based on the bottom interdigitated tissue electrode and the top bridge of a microporous tissue/carbon nanotube composite was proposed. Such pressure sensors exhibited ultrahigh sensitivity (≈1911.4 kPa−1), fast response time (