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Cervical cancer is one of the major diseases that seriously threaten women’s health. Cervical cancer automatic screening technology is of great significance to reduce the incidence of cervical cancer. However, the current method has shortcomings: low efficiency, low accuracy, and weak generalization ability, especially in complex situation. This paper innovatively applies the YOLO algorithm to the detection of abnormal cervical cells to ensure the rapidity and accuracy of the detection. For cellular classification of small targets, complex background and irregular shapes, we add the dense block and S3Pool algorithm on the basis of the feature extraction network Darknet-53 to improve the generalization ability of the model to cell features. The improved algorithm k-means++ is used to replace the clustering algorithm k-means in the original yolov3 to cluster the target frame of the cell data set, set reasonable anchors size, reconstruct the prediction scale creatively. Moreover, the Focal Loss and balanced cross entropy function are employed to improve the detection effect of the model against complex backgrounds, tight cell clusters, and uneven number of cell types. The NMS algorithm with linear attenuation is used to post-processing the model to improve the detection accuracy of cells in the occlusion situation. Experimental verification shows that the network achieved MAP of 78.87%, which is 8.02%, 8.22% and 4.83% higher than SSD (Single Shot Multi-Box Detector), YOLOv3(You Only Look Once) and ResNet50. The optimization method proposed in this paper improves the network sensitivity and the overall accuracy, especially in complex background. The research in this paper will have significance for the future design of an automatic cervical cancer diagnosis system.

The issue of low physical activity (PA) levels among the youth is a longstanding concern. Smartphone applications offer a promising avenue for delivering interventions that are both accessible and engaging. Up to now, there appears to be a gap in the literature, with no systematic reviews assessing the efficacy of smartphone apps in encouraging increased physical activity among healthy young adults. To synthesize the effects of a smartphone app-based intervention on PA and PA-related psychological correlates in healthy young adults (18-35 years old). A search was conducted on eighteen databases: PubMed, Medline, Web of Science, SPORTDiscus, Scopus, Academic Search Premier, Communication and Mass Media Complete, Article First, Biomed Central, BioOne, EBSCOHost, JSTOR, ProQuest, SAGE Reference Online, ScienceDirect, SpringerLink, Taylor&Francis, and Wiley Online. The search covered the period up until December 2023. This research included all randomized controlled trials (RCTs) that evaluated the effectiveness of smartphone app-based interventions on PA and PA related psychological outcomes in healthy young adults. The overall impact was determined by vote counting based on the direction of effect and aggregating p values. The quality of the evidence was evaluated using an 8-item scale. This study has been registered in the PROSPERO database with the identification number CRD42023390033. A total of 8403 articles were retrieved, and based on the predefined inclusion and exclusion criteria, seven articles were selected for inclusion. Among these articles, four high-quality RCTs were identified, and the results of vote counting and combining p values methods suggested that smartphone-based app interventions did not demonstrate significant effectiveness in improving PA and PA-related psychological outcomes. However, some improvements were observed. The analysis results, which were categorized into fitness apps and health apps based on the characteristics of the interventions, also failed to demonstrate significant intervention effects. The findings indicate that, currently, there are no significant effects of smartphone app interventions on improving PA and PA-related psychological outcomes in healthy young adults aged 18-35 years. It is important to note that these findings should be interpreted with caution due to the limited number of included studies. Future research should focus on employing high-quality study designs to determine the true effects of interventions and analyze various smartphone app interventions. These analyses should encompass different app characteristics (e.g., fitness app and health app), various combinations (e.g., fitness app alone and fitness app in combination with other interventions), diverse intervention goals (e.g., PA and PA along with other outcomes), and multiple intervention characteristics (e.g., frequency and duration).

Multi-object tracking still faces significant challenges in complex conditions such as dense scenes, occlusion environments, and non-linear motion, especially regarding the detection and identity maintenance of small objects. To tackle these issues, this paper proposes a multi-modal fusion tracking framework that realizes high-precision tracking in complex scenarios by collaboratively optimizing feature enhancement and motion prediction. Firstly, a multi-scale feature adaptive enhancement (MS-FAE) module is designed, integrating multi-level features and introducing a small object adaptive attention mechanism to enhance the representation ability for small objects. Secondly, a cross-frame feature association module (CFAM) is put forward, constructing a global semantic association network via grouped cross-attention and a memory recall mechanism to solve the matching difficulties in occlusion and dense scenes. Thirdly, a Dynamic Motion Model (DMM) is developed, enabling the robust prediction of non-linear motion based on an improved Kalman filter framework. Finally, a Bi-modal dynamic decision method (BDDM) is devised to fuse appearance and motion information for hierarchical decision making. Experiments conducted on multiple public datasets, including MOT17, MOT20, and VisDrone-MOT, demonstrate that this method remarkably improves tracking accuracy while maintaining real-time performance. On the MOT17 test set, it achieves 63.7% in HOTA, 61.4 FPS in processing speed, and 79.4% in IDF1, outperforming current state-of-the-art tracking algorithms.

Achieving vibration-free smooth motion of industrial robotic arms in a short period is an important research topic. Existing path planning algorithms often sacrifice smoothness in pursuit of efficient motion. A robotic trajectory planning particle swarm optimization algorithm (RTPPSO) is introduced for optimizing joint angles or paths of mechanical arm movements. The RTPPSO algorithm is enhanced through the introduction of adaptive weight strategies and random perturbation terms. Subsequently, the RTPPSO algorithm is utilized to plan selected parameters of an S-shaped velocity profile, iterating to obtain the optimal solution. Experimental results demonstrate that this velocity planning algorithm significantly improves the acceleration of the robotic arm, surpassing traditional trial-and-error velocity planning methods.

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data from glioblastoma (GBM) and low-grade glioma (LGG) samples, we identified 55 distinct cell states via the EcoTyper framework, validated for stability and prognostic impact in an independent cohort. We constructed multi-omics datasets of 620 samples, integrating transcriptomic, copy number variation (CNV), somatic mutation (MUT), Microbe (MIC), EcoTyper result data. A scRNA-seq enhanced Self-Normalizing Network-based glioma prognosis model achieved a C-index of 0.822 (training) and 0.817 (test), with AUC values of 0.867, 0.876, and 0.844 at 1, 3, and 5 years in the training set, and 0.820, 0.947, and 0.936 in the test set. Gradient attribution analysis enhanced the interpretability of the model and identified key molecular markers. The classification into high- and low-risk groups was validated as an independent prognostic factor. HDAC inhibitors are proposed as potential treatments. This study demonstrates the potential of integrating scRNA-seq and multi-omics data for robust glioma prognosis and clinical decision-making support.

Protein misfolding and aggregation are associated with more than twenty diseases, such as neurodegenerative diseases and metabolic diseases. The amyloid oligomers and fibrils may induce cell membrane disruption and lead to cell apoptosis. A great number of studies have focused on discovery of amyloid inhibitors which may prevent or treat amyloidosis diseases. Polyphenols have been extensively studied as a class of amyloid inhibitors, with several polyphenols under clinical trials as anti-neurodegenerative drugs. As oxidative intermediates of natural polyphenols, quinones widely exist in medicinal plants or food. In this study, we used insulin as an amyloid model to test the anti-amyloid effects of four simple quinones and four natural anthraquinone derivatives from rhubarb , a traditional herbal medicine used for treating Alzheimer's disease. Our results demonstrated that all eight quinones show inhibitory effects to different extent on insulin oligomerization, especially for 1,4-benzoquinone and 1,4-naphthoquinone. Significantly attenuated oligomerization, reduced amount of amyloid fibrils and reduced hemolysis levels were found after quinones treatments, indicating quinones may inhibit insulin from forming toxic oligomeric species. The results suggest a potential action of native anthraquinone derivatives in preventing protein misfolding diseases, the quinone skeleton may thus be further explored for designing effective anti-amyloidosis compounds.

The designation of yolk–shell structure is possessing advantages such as more active sites available and hierarchical space for volume expansion in the practical electrochemical applications. However, 3D yolk–shell nanostructures, with overall sizes over 100 nm, are still considered to be a certain degree of difficulty. Herein, we present the synthesis of novel yolk–shell Co3O4 microspheres in situ derived from Co-BTC/ZIF precursor, which is further decorated with graphitic carbon quantum dots coating layer (designated as YS-Co3O4/CQD). As expected, the yolk–shell structure with hollow cavity and interior porosity provides numerous active sites for enhanced electrochemical kinetics and effectively alleviates the volume effect for structural integrity. Moreover, the introduction of CQDs-decorated layer further improved the ionic/electric conductivity and the structural stability of YS-Co3O4 microspheres. Thus, the YS-Co3O4/CQD anode displays a high reversible specific capacity of around 1027 mAh g−1 at 0.1 A g−1, good cyclic stability up to 300 cycles at 1.0 A g−1 and superior rate capacity of 672.7, 550.3, 399.9 and 282.8 mAh g−1 at 4.0, 6.0, 8.0 and 10.0 A g−1, respectively.1. We synthesize Co-BTC/ZIF derivative YS-Co3O4 decorated with CQDs coating.2. Specific yolk–shell structure endows composite with excellent cycling stability.3. The CQDs coating layer contributes to fast lithium storage kinetics.4. YS-Co3O4/CQD exhibits ultrahigh rate capacity and good cyclic stability for LIBs.

Objective To explore the impact of the digital implementation of the Otago Exercise Program (OEP) on balance ability (static and dynamic), muscle strength, and fall efficacy in elderly people; and analyze different potential influencing factors in subgroups to find the most suitable training plan. Methods EBSCO, PubMed, Web of Science, and China Knowledge Network databases (core) were searched up to August 1, 2023. Experimental studies of implementing OEP based on digital technology to improve outcomes related to falls in the elderly were included. Bias risks were assessed using the Cochrane collaboration tool. Meta-analysis was performed to assess the pooled effect of balance ability (static and dynamic), muscle strength, and fall efficacy using a random effects model. Subgroup analyses were conducted to examine the potential modifying effects of different factors (e.g., training period, frequency, duration, age). Results Twelve articles were included from the literature, including 10 randomized controlled trials, one single-group quasi-experimental study, and one case report. Digital technologies used in the studies were categorized into three types: (1) online interventions (Zoom, WeChat), (2) recorded videos (via computers, TVs, DVDs), and (3) wearable technologies (motion sensors, augmented reality systems). The implementation of OEP based on digital technology showed significantly improved on static balance (SMD = 0.86, 95% CI 0.35–1.37), dynamic balance (SMD = 1.07, 95% CI 0.90–1.24), muscular strength (SMD = 0.43, 95% CI 0.17–0.69), and fall efficacy (SMD=-0.70, 95% CI -0.98, -0.41); Subgroup analysis by period ‘≥12 weeks’, frequency ‘≥3 times/week’, and duration ‘≤45 minutes per session’, respectively, showed significant improvements on static balance (SMD = 0.73, 95% CI 0.21–1.25; SMD = 0.86, 95% CI 0.35–1.37; SMD = 1.10, 95% CI 0.31–1.89), dynamic balance (SMD = 1.08, 95% CI 0.88–1.28; SMD = 1.01, 95% CI 0.93–1.27; SMD = 1.07, 95% CI 0.89–1.25), muscle strength (SMD = 0.43, 95% CI 0.10–0.75; SMD = 0.54, 95% CI 0.30–0.77; SMD = 0.53, 95% CI 0.19–0.87), and fall efficacy (SMD=-0.75, 95% CI -1.39, -0.11; SMD=-0.70, 95% CI -0.98, -0.41; SMD=-0.74, 95% CI -1.10, -0.39). Conclusions OEP implemented through digital technology effectively enhances static and dynamic balance, muscle strength, and self-efficacy in older adults. A training regimen of 12 weeks or more, with sessions occurring three or more times per week for 30 to 45 min, appears to be an effective approach for improving these outcomes based on the available evidence from the included studies. Future research should prioritize specific digital technologies and target populations, employing high-quality research designs to further explore these interventions, and consider new technologies such as wearables, to assess changes in fall prevalence.

Speech recognition, as a key driver of artificial intelligence and global communication, has advanced rapidly in major languages, while studies on low-resource languages remain limited. Tongan, a representative Polynesian language, carries significant cultural value. However, Tongan speech recognition faces three main challenges: data scarcity, limited adaptability of transfer learning, and weak dictionary modeling. This study proposes improvements in adaptive transfer learning and NBPE-based dictionary modeling to address these issues. An adaptive transfer learning strategy with layer-wise unfreezing and dynamic learning rate adjustment is introduced, enabling effective adaptation of pretrained models to the target language while improving accuracy and efficiency. In addition, the MEA-AGA is developed by combining the Mind Evolutionary Algorithm (MEA) with the Adaptive Genetic Algorithm (AGA) to optimize the number of byte-pair encoding (NBPE) parameters, thereby enhancing recognition accuracy and speed. The collected Tongan speech data were expanded and preprocessed, after which the experiments were conducted on an NVIDIA RTX 4070 GPU (16 GB) using CUDA 11.8 under the Ubuntu 18.04 operating system. Experimental results show that the proposed method achieved a word error rate (WER) of 26.18% and a word-per-second (WPS) rate of 68, demonstrating clear advantages over baseline methods and confirming its effectiveness for low-resource language applications. Although the proposed approach demonstrates promising performance, this study is still limited by the relatively small corpus size and the early stage of research exploration. Future work will focus on expanding the dataset, refining adaptive transfer strategies, and enhancing cross-lingual generalization to further improve the robustness and scalability of the model.

The alleviation of oxidative stress is considered an effective treatment for acetaminophen (APAP)-induced acute liver injury (AILI). However, it remains unknow whether the potential antioxidant Smilax china L. polysaccharide (SCLP) protects against AILI. In this study, in vitro and in vivo experiments were conducted to verify the hepatoprotective effect of SCLP against AILI and explore the potential mechanism. We found that SCLP relieved liver histopathological changes; reversed the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), malondialdehyde (MDA) and reactive oxygen species (ROS); reversed the change in liver myeloperoxidase (MPO) activity; and enhanced liver antioxidant (GSH, GSH-Px, and t-SOD) levels in APAP-treated mice, thereby significantly reducing APAP-induced liver toxicity. SCLP rescued the cell viability and alleviated oxidative stress in H 2 O 2 -treated mouse AML12 (Alpha mouse liver 12) hepatocytes. The results of the mechanistic studies showed that SCLP upregulated nuclear factor E2 related factor (Nrf2) expression, promoted Nrf2 nuclear translocation, and enhanced the ability of Nrf2 to bind antioxidant response elements (AREs). Furthermore, SCLP activated Nrf2-ARE pathway, thus upregulating the expression of oxidative stress-related proteins heme oxygenase 1(HO-1), NAD(P)H quinone dehydrogenase 1(NQO-1) and glutamic acid cysteine ligase catalytic subunit (GCLC). In conclusion, this study confirmed the close correlation between liver protection by SCLP upon exposure to APAP and activated of the Nrf2-ARE pathway. These findings suggest that SCLP is an attractive therapeutic candidate drug for the treatment of AILI.