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Banana is a significant crop, and three banana leaf diseases, including Sigatoka, Cordana and Pestalotiopsis, have the potential to have a serious impact on banana production. Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases. Therefore, this paper proposes a novel method to identify banana leaf diseases. First, a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images. The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds, the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image. Then, this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50. In this, the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed; the ResNeSt Module adjusts the weight of each channel, increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification; the model's computational speed is increased using the hybrid activation function of RReLU and Swish. Our model achieves an average accuracy of 96.98% and a precision of 89.31% applied to 13,021 images, demonstrating that the proposed method can effectively identify banana leaf diseases.

In recent years, China's modern coal industry governance system cannot satisfy the high-quality development requirements of coal industry. To improve the capacity of government regulation, a complete set of coal industry governance platforms should be set up by the new generation of information technologies. The governance platform can help to overcome the bottlenecks in market cooperation, set up international channels of dialogue, and eventually support the practical reform of China's coal industry mechanism and implementation of relevant policies. This paper analyzes the technical characteristics, the current development, and trends of the new generation information technologies, including 5G, artificial intelligence, big data, and block chain. The topological structure of the coal industry governance platform is also proposed. The topological structure contains the integrated public and private 5G coal industry network, coal big data and intelligent information processing platform, and blockchain coal industry cooperation supervision platform. Furthermore, the key technologies that can be applied in the coal industry governance platform are also analyzed in depth, providing practical and feasible solutions for the construction of the modern coal mining governance platform.

Sepsis, characterized by a widespread and dysregulated immune response to infection leading to organ dysfunction, presents significant challenges in diagnosis and treatment. In this study, we investigated 203 coagulation-related genes in sepsis patients to explore their roles in the disease. Through differential gene expression analysis, we identified 20 genes with altered expression patterns. Subsequent correlation analysis, visualized through circos plots and heatmaps, revealed significant relationships among these genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these genes are involved in immune response activation, coagulation, and immune receptor activity. Disease Ontology (DO) enrichment analysis further linked these genes to autoimmune hemolytic anemia and tumor-related signaling pathways. Additionally, the CIBERSORT analysis highlighted differences in immune cell composition in sepsis patients, revealing an increase in neutrophils and monocytes and a decrease in inactive NK cells, CD8 T cells, and B cells. We employed machine learning techniques, including random forest and SVM, to construct a diagnostic model, identifying FCER1G and FYN as key biomarkers. These biomarkers were validated through their expression levels and ROC curve analysis in an independent validation cohort, demonstrating strong diagnostic potential. Single-cell analysis from the GSE167363 dataset further confirmed the distinct expression profiles of these genes across various cell types, with FCER1G predominantly expressed in monocytes, NK cells, and platelets, and FYN in CD4+ T cells and NK cells. Enrichment analysis via GSEA and ssGSEA revealed that these genes are involved in critical pathways, including intestinal immune networks, fatty acid synthesis, and antigen processing. In conclusion, our comprehensive analysis identifies FCER1G and FYN as promising biomarkers for sepsis, providing valuable insights into the molecular mechanisms of this complex condition. These findings offer new avenues for the development of targeted diagnostic and therapeutic strategies in sepsis management.

BackgroundWhile vitamin deficiencies can pose serious health consequences for the body, excessive intake of vitamins can also lead to health risks. However, there is limited data about the impact of multivitamins on neurological and growth disorders. This study aimed to investigate the relationship between multivitamins and neurological and growth disorders.MethodsA cross-sectional study was conducted with 16,921 subjects who visited the Children’s Hospital of Nanjing Medical University from 2019 to 2021. The subjects were categorized into two groups based on their health status including 9,368 cases (4,484 with neurological disorders and 4,884 with growth disorders) and 7,553 healthy controls. Statistical tests including the T-test, Wilcoxon Rank Sum test, and Chi-Square test were employed to compare the groups, and logistic regression and Weighted Quantile Sum (WQS) regression were used to identify associations.ResultsIn the adjusted logistic regression, serum 25 hydroxyvitamin D [25(OH)D], vitamin B2, and vitamin B9 were associated with decreasing risks of neurological disorders, whereas vitamin A, vitamin B1, and vitamin B12 were associated with increasing risks of neurological disorders. Nevertheless, vitamin A and vitamin B2 were associated with increasing risks of growth disorders. In the WQS model, nine multivitamins were positively associated with risks of neurological disorders, and Vitamins D and C were weighted the most. In addition, the inverse association but not statistically significant was observed between multivitamins and growth disorders, particularly growth retardation revealed a negative association, and some individual growth disorders revealed positive associations including obesity and malnutrition.ConclusionIn general, the study observed that multivitamins may be associated with neurological and growth disorders either positive or negative depending on the type of disorder.

Cancer is one of the top 10 fatal diseases worldwide, among which advanced metastatic carcinoma has the highest mortality rate. Sunitinib and immune checkpoint blockers are commonly used to treat metastatic renal carcinoma with limited efficacy. Therefore, there is an urgent need to develop novel targeted therapies for metastatic renal cancer. In this study, we designed an antibody fusion protein, 57103, that simultaneously targeted the cluster of differentiation 24 (CD24), interleukin 4 receptor (IL-4R), and integrin receptors αvβ3 and α5β1. In vitro assays showed that 57103 significantly suppressed the proliferation, migration, invasion, colony formation, and adhesion abilities of renal cancer cells, resulting in a comprehensive and significant antitumor effect. Furthermore, 57103 inhibited angiogenesis, promoted THP1-derived M0-type macrophage phagocytosis, and enhanced the antibody-dependent cellular cytotoxicity of peripheral blood mononuclear and NK92MI-CD16a cells. In vivo experiments revealed significant inhibition of tumor growth in ACHN cell xenograft nude mice and an MC38-hCD24 tumor-bearing mouse model. Immunohistochemical analysis showed that 57103 decreased the proliferation and induced the apoptosis of renal cancer cells, while inhibiting angiogenesis. The MC38-hPDL1 and MC38-hCD24-hPDL1 tumor-bearing mouse models further offer the possibility of combining 57103 with the PDL1 antagonist atezolizumab. In conclusion, 57103 is a potential candidate drug for the treatment of metastatic renal carcinoma or PDL1-overexpressing cancer.

ObjectiveThe objective of this study is to investigate the factors that influence the live birth rate (LBR) of the first single euploid frozen-thawed blastocyst transfer (FBT) cycles after preimplantation genetic testing for structural rearrangements (PGT-SR) in couples with balanced chromosomal translocations (BCT).DesignSingle center, retrospective and observational study.MethodsA total of 336 PGT-SR and the first single euploid FBT cycles between July 2016 and December 2022 were included in this study. The patients were divided into two groups according to the live birth outcomes. The parameters of the study population, controlled ovarian stimulation cycles, and FBT cycles were analyzed. Multivariable binary logistic regression was performed to find the factors that affected the LBR.ResultsThe percentage of blastocysts at developmental stage Day 5 compared to Day 6 (51.8% vs. 30.8%; P

Abstract Cuproptosis is a recently described copper-dependent cell death pathway. Consequently, there are still few studies on lung adenocarcinoma (LUAD)-related cuproptosis, and we aimed to deepen in this matter. In this study, data from 503 patients with lung cancer from the TCGA-LUAD cohort data collection and 11 LUAD single-cells from GSE131907 as well as from 10 genes associated with cuproptosis were analyzed. The AUCell R package was used to determine the copper-dependent cell death pathway activity for each cell subpopulation, calculate the CellChat score, and display cell communication for each cell subpopulation. The PROGENy score was calculated to show the scores of tumor-related pathways in different cell populations. GO and KEGG analyses were used to calculate pathway activity. Univariate COX and random forest analyses were used to screen prognosis-associated genes and construct models. The ssGSEA and xCell algorithms were used to calculate the immunocyte infiltration score. Based on data from the GDSC database, the drug sensitivity score was calculated using oncoPredict. Finally, in vitro experiments were performed to determine the role of TLE1, the most important gene in the prognostic model. The 11 LUAD single-cell samples were classified into 8 different cell populations, from which epithelial cells showed the highest copper-dependent cell death pathway activity. Epithelial cell subsets were significantly positively correlated with MAKP, hypoxia, and other pathways. In addition, cell subgroup communication showed highly active collagen and APP pathways. Using the Findmark algorithm, differentially expressed genes (DEGs) between epithelial and other cell types were identified. Combined with the bulk data in the TCGA-LUAD database, DEGs were enriched in pathways such as EGFR tyrosine kinase inhibitor resistance, Hippo signaling pathway, and tight junction. Subsequently, we selected 4 genes (out of 112) with prognostic significance, ANKRD29, RHOV, TLE1, and NPAS2, and used them to construct a prognostic model. The high- and low-risk groups, distinguished by the median risk score, showed significantly different prognoses. Finally, we chose TLE1 as a biomarker based on the relative importance score in the prognostic model. In vitro experiments showed that TLE1 promotes tumor proliferation and migration and inhibits apoptosis.

Precise disease detection is crucial in modern precision agriculture, especially in ensuring the health of tomato crops and enhancing agricultural productivity and product quality. Although most existing disease detection methods have helped growers identify tomato leaf diseases to some extent, these methods typically target fixed categories. When faced with new diseases, extensive and costly manual annotation is required to retrain the dataset. To overcome these limitations, this study proposes a multimodal model PDC-VLD based on the open-vocabulary object detection (OVD) technology within the VLDet framework, which can accurately identify new tomato leaf diseases without manual annotation by using only image–text pairs. First, we developed a progressive visual transformer-convolutional pyramid module (PVT-C) that effectively extracts tomato leaf disease features and optimizes anchor box positioning using the self-supervised learning algorithm DINO, suppressing interference from irrelevant backgrounds. Then, a context feature guided module (CFG) was adopted to address the low adaptability and recognition accuracy of the model in data-scarce environments. To validate the model’s effectiveness, we constructed a tomato leaf disease image dataset containing 4 base classes and 2 new categories. Experimental results show that the PDC-VLD model achieved 61.2% on the main evaluation metric [Formula: see text], and 56.4% on [Formula: see text], 87.7% on [Formula: see text], 81.0% on [Formula: see text], and 45.5% on average recall, outperforming existing OVD models. Our research provides an innovative solution for efficiently and accurately detecting new diseases, substantially reducing the need for manual annotation, and offering critical technical support and practical reference for agricultural workers.

Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model’s adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image–text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination–hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model’s convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.

The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages.

ObjectivesTo analyze the relationship between sleep quality, snoring symptoms, night shift and risk of stroke in Chinese population over 40 years old.MethodsBased on the national screening and intervention program for high-risk population of stroke in 2016, 15,016 people completed the study of “the association between sleep and stroke,” 58,696 people completed the snoring questionnaire, and 58,637 people completed the night shift questionnaire.ResultsThe proportion of coronary heart disease, hypertension, hyperlipidemia, diabetes, snoring, atrial fibrillation, stroke and high-risk group of stroke risk rating were higher in the group with poor sleep quality (p < 0.05). The proportion of high blood pressure, hyperlipidemia, diabetes, atrial fibrillation, transient ischemic attack (TIA), or high-risk group of stroke risk rating was higher in snoring group (p < 0.05). The body mass index (BMI), waist circumference, neck circumference, fasting blood glucose, triglyceride (TG), total cholesterol (TC), low density lipoprotein (LDL) and homocysteine (Hcy) levels in snoring group were higher than the non-snoring group, and high density lipoprotein (HDL) levels were lower (p < 0.05). People with TIA, high risk for stroke, and high blood pressure were higher in night shift workers than non-night shift workers (p < 0.05). The levels of BMI, fasting blood glucose, 2 h postprandial blood glucose, glycated hemoglobin, TG, TC, LDL, HDL and Hcy in night shift group were lower than the non-night shift group (p < 0.05).ConclusionSleep quality, snoring and night shift might be related to the risk factors of stroke.

Sepsis has a high mortality rate, and treating sepsis remains a significant challenge worldwide. In former studies, our group found that traditional Chinese medicine, Shen FuHuang formula (SFH), is a promising medicine in treating coronavirus disease 2019 (COVID-19) patients with the septic syndrome. However, the underlying mechanisms remain elusive. In the present study, we first investigated the therapeutic effects of SFH on septic mice. To investigate the mechanisms of SFH-treated sepsis, we identified the gut microbiome profile and exploited untargeted metabolomics analyses. The results demonstrated that SFH significantly enhanced the mice’s 7-day survival rate and hindered the release of inflammatory mediators, i.e., TNF-α, IL-6, and IL-1β. 16S rDNA sequencing further deciphered that SFH decreased the proportion of Campylobacterota and Proteobacteria at the phylum level. LEfSe analysis revealed that the treatment of SFH enriched Blautia while decreased Escherichia_Shigella. Furthermore, serum untargeted metabolomics analysis indicated that SFH could regulate the glucagon signaling pathway, PPAR signaling pathway, galactose metabolism, and pyrimidine metabolism. Finally, we found the relative abundance of Bacteroides, Lachnospiraceae_NK4A136_group, Escherichia_Shigella, Blautia, Ruminococcus, and Prevotella were closely related to the enrichment of the metabolic signaling pathways, including L-tryptophan, uracil, glucuronic acid, protocatechuic acid, and gamma-Glutamylcysteine. In conclusion, our study demonstrated that SFH alleviated sepsis by suppressing the inflammatory response and hence reduced mortality. The mechanism of SFH for treating sepsis may be ascribed to the enrichment of beneficial gut flora and modulation in glucagon signaling pathway, PPAR signaling pathway, galactose metabolism, and pyrimidine metabolism. To sum up, these findings provide a new scientific perspective for the clinical application of SFH in treating sepsis.

In modern air combat, acquiring the opponent’s air combat intention is one of essential prerequisites to evaluate the air combat situation effectively and master the battlefield initiative. On account of multi-dimensional and temporal characteristics of the target state, a recognition model is proposed to identify tactical intention of aerial target based on a multi-sense-scaled attention architecture. First of all, the multi-dimensional feature information, including target state attributes, battlefield environment, target attributes and so on, is constructed as target feature. Secondly, the non-numerical information, such as battlefield environment characteristics, enemy and friend attributes of targets, radar status, maneuver type, etc, is transformed into numerical data. For the purpose of subsequent data processing, the flight speed, altitude, RCS, etc, in the target status information are normalized into the same dimension. Furthermore, a target intention recognition model with multiple sense-scaled attention mechanism is designed to depict the target state, attributes and the information of the battlefield environment from multiple dimensions, which is convenient to be close to the actual combat. The BiLSTM neural network is used to learn the deep-seated information in the air combat intention feature vector, and the attention mechanism is used to adaptively allocate the network weights. The air combat feature information with different weights is introduced into the softmax function layer for intention recognition. Compared with the traditional air tactical target intention recognition model, the proposed model effectively improves the efficiency of air target tactical intention recognition as well as affords important theoretical significance and reference value for the auxiliary combat system.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of environments, which can lead to intraclass variability and interclass similarity in the disease. Tomato plants are commonly planted in soil. When a disease occurs near the leaf’s edge, the soil backdrop in the image tends to interfere with the infected region. These problems can make tomato detection challenging. In this paper, we propose a precise image-based tomato leaf disease detection approach using PLPNet. First, a perceptual adaptive convolution module is proposed. It can effectively extract the disease’s defining characteristics. Second, a location reinforcement attention mechanism is proposed at the neck of the network. It suppresses the interference of the soil backdrop and prevents extraneous information from accessing the network’s feature fusion phase. Then, a proximity feature aggregation network with switchable atrous convolution and deconvolution is proposed by combining the mechanisms of secondary observation and feature consistency. The network solves the problem of disease interclass similarities. Finally, the experimental results show that PLPNet achieved 94.5% mean average precision with 50% thresholds (mAP50), 54.4% average recall (AR), and 25.45 frames per second (FPS) on a self-built dataset. The model is more accurate and specific for the detection of tomato leaf diseases than other popular detectors. Our proposed method may effectively improve conventional tomato leaf disease detection and provide modern tomato cultivation management with reference experience.

Hepatic steatosis is a highly prevalent liver disease, yet research on it is hampered by the lack of tractable cellular models in poultry. To examine the possibility of using organoids to model steatosis and detect it efficiently in leghorn male hepatocellular (LMH) cells, we first established steatosis using different concentrations of oleic acid (OA) (0.05–0.75 mmol/L) for 12 or 24 h. The subsequent detections found that the treatment of LMH cells with OA resulted in a dramatic increase in intracellular triglyceride (TG) concentrations, which was positively associated with the concentration of the inducing OA (R2 > 0.9). Then, the modeled steatosis was detected by flow cytometry after NileRed staining and it was found that the intensity of NileRed-A was positively correlated with the TG concentration (R2 > 0.93), which demonstrates that the flow cytometry is suitable for the detection of steatosis in LMH cells. According to the detection results of the different steatosis models, we confirmed that the optimal induction condition for the establishment of the steatosis model in LMH cells is OA (0.375 mmol/L) incubation for 12 h. Finally, the transcription and protein content of fat metabolism-related genes in steatosis model cells were detected. It was found that OA-induced steatosis could significantly decrease the expression of nuclear receptor PPAR-γ and the synthesis of fatty acids (SREBP-1C, ACC1, FASN), increasing the oxidative decomposition of triglycerides (CPT1A) and the assembly of low-density lipoproteins (MTTP, ApoB). Sterol metabolism in model cells was also significantly enhanced (HMGR, ABCA1, L-BABP). This study established, detected, and analyzed an OA-induced steatosis model in LMH cells, which provides a stable model and detection method for the study of poultry steatosis-related diseases.

Qin-Qiao-Xiao-Du (QQXD), a traditional Chinese medicine (TCM) formula, has been used in the clinical treatment of influenza virus pneumonia. However, the effects and mechanisms of QQXD on influenza virus pneumonia remain unknown. Therefore, this study explores the mechanisms of QQXD in the treatment of influenza virus pneumonia from the point of view of intestinal flora and metabolism. The results showed that QQXD was able to reduce mortality, weight loss, lung viral load, lung index, and lung injury in influenza virus mice. A cytokine array found that the QQXD attenuated the expression of serum IL-1α, IL-4, IL-12(P70), and TNF-α. Subsequently, 16s rRNA gene sequencing showed that QQXD could increase the relative abundances of Gemmiger, Anaerofustis, Adlercreutzia, and Streptococcus and decrease those of Dehalobacteriu, Burkholderia, Prevotella, Butyrimimonas, Delftia, and others. Meanwhile, targeted metabolic profiling analysis showed that QQXD could regulate nitrogen metabolism, phenylalanine metabolism, valine, leucine, and isoleucine biosynthesis. Correlation analysis demonstrated that the regulatory effect of QQXD on the cyanoamino acid metabolism pathway was associated with changes in the abundance of Parabacteroides, Pediococcus, and Clostridium in influenza mice. In conclusion, our study revealed that QQXD can inhibit influenza virus replication, suppress cytokine storms, and protect mice from influenza virus infection pneumonia. The mechanisms are likely to be related to improved gut microbiota dysbiosis, increased intestinal carbohydrate metabolism, and up-regulated cyanoamino acid metabolism pathways.

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface defect detection under small sample conditions, we designed an apple surface defect detection network (ASDINet) suitable for small sample learning. The self-developed apple sorting system collected RGB images of 50 apple samples for model verification, including non-defective and defective apples (rot, disease, lacerations, and mechanical damage). First, a segmentation network (AU-Net) with a stronger ability to capture small details was designed, and a Dep-conv module that could expand the feature capacity of the receptive field was inserted in its down-sampling path. Among them, the number of convolutional layers in the single-layer convolutional module was positively correlated with the network depth. Next, to achieve real-time segmentation, we replaced the flooding of feature maps with mask output in the 13th layer of the network. Finally, we designed a global decision module (GDM) with global properties, which inserted the global spatial domain attention mechanism (GSAM) and performed fast prediction on abnormal images through the input of masks. In the comparison experiment with state-of-the-art models, our network achieved an AP of 98.8%, and a 97.75% F1-score, which were higher than those of most of the state-of-the-art networks; the detection speed reached 39ms per frame, achieving accuracy-easy deployment and substantial trade-offs that are in line with actual production needs. In the data sensitivity experiment, the ASDINet achieved results that met the production needs under the training of 42 defective pictures. In addition, we also discussed the effect of the ASDINet in actual production, and the test results showed that our proposed network demonstrated excellent performance consistent with the theory in actual production.

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases.

Aiming at the problems of noise, background interference and low detection in peach disease image, this paper proposes a detection method of peach disease based on the asymptotic non-local means (ANLM) image algorithm and the fusion of parallel convolution neural network (PCNN) and extreme learning machine(ELM) optimized by linear particle swarm optimization(IPSO). Firstly, the method uses the ANLM image denoising algorithm to reduce the interference of the complex background in the image, then uses the parallel convolution neural network proposed by this paper to identify the characteristics of peach disease, uses the improved elu activation function instead of the conventional ReLu activation function, and uses the linear particle swarm optimized ELM (IPELM) proposed by this paper in the last layer instead of the traditional softmax layer, the update method is improved from two aspects to improve the convergence speed and accuracy of the network effectively. The results of 25513 images showed that the highest detection accuracy of brown rot, black spot, anthracnose, scab and normal peach were 89.02, 90.56, 85.37, 86.70 and 89.91 percent respectively, which indicated that this method was an effective method for peach disease detection.

The identification of maize leaf diseases will meet great challenges because of the difficulties in extracting lesion features from the constant-changing environment, uneven illumination reflection of the incident light source and many other factors. In this paper, a novel maize leaf disease recognition method is proposed. In this method, we first designed a maize leaf feature enhancement framework with the capability of enhancing the features of maize under the complex environment. Then a novel neural network is designed based on backbone Alexnet architecture, named DMS-Robust Alexnet. In the DMS-Robust Alexnet, dilated convolution and multi-scale convolution are combined to improve the capability of feature extraction. Batch normalization is performed to prevent network over-fitting while enhancing the robustness of the model. PRelu activation function and Adabound optimizer are employed to improve both convergence and accuracy. In experiments, it is validated from different perspectives that the maize leaf disease feature enhancement algorithm is conducive to improving the capability of the DMS-Robust Alexnet identification. Our method demonstrates strong robustness for maize disease images collected in the natural environment, providing a reference for the intelligent diagnosis of other plant leaf diseases.

In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of noise, background interference, and low detection accuracy of maize leaf disease images. To begin, a processing layer called Wavelet threshold guided bilateral filtering (WT-GBF) based on the WG-MARNet model is employed to reduce image noise and perform high and low-frequency decomposition of the input image using WT-GBF. This increases the input image's resistance to environmental interference and feature extraction capability. Secondly, for the multiscale feature fusion technique, an average down-sampling and tiling method is employed to increase feature representation and limit the risk of overfitting. Then, on high and low-frequency multi-channel, an attenuation factor is introduced to optimize the performance instability during training of the deep network. Finally, when the convergence and accuracy are compared, PRelu and Adabound are used instead of the Relu activation function and the Adam optimizer. The experimental results revealed that our method's average recognition accuracy was 97.96%, and the detection time for a single image was 0.278 seconds. The average detection accuracy has been increased. The method lays the groundwork for the precise control of maize diseases in the field.

The clinical application of anastomotic instruments improves the efficiency of the digestive tract surgery. However, the stapler with titanium nails implanted is still controversial in terms of anastomotic complications, and further improvement and optimization are needed. The purpose of this study was to explore the optimal microtextured parameters that could enhance the bioactivity of titanium implants in vitro. Laser microtexturing technology was used to construct the groove-type microstructural surfaces with different parameters, and human gastric mucosal epithelial cells (GES-1 cells) and mouse fibroblasts (3T3 cells) were cultured on the surface of the titanium plates in vitro. The data of cell adhesion, cell proliferation and cell activity were obtained and statistically analyzed. The textured titanium plates meet the expected design. GES-1 and 3T3 cell adhesion were better in the surface of titanium plates in microstructural group than that in the polished group. GES-1 and 3T3 cells also showed higher proliferative activity in the microstructural group compared with the polished group. The laser textured titanium plates have good groove-type microstructure, which increase the surface roughness, change the surface wettability, promote the adhesion, proliferating and orderly growth of GES-1 and 3T3 cells, and show good biological properties.

Rothia nasimurium is a facultative anaerobic Gram-positive coccus belonging to the Rothia genus of the Micrococcaceae family. While Rothia nasimurium is considered an opportunistic pathogen, to date few studies have investigated its pathogenicity and drug resistance. In January 2022, chickens at a poultry farm in China’s Xinjiang Uygur Autonomous Region became ill and died. Treatment with commonly used Chinese medicines and antibiotics was ineffective, causing economic losses to the poultry farm. In order to determine the cause of the disease in these poultry farm chickens, the isolation and identification of the pathogens in the livers and other internal organs of the sick and dead chickens were performed. Further, animal pathogenicity tests, antibiotic susceptibility tests, and the detection of antibiotic resistance genes were carried out to analyze the pathogenicity and drug resistance of the identified pathogens. A Gram-positive coccus was isolated from the livers of the diseased chickens. The isolate was resistant to 17 antibiotics, including ciprofloxacin, chloramphenicol, and florfenicol, and was only sensitive to penicillin, amikacin, and tigecycline, to varying degrees. The results of the drug resistance gene testing indicated that the isolated bacterium carried 13 kinds of resistance genes. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, morphological observations, biochemical tests, and 16S rRNA gene sequence analysis were performed on the isolated bacterium, and it was determined that the isolated bacterial strain was Rothia nasimurium. The animal pathogenicity tests showed that the isolate caused feather loss and death in chicks; the clinical symptoms and necropsy lesions of the test chicks were consistent with those observed in the farmed chickens. A review of the literature revealed that, to date, there are no reports of infection with Rothia nasimurium in chickens. Thus, in this study, Rothia nasimurium was isolated from chickens for the first time and an investigation of the biological characteristics of the bacterium was carried out in order to provide a reference for the clinical treatment, prevention, and control of Rothia nasimurium infection.

Abstract Background The dysfunction of airway epithelial barrier is closely related to the pathogenesis of asthma. Secreted Hsp90α participates in inflammation and Hsp90 inhibitor protects endothelial dysfunction. In the current study, we aimed to explore the role of secreted Hsp90α in asthmatic airway epithelial barrier function. Methods Male BALB/c mice were sensitized and challenged with HDM to generate asthma model. The 16HBE and Hsp90α-knockdown cells were cultured and treated according to the experiment requirements. Transepithelial Electric Resistance (TEER) and permeability of epithelial layer in vitro, distribution and expression of junction proteins both in vivo and in vitro were used to evaluate the epithelial barrier function. Western Blot was used to evaluate the expression of junction proteins and phosphorylated AKT in cells and lung tissues while ELISA were used to evaluate the Hsp90α expression and cytokines release in the lung homogenate. Results HDM resulted in a dysfunction of airway epithelial barrier both in vivo and in vitro, paralleled with the increased expression and release of Hsp90α. All of which were rescued in Hsp90α-knockdown cells or co-administration of 1G6-D7. Furthermore, either 1G6-D7 or PI3K inhibitor LY294002 suppressed the significant phosphorylation of AKT, which caused by secreted and recombinant Hsp90α, resulting in the restoration of epithelial barrier function. Conclusions Secreted Hsp90α medicates HDM-induced asthmatic airway epithelial barrier dysfunction via PI3K/AKT pathway, indicating that anti-secreted Hsp90α therapy might be a potential treatment to asthma in future.

The probability of wind-induced failure accidents in three-axle trucks under pulsating strong crosswinds and the corresponding critical safe speed are investigated in this study. Reliability theory and random fuzzy methods are utilized to establish the membership function of the failure probability in the series system (FPSS) composed of rollover, side-slip, and rotation failure accidents. The Kaman spectrum is used to realistically simulate the fluctuating wind time history curves of different average speeds. Four factors affecting the six-component force coefficient of the three-axle truck and the crosswind load are considered: fluctuating average wind speed, wind direction (angle), truck driving speed, and road adhesion coefficient. A three-axle truck nonlinear model is established accordingly. The model is used to obtain the dynamic response of the three-axle truck under strong crosswind conditions as per the time-varying curves of the vertical load of the truck, the time-varying curves of the lateral displacement of the center of mass, and the time-varying curves of the heading angle. An advanced Monte Carlo simulation algorithm based on importance sampling is used to determine the probability of a three-axle truck with FPSS under strong crosswinds; the given acceptable probability of failure (accident) is used to obtain the critical safety speed. The sensitivity analysis of random variables reveals that the possibility of three truck failures of the three-axle truck in strong crosswinds is, from largest to smallest, rollover, side-slip, and rotation. This research may provide useful guidance for exploring the probability of wind-induced accidents and the critical safety speeds of vehicles, as well as useful general information for road transportation management departments.

“Two ecological barriers and three shelters” (TEBTS), which has the effect of relieving ecological pressure, is the national ecological security pattern in China. Calculating the value of TEBTS ecosystem services, clarifying the synergy/trade-off relationships between ecosystem services, and maximizing the value of regional ecosystem services are of great significance for maintaining the security of the ecological civilization. At present, the research on ecosystem service synergy/trade-off has become the frontier field of ecology and related disciplines at home and abroad, and many research results have been obtained. However, there is still room and significance for continuing research to think about the synergy/trade-off relationship of ecosystems from the perspective of temporal and spatial heterogeneity: clarifying the spatial scope and spatial transmission characteristics of ecosystem service synergy/trade-off; exploring the trend of ecosystem service synergy/trade-off, and simulating the dynamic characteristics of natural factors affecting ecosystem services; and analyzing the characteristics of different spatial attributes that lead to the synergy/trade-off of ecosystem services. In this study, the Songhua River Basin (SRB), where the NFB is located, is used as the research area, the ecosystem services are simulated through the ecosystem assessment model, ecological unit (EU) is constructed as a research carrier, which is used to define the spatial scope of ecosystem services, and the influence of spatial characteristics and attribute characteristics on the change trend of the ecosystem service synergy/trade-off relationship is analyzed. The research found that water retention, soil conservation, and biodiversity did not change much from 2000 to 2015, and these ecosystem services have a greater value in the NFZ. The amount of carbon sequestration increased rapidly from 2010 to 2015. Crop production showed an increasing trend year by year. As the main grain production area, the Songnen Plain provides the main crop production function, which is greatly affected by humans. In the spatial characteristic, water retention, soil sequestration, and biodiversity present a very significant synergistic relationship, which is manifested in the obvious high-value aggregation characteristics in the NFZ, and crop production and the other four types of ecosystem services are in a trade-off relationship. At the time scale, the four types of ecosystem services, including water retention, soil conservation, biodiversity, and carbon sequestration, are synergistic, and crop production and water retention are synergistic. The vegetation types exhibiting a synergy/trade-off relationship are mainly broad-leaved forests, and the soil types are mainly luvisols and phaeozems. These EUs are mainly distributed in the NFZ and have spatial topological characteristics: the area and circumference of these EUs are smaller, the radius of gyration is also significantly smaller than that of other EUs, and the shape is more regular. By focusing on the spatial aggregation characteristics and changing trends of the ecosystem service synergy/trade-off and clarifying the influencing factors of the ecosystem service synergy/trade-off, the ecosystem services can be integrated, and the ecosystem can be optimized. Thus, the value of regional ecosystem services can be maximized, and a certain data foundation and theoretical support can be provided for major projects, such as ecological restoration and ecological environment governance, which is of great significance for improving the pattern of ecological security.

Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Network based on Multi-rate Dilated Attention Mechanism and Multi-granularity Feature Sharer (MRDA-MGFSNet). First, in this network, in order to effectively identify similar patterns between butterflies and suppress the information that is similar to the butterfly’s features in the background but is invalid, a Multi-rate Dilated Attention Mechanism (MRDA) with a symmetrical structure which assigns different weights to channel and spatial features is designed. Second, fusing the multi-scale receptive field module with the depthwise separable convolution module, a Multi-granularity Feature Sharer (MGFS), which can better solve the recognition problem of a small inter-class variance and reduce the increase in parameters caused by multi-scale receptive fields, is proposed. In order to verify the feasibility and effectiveness of the model in a complex environment, compared with the existing methods, our proposed method obtained a mAP of 96.64%, and an F1 value of 95.44%, which showed that the method proposed in this paper has a good effect on the fine-grained classification of butterflies.

Acetaminophen (APAP) overdose causes acute liver injury and leads to fatal liver damage. However, the therapies are quite limited. Shikonin is a natural product with antioxidant and anti-inflammatory activities. In the present study, the hepatoprotective effects and the underlying mechanisms of shikonin in APAP-induced hepatotoxicity in vivo and in vitro were investigated. APAP-induced acute liver injury and shikonin pretreatment models were established in vivo and in vitro, as evidenced by serum hepatic enzymes, histological changes, oxidative stress indicators and proinflammatory cytokines. The results revealed that shikonin pretreatment prevented the elevation of serum alanine transaminase (ALT), aspartate transaminase (AST) and lactate dehydrogenase (LDH) levels and markedly reduced APAP-induced histological alterations in liver tissues. Additionally, shikonin restored superoxide dismutase (SOD) expression and glutathione (GSH) content in line with the blockade of oxidative stress. The changes in gene expression involved in oxidative stress including methionine sulfoxide reductase (such as MsrA and MsrB1), heme oxygenase-1 (HO-1), SOD2 and cytochrome P450 2E1 (CYP2E1), were markedly reversed after shikonin therapy. Furthermore, shikonin markedly attenuated the APAP-induced production of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-1β (IL-1β) and suppressed the expression of genes related to inflammation. In AML-12 cells, shikonin pretreatment decreased APAP-induced cytotoxicity as measured by CCK-8 assay and LDH release. The changes in gene expression involved in oxidative stress and the inflammatory response were consistent with those in mouse livers. This study indicated that shikonin attenuated APAP-induced acute liver injury via inhibiting oxidative stress and inflammatory responses in vivo and in vitro. These findings offer new insights into the potential therapy for APAP hepatotoxicity.

The water cycle in the key agricultural and pastoral zones (KAPZs) is an important factor for maintaining the stability of the ecosystem. Groundwater collection and lateral seepage are indispensable parts of the water cycle, and it is difficult to monitor the groundwater situation in each area. The strength of the alternate circulation of groundwater is directly related to the utilization value and development prospects of groundwater; therefore, creating an effective method for the detection of groundwater burial depth has become an issue of increasing concern. In this paper, we attempt to create a method for the detection of groundwater burial depth that combines cokriging interpolation, spatial autocorrelation, geographically weighted regression, and other methods to construct a quantitative relationship between different land cover types and groundwater depth. By calculating the band index of the land cover type, the groundwater depthinformation of the unknown area can be obtained more accurately. Through collaborative kriging interpolation, normalized difference vegetation index (NDVI), precipitation, and hydrogeological conditions were used as covariates. The groundwater burial depth of Wengniute Banner in 2005, 2009, 2013, and 2017 was the response variable, and the groundwater burial depth in the study area was calculated. The groundwater burial depth data after the cokriging interpolation was used to transform the raster data into vector data in space using the improved hydrological response unit (HRU) model to make it more suitable for the actual groundwater confluence. Subsequently, 551 minimum response units (MHRUs) were obtained by division, and the spatial autocorrelation analysis was performed accordingly. The groundwater burial depth in the study area is spatially distinct from east to west, and the groundwater level shows a trend of being high in the west and low in the east, gradually increasing due to precipitation and rivers. The average change of groundwater depth in the time series is not significant, but it does gradually show a trend of accumulation. According to the aggregation characteristics of spatial autocorrelation analysis, a geographically weighted regression model of groundwater depth and NDVI, normalized difference drought index (NDDI), and net relatedness index (NRI) was established. The NDVI representing the forest land and the Adjusted R2 of the groundwater depth is 0.67. The NRI representing the cultivated land and the Adjusted R2 of the groundwater depth is 0.8675. The NDDI representing the bare land and the Adjusted R2 of the groundwater depth is 0.7875. It shows that the band index representing the ground type has a good fitting effect with the groundwater burial depth.

A molecular dynamics model of the diamond abrasive polishing the single crystal silicon is established. Crystal surfaces of the single crystal silicon in the Y-direction are (010), (011), and (111) surfaces, respectively. The effects of crystallographic orientations on polishing the non-continuous single crystal silicon surfaces are discussed from the aspects of surface morphology, displacement, polishing force, and phase transformation. The simulation results show that the Si(010) surface accumulates chips more easily than Si(011) and Si(111) surfaces. Si(010) and Si(011) workpieces are deformed in the entire pore walls on the entry areas of pores, while the Si(111) workpiece is a local large deformation on entry areas of the pores. Comparing the recovery value of the displacement in different workpieces, it can be seen that the elastic deformation of the A side in the Si(011) workpiece is larger than that of the A side in other workpieces. Pores cause the tangential force and normal force to fluctuate. The fluctuation range of the tangential force is small, and the fluctuation range of the normal force is large. Crystallographic orientations mainly affect the position where the tangential force reaches the maximum and minimum values and the magnitude of the decrease in the tangential force near the pores. The position of the normal force reaching the maximum and minimum values near the pores is basically the same, and different crystallographic orientations have no obvious effect on the drop of the normal force, except for a slight fluctuation in the value. The high-pressure phase transformation is the main way to change the crystal structure. The Si(111) surface is the cleavage surface of single crystal silicon, and the total number of main phase transformation atoms on the Si(111) surface is the largest among the three types of workpieces. In addition, the phase transformation in Si(010) and Si(011) workpieces extends to the bottom of pores, and the Si(111) workpiece does not extend to the bottom of pores.

Urban forests affect the filtration and absorption of airborne particulate matter, which can minimize the harmful effects to human health caused by airborne particulate pollution. Evergreen plants in urban forests play a major role in absorbing dust and purifying the air, especially in winter. Studying the spatial distribution of leaf dust and exploring the dust retention effect of evergreen shrubs are important for scientifically guiding urban forest construction and improving the living environment of cities in winter. The purpose of this study was to establish a dust inversion model by correlation analysis of spectral reflectance and the amount of dust absorption (ADA) of vegetation, using Sentinel-2 satellite remote-sensing images to obtain the dust distribution of the evergreen vegetation (mainly Euonymus japonicus Thunb.) in the Beijing urban area, and to determine the effect of the spatial pattern of E. japonicus woodland on ADA intensity. The result showed that the red band and near-infrared band are most sensitive to dust. The normalized difference phenology index (NDPI) is more suitable for building an inversion model, where the determination coefficient (R2) of the inversion model constructed by the ratio of the NDPI (RNDPI) was 0.879. The inversion results show that the mean ADA in the enclosed area is smaller than that in semi-enclosed and open areas, and the regional distribution of high ADA in the urban area of Beijing was higher in the south with a tendency of the ADA to decrease from city center to the surrounding area. The size, shape, and percentage of landscape (PLAND) of E. japonicus woodland have a significant effect on ADA intensity. We found that, in the study area, when the PLAND of E. japonicus woodland is higher than 40%, its ADA intensity remains basically unchanged. When the vegetation coverage is fixed, the landscape shape index is negatively correlated with ADA intensity, and reduction of the overall shape complexity of forestland can enhance its dust retention effect and improve the air environment of the surrounding areas. The results of this study can be used as a reference for urban planners and landscape architects when building urban forests, providing a scientific basis for controlling and reducing air particulate pollution in Beijing’s winter and improving the living environment.

The strawberry vein banding virus (SVBV) open reading frame (ORF) VI encodes a P6 protein known as the RNA silencing suppressor. This protein is known to form inclusion like granules of various sizes and accumulate in both the nuclei and the cytoplasm of SVBV-infected plant cells. In this study, we have determined that the P6 protein is the only trans-activator (TAV) encoded by SVBV, and can efficiently trans-activate the translation of downstream gfp mRNA in a bicistron derived from the SVBV. Furthermore, the P6 protein can trans-activate the expression of different bicistrons expressed by different caulimovirus promoters. The P6 protein encoded by SVBV from an infectious clone can also trans-activate the expression of bicistron. Through protein-protein interaction assays, we determined that the P6 protein could interact with the cell translation initiation factor FveIF3g of Fragaria vesca and co-localize with it in the nuclei of Nicotiana benthamiana cells. This interaction reduced the formation of P6 granules in cells and its trans-activation activity on translation.

This paper describes the innovative design of a three-dimensional (3D) motion device based on a flexible mechanism, which is used primarily to produce accurate and fast micro-displacement. For example, the rapid contact and separation of the tool and the workpiece are realized by the operation of the 3D motion device in the machining process. This paper mainly concerns the device performance. A theoretical model for the static performance of the device was established using the matrix-based compliance modeling (MCM) method, and the static characteristics of the device were numerically simulated by finite element analysis (FEA). The Lagrangian principle and the finite element analysis method for device dynamics are used for prediction to obtain the natural frequency of the device. Under no-load conditions, the dynamic response performance and linear motion performance of the three directions were tested and analyzed with different input signals, and three sets of vibration trajectories were obtained. Finally, the scratching experiment was carried out. The detection of the workpiece reveals a pronounced periodic texture on the surface, which verifies that the vibration device can generate an ideal 3D vibration trajectory.

BACKGROUND:Multidrug efflux transporter P-glycoprotein (P-gp) is highly expressed on membrane of tumor cells and is implicated in resistance to tumor chemotherapy. HZ08 is synthesized and studied in order to find a novel P-gp inhibitor. METHODS:MDCK-MDR1 monolayer transport, calcein-AM P-gp inhibition and P-gp ATPase assays were used to confirm the P-gp inhibition capability of HZ08. Furthermore, KB-WT and KB-VCR cells were used to evaluate the P-gp inhibitory activity of HZ08 both in vitro and in vivo. RESULTS:Results showed that HZ08 was more potent than verapamil in MDCK-MDR1 monolayer transportation model. Meanwhile, P-gp ATPase assay and calcein-AM P-gp inhibition assay confirmed that HZ08 inhibited P-gp ATPase with a calcein-AM IC50 of 2.44±0.31μM. In addition, significantly greater in vitro multidrug resistance reversing effects were observed when vincristine or paclitaxel was used in combination with 10μM HZ08 compared with 10μM verapamil. Moreover, HZ08 could significantly enhance the sensitivity of vincristine with a similar effect like verapamil in both KB-WT and KB-VCR tumor xenograft models. CONCLUSIONS:The novel structure HZ08 could be a potent P-gp inhibitor.

Background: Tumor compression-induced pain (TCIP) is a complex pathological cancer pain. Spinal glial cells play a critical role in maintenance of cancer pain by releasing proinflammatory cytokines and chemokines. In this study, we verified the role of levo-corydalmine (l-CDL) on TCIP. Methods: Spontaneous pain, paw withdrawal threshold and latency were assessed using TCIP mouse model. Immunofluorescence was used to identify the reactions of glia. RT-PCR and western blot or ELISA were used to determine mRNA or protein expression of tumor necrosis factor-α (TNF-α), interlukin-1β (IL-1β), CC chemokine ligand 2 (CCL2) and chemotactic cytokine receptor 2 (CCR2) in vivo and in vitro. Results: l-CDL significantly attenuated TCIP hypersensitivity, accompanying with downregulation of TNF-α and IL-1β expression levels and declined astrocytes and microglial activation. It also significantly decreased the expression of the mRNA and protein level for CCL2 and CCR2. Further, l-CDL could suppress TNF-α-induced astrocytes activation and IL-1β expression through downregulating the CCL2/CCR2. Besides, CCL2-induced BV-microglia activation and inflammatory factors secretion were suppressed by l-CDL via CCR2. Conclusions: Suppression of CCL2/CCR2 by l-CDL may contribute to alleviate TCIP, offering an alternative medication for TCIP.