Active ingredient identification and purification of organosilicon spray adjuvant and its uptake and translocation in rice (Oryza sativa L.).

The assessment of residue, absorption, conduction, and degradation of agricultural organosilicon surfactants in the environment is hindered by the lack of information on active ingredients and corresponding quantitative standards for organosilicon spray adjuvants. The spray adjuvant 'Jiexiaoli,' a primary organosilicon spray agent in China, was identified as hydroxy (polyethylene) propyl-heptamethyl trisiloxane (TSS-H) with 3-15 ethoxy (EO) groups. Purification of TSS-H was achieved through semi-preparative separation using high-performance liquid chromatography (HPLC), resulting in TSS-H purity exceeding 96%. An accurate residual detection method for nine oligomers (4-12 EO) of TSS-H in rice roots, stems, leaves, and culture solution samples was developed using HPLC tandem high-resolution mass spectrometry (HPLC-HRMS). Recoveries for nine oligomers of TSS-H in the four matrices ranged from 80.22% to 104.01%. Foliar application experiments demonstrated that TSS-H did not transfer from the upper to the lower parts of the rice plant. The half-lives of each oligomer (4-12 EO) in leaves were less than 3.21 days. Root application experiments revealed a root concentration factor (RCF) ranging from 0.20 to 0.56, a biological enrichment factor (BCF) ranging from 0.36 to 0.68, a transpiration factor (TSCF) ranging from 0.069 to 0.086, and a transport factor (TF) ranging from 0.08 to 0.43. These results indicated that TSS-H could be absorbed by rice roots and conducted to the above-ground parts of rice plants. This study fills the data gap in the environmental risk and food safety assessment of agricultural silicone spray adjuvants. © 2024 Society of Chemical Industry.

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model.

Wood borers, such as the emerald ash borer and holcocerus insularis staudinger, pose a significant threat to forest ecosystems, causing damage to trees and impacting biodiversity. This paper proposes a neural network for detecting and classifying wood borers based on their feeding vibration signals. We utilize piezoelectric ceramic sensors to collect drilling vibration signals and introduce a novel convolutional neural network (CNN) architecture named Residual Mixed Domain Attention Module Network (RMAMNet).The RMAMNet employs both channel-domain attention and time-domain attention mechanisms to enhance the network's capability to learn meaningful features. The proposed system outperforms established networks, such as ResNet and VGG, achieving a recognition accuracy of 95.34% and an F1 score of 0.95. Our findings demonstrate that RMAMNet significantly improves the accuracy of wood borer classification, indicating its potential for effective pest monitoring and classification tasks. This study provides a new perspective and technical support for the automatic detection, classification, and early warning of wood-boring pests in forestry.

Microbiota-microglia crosstalk between Blautia producta and neuroinflammation of Parkinson's disease: A bench-to-bedside translational approach.

Parkinson's disease (PD) is intricately linked to abnormal gut microbiota, yet the specific microbiota influencing clinical outcomes remain poorly understood. Our study identified a deficiency in the microbiota genus Blautia and a reduction in fecal short-chain fatty acid (SCFA) butyrate level in PD patients compared to healthy controls. The abundance of Blautia correlated with the clinical severity of PD. Supplementation with butyrate-producing bacterium B. producta demonstrated neuroprotective effects, attenuating neuroinflammation and dopaminergic neuronal death in mice, consequently ameliorating motor dysfunction. A pivotal inflammatory signaling pathway, the RAS-related pathway, modulated by butyrate, emerged as a key mechanism inhibiting microglial activation in PD. The change of RAS-NF-κB pathway in PD patients was observed. Furthermore, B. producta-derived butyrate demonstrated the inhibition of microglial activation in PD through regulation of the RAS-NF-κB pathway. These findings elucidate the causal relationship between specific gut microbiota and PD, presenting a novel microbiota-based treatment perspective for PD.

Protein structuromics: A new method for protein structure-function crosstalk in glioma.

Glioma is a type of tumor that starts in the glial cells of the brain or spine. Since the 1800s, when the disease was first named, its survival rates have always been unsatisfactory. Despite great advances in molecular biology and traditional treatment methods, many questions regarding cancer occurrence and the underlying mechanism remain to be answered. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti-oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure-function relationships of proteins. All of these results directly indicate that unfavorable group proteins show more complex structures than favorable group proteins. As the tumor cell microenvironment changes, the balance of oncogene-related and anti-oncogene-related proteins is disrupted, and most of the structures of the two groups of proteins will be disrupted. However, more unfavorable group proteins will maintain and refold to achieve their correct shape faster and perform their functions more quickly than favorable group proteins, and the former thus support cancer development. We hope that these analyses will help promote mechanistic research and the development of new treatments for glioma.

Malnutrition and cerebral intraparenchymal damage in patients with thrombosis of dural sinuses and/or cerebral veins.

BACKGROUNDS: Thrombosis of dural sinuses and/or cerebral veins (CVT) is an uncommon form of cerebrovascular disease. Malnutrition is common in patients with cerebrovascular disease, and early assessment of malnutrition and individualized nutritional treatment have been reported to improve functional outcomes of these patients. As for CVT patients, little is known about whether these patients would suffer from malnutrition. Also, the correlation between malnutrition and cerebral intraparenchymal damage (CID) in CVT patients was rarely studied. METHODS: Patients with CVT were retrospectively included in this observational study. Multivariate logistic regressions were used to investigate the effects of nutritional indexes on the risk of CID. Subsequently, we used the independent risk factors to construct the nomogram model, and the consistency index (C-index), calibration curve and decision curve analysis (DCA) to assess the reliability and applicability of the model. RESULTS: A total of 165 patients were included in the final analysis. Approximately 72.7% of CVT patients were regarded as malnourished by our malnutrition screening tools, and malnutrition is associated with an increased risk of CID. Prognostic Nutritional Index (PNI) (OR = 0.873; CI: 0.791, 0.963, p = 0.007) remained as an independent predictor for CID after adjustment for other risk factors. The nomogram model showed that PNI and gender have a great contribution to prediction. Besides, the nomogram model was consistent with the actual observations of CID risk (C-index = 0.65) and was of clinical significance. CONCLUSIONS: We reported that malnutrition, as indicated by PNI, was associated with a higher incidence of CID in CVT patients. Also, we have constructed a nomogram for predicting the risk of CID in these patients.

The impact of perceived school climate on exercise behavior engagement among obese adolescents: a dual mediation effect test of exercise benefits and perseverance qualities.

Introduction: This study explores the relationship between perceived school climate and exercise behavior among obese adolescents, as well as the multiple mediating effects of perseverance qualities and exercise benefits. Methods: A survey was conducted on 586 obese adolescents in Beijing, with an age range of 13-18 years old and an average age of 15.40 ± 1.824, among who 337 were male, 249 were female, 303 were high school students and 238 were middle school students. A standard scale was used to evaluate perceived school climate, exercise benefits, perseverance qualities, and exercise behaviors. The data was analyzed by independent samples t-test, bivariate correlation analysis, descriptive statistical analysis, and structural equation model (SEM). Results: (1) Perceived school climate among obese adolescents positively predicted exercise behavior (Z = 2.870, p < 0.01), perseverance qualities (Z = 3.107, p < 0.01) and exercise benefits (Z = 4.290, p < 0.001); perseverance qualities positively predicted exercise behavior in obese adolescents (Z = 4.431, p < 0.001); exercise benefits positively predicted the obese adolescents' exercise behavior (Z = 4.267, p < 0.001). (2) Perseverance qualities (Z = 2.282, 95% CI [0.032, 0.191], [0.028, 0.179]) and exercise benefits (Z = 2.518, 95% CI [0.060, 0.287], [0.053, 0.271]) play a mediating role in the obese adolescents' perceived school climate and exercise behavior, respectively. These two factors have parallel multiple mediating effects between obese adolescents' perceived school climate and exercise behavior, with mediating effects accounting for 16 and 25%, respectively. The mediating effect of exercise benefits is greater than that of perseverance qualities. (3) There is no difference in the specific indirect effects of perseverance qualities and exercise benefits (Z = -0.800, 95% CI [- 0.198, 0.064], [-0.190, 0.068]). Conclusion: Obese adolescents' perception of school climate can effectively enhance their motivation to participate in exercise behavior and indirectly influence exercise behavior through exercise benefits and perseverance qualities, cultivate good physical exercise behavior among obese adolescents, and effectively prevent and intervene in the occurrence of obesity.

Fecal microbiota transplantation inhibited neuroinflammation of traumatic brain injury in mice via regulating the gut-brain axis.

Introduction: Recent studies have highlighted the vital role of gut microbiota in traumatic brain injury (TBI). Fecal microbiota transplantation (FMT) is an effective means of regulating the microbiota-gut-brain axis, while the beneficial effect and potential mechanisms of FMT against TBI remain unclear. Here, we elucidated the anti-neuroinflammatory effect and possible mechanism of FMT against TBI in mice via regulating the microbiota-gut-brain axis. Methods: The TBI mouse model was established by heavy object falling impact and then treated with FMT. The neurological deficits, neuropathological change, synaptic damage, microglia activation, and neuroinflammatory cytokine production were assessed, and the intestinal pathological change and gut microbiota composition were also evaluated. Moreover, the population of Treg cells in the spleen was measured. Results: Our results showed that FMT treatment significantly alleviated neurological deficits and neuropathological changes and improved synaptic damage by increasing the levels of the synaptic plasticity-related protein such as postsynaptic density protein 95 (PSD-95) and synapsin I in the TBI mice model. Moreover, FMT could inhibit the activation of microglia and reduce the production of the inflammatory cytokine TNF-α, alleviating the inflammatory response of TBI mice. Meanwhile, FMT treatment could attenuate intestinal histopathologic changes and gut microbiota dysbiosis and increase the Treg cell population in TBI mice. Conclusion: These findings elucidated that FMT treatment effectively suppressed the TBI-induced neuroinflammation via regulating the gut microbiota-gut-brain axis, and its mechanism was involved in the regulation of peripheral immune cells, which implied a novel strategy against TBI.

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas.

Background: Sperm-associated antigen 5 (SPAG5) has been identified as a novel driver oncogene involved in multiple cancers; however, its role in lung adenocarcinoma (LUAD) needs further investigation. Our study aims to elucidate the potential significance of SPAG5 in LUAD prognosis and its implications for the efficacy of immunotherapy. Methods: In this study, we used bioinformatics analysis and tissue microarray (TMA) staining to examine the potential role of SPAG5 in LUAD survival and response to immunotherapy. We used the Oncomine, TIMER2.0, Gene Expression Profiling Interactive Analysis (GEPIA), Sangerbox, PredicScan, and Kaplan-Meier Plotter databases to examine the expression and prognostic role of SPAG5 in the LUAD of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases. We also used Cancer Single-cell State Atlas (CancerSEA) and Tumor Immune Estimation Resource (TIMER2.0) to analyze the association of SPAG5 with malignant phenotype and tumor immune microenvironment. Furthermore, Immune Cell Abundance Identifier (ImmuCellAI) analysis of TCGA sequencing data was used to predict the role of SPAG5 in determining the response to immune checkpoint blockade (ICB) treatment in LUAD. Co-expression analysis of programmed death-ligand 1 (PD-L1) and SPAG5 was performed using LUAD TMA immunohistochemistry (IHC) analysis. Results: Our findings indicate that SPAG5 is overexpressed in LUAD and is positively correlated with advanced clinical stage, poor overall survival, relapse-free survival, and progression-free survival outcomes. SPAG5 may be involved in regulating the cell cycle, proliferation, invasion, DNA damage and repair, and tumor immunosuppression. Furthermore, TMA IHC analysis showed a positive correlation between PD-L1 expression in LUAD and SPAG5 which suggests that SPAG5 may serve as a potential predictor of response to ICB therapy in LUAD. Conclusions: Our results highlight the role of SAPG5 in promoting a tumor malignancy phenotype and immunosuppression in LUAD and suggest that SPAG5 may serve as a potential response marker for ICB therapy.

The Influence of Patent Foramen Ovale on the Clinical Features of Migraine without Aura: A Cross-Sectional Study.

Objective: To investigate the influence of patent foramen ovale (PFO) on the clinical features of migraine without aura (MoA). Methods: We consecutively enrolled 390 MoA patients and compared the frequency of headache, episode duration, and the Visual Analogue Scale (VAS), Headache Impact Test 6 (HIT-6), and European Health Interview Survey-Quality of Life 8-item index (EUROHIS-QOL8) scores of patients with and without PFO, those with the mild right-to-left shunt (RLS) and moderate to large RLS, and those with permanent RLS and latent RLS using a nonparametric Mann-Whitney U-test. In addition, we analyzed the clinical features of migraine in 39 MoA patients before and after PFO closure treatment using the paired Wilcoxon test. Results: The prevalence of PFO in the 390 MoA patients was 44.4%. Patients with PFO had significantly higher frequency of headaches, VAS scores, HIT-6 scores, and incidence of white matter lesions than those without PFO (all p< 0.05). Patients with moderate to large RLS had significantly higher VAS scores than those with mild RLS (p = 0.002). Additionally, 39 MoA patients underwent PFO closure, which remarkably decreased their frequency of headache, episode duration, VAS scores, and HIT-6 scores, and increased their EUROHIS-QOL8 scores. Conclusion: The migraine features in MoA patients could be influenced by PFO, especially in patients with moderate to large shunt, in whom PFO closure improved the symptoms.

Classification of Camellia oleifera Diseases in Complex Environments by Attention and Multi-Dimensional Feature Fusion Neural Network.

The use of neural networks for plant disease identification is a hot topic of current research. However, unlike the classification of ordinary objects, the features of plant diseases frequently vary, resulting in substantial intra-class variation; in addition, the complex environmental noise makes it more challenging for the model to categorize the diseases. In this paper, an attention and multidimensional feature fusion neural network (AMDFNet) is proposed for Camellia oleifera disease classification network based on multidimensional feature fusion and attentional mechanism, which improves the classification ability of the model by fusing features to each layer of the Inception structure and enhancing the fused features with attentional enhancement. The model was compared with the classical convolutional neural networks GoogLeNet, Inception V3, ResNet50, and DenseNet121 and the latest disease image classification network DICNN in a self-built camellia disease dataset. The experimental results show that the recognition accuracy of the new model reaches 86.78% under the same experimental conditions, which is 2.3% higher than that of GoogLeNet with a simple Inception structure, and the number of parameters is reduced to one-fourth compared to large models such as ResNet50. The method proposed in this paper can be run on mobile with higher identification accuracy and a smaller model parameter number.

A CNN-Based Method for Enhancing Boring Vibration with Time-Domain Convolution-Augmented Transformer.

Recording vibration signals induced by larvae activity in the trunk has proven to be an efficient method for detecting trunk-boring insects. However, the accuracy of the detection is often limited because the signals collected in real-world environments are heavily disrupted by environmental noises. To deal with this problem, we propose a deep-learning-based model that enhances trunk-boring vibration signals, incorporating an attention mechanism to optimize its performance. The training data utilized in this research consist of the boring vibrations of Agrilus planipennis larvae recorded within trunk sections, as well as various environmental noises that are typical of the natural habitats of trees. We mixed them at different signal-to-noise ratios (SNRs) to simulate the realistically collected sounds. The SNR of the enhanced boring vibrations can reach up to 17.84 dB after being enhanced by our model, and this model can restore the details of the vibration signals remarkably. Consequently, our model's enhancement procedure led to a significant increase in accuracy for VGG16, a commonly used classification model. All results demonstrate the effectiveness of our approach for enhancing the detection of larvae using boring vibration signals.

Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression.

Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussian-scale flow for prediction, which cannot support fine-grained adaptation to diverse motion content. Towards more content-adaptive prediction, we propose a novel cross-scale prediction module that achieves more effective motion compensation. Specifically, on the one hand, we produce a reference feature pyramid as prediction sources and then transmit cross-scale flows that leverage the feature scale to control the precision of prediction. On the other hand, for the first time, a weighted prediction mechanism is introduced even if only a single reference frame is available, which can help synthesize a fine prediction result by transmitting cross-scale weight maps. In addition to the cross-scale prediction module, we further propose a multi-stage quantization strategy, which improves the rate-distortion performance with no extra computational penalty during inference. We show the encouraging performance of our efficient neural video codec (ENVC) on several benchmark datasets. In particular, the proposed ENVC can compete with the latest coding standard H.266/VVC in terms of sRGB PSNR on UVG dataset for the low-latency mode. We also analyze in detail the effectiveness of the cross-scale prediction module in handling various video content, and provide a comprehensive ablation study to analyze those important components. Test code is available at https://github.com/USTC-IMCL/ENVC.

Structure-function crosstalk in liver cancer research: Protein structuromics.

Liver cancer can be primary (starting in the liver) or secondary (cancer that has spread from elsewhere to the liver, known as liver metastasis). Liver metastasis is more common than primary liver cancer. Despite great advances in molecular biology methods and treatments, liver cancer is still associated with a poor survival rate and a high death rate, and there is no cure. Many questions remain regarding the mechanisms of liver cancer occurrence and development as well as tumor reoccurrence after treatment. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti-oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure-function relationships of proteins. Our aim was to provide new insights that may inform research on the development and treatment of liver cancer.

The interpretability of the activity signal detection model for wood-boring pests Semanotus bifasciatus in the larval stage.

BACKGROUND: The acoustic detection model of activity signals based on deep learning could detect wood-boring pests accurately and reliably. However, the black-box characteristics of the deep learning model have limited the credibility of the results and hindered its application. Aiming to address the reliability and interpretability of the model, this paper designed an active interpretable model called Dynamic Acoustic Larvae Prototype Network (DalPNet), which used the prototype to assist model decisions and achieve more flexible model explanation through dynamic feature patch computation. RESULTS: In the experiments, the average recognition accuracy of the DalPNet on the simple test set and anti-noise test set for Semanotus bifasciatus larval activity signals reached 99.3% and 98.5%, respectively. The quantitative evaluation of interpretability was measured by the relative area under the curve (RAUC) and the cumulative slope (CS) of the accuracy change curve in this paper. In the experiments, the RAUC and the CS of DalPNet were 0.2923 and -2.0105, respectively. Additionally, according to the visualization results, the explanation results of DalPNet were more accurate in locating the bite pulses of the larvae and could better focus on multiple bite pulses in one signal, which showed better performance compared to the baseline model. CONCLUSION: The experimental results demonstrated that the proposed DalPNet had better explanation while ensuring recognition accuracy. In view of that, it could improve the trust of forestry custodians in the activity signals detection model and aid in the practical application of the model in the forestry field. © 2023 Society of Chemical Industry.

A Fine-Grained Bird Classification Method Based on Attention and Decoupled Knowledge Distillation.

Classifying birds accurately is essential for ecological monitoring. In recent years, bird image classification has become an emerging method for bird recognition. However, the bird image classification task needs to face the challenges of high intraclass variance and low inter-class variance among birds, as well as low model efficiency. In this paper, we propose a fine-grained bird classification method based on attention and decoupled knowledge distillation. First of all, we propose an attention-guided data augmentation method. Specifically, the method obtains images of the object's key part regions through attention. It enables the model to learn and distinguish fine features. At the same time, based on the localization-recognition method, the bird category is predicted using the object image with finer features, which reduces the influence of background noise. In addition, we propose a model compression method of decoupled knowledge distillation. We distill the target and nontarget class knowledge separately to eliminate the influence of the target class prediction results on the transfer of the nontarget class knowledge. This approach achieves efficient model compression. With 67% fewer parameters and only 1.2 G of computation, the model proposed in this paper still has a 87.6% success rate, while improving the model inference speed.

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.

BACKGROUND: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT. METHODS: We searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs. RESULTS: In this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8. CONCLUSION: The contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.

The association between ethylene oxide exposure and asthma risk: a population-based study.

Ethylene oxide (EO) is a reactive epoxide. However, the association between EO exposure and the risk of developing asthma in humans is unknown. The aim of this study was to investigate the relationship between EO exposure and the risk of developing asthma in the general US population. In this cross-sectional study, data of 2542 patients from the National Health and Nutrition Examination Survey (NHANES) between 2013 and 2016 were obtained and analyzed. Hemoglobin adducts of EO (HbEO) level be used as the main factor for predicting EO exposure. The association between the level of EO exposure and the risk of developing asthma was evaluated with logistic regression models and dose-response analysis curves of restricted cubic spline function. Mediation analysis and linear regression analysis were utilized to evaluate the association between EO exposure and inflammation indicators. According to the quartiles of HbEO level, the patients were divided into four groups. The results indicated that an increased HbEO level was associated with a higher risk of asthma onset. Compared with the lowest quartile, the odds ratio (OR) with the 95% confidence interval (CI) for the highest quartile was 1.960 (95% CI: 1.348-2.849, P = 0.003). After being adjusted for numerous potential confounders, the OR of quartile 4 relative to quartile 1 was 1.991 (95% CI: 1.359-2.916, P = 0.001). Consistent results were also obtained in most subgroup analyses and dose-response analysis curves. In addition, EO levels were positively correlated with the inflammatory indicators (P = 0.006 for WBC, P = 0.015 for lymphocyte, and P = 0.015 for neutrophil). This study revealed a positive correlation between the level of EO exposure and the risk of asthma in a representative US population. In addition, inflammatory response may prove to be a potential biological mechanism underlying EO-induced asthma.

Trajectory Optimization of Laser-Charged UAVs for Charging Wireless Rechargeable Sensor Networks.

This paper considers a laser-powered unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system. In the system, a UAV is dispatched as an energy transmitter to replenish energy for battery-limited sensors in a wireless rechargeable sensor network (WRSN) by transferring radio frequency (RF) signals, and a mobile unmanned vehicle (MUV)-loaded laser transmitter travels on a fixed path to charge the on-board energy-limited UAV when it arrives just below the UAV. Based on the system, we investigate the trajectory optimization of laser-charged UAVs for charging WRSNs (TOLC problem), which aims to optimize the flight trajectories of a UAV and the travel plans of an MUV cooperatively to minimize the total working time of the UAV so that the energy of every sensor is greater than or equal to the threshold. Then, we prove that the problem is NP-hard. To solve the TOLC problem, we first propose the weighted centered minimum coverage (WCMC) algorithm to cluster the sensors and compute the weighted center of each cluster. Based on the WCMC algorithm, we propose the TOLC algorithm (TOLCA) to design the detailed flight trajectory of a UAV and the travel plans of an MUV, which consists of the flight trajectory of a UAV, the hovering points of a UAV with the corresponding hovering times used for the charging sensors, the hovering points of a UAV with the corresponding hovering times used for replenishing energy itself, and the hovering times of a UAV waiting for an MUV. Numerical results are provided to verify that the suggested strategy provides an effective method for supplying wireless rechargeable sensor networks with sustainable energy.

Differential expression pattern, bioinformatics analysis, and validation of circRNA and mRNA in patients with arteriosclerosis.

Background: Lower limb arteriosclerosis obliterans (ASO) is the formation of atherosclerotic plaques in lower limb arteries, leading to vascular stenosis and occlusion, and is a major factor leading to lower limb amputation. The ASO seriously endangers the physical and mental health of patients. As living standards improve, the disease tends to occur in younger patients, and the incidence keeps increasing year by year. The circular RNAs (circRNAs) have been found to be tissue-specific, and they play an important role in a variety of diseases, but there are few studies on the pathogenic role and expression of circRNAs in ASOs. Method: Three diseased arteries from patients with ASO and three healthy arteries from healthy donors were collected for second-generation sequencing, and the pathogenic pathways and possible pathogenic circRNAs related to ASO were screened through bioinformatics analysis. PCR and agarose gel electrophoresis were used to validate the sequencing results. The expression of circRNA-0008706 in human arterial smooth muscle cells (HASMCs) was knocked down using siRNA technology to explore its function. Result: We identified 480 differentially expressed (DE) circRNAs and 2,997 DEmRNAs. Functional analysis revealed that epithelial-to-mesenchymal transition (EMT), lipid transport, regulation of extracellular matrix disassembly, regulation of cardiac muscle cell proliferation, branched-chain amino acid biosynthetic process, and positive regulation of cell growth and migration were enriched. Based on our previous microRNA array results, we constructed an ASO disease-specific competing endogenous (ceRNA) network. After validation, circRNA-0008706 was selected for functional analysis. Knockdown of circRNA-0008706 significantly suppressed the proliferation and migration phenotype of HASMCs and decreased the BCAT1 expression, which may be due to the specific binding of circRNA-0008706 to microRNA-125b-5p. Conclusion: This study is the first to compare the circRNA and mRNA expression profiles of ASOs and healthy arterial specimens and to construct a disease-specific ceRNA network for ASOs. This study may provide a new therapeutic target for ASO.

A Waveform Mapping-Based Approach for Enhancement of Trunk Borers' Vibration Signals Using Deep Learning Model.

The larvae of some trunk-boring beetles barely leave traces on the outside of trunks when feeding within, rendering the detection of them rather difficult. One approach to solving this problem involves the use of a probe to pick up boring vibrations inside the trunk and distinguish larvae activity according to the vibrations. Clean boring vibration signals without noise are critical for accurate judgement. Unfortunately, these environments are filled with natural or artificial noise. To address this issue, we constructed a boring vibration enhancement model named VibDenoiser, which makes a significant contribution to this rarely studied domain. This model is built using the technology of deep learning-based speech enhancement. It consists of convolutional encoder and decoder layers with skip connections, and two layers of SRU++ for sequence modeling. The dataset constructed for study is made up of boring vibrations of Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae) and environmental noise. Our VibDenoiser achieves an improvement of 18.57 in SNR, and it runs in real-time on a laptop CPU. The accuracy of the four classification models increased by a large margin using vibration clips enhanced by our model. The results demonstrate the great enhancement performance of our model, and the contribution of our work to better boring vibration detection.