This investigation was aimed at extrapolating whether and how lncRNA GAS5, miR-196a-5p and HOXA5 altered progression of ovarian cancer (OA).
Totally 195 pairs of OA tissues and adjacent normal ...tissues were collected. Also si-GAS5, pcDNA-GAS5, miR-196a-5p mimic, miR-196a-5p inhibitor and negative control (NC) were, respectively, transfected into OA cells. Besides, dual-luciferase reporter gene assay was performed to validate the targeted relationships between GAS5 and miR-196a-5p, as well as between miR-196a-5p and HOXA5. The impacts of GAS5, miR-196a-5p and HOXA5 on viability, proliferation and apoptosis of OA cells were appraised via conduction of colony formation assay, MTT assay and flow cytometry assay.
Lower GAS5 expression and higher miR-196a-5p expression were associated with larger tumor size (≥5 cm) and more advanced FIGO stage (III-IV) of OA patients (P < 0.05). Transfection of si-GAS5, miR-196a-5p mimic or si-HOXA5 conferred OA cells with stronger viability, faster proliferation and smaller percentage of apoptosis (P < 0.05). After injecting mice models with si-GAS5, miR-196a-5p mimic or si-HOXA5, a larger tumor size was also observed within the rats (P < 0.05). GAS5 was indicated to directly target miR-196a-5p and modify its expression, and the targeted relationship also seemed to exist between miR-196a-5p and HOXA5 (P < 0.05). The HOXA5 was found to reverse the effects imposed by miR-196a-5p on viability, proliferation and apoptosis of OA cells (P < 0.05).
LncRNA GAS5 depressed OA development by targeting miR-196a-5p and thereby down-regulating HOXA5 expression, providing substance for developing lncRNA-based strategies to treat OA.
•Lower GAS5 expression and higher miR-196a-5p expression were associated with ovarian cancer patients.•Si-GAS5, miR-196a-5p mimics and si-HOXA5 conferred with higher viability, proliferation, EMT and lower apoptotic rate.•HOXA5 could reverse the effects imposed by miR-196a-5p on viability, proliferation, EMT and apoptosis of ovarian cancer cells.•LncRNA GAS5/miR-196a-5p/HOXA5 signaling could turn as potential diagnostic and treatment targets for ovarian cancer.
This paper investigates the application of the YOLOv7 object detection model combined with knowledge distillation techniques in forest fire detection. As an advanced object detection model, YOLOv7 ...boasts efficient real-time detection capabilities. However, its performance may be constrained in resource-limited environments. To address this challenge, this research proposes a novel approach: considering that deep neural networks undergo multi-layer mapping from the input to the output space, we define the knowledge propagation between layers by evaluating the dot product of features extracted from two different layers. To this end, we utilize the Flow of Solution Procedure (FSP) matrix based on the Gram matrix and redesign the distillation loss using the Pearson correlation coefficient, presenting a new knowledge distillation method termed ILKDG (Intermediate Layer Knowledge Distillation with Gram Matrix-based Feature Flow). Compared with the classical knowledge distillation algorithm, KD, ILKDG achieved a significant performance improvement on a self-created forest fire detection dataset. Specifically, without altering the student network’s parameters or network layers, mAP@0.5 improved by 2.9%, and mAP@0.5:0.95 increased by 2.7%. These results indicate that the proposed ILKDG method effectively enhances the accuracy and performance of forest fire detection without introducing additional parameters. The ILKDG method, based on the Gram matrix and Pearson correlation coefficient, presents a novel knowledge distillation approach, providing a fresh avenue for future research. Researchers can further optimize and refine this method to achieve superior results in fire detection.
Learning based hashing approaches have achieved considerable success in large-scale image retrieval due to the query effectiveness and efficiency. However, most studies highly rely on supervised ...knowledge like data labels, thus might fail in unsupervised setting. To address this issue, we propose a self-collaborative unsupervised hashing method (SCUH), which jointly learns hashing function and virtual labels in a self-collaborative manner. Different from existing unsupervised counterparts, SCUH treats sample features and the learned virtual labels as two groups of different features in a fair view. This results in two groups of transformation matrices, which have proven beneficial for capturing diversity information of different features to better advance binary codes. Moreover, we devise an alternate optimization algorithm to solve SCUH, in which each sub-problem has a closed-form solution as a byproduct. Experiments of image retrieval on three datasets show that SCUH performs favorably over eight comparative methods including unsupervised counterparts.
Asset price jumps are very common in financial markets, and they are essential to accurately predict volatility. This article focuses on 50 randomly selected stocks from the Chinese stock market, ...utilising high-frequency data to construct two jump models, the heterogeneous autoregressive quarticity jump model (HARQ-J) and the full heterogeneous autoregressive quarticity jump model (HARQ-F-J), which take into account jump variables based on existing models (HARQ and HARQ-F). To further enhance the accuracy of our volatility forecasts, the study combines the newly constructed models with the machine learning (ML) to form a hybrid model. Finally, the empirical research shows that the new hybrid model performs better than existing traditional prediction methods. In particular, the long- and short-term memory (LSTM) function is significantly better than other machine learning functions. Among all the LSTM models tested by the model confidence set (MCS), the HARQ-F-J-LSTM model has the highest prediction accuracy, followed by the HARQ-J-LSTM model.
To explore the predictive value of interleukin-6 (IL-6) combined with human neutrophil lipocalin (HNL) of stroke-associated pneumonia (SAP) in patients who were diagnosed with acute ischemic stroke ...(AIS).
108patients were divided into two groups: pneumonia group (52 cases) and non-pneumonia group (56 cases), according to whether the patients developed SAP within 7 days of admission. General information was compared between the two groups, like age, gender, history of hypertension, diabetes mellitus, cardiovascular disease, dysphagia, smoking and alcoholhistory. Clinical data were recorded and compared, including lipid profile, interleukin-6 (IL-6), homocysteine (Hcy), National Institutes of Health Stroke Scale (NIHSS) score, and HNL. Multivariate Logistic regression analysis was used to screen the risk factors of AIS-AP, and the predictive value of IL-6 and HNL alone and in combination was evaluated by receiver operating characteristic curve (ROC curve).
Logistic regression analysis showed that dysphagia (OR,0.018; 95% CI, 0.001 ~ 0.427; P = 0.013), increased NIHSS scores(OR,0.012; 95% CI, 0.000 ~ 0.434; P = 0.016), and high levels of IL-6 (OR,0.014; 95% CI, 0.000 ~ 0.695; P = 0.032)and HNL (OR,0.006; 95% CI, 0.000 ~ 0.280; P = 0.009) were independent risk factors for SAP with significant difference (all P < 0.05). According to the ROC curve analysis of IL-6, the area under the curve (AUC) was 0.881 (95% CI: 0.820 ~ 0.942), and the optimal cutoff value was 6.89 pg/mL with the sensitivity of 73.1% and specificity of 85.7%. As for the ROC curve analysis of HNL, the AUC was 0.896 (95% CI: 0.839 ~ 0.954), and the best cutoff value was 99.66ng/mL with the sensitivity of 76.9% and specificity of 89.3%. The AUC of the combination of IL-6 and HNL increased to 0.952 (95% CI: 0.914 ~ 0.989), and the sensitivity and specificity increased to 80.8% and 92.9%, respectively.
In this research, the levels of IL-6 ≥ 6.89 pg/mL and HNL ≥ 99.66ng/mL were considered as risk factors for AIS patients complicated with SAP. The combined detection had higher predictive value for patients with SAP, which may help to identify who were in highrisk.
Studying the effects of maize and soybean intercropping for improving the maize yield and sustaining stability of the maize yield under different phosphate (P) application rates in red soil is ...crucial for promoting maize productivity, improving soil fertility and optimizing P nutrient management in southwest China. The objective of this study was to evaluate the dynamic changes in maize yield, yield stability and soil fertility under monoculture and intercropping maize with different P application rates. A six-year field experiment was conducted from 2017 to 2022 to investigate the effects of maize intercropping with soybean on the yield stability and sustainability of maize according to the changes in the maize yield, biomass, partial land equivalent ratio of yield (pLERY), actual yield loss index (AYL), contribution rate of soil capacity and fertilizer (SCR, SFCR) over time, as well as the differences in the coefficient of variation (CV) and sustainable yield index (SYI) at four P application rates (0 kg P2O5 ha−1, P0; 60 kg P2O5 ha−1, P1; 90 kg P2O5 ha−1, P2; and 120 kg P2O5 ha−1, P3) based on the two-factor randomized block design. The linear-platform model was utilized to simulate the relationship between the grain yield, the SYI and the amount of P fertilizer under different P application rates. The maize yield in intercropping was significantly superior to the maize yield in monoculture throughout the entire six-year experiment. For all planting years, the yield and biomass of the intercropping were higher than those of the matched monoculture average by 56.0% and 56.1%, respectively. Intercropping had an advantage of pLERY and AYL for maize. Otherwise, intercropping reduced the CV by 30.8% and 39.1% and increased the SYI by 39.4% and 23.0% in P0 and P3 compared with the matched monoculture, respectively. For all planting years, the average SFCR in intercropping treatment was higher than that in monoculture treatment. The linear-plateau model fitted showed that intercropping increased the yield and SYI by 19.8% and 40.7% on the platform and reduced the P application rate by 37.8% and 11.9% at the inflection point, respectively. These results demonstrate that maize and soybean intercropping could achieve a higher yield, a higher yield stability and an SYI with a lower P input than monoculture. Maize and soybean intercropping could be a sustainable practice for promoting the maize productivity and the yield sustainability in the red soil of southwest China.
The identification of tomato leaf diseases is easily affected by complex backgrounds, small differences between different diseases, and large differences between the same diseases. Therefore, we ...propose a novel classification network for tomato leaf disease, the Dense Inception MobileNet-V2 parallel convolutional block attention module network (DIMPCNET). To begin, we collected a total of 1256 original images of 5 tomato leaf diseases and expanded them to 8190 using data enhancement techniques. Next, an improved bilateral filtering and threshold function (IBFTF) algorithm is designed to effectively remove noise. Then, the Dense Inception convolutional neural network module (DI) was designed to alleviate the problem of large intra-class differences and small inter-class differences. Then, a parallel convolutional block attention module (PCBAM) was added to MobileNet-V2 to reduce the impact of complex backgrounds. Finally, the experimental results show that the recognition accuracy and F1-score obtained by DIMPCNET are 94.44% and 0.9475. The loss is approximately 0.28%. This method is the most advanced and provides a new idea for the identification of crop diseases, such as tomatoes, and the development of smart agriculture.
Polycystic ovary syndrome (PCOS) is a frequent reproductive and metabolic disorder associated with insulin resistance (IR). Berberine (BBR) is an isoquinoline derivative alkaloid extracted from ...Chinese medicinal herbs that has been used as an insulin sensitizer. BBR may have a potential therapeutic value for PCOS. The aim of this study was to evaluate the effects of BBR in comparison to metformin (MET) on the metabolic features of women with PCOS.
Eighty-nine subjects with PCOS and IR subjects were randomized into one of three treatment groups: BBR+compound cyproterone acetate (CPA; n=31), MET+CPA (n=30), and placebo+CPA (n=28) for 3 months. Clinical characteristics of the women and metabolic and hormonal parameters were assessed before and after the period of treatment.
Treatment with BBR in comparison to MET showed decrease in waist circumference and waist-to-hip ratio (WHR; P<0.01), total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDLC; P<0.05) as well as increase in high-density lipoprotein cholesterol (HDLC) and sex hormone-binding globulin (SHBG; P<0.05). Similarly, treatment with BBR in comparison to placebo showed decrease in WHR, fasting plasma glucose, fasting insulin, homeostasis model assessment for IR, area under the curve of insulin, TC, LDLC, and TG (P<0.05) as well as increase in HDLC and SHBG (P<0.01).
Intake of BBR improved some of the metabolic and hormonal derangements in a group of treated Chinese women with PCOS. Main effects could be related to the changes in body composition in obesity and dyslipidemia. Further controlled studies are needed for the assessment of the potential favorable metabolic effects of BBR in women with PCOS.
Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. ...However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers.
Apple leaf diseases are one of the most important factors that reduce apple quality and yield. The object detection technology based on deep learning can detect diseases in a timely manner and help ...automate disease control, thereby reducing economic losses. In the natural environment, tiny apple leaf disease targets (a resolution is less than 32 × 32 pixel
) are easily overlooked. To address the problems of complex background interference, difficult detection of tiny targets and biased detection of prediction boxes that exist in standard detectors, in this paper, we constructed a tiny target dataset TTALDD-4 containing four types of diseases, which include Alternaria leaf spot, Frogeye leaf spot, Grey spot and Rust, and proposed the HSSNet detector based on the YOLOv7-tiny benchmark for professional detection of apple leaf disease tiny targets. Firstly, the H-SimAM attention mechanism is proposed to focus on the foreground lesions in the complex background of the image. Secondly, SP-BiFormer Block is proposed to enhance the ability of the model to perceive tiny targets of leaf diseases. Finally, we use the SIOU loss to improve the case of prediction box bias. The experimental results show that HSSNet achieves 85.04% mAP (mean average precision), 67.53% AR (average recall), and 83 FPS (frames per second). Compared with other standard detectors, HSSNet maintains high real-time detection speed with higher detection accuracy. This provides a reference for the automated control of apple leaf diseases.
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