A Counting Method of Red Jujube Based on Improved YOLOv5s

Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision

Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study

Background. There are few reports on the relationship between dietary patterns and cardiovascular disease (CVD) risk in patients with type 2 diabetes (T2D). This study aimed to explore relationships between dietary patterns and CVD risk in the T2D population using multiple statistical analysis methods. Methods. A total of 2,984 patients with T2D from the Xinjiang Multi-Ethnic Cohort, 555 of whom were suffering from CVD, were enrolled in this study. Participants’ dietary intake was measured by the semiquantitative food frequency questionnaire (FFQ). Three statistical methods were used to construct dietary patterns, including principal component analysis (PCA) method, reduced-rank regressions (RRR) method, and partial least-squares regression (PLS) method. Then, the association between dietary patterns and CVD risk in T2D patients was analyzed by logistic regression. After excluding participants with CVD, the associations between dietary patterns and 10-year CVD risk scores were subsequently evaluated to reduce reverse causality. Results. In this study, four dietary patterns were identified by three methods. Adjustment for confounding factors, subjects with the highest scores on the “high-protein and high-carbohydrate” patterns derived from PCA, RRR, and PLS had higher odds of CVD than those with the lowest scores (OR: 2.89, 95% CI: 2.11–3.96, P t r e n d < 0.001 ; OR: 2.96, 95% CI: 2.17–4.03, P t r e n d < 0.001 ; OR: 2.01, 95% CI: 1.50–2.70, P t r e n d < 0.001 , respectively). However, the dietary pattern of PCA-prudent was not significantly related to the odds of having CVD in T2D patients (adjusted ORQ4vsQ1: 0.93, 95% CI: 0.70–1.24, P t r e n d = 0.474 ). Interestingly, we also found significant associations between “high-protein and high-carbohydrate” patterns and the elevated predicted 10-year CVD risk in T2D patients (all P t r e n d < 0.05 ). Conclusion. The positive correlation between “high-protein and high-carbohydrate” patterns and CVD risk in T2D patients was robust across all three data-driven approaches. These findings may have public health significance, encouraging an emphasis on food choices in the usual diet and promoting nutritional interventions for patients with T2D to prevent CVD

A Counting Method of Red Jujube Based on Improved YOLOv5s

Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision

Effect of Carbon Nanotubes on the Mechanical, Crystallization, Electrical and Thermal Conductivity Properties of CNT/CCF/PEKK Composites

Carbon nanotube/continuous carbon fiber–reinforced poly(etherketoneketone) (CNT/CCF/PEKK) prepreg tapes were prepared by the wet powder impregnation method, and then the prepreg tapes were molded into laminates. The effects of carbon nanotubes on the mechanical properties, conductivity, thermal conductivity and crystallinity of the composites were studied by universal testing machine, thermal conductivity and resistivity tester, dynamic mechanical analyzer (DMA) and differential scanning calorimeter (DSC). The results show that when the content of carbon nanotubes is 0.5 wt% (relative to the mass of PEKK resin, the same below), the flexural strength and interlaminar shear strength of the laminates reach the maximum, which are increased by 15.99% and 18.16%, respectively, compared with the laminates without carbon nanotubes. The results of conductivity and thermal conductivity show that the higher the content of carbon nanotubes, the better the conductivity and thermal conductivity of the material. DSC results show that the addition of CNT promoted the crystallization of PEKK in the material and decreased the cold crystallization of PEKK. DMA results show that the deformation resistance of the material can be improved by adding an appropriate amount of CNT and the bonding between CF and PEKK can be enhanced, while excessive CNT destroys this phenomenon

Osteopontin alters DNA methylation through up-regulating DNMT1 and sensitizes CD133+/CD44+cancer stem cells to 5 azacytidine in hepatocellular carcinoma

Background: In hepatocellular carcinoma (HCC), CD133+/CD44+ cells are one subgroup with high stemness and responsible for metastatic relapse and resistance to treatment. Our previous studies have demonstrated that osteopontin (OPN) plays critical roles in HCC metastasis. We further investigated the molecular mechanism underlying the role of OPN in regulating the stemness of HCC epigenetically and explored possible targeting strategy. Methods: CD133+/CD44+ subgroup sorting from HCC cell lines and HCC tissues was used to investigate the effects of OPN knockdown on stemness. iTRAQ and MedIP-sequencing were applied to detect the protein profile and epigenetic modification of CD133+/CD44+ subgroup with or without OPN knockdown. The antitumor effects of 5 Azacytidine were examined in cultured HCC cells and patient derived xenograft (PDX) models. Results: OPN was accumulated in CD133+/CD44+ subgroup of HCC cells. Knocking down OPN significantly inhibited the sphere formation and stemness-related genes expression, and delayed tumor initiation of CD133+/CD44+ subgroup of HCC cells. Employing MedIP-sequencing, dot blot and iTRAQ analyses of CD133+/CD44+ SCR and CD133+/CDM44+ shOPN cells, we found that OPN knockdown leaded to reduction in DNA methylation with particular enrichment in CGI. Meanwhile, DNA (cytosine-5)-methyltransferase 1 (DNMT1), the main methylation maintainer, was downregulated via proteomics analysis, which mediated OPN altering DNA methylation. Furthermore, DNMT1 upregulation could partially rescue the properties of CD133+/CD44+ shOPN cells. Both in vitro and in vivo assays showed that CD133+/CD44+ cells with high OPN levels were more sensitive to DNA methylation inhibitor, 5 Azacytidine (5 Aza). The above findings were validated in HCC primary cells, a more clinically relevant model. Conclusions: OPN induces methylome reprogramming to enhance the stemness of CD133+/CD44+ subgroup and provides the therapeutic benefits to DNMT1 targeting treatment in HCC