The aim of the present study was to investigate the effects of various decellularization methods on the histological and biomechanical properties of rabbit tendons. In total, six chemical reagents, including 1% t-octyl-phenoxypolyethoxyethanol (Triton-X 100), 0.5% sodium dodecyl sulfate (SDS), 1% tri-n-butyl phosphate (TnBP), 1% Triton-X 100 + 0.5% SDS, 1% TnBP + 0.5% SDS and 1% TnBP + 1% Triton-X 100, were used on rabbit semitendinosus muscles and flexor digitorum tendons for 24 h to remove cells. Hematoxylin and eosin staining was applied for histological observation, while tension testing was used for biomechanical studies. The effects of the various decellularization methods on the histological structure and biomechanical properties of rabbit tendons were evaluated. A group of fresh tendons treated with phosphate-buffered saline served as controls. The various decellularization methods resulted in different effects on the tendons. All the treatment groups exhibited a decrease in tendon biomechanical properties, but no statistically significant differences were observed among the experimental groups. The extensibility of the 1% TnBP-treated group was found to be greater than that of the other groups; however, the difference was not statistically significant. Histologically, the 1% TnBP + 0.5% SDS treatment was shown to have the least impact on the rabbit tendon structure, with good decellularization and no clear cellular remnants observed. The 1% Triton-X 100 + 0.5% SDS treatment had a pronounced effect on the tendon collagen structure and a number of collagen ruptures were observed. Overall, 1% TnBP + 0.5% SDS was found to be the most effective compared with the other treatments, as this treatment preserved the tendon collagen structure while completely removing the cells. Tendons treated with 1% TnBP + 0.5% SDS were histologically similar to normal tendon tissue and biomechanically similar to the tendons in the control group.
Chemically amplified resists were tested for preparing universal templates to bind various organic species in micron-scale patterns (functional images). UV exposure and heating of a poly(di-tert-butyl fumarate-co-styrene):onium salt resist produced alternating areas of nonpolar ester and polar acid/anhydride polymers. Various compounds that were fluorescent dyes or amines could then be placed selectively either into or onto exposed or nonexposed areas, according to the nature of solvents and binding species. The ease and extent of immobilization were found to depend on (i) resist composition, (ii) matrix wettability and permeability by a solvent, and (iii) covalent, ionic, dipole-dipole, or other sorbate-matrix binding interactions.
The degree of conversion of a flexible 2,2'-diacylbiphenyl unit into a rigid phenanthrene ring in poly(arylene ether) can be controlled by varying the amount of thionating agent, e.g., Lawesson's reagent, used in the polymer transformation. When less than 1.5 molar equiv of Lawesson's reagent is used, partial polymer transformation can be achieved, which affords random copolymers containing a different amount of phenanthrene units. The increase in the phenanthrene content in the polymer results in significant increases in the glass transition temperature, the thermal stability, and the solvent resistance.
The occurrence of cucumber downy mildew in solar greenhouses directly affects the yield and quality of cucumber. Chemical control methods may cause excessive pesticide residues, endanger food quality and safety, pollute the ecological environment, etc. Therefore, it is very important to predict the disease before its occurrence. To provide farmers with better and effective guidance for the prevention and control work, minimize the loss of disease damage, this article took cucumber ‘Lyujingling No. 2′ as the experimental material and acquired greenhouse environmental factors data by wireless sensors, including Temp (Temperature), RH (Relative Humidity), ST (Soil Temperature) and SR (Solar Radiation). LSTM (Long Short-Term Memory) neural network structure was constructed based on Keras deep learning framework to develop a prediction model with time-series environmental factors. Combined with the occurrence of downy mildew from manual investigation and statistics, through debugging the parameters, this article developed an occurrence prediction model for cucumber downy mildew and compared it with KNN (K-Nearest Neighbors Classification) and ANN (Artificial Neural Network). In the prediction model, the forecasted results of the four environmental factors were consistent with the true value distributions, and R2 (R-Squared) were all above 0.95. Among them, the ST variable predicted the best results, e.g., R2 = 0.9982, RMSE (Root Mean Square Error) = 0.08 °C, and MAE (Mean Absolute Error) = 0.05 °C. In the disease occurrence prediction model, the training accuracy was 95.99%, the Loss value was 0.0159, the disease occurrence prediction Accuracy was 90%, Precision was 94%, Recall was 89%, F1-score was 91%, the AUC (Area Under Curve) value was 90.15%, and Kappa coefficient was 0.80. It also had obvious advantages over other different models. In summary, the model had a high classification accuracy and performance, and it can provide a reference for the occurrence prediction of cucumber downy mildew in actual production.
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