Thin films of silk fibroin were prepared by solvent evaporation from calcium chloride/ethanol aqueous solution. The influence of alcohol treatments on thermal, mechanical and optical properties of silkfibroin-based film is presented. To understand the conformal structure of the alcohol-treated silk fibroin film, the IR spectral decomposition method is employed. The optical properties especially the optical transparency, haze and fluorescence emission of alcohol-treated silk fibroin film is systematically investigated together with the conformal structure to understand the effect of the fibril such as the betasheet influencing the optical properties. Monohydric alcohol treatment increased beta-turn content in the regenerated silk fibroin structure. These affected the amount of light diffusion and scattering within silk-fibroin films. With alcohol-treatment, all the silk-fibroin films exhibit exceptional optical transparency (>90%) with different levels of optical haze (2.56-14.17%). In particular, ethanol-treated silk-fibroin films contain the highest content of beta-turns (22.8%). The ethanol-treated silk-fibroin films displayed a distinct interference of oscillating crests and troughs in the UV-Vis transmittance spectra, thereby showing the lowest optical haze of 2.56%. In contrast, the silk-fibroin films treated with methanol and propanol exhibit the highest (14.17%) and second-highest (10.29%) optical transmittance haze, respectively. The beta-turn content of the silk-fibroin films treated with methanol is the lowest (20.5%).These results show the relationship between the beta-turn content and optical haze properties. The results manifestly provide a method to manufacture exceptional optically transparent silk-fibroin films with adjustable light diffusion and scattering which can be designed to meet specific applications with the potential to provide UV-shielding protection via monohydric alcohol treatment. View Article Onlinea Intermolecular beta-sheets. b Intramolecular beta-sheets.15918 | RSC Adv., 2020, 10, 15913-15923This journal is
Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.
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