Retinal levels of vascular endothelial growth factor (VEGF) and pigment epithelium-derived factor (PEDF), an angiogenic inhibitor, were measured and correlated with the ischemia-induced retinal neovascularization in rats. The retinas with neovascularization showed a 5-fold increase in VEGF while 2-fold decrease in PEDF, compared to the age-matched controls, resulting in an increased VEGF/PEDF ratio. The time course of the VEGF/PEDF ratio change correlated with the progression of retinal neovascularization. Changes in the VEGF and PEDF mRNAs preceded their protein level changes. These results suggest that an unbalance between angiogenic stimulators and inhibitors may contribute to retinal neovascularization. ß

The protein RPE65 has an important role in retinoid processing and/or retinoid transport in the eye. Retinoids are involved in cell differentiation, embryogenesis and carcinogenesis. Since the kidney is known as an important site for retinoid metabolism, the expression of RPE65 in normal kidney and transformed kidney cells has been examined. The RPE65 mRNA was detected in transformed kidney cell lines including the human embryonic kidney cell line HEK293 and the African green monkey kidney cell lines COS-1 and COS-7 by reverse transcription PCR. In contrast, it was not detected in human primary kidney cells or monkey kidney tissues under the same PCR conditions. The RPE65 protein was also identified in COS-7 and HEK293 cells by Western blot analysis using a monoclonal antibody to RPE65, but not in the primary kidney cells or kidney tissues. The RPE65 cDNA containing the fulllength encoding region was amplified from HEK293 and COS-7 cells. DNA sequencing showed that the RPE65 cDNA from HEK293 cells is identical to the RPE65 cDNA from the human retinal pigment epithelium. The RPE65 from COS-7 cells shares 98 and 99% sequence identity with human RPE65 at the nucleotide and amino acid levels, respectively. Moreover, the RPE65 mRNA was detected in three out of four renal tumor cultures analyzed including congenital mesoblastic nephroma and clear cell sarcoma of the kidney. These results demonstrated that transformed kidney cells express this retinoid processing protein, suggesting that these transformed cells may have an alternative retinoid metabolism not present in normal kidney cells.z 1999 Federation of European Biochemical Societies.

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This study develops and tests an automatic pixel-level image recognition model to reduce the amount of manual labor required to collect data for road maintenance. Firstly, images of six kinds of pavement distresses, namely, transverse cracks, longitudinal cracks, alligator cracks, block cracks, potholes, and patches, are collected from four asphalt highways in three provinces in China to build a labeled pixel-level dataset containing 10,097 images. Secondly, the U-net model, one of the most advanced deep neural networks for image segmentation, is combined with the ResNet neural network as the basic classification network to recognize distressed areas in the images. Data augmentation, batch normalization, momentum, transfer learning, and discriminative learning rates are used to train the model. Thirdly, the trained models are validated on the test dataset, and the results of experiments show the following: if the types of pavement distresses are not distinguished, the pixel accuracy (PA) values of the recognition models using ResNet-34 and ResNet-50 as basic classification networks are 97.336% and 95.772%, respectively, on the validation set. When the types of distresses are distinguished, the PA values of models using the two classification networks are 66.103% and 44.953%, respectively. For the model using ResNet-34, the category pixel accuracy (CPA) and intersection over union (IoU) of the identification of areas with no distress are 99.276% and 99.059%, respectively. For areas featuring distresses in the images, the CPA and IoU of the model are the highest for the identification of patches, at 82.774% and 73.778%, and are the lowest for alligator cracks, at 14.077% and 12.581%, respectively.

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