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Aims:  Development and optimization of an efficient and inexpensive biotransformation process for ginsenoside compound K production by Paecilomyces bainier sp. 229. Methods and Results:  We have determined the optimum culture conditions required for the efficient production of ginsenoside compound K by P. bainier sp. 229 via biotransformation of ginseng saponin substrate. The optimal medium constituents were determined to be: 30 g sucrose, 30 g soybean steep powder, 1 g wheat bran powder, 1 g (NH4)2SO4, 2 g MgSO4·7H2O and 1 g CaCl2 in 1 l of distilled water. An inoculum size of 5–7·5% with an optimal pH range of 4·5–5·5 was essential for high yield. Conclusions:  The Mol conversion quotient of ginseng saponins increased from 21·2% to 72·7% by optimization of the cultural conditions. Scale‐up in a 10 l fermentor, under conditions of controlled pH and continuous air supply in the optimal medium, resulted in an 82·6% yield of ginsenoside compound K. Significant and Impact of the Study:  This is the first report on the optimization of culture conditions for the production of ginsenoside compound K by fungal biotransformation. The degree of conversion is significantly higher than previous reports. Our method describes an inexpensive, rapid and efficient biotransformation system for the production of ginsenoside compound K.

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One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions such as illumination and precipitation can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate into poor semantic pixel classification which can potentially lead to catastrophic consequences when driving autonomously. This paper presents a novel method for analysing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions. The process incorporates an additional sensor (lidar) to automate the process, eliminating the need for labour-intensive hand labelling of validation data. The system integrity can be monitored as the performance of the vision sensors are validated against a different sensor modality. This is necessary for detecting failures that are inherent to vision technology. Experimental results are presented based on multiple datasets collected at different times of the year with different environmental conditions. These results show that the semantic segmentation performance varies depending on the weather, camera parameters, existence of shadows, etc.. The results also demonstrate how the metrics can be used to compare and validate the performance after making improvements to a model, and compare the performance of different networks.

The precise segmentation of retinal blood vessel is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and then multiply the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employees a kind of structured dropout convolutional block instead of the original convolutional block of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that our proposed SA-UNet achieves the state-of-the-art retinal vessel segmentation accuracy on both datasets. 

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Covalent drugs have attracted increasing attention in recent years due to good inhibitory activity and selectivity. Targeting noncatalytic cysteines with irreversible inhibitors is a powerful approach for enhancing pharmacological potency and selectivity because cysteines can form covalent bonds with inhibitors through their nucleophilic thiol groups. However, most human kinases have multiple noncatalytic cysteines within the active site; to accurately predict which cysteine is most likely to form covalent bonds is of great importance but remains a challenge when designing irreversible inhibitors. In this work, FTMap was first applied to check its ability in predicting covalent binding site defined as the region where covalent bonds are formed between cysteines and irreversible inhibitors. Results show that it has excellent performance in detecting the hot spots within the binding pocket, and its hydrogen bond interaction frequency analysis could give us some interesting instructions for identification of covalent binding cysteines. Furthermore, we proposed a simple but useful covalent fragment probing approach and showed that it successfully predicted the covalent binding site of seven targets. By adopting a distance-based method, we observed that the closer the nucleophiles of covalent warheads are to the thiol group of a cysteine, the higher the possibility that a cysteine is prone to form a covalent bond. We believe that the combination of FTMap and our distance-based covalent fragment probing method can become a useful tool in detecting the covalent binding site of these targets.

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