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Deep ocean water (DOW) is rich in mineral nutrients, and our previous studies have confirmed that it promotes fungal growth and metabolite production. Cordycepin is a functional metabolite of Cordyceps with anti-inflammatory and antitumor properties. Although Cordyceps militaris can produce a large amount of cordycepin when DOW is used as the culture water, no study has explored the relevant regulatory mechanism through which DOW increases the adenosine and cordycepin contents. Therefore, in this study, we used 2D electrophoresis and proteomic analysis to investigate the effects of DOW with different mineral compositions on proteins in the fruiting bodies of C. militaris and to explore how DOW regulates cordycepin biosynthesis. The results indicated that the use of DOW with high magnesium and calcium contents can increase the production of cordycepin from 6.62 mg/g to 11.40 mg/g by C. militaris fruiting bodies. According to the 2D electrophoresis and proteomic analysis results of the fruiting body proteins of C. militaris cultured with DOW with different concentrations, the expression of 17 and 9 proteins was proportional to the cordycepin content and the concentration of DOW, respectively. Magnesium, sulfate, and other minerals of DOW stimulated the production of cordycepin by promoting the glycolytic pathway, producing more ATP and adenosine. It also increased the production of nucleic acids and adenosine by promoting the pentose phosphate pathway and methionine cycle, thereby promoting the growth and cordycepin production of fruiting bodies.

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission.

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.