The human estrogen receptor has been used for about thirty years, in the yeast S. cerevisiae, as a component of chimeric transcription factors. Its ligand, β-estradiol, permits to control the protein translocation into the nucleus and, as a consequence, the expression of the gene(s) targeted by the synthetic transcription factor. Activators that are orthogonal to the yeast genome have been realized by fusing the human estrogen receptor to an activation and a DNA-binding domain from bacteria, viruses, or higher eukaryotes. In this work, we optimized the working of a β-estradiol-sensing device—in terms of detection range and maximal output signal—where the human estrogen receptor is flanked by the bacterial protein LexA and either the strong VP64 (from herpes simplex virus) or the weaker B42 (from E. coli) activation domain. We enhanced the biosensor performance by thoroughly engineering both the chimeric activator and the reporter protein expression cassette. In particular, we constructed a synthetic promoter—where transcription is induced by the chimeric activators—based on the core sequence of the yeast CYC1 promoter, by tuning parameters such as the length of the 5′ UTR, the distance between adjacent LexA binding sites (operators), and the spacing between the whole operator region and the main promoter TATA box. We found a configuration that works both as a highly sensitive biosensor and a sharp switch depending on the concentration of the chimeric activator and the strength of its activation domain.
In the Seventh Freescale Cup National College Smart Car Competition, we designed and implemented a smart car to realize the autonomous driving based on road tracking method. This paper presents three main aspects of the smart car, the mechanical structure; hardware system; software framework and algorithms employed for road tracking and PID speed control. The image-based road tracking system was used to control the steering angle and the speed of the smart car. The images captured by an analog CMOS camera were preprocessed by a monochrome binarization circuit, and then the binarization signals were fed into the MCU as well as the speed feedback signals obtained by a rotary photoelectric encoder, as input variables for controlling the motor, steering gear etc, by the MCU. Experimental results showed that this smart car can accurately recognize the images of various road elements to precisely adjust the steering angle, and steadily run at a maximum controllable speed.
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