Browsing by Author "Liu, Huan"

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    Cocktail biosynthesis of triacylglycerol by rational modulation of diacylglycerol acyltransferases in industrial oleaginous Aurantiochytrium
    (2021-12-27) Lan, Chuanzeng; Wang, Sen; Zhang, Huidan; Wang, Zhuojun; Wan, Weijian; Liu, Huan; Hu, Yang; Cui, Qiu; Song, Xiaojin
    Abstract Background Triacylglycerol (TAG) is an important storage lipid in organisms, depending on the degree of unsaturation of fatty acid molecules attached to glycerol; it is usually used as the feedstock for nutrition or biodiesel. However, the mechanism of assembly of saturated fatty acids (SFAs) or polyunsaturated fatty acids (PUFAs) into TAGs remains unclear for industrial oleaginous microorganism. Results Diacylglycerol acyltransferase (DGAT) is a key enzyme for TAG synthesis. Hence, ex vivo (in yeast), and in vivo functions of four DGAT2s (DGAT2A, DGAT2B, DGAT2C, and DGAT2D) in industrial oleaginous thraustochytrid Aurantiochytrium sp. SD116 were analyzed. Results revealed that DGAT2C was mainly responsible for connecting PUFA to the sn-3 position of TAG molecules. However, DGAT2A and DGAT2D target SFA and/or MUFA. Conclusions There are two specific TAG assembly routes in Aurantiochytrium. The “saturated fatty acid (SFA) TAG lane” primarily produces SFA-TAGs mainly mediated by DGAT2D whose function is complemented by DGAT2A. And, the “polyunsaturated fatty acid (PUFA) TAG lane” primarily produces PUFA-TAGs via DGAT2C. In this study, we demonstrated the functional distribution pattern of four DGAT2s in oleaginous thraustochytrid Aurantiochytrium, and provided a promising target to rationally design TAG molecular with the desired characteristics.
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    Spatial-temporal variation and risk factor analysis of hand, foot, and mouth disease in children under 5 years old in Guangxi, China
    (2019-11-08) Liu, Huan; Song, Genxin; He, Nan; Zhai, Shiyan; Song, Hongquan; Kong, Yunfeng; Liang, Lizhong; Liu, Xiaoxiao
    Abstract Background Hand, foot and mouth disease (HFMD) incidence is a critical challenge to disease control and prevention in parts of China, particularly Guangxi. However, the association between socioeconomic factors and meteorological factors on HFMD is still unclear. Methods This study applied global and local Moran’s I to examine the spatial pattern of HFMD and series analysis to explore the temporal pattern. The effects of meteorological factors and socioeconomic factors on HFMD incidence in Guangxi, China were analyzed using GeoDetector Model. Results This study collected 45,522 cases from 87 counties in Guangxi during 2015, among which 43,711 cases were children aged 0–4 years. Temporally, there were two HFMD risk peaks in 2015. One peak was in September with 7890 cases. The other appeared in May with 4687 cases of HFMD. A high-risk cluster was located in the valley areas. The tertiary industry, precipitation and second industry had more influence than other risk factors on HFMD incidence with explanatory powers of 0.24, 0.23 and 0.21, respectively. The interactive effect of any two risk factors would enhance the risk of HFMD. Conclusions This study suggests that precipitation and tertiary industry factors might have stronger effects on the HFMD incidence in Guangxi, China, compared with other factors. High-risk of HFMD was identified in the valley areas characterized by high temperature and humidity. Local government should pay more attention and strengthen public health services level in this area.
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    Subsurface Sensing Through Data Fusion of Redundant IMU Sensors with Supervised Learning
    (2019-09-16) Liu, Huan; Park, Simon S.; Shor, Roman J.; Kim, Jeong-woo; Ramírez Serrano, Alejandro; Gao, Yang; Chen, Dongmei
    In this thesis we obtain angular displacements using two different approaches to improve sensor robustness to magnetic and shock disturbances; also, we discuss the pros and cons of these two different approaches. The first approach is the supervised learning filter (SLF) approach, and the second is the supervised learning-Kalman filter (SL-KF) approach. In SLF, azimuth angle errors obtained from different sensors (magnetometers, accelerometers, and gyroscopes) are compared under magnetic and shock disturbance conditions; then, we employ an adaptive neuro fuzzy inference system (ANFIS) to calculate the error models of the sensors. Based on these sensors’ error models, the proper weights of the azimuth angles obtained from different sensors are computed and applied to the azimuth angles to output a final azimuth angle. However, to achieve the best results of SLF, we assume that at least one magnetometer is not affected by interferences at the same time interval (two magnetometers are separated by a distance D, and D can prevent both magnetometers from being affected by a magnetic disturbance at the same time). Therefore, SL-KF combines SLF with a KF to further reduce the effect of disturbances on sensors. SLF computes the corrected rotational angles and angular velocities that are subsequently fed into a global filter KF, which performs further corrections. The present subsurface positioning (directional drilling) relies on angular displacements and values of measurement depth (drill string length) to estimate a well path. However, these methods have limitations to apply in working conditions (for example drill string length maybe inaccurate caused by steel expands with increased temperature and stress). To deal with the drill string length inaccuracy problem, instead of using real external measurement signals (drill string length), we use correction signals designed based on the dual acceleration difference (DAD) method to correct the positions.