【目的】利用P2C可以定向进入卵巢以及Gal4蛋白可与UAS序列稳定结合的特点,在中华按蚊Anopheles sinensis中建立高效的非胚胎期外源DNA投递技术系统。【方法】注射P2C-Gal4-DsRed重组蛋白至吸血后20 h时的中华按蚊雌成蚊腹部,通过冰冻...【目的】利用P2C可以定向进入卵巢以及Gal4蛋白可与UAS序列稳定结合的特点,在中华按蚊Anopheles sinensis中建立高效的非胚胎期外源DNA投递技术系统。【方法】注射P2C-Gal4-DsRed重组蛋白至吸血后20 h时的中华按蚊雌成蚊腹部,通过冰冻切片荧光观察和Western blot检测分析重组蛋白P2C-Gal4-DsRed在卵巢中的投递效率;制备P2C-Gal4 DNA BINDING重组蛋白,构建包含12×UAS重复基序的转基因质粒和辅助质粒,通过电泳迁移实验分析重组蛋白P2C-Gal4 DNA BINDING和12×UAS重复基序间的体外结合;分别将体外孵育的P2C-Gal4 DNA BINDING+辅助质粒ITF36-12×UAS和P2C-Gal4 DNA BINDING+转基因质粒ITF2-12×UAS afm复合物注射入吸血后20 h时的中华按蚊雌成蚊腹部,于血餐后40 h时提取其卵巢组织DNA,并通过特异性引物PCR扩增和测序分析外源DNA在活体中的投递情况。【结果】100%注射P2C-Gal4-DsRed的中华按蚊雌成蚊卵巢在绿色滤光片下呈现明显的红色荧光,表明P2C-Gal4-DsRed重组蛋白能够被高效地导入雌成蚊卵巢中;P2C-Gal4 DNA BINDING重组蛋白能够与12×UAS重复基序以及含有该重复基序片段的质粒稳定结合;分别有91%和93%的注射了P2C-Gal4 DNA BINDING+ITF36-12×UAS和P2C-Gal4 DNA BINDING+ITF2-12×UAS afm的雌成蚊卵巢组织中能够检测到外源DNA片段。【结论】在中华按蚊中成功建立了基于P2C卵巢导向肽和Gal4-12×UAS重复基序结合特性的外源DNA投递技术体系;通过此技术平台能够便捷、快速和高效地实现质粒等DNA分子在中华按蚊卵巢中的投递,这为进一步简化转基因、过表达及基因敲入等遗传操作奠定了基础。展开更多
Fuel cell is a device that converts the chemical energy in the reactants into the electrical energy after steps of sequential electrochemical processes with no significant impact on the environment. For high altitude ...Fuel cell is a device that converts the chemical energy in the reactants into the electrical energy after steps of sequential electrochemical processes with no significant impact on the environment. For high altitude long endurance (HALE) of unmanned aircraft system (UAS) where fuel cell operates as a prime source of power, the operation and performance of a PEM fuel cell at different level of altitudes is vitally important. In this paper, the impact of direct using extracted air from high altitudes atmosphere in order to feed the stack is investigated, and the governing equations of the supplied air and oxygen to the PEM fuel cell stack are developed. The impact of high altitudes upon the operation and the consumption of air are determined in order to maintain certain level of delivered power to the load. Also the implications associated with operating the PEM fuel cell stack at high altitudes and different technical solutions are proposed. Various modes of Integral, Proportional-Integral, and Proportional-Integral-Derivative controller are introduced and examined for different time setting responses in order to determine the most adequate trade-off choice between fast response and reactants consumption which provides the necessary optimization of the air consumption for the developed model of PEM fuel cell used for UAS operation.展开更多
An Unmanned Aircraft System （UAS） is an aircraft or ground station that can be either remote controlled manually or is capable of flying autonomously under the guidance of pre-programmed Global Positioning System （...An Unmanned Aircraft System （UAS） is an aircraft or ground station that can be either remote controlled manually or is capable of flying autonomously under the guidance of pre-programmed Global Positioning System （GPS） waypoint flight plans or more complex onboard intelligent systems. The UAS aircrafts have recently found extensive applications in military reconnaissance and surveillance, homeland security, precision agriculture, fire monitoring and analysis, and other different kinds of aids needed in disasters. Through surveillance videos captured by a UAS digital imaging payload over the interest areas, the corresponding UAS missions can be conducted. In this paper, the authors present an effective method to detect and extract architectural buildings under rural environment from UAS video sequences. The SIFT points are chosen as image features. The planar homography is adopted as the motion model between different image frames. The proposed algorithm is tested on real UAS video data.展开更多
Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based ...Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m<sup>2</sup> hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m<sup>2</sup>, 95% CI 10,613 - 23,952 m<sup>2</sup>) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m<sup>2</sup>, 95% CI 13,054 - 20,133 m<sup>2</sup>). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.展开更多
The aim of the present work is to realize an identification algorithm especially devoted to UAS （unmanned aerial systems）. Because UAS employ low cost sensor, very high measurement noise has to be taken into account...The aim of the present work is to realize an identification algorithm especially devoted to UAS （unmanned aerial systems）. Because UAS employ low cost sensor, very high measurement noise has to be taken into account. Therefore, due to both modelling errors and atmospheric turbulence, noticeable system noise has also to be considered. To cope with both the measurement and system noise, the identification problem addressed in this work is solved by using the FEM （filter error method） approach. A nonlinear mathematical model of the subject aircraft longitudinal dynamics has been tuned up through semi-empirical methods, numerical simulations and ground tests. To take into account model nonlinearities, an EKF （extended Kalman filter） has been implemented to propagate the state. A procedure has been tuned up to determine either aircraft parameters or the process noise. It is noticeable that, because the system noise is treated as unknown parameter, it is possible to identify system affected by noticeable modelling errors. Therefore, the obtained values of process noise covariance matrix can be used to highlight system failure. The obtained results show that the algorithm requires a short computation time to determine aircraft parameter with noticeable precision by using low computation power. The present procedure could be employed to determine the system noise for various mechanical systems, since it is particularly devoted to systems which present dynamics that are difficult to model. Finally, the tuned up off-line EKF should be employed to on-line estimation of either state or unmeasurable inputs like atmospheric turbulence.展开更多
文摘【目的】利用P2C可以定向进入卵巢以及Gal4蛋白可与UAS序列稳定结合的特点,在中华按蚊Anopheles sinensis中建立高效的非胚胎期外源DNA投递技术系统。【方法】注射P2C-Gal4-DsRed重组蛋白至吸血后20 h时的中华按蚊雌成蚊腹部,通过冰冻切片荧光观察和Western blot检测分析重组蛋白P2C-Gal4-DsRed在卵巢中的投递效率;制备P2C-Gal4 DNA BINDING重组蛋白,构建包含12×UAS重复基序的转基因质粒和辅助质粒,通过电泳迁移实验分析重组蛋白P2C-Gal4 DNA BINDING和12×UAS重复基序间的体外结合;分别将体外孵育的P2C-Gal4 DNA BINDING+辅助质粒ITF36-12×UAS和P2C-Gal4 DNA BINDING+转基因质粒ITF2-12×UAS afm复合物注射入吸血后20 h时的中华按蚊雌成蚊腹部,于血餐后40 h时提取其卵巢组织DNA,并通过特异性引物PCR扩增和测序分析外源DNA在活体中的投递情况。【结果】100%注射P2C-Gal4-DsRed的中华按蚊雌成蚊卵巢在绿色滤光片下呈现明显的红色荧光,表明P2C-Gal4-DsRed重组蛋白能够被高效地导入雌成蚊卵巢中;P2C-Gal4 DNA BINDING重组蛋白能够与12×UAS重复基序以及含有该重复基序片段的质粒稳定结合;分别有91%和93%的注射了P2C-Gal4 DNA BINDING+ITF36-12×UAS和P2C-Gal4 DNA BINDING+ITF2-12×UAS afm的雌成蚊卵巢组织中能够检测到外源DNA片段。【结论】在中华按蚊中成功建立了基于P2C卵巢导向肽和Gal4-12×UAS重复基序结合特性的外源DNA投递技术体系;通过此技术平台能够便捷、快速和高效地实现质粒等DNA分子在中华按蚊卵巢中的投递,这为进一步简化转基因、过表达及基因敲入等遗传操作奠定了基础。
文摘Fuel cell is a device that converts the chemical energy in the reactants into the electrical energy after steps of sequential electrochemical processes with no significant impact on the environment. For high altitude long endurance (HALE) of unmanned aircraft system (UAS) where fuel cell operates as a prime source of power, the operation and performance of a PEM fuel cell at different level of altitudes is vitally important. In this paper, the impact of direct using extracted air from high altitudes atmosphere in order to feed the stack is investigated, and the governing equations of the supplied air and oxygen to the PEM fuel cell stack are developed. The impact of high altitudes upon the operation and the consumption of air are determined in order to maintain certain level of delivered power to the load. Also the implications associated with operating the PEM fuel cell stack at high altitudes and different technical solutions are proposed. Various modes of Integral, Proportional-Integral, and Proportional-Integral-Derivative controller are introduced and examined for different time setting responses in order to determine the most adequate trade-off choice between fast response and reactants consumption which provides the necessary optimization of the air consumption for the developed model of PEM fuel cell used for UAS operation.
文摘An Unmanned Aircraft System （UAS） is an aircraft or ground station that can be either remote controlled manually or is capable of flying autonomously under the guidance of pre-programmed Global Positioning System （GPS） waypoint flight plans or more complex onboard intelligent systems. The UAS aircrafts have recently found extensive applications in military reconnaissance and surveillance, homeland security, precision agriculture, fire monitoring and analysis, and other different kinds of aids needed in disasters. Through surveillance videos captured by a UAS digital imaging payload over the interest areas, the corresponding UAS missions can be conducted. In this paper, the authors present an effective method to detect and extract architectural buildings under rural environment from UAS video sequences. The SIFT points are chosen as image features. The planar homography is adopted as the motion model between different image frames. The proposed algorithm is tested on real UAS video data.
文摘Large in-stream wood (LW) is a critical component of riparian systems that increases heterogeneity of flow regimes and provides high quality habitat for salmonids and other fishes. We present four sampling-based methods to estimate two-dimensional LW for a 61-hectare river restoration project on the South Fork McKenzie River near Rainbow, OR (USA). We manually delineated LW area, from unoccupied aircraft systems (UAS) multispectral imagery for 40 randomly selected 51.46 m<sup>2</sup> hexagonal plots. Seven auxiliary variables were extracted from the imagery and imagery derivatives to be incorporated in four estimators by summarizing spectral statistics for each plot including Random forest (RF) classification of segmented imagery (Cohen’s kappa = 0.75, balanced accuracy = 0.86). The four estimators were: difference estimator, simple linear regression estimator with one auxiliary variable, general regression estimator with seven auxiliary variables, and simple random sample without replacement. We assessed variance of the estimators and found that the simple random sample without replacement produced the largest estimate for LW area and widest confidence interval (17,283 m<sup>2</sup>, 95% CI 10,613 - 23,952 m<sup>2</sup>) while the generalized regression approach resulted in the smallest estimate and narrowest confidence interval (16,593 m<sup>2</sup>, 95% CI 13,054 - 20,133 m<sup>2</sup>). These methods facilitate efficient estimates of critical habitat components, that are especially suited to efforts that seek to quantify large amounts of these components through time. When combined with traditional sampling methods, classified imagery acquired via UAS promises to enhance the temporal resolution of the data products associated with restoration efforts while minimizing the necessity for potentially hazardous field work.
文摘The aim of the present work is to realize an identification algorithm especially devoted to UAS （unmanned aerial systems）. Because UAS employ low cost sensor, very high measurement noise has to be taken into account. Therefore, due to both modelling errors and atmospheric turbulence, noticeable system noise has also to be considered. To cope with both the measurement and system noise, the identification problem addressed in this work is solved by using the FEM （filter error method） approach. A nonlinear mathematical model of the subject aircraft longitudinal dynamics has been tuned up through semi-empirical methods, numerical simulations and ground tests. To take into account model nonlinearities, an EKF （extended Kalman filter） has been implemented to propagate the state. A procedure has been tuned up to determine either aircraft parameters or the process noise. It is noticeable that, because the system noise is treated as unknown parameter, it is possible to identify system affected by noticeable modelling errors. Therefore, the obtained values of process noise covariance matrix can be used to highlight system failure. The obtained results show that the algorithm requires a short computation time to determine aircraft parameter with noticeable precision by using low computation power. The present procedure could be employed to determine the system noise for various mechanical systems, since it is particularly devoted to systems which present dynamics that are difficult to model. Finally, the tuned up off-line EKF should be employed to on-line estimation of either state or unmeasurable inputs like atmospheric turbulence.
