Effects of aflatoxin on hepatic gene expression in a poultry model

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on September 19, 2008)Thesis (M.S.) University of Missouri-Columbia 2008.To determine the effects of dietary aflatoxin on hepatic gene expression in chicks, seventy five day old-male broiler chicks were assigned to three dietary treatments (0, 1 and 2 mg/kg aflatoxin/kg of feed) from hatch to day 21. At the end of the study, feed intake, body and liver weights and serum chemistry were evaluated and hepatic RNA was used to determine the hepatic gene expression associated with aflatoxin diet intake using microarrays and real time PCR. Aflatoxin diet reduced feed intake, body weight, serum total proteins, calcium and phosphorus and increased liver weights in dose dependent manner. The diet down-regulated the genes associated with energy metabolism, growth and development, antioxidant and immune protection and up-regulated the genes associated with cell proliferation. In the subsequent study, beneficial effects of boosting the antioxidant system through feeding a diet containing turmeric (1.48% Curcuma longa) to aflatoxin diet fed birds on physiological and hepatic gene expression was evaluated. Aflatoxin reduced feed intake and body weight gain, and increased relative liver weight. Addition of curcumin to the AF diet ameliorated the negative effects of AF on growth performance and liver weight. Decreased expression of antioxidants genes and increased expression of interleukins and cytochrome P450s due to AF was alleviated by the inclusion of curcumin in the diet. The current study demonstrates a protective effect of curcumin on gene expression in livers of chicks fed AF.Includes bibliographical reference

Enhancing path following drone : using image-based sensor matrix

Drones are a fascinating form of transport and have the potential to dominate transportation in the future. Drones are usually faster and are unstable when flying in the air. Drones can be automated in many ways to realize various applications, one of which is a path following drone. Path following drones can follow a path that can be distinguished from the background. This kind of drone finds many applications for example following rivers or roadways for mapping, surveillance in the military, and medicine or blood transportation in healthcare. Our thesis aims to implement a new technique and evaluate the performance of the path following drone. The main objective is to make the drone more responsive to sudden changes in the path. To achieve this we employed a higher-order matrix analysis of the image retrieved from the drone. Throughout the thesis the image from the drone is divided into a 3x3 matrix of sections and each section is assigned a value. These matrices are further analyzed to plan the movement of the drone.  The concept of masks was deployed which reduced the computation to a great extent as compared to look-up tables with all possible matrices. Each mask is again a 3x3 matrix and represents a particular direction and speed of the drone. Each time all masks are applied to the image from the drone and the mask which is closest to the drone image matrix is chosen and the drone is controlled accordingly to the closest mask. We also find the contour of the path regularly and find the center of such contour. This center helps the drone to get back to the course of the path if the drone is going too far away from the path. The algorithm is coded in Python programming language and the OpenCV library is used for image processing tasks.  The results of our work show that we could improve the performance of the path following drone. Though the performance of the drone is limited by some factors such as the drone’s bias in a particular direction, lighting conditions, etc. Also, the drone’s performance is inhibited by the choice of parameters like threshold, speed, and angle vectors. Overall the drone is found to be responsive to rapid changes in the path with the implementation of higher-order matrix analysis, contouring, and the concept of masks

Enhancing path following drone : using image-based sensor matrix

Drones are a fascinating form of transport and have the potential to dominate transportation in the future. Drones are usually faster and are unstable when flying in the air. Drones can be automated in many ways to realize various applications, one of which is a path following drone. Path following drones can follow a path that can be distinguished from the background. This kind of drone finds many applications for example following rivers or roadways for mapping, surveillance in the military, and medicine or blood transportation in healthcare. Our thesis aims to implement a new technique and evaluate the performance of the path following drone. The main objective is to make the drone more responsive to sudden changes in the path. To achieve this we employed a higher-order matrix analysis of the image retrieved from the drone. Throughout the thesis the image from the drone is divided into a 3x3 matrix of sections and each section is assigned a value. These matrices are further analyzed to plan the movement of the drone.  The concept of masks was deployed which reduced the computation to a great extent as compared to look-up tables with all possible matrices. Each mask is again a 3x3 matrix and represents a particular direction and speed of the drone. Each time all masks are applied to the image from the drone and the mask which is closest to the drone image matrix is chosen and the drone is controlled accordingly to the closest mask. We also find the contour of the path regularly and find the center of such contour. This center helps the drone to get back to the course of the path if the drone is going too far away from the path. The algorithm is coded in Python programming language and the OpenCV library is used for image processing tasks.  The results of our work show that we could improve the performance of the path following drone. Though the performance of the drone is limited by some factors such as the drone’s bias in a particular direction, lighting conditions, etc. Also, the drone’s performance is inhibited by the choice of parameters like threshold, speed, and angle vectors. Overall the drone is found to be responsive to rapid changes in the path with the implementation of higher-order matrix analysis, contouring, and the concept of masks