Spectral Band Selection for Ensemble Classification of Hyperspectral Images with Applications to Agriculture and Food Safety

In this dissertation, an ensemble non-uniform spectral feature selection and a kernel density decision fusion framework are proposed for the classification of hyperspectral data using a support vector machine classifier. Hyperspectral data has more number of bands and they are always highly correlated. To utilize the complete potential, a feature selection step is necessary. In an ensemble situation, there are mainly two challenges: (1) Creating diverse set of classifiers in order to achieve a higher classification accuracy when compared to a single classifier. This can either be achieved by having different classifiers or by having different subsets of features for each classifier in the ensemble. (2) Designing a robust decision fusion stage to fully utilize the decision produced by individual classifiers. This dissertation tests the efficacy of the proposed approach to classify hyperspectral data from different applications. Since these datasets have a small number of training samples with larger number of highly correlated features, conventional feature selection approaches such as random feature selection cannot utilize the variability in the correlation level between bands to achieve diverse subsets for classification. In contrast, the approach proposed in this dissertation utilizes the variability in the correlation between bands by dividing the spectrum into groups and selecting bands from each group according to its size. The intelligent decision fusion proposed in this approach uses the probability density of training classes to produce a final class label. The experimental results demonstrate the validity of the proposed framework that results in improvements in the overall, user, and producer accuracies compared to other state-of-the-art techniques. The experiments demonstrate the ability of the proposed approach to produce more diverse feature selection over conventional approaches

ANTIOXIDANT AND CHEMOTHERAPEUTIC POTENTIAL OF CURCUMA AMADA RHIZOME EXTRACT ON BENZO(A)PYRENE INDUCED CERVICAL CARCINOMA IN SPRAGUE DAWLEY RATS

ABSTRACTObjective: To evaluate the antioxidant and chemotherapeutic potential of Curcuma amada Rhizome extract on benzo(a)pyrene (BaP) induced cervicalcarcinoma in Sprague Dawley rats.Methods: A total of 30 female Sprague Dawley rats were selected to establish cervical cancer model and then divided into 5 groups at random withsix mice in each group. Group 1 control, Group 2 BaP (oral), Group 3 BaP for 8 weeks and post-treated with cisplatin (intravenous administration),Group 4 BaP for 8 weeks and post-treated with 250 mg of ethanol extract of C. amada (oral), Group 5 BaP for 8 weeks and post-treated with 500 mgof ethanol extract of C. amada (oral). 4 weeks after the treatment, the animals were sacrificed, serum separated, and cervical tissues were dissected.Antioxidants and the markers carcinoembryonic antigen (CEA), cancer antigens (CAs) 125, gamma glutamyltransferase (GTT) were assayed in serumand the tissue was used for analyzing tumor burden and sectioned for histopathological assays.10% tissue homogenate was estimated for antioxidantsand membrane-bound enzymes.Results: BaP treated group showed significant (p<0.001) incidence of tumor burden, decreased activities of antioxidants, elevated lipid peroxidation,Na+/K+ adenosine triphosphatase (Na+K+ATPase), Calcium adenosine triphosphatase (Ca2+ATPase), Magnesium adenosine triphosphatase (Mg2+ ATPase),CEA, CA 125, GTT. Treatment with C. amada rhizome extract and standard drug cisplatin reverted the antioxidants, serum markers and tissue enzymes.Conclusion: From the results, it can be concluded that C. amada Rhizome extract ameliorated BaP induced oxidative stress in the cervicalcarcinogenicity of rats.Keywords: Curcuma amada, In vivo antioxidant, Chemotherapy, Benzo(a)pyrene, Cervical carcinoma, Tumor markers

Interaction of some green muscardine fungi with laboratory cultured beneficial insects

The entomopathogenic fungi  Metarhizium flavoviride  and Nomuraea rileyi used for the management of fruit borer were tested for their safety to some of the laboratory reared beneficial insects viz., Trichogramma chilonis(Ishii.), egg-larval parasitoid, Chelonus blackburni(Cam.) and  pupal parasitoid, Tetrastichus israeli (Fer.). In the laboratory experiment there was no significant harmful effect on T. chilonis. The variations observed in  parasitoids  were also not significant for many parameters observed. Thus, infection by N. rileyi  did not affect the  development  of parasitoid in the host eggs. Emergence of egg-larval parasitoid C. blackburni from  M. flavoviride  infected  Helicoverpa  larvae was affected by  fungi in laboratory conditions. The emergence of parasitoid from 100 host eggs was  12 to 46 per cent in comparison with control (65%). The fungi  M. flavoviride  and  N. rileyi were not harmful to the adults of  T. israeli  and the variation recorded from control was not significant although adult emergence was affected in the laboratory conditions. As far as  M. flavoviride   was concerned, there was no variations from  control

Microbial Infections and Male Infertility

Microbial infections can happen on a daily basis. There are specific microbial infections that are associated with the reproductive health. Infertility is a devastating health problem. It is estimated that out of 15% of infertility worldwide, around 50% is due to male partner. Infections due to Chlamydia trachomatis, Mycoplasma genitalium, hepatitis B virus, tuberculosis, Streptococcus faecalis, and mumps are found to be associated with male infertility. Although most of the life-threatening microbial infections have dramatically declined since the introduction of the childhood vaccination program, there are concerns about few outbreaks and the associated risk of male infertility. This chapter deals with few microbial infections, their association with male infertility, prevention, symptoms, diagnosis, treatment, and control measures

Insertion of transmembrane 5-lipoxygenase activating protein (FLAP) into nanodiscs towards structure function studies of complex formation with soluble proteins

Leukotrienes are specialized lipid molecules derived from arachidonic acid that have severe pathological roles in inflammatory diseases like asthma, allergy and in the formation of cardiovascular diseases. Since these diseases can be fatal if not treated, it is significant to study leukotriene products and how they form. The formation of leukotrienes from arachidonic acid is a multi step process. This event is aided by different enzymes. Two such enzymes are 5–lipoxygenase (5-LO) and 5-lipoxygenase activating protein (FLAP). FLAP is an integral membrane protein and belongs to the MAPEG super family (Membrane Associated proteins in Eicosanoid and Glutathione metabolism). In the leukotriene synthesis pathway, the only function of FLAP is to increase the oxygenation reaction by assisting the transfer of arachidonic acid from nuclear membrane to 5-LO. In fact, FLAP doesn’t seem to have any enzymatic/mechanistic activity on its own. Many biological questions in this pathway are unanswered. Is there any functional activity of FLAP? Is there any physical contact between FLAP and 5-LO in the leukotriene formation? To address these questions, we introduced a novel method of studying the pathway by constructing nanodiscs that are small soluble pieces of membrane of defined sizes. The aim of the project is to develop a protocol for generation of nanodiscs that contain FLAP (so called reconstitution of FLAP) and to verify the presence of FLAP in the nanodiscs by practical techniques such as blue native PAGE and Western Blot. Images from transmission electron microscopy were less conclusive but improvement can be done in future by use of antibodies to visualize FLAP nanodiscs. Ultimately, reconstitution of FLAP was successfully completed for further structural and functional studies. To conclude, this project paves way for the studies of complex formation of integral membrane proteins with soluble proteins and further applications on pharmaceutical front

How Can Insurers Become Effective E-Carriers? An exploratory study in Malaysia

Internet retailing offer consumers with unparalleled opportunity and benefits to locate, to compare and customize the product need, has tremendously changed the way things are bought and sold in the markets. In insurance, firms are beginning to offer a wide array of online services, including online sales, needs analysis, and customer service. Together with other important trends such as globalization and regulatory reform, these developments are forcing far-reaching changes upon the insurance industry and making it more competitive. While insurance purchasers have more options available today, it is unclear if and when the Internet channel will dominate the traditional agent-led distribution channels. Therefore insurers face the challenge to understand the customer segment that is more prone to transacting via the internet, the type of products preferred by the consumer for online purchase and the quality dimensions they look forward to whilst making an online purchase. In this research, the afore mentioned elements have been examined in the Malaysian context. The results of the survey supports that consumers with prior experience of purchasing via the internet have higher propensity to purchase insurance online. It has also revealed that simple and straightforward products, such as travel, personal accident and auto insurance are preferred to be purchased over the internet as compared to life and health insurance. The study has also revealed that insurers should pay much attention to the infrastructure and web design of the portal to be used for online transactions. Download speed and ease of navigation are important factors to an online consumer. Therefore, insurers should have the right strategies in venturing into online retailing in order to maximize their investment

Improvement of crop and soil management practices through mulching for enhancement of soil fertility and environmental sustainability: A review

The logarithmic phase of the human population creates high food demand near the future throughout the world. On the flip side, improved crop production requires uninterrupted water irrigation. Therefore, sensible agricultural inputs are needed to overcome these concerns. New technology-based innovative agronomic research steps will boost the contemporary agriculture practices in developed and developing countries. Agricultural cropping systems could follow mulching practices as one of the best crop management practices for its water and nutrient management potential. It is primarily to accomplish healthy economic and environmental bonds. By covering the soil's surface with biodegradable resources such as organic and inorganic materials, mulching improves the physicochemical characteristics of the soil. This approach provides a favorable environment for the development of plant growth and fosters the activities of microbial communities. Additionally, it reduces the growth of weeds, manages erosion, gets rid of pesticide residue, and increases soil fertility. Mulching the soil surface has profound benefits in improving the soil moisture levels due to a reduced evaporation rate. This method is a practical agronomic entrance to reduce water scarcity and raise the chance of water conservation, notably in arid and semiarid regions. It can also boost crop security and production to meet the global food requirements. This review significantly focuses on the current influence and advantages of organic mulches for crop establishment in the agriculture sector, which can close the production gap between achievable and actual yield

Estimating Waterbird Abundance on Catfish Aquaculture Ponds Using an Unmanned Aerial System

In this study, we examined the use of an unmanned aerial system (UAS) to monitor fish-eating birds on catfish (Ictalurus spp.) aquaculture facilities in Mississippi, USA. We tested 2 automated computer algorithms to identify bird species using mosaicked imagery taken from a UAS platform. One algorithm identified birds based on color alone (color segmentation), and the other algorithm used shape recognition (template matching), and the results of each algorithm were compared directly to manual counts of the same imagery. We captured digital imagery of great egrets (Ardea alba), great blue herons (A. herodias), and double-crested cormorants (Phalacrocorax auritus) on aquaculture facilities in Mississippi. When all species were combined, template matching algorithm produced an average accuracy of 0.80 (SD = 0.58), and color segmentation algorithm produced an average accuracy of 0.67 (SD = 0.67), but each was highly dependent on weather, image quality, habitat characteristics, and characteristics of the birds themselves. Egrets were successfully counted using both color segmentation and template matching. Template matching performed best for great blue herons compared to color segmentation, and neither algorithm performed well for cormorants. Although the computer-guided identification in this study was highly variable, UAS show promise as an alternative monitoring tool for birds at aquaculture facilities

Improving animal monitoring using small unmanned aircraft systems (sUAS) and deep learning networks

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and whitetailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals