H5 antibody detection by blocking enzyme-linked immunosorbent assay using a monoclonal antibody

Many commercial enzyme-linked immunosorbent assays (ELISAs) are unable to differentiate antibody responses to different avian influenza virus (AIV) subtypes. Developing an ELISA for specifically detecting the H5 antibody is the purpose of this study. Four monoclonal antibodies (Mabs) were raised using A/duck/Yunlin/04 (H5N2). They were confirmed as being specific to H5. Two of these antibodies showed hemagglutination inhibition (HI) activity using the HI test. Using immunodot blot assays, three Mabs recognized both Eurasian and American H5, whereas the other Mab recognized only the tested Eurasian H5 virus. When testing denatured H5 antigen, one of the Mabs lost its antigen binding activity using Western blotting. For detecting the H5 humoral response in serum, one monoclonal antibody was purified and labeled with horseradish peroxidase to set up a blocking ELISA. Chicken sera that blocked H5 Mab binding by > 29% were considered H5 antibody positive. Inhibition percentages for sera from chickens infected with other AIV subtypes, H1 to H15, were < 29%. This blocking ELISA was used for 478 field chicken serum samples. The results showed that the sensitivity and specificity of this ELISA were 98.3% (232/236) and 95.9% (232/242), respectively. This blocking ELISA could be used specifically for detecting the H5 humoral responses in chickens.     

A wireless sensor network-based monitoring system with dynamic convergecast tree algorithm for precision cultivation management in orchid greenhouses

The orchid industry in Taiwan has established large-scale orchid greenhouses to achieve high-precision cultivation of orchids, especially for Phalaenopsis. The wireless sensor network (WSN) technology has been shown to be able to play an important and useful role for effectively acquiring environmental parameters in real-time. However, the mobile benches equipped with different sensors used in an orchid greenhouse create a problem of susceptible dynamic network topology. To meet the requirements of reliable data acquisition in the monitoring of orchid growth, a novel dynamic convergecast tree algorithm (DCTA) based on a tree-like topology was designed and implemented in the WSN-based monitoring system. The proposed WSN algorithm uses the information of the received signal strength indication and hop count to dynamically adjust the routing path of each sensor node. The proposed algorithm includes a flexible scheduling-based design for the medium access control protocol to guarantee higher transmission reliability of the sensor data. An extensive series of experiments, including tests in the lab and an orchid greenhouse, were conducted to examine the performance of the proposed DCTA. The experimental results show that the proposed algorithm can reliably collect environmental data; average successful data delivery rates up to 92.5 % of the entire tested networks with multiple mobile nodes in the greenhouse can be achieved. The WSN-based monitoring system equipped with the proposed DCTA provides environmental measurements with better spatio-temporal resolution to achieve precision cultivation management for orchids.     

Precisely forecasting population dynamics of agricultural pests based on an interval type-2 fuzzy logic system: case study for oriental fruit flies and the tobacco cutworms

Traditional pest control approaches rely mostly on the experience of farmers, which may not be effective due to lack of scientific information regarding the environment where crops grow. Farmers can initiate a more effective integrated pest management program when precise and quantified results of forecasting pest population outbreaks are provided. Previous studies generally utilize long-term data to predict pest populations, but such a prediction approach might not be useful for farmers who grow fruit and vegetables with shorter life cycles. This paper therefore proposes an interval type-2 fuzzy logic system (IT2FLS) with short-term data to forecast the population dynamics of the oriental fruit fly (OFF, Bactrocera dorsalis (Hendel)) and the tobacco cutworm (TC, Spodoptera litura (Fabricius)). Two automatic monitoring systems are used to collect the data of the population dynamics of OFFs and TCs and the environmental parameters in farming areas. A univariate fuzzy time series forecasting model with difference-based intervals (UFTSFM_DI) and a bivariate fuzzy time series forecasting model with difference-based intervals (BFTSFM_DI) are developed, and integrated into the proposed IT2FLS. It is found that the BFTSFM_DI model yields better performances of forecasting OFF and TC populations when the atmospheric temperature data are employed. With the forecasting results, farmers will have a better understanding of the population dynamics of the OFF and TC in farming areas, so they can take proper measures, such as bagging their fruits and spraying pesticides, before pest outbreaks occur.