Genetic diversity for mycorrhizal symbiosis and phosphate transporters in rice

Phosphorus （P） is a major plant nutrient and developing crops with higher P-use efficiency is an important breeding goal. In this context we have conducted a comparative study of irrigated and rainfed rice varieties to assess genotypic differences in colonization with arbuscular mycorrhizal （AM） fungi and expression of different P trans- porter genes. Plants were grown in three different soil samples from a rice farm in the Philippines. The data show that AM symbiosis in all varieties was established after 4 weeks of growth under aerobic conditions and that, in soil derived from a rice paddy, natural AM populations recovered within 6 weeks. The analysis of AM marker genes （AM1, AM3, AM14） and P transporter genes for the direct Pi uptake （PT2, PT6） and AM-mediated pathway （PTll, PT13） were largely in agreement with the observed root AM colonization providing a useful tool for diversity studies. Interestingly, delayed AM colonization was observed in the aus-type rice varieties which might be due to their different root structure and might confer an advantage for weed competition in the field. The data further showed that P-starvation induced root growth and expression of the high-affinity P transporter PT6 was highest in the irrigated variety IR66 which a]so maintained grain yield under P-deficient field conditions.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.