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Agriculture is one of the major revenue-producing fields and a source of livelihood in India. On the largest regions in India, rice is cultivated as an essential food. It is observed that rice crops are strongly affected by diseases, that causes major loses in agriculture sector. Plant pathologists are searching for an accurate and reliable diagnosis method for rice plant disease. The machine learning has been used effectively in various areas of crop remote sensing, particularly in the classification of crop diseases. At present time, deep learning is a hot research topic for crop disease identification. In this research, we developed an efficient rice plant disease detection method based on convolution neural network approach. This paper focuses mainly on three well known rice diseases, namely, leaf smut and brown spot caused by fungus and bacterial leaf blight caused by bacteria. This article proposes an effective approach for recognition and identification of rice plant disease based on size, shape and color of lesions in the leaf image. The proposed model applies Otsu’s global thresholding technique to perform image binarization to remove background noise of the image. The proposed method based on fully connected CNN was trained using 4000 image samples of each diseased leaves and 4000 image samples of healthy rice leaves, to detect the three rice diseases. The analysis of result shows that the proposed fully connected CNN is fast and effective approach, which provides an accuracy of 99.7% on the dataset. This accuracy is significantly higher than that for existing plant disease recognition and classification approaches.

ABSTRACTHypoxia triggers reactive oxygen species (ROS)-induced elevation in cytoplasmic calcium (Ca2+) in the plant cells. Calcium-dependent protein kinase 12 (CPK12) acts as a sensor to recognize the Ca2+signature and is activated by autophosphorylation. Then, the CPK12 moves into the nucleus with the help of phosphatidic acid (PA) and phosphorylates ERF-VII family proteins that activate hypoxia signaling and response. The study provides a novel mechanism of hypoxia signaling in plants. Moreover, the mechanism of hypoxia-specific Ca2+signature generation remains elusive.

ABSTRACTPhtheirospermum japonicumshows induced expression of PjPMEand PjPMEIgenes during haustoria development in rice and Arabidopsis with increased PME activity, which leads to the modulated cell wall during parasitism. Moreover, how PME and PMEI proteins interact and balance during haustoria development remains elusive.

Hexanucleotide repeat expansions in C9ORF72are the most common genetic cause of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Studies have shown that the hexanucleotide expansions cause the noncanonical translation of C9ORF72transcripts into neurotoxic dipeptide repeat proteins (DPRs) that contribute to neurodegeneration. We show that a cell-penetrant peptide blocked the nuclear export of C9ORF72-repeat transcripts in HEK293T cells by competing with the interaction between SR-rich splicing factor 1 (SRSF1) and nuclear export factor 1 (NXF1). The cell-penetrant peptide also blocked the translation of toxic DPRs in neurons differentiated from induced neural progenitor cells (iNPCs), which were derived from individuals carrying C9ORF72-linked ALS mutations. This peptide also increased survival of iNPC-differentiated C9ORF72-ALS motor neurons cocultured with astrocytes. Oral administration of the cell-penetrant peptide reduced DPR translation and rescued locomotor deficits in a Drosophilamodel of mutant C9ORF72-mediated ALS/FTD. Intrathecal injection of this peptide into the brains of ALS/FTD mice carrying a C9ORF72mutation resulted in reduced expression of DPRs in mouse brains. These findings demonstrate that disrupting the production of DPRs in cellular and animal models of ALS/FTD might be a strategy to ameliorate neurodegeneration in these diseases.