RESUMEN
A serious medical issue reported at the center of media worldwide, Since December, 2019 is the Covid19 pandemic. As declared by World Health Organization, confirmed cases of Covid19 have been 579,893,790 including 6,415,070 deaths as of 29 July 2022. Even new cases reported in last 24 hours are 20,409 in India. This needs to diagnose and timely treatment of Covid-19 is essential to prevent hurdles including death. The author developed deep learning based Covid19 diagnosis and severity prediction models using x-ray images with hope that this technology can increase access to radiology expertise in remote places where availability of expert radiologist is limited. The researchers proposed and implemented Attentive Multi Scale Feature map based deep Network (AMSF-Net) for x- ray image classification with improved accuracy. In binary classification, x-ray images are classified as normal or Covid19. Multiclass classification classifies x-ray images into mild, moderate or severe infection of Covid19. The researchers utilized lower layers features in addition to features from highest level with different scale to increase ability of CNN to learn fine-grained features. Channel attention also incorporated to amplify features of important channels. ROI based cropping and AHE employed to enhance content of training image. Image augmentation utilized to increase dataset size. To address the issue of the class imbalance problem, focal loss has been applied. Sensitivity, precision, accuracy and F1 score metrics are used for performance evaluation. The author achieved 78% accuracy for binary classification. Precision, recall and F1 score values for positive class is 85, 67 and 75, respectively while 73, 88 and 80 for negative class. Classification accuracy of mild, moderate and sever class is 90, 97 and 96. Average accuracy of 95 % achieved with superior performance compared to existing methods.
RESUMEN
Among the various biotic factors that disrupt crop yield, Xanthomonas oryzae pv oryzae (Xoo) is the most ruinous microbe of rice and causes bacterial leaf blight (BLB) disease. The present study focused on the utilization of copper nanoparticles (Cu-NPs) to control BLB. The copper nanosuspension (259.7 nm) prepared using Na-CMC, CuSO4·7H2O, and NaOH showed effectively inhibited Xoo (65.0 µg/ml). The performance of Cu-NPs in vivo showed enhanced plant attributes (127.9% root length and 53.9% shoot length) compared to the control and CuSO4 treated seedling. Furthermore, Cu-NPs treated seedlings showed 23.01% disease incidence (DI) compared to CuSO4 (85.71%) treated and control plants (91.83%). In addition to enhancing the growth parameters and reducing DI, seed priming with Cu-NPs improved the total chlorophyll content to 36.0% compared to the control. The assessment of antioxidant enzymes such as superoxide dismutase (1.9 U), polyphenol oxidase, peroxidase, and phenylalanine ammonia-lyase (two- to three-fold) in roots and shoots of rice plants revealed significant enhancement in Cu-NPs treated seedlings (P < 0.05). The present study suggests that Cu-NPs can be used to control Xoo and enhance rice growth.