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Ovaries play a vital role in the female reproductive system as they are responsible for the production of egg or ovum required during the fertilization. The female ovaries very often get affected with cyst. An enlarged ovarian cyst can lead to torsion, infertility and even cancer. Therefore, it is very important to diagnose it as soon as possible. For the diagnosis of an ovarian cyst, ultrasound test is conducted. We collected the sample ultrasound images of ovaries of different women and detected whether ovarian cyst is present or not. The proposed work employs the traditional VGG-16 model fine-tuned with our very own dataset of ultrasound images. A VGG-16 model is a 16-layer deep learning neural network trained on ImageNet dataset. Fine-tuning is done by modifying the last four layers of VGG-16 network. Our model is able to determine whether the ultrasound images shows ovarian cyst or not. An accuracy of 92.11% is obtained. The accuracy and loss curves are also plotted for the proposed model.

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Citrobacter species often occur in sewage, food, soil, wastewater, and in the intestinal tract of animals and humans. Citrobacter spp. cause urinary tract infections (UTIs) and infantile meningitis in humans. Due to the presence of plasmid-encoded resistance genes, Citrobacter spp. are often resistant to many antibiotics. In this study, Citrobacter virus HCF1, a novel virulent bacteriophage capable of killing Citrobacter amalonaticus and Citrobacter freundii, was isolated from the sewage water. The isolated bacteriophage was characterized with respect to transmission electron microscopy, one-step growth curve, host range, in vitro efficacy, storage stability, and environmental stress tolerance. The one-step growth curve analysis revealed that the latent period of HCF1 was 30 min and the estimated burst size was 121 plaque-forming units (PFU) per bacterial cell. Host range testing indicated that the HCF1 was specific to the Citrobacter genus. In vitro efficacy assay in the effluent of an anaerobic biodigester showed that the HCF1 completely eliminated the host within 4 and 5 h at MOI:100 and MOI:10, respectively, thereby indicating its potential for combating C. amalonaticus infections. The isolated bacteriophage is considerably stable and tolerant to environmental stress. Furthermore, the complete genome of HCF1 was sequenced using Oxford Nanopore sequencing and the data were subjected to detailed bioinformatic analyses. NCBI-BLASTn analysis revealed that the HCF1 genome had a query coverage of 15–21% and a maximum similarity of 77.27–78.49% with 11 bacteriophages of the Drexlerviridae family. Detailed bioinformatic analysis of the genome profile suggests that HCF1 is a novel T1svirus belonging to the Tempevirinae subfamily of the Drexlerviridae family.

Sugarcane, belonging to the grass family Poaceae, is rich in sugar sucrose, thereby used for making white sugar, jaggery and other by-products like molasses and bagasse. However, a diseased sugarcane plant is of no use, so it needs to be detected as soon as possible. A novel deep learning framework approach is proposed in this paper to detect whether a sugarcane plant is diseased or not by analyzing its leaves, stem, color, etc. The study comprises three scenarios based on different feature extractors namely Inception v3, VGG-16 and VGG-19. These are the pertained models on which different classifiers are trained. The state-of-the-art algorithms (SVM, SGD, ANN, naive Bayes, KNN and logistic regression) are compared with deep learning algorithms like neural network and hybrid AdaBoost. Several statistical measures such as accuracy, precision, specificity, AUC and sensitivity are calculated using Orange software, and the scenario having the highest accuracy is chosen. The receiver operating characteristic curve is computed in order to assess accuracy. An AUC of 90.2% is obtained using VGG-16 as the feature extractor and SVM as the classifier.