The nonylphenol-degrading bacterium Sphingomonas sp. strain NP5 has a very unique monooxygenase that can attack a wide range of 4-alkylphenols with a branched side chain. Due to the structural similarity, it can also attack bisphenolic compounds, which are very important materials for the synthesis of plastics and resins, but many of them are known to or suspected to have endocrine disrupting effects to fish and animals. In this study, to clarify the substrate specificity of the enzyme (NmoA) for bisphenolic compounds, degradation tests using the cell suspension of Pseudomonas putida harboring the nonylphenol monooxygenase gene (nmoA) were conducted. The cell suspension degraded several bisphenols including bisphenol F, bisphenol S, 4,4′-dihydroxybenzophenone, 4,4′-dihydroxydiphenylether, and 4,4′-thiodiphenol, indicating that this monooxygenase has a broad substrate specificity for compounds with a bisphenolic structure.

Objectives: Although breast ultrasound imaging is powerful and effective tool to detect breast lesions and have been widely performed worldwide, it is an operator-dependent test, hence the accuracy for detection and diagnosis of breast lesions depend on the operator. We develop a computer-aided detection system for masses in ultrasound image using deep convolutional neural network. Methods: A data set containing 818 ultrasound images were collected from our institute. Breast lesions on these images were cropped as 256x256 pixel images manually. All images were flipped horizontally and were collected to make new images. After data augmentation, a total of 2604 images were obtained. These images were labeled and classified by histological tissue type: cyst, concentrated cyst, ductal carcinoma in situ, fibroadenoma, intraductal papilloma, lymph node, invasive lobular carcinoma, mastitis, mastopathic change, mucinous carcinoma, invasive papillotubular carcinoma, phyllodes, tumor, invasive scirrhous carcinoma or invasive solidtubular carcinoma. For training and testing, the data set was randomly divided into a training set and an independent test set with a ratio of 80:20. Classification of data set images were trained using convolutional neural network(CNN) with ten hidden layers including convolutional layers and pooling layers. Its accuracy of classification was evaluated. Data set was also classified as either benign or malignant, and trained using CNN. Its accuracy, sensitivity and specificity were also evaluated. Results: Images were classified as 14 tissue type and trained. The accuracy for classification of each tissue type was 86.9%. Images were classified as either benign or malignant. The accuracy, sensitivity and specificity were 95.4%, 93.2% and 96.4%, respectively. Conclusions: We achieved 95% of accuracy for classification from ultrasound imaging using CNN. Deep learning could potentially help detecting and diagnosing the breast cancer, improving accuracy and productivity of diagnosing breast cancer by physician.

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