Benign metastasizing leiomyomatosis (BML) is a rare cause of pulmonary lesions found in reproductive age women who have undergone a hysterectomy for uterine leiomyoma. Given the relative rarity of the disease, the management of these lesions varies from surgical (oopherectomy) or medical antiestrogen hormonal therapy to clinical observation and survelliance. The disease generally presents asymptomatically with multiple, well-defined pulmonary nodules discovered incidentally on imaging. We report an atypical presentation of a 46-year-old woman with incidentally found bilateral pulmonary cavitating nodules and cysts, concerning for lymphangioleiomyomatosis (LAM), who was ultimately diagnosed with BML.

Skin diseases are the most common types of health illness faced by people of different age groups. The identification and classification of skin disease problems relies on highly expert doctors and high level instruments which is a time consuming process. To avoid this delay in diagnosis, an automated system is required to identify and classify this skin disease. This paper proposes a convolutional neural network based intelligent system for multi-class skin disease categorization. Three pre-trained deep learning based convolutional neural network models, VGG16, MobileNet, and Inception V3, are used in this study to classify skin disease images. The Dermoscopic image dataset HAM10000 is used for training, validating, and testing. The proposed system is designed, implemented, and tested to classify skin lesion image into one of seven categories. The implementation result of these models using HAM10000 Dataset is obtain as MobileNet accuracy 85.72%,VGG16 accuracy 73.63% and Inception V3accuracy 75.80%.

We report on the fabrication of integrated AlGaN‐on‐Si‐based ultraviolet photodetectors. Devices were interconnected with dedicated Si‐based fanout structures using flip‐chip bonding. The interconnects were In solder bumps with a 20 µm pitch. Backside illumination was enabled by removing the Si substrate over the active area, leaving an ultrathin AlGaN membrane with the thickness of 400 nm and the area of 4x4 mm2 supported by an array of In bumps. The presented devices act as a test vehicle for the 10 µm pitch hybrid AlGaN Extreme Ultraviolet (EUV) imagers and prove the feasibility of backside illumination in an integrated device. The use of AlGaN as the active layer provides intrinsic solar blindness with a high rejection ratio of visible and infrared spectra, which is one of the most critical limitations of the commonly used Si‐based devices. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

The present work was designed to study bifunctional reactive dyes by printing and cold pad batch method also evaluated their antibacterial activity. The main object of this research paper is to highlight synthesized dyes having excellent fixation abilities for the dyeing and printing industries to further invest upon. All dyes were reported earlier, In the synthesis of reactive dyes phenyl urea derivatives introduced in triazine ring, the dyes have excellent solubility. Synthesized dyes Z1-10 were applied on cotton fabric with cold pad batch dyeing and printing method. The fixation value of dyes in the CPB method and percentage of absorbance in the printing method of dyes were evaluated. Ten synthesized reactive dyes had a 64 % absorbance value in the print method due to the solubility behavior of dyes. The colorimetric data of dyed and printed fabric were examined in the CIELab system. The Fastness properties of dyed and printed fabrics were evaluated through standard test method. Antibacterial activity of synthesized dyes was investigated against different kinds of bacteria on dyed fabric. The synthesized dyes had good Antibacterial affinity compared with selected antibiotics as a reference standard.

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