The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generate a large amount of data without human intervention. Learning-based sophisticated models are required to extract meaningful information from this massive surge of data. In this context, Deep Neural Network (DNN) has been proven to be a powerful tool for disease detection. Pulmonary Embolism (PE) is considered the leading cause of death disease, with a death toll of 180,000 per year in the US alone. It appears due to a blood clot in pulmonary arteries, which blocks the blood supply to the lungs or a part of the lung. An early diagnosis and treatment of PE could reduce the mortality rate. Doctors and radiologists prefer Computed Tomography (CT) scans as a first-hand tool, which contain 200 to 300 images of a single study for diagnosis. Most of the time, it becomes difficult for a doctor and radiologist to maintain concentration going through all the scans and giving the correct diagnosis, resulting in a misdiagnosis or false diagnosis. Given this, there is a need for an automatic Computer-Aided Diagnosis (CAD) system to assist doctors and radiologists in decision-making. To develop such a system, in this paper, we proposed a deep learning framework based on DenseNet201 to classify PE into nine classes in CT scans. We utilized DenseNet201 as a feature extractor and customized fully connected decision-making layers. The model was trained on the Radiological Society of North America (RSNA)-Pulmonary Embolism Detection Challenge (2020) Kaggle dataset and achieved promising results of 88%, 88%, 89%, and 90% in terms of the accuracy, sensitivity, specificity, and Area Under the Curve (AUC), respectively.

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The ultrastructure and immunohistochemistry of secretory proteins of sublingual glands were studied in mice flown on the US space shuttles Discovery [Space Transportation System (STS)-131] and Atlantis (STS-135). No differences in mucous acinar or serous demilune cell structure were observed between sublingual glands of ground (control) and flight mice. In contrast, previous studies showed autophagy and apoptosis of parotid serous acinar cells in flight mice. The expression of parotid secretory protein (PSP) in sublingual demilune cells of STS-131 flight mice was significantly increased compared with ground (control) mice but decreased in STS-135 flight mice. Similarly, expression of mucin (MUC-19) in acinar cells and expression of the type II regulatory subunit of protein kinase A (PKA-RII) in demilune cells were increased in STS-131 flight mice and decreased in STS-135 flight mice, but not significantly. Demilune cell and parotid protein (DCPP) was slightly decreased in mice from both flights, and nuclear PKA-RII was slightly increased. These results indicate that the response of salivary glands to spaceflight conditions varies among the different glands, cell types, and secretory proteins. Additionally, the spaceflight environment, including the effects of microgravity, modifies protein expression. Determining changes in salivary proteins may lead to development of non-invasive methods to assess the physiological status of astronauts.

CaTi1-x(Nb1/2Al1/2)xO3 with x=0.1-0.5 ceramics were processed  through solid state sintering. X-rays diffraction (XRD) patterns of the compositions showed that the samples have orthorhombic crystal structure with symmetry (Pbnm). The symmetry was further confirmed using Raman spectroscopy. A total of 13 Raman modes were detected, which were in agreement with the XRD results. Microstructure analysis of the samples showed porosity in the samples, presumably due to the substitution of Al, having high melting point. As the concentration of Al and Nb increased, relative permittivity (er), quality factor (Q×fo) and temperature coefficient of resonance frequency decreased. Optimum microwave dielectric properties were achieved for the composition x=0.5 sintered at 1650 °C for 8 h i.e., er ~27.09, Q×fo ~17378 GHz and tf ~ -2.5 ppm/°C.