Combined Treatment with Cryptocaryone and Ultraviolet C Promotes Antiproliferation and Apoptosis of Oral Cancer Cells

Cryptocaryone (CPC) was previously reported as preferential for killing natural products in oral cancer cells. However, its radiosensitizing potential combined with ultraviolet C (UVC) cell killing of oral cancer cells remains unclear. This study evaluates the combined anti-proliferation effect and clarifies the mechanism of combined UVC/CPC effects on oral cancer cells. UVC/CPC shows higher anti-proliferation than individual and control treatments in a low cytotoxic environment on normal oral cells. Mechanistically, combined UVC/CPC generates high levels of reactive oxygen species and induces mitochondrial dysfunction by generating mitochondrial superoxide, increasing mitochondrial mass and causing the potential destruction of the mitochondrial membrane compared to individual treatments. Moreover, combined UVC/CPC causes higher G2/M arrest and triggers apoptosis, with greater evidence of cell cycle disturbance, annexin V, pancaspase, caspases 3/7 expression or activity in oral cancer cells than individual treatments. Western blotting further indicates that UVC/CPC induces overexpression for cleaved types of poly (ADP-ribose) polymerase and caspase 3 more than individual treatments. Additionally, UVC/CPC highly induces γH2AX and 8-hydroxy-2’-deoxyguanosine adducts as DNA damage in oral cancer cells. Taken together, CPC shows a radiosensitizing anti-proliferation effect on UVC irradiated oral cancer cells with combined effects through oxidative stress, apoptosis and DNA damage.     

Multilayer Partially Homomorphic Encryption Text Steganography (MLPHE-TS): A Zero Steganography Approach

Wireless Personal Communications - This paper presents a Multilayer Partially Homomorphic Encryption Text Steganography, an invisible approach for covert communication. Existing text-based schemes...     

Wide-band,passively Q-switched Yb- and Tm-doped fibre laser using WSSe saturable absorber

A wide-band, passively Q-switched fibre lasers based on ytterbium- and thulium-doped fibre gain medium are proposed and demonstrated. The lasers employ a transition metal dichalcogenide, tungsten sulphoselenide as a saturable absorber (SA) which is prepared by mechanical exfoliation technique. By integrating the SA in the laser cavities, self-starting Q-switched at 1038 and 1982?nm are obtained. The generated pulses exhibit a microsecond pulse duration with few kilohertz repetition rate. The proposed SA has high potential as a new material to cater to the needs of various scientific, industrial and biomedical applications.     

Optimize adaptive media playout using dynamic fuzzy logic control for video streaming

Multimedia Tools and Applications - Adaptive Media Playout (AMP) controls adapt playout rate to prevent buffer outage and to reduce delay in playout. Most AMP techniques use buffer fullness or its...     

Imaging the Curie Point isothermal surface and predicting its impact on the geothermal regime within the Nile Delta,Egypt

The available aeromagnetic data together with information from some available wells have been used in the current study to shed the light on the geothermal setup of the Nile Delta Province. The aeromagnetic data was reduced-to-pole and critically analyzed using the Spectral Analysis Technique through the Fast Fourier Transform (FFT), to determine the expected depths to the Basement and Curie Point (CPD) surfaces. The geothermal gradient between CPD and earth's surface was estimated, and accordingly the heat flow was evaluated using the proposed thermal conductivity. Then, the Basement Surface Temperature (BST) was estimated, by which the geothermal gradient and heat flow for the igneous-rocky basement and sedimentary-rocky horizon was independently predicted. By which, it could be possible to distinguish between the heat flow contribution from each horizon separately.     

Enhanced backstepping sliding mode controller for motion tracking of a nonlinear 2-DOF piezo-actuated micromanipulation system

Microsystem Technologies - In this paper a robust backstepping sliding mode controller is developed for tracking control of 2-DOF piezo-actuated micromanipulation system. The control approach is...     

Parallel query execution over encrypted data in database-as-a-service (DaaS)

The Journal of Supercomputing - The main challenge in database-as-a-service is the security and privacy of data because service providers are not usually considered as trustworthy. So, the data...     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

A novel image encryption scheme based on orthogonal matrix,skew tent map,and XOR operation

Neural Computing and Applications - Content protection is considered as an important issue in today’s world. Therefore, encryption of such contents is a challenging task for researchers. They...