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Methicillin-resistant and Vancomycin-resistant Staphylococcus aureus bacteria (MRSA and VRSA, respectively) can seriously jeopardizes bone implants. This research aimed to examine the potential synergistic effects of Melittin and vancomycin in preventing MRSA and VRSA associated bone implant infections. Chitosan/bioactive glass nanoparticles/vancomycin composites were coated on hydrothermally etched titanium substrates by casting method. The composite coatings were coated by Melittin through drop casting technique. Melittin raised the proliferation of MC3T3 cells, making it an appropriate option as osteoinductive and antibacterial substance in coatings of orthopedic implants. Composite coatings having combined vancomycin and Melittin eliminated both planktonic and adherent MRSA and VRSA bacteria, whereas coatings containing one of them failed to kill the whole VRSA bacteria. Therefore, chitosan/bioactive glass/vancomycin/Melittin coating can be used as a bone implant coating because of its anti-infective properties.

Fake faces generated with Generative Adversarial Networks (GANs) are becoming more and more realistic and getting harder to be identified directly by human beings. However, CNN (Convolutional Neural network) based deep learning architecture can achieve almost perfect detection accuracy on such fake faces. In this paper we present a study of fake face detection with the exploration of the global texture features based on the empirical knowledge that the textures of fake faces are quite different from those of real faces. A new architecture, LBP (Local Binary Pattern)-Net, is designed to utilize binary representation image texture for the effective identification of fake images. Experimental results show that the proposed method is more robust than existing algorithms for detecting fake images edited by different image augmentation methods, such as blurring, cutout, brightness and color changing, equalization, etc. Ensemble models are also experimented to combine advantages of individual models. The most significant effect of ensemble models is the robustness for detecting edited fake images compared to single models. Experimental results show that our ensemble models outperform single models for detecting fake images.