RESUMEN
Image-to-image conversion based on deep learning techniques is a topic of interest in the fields of robotics and computer vision. A series of typical tasks, such as applying semantic labels to building photos, edges to photos, and raining to de-raining, can be seen as paired image-to-image conversion problems. In such problems, the image generation network learns from the information in the form of input images. The input images and the corresponding targeted images must share the same basic structure to perfectly generate target-oriented output images. However, the shared basic structure between paired images is not as ideal as assumed, which can significantly affect the output of the generating model. Therefore, we propose a novel Input-Perceptual and Reconstruction Adversarial Network (IP-RAN) as an all-purpose framework for imperfect paired image-to-image conversion problems. We demonstrate, through the experimental results, that our IP-RAN method significantly outperforms the current state-of-the-art techniques.
RESUMEN
In mobile wireless sensor network (MWSN), the lifetime of the network largely depends on energy efficient routing protocol. In the literature, cluster leader (CL) is selected based on remaining energy of mobile sensor nodes to enhance sensor network lifetime. In this study, a novel connectivity-based Low-Energy Adaptive Clustering Hierarchy-Mobile Energy Efficient and Connected (LEACH-MEEC) routing protocol was proposed, where CL is selected based on connectivity among neighboring nodes and the remaining energy of mobile sensor nodes. Consequently, it improves data delivery, network lifetime and balances the energy consumption. We studied various performance metrics including the number of alive nodes (NAN), remaining energy (RE) and packet delivery ratio (PDR). Our proposed LEACH-MEEC outperforms all other algorithms due to the connectivity metric. Moreover, the performance of mobility models was investigated through graphical and statistically tabulated results. The results show that Reference Point Group Mobility model (RPGM) is better than other mobility models.
RESUMEN
BACKGROUND: Multimeric naphthoquinones are redox-active compounds that exhibit antineoplastic, antiprotozoal, and antiviral activities. Due to their multimodal effect on perturbation of cellular oxidative state, these compounds hold great potential as therapeutic agents against highly proliferative neoplastic cells. In our previous work, we developed a series of novel dimeric naphthoquinones and showed that they were selectively cytotoxic to human acute myeloid leukemia (AML), breast and prostate cancer cell lines. We subsequently identified the oxidoreductase NAD(P)H dehydrogenase, quinone 1 (NQO1) as the major target of dimeric naphthoquinones and proposed a mechanism of action that entailed induction of a futile redox cycling. RESULTS: Here, for the first time, we describe a direct physical interaction between the bromohydroxy dimeric naphthoquinone E6a and NQO1. Moreover, our studies reveal an extensive binding interface between E6a and the isoalloxazine ring of the flavin adenine dinucleotide (FAD) cofactor of NQO1 in addition to interactions with protein side chains in the active site. We also present biochemical evidence that dimeric naphthoquinones affect the redox state of the FAD cofactor of NQO1. Comparison of the mode of binding of E6a with those of other chemotherapeutics reveals unique characteristics of the interaction that can be leveraged in future drug optimization efforts. CONCLUSION: The first structure of a dimeric naphthoquinone-NQO1 complex was reported, which can be used for design and synthesis of more potent next generation dimeric naphthoquinones to target NQO1 with higher affinity and specificity.