N-[(E)-4-Pyridylmethyl­ene]-4-[(E)-4-(4-pyridylmethyl­eneamino)benz­yl]aniline tetra­hydrate

The title compound, C25H20N4·4H2O, crystallizes with the organic mol­ecule lying on a twofold rotation axis through the methyl­ene bridge C atom; there are also two water molecules in the asymmetric unit. The crystal structure is stabilized by C—H⋯O, O—H⋯O and O—H⋯N hydrogen bonds, linking the water mol­ecules to each other and to the pyridine N atom

Edge-aware Feature Aggregation Network for Polyp Segmentation

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.Comment: 20 pages 8 figure

Effective-stress finite element analysis of spudcan penetration with lattice leg in clay

10.1063/1.4826009AIP Conference Proceedings15582337-234

Studying antibiotic–membrane interactions via X-ray diffraction and fluorescence microscopy

AbstractAntibiotic drug resistance is a serious issue for the treatment of bacterial infection. Understanding the resistance to antibiotics is a key issue for developing new drugs. We used penicillin and sulbactam as model antibiotics to study their interaction with model membranes. Cholesterol was used to target the membrane for comparison with the well-known insertion model. Lamellar X-ray diffraction (LXD) was used to determine membrane thickness using successive drug-to-lipid molar ratios. The aspiration method for a single giant unilamellar vesicle (GUV) was used to monitor the kinetic binding process of antibiotic–membrane interactions in an aqueous solution. Both penicillin and sulbactam are found positioned outside the model membrane, while cholesterol inserts perpendicularly into the hydrophobic region of the membrane in aqueous solution. This result provides structural insights for understanding the antibiotic–membrane interaction and the mechanism of antibiotics