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Superhydrophobic and superoleophilic polyester materials are successfully prepared by one‐step growth of silicone nanofilaments onto the textile via chemical vapor deposition of trichloromethylsilane. The successful growth of silicone nanofilaments is confirmed with scanning electron microscopy, energy‐dispersive X‐ray analysis, and investigation of the wetting behavior of water on the textile. Even microfibers deeply imbedded inside a woven material could be coated very well with the nanofilaments. The coated textile is water repellant and could only be wetted by liquids of low surface tension. The applications of the coated textile as a membrane for oil/water separation and as a bag for selective oil absorption from water are studied in detail. Owing to the superwetting properties and flexibility of the coated textile, excellent reusability, oil/water separation efficiency, and selective oil absorption capacity are observed, which make it very promising material, e.g., for practical oil absorption.

As a unique biometric feature that can be recognized at a distance, gait has broad applications in crime prevention, forensic identification and social security. To portray a gait, existing gait recognition methods utilize either a gait template, where temporal information is hard to preserve, or a gait sequence, which must keep unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames. We propose a new network named GaitSet to learn identity information from the set. Based on the set perspective, our method is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results represent new state-of-the-art recognition accuracy. On various complex scenarios, our model exhibits a significant level of robustness. It achieves accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing walking conditions, respectively. These outperform the existing best methods by a large margin. The method presented can also achieve a satisfactory accuracy with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.

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Hepatocyte nuclear factor 4α (HNF4α) is a transcription factor that plays a key role in hepatocyte differentiation and the maintenance of hepatic function, but its role in hepatocarcinogenesis has yet to be examined. Here, we report evidence of a suppressor role for HNF4α in liver cancer. HNF4α expression was progressively decreased in the diethylinitrosamine-induced rat model of liver carcinogenesis. In human liver tissues, HNF4α expression was decreased in cirrhotic tissue and further decreased in hepatocarcinoma relative to healthy tissue. Notably, an inverse correlation existed with epithelial-mesenchymal transition (EMT). Enforced expression of HNF4α attenuated hepatocyte EMT during hepatocarcinogenesis, alleviated hepatic fibrosis, and blocked hepatocellular carcinoma (HCC) occurrence. In parallel, stem cell marker gene expression was inhibited along with cancer stem/progenitor cell generation. Further, enforced expression of HNF4α inhibited activation of β-catenin, which is closely associated with EMT and hepatocarcinogenesis. Taken together, our results suggest that the inhibitory effect of HNF4α on HCC development might be attributed to suppression of hepatocyte EMT and cancer stem cell generation through an inhibition of β-catenin signaling pathways. More generally, our findings broaden knowledge on the biological significance of HNF4α in HCC development, and they imply novel strategies for HCC prevention through the manipulation of differentiation-determining transcription factors in various types of carcinomas.

We report the use of a self-assembled two-dimensional (2D) DNA nanogrid as a template to organize 5-nm gold nanoparticles (Au NPs) into periodic square lattices. Each particle sits on only a single DNA tile. The center-to-center interparticle spacing between neighboring particles is controlled to be approximately 38 nm. These evenly distributed Au NP arrangements with accurate control of interparticle spacing may find applications in nanoelectronic and nanophotonic devices.

Chronic liver injury can cause cirrhosis and impaired liver regeneration, impairing organ function. Adult livers can regenerate in response to parenchymal insults, and multiple cellular sources have been reported to contribute to this response. In this study, we modeled human chronic liver injuries, in which such responses are blunted, without genetic manipulations, and assessed potential contributions of non-parenchymal cells (NPCs) to hepatocyte regeneration. We show that NPC-derived hepatocytes replenish a large fraction of the liver parenchyma following severe injuries induced by long-term thioacetamide (TAA) or 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) treatment. Through lineage tracing of biliary epithelial cells (BECs), we show that BECs are a source of new hepatocytes and gain an Hnf4αCK19 bi-phenotypic state in periportal regions and fibrotic septa. Bi-phenotypic cells were also detected in cirrhotic human livers. Together, these data provide further support for hepatocyte regeneration from BECs without genetic interventions and show their cellular plasticity during severe liver injury.