We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zeroshot and N -shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.

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Owing to its mechanical performance, thermal stability, and size effects, single or few-layer black phosphorus (BP) has the potential to prepare the polymer nanocomposites as a candidate of nanoadditives, similar to graphene. The step to realize the scalable exfoliation of single or few-layer BP nanosheets is crucial to BP applications. Herein, we utilized a facile, green, and scalable electrochemical strategy for generating cobaltous phytate-functionalized BP nanosheets (BP-EC-Exf) wherein the BP crystal served as the cathode and phytic acid served as a modifier and an electrolyte simultaneously. Moreover, high-performance polyurethane acrylate/BP-EC-Exf (PUA/BP-EC) nanocomposites are easily prepared by a convenient UV-curable strategy for the first time. Significantly, the conclusion of introducing BP-EC-Exf into the PUA matrix resulted in enhancement in mechanical properties of PUA in terms of the tensile strength (increased by 59.8%) and tensile fracture strain (increased by 88.1%), in the distinct improvement in flame retardancy of PUA in terms of the decreased peak heat release rate (reduced by 44.5%) and total heat release (decreased by 34.5%), and in lower intensities of pyrolysis products including toxic CO. Moreover, it was confirmed by X-ray diffraction and Raman spectra that the air stability of PUA/BP-EC nanocomposites was maintained after exposure to environmental conditions for 4 months. The air-stable BP nanosheets, which were wrapped and embedded in the PUA matrix, can achieve the isolation and protection effect. This modified electrochemical method toward the simultaneous exfoliation and functionalization of BP nanosheets provides an efficient approach for fabricating BP-polymer-based nanocomposites.

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SOX genes play an important role in a number of developmental processes. The transcription factor SOX11 is one of the members of the SOX family emerging as important transcriptional regulators. The aim of this study was to investigate the role of SOX11 in prostate cancer (PCa) and its expression pattern and clinical significance. The gene expression of SOX11 in human PCa tissues compared with benign prostate hyperplasia (BPH) tissues was detected using real-time quantitative reverse transcriptase-polymerase chain reaction (QRT-PCR) analysis and immunohositochemistry. SOX11 overexpression cell model was used to examine the role of SOX11 in cell growth and metastasis in vitro. The results showed that the positive rate of SOX11 staining was 16.67 % (10/60) in cases of prostatic carcinoma and 81.67 % (49/60) in cases of BPH, and the difference of SOX11 expression between PCa and BPH was statistically significant (P < 0.001). SOX11 mRNA level was lowly expressed in PCa cell lines compared to RWPE-1. SOX11 overexpression suppresses PCa cell migration and invasion. In conclusion, our findings demonstrate that SOX11 could suppress cell proliferation, migration, and invasion of PCa in vitro.