NiCo2O4/C Nanocomposite as a highly reversible anode material for lithium-ion batteries

A NiCo2O4/C nanocomposite has been synthesized by a hydrothermal method followed by a calcination. X-ray powder diffraction and transmission electron microscopy measurements demonstrated the composite was composed of crystalline NiCo2O4 and amorphous carbon, and NiCo2O4 and carbon particles amalgamated together with good affinity. The electrochemical results showed as high as 914.5 mAh/g reversible capacity could be achieved at 40 mA/g current density in the potential range of 0.01-3.0 V. The initial coulombic efficiency of the composite was 79.2%, and the capacity retention was 78.3% up to 50 cycles. The superior electrochemical performance indicated that the NiCo2O4/C nanocomposite might be a promising alternative to conventional graphite-based anode materials for lithium-ion batteries

DLNet: Accurate segmentation of green fruit in obscured environments

o achieve more accurate recognition and segmentation of obscured fruit in natural orchard environments, DLNet model is proposed. The model is improved for the more challenging problem of segmenting overlapping fruit from homochromatic backgrounds without considering various damages. This approach is tantamount to construct the detection network RS-RFP and the segmentation network DLNet. RS-RFP extends Full Convolutional One-Stage Object Detection (FCOS). Specifically, Feature Pyramid Network (FPN) by adding Gaussian non-local attention mechanism to build Refined Pyramid Network (RFP) for refining semantic features generated continuously by Residual Network (ResNet) and FPN. The DLNet segmentation framework is composed of a dual-layer Graph Attention Networks (GAT) layer is constructed to model the image as two overlapping layers, where the top GAT layer detects the occluded object (occluded) and the bottom GAT layer infers the partially occluded instance (occlude). Display modeling of the two-layer structure occlusion relationship can naturally the boundaries between the occluded and occlude instances and consider their interactions. The experimental results show that the method outperforms earlier segmentation models and achieves metric values of 80.9% and 81.2% for Average Precision (AP) box and AP mask respectively. In a reasonable running time, it meets the requirements of accuracy and robustness for picking robots and provides a reference for segmentation of other fruits and vegetables

PTHrP/PTHR1 and TGF-β Levels Are Inversely Associated in Liver Regeneration

Background. Transforming growth factor (TGF)-β provides growth control in liver regeneration. We have recently demonstrated that TGF-β induced parathyroid hormone-related protein (PTHrP) expression and secretion, and PTHrP mediated TGF-β-induced apoptosis in liver cells. However, whether PTHrP signaling pathway is altered during liver regeneration is unknown. Therefore we used a murine hepatectomy model in this study and tested the hypothesis that both PTHrP and TGF-β signaling pathways are upregulated during liver regeneration.    Methods. Swiss Webster mice received 70% hepatectomy or sham operation and euthanized at different time points post-surgery for analyses. Liver regeneration was determined by liver/body weight and proliferating cell nuclear antigen (PCNA) staining. mRNA levels of TGF-β1, TGF-β receptors, PTHrP, and PTHrP receptor 1 (PTHR1) were measured by real-time quantitative PCR. Protein levels of TGF-β1 were measured by ELISA and PTHrP and PTHR1 were measured by Western blotting.      Results. After 70% hepatectomy, the liver regeneration began at 24 hours and was restored to 82% of original liver mass at day 7. TGF-β1 and its receptor levels increased at 24 and 48 hours after hepatectomy, while PTHrP levels decreased at 12 hours and PTHR1 levels decreased at 12, 24 and 48 hours after hepatectomy. The levels of these molecules returned to similar levels as that in sham animals thereafter.    Conclusion. We demonstrated that an upregulation of the TGF-β and its receptors were associated with a down-regulation of PTHrP/PTHR1 expression during liver regeneration, which may contribute to hepatocyte proliferation and regeneration after hepatectomy

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.Comment: 7 pages, 11 figures, accepted by the 2019 World Wide Web Conference (WWW '19