The Expressions of IL-7 and IL-7R and the Relationship between them with Lymph Node Metastasis and Prognosis in Non-small Cell Lung Cancer

Background and objective It has been proven that lymph node metastasis was closely related to prognosis of lung cancer. Interleukin-7 (IL-7) and interleukin-7 receptor (IL-7R) could promote lymph node metastasis through vascular endothelial growth factor-D (VEGF-D). The aim of this study is to explore the expressions of IL-7 and IL-7R in lung cancer and the relationship between them with lymph node metastasis and prognosis in non-small cell lung cancer (NSCLC). Methods The expressions of IL-7 and IL-7R in 95 cases of NSCLC were detected with immunohistochemistry method and the relationship between IL-7 and IL-7R and their impact on lung cancer patients’ outcomes were analyzed. Results In 95 cases of NSCLC, the high expression rates of IL-7, IL-7R and VEGF-D were 63.16%, 61.05% and 58.95%. The expressions of IL-7 and IL-7R were correlated closely with clinic stage and lymph node metastasis, but had no relationship with age, gender, histological type and differentiation degree. The lymphatic vessel density (LVD) mean of the group with high expressions of IL-7 and IL-7R was higher than that with low or negative expressions of IL-7 and IL-7R, and they were significant different in statistics. Log-rank analysis showed that the postoperative survival period was significantly shorter in high expression groups IL-7, IL-7R and VEGF-D comparing with that in low or negative groups. Conclusion The high expression of IL-7 and IL-7R is highly positie correlated with clinic stage, lymph node metastasis, VEGF-D, LVD and poor prognosis in Non-small cell lung cancer

Highly Efficient and Selective Photocatalytic Nonoxidative Coupling of Methane to Ethylene over Pd-Zn Synergistic Catalytic Sites

Photocatalytic nonoxidative coupling of CH4 to multicarbon (C2+) hydrocarbons (e.g., C2H4) and H2 under ambient conditions provides a promising energy-conserving approach for utilization of carbon resource. However, as the methyl intermediates prefer to undergo self-coupling to produce ethane, it is a challenging task to control the selective conversion of CH4 to higher value-added C2H4. Herein, we adopt a synergistic catalysis strategy by integrating Pd-Zn active sites on visible light-responsive defective WO3 nanosheets for synergizing the adsorption, activation, and dehydrogenation processes in CH4 to C2H4 conversion. Benefiting from the synergy, our model catalyst achieves a remarkable C2+ compounds yield of 31.85 mu mol center dot g-1 center dot h-1 with an exceptionally high C2H4 selectivity of 75.3% and a stoichiometric H2 evolution. In situ spectroscopic studies reveal that the Zn sites promote the adsorption and activation of CH4 molecules to generate methyl and methoxy intermediates with the assistance of lattice oxygen, while the Pd sites facilitate the dehydrogenation of methoxy to methylene radicals for producing C2H4 and suppress overoxidation. This work demonstrates a strategy for designing efficient photocatalysts toward selective coupling of CH4 to higher value-added chemicals and highlights the importance of synergistic active sites to the synergy of key steps in catalytic reactions.Peer reviewe

Erythritol attenuates postprandial blood glucose by inhibiting α-glucosidase

This work was supported by grants from Natural Science Foundation of Qinghai (No. 2016-ZJ-942Q), West Light Foundation of the Chinese Academy of Sciences (No. Y629071211), National Natural Science Foundation of China (No. 31701243), International Cooperative Projects of Qinghai province (No. 2017-HZ-811), Project of Discovery, Evaluation and Transformation of Active Natural Compounds, Strategic Biological Resources Service Network Program of Chinese Academy of Sciences (No. ZSTH-027), Major Special Science and Technology Projects in Qinghai Province (2014-GX-A3A-01).Diabetes mellitus (DM) is a serious metabolic disorder where impaired postprandial blood glucose regulation often leads to severe health complications. The natural chemical, erythritol is a C4 polyol approved by FDA for use as a sweetener. Here we examined a potential role for erythritol in the control of postprandial blood glucose levels in DM. An anti-postprandial hyperglycemia effect upon erythritol administration (500 mg kg-1) was demonstrated in alloxan-induced DM model mice by monitoring changes in blood glucose after intragastric administration of drugs and starch. We also found that erythritol most likely exerts its anti-postprandial hyperglycemic activities by inhibiting α-glucosidase in a competitive manner. This was supported by enzyme activity assays and molecular modelling experiments. In the latter experiments it was possible to successful dock erythritol into the catalytic pocket of α-glucosidase, with the resultant interaction likely to be driven by electrostatic interactions involving Asp 215, Asp69 and Arg446 residues. This study suggests that erythritol may not only serve as a glucose substitute but may also be a useful agent in the treatment of diabetes mellitus to help manage postprandial blood glucose levels.PostprintPeer reviewe

Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network

Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities

Antimicrobial peptides sourced from post-butter processing waste yak milk protein hydrolysates

Abstract Yak butter is one of the most important foods for the Tibetan people. Of note, its production yields waste yak milk as a by-product. In this work, waste yak milk protein hydrolysates made via Pepsin hydrolysis were shown to have antimicrobial activity. Furthermore, an innovative method of magnetic liposome adsorption combined with reversed-phase high performance liquid chromatography (RP-HPLC) was developed to screen for and purify the antimicrobial peptides. Two antimicrobial peptides were obtained and their amino acid sequences were determined by N-sequencing, namely Arg-Val-Met-Phe-Lys-Trp-Ala and Lys-Val-Ile-Ser-Met-Ile. The antimicrobial activity spectra of Arg-Val-Met-Phe-Lys-Trp-Ala included Bacillus subtilis, Staphylcoccus aureus, Listeria innocua, Escherichia coli, Enterobacter cloacae and Salmonella paratyphi, while the Lys-Val-Ile-Ser-Met-Ile peptide shows not only bacterial growth inhibition but also of fungi. Haemolytic testing suggested that these two antimicrobial peptides could be considered to have no haemolytic effect at their minimum inhibitory concentrations (MICs)

Structure-Texture Dual Preserving for Remote Sensing Image Super Resolution

Most of the existing remote sensing image super-resolution (SR) methods based on deep learning tend to learn the mapping from low-resolution (LR) images to high-resolution (HR) images directly. But they ignore the potential structure and texture consistency of LR and HR spaces, which cause the loss of high-frequency information and produce artifacts. A structure-texture dual preserving method is proposed to solve this problem and generate pleasing details. Specifically, we propose a novel edge prior enhancement strategy that uses the edges of LR images and the proposed interactive supervised attention module (ISAM) to guide SR reconstruction. First, we introduce the LR edge map as a prior structural expression for SR reconstruction, which further enhances the SR process with edge preservation capability. In addition, to obtain finer texture edge information, we propose a novel ISAM in order to correct the initial LR edge map with high-frequency information. By introducing LR edges and ISAM-corrected HR edges, we build LR–HR edge mapping to preserve the consistency of LR and HR edge structure and texture, which provides supervised information for SR reconstruction. Finally, we explore the salient features of the image and its edges in the ascending space, and restored the difference between LR and HR images by residual and dense learning. A large number of experimental results on Draper and NWPU-RESISC45 datasets show that our model is superior to several advanced SR algorithms in both objective and subjective image quality

Feature Matching for Remote-Sensing Image Registration via Neighborhood Topological and Affine Consistency

Feature matching is a key method of feature-based image registration, which refers to establishing reliable correspondence between feature points extracted from two images. In order to eliminate false matchings from the initial matchings, we propose a simple and efficient method. The key principle of our method is to maintain the topological and affine transformation consistency among the neighborhood matches. We formulate this problem as a mathematical model and derive a closed solution with linear time and space complexity. More specifically, our method can remove mismatches from thousands of hypothetical correspondences within a few milliseconds. We conduct qualitative and quantitative experiments on our method on different types of remote-sensing datasets. The experimental results show that our method is general, and it can deal with all kinds of remote-sensing image pairs, whether rigid or non-rigid image deformation or image pairs with various shadow, projection distortion, noise, and geometric distortion. Furthermore, it is two orders of magnitude faster and more accurate than state-of-the-art methods and can be used for real-time applications