Institution
Xuzhou Institute of Technology
Education•Xuzhou, China•
About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.
Topics: Catalysis, Computer science, Adsorption, Microstructure, Coal mining
Papers published on a yearly basis
Papers
More filters
••
TL;DR: In this paper, the static shear behavior of large-headed studs embedded in ultra-high-performance concrete was investigated by push-out test and numerical analysis, and a total of nine pushout tests were performed.
Abstract: In this article, the static shear behavior of large-headed studs embedded in ultra-high-performance concrete was investigated by push-out test and numerical analysis. A total of nine push-out speci...
16 citations
••
TL;DR: The findings of the present study indicated that the T allele of rs2241766 polymorphism is the susceptibility locus of T2DM in the WestAsian population, but has a protective effect in the South Asian population, albeit further studies are needed in other populations.
Abstract: Objective The present study aimed to determine whether the polymorphisms at rs2241766 and rs1501299 on the ADIPOQ gene were related to the susceptibility of type 2 diabetes mellitus (T2DM). Methods Eight databases, PubMed, GWAS, Embase, Lochrane, Ebsco, CNKI (Chinese National Knowledge Infrastructure), VIP (Viper Database) and ChinaInfo were searched, and a meta-analysis of susceptibility was conducted between SNP45, SNP276 polymorphisms and T2DM. Furthermore, HWE test was conducted to assess the genetic balance of the study, evaluate the quality of Newcastle-Ottawa quality assessment scale (NOS), and establishing allelic, dominant, recessive, heterozygous, and homozygous gene models. Results This meta-analysis included 53 articles, encompassing 9285 cases with rs2241766 and 14156 controls and 7747 cases with rs1501299 and 10607 controls. For the rs2241766 locus, a significant correlation was found in the three models by the subgroup analysis. Western Asians: dominant gene model (TT + TG vs. GG, P = 0.01); heterozygous gene model (TG vs. GG, P = 0.02); homozygous gene model (TT vs. GG, P = 0.01). South Asians: dominant gene model (TT + TG vs. GG, P = 0.004); heterozygous gene model (TG vs. GG, P = 0.009); homozygous gene model (TT vs. GG, P = 0.005). However, no statistically significant correlation was established among the five genetic models for rs1501299 locus. Conclusion The findings of the present study indicated that the T allele of rs2241766 polymorphism is the susceptibility locus of T2DM in the West Asian population, but has a protective effect in the South Asian population, albeit further studies are needed in other populations. Also, no association was found between the ADIPOQ rs1501299 polymorphism and T2DM.
16 citations
••
TL;DR: A method, named MSIT, utilized the amino acid residues and profile information to identify the lysine malonylation sites with the tree structural neural network in the peptides sequence level and it can be demonstrated that MSIT will be helpful in identifying candidate maloneylation sites.
Abstract: Post-Translational Modifications (PTMs), which include more than 450 types, can be regarded as the fundamental cellular regulation.Recently, experiments demonstrated that the lysine malonylation modification is a significant process in several organisms and cells. Meanwhile, malonylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions.Identification of malonylation will contribute to understanding the molecular mechanism in the field of biology. Nevertheless, several existing experimental approaches, which can hardly meet the need of the high speed data generation, are expensive and time-consuming. Moreover, some machine learning methods can hardly meet the high-accuracy need in this issue.In this study, we proposed a method, named MSIT that means malonylation sites identification tree, utilized the amino acid residues and profile information to identify the lysine malonylation sites with the tree structural neural network in the peptides sequence level.The proposed algorithm can get 0.8699 of F1 score and 89.34% in true positive ratio in E. coli. MSIT outperformed existing malonylation site identification methods and features on different species datasets.Based on these measures, it can be demonstrated that MSIT will be helpful in identifying candidate malonylation sites.
16 citations
••
TL;DR: A wavelength band-pass filter with asymmetric dual circular ring resonators in a metal-insulator-metal (MIM) structure that can be easily extended to other similar compact structures to realize the filtering task is proposed and numerically simulated.
Abstract: A wavelength band-pass filter with asymmetric dual circular ring resonators in a metal-insulator-metal (MIM) structure is proposed and numerically simulated. For the interaction of the local discrete state and the continuous spectrum caused by the side-coupled resonators and the baffle, respectively, the transmission spectrum exhibits a sharp and asymmetric profile. By adjusting the radius and material imbedded in one ring cavity, the off-to-on plasmon-induced absorption (PIA) optical response can be tunable achieved. In addition, the structure can be easily extended to other similar compact structures to realize the filtering task. Our structures have important potential applications for filters and sensors at visible and near-infrared regions.
16 citations
••
TL;DR: Wang et al. as mentioned in this paper investigated the pollution of microplastics in water and sediment of Xi'an, the largest city in northwest China, and found that the MPs in these parks were mainly in four kinds of shapes, namely fiber, pellet, fragment and film, and dominated by fibers and fragments.
16 citations
Authors
Showing all 1711 results
Name | H-index | Papers | Citations |
---|---|---|---|
Peng Wang | 108 | 1672 | 54529 |
Qiong Wu | 51 | 316 | 12933 |
Wenping Cao | 34 | 176 | 4093 |
Bin Hu | 30 | 213 | 3121 |
Syed Abdul Rehman Khan | 29 | 131 | 2733 |
Jingui Duan | 29 | 93 | 3807 |
Vivian C.H. Wu | 25 | 105 | 2566 |
Lei Chen | 16 | 99 | 1062 |
Chao Wang | 16 | 74 | 741 |
Wenbin Gong | 16 | 27 | 953 |
Jing Li | 16 | 40 | 1025 |
Chao Liu | 15 | 43 | 737 |
Qinglin Wang | 14 | 72 | 595 |
Yaocheng Zhang | 14 | 54 | 566 |
Chao Wang | 13 | 25 | 774 |