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Two-dimensional materials with novel mechanical and thermal properties are available for sensors, photodetectors, thermoelectric, crystal diode and flexible nanodevices. In this investigation, the mechanical and thermal properties of pristine SiC and GeC are explored by molecular dynamics simulations. First, the fracture strength and fracture strain behaviors are addressed in the zigzag and armchair directions at 300 K. The excellent toughness of the SiC and GeC is obtained by the maximal fracture strain as 0.43 and 0.47 in the zigzag direction, respectively. Interestingly, the temperature-tunable tensile strength of the SiC and GeC is also investigated. Then, using non-equilibrium molecular dynamics (NEMD) calculations, the thermal performances of the SiC and GeC are explored. In particular, the thermal conductivity of the SiC and GeC present a pronounced size-dependent thermal conductivity, which can be enhanced up to 85.67 Wm–1K–1 and 34.37 Wm–1K–1, respectively. The goal of our work is to provide a theoretical framework that can be used in the near future. This will enable us to design an efficient thermal management scheme for 2D materials in electronics and optoelectronics.

It is well known that the T Helper (Th)2 bias plays a critical role in allergic asthma. Whereas the Th2 bias is maintained in the local tissues is uncertain. IL-33 is vital for the development of the Th2 polarization. TWIST-1 has an effect on regulating cellular functions. The aberrant activation of RAS sustains certain cellular activities. The aim of this study is to study the role of the interaction between activation of TWIST1 and RAS in inducing and maintaining Th2 polarization in allergic asthma. The epithelial cells of the airways (AEC) were isolated from the broncho-alveolar lavage fluids in patients with asthma. The mediators involved in the over-expression of IL-33 were determined by RNA sequencing. A mouse model was established to test the role of TWIST1 and RAS in developing allergic asthma. We observed a strong expression of TWIST1 in patients with allergic asthma that showed a positive correlation with asthmatic responses. TWIST1 favored the expression of the IL-33 in the AEC. Twist1-deficient AEC-carrying mice did not induce Th2 polarization in the airways. The expression TWIST1 in AECs was positively associated with RAS activation in AECs in patients with allergic asthma. The interaction between RAS and TWIST1 in AECs sustained airway allergic inflammation. Inhibition of TWIST1 or RAS prevented asthma-like inflammation in the mouse airways. In summary, the interaction between TWIST1 and RAS induces and maintains IL-33 expression in AECs to facilitate allergic inflammation in the respiratory tract. Inhibition of TWIST1 or RAS can prevent experimental allergic asthma.

In the last few decades, there has been a progressive increase in the prevalence of allergic rhinitis (AR) in China, where it now affects approximately 250 million people. AR prevention and treatment include allergen avoidance, pharmacotherapy, allergen immunotherapy (AIT), and patient education, among which AIT is the only curative intervention. AIT targets the disease etiology and may potentially modify the immune system as well as induce allergen-specific immune tolerance in patients with AR. In 2017, a team of experts from the Chinese Society of Allergy (CSA) and the Chinese Allergic Rhinitis Collaborative Research Group (C2AR2G) produced the first English version of Chinese AIT guidelines for AR. Since then, there has been considerable progress in basic research of and clinical practice for AIT, especially regarding the role of follicular regulatory T (TFR) cells in the pathogenesis of AR and the use of allergen-specific immunoglobulin E (sIgE) in nasal secretions for the diagnosis of AR. Additionally, potential biomarkers, including TFR cells, sIgG4, and sIgE, have been used to monitor the incidence and progression of AR. Moreover, there has been a novel understanding of AIT during the coronavirus disease 2019 pandemic. Hence, there was an urgent need to update the AIT guideline for AR by a team of experts from CSA and C2AR2G. This document aims to serve as professional reference material on AIT for AR treatment in China, thus improving the development of AIT across the world.

Fifteen new triterpenoids (1-15), along with twenty known ones (16-35), were isolated from Pseudolarix amabilis. The triterpenoid structures include multiple skeleton types, such as 2,3-seco-cycloartane, 3,4-seco-cycloartane, 3,4:9,10-diseco-cycloartane, and 3,4:8,9:9,10-triseco-cycloartane, as elucidated by extensive spectroscopy (1D NMR, 2D NMR, HRESIMS, and IR) and single-crystal X-ray diffraction. The anti-inflammatory activities of compounds 1-35 were evaluated. Compounds 3, 11, 16, 24, 25, and 26 suppressed the transcription of the NF-κB-dependent reporter gene in LPS-induced 293T/NF-κB-Luc cells with IC

The present guideline on sublingual immunotherapy (SLIT) for allergic rhinitis (AR) and allergic asthma (AS) has been developed by a panel of experts on behalf of the Chinese Society of Allergy to guide the clinical practice of SLIT in China. The panel included eminent clinical physicians and scientific specialists, who majored in the fields of Otolaryngology, Respirology, Pediatrics, Allergy and Clinical Immunology, and focused their investigations on two common allergic airway diseases (namely AR and AS) in China. During the past few decades, AR and AS have shown increasing prevalence worldwide, and resulted in substantial direct and indirect costs (1).

As one of the key technologies to realize the autonomy of UAVs, path planning is one of the key issues in the field of UAVs. The results of the existing traditional algorithms for path planning are difficult to meet the increasingly complex environment and task requirements. Aiming at the shortcomings of traditional particle swarm algorithm, this research improves the weight and learning factor of particle swarm. The proposed new algorithm can efficiently plan autonomous paths for UAVs, thereby effectively improving the operating environment of UAVs and providing help for applications in related industries. Finally, this research was simulated by MATLAB software to verify the feasibility and effectiveness of the proposed algorithm.

In the modern society where artificial intelligence is gradually being used, identity recognition is widely used in various places. Footprint recognition is different from face recognition, its feature extraction is more difficult, and it is often used in related criminal investigation fields. In this study, Haar and Local Binary Pattern (LBP) are selected as the feature extraction methods. These two methods are used to extract the feature vectors of digging and tread marks respectively, and Adaptive Boosting (Adaboost) is selected as the classification algorithm, based on Haar features and LBP feature training respectively. Algorithms are developed and tested for detecting pick marks and tread marks. The test results show that for pick marks and tread marks, the artificial intelligence method using Haar and LBP features has a better detection effect.

Fingerprint image enhancement is an important part of the pre-processing step of the fingerprint recognition process. The purpose is to repair the degraded fingerprint image, improve the contrast of the fingerprint image, and connect the broken lines. After careful study of the anisotropic diffusion (AD) algorithm, combined with the shock filtering (SF) algorithm, a two-stage fingerprint image enhancement algorithm based on anisotropic diffusion and shock filtering (ADSF) is proposed. The experimental results show that the method can not only preserve the advantages of repairing the ridge gap of coherent enhanced diffusion, but also sharpen the edge of the fingerprint ridge and enhance the contrast of the ridge line.

Gene expression analysis is crucial for uncovering components underlying important biological processes for a focal organism. Large-scale gene co-expression networks generally exhibit small-world, scale-free properties; and the degree distributions of these networks follow power-law forms. Topology properties are often informative for determining the key components of the biological systems and their genetic mechanisms. Some basic topology properties display dissimilarity in different tissues, which helps to elucidate the different genetic mechanisms underlying important biological processes among tissues from the top to bottom of trees. In this study, the topology properties of gene co-expression networks were compared in leaf, shoot, wood, and root tissues of poplar. The comparison results demonstrated that the differences of topology properties exist among tissues and the root tissue displays larger average degree and network density, indicating that genes in root tissue are more highly co-expressed than those in the other three tissues. The nodes with a large degree, also known as hub genes, were annotated by the NetAffx Analysis Center and the agriGO tool, with annotation results that these highly interconnected genes are involved in the key biological processes in each tissue. This study also revealed the topology properties’ differences between gene co-expression networks and random network, suggesting the existence of hierarchically organized module and small-world organizations in networks of poplar. The approach described in this research offers an effective strategy for identifying key genes involved in the important biological processes in poplar.

Capital structure determination is considered as one of the key corporate financing decisions and managers often face difficulty in finding the optimal one. There are various theories regarding this phenomenon in the finance literature and this issue has been discussed since long. No theory can be regarded as the conclusive one as varying evidences found regarding this complex issue. Presently, the need to determine an optimal capital structure has become more troublesome as well as important due to the emergence of a need of the best corporate governance practices. To mitigate agency problem, the organizations may implement code of corporate governance. This study aims to investigate about the impact of corporate governance on capital structure determination. Secondly, this study focuses on three well known capital structure theories i.e. trade-off theory, agency theory and pecking-order theory. Quantitative research design is used for this empirical study. Sample consists of panel data of the non-financial sector companies listed at Pakistan Stock Exchange for five years 2009-2013. The data of the variables of interest are collected from annual reports published by companies and the publications of State Bank of Pakistan. The companies are selected by taking a representative sample from the whole non-financial sector. Stratified Random Sampling technique is used by taking 10% of each sector and final selected firms are 40.Then, simple random sampling technique is used for the selection of a representative sample by using random number method. Panel data analysis and Hausman test reveal that fixed effects model is better than other options and Board size has a significant impact on Debt to equity ratio in positive direction in case of Pakistani firms operating in nonfinancial sector. We may infer that Pakistani firms have positive relationship between managerial ownership and capital structure. Since this relationship is insignificant in all regressions. The negative relationship of return on assets (ROA) and debt to equity ratio suggests that Pakistani firms earn higher returns on assets and such firms rely more on internal financing resulting in less use of debt, strong negative association has been found between liquidity and debt to equity whereas the relationship with firm size is observed negative as well as insignificant. The findings of this study can help to policy makers to give importance to Board size as it is an important determinant of capital structure as larger the size of board is, the better the monitoring and decision making process

Micranthanosides I and II (1-2), two diterpenoid glucosides featuring a new 1,10-secograyanane skeleton, thirteen new diterpenoid glycosides (3-15), and 21 known analogues were obtained from the ethanol extract of the leaves and twigs of

Face detection based on the YOLOV2 algorithm can achieve a higher level of accuracy and show a faster detection speed. Therefore, the YOLOV2 algorithm appears more frequently in the field of target real-time detection. The traditional YOLOV2 algorithm uses the Darknet-19 network. Based on this, this paper builds a new network by merging the residual network (ResNet). In addition, in order to further make up for the defects exposed by the YOLOV2 algorithm in face detection, we change the weight of the loss function and implement an image enhancement scheme on the training set. The YOLOV2 algorithm and the improved YOLOV2 algorithm are compared and tested on the Wider Face dataset, and then we use face data sets from different angles to test the performance of the algorithm. According to the analysis of the experimental results, the improved YOLOV2 algorithm can show higher accuracy and robustness in multi-angle face detection.

As Chinese economy develops, China is the second largest economy in the world with $13.01 trillion nominal GDP in 2017. However, there are always several different voices on Chinese products and Chinese technical economy, even on Chinese internal problems, such as environmental problems, food safety problems. This paper wants to study the relation between Chinese technical economy and global economy, and finally wants to argue for the goods of Chinese technical economy and Chinese product. Firstly, this paper uses literature review method to renew the definition of Chinese technical economy. Secondly, this paper uses linear regression analysis to test the relation between Chinese technical economy and global economy. At last, this paper gives its own policy advices to improve the quality of Chinese technical economy.

In poplar, genetic research on wood properties is very important for the improvement of wood quality. Studies of wood formation genes at each developmental stage using modern biotechnology have often been limited to several genes or gene families. Because of the complex regulatory network involved in the co-expression and interactions of thousands of genes, however, the genetic mechanisms of wood formation must be surveyed on a genome-wide scale. In this study, we identified wood formation-related genes using a differentially co-expressed (DCE) gene subset approach based on biological networks inferred from microarray data. Gene co-expression networks in leaf, root, and wood tissues were first constructed and topologically analyzed using microarray data collected from the Gene Expression Omnibus. The DCE gene modules in wood-forming tissue were then detected based on graph theory, which was followed by gene ontology (GO) enrichment analysis and GO annotation of probe sets. Finally, 72 probe sets were identified in the largest cohesive subgroup of the DCE gene network in wood tissue, with most of the probe sets associated with wood formation-related biological processes and GO cellular component categories. The approach described in this paper provides an effective strategy to identify wood formation genes in poplar and should contribute to the better understanding of the genetic and molecular mechanisms underlying wood properties in trees.

Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods.

This research aims to explore the mediating effect of socialization on the relationship between personality and job satisfaction. Furthermore, to explore which personality traits can serve as predictors towards job satisfaction, targeting the bank branch managers in Pakistan by questionnaire in this Cross Section study. From the analysis, we found: (1) socialization has a mediating effect on the relationship between personality and job satisfaction, (2) Among five factors of Personality, Extraversion, Conscientiousness and Neuroticism are found to be significant predictors to determine Job Satisfaction. In conclusion, this research discusses the theoretical and practical connotations of the research results and proposed recommendations for follow-up studies.

Identifying potential disease genes for complex disease is very important for understanding disease pathogenesis and providing preventive measures. The computational gene prioritization tools provide a promising and effective way for disease gene identification. Gene GAD2 on Chromosome 10p12 is a disputing candidate disease gene for obesity because of the contradictory conclusions obtained by association studies of different researchers. In this paper, a computational tool ENDEAVOUR is used to verify the probability of gene GAD2 being an obesity candidate disease gene. ENDEAVOUR evaluates each candidate gene based on its similarity with the training genes (known disease genes). The high prioritization of gene GAD2 in the computational results means that the gene GAD2 has high probability to be a disease gene for obesity.

Computational analysis of microarray data has provided an effective way to identify disease-related genes. Traditional disease gene selection methods from microarray data such as statistical test always focus on differentially expressed genes in different samples by individual gene prioritization. These traditional methods might miss differentially coexpressed (DCE) gene subsets because they ignore the interaction between genes. In this paper, MIClique algorithm is proposed to identify DEC gene subsets based on mutual information and clique analysis. Mutual information is used to measure the coexpression relationship between each pair of genes in two different kinds of samples. Clique analysis is a commonly used method in biological network, which generally represents biological module of similar function. By applying the MIClique algorithm to real gene expression data, some DEC gene subsets which correlated under one experimental condition but uncorrelated under another condition are detected from the graph of colon dataset and leukemia dataset.