An information model for highway operational risk management based on the IFC-Brick schema

With the development of highways, new technologies should be continuously introduced to improve highway traffic safety. Digital twin (DT) has been an emerging field of research in recent years. To develop a digital twin management system, a data model is essential. In the field of highway operational risk management (HORM), however, the development of data models is still in its infancy. Motivated by the concept of linked data, in this paper, we attempt to propose an information model for HORM. The main achievements of this paper include data architecture, identification and classification code methods, data interaction method, and the developed system. Based on data needs analysis, the highway information model architecture for risk management is defined as five layers: basic highway products, traffic sensors and equipment, traffic rules, traffic flow, and weather. Furthermore, according to the concepts of semantic data, these five layers can be classified into three categories: highway product data, topology data, and sensor data. Although the Industry Foundation Classes (IFC) standard and Brick schema were first proposed and applied in the building domain, some of their entities and relationships can also be applied to highways. To this end, we defined some new classes, a specific ontology, and an integrated framework for HORM. Finally, a case study was carried out. Applying such information model to highways has broad potential. It changes the file-based exchange method to the data-based one, which can promote highway data exchange and applications. The proposed information model could be of great significance for HORM.</p

The spontaneous escape behavior of silver from graphite-like carbon coatings and its effect on corrosion resistance

Silver-doped graphite-like carbon (Ag-GLC) coatings were prepared on the surface of aluminum alloy and single-crystal silicon by magnetron sputtering under different deposition parameters. The effects of silver target current and deposition temperature, as well as of the addition of CH4 gas flow, on the spontaneous escape behavior of silver from the GLC coatings were investigated. Furthermore, the corrosion resistance of the Ag-GLC coatings were evaluated. The results showed that the spontaneous escape phenomenon of silver could take place at the GLC coating, regardless of preparation condition. These three preparation factors all had an influence on the size, number and distribution of the escaped silver particles. However, in contrast with the silver target current and the addition of CH4 gas flow, only the change in deposition temperature had a significant positive effect on the corrosion resistance of the Ag-GLC coatings. The Ag-GLC coating showed the best corrosion resistance when the deposition temperature was 500 °C, which was due to the fact that increasing the deposition temperature effectively reduced the number of silver particles escaping from the Ag-GLC coating

Glycomics: Immunoglobulin GN-glycosylation associated with mammary gland hyperplasia in women

© Copyright 2020, Mary Ann Liebert, Inc., publishers 2020. Mammary gland hyperplasia (MGH) is very common, especially among young and middle-aged women. New diagnostics and biomarkers for MGH are needed for rational clinical management and precision medicine. We report, in this study, new findings using a glycomics approach, with a focus on immunoglobulin G (IgG) N-glycosylation. A cross-sectional study was conducted in a community-based population sample in Beijing, China. A total of 387 participants 40-65 years of age were enrolled in this study, including 194 women with MGH (cases) and 193 women who had no MGH (controls). IgG N-glycans were characterized in the serum by ultra-performance liquid chromatography. The levels of the glycan peaks (GPs) GP2, GP5, GP6, and GP7 were lower in the MGH group compared with the control group, whereas GP14 was significantly higher in the MGH group (p \u3c 0.05). A predictive model using GP5, GP21, and age was established and a receiver operating characteristic curve analysis was performed. The sensitivity and specificity of the model for MGH was 61.3% and 63.2%, respectively, likely owing to receptor mechanisms and/or inflammation regulation. To the best of our knowledge, this is the first study reporting on an association between IgG N-glycosylation and MGH. We suggest person-to-person variations in IgG N-glycans and their combination with multiomics biomarker strategies offer a promising avenue to identify novel diagnostics and individuals at increased risk of MGH

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method.Comment: 9 pages, 6 figure

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm’s searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems

Cord blood-derived CD19-specific chimeric antigen receptor T cells: an off-the-shelf promising therapeutic option for treatment of diffuse large B-cell lymphoma

PurposeAutologous chimeric antigen receptor (CAR) T cell therapy is one of the most significant breakthroughs in hematological malignancies. However, a three-week manufacturing cycle and ineffective T cell dysfunction in some patients hinder the widespread application of auto-CAR T cell therapy. Studies suggest that cord blood (CB), with its unique biological properties, could be an optimal source for CAR T cells, providing a product with ‘off-the-shelf’ availability. Therefore, exploring the potential of CB as an immunotherapeutic agent is essential for understanding and promoting the further use of CAR T cell therapy.Experimental designWe used CB to generate CB-derived CD19-targeting CAR T (CB CD19-CAR T) cells. We assessed the anti-tumor capacity of CB CD19-CAR T cells to kill diffuse large B cell lymphoma (DLBCL) in vitro and in vivo.ResultsCB CD19-CAR T cells showed the target-specific killing of CD19+ T cell lymphoma cell line BV173 and CD19+ DLBCL cell line SUDHL-4, activated various effector functions, and inhibited tumor progression in a mouse (BALB/c nude) model. However, some exhaustion-associated genes were involved in off-tumor cytotoxicity towards activated lymphocytes. Gene expression profiles confirmed increased chemokines/chemokine receptors and exhaustion genes in CB CD19-CAR T cells upon tumor stimulation compared to CB T cells. They indicated inherent changes in the associated signaling pathways in the constructed CB CAR T cells and targeted tumor processes.ConclusionCB CD19-CAR T cells represent a promising therapeutic strategy for treating DLBCL. The unique biological properties and high availability of CB CD19-CAR T cells make this approach feasible

Development and Utilization of Introgression Lines Using Synthetic Octaploid Wheat (Aegilops tauschii × Hexaploid Wheat) as Donor

As the diploid progenitor of common wheat, Aegilops tauschii Cosson (DD, 2n = 2x = 14) is considered to be a promising genetic resource for the improvement of common wheat. In this work, we demonstrated that the efficiency of transferring A. tauschii segments to common wheat was clearly improved through the use of synthetic octaploid wheat (AABBDDDD, 2n = 8x = 56) as a “bridge.” The synthetic octaploid was obtained by chromosome doubling of hybrid F1 (A. tauschii T015 × common wheat Zhoumai 18). A set of introgression lines (BC1F8) containing 6016 A. tauschii segments was developed and displayed significant phenotype variance among lines. Twelve agronomic traits, including growth duration, panicle traits, grain traits, and plant height (PH), were evaluated. And transgressive segregation was identified in partial lines. Additionally, better agronomic traits could be observed in some lines, compared to the recurrent parent Zhoumai 18. To verify that the significant variance of those agronomic traits was supposedly controlled by A. tauschii segments, 14 quantitative trait loci (QTLs) for three important agronomic traits (thousand kernel weight, spike length, and PH) were further located in the two environments (Huixian and Zhongmou), indicating the introgression of favorable alleles from A. tauschii into common wheat. This study provides an ameliorated strategy to improve common wheat utilizing a single A. tauschii genome

Preventive Effects of the Intestine Function Recovery Decoction, a Traditional Chinese Medicine, on Postoperative Intra-Abdominal Adhesion Formation in a Rat Model

The intestine function recovery decoction (IFRD) is a traditional Chinese medicine that has been used for the treatment of adhesive intestinal obstruction. In this study, the preventative effects and probable mechanism of the IFRD were investigated in a rat model. We randomly assigned rats to five groups: normal, model, control, low dose IFRD, and high dose IFRD. In the animal model, the caecum wall and parietal peritoneum were abraded to induce intra-abdominal adhesion formation. Seven days after surgery, adhesion scores were assessed using a visual scoring system, and histopathological samples were examined. The levels of serum interleukin-6 (IL-6) and transforming growth factor beta-1 (TGF-β1) were analysed by an enzyme-linked immunosorbent assay (ELISA). The results showed that a high dose of IFRD reduced the grade of intra-abdominal adhesion in rats. Furthermore, the grades of inflammation, fibrosis, and neovascularization in the high dose IFRD group were significantly lower than those in the control group. The results indicate that the IFRD can prevent intra-abdominal adhesion formation in a rat model. These data suggest that the IFRD may be an effective antiadhesion agent