A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction

Traditional credit risk prediction models mainly rely on financial data. However, technological innovation is the main driving force for the development of enterprises in strategic emerging industries, which is closely related to enterprise credit risk. In this paper, a novel prediction framework utilizing technological innovation text mining data and ensemble learning is proposed. The empirical data from China listed enterprises in strategic emerging industries were applied to construct prediction models using the classification and regression tree model, the random forest model and extreme gradient boosting model. The results show that the model uses the technological innovation text mining data proven to have significant predict ability, and top management teamꞌs attention to innovation variables offer the best prediction capacities. This work improves the application value of enterprise credit risk prediction models in strategic emerging industries by embedding the mining of technological innovation text information

A Hybrid Technological Innovation Text Mining, Ensemble Learning and Risk Scorecard Approach for Enterprise Credit Risk Assessment

Enterprise credit risk assessment models typically use financial-based information as a predictor variable, relying on backward-looking historical information rather than forward-looking information for risk assessment. We propose a novel hybrid assessment of credit risk that uses technological innovation information as a predictor variable. Text mining techniques are used to extract this information for each enterprise. A combination of random forest and extreme gradient boosting are used for indicator screening, and finally, risk scorecard based on logistic regression is used for credit risk scoring. Our results show that technological innovation indicators obtained through text mining provide valuable information for credit risk assessment, and that the combination of ensemble learning from random forest and extreme gradient boosting combinations with logistic regression models outperforms other traditional methods. The best results achieved 0.9129 area under receiver operating characteristic. In addition, our approach provides meaningful scoring rules for credit risk assessment of technology innovation enterprises

Fatty acid metabolism is related to the immune microenvironment changes of gastric cancer and RGS2 is a new tumor biomarker

BackgroundAlterations in lipid metabolism promote tumor progression. However, the role of lipid metabolism in the occurrence and development of gastric cancer have not been fully clarifiedMethodHere, genes that are related to fatty acid metabolism and differentially-expressed between normal and gastric cancer tissues were identified in the TCGA-STAD cohort. The intersection of identified differentially-expressed genes with Geneset was determined to obtain 78 fatty acid metabolism-related genes. The ConsensusClusterPlus R package was used to perform differentially-expressed genes, which yielded divided two gastric cancer subtypes termed cluster 1 and cluster 2.ResultsPatients in cluster 2 was found to display poorer prognosis than patients in cluster 1. Using machine learning method to select 8 differentially expressed genes among subtypes to construct fatty acid prognostic risk score model (FARS), which was found to display good prognostic efficacy. We also identified that certain anticancer drugs, such as bortezomib, elesclomol, GW843682X, and nilotinib, showed significant sensitivity in the high FARS score group. RGS2 was selected as the core gene upon an analysis of the gastric cancer single-cell, and Western blotting and immunofluorescence staining results revealed high level of expression of this gene in gastric cancer cells. The results of immunohistochemical staining showed that a large amount of RGS2 was deposited in the stroma in gastric cancer. A pan-cancer analysis also revealed a significant association of RGS2 with TMB, TIDE, and CD8+ T-cell infiltration in other cancer types as well. RGS2 may thus be studied further as a new target for immunotherapy in future studies on gastric cancer.ConclusionIn summary, the FARS model developed here enhances our understanding of lipid metabolism in the TME in gastric cancer, and provides a theoretical basis for predicting tumor prognosis and clinical treatment

Role of Hydrogen Bonding in Green Fluorescent Protein-like Chromophore Emission.

The fluorescence emission from green fluorescent protein (GFP) is known to be heavily influenced by hydrogen bonding between the core fluorophore and the surrounding side chains or water molecules. Yet how to utilize this feature for modulating the fluorescence of GFP chromophore or GFP-like fluorophore still remains elusive. Here we present theoretical calculations to predict how hydrogen bonding could influence the excited states of the GFP-like fluorophores. These studies provide both a new perspective for understanding the photophysical properties of GFP as well as a solid basis for the rational design of GFP-based fluorophores

Parameter Optimization for Preparing Carbon Fiber/Epoxy Composites by Selective Laser Sintering

Carbon fiber (CF) reinforced thermosetting resin composites offer a wide range of high performance features including excellent strength, modulus and thermal resistance and light weight. Consequently, they are increasingly demanded by aerospace and automotive industries due to the tighter requirements of the transport vehicles for lightweight as well as higher payloads. Although thermoplastics and their composites have been widely used in additive manufacturing (AM), to date it is difficult to manufacture carbon fibers reinforced thermosetting composite parts via AM technologies. Therefore, this study developed a novel method based on selective laser sintering (SLS) to fabricate high-performance carbon fiber/epoxy resin composites. The response surface method was employed to study the processing parameters affecting the quality of final parts, and an optimized processing condition was obtained.Mechanical Engineerin

Revealing in situ stress-induced short- and medium-range atomic structure evolution in a multicomponent metallic glassy alloy

Deformation behaviour of multicomponent metallic glasses are determined by the evolution/reconfiguration of the short- and medium-range order (SRO and MRO) atomic structures. A precise understanding of how different atom species rearrange themselves in different stress states is still a great challenge in materials science and engineering. Here, we report a systematic and synergetic research of using electron microscopy imaging, synchrotron X-ray total scattering plus empirical potential structure refinement (EPSR) modelling to study in situ the deformation of a Zr-based multicomponent metallic glassy alloy with 5 elements. Systematic and comprehensive analyses on the characteristics of the SRO and MRO structures in 3D and the decoupled 15 partial PDFs at each stress level reveal quantitatively how the SRO and MRO structures evolve or reconfigure in 3D space in the tensile and compressive stress states. The results show that the Zr-centred atom clusters have low degree of icosahedra and are the preferred atom clusters to rearrange themselves under the tensile and compressive stresses. The Zr-Zr is the dominant atom pair in controlling the shear band's initiation and propagation. The evolution and reconfiguration of the MRO clusters under different stress states are realised by changing the connection modes between the Zr-centred atom clusters. The coordinated changes of both bond angles and bond lengths of the Zr-centred clusters are the dominant factors in accommodating the tensile or compressive strains. While other solute-centred MRO clusters only play minor roles in the atomic structure reconfiguration/evolution. The research has demonstrated a synergetic and multimodal materials operando characterization methodology that has great application potential in design and development of high performance multiple-component engineering alloys

Effect of NETs/COX-2 pathway on immune microenvironment and metastasis in gastric cancer

BackgroundNeutrophil extracellular traps (NETs) are crucial in the progression of several cancers. The formation of NETs is closely related to reactive oxygen species (ROS), and the granule proteins involved in nucleosome depolymerization under the action of ROS together with the loosened DNA compose the basic structure of NETs. This study aims to investigate the specific mechanisms of NETs promoting gastric cancer metastasis in order to perfect the existing immunotherapy strategies.MethodsIn this study, the cells and tumor tissues of gastric cancer were detected by immunological experiments, real-time polymerase chain reaction and cytology experiments. Besides, bioinformatics analysis was used to analyze the correlation between cyclooxygenase-2 (COX-2) and the immune microenvironment of gastric cancer, as well as its effect on immunotherapy.ResultsExamination of clinical specimens showed that NETs were deposited in tumor tissues of patients with gastric cancer and their expression was significantly correlated with tumor staging. Bioinformatics analysis showed that COX-2 was involved in gastric cancer progression and was associated with immune cell infiltration as well as immunotherapy. In vitro experiments, we demonstrated that NETs could activate COX-2 through Toll-like receptor 2 (TLR2) and thus enhance the metastatic ability of gastric cancer cells. In addition, in a liver metastasis model of nude mice we also demonstrated the critical role of NETs and COX-2 in the distant metastasis of gastric cancer.ConclusionNETs can promote gastric cancer metastasis by initiating COX-2 through TLR2, and COX-2 may become a target for gastric cancer immunotherapy