A deep learning approach of financial distress recognition combining text

The financial distress of listed companies not only harms the interests of internal managers and employees but also brings considerable risks to external investors and other stakeholders. Therefore, it is crucial to construct an efficient financial distress prediction model. However, most existing studies use financial indicators or text features without contextual information to predict financial distress and fail to extract critical details disclosed in Chinese long texts for research. This research introduces an attention mechanism into the deep learning text classification model to deal with the classification of Chinese long text sequences. We combine the financial data and management discussion and analysis Chinese text data in the annual reports of 1642 listed companies in China from 2017 to 2020 in the model and compare the effects of the data on different models. The empirical results show that the performance of deep learning models in financial distress prediction overcomes traditional machine learning models. The addition of the attention mechanism improved the effectiveness of the deep learning model in financial distress prediction. Among the models constructed in this study, the Bi-LSTM+Attention model achieves the best performance in financial distress prediction

Different male mate location behaviour of the Glanville fritillary butterfly in different landscapes in the Tianshan Mountains, northwestern China

In a previous study most males of the Glanville fritillary butterfly (Melitaea cinxia) were caught in valleys, whereas almost all females were distributed on slopes in the Tianshan Mountains, northwestern China. To help understand this phenomenon, male mate location behaviours were observed in different landscapes of the Tianshan Mountains. In valleys, males exhibited perching behaviour. On slopes, spatial distribution of males showed patrolling behaviour on meadows, but intermediate behaviour between perching and patrolling at forest edge. The temporal distribution of males also varied, being found on slopes from 7:00 to 18:00, but in valleys from 8:00 to 13:00 each day. Ambient temperatures were higher on slopes than those in valleys between 8:00 to 13:00. Males exhibited lower tolerance to high temperature than females, leading to the conclusion that valleys are more likely to be used by males as thermoregulation sites, rather than for mating

Serum Amyloid A Impairs the Antiinflammatory Properties of HDL

HDL from healthy humans and lean mice inhibits palmitate-induced adipocyte inflammation; however, the effect of the inflammatory state on the functional properties of HDL on adipocytes is unknown. Here, we found that HDL from mice injected with AgNO3 fails to inhibit palmitate-induced inflammation and reduces cholesterol efflux from 3T3-L1 adipocytes. Moreover, HDL isolated from obese mice with moderate inflammation and humans with systemic lupus erythematosus had similar effects. Since serum amyloid A (SAA) concentrations in HDL increase with inflammation, we investigated whether elevated SAA is a causal factor in HDL dysfunction. HDL from AgNO3-injected mice lacking Saa1.1 and Saa2.1 exhibited a partial restoration of antiinflammatory and cholesterol efflux properties in adipocytes. Conversely, incorporation of SAA into HDL preparations reduced antiinflammatory properties but not to the same extent as HDL from AgNO3-injected mice. SAA-enriched HDL colocalized with cell surface–associated extracellular matrix (ECM) of adipocytes, suggesting impaired access to the plasma membrane. Enzymatic digestion of proteoglycans in the ECM restored the ability of SAA-containing HDL to inhibit palmitate-induced inflammation and cholesterol efflux. Collectively, these findings indicate that inflammation results in a loss of the antiinflammatory properties of HDL on adipocytes, which appears to partially result from the SAA component of HDL binding to cell-surface proteoglycans, thereby preventing access of HDL to the plasma membrane

A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer

In this paper, we introduce a transformer into the field of credit scoring based on user online behavioral data and develop an end-to-end feature embedded transformer (FE-Transformer) credit scoring approach. The FE-Transformer neural network is composed of two parts: a wide part and a deep part. The deep part uses the transformer deep neural network. The output of the deep neural network and the feature data of the wide part are concentrated in a fusion layer. The experimental results show that the FE-Transformer deep learning model proposed in this paper outperforms the LR, XGBoost, LSTM, and AM-LSTM comparison methods in terms of area under the receiver operating characteristic curve (AUC) and the Kolmogorov–Smirnov (KS). This shows that the FE-Transformer deep learning model proposed in this paper can accurately predict user default risk

Three amino acid substitutions contributing to thermostability of phosphoglucose isomerase in the Glanville fritillary butterfly

Abstract Temperature is one of the most important environmental factors that affect organisms, especially ectotherms, due to its effects on protein stability. Understanding the general rules that govern thermostability changes in proteins to adapt high-temperature environments is crucial. Here, we report the amino acid substitutions of phosphoglucose isomerase (PGI) related to thermostability in the Glanville fritillary butterfly (Melitaea cinxia, Lepidoptera: Nymphalidae). The PGI encoded by the most common allele in M. cinxia in the Chinese population (G3-PGI), which is more thermal-tolerant, is more stable under heat stress than that in the Finnish population (D1-PGI). There are five amino acid substitutions between G3-PGI and D1-PGI. Site-directed mutagenesis revealed that the combination of amino acid substitutions of H35Q, M49T and I64V may increase PGI thermostability. These substitutions alter the 3D structure to increase the interaction between two monomers of PGI. Through molecular dynamics simulations, it was found that the amino acid at site 421 is more stable in G3-PGI, confining the motion of the α-helix 420?441 and stabilizing the interaction between two PGI monomers. The strategy for high-temperature adaptation through these three amino acid substitutions is also adopted by other butterfly species (Boloria eunomia, Aglais urticae, Colias erate and Polycaena lua) concurrent with M. cinxia in the Tianshan Mountains of China, i.e., convergent evolution in butterflies. This article is protected by copyright. All rights reservedPeer reviewe