The Determinants of Bankruptcy for Chinese Firms

The global financial crisis in 2008 increased the number of business failures in the U.S. as well as in China. The Chinese economy has also been affected by the recent global financial crisis given the fact that the Chinese economy depends heavily on international trade. Our study tries to find the determinants of bankruptcy in Chinese firms. Both logit and survival model analyses provide consistent results on the determinants in predicting distressed firms in China. Our results suggest that firms with liquidity problems and firms experiencing a decline in profits are more likely to file for bankruptcy. In addition, we find that, compared to state-owned enterprises (SOEs), collectively-owned enterprises, private-owned enterprises, and foreign-owned businesses are more likely to file for bankruptcy. This conclusion is robust after controlling for regional differences. The findings of this study show that the financial variables developed by Altman [Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(3), 589–609] and Ohlson [Financial ratios and probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131] perform reasonably well in determining business failures of Chinese firms even though SOEs and shadow financing exist in China

Solid Dynamic Models for Analysis of Stress and Strain in Human Hearts

This paper proposes a solid model based on four-dimensional trivariate B-spline for strain and stress analysis of ventricular myocardium. With a series of processing steps in the four-dimensional medical images, the feature points of ventricular inner and outer wall are obtained. A B-spline surface is then used to build the dynamic deformation model of the myocardial walls. With such a surface model, a hexahedron control mesh can be constructed by sweeping the cloud data, and the ventricular solid model is built by fitting the trivariate B-spline parameters. Based on these models, a method of isogeometric analysis can be applied to calculate the stress and strain continuously distributed in the ventricle. The model is represented smoothly in the cylindrical coordinate system and is easy to measure myocardium dynamics for finding abnormal motion. Experiments are carried out for comparing the stress and strain distribution. It is found that the solid model can determine ventricular dynamics which can well reflect the deformation distribution in the heart and imply early clues of cardiac diseases

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

RNA sequencing analysis to capture the transcriptome landscape during skin ulceration syndrome progression in sea cucumber Apostichopus japonicus

Complement and coagulation cascades pathways (tif). Red boxes represent up-regulated genes, and green boxes represent down-regulated genes. (TIF 627 kb

B7 family protein glycosylation: Promising novel targets in tumor treatment

Cancer immunotherapy, including the inhibition of immune checkpoints, improves the tumor immune microenvironment and is an effective tool for cancer therapy. More effective and alternative inhibitory targets are critical for successful immune checkpoint blockade therapy. The interaction of the immunomodulatory ligand B7 family with corresponding receptors induces or inhibits T cell responses by sending co-stimulatory and co-inhibitory signals respectively. Blocking the glycosylation of the B7 family members PD-L1, PD-L2, B7-H3, and B7-H4 inhibited the self-stability and receptor binding of these immune checkpoint proteins, leading to immunosuppression and rapid tumor progression. Therefore, regulation of glycosylation may be the “golden key” to relieve tumor immunosuppression. The exploration of a more precise glycosylation regulation mechanism and glycan structure of B7 family proteins is conducive to the discovery and clinical application of antibodies and small molecule inhibitors