The COVID-19 outbreak has greatly impacted the stability of the global financial markets. In the post-COVID-19 pandemic era, the risk contagion patterns of the global financial markets may change. This paper utilizes the conditional value-at-risk (ΔCoVaR) model to measure the risk level of the financial markets in various economies and uses the TVP-VAR-CONNECTEDNESS approach to construct a time-varying spillover index. Based on the dimensions of time and space, we explored the contagion path, contagion status, and contagion structure characteristics of global financial market risk before and during the COVID-19 pandemic. The results entail several conclusions. (i) The COVID-19 pandemic increased the spillover level of global financial market risk and the risk connectedness of financial markets in different countries. In addition, during the concentrated outbreak period of COVID-19, the risk spillover level in developing countries rose rapidly, while the financial risk spillover level in developed countries decreased significantly. (ii) The impact of the COVID-19 pandemic on the spillover of the global financial market risk is time-varying, and there is a strong correlation between the risk spillover level of the financial markets of the world and the severity of the COVID-19 pandemic. (iii) Due to the impact of the COVID-19 pandemic, Brazil, Canada, and Russia have become new risk spillover centers; in the post-COVID-19 pandemic era, China's spillover to developed countries has increased, and the financial influence of China has also gradually increased. In addition, the risk contagion capacity of financial markets among European countries is gradually converging. (iv) During the concentrated outbreak of the COVID-19 pandemic, the Americas were the main exporter of global financial market risk, while Europe played a role in risk absorption.

Convertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides, embedded options, such as conversion, call, and put provisions are often restricted to certain periods, may vary over time, and are subject to additional path-dependent features of the state variables. Moreover, the most challenging problem in convertible bond valuation is the underlying stock return process modeling as it retains various complex statistical properties. In this paper, we propose DeepPricing, a novel data-driven convertible bonds pricing model, which is inspired by the recent success of generative adversarial networks (GAN), to address the above challenges. The method introduces a new financial time-series generative adversarial networks (FinGAN), which is able to reproduce risk-neutral stock return process that retains the unique statistical properties such as the fat-tailed distributions, the long-range dependence, and the asymmetry structure etc., and then transit to its risk-neutral distribution. Thus it is more flexible and accurate to capture the dynamics of the underlying stock return process and keep the rich set of real-world convertible bond specifications compared with previous model-driven models. The experiments on the Chinese convertible bond market demonstrate the effectiveness of DeepPricing model. Compared with the convertible bond market prices, our model has a better convertible bonds pricing performance than both model-driven models, i.e. Black-Scholes, the constant elasticity of variance, GARCH, and the state-of-the-art GAN-based models, i.e. FinGAN-MLP, FinGAN-LSTM. Moreover, our model has a better fitting capacity for higher-volatility convertible bonds and the overall convertible bond market implied volatility smirk, especially for equity-liked convertible bonds, convertible bonds trading in the bull market, and out-of-the-money convertible bonds. Furthermore, the Long-Short and Long-Only investment strategies based on our model earn a significant annualized return with 41.16% and 31.06%, respectively, for the equally-weighted portfolio during the sample period.

Previous studies have shown that forkhead box P4 antisense RNA 1 (FOXP4‐AS1) is dysregulated in tumor tissues and can serve as a prognostic indicator for multiple cancers. However, the clinical significance of FOXP4‐AS1 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. The goal of this study is to recognize the possible clinical significance of long noncoding RNA FOXP4‐AS1 in patients with early stage PDAC. A total of 112 patients from The Cancer Genome Atlas (TCGA) PDAC cohort, receiving RNA sequencing, were involved in the study. Survival analysis, functional mechanism, and potential small molecule drugs of target therapy of FOXP4‐AS1 were performed in this study. Survival analysis in TCGA PDAC cohort suggested that patients with high FOXP4‐AS1 expression had significantly augmented possibility of death than in PDAC patients with lower FOXP4‐AS1 expression (adjusted P = .008; adjusted HR = 2.143, 95% CI = 1.221‐3.760). In this study, a genome‐wide RNA sequencing dataset was used to identify 927 genes co‐expressing with FOXP4‐AS1 in PDAC tumor tissues. A total of 676 differentially expressed genes were identified between different FOXP4‐AS1 expression groups. Functional enrichment analysis of these genes and gene set enrichment analysis for PDAC genome‐wide RNA sequencing dataset was done. We have found that FOXP4‐AS1 may function in PDAC by participating in biological processes and pathways including oxidative phosphorylation, tricarboxylic acid cycle, classical tumor‐related pathways such as NF‐kappaB as well as Janus kinase/signal transducers in addition to activators of transcription, cell proliferation, and adhesion. In addition, we also screened two potential targeted therapeutic small molecule drugs (dimenhydrinate and metanephrine) for FOXP4‐AS1 in PDAC. In conclusion, our present study demonstrated that higher expression of FOXP4‐AS1 in PDAC tumor tissues were related with an inferior medical outcome. Through multiple genome‐wide approaches, we identified the potential molecular mechanisms of FOXP4‐AS1 in PDAC and two targeted therapeutic drugs for it.

Reactive oxygen species (ROS) are significant active species in living organisms, and their coordination maintains the function of organelles to resist the invasion of foreign substances. Hypochlorous acid (HClO) is not only an eventful signaling species but also a kind of ROS, which plays an irreplaceable role in the immune system. However, its abnormal levels can cause cell damage or even apoptosis, which in turn leads to the onset of a series of diseases such as inflammation, neurological diseases, and even cancer. Based on this, we designed a near-infrared fluorescent probe with a large Stokes shift for ultrafast response to HClO. Furthermore, the probe exhibits excellent sensitivity and selectivity toward HClO over other species. The probe was successfully applied to visualize endogenous and exogenous HClO in living cells and in zebrafish. This unique study is the key to providing a trustworthy tool for imaging based on the in vitro and in vivo imaging of endogenous HClO, which possesses great potential for the use in future studies of HClO-related biology and pathology.

This paper extends the traditional jump-diffusion model to a comprehensive general Lévy process model with the stochastic interest rate for European-style options pricing. By using the Girsanov theorem and Itô formula, we derive the uniform formalized pricing formulas under the equivalent martingale measure. This model contains not only the traditional jump-diffusion model, such as the compound Poisson model, the renewal model, the pure-birth jump-diffusion model, but also the infinite activities Lévy model.