DeepPricing:pricing convertible bonds based on financial time-series generative adversarial networks

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.

Analysis on the medication rule of traditional Chinese medicine in the treatment of COVID-19 patients

Objective: To describe patterns of utilization of traditional Chinese medicine(TCM) in the treatment of patients with coronavirus disease 2019(COVID-19).Methods: Adult patients with COVID-19 who received TCM treatment were divided into a non-serious group(mild and moderate types) and a serious group(severe and critical types) according to their admission conditions. The medical records and prescriptions of these patients were investigated to determine their TCM utilization patterns.Results: In all, 3,872 COVID-19 patients were included. Oral Chinese traditional patent medicine(CPM) was the most commonly used type of TCM(83.2%), fol owed by decoction(64.4%). As for medication pattern, the proportion of multi-drug combinations was higher than single drug use(55.0% vs. 45.0%). Decoction combined with oral CPM was the most common combination(39.1%,1,514/3,872). Oral y administered, injected, and external y applied CPM were significantly more common in the serious group than in the non-serious, while decoction and non-drug TCM treatments were more common in the non-serious than in the serious group.Multi-drug combinations were used for the majority of patients in both groups, mainly in the form of decoction combined with oral CPM.Serious patients were analysed by three groups of different prognoses and outcomes, including discharged after improvement,discharged after relapse and improvement, died final y. The two most common medication patterns were decoction combined with oral CPM and oral CPM alone in the two final y discharged groups. Oral CPM alone or used in combination with injected CPM were seen most commonly in the death group. Significant differences were established in medication patterns among patients in these three groups.Conclusions: Oral CPM was the mainly used TCM for COVID-19 patients, followed by decoction. More common medication pattern was multi-drug combination, especially decoction combined with oral CPM. The treatment measures and medication patterns of TCM commonly used in COVID-19 patients with the range of conditions found in this study should be further explored in the future to provide a more complete reference for COVID-19 treatment.

以循环标准判定死亡的遗体器官捐献分类标准、实施流程及需要注意的问题

近十年来我国脑死亡器官捐献意识和能力均有了显著提升。在脑死亡器官捐献持续增量的同时,科学规范地推动以循环标准判定死亡的遗体器官捐献同样是未来发展的方向。本文结合多个国家的相关指南、国际上相关领域最新研究进展以及作者经验,对相关定义的变迁、分类标准的完善、实施流程以及实施过程需要注意的问题等进行了阐述,旨在为以循环标准判定死亡的遗体器官捐献与获取工作提供科学规范的实践指导,为进一步提高遗体器官捐献率和捐献器官利用率奠定相关基础。

Wnt/β连环蛋白信号通路在体表创面愈合中的作用机制研究进展

创面愈合是一个缓慢而复杂的生物学过程,包括炎症反应、细胞增殖分化和迁移、血管新生、细胞外基质沉积和组织重塑等。Wnt信号通路可分为经典通路和非经典通路,其中Wnt经典通路又称Wnt/β连环蛋白信号通路,其在细胞分化、迁移和组织稳态维持过程中发挥重要作用。许多炎症因子、生长因子等都参与该通路的上游调控。Wnt/β连环蛋白信号通路的激活在皮肤创面的发生、发展、再生、修复及相关治疗的过程中,起到了重要作用。该文综述了Wnt/β连环蛋白信号通路与创面愈合的关系,并总结了其对炎症反应、细胞增殖、血管新生、毛囊再生和皮肤纤维化等创面愈合重要过程的影响以及Wnt信号通路抑制因子在创面愈合中的作用。

Implementation of slow and smooth etching of GaN by inductively coupled plasma

Slow and smooth etching of gallium nitride（GaN） by BCl;/Cl;-based inductively coupled plasma（ICP）is investigated in this paper. The effects of etch parameters, including ICP power, radio frequency（RF） power, the flow rate of Cl;and BCl;, on GaN etch rate and etch surface roughness RMS are discussed. A new model is suggested to explain the impact mechanism of the BCl;flow rate on etch surface roughness. An optimized etch result of a slow and smooth etch surface was obtained; the etch rate and RMS were 0.36 ?/s and 0.9 nm, respectively.

Immunogenicity and reactogenicity of heterologous immunization schedules with COVID-19 vaccines:a systematic review and network meta-analysis

Background:Data on the immunogenicity and safety of heterologous immunization schedules are inconsistent.This study aimed to evaluate the immunogenicity and safety of homologous and heterologous immunization schedules.Methods:Multiple databases with relevant studies were searched with an end date of October 31,2021,and a website including a series of Coronavirus disease 2019 studies was examined for studies before March 31,2022.Randomized controlled trials(RCTs)that compared different heterologous and homologous regimens among adults that reported immunogenicity and safety outcomes were reviewed.Primary outcomes included neutralizing antibodies against the original strain and serious adverse events(SAEs).A network meta-analysis(NMA)was conducted using a random-effects model.Results:In all,11 RCTs were included in the systematic review,and nine were ultimately included in the NMA.Among participants who received two doses of CoronaVac,another dose of mRNA or a non-replicating viral vector vaccine resulted in a significantly higher level of neutralizing antibody than a third CoronaVac 600 sino unit(SU);a dose of BNT162b2 induced the highest geometric mean ratio(GMR)of 15.24,95%confidence interval[CI]:9.53–24.39.Following one dose of BNT162b2 vaccination,a dose of mRNA-1273 generated a significantly higher level of neutralizing antibody than BNT162b2 alone(GMR=1.32;95%CI:1.06–1.64),NVX-CoV2373(GMR=1.60;95%CI:1.16–2.21),or ChAdOx1(GMR=1.80;95%CI:1.25–2.59).Following one dose of ChAdOx1,a dose of mRNA-1273 was also more effective for improving antibody levels than ChAdOx1(GMR=11.09;95%CI:8.36–14.71)or NVX-CoV2373(GMR=2.87;95%CI:1.08–3.91).No significant difference in the risk for SAEs was found in any comparisons.Conclusions:Relative to vaccination with two doses of CoronaVac,a dose of BNT162b2 as a booster substantially enhances immunogenicity reactions and has a relatively acceptable risk for SAEs relative to other vaccines.For primary vaccination,schedules including mRNA vaccines induce a greater immune response.However,the comparatively higher risk for local and systemic adverse events introduced by mRNA vaccines should be noted.Registration:PROSPERO;https://www.crd.york.ac.uk/PROSPERO/;No.CRD42021278149.