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Background: Last winter, a new coronavirus-induced pneumonia, COVID-19, broke out in Wuhan, China, and spread rapidly throughout the country due to its high inf

This paper focuses on the investor sentiment and stocks performance, but we measure investor sentiments, specially, with advice, comments and opinion from professional investors, which are official social networks about community of individual stock, named HDY (互动易), administered by Shanghai Stock Exchange and Shenzhen Stock Exchange. We find that, basically, sentiments of professional query could be a significant factor, negative sentiment has more remarkable predictability than positive sentiment. Furthermore, specially, dividing into 5 quantiles, the best quantile can obtains more than 30% cumulative return in sample. So sentiment factor from HDY platform shows remarkable Cumulative Return, which also shows robustness, after controlling Fama-French 3/5 factors and testing with Fama-Macbeth method.

Background: In December 2019, a novel coronavirus (SARS-CoV-2) caused infectious disease, termed COVID-19, outbroke in Wuhan, China. COVID-19 patients manifested as lung injury with complications in other organs, such as liver, heart, gastrointestinal tract, especially for severe cases. However, whether COVID-19 causes significant acute kidney injury (AKI) remained controversial. Methods: We retrospectively analyzed the clinical characteristics, urine and blood routine tests and other laboratory parameters of hospitalized COVID-19 patients in Wuhan Union Hospital. Findings: 178 patients, admitted to Wuhan Union hospital from February 02 to February 29, 2020, were included in this study. No patient (0 [0%]) presented increased serum creatinine (Scr), and 5 (2.8%) patients showed increased blood urea nitrogen (BUN), indicating few cases with “kidney dysfunction”. However，for patients (83) with no history of kidney disease who received routine urine test upon hospitalization, 45 (54.2%) patients displayed abnormality in urinalysis, such as proteinuria, hematuria and leukocyturia, while none of the patients was recorded to have acute kidney injury (AKI) throughout the study. Meanwhile, the patients with abnormal urinalysis usually had worse disease progression reflecting by laboratory parameters presentations, including markers of liver injury, inflammation, and coagulation. Interpretation: Many patients manifested by abnormal urinalysis on admission, including proteinuria or hematuria. Our results revealed that urinalysis is better in unveiling potential kidney impairment of COVID-19 patients than blood chemistry test and urinalysis could be used to reflect and predict the disease severity. We therefore recommend pay more attention in urinalysis and kidney impairment in COVID-19 patients. Funding Statement: This study was funded by the grants from the project of Thousand Youth Talents for D.H.; and from the China National Natural Science Foundation (Nos. 31770983 and 81974249 to D.H., No. 81601747 to S.L.). Declaration of Interests: The authors have no conflicts of interest. Ethics Approval Statement: This study was approved by the Institutional Ethics Board of Wuhan Union Hospital of Tongji Medical College, Huazhong University of Science and Technology (No. Union Hospital-0093). Written informed consent was waived by the Ethics Commission of the designated hospital for the emerging infectious diseases.

Background: The pandemic of the coronavirus disease 2019 (COVID-19) has brought a global public health crisis. However, the pathogenesis underlying COVID-19 are barely understood. Methods: In this study, we performed proteomic analyses of airway mucus obtained by bronchoscopy from severe COVID-19 patients. In total, 2351 and 2073 proteins were identified and quantified in COVID-19 patients and healthy controls, respectively. Results: Among them, 92 differentiated expressed proteins (DEPs) (46 up-regulated and 46 down-regulated) were found with a fold change > 1.5 or < 0.67 and a p-value < 0.05, and 375 proteins were uniquely present in airway mucus from COVID-19 patients. Pathway and network enrichment analyses revealed that the 92 DEPs were mostly associated with metabolic, complement and coagulation cascades, lysosome, and cholesterol metabolism pathways, and the 375 COVID-19 only proteins were mainly enriched in amino acid degradation (Valine, Leucine and Isoleucine degradation), amino acid metabolism (beta-Alanine, Tryptophan, Cysteine and Methionine metabolism), oxidative phosphorylation, phagosome, and cholesterol metabolism pathways. Conclusions: This study aims to provide fundamental data for elucidating proteomic changes of COVID-19, which may implicate further investigation of molecular targets directing at specific therapy. Funding Statement: This work was supported by grants from the National Key R&D Program of China (2016YFC0903700), the National Natural Science Foundation of China (81520108001 and 81770043), and grant specific for COVID-19 study from Guangzhou Institute of Respiratory Health. Declaration of Interests: The authors have no conflict of interest to declare. Ethics Approval Statement: All the procedures were approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (No. 2020-65). Verbal informed consent were obtained from all participants because the family members were in quarantine.

An integrated analysis of human microarray and rat transcriptomic data was performed to illustrate the pathologic effects of cigarette smoking (CS) on the gene expression profile and identify new gene candidates for chronic obstructive pulmonary disease (COPD) progression. Small airway epithelial samples of Human with different smoking status using fiberoptic bronchoscopy and corresponding rat lung tissues following 0, 3, and 6 months of CS exposure were obtained. Differential expression analysis was performed to identify differentially expressed genes (DEGs). Using qRT-PCR, the expression of the significant overlapping genes between human and rats were confirmed in 16HBE cells. 8 relevant studies comprising 293 individuals including phenotypically normal non-smokers, normal smokers, and COPD smokers were involved by searching the public databases. The integrated bioinformatic analysis of 8 human GEO datasets and rat transcriptome databases revealed 13 overlapping genes between humans and rats in response to CS exposure during COPD progression. Of these, 5 genes (AKR1C3, ERP27, AHRR, KCNMB2, and MRC1) were consistently identified in both the human and rat and validated by qRT-PCR. Among them, ERP27, KCNMB2, and MRC1 were newly identified candidate genes for COPD progression in response to CS. In addition, we also found that DEGs obtained from each expression profile dataset was better than combined analysis as more genes could be identified. The findings of the present study illustrated that integration of human microarray and rat transcriptome data might facilitate the discovery of new DEGs candidate of COPD progression in response to smoking. Funding Statement: This study was supported by grants from the National Key R&D Program of China (2016YFC0903700), National Natural Science Foundation of China (81520108001, 81770043 and 81700043), Guangzhou Department of Education (1201620007), Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01S155), the National Key R&D Program of China (2018YFC1311900), the Guangdong Natural Science Foundation (2020A1515010076), and the Research Projects of SKLRD (OP-201808, QN-201706, QN-201917, OP-201912). Declaration of Interests: The authors have no conflict of interest to declare. Ethics Approval Statement: The study was conducted according to the criteria set by the declaration of Helsinki, and written informed consent was obtained from all participants before data collection. The study was approved by the Institutional Review Board (GZMC 2009-08-1336).

With the progress in natural language processing and machine learning, using deep learning techniques to extract valuable information from financial texts has gained popularity among researchers. However, as deep learning models require large amounts of labelled data, current models for financial text mining are sensitive to noise and prone to overfitting due to the lack of labelled data in financial fields. We address this issue by using pretrained word representations and BERT model to leverage knowledge acquired from huge text corpus. In particular, we design a deep neural network model that combines CNN, Att-BLSTM and BERT for predicting financial news sentiment polarity. Then, the news sentiment indices are constructed based on the predictive model. In terms of predicting news sentiment polarity, we show that our model outperforms state-of-the-art competitors. Furthermore, we show that the news sentiment exhibits a significant relationship with stock market returns.

The implied volatility smile surface is the basis of option pricing, and the dynamic evolution of the option volatility smile surface is difficult to predict. In this paper, attention mechanism is introduced into LSTM, and a volatility surface prediction method combining deep learning and attention mechanism is pioneeringly established. LSTM's forgetting gate makes it have strong generalization ability, and its feedback structure enables it to characterize the long memory of financial volatility. The application of attention mechanism in LSTM networks can significantly enhance the ability of LSTM networks to select input features. This paper considers the discrete points of the implied volatility smile surface as an overall prediction target, extracts the daily, weekly, and monthly option implied volatility as input features and establishes a set of LSTM-Attention deep learning systems. Using the dropout mechanism in training reduces the risk of over-fitting. For the prediction results, we use arbitrage-free smoothing to form the final implied volatility smile surface. This article uses the S&P 500 option market to conduct an empirical study. The research shows that the error curve of the LSTM-attention prediction system converges, and the prediction of the implied volatility surface is more accurate than other predicting system. According to the implied volatility surface of the 3-year rolling forecast, the BS formula is used to pricing the option contract, and then a time spread strategy and a butterfly spread strategy are constructed respectively. The experimental results show that the two strategies constructed using the predicted implied volatility surfaces have higher returns and sharp ratios than that the volatility surfaces are not predicted. This paper confirms that the use of AI to predict the implied volatility surface has theoretical and economic value. The research method provides a new reference for option pricing and strategy.

The price of Bitcoin reached its peak a mere few days after the introduction of Bitcoin futures and suffered an 80% loss in the following year. In this paper, we find a significant and negative relationship between the introduction of Bitcoin futures and Bitcoin returns, and an insignificant or positive relationship for other 7 major non-Bitcoin cryptocurrencies. Within the first 45 days after the futures launch, Bitcoin suffered a -26.50% loss, while other cryptocurrencies could still provide positive returns. We presume that the launch of Bitcoin futures was to an extent responsible for the crash of Bitcoin.

Quality-minus-junk (QMJ) is a long-short factor that captures the time varying premium of high quality assets. The long side (Quality) is stocks with high quality and the short side (Junk) is stocks with low quality. In this paper, we find that both Quality and Junk have a significant Halloween effect: the portfolios have a relatively better performance during winter as compared with summer. In terms of the magnitude of Halloween effect, Junk is greater than Quality. As a result, the long-short QMJ factor exhibits a pattern of reversal Halloween effect and this result is robust under different study samples. In addition, we also find that the Halloween effect of the stock market (MKT) can be largely explained by its negative exposure on QMJ. After controlling for QMJ, the magnitude of Halloween effect for MKT diminishes more than 60%.

Chinese Abstract: 本文探讨了当投资者具有有限关注度时，因科技价值信息的延迟扩散带来的投资机会。在有限关注度假设下，投资者倾向于忽略企业间的科技关联，导致科技价值在同类科技公司中的传递产生延迟，从而带来投资机会。然而，在我国股票收益率受非理性因素影响过多，不适合用作科技溢出效应的代理变量。本文首次提出，分析师一致盈余预测修正比股票收益率更适合用来衡量上市公司的科技价值。本文根据上市公司持有的专利计算了不同公司间的科技关联，并结合分析师一致盈余预测修正设计了“科技修正因子”。通过该因子构建的套利组合可以获得1.00%的月均收益。本文不仅验证了科技关联在我国股票市场的信息传递能力，而且深入探讨了投资者有限关注度在此过程中起到的作用。本文发现科技修正因子的投资价值一方面来源于其能够预测未来的分析师一致盈余预测修正，一方面来源于其能够预测公司未来的盈余变化。本文首次将分析师行为信息与上市公司间的科技关联结合起来运用，该思路不仅有助于增加投资者对科技关联的应用，而且有助于更加充分地挖掘分析师行为信息的价值。 English Abstract: This paper investigates the investment opportunities from the delayed information diffusion through technological links among firms. Under the assumption of limited attention, investors tend to neglect the technological linkages among firms, resulting in delayed information diffusion among technological peers, thus bringing investment opportunities. However, the stock return is affected by too many irrational factors in China, which makes it unsuitable to be treated as proxy for the spillover effect of technology development. This paper proposes for the first time that analyst consensus earnings forecast revision is more suitable than stock return to measure technology value of listed companies. Using analyst consensus earnings forecast revision, this paper proposes the TechRev factor. Then the roles of technological links and limited attention in asset pricing are investigated. Firstly, this paper defined the technological correlation between firms based on the patents belonging to the focal firm. Then the TechRev for the focal firm can be calculated by averaging the analyst consensus earnings forecast revision of its technological peers, using the technological correlations as the weights. The empirical results show that: (1) the TechRev factor has significant investment value and the long-short strategy based on it yields monthly return of 100 basis points; (2) both portfolio test and the Fama-Macbeth regression verify that the investment value of TechRev is not derived from the well-known anomaly factors, but based on the specific information it contains essentially; (3) the TechRev factor is more efficient among the firms with stronger patent intensity or less analyst reports coverage, which means that the limited attention mechanism plays an important role; (4) the predictability of TechRev factor for the future stock return of the focal firm is derived from its predictability for the analyst consensus forecast revisions and the unexpected earnings of the focal firm; (5) the TechRev anomaly is more attributed to mispricing instead of risk premium. Because of the huge difference between the stock markets of China and developed countries, many effective investment strategies in mature markets lose their effects in China. However, this does not mean that the operation mode of China's stock market does not conform to the basic economic or financial laws. At least, the limited attention paradigm embodies well in the markets of both China and the US. And the securities analysts play indispensable roles in the stock market, although the independence of China's securities analysts has been suspected for a long time. As the information medium between listed companies and investors, securities analysts can not only the transmit information among the market, but also provide deeper and more effective investment information for the majority of investors based on their professional investment knowledge. At present, the market still cannot make full use of the analyst behavior information. This paper opens a new perspective by combining the analyst behavior information with the technological linkages of the listed firms, which is not only conducive to providing investors with new investment strategies, but also conducive to improving the efficiency of the whole stock market in China.

Background: Recently, long non-coding RNA-H19 was reported to mediate fatty acids and bile acids metabolism in liver tissue and play important role in fatty liver and cholestatic liver injury. However, little was known about H19 function and mechanism in liver fibrosis. Methods: ICR mice were intoxicated with CCl4 for evaluating H19 and Dihydroartemisinin effects in vivo. HSC-LX2 was cultured and multiple molecular experiments including real-time PCR, western blot, immunofluorescence, oil red staining, nile red staining, dual-luciferase reporter assay, fluorescence in situ hybridization, RNA immunoprecipitation and co-immunoprecipitation were used to elucidate the underlying mechanisms. Results: Decreased expression of H19 significantly ameliorated histopathological feature of liver fibrosis and inhibited HSC activation. This role of H19 was associated with lipid droplet metabolism dependent on AMPKα pathway in HSC. Interestingly, Dihydroartemisinin (DHA) inhibited H19 expression level in a dose-dependent manner. DHA decreased HIF-1α expression level to prevent HIF-1α binding to the promoter of H19. Mechanically, H19 drove AMPKα to interact with LKB1, resulting in the formation of the LKB1/AMPKα complex to facilitate phosphorylation of LKB1 towards AMPKα, causing obvious LDs consumption in HSC, but which was inhibited by DHA. Conclusions: Collectively, H19-induced AMPKα/LKB1 complex was required for DHA to regulate LD metabolism in activated HSC. This study highlighted a new molecular mechanism of DHA against liver fibrosis. Funding Statement: This study was supported by the National Natural Science Foundation of China (81270514, 31571455, 31401210, 31600653, and 81600483), the Natural Science Foundation of Jiangsu Province (BK20140955), the Open Project Program of Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica (No. JKLPSE201804), the Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Postgraduate Research& Practice Innovation Program of Jiangsu Province (KYCX19_1262). Declaration of Interests: The authors claim that they have no conflicts of interest. Ethics Approval Statement: All experimental procedures were approved by the Institutional and Local Committee on the Care and Use of Animals of Nanjing University of Chinese Medicine, and all animals received humane care according to the National Institutes of Health (USA) guidelines.

The aberrant expression of Matrix Metalloproteinases (MMPs) is known to contribute to the pathogenesis of airway remodeling and alveolar disruption in chronic obstructive pulmonary diseases (COPD). This study explored the relationship between MMP genetic variants and the risk of COPD in family and population based studies. For the discovery stages, eleven COPD patients from five families were subjected to whole genome sequencing (WGS), and 21 common mutations in MMPs and TIMPs were identified. Of these mutations, two SNPs rs2664370 and rs2664369 in MMP16 remained significant differences (P C variant decreases the risk of COPD likely thorough increasing the interaction with hsa-miR-576-5p and reducing the expression of MMP16. Therefore, common mutations in MMP16 reduce COPD risk and improve plasma blood gas levels. Funding Statement: This work was supported by grants from the National Natural Science Foundation of China (81520108001, 81700043, 81770043, and 81220108001), Guangdong Natural Science Foundation (2016A030313593), Guangzhou Science and Technology Programs for Science Study (201607020030), the National Key R&D Project (2016YFC 0903700), the 973 Key Scheme of China (2015CB553406), Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2014, for WL), Guangdong Province Universities and Colleges Key Grant for Innovative Research (cxzd1142), Guangzhou Department of Education (1201620007, 13C08, and 12A001S), Open Project of State Key Laboratory of Respiratory Disease (SKLRD-OP-201808, SKLRD-QN-201719), Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017BT01S155), and the National Key R&D Program of China (2018YFC1311900). Declaration of Interests: The authors declare: "None." Ethics Approval Statement: The study was approved by the Institutional Review Board of Guangzhou Medical University (Ethics Committee of the First Affiliated Hospital (GZMC2009-08-1336)) and adhered to the Declaration of Helsinki as previously described.

Chinese Abstract: 本文对比了中国与美国股票市场的长期β与α。实证结果显示，从长期来看，美国股市全市场的β在6%-7%之间，中国股市全市场的β在5%左右。相比中国市场，美国股市的β更高，波动率更低。美国股票型共同基金的年化超额收益α在-1%至-2%之间。美国市场高度成熟，早在1945年-1965年期间，股票型共同基金就已经无法在市场上获得超额收益α了。中国市场的年化β为6.53%，同期普通股票型公募基金的年化α为4.78%，偏股混合型公募基金的年化α为2.98%。相比高度成熟的美国市场，机构投资者仍可以在中国市场上获得超额收益。 English Abstract: This paper compares the long-term β and α of the Chinese and US stock markets. The empirical results show that in the long run, the β of the US stock market is between 6% and 7%, and the β of the Chinese stock market is around 5%. Compared with the Chinese market, the US stock market has a higher β and a lower volatility. The annualized excess return α of US equity mutual funds is between -1% and -2%. The US market is highly efficient: as early as 1945-1965, stock mutual funds were unable to obtain excess returns. The annualized β of the Chinese market is 6.53%. In China, the annualized α of stock mutual funds is 4.78%, and the annualized α of blended mutual funds is 2.98%. Institutional investors can still get excess returns in the Chinese market compared to the highly efficient US market.