The Relationship Between Investor Sentiment and Stock Market Volatility: Based on the VAR Model

Using web crawling technology crawls investors’ comments of SANY stock(Stock Code: 600031) and Fujian Expressway stock(Stock Code: 600033) from February 11, 2015 to August 16, 2017. Then using semi-supervised machine learning method construct investor sentiment index. Moreover, collecting the daily closing stock price and trading volume data from Qianlong software explore the relationship between investor sentiment and stock market volatility based on VAR model and Granger Test Method. The results show that the rate of return and trading volume have a two-way Granger causality, while negative emotion and the rate of return have a one-way Granger causality. Furthermore, with the impulse response function and variance decomposition, the results show that trading volume has significant effects on rate of return and negative emotions of investors have significant negative effects on rate of return and trading volume

Super resolution dual-layer CBCT imaging with model-guided deep learning

Objective: This study aims at investigating a novel super resolution CBCT imaging technique with the dual-layer flat panel detector (DL-FPD). Approach: In DL-FPD based CBCT imaging, the low-energy and high-energy projections acquired from the top and bottom detector layers contain intrinsically mismatched spatial information, from which super resolution CBCT images can be generated. To explain, a simple mathematical model is established according to the signal formation procedure in DL-FPD. Next, a dedicated recurrent neural network (RNN), named as suRi-Net, is designed by referring to the above imaging model to retrieve the high resolution dual-energy information. Different phantom experiments are conducted to validate the performance of this newly developed super resolution CBCT imaging method. Main Results: Results show that the proposed suRi-Net can retrieve high spatial resolution information accurately from the low-energy and high-energy projections having lower spatial resolution. Quantitatively, the spatial resolution of the reconstructed CBCT images of the top and bottom detector layers is increased by about 45% and 54%, respectively. Significance: In future, suRi-Net provides a new approach to achieve high spatial resolution dual-energy imaging in DL-FPD based CBCT systems

Analysis of 142 genes resolves the rapid diversification of the rice genus

The relationships among all diploid genome types of the rice genus were clarified using 142 single-copy gene

Evaluation of human enterovirus 71 and coxsackievirus A16 specific immunoglobulin M antibodies for diagnosis of hand-foot-and-mouth disease

<p>Abstract</p> <p>Background</p> <p>Hand-foot-and-mouth disease (HFMD) is caused mainly by the human enterovirus type 71 (HEV71) and the Coxsackievirus A group type 16 (CVA16). Large outbreaks of disease have occurred frequently in the Asia-Pacific region. Reliable methods are needed for diagnosis of HFMD in childen. IgM-capture ELISA, with its notable advantages of convenience and low cost, provides a potentially frontline assay. We aimed to evaluate the newly developed IgM-capture ELISAs for HEV71 and CVA16 in the diagnosis of HFMD, and to measure the kinetics of IgM over the course of HEV71 or CVA16 infections.</p> <p>Results</p> <p>We mapped, for the first time, the kinetics of IgM in HEV71 and CVA16 infection. HEV71- and CVA16-IgM were both detectable in some patients on day 1 of illness, and in 100% of patients by day 5 (HEV71) and day 8 (CVA16) respectively; both IgMs persisted for several weeks. The IgM detection rates were 90.2% (138 of 153 sera) and 68.0% (66 of 97 sera) for HEV71 and CVA16 infections, respectively, during the first 7 days of diseases. During the first 90 days after onset these values were 93.6% (233 of 249 sera) and 72.8% (91 of 125 sera) for HEV71 and CVA16 infections, respectively. Some cross-reactivity was observed between HEV71- and CVA16-IgM ELISAs. HEV71-IgM was positive in 38 of 122 (31.1%) CVA16 infections, 14 of 49 (28.6%) other enteroviral infections and 2 of 105 (1.9%) for other respiratory virus infected sera. Similarly, CVA16-IgM was apparently positive in 58 of 211 (27.5%) HEV71 infections, 16 of 48 (33.3%) other enterovirus infections and 3 of 105 (2.9%) other respiratory virus infected sera. Nevertheless, the ELISA yielded the higher OD₄₅₀value of main antibody than that of cross-reaction antibody, successfully identifying the enteroviral infection in 96.6% (HEV71) and 91.7% (CVA16) cases. When blood and rectal swabs were collected on the same day, the data showed that the agreement between IgM-capture ELISA and real-time RT-PCR in HEV71 was high (Kappa value = 0.729) while CVA16 somewhat lower (Kappa value = 0.300).</p> <p>Conclusions</p> <p>HEV71- and CVA16-IgM ELISAs can be deployed successfully as a convenient and cost-effective diagnostic tool for HFMD in clinical laboratories.</p

Noninvasive Submillimeter-Precision Brain Stimulation by Optically-Driven Focused Ultrasound

High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological diseases. Current non-invasive neuromodulation methods offer limited millimeter-level precision. Here, we report an optically-driven focused ultrasound (OFUS) for non-invasive brain stimulation with submillimeter precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane film. SOAP generates a transcranial ultrasound focus at 15 MHz with a lateral resolution of 83 micrometers, which is two orders of magnitude smaller than that of conventional transcranial focused ultrasound (tFUS). Effective OFUS neurostimulation in vitro with a single ultrasound cycle is shown. Submillimeter transcranial stimulation of mouse motor cortex in vivo is demonstrated. An acoustic energy of 0.02 J/cm^2, two orders of magnitude less than that of tFUS, is sufficient for successful OFUS neurostimulation. By delivering a submillimeter focus non-invasively, OFUS opens a new way for neuroscience studies and disease treatments.Comment: 36 pages, 5 main figures, 13 supplementary figure