This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices of the Unites States (US) stock market over the period from 21st of January 2020 to 20th of May 2020. While extensive research on sentiment analysis for predicting stock market movement use tweeter data, not much has used DNSI or Google Trends data. In addition, this study examines whether changes in DNSI predict US industry returns differently by estimating the time series regression model with excess returns of industry as the dependent variable. The excess returns are obtained from the Fama-French three factor model. The results of this study offer a comprehensive view of the initial impact of COVID-19 sentiment on the US stock market by industry and furthermore suggests the strategic investment planning considering the time lag perspectives by visualizing changes in the correlation level by time lag differences.

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The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real estate auction prices using artificial intelligence and statistical methodologies. The forecasting models are developed through a regression model, an artificial neural network and a genetic algorithm. For empirical analysis, we use Seoul apartment auction data from 2013 to 2017 to predict the auction prices and compare the forecasting accuracy of the models. The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy.

Clostridium perfringens produces diverse virulent toxins that cause necrotic enteritis in poultry, resulting in a great negative impact on the poultry industry. To study the characteristics of C. perfringens in chickens, we isolated 88 strains from chickens (1 strain per flock) with necrotic enteritis. The isolated bacterial strains were screened for toxin type and antimicrobial susceptibility. Necropsy of 17 chickens that died from necrotic enteritis revealed that their intestines were dilated with inflammatory exudates and characterized by mucosal necrosis. All the isolated strains were identified as toxin type A using multiplex PCR for toxin typing. We found that the rate of netB-positive strains isolated from dead chickens was significantly higher (8 of 17) than the rate among healthy chickens (2 of 50). We performed antimicrobial susceptibility test with 20 selected antimicrobial agents using the disk diffusion test and found that 30 tested strains were completely resistant to 5 antibiotics and partially resistant to 6 antibiotics whereas all the strains were susceptible to 9 antimicrobial agents. Using pulsed-field gel electrophoresis analysis, the 17 strains were divided into 13 genetic clusters showing high genetic diversity. In conclusion, C. perfringens strains isolated from Korean poultry showed a high resistance to antimicrobial drugs and high genetic diversity, suggesting that continuous monitoring is essential to prevent outbreaks of necrotic enteritis in chickens.

Salmonella (S.) Typhimurium and S. Enteritidis are the major causative agents of food-borne illnesses worldwide. Currently, a rapid detection system using multiplex real-time polymerase chain reaction (PCR) has been applied for other food-borne pathogens such as Escherichia coli, Staphylococcus aureus and Streptococcus spp. A multiplex real-time PCR was developed for the simultaneous detection of Salmonella spp., especially S. Typhimurium and S. Enteritidis, in beef and pork. For the specific and sensitive multiplex real-time PCR, three representative primers and probes were designed based on sequence data from Genbank. Among the three DNA extraction methods (boiling, alkaline lysis, and QIAamp DNA Mini Kit), the QIAamp DNA Mini Kit was the most sensitive in this study. The optimized multiplex real-time PCR was applied to artificially inoculated beef or pork. The detection sensitivity of the multiplex real-time PCR was increased. The specificity of the multiplex real-time PCR assay, using 128 pure-cultured bacteria including 110 Salmonella isolates and 18 non-Salmonella isolates, was 100%, 100% and 99.1% for Salmonella spp., S. Typhimurium and S. Enteritidis, respectively. The sensitivity was 100%, 100% and 91.7% for Salmonella spp., S. Typhimurium and S. Enteritidis, respectively. The multiplex real-time PCR assay developed in this study could detect up to 0.54 ± 0.09 and 0.65 ± 0.07 log10 CFU/ml for S. Typhimurium and S. Enteritidis for beef, 1.45 ± 0.21 and 1.65 ± 0.07 log10 CFU/ml for S. Typhimurium and S. Enteritidis for pork, respectively, with all conditions optimized. Our results indicated that the multiplex real-time PCR assay developed in this study could sensitively detect Salmonella spp. and specifically differentiate S. Typhimurium from S. Enteritidis in meats.