The reduced cost and improved efficiency of whole genome sequencing (WGS) is drastically improving the development of cats as biomedical models. Persian cats are models for Leber’s congenital amaurosis (LCA), the most severe and earliest onset form of visual impairment in humans. Cats with innocuous breed-defining traits, such as a bobbed tail, can also be models for somite segmentation and vertebral column development. The first WGS in cats was conducted on a trio segregating for LCA and the bobbed tail abnormality. Variants were identified using FreeBayes and effects predicted using SnpEff. Variants within a known haplotype block for cat LCA and specific candidate genes for both phenotypes were prioritized by the predicted variant effect on the proteins and concordant segregation within the trio. The efficiency of WGS of a single trio of domestic cats was evaluated. A stop gain was identified at position c.577C > T in cat AIPL1, a predicted p.Arg193*. A c.5A > G variant causing a p.V2A was identified in HES7. The variants segregated concordantly in a Persian – Japanese bobtail pedigree. Over 1700 cats from 40 different breeds and populations were genotyped for the AIPL1 variant, defining an allelic frequency in only Persian –related breeds of 1.15 %. A sub-set of cats was genotyped for the HES7 variant, supporting the variant as private to the Japanese bobtail breed. Approximately 18 million SNPs were identified for application in cat research. The cat AIPL1 variant would have been considered a high priority variant for evaluation, regardless of a priori knowledge from previous genetic studies. This study represents the first effort of the 99 Lives Cat Genome Sequencing Initiative to identify disease - causing variants in the domestic cat using WGS. The current cat reference assembly is efficient for gene and variant identification. However, as the feline variant database improves, development of cats as biomedical models for human disease will be more efficient, providing an alternative, large animal model for drug and gene therapy trials. Undiagnosed human patients with early-onset blindness should be screened for this AIPL1 variant. The HES7 variant should further calibrate the somite segmentation clock.

Cat domestication likely initiated as a symbiotic relationship between wildcats (Felis silvestris subspecies) and the peoples of developing agrarian societies in the Fertile Crescent. As humans transitioned from hunter-gatherers to farmers ~12,000 years ago, bold wildcats likely capitalized on increased prey density (i.e., rodents). Humans benefited from the cats’ predation on these vermin. To refine the site(s) of cat domestication, over 1000 random-bred cats of primarily Eurasian descent were genotyped for single-nucleotide variants and short tandem repeats. The overall cat population structure suggested a single worldwide population with significant isolation by the distance of peripheral subpopulations. The cat population heterozygosity decreased as genetic distance from the proposed cat progenitor’s (F.s. lybica) natural habitat increased. Domestic cat origins are focused in the eastern Mediterranean Basin, spreading to nearby islands, and southernly via the Levantine coast into the Nile Valley. Cat population diversity supports the migration patterns of humans and other symbiotic species.

Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

It is believed that the huge amount of information delivered to the consumers through mass media, including television and social networks, may affect consumers’ behavior. The purpose of this study was to forecast the amount required to purchase pork belly meat by using unstructured data such as broadcast news, TV programs/shows and social network as well as structured data such as consumer panel data, retail and wholesale prices and production outputs in order to prove that mass media data release can occur ahead of actual economic activities and consumer behavior can be predicted by using these data. By using structured and unstructured data from 2010 to 2016 and five forecasting algorithms (autoregressive exogenous model and vector error correction model for time series, gradient boosting and random forest for machine learning, and long short-term memory for recurrent neural network), the amounts required to purchase pork belly meat in 2017 were forecasted and compared with the actual amounts to validate model accuracy. Our findings suggest that when unstructured data were combined with structured data, the forecast pattern is improved. To date, our study is the first report that forecasts the demand of pork meat by using structured and unstructured data.

More than 110,000 companion animals are sent to shelters each year due to abandonment in Republic of Korea, and there is a need to analyze the causes of the relinquishment of animals and implement appropriate policies. Veterinary costs have been blamed for this issue in Republic of Korea above the reported leading causes of socioeconomic status of owners, cost and behavior issues of the animals, or housing restrictions. However, it is rare to find supporting evidence. In this study, we aimed to determine whether veterinary costs and socioeconomic factors are related to animal relinquishment in Republic of Korea. Multiple regression models were used to test if veterinary costs and socioeconomic indicators can account for relinquishment in 128 regions of Republic of Korea in 2020 and 2021. When five independent variables (two veterinary cost data and three socioeconomic indicators) were included, the regression model showed significance in explaining pet relinquishment in 2020, with an adjusted coefficient of determination of 0.3956. Pet relinquishment can also be explained by the same five variables for 2021, with an adjusted coefficient of determination of 0.391 with p < 0.0001. The findings suggest that intervention to reduce companion animal relinquishment in Republic of Korea should focus on lightening the financial burdens of owners as the socioeconomic status of a community worsens.

Acute coronary syndrome (ACS) has been one of the most important issues in global public health. The high recurrence risk of patients with coronary heart disease (CHD) has led to the importance of post-discharge care and secondary prevention of CHD. Previous studies provided binary results of ACS recurrence risk; however, studies providing the recurrence risk of an individual patient are rare. In this study, we conducted a model which provides the recurrence risk probability for each patient, along with the binary result, with two datasets from the Korea Health Insurance Review and Assessment Service and Chungbuk National University Hospital. The total data of 6,535 patients who had been diagnosed with ACS were used to build a machine learning model by using logistic regression. Data including age, gender, procedure codes, procedure reason, prescription drug codes, and condition codes were used as the model predictors. The model performance showed 0.893, 0.894, 0.851, 0.869, and 0.921 for accuracy, precision, recall, F1-score, and AUC, respectively. Our model provides the ACS recurrence probability of each patient as a personalized ACS recurrence risk, which may help motivate the patient to reduce their own ACS recurrence risk. The model also shows that acute transmural myocardial infarction of an unspecified site, and other sites and acute transmural myocardial infarction of an unspecified site contributed most significantly to ACS recurrence with an odds ratio of 97.908 as a procedure reason code and with an odds ratio of 58.215 as a condition code, respectively.