Evaluation of Virus-Free Chrysanthemum 'Hangju' Productivity and Response to Virus Reinfection in the Field: Molecular Insights into Virus-Host Interactions.

The shoot apical meristem culture has been used widely to produce virus-free plantlets which have the advantages of strong disease resistance, high yield, and prosperous growth potential. However, this virus-free plant will be naturally reinfected in the field. The physiological and metabolic responses in the reinfected plant are still unknown. The flower of chrysanthemum 'Hangju' is a traditional medicine which is unique to China. In this study, we found that the virus-free 'Hangju' (VFH) was reinfected with chrysanthemum virus B/R in the field. However, the reinfected VFH (RVFH) exhibited an increased yield and medicinal components compared with virus-infected 'Hangju' (VIH). Comparative analysis of transcriptomes was performed to explore the molecular response mechanisms of the RVFH to CVB infection. A total of 6223 differentially expressed genes (DEGs) were identified in the RVFH vs. the VIH. KEGG enrichment and physiological analyses indicated that treatment with the virus-free technology significantly mitigated the plants' lipid and galactose metabolic stress responses in the RVFH. Furthermore, GO enrichment showed that plant viral diseases affected salicylic acid (SA)-related processes in the RVFH. Specifically, we found that phenylalanine ammonia-lyase (PAL) genes played a major role in defense-related SA biosynthesis in 'Hangju'. These findings provided new insights into the molecular mechanisms underlying plant virus-host interactions and have implications for developing strategies to improve plant resistance against viruses.

A Scoping Review of Tools and Techniques on Evaluating Population Health and Healthy Life Expectancy.

The concept of healthy life expectancy (HLE) integrates the ideas of life expectancy and health status, providing a valuable metric to evaluate both the length and quality of life. This paper seeks to aid policymakers in creating an inclusive HLE indicator system through a systematic review of methodologies for defining and measuring HLE, along with relevant published studies' descriptions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement, two English language literature databases were researched from January 2020 to April 2023. Findings from empirical HLE-related studies were analyzed by extracting data on the study area, design, population, healthy state measurement tools, and results of studies using HLE indicators. The current analysis encompassed 48 empirical studies. Researchers discerned 11 unique HLE indicators within this corpus, each concentrating on a particular aspect. Furthermore, the analysis revealed 18 diverse instruments for evaluating health statuses, each varying in its definition of a healthy state, dimensions of measurement, and the categories of data employed. Therefore, merging global health concepts, HLE indicators, methodologies for assessing healthy states, and applied research demonstrations are essential for a consolidated HLE indicator system creation.

Production of Virus-Free Chrysanthemum (Chrysanthemum morifolium Ramat) by Tissue Culture Techniques.

Almost all plants in their natural environment are commonly infected by viruses. These viral infections can cause devastating diseases and result in severe yield and economic losses, making viral diseases an important limiting factor for agricultural production and sustainable development. However, these losses can be effectively reduced through the productions and applications of virus-free plantlets. In vitro culture techniques are the most successful approaches for efficient eradication of various viruses from almost all the most economically important crops. Techniques for producing virus-free plantlets include meristem tip culture, somatic embryogenesis, chemotherapy, thermotherapy, electrotherapy, shoot tip cryotherapy, and micrografting. Among them, meristem tip culture is currently the most widely used. Here, we describe a detailed protocol for producing virus-free plantlets of Chrysanthemum morifolium Ramat using tissue culture techniques.

Optimized Neural Network Based on Genetic Algorithm to Construct Hand-Foot-and-Mouth Disease Prediction and Early-Warning Model.

Accompanied by the rapid economic and social development, there is a phenomenon of the crazy spread of many infectious diseases. It has brought the rapid growth of the number of people infected with hand-foot-and-mouth disease (HFMD), and children, especially infants and young children's health is at great risk. So it is very important to predict the number of HFMD infections and realize the regional early-warning of HFMD based on big data. However, in the current field of infectious diseases, the research on the prevalence of HFMD mainly predicts the number of future cases based on the number of historical cases in various places, and the influence of many related factors that affect the prevalence of HFMD is ignored. The current early-warning research of HFMD mainly uses direct case report, which uses statistical methods in time and space to have early-warnings of outbreaks separately. It leads to a high error rate and low confidence in the early-warning results. This paper uses machine learning methods to establish a HFMD epidemic prediction model and explore constructing a variety of early-warning models. By comparison of experimental results, we finally verify that the HFMD prediction algorithm proposed in this paper has higher accuracy. At the same time, the early-warning algorithm based on the comparison of threshold has good results.

How to Determine the Early Warning Threshold Value of Meteorological Factors on Influenza through Big Data Analysis and Machine Learning.

Infectious diseases are a major health challenge for the worldwide population. Since their rapid spread can cause great distress to the real world, in addition to taking appropriate measures to curb the spread of infectious diseases in the event of an outbreak, proper prediction and early warning before the outbreak of the threat of infectious diseases can provide an important basis for early and reasonable response by the government health sector, reduce morbidity and mortality, and greatly reduce national losses. However, if only traditional medical data is involved, it may be too late or too difficult to implement prediction and early warning of an infectious outbreak. Recently, medical big data has become a research hotspot and has played an increasingly important role in public health, precision medicine, and disease prediction. In this paper, we focus on exploring a prediction and early warning method for influenza with the help of medical big data. It is well known that meteorological conditions have an influence on influenza outbreaks. So, we try to find a way to determine the early warning threshold value of influenza outbreaks through big data analysis concerning meteorological factors. Results show that, based on analysis of meteorological conditions combined with influenza outbreak history data, the early warning threshold of influenza outbreaks could be established with reasonable high accuracy.

The Prediction of Hepatitis E through Ensemble Learning.

According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This paper presents an ensemble learning model for Hepatitis E prediction by studying the correlation between historical epidemic cases of hepatitis E and environmental factors (water quality and meteorological data). Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random Forest for training and prediction. Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical time series prediction model. It is concluded that the ensemble learning model has a better prediction effect than the classical model, and the prediction effectiveness can be improved by exploiting water quality and meteorological factors (radiation, air pressure, precipitation).