Logistic Models Derived via LASSO Methods for Quantifying the Risk of Natural Contamination of Maize Grain with Deoxynivalenol

Weather-Based Logistic Regression Models for Predicting Wheat Head Blast Epidemics

Wheat head blast is a major disease of wheat in the Brazilian Cerrado. Empirical models for predicting epidemics were developed using data from field trials conducted in Patos de Minas (2013 to 2019) and trials conducted across 10 other sites (2012 to 2020) in Brazil, resulting in 143 epidemics, with each being classified as either outbreak (≥20% head blast incidence) or nonoutbreak. Daily weather variables were collected from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) website and summarized for each epidemic. Wheat heading date (WHD) served to define four time windows, with each comprising two 7-day intervals (before and after WHD), which combined with weather-based variables resulted in 36 predictors (nine weather variables × four windows). Logistic regression models were fitted to binary data, with variable selection using least absolute shrinkage and selection operator (LASSO) and sequentially best subset analyses. The models were validated using the leave-one-out cross-validation (LOOCV) technique, and their statistical performance was compared. One model was selected, implemented in a 24-year series, and assessed by experts and literature. Models with two to five predictors showed accuracies between 0.80 and 0.85, sensitivities from 0.80 to 0.91, specificities from 0.72 to 0.86, and area under the curve (AUC) from 0.89 to 0.91. The accuracy of LOOCV ranged from 0.76 to 0.81. The model applied to a historical series included temperature and relative humidity in preheading date, as well as postheading precipitation. The model accurately predicted the occurrence of outbreaks, aligning closely with real-world observations, specifically tailored for locations with tropical and subtropical climates.

Screening Potential Diagnostic Biomarkers for Age-Related Sarcopenia in the Elderly Population by WGCNA and LASSO

Background

Sarcopenia is a common chronic disease characterized by age-related decline in skeletal muscle mass and function, and the lack of diagnostic biomarkers makes community-based screening problematic.

Methods

Three gene expression profiles related with sarcopenia were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and eigengenes of a module in the merged dataset were identified by differential expression analysis and weighted gene coexpression network analysis (WGCNA), and common genes (CGs) were defined as the intersection of DEGs and eigengenes of a module. CGs were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) analysis was performed to screen the CGs for identifying the diagnostic biomarkers of sarcopenia. Based on the diagnostic biomarkers, we established a novel nomogram model of sarcopenia. At last, we validated the diagnostic biomarkers and evaluated the diagnostic performance of the nomogram model by the area under curve (AUC) value.

Results

We screened out 107 DEGs and 788 eigengenes in the turquoise module, and 72 genes were selected as CGs of sarcopenia by intersection. GO analysis showed that CGs were mainly involved in metal ion detoxification and mitochondrial structure, and KEGG analysis revealed that CGs were mainly enriched in the mineral absorption, glucagon signaling pathway, FoxO signaling pathway, insulin signaling pathway, AMPK signaling pathway, and estrogen signaling pathway. Then, six diagnostic biomarkers (ARHGAP36, FAM171A1, GPCPD1, MT1X, ZNF415, and RXRG) were identified by LASSO analysis. Finally, the validation AUC values indicated that the six diagnostic biomarkers had high diagnostic accuracy for sarcopenia.

Conclusion

We identified six diagnostic biomarkers with high diagnostic performance, providing new insights into the incidence and progression of sarcopenia in future research.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

Presence and Correlation of Fusarium graminearum and Deoxynivalenol Accumulation in Silage Corn Plant Parts

Corn silage, made from Zea mays, is a high-energy feed that is important for feeding dairy cows. Plant diseases, such as those caused by Fusarium graminearum, can decrease silage corn yields and quality. F. graminearum (teleomorph Gibberella zeae) is an ascomycete fungus that causes Gibberella ear and stalk rot in corn. F. graminearum produces deoxynivalenol (DON), a secondary metabolite toxic to humans and animals. An understanding of the distribution of DON and F. graminearum throughout the corn plant is important for determining the quality of corn silage. A partitioned sample experiment that included two brown midrib silage hybrids and three fungicide treatments was conducted in research plots located in Arlington, WI, U.S.A., in 2018 and 2019. At harvest, stalk and ear parts were physically separated, dried, and ground for analysis. DON concentration (in parts per million) was determined using an enzyme-linked immunosorbent assay, and F. graminearum DNA concentration (in picograms per nanogram) was determined using quantitative PCR. DON and F. graminearum DNA were detected in all samples, demonstrating accumulation of the fungus in stalks and ears of the plant. In 2018, DON contamination was as high as 30 ppm and varied drastically between stalks and ears. In 2019, DON concentrations were much lower (<5 ppm), but were consistently higher in stalk samples than ear samples. Across all samples, DON concentrations and F. graminearum accumulation were highly correlated within the separated stalk (r = 0.78) and ear portions (r = 0.87) but were not correlated between ears and stalks. Depending on the weather and planting conditions in a given year, stalk infections or ear infections may occur by F. graminearum, leading to subsequent DON increases in those respective parts that are independent of each other.