Global genomic signature reveals the evolution of fall armyworm in the Eastern hemisphere.

The major plant pest fall armyworm (FAW), Spodoptera frugiperda, is native to the Americas and has colonized Africa and Asia within the Eastern hemisphere since 2016, causing severe damage to multiple agricultural crop species. However, the genetic origin of these invasive populations requires more in-depth exploration. We analysed genetic variation across the genomes of 280 FAW individuals from both the Eastern hemisphere and the Americas. The global range-wide genetic structure of FAW shows that the FAW in America has experienced deep differentiation, largely consistent with the Z-chromosomal Tpi haplotypes commonly used to differentiate 'corn-strain' and 'rice-strain' populations. The invasive populations from Africa and Asia are different from the American ones and have a relatively homogeneous population structure, consistent with the common origin and recent spreading from Africa to Asia. Our analyses suggest that north- and central American 'corn-strain' FAW are the most likely sources of the invasion into the Eastern hemisphere. Furthermore, evidence based on genomic, transcriptomic and mitochondrial haplotype network analyses indicates an earlier, independent introduction of FAW into Africa, with subsequent migration into the recent invasive population.

The distribution of covert microbial natural enemies of a globally invasive crop pest, fall armyworm, in Africa: Enemy release and spillover events.

Invasive species pose a significant threat to biodiversity and agriculture world-wide. Natural enemies play an important part in controlling pest populations, yet we understand very little about the presence and prevalence of natural enemies during the early invasion stages. Microbial natural enemies of fall armyworm Spodoptera frugiperda are known in its native region, however, they have not yet been identified in Africa where fall armyworm has been an invasive crop pest since 2016. Larval samples were screened from Malawi, Rwanda, Kenya, Zambia, Sudan and Ghana for the presence of four different microbial natural enemies; two nucleopolyhedroviruses, Spodoptera frugiperda NPV (SfMNPV) and Spodoptera exempta NPV (SpexNPV); the fungal pathogen Metarhizium rileyi; and the bacterium Wolbachia. This study aimed to identify which microbial pathogens are present in invasive fall armyworm, and determine the geographical, meteorological and temporal variables that influence prevalence. Within 3 years of arrival, fall armyworm was exposed to all four microbial natural enemies. SfMNPV probably arrived with fall armyworm from the Americas, but this is the first putative evidence of host spillover from Spodoptera exempta (African armyworm) to fall armyworm for the endemic pathogen SpexNPV and for Wolbachia. It is also the first confirmed incidence of M. rileyi infecting fall armyworm in Africa. Natural enemies were localised, with variation being observed both nationally and temporally. The prevalence of SfMNPV (the most common natural enemy) was predominantly explained by variables associated with the weather; declining with increasing rainfall and increasing with temperature. However, virus prevalence also increased as the growing season progressed. The infection of an invasive species with a natural enemy from its native range and novel pathogens specific to its new range has important consequences for understanding the population ecology of invasive species and insect-pathogen interactions. Additionally, while it is widely known that temporal and geographic factors affect insect populations, this study reveals that these are important in understanding the distribution of microbial natural enemies associated with invasive pests during the early stages of invasion, and provide baseline data for future studies.

The role of national nutrition programs on stunting reduction in Rwanda using machine learning classifiers: a retrospective study.

BACKGROUND: In Rwanda, the prevalence of childhood stunting has slightly decreased over the past five years, from 38% in 2015 to about 33% in 2020. It is evident whether Rwanda's multi-sectorial approach to reducing child stunting is consistent with the available scientific knowledge. The study was to examine the benefits of national nutrition programs on stunting reduction under two years in Rwanda using machine learning classifiers. METHODS: Data from the Rwanda DHS 2015-2020, MEIS and LODA household survey were used. By evaluating the best method for predicting the stunting reduction status of children under two years old, the five machine learning algorithms were modelled: Support Vector Machine, Logistic Regression, K-Near Neighbor, Random Forest, and Decision Tree. The study estimated the hazard ratio for the Cox Proportional Hazard Model and drew the Kaplan-Meier curve to compare the survivor risk of being stunted between program beneficiaries and non-beneficiaries. Logistic regression was used to identify the nutrition programs related to stunting reduction. Precision, recall, F1 score, accuracy, and Area under the Curve (AUC) are the metrics that were used to evaluate each classifier's performance to find the best one. RESULTS: Based on the provided data, the study revealed that the early childhood development (ECD) program (p-value = 0.041), nutrition sensitive direct support (NSDS) program (p-value = 0.03), ubudehe category (p-value = 0.000), toilet facility (p-value = 0.000), antenatal care (ANC) 4 visits (p-value = 0.002), fortified blended food (FBF) program (p-value = 0.038) and vaccination (p-value = 0.04) were found to be significant predictors of stunting reduction among under two children in Rwanda. Additionally, beneficiaries of early childhood development (p < .0001), nutrition sensitive direct support (p = 0.0055), antenatal care (p = 0.0343), Fortified Blended Food (p = 0.0136) and vaccination (p = 0.0355) had a lower risk of stunting than non-beneficiaries. Finally, Random Forest performed better than other classifiers, with precision scores of 83.7%, recall scores of 90.7%, F1 scores of 87.1%, accuracy scores of 83.9%, and AUC scores of 82.4%. CONCLUSION: The early childhood development (ECD) program, receiving the nutrition sensitive direct support (NSDS) program, focusing on households with the lowest wealth quintile (ubudehe category), sanitation facilities, visiting health care providers four times, receiving fortified blended food (FBF), and receiving all necessary vaccines are what determine the stunting reduction under two among the 17 districts of Rwanda. Finally, when compared to other models, Random Forest was shown to be the best machine learning (ML) classifier. Random forest is the best classifier for predicting the stunting reduction status of children under two years old.