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Rice yellow mottle virus (RYMV) is a major pathogen of rice in Africa. RYMV has a narrow host range limited to rice and a few related poaceae species. We explore the links between the spread of RYMV in East Africa and rice history since the second half of the 19 th century. The phylogeography of RYMV in East Africa was reconstructed from coat protein gene sequences (ORF4) of 335 isolates sampled over two million square kilometers between 1966 and 2020. Dispersal patterns obtained from ORF2a and ORF2b, and full-length sequences converged to the same scenario. The following imprints of rice cultivation on RYMV epidemiology were unveiled. RYMV emerged in the middle of the 19 th century in the Eastern Arc Mountains where slash-and-burn rice cultivation was practiced. Several spillovers from wild hosts to cultivated rice occurred. RYMV was then rapidly introduced into the nearby large rice growing Kilombero valley and Morogoro region. Harvested seeds are contaminated by debris of virus infected plants that subsist after threshing and winnowing. Long-distance dispersal of RYMV is consistent (i) with rice introduction along the caravan routes from the Indian Ocean Coast to Lake Victoria in the second half of the 19 th century, (ii) seed movement from East Africa to West Africa at the end of the 19 th century, from Lake Victoria to the north of Ethiopia in the second half of the 20 th century and to Madagascar at the end of the 20 th century, (iii) and, unexpectedly, with rice transport at the end of the First World War as a troop staple food from the Kilombero valley towards the South of Lake Malawi. Overall, RYMV dispersal was associated to a broad range of human activities, some unsuspected. Consequently, RYMV has a wide dispersal capacity. Its dispersal metrics estimated from phylogeographic reconstructions are similar to those of highly mobile zoonotic viruses.

This study assessed the relative influence of agroecological zones (AEZs) and altitude on soil nutrient dynamics to guide site‑specific management. Soil samples from 20 marshlands across seven AEZs, spanning 944 to 1900 m a.s.l., were collected from 0 – 30 cm and analysed for pH, exchangeable bases (Ca, Mg, K, and Na), organic carbon (OC), total nitrogen (TN), available phosphorus (P), and texture using ANOVA, Welch’s tests, and PCA. Fertility varied markedly among AEZs: the Imbo zone exhibited near‑neutral pH and higher base cations, while and the were acidic and nutrient‑poor. Low‑elevation sites (944 – 1044 m a.s.l.) had higher pH (6.32) and base cations, whereas high‑altitude soils (>1800 m a.s.l.) were acidic (pH 4.92) and depleted. Effect size analysis confirmed altitude’s stronger explanatory power for Ca ( = 0.53 vs. = 0.49), Mg ( = 0.54 vs. = 0.37), K ( = 0.47 vs. = 0.29) and pH ( = 0.45 vs. = 0.33), while AEZs better explained TN ( = 0.27 vs. = 0.09), though altitude produced a notable mid‑elevation TN peak (1345 – 1544 m a.s.l.), and OC ( = 0.31 vs. = 0.10). Integrating altitude with AEZ‑based frameworks and applying organic amendments in high‑altitude acidic zones can improve nutrient use efficiency, rice yield, and sustainable marshland productivity in Rwanda.

ABSTRACTThe complete sequence of the isolate Mw10 of Rice yellow mottle virus was determined. Sequence comparisons revealed 8.4% to 10.7% nucleotide divergence from the published sequences, resulting in the definition of the strain S7. Importantly, a putative recombination event was identified encompassing the viral genome-linked protein involved in host adaptation.

Rice yellow mottle virus (RYMV) is a major pathogen of rice in Africa. RYMV has a narrow host range limited to rice and a few related poaceae species. We explore the links between the spread of RYMV in East Africa and rice history since the second half of the 19th century. The phylogeography of RYMV in East Africa was reconstructed from coat protein gene sequences (ORF4) of 335 isolates sampled over two million square kilometers between 1966 and 2020. Dispersal patterns obtained from ORF2a and ORF2b, and full-length sequences converged to the same scenario. The following imprints of rice cultivation on RYMV epidemiology were unveiled. RYMV emerged in the middle of the 19th century in the Eastern Arc Mountains where slash-and-burn rice cultivation was practiced. Several spillovers from wild hosts to cultivated rice occurred. RYMV was then rapidly introduced into the adjacent large rice growing Kilombero valley. Harvested seeds are contaminated by debris of virus infected plants that subsist after threshing and winnowing. Long-distance dispersal of RYMV is consistent (i) with rice introduction along the caravan routes from the Indian Ocean Coast to Lake Victoria in the second half of the 19th century, (ii) seed movement from East Africa to West Africa at the end of the 19th century, from Lake Victoria to the north of Ethiopia in the second half of the 20th century and to Madagascar at the end of the 20th century, (iii) and, unexpectedly, with rice transport at the end of the First World War as a troop staple food from the Kilombero valley towards the South of Lake Malawi. Overall, RYMV dispersal was associated to a broad range of human activities, some unsuspected. Consequently, RYMV has a wide dispersal capacity, its dispersal metrics estimated from phylogeographic reconstructions are similar to those of highly mobile zoonotic viruses. Rice yellow mottle virus (RYMV) poses a major threat to rice production in Africa. We explored through a multidisciplinary approach the links between the history of rice in East Africa since the second half of the 19th century and the spread of RYMV. The results illuminate the causes of RYMV diffusion. We show the role of long-distance caravan trade, the impact of the First World War and the consequences of seed exchange in the dispersal of RYMV. The paradoxical role of seeds in the spread of RYMV - which is vector transmitted and not seed transmitted – is explained in the light of rice biology and agronomy. Overall, this study reveals the wide range of transmission ways, some unexpected, in the dispersal of plant viruses. It also highlights the role of human transmission of pathogens, even vector-borne, and sheds light on the risk of transmission of RYMV and of other plant viruses from Africa to other continents.

Background Despite substantial progress in maternal and neonatal health, Rwanda’s mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electronic Medical Records, this study explores the application of machine learning models to predict adverse pregnancy outcomes, thereby improving risk assessment and enhancing care delivery. Methods This study utilized retrospective cohort data from the electronic medical record (EMR) system of 25 hospitals in Rwanda from 2020 to 2023. The independent variables included socioeconomic status, health status, reproductive health, and pregnancy-related factors. The outcome variable was a binary composite feature that combined adverse pregnancy outcomes in both the mother and the newborn. Extensive data cleaning was performed, with missing values addressed through various strategies, including the exclusion of variables and instances, imputation techniques using K-Nearest Neighbors and Multiple Imputation by Chained Equations. Data imbalance was managed using a synthetic minority oversampling technique. Six machine learning models—Logistic Regression, Decision Trees, Support Vector Machine, Gradient Boosting, Random Forest, and Multilayer Perceptron—were trained using 10-fold cross-validation and evaluated on an unseen dataset with–70 − 30 training and evaluation splits. Results Data from 117,069 women across 25 hospitals in Rwanda were analyzed, leading to a final dataset of 32,783 women after removing entries with significant missing values. Among these women, 5,424 (16.5%) experienced adverse pregnancy outcomes. Random Forest and Gradient Boosting Classifiers demonstrated high accuracy and precision. After hyperparameter tuning, the Random Forest model achieved an accuracy of 90.6% and an ROC-AUC score of 0.85, underscoring its effectiveness in predicting adverse outcomes. However, a recall rate of 46.5% suggests challenges in detecting all the adverse cases. Key predictors of adverse outcomes identified in this study included gestational age, number of pregnancies, antenatal care visits, maternal age, vital signs, and delivery methods. Conclusions This study recommends enhancing EMR data quality, integrating machine learning into routine practice, and conducting further research to refine predictive models and address evolving pregnancy outcomes. In addition, this study recommends the design of AI-based interventions for high-risk pregnancies. Clinical trial number Not applicable.