Emergence and Rising Prevalence of Artemisinin Partial Resistance Marker Kelch13 P441L in a Low Malaria Transmission Setting in Southern Zambia.

Increasing artemisinin partial resistance (ArtR) due to mutations in the gene encoding Kelch13 () protein in eastern Africa is of urgent concern, and mutations, such as P441L, continue to emerge. We used an amplicon deep-sequencing panel to estimate the prevalence of ArtR mutations in samples collected between 2018 and 2023 in southern Zambia. P441L was present in 30 of 501 samples (6%), and prevalence increased over time (0% to 7.

Application of a new highly multiplexed amplicon sequencing tool to evaluate antimalarial resistance and relatedness in individual and pooled samples from Dschang, Cameroon.

Background: Resistance to antimalarial drugs remains a major obstacle to malaria elimination. Multiplexed, targeted amplicon sequencing is being adopted for surveilling resistance and dissecting the genetics of complex malaria infections. Moreover, genotyping of parasites and detection of molecular markers drug resistance in resource-limited regions requires open-source protocols for processing samples, using accessible reagents, and rapid methods for processing numerous samples including pooled sequencing.

Mapping multi-dimensional variability in water stress strategies across temperate forests.

Increasing water stress is emerging as a global phenomenon, and is anticipated to have a marked impact on forest function. The role of tree functional strategies is pivotal in regulating forest fitness and their ability to cope with water stress. However, how the functional strategies found at the tree or species level scale up to characterise forest communities and their variation across regions is not yet well-established.

Predictive Understanding of Stream Salinization in a Developed Watershed Using Machine Learning.

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed. We compare the predictive performance of space- and time-unaware Random Forest models and space- and time-aware Recurrent Graph Convolution Neural Network models (KGE: 0.