BackgroundWhile the utility of parasite genotyping for malaria elimination has been extensively documented in low to moderate transmission settings, it has been less well-characterized in holoendemic regions. High malaria burden settings have received renewed attention acknowledging their critical role in malaria elimination. Defining the role for parasite genomics in driving these high burden settings towards elimination will enhance future control programme planning.MethodsAmplicon deep sequencing was used to characterize parasite population genetic diversity at polymorphic Plasmodium falciparum loci, Pfama1 and Pfcsp, at two timepoints in June–July 2016 and January–March 2017 in a high transmission region along the international border between Luapula Province, Zambia and Haut-Katanga Province, the Democratic Republic of the Congo (DRC).ResultsHigh genetic diversity was observed across both seasons and in both countries. No evidence of population structure was observed between parasite populations on either side of the border, suggesting that this region may be one contiguous transmission zone. Despite a decline in parasite prevalence at the sampling locations in Haut-Katanga Province, no genetic signatures of a population bottleneck were detected, suggesting that larger declines in transmission may be required to reduce parasite genetic diversity. Analysing rare variants may be a suitable alternative approach for detecting epidemiologically important genetic signatures in highly diverse populations; however, the challenge is distinguishing true signals from potential artifacts introduced by small sample sizes.ConclusionsContinuing to explore and document the utility of various parasite genotyping approaches for understanding malaria transmission in holoendemic settings will be valuable to future control and elimination programmes, empowering evidence-based selection of tools and methods to address pertinent questions, thus enabling more efficient resource allocation.

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We show that the higher rank lamplighter groups, or Diestel-Leader groups Γ d (q) for d ≥ 3, are graph automatic. As these are not automatic groups, this introduces a new family of graph automatic groups which are not automatic.

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within‐sample ranks of protein levels can mitigate the impact of between‐sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.