ABSTRACT
Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection's country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.
Subject(s)
Malaria, Vivax , Malaria , Humans , Malaria, Vivax/diagnosis , Malaria, Vivax/genetics , Likelihood Functions , Plasmodium vivax/genetics , InternetABSTRACT
We suggest a simple approach to calculate the local density of states that effectively applies to any structure created by an axially symmetric potential on a continuous graphene sheet such as circular graphene quantum dots or rings. Calculations performed for the graphene quantum dot studied in a recent scanning tunneling microscopy measurement (Gutierrez et al 2016 Nat. Phys. 12 1069-75) show an excellent experimental-theoretical agreement.
ABSTRACT
The standard T-matrix method can be effectively used for studying the dynamics of Dirac electrons under one-dimensional potentials in graphene. The transmission probability expressed in terms of T-matrices and the corresponding ballistic current are derived for any smooth one-dimensional potential, taking into account the chirality of Dirac massless carriers. Numerical calculations are illustrated for the potential approximately describing graphene n-p junctions.
