Late blight caused by Phytophthora infestans greatly constrains potato production. Many Resistance (R) genes were cloned from wild Solanum species and/or introduced into potato cultivars by breeding. However, individual R genes have been overcome by P. infestans evolution; durable resistance remains elusive. We positionally cloned a new R gene, Rpi-amr1, from Solanum americanum , that encodes an NRC helper-dependent CC-NLR protein. Rpi-amr1 confers resistance in potato to all 19 P. infestans isolates tested. Using association genomics and long-read RenSeq, we defined eight additional Rpi-amr1 alleles from different S. americanum and related species.Despite only ~90% identity between Rpi-amr1 proteins, all confer late blight resistance but differentially recognize Avramr1 orthologs and paralogs. We propose that Rpi-amr1 gene family diversity assists detection of diverse paralogs and alleles of the recognized effector, facilitating durable resistance against P. infestans .

Visceral leishmaniasis (VL), one of the deadliest parasitic diseases in the world, causes more than 50,000 human deaths each year and afflicts millions of people throughout South America, East Africa, South Asia, and Mediterranean Region. In 2015 the World Health Organization classified VL as a neglected tropical disease (NTD), prompting concentrated study of the VL epidemic using mathematical and simulation models. This paper reviews literature related to prevalence and prevention control strategies. More than thirty current research works were reviewed and classified based on VL epidemic study methods, including modeling approaches, control strategies, and simulation techniques since 2013. A summarization of these technical methods, major findings, and contributions from existing works revealed that VL epidemic research efforts must improve in the areas of validating and verifying VL mathematical models with real-world epidemic data. In addition, more dynamic disease control strategies must be explored and advanced simulation techniques must be used to predict VL pandemics.