Machine learning approach to support taxonomic species discrimination based on helminth collections data.

BACKGROUND: There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Eggs are the parasitic structures most viewed in coprological analysis in both modern and ancient samples; consequently, their presence is indicative of positive diagnosis for infection. The structure of the egg could play a role in genera or species discrimination. Institutional biological collections are taxonomic repositories of specimens described and strictly identified by systematics specialists. METHODS: The present work aims to characterize eggs of capillariid species deposited in institutional helminth collections and to process the morphological, morphometric and ecological data using machine learning (ML) as a new approach for taxonomic identification. Specimens of 28 species and 8 genera deposited at Coleção Helmintológica do Instituto Oswaldo Cruz (CHIOC, IOC/FIOCRUZ/Brazil) and Collection de Nématodes Zooparasites du Muséum National d'Histoire Naturelle de Paris (MNHN/France) were examined under light microscopy. In the morphological and morphometric analyses (MM), the total length and width of eggs as well as plugs and shell thickness were considered. In addition, eggshell ornamentations and ecological parameters of the geographical location (GL) and host (H) were included. RESULTS: The performance of the logistic model tree (LMT) algorithm showed the highest values in all metrics compared with the other algorithms. Algorithm J48 produced the most reliable decision tree for species identification alongside REPTree. The Majority Voting algorithm showed high metric values, but the combined classifiers did not attenuate the errors revealed in each algorithm alone. The statistical evaluation of the dataset indicated a significant difference between trees, with GL + H + MM and MM only with the best scores. CONCLUSIONS: The present research proposed a novel procedure for taxonomic species identification, integrating data from centenary biological collections and the logic of artificial intelligence techniques. This study will support future research on taxonomic identification and diagnosis of both modern and archaeological capillariids.

A new species of Litomosoides (Nematoda: Onchocercidae), parasite of Nectomys palmipes (Rodentia: Cricetidae: Sigmodontinae) from Venezuela: description, molecular evidence, Wolbachia pipientis screening.

Abstract: The onchocercid filaria Litomosoides taylori sp. n. is described from the sigmodontine cricetid Nectomys palmipes Allen et Chapman in northeast Venezuela. A voucher specimen of the new species was used for molecular analysis of the coxI and 12S rDNA genes, and screened for the presence of the endobacterium Wolbachia pipientis. Litomosoides taylori belongs to the "sigmodontis group" of Litomosoides and a combination of characters can be used to distinguish it from the remaining 18 species forming this group. Among the five Nectomys species, all living near running water, N. squamipes also harbours Litomosoides species, L. khonae in Brazil and L. navonae in Argentina. These three Litomosoides species of the "sigmodontis group" do not share any particular characters. Gene sequences of L. taylori differ from those of the five Litomosoides species available, the three of the "carinii group" being the most distant. The new species harbours W pipientis, which is concurrent with the great majority of Litomosoides species screened to date.