Expression of Cysteine Proteinases by Metacyclic Promastigotes of Leishmania mexicana

ABSTRACT. The expression of cysteine proteinases by metacyclic promastigotes of Leishmania mexicana was investigated using gelatin polyacrylamide gel electrophoresis. Two prominent bands were detected which distinguished metacyclics from multiplicative promastigotes, lacking detectable cysteine proteinase activity, and amastigotes, with a distinct banding pattern composed of multiple enzymes. A correlation between relative activity of the metacyclic-specific bands and the prevalence of metacyclics was found both during the growth cycle in vitro as metacyclogenesis occurred, and by comparison of stationary phase populations from consecutive subpassages in vitro. Irreversible inhibition of the metacyclic activities using N-benzyloxycarbonyl-phenylalanyl-alanyl diazomethane did not inhibit metacyclic to amastigote transformation in vitro. These activities provide a useful biochemical marker for the metacyclic promastigotes of L. mexicana .     

Machine vision automated species identification scaled towards production levels

Computer‐automated identification of insect species has long been sought to support activities such as environmental monitoring, forensics, pest diagnostics, border security and vector epidemiology, to name just a few. In order to succeed, an automated identification programme capable of addressing the needs of the end user should be able to classify hundreds of taxa, if not thousands, and is expected to distinguish closely related and hence morphologically similar species. However, it remains unknown how automated identification methods might handle an increase in data quantity, be it in reference imagery or taxonomic diversity. We sought to test the scalability of an automated identification method in terms of the number of reference specimens used to train the classifier and the number of taxa into which the classifier should assign unknown specimens. Is there an optimal number of reference images, where the cost of acquiring more images becomes greater than the marginal increase in identification success? Does increasing taxonomic diversity affect identification success, whether negatively or positively? In order to test the scalability of the automated insect identification enterprise, we used a sparse processing technique and support vector machine to test the largest dataset to date: 72 species of fruit flies (Diptera: Tephritidae) and 76 species of mosquitoes (Diptera: Culicidae). We found that: (i) machine vision methods are capable of correctly classifying large numbers of closely related species; (ii) when the misclassification of a specimen occurs at the species level, it is often classified in the correct genus; (iii) classification success increases asymptotically as new training images are added to the dataset; (iv) broad taxon sampling outside a focal group can increase classification success within it.