Automatic method based on deep learning to identify and account Rhipicephalus microplus larval hatching.

Reports of Rhipicephalus microplus resistant populations worldwide have increased extensively, making it difficult to control this ectoparasite. The adult immersion test, commonly used to screen for acaricide resistance, produces the results only after 40 days of the tick collection because it needs the eggs to be laid and larvae to hatch. The present study aims to develop an automatic method, based on deep learning, to predict the hatching of R. microplus larva based on egg morphology. Initially, the time course of embryonic development of tick eggs was performed to discriminate between viable and non-viable eggs. Secondly, using artificial intelligence deep learning techniques, a method was developed to classify and count the eggs. The larval hatching rate of three populations of R. microplus was evaluated for the software validation process. Groups of three and six images of eggs with 12 days of embryonic development were submitted to the software to predict the larval hatching percent automatically. The results obtained by the software were compared with the prediction results of the hatching percentage performed manually by the specialist and with the results of the hatching percentage of larvae obtained in the biological assay. The group with three images of each population submitted to the software for automatic prediction of the larval hatching percent presented mean values of 96.35% ± 3.33 (Piracanjuba population), 95.98% ± 3.5 (Desterro population) and 0.0% ± 0.0 (Barbalha population). For groups with six images, the values were 94.41% ± 3.84 (Piracanjuba population), 95.93% ± 2.36 (Desterro population) and 0.0% ± 0.0 (Barbalha population). Biological assays showed the following hatching percentage values: 98% ± 1.73 (Piracanjuba population); 96% ± 2.1 (Desterro population); and 0.14% ± 0.25 (Barbalha population). There was no statistical difference between the evaluated methods. The automatic method for predicting the hatching percentage of R. microplus larvae was validated and proved to be effective, with considerable reduction in time to obtain results.

Short-term variation in sperm competition causes sperm-mediated epigenetic effects on early offspring performance in the zebrafish.

The inheritance of non-genetic factors is increasingly seen to play a major role in ecology and evolution. While the causes and consequences of epigenetic effects transmitted from the mother to the offspring have received ample attention, much less is known about how variation in the condition of the father affects the offspring. Here, we manipulated the intensity of sperm competition experienced by male zebrafish Danio rerio to investigate the potential for sperm-mediated epigenetic effects over a relatively short period of time. We found that the rapid responses of males to varying intensity of sperm competition not only affected sperm traits as shown previously, but also the performance of the resulting offspring. We observed that males exposed to high intensity of sperm competition produced faster swimming and more motile sperm, and sired offspring that hatched over a narrower time frame but exhibited a lower survival rate than males exposed to low intensity of sperm competition. Our results provide striking evidence for short-term paternal effects and the possible fitness consequences of such sperm-mediated non-genetic factors not only for the resulting offspring but also for the female.