A model for the relationship between wing fray and chronological and ovarian ages in tsetse (Glossina spp)

Improved models for the relationship between age and the probability of trypanosome infection in female tsetse, Glossina pallidipes Austen

Between 1990 and 1999, at Rekomitjie Research Station, Zambezi Valley, Zimbabwe, 29,360 female G. pallidipes were dissected to determine their ovarian category and trypanosome infection status. Overall prevalences were 3.45 and 2.66% for T. vivax and T. congolense, respectively, declining during each year as temperatures increased from July - December. Fits to age-prevalence data using Susceptible-Exposed-Infective (SEI) and SI compartmental models were statistically better than those obtained using a published catalytic model, which made the unrealistic assumption that no female tsetse survived more than seven ovulations. The improved models require knowledge of fly mortality, estimated separately from ovarian category distributions. Infection rates were not significantly higher for T. vivax than for T. congolense. For T. congolense in field-sampled female G. pallidipes, we found no statistical support for a model where the force of infection was higher at the first feed than subsequently. The long survival of adult female tsetse, combined with feeding at intervals ≤3 days, ensures that post-teneral feeds, rather than the first feed, play the dominant role in the epidemiology of T. congolense infections in G. pallidipes. This is supported by estimates that only about 3% of wild hosts at Rekomitjie were harbouring sufficient T. congolense to ensure that tsetse feeding off them take an infected meal, so that the probability of ingesting an infected meal is low at every meal.

Deep learning approaches to landmark detection in tsetse wing images

Morphometric analysis of wings has been suggested for identifying and controlling isolated populations of tsetse (Glossina spp), vectors of human and animal trypanosomiasis in Africa. Single-wing images were captured from an extensive data set of field-collected tsetse wings of species Glossina pallidipes and G. m. morsitans. Morphometric analysis required locating 11 anatomical landmarks on each wing. The manual location of landmarks is time-consuming, prone to error, and infeasible for large data sets. We developed a two-tier method using deep learning architectures to classify images and make accurate landmark predictions. The first tier used a classification convolutional neural network to remove most wings that were missing landmarks. The second tier provided landmark coordinates for the remaining wings. We compared direct coordinate regression using a convolutional neural network and segmentation using a fully convolutional network for the second tier. For the resulting landmark predictions, we evaluate shape bias using Procrustes analysis. We pay particular attention to consistent labelling to improve model performance. For an image size of 1024 × 1280, data augmentation reduced the mean pixel distance error from 8.3 (95% confidence interval [4.4,10.3]) to 5.34 (95% confidence interval [3.0,7.0]) for the regression model. For the segmentation model, data augmentation did not alter the mean pixel distance error of 3.43 (95% confidence interval [1.9,4.4]). Segmentation had a higher computational complexity and some large outliers. Both models showed minimal shape bias. We deployed the regression model on the complete unannotated data consisting of 14,354 pairs of wing images since this model had a lower computational cost and more stable predictions than the segmentation model. The resulting landmark data set was provided for future morphometric analysis. The methods we have developed could provide a starting point to studying the wings of other insect species. All the code used in this study has been written in Python and open sourced.

Flight activity and age cause wing damage in house flies

Wing damage attenuates aerial performance in many flying animals such as birds, bats and insects. Especially insect wings are fragile and light in order to reduce inertial power requirements for flight at elevated wing flapping frequencies. There is a continuing debate on the factors causing wing damage in insects including collisions with objects, mechanical stress during flight activity, and aging. This experimental study is engaged with the reasons and significance of wing damage for flight in the house fly Musca domestica. We determined natural wing area loss under two housing conditions and recorded flight activity and flight ability throughout the animals' lifetime. Our data show that wing damage occurs on average after 6 h of flight, is sex-specific, and depends on housing conditions. Statistical tests show that both physiological age and flight activity have similar significance as predictors for wing damage. Tests on freely flying flies showed that minimum wing area for active flight is approximately 10-34% below the initial area and requires a left-right wing area asymmetry of less than approximately 25%. Our findings broadly confirm predictions from simple aerodynamic theory based on mean wing velocity and area, and are also consistent with previous wing damage measurements in other insect species.

A gene expression panel for estimating age in males and females of the sleeping sickness vector Glossina morsitans

Many vector-borne diseases are controlled by methods that kill the insect vectors responsible for disease transmission. Recording the age structure of vector populations provides information on mortality rates and vectorial capacity, and should form part of the detailed monitoring that occurs in the wake of control programmes, yet tools for obtaining estimates of individual age remain limited. We investigate the potential of using markers of gene expression to predict age in tsetse flies, which are the vectors of deadly and economically damaging African trypanosomiases. We use RNAseq to identify candidate expression markers, and test these markers using qPCR in laboratory-reared Glossina morsitans morsitans of known age. Measuring the expression of six genes was sufficient to obtain a prediction of age with root mean squared error of less than 8 days, while just two genes were sufficient to classify flies into age categories of ≤15 and >15 days old. Further testing of these markers in field-caught samples and in other species will determine the accuracy of these markers in the field.

Time Flies-Age Grading of Adult Flies for the Estimation of the Post-Mortem Interval

The estimation of the minimum time since death is one of the main applications of forensic entomology. This can be done by calculating the age of the immature stage of necrophagous flies developing on the corpse, which is confined to approximately 2–4 weeks, depending on temperature and species of the first colonizing wave of flies. Adding the age of the adult flies developed on the dead body could extend this time frame up to several weeks when the body is in a building or closed premise. However, the techniques for accurately estimating the age of adult flies are still in their beginning stages or not sufficiently validated. Here we review the current state of the art of analysing the aging of flies by evaluating the ovarian development, the amount of pteridine in the eyes, the degree of wing damage, the modification of their cuticular hydrocarbon patterns, and the increasing number of growth layers in the cuticula. New approaches, including the use of age specific molecular profiles based on the levels of gene and protein expression and the application of near infrared spectroscopy, are introduced, and the forensic relevance of these methods is discussed.