Morphometric diagnosis of Glossina palpalis (Diptera: Glossinidae) population structure in Ghana

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.

Morphometrics of the Tropical Bed Bug (Hemiptera: Cimicidae) From Cape Coast, Ghana

Bed bugs, Cimex lectularius (L.) (Hemiptera: Cimicidae) and Cimex hemipterus (F.), have become established worldwide in recent years largely due to the development of insecticide resistance. However, limited attention has been given to ongoing morphological and macroevolutionary changes within the species and their populations, which could have implications for their control. Here, we evaluated whether bed bugs of the species C. hemipterus inhabiting different communities in Cape Coast, Ghana are undergoing segregation, which could lead to possible speciation. We also aimed to provide a morphometric description of all nymphal stages. Nine-bed bug populations of C. hemipterus were field-collected in Cape Coast and were subjected to geometric morphometric analysis. The multivariate parameters applied distinguished various populations from each of the locations, indicating the presence of morphologically distinct subpopulations of C. hemipterus. Shape-based segregation and shape changes associated with the insect pronotum (which is an important taxonomic character in the Cimicidae) were evident across the populations. Through this comparative study of C. hemipterus, we showed that possible subpopulations of this bed bug are being spread from Ghana. The nymphal stages (first-fifth) of C. hemipterus were distinguished by the length of the last three antennal segment and pronota width; such information contributes to the taxonomic knowledge of the species.

Intraspecific variation in wing geometry among Tabanus rubidus (Diptera: Tabanidae) populations in Thailand

Tabanus rubidus (Wiedemann, 1821) (Diptera: Tabanidae) is a hematophagous insect of veterinary and medical importance and is the predominant Tabanus spp. in Thailand. It is a potential mechanical vector of Trypanosoma evansi, which causes surra in domestic and wild animals. Wing geometric morphometrics is widely used as morphological markers for species identification and to assess the insect population structure. Herein, we investigated the intraspecific variation in wing geometry among T. rubidus populations in Thailand using landmark-based geometric morphometric analysis. Tabanus rubidus females were collected from five populations in four geographical regions in Thailand. The left wings of 240 specimens were removed and digitized using 22 landmarks for analysis. While wing size variations were found between some populations, wing shape variations were detected in all. These intraspecific variations in T. rubidus populations indicate an adaptive response to the local environmental conditions.

Vector-borne protozoal infections of the CNS: cerebral malaria, sleeping sickness and Chagas disease

PURPOSE OF REVIEW

Malaria, Chagas Disease and Human African Trypanosomiasis are vector-borne protozoan illnesses, frequently associated with neurological manifestations. Intriguing but ignored, limited mainly to resource-limited, tropical settings, these disorders are now coming to light because of globalisation and improved diagnosis and treatment. Enhanced understanding of these illnesses has prompted this review.

RECENT FINDINGS

Methods of diagnosis have currently transitioned from blood smear examinations to immunological assays and molecular methods. Tools to assess neurological involvement, such as magnetic resonance imaging, are now increasingly available in regions and countries with high infection loads. Sleep and other electrophysiological technologies (electroencephalography, actigraphy) are also promising diagnostic tools but requiring field-validation. Access to treatments was formerly limited, even as limitations of agents used in the treatment are increasingly recognised. Newer agents are now being developed and trialled, encouraged by improved understanding of the disorders' molecular underpinnings.

SUMMARY

Prompt diagnosis and treatment are crucial in ensuring cure from the infections. Attention should also be due to the development of globally applicable treatment guidelines, the burden of neurological sequelae and elimination of the zoonoses from currently endemic regions.

The Actigraphy Sleep Score: A New Biomarker for Diagnosis, Disease Staging, and Monitoring in Human African Trypanosomiasis

Human African trypanosomiasis (HAT) remains a serious public health problem with diagnostic and treatment challenges in many African countries. The absence of a gold-standard biomarker has been a major difficulty for accurate disease staging and treatment follow-up. We therefore attempted to develop a simple, affordable, and noninvasive biomarker for HAT diagnosis and staging. Simultaneous actigraphy and polysomnography as well as cerebrospinal fluid (CSF) white blood cell (WBC) count, trypanosome presence, and C-X-C motif ligand (CXCL)-10 cytokine levels were performed in 20 HAT patients and nine healthy individuals (controls) using standard procedures. The International HIV Dementia Scale (IHDS) was scored in some patients as a surrogate for clinical assessment. From actigraphic parameters, we developed a novel sleep score and used it to determine correlations with other HAT markers, and compared their performance in differentiating between patients and controls and between HAT stages. The novel actigraphy sleep score (ASS) had the following ranges: 0-25 (healthy controls), 67-103 (HAT stage I), 111-126 (HAT intermediate), and 133-250 (HAT stage II). Compared with controls, stage I patients displayed a 7-fold increase in the ASS (P < 0.01), intermediate stage patients a 10-fold increase (P < 0.001), and HAT stage II patients an almost 20-fold increase (P < 0.001). CXCL-10 showed high interindividual differences. White blood cell counts were only marked in HAT stage II patients with a high interindividual variability. The International HIV Dementia Scale score negatively correlated with the ASS. We report the development and better performance of a new biomarker, ASS, for HAT diagnosis, disease staging, and monitoring that needs to be confirmed in large cohort studies.