Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiae complex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance.

Using a Logic Model to Direct Backward Design of Curriculum.

Contemporary approaches to STEM course design typically encourage the backward design of curricula. This is to say that the learning activities and assessments of the course are explicitly guided by the learning outcomes of the course. Less discussed is the fact that this paradigm is also used in nonacademic settings. From this perspective, drawing from the nonacademic world, we discuss the use of a logic model approach as a structured, orderly way to implement backward design. We use the design and implementation of an introductory biology class to illustrate how a logic model template helped frame our inclusive, Freirean approach to teaching and learning.