Vector-borne Chagas disease is endemic to the Americas and imposes significant economic and social burdens on public health. In a previous contribution, we presented an automated identification system that was able to discriminate among 12 Mexican and 39 Brazilian triatomine (Hemiptera: Reduviidae) species from digital images. To explore the same data more deeply using machine-learning approaches, hoping for improvements in classification, we employed TensorFlow, an open-source software platform for a deep learning algorithm. We trained the algorithm based on 405 images for Mexican triatomine species and 1,584 images for Brazilian triatomine species. Our system achieved 83.0 and 86.7% correct identification rates across all Mexican and Brazilian species, respectively, an improvement over comparable rates from statistical classifiers (80.3 and 83.9%, respectively). Incorporating distributional information to reduce numbers of species in analyses improved identification rates to 95.8% for Mexican species and 98.9% for Brazilian species. Given the ‘taxonomic impediment’ and difficulties in providing entomological expertise necessary to control such diseases, automating the identification process offers a potential partial solution to crucial challenges.

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

NUP98 fusion oncoproteins (FO) are a hallmark of childhood acute myeloid leukemia. NUP98 FOs drive leukemogenesis through phase-separated condensate formation and maintenance of an active chromatin landscape at stem cell–associated genes in cooperation with epigenetic regulators. In this study, we show that MYST family histone acetyltransferase (HAT) complex proteins, including KAT6A/MOZ, KAT7/HBO1, and the common KAT6A/7 complex subunit BRPF1, associate with NUP98 FOs on chromatin and within condensates. MYST HATs are molecular dependencies in NUP98-rearranged (NUP98-r) leukemia, and genetic inactivation or pharmacologic inhibition of KAT6A and KAT7 impairs NUP98-r cell fitness. KAT6A/7 inhibition decreased global H3K23ac levels, displaced NUP98::HOXA9 from chromatin at the Meis1 locus, and led to myeloid cell differentiation. Additionally, KAT6A/7 inhibition decreased leukemic burden in multiple NUP98-r leukemia xenograft mouse models, synergized with menin inhibitor treatment, and was efficacious in menin inhibitor–resistant cells. In summary, we show that MYST family HATs are therapeutically actionable dependencies in NUP98-r acute myeloid leukemia. Significance: KAT6A and KAT7 associate with NUP98 FOs to drive leukemogenesis. Inhibition of their HAT activity is an effective therapeutic strategy in NUP98-r leukemias, including those resistant to menin inhibition. Moreover, combined KAT6A/7 and menin inhibition is synergistic, supporting clinical translation to improve outcomes for NUP98 FO–driven leukemias.

Rostrhamus sociabilis (Snail Kite) have recently expanded their range in Florida, tracking the invasion of a Pomacea snail (P. maculata), and exhibiting considerable changes in bill size and feeding niche. This range expansion is not aligned with changes in climatic conditions or the distribution of their historic prey (P. paludosa). The Eltonian Noise Hypothesis (ENH), which posits that interactive (biotic) factors have stronger effects on species’ distributions at local scales, predicts that non-interactive (abiotic) factors are generally more relevant at geographic extents. However, in this study, we explore the R. sociabilis range shift as a potential counterexample of the ENH. Under the biotic-abiotic-mobility (BAM) framework, we explore the role of biotic and abiotic factors in the northward range expansion of this endangered species. Over the past 15 years, R. sociabilis have begun consuming the more-abundant invasive snails more often, while increasing in bill size, expanding ~175 km northward from previous range limits in the Kissimmee River Valley. We developed ecological niche models using 3 algorithms (Maxent, GLM, ellipsoids) and found stability in climatic suitability between past and present models. Moreover, although native snails occur in northern Florida, R. sociabilis have had a historically patchy northern distribution due in part to the availability of appropriate wetland conditions. We found a strong latitudinal cline, with bill length increasing with latitude at least through 2020, suggesting that this morphological change broadened the species’ biotic suitable area and distributional potential. The interplay between changes in phenotype and biotic interactions has been poorly documented in distributional ecology, given a lack of rich occurrence datasets. Here, we highlight a case in which a biological invasion and subsequent changes in morphology and diet have facilitated expansion of a specialized predator into areas that were unsuitable until recently.