The long-distance dispersal of the invasive disease vectors Aedes aegypti and Aedes albopictus has introduced arthropod-borne viruses into new geographical regions, causing a significant medical and economic burden. The used-tire industry is an effective means of Aedes dispersal, yet studies to determine Aedes occurrence and the factors influencing their distribution along local transport networks are lacking. To assess infestation along the primary transport network of Panama we documented all existing garages that trade used tires on the highway and surveyed a subset for Ae. aegypti and Ae. albopictus. We also assess the ability of a mass spectrometry approach to classify mosquito eggs by comparing our findings to those based on traditional larval surveillance. Both Aedes species had a high infestation rate in garages trading used tires along the highways, providing a conduit for rapid dispersal across Panama. However, generalized linear models revealed that the presence of Ae. aegypti is associated with an increase in road density by a log-odds of 0.44 (0.73 ± 0.16; P = 0.002), while the presence of Ae. albopictus is associated with a decrease in road density by a log-odds of 0.36 (0.09 ± 0.63; P = 0.008). Identification of mosquito eggs by mass spectrometry depicted similar occurrence patterns for both Aedes species as that obtained with traditional rearing methods. Garages trading used tires along highways should be targeted for the surveillance and control of Aedes-mosquitoes and the diseases they transmit. The identification of mosquito eggs using mass spectrometry allows for the rapid evaluation of Aedes presence, affording time and cost advantages over traditional vector surveillance; this is of importance for disease risk assessment.

We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.

This work presents a methodology to automatically detect and identify manatee vocalizations in continuous passive acoustic underwater recordings. Given that vocalizations of each manatee present a slightly different frequency content, it is possible to identify individuals using a non-invasive acoustic approach. The recordings are processed in four stages, including detection, denoising, classification, and manatee counting and identification by vocalization clustering. The main contribution of this work is considering the vocalization spectrogram as an image (i.e., two-dimensional pattern) and representing it in terms of principal component analysis coefficients that feed a clustering approach. A performance study is carried out for each stage of the scheme. The methodology is tested to analyze three years of recordings from two wetlands in Panama to support ongoing efforts to estimate the manatee population.

Background: Angiopoietin-1 (Ang-1) is the main ligand of Tie-2 receptors. It promotes endothelial cell (EC) survival, migration, and differentiation. Little is known about the transcription factors (TFs) in ECs that are downstream from Tie-2 receptors.Objective: The main objective of this study is to identify the roles of the ETS family of TFs in Ang-1 signaling and the angiogenic response.Methods: In silico enrichment analyses that were designed to predict TF binding sites of the promotors of eighty-six Ang-1-upregulated genes showed significant enrichment of ETS1, ELK1, and ETV4 binding sites in ECs. Human umbilical vein endothelial cells (HUVECs) were exposed for different time periods to recombinant Ang-1 protein and mRNA levels of ETS1, ELK1, and ETV4 were measured with qPCR and intracellular localization of these transcription factors was assessed with immunofluorescence. Electrophoretic mobility shift assays and reporter assays were used to assess activation of ETS1, ELK1, and ETV4 in response to Ang-1 exposure. The functional roles of these TFs in Ang-1-induced endothelial cell survival, migration, differentiation, and gene regulation were evaluated by using a loss-of-function approach (transfection with siRNA oligos).Results: Ang-1 exposure increased ETS1 mRNA levels but had no effect on ELK1 or ETV4 levels. Immunostaining revealed that in control ECs, ETS1 has nuclear localization whereas ELK1 and ETV4 are localized to the nucleus and the cytosol. Ang-1 exposure increased nuclear intensity of ETS1 protein and enhanced nuclear mobilization of ELK1 and ETV4. Selective siRNA knockdown of ETS1, ELK1, and ETV4 showed that these TFs are required for Ang-1-induced EC survival and differentiation of cells, while ETS1 and ETV4 are required for Ang-1-induced EC migration. Moreover, ETS1, ELK1, and ETV4 knockdown inhibited Ang-1-induced upregulation of thirteen, eight, and nine pro-angiogenesis genes, respectively.Conclusion: We conclude that ETS1, ELK1, and ETV4 transcription factors play significant angiogenic roles in Ang-1 signaling in ECs.