Mosquito Host Seeking in 3D Using a Versatile Climate-Controlled Wind Tunnel System

Future anthropogenic climate change is predicted to impact sensory-driven behaviors. Building on recent improvements in computational power and tracking technology, we have developed a versatile climate-controlled wind tunnel system, in which to study the effect of climate parameters, including temperature, precipitation, and elevated greenhouse gas levels, on odor-mediated behaviors in insects. To establish a baseline for future studies, we here analyzed the host-seeking behavior of the major malaria vector mosquito, Anopheles gambiae sensu strico, to human odor and carbon dioxide (CO2), under tightly controlled climatic conditions, and isolated from potential background contamination by the presence of an experimenter. When presented with a combination of human foot odor and CO2 (case study I), mosquitoes engaged in faster crosswind flight, spent more time in the filamentous odor plume and targeted the odor source more successfully. In contrast, female An. gambiae s. s. presented with different concentrations of CO2 alone, did not display host-seeking behavior (case study II). These observations support previous findings on the role of human host-associated cues in host seeking and confirm the role of CO2 as a synergist, but not a host-seeking cue on its own. Future studies are aimed at investigating the effect of climate change on odor-mediated behavior in mosquitoes and other insects. Moreover, the system will be used to investigate detection and processing of olfactory information in various behavioral contexts, by providing a fine-scale analysis of flight behavior

Generating Headlines with Recurrent Neural Networks

This report describes the implementation and evaluation of two natural language models using the machine learning technique deep learning. More specifically, two different models describing recurrent artificial neural networks (RNNs) were implemented, capable of generating news article headlines. One model focused on the generation of random (unconditioned) headlines, and the other one on the generation of headlines based (conditioned) on a given news article. Both models were then trained and evaluated on a data set of approximately 500,000 pairs of news articles and their corresponding headlines. The task of summarizing large bodies of text into smaller ones, while maintaining the key points of the original text, has many applications. Quickly and automatically obtaining condensed versions of for example medical journals, scientific papers, and news articles can be of great value for the users of such content. The unconditioned model, implemented using a multi-layer RNN consisting of LSTM cells, was able to produce headlines of moderate plausibility, a majority being syntactically correct. The conditioned model was implemented using two RNNs consisting of GRU cells in an encoder-decodernetwork with an attention mechanism, allowing the network to learn what words to focus on during headlining. Although the model managed to identify important keywords in the articles, it seldomly managed to produce meaningful sentences with them. We conclude that the techniques and models described in this report could be used to generate plausible news headlines. However, for the purpose of generating conditioned headlines, we think that additional modifications are needed to obtain satisfying results