Host-Feeding Behavior of Mosquitoes in the Florida Everglades.

Background: West Nile virus (WNV), Everglades virus (EVEV), and five species of Orthobunyavirus were isolated from mosquitoes collected in the Everglades in 2016-2017. Prior studies of blood meals of mosquitoes in southern Florida have related findings to acquisition and transmission of EVEV, St. Louis encephalitis virus, and WNV, but not the Orthobunyavirus viruses associated with the subgenus Melanoconion of the genus Culex. Materials and Methods: In the present study, blood-fed mosquitoes were collected in the Everglades in 2016, 2017, 2021, and 2022, and from an industrial site in Naples, FL in 2017. Blood meals were identified to host species by PCR assays using mitochondrial cytochrome b gene. Results: Blood meals were identified from Anopheles crucians complex and 11 mosquito species captured in the Florida Everglades and from 3 species collected from an industrial site. The largest numbers of blood-fed specimens were from Culex nigripalpus, Culex erraticus, Culex cedecei, and Aedes taeniorhynchus. Cx. erraticus fed on mammals, birds, and reptiles, particularly American alligator. This mosquito species could transmit WNV to American alligator in the wild. Cx. nigripalpus acquired blood meals primarily from birds and mammals and frequently fed on medium-sized mammals and white-tailed deer. Water and wading birds were the primary avian hosts for Cx. nigripalpus and Cx. erraticus in the Everglades. Wading birds are susceptible to WNV and could serve as reservoir hosts. Cx. cedecei fed on five species of rodents, particularly black and hispid cotton rats. EVEV and three different species of Orthobunyavirus have been isolated from the hispid cotton rat and Cx. cedecei in the Everglades. Cx. cedecei is likely acquiring and transmitting these viruses among hispid cotton rats and other rodents. The marsh rabbit was a frequent host for An. crucians complex. An. crucians complex, and other species could acquire Tensaw virus from rabbits. Conclusions: Our study contributes to a better understanding of the host and viral associations of mosquito species in southwestern Florida.

Generative models of birdsong learning link circadian fluctuations in song variability to changes in performance.

Learning skilled behaviors requires intensive practice over days, months, or years. Behavioral hallmarks of practice include exploratory variation and long-term improvements, both of which can be impacted by circadian processes. During weeks of vocal practice, the juvenile male zebra finch transforms highly variable and simple song into a stable and precise copy of an adult tutor's complex song. Song variability and performance in juvenile finches also exhibit circadian structure that could influence this long-term learning process. In fact, one influential study reported juvenile song regresses towards immature performance overnight, while another suggested a more complex pattern of overnight change. However, neither of these studies thoroughly examined how circadian patterns of variability may structure the production of more or less mature songs. Here we relate the circadian dynamics of song maturation to circadian patterns of song variation, leveraging a combination of data-driven approaches. In particular we analyze juvenile singing in learned feature space that supports both data-driven measures of song maturity and generative developmental models of song production. These models reveal that circadian fluctuations in variability lead to especially regressive morning variants even without overall overnight regression, and highlight the utility of data-driven generative models for untangling these contributions.

Where is that smell coming from?

Computational model reveals why pausing to sniff the air helps animals track a scent when they are far away from the source.

Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.

Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from several crucial limitations. For example, handpicked features may miss important dimensions of variability, and correlations among them complicate statistical testing. Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning method, to learn features directly from data and quantify the vocal behavior of two model species: the laboratory mouse and the zebra finch. The VAE converges on a parsimonious representation that outperforms handpicked features on a variety of common analysis tasks, enables the measurement of moment-by-moment vocal variability on the timescale of tens of milliseconds in the zebra finch, provides strong evidence that mouse ultrasonic vocalizations do not cluster as is commonly believed, and captures the similarity of tutor and pupil birdsong with qualitatively higher fidelity than previous approaches. In all, we demonstrate the utility of modern unsupervised learning approaches to the quantification of complex and high-dimensional vocal behavior.

Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use.

In sensorimotor adaptation tasks, feedback delays can cause significant reductions in the rate of learning. This constraint is puzzling given that many skilled behaviors have inherently long delays (e.g., hitting a golf ball). One difference in these task domains is that adaptation is primarily driven by error-based feedback, whereas skilled performance may also rely to a large extent on outcome-based feedback. This difference suggests that error- and outcome-based feedback may engage different learning processes, and these processes may be associated with different temporal constraints. We tested this hypothesis in a visuomotor adaptation task. Error feedback was indicated by the terminal position of a cursor, while outcome feedback was indicated by points. In separate groups of participants, the two feedback signals were presented immediately at the end of the movement, after a delay, or with just the error feedback delayed. Participants learned to counter the rotation in a similar manner regardless of feedback delay. However, the aftereffect, an indicator of implicit motor adaptation, was attenuated with delayed error feedback, consistent with the hypothesis that a different learning process supports performance under delay. We tested this by employing a task that dissociates the contribution of explicit strategies and implicit adaptation. We find that explicit aiming strategies contribute to the majority of the learning curve, regardless of delay; however, implicit learning, measured over the course of learning and by aftereffects, was significantly attenuated with delayed error-based feedback. These experiments offer new insight into the temporal constraints associated with different motor learning processes.

Electrophysiological Correlates of Refreshing: Event-related Potentials Associated with Directing Reflective Attention to Face, Scene, or Word Representations.

Refreshing is the component cognitive process of directing reflective attention to one of several active mental representations. Previous studies using fMRI suggested that refresh tasks involve a component process of initiating refreshing as well as the top-down modulation of representational regions central to refreshing. However, those studies were limited by fMRI's low temporal resolution. In this study, we used EEG to examine the time course of refreshing on the scale of milliseconds rather than seconds. ERP analyses showed that a typical refresh task does have a distinct electrophysiological response as compared to a control condition and includes at least two main temporal components: an earlier (∼400 msec) positive peak reminiscent of a P3 response and a later (∼800-1400 msec) sustained positivity over several sites reminiscent of the late directing attention positivity. Overall, the evoked potentials for refreshing representations from three different visual categories (faces, scenes, words) were similar, but multivariate pattern analysis showed that some category information was nonetheless present in the EEG signal. When related to previous fMRI studies, these results are consistent with a two-phase model, with the first phase dominated by frontal control signals involved in initiating refreshing and the second by the top-down modulation of posterior perceptual cortical areas that constitutes refreshing a representation. This study also lays the foundation for future studies of the neural correlates of reflective attention at a finer temporal resolution than is possible using fMRI.