Microbial odors are essential for attracting mosquitoes to their hosts, but their role in attraction to other nutrient sources remains unknown. Sugar sources provide nutrients that lengthen the lifespan of both male and female mosquitoes and increase the vectorial capacity in host-seeking female mosquitoes. Microbial odors have been shown to act as proxy signals for the availability and composition of certain nutrients, such as amino acids and mineral ions, found in nectar and fruit sap. As microbes are abundant in flowers and fruits, we hypothesize that Microbial Volatile Organic Compounds (mVOCs) from these nutrient sources play an important role in the feeding behavior of mosquitos. To test this, we analyzed the microbial community of a known attractive fruit, Mangifera indica ‘Keitt.’ Microbial species were identified from our environmental samples through amplicon sequencing of the 16S and 26S regions. Isolates of the most abundant and relevant species were cultured for mVOC collection and chemical analysis via Gas Chromatography and Mass Spectroscopy (GC-MS). Behavioral assays were then performed with Aedes aegypti mosquitoes to observe the effect of microbial odors on the attractiveness of nutrient sources. Through the identification of behaviorally-relevant microbial compounds, we can gain a stronger understanding of the ecological dynamics of mosquito chemoreception and microbial community signaling, which can help inform future mosquito-control measures.

Adaptive algorithms used in brain-computer interfaces (BCIs) adjust to user strategies by dynamically adjusting how BCIs decode neural data throughout an experiment. Current adaptive algorithms continuously update the decoder using all available data during training. However, if users are unfocused or inattentive, it is likely that some of the training data is unhelpful towards decoder training and could lead to poor decoder performance. Unfortunately, determining attentiveness in a subject is difficult. Non-human animals cannot self-report attention levels, and even in human trials, self-reporting often leads to subjective data that varies between subjects. A non-invasive estimate of subject attentiveness could improve data selection for decoder training. Pupil size is correlated with a participant’s perception of task difficulty, and participants involved in attention-grabbing tasks display blinking rate-inhibition (Kucewicz et al., 2018; Maffei et al., 2019). I hypothesize that these eye data could be used to estimate a subject’s task engagement. I explored data from a novel task where both human and non-human primate subjects controlled a cursor on a 2D screen with 3D hand motions through some unknown mapping. Due to the unknown 3D-to-2D mapping, this task required constant cognitive attention in order for subjects to succeed. I looked at the subjects’ data to identify trends in pupil size and blink frequency across multiple days of task performance. In the future, I aim to build engagement classification models to better select training data for adaptive algorithms and apply these algorithms to realtime BCI experiments.

Commercial pet food (CPF) is not what most people think it is. In the U.S. about 90% of pet owners raise their dogs on CPF, claimed to be complete and nutritionally balanced. Major pet food companies make efforts to hide the fact that their products consist of contaminants such as heavy metals, aflatoxins, heterocyclic amines, and acrylamides. As investigated by Dr. Richard Pitcairn, feeding CPF to animals increases chances of developing cancer and other degenerative diseases. Due to toxins combined with insufficient nutrients, CPF is likely a major factor negatively affecting dogs’ physical health, shortening healthspan (period in life considered healthy), and causing premature deaths. Top causes of death in dogs are illnesses in which veterinarians generally prescribe drugs to treat only the symptoms of these common ailments. However, a focused approach to reduce dog’s susceptibility to disease, by eliminating the potential root cause, should be considered instead. This literature review investigates various threats posed by CPF by analyzing its risk factors and their impact on dogs’ healthspan. Returning dogs' natural diet by including living, raw, nutrient dense food may be the answer in improving dogs’ overall health and lengthening healthspan. Eliminating the overly processed, cooked, and toxic feed and transitioning to a diet of raw food as an alternative poses a chance to reduce common indicators of illness, ultimately resulting in fewer stressful vet visits and allowing owners to spend more time with their pets. Identifying the research gap surrounding the ambiguity of the effects of contaminants in CPF on dog health, this analysis helps to urge studies to be conducted on these contaminants to further pave the path towards creating a healthier dog diet; in addition to prompting more unbiased research on what types of diets are most efficient and effective at maximizing dogs’ healthspan.

Visual perspective-taking (PT) is a fundamental spatial cognition task, requiring an individual to adopt another’s viewpoint. Previous experiments have shown that response times increase as the angular difference between viewer and reference perspectives grows. Preliminary fMRI results suggest that neural activity in specific brain regions follows a similar pattern, their activity increases as a factor of angular difference, reflecting the cognitive demands of mental perspective transformation. However, little is known about how eye-gaze behavior varies in this task. In this study, we analyze eye-tracking data collected during fMRI scans with an Eyelink 1000 to examine the relationship between gaze patterns and perspective alignment. Specifically, we investigate whether eye-gaze behavior differs between aligned and unaligned trials and whether angular difference influences gaze dynamics. Gaze coordinates (xpos, ypos) will be analyzed trial-by-trial to determine how visual attention is modulated during perspective-taking. Understanding these gaze patterns may provide insights into the strategies used in spatial perspective shifts and their neural underpinnings.

Visual Perspective-taking (VPT) is the ability to recognize another’s viewpoint, and can play a role in communication and empathy. Previous research supports that VPT in Autism Spectrum Disorder (ASD) populations is altered compared to neurotypicals (NT), but the traits within both populations that contribute to VPT differences remain unknown. This study investigates how VPT differs in ASD compared to NT adults using both animate and inanimate target objects. We also explore how these differences might be associated with ASD traits, measured by the Social Responsiveness Scale-2 (SRS-2). Participants complete computerized tasks that evaluate how stimuli appear from a different perspective. Psychophysical tests determine participants' ability to identify the position of an object from the perspective of an animate object (an avatar in the image) and an inanimate object (a chair), measuring accuracy and reaction time. We expect to replicate past findings of increased reaction time with greater angular disparity between the participants’ viewpoint and the viewpoint of the target object, for both ASD and NT subjects. We hypothesize this interaction between reaction time and angular perspective for both populations may interact with the type of reference object (animate vs. inanimate) and SRS-2 scores. We believe that NT participants will demonstrate greater accuracy and faster reaction times than ASD participants in both animate and inanimate conditions, with the difference being evident in the animate condition for ASD participants, possibly due to challenges in processing social cues reflected by higher scores on the SRS-2. This research can increase the understanding of the psychological disparities in individuals with ASD compared to NT contributing to diagnostic tools and targeted interventions for improving social cognition in ASD populations and potentially other neurodivergent populations with VPT differences.

Most real-world motor tasks involve a many-to-few input-output relationship, such as many neurons firing or muscles contracting to control a few degrees of freedom of the arm. The brain must form an internal model of outputs to inputs when there are fewer dimensions of feedback than dimensions of inputs to control ("redundancy"). However, motor learning is typically studied in laboratory contexts with one-to-one input-output tasks (Krakauer et al., 2019). To investigate how redundancy influences motor learning, I developed a novel virtual reality (VR) visuomotor perturbation task that can either be fully dimensioned or redundant. Participants are trained on a point-to-point reaching task controlled by hand movements in 3D space. In the 3D version of the task, the 3D cursor motion results from the 3D movements of the hand according to some unknown spatial rotation that the participant will have to learn in order to get to targets. In the redundant (2D) version, 3D hand motions are projected onto a 2D plane oriented somewhere in space that the participant has to learn. In both cases, targets are represented as infinite cylinders such that there is a task-irrelevant dimension, but in the redundant version of the task, the participant receives no visual feedback from this dimension. I hypothesize that providing 3D cursor feedback will enhance the learning of the task-relevant 2D plane by allowing participants to better map redundant hand movements in 3D space onto the constrained 2D plane. In contrast, restricting feedback to only the 2D plane will limit available sensory information, making it more difficult to learn the correct movement strategy. By comparing performance across these two tasks, I aim to clarify how task redundancy influences internal model formation and adaptation, with implications for designing more effective motor rehabilitation and VR-based training protocols.