Seed dispersal is a crucial phase of plant lifecycles. Effective dispersal is important to the ecosystem as a whole because it affects composition of the community, ecological succession, and response to climate change. Given the importance of seed dispersal, understanding the factors that contribute to the evolution of varied dispersal modes and promote convergence on specific dispersal strategies is particularly important to understanding plant ecology. We think habitat might be one of these important factors. Here, to explore the relationship between habitat and dispersal mode, we are studying the onion grasses (Melica), a small genus of perennial grasses primarily distributed in temperate regions, and their relatives. These grasses are found in a wide variety of habitats and possess a remarkable diversity of seed dispersal strategies. These traits make them a useful case study for better understanding the factors that influence the evolution of dispersal strategy. We are testing the hypothesis that evolution in traits associated with seed dispersal is correlated with changes in habitat. In particular, we hypothesize that the evolution of wind dispersed seeds follows transitions into open habitats. Seed dispersal structures (diaspores) were collected from 28 grass species (14 Melica and 14 outgroup). To assess wind dispersal potential, we quantify falling velocity by filming seed descent at 1000 fps. Lower falling velocities are associated with higher wind dispersal potential. Diaspores were photographed and the images were used to measure surface roughness, which is associated with adhesive dispersal potential. Habitat data were obtained for each species via a literature survey. These data, along with several other traits associated with seed dispersal processes, were mapped onto the evolutionary tree of the onion grasses. Initial results indicate that convergence upon wind dispersal may be in part driven by convergence upon disturbed habitat types.

Neurons in the brain are dynamical in nature, maintaining constantly changing states. Neurons modulate voltage based on input currents and produce spikes when the voltage exceeds a certain threshold. Additional dynamics after spiking, called evoked after-spike currents, are important for computation and memory over time scales. The diversity of neuronal dynamics and the variability in parameters underlying them give rise to rich and varied dynamics across networks. We hypothesize that the complexity and diversity of biological dynamics in the brain play a critical role in predictive coding of temporally complex systems, and that diverse forms of after-spike currents enable computation over variable timescales. Current artificial neural networks (ANNs), that emulate the structure of biological neural networks, successfully learn relationships between static patterns but have difficulty learning dynamic patterns that change over time. We aim to incorporate complex biological dynamics and diversity in ANNs and thereby systematically explore the function of such dynamics in network computation and learning. To this end, we construct ANNs that express biologically realistic dynamics, developing methods to learn dynamics-generating parameters, such as membrane capacitance and threshold, in individual neurons. Theoretically, diverse dynamics of individual neurons will enable even more complex dynamics when combined in networks and may improve performance on tasks requiring computation over complex timescales, such as determining actions based on temporal patterns of cues. Hence, we hypothesize that when trained on temporally challenging tasks, our networks will learn diverse dynamics across neurons. We present the diversity of parameters learned and the resulting distribution of firing patterns and compare performance between our neural networks and traditional networks that only learn connection weights. This research will inform learning methods for training novel biologically inspired neural networks and will also shed light on the physiological role of diversity in the brain.

Plants have evolved to represent a diversity of species, characterized by functional traits that dictate their performance in response to changing environments. One such leaf functional trait that relates to plant ecological strategy and is strongly correlated with photosynthetic rates, is minor leaf vein density (LVD). Our study will assess how ecological strategies within plant communities shifted in response to Earth’s most recent major global warming event, the Middle Miocene Climatic Optimum (MMCO) from 17-14 Ma, where rises in both global temperature and atmospheric CO₂ levels occurred. We hypothesize that global warming led to longer growing seasons and ecological strategies that prioritized persistence over productivity became dominant, and more favorable climates increased the diversity of ecological strategies present within the community. We will be traveling to various museums to photograph fossils that have all their minor veins preserved, collected from sites representing before, during, and after the MMCO in the Pacific Northwest region. Our goal will be to include several species per site, and photograph their fossil leaves under a stereo microscope. From there we will measure LVD using the program ImageJ using standard protocols. Once we have that data, we will calculate measures of the community-level distribution of this trait (mean, variance, kurtosis), and then compare those values between sites, and thus across the MMCO. In support of our hypothesis, we predict to see a lower mean and kurtosis, and higher variance of LVD values in MMCO plant communities, relative to those existing before or after the warming event. Overall this research is important to not only understanding how plant communities responded in ancient times to rising temperature but how plant communities could potentially respond to the rising temperatures in the future.

 Phytoliths are microscopic silica structures produced in plant tissue, and palms (family Arecaceae) are prolific producers of them. The stability of these structures contributes to the abundance of palms within the fossil record, as these structures can fossilize in sediments where leaves cannot, providing a wider range of evidence of the past palm geographic distribution. Palms generally grow in warmer and wetter environments, but certain species can live in more extreme climates (i.e., cold, dry). However, because 90% of palms are currently distributed around the equator and tropical rainforests, they are often used as an indicator of warmer environments in the fossil record. The current understanding of the taxonomic groups below the family level is limited, and it is unclear whether palm phytolith shape can be used to identify different palm groups in the fossil record. The Palm Project hopes to address this gap by testing phytolith classification using a morphometric approach. Over 100 species of known modern Arecaceae (~30 images/species) phytoliths have been imaged with a confocal microscope, and a semi-automated script in ImageJ quantifies the overall shape (globular or hat), density, and size of the phytolith ornamentations. Next, multivariate analysis on the morphometric data attempts to distinguish the phytoliths of various modern subfamilies based on distinct morphological traits, forming a quantitative baseline to describe and classify phytoliths. When combined with the collected ecological data, this could potentially identify signals between the environment of modern palm species and the phytoliths they form. Once the morphological differences between groups of modern palms are quantified, the same analysis can be applied to fossil phytoliths of unknown origins. This could reveal which taxonomic and ecological groups they belong to based on the most similar modern phytolith, giving insight into the phylogeny of Arecaceae and the paleoenvironment the fossils came from.

Despite recent advancements in cancer treatment, the 5-year survival rate for glioblastoma is only 22%. The greatest challenge in brain cancer treatment is the blood-brain barrier (BBB), a physical barrier that protects the central nervous system (CNS) from circulating solutes in the blood, but in cancer, prevents therapeutics from entering the brain space. While surgery is the gold-standard treatment, this procedure is high-risk. Thus, we are investigating injectable therapies to treat brain tumors by crossing the BBB. To do this, we are developing nanoparticles (NPs) to cross the BBB via receptor-mediated transcytosis (RMT). In RMT, ligands (keys) bind to receptors (locks) expressed by the BBB to gain entrance into the CNS. By attaching the ligand transferrin, we can trick the BBB into granting our NPs access to the brain. Prior to testing particles in vivo, I am developing a cellular model of the BBB to assess the ability of different NP formulations to cross the BBB in vitro. The transwell model comprises two main compartments: the donor and acceptor compartments, separated by a cellular monolayer that only allows transport between compartments via RMT. Here, I developed a co-culture consisting of brain endothelial cells and astrocytes. This co-culture model is more representative of the BBB and provides higher monolayer integrity than single-cell models. In parallel, I am investigating the use of acidification inhibitors to enhance the ability of NPs to cross the BBB. If successful, these inhibitors will be incorporated into a new NP design. Through this project, I am (i) developing a more representative in vitro model of the BBB and (ii) exploring alternative mechanisms to enhance NP transport through the BBB. Ultimately, these two aims will enable us to better direct NP behavior in vivo and cross the BBB.

Seed dispersal is an important component in the lifecycle of plants and aids in establishment of successive generations. Flowering plants have developed multiple ways of dispersing their progeny, including via wind, a strategy known as anemochory. In this presentation, I evaluate and compare wind dispersal potential of ecologically dominant grasses of the tropical savannas of Venezuela, Cerrado region in Brazil, and Serengeti region in Tanzania. Due to increased canopy cover in regions of the Cerrado and Venezuelan savannah relative to the Serengeti, I predict that conditions in the Serengeti would favor wind dispersal. Dispersal structures, known as diaspores, were sampled from specimens obtained from various herbaria. I measured falling velocity as a proxy for wind dispersal ability. I dropped diaspores from a chute and recorded them on high-speed video, which I analyzed to determine the speed of the falling specimen. My data analysis so far has consisted of obtaining and comparing average falling velocity for communities. Contrary to my original prediction, preliminary data suggest that wind dispersal is favored in the Cerrado. This may be due in part to the relative abundance of megafauna in the Serengeti, which would allow for seed dispersal via animal adhesion (epizoochory) or consumption (endozoochory). Diaspores using these dispersal mechanisms may not be as likely to have low falling velocities associated with anemochory. To further evaluate epizoochorous and endozoochorous potential, I am currently analyzing surface roughness using photographs of dispersal units.

Trait-based plant ecology can serve as a means to better understand shifts in ecological strategies within plant communities, and how that affects greater ecosystem processes, such as productivity, before, during, and after a period of major climate change. Apart from modern anthropogenic activity regarding greenhouse gases, the most recent major global warming event was the Middle Miocene Climatic Optimum (MMCO). 17 14 million years ago, this event was a short aberration to a long-term cooling trend that lasted over the last 53 million years, with global temperature averages during the MMCO estimated to be about 8°C warmer than preindustrial averages. Through statistical analysis of leaf functional trait measurements such as leaf area, leaf perimeter, tooth count, and petiole width, we seek to help document an example of how global warming affected vegetation in Earth’s past by comparing changes in leaf traits across the MMCO to modern leaves. While studying Miocene fossil leaves, we are creating a modern leaf database to better interpret trait trends analyzed from samples. This is building off of a previous study’s global dataset to create a model that can be used as an analog for categorizing fossil plant assemblages into different vegetation types using functional trait distributions, as well as assist in trait trend interpretation. At this point in time, we have analyzed enough modern samples to be prepared to interpret trait trends preserved in MMCO leaf fossils. Our objective during the 2021-2022 school year will be to collect enough MMCO trait trend data to make stronger predictions about how modern plant communities will be affected by climate change.

Digital badges are part of a larger informal credentialing system with many uses for tracking and rewarding extracurricular achievement. Scout badges, videogame trophies, and badges on educational websites like Khan Academy are all examples of microcredentials that represent one’s experiences and skills. This research is part of a larger six-year study that has explored the role of digital badges in encouraging youth to connect their out-of-school science learning to other aspects of their lives. My work specifically focuses on uncovering sources of support students receive in their daily lives to pursue science as a career or field of study, and how digital badges fit into their existing support systems. This study aimed to answer the following three research questions: 1. What factors influence students’ science identities? 2. What are the primary supports students receive to develop their science identities? 3. How do students perceive the role of badges in supporting their science identities? Using interviews, case studies, and surveys I was able to assess students’ relationships with science and the support they received from various sources to continue developing their interest in science. I used qualitative coding methods to identify common themes in each participant’s experience using the badge system. This study’s preliminary results indicate that students did not find the badge system useful in developing their science identities. Data from the participant interviews suggests that this is partially because the badges were not integrated into participants’ existing support systems like their friend groups and families. The findings of this study support a central idea of sociocultural learning theory—that students’ interest in topics is sustained through their interactions with others. Insights from this study could be used to inform the approach of programs that encourage students to participate in STEM.