Biology Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2749
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Item SENSORY AND HORMONAL MECHANISMS OF EARLY LIFE BEHAVIOR IN A SOCIAL CICHLID FISH(2024) Westbrook, Molly; Juntti, Scott; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Studying the ontogeny of animal behavior is fundamental to ethology and allows understanding how behaviors in early life may affect later life success. The social cichlid Astatotilapia burtoni is an excellent model for examining the mechanisms of early life aggression due to the robust social hierarchy enforced by stereotyped, measurable social behaviors. We examine how hormonal signaling affects early life aggression through pharmacology and CRISPR-Cas9 mutants. We test which sensory pathways convey aggression-eliciting stimuli through sensory deprivation experiments. And we identify kinematic features that predict aggression through machine-learning video tracking algorithms. We observe that aggressive behaviors emerge around 17 days post fertilization (dpf), correlating with when the animals transition to free swimming away from the mother. We find that sex steroids subtly organize behavioral circuits for aggression and suggest that unknown additional mechanisms play a leading role. We show that thyroid hormone is not necessary or sufficient for the transition to aggressive behavior. We show that visual signals are necessary for the full expression of aggression, but in the absence of visual signal, low levels of aggression remain. We show that ciliated olfactory receptor signaling maintains low levels of aggression, as mutant animals display higher levels of aggressive behavior between 17 and 24 dpf. Finally, we demonstrate that swimming velocity has potential to predict aggressive instances of behavior. Together, we find multiple levels of control for early life aggressive bouts from sensory input to hormonal organization of brain circuits.Item EVOLUTION OF THE CRISPR IMMUNE SYSTEM FROM ECOLOGICAL TO MOLECULAR SCALES(2024) Xiao, Wei; Johnson, Philip LF; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bacteria and archaea inhabit environments that constantly face viral infections and other external genetic threats. They have evolved an arsenal of defense strategies to protect themselves. My research delves into the CRISPR immune system, the only known adaptive immune system of prokaryotes. My work explores three different dimensions of the CRISPR immune system, ranging from ecological to molecular scales.From an evolutionary perspective, CRISPR is widely distributed across the prokaryotic tree, underscoring its immune effectiveness. However, the CRISPR distribution is uneven and some lineages are devoid of CRISPR. Here, I identify two ecological drivers of the CRISPR immune system. By analyzing both 16S rRNA data and metagenomic data, I find the CRISPR system is favored in less abundant prokaryotes in the saltwater environment and higher diverse prokaryote communities in the human oral environment. On the molecular level, the CRISPR system selects and cleaves its “favorite” DNA segments (also known as “spacers”) from invading viral genomes to form immune memories. I explore how the spacer sequence composition affects its acquisition rate by the CRISPR system. I develop a convolutional neural network model to predict the spacer acquisition rate based on the spacer sequence composition in natural microbial communities. The model interpretation reveals that the PAM-proximal end of the spacer is more important in predicting the spacer abundance, which is consistent with previous findings from controlled experimental studies. Combining these scales, CRISPR repeat sequences coevolve with the rest of the genome. Thus, I explore the potential of utilizing CRISPR repeat sequences for taxonomy profiling. I find a strong relationship between unique repeat sequences and taxonomy in both the RefSeq database and a human metagenomic dataset. Then I show high accuracy when utilizing repeat sequences in taxonomy annotation of human metagenomic contigs. This novel method not only aids in annotating CRISPR arrays but also introduces a novel tool for metagenomic sequence annotation.Item Application of advanced machine learning strategies for biomedical research(2023) Chou, Renee Ti; Cummings, Michael P.; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biomedical research delves deeply into understanding individual health and disease mechanisms. Recent advancements in technologies have further transformed the field with large-scale data sets, enabling data-driven approaches to identify important patterns and relationships from large data sets. However, these data sets are often noisy and unstructured. Moreover, missing values and high dimensionality further complicate the analysis processes aimed at yielding meaningful results. With examples in ocular diseases and malaria, this dissertation presents novel strategies employing machine learning to tackle some of the challenges in biomedical research. In ocular diseases, sustained ocular drug delivery is critical to retain therapeutic levels and improve patient adherence to dosing schedules. To enhance the sustained delivery system, we engineer peptide sequences as an adapter to impart desired properties to ocular drugs. Specifically, we develop machine learning models separately for three properties–melanin binding, cell-penetration, and non-toxicity. We employ data reduction techniques to reduce the number of features while maintaining the machine learning model performance and apply interpretable machine learning techniques to explain model predictions on the three properties. Experimental validation in rabbits show two-fold increase in drug retention time with the selected peptide candidate. The developed machine learning framework can be further tailored to engineer other properties in molecular sequences with a wide variety of potential in biomedical applications. Malaria is an infectious disease caused by protozoan of the genus Plasmodium and has been a burden in global health. Developing malaria vaccines is challenging due to the diversity in parasite antigen sequences, which may lead to immune escape. To facilitate the vaccine development process, we leverage the wealth of systems data collected from various sources. For facile data management, a database is constructed to store the structured data processed from the results of the bioinformatics tools. Due to the small fraction of Plasmodium proteins labeled as known antigens, and the remaining proteins unknown of being antigens or non-antigens, a positive-unlabeled machine learning method is applied to identify potential vaccine antigen candidates. Beyond malaria, our approach provides a promising framework for identifying and prioritizing vaccine antigen candidates for a broad range of disease pathogens.Item ECOLOGICAL APPLICATIONS OF MACHINE LEARNING TO DIGITIZED NATURAL HISTORY DATA(2022) Robillard, Alexander John; Rowe, Christopher; Bailey, Helen; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Natural history collections are a valuable resource for assessment of biodiversity and species decline. Over the past few decades, digitization of specimens has increased the accessibility and value of these collections. As such the number and size of these digitized data sets have outpaced the tools needed to evaluate them. To address this, researchers have turned to machine learning to automate data-driven decisions. Specifically, applications of deep learning to complex ecological problems is becoming more common. As such, this dissertation aims to contribute to this trend by addressing, in three distinct chapters, conservation, evolutionary and ecological questions using deep learning models. For example, in the first chapter we focus on current regulations prohibiting the sale and distribution of hawksbill sea turtle derived products, which continues internationally in physical and online marketplaces. To curb the sale of illegal tortoiseshell, application of new technologies like convolutional neural networks (CNNs) is needed. Therein we describe a curated data set (n = 4,428) which was used to develop a CNN application we are calling “SEE Shell”, which can identify real and faux hawksbill derived products from image data. Developed on a MobileNetV2 using TensorFlow, SEE Shell was tested against a validation (n = 665) and test (n = 649) set where it achieved an accuracy between 82.6-92.2% correctness depending on the certainty threshold used. We expect SEE Shell will give potential buyers more agency in their purchasing decision, in addition to enabling retailers to rapidly filter their online marketplaces. In the second chapter we focus on recent research which utilized geometric morphometrics, associated genetic data, and Principal Component Analysis to successfully delineate Chelonia mydas (green sea turtle) morphotypes from carapace measurements. Therein we demonstrate a similar, yet more rapid approach to this analysis using computer vision models. We applied a U-Net to isolate carapace pixels of (n = 204) of juvenile C. mydas from multiple foraging grounds across the Eastern Pacific, Western Pacific, and Western Atlantic. These images were then sorted based on general alignment (shape) and coloration of the pixels within the image using a pre-trained computer vision model (MobileNetV2). The dimensions of these data were then reduced and projected using Universal Manifold Approximation and Projection. Associated vectors were then compared to simple genetic distance using a Mantel test. Data points were then labeled post-hoc for exploratory analysis. We found clear congruence between carapace morphology and genetic distance between haplotypes, suggesting that our image data have biological relevance. Our findings also suggest that carapace morphotype is associated with specific haplotypes within C. mydas. Our cluster analysis (k = 3) corroborates past research which suggests there are at least three morphotypes from across the Eastern Pacific, Western Pacific, and Western Atlantic. Finally, within the third chapter we discuss the sharp increase in agricultural and infrastructure development and the paucity of widespread data available to support conservation management decisions around the Amazon. To address these issues, we outline a more rapid and accurate tool for identifying fish fauna in the world's largest freshwater ecosystem, the Amazon. Current strategies for identification of freshwater fishes require high levels of training and taxonomic expertise for morphological identification or genetic testing for species recognition at a molecular level. To overcome these challenges, we built an image masking model (U-Net) and a CNN to mask and classify Amazonian fish in photographs. Fish used to generate training data were collected and photographed in tributaries in seasonally flooded forests of the upper Morona River valley in Loreto, Peru in 2018 and 2019. Species identifications in the training images (n = 3,068) were verified by expert ichthyologists. These images were supplemented with photographs taken of additional Amazonian fish specimens housed in the ichthyological collection of the Smithsonian’s National Museum of Natural History. We generated a CNN model that identified 33 genera of fishes with a mean accuracy of 97.9%. Wider availability of accurate freshwater fish image recognition tools, such as the one described here, will enable fishermen, local communities, and citizen scientists to more effectively participate in collecting and sharing data from their territories to inform policy and management decisions that impact them directly.