18525 research outputs found
Sort by
Incorporating Data Triangulation to Promote Generalizable Outcomes for Function-Based Treatment of Severe Challenging Behavior
No full textAlthough function-based treatments have been proven to decrease challenging behavior, it is unclear whether these results translate to socially significant outcomes. In fact, researchers rarely analyze what happens outside the highly controlled setting in which behavior analytic sessions typically occur. In our first study, we used a changing criterion design to evaluate the effectiveness of using the Function-Informed Mechanisms-Based (FIMB) framework to increase levels of replacement behaviors and decrease tantrum behavior for three children with IDD (i.e., a single sibling set) and a history of severe challenging behavior. In the second study, we discussed caregiver reports of the children’s behavior outside of treatment, offering a more nuanced understanding of data patterns included in Study 1. By embedding a structured process of formative data triangulation, researchers expanded the existing treatment plan to promote generalizable decreases in challenging behavior and socially significant improvements in the family’s quality of life
Studies of the Temporal Dynamics of Brain Networks in Typical and Atypical Development
No full textResting-state EEG microstates have been used to study the temporal dynamics of brain networks across various functional states and pathophysiology in adults. Differences in brain network activity have been observed at rest in fMRI studies as a function of different resting-state conditions (e.g. eyes-open versus eyes-closed), age and between individuals with and without Autism Spectrum Disorders (ASD). To date only one study comparing these resting-state conditions has been performed using EEG microstates in healthy adults and very few studies of temporal dynamics in ASD exist in literature. My dissertation studies have sought to address how EEG microstate architecture is altered 1) in typically developing (TD) children across resting-state conditions 2) between TD and ASD children across resting-state conditions 3) across various EEG preprocessing pipelines. The results from these studies suggest that, in TD children, the availability of visual information during resting state impacts not only the temporal dynamics of the microstate associated with the visual network but also those related to other brain networks. In children with ASD, the microstate associated with the default mode network shows atypical architecture in eyes-closed resting-state and may be an indicator of altered interoceptive processes in individuals with ASD. These results are consistent with the findings from fMRI studies and provide additional insight on the temporal characteristics of the brain networks
Pathways to Heterogeneity in Uropathogenic Escherichia coli
No full textInfectious diseases often result from invasive bacteria that replicate within specific anatomical sites. The replication of these bacteria provokes immune responses, inflammation, and contributes to the onset of various disease symptoms. While traditionally, disease-promoting bacterial populations arising from a single progenitor were thought to be uniform in genotype and phenotype, this notion has been over-turned in recent years Variations in the microenvironment, including metabolic factors and stress-inducing cues, exert selective pressure on mixed isogenic bacterial populations, favoring phenotypes that enhance colonization. In this work, I investigate one such example of heterogeneity in this thesis dissertation, using uropathogenic Escherichia coli (UPEC), the predominant cause of urinary tract infections worldwide. Recurrent UTIs (rUTI) – which affect approximately 25% of women who experience a primary UTI – are primarily fueled by UPEC reservoirs established in the host. The contribution of heterogeneity to reservoir formation and recurrent UTI remains poorly understood. This dissertation focuses on an example of UPEC heterogeneity, identified in strains isolated from rUTI patients. I report a “peppermint” heterogeneous phenotype strain that results from differential expression of exopolymeric substances in UPEC colonies and subsequent differential uptake of the Congo red dye. Following the isolation of peppermint subpopulations, next-generation sequencing revealed a regulatory network controlling curli and cellulose, the exopolymeric substances that bind Congo red in E. coli. Moreover, I report that the subpopulations isolated during this thesis work have differing colonization potentials in reservoir niches, specifically nlpI- and waaO- deficient mutants. My work emphasizes the characterization of heterogeneous clinical UPEC morphotypes and their implications as a potential source for UPEC persistence
Conformational Dynamics of Neurotransmitter:Sodium Symporters: Insights from MhsT and hSERT
No full textNeurotransmitter:sodium symporters (NSSs) play critical roles in neural signaling by regulating neurotransmitter uptake into cells powered by sodium electrochemical gradients. Due to their significance in disease and as drug targets, extensive research has focused on understanding the underlying functional mechanisms of NSSs and their bacterial homologs, with a focus on the alternating access model. In this dissertation, we investigated the dynamics of two NSS family members, MhsT from Bacillus halodurans and the human serotonin transporter (hSERT), using a combination of electron paramagnetic resonance (EPR) spectroscopy and AlphaFold2 (AF2) techniques. The core of this work centers on comprehensive double electron-electron resonance (DEER) spectroscopy experiments on MhsT in both detergent and lipid nanodisc environments. We developed a novel computational/experimental methodology to illuminate the conformational landscape of MhsT alternating access, which can be broadly applied across a diverse spectrum of transporters. While our model of MhsT transport highlights conserved features of alternating access shared with hSERT, the N-terminal regions of mammalian NSSs introduce more complex functions not present in their bacterial counterparts. Therefore, we focused on evaluating hSERT’s N-terminal structural dynamics. However, much of our effort involved optimizing procedures to facilitate EPR studies. This work culminated in preliminary EPR data for five sites along the N-terminus, providing insights into its structural flexibility. Additionally, our data reveal differences in N-terminal dynamics between detergent and proteoliposome environments. Our research on both transporters emphasizes the importance of the lipid environment in modulating NSS dynamics. Collectively, these findings contribute to a deeper understanding of NSS function, highlighting the conserved features that bridge bacterial and mammalian systems as well as their distinct differences, showcasing the broad spectrum of neurotransmitter transport systems
Structure from Dynamics: Machine Learning and Modeling in Cells Undergoing Multi-scale Self-organization
No full textLiving things are complex, self-assembling systems capable of generating structured patterns that span nanosecond-scale behavior of amino acids to trillions of cells organized in a decades-long process of development. With computational models we can represent biological processes as mathematical relationships, and simulations allow us to observe and predict their behavior. Here we confront several challenges in the process of constructing and making sense of computational biological models.
We present a vision for how emerging computational tools and frameworks can enable investigators to construct models that are both larger and more detailed than previously possible. We demonstrate that these energy-rule based structural models can explain how changes in individual protein conformations lead to adaptive resistance to targeted cancer therapy. Further, we envision a road map for the next generation of models, combining computationally predicted protein structures, machine reading to encode molecular interactions, biophysical constraints, and automated mining of massive in-silico experiments to provide unprecedented biological understanding.
We then focus on applications of machine learning tools to the challenge of understanding the biological regulatory circuits that govern early development. We present MC-Boomer, a reinforcement learning algorithm that automatically constructs Boolean models with specified behaviors. We describe a model analysis framework that identifies families of structurally similar models and extracts the interactions that drive their behavior. We then introduce TangleFlow, a deep learning architecture that models the dynamics of cells on the developmental landscape. Applied to a mouse model of Cornelia de Lange syndrome (CdLS), TangleFlow accurately captures subtle differences in early cell fate commitment that underlie large-scale phenotypic differences in CdLS patients. We further present a novel analysis technique, relying on the sparsity of the TangleFlow architecture, that allows us to identify multi-gene regulatory circuits guiding cell fate commitment.
Together, this work aims to both propose and implement a novel vision for constructing and analyzing data-driven models of biological systems. We aim to build computational bridges from atomistic biophysical descriptions of protein conformations to tissue-scale cell fate organization, helping us to manipulate biological systems to restore their health and function
A Central-California Stalagmite Record of Hydroclimate Variability During the Holocene from White Moon Cave, CA
No full textI present a precisely dated, high-resolution stable isotope (δ13C and δ18O) and trace element (Mg/Ca, Ba/Ca, Sr/Ca, Zn/Ca) record of mid-Holocene hydroclimate variability using a speleothem (WMC5) from White Moon Cave (WMC) in central California. Geochemical analyses of WMC5, which grew from ~8,000–5,900 cal. years BP, reveal a multi-centennial baseline drying trend from ~6400–5,900 cal. years BP. This trend is indicated by more positive δ¹³C and increases in Mg/Ca, Ba/Ca, and Sr/Ca, along with a decrease in Zn/Ca, suggesting the potential regional onset of the mid-Holocene Warm Period (MHWP), a globally expressed yet asynchronous event. Comparison with previously published stable isotope and trace element data from another WMC speleothem (WMC1) demonstrate that WMC5 proxies exhibit a positive offset and higher range of variability. These offsets, alongside weak correlations between WMC5 and WMC1 δ¹³C and δ¹⁸O during coeval growth, suggest complex controls on WMC proxies. Statistical comparison of WMC5 proxy records with coeval paleoclimate records from the western US reveals strong positive and moderate negative correlations with Nevada and Oregon speleothem proxy records, respectively; though, statistical comparison is constrained by the WMC5 record’s comparatively higher temporal resolution. Visual comparison shows that mid-Holocene drying at WMC ~6,400 cal. years BP overlaps warming and drying trends in coeval western US paleoclimate records and wetter conditions in a Pacific Northwest record. Future work extending WMC1 proxy record temporal coverage and resolution will elucidate regional hydroclimate volatility, variable hydroclimate expressions in WMC speleothems, and spatio-temporal dynamics of the MHWP as recorded at WMC
The Genetic Architecture of Diabetic Retinopathy
No full textThe goal of this dissertation was to utilize techniques to understand the genetic architecture of diabetic retinopathy with three aims: 1) develop a phenotyping algorithm to identify diabetic retinopathy cases in electronic health records (EHR), 2) examine the associations between genetic variants and diabetic retinopathy, and 3) validate results from genetic predictors of diabetic retinopathy. By developing algorithms to identify individuals with diabetes, with and without diabetic retinopathy, we show the potential of leveraging existing EHR systems to advance our understanding of etiology of diabetic retinopathy. We demonstrate the EHR-based algorithms to be portable and accurate across separate EHR systems and this represents a vast improvement over existing high-throughput method such as PheWAS. We used summary statistics from a previous GWAS along with results from the MVP, BioVU, UKBB, and the MGBB to evaluate genetic associations with diabetic retinopathy. We identified nine novel loci, localizing to MFSD4A, TENM2, GRHL2, TCF7L2, FBXW8, COL4A2, SLC16A3, TMPRSS6, and G6PD, that either increase or decrease risk for diabetic retinopathy. Additionally, there was a high genetic correlation between African- and European-ancestry meta-analyses, even though some loci only appeared in a single ancestry. Validation of our findings raise several practical and clinical implications for management of diabetes. Given the common occurrence of G6PD deficiency and its impact on diabetic complications, screening African Americans with diabetes for this deficiency may be of high priority. If screening African Americans for this variant is cost-prohibitive, then concurrently checking blood glucose levels along with HbA1c could be another strategy. Our estimates suggest that with comprehensive genetic screening of African Americans and subsequent standard-of-care treatment, possibly aimed at glucose rather than HbA1c targets, ~12% of diabetic retinopathy cases could be avoided in U.S. African Americans alone. Based on the evidence from this study, we suggest that implementation of this screening would mitigate disparity of prevalence and severity for other diabetes sequelae among African American
An Analysis of Local Neighborhood-based Paradoxes in Signed Social Networks
No full textGiven the ubiquity and significance of social network systems, comprehending the network topology is essential for a deeper understanding of these networks and can help guide online user interactions. One notable phenomenon in social networks, Friendship Paradox (FP), has been extensively studied and has led to the Generalized Friendship Paradox (GFP), which states that an individual’s neighbors, on average, have more of some measurable characteristic or quantity than the individual (e.g., friends/degree in the original FP). However, most of the existing works on FP and GFP only focus on positive relationships on an unsigned network setting. However, users in online social networks have negative relationships just as they do in the real world (i.e. reporting, blocking, leaving a negative review). Thus social networks can be more accurately modeled and information rich with signed and directed edges. To bridge this crucial gap, we investigate (G)FP in signed networks which contain both positive and negative relationships (e.g., friends and foes). Specifically, we propose the Signed Neighbor Paradox metric and its generalized version based on the traditional (G)FP that not only considers homogeneous link relations but also heterogeneous link relations. The Signed Neighbor Paradox aims to capture how users’ representative positive and negative relations compare to that of their friends and foes. To further understand this paradox and the relationship between a node and its neighborhood, we analyze the impact that a node’s local topology, such as degree and local clustering coefficient, have on the signed neighbor paradox. We additionally illustrate how the Signed Neighbor Paradox evolves with respect to time and increasing relationships. Furthermore, we develop a Signed Neighbor-Neighbor Paradox metric to study the relationship between an individual’s positive and negative neighborhood sets. Our analysis is performed on five representative signed social networks and we conclude with discussing numerous future directions
