12466 research outputs found
Sort by
Patterns of Synonymous Codons Use in Highly Conserved Proteins
Get PDFSynonymous codons have been at the forefront of recent biological research in the pursuit of understanding evolutionary history and furthering pharmacology. Utilizing a dataset of 2,254 protein chains paired with every possible match found on NCBI’s Nucleotide collection, a database was created to house a compilation of genetic sequences sharing at least one instance of one synonymous codon with another and the same protein chain. Through a data transformation process and several analyses, emerging patterns regarding historical mutations of species were observed. Most notable is how various chains and classification groups maintain conservation of specific codons while others appear much more susceptible to mutation. Evolutionary synonymous codons can be found in a wide array of species, especially eukaryotic chordates, and the abundance in count is dependent on how well conserved and commonly used the protein is throughout a phylogenetic tree
ANALYSIS OF FLOW DIVERSION TREATMENT AND FLOW EFFECTS IN CEREBRAL ANEURYSMS
Get PDFA cerebral aneurysm (CA) is a pathological enlargement of a weakened arterial brain wall, which occurs in 2-5% of adults. Subarachnoid hemorrhage (SAH) due to aneurysm rupture has high mortality and disability rates. Asymptomatic aneurysms, detected incidentally, can be treated with surgical clipping, endovascular coiling, and flow diverters (FDs) deployment to prevent unruptured aneurysms from rupturing. FDs are one of the most popular treatments which reduce inflow into aneurysm sac and lead to thrombosis without blocking the vessel.Complications of aneurysm treatments with FDs include Delayed intraparenchymal hemorrhage (DIPH) which are often fatal, and incomplete occlusion after FDs deployment. To address these problems, this dissertation aimed to analyze the distal hemodynamic alterations induced by the treatment of CAs with FDs and to predict which aneurysms are more likely to occlude quickly after deploying FD using computational fluid dynamics xix (CFD) simulations. The study used a model of the brain arterial network and found that flow diverters treatment can cause flow reversal in distal collaterals which may lead to detrimental effect on these collateral vessels, as well as DIPHs. It also found that CFD models can predict aneurysm occlusion, allowing physicians to manage treatment strategies more effectively. Due to these complications and challenges of aneurysm treatments, identifying high-risk aneurysms for immediate treatment and avoiding unnecessary treatment are crucial in clinical decision-making. Understanding the mechanisms of aneurysm development and growth is essential for identifying high-risk aneurysms and determining the appropriate treatment. This dissertation investigated the mechanisms of aneurysm wall enhancement using CFD simulations and high-resolution medical images. The study found that on average, wall shear stress was lower in enhanced regions, and that this association varied depending on the location of the region on the aneurysm sac. The study also looked into the effects of blood flow stagnation in aneurysms and developed a CFD-based virtual angiogram model to identify and quantify aneurysms with significant flow stagnation. This dissertation also aimed to evaluate the predictive power of machine learning models developed with cross-sectional data in identifying aneurysms with a high risk of focal growth. As, collecting longitudinal data is difficult compared to cross-sectional data, the study focused on using cross-sectional data to develop the models. The results of the study showed that the models based on cross-sectional data can effectively identify aneurysms at a higher risk of future focalized growth and may be useful as early indicators of future risk in clinical practice. xx In overall, the objective of this dissertation was to better understand the mechanisms of aneurysm progression and growth and to predict the efficacy of flow diverters as a treatment option for aneurysms. By implementing these results, it is hoped that clinicians will be able to better understand their patients' conditions and make more informed and effective treatment decisions
Building Peace for the Peacebuilders: A Psychological Profile of Resilience within Insider Peacebuilders Supporting Global Reconciliation Processes
Get PDFThis work is embargoed by the author and will not be publicly available until December 2025.‘Insider peacebuilders’ are (a) deeply connected to and are trusted by one or more communities experiencing conflict and (b) support or lead peacebuilding processes, such as peri-/post-conflict reconciliation, within this conflict context. Theoretical, anecdotal, and ethnographic accounts of insider peacebuilders suggest this work can enact a profound toll on their mental health and wellbeing and, by extension, negatively impact the effectiveness and sustainability of peacebuilding processes. Building upon these studies’ insights and limitations, I sought to answer the following research questions for this dissertation project: (1) How does supporting peacebuilding processes shape insiders’ resilience, and (2) how can insiders’ wellbeing be better supported in this line of work? To answer these questions, I conducted a secondary analysis of interview data from forty-one (41) insider peacebuilders supporting global reconciliation processes, initially collected by a team of researchers I led at George Mason University’s Mary Hoch Center for Reconciliation between 2021-2023. Our project deployed a participatory action research methodology to understand insider peacebuilders’ conceptions of peacebuilding and reconciliation, history and experiences in the field, and the interrelations between their work and wellbeing. Using an inductive thematic analysis of interview data, I investigate the following main themes that inform this dissertation project: (a) psychological profiles of three typologies of insider peacebuilders; (b) substantial challenges insider peacebuilders face that negatively impact their wellbeing; and (c) insiders’ innovative practices of biological, psychological, social, cultural, and structural resilience. These findings have crucial implications for individual, organizational, and institutional support mechanisms for insiders, their loved ones, and their communities during peacebuilding processes. I conclude this dissertation with an exploration of the methodological, theoretical, and practical implications of this research, including a novel theoretical model explaining resilience in insider peacebuilders; a framework for constructing healing-centered peacebuilding processes; and a future research and practice agenda that empowers peacebuilders to be healthier, happier, safer, and more sustainable in their work and lives.2025-12-1
Structural Analysis of Reinforced Concrete Columns Subjected to Underwater Explosions
Get PDFThis thesis presents a study on structural modeling of reinforced concrete (RC) columns subjected to underwater explosions (UNDEX). With the heightened tensions stemming from warfare as well as accidents such as gas explosions or construction overblasting, understanding of UNDEX is critical to minimizing damage to infrastructure and human lives. Previous research was motivated by defense and is still a driving factor for blast research in the current day. Physical experimentation of UNDEX is expensive and computational analysis plays an important role in analyzing structures subjected to UNDEX. Published research on the effect of UNDEX in civil engineering structures is relatively scant. This thesis provides a review of current literature concerning UNDEX effects, a proposed computational framework for assisting simulation of UNDEX effects, and a series of validation and sensitivity studies to examine a concrete material model in a finite element analysis focused on reinforced columns. First, a literature review discussing the current understanding of UNDEX phenomena and effects on concrete structures is explored with considerations towards computational methods. Then a computational framework used to analyze complex loadings in ABAQUS is discussed. Additionally, an examination of empirical pressure equations used for dynamic analysis is presented and compared with existing experimental data. Lastly, the concrete damage plasticity (CDP) model is examined with sensitivity studies conducted on its input parameters. The effect of varying CDP input parameters on maximum displacement response is outlined
Vegetation structure predicts avian breeding activity, space use, and diversity
No full textEcosystem decay is responsible for biodiversity declines following forest fragmentation, as initially abundant species may become rare, or experience delayed local extinctions. However, the underlying mechanisms behind the delayed local extinction of certain species following fragmentation are unknown. The purpose of this dissertation is to uncover potential mechanisms that are responsible for the delayed local extinctions of birds from forest fragments in the Amazon rainforest. In addition, this dissertation seeks to study the influence of vegetation structure on avian breeding activity, space, use, and diversity, in order to inform adaptive management strategies that promote the return of sensitive avian species to disturbed habitats. In the first study, I used 10 years of banding data at the Biological Dynamics of Forest Fragments Project (BDFFP) to analyze potential mechanisms responsible for the delayed local extinction of certain avian species after forest fragment isolation. This study conclusively identified decreased breeding activity as a mechanism contributing to the delayed local extinction of avian species after forest fragment isolation. I also found no significant difference in the number of young birds after fragment isolation, eliminating reduced recruitment of young birds as a contributing mechanism to delayed local extinctions. For the second study, I used light detection and ranging (LiDAR) technology to analyze the influence of vegetation structure on the space use of understory mixed-species bird flocks. I found that flocks often used locations with lower elevations and greater structural complexity in the subcanopy (16-25 m). However, these general tendencies varied across habitats, suggesting that flock vegetative preferences are likely flexible within and across habitats. Using both the behavioral observations of horizontal and vertical flock movements, coupled with three-dimensional LiDAR technology, I provide robust estimates of habitat preference for avian mixed-species flocks along a disturbance gradient. Finally, the third study, I extended my analysis of vegetative predictors of flock space use to use LiDAR technology to assess vertical vegetation structure to determine if any vegetative predictors are positively correlated with species richness and functional diversity in mixed-species flocks, to determine if recovering secondary forest is a viable forest type for preserving biodiversity and ecosystem services in disturbed landscapes. I found that areas with denser and more complex understory and subcanopy vegetation were associated with greater flock species richness and functional diversity. Elevation was also an important predictor of species richness and functional richness in both forest types, as lower elevations were negatively correlated with species richness and positively correlated with functional richness. I also found that leaf area density in the subcanopy and understory were especially important vegetative metrics for maintaining or attracting functional richness in mixed-species flocks in both disturbed and undisturbed habitats. The results of this dissertation carry important implications for forest restoration efforts that aim to conserve biodiversity in the Amazon. Adaptive management strategies can integrate the results of this dissertation into reforestation plans to facilitate the return of avian species richness and functional diversity to regenerating secondary forests in an effort to maximize the socio-ecological benefits that these ecosystems offer
Love
No full textLove is not one thing today, and it was never one thing in the past. In this short course I talk about different kinds of loves, related to and yet defined against one another and therefore often in conflict. Unlike most surveys of love, my focus is not on theories but rather on the fantasies that guide us, setting up expectations for ourselves and others
Interactive Numerical Optimization and Predictive Geometry Registration
Get PDFShape similarity is a fundamental problem in geometry processing, enabling applications such as surface correspondence, segmentation, and edit propagation. For example, a user may paint a stroke on one finger of a model and desire the edit to propagate to all fingers. Automatic approaches have difficulty matching user expectations, either due to an algorithm's inability to guess the scale at which the user is intending to edit or due to underlying deficiencies in the similarity metric (e.g., semantic information not present in the geometry). We propose an approach to interactively design self-similarity maps. We investigate two primitive operations, useful in a variety of scenarios: region and curve similarity. Users select example similar and dissimilar regions. Starting with an automatically generated multi-scale shape signature, our approach solves for a scale parameter and thresholds that group the example regions as specified. We propose a new Smooth Shape Diameter Signature (SSDS) as a more efficient alternative to the Heat or Wave Kernel Signature. If no such parameters can be found, our approach modifies the shape signature itself. Given a curve drawn on the surface, we perform hybrid discrete/continuous optimization to find similar curves elsewhere. We apply our approach for interactive editing scenarios: propagating mesh geometry, patterns duplication, and segmentation. These demonstrations lead to observations regarding the nature of optimization procedures within user-driven applications. In many interactive systems, user input initializes and launches an iterative optimization procedure. The goal is to provide assistive feedback to some creation/editing process. Examples include constraint-based GUI layout and complex snapping scenarios. Many geometric problems, such as fitting a shape to data, involve optimizations which may take seconds to complete (or even longer), yet require human guidance. In order to make these optimization routines practical in interactive sessions, simplifications or sacrifices must be made. Canonically, non-convex optimization problems are solved iteratively by taking a series of steps towards a solution. By their nature, there are many locally optimal solutions; which solution is found is highly dependent on an initial guess. There is a fundamental conflict between optimization and interactivity. Interrupting and restarting the optimization every time the user, e.g., moves the mouse prevents any solution from being computed until the user ceases interaction. Continuing to run the optimization procedure computes a perpetually outdated solution. This presents a particular unsolved challenge with respect to direct manipulation. Every time the user, e.g., moves the mouse, the entire optimization must be restarted with the new user input, since returning a stale result associated with the previous user state is undesirable. We propose predictive short-circuiting to reduce this fundamental tension. Our approach memoizes paths in the optimization's configuration space and predicts the trajectory of future optimization in real-time, leveraging common continuity assumptions. This enables direct manipulation of formerly sluggish interactions. We demonstrate our approach on geometric fitting tasks. Additionally, we evaluate complementary mouse motion prediction algorithms as a means to discard or skip optimization problems that are irrelevant to the user's intended initial configuration for a targeted optimization procedure. Predicting where the mouse cursor will be located at the end of an operation, such as dragging a model into a scene for alignment, allows us to preemptively begin solving the targeted problem before the user finishes their movement. We take advantage of the fact that the prediction indicates the approximate energy basin the optimization procedure will need to explore
GENERATIVE AI FOR PROTEIN MODELING: SAMPLING PROTEIN CONFORMATION SPACE
Get PDFProtein modeling has benefitted greatly from machine learning methods over the years,particularly in well-defined prediction tasks attempting to connect the chemical characteristics and makeup of protein molecules with their biological activities and function in the cell. Many decades’ worth of experimental, computational, and theoretical studies, however, have informed us both on the rich set of activities a protein molecule can carry out in the cell, as well as on the intrinsic structural plasticity that allows a protein molecule to interact with diverse molecular partners. Accounting for such plasticity by sampling, for instance, the potentially rich space of conformations of a protein molecule has motivated much computational research over the years and remains a challenging problem. In this dissertation, we build over the generative AI platform, harnessing the growing sophistication of generative deep learning to address protein structure modeling at increasing complexity. Specifically, this dissertation proposes, implements, and rigorously evaluates deep generative models of various architectures for sampling protein conformation space. The work presented in this dissertation work advances protein structure modeling and, more broadly, generative AI for science
Towards Elastic and Cost-effective Stateful Serverless Systems
Get PDFServerless computing has emerged as a new paradigm that transforms conventional VM-based cloud computing by offering fine-grained computational resources with high elasticity while abstracting away complex system administration tasks. This computing model has garnered significant interest due to its autoscaling capabilities, massive parallelism, and fine-grained pay-per-use billing. Function-as-a-Service (FaaS) is a state-of-the-art example of the serverless service, which provides developers with cloud functions running relatively small pieces of code written in a high-level language to construct stateless serverless applications. In this dissertation, we develop a series of solutions to extend serverless computingbenefits to stateful systems. Our research starts with utilizing an existing stateless FaaS offering to create a stateful in-memory cache. To address the statelessness of cloud functions, we study function behavior. FaaS providers often cache functions to mitigate cold start penalties. Our six-month-long study reveals that this caching policy can be used for retaining data through multiplexed periodic function invocations. By integrating more fault-tolerant techniques such as erasure coding and periodic delta backup, we introduce the InfiniCache, a first-of-its-kind, cost-effective in-memory object cache based on stateless serverless functions. It is the first step towards building stateful serverless systems. The InfiniCache gains 31 − 96× tenant-side cost savings compared to AWS ElastiCachefor a large-object-only production workload, maintains an effective 95.4% data durability for each one hour window, and offers competitive performance comparable to typical in-memory caches. Next, we present InfiniStore, a serverless cloud storage with the ServerlessMemoryservice. Building upon our initial solution, InfiniCache, ServerlessMemory employs a sliding-window-based memory management strategy, inspired by the garbage collection mechanisms in programming languages, to effectively segregate hot and cold data. This approach provides fine-grained elasticity, good performance, and an extremely low-cost pay-per-access cost model. Combined with an inexpensive cloud object layer, InfiniStore ensures durability despite function failures through a fast parallel recovery scheme built on the auto-scaling functionality of a FaaS platform. The fine-grained elasticity achieved results of 26.26% cost benefits compared to our initial solution. Lastly, we propose InfiniMLT, a GPU-enabled stateful serverless service for OnlineInteractive Machine Learning Training Service (OIMLTS), designed to optimize task interactivity, GPU utilization, and cost. All three solutions demonstrate the feasibility of leveraging serverless computing for stateful applications while maintaining key properties such as elasticity, cost-effectiveness, and pay-per-use
Ground-based light curve follow-up validation observations of TESS object of interest TOI 3616.01
Get PDFTESS object of interest TOI 3616 was investigated using George Mason University’s ground-based telescope. The goal of this investigation is to provide follow-up research on TOI 3616.01. Using AstroImageJ, a light curve was generated to provide more insight into the flux data. Due to the large amount of exposures needed to be removed, the data remains inconclusive. However, the generated light curve may produce viable data for future analyses