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Application Of Functional Near-Infrared Spectroscopy To Examine The Neurophysiology Of The Injured Adolescent Brain
No full textOver 1.9 million children sustain a concussion in the US annually, with adolescents accounting for over 50% of these injuries. Common sequelae of concussion include neurocognitive deficits, which can negatively affect daily activities, and persist past symptom resolution, the primary method for gauging recovery. There are currently few methods for quantifying cognitive recovery after concussion in adolescents. Functional near-infrared spectroscopy (fNIRS) is a portable, optical, neuroimaging technology that can quantitatively assess cognitive deficits by measuring brain activation in the prefrontal cortex, a brain region implicated in cognition and undergoing rapid development in adolescence. fNIRS has been applied to adult populations; however, because of the variable rates of development, fNIRS findings in adults cannot be directly translated to adolescents. To quantify prefrontal cortical response in adolescence due to concussion or repetitive head acceleration event exposure (RHAEE) due to sport participation, three studies were undertaken. First, in a sample of uninjured high school soccer players, in which RHAEE was quantified over a season, fNIRS was used pre- and post-season to measure prefrontal cortical response during cognitive assessments. Adolescents with greater RHAEE displayed greater prefrontal cortical activation during the most cognitively complex portions of the assessments at post-season, compared to pre-season, indicating a reliance on aberrant neural networks after a season of RHAEE. This trend continued in the second study when, compared to uninjured youth, concussed adolescents displayed lower neural efficiency, a measure that contextualizes cortical activation with behavioral performance, during the dual motor-cognitive condition of a gait assessment, but not during simpler single-task conditions. Concussed adolescents also displayed less efficient motor unit recruitment during walking conditions compared to uninjured adolescents. Finally, in the third study during a validated simulated driving assessment, concussed adolescents displayed lower neural efficiency during certain distracted driving scenarios. Collectively, the studies suggest cognitive deficits in adolescents with greater RHAEE or a concussion can be identified via cortical activation and neural efficiency metrics from fNIRS and manifest during complex ecologically valid assessments that mimic real-world activities
Hyperscale Data Processing With Network-Centric Designs
Get PDFToday’s largest data processing workloads are hosted in cloud data centers. Due to unprecedented data growth and the end of Moore’s Law, these workloads have ballooned to the hyperscale level, encompassing billions to trillions of data items and hundreds to thousands of machines per query. Enabling and expanding with these workloads are highly scalable data center networks that connect up to hundreds of thousands of networked servers. These massive scales fundamentally challenge the designs of both data processing systems and data center networks, and the classic layered designs are no longer sustainable.
Rather than optimize these massive layers in silos, we build systems across them with principled network-centric designs. In current networks, we redesign data processing systems with network-awareness to minimize the cost of moving data in the network. In future networks, we propose new interfaces and services that the cloud infrastructure offers to applications and codesign data processing systems to achieve optimal query processing performance. To transform the network to future designs, we facilitate network innovation at scale.
This dissertation presents a line of systems work that covers all three directions. It first discusses GraphRex, a network-aware system that combines classic database and systems techniques to push the performance of massive graph queries in current data centers. It then introduces data processing in disaggregated data centers, a promising new cloud proposal. It details TELEPORT, a compute pushdown feature that eliminates data processing performance bottlenecks in disaggregated data centers, and Redy, which provides high-performance caches using remote disaggregated memory. Finally, it presents MimicNet, a fine-grained simulation framework that evaluates network proposals at datacenter scale with machine learning approximation. These systems demonstrate that our ideas in network-centric designs achieve orders of magnitude higher efficiency compared to the state of the art at hyperscale
Mechanized Reasoning About how Using Functional Programs And Embeddings
Get PDFEmbedding describes the process of encoding a program\u27s syntax and/or semantics in another language---typically a theorem prover in the context of mechanized reasoning. Among different embedding styles, deep embeddings are generally preferred as they enable the most faithful modeling of the original language. However, deep embeddings are also the most complex, and working with them requires additional effort. In light of that, this dissertation aims to draw more attention to alternative styles, namely shallow and mixed embeddings, by studying their use in mechanized reasoning about programs\u27 properties that are related to how . More specifically, I present a simple shallow embedding for reasoning about computation costs of lazy programs, and a class of mixed embeddings that are useful for reasoning about properties of general computation patterns in effectful programs. I show the usefulness of these embedding styles with examples based on real-world applications
The Network Science Of Distributed Representational Systems
Get PDFFrom brains to science itself, distributed representational systems store and process information about the world. In brains, complex cognitive functions emerge from the collective activity of billions of neurons, and in science, new knowledge is discovered by building on previous discoveries. In both systems, many small individual units—neurons and scientific concepts—interact to inform complex behaviors in the systems they comprise. The patterns in the interactions between units are telling; pairwise interactions not only trivially affect pairs of units, but they also form structural and dynamic patterns with more than just pairs, on a larger scale of the network. Recently, network science adapted methods from graph theory, statistical mechanics, information theory, algebraic topology, and dynamical systems theory to study such complex systems. In this dissertation, we use such cutting-edge methods in network science to study complex distributed representational systems in two domains: cascading neural networks in the domain of neuroscience and concept networks in the domain of science of science.
In the domain of neuroscience, the brain is a system that supports complex behavior by storing and processing information from the environment on long time scales. Underlying such behavior is a network of millions of interacting neurons. Many recent studies measure neural activity on the scale of the whole brain with brain regions as units or on the scale of brain regions with individual neurons as units. While many studies have explored the neural correlates of behaviors on these scales, it is less explored how neural activity can be decomposed into low-level patterns. Network science has shown potential to advance our understanding of large-scale brain networks, and here, we apply network science to further our understanding of low-level patterns in small-scale neural networks. Specifically, we explore how the structure and dynamics of biological neural networks support information storage and computation in spontaneous neural activity in slice recordings of rodent brains. Our results illustrate the relationships between network structure, dynamics, and information processing in neural systems.
In the domain of science of science, the practice of science itself is a system that discovers and curates information about the physical and social world. For centuries, philosophers, historians, and sociologists of science have theorized about the process and practice of scientific discovery. Recently, the field of science of science has emerged to use a more data-driven approach to quantify the process of science. However, it remains unclear how recent advances in science of science either support or refute the various theories from the philosophies of science. Here, we use a network science approach to operationalize theories from prominent philosophers of science, and we test those theories using networks of hyperlinked articles in Wikipedia, the largest online encyclopedia. Our results support a nuanced view of philosophies of science—that science does not grow outward, as many may intuit, but by filling in gaps in knowledge.
In this dissertation, we examine cascading neural networks first in Chapters 2 through 4 and then concept networks in Chapter 5. The studies in Chapters 2 to 4 highlight the role of patterns in the connections of neural networks in storing information and performing computations. The study in Chapter 5 describes patterns in the historical growth of concept networks of scientific knowledge from Wikipedia. Together, these analyses aim to shed light on the network science of distributed representational systems that store and process information about the world
No Such Thing As A Free Lunch? A Three Part Analysis Of Free School Meal Programs Under The Community Eligibility Provision
Get PDFTraditional federal school meals help mitigate food insecurity among students (Hinrichs, 2010) but do not fully eliminate it. The Community Eligibility Provision (CEP) is a federal attempt to expand access to school meals in areas of targeted need. Schools that opt into CEP offer meals at no cost to all students regardless of individual need, thus replacing free and reduced-price meal applications. However, by virtue of the funding design, schools with lower levels of documented poverty are financially disincentivized from participating in CEP and despite promising benefits, many of these schools do not take up the program. Importantly, even though these schools demonstrate “lower” need, their needs may still be persistent and severe as qualification standards may under-diagnose poverty, especially in specific communities. I conduct a three-part analysis of CEP. Part one is a systematic review of existing CEP literature. CEP has shown promise in initial research to benefit students with positive outcomes on student participation in meal programs, improved nutrition quality, improved test scores, and improved attendance and taken cumulatively, indicate a reduction in anti-poverty stigma. In part two, I conduct a novel analysis of schools that opt into CEP before subsequently opting out. I find that students miss more school when CEP is taken away, an effect driven largely by students who are economically disadvantaged. In part three, I analyze the economic implications of policy proposals that expand or contract CEP. Results indicate that CEP could be expanded to provide access to nearly 20 million more students with a net federal school meal expenditure change of between 11-15.3%. Taken together, CEP is a program that benefits economically disadvantaged students in spite of a sliding scale finance schedule that disadvantages schools. Policy changes that would improve this sliding scale feature are reasonably feasible and would impact millions of economically disadvantaged students. These analyses are timely, given recent interest in the expansion of CEP and have the potential to contribute to important conversations on the future of federal school meal policy
Engineering Injectable, Radiopaque Hydrogels For X-Ray Imaging And Therapeutic Delivery For Cancer Treatments
No full textCancer is the leading cause of death globally and was responsible for an estimated 9.6 million deaths in 2018. Depending on the type and stage of cancer, treatments to eradicate the tumor or slow its growth include surgical intervention, chemotherapy, radiation therapy, immunotherapy, but these treatments may result in severe side effects,. Local sustained and controlled release of these therapeutic agents can significantly improve therapeutic efficacy and alleviate side effects. To achieve local controlled therapeutics release, novel drug delivery systems for the combination of cancer therapies need to be developed, and hydrogels are an attractive vehicle for this purpose. Hydrogels are three-dimensional hydrophilic polymeric networks that can swell with water. Their porosity permits loading of drugs and other cargoes into the gel matrix. One of the most significant developments of hydrogels is the emergence of stimuli-responsive hydrogels. Hydrogels can be designed to be injectable and formed in situ upon temperature changes or be stimuli-responsive serving as an on-demand drug delivery platform, allowing clinicians to modulate the timing and extent of drug release and plan for cancer treatment regimens, such as radiotherapy or immunotherapy. Thus, we herein propose the development of thermosensitive tumor casting hydrogels for treatment of hepatocellular carcinoma, and FLASH radiotherapy responsive hydrogels for immunotherapy of melanoma,Unfortunately, hydrogels present challenges when attempting to assess their properties in tissue, as crucial information involving hydrogel administration site and degradation degree is often lacking. To address this issue, evaluating and monitoring hydrogels with noninvasive imaging modalities is on the rise. Therefore, we incorporated gold nanoparticles (AuNP) into the previously mentioned hydrogels making them radiopaque. We recently surveyed elements that could potentially be used as SPCCT contrast agents and discovered that ytterbium can produce twice the contrast of the element most used for CT and SPCCT contrast agents, gold. Therefore, finally, we developed ultra-small ytterbium nanoparticles that can be used as contrast agents for both CT and SPCCT and can be used in the hydrogel system
it\u27s Only Fellow Refugees Who Assist: Social Capital, Social Institutions, And Self-Reliance In Kakuma Refugee Camp, Kenya
No full textStudies have increasingly examined the economic lives of refugees, revealing a wide range of economic activity in displacement. Seeking to promote a neoliberal ethos of self-reliance, policymakers have focused on the role of employment on individual life trajectories. Yet, research also suggests the crucial role of humanitarian organizations and social networks. Drawing on ethnographic observation and interviews with refugee households and humanitarian workers in Kakuma refugee camp in Kenya, along with weekly questionnaires with refugee families, this dissertation brings together three papers on refugees’ economic lives. In Kakuma, a broad range of social institutions played a key role in the distribution of resources—from humanitarian organizations to refugee-led groups, such as churches and women’s groups, which structured social networks. Yet, sources of support could become sources of instability, as refugees’ faced barriers in accessing assistance. First, although employment and income are important economic drivers for self-reliance, refugee households across social class backgrounds reported successive economic shocks across sectors. These shocks reverberated across communities, destabilizing even refugee households with relatively stable employment. Thus, a household’s ability to cope with any one shock depended not only on their income, but also on the broader context of multiple intersecting shocks. Second, while refugee social networks provided crucial resources to overcome shocks, social ties also faced resource downturns. As a result, their support varied over time. Thus, the presence of a social tie did not equate to access to their resources, rather social capital was contingent on the success of mobilizing a tie based on their changing resource levels and resource demands. Third, refugees might have turned to humanitarian institutions; yet, facing overwhelming need with insufficient resources, front-line workers in humanitarian institutions distanced themselves from refugee clients through informal work practices. These practices marginalized refugee workers within humanitarian organizations and created barriers for refugees to access organizational assistance. Thus, models of refugees’ economic trajectories need to take into account how sources of support often became sources of instability
Characterizing Viral Antigenic Drift And Using Immunoglobulin G3 To Increase Antibody Breadth
No full textAntigenic drift, the sequential accumulation of substitutions that enable escape from population immunity, is a constant problem for RNA viruses that circulate in the human population. Developing effective vaccines against such pathogens is challenging and requires extensive knowledge of virological and antigenic properties of circulating strains. Here we complete studies on seasonal influenza viruses of distinct lineages to evaluate recent changes in these viruses. We show that H3N2 and influenza B viruses have recently acquired substitutions in the receptor binding protein, hemagglutinin (HA). We demonstrate that these viruses maintain binding to human-type receptors, and maintain efficient growth in primary human airway cells. Additionally, one of these novel virus variants encoded for changes that significantly altered the antigenicity of the HA protein, which led to a major vaccine mismatch in 2021. These data highlighted the plasticity of the influenza HA protein in maintaining its essential functions for continued circulation in the human population. Next, we study monoclonal antibodies expressed as the different IgG subclasses to evaluate the role of IgG constant domains in altering binding and neutralization of antibodies. We found that many influenza virus-specific mAbs have altered binding and neutralization potency depending on the IgG subclass encoded, and that these differences result from unique avidity differences among the subclasses. Importantly, subclass differences in antibody binding and neutralization were greatest when the affinity for the target antigen was reduced through antigenic mismatch. We found that antibodies expressed as IgG3 bound and neutralized antigenically drifted influenza viruses more effectively. We obtained similar results using a panel of SARS-CoV-2-specific mAbs and an antigenically drifted strain of SARS-CoV-2. These data highlight the utility of IgG3 as a molecule with increased breadth, which could prove useful for therapeutic antibodies. Overall, the work presented here provides granularity to the problem of antigenic drift of viruses, and provides a potential solution in eliciting or utilizing mAbs with a natural propensity for increased breadth
Effects Of Inter-Molecular And Intra-Molecular Factors On The Properties Of Simulated Glasses
Get PDFGlasses of small organic molecules are a ubiquitous material type of wide interest due to their unique amorphous packings. However, their properties can vary widely based on preparation method, aging time, or type of molecule. Stable glasses, prepared via physical vapor-deposition, have been shown to exhibit properties equivalent to those that been aged for thousands of years. Molecular dynamics simulations of coarse-grained molecules provide a simple model for closely examining the relevant properties of glasses, both traditionally liquid-quenched and vapor-deposited. In this dissertation, several projects are presented focusing on systematic changes to inter-molecular interactions and intra-molecular degrees of freedom and how they impact glass properties . In Chapter 2, we study the effects of inter-molecular interactions and microstructure formation on vapor-deposited glass films, using a coarse-grained model of molecules with fluorinated tails. By altering the length of the tail, we can tune the degree of microstructure formation, and we observe how this affects vapor-deposited glass stability, while also proposing, and supporting a mechanism for this behavior. In Chapter 3, models of organic molecules are developed focusing on the strength of the rotational barriers placed on their side groups in order to study intra-molecular degrees of freedom. Here we see the effect this has on vapor-deposited glass stability and make connections to surface mobility and the depth of the mobile region. In Chapter 4, vapor-deposited and liquid-quenched glasses are tested for their mechanical response to shear. The large differences in their properties, as well as the length scales over which they take place, are correlated with local particle mobility. Finally, in Chapter 5, the same molecular models are used to tune fragility behavior. The machine-learned structural property, softness, is implemented for a molecular system for the first time and provides new insight into the origins of glass fragility
Practical Network Programming Automation
Get PDFNetwork configurations are notoriously hard to write and maintain correctly. It requiresexpertise about the domain to write, frequent and laborious updates, and sometimes formal proof to ensure the absence of certain mistakes. The problem becomes more challenging with the popularity of software-defined network(SDN) in recent years, which aims to give users more flexible control over the network’s dynamic behaviors. There has been research on automating the process of configuring the network. However, much of it requires users to learn a specific programming abstraction or interface. Since network operators are a group generally unfamiliar with programming, using these systems may go beyond their abilities. It is also hard to ensure these systems are scalable and accurate enough for real-world usecases. They mostly lack both design considerations to address scalability and accuracy, and also a systematic evaluation of the two metrics in practical scenarios. In this work, we propose a series of approaches to automate network programming. They are based on specifications that are easy and natural to obtain by network operators. We also apply novel program analysis techniques to speed up the process of finding a program that can accurately capture the intention of the specification. We have evaluated our systems on a broad range of benchmarks obtained from real-world data. They have shown ability to finish complex programming tasks within minutes and achieved very high accuracy