Browsing by Subject "Aggregation"

Now showing 1 - 9 of 9
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    2018 Undergraduate Research Symposium Presentation on Method for Detection for Protein Aggregation Propensity
    (2018) Fransen, Katharina A; Ritter, Seth C; Hackel, Benjamin
    Engineered proteins are valuable tools for clinical therapeutics and diagnostics as well as for other biotechnology applications. The physicochemical robustness of a protein is an important factor in its utility. For example, protein solubility is advantageous for production, conjugation, formulation, and use; yet numerous engineered proteins exhibit suboptimal solubility due to the formation of protein aggregates. For the technological aim of identifying superior reagents and the scientific goal of elucidating protein sequence – function relationships, we are developing a method for high-throughput analysis of protein aggregation. The method uses yeast surface display in which the protein of interest (POI) is coupled to the surface of a S. cerevisiae yeast cell via an extended ‘PAS40’ polypeptide linker to the native yeast mating protein Aga2p, which binds to the yeast-bound Aga1 membrane protein. Meanwhile, the DNA encoding the POI is retained inside the yeast thereby creating a phenotype-genotype linkage that enables independent evaluation of millions of POI variants with simple DNA sequencing to reveal the identity of functional variants after aggregation analysis. Aggregation analysis utilizes Foerster resonance energy transfer (FRET) in which an excited donor fluorophore transfers energy to a nearby acceptor fluorophore. The acceptor and donor fluorophores are attached near the N-terminus of the POIs, allowing aggregating POIs to bring the fluorophores within the necessary radius for energy transfer and subsequent acceptor fluorescence. Measurement of the fluorescence of the acceptor protein allows for the aggregation analysis of the POI, which can be performed in high-throughput (100 million per hour) via flow cytometry.
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    Community and aggregation in the Upper Mississippi River Valley: the Red Wing Locality.
    (2009-06) Fleming, Edward Paul
    The Red Wing Locality is a cluster of Late Precontact villages located in the Upper Mississippi valley of the Midwestern United States. It has long been interpreted as a monolithic presence within the broad regional context of Late Precontact times. While these studies place Red Wing into a broader context relative to a presumed dominant, Mississippian culture and other cultural entities, they have been at the expense of addressing cultural relationships within the Red Wing region itself. The research presented in this dissertation is a community-based, inside-out approach to understanding how the Red Wing Locality functioned for the populations it served. The core focus is the nature of the relationship of Red Wing Locality villages to one another and to their hinterlands. For decades, scholars have recognized the Red Wing Locality as a locale of intense social interaction, and the processes of social aggregation at central places provide an explanatory model for this phenomenon. A diverse range of materials are examined that highlight similarities and differences among villages in the Red Wing Locality. These data demonstrate that contemporary villages on opposite sides of the river had different hinterland contacts and access to resources. One conclusion of this research is that interactions and mobility patterns into and out of the Locality were structured by the Mississippi River. Finally, the Red Wing Locality is examined in the light of a three-tiered non-hierarchical community conceptual framework that at once separated individual settlements, combines the settlement cluster, and ties individual settlements to a broader region that included supporting hinterland populations that aggregated at Red Wing villages. A major contribution of this research is that it provides a new, holistic perspective of the archaeology of the Red Wing Locality and the Upper Mississippi River valley.
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    Computational and experimental studies of dye sensitized solar cells
    (2013-09) Vatassery, Rajan Nayar
    The dye-sensitized solar cell (DSSC) has been studied by observing charge transfer from an organic terthiophene dye into a CdS nanoparticle. Using NMR and UV-Vis, we find characteristics of dye aggregation and a concomitant reduction in the electron transfer efficiency as measured by ultrafast transient absorption (TA) spectroscopy. Specifically, the NMR and UV-Vis spectra of the dye molecules indicate aggregates are readily formed at high surface loading, or roughly a 20:1 dye:nanoparticle ratio. Upon analysis by TA spectroscopy the same samples show a dominant S1 state quenching process separate from the expected intersystem crossing and electron transfer (ET) S1 quenching pathways. We propose that the dominant process is concentration-quenching because it only appears at high surface coverage where aggregates are detected spectroscopically; at lower surface coverage (ratios of dye:nanoparticle of roughly 1:1) the ET mechanism is the dominant pathway for S1 reduction and the parasitic concentration-quenching pathway is not observed. We therefore suggest that planar oligothiophene dyes should be modified to frustrate packing on the surface in an effort to avoid concentration quenching losses, or that dye loading be considered when creating a DSSC from planar dye molecules. Classical molecular dynamics (MD) simulations are also presented to corroborate the experimental picture described above. These simulations show that dyes aggregate in a variety of orientations, and that dye molecules are stabilized by these aggregation events even in the presence of explicit solvent. The ability of the dye molecules to pack more densely than is found experimentally shows that the surface of the CdS nanoparticle is likely undersaturated. In this situation, dye molecules can be either uniformly distributed around the surface of the nanoparticle, or they can be concentrated in islands on certain crystallographic faces, leaving other faces unoccupied. The experimental signs of aggregation support the latter.
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    Mass, momentum and energy transfer in aggregated particulate media
    (2013-12) Thajudeen, Thaseem
    Synthesis of nanoparticles in gas phase systems often results in the formation of non spherical particles which are commonly found as clusters of spherical particles, termed as aggregates. Prior studies have shown that these aggregates can be accurately modeled using statistical scaling law. While theories are available for determining the transport properties of spherical particles, the effect of the morphology of the particles has not been well studied. This dissertation focuses on studying how exactly the morphology of the aggregates arises in a given synthesis system and calculation of the transport properties of the formed aggregates. Given the particle morphology, this study also investigates the effect the aggregates have on altering the bulk properties of a system into which they are embedded. The study is computational, experimental and analytical in nature with specific emphasis on studying aggregate formation and transport properties of non spherical particles. An overview of the dissertation is given in Chapter 1. In Chapter 2, an expression is proposed for calculating the drag on non spherical particles (that determines their motion in gas phase systems) and is experimentally validated with a study on flame synthesized Titania aggregates. Chapter 3 looks at calculating collision rates between non spherical particles taking into account the morphology of both the colliding entities for all mass transfer regimes and in chapter 4; aerosol filtration process is studied as quintessentially a collision process. The proposed expressions are validated using a numerical study using Brownian Dynamics simulations. In chapter 5, aggregation process is studied in detail, with specific emphasis on aggregate formation and calculation of the transport properties of the formed aggregates, with the aggregation process occurring in different mass and momentum transfer regimes. Given the particle morphology, its effect in altering the bulk properties of the host medium into which they are embedded is dealt with in chapters 6 and 7, specifically looking into their effect on the thermal conductivity and convective heat transfer. The main conclusions from the study and suggestions for possible future studies based on this dissertation are explained in chapter 8.
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    Minimax estimation and model identification for high-dimensional regression.
    (2012-08) Wang, Zhan
    This dissertation consists of two parts. In Part I, adaptive minimax estimation over sparse `q-hulls is studied. Given a dictionary of Mn initial estimates of the unknown regression function, we aim to construct linearly aggregated estimators that target the best performance among all linear combinations under a sparse q-norm (0 <_ q <_ 1) constraint. Besides identifying the optimal rates of aggregation for these `q-aggregation problems, our multi-directional (or adaptive) strategies by model mixing or model selection achieve the optimal rates simultaneously over the full range of 0 <_ q <_ 1 for general Mn and upper bound tn of the q-norm. Both random and fixed designs, with known or unknown error variance, are handled, and the `q-aggregations examined in this work cover major types of aggregation problems previously studied in the literature. Consequences on minimax-rate adaptive regression under `q- constrained coefficients are also provided. In Part II, the relationship between consistency and minimax-rate optimality in possibly high-dimensional regression estimation is investigated. In model selection where the true model is fixed, it is now well-known that if a model selection method is consistent, it cannot be minimax-rate optimal at the same time. We investigate this con ict in a high-dimensional regression setting where the true model is a changing target, and show that consistency and minimaxrate optimality may co-exist in a single model selection method. Our results provide a comprehensive guideline for characteristics of a model selection method which can be consistent and minimax-rate optimality at the same time.
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    Optimizing Aggregation and Join Queries in Geo-Distributed Data Analytics
    (2022-03) Kumar, Dhruv
    Large-scale data analytics services require collection and analysis of data from end-user applications and devices distributed around the globe. These services are increasingly deployed on a geographically distributed infrastructure comprising a multi-tier topology of edge servers and cloud data centers (DCs). Such geo-distributed analytics (GDA) involves data transfer over the wide area network (WAN) links connecting the various processing sites (edges and DCs). These WAN links are highly constrained and heterogeneous in nature, making the data transfer over the WAN slow and costly. Additionally, the edge nodes can also be constrained in terms of compute capacity. While the prior work on GDA has tried to address these challenges to some degree, this thesis identifies and solves a number of unidentified challenges associated with two fundamental operations in any GDA system: data aggregation and relational joins. Real-time aggregation and processing of geo-distributed data streams continuously over time often has two competing requirements: first, the results be available at the center within a certain acceptable delay bound and second, the WAN traffic needs to be minimized due to constrained and expensive WAN bandwidth. This delay-traffic tradeoff forms a fundamental component of streaming analytics. This thesis proposes a Time-To-Live (TTL-) based aggregation model which provides a theoretical basis for understanding the aforementioned delay-traffic tradeoff. The TTL-based aggregation model is then utilized to solve a variety of optimization problems such as jointly minimizing the delay and traffic costs, minimizing delay subject to a traffic bound and minimizing traffic subject to a delay bound in the context of hub-and-spoke like edge-cloud infrastructure where multiple edges are connected to a central cloud data center. Next, this thesis also proposes aggregation networks to efficiently perform continuous aggregation over a general multi-tiered distributed edge-cloud infrastructure. In doing so, it identifies a number of less studied tradeoffs such as tradeoff between traffic and traffic cost. The identified tradeoffs are then utilized to propose AggNet, a cost-aware system for minimizing traffic cost across aggregation networks while satisfying the resource constraints in the network as well as the delay sensitivity of the streaming aggregation queries. Computing joins in a geo-distributed setting remains a challenging problem, as joins often form the most heavyweight component in an analytics query, both in terms of compute and data shuffle over the WAN. This thesis first looks at queries comprising both join and aggregation operators. It proposes AggFirstJoin, an approach to minimize the cost of geo-distributed joins using a theoretically sound query transformation technique. The optimization approach takes a combined view of the join and aggregation operations which are often part of the same query, and pushes (a transformed) aggregation before join so as to produce the same results as the original query. The query transformation technique is further augmented with a WAN-aware task placement and a Bloom filtering approach to further reduce query execution time and WAN usage respectively. Next, this thesis studies queries with join operators on their own. Computing exact results for such queries is much more challenging since there are no aggregation operators which could have reduced the data shuffle over WAN. Hence, this thesis proposes a geo-distributed join sampling approach which can efficiently generate random samples from geo-distributed tables in order to finally produce a random sample of the joined result. All of the proposed techniques in this thesis are implemented on top of popular data analytics engines such as Apache Spark and Apache Flink. Evaluations are carried out using both real and synthetic traces on a real geo-distributed testbed on AWS as well as an emulated test-bed. The proposed techniques show remarkable improvements over the existing state-of-the-art.
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    Sensory and chemical basis of off-host aggregation behavior by bed bugs, Cimex lectularius
    (2015-02) Olson, Joelle F.
    After feeding on hosts, bed bugs, Cimex lectularius L., aggregate in cracks and crevices near their hosts. Off-host aggregation is mediated by sensory organs on the bed bug antennae and chemical stimuli associated with bug feces. This dissertation examined the sensory bases of bed bug off-host aggregation behavior, and results are presented in four chapters. Chapter one provides a basic overview of existing literature on the sensory structures located on adult antennae and the chemical stimuli that influence bed bug behavior. The chapter concludes with a discussion of practical applications for bed bug control. In chapter two, behavioral assays and microscopy were used to study sensilla on the bed bug antenna. A multi-choice behavioral assay using fecal stained filter papers determined which antennal segments mediate off-host aggregation. Both scanning and transmission electron microscopy techniques were used to determine the type and function of sensilla on the pedicel of adults and nymphs. In addition to an abundance of serrated hairs, several smooth hairs with gustatory function were sparsely distributed throughout the segment and a distal patch of sensilla with olfactory function was also described. The identification of sensilla with olfactory and gustatory function on the pedicel suggests off-host aggregation by bed bugs may be mediated by a volatile or semi-volatile compound or compounds. In chapter three, the chemical stimulus associated with bed bug feces was analyzed, including stimulus volatility, extraction, isolation, and separation of component molecules. Solid phase microextration (SPME) techniques were used to assess the presence of known bed bug pheromones, (E)-2-hexenal (E2H) and (E)-2-octenal (E2O) on fecal stained papers that were heat treated for several days. In addition, multi-choice behavioral assays were used to assess aggregation response to fecal stained papers that were heated, to papers washed in various solvents, and to concentrated methanol extracts and extracts separated by solid phase extraction (SPE) techniques. Results demonstrated that E2H and E2O decrease significantly with heat exposure; however, aggregation response to fecal stained disks remained relatively constant, suggesting that the chemical stimulus is less volatile compared to previous reports. The chemical stimulus was soluble in methanol and water, with bed bug response greatest to concentrations of fecal extracts above 30 mg/ml methanol. Separation of the active components was possible using a C18 SPE cartridge and gas chromatography techniques, which prepared the chemical stimulus for further identification. In chapter four, crude extracts from bed bug feces were analyzed by a gas chromatograph coupled with an electro-antennogram detector (GC-EAD) and mass spectrometer (GC-MS) to identify essential components of the off-host aggregation pheromone. Adult antennae responded to compounds associated with three elution regions of the crude extract. Several chemical compounds were identified in each of the active regions, and selected groups of compounds were evaluated in multiple choice assays to assess aggregation response. A combination of two compounds, dimethyl trisulfide (DMTS) and methyldiethanolamine (MDEA) resulted in an aggregation response that was equivalent to original extract. This final chapter concludes with a discussion of potential applications of a synthetic aggregation pheromone for surveillance and bed bug control.
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    Streptococcus sanguinis Ecto-5'-nucleotidase modulates platelet aggregation.
    (2011-05) Fan, Jingyuan
    Streptococcus sanguinis, an oral commensal bacterium, is the leading cause of infective endocarditis (IE). In an animal model, the abilities of S. sanguinis to adhere to and activate platelets are correlated with the increased severity of IE. In response to S. sanguinis, platelet activation is associated with secretion of adenosine triphosphate (ATP) and adenosine diphosphate (ADP) from dense granules. The extracellular ADP is a potent platelet agonist and amplifies platelet aggregation induced by other pro-thrombotic agonists, whereas, the final product of hydrolysis of adenine nucleotides, adenosine, is a platelet aggregation antagonist. Here, we show that cell surface ecto-5'-nucleotidase (NT5E) of S. sanguinis can hydrolyze adenine nucleotides ATP to ADP, adenosine monophosphate (AMP) and finally adenosine. Therefore, we hypothesize that S. sanguinis ecto-5'-nucleotidase modulates platelet aggregation. A nt5e deletion mutant of S. sanguinis 133-79 showed significantly shorter lag time to onset of platelet aggregation than the wild-type strain (wt). However, the nt5e deletion mutant adhered to human platelets indistinguishably from the wild-type and complemented strains. By hydrolyzing the released ATP and ADP from dense granules of activated platelets, therefore, NT5E modulates S. sanguinis-induced platelet aggregation in vitro. In addition, strains of S. sanguinis showed different cell surface enzymatic activities for hydrolysis of adenine nucleotides, which may contribute to the determination of the platelet interactivity phenotypes. To further elucidate the mechanism, we distinguished the roles of ADP and adenosine receptors on streptococcal-platelet interactions using specific antagonists. We showed that the ADP receptors, P2Y1 and P2Y12, and the adenosine receptor A2a were all involved in S. sanguinis-induced platelet aggregation. Downstream of P2Y12, platelet activation involved two waves of Akt phosphorylation in response to S. sanguinis. NT5E also modulates platelet aggregation by indirectly signaling Rap1 activity. Through these pathways, S. sanguinis NT5E slows down platelet aggregation by removing ADP and generating adenosine. Using a rabbit endocarditis model, we found that in the absence of nt5e, the mass of the vegetations and recovered bacterial loads were greatly decreased, suggesting a contribution of NT5E to the virulence of S. sanguinis in vivo. Similar to the release of ADP, activated platelets secrete platelet microbicidal proteins (PMPs), which antagonize a broad range of pathogens. These data, therefore, indicate that NT5E-mediated inhibition of platelet aggregation might delay presentation of PMPs to infecting bacteria on heart valves. The delay would enable the infecting bacteria to colonize in the absence of this innate immune effector. Extracellular adenine nucleotides are also important signaling molecules that mediate both inflammatory and anti-inflammatory processes. By hydrolyzing ATP, a pro-inflammatory molecule, and generating adenosine, an immunosuppressive molecule, NT5E might inhibit phagocytic monocyte/macrophages associated with valvular vegetations, promoting the survival of infecting S. sanguinis. In conclusion, we now show for the first time that streptococcal NT5E modulates S. sanguinis-induced platelet aggregation and contributes to the virulence of streptococci in IE. These findings expand our knowledge of bacterial-host interactions and may suggest novel therapeutics for cardiovascular infectious diseases.
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    Use of Aggregated Covariates In Propensity Score Analysis of Clustered Data
    (2020-06) Nickodem, Kyle
    Propensity score methods can be used to reduce selection bias and improve causal inferencing with nonrandomized data. However, there is little guidance for implementing a propensity score analysis when treatment exposure is a property of clusters rather than subjects. For example, education policies and practices are often implemented by school or district rather than by individual student. The three studies in this dissertation strive to clarify procedural quandaries for a propensity score analysis with cluster-level treatment exposure and subject-level outcomes. Additionally, omission of a true confounder from a propensity score analysis can bias treatment effect estimation. My dissertation also explores the utility of aggregated covariates as replacements for missing true cluster-level confounders. The first simulation study compared four procedures for generating aggregated covariates. The results highlight that: 1) researchers need to verify the comparability of generated samples to real world contexts; 2) a propensity score analysis with cluster-level treatment exposure requires at least 60 clusters. The second simulation compared covariate balance and treatment effect estimation when appraising treatment exposure by subjects or by clusters and including aggregated covariates of varying quality. Treatment appraisal by subjects outperformed appraisal by clusters under certain conditions. When highly correlated (r = .92 - .98) with the missing true confounders, aggregated covariates were viable replacements. The last study applied the guidance from the simulations to statewide survey data. The investigation found no association between the presence of a school resource officer and students’ social-emotional well-being and academic performance. A critical caveat is the results may not generalize to student populations that have historically been targeted by discrimination and school violence.