12,797 research outputs found

    Maximizing Activity in Ising Networks via the TAP Approximation

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    A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the na\"{i}ve mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of 11/e1-1/e. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system

    Directing the paracrine actions of adipose stem cells for cartilage regeneration

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    Current cartilage repair methods are ineffective in restoring the mechanical and biological functions of native hyaline cartilage. Therefore, using the paracrine actions of stem cell therapies to stimulate endogenous cartilage regeneration has gained momentum. Adipose stem cells (ASCs) are an attractive option for this endeavor because of their accessibility, chondrogenic potential, and secretion of factors that promote connective tissue repair. In order to use the factors secreted by ASCs to stimulate cartilage regeneration, the signaling pathways that affect postnatal cartilage development and morphology need to be understood. Next, approaches need to be developed to tailor the secretory profile of ASCs to promote cartilage regeneration. Finally, delivery methods that localize ASCs within a defect site while facilitating paracrine factor secretion need to be optimized. The overall objective of this thesis was to develop an ASC therapy that could be effectively delivered in cartilage defects and stimulate regeneration via its paracrine actions. The general hypothesis was that the secretory profile of ASCs can be tailored to enhance cartilage regeneration and be effectively delivered to regenerate cartilage in vivo. The overall approach used the growth plate as an initial model to study changes in postnatal cartilage morphology and the molecular mechanisms that regulate it, different media treatments and microencapsulation to tailor growth factor production, and alginate microbeads to deliver ASCs in vivo to repair cartilage focal defects.PhDCommittee Chair: Boyan, Barbara D.; Committee Member: Guldberg, Robert E.; Committee Member: Murphy, Mary; Committee Member: Sambanis, Anthanassios ; Committee Member: Schwartz, Zv

    Ecological and physical characteristics of the Te Awa O Katapaki Stream, Flagstaff, Waikato

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    1. The fish, macroinvertebrates, aquatic vegetation, and water quality indicate that the Te Awa O Katapaki Stream is an unpolluted, pastureland stream that is typical of the Waikato region. 2. The stream has very high nutrient concentrations that probably result from the dairy farming upstream. 3. The fish fauna is dominated by the native shortfinned eels. The presence of the migratory common smelt indicates that swimming fish species also have free access to the stream from the Waikato River. 4. Fish of high conservation value, such as giant or banded kokopu (Galaxias argenteus or G. fasciatus) were absent, which is predictable given the warm, unshaded nature of the stream. 5. Fish and invertebrates would soon recolonise the restored stream following any work in the streambed

    On the Insignificance of Photochemical Hydrocarbon Aerosols in the Atmospheres of Close-in Extrasolar Giant Planets

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    The close-in extrasolar giant planets (CEGPs) reside in irradiated environments much more intense than that of the giant planets in our solar system. The high UV irradiance strongly influences their photochemistry and the general current view believed that this high UV flux will greatly enhance photochemical production of hydrocarbon aerosols. In this letter, we investigate hydrocarbon aerosol formation in the atmospheres of CEGPs. We find that the abundances of hydrocarbons in the atmospheres of CEGPs are significantly less than that of Jupiter except for models in which the CH4_4 abundance is unreasonably high (as high as CO) for the hot (effective temperatures 1000\gtrsim 1000 K) atmospheres. Moreover, the hydrocarbons will be condensed out to form aerosols only when the temperature-pressure profiles of the species intersect with the saturation profiles--a case almost certainly not realized in the hot CEGPs atmospheres. Hence our models show that photochemical hydrocarbon aerosols are insignificant in the atmospheres of CEGPs. In contrast, Jupiter and Saturn have a much higher abundance of hydrocarbon aerosols in their atmospheres which are responsible for strong absorption shortward of 600 nm. Thus the insignificance of photochemical hydrocarbon aerosols in the atmospheres of CEGPs rules out one class of models with low albedos and featureless spectra shortward of 600 nm.Comment: ApJL accepte

    Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

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    We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle clusters from multiple structural motifs, and individually fits them to experimental PDFs. Each cluster-fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles
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