1,242 research outputs found

    Simplified half-life methods for the analysis of kinetic data

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    The analysis of reaction rate data has as its goal the determination of the order rate constant which characterize the data. Chemical reactions with one reactant and present simplified methods for accomplishing this goal are considered. The approaches presented involve the use of half lives or other fractional lives. These methods are particularly useful for the more elementary discussions of kinetics found in general and physical chemistry courses

    Simplified rate-law integration for reactions which are first order in each of two reactants

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    A simple procedure for integrating the rate law for chemical reactions which are first order in each of two reactants is presented

    Identification of astrocytoma associated genes including cell surface markers

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    BACKGROUND: Despite intense effort the treatment options for the invasive astrocytic tumors are still limited to surgery and radiation therapy, with chemotherapy showing little or no increase in survival. The generation of Serial Analysis of Gene Expression (SAGE) profiles is expected to aid in the identification of astrocytoma-associated genes and highly expressed cell surface genes as molecular therapeutic targets. SAGE tag counts can be easily added to public expression databases and quickly disseminated to research efforts worldwide. METHODS: We generated and analyzed the SAGE transcription profiles of 25 primary grade II, III and IV astrocytomas [1]. These profiles were produced as part of the Cancer Genome Anatomy Project's SAGE Genie [2], and were used in an in silico search for candidate therapeutic targets by comparing astrocytoma to normal brain transcription. Real-time PCR and immunohistochemistry were used for the validation of selected candidate target genes in 2 independent sets of primary tumors. RESULTS: A restricted set of tumor-associated genes was identified for each grade that included genes not previously associated with astrocytomas (e.g. VCAM1, SMOC1, and thymidylate synthetase), with a high percentage of cell surface genes. Two genes with available antibodies, Aquaporin 1 and Topoisomerase 2A, showed protein expression consistent with transcript level predictions. CONCLUSIONS: This survey of transcription in malignant and normal brain tissues reveals a small subset of human genes that are activated in malignant astrocytomas. In addition to providing insights into pathway biology, we have revealed and quantified expression for a significant portion of cell surface and extra-cellular astrocytoma genes

    Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization

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    We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.Comment: 12 pages, 4 figures; accepted for publication in PLoS ON

    Wave Amplitude Dependent Engineering Model of Propellant Slosh in Spherical Tanks

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    Liquid propellant slosh is often a concern for the controllability of flight vehicles. Anti-slosh devices are traditionally included in propellant tank designs to limit the amount of sloshing allowed during flight. These devices and any necessary supports can be quite heavy to meet various structural requirements. Some of the burden on anti-slosh devices can be relieved by exploiting the nonlinear behavior of slosh waves in bare smooth wall tanks. A nonlinear regime slosh model for bare spherical tanks was developed through a joint analytical and experimental effort by NASA/MSFC. The developed slosh model accounts for the large damping inherent in nonlinear slosh waves which is more accurate and drives conservatism from vehicle stability analyses that use traditional bare tank slosh models. A more accurate slosh model will result in more realistic predicted slosh forces during flight reducing or removing the need for active controls during a maneuver or baffles in the tank design. Lower control gains and smaller or fewer tank baffles can reduce cost and system complexity while increasing vehicle performance. Both Computational Fluid Dynamics (CFD) simulation and slosh testing of three different spherical tank geometries were performed to develop the proposed slosh model. Several important findings were made during this effort in addition to determining the parameters to the nonlinear regime slosh model. The linear regime slosh damping trend for spherical tanks reported in NASA SP-106 was shown to be inaccurate for certain regions of a tank. Additionally, transition to the nonlinear regime for spherical tanks was only found to occur at very large wave amplitudes in the lower hemisphere and was a strong function of the propellant fill level in the upper hemisphere. The nonlinear regime damping trend was also found to be a function of the propellant fill level

    Effect of a Novel Nonviral Gene Delivery of BMP-2 on Bone Healing

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    Background. Gene therapeutic drug delivery approaches have been introduced to improve the efficiency of growth factors at the site of interest. This study investigated the efficacy and safety of a new nonviral copolymer-protected gene vector (COPROG) for the stimulation of bone healing. Methods. In vitro, rat osteoblasts were transfected with COPROG + luciferase plasmid or COPROG + hBMP-2 plasmid. In vivo, rat tibial fractures were intramedullary stabilized with uncoated versus COPROG+hBMP-2-plasmid-coated titanium K-wires. The tibiae were prepared for biomechanical and histological analyses at days 28 and 42 and for transfection/safety study at days 2, 4, 7, 28, and 42. Results. In vitro results showed luciferase expression until day 21, and hBMP-2-protein was measured from day 2 – day 10. In vivo, the local application of hBMP-2-plasmid showed a significantly higher maximum load after 42 days compared to that in the control. The histomorphometric analysis revealed a significantly less mineralized periosteal callus area in the BMP-2 group compared to the control at day 28. The rt-PCR showed no systemic biodistribution of luciferase RNA. Conclusion. A positive effect on fracture healing by nonviral BMP-2 plasmid application from COPROG-coated implants could be shown in this study; however, the effect of the vector may be improved with higher plasmid concentrations. Transfection showed no biodistribution to distant organs and was considered to be safe

    State Transition Algorithm

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    In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.Comment: 18 pages, 28 figure

    Ab initio Random Structure Searching

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    It is essential to know the arrangement of the atoms in a material in order to compute and understand its properties. Searching for stable structures of materials using first-principles electronic structure methods, such as density functional theory (DFT), is a rapidly growing field. Here we describe our simple, elegant and powerful approach to searching for structures with DFT which we call ab initio random structure searching (AIRSS). Applications to discovering structures of solids, point defects, surfaces, and clusters are reviewed. New results for iron clusters on graphene, silicon clusters, polymeric nitrogen, hydrogen-rich lithium hydrides, and boron are presented.Comment: 44 pages, 23 figure
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