1,025 research outputs found

    RNA interference approaches for treatment of HIV-1 infection.

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    HIV/AIDS is a chronic and debilitating disease that cannot be cured with current antiretroviral drugs. While combinatorial antiretroviral therapy (cART) can potently suppress HIV-1 replication and delay the onset of AIDS, viral mutagenesis often leads to viral escape from multiple drugs. In addition to the pharmacological agents that comprise cART drug cocktails, new biological therapeutics are reaching the clinic. These include gene-based therapies that utilize RNA interference (RNAi) to silence the expression of viral or host mRNA targets that are required for HIV-1 infection and/or replication. RNAi allows sequence-specific design to compensate for viral mutants and natural variants, thereby drastically expanding the number of therapeutic targets beyond the capabilities of cART. Recent advances in clinical and preclinical studies have demonstrated the promise of RNAi therapeutics, reinforcing the concept that RNAi-based agents might offer a safe, effective, and more durable approach for the treatment of HIV/AIDS. Nevertheless, there are challenges that must be overcome in order for RNAi therapeutics to reach their clinical potential. These include the refinement of strategies for delivery and to reduce the risk of mutational escape. In this review, we provide an overview of RNAi-based therapies for HIV-1, examine a variety of combinatorial RNAi strategies, and discuss approaches for ex vivo delivery and in vivo delivery

    U.S. State-Level Carbon Dioxide Emissions: A Spatial-Temporal Econometric Approach of the Environmental Kuznets Curve

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    One of the major criticisms of past environmental Kuznets curve (EKC) studies is that the spatiotemporal aspects within the data have largely been ignored. By ignoring the spatial aspect of pollution emissions past estimates of the EKC implicitly assume that a region’s emissions are unaffected by events in neighboring regions (i.e., assume there are no transboundary pollution emissions between neighbors). By ignoring the spatial aspects within the data several past estimates of the EKC could have generated biased or inconsistent regression results. By ignoring the temporal aspect within the data several past estimates of the EKC could have generated spurious regression results or misspecified t and F statistics. To address this potential misspecification we estimate the relationship between state-level carbon dioxide emissions and income (GDP) accounting for both the spatiotemporal components within the data. Specifically, we estimate a dynamic spatiotemporal panel model using a newly proposed robust, spatial fixed effects model. This new estimation scheme is appropriate for panels with large N and T. Consistent with the EKC hypothesis we find the inverted-U shaped relationship between CO2 emissions and income. Further, we find adequate evidence that carbon dioxide emissions and state-level GDP are temporally and spatially dependent. These findings offer policy implications for both interstate energy trade and pollution emission regulations. These implications are particularly important for the formulation of national policies related to the 2009 Copenhagen Treaty in which the U.S. has committed to significantly reduce greenhouse gas emissions over the next twenty years.Environmental Kuznets Curve, Carbon Dioxide, Spatial Econometrics, Panel Data Econometrics, Time Series Analysis, Environmental Economics, Pollution Economics, Environmental Economics and Policy, Q50, Q53, Q43, C01, C33,

    A Spatiotemporal Fixed Effects Estimation of U.S. State-Level Carbon Dioxide Emissions

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    One of the major shortcommings of past environmental Kuznets curve (EKC) studies is that the spatiotemporal aspects within the data have largely been ignored. By ignoring the spatial aspect of pollution emissions past estimates of the EKC implicitly assume that a region’s emissions are unaffected by events in neighboring regions (i.e., assume there are no transboundary pollution emissions between neighbors). By ignoring the spatial aspects within the data several past estimates of the EKC could have generated biased or inconsistent regression results. By ignoring the temporal aspect within the data several past estimates of the EKC could have generated spurious regression results or misspecified t and F statistics. To address this potential misspecification we estimate the relationship between state-level carbon dioxide emissions and income (GDP) accounting for both the spatiotemporal components within the data. Specifically, we estimate a dynamic spatiotemporal panel model using a newly proposed robust, spatial fixed effects model. This new estimation scheme is appropriate for panels with large N and T. Consistent with the EKC hypothesis we find the inverted-U shaped relationship between CO2 emissions and income. Further, we find adequate evidence that the underlying economic processes driving carbon dioxide emissions and state-level GDP are temporally and spatially dependent. These findings offer policy implications for both interstate energy trade and pollution emission regulations. These implications are particularly important for the formulation of national policies related to the 2009 Copenhagen Treaty in which the U.S. has committed to significantly reduce greenhouse gas emissions over the next twenty years.Pollution Economics, Environmental Kuznets Curve, Spatial Econometrics, Dynamic Panel Data, Carbon Dioxide Emissions, Global Climate Change, Environmental Economics and Policy, Research Methods/ Statistical Methods, Resource /Energy Economics and Policy, C33, C51, Q43, Q50, Q53, Q58,

    Stochastic Gene Expression in a Lentiviral Positive Feedback Loop: HIV-1 Tat Fluctuations Drive Phenotypic Diversity

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    Stochastic gene expression has been implicated in a variety of cellular processes, including cell differentiation and disease. In this issue of Cell, Weinberger et al. (2005) take an integrated computational-experimental approach to study the Tat transactivation feedback loop in HIV-1 and show that fluctuations in a key regulator, Tat, can result in a phenotypic bifurcation. This phenomenon is observed in an isogenic population where individual cells display two distinct expression states corresponding to latent and productive infection by HIV-1. These findings demonstrate the importance of stochastic gene expression in molecular "decision-making."Comment: Supplemental data available as q-bio.MN/060800
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