796 research outputs found

    Phosphorylation of U24 from Human Herpes Virus type 6 (HHV-6) and its potential role in mimicking myelin basic protein (MBP) in multiple sclerosis

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    AbstractMyelin basic protein (MBP) from multiple sclerosis (MS) patients contains lower levels of phosphorylation at Thr97 than normal individuals. The significance of phosphorylation at this site is not fully understood, but it is proposed to play a role in the normal functioning of MBP. Human Herpesvirus Type 6 encodes the protein U24, which has tentatively been implicated in the pathology of MS. U24 shares a 7 amino acid stretch encompassing the Thr97 phosphorylation site of MBP: PRTPPPS. We demonstrate using a combination of mass spectrometry, thin layer chromatography and autoradiography, that U24 can be phosphorylated at the equivalent threonine. Phospho-U24 may confound signalling or other pathways in which phosphorylated MBP may participate, precipitating a pathological process.Structured summaryMINT-6613181:MAPK (uniprotkb:P28482) phosphorylates (MI:0217) MBP (uniprotkb:P02687) by protein kinase assay (MI:0424)MINT-6613171, MINT-6613190:MAPK (uniprotkb:P28482) phosphorylates (MI:0217) U24 (uniprotkb:Q69559) by protein kinase assay (MI:0424

    Association of U.S. tornado occurrence with monthly environmental parameters

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    Monthly U.S. tornado numbers are here related to observation-based monthly averaged atmospheric parameters. Poisson regression is used to form an index which captures the climatological spatial distribution and seasonal variation of tornado occurrence, as well as year-to-year variability, and provides a framework for extended range forecasts of tornado activity. Computing the same index with predicted atmospheric parameters from a comprehensive forecast model gives some evidence of the predictability of monthly tornado activity

    Tropical cyclone genesis potential using a ventilated potential intensity

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    Genesis potential indices (GPIs) are widely used to understand the climatology of tropical cyclones (TCs). However, the sign of projected future changes depends on how they incorporate environmental moisture. Recent theory combines potential intensity and mid-tropospheric moisture into a single quantity called the ventilated potential intensity, which removes this ambiguity. This work proposes a new GPI (GPIvGPI_v) that is proportional to the product of the ventilated potential intensity and the absolute vorticity raised to a power. This power is estimated to be approximately 5 by fitting observed tropical cyclone best-track and ECMWF Reanalysis v5 (ERA5) data. Fitting the model with separate exponents yields nearly identical values, indicating that their product likely constitutes a single joint parameter. Likewise, results are nearly identical for a Poisson model as for the power law. GPIvGPI_v performs comparably well to existing indices in reproducing the climatological distribution of tropical cyclone genesis and its covariability with El Ni\~no-Southern Oscillation, while only requiring a single fitting exponent. When applied to Coupled Model Intercomparison Project Phase 6 (CMIP6) projections, GPIvGPI_v predicts that environments globally will become gradually more favorable for TC genesis with warming, consistent with prior work based on the normalized entropy deficit, though significant changes emerge only at higher latitudes under relatively strong warming. GPIvGPI_v helps resolve the debate over the treatment of the moisture term and its implication for changes in TC genesis favorability with warming, and its clearer physical interpretation may offer a step forward towards a theory for genesis across climate states

    Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity

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    The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used
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