The role of hypothalamus-pituitary-adrenal (HPA)-like axis in inflammatory pilosebaceous disorders

Skin is the largest peripheral endocrine organ and functions as a hormone target and endocrine gland. A cutaneous hypothalamus-pituitary-adrenal (HPA)-like axis enables the skin to respond to stress and regulates its steroidogenic activity. The pilosebaceous unit is a site for production and metabolism of a number of steroid hormones, including stress and sex hormones. This is an overview of the important role that the cutaneous HPA-like-axis plays in the pathogenesis and treatment of inflammatory pilosebaceous disorders, including acne, rosacea, seborrheic dermatitis, and hidradenitis suppurativa

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

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

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

A Poisson Regression Index for Tropical Cyclone Genesis and the Role of Large-Scale Vorticity in Genesis

A Poisson regression between the observed climatology of tropical cyclogenesis (TCG) and large-scale climate variables is used to construct a TCG index. The regression methodology is objective and provides a framework for the selection of the climate variables in the index. Broadly following earlier work, four climate variables appear in the index: low-level absolute vorticity, relative humidity, relative sea surface temperature (SST), and vertical shear. Several variants in the choice of predictors are explored, including relative SST versus potential intensity and satellite-based column-integrated relative humidity versus reanalysis relative humidity at a single level; these choices lead to modest differences in the performance of the index. The feature of the new index that leads to the greatest improvement is a functional dependence on low-level absolute vorticity that causes the index response to absolute vorticity to saturate when absolute vorticity exceeds a threshold. This feature reduces some biases of the index and improves the fidelity of its spatial distribution. Physically, this result suggests that once low-level environmental vorticity reaches a sufficiently large value, other factors become rate limiting so that further increases in vorticity (at least on a monthly mean basis) do not increase the probability of genesis. Although the index is fit to climatological data, it reproduces some aspects of interannual variability when applied to interannually varying data. Overall, the new index compares positively to the genesis potential index (GPI), whose derivation, computation, and analysis is more complex in part because of its dependence on potential intensity

Are Midtwentieth Century Forced Changes in North Atlantic Hurricane Potential Intensity Detectable?

The impact of anthropogenic forcings on tropical North Atlantic hurricane potential intensity (PI) is evaluated in Climate Model Intercomparison Project 5 models for the period 1958–2005. Eleven models are examined, but only seven models have a forced response that is distinguishable from internal variability. The use of discriminant analysis to optimize detectability does not yield a clear, common climate change signal. Of the seven models with a significant response, one has a negative linear trend while two have a positive linear trend. The trend in PI is not even consistent among reanalyses, although this difference is not statistically significant because of large uncertainties. Furthermore, estimates of PI internal variability have significantly different variances among different reanalysis products. These disagreements between models, reanalysis products, and between models and reanalyses, in conjunction with relatively large uncertainties, highlight the difficulty of detecting and attributing observed changes in North Atlantic hurricane potential intensity

Autoregressive Modeling for Tropical Cyclone Intensity Climatology

An autoregressive model is developed to simulate the climatological distribution of global tropical cyclone (TC) intensity. The model consists of two components: a regression-based deterministic component that advances the TC intensity in time and depends on the storm state and surrounding large-scale environment and a stochastic forcing. Potential intensity, deep-layer mean vertical shear, and midlevel relative humidity are the environmental variables included in the deterministic component. Given a storm track and its environment, the model is initialized and then iterated along the track. Model performance is evaluated by its ability to represent the observed global and basin distributions of TC intensity as well as lifetime maximum intensity (LMI). The deterministic model alone captures the spatial features of the climatological TC intensity distribution but with intensities that remain below 100 kt (1 kt ≈ 0.51 m s−1). Addition of white (uncorrelated in time) stochastic forcing reduces this bias by improving the simulated intensification rates and the frequency of major storms. The model simulates a realistic range of intensities, but the frequency of major storms remains too low in some basins

Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity

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

An Empirical Relation between U.S. Tornado Activity and Monthly Environmental Parameters

In previous work the authors demonstrated an empirical relation, in the form of an index, between U.S. monthly tornado activity and monthly averaged environmental parameters. Here a detailed comparison is made between the index and reported tornado activity. The index is a function of two environmental parameters taken from the North American Regional Reanalysis: convective precipitation (cPrcp) and storm relative helicity (SRH). Additional environmental parameters are considered for inclusion in the index, among them convective available potential energy, but their inclusion does not significantly improve the overall climatological performance of the index. The aggregate climatological dependence of reported monthly U.S. tornado numbers on cPrcp and SRH is well described by the index, although it fails to capture nonsupercell and cool season tornadoes. The contributions of the two environmental parameters to the index annual cycle and spatial distribution are examined with the seasonality of cPrcp (maximum during summer) relative to SRH (maximum in winter) accounting for the index peak value in May. The spatial distribution of SRH establishes the central U.S. “tornado alley” of the index, while the spatial distribution of cPrcp enhances index values in the South and Southeast and suppresses them west of the Rockies and over elevation. At the scale of the NOAA climate regions, the largest deficiency of the index climatology occurs over the central region where the index peak in spring is too low and where the late summer drop-off in the reported number of tornadoes is poorly captured. This index deficiency is related to its sensitivity to SRH, and increasing the index sensitivity to SRH improves the representation of the annual cycle in this region. The ability of the index to represent the interannual variability of the monthly number of U.S. tornadoes can be ascribed during most times of the year to interannual variations of cPrcp rather than of SRH. However, both factors are important during the peak spring period. The index shows some skill in representing the interannual variability of monthly tornado numbers at the scale of NOAA climate regions

Rapid intensification and the bimodal distribution of tropical cyclone intensity

The severity of a tropical cyclone (TC) is often summarized by its lifetime maximum intensity (LMI), and the climatological LMI distribution is a fundamental feature of the climate system. The distinctive bimodality of the LMI distribution means that major storms (LMI >96 kt) are not very rare compared with less intense storms. Rapid intensification (RI) is the dramatic strengthening of a TC in a short time, and is notoriously difficult to forecast or simulate. Here we show that the bimodality of the LMI distribution reflects two types of storms: those that undergo RI during their lifetime (RI storms) and those that do not (non-RI storms). The vast majority (79%) of major storms are RI storms. Few non-RI storms (6%) become major storms. While the importance of RI has been recognized in weather forecasting, our results demonstrate that RI also plays a crucial role in the TC climatology