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

Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat

The liver—a central metabolic organ that integrates whole-body metabolism to maintain glucose and fatty-acid regulation, and detoxify ammonia—is susceptible to injuries induced by drugs and toxic substances. Although plasma metabolite profiles are increasingly investigated for their potential to detect liver injury earlier than current clinical markers, their utility may be compromised because such profiles are affected by the nutritional state and the physiological state of the animal, and by contributions from extrahepatic sources. To tease apart the contributions of liver and non-liver sources to alterations in plasma metabolite profiles, here we sought to computationally isolate the plasma metabolite changes originating in the liver during short-term fasting. We used a constraint-based metabolic modeling approach to integrate central carbon fluxes measured in our study, and physiological flux boundary conditions gathered from the literature, into a genome-scale model of rat liver metabolism. We then measured plasma metabolite profiles in rats fasted for 5–7 or 10–13 h to test our model predictions. Our computational model accounted for two-thirds of the observed directions of change (an increase or decrease) in plasma metabolites, indicating their origin in the liver. Specifically, our work suggests that changes in plasma lipid metabolites, which are reliably predicted by our liver metabolism model, are key features of short-term fasting. Our approach provides a mechanistic model for identifying plasma metabolite changes originating in the liver

Protein profiling of the dimorphic, pathogenic fungus, Penicillium marneffei

<p>Abstract</p> <p>Background</p> <p><it>Penicillium marneffei </it>is a pathogenic fungus that afflicts immunocompromised individuals having lived or traveled in Southeast Asia. This species is unique in that it is the only dimorphic member of the genus. Dimorphism results from a process, termed phase transition, which is regulated by temperature of incubation. At room temperature, the fungus grows filamentously (mould phase), but at body temperature (37°C), a uninucleate yeast form develops that reproduces by fission. Formation of the yeast phase appears to be a requisite for pathogenicity. To date, no genes have been identified in <it>P. marneffei </it>that strictly induce mould-to-yeast phase conversion. In an effort to help identify potential gene products associated with morphogenesis, protein profiles were generated from the yeast and mould phases of <it>P. marneffei</it>.</p> <p>Results</p> <p>Whole cell proteins from the early stages of mould and yeast development in <it>P. marneffei </it>were resolved by two-dimensional gel electrophoresis. Selected proteins were recovered and sequenced by capillary-liquid chromatography-nanospray tandem mass spectrometry. Putative identifications were derived by searching available databases for homologous fungal sequences. Proteins found common to both mould and yeast phases included the signal transduction proteins cyclophilin and a RACK1-like ortholog, as well as those related to general metabolism, energy production, and protection from oxygen radicals. Many of the mould-specific proteins identified possessed similar functions. By comparison, proteins exhibiting increased expression during development of the parasitic yeast phase comprised those involved in heat-shock responses, general metabolism, and cell-wall biosynthesis, as well as a small GTPase that regulates nuclear membrane transport and mitotic processes in fungi. The cognate gene encoding the latter protein, designated <it>RanA</it>, was subsequently cloned and characterized. The <it>P. marneffei </it>RanA protein sequence, which contained the signature motif of Ran-GTPases, exhibited 90% homology to homologous <it>Aspergillus </it>proteins.</p> <p>Conclusion</p> <p>This study clearly demonstrates the utility of proteomic approaches to studying dimorphism in <it>P. marneffei</it>. Moreover, this strategy complements and extends current genetic methodologies directed towards understanding the molecular mechanisms of phase transition. Finally, the documented increased levels of RanA expression suggest that cellular development in this fungus involves additional signaling mechanisms than have been previously described in <it>P. marneffei</it>.</p

Recent progress in understanding and predicting Atlantic decadal climate variability

Recent Atlantic climate prediction studies are an exciting new contribution to an extensive body of research on Atlantic decadal variability and predictability that has long emphasized the unique role of the Atlantic Ocean in modulating the surface climate. We present a survey of the foundations and frontiers in our understanding of Atlantic variability mechanisms, the role of the Atlantic Meridional Overturning Circulation (AMOC), and our present capacity for putting that understanding into practice in actual climate prediction systems

The Jeep as a Farm Truck-Tractor for the Post-War Period

Topics include specifications as of 1943, road fuel economy studies, hitching to vehicle, maximum load tests, potato digging, hauling baled hay from field to college barns, apple harvesting, road tests from orchard to cold-storage plant, field tests in the lower Yakima valley, and general observation

A new method for determining the optimal lagged ensemble

Abstract We propose a general methodology for determining the lagged ensemble that minimizes the mean square forecast error. The MSE of a lagged ensemble is shown to depend only on a quantity called the cross‐lead error covariance matrix, which can be estimated from a short hindcast data set and parameterized in terms of analytic functions of time. The resulting parameterization allows the skill of forecasts to be evaluated for an arbitrary ensemble size and initialization frequency. Remarkably, the parameterization also can estimate the MSE of a burst ensemble simply by taking the limit of an infinitely small interval between initialization times. This methodology is applied to forecasts of the Madden Julian Oscillation (MJO) from version 2 of the Climate Forecast System version 2 (CFSv2). For leads greater than a week, little improvement is found in the MJO forecast skill when ensembles larger than 5 days are used or initializations greater than 4 times per day. We find that if the initialization frequency is too infrequent, important structures of the lagged error covariance matrix are lost. Lastly, we demonstrate that the forecast error at leads ≥10 days can be reduced by optimally weighting the lagged ensemble members. The weights are shown to depend only on the cross‐lead error covariance matrix. While the methodology developed here is applied to CFSv2, the technique can be easily adapted to other forecast systems