The kinases MSK1 and MSK2 act as negative regulators of Toll-like receptor signaling

The kinases MSK1 and MSK2 are activated 'downstream' of the p38 and Erk1/2 mitogen-activated protein kinases. Here we found that MSK1 and MSK2 were needed to limit the production of proinflammatory cytokines in response to stimulation of primary macrophages with lipopolysaccharide. By inducing transcription of the mitogen-activated protein kinase phosphatase DUSP1 and the anti-inflammatory cytokine interleukin 10, MSK1 and MSK2 exerted many negative feedback mechanisms. Deficiency in MSK1 and MSK2 prevented the binding of phosphorylated transcription factors CREB and ATF1 to the promoters of the genes encoding interleukin 10 and DUSP1. Mice doubly deficient in MSK1 and MSK2 were hypersensitive to lipopolysaccharide-induced endotoxic shock and showed prolonged inflammation in a model of toxic contact eczema induced by phorbol 12-myristate 13-acetate. Our results establish MSK1 and MSK2 as key components of negative feedback mechanisms needed to limit Toll-like receptor-driven inflammation.</p

Charged-Higgs phenomenology in the Aligned two-Higgs-doublet model

The alignment in flavour space of the Yukawa matrices of a general two-Higgs-doublet model results in the absence of tree-level flavour-changing neutral currents. In addition to the usual fermion masses and mixings, the aligned Yukawa structure only contains three complex parameters, which are potential new sources of CP violation. For particular values of these three parameters all known specific implementations of the model based on discrete Z_2 symmetries are recovered. One of the most distinctive features of the two-Higgs-doublet model is the presence of a charged scalar. In this work, we discuss its main phenomenological consequences in flavour-changing processes at low energies and derive the corresponding constraints on the parameters of the aligned two-Higgs-doublet model.Comment: 46 pages, 19 figures. Version accepted for publication in JHEP. References added. Discussion slightly extended. Conclusions unchange

Optimal stomatal behaviour around the world

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this recordStomatal conductance (g s) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of g s in predictions of global water and carbon cycle changes, a global-scale database and an associated globally applicable model of g s that allow predictions of stomatal behaviour are lacking. Here, we present a database of globally distributed g s obtained in the field for a wide range of plant functional types (PFTs) and biomes. We find that stomatal behaviour differs among PFTs according to their marginal carbon cost of water use, as predicted by the theory underpinning the optimal stomatal model and the leaf and wood economics spectrum. We also demonstrate a global relationship with climate. These findings provide a robust theoretical framework for understanding and predicting the behaviour of g s across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of ecosystem productivity, energy balance and ecohydrological processes in a future changing climate.This research was supported by the Australian Research Council (ARC MIA Discovery Project 1433500-2012-14). A.R. was financially supported in part by The Next-Generation Ecosystem Experiments (NGEE-Arctic) project, which is supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, and through the United States Department of Energy contract No. DE-AC02-98CH10886 to Brookhaven National Laboratory. M.O.d.B. acknowledges that the Brassica data were obtained within a research project financed by the Belgian Science Policy (OFFQ, contract number SD/AF/02) and coordinated by K. Vandermeiren at the Open-Top Chamber research facilities of CODA-CERVA (Tervuren, Belgium)

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Monazite trumps zircon: applying SHRIMP U–Pb geochronology to systematically evaluate emplacement ages of leucocratic, low-temperature granites in a complex Precambrian orogen

Although zircon is the most widely used geochronometer to determine the crystallisation ages of granites, it can be unreliable for low-temperature melts because they may not crystallise new zircon. For leucocratic granites U–Pb zircon dates, therefore, may reflect the ages of the source rocks rather than the igneous crystallisation age. In the Proterozoic Capricorn Orogen of Western Australia, leucocratic granites are associated with several pulses of intracontinental magmatism spanning ~800 million years. In several instances, SHRIMP U–Pb zircon dating of these leucocratic granites either yielded ages that were inconclusive (e.g., multiple concordant ages) or incompatible with other geochronological data. To overcome this we used SHRIMP U–Th–Pb monazite geochronology to obtain igneous crystallisation ages that are consistent with the geological and geochronological framework of the orogen. The U–Th–Pb monazite geochronology has resolved the time interval over which two granitic supersuites were emplaced; a Paleoproterozoic supersuite thought to span ~80 million years was emplaced in less than half that time (1688–1659 Ma) and a small Meso- to Neoproterozoic supersuite considered to have been intruded over ~70 million years was instead assembled over ~130 million years and outlasted associated regional metamorphism by ~100 million years. Both findings have consequences for the duration of associated orogenic events and any estimates for magma generation rates. The monazite geochronology has contributed to a more reliable tectonic history for a complex, long-lived orogen. Our results emphasise the benefit of monazite as a geochronometer for leucocratic granites derived by low-temperature crustal melting and are relevant to other orogens worldwide

The association between parity, infant gender, higher level of paternal education and preterm birth in Pakistan: a cohort study

<p>Abstract</p> <p>Background</p> <p>High rates of antenatal depression and preterm birth have been reported in Pakistan. Self reported maternal stress and depression have been associated with preterm birth; however findings are inconsistent. Cortisol is a biological marker of stress and depression, and its measurement may assist in understanding the influence of self reported maternal stress and depression on preterm birth.</p> <p>Methods</p> <p>In a prospective cohort study pregnant women between 28 to 30 weeks of gestation from the Aga Khan Hospital for Women and Children completed the A-Z Stress Scale and the Centre for Epidemiology Studies Depression Scale to assess stress and depression respectively, and had a blood cortisol level drawn. Women were followed up after delivery to determine birth outcomes. Correlation coefficients and Wilcoxon rank sum test was used to assess relationship between preterm birth, stress, depression and cortisol. Logistic regression analysis was used to determine the key factors predictive of preterm birth.</p> <p>Results</p> <p>132 pregnant women participated of whom 125 pregnant women had both questionnaire and cortisol level data and an additional seven had questionnaire data only. Almost 20% of pregnant women (19·7%, 95% CI 13·3-27·5) experienced a high level of stress and nearly twice as many (40·9%, 95% CI 32·4-49·8%) experienced depressive symptoms. The median of cortisol level was 27·40 ug/dl (IQR 22·5-34·2). The preterm birth rate was 11·4% (95% CI 6·5-18). There was no relationship between cortisol values and stress scale or depression. There was a significant positive relationship between maternal depression and stress. Preterm birth was associated with higher parity, past delivery of a male infant, and higher levels of paternal education. Insufficient numbers of preterm births were available to warrant the development of a multivariable logistic regression model.</p> <p>Conclusions</p> <p>Preterm birth was associated with higher parity, past delivery of a male infant, and higher levels of paternal education. There was no relationship between stress, and depression, cortisol and preterm birth. There were high rates of stress and depression among this sample suggesting that there are missed opportunities to address mental health needs in the prenatal period. Improved methods of measurement are required to better understand the psychobiological basis of preterm birth.</p