Trace Element Zoning and Incipient Metamictization in a Lunar Zircon: Application of Three Microprobe Techniques

We have determined major (Si, Zr, Hf), minor (Al, Y, Fe, P), and trace element (Ca, Sc, Ti, Ba, REE, Th, U) concentrations and Raman spectra of a zoned, 200 microns zircon grain in lunar sample 14161,7069, a quartz monzodiorite breccia collected at the Apollo 14 site. Analyses were obtained on a thin section in situ with an ion microprobe, an electron microprobe, and a laser Raman microprobe. The zircon grain is optically zoned in birefringence, a reflection of variable (incomplete) metamictization resulting from zo- nation in U and Th concentrations. Variations in the concentrations of U and Th correlate strongly with those of other high-field-strength trace elements and with changes in Raman spectral parameters. Concentrations of U and Th range from 21 to 55 ppm and 6 to 31 ppm, respectively, and correlate with lower Raman peak intensities, wider Raman peaks, and shifted Si-O peak positions. Concentrations of heavy rare earth elements range over a factor of three to four and correlate with intensities of fluorescence peaks. Correlated variations in trace element concentrations reflect the original magmatic differentiation of the parental melt approx. 4 b.y. ago. Degradation of the zircon structure, as reflected by the observed Raman spectral parameters, has occurred in this sample over a range of alpha-decay event dose from approx. 5.2 x 10(exp 14) to 1.4 x 10(exp 15) decay events per milligram of zircon, as calculated from the U and Th concentrations. This dose is well below the approx. 10(exp 16) events per milligram cumulative dose that causes complete metamictization and indicates that laser Raman microprobe spectroscopy is an analytical technique that is very sensitive to the radiation-induced damage in zircon

Supervised inference of gene-regulatory networks

<p>Abstract</p> <p>Background</p> <p>Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.</p> <p>Results</p> <p>The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.</p> <p>Conclusion</p> <p>Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.</p

Elephants in pyjamas: testing the weak central coherence account of autism spectrum disorders using a syntactic disambiguation task

According to the weak central coherence (CC) account individuals with autism spectrum disorders (ASD) exhibit enhanced local processing and weak part-whole integration. CC was investigated in the verbal domain. Adolescents, recruited using a 2 (ASD status) by 2 (language impairment status) design, completed an aural forced choice comprehension task involving syntactically ambiguous sentences. Half the picture targets depicted the least plausible interpretation, resulting in longer RTs across groups. These were assumed to reflect local processing. There was no ASD by plausibility interaction and consequently little evidence for weak CC in the verbal domain when conceptualised as enhanced local processing. Furthermore, there was little evidence that the processing of syntactically ambiguous sentences differed as a function of ASD or language-impairment status

How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)

Physical activity, cardiorespiratory fitness, and metabolic syndrome in adolescents: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>In adults, there is a substantial body of evidence that physical inactivity or low cardiorespiratory fitness levels are strongly associated with the development of metabolic syndrome. Although this association has been studied extensively in adults, little is known regarding this association in adolescents. The aim of this study was to analyze the association between physical activity and cardiorespiratory fitness levels with metabolic syndrome in Brazilian adolescents.</p> <p>Methods</p> <p>A random sample of 223 girls (mean age, 14.4 ± 1.6 years) and 233 boys (mean age, 14.6 ± 1.6 years) was selected for the study. The level of physical activity was determined by the Bouchard three-day physical activity record. Cardiorespiratory fitness was estimated by the Leger 20-meter shuttle run test. The metabolic syndrome components assessed included waist circumference, blood pressure, HDL-cholesterol, triglycerides, and fasting plasma glucose levels. Independent Student <it>t</it>-tests were used to assess gender differences. The associations between physical activity and cardiorespiratory fitness with the presence of metabolic syndrome were calculated using logistic regression models adjusted for age and gender.</p> <p>Results</p> <p>A high prevalence of metabolic syndrome was observed in inactive adolescents (males, 11.4%; females, 7.2%) and adolescents with low cardiorespiratory fitness levels (males, 13.9%; females, 8.6%). A significant relationship existed between metabolic syndrome and low cardiorespiratory fitness (OR, 3.0 [1.13-7.94]).</p> <p>Conclusion</p> <p>The prevalence of metabolic syndrome is high among adolescents who are inactive and those with low cardiorespiratory fitness. Prevention strategies for metabolic syndrome should concentrate on enhancing fitness levels early in life.</p