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

Spectral high resolution feature selection for retrieval of combustion temperature profiles

Proceeding of: 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 (Burgos, Spain, September 20-23, 2006)The use of high spectral resolution measurements to obtain a retrieval of certain physical properties related with the radiative transfer of energy leads a priori to a better accuracy. But this improvement in accuracy is not easy to achieve due to the great amount of data which makes difficult any treatment over it and it's redundancies. To solve this problem, a pick selection based on principal component analysis has been adopted in order to make the mandatory feature selection over the different channels. In this paper, the capability to retrieve the temperature profile in a combustion environment using neural networks jointly with this spectral high resolution feature selection method is studied.Publicad

Nearest neighbours in least-squares data imputation algorithms with different missing patterns

Methods for imputation of missing data in the so-called least-squares approximation approach, a non-parametric computationally efficient multidimensional technique, are experimentally compared. Contributions are made to each of the three components of the experiment setting: (a) algorithms to be compared, (b) data generation, and (c) patterns of missing data. Specifically, "global" methods for least-squares data imputation are reviewed and extensions to them are proposed based on the nearest neighbours (NN) approach. A conventional generator of mixtures of Gaussian distributions is theoretically analysed and, then, modified to scale clusters differently. Patterns of missing data are defined in terms of rows and columns according to three different mechanisms that are referred to as Random missings, Restricted random missings, and Merged database. It appears that NN-based versions almost always outperform their global counterparts. With the Random missings pattern, the winner is always the authors' two-stage method M, which combines global and local imputation algorithms

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

Altered Metabolic Signature in Pre-Diabetic NOD Mice

Altered metabolism proceeding seroconversion in children progressing to Type 1 diabetes has previously been demonstrated. We tested the hypothesis that non-obese diabetic (NOD) mice show a similarly altered metabolic profile compared to C57BL/6 mice. Blood samples from NOD and C57BL/6 female mice was collected at 0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13 and 15 weeks and the metabolite content was analyzed using GC-MS. Based on the data of 89 identified metabolites OPLS-DA analysis was employed to determine the most discriminative metabolites. In silico analysis of potential involved metabolic enzymes was performed using the dbSNP data base. Already at 0 weeks NOD mice displayed a unique metabolic signature compared to C57BL/6. A shift in the metabolism was observed for both strains the first weeks of life, a pattern that stabilized after 5 weeks of age. Multivariate analysis revealed the most discriminative metabolites, which included inosine and glutamic acid. In silico analysis of the genes in the involved metabolic pathways revealed several SNPs in either regulatory or coding regions, some in previously defined insulin dependent diabetes (Idd) regions. Our result shows that NOD mice display an altered metabolic profile that is partly resembling the previously observation made in children progressing to Type 1 diabetes. The level of glutamic acid was one of the most discriminative metabolites in addition to several metabolites in the TCA cycle and nucleic acid components. The in silico analysis indicated that the genes responsible for this reside within previously defined Idd regions

Parametric Investigation of Traditional Vaulted Roofs in Hot-Arid Climates

In the Mediterranean and North African regions, traditional vaulted roof forms have been widely used due to their significant influence on enhancing thermal indoor conditions. This research parametrically investigates the thermal performance of vaulted roofs, seeking a better understanding of the reciprocal relationship between the solar irradiance received by these roofs and the resulting energy consumption in the hot-arid city of Aswan (23.58oN), Egypt. The methodological procedure is realized through two phases. The annual simulations of solar irradiance and energy consumption are carried out in the first phase, where the quantitative performance of 2,310 different cases are predicted in terms of six vaulted roof forms against eleven key influencing variables. The unsupervised technique of Principal Component Analysis is used in the second phase to reduce the higher dimensionality of the resulting dataset and extract important information from newly established orthogonal principal components. The outcomes of this work aim to provide architects and practitioners with an optimized dataset to use in the design and application of vaulted roof forms and support decision makers addressing the development strategies by providing essential data for setting regulations of newly built environments in harsh hot-arid contexts

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

Filling Kinetic Gaps: Dynamic Modeling of Metabolism Where Detailed Kinetic Information Is Lacking

Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation.In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically nulls. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages

Local linear embedded regression in the quantitative analysis of glucose in near infrared spectra

This paper investigates the use of Local Linear Embedded Regression (LLER) for the quantitative analysis of glucose from near infrared spectra. The performance of the LLER model is evaluated and compared with the regression techniques Principal Component Regression (PCR), Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) both with and without pre-processing. The prediction capability of the proposed model has been validated to predict the glucose concentration in an aqueous solution composed of three components (urea, triacetin and glucose). The results show that the LLER method offers improvements in comparison to PCR, PLSR and SVR

A pathway-specific microarray analysis highlights the complex and co-ordinated transcriptional networks of the developing grain of field-grown barley

The aim of the study was to describe the molecular and biochemical interactions associated with amino acid biosynthesis and storage protein accumulation in the developing grains of field-grown barley. Our strategy was to analyse the transcription of genes associated with the biosynthesis of storage products during the development of field-grown barley grains using a grain-specific microarray assembled in our laboratory. To identify co-regulated genes, a distance matrix was constructed which enabled the identification of three clusters corresponding to early, middle, and late grain development. The gene expression pattern associated with the clusters was investigated using pathway-specific analysis with specific reference to the temporal expression levels of a range of genes involved mainly in the photosynthesis process, amino acid and storage protein metabolism. It is concluded that the grain-specific microarray is a reliable and cost-effective tool for monitoring temporal changes in the transcriptome of the major metabolic pathways in the barley grain. Moreover, it was sensitive enough to monitor differences in the gene expression profiles of different homologues from the storage protein families. The study described here should provide a strong complement to existing knowledge assisting further understanding of grain development and thereby provide a foundation for plant breeding towards storage proteins with improved nutritional quality