An experiment in the Assimilation of Data in Dynamical Analysis

A system for continuous data assimilation is presented and discussed. To simulate the dynamical development a channel version of a balanced barotropic model is used and geopotential (height) data are assimilated into the models computations as data become available. In the first experiment the updating is performed every 24th, 12th and 6th hours with a given network. The stations are distributed at random in 4 groups in order to simulate 4 areas with different density of stations. Optimum interpolation is performed for the difference between the forecast and the valid observations. The RMS-error of the analyses is reduced in time, and the error being smaller the more frequent the updating is performed. The updating every 6th hour yields an error in the analysis less than the RMS-error of the observation. In a second experiment the updating is performed by data from a moving satellite with a side-scan capability of about 15°. If the satellite data are analysed at every time step before they are introduced into the system the error of the analysis is reduced to a value below the RMS-error of the observation already after 24 hours and yields as a whole a better result than updating from a fixed network. If the satellite data are introduced without any modification the error of the analysis is reduced much slower and it takes about 4 days to reach a comparable result to the one where the data have been analysed.

Identification of reference genes for expression analysis by real-time quantitative PCR in drought-stressed soybean

O objetivo deste trabalho foi validar, pela técnica de PCR quantitativo em tempo real (RT-qPCR) genes para serem utilizados como referência em estudos de expressão gênica em soja, em ensaios de estresse hídrico. Foram avaliados quatro genes comumente utilizados em soja: Gmβ-actin, GmGAPDH, GmLectin e GmRNAr18S. O RNA total foi extraído de seis amostras: três amostras de raízes em sistema de hidroponia com diferentes intensidades de déficit hídrico (0, 25, 50, 75 e 100 minutos de estresse hídrico), e três amostras de folhas de plantas cultivadas em areia com diferentes umidades do solo (15, 5 e 2,5% de umidade gravimétrica). Os dados brutos do intervalo cycle threshold (Ct) foram analisados, e a eficiência de cada iniciador foi calculada para uma analise da Ct entre as diferentes amostras. A aplicação do programa GeNorm foi utilizada para a avaliação dos melhores genes de referência, de acordo com a estabilidade. O GmGAPDH foi o gene menos estável, com o maior valor médio de estabilidade de expressão (M), e os genes mais estáveis, com menor valor de M, foram o Gmβ-actin e GmRNAr18S, tanto nas amostras de raízes como nas de folhas. Estes genes podem ser usados em RT-qPCR como gens de referência para análises de expressão gênica.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Systemic analysis of microarray data sets towards identifying genes regulating root development

Nitrogen and water are essential for plant growth and development. In this study, we designed experiments to produce gene expression data of poplar roots under nitrogen starvation and water deprivation conditions. We found low concentration of nitrogen led first to increased root elongation followed by lateral root proliferation and eventually increased root biomass. To identify genes regulating root growth and development under nitrogen starvation and water deprivation, we designed a series of data analysis procedures, through which, we have successfully identified biologically important genes. Differentially Expressed Genes (DEGs) analysis identified the genes that are differentially expressed under nitrogen starvation or drought. Protein domain enrichment analysis identified enriched themes (in same domains) that are highly interactive during the treatment. Gene Ontology (GO) enrichment analysis allowed us to identify biological process changed during nitrogen starvation. Based on the above analyses, we examined the local Gene Regulatory Network (GRN) and identified a number of transcription factors. After testing, one of them is a high hierarchically ranked transcription factor that affects root growth under nitrogen starvation. It is very tedious and time-consuming to analyze gene expression data. To avoid doing analysis manually, we attempt to automate a computational pipeline that now can be used for identification of DEGs and protein domain analysis in a single run. It is implemented in scripts of Perl and R.

A simulation study using the hybrid statistic and the meta-analysis t-test for data with matched and unmatched subjects in the interim analysis of clinical trials

An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

Palynofacies analysis of sediments in the eastern Equatorial Atlantic

Analyses of the palynofacies and sporomorph thermal alteration indices (TAI) of sediments from Ocean Drilling Program (ODP) Sites 959 to 962 in the Cote d'Ivoire-Ghana Transform Margin, West Africa were undertaken to (1) determine the source and depositional conditions of the organic matter in the sediments, (2) refine a paleobathymetric curve derived from other data for Site 959, which drilled the most continuous sedimentary sequence from Pleistocene to Albian and (3) interpret the paleothermal history of the area. Twelve types of dispersed organic matter were identified: amorphous organic matter (AOM), marine palynomorphs, algae, resins, black debris, yellow-brown fragments, black-brown fragments, cuticles, plant tissue, wood, sporomorphs and fungi, The relative abundances of these organic matter components at each site were analyzed using cluster analysis, resulting in the identification of seven palynofacies assemblages at Site 959, five each at sites 960 and 961, and four at Site 962. Amorphous organic matter (which is chiefly marine derived), black debris and wood have played the most significant role in defining palynofacies assemblages. The palynofacies assemblages show some correlation with lithologic units, sediment sources and depositional environments. Previous palynofacies studies in passive margins have demonstrated that changes in the ratio of AOM to terrestrial organic matter are related primarily to proximal-distal positions of depositional environments relative to the shoreline. However, this assumption does not always hold true for a transform margin where tectonic factors play an important role in the organic matter distribution, at least in the early stages of evolution. Lithofacies, CCD paleodepths for the North Atlantic, trace fossil association, benthic foraminifera and palynofacies data were the criteria used for reconstructing a paleobathymetric curve for Site 959. A cyclicity in the organic matter distribution of the Upper Miocene to Lower Pliocene pelagic sediments could be related to fluctuations in productivity of biosiliceous and calcareous organisms, and sedimentation rates. A drastic increase in the amount of AOM and a decrease in black debris and wood in the carbonate and clastic rocks (Lithologic Unit IV) overlying the tectonized Albian sediments (Lithologic Unit V) at Sites 959 and 960 coincide with the presence of an unconformity. Qualitative color analysis of palynomorphs was undertaken for all sites, although the main focus was on Site 959 where detailed organic geochemical data were available. At Site 959, TAI values indicate an immature stage of organic maturation (<2) down to the black claystones of Lithologic Unit III at about 918.47 mbsf. Below this, samples show an increase with depth to a moderately mature stage (>2 except for the claystone samples between 1012.52 and 1036.5 mbsf, and one limestone sample at 1043.4 mbsf), reaching peak levels of 2.58 to 3.0 in the tectonized Albian sediments below the unconformity. These TAI values show a positive correlation with the Tma x values derived from Rock-Eval pyrolysis data. The highest values recorded in the basal tectonized units at all the sites (Sites 960-962 have mean values between 2.25 and 3.13) may be related to high heat flow during the intracontinental to syntransform basin stage in the region.

An introduction to analysis of variance (ANOVA) with special reference to data from clinical experiments in optometry

This article is aimed primarily at eye care practitioners who are undertaking advanced clinical research, and who wish to apply analysis of variance (ANOVA) to their data. ANOVA is a data analysis method of great utility and flexibility. This article describes why and how ANOVA was developed, the basic logic which underlies the method and the assumptions that the method makes for it to be validly applied to data from clinical experiments in optometry. The application of the method to the analysis of a simple data set is then described. In addition, the methods available for making planned comparisons between treatment means and for making post hoc tests are evaluated. The problem of determining the number of replicates or patients required in a given experimental situation is also discussed. Copyright (C) 2000 The College of Optometrists.

Data methods in optometry Part 9: experimental design and analysis of variance

The key to the correct application of ANOVA is careful experimental design and matching the correct analysis to that design. The following points should therefore, be considered before designing any experiment: 1. In a single factor design, ensure that the factor is identified as a 'fixed' or 'random effect' factor. 2. In more complex designs, with more than one factor, there may be a mixture of fixed and random effect factors present, so ensure that each factor is clearly identified. 3. Where replicates can be grouped or blocked, the advantages of a randomised blocks design should be considered. There should be evidence, however, that blocking can sufficiently reduce the error variation to counter the loss of DF compared with a randomised design. 4. Where different treatments are applied sequentially to a patient, the advantages of a three-way design in which the different orders of the treatments are included as an 'effect' should be considered. 5. Combining different factors to make a more efficient experiment and to measure possible factor interactions should always be considered. 6. The effect of 'internal replication' should be taken into account in a factorial design in deciding the number of replications to be used. Where possible, each error term of the ANOVA should have at least 15 DF. 7. Consider carefully whether a particular factorial design can be considered to be a split-plot or a repeated measures design. If such a design is appropriate, consider how to continue the analysis bearing in mind the problem of using post hoc tests in this situation.

Options to advance co-ordination and analysis of climate data in Europe

Systematic, high-quality observations of the atmosphere, oceans and terrestrial environments are required to improve understanding of climate characteristics and the consequences of climate change. The overall aim of this report is to carry out a comparative assessment of approaches taken to addressing the state of European observations systems and related data analysis by some leading actors in the field. This research reports on approaches to climate observations and analyses in Ireland, Switzerland, Germany, The Netherlands and Austria and explores options for a more coordinated approach to national responses to climate observations in Europe. The key aspects addressed are: an assessment of approaches to develop GCOS and provision of analysis of GCOS data; an evaluation of how these countries are reporting development of GCOS; highlighting best practice in advancing GCOS implementation including analysis of Essential Climate Variables (ECVs); a comparative summary of the differences and synergies in terms of the reporting of climate observations; an overview of relevant European initiatives and recommendations on how identified gaps might be addressed in the short to medium term.

Governing Big Data: The Political Economy of Power, Knowledge and Consumer Finance in the Digital Age

This dissertation offers a critical international political economy (IPE) analysis of the ways in which consumer information has been governed throughout the formal history of consumer finance (1840 – present). Drawing primarily on the United States, this project problematizes the notion of consumer financial big data as a ‘new era’ by tracing its roots historically from late nineteenth century through to the present. Using a qualitative case study approach, this project applies a unique theoretical framework to three instances of governance in consumer credit big data. Throughout, the historically specific means used to govern consumer credit data are rooted in dominant ideas, institutions and material factors.

Expedition in Data and Harmonic Analysis on Graphs

The graph Laplacian operator is widely studied in spectral graph theory largely due to its importance in modern data analysis. Recently, the Fourier transform and other time-frequency operators have been defined on graphs using Laplacian eigenvalues and eigenvectors. We extend these results and prove that the translation operator to the i’th node is invertible if and only if all eigenvectors are nonzero on the i’th node. Because of this dependency on the support of eigenvectors we study the characteristic set of Laplacian eigenvectors. We prove that the Fiedler vector of a planar graph cannot vanish on large neighborhoods and then explicitly construct a family of non-planar graphs that do exhibit this property. We then prove original results in modern analysis on graphs. We extend results on spectral graph wavelets to create vertex-dyanamic spectral graph wavelets whose support depends on both scale and translation parameters. We prove that Spielman’s Twice-Ramanujan graph sparsifying algorithm cannot outperform his conjectured optimal sparsification constant. Finally, we present numerical results on graph conditioning, in which edges of a graph are rescaled to best approximate the complete graph and reduce average commute time.

What Are Effective Program Characteristics of Self-Management Interventions in Patients With Heart Failure? : An Individual Patient Data Meta-analysis

Background To identify those characteristics of self-management interventions in patients with heart failure (HF) that are effective in influencing health-related quality of life, mortality, and hospitalizations. Methods and Results Randomized trials on self-management interventions conducted between January 1985 and June 2013 were identified and individual patient data were requested for meta-analysis. Generalized mixed effects models and Cox proportional hazard models including frailty terms were used to assess the relation between characteristics of interventions and health-related outcomes. Twenty randomized trials (5624 patients) were included. Longer intervention duration reduced mortality risk (hazard ratio 0.99, 95% confidence interval [CI] 0.97–0.999 per month increase in duration), risk of HF-related hospitalization (hazard ratio 0.98, 95% CI 0.96–0.99), and HF-related hospitalization at 6 months (risk ratio 0.96, 95% CI 0.92–0.995). Although results were not consistent across outcomes, interventions comprising standardized training of interventionists, peer contact, log keeping, or goal-setting skills appeared less effective than interventions without these characteristics. Conclusion No specific program characteristics were consistently associated with better effects of self-management interventions, but longer duration seemed to improve the effect of self-management interventions on several outcomes. Future research using factorial trial designs and process evaluations is needed to understand the working mechanism of specific program characteristics of self-management interventions in HF patients.

A spatial data warehouse to predict lithic sources of tombs from South of Portugal: mixing geochemistry, petrology, cartography and archaeology in spatial analysis

MEGAGEO - Moving megaliths in the Neolithic is a project that intends to find the provenience of lithic materials in the construction of tombs. A multidisciplinary approach is carried out, with researchers from several of the knowledge fields involved. This work presents a spatial data warehouse specially developed for this project that comprises information from national archaeological databases, geographic and geological information and new geochemical and petrographic data obtained during the project. The use of the spatial data warehouse proved to be essential in the data analysis phase of the project. The Redondo Area is presented as a case study for the application of the spatial data warehouse to analyze the relations between geochemistry, geology and the tombs in this area.

Analysis of spatial variation of tree heights in an urban area using LiDAR data.

Estimating with greater precision and accuracy the height of plants has been a challenge for the scientific community. The objective this study is to evaluate the spatial variation of tree heights at different spatial scales in areas of the city of Recife, Brazil, using LiDAR remote sensing data. The LiDAR data were processed in the QT Modeler (Quick Terrain Modeler v. 8.0.2) software from Applied Imagery. The TreeVaW software was utilized to estimate the heights and crown diameters of trees. The results obtained for tree height were consistent with field measurements.