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.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Statistical Analysis of Data on Linker Histones/DNA Interactions

2000 Mathematics Subject Classification: 62P10, 92C40

Carbon analysis of surface sediments from the Laptev Se

During the past six years organic geochemical, micropaleontological, and sedimentological investigations were carried out within the framework of the multidisciplinary bilateral German-Russian research project ''System Laptev Sea'' and detailed biological investigations within the project ''German-Russian Investigations of the Marginal Seas of the Eurasian Arctic'', In order to understand the Laptev Sea ecosystem and to obtain information about sources and fate of organic carbon, the distribution of phyto- and zooplankton, diatoms, chlorophyll a benthic macrofauna, palynomorphs, grain size, total organic carbon, d13Corg and biomarkers (n-alkanes, fatty acids) were determined. In general, the influence of the major rivers draining into the Laptev Sea, is reflected in the water column as well as in the surface sediments. In both habitats three ecological provinces can be distinguished, i.e., the southeastern Laptev Sea, the central Laptev Sea, and the northern Laptev Sea. Additionally, clear differences between the western and the eastern Laptev Sea occur. The comparison of the different data sets of the water column and the surface sediments provide information about organic carbon sources and pathways in the Laptev Sea shelf and continental slope area.

Savani: A variable resolution whole-mantle model of anisotropic shear velocity variations based on multiple data sets

International audience

Cross-sensors analysis of multispectral Earth observation data for environmental monitoring

Time Series Analysis of multispectral satellite data offers an innovative way to extract valuable information of our changing planet. This is now a real option for scientists thanks to data availability as well as innovative cloud-computing platforms, such as Google Earth Engine. The integration of different missions would mitigate known issues in multispectral time series construction, such as gaps due to clouds or other atmospheric effects. With this purpose, harmonization among Landsat-like missions is possible through statistical analysis. This research offers an overview of the different instruments from Landsat and Sentinel missions (TM, ETM, OLI, OLI-2 and MSI sensors) and products levels (Collection-2 Level-1 and Surface Reflectance for Landsat and Level-1C and Level-2A for Sentinel-2). Moreover, a cross-sensors comparison was performed to assess the interoperability of the sensors on-board Landsat and Sentinel-2 constellations, having in mind a possible combined use for time series analysis. Firstly, more than 20,000 pairs of images almost simultaneously acquired all over Europe were selected over a period of several years. The study performed a cross-comparison analysis on these data, and provided an assessment of the calibration coefficients that can be used to minimize differences in the combined use. Four of the most popular vegetation indexes were selected for the study: NDVI, EVI, SAVI and NDMI. As a result, it is possible to reconstruct a longer and denser harmonized time series since 1984, useful for vegetation monitoring purposes. Secondly, the spectral characteristics of the recent Landsat-9 mission were assessed for a combined use with Landsat-8 and Sentinel-2. A cross-sensor analysis of common bands of more than 3,000 almost simultaneous acquisitions verified a high consistency between datasets. The most relevant discrepancy has been observed in the blue and SWIRS bands, often used in vegetation and water related studies. This analysis was supported with spectroradiometer ground measurements.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Fractional Analysis of the DNA Information

Proceedings of the 12th Conference on 'Dynamical Systems -Theory and Applications'

Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers.

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.

Meta-analysis of heat- and chemically upregulated chaperone genes in plant and human cells.

Molecular chaperones are central to cellular protein homeostasis. In mammals, protein misfolding diseases and aging cause inflammation and progressive tissue loss, in correlation with the accumulation of toxic protein aggregates and the defective expression of chaperone genes. Bacteria and non-diseased, non-aged eukaryotic cells effectively respond to heat shock by inducing the accumulation of heat-shock proteins (HSPs), many of which molecular chaperones involved in protein homeostasis, in reducing stress damages and promoting cellular recovery and thermotolerance. We performed a meta-analysis of published microarray data and compared expression profiles of HSP genes from mammalian and plant cells in response to heat or isothermal treatments with drugs. The differences and overlaps between HSP and chaperone genes were analyzed, and expression patterns were clustered and organized in a network. HSPs and chaperones only partly overlapped. Heat-shock induced a subset of chaperones primarily targeted to the cytoplasm and organelles but not to the endoplasmic reticulum, which organized into a network with a central core of Hsp90s, Hsp70s, and sHSPs. Heat was best mimicked by isothermal treatments with Hsp90 inhibitors, whereas less toxic drugs, some of which non-steroidal anti-inflammatory drugs, weakly expressed different subsets of Hsp chaperones. This type of analysis may uncover new HSP-inducing drugs to improve protein homeostasis in misfolding and aging diseases.

Comparative modular analysis of gene expression in vertebrate development

The focus of my PhD research was the concept of modularity. In the last 15 years, modularity has become a classic term in different fields of biology. On the conceptual level, a module is a set of interacting elements that remain mostly independent from the elements outside of the module. I used modular analysis techniques to study gene expression evolution in vertebrates. In particular, I identified ``natural'' modules of gene expression in mouse and human, and I showed that expression of organ-specific and system-specific genes tends to be conserved between such distance vertebrates as mammals and fishes. Also with a modular approach, I studied patterns of developmental constraints on transcriptome evolution. I showed that none of the two commonly accepted models of the evolution of embryonic development (``evo-devo'') are exclusively valid. In particular, I found that the conservation of the sequences of regulatory regions is highest during mid-development of zebrafish, and thus it supports the ``hourglass model''. In contrast, events of gene duplication and new gene introduction are most rare in early development, which supports the ``early conservation model''. In addition to the biological insights on transcriptome evolution, I have also discussed in detail the advantages of modular approaches in large-scale data analysis. Moreover, I re-analyzed several studies (published in high-ranking journals), and showed that their conclusions do not hold out under a detailed analysis. This demonstrates that complex analysis of high-throughput data requires a co-operation between biologists, bioinformaticians, and statisticians.

A Comparative Policy Analysis of Seat Belt Laws, RB37-013, 2014

This analysis examined data from a variety of sources to estimate the benefit of enhancing Iowa’s current law to require all passengers to use seat belts. In addition to assessing Iowans’ opinions about changing the law, a literature review, a legislative policy review, and analysis of Iowa crash data were completed. Currently 28 states enforce seat belt laws for all passengers. Belted passengers riding with an unbelted passenger are 2 to 5 times more likely to suffer fatal injuries in a crash relative to when all occupants are using seat belts. Iowans are highly compliant (90%-94%) with the current seat belt law for front seat occupants. Of more than 1000 Iowans surveyed, 85% said they always use a seat belt when riding in the front seat, but only 36% always do so when they ride in the back seat. The most common reasons given for not using seat belts in the back seat are forgetting to buckle up and because it is not the law. Iowans widely support strengthening Iowa’s seat belt law — 62% said Iowa law should require all rear seat passengers to use seat belts. Four out of five respondents said they would use seat belts more often when sitting in the rear seat if it was the law. It is estimated rear seat fatalities would decrease about 48%, from 13 to 7 fatalities annually, if an all-passenger law was implemented in Iowa.

Spectral analysis of the angular distribution function of back reflectors for thin film silicon solar cells

Nowadays, one of the most important challenges to enhance the efficiency of thin film silicon solar cells is to increase the short circuit intensity by means of optical confinement methods, such as textured back-reflector structures. In this work, two possible textured structures to be used as back reflectors for n-i-p solar cells have been optically analyzed and compared to a smooth one by using a system which is able to measure the angular distribution function (ADF) of the scattered light in a wide spectral range (350-1000 nm). The accurate analysis of the ADF data corresponding to the reflector structures and to the μc-Si:H films deposited onto them allows the optical losses due to the reflector absorption and its effectiveness in increasing light absorption in the μc-Si:H layer, mainly at long wavelengths, to be quantified.

Analysis of bulk and surface contributions in the neutron skin of nuclei

The neutron skin thickness of nuclei is a sensitive probe of the nuclear symmetry energy and has multiple implications for nuclear and astrophysical studies. However, precision measurements of this observable are difficult to obtain. The analysis of the experimental data may imply some assumptions about the bulk or surface nature of the formation of the neutron skin. Here we study the bulk or surface character of neutron skins of nuclei following from calculations with Gogny, Skyrme, and covariant nuclear mean-field interactions. These interactions are successful in describing nuclear charge radii and binding energies but predict different values for neutron skins. We perform the study by fitting two-parameter Fermi distributions to the calculated self-consistent neutron and proton densities. We note that the equivalent sharp radius is a more suitable reference quantity than the half-density radius parameter of the Fermi distributions to discern between the bulk and surface contributions in neutron skins. We present calculations for nuclei in the stability valley and for the isotopic chains of Sn and Pb.

Detection and analysis of apoptosis in peripheral blood cells from breast cancer patients

Apoptosis is a well-known specific process of cell death that normally occurs in physiological situations such as tissue or organ development and involution. During tumor growth there is a balance between proliferation and cell death which involves apoptotic mechanisms. In the present study genomic DNAs from 120 breast tumor biopsies were analyzed by agarose gel electrophoresis and none of them presented the fragmentation pattern characteristic of the apoptosis process. However, 33% of the 105 breast cancer patients clearly showed the apoptotic pattern when DNA from blood cells was analyzed. None of the DNAs from healthy volunteer blood cells showed any trace of apoptosis. Since the breast cancer patients were not receiving chemo- or hormone therapy, the possible relationship between blood cortisol levels and the apoptotic pattern found in patient blood cells was investigated. Using a chemoluminescence immunodetection assay, similar cortisol levels were observed in breast cancer patient sera presenting or not apoptotic blood cells and in healthy volunteer sera. Analysis of the clinical data obtained from 60 of these patients showed that patients bearing tumors of smaller size (under 20 mm) were more susceptible to the apoptotic effect in blood cells. According to the Elston grade, it was observed that 7 of 12 patients with grade III tumors (58%) presented apoptotic peripheral blood cells, in contrast to 10 of 48 patients with grade I and grade II tumors. These observations may reflect the immunosuppression characteristic of some breast cancer patients, which may contribute to tumor growth. Therefore, further studies are necessary to elucidate the factor(s) involved in such massive blood cell death.