Variance estimation in ranked set sampling using a concomitant variable

We propose a nonparametric variance estimator when ranked set sampling (RSS) and judgment post stratification (JPS) are applied by measuring a concomitant variable. Our proposed estimator is obtained by conditioning on observed concomitant values and using nonparametric kernel regression.

Quick divergence but slow convergence during ecotype formation in lake and stream stickleback pairs of variable age

When genetic constraints restrict phenotypic evolution, diversification can be predicted to evolve along so-called lines of least resistance. To address the importance of such constraints and their resolution, studies of parallel phenotypic divergence that differ in their age are valuable. Here, we investigate the parapatric evolution of six lake and stream threespine stickleback systems from Iceland and Switzerland, ranging in age from a few decades to several millennia. Using phenotypic data, we test for parallelism in ecotypic divergence between parapatric lake and stream populations and compare the observed patterns to an ancestral-like marine population. We find strong and consistent phenotypic divergence, both among lake and stream populations and between our freshwater populations and the marine population. Interestingly, ecotypic divergence in low-dimensional phenotype space (i.e. single traits) is rapid and seems to be often completed within 100 years. Yet, the dimensionality of ecotypic divergence was highest in our oldest systems and only there parallel evolution of unrelated ecotypes was strong enough to overwrite phylogenetic contingency. Moreover, the dimensionality of divergence in different systems varies between trait complexes, suggesting different constraints and evolutionary pathways to their resolution among freshwater systems.

Continuous eclogite melting and variable refertilisation in upwelling heterogeneous mantle

Large-scale tectonic processes introduce a range of crustal lithologies into the Earth's mantle. These lithologies have been implicated as sources of compositional heterogeneity in mantle-derived magmas. The model being explored here assumes the presence of widely dispersed fragments of residual eclogite (derived from recycled oceanic crust), stretched and stirred by convection in the mantle. Here we show with an experimental study that these residual eclogites continuously melt during upwelling of such heterogeneous mantle and we characterize the melting reactions and compositional changes in the residue minerals. The chemical exchange between these partial melts and more refractory peridotite leads to a variably metasomatised mantle. Re-melting of these metasomatised peridotite lithologies at given pressures and temperatures results in diverse melt compositions, which may contribute to the observed heterogeneity of oceanic basalt suites. We also show that heterogeneous upwelling mantle is subject to diverse local freezing, hybridization and carbonate-carbon-silicate redox reactions along a mantle adiabat.

Eclogitic diamonds from variable crustal protoliths in the northeastern Siberian craton: trace elements and coupled δ13C – δ18O signatures in diamonds and garnet inclusions

Diamonds of eclogitic assemblages are dominant in the placer diamond deposits of the northeastern Siberian platform. In this study we present new trace elements and stable isotopes (δ13C and δ18O) data for alluvial diamonds and their garnet inclusions from this locality. Cr-rich garnets of peridotitic affinity in the studied diamonds have a narrow range of δ18O values from 5.7‰ to 6.2‰, which is largely overlapping with the accepted mantle range. This narrow range suggests that the garnet inclusions showing different REE patterns and little variations in oxygen isotopes may have formed by different processes involving fluid/melts that, however, were in oxygen isotopic equilibrium with the mantle. The trace element composition of the eclogitic garnet inclusions supports a crustal origin for at least the high-Ca garnets, which show flat HREE patterns and in some cases a positive Eu-anomaly. High-Ca eclogitic garnets generally show heavier oxygen isotope compositions (δ18O 6.5–9.6‰) than what is observed in low-Ca garnets (δ18O 5.7–7.4‰). The variability in oxygen isotopes and trace elements is suggested to be inherited from contrasting crustal protoliths. The relationship between the high δ18O values of inclusions and the low δ13C values of the host diamonds implies that the high-Ca garnet inclusions were derived from intensely hydrated (e.g., δ18O > 7‰) and typically oxidised basaltic rock close to the seawater interface, and that the carbon for diamonds was closely associated with this protolith.

Variable phenotypic expressivity in inbred retinal degeneration mouse lines: A comparative study of C3H/HeOu and FVB/N rd1 mice

PURPOSE Recent advances in optogenetics and gene therapy have led to promising new treatment strategies for blindness caused by retinal photoreceptor loss. Preclinical studies often rely on the retinal degeneration 1 (rd1 or Pde6b(rd1)) retinitis pigmentosa (RP) mouse model. The rd1 founder mutation is present in more than 100 actively used mouse lines. Since secondary genetic traits are well-known to modify the phenotypic progression of photoreceptor degeneration in animal models and human patients with RP, negligence of the genetic background in the rd1 mouse model is unwarranted. Moreover, the success of various potential therapies, including optogenetic gene therapy and prosthetic implants, depends on the progress of retinal degeneration, which might differ between rd1 mice. To examine the prospect of phenotypic expressivity in the rd1 mouse model, we compared the progress of retinal degeneration in two common rd1 lines, C3H/HeOu and FVB/N. METHODS We followed retinal degeneration over 24 weeks in FVB/N, C3H/HeOu, and congenic Pde6b(+) seeing mouse lines, using a range of experimental techniques including extracellular recordings from retinal ganglion cells, PCR quantification of cone opsin and Pde6b transcripts, in vivo flash electroretinogram (ERG), and behavioral optokinetic reflex (OKR) recordings. RESULTS We demonstrated a substantial difference in the speed of retinal degeneration and accompanying loss of visual function between the two rd1 lines. Photoreceptor degeneration and loss of vision were faster with an earlier onset in the FVB/N mice compared to C3H/HeOu mice, whereas the performance of the Pde6b(+) mice did not differ significantly in any of the tests. By postnatal week 4, the FVB/N mice expressed significantly less cone opsin and Pde6b mRNA and had neither ERG nor OKR responses. At 12 weeks of age, the retinal ganglion cells of the FVB/N mice had lost all light responses. In contrast, 4-week-old C3H/HeOu mice still had ERG and OKR responses, and we still recorded light responses from C3H/HeOu retinal ganglion cells until the age of 24 weeks. These results show that genetic background plays an important role in the rd1 mouse pathology. CONCLUSIONS Analogous to human RP, the mouse genetic background strongly influences the rd1 phenotype. Thus, different rd1 mouse lines may follow different timelines of retinal degeneration, making exact knowledge of genetic background imperative in all studies that use rd1 models.

Variable effects of the conserved RNA hairpin element upon 3' end processing of histone pre-mRNA in vitro.

We have studied the requirements for efficient histone-specific RNA 3' processing in nuclear extract from mammalian tissue culture cells. Processing is strongly impaired by mutations in the pre-mRNA spacer element that reduce the base-pairing potential with U7 RNA. Moreover, by exchanging the hairpin and spacer elements of two differently processed H4 genes, we find that this difference is exclusively due to the spacer element. Finally, processing is inhibited by the addition of competitor RNAs, if these contain a wild-type spacer sequence, but not if their spacer element is mutated. Conversely, the importance of the hairpin for histone RNA 3' processing is highly variable: A hairpin mutant of the H4-12 gene is processed with almost wild-type efficiency in extract from K21 mouse mastocytoma cells but is strongly affected in HeLa cell extract, whereas an identical hairpin mutant of the H4-1 gene is affected in both extracts. The hairpin defect of H4-12-specific RNA in HeLa cells can be overcome by a compensatory mutation that increases the base complementarity to U7 snRNA. Very similar results were also obtained in RNA competition experiments: processing of H4-12-specific RNA can be competed by RNA carrying a wild-type hairpin element in extract from HeLa, but not K21 cells, whereas processing of H4-1-specific RNA can be competed in both extracts. With two additional histone genes we obtained results that were in one case intermediate and in the other similar to those obtained with H4-1. These results suggest that hairpin binding factor(s) can cooperatively support the ability of U7 snRNPs to form an active processing complex, but is(are) not directly involved in the processing mechanism.

Increased spread and replication efficiency of Listeria monocytogenes in organotypic brain-slices is related to multilocus variable number of tandem repeat analysis (MLVA) complex.

BACKGROUND Listeria (L.) monocytogenes causes fatal infections in many species including ruminants and humans. In ruminants, rhombencephalitis is the most prevalent form of listeriosis. Using multilocus variable number tandem repeat analysis (MLVA) we recently showed that L. monocytogenes isolates from ruminant rhombencephalitis cases are distributed over three genetic complexes (designated A, B and C). However, the majority of rhombencephalitis strains and virtually all those isolated from cattle cluster in MLVA complex A, indicating that strains of this complex may have increased neurotropism and neurovirulence. The aim of this study was to investigate whether ruminant rhombencephalitis strains have an increased ability to propagate in the bovine hippocampal brain-slice model and can be discriminated from strains of other sources. For this study, forty-seven strains were selected and assayed on brain-slice cultures, a bovine macrophage cell line (BoMac) and a human colorectal adenocarcinoma cell line (Caco-2). They were isolated from ruminant rhombencephalitis cases (n = 21) and other sources including the environment, food, human neurolisteriosis cases and ruminant/human non-encephalitic infection cases (n = 26). RESULTS All but one L. monocytogenes strain replicated in brain slices, irrespectively of the source of the isolate or MLVA complex. The replication of strains from MLVA complex A was increased in hippocampal brain-slice cultures compared to complex C. Immunofluorescence revealed that microglia are the main target cells for L. monocytogenes and that strains from MLVA complex A caused larger infection foci than strains from MLVA complex C. Additionally, they caused larger plaques in BoMac cells, but not CaCo-2 cells. CONCLUSIONS Our brain slice model data shows that all L. monocytogenes strains should be considered potentially neurovirulent. Secondly, encephalitis strains cannot be conclusively discriminated from non-encephalitis strains with the bovine organotypic brain slice model. The data indicates that MLVA complex A strains are particularly adept at establishing encephalitis possibly by virtue of their higher resistance to antibacterial defense mechanisms in microglia cells, the main target of L. monocytogenes.

Revisiting the ISN Flow Parameters, using a variable IBEX pointing strategy

The Interstellar Boundary Explorer (IBEX) has observed the interstellar neutral (ISN) gas flow over the past 6 yr during winter/spring when the Earth's motion opposes the ISN flow. Since IBEX observes the interstellar atom trajectories near their perihelion, we can use an analytical model based upon orbital mechanics to determine the interstellar parameters. Interstellar flow latitude, velocity, and temperature are coupled to the flow longitude and are restricted by the IBEX observations to a narrow tube in this parameter space. In our original analysis we found that pointing the spacecraft spin axis slightly out of the ecliptic plane significantly influences the ISN flow vector determination. Introducing the spacecraft spin axis tilt into the analytical model has shown that IBEX observations with various spin axis tilt orientations can substantially reduce the range of acceptable solutions to the ISN flow parameters as a function of flow longitude. The IBEX operations team pointed the IBEX spin axis almost exactly within the ecliptic plane during the 2012-2014 seasons, and about 5° below the ecliptic for half of the 2014 season. In its current implementation the analytical model describes the ISN flow most precisely for the spin axis orientation exactly in the ecliptic. This analysis refines the derived ISN flow parameters with a possible reconciliation between velocity vectors found with IBEX and Ulysses, resulting in a flow longitude lambda∞ = 74.°5 ± 1.°7 and latitude beta∞ = -5.°2 ± 0.°3, but at a substantially higher ISN temperature than previously reported.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^