Approximate decoding approaches for network coded correlated data

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

Evaluation of the accuracy of multilevel analysis parameter estimates for intra-cluster correlation in correlated binary data

Many studies in biostatistics deal with binary data. Some of these studies involve correlated observations, which can complicate the analysis of the resulting data. Studies of this kind typically arise when a high degree of commonality exists between test subjects. If there exists a natural hierarchy in the data, multilevel analysis is an appropriate tool for the analysis. Two examples are the measurements on identical twins, or the study of symmetrical organs or appendages such as in the case of ophthalmic studies. Although this type of matching appears ideal for the purposes of comparison, analysis of the resulting data while ignoring the effect of intra-cluster correlation has been shown to produce biased results.^ This paper will explore the use of multilevel modeling of simulated binary data with predetermined levels of correlation. Data will be generated using the Beta-Binomial method with varying degrees of correlation between the lower level observations. The data will be analyzed using the multilevel software package MlwiN (Woodhouse, et al, 1995). Comparisons between the specified intra-cluster correlation of these data and the estimated correlations, using multilevel analysis, will be used to examine the accuracy of this technique in analyzing this type of data. ^

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

Immunostaining of a heterodimeric dermatan sulphate proteoglycan is correlated with smooth muscles and some basement membranes.

A heterodimeric 760-kDa dermatan sulphate proteoglycan tentatively named PG-760 was characterized as a product of keratinocytes, endothelial cells, and fibroblasts. The two core proteins of 460 kDa and 300 kDa are linked by disulphide bridges, and both carry one or only very few dermatan sulphate chains. Different antisera against PG-760 were used in the present study to investigate the distribution in selected murine tissues by light and electron microscopy. PG-760 immunostaining was observed in cornea (epithelium including basement membrane, stroma, and Descemet's membrane), skin, mucosa of the small intestine, Engelbreth-Holm-Swarm (EHS)-tumour (matrix and cells), and the smooth muscle layers of uterus, small intestine, and blood vessels. No staining was observed in capillaries, striated muscles, and liver parenchyma including the central vein. The expression of PG-760 in EHS-tumour was also demonstrated after extraction with 4 M guanidine and partial purification by diethylaminoethyl (DEAE)-chromatography. We conclude that this novel proteoglycan exhibits a unique tissue distribution being a constituent of some but not all basement membranes, of some other extracellular matrices, and additionally, of all investigated smooth muscle layers.

Myoglobin expression in prostate cancer is correlated to androgen receptor expression and markers of tumor hypoxia.

Recent studies identified unexpected expression and transcriptional complexity of the hemoprotein myoglobin (MB) in human breast cancer but its role in prostate cancer is still unclear. Expression of MB was immunohistochemically analyzed in three independent cohorts of radical prostatectomy specimens (n = 409, n = 625, and n = 237). MB expression data were correlated with clinicopathological parameters and molecular parameters of androgen and hypoxia signaling. Expression levels of novel tumor-associated MB transcript variants and the VEGF gene as a hypoxia marker were analyzed using qRT-PCR. Fifty-three percent of the prostate cancer cases were MB positive and significantly correlated with androgen receptor (AR) expression (p < 0.001). The positive correlation with CAIX (p < 0.001) and FASN (p = 0.008) as well as the paralleled increased expression of the tumor-associated MB transcript variants and VEGF suggest that hypoxia participates in MB expression regulation. Analogous to breast cancer, MB expression in prostate cancer is associated with steroid hormone signaling and markers of hypoxia. Further studies must elucidate the novel functional roles of MB in human carcinomas, which probably extend beyond its classic intramuscular function in oxygen storage.

Competitive fitness in coronaviruses is not correlated with size or number of double-membrane vesicles under reduced-temperature growth conditions.

Positive-stranded viruses synthesize their RNA in membrane-bound organelles, but it is not clear how this benefits the virus or the host. For coronaviruses, these organelles take the form of double-membrane vesicles (DMVs) interconnected by a convoluted membrane network. We used electron microscopy to identify murine coronaviruses with mutations in nsp3 and nsp14 that replicated normally while producing only half the normal amount of DMVs under low-temperature growth conditions. Viruses with mutations in nsp5 and nsp16 produced small DMVs but also replicated normally. Quantitative reverse transcriptase PCR (RT-PCR) confirmed that the most strongly affected of these, the nsp3 mutant, produced more viral RNA than wild-type virus. Competitive growth assays were carried out in both continuous and primary cells to better understand the contribution of DMVs to viral fitness. Surprisingly, several viruses that produced fewer or smaller DMVs showed a higher fitness than wild-type virus at the reduced temperature, suggesting that larger and more numerous DMVs do not necessarily confer a competitive advantage in primary or continuous cell culture. For the first time, this directly demonstrates that replication and organelle formation may be, at least in part, studied separately during infection with positive-stranded RNA virus. IMPORTANCE The viruses that cause severe acute respiratory syndrome (SARS), poliomyelitis, and hepatitis C all replicate in double-membrane vesicles (DMVs). The big question about DMVs is why they exist in the first place. In this study, we looked at thousands of infected cells and identified two coronavirus mutants that made half as many organelles as normal and two others that made typical numbers but smaller organelles. Despite differences in DMV size and number, all four mutants replicated as efficiently as wild-type virus. To better understand the relative importance of replicative organelles, we carried out competitive fitness experiments. None of these viruses was found to be significantly less fit than wild-type, and two were actually fitter in tests in two kinds of cells. This suggests that viruses have evolved to have tremendous plasticity in the ability to form membrane-associated replication complexes and that large and numerous DMVs are not exclusively associated with efficient coronavirus replication.

Implicit probabilistic sequence learning: Correlated streams and sequence length matter

We investigated whether a pure perceptual stream is sufficient for probabilistic sequence learning to occur within a single session or whether correlated streams are necessary, whether learning is affected by the transition probability between sequence elements, and how the sequence length influences learning. In each of three experiments, we used six horizontally arranged stimulus displays which consisted of randomly ordered bigrams xo and ox. The probability of the next possible target location out of two was either .50/.50 or .75/.25 and was marked by an underline. In Experiment 1, a left vs. right key response was required for the x of a marked bigram in the pure perceptual learning condition and a response key press corresponding to the marked bigram location (out of 6) was required in the correlated streams condition (i.e., the ring, middle, or index finger of the left and right hand, respectively). The same probabilistic 3-element sequence was used in both conditions. Learning occurred only in the correlated streams condition. In Experiment 2, we investigated whether sequence length affected learning correlated sequences by contrasting the 3-elements sequence with a 6-elements sequence. Significant sequence learning occurred in all conditions. In Experiment 3, we removed a potential confound, that is, the sequence of hand changes. Under these conditions, learning occurred for the 3-element sequence only and transition probability did not affect the amount of learning. Together, these results indicate that correlated streams are necessary for probabilistic sequence learning within a single session and that sequence length can reduce the chances for learning to occur.

Correlated cosmogenic W and Os isotopic variations in Carbo and implications for Hf-W chronology

An obstacle for establishing the chronology of iron meteorite formation using 182Hf-182W systematics (t1/2 = 8.9 Myr) is to find proper neutron fluence monitors to correct for cosmic ray modification of W isotopic composition. Recent studies showed that siderophile elements such as Pt and Os could serve such a purpose. To test and calibrate these neutron dosimeters, the isotopic compositions of W and Os were measured in a slab of the IID iron meteorite Carbo. This slab has a well-characterized noble gas depth profile reflecting different degrees of shielding to cosmic rays. The results show that W and Os isotopic ratios correlate with distance from the pre-atmospheric center. Negative correlations, barely resolved within error, were found between epsilo190Os-epsilo189Os and epsilo186Os-epsilo189Os with slopes of -0.64 ± 0.45 and -1.8(+1.9/-2.1), respectively. These Os isotope correlations broadly agree with model predictions for capture of secondary neutrons produced by cosmic ray irradiation and results reported previously for other groups of iron meteorites. Correlations were also found between epsilo182W-epsilo189Os (slope = 1.02 ± 0.37) and epsilo182W-epsilo190Os (slope = -1.38 ± 0.58). Intercepts of these two correlations yield pre-exposure epsilo182W values of -3.32 ± 0.51 and -3.62 ± 0.23, respectively (weighted average epsilo182W = -3.57 ± 0.21). This value relies on a large extrapolation leading to a large uncertainty but gives a metal-silicate segregation age of -0.5 ± 2.4 Myr after formation of the solar system. Combining the iron meteorite measurements with simulations of cosmogenic effects in iron meteorites, equations are presented to calculate and correct for cosmogenic effects on 182W using Os isotopes.

Bayesian models for the analysis of genetic structure when populations are correlated

Motivation: Population allele frequencies are correlated when populations have a shared history or when they exchange genes. Unfortunately, most models for allele frequency and inference about population structure ignore this correlation. Recent analytical results show that among populations, correlations can be very high, which could affect estimates of population genetic structure. In this study, we propose a mixture beta model to characterize the allele frequency distribution among populations. This formulation incorporates the correlation among populations as well as extending the model to data with different clusters of populations. Results: Using simulated data, we show that in general, the mixture model provides a good approximation of the among-population allele frequency distribution and a good estimate of correlation among populations. Results from fitting the mixture model to a dataset of genotypes at 377 autosomal microsatellite loci from human populations indicate high correlation among populations, which may not be appropriate to neglect. Traditional measures of population structure tend to over-estimate the amount of genetic differentiation when correlation is neglected. Inference is performed in a Bayesian framework.

Marriage Matching with Correlated Preferences

Authors of experimental, empirical, theoretical and computational studies of two-sided matching markets have recognized the importance of correlated preferences. We develop a general method for the study of the effect of correlation of preferences on the outcomes generated by two-sided matching mechanisms. We then illustrate our method by using it to quantify the effect of correlation of preferences on satisfaction with the men-propose Gale-Shapley matching for a simple one-to-one matching problem.

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Multivariate methods for correlated effects sizes

Current statistical methods for estimation of parametric effect sizes from a series of experiments are generally restricted to univariate comparisons of standardized mean differences between two treatments. Multivariate methods are presented for the case in which effect size is a vector of standardized multivariate mean differences and the number of treatment groups is two or more. The proposed methods employ a vector of independent sample means for each response variable that leads to a covariance structure which depends only on correlations among the $p$ responses on each subject. Using weighted least squares theory and the assumption that the observations are from normally distributed populations, multivariate hypotheses analogous to common hypotheses used for testing effect sizes were formulated and tested for treatment effects which are correlated through a common control group, through multiple response variables observed on each subject, or both conditions.^ The asymptotic multivariate distribution for correlated effect sizes is obtained by extending univariate methods for estimating effect sizes which are correlated through common control groups. The joint distribution of vectors of effect sizes (from $p$ responses on each subject) from one treatment and one control group and from several treatment groups sharing a common control group are derived. Methods are given for estimation of linear combinations of effect sizes when certain homogeneity conditions are met, and for estimation of vectors of effect sizes and confidence intervals from $p$ responses on each subject. Computational illustrations are provided using data from studies of effects of electric field exposure on small laboratory animals. ^