A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing

Background Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called “dropout”, which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the “dropout” effect in scRNA-seq data using a novel graph-based k-partitioning method by merging transcriptomically similar cells. Results We observed that the number of zero values was reduced among the merged transcriptomically similar cell clusters. Motivated by this observation, we leveraged a graph-based algorithm and develop an R package, scCorr, to recover the missing gene-gene correlation in scRNA-seq data that enables the reliable acquisition of cluster-based gene-gene correlations in three independent scRNA-seq datasets. The graphically partitioned cell clusters did not change the local cell community. For example, in scRNA-seq data from peripheral blood mononuclear cells (PBMCs), the gene-gene correlation estimated by scCorr outperformed the correlation estimated by the nonclustering method. Among 85 correlated gene pairs in a set of 100 clusters, scCorr detected 71 gene pairs, while the nonclustering method detected only 4 pairs of a dataset from PBMCs. The performance of scCorr was comparable to those of three previously published methods. As an example of downstream analysis using scCorr, we show that scCorr accurately identified a known cell type (i.e., CD4+ T cells) in PBMCs with a receiver operating characteristic area under the curve of 0.96. Conclusions Our results demonstrate that scCorr is a robust and reliable graph-based method for identifying correlated gene pairs, which is fundamental to network construction, gene-gene interaction, and cellular omic analyses. scCorr can be quickly and easily implemented to minimize zero values in scRNA-seq analysis and is freely available at https://github.com/CBIIT-CGBB/scCorr.

Polarization Characterization of LEDs by Stokes Parameters Measurements

Stokes parameters have been measured by using a polarimeter consisting of a rotating phase plate before a fixed polarizer for bullet-shaped red, green and blue LEDs at 3 different directions of 0°, 45° and 90° from the principal axis. The degree of polarization is minimum at the observation angle 0° (observed head-on) for all colors as expected but has non-zero values (1-9%). As for the possible cause for the partial polarization, it is likely to be brought by striae inside the transparent epoxy resin that can be easily visible. Data at observation angle 90° have features common for all colors; the degree of polarization is highest, the long axis azimuth of the polarization ellipse is nearly in the horizontal direction, and the ellipticity is small. These features can be explained as follows. At observation angle 90°, only small fraction of the beam emitted nearly horizontally is detected possibly through multireflection (the plane of incidence is in the vertical plane) inside the top- and bottom-surfaces (in the horizontal direction) of the chip substrate. Since the reflectance for s-polarization (horizontal component) is higher than that for p-polarization, the emerging beam becomes horizontally polarized. The hypotheses that geometrical asymmetry generates polarization is experimentally supported.

An analytical model for mountain wave in stratified atmosphere

An analytical, two-dimensional computer model has been developed for real time prediction of 'mountain wave due to Principal mountains over Kashmir valley. Simulation of the L2 profile has been made with realistic, non-zero values at higher levels and exponentially decreasing values at lower levels. Unlike Doos (1961), present solution has no restriction on the value of wave number (k). Validity of the model has been tested with the satellite observed waves in seven cases and actual aircraft report in one case.

Effect of Cobalt and Chromium Ions on the Chlorhexidine Digluconate as Seen by Intermolecular Diffusion

Metal ions such as cobalt (II) and chromium (III) might be present in the oral cavity, as a consequence of the corrosion of Co-Cr dental alloys. The diffusion of such metal ions into the organism, carried by saliva, can cause health problems as a consequence of their toxicity, enhanced by a cumulative effect in the body. The effect of the chlorhexidine digluconate, which is commonly used in mouthwash formulations, on the transport of these salts is evaluated in this paper by using the Taylor dispersion technique, which will allow an assessment of how the presence of chlorhexidine digluconate (either in aqueous solution or in a commercial formulation) may affect the diffusion of metal ions. The ternary mutual diffusion coefficients of metal ions (Co and Cr) in the presence of chlorhexidine digluconate, in an artificial saliva media, were measured. Significant coupled diffusion of CoCl2 (and CrCl3) and chlorhexidine digluconate is observed by analysis of the non-zero values of the cross-diffusion coefficients, D12 and D21. The observed interactions between metal ions and chlorhexidine digluconate suggest that the latter might be considered as an advantageous therapeutic agent, once they contribute to the reduction of the concentration of those ions inside the mouth.

Modelling annual earnings with unemployment: Non-random selection in female workers

<p>Female earnings are underrepresented in the earnings and earnings dynamics literature. This underrepresentation is largely a result of the di erences in participation rates between male and female workers. Female workers tend to have more frequent changes in employment status, and more periods of unemployment than their male counterparts. These periods of unemployment result in observations with zero earnings, and common transformations such as the logarithm are not de ned for zero values. This means that any analysis of the logarithm of earnings is forced to exclude periods where an individual does not work, and cannot take into account the e ect of moving into or out of employment. The higher rate of unemployment in female workers also increases the risk of sample selection bias. If selection into employment is non-random, then estimating earnings equations based on only workers will result in biased estimates. This thesis takes a novel approach by focusing on the annual earnings of females, and in doing so introduces two methods for addressing the issues associated with zero earnings observations. First, the Inverse Hyperbolic Sine (IHS) function is introduced as an alternative to the logarithm. The IHS is de ned for zero values, allowing for the creation of descriptive statistics that take into account periods of unemployment and changes in employment status. While the IHS has many properties that are useful when working with annual earnings, this thesis also highlights a number of estimation issues that can arise when using the function that have not previously been mentioned in the literature. Second, a new correction for sample selection bias that has been proposed by Semykina and Wooldridge (2013) is used to model the annual earnings of female workers. Both the sample selection bias correction and the IHS are applied to data on prime aged females from the Survey of Families, Income, and Employment (SoFIE) data set.</p>

Modelling annual earnings with unemployment: Non-random selection in female workers

<p>Female earnings are underrepresented in the earnings and earnings dynamics literature. This underrepresentation is largely a result of the di erences in participation rates between male and female workers. Female workers tend to have more frequent changes in employment status, and more periods of unemployment than their male counterparts. These periods of unemployment result in observations with zero earnings, and common transformations such as the logarithm are not de ned for zero values. This means that any analysis of the logarithm of earnings is forced to exclude periods where an individual does not work, and cannot take into account the e ect of moving into or out of employment. The higher rate of unemployment in female workers also increases the risk of sample selection bias. If selection into employment is non-random, then estimating earnings equations based on only workers will result in biased estimates. This thesis takes a novel approach by focusing on the annual earnings of females, and in doing so introduces two methods for addressing the issues associated with zero earnings observations. First, the Inverse Hyperbolic Sine (IHS) function is introduced as an alternative to the logarithm. The IHS is de ned for zero values, allowing for the creation of descriptive statistics that take into account periods of unemployment and changes in employment status. While the IHS has many properties that are useful when working with annual earnings, this thesis also highlights a number of estimation issues that can arise when using the function that have not previously been mentioned in the literature. Second, a new correction for sample selection bias that has been proposed by Semykina and Wooldridge (2013) is used to model the annual earnings of female workers. Both the sample selection bias correction and the IHS are applied to data on prime aged females from the Survey of Families, Income, and Employment (SoFIE) data set.</p>

Analysis of zero inflated dichotomous variables from a Bayesian perspective: application to occupational health

Background Zero-inflated models are generally aimed to addressing the problem that arises from having two different sources that generate the zero values observed in a distribution. In practice, this is due to the fact that the population studied actually consists of two subpopulations: one in which the value zero is by default (structural zero) and the other is circumstantial (sample zero). Methods This work proposes a new methodology to fit zero inflated Bernoulli data from a Bayesian approach, able to distinguish between two potential sources of zeros (structural and non-structural). Results The proposed methodology performance has been evaluated through a comprehensive simulation study, and it has been compiled as an R package freely available to the community. Its usage is illustrated by means of a real example from the field of occupational health as the phenomenon of sickness presenteeism, in which it is reasonable to think that some individuals will never be at risk of suffering it because they have not been sick in the period of study (structural zeros). Without separating structural and non-structural zeros one would be studying jointly the general health status and the presenteeism itself, and therefore obtaining potentially biased estimates as the phenomenon is being implicitly underestimated by diluting it into the general health status. Conclusions The proposed methodology is able to distinguish two different sources of zeros (structural and non-structural) from dichotomous data with or without covariates in a Bayesian framework, and has been made available to any interested researcher in the form of the bayesZIB R package (https://cran.r-project.org/package=bayesZIB).

The influence of synthetic foaming agents on seed germination of coniferous species

Water is the most frequently used substance for extinguishing of wildfires. Ones of the most commonly used additives enhancing the extinguishing efficiency are foaming agents. This article deals with the influence of foaming agents on germination of coniferous species. Foaming agents Moussol-APS F-15 and Sthamex F-15, foaming solutions of various concentrations were used for the tests. Germination of seeds of Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) was observed. The percentage of germinating seeds was recorded every 7 days. The results were evaluated in the form of graphs. When the concentration levels of foaming solutions ranged from 0.1 vol% to 0.25 vol%, their influence on germination of both coniferous species seeds varied from negligible to slightly positive. Subsequently, the negative effect increased considerably and with concentrations exceeding 1 vol% - 1.5 vol% the germination of samples reached zero values. While foaming solutions of Sthamex F-15 showed less significant influence on germination of the Scots pine seeds, seeds of the Norway spruce were less influenced by foaming agent Moussol-APS F-15. Based on obtained results it is possible to recommend minimisation of foaming agents amounts, eventually application of substances with less negative effect on plants germination for extinguishing of wildland fires, in order to gain restoration of affected area as fast as possible. Key words: foaming agent, germination, wildfire (wildland fire), foaming solution

Marginalized Two-Part Joint Modeling of Longitudinal Semi-Continuous Responses and Survival Data: With Application to Medical Costs

Non-negative continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical costs data. It is thus critical to incorporate the potential dependence of survival status and longitudinal medical costs in joint modeling, where censorship is death-related. Despite the wide use of conventional two-part joint models (CTJMs) to capture zero-inflation, they are limited to conditional interpretations of the regression coefficients in the model’s continuous part. In this paper, we propose a marginalized two-part joint model (MTJM) to jointly analyze semi-continuous longitudinal costs data and survival data. We compare it to the conventional two-part joint model (CTJM) for handling marginal inferences about covariate effects on average costs. We conducted a series of simulation studies to evaluate the superior performance of the proposed MTJM over the CTJM. To illustrate the applicability of the MTJM, we applied the model to a set of real electronic health record (EHR) data recently collected in Iran. We found that the MTJM yielded a smaller standard error, root-mean-square error of estimates, and AIC value, with unbiased parameter estimates. With this MTJM, we identified a significant positive correlation between costs and survival, which was consistent with the simulation results.