Comprehensive Assessment for the Impacts of S/IVOC Emissions from Mobile Sources on SOA Formation in China.

Semivolatile/intermediate-volatility organic compounds (S/IVOCs) from mobile sources are essential SOA contributors. However, few studies have comprehensively evaluated the SOA contributions of S/IVOCs by simultaneously comparing different parameterization schemes. This study used three SOA schemes in the CMAQ model with a measurement-based emission inventory to quantify the mobile source S/IVOC-induced SOA (MS-SI-SOA) for 2018 in China. Among different SOA schemes, SOA predicted by the 2D-VBS scheme was in the best agreement with observations, but there were still large deviations in a few regions. Three SOA schemes showed the peak value of annual average MS-SI-SOA was up to 0.6 ± 0.3 µg/m3. High concentrations of MS-SI-SOA were detected in autumn, while the notable relative contribution of MS-SI-SOA to total SOA was predicted in the coastal areas in summer, with a regional average contribution up to 20 ± 10% in Shanghai. MS-SI-SOA concentrations varied by up to 2 times among three SOA schemes, mainly due to the discrepancy in SOA precursor emissions and chemical reactions, suggesting that the differences between SOA schemes should also be considered in modeling studies. These findings identify the hotspot areas and periods for MS-SI-SOA, highlighting the importance of S/IVOC emission control in the future upgrading of emission standards.

Common risk difference test and interval estimation of risk difference for stratified bilateral correlated data.

Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, and the measurements from such paired organs are generally highly correlated. Various statistical methods have been developed to tackle intra-class correlation on bilateral correlated data analysis. In practice, it is very important to adjust the effect of confounder on statistical inferences, since either ignoring the intra-class correlation or confounding effect may lead to biased results. In this article, we propose three approaches for testing common risk difference for stratified bilateral correlated data under the assumption of equal correlation. Five confidence intervals of common difference of two proportions are derived. The performance of the proposed test methods and confidence interval estimations is evaluated by Monte Carlo simulations. The simulation results show that the score test statistic outperforms other statistics in the sense that the former has robust type I error rates with high powers. The score confidence interval induced from the score test statistic performs satisfactorily in terms of coverage probabilities with reasonable interval widths. A real data set from an otolaryngologic study is used to illustrate the proposed methodologies.

Confidence intervals for proportion ratios of stratified correlated bilateral data.

In stratified bilateral studies, responses from two paired body parts are correlated. Confidence intervals (CIs), which reveal various features of the data, should take the correlations into account. In this article, five CI methods (sample-size weighted naïve Maximum likelihood estimation (MLE)-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, MLE-based score CI and pooled MLE-based Wald-type CI) are derived for proportion ratios under the assumption of equal correlation coefficient within each stratum. Monte Carlo simulation shows that the complete MLE-based Wald-type CI approach generally produces the shortest mean interval width and satisfactory empirical coverage probability with close form solution; while the profile likelihood CI and the MLE-based score CI provide preferred ratio of non coverage probability and are more symmetric. Two real examples are used to demonstrate the performance of the proposed methods.

Testing equality of proportions for correlated binary data in ophthalmologic studies.

In ophthalmologic studies, bilateral correlated data often arise when information involving paired organs (e.g., eyes) are measured from each subject. Adjusted chi-square approach for testing the equality of proportions has been proposed in the literature. In this article, we investigate and derive three alter- native testing procedures for the problem. Our simulation results show the score testing procedure usually produces satisfactory type I error control with higher power, and therefore is recommended. Examples from ophthalmologic studies are used to illustrate our proposed methods.

[Assessment of PM2.5 Pollution Mitigation due to Emission Reduction from Main Emission Sources in the Bejing-Tianjin-Hebei Region].

This study chose two months (January and July) in 2012 which represent winter and summer respectively, to assess the effects of fine particle(PM2.5) pollution elimination due to emission control from different sectors in the Bejing-Tianjin-Hebei region by using CMAQ/2D-VBS modeling system. The results showed that, industrial emissions contributed most to PM2.5 pollution in the Beijing-Tianjin-Hebei region, followed by domestic emissions, while the contribution of per ton emission reduced for industrial sectors subject to domestic sectors. The total contribution and contribution of per ton emission reduced for transportation and power plant were both at low level. Among industrial sectors, the iron, steel and metallurgical industry was the greatest contributor, followed by cement industry, industrial boiler, coking industry, lime and bricks industry and chemical industry. It was found that the contribution of each emission source had significant association with its primary PM2.5 emission level. The control of NOx emissions would promote the formation of PM2.5, and atmospheric vertical diffusion effect was weak during wintertime in the Beijing-Tianjin-Hebei region. As a result, emission control of various sectors was universally more effective for PM2.5 pollution mitigation in summer than in winter. Emission control in summer was significantly more effective for transportation, powerplant, cement industry, industrial boiler and lime and bricks industry. Due to considerable emissions in heating season, domestic emissions showed more contribution in winter. Agricultural sources showed greater contribution per emission reduction in winter by the reason of substantial emissions from straw open burning during this time. With respect to a certain reduction ratio of emission, future control strategies should pay more attention to industrial emissions, especially to the primary PM2.5 emissions. In details, priorities should be given to NOx and SO2 emission control for iron, steel and metallurgical industry, NOx emission control for cement industry and SO2 and NMVOC emission control for coking industry. Besides, domestic emission control should also be taken into consideration, and it will be more effective in winter.

A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait.

The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by-environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.

The risk of birth defects in multiple births: a population-based study.

OBJECTIVES: To determine if multiple births have higher risks of birth defects compared to singletons and to identify types of birth defects that occur more frequently in multiple births, controlling for seven sociodemographic and health-related variables. METHODS: A retrospective cohort study was conducted of all resident live births in Florida during 1996-2000 using data from a population-based surveillance system. Birth defects were defined as in the 9th edition of the International Classification of Diseases-Clinical Modification (ICD-9-CM) code for the 42 reportable categories in the Centers for Disease Control and Prevention (CDC) Birth Defects Registry list and eight major birth defects classifications. Relative risks (RR) before and after adjusting for control variables and 95% confidence intervals (95% CI) were calculated. The control variables included mother's race, age, previous adverse pregnancy experience, education, Medicaid participation during pregnancy, infant's sex and number of siblings. RESULTS: This study included 972,694 live births (27,727 multiple births and 944,967 singletons). Birth defects prevalence per 10,000 live births was 358.50 for multiple births and 250.54 for singletons. After adjusting for control variables, multiple births had a 46% increased risk of birth defects compared to singletons. Higher risks were found in 23 of 40 birth defects for multiple births. Five highest adjusted relative risks for birth defects among multiple births were: anencephalus, biliary atresia, hydrocephalus without spina bifida, pulmonary valve atresia and stenosis, and bladder exstrophy. Increased risks were also found in 6 out of 8 major birth defects classifications. CONCLUSIONS: Multiple births have increased risks of birth defects compared to singletons.

A general framework for statistical linkage analysis in multivalent tetraploids.

In multivalent polyploids, simultaneous pairings among homologous chromosomes at meiosis result in a unique cytological phenomenon-double reduction. Double reduction casts an impact on chromosome evolution in higher plants, but because of its confounded effect on the pattern of gene cosegregation, it complicates linkage analysis and map construction with polymorphic molecular markers. In this article, we have proposed a general statistical model for simultaneously estimating the frequencies of double reduction, the recombination fraction, and optimal parental linkage phases between any types of markers, both fully and partially informative, or dominant and codominant, for a tetraploid species that undergoes only multivalent pairing. This model provides an in-depth extension of our earlier linkage model that was built upon Fisher's classifications for different gamete formation modes during the polysomic inheritance of a multivalent polyploid. By implementing a two-stage hierarchical EM algorithm, we derived a closed-form solution for estimating the frequencies of double reduction through the estimation of gamete mode frequencies and the recombination fraction. We performed different settings of simulation studies to demonstrate the statistical properties of our model for estimating and testing double reduction and the linkage in multivalent tetraploids. As shown by a comparative analysis, our model provides a general framework that covers existing statistical approaches for linkage mapping in polyploids that are predominantly multivalent. The model will have great implications for understanding the genome structure and organization of polyploid species.

Functional mapping of quantitative trait loci underlying growth trajectories using a transform-both-sides logistic model.

The incorporation of developmental control mechanisms of growth has proven to be a powerful tool in mapping quantitative trait loci (QTL) underlying growth trajectories. A theoretical framework for implementing a QTL mapping strategy with growth laws has been established. This framework can be generalized to an arbitrary number of time points, where growth is measured, and becomes computationally more tractable, when the assumption of variance stationarity is made. In practice, however, this assumption is likely to be violated for age-specific growth traits due to a scale effect. In this article, we present a new statistical model for mapping growth QTL, which also addresses the problem of variance stationarity, by using a transform-both-sides (TBS) model advocated by Carroll and Ruppert (1984, Journal of the American Statistical Association 79, 321-328). The TBS-based model for mapping growth QTL cannot only maintain the original biological properties of a growth model, but also can increase the accuracy and precision of parameter estimation and the power to detect a QTL responsible for growth differentiation. Using the TBS-based model, we successfully map a QTL governing growth trajectories to a linkage group in an example of forest trees. The statistical and biological properties of the estimates of this growth QTL position and effect are investigated using Monte Carlo simulation studies. The implications of our model for understanding the genetic architecture of growth are discussed.

Statistical methods for dissecting triploid endosperm traits using molecular markers. An autogamous model.

The endosperm, a result of double fertilization in flowering plants, is a triploid tissue whose genetic composition is more complex than diploid tissue. We present a new maximum-likelihood-based statistical method for mapping quantitative trait loci (QTL) underlying endosperm traits in an autogamous plant. Genetic mapping of quantitative endosperm traits is qualitatively different from traits for other plant organs because the endosperm displays complicated trisomic inheritance and represents a younger generation than its mother plant. Our endosperm mapping method is based on two different experimental designs: (1) a one-stage design in which marker information is derived from the maternal genome and (2) a two-stage hierarchical design in which marker information is derived from both the maternal and offspring genomes (embryos). Under the one-stage design, the position and additive effect of a putative QTL can be well estimated, but the estimates of the dominant and epistatic effects are upward biased and imprecise. The two-stage hierarchical design, which extracts more genetic information from the material, typically improves the accuracy and precision of the dominant and epistatic effects for an endosperm trait. We discuss the effects on the estimation of QTL parameters of different sampling strategies under the two-stage hierarchical design. Our method will be broadly useful in mapping endosperm traits for many agriculturally important crop plants and also make it possible to study the genetic significance of double fertilization in the evolution of higher plants.

Simultaneous maximum likelihood estimation of linkage and linkage phases in outcrossing species.

With the advent of new molecular marker technologies, it is now feasible to initiate genome projects for outcrossing plant species, which have not received much attention in genetic research, despite their great agricultural and environmental value. Because outcrossing species typically have heterogeneous genomes, data structure for molecular markers representing an entire genome is complex: some markers may have more alleles than others, some markers are codominant whereas others are dominant, and some markers are heterozygous in one parent but fixed in the other parent whereas the opposite can be true for other markers. A major difficulty in analyzing these different types of marker at the same time arises from uncertainty about parental linkage phases over markers. In this paper, we present a general maximum-likelihood-based algorithm for simultaneously estimating linkage and linkage phases for a mixed set of different marker types containing fully informative markers (segregating 1:1:1:1) and partially informative markers (or missing markers, segregating 1:2:1, 3:1, and 1:1) in a full-sib family derived from two outbred parent plants. The characterization of linkage phases is based on the posterior probability distribution of the assignment of alternative alleles at given markers to two homologous chromosomes of each parent, conditional on the observed phenotypes of the markers. Two- and multi-point analyses are performed to estimate the recombination fraction and determine the most likely linkage phase between different types of markers. A numerical example is presented to demonstrate the statistical properties of the model for characterizing the linkage phase between markers.