Element concentrations of aluminosilicate components from ODP sites in the Pacific Ocean

We measured major and trace element concentrations in the operationally defined, chemically extracted, residual aluminosilicate component of sediment from Ocean Drilling Program Sites 1215 and 1256 in the central and eastern equatorial Pacific Ocean and found that this residual component contains volcanogenic and authigenic aluminosilicates in addition to inferred eolian material. While the residual component younger than 20 Ma from the central Pacific (ODP Site 1215) is similar compositionally to upper continental crust and suggests an increase in the delivery of Asian dust material since 20 Ma, the residual in sediment older than 20 Ma indicates significant amounts of volcanogenic and authigenic materials. Volcanogenic debris comprises as much as ~ 40% of the residual between 23-40 Ma, which coincides with the mid-Tertiary "ignimbrite flare-up" that occurred in much of western North America. The residual component extracted from the 50 Ma biogenic sediment reflects authigenic signatures (seawater-like negative cerium anomalies and elevated Fe/Si ratios). The previously interpreted increase in an andesitic detrital source in North Pacific locations may instead be authigenic material, presenting significant challenges for many paleoclimate proxies. Additionally, in the eastern Pacific (ODP Site 1256), the residual component contains ~70% of volcanogenic material, most likely originating from Central America, and also includes refractory barite. The ability to separately identify eolian, volcanogenic, and authigenic materials in the aluminosilicate component of pelagic sediment allows resolution, respectively, of the climatic, geologic, and chemical processes contributing to the paleoceanographic archive in this critical oceanic region.

(Table 1) Content of sand fraction components in sediments of the interglacial MIS 7 optimum from Core LV28-42-4

Distribution of diatoms, radiolarians, planktonic and benthic foraminifers, and sediment components in fraction >0.125 mm was analyzed in a core obtained from the central Sea of Okhotsk within frameworks of the Russian-German KOMEX Project. The core section characterizes the period 190-350 ka, which corresponds to marine-isotopic stages (MIS) 7 to 10. During glacial MIS 10 and MIS 8, the basin accumulated terrigenous material lacking microfossils or containing them in low abundance, which reflects, along with their composition, heavy sea-ice conditions, suppressed bioproductivity, and bottom environment aggressive toward calcium carbonate. Interglacial MIS 9 was characterized by elevated bioproductivity with accumulation of diatomaceous ooze during the climatic optimum (328 to 320 ka). Water exchange with the Pacific was maximal from 328 to 324 ka ago. Environment became moderate and close to the present-day one at the end of the optimum exhibiting possible existence of a dichothermal layer with substantial amounts of surface Pacific water still flowing into the basin. Similar to interglacial MIS 5e and MIS 1, ''old'' Pacific water determined near-bottom environment in the central Sea of Okhotsk during that period, although influx of terrigenous material was higher, probably reflecting more humid climate of the region. Slight warming marked the terminal MIS 8 (approximately 260 ka ago). Paleoceanographic situation during the interglacial MIS 7 was highly variable: from warm-water to almost glacial. The main climatic optimum of MIS 7 occurred within 220-210 ka, when subsurface stratification increased and the dichothermal layer developed. Bottom environment during the studied time interval, except for the optimum of interglacial MIS 9, resembled those characteristic of glacial periods: actively formed ''young'' Okhotsk water displaced ''old'' Pacific deep water.

Development of Cookstove Emissions and Performance Testing Suite with Time-resolved Particulate Matter Analysis and Excess Air Estimation

Thesis (Master's)--University of Washington, 2016-06

Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.

A Mixture model with random-effects components for clustering correlated gene-expression profiles

Motivation: The clustering of gene profiles across some experimental conditions of interest contributes significantly to the elucidation of unknown gene function, the validation of gene discoveries and the interpretation of biological processes. However, this clustering problem is not straightforward as the profiles of the genes are not all independently distributed and the expression levels may have been obtained from an experimental design involving replicated arrays. Ignoring the dependence between the gene profiles and the structure of the replicated data can result in important sources of variability in the experiments being overlooked in the analysis, with the consequent possibility of misleading inferences being made. We propose a random-effects model that provides a unified approach to the clustering of genes with correlated expression levels measured in a wide variety of experimental situations. Our model is an extension of the normal mixture model to account for the correlations between the gene profiles and to enable covariate information to be incorporated into the clustering process. Hence the model is applicable to longitudinal studies with or without replication, for example, time-course experiments by using time as a covariate, and to cross-sectional experiments by using categorical covariates to represent the different experimental classes. Results: We show that our random-effects model can be fitted by maximum likelihood via the EM algorithm for which the E(expectation) and M(maximization) steps can be implemented in closed form. Hence our model can be fitted deterministically without the need for time-consuming Monte Carlo approximations. The effectiveness of our model-based procedure for the clustering of correlated gene profiles is demonstrated on three real datasets, representing typical microarray experimental designs, covering time-course, repeated-measurement and cross-sectional data. In these examples, relevant clusters of the genes are obtained, which are supported by existing gene-function annotation. A synthetic dataset is considered too.