Organic aerosols were studied at the molecular level in 14 coastal and inland mega-cities in China during winter and summer 2003. They are characterized by the abundant presence of n-alkanes (annual average, 340 ng m(-3)), fatty acids (769 ng m(-3)), sugars (412 ng m(-3)), and phthalates (387 ng m(-3)). In contrast, fatty alcohols, polyols/polyacids, lignin and resin products, sterols, polycyclic aromatic hydrocarbons (PAHs), and hopanes were detected as relatively minor components. n-Alkanes show a weak odd/even carbon predominance (CPI = 1.1) and PAHs show a predominance of benzo(b)fluoranthene, suggesting a serious contribution from fossil fuel ( mainly coal) combustion. Their concentrations ( except for phthalates and polyols/polyacids) were 2-15 times higher in winter than summer due to a significant usage of coal burning and an enhancement of atmospheric inversion layers. Phthalates were found to be more abundant in summer than winter, probably due to enhanced vaporization from plastics followed by adsorptive deposition on the pre-existing particles. Concentrations of total quantified compounds are extremely high (similar to 10 mu g m(-3)) in the midwest (Chongqing and Xi'an) where active industrialization/urbanization is going on. This study shows that concentrations of the compounds detected are 1-3 orders of magnitude higher than those reported from developed countries.
This article presents a conceptual analysis of collaboration scripts used in face-to-face and computer-mediated collaborative learning. Collaboration scripts are scaffolds that aim to improve collaboration through structuring the interactive processes between two or more learning partners. Collaboration scripts consist of at least five components: (a) learning objectives, (b) type of activities, (c) sequencing, (d) role distribution, and (e) type of representation. These components serve as a basis for comparing prototypical collaboration script approaches for face-to-face vs. computer-mediated learning. As our analysis reveals, collaboration scripts for face-to-face learning often focus on supporting collaborators to engage in activities that are specifically related to individual knowledge acquisition. Scripts for computer-mediated collaboration are typically concerned with facilitating communicative-coordinative processes that occur among group members. The two research lines can be consolidated to facilitate the design of collaboration scripts which both support participation and coordination and induce learning activities closely related to individual knowledge acquisition and metacognition. However, research on collaboration scripts needs to consider the learners' internal collaboration scripts as a further determinant of collaboration behavior.
Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings. We first show that PCA can be formulated as a regression-type optimization problem; sparse loadings are then obtained by imposing the lasso (elastic net) constraint on the regression coefficients. Efficient algorithms are proposed to fit our SPCA models for both regular multivariate data and gene expression arrays. We also give a new formula to compute the total variance of modified principal components. As illustrations, SPCA is applied to real and simulated data with encouraging results.
Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (πsamples) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (πsamples) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (πsamples ≥ 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (πmarkers ≥ 0.01).
Traditional Chinese Herbal Medicine (TCHM) contain multiple botanicals, each of which contains many compounds that may be relevant to the medicine's putative activity. Therefore, analytical techniques that look at a suite of compounds, including their respective ratios, provide a more rational approach to the authentication and quality assessment of TCHM. In this paper we present several examples of applying chromatographic fingerprint analysis for determining the identity, stability, and consistency of TCHM as well as the identification of adulterants as follows: (1) species authentication of various species of ginseng ( , , ) and stability of ginseng preparations using high performance thin-layer chromatography (HPTLC) fingerprint analysis; (2) batch-to-batch consistency of extracts of Total Glycosides of Peony (TGP), to be used as a raw material and in finished products (TGP powdered extract products), using high performance liquid chromatography (HPLC) fingerprint analysis with a pattern recognition software interface (CASE); (3) documenting the representative HPLC fingerprints of Immature Fruits of (IFTC) through the assessment of raw material, in-process assay of the extracts, and the analysis of the finished product (tablets); (4) HPLC fingerprint study demonstrating the consistent quality of total flavonoids of commercial extracts of ginkgo ( ) leaves (EGb) along with detection of adulterations. The experimental conditions as well as general comments on the application of chromatographic fingerprint analysis are discussed.