Large-scale structure of time evolving citation networks

In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network, and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape.Comment: 10 pages, 6 figures; journal names for 4 references fixe

UV Photo-Oxidation of Polybenzimidazole (PBI)

Since polybenzimidazole (PBI) is often used in the aerospace industry and in high temperature fuel cells, this research investigated the surface modification of PBI film with 253.7 and 184.9 nm UV photo-oxidation. As observed by X-ray photoelectron spectroscopy (XPS), the oxygen concentration on the surface increased up to a saturation level of 20.2 ± 0.7 at %. With increasing treatment time, there were significant decreases in the concentrations of C-C sp2 and C=N groups and increases in the concentration of C=O, O-C=O, O-(C=O)-O, C-N, N-O, and N-C=O containing moieties due to 253.7 nm photo-oxidation of the aromatic groups of PBI and reaction with ozone produced by 184. 9 nm photo-dissociation of oxygen. Because no significant changes in surface topography were detected by AFM and SEM measurements, the observed decrease in the water contact angle down to ca. 44°, i.e., increase in hydrophilic, was due to the chemical changes on the surface

Labour supply and skills demands in fashion retailing

If, as Adam Smith once famously suggested, Britain was a nation of shopkeepers then it is now a nation of shopworkers. Retail is now a significant part of the UK economy, accounting for £256 billion in sales and one-third of all consumer spending (Skillsmart, 2007). It is the largest private sector employer in the UK, employing 3m workers, or 1 in 10 of the working population. For future job creation in the UK economy retail is also similarly prominent and the sector is expected to create a further 250,000 jobs to 2014 (Skillsmart, 2007). The centrality of retail to economic success and job creation is apparent in other advanced economies. For example, within the US, retail sales is the occupation with the largest projected job growth in the period 2004-2014 (Gatta et al., 2009) and in Australia retail accounts for 1 in 6 workers (Buchanan et al., 2003). Within the UK these workers are employed in approximately 290,000 businesses, encompassing large and small organizations and also a number of sub-sectors. This variance suggests that retail should not be regarded as homogenous in its labour demands. Hart et al. (2007) note how skill requirements and the types of workers employed may differ across the sector. This chapter further opens up this point, providing an analysis of the labour supply and skills demands for the sub-sectors of clothing, footwear and leather goods, which are described by Skillsmart (2007: 48) as being 'significant categories in UK retailing'

Drivers of jaguar (Panthera onca) and puma (Puma concolor) predation on endangered primates within a transformed landscape in southern Mexico

Human pressures have increasingly placed keystone species, such as large cats, under threat. Together with forest loss, prey depletion is one of the main threats to the survival of jaguars (Panthera onca) and pumas (Puma concolor) throughout the Neotropics. Generally, primates are not considered main prey for jaguar and puma, and their inclusion in the diet could be indicative of ongoing prey species decline. Here, we investigate the effect of habitat type and disturbance on primate predation by large cats. Surveys took place during the dry seasons (March to June) of 2010 and 2011, covering a total of 608.5 km across 24 localities in the Uxpanapa Valley, Mexico. We found 65 felid scat samples with the aid of a wildlife scat detection dog, and then examined them to identify predator species and classify the prey remains they contained. Primates represented the most frequent prey (35%) for both jaguar and puma in our study site and constituted approximately half of the biomass consumed by these felines in the area. Primate remains were more likely to be found in scats surrounded by the lowest percentage of conserved forest or in areas surrounded by more villages, showing the potential effects of human activities on these species' populations. The high proportion of primates found in scats within our study site could be an early indication that populations of ungulates and other “typical” prey are beginning to collapse, and urgent conservation interventions are needed for both large cats and primates before they become locally extinct

Optimally splitting cases for training and testing high dimensional classifiers

<p>Abstract</p> <p>Background</p> <p>We consider the problem of designing a study to develop a predictive classifier from high dimensional data. A common study design is to split the sample into a training set and an independent test set, where the former is used to develop the classifier and the latter to evaluate its performance. In this paper we address the question of what proportion of the samples should be devoted to the training set. How does this proportion impact the mean squared error (MSE) of the prediction accuracy estimate?</p> <p>Results</p> <p>We develop a non-parametric algorithm for determining an optimal splitting proportion that can be applied with a specific dataset and classifier algorithm. We also perform a broad simulation study for the purpose of better understanding the factors that determine the best split proportions and to evaluate commonly used splitting strategies (1/2 training or 2/3 training) under a wide variety of conditions. These methods are based on a decomposition of the MSE into three intuitive component parts.</p> <p>Conclusions</p> <p>By applying these approaches to a number of synthetic and real microarray datasets we show that for linear classifiers the optimal proportion depends on the overall number of samples available and the degree of differential expression between the classes. The optimal proportion was found to depend on the full dataset size (n) and classification accuracy - with higher accuracy and smaller <it>n </it>resulting in more assigned to the training set. The commonly used strategy of allocating 2/3rd of cases for training was close to optimal for reasonable sized datasets (<it>n </it>≥ 100) with strong signals (i.e. 85% or greater full dataset accuracy). In general, we recommend use of our nonparametric resampling approach for determing the optimal split. This approach can be applied to any dataset, using any predictor development method, to determine the best split.</p

Principal-Oscillation-Pattern Analysis of Gene Expression

Principal-oscillation-pattern (POP) analysis is a multivariate and systematic technique for identifying the dynamic characteristics of a system from time-series data. In this study, we demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. We use POP analysis to infer oscillation patterns in gene expression. Typically, a genomic system matrix cannot be directly estimated because the number of genes is usually much larger than the number of time points in a genomic study. Thus, we first identify the POPs of the eigen-genomic system that consists of the first few significant eigengenes obtained by singular value decomposition. By using the linear relationship between eigengenes and genes, we then infer the POPs of the genes. Both simulation data and real-world data are used in this study to demonstrate the applicability of POP analysis to genomic data. We show that POP analysis not only compares favorably with experiments and existing computational methods, but that it also provides complementary information relative to other approaches

Spectrum of Oncogenic Driver Mutations in Lung Adenocarcinomas from East Asian Never Smokers

PURPOSE:We previously showed that 90% (47 of 52; 95% CI, 0.79 to 0.96) of lung adenocarcinomas from East Asian never-smokers harbored well-known oncogenic mutations in just four genes: EGFR, HER2, ALK, and KRAS. Here, we sought to extend these findings to more samples and identify driver alterations in tumors negative for these mutations. EXPERIMENTAL DESIGN:We have collected and analyzed 202 resected lung adenocarcinomas from never smokers seen at Fudan University Shanghai Cancer Center. Since mutations were mutually exclusive in the first 52 examined, we determined the status of EGFR, KRAS, HER2, ALK, and BRAF in stepwise fashion as previously described. Samples negative for mutations in these 5 genes were subsequently examined for known ROS1 fusions by RT-PCR and direct sequencing. RESULTS:152 tumors (75.3%) harbored EGFR mutations, 12 (6%) had HER2 mutations, 10 (5%) had ALK fusions all involving EML4 as the 5' partner, 4 (2%) had KRAS mutations, and 2 (1%) harbored ROS1 fusions. No BRAF mutation were detected. CONCLUSION:The vast majority (176 of 202; 87.1%, 95% CI: 0.82 to 0.91) of lung adenocarcinomas from never smokers harbor mutant kinases sensitive to available TKIs. Interestingly, patients with EGFR mutant patients tend to be older than those without EGFR mutations (58.3 Vs 54.3, P = 0.016) and patient without any known oncogenic driver tend to be diagnosed at a younger age (52.3 Vs 57.9, P = 0.013). Collectively, these data indicate that the majority of never smokers with lung adenocarcinoma could benefit from treatment with a specific tyrosine kinase inhibitor

Gene expression model (in)validation by Fourier analysis

The determination of the right model structure describing a gene regulation network and the identification of its parameters are major goals in systems biology. The task is often hampered by the lack of relevant experimental data with sufficiently low noise level, but the subset of genes whose concentration levels exhibit an oscillatory behavior in time can readily be analyzed on the basis of their Fourier spectrum, known to turn complex signals into few relatively noise-free parameters. Such genes therefore offer opportunities of understanding gene regulation quantitatively.Journal ArticleResearch Support, Non-U.S. Gov'tValidation StudiesSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Selective Targeting of Tumorigenic Cancer Cell Lines by Microtubule Inhibitors

For anticancer drug therapy, it is critical to kill those cells with highest tumorigenic potential, even when they comprise a relatively small fraction of the overall tumor cell population. We have used the established NCI/DTP 60 cell line growth inhibition assay as a platform for exploring the relationship between chemical structure and growth inhibition in both tumorigenic and non-tumorigenic cancer cell lines. Using experimental measurements of “take rate” in ectopic implants as a proxy for tumorigenic potential, we identified eight chemical agents that appear to strongly and selectively inhibit the growth of the most tumorigenic cell lines. Biochemical assay data and structure-activity relationships indicate that these compounds act by inhibiting tubulin polymerization. Yet, their activity against tumorigenic cell lines is more selective than that of the other microtubule inhibitors in clinical use. Biochemical differences in the tubulin subunits that make up microtubules, or differences in the function of microtubules in mitotic spindle assembly or cell division may be associated with the selectivity of these compounds

Metagenes Associated with Survival in Non-Small Cell Lung Cancer

NSCLC (non-small cell lung cancer) comprises about 80% of all lung cancer cases worldwide. Surgery is most effective treatment for patients with early-stage disease. However, 30%–55% of these patients develop recurrence within 5 years. Therefore, markers that can be used to accurately classify early-stage NSCLC patients into different prognostic groups may be helpful in selecting patients who should receive specific therapies