The conceptual framework of child-friendly cities guarantees children’s equal access to public urban services. Despite the widespread application of geographical information systems (GISs) and pedestrian network analysis, studies have yet to analyze children’s comprehensive pedestrian access to urban services in a large-scale city. This study demonstrates GIS-based approaches to measuring children’s pedestrian access to urban services using a pedestrian path layer and the spatial layers of social infrastructure locations in Seoul, South Korea. We show the spatial inequities in children’s access to urban services, which depend on the locational characteristics of social infrastructures and the urban development patterns around children. We analyze how children’s access to social infrastructures is differentiated by land use composition. Our statistical analysis finds that low-rise residential areas, consisting of impermeable street patterns, increase children’s walking distance and restrict children from accessing urban services within their walkable area. In addition, there is potential for key infrastructures such as schools and local community centers to promote pedestrian access to urban services for children. Considering pedestrian access at the street level will help pinpoint vulnerable areas with children who have less access overall and maximize the users served within the service areas of infrastructures.

BACKGROUND: Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient's tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients' drug responses based on gene expression and drug activity data from PDX models. RESULTS: Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials. CONCLUSION: Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org .

MOTIVATION: ChIP-chip has been widely used for various genome-wide biological investigations. Given the small number of replicates (typically two to three) per biological sample, methods of analysis that control the variance are desirable but in short supply. We propose a double error shrinkage (DES) method by using moving average statistics based on local-pooled error estimates which effectively control both heterogeneous error variances and correlation structures of an extremely large number of individual probes on tiling arrays. RESULTS: Applying DES to ChIP-chip tiling array study for discovering genome-wide protein-binding sites, we identified 8400 target regions that include highly likely TFIID binding sites. About 33% of these were well matched with the known transcription starting sites on the DBTSS library, while many other newly identified sites have a high chance to be real binding sites based on a high positive predictive value of DES. We also showed the superior performance of DES compared with other commonly used methods for detecting actual protein binding sites.

MOTIVATION: The identification and characterization of susceptibility genes that influence the risk of common and complex diseases remains a statistical and computational challenge in genetic association studies. This is partly because the effect of any single genetic variant for a common and complex disease may be dependent on other genetic variants (gene-gene interaction) and environmental factors (gene-environment interaction). To address this problem, the multifactor dimensionality reduction (MDR) method has been proposed by Ritchie et al. to detect gene-gene interactions or gene-environment interactions. The MDR method identifies polymorphism combinations associated with the common and complex multifactorial diseases by collapsing high-dimensional genetic factors into a single dimension. That is, the MDR method classifies the combination of multilocus genotypes into high-risk and low-risk groups based on a comparison of the ratios of the numbers of cases and controls. When a high-order interaction model is considered with multi-dimensional factors, however, there may be many sparse or empty cells in the contingency tables. The MDR method cannot classify an empty cell as high risk or low risk and leaves it as undetermined. RESULTS: In this article, we propose the log-linear model-based multifactor dimensionality reduction (LM MDR) method to improve the MDR in classifying sparse or empty cells. The LM MDR method estimates frequencies for empty cells from a parsimonious log-linear model so that they can be assigned to high-and low-risk groups. In addition, LM MDR includes MDR as a special case when the saturated log-linear model is fitted. Simulation studies show that the LM MDR method has greater power and smaller error rates than the MDR method. The LM MDR method is also compared with the MDR method using as an example sporadic Alzheimer's disease.

The adaptive use case methodology for software development proposed by Carlson and Hurlbutt [1997, 1998] forms the backdrop for this paper. Their methodology integrates the software design, development and testing processes through a series of design preserving, algorithmic transformations. This paper focuses on the software testing metrics used in the generation of object oriented test plans as part of that methodology. During the design phase, interaction diagrams are developed from which use case action matrices are then generated. A use case action matrix contains a collection of related scenarios each describing a specific variant of an executable sequence of use case actions. The proposed software testing metrics are employed to improve the productivity of the testing process through scenario prioritization.