Statistical Tests of Group Differences in ALSCAL-Derived Subject Weights: Some Monte Carlo Results

Several techniques to test for group differences in weighted multidimensional scaling (MDS) subject weights have recently been proposed. The present study presents monte carlo results to evaluate the op erating characteristics of two of these with ALSCAL- derived subject weights. The first uses the analysis of angular variation (ANAVA) on raw subject weights. The second applies the analysis of variance (ANOVA) to the flattened subject weights generated by ALSCAL. The ANOVA on flattened weights was less affected by the presence of error and by distortions caused by ALSCAL'S normalization routine than was the ANAVA.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Implicit media frames: Automated analysis of public debate on artificial sweeteners

The framing of issues in the mass media plays a crucial role in the public understanding of science and technology. This article contributes to research concerned with diachronic analysis of media frames by making an analytical distinction between implicit and explicit media frames, and by introducing an automated method for analysing diachronic changes of implicit frames. In particular, we apply a semantic maps method to a case study on the newspaper debate about artificial sweeteners, published in The New York Times (NYT) between 1980 and 2006. Our results show that the analysis of semantic changes enables us to filter out the dynamics of implicit frames, and to detect emerging metaphors in public debates. Theoretically, we discuss the relation between implicit frames in public debates and codification of information in scientific discourses, and suggest further avenues for research interested in the automated analysis of frame changes and trends in public debates

Malnutrition as assessed by nutritional risk index is associated with worse outcome in patients admitted with acute decompensated heart failure: an ACAP-HF data analysis

Malnutrition is common at hospital admission and tends to worsen during hospitalization. This controlled population study aimed to determine if serum albumin or moderate and severe nutritional depletion by Nutritional Risk Index (NRI) at hospital admission are associated with increased length of hospital stay (LOS) in patients admitted with acute decompensated heart failure (ADHF). Serum albumin levels and lymphocyte counts were retrospectively determined at hospital admission in 1740 consecutive patients admitted with primary and secondary diagnosis of ADHF. The Nutrition Risk Score (NRI) developed originally in AIDS and cancer populations was derived from the serum albumin concentration and the ratio of actual to usual weight, as follows: NRI = (1.519 × serum albumin, g/dL) + {41.7 × present weight (kg)/ideal body weight(kg)}. Patients were classified into four groups as no, mild, moderate or severe risk by NRI. Multiple logistic regressions were used to determine the association between nutritional risk category and LOS

Random effects diagonal metric multidimensional scaling models

By assuming a distribution for the subject weights in a diagonal metric (INDSCAL) multidimensional scaling model, the subject weights become random effects. Including random effects in multidimensional scaling models offers several advantages over traditional diagonal metric models such as those fitted by the INDSCAL, ALSCAL, and other multidimensional scaling programs. Unlike traditional models, the number of parameters does not increase with the number of subjects, and, because the distribution of the subject weights is modeled, the construction of linear models of the subject weights and the testing of those models is immediate. Here we define a random effects diagonal metric multidimensional scaling model, give computational algorithms, describe our experiences with these algorithms, and provide an example illustrating the use of the model and algorithms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45758/1/11336_2005_Article_BF02295730.pd

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context