Cultural difference in attitudes towards stuttering among British, Arab and Chinese students: considering home and host cultures

Background Geographical and cultural differences have been shown to affect public attitudes towards stuttering. However, increasingly for many individuals in the world one's birthplace culture (or home culture) and culture in their local geographical environment (or host culture) are not the same. Aims The effects of home culture and host culture in shaping the attitudes towards stuttering among students with British, Arab and Chinese home cultures attending one British university were explored. The effects of host culture were investigated by considering the time lived in the UK for Arab and Chinese students. Methods & Procedures The study used a descriptive survey design that included a standardized self‐delivered questionnaire: the Public Opinion Survey of Human Attributes—Stuttering (POSHA‐S). Purposive sampling was carried out thorough volunteer mailing lists, student societies and personal contact. The final sample of 156 university students included 51 British, 52 Arab and 53 Chinese students. Outcomes & Results Overall stuttering score (OSS), which is indicative of attitudes towards stuttering, was highest for British participants (mean = 30) and lowest for Chinese participants (mean = 13), with Arab participants falling in the middle (mean = 21). The differences in attitudes between the three groups were statistically significant, suggesting that home culture is a contributor to attitudes towards stuttering. A post‐hoc item analysis of the POSHA‐S revealed numerous specific differences in attitudes towards stuttering between the three groups, including differences in the attribution of the aetiology of stuttering, their role in helping people who stutter (PWS) and sympathy toward PWS. Time lived in the UK—a proxy measure for the role of host culture—did not significantly influence the attitudes of Arab and Chinese respondents. Conclusions & Implications To varying degrees, all three groups had evidence of stereotypical stuttering attitudes. Nevertheless, given similar ages and student status in the same university, observed respondent differences confirm previous research documenting geographical influences on stuttering attitudes in Western versus East Asian and Middle Eastern samples. The study also provides evidence that home culture was influential in shaping attitudes towards stuttering, but host culture was not a significant contributor. What this paper adds What is already known on the subject Public stereotypical beliefs towards stuttering are found across the world and hinder the quality of life among PWS. Different cultures have unique stereotypical beliefs towards PWS. What this study adds to existing knowledge To the best of our knowledge, no other study has investigated specifically if individuals who live in the same geographical location but have different home cultures, have similar or differing attitudes towards PWS. Results provide preliminary evidence that the home culture of an individual was influential in shaping attitudes towards PWS, but host culture, measured as the length of time living in the current geographical location, did not have a significant relationship with attitudes towards stuttering. What are the potential or actual clinical implications of this work This study highlights that culturally sensitive clinical practice should not be based on just the culture of the region but should take home culture into consideration as well, and clinicians should discuss cultural perceptions of stuttering with clients in clinical practice

A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes

A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities

Network-Free Inference of Knockout Effects in Yeast

Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels. We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data, and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating. Differing from previous approaches, our prediction approach does not depend on physical network information; the latter is used only for the annotation task. Consequently, it is both more efficient and of wider applicability than previous methods. We evaluate our approach using a large scale collection of gene knockout experiments in yeast, comparing it to the state-of-the-art SPINE algorithm. In cross validation tests, our algorithm exhibits superior prediction accuracy, while at the same time increasing the coverage by over 25-fold. Significant coverage gains are obtained also in the annotation of the physical network

Sign assignment problems on protein networks

Abstract. In a maximum sign assignment problem one is given an undirected graph and a set of signed source-target vertex pairs. The goal is to assign signs to the graph’s edges so that a maximum number of pairs admit a source-to-target path whose aggregate sign (product of its edge signs) equals the pair’s sign. This problem arises in the annotation of physical interaction networks with activation/repression signs. It is known to be NP-complete and most previous approaches to tackle it were limited to considering very short paths in the network. Here we provide a sign assignment algorithm that solves the problem to optimality by reformulating it as an integer program. We apply our algorithm to sign physical interactions in yeast and measure our performance using edges whose activation/repression signs are known. We find that our algorithm achieves high accuracy (89%), outperforming a state-of-the-art method by a significant margin. Key words: network annotation, protein-protein interaction, activation, repression, integer linear program.

Optimally orienting physical networks

Abstract. In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This problem is NP-complete and no approximation algorithms are known for it. It arises in the context of analyzing physical networks of protein-protein and protein-dna interactions. While the latter are naturally directed from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ilp formulation for this problem, which can be efficiently solved on current networks. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified algorithmic variants that do not use information on edge directions. The obtained orientations can lead to better understanding of the structure and function of the network. Key words: network orientation, protein-protein interaction, proteindna interaction, integer linear program, mixed graph

Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

Signaling networks are essential for cells to control processes such as growth and response to stimuli. Although many “omic” data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases. We overcome these problems and use “omic” data to simultaneously reconstruct multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees each of which is rooted in a different cell surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies.National Institutes of Health (U.S.) (NIH grant U54CA112967)National Institutes of Health (U.S.) (NIH grant R01GM089903)Massachusetts Institute of Technology (Eugene Bell Career Development Chair)National Science Foundation (U.S.) (Award No. DB1- 082139)European Research Council (ERC grant OPTINF 267915)European Commission (EC grant STAMINA 265496