A Comparison of U. S. and European University-Industry Relations in the Life Sciences

We draw on diverse data sets to compare the institutional organization of upstream life science research across the United States and Europe. Understanding cross-national differences in the organization of innovative labor in the life sciences requires attention to the structure and evolution of biomedical networks involving public research organizations (universities, government laboratories, nonprofit research institutes, and research hospitals), science-based biotechnology firms, and multinational pharmaceutical corporations. We use network visualization methods and correspondence analyses to demonstrate that innovative research in biomedicine has its origins in regional clusters in the United States and in European nations. But the scientific and organizational composition of these regions varies in consequential ways. In the United States, public research organizations and small firms conduct R&D across multiple therapeutic areas and stages of the development process. Ties within and across these regions link small firms and diverse public institutions, contributing to the development of a robust national network. In contrast, the European story is one of regional specialization with a less diverse group of public research organizations working in a smaller number of therapeutic areas. European institutes develop local connections to small firms working on similar scientific problems, while cross-national linkages of European regional clusters typically involve large pharmaceutical corporations. We show that the roles of large and small firms differ in the United States and Europe, arguing that the greater heterogeneity of the U. S. system is based on much closer integration of basic science and clinical development

eBay users form stable groups of common interest

Market segmentation of an online auction site is studied by analyzing the users' bidding behavior. The distribution of user activity is investigated and a network of bidders connected by common interest in individual articles is constructed. The network's cluster structure corresponds to the main user groups according to common interest, exhibiting hierarchy and overlap. Key feature of the analysis is its independence of any similarity measure between the articles offered on eBay, as such a measure would only introduce bias in the analysis. Results are compared to null models based on random networks and clusters are validated and interpreted using the taxonomic classifications of eBay categories. We find clear-cut and coherent interest profiles for the bidders in each cluster. The interest profiles of bidder groups are compared to the classification of articles actually bought by these users during the time span 6-9 months after the initial grouping. The interest profiles discovered remain stable, indicating typical interest profiles in society. Our results show how network theory can be applied successfully to problems of market segmentation and sociological milieu studies with sparse, high dimensional data.Comment: Major revision of the manuscript. Methodological improvements and inclusion of analysis of temporal development of user interests. 19 pages, 12 figures, 5 table

Factors Affecting Synonymous Codon Usage Bias in Chloroplast Genome of Oncidium Gower Ramsey

Oncidium Gower Ramsey is a fascinating and important ornamental flower in floral industry. In this research, the complete nucleotide sequence of the chloroplast genome in Oncidium Gower Ramsey was studied, then analyzed using Codonw software. Correspondence analysis and method of effective number of codon as Nc-plot were conducted to analyze synonymous codon usage. According to the corresponding analysis, codon bias in the chloroplast genome of Oncidium Gower Ramsey is related to their gene length, mutation bias, gene hydropathy level of each protein, gene function and selection or gene expression only subtly affect codon usage. This study will provide insights into the molecular evolution study and high-level transgene expression

Euclidean Distances, soft and spectral Clustering on Weighted Graphs

We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow data, properly conditioned, illustrate the procedure as well as visualization aspects.Comment: accepted for presentation (and further publication) at the ECML PKDD 2010 conferenc

Risk of Congenital Anomalies after the Opening of Landfill Sites

Concern that living near a particular landfill site in Wales caused increased risk of births with congenital malformations led us to examine whether residents living close to 24 landfill sites in Wales experienced increased rates of congenital anomalies after the landfills opened compared with before they opened. We carried out a small-area study in which expected rates of congenital anomalies in births to mothers living within 2 km of the sites, before and after opening of the sites, were estimated from a logistic regression model fitted to all births in residents living at least 4 km away from these sites and hence not likely to be subject to contamination from a landfill, adjusting for hospital catchment area, year of birth, sex, maternal age, and socioeconomic deprivation score. We investigated all births from 1983 through 1997 with at least one recorded congenital anomaly [International Classification of Diseases, Ninth Revision (ICD-9), codes 7400–7599; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), codes Q000–Q999]. The ratio of the observed to expected rates of congenital anomalies before landfills opened was 0.87 [95% confidence interval (CI), 0.75–1.00], and this increased to 1.21 (95% CI, 1.04–1.40) after opening, giving a standardized risk ratio of 1.39 (95% CI, 1.12–1.72). Enhanced congenital malformation surveillance data collected from 1998 through 2000 showed a standardized risk ratio of 1.04 (95% CI, 0.88–1.21). Causal inferences are difficult because of possible biases from incomplete case ascertainment, lack of data on individual-level exposures, and other socioeconomic and lifestyle factors that may confound a relationship with area of residence. However, the increase in risk after the sites opened requires continued enhanced surveillance of congenital anomalies, and site-specific chemical exposure studies

Does designation as a UNESCO World Heritage Site influence tourist evaluation of a local destination?

The purpose of this study is to explore whether the UNESCO World Heritage Site (WHS) designation affects tourists’ evaluation of the local destination hosting the site, building on a large sample of about 0.8 million tourists who visited Italy over the period 1997-2015. We find that the inscription onto the UNESCO World Heritage List exerts surprisingly a negative effect on the overall evaluation of the destination and also on the evaluation of its artistic assets though the magnitude of the latter is lower. The effect is heterogeneous across visitors, depending on evaluation levels, as well as origin/destinations and demographics. Nonetheless, the presence of multiple WHSs in the same destination tends to increase evaluation suggesting that destination stakeholders with previous experience in dealing with WHS designations are better equipped to manage the complicated relationship between tourism and preservation. Managerial and policy-making implications are discussed

Dominant cognitive frames and the innovative power of social networks

In this paper, we illustrate the link between social network structures, dominant cognitive frames on network purpose and the innovative power of a network, through a mixed-method comparative analysis of two Knowledge Translation Networks (KTNs)in the English National Health Service (NHS). Our findings illustrate several challenges for networked forms of organization linked to different manifestations of social networks (centralized/decentralized) and dominant cognitive frames (polarizing/loosely clustered). Our paper contributes a better understanding of how dominant frames on network purpose emerge alongside the development of network structure itself, and explores how this interplay between dominant frames and social networks impacts upon the collaborative work that supports the networks’ innovative power

On the Schoenberg Transformations in Data Analysis: Theory and Illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A simple distance-based discriminant algorithm illustrates the theory, intimately connected to the Gaussian kernels of Machine Learning