University of Pennsylvania

About: University of Pennsylvania is a education organization based out in Philadelphia, Pennsylvania, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 109318 authors who have published 257688 publications receiving 14150562 citations. The organization is also known as: UPenn & Penn.

Absorptive capacity: a new perspective on learning and innovation

Abstract: In this paper, we argue that the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities. We label this capability a firm's absorptive capacity and suggest that it is largely a function of the firm's level of prior related knowledge. The discussion focuses first on the cognitive basis for an individual's absorptive capacity including, in particular, prior related knowledge and diversity of background. We then characterize the factors that influence absorptive capacity at the organizational level, how an organization's absorptive capacity differs from that of its individual members, and the role of diversity of expertise within an organization. We argue that the development of absorptive capacity, and, in turn, innovative performance are history- or path-dependent and argue how lack of investment in an area of expertise early on may foreclose the future development of a technical capability in that area. We formulate a model of firm investment in research and development (R&D), in which R&D contributes to a firm's absorptive capacity, and test predictions relating a firm's investment in R&D to the knowledge underlying technical change within an industry. Discussion focuses on the implications of absorptive capacity for the analysis of other related innovative activities, including basic research, the adoption and diffusion of innovations, and decisions to participate in cooperative R&D ventures. **

QIIME allows analysis of high-throughput community sequencing data.

Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.

An Evolutionary Theory of Economic Change

Abstract: This study develops an evolutionary theory of the capabilities and behavior of business firms operating in a market environment. It includes both general discussion and the manipulation of specific simulation models consistent with that theory. The analysis outlines the differences between an evolutionary theory of organizational and industrial change and a neoclassical microeconomic theory. The antecedents to the former are studies by economists like Schumpeter (1934) and Alchian (1950). It is contrasted with the orthodox theory in the following aspects: while the evolutionary theory views firms as motivated by profit, their actions are not assumed to be profit maximizing, as in orthodox theory; the evolutionary theory stresses the tendency of most profitable firms to drive other firms out of business, but, in contrast to orthodox theory, does not concentrate on the state of industry equilibrium; and evolutionary theory is related to behavioral theory: it views firms, at any given time, as having certain capabilities and decision rules, as well as engaging in various ‘search' operations, which determines their behavior; while orthodox theory views firm behavior as relying on the use of the usual calculus maximization techniques. The theory is then made operational by the use of simulation methods. These models use Markov processes and analyze selection equilibrium, responses to changing factor prices, economic growth with endogenous technical change, Schumpeterian competition, and Schumpeterian tradeoff between static Pareto-efficiency and innovation. The study's discussion of search behavior complicates the evolutionary theory. With search, the decision making process in a firm relies as much on past experience as on innovative alternatives to past behavior. This view combines Darwinian and Lamarkian views on evolution; firms are seen as both passive with regard to their environment, and actively seeking alternatives that affect their environment. The simulation techniques used to model Schumpeterian competition reveal that there are usually winners and losers in industries, and that the high productivity and profitability of winners confer advantages that make further success more likely, while decline breeds further decline. This process creates a tendency for concentration to develop even in an industry initially composed of many equal-sized firms. However, the experiments conducted reveal that the growth of concentration is not inevitable; for example, it tends to be smaller when firms focus their searches on imitating rather than innovating. At the same time, industries with rapid technological change tend to grow more concentrated than those with slower progress. The abstract model of Schumpeterian competition presented in the study also allows to see more clearly the public policy issues concerning the relationship between technical progress and market structure. The analysis addresses the pervasive question of whether industry concentration, with its associated monopoly profits and reduced social welfare, is a necessary cost if societies are to obtain the benefits of technological innovation. (AT)

A New Equation to Estimate Glomerular Filtration Rate

Abstract: The Modification of Diet in Renal Disease (MDRD) Study equation underestimates glomerular filtration rate (GFR) in patients with mild kidney disease. Levey and associates therefore developed and va...

Meta-analysis of observational studies in epidemiology - A proposal for reporting

Abstract: ObjectiveBecause of the pressure for timely, informed decisions in public health and clinical practice and the explosion of information in the scientific literature, research results must be synthesized. Meta-analyses are increasingly used to address this problem, and they often evaluate observational studies. A workshop was held in Atlanta, Ga, in April 1997, to examine the reporting of meta-analyses of observational studies and to make recommendations to aid authors, reviewers, editors, and readers.ParticipantsTwenty-seven participants were selected by a steering committee, based on expertise in clinical practice, trials, statistics, epidemiology, social sciences, and biomedical editing. Deliberations of the workshop were open to other interested scientists. Funding for this activity was provided by the Centers for Disease Control and Prevention.EvidenceWe conducted a systematic review of the published literature on the conduct and reporting of meta-analyses in observational studies using MEDLINE, Educational Research Information Center (ERIC), PsycLIT, and the Current Index to Statistics. We also examined reference lists of the 32 studies retrieved and contacted experts in the field. Participants were assigned to small-group discussions on the subjects of bias, searching and abstracting, heterogeneity, study categorization, and statistical methods.Consensus ProcessFrom the material presented at the workshop, the authors developed a checklist summarizing recommendations for reporting meta-analyses of observational studies. The checklist and supporting evidence were circulated to all conference attendees and additional experts. All suggestions for revisions were addressed.ConclusionsThe proposed checklist contains specifications for reporting of meta-analyses of observational studies in epidemiology, including background, search strategy, methods, results, discussion, and conclusion. Use of the checklist should improve the usefulness of meta-analyses for authors, reviewers, editors, readers, and decision makers. An evaluation plan is suggested and research areas are explored.