Technology Policy: A Fixture on the National Agenda

Federal government research and development priorities have shifted in recent years away from areas of national security and agency missions, and toward the enhancement of industrial competitiveness in the global economy. This shift has stirred ideological controversy over whether the federal government should be in the business of picking winners and losers, even prompting some to label this practice “corporate welfare.” Rycroft, Kash, and Adams suggest that the central issues at stake have little to do with ideological differences and a great deal to do with whether the U.S.will continue to lead the world in technological innovation. They describe a new economic reality, driven by technology, that calls for basic changes to our national technology policy

Specific strains of Escherichia coli are pathogenic for the endometrium of cattle and cause pelvic inflammatory disease in cattle and mice.

BACKGROUND: Escherichia coli are widespread in the environment and pathogenic strains cause diseases of mucosal surfaces including the female genital tract. Pelvic inflammatory disease (PID; metritis) or endometritis affects ∼40% of cattle after parturition. We tested the expectation that multiple genetically diverse E. coli from the environment opportunistically contaminate the uterine lumen after parturition to establish PID. METHODOLOGY/PRINCIPAL FINDINGS: Distinct clonal groups of E. coli were identified by Random Amplification of Polymorphic DNA (RAPD) and Multilocus sequence typing (MLST) from animals with uterine disease and these differed from known diarrhoeic or extra-intestinal pathogenic E. coli. The endometrial pathogenic E. coli (EnPEC) were more adherent and invasive for endometrial epithelial and stromal cells, compared with E. coli isolated from the uterus of clinically unaffected animals. The endometrial epithelial and stromal cells produced more prostaglandin E(2) and interleukin-8 in response to lipopolysaccharide (LPS) purified from EnPEC compared with non-pathogenic E. coli. The EnPEC or their LPS also caused PID when infused into the uterus of mice with accumulation of neutrophils and macrophages in the endometrium. Infusion of EnPEC was only associated with bacterial invasion of the endometrium and myometrium. Despite their ability to invade cultured cells, elicit host cell responses and establish PID, EnPEC lacked sixteen genes commonly associated with adhesion and invasion by enteric or extraintestinal pathogenic E. coli, though the ferric yersiniabactin uptake gene (fyuA) was present in PID-associated EnPEC. Endometrial epithelial or stromal cells from wild type but not Toll-like receptor 4 (TLR4) null mice secreted prostaglandin E(2) and chemokine (C-X-C motif) ligand 1 (CXCL1) in response to LPS from EnPEC, highlighting the key role of LPS in PID. CONCLUSIONS/SIGNIFICANCE: The implication arising from the discovery of EnPEC is that development of treatments or vaccines for PID should focus specifically on EnPEC and not other strains of E. coli

Organizational factors and depression management in community-based primary care settings

Abstract Background Evidence-based quality improvement models for depression have not been fully implemented in routine primary care settings. To date, few studies have examined the organizational factors associated with depression management in real-world primary care practice. To successfully implement quality improvement models for depression, there must be a better understanding of the relevant organizational structure and processes of the primary care setting. The objective of this study is to describe these organizational features of routine primary care practice, and the organization of depression care, using survey questions derived from an evidence-based framework. Methods We used this framework to implement a survey of 27 practices comprised of 49 unique offices within a large primary care practice network in western Pennsylvania. Survey questions addressed practice structure (e.g., human resources, leadership, information technology (IT) infrastructure, and external incentives) and process features (e.g., staff performance, degree of integrated depression care, and IT performance). Results The results of our survey demonstrated substantial variation across the practice network of organizational factors pertinent to implementation of evidence-based depression management. Notably, quality improvement capability and IT infrastructure were widespread, but specific application to depression care differed between practices, as did coordination and communication tasks surrounding depression treatment. Conclusions The primary care practices in the network that we surveyed are at differing stages in their organization and implementation of evidence-based depression management. Practical surveys such as this may serve to better direct implementation of these quality improvement strategies for depression by improving understanding of the organizational barriers and facilitators that exist within both practices and practice networks. In addition, survey information can inform efforts of individual primary care practices in customizing intervention strategies to improve depression management.http://deepblue.lib.umich.edu/bitstream/2027.42/78269/1/1748-5908-4-84.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78269/2/1748-5908-4-84-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78269/3/1748-5908-4-84.pdfPeer Reviewe

A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA

ECONOMIC EFFICIENCY IN ENERGY POLICY

Pietro S. Nivola. The Politics of Energy Conservation Copyright 1987 by The Policy Studies Organization.