Panel Two: Information Policy Making

The second panel of From Conduit to Content: The Emergence of Information Policy and Law addresses the government\u27s response to the policy making challenges presented by information. Panelists from the government and academia explore the question: How has, and how should, the policy-making process respond to the diversity of issues, interests, and policymakers? Participants include Fred H. Cate, Allen S. Hammond, Bruce W. McConnell, Michael Nelson, Janice Obuchowski, and Marc Rotenbergaddresses the government\u27s response to the policy making challenges presented by information. Panelists from the government and academia explore the question: How has, and how should, the policy-making process respond to the diversity of issues, interests, and policymakers? Participants include Fred H. Cate, Allen S. Hammond, Bruce W. McConnell, Michael Nelson, Janice Obuchowski, and Marc Rotenberg. From Conduit to Content: The Emergence of Information Policy and Law. The Annenberg Washington Program. Friday, March 3 1995, Washington, D.C

Panel Two: Information Policy Making

The second panel of From Conduit to Content: The Emergence of Information Policy and Law addresses the government\u27s response to the policy making challenges presented by information. Panelists from the government and academia explore the question: How has, and how should, the policy-making process respond to the diversity of issues, interests, and policymakers? Participants include Fred H. Cate, Allen S. Hammond, Bruce W. McConnell, Michael Nelson, Janice Obuchowski, and Marc Rotenbergaddresses the government\u27s response to the policy making challenges presented by information. Panelists from the government and academia explore the question: How has, and how should, the policy-making process respond to the diversity of issues, interests, and policymakers? Participants include Fred H. Cate, Allen S. Hammond, Bruce W. McConnell, Michael Nelson, Janice Obuchowski, and Marc Rotenberg. From Conduit to Content: The Emergence of Information Policy and Law. The Annenberg Washington Program. Friday, March 3 1995, Washington, D.C

Evolution of a physiological pH 6.8 bicarbonate buffer system: application to the dissolution testing of enteric coated products.

The use of compendial pH 6.8 phosphate buffer to assess dissolution of enteric coated products gives rise to poor in vitro-in vivo correlations because of the inadequacy of the buffer to resemble small intestinal fluids. A more representative and physiological medium, pH 6.8 bicarbonate buffer, was developed to evaluate the dissolution behaviour of enteric coatings. The bicarbonate system was evolved from pH7.4 Hanks balanced salt solution to produce a pH 6.8 bicarbonate buffer (modified Hanks buffer, mHanks), which resembles the ionic composition and buffer capacity of intestinal milieu. Prednisolone tablets were coated with a range of enteric polymers: hypromellose phthalate (HP-50 and HP-55), cellulose acetate phthalate (CAP), hypromellose acetate succinate (HPMCAS-LF and HPMCAS-MF), methacrylic acid copolymers (EUDRAGIT® L100-55, EUDRAGIT® L30D-55 and EUDRAGIT® L100) and polyvinyl acetate phthalate (PVAP). Dissolution of coated tablets was carried out using USP-II apparatus in 0.1M HCl for 2h followed by pH 6.8 phosphate buffer or pH 6.8 mHanks bicarbonate buffer. In pH 6.8 phosphate buffer, the various enteric polymer coated products displayed rapid and comparable dissolution profiles. In pH 6.8 mHanks buffer, drug release was delayed and marked differences were observed between the various coated tablets, which is comparable to the delayed disintegration times reported in the literature for enteric coated products in the human small intestine. In summary, the use of pH 6.8 physiological bicarbonate buffer (mHanks) provides more realistic and discriminative in vitro release assessment of enteric coated formulations compared to compendial phosphate buffer

Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups

<p>Abstract</p> <p>Background</p> <p>Variations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures.</p> <p>Methods</p> <p>A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994–95 (N ~1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO₂) surface based on a network of 100 passive NO₂monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking.</p> <p>Results</p> <p>There were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO₂LUR OR = 1.86 (95%CI, 1.59–2.16) in all girls and OR = 2.98 (95%CI, 0.98–9.06) for older girls, over an interquartile range increase and controlling for confounders.</p> <p>Conclusion</p> <p>Our findings indicate that traffic-related pollutants, such as NO₂, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.</p

Rhabdomyoblastic Differentiation in Head and Neck Malignancies Other Than Rhabdomyosarcoma

Rhabdomyosarcoma is a relatively common soft tissue sarcoma that frequently affects children and adolescents and may involve the head and neck. Rhabdomyosarcoma is defined by skeletal muscle differentiation which can be suggested by routine histology and confirmed by immunohistochemistry for the skeletal muscle-specific markers myogenin or myoD1. At the same time, it must be remembered that when it comes to head and neck malignancies, skeletal muscle differentiation is not limited to rhabdomyosarcoma. A lack of awareness of this phenomenon could lead to misdiagnosis and, subsequently, inappropriate therapeutic interventions. This review focuses on malignant neoplasms of the head and neck other than rhabdomyosarcoma that may exhibit rhabdomyoblastic differentiation, with an emphasis on strategies to resolve the diagnostic dilemmas these tumors may present. Axiomatically, no primary central nervous system tumors will be discussed.info:eu-repo/semantics/publishedVersio

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Prevalence and architecture of de novo mutations in developmental disorders.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages