Moses of South Carolina

Franklin Moses Jr. is one of the great forgotten figures in American history. Scion of a distinguished Jewish family in South Carolina, he was a firebrand supporter of secession and an officer in the Confederate army. Moses then reversed course. As Reconstruction governor of South Carolina, he shocked and outraged his white constituents by championing racial equality and socializing freely with former slaves. Friends denounced him, his family disowned him, and enemies ultimately drove him from his home state.In Moses of South Carolina, Benjamin Ginsberg rescues this protean figure and his fascinating story from obscurity. Though Moses was far from a saint—he was known as the “robber governor” for his corrupt ways—Ginsberg suggests that Moses nonetheless deserves better treatment in the historical record. Despite his moral lapses, Moses launched social programs, integrated state institutions, and made it possible for blacks to attend the state university.As a Jew, Moses grew up on the fringe of southern plantation society. After the Civil War, Moses envisioned a culture different from the one in which he had been raised, one that included the newly freed slaves. From the margins of southern society, Franklin Moses built America’s first black-Jewish alliance, a model, argues Ginsberg, for the coalitions that would help reshape American politics in the decades to come. Revisiting the story of the South's “most perfect scalawag,” Ginsberg contributes to a broader understanding of the essential role southern Jews played during the Civil War and Reconstruction

Downsizing Democracy

Originally publushed in 2002. In Downsizing Democracy, Matthew A. Crenson and Benjamin Ginsberg describe how the once powerful idea of a collective citizenry has given way to a concept of personal, autonomous democracy. Today, political change is effected through litigation, lobbying, and term limits, rather than active participation in the political process, resulting in narrow special interest groups dominating state and federal decision-making. At a time when an American's investment in the democratic process has largely been reduced to an annual contribution to a political party or organization, Downsizing Democracy offers a critical reassessment of American democracy

Prevention, screening and treatment of colorectal cancer: a global and regional generalized cost effectiveness analysis

<p>Abstract</p> <p>Background</p> <p>Regional generalized cost-effectiveness estimates of prevention, screening and treatment interventions for colorectal cancer are presented.</p> <p>Methods</p> <p>Standardised WHO-CHOICE methodology was used. A colorectal cancer model was employed to provide estimates of screening and treatment effectiveness. Intervention effectiveness was determined via a population state-transition model (PopMod) that simulates the evolution of a sub-regional population accounting for births, deaths and disease epidemiology. Economic costs of procedures and treatment were estimated, including programme overhead and training costs.</p> <p>Results</p> <p>In regions characterised by high income, low mortality and high existing treatment coverage, the addition of screening to the current high treatment levels is very cost-effective, although no particular intervention stands out in cost-effectiveness terms relative to the others.</p> <p>In regions characterised by low income, low mortality with existing treatment coverage around 50%, expanding treatment with or without screening is cost-effective or very cost-effective. Abandoning treatment in favour of screening (no treatment scenario) would not be cost effective.</p> <p>In regions characterised by low income, high mortality and low treatment levels, the most cost-effective intervention is expanding treatment.</p> <p>Conclusions</p> <p>From a cost-effectiveness standpoint, screening programmes should be expanded in developed regions and treatment programmes should be established for colorectal cancer in regions with low treatment coverage.</p

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and general additive modeling

Background A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Methods Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. Results The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. Conclusions The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months

α4β1-dependent adhesion strengthening under mechanical strain is regulated by paxillin association with the α4-cytoplasmic domain

The capacity of integrins to mediate adhesiveness is modulated by their cytoplasmic associations. In this study, we describe a novel mechanism by which α4-integrin adhesiveness is regulated by the cytoskeletal adaptor paxillin. A mutation of the α4 tail that disrupts paxillin binding, α4(Y991A), reduced talin association to the α4β1 heterodimer, impaired integrin anchorage to the cytoskeleton, and suppressed α4β1-dependent capture and adhesion strengthening of Jurkat T cells to VCAM-1 under shear stress. The mutant retained intrinsic avidity to soluble or bead-immobilized VCAM-1, supported normal cell spreading at short-lived contacts, had normal α4-microvillar distribution, and responded to inside-out signals. This is the first demonstration that cytoskeletal anchorage of an integrin enhances the mechanical stability of its adhesive bonds under strain and, thereby, promotes its ability to mediate leukocyte adhesion under physiological shear stress conditions

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling

A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.https://doi.org/10.1186/1476-069X-12-9