Marine Archeological Survey of the Proposed Bolivar Ferry Dredge Spoil Pile Expansion Area, Galveston County, Texas

Archeologists from AmaTerra Environmental, Inc. (AmaTerra) and Southeastern Archaeological Research, Inc. (SEARCH; collectively the Team) conducted intensive underwater archeological remote sensing survey on behalf of the Texas Department of Transportation (TxDOT) on a proposed dredge spoil expansion area south of the Bolivar Peninsula at the mouth of Galveston Bay, Galveston County, Texas. The survey was required for compliance with the National Historic Preservation Act of 1966, as amended, due to dredge permits to be issued by the US Fish and Wildlife Service, and the Antiquities Code of Texas, due to its location on lands owned by a political subdivision of the State of Texas (the Texas General Land Office). Completed under Antiquities Permit 6272, the survey area includes the 51-acre dredge spoil expansion footprint and the original 16-acre dredge spoil pile (67 acres total). The archeologists utilized towed marine magnetometer and side-scan sonar survey along 12 parallel, 20-meter-interval transects to assess the full archeological Area of Potential Effects. The survey area was found to be littered with isolated modern metallic debris. Side-scan sonar imagery revealed a submerged surface that was generally flat with no indications of shipwrecks and a large oyster reef along the southwestern quarter. One large metallic feature located within the survey area (Anomaly M1) was identified as a fallen modern navigational sighting marker tower. None of the archeological survey data, sensor readings, or imagery from the proposed expansion area is consistent with expected signatures of historic-age shipwrecks. Archeologists did re-identify the previously-documented magnetic anomaly M44 that was recommended for avoidance or testing within the original dredge pile area. If the avoidance zone continues to be observed, it will not be impacted by the current undertaking. As such, the project is recommended to proceed with no Historic Properties, State Archeological Landmarks, or archeological sites impacted in accordance with guidelines outlined in 36 CFR 800 and 13 TAC 26. No sites were identified and no artifacts were collected during the course of the survey. Accordingly, all project-generated notes, forms, and other materials will be permanently curated at AmaTerra’s office in Austin, Texas and SEARCH’s office in Pensacola, Florida

Risk‐based strategies for surveillance of bovine Tuberculosis infection in cattle for low risk areas in England and Scotland

Disease surveillance can be made more effective by either improving disease detection, providing cost savings, or doing both. Currently, cattle herds in low-risk areas for bovine tuberculosis (bTB) in England (LRAs) are tested once every four years. In Scotland, the default herd testing frequency is also four years, but a risk-based system exempts some herds from testing altogether. To extend this approach to other areas, a bespoke understanding of at-risk herds and how risk-based surveillance can affect bTB detection is required. Here, we use a generalized linear mixed model (GLMM) to inform a Bayesian probabilistic model of freedom from infection and explore risk-based surveillance strategies in LRAs and Scotland. Our analyses show that in both areas the primary herd-level risk factors for bTB infection are the size of the herd and purchasing cattle from high-risk areas of Great Britain and/or Ireland. A risk-based approach can improve the current surveillance system by both increasing detection (9% and 7% fewer latent infections), and reducing testing burden (6 % and 26% fewer animal tests) in LRAs and Scotland, respectively. Testing at-risk herds more frequently can also improve the level of detection by identifying more infected cases and reducing the hidden burden of the disease, and reduce surveillance effort by exempting low-risk herds from testing

Classifying Chronic Lower Respiratory Disease Events in Epidemiologic Cohort Studies

Rationale: One in 12 adults has chronic obstructive pulmonary disease or asthma. Acute exacerbations of these chronic lower respiratory diseases (CLRDs) are a major cause of morbidity and mortality. Valid approaches to classifying cases and exacerbations in the general population are needed to facilitate prevention research

Induction of Asthma and the Environment: What We Know and Need to Know

The prevalence of asthma has increased dramatically over the last 25 years in the United States and in other nations as a result of ill-defined changes in living conditions in modern society. On 18 and 19 October 2004 the U.S. Environmental Protection Agency and the National Institute of Environmental Health Sciences sponsored the workshop “Environmental Influences on the Induction and Incidence of Asthma” to review current scientific evidence with respect to factors that may contribute to the induction of asthma. Participants addressed two broad questions: a) What does the science suggest that regulatory and public health agencies could do now to reduce the incidence of asthma? and b) What research is needed to improve our understanding of the factors that contribute to the induction of asthma and our ability to manage this problem? In this article (one of four articles resulting from the workshop), we briefly characterize asthma and its public health and economic impacts, and intervention strategies that have been successfully used to prevent induction of asthma in the workplace. We conclude with the findings of seven working groups that focus on ambient air, indoor pollutants (biologics), occupational exposures, early life stages, older adults, intrinsic susceptibility, and lifestyle. These groups found strong scientific support for public health efforts to limit in utero and postnatal exposure to cigarette smoke. However, with respect to other potential types of interventions, participants noted many scientific questions, which are summarized in this article. Research to address these questions could have a significant public health and economic impact that would be well worth the investment

Transfer of K-types on local theta lifts of characters and unitary lowest weight modules

In this paper we study representations of the indefinite orthogonal group O(n,m) which are local theta lifts of one dimensional characters or unitary lowest weight modules of the double covers of the symplectic groups. We apply the transfer of K-types on these representations of O(n,m), and we study their effects on the dual pair correspondences. These results provide examples that the theta lifting is compatible with the transfer of K-types. Finally we will use these results to study subquotients of some cohomologically induced modules

Modeling the effectiveness of targeting Rift Valley fever virus vaccination using imperfect network information

Livestock movements contribute to the spread of several infectious diseases. Data on livestock movements can therefore be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases, including Rift Valley fever (RVF)—a vaccine-preventable arboviral fever. Detailed livestock movement data are known to be useful for targeting control efforts including vaccination. These data are available in many countries, however, such data are generally lacking in others, including many in East Africa, where multiple RVF outbreaks have been reported in recent years. Available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing targeting of vaccination is not fully understood. Here, we used a network simulation model to describe the spread of RVF within and between 398 wards in northern Tanzania connected by cattle movements, on which we evaluated the impact of targeting vaccination using imperfect movement data. We show that pre-emptive vaccination guided by only market movement permit data could prevent large outbreaks. Targeted control (either by the risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Our modeling approach demonstrates how targeted interventions can be effectively used to inform animal and public health policies for disease control planning. This is particularly valuable in settings where detailed data on livestock movements are either unavailable or imperfect due to resource limitations in data collection, as well as challenges associated with poor compliance