A geochemical, taxonomic, and phylogenetic investigation of paleozoic arthropods : case studies on the cambrian spice event and the silurian waukesha lagerstatte

This thesis details two stand-alone projects: Chapters 1 and 2 comprise a quantitative investigation of controls on the [delta]13C record of the Steptoean Positive Carbon Isotope Excursion (SPICE); Chapter 3 describes a taxonomic and phylogenetic assessment of a previously unnamed arthropod from the Silurian Waukesha Lagerstatte. Despite the disparate nature of these projects, they are united by an underpinning theme of Paleozoic arthropods. The SPICE event is a prominent, global disturbance in the [delta]13C record of the Cambrian that is temporally linked to trilobite biomere turnover at the Marjuman--Steptoean and equivalent Guzhangian--Paibian stage boundaries. The SPICE has been used as a tool for global chemostratigraphic correlation despite the fact that individual [delta]13C records display marked variability. To quantitatively evaluate its utility as a chemostratigraphic marker, we constructed the Steptoean Positive Carbon Isotope Excursion repository for quantitative analysis (the SPICEraq) containing [delta]13C values and associated metadata from known records of the SPICE. Results from Wilcoxon Rank Sum and x2 tests indicate that regional/local conditions do have a statistically significant impact on the expression of the SPICE, with paleolatitude and approximate water depth bearing the most appreciable impact. Furthermore, this investigation confirms that the SPICE event is immediately preceded by a short-lived negative [delta]13C excursion which potentially has bearing on the associated trilobite extinction event.Includes bibliographical references

Statistical Analyses of Second Indoor Bio-Release Field Evaluation Study at Idaho National Laboratory

In September 2008 a large-scale testing operation (referred to as the INL-2 test) was performed within a two-story building (PBF-632) at the Idaho National Laboratory (INL). The report “Operational Observations on the INL-2 Experiment” defines the seven objectives for this test and discusses the results and conclusions. This is further discussed in the introduction of this report. The INL-2 test consisted of five tests (events) in which a floor (level) of the building was contaminated with the harmless biological warfare agent simulant Bg and samples were taken in most, if not all, of the rooms on the contaminated floor. After the sampling, the building was decontaminated, and the next test performed. Judgmental samples and probabilistic samples were determined and taken during each test. Vacuum, wipe, and swab samples were taken within each room. The purpose of this report is to study an additional four topics that were not within the scope of the original report. These topics are: 1) assess the quantitative assumptions about the data being normally or log-normally distributed; 2) evaluate differences and quantify the sample to sample variability within a room and across the rooms; 3) perform geostatistical types of analyses to study spatial correlations; and 4) quantify the differences observed between surface types and sampling methods for each scenario and study the consistency across the scenarios. The following four paragraphs summarize the results of each of the four additional analyses. All samples after decontamination came back negative. Because of this, it was not appropriate to determine if these clearance samples were normally distributed. As Table 1 shows, the characterization data consists of values between and inclusive of 0 and 100 CFU/cm2 (100 was the value assigned when the number is too numerous to count). The 100 values are generally much bigger than the rest of the data, causing the data to be right skewed. There are also a significant number of zeros. Using QQ plots these data characteristics show a lack of normality from the data after contamination. Normality is improved when looking at log(CFU/cm2). Variance component analysis (VCA) and analysis of variance (ANOVA) were used to estimate the amount of variance due to each source and to determine which sources of variability were statistically significant. In general, the sampling methods interacted with the across event variability and with the across room variability. For this reason, it was decided to do analyses for each sampling method, individually. The between event variability and between room variability were significant for each method, except for the between event variability for the swabs. For both the wipes and vacuums, the within room standard deviation was much larger (26.9 for wipes and 7.086 for vacuums) than the between event standard deviation (6.552 for wipes and 1.348 for vacuums) and the between room standard deviation (6.783 for wipes and 1.040 for vacuums). Swabs between room standard deviation was 0.151, while both the within room and between event standard deviations are less than 0.10 (all measurements in CFU/cm2)

Monterey Strategy Seminar: Day 1: Capabilities Based Planning.

Monterey Strategy Seminar: Day 1: Capabilities Based Planning. Day 2: Dissuasion in the U.S. Defense Strategy. Day 3: Global Strike WarfareNaval Postgraduate SchoolCenter for Contemporary Conflict (CCC

Using airborne LiDAR Survey to explore historic-era archaeological landscapes of Montserrat in the eastern Caribbean

This article describes what appears to be the first archaeological application of airborne LiDAR survey to historic-era landscapes in the Caribbean archipelago, on the island of Montserrat. LiDAR is proving invaluable in extending the reach of traditional pedestrian survey into less favorable areas, such as those covered by dense neotropical forest and by ashfall from the past two decades of active eruptions by the Soufrière Hills volcano, and to sites in localities that are inaccessible on account of volcanic dangers. Emphasis is placed on two aspects of the research: first, the importance of ongoing, real-time interaction between the LiDAR analyst and the archaeological team in the field; and second, the advantages of exploiting the full potential of the three-dimensional LiDAR point cloud data for purposes of the visualization of archaeological sites and features

A Framework for Evaluating Innovative Statistical and Risk Assessment Tools to Solve Environment Restoration Problems

Environmental restoration activities at the US Department of Energy (DOE) Hanford site face complex issues due to history of varied past contaminant disposal practices. Data collection and analysis required for site characterization, pathway modeling, and remediation selection decisions must deal with inherent uncertainties and unique problems associated with the restoration. A framework for working through the statistical aspects of the site characterization and remediation selection problems is needed. This framework would facilitate the selection of appropriate statistical tools for solving unique aspects of the environmental restoration problem. This paper presents a framework for selecting appropriate statistical and risk assessment methods. The following points will be made: (1) pathway modelers and risk assessors often recognize that some type'' of statistical methods are required but don't work with statisticians on tools development in the early planning phases of the project; (2) statistical tools selection and development are problem-specific and often site-specific, further indicating a need for up-front involvement of statisticians; and (3) the right tool, applied in the right way can minimize sampling costs, get as much information as possible out of the data that does exist, provide consistency and defensibility for the results, and given structure and quantitative measures to decision risks and uncertainties

National Cancer Institute–National Heart, Lung and Blood Institute/Pediatric Blood and Marrow Transplant Consortium First International Consensus Conference on Late Effects After Pediatric Hematopoietic Cell Transplantation: Long-Term Organ Damage and Dysfunction

Long-term complications after hematopoietic cell transplantation (HCT) have been studied in detail. Although virtually every organ system can be adversely affected after HCT, the underlying pathophysiology of these late effects remain incompletely understood. This article describes our current understanding of the pathophysiology of late effects involving the gastrointestinal, renal, cardiac, and pulmonary systems, and discusses post-HCT metabolic syndrome studies. Underlying diseases, pretransplantation exposures, transplantation conditioning regimens, graft-versus-host disease, and other treatments contribute to these problems. Because organ systems are interdependent, long-term complications with similar pathophysiologic mechanisms often involve multiple organ systems. Current data suggest that post-HCT organ complications result from cellular damage that leads to a cascade of complex events. The interplay between inflammatory processes and dysregulated cellular repair likely contributes to end-organ fibrosis and dysfunction. Although many long-term problems cannot be prevented, appropriate monitoring can enable detection and organ-preserving medical management at earlier stages. Current management strategies are aimed at minimizing symptoms and optimizing function. There remain significant gaps in our knowledge of the pathophysiology of therapy-related organ toxicities disease after HCT. These gaps can be addressed by closely examining disease biology and identifying those patients at greatest risk for adverse outcomes. In addition, strategies are needed for targeted disease prevention and health promotion efforts for individuals deemed at high risk because of their genetic makeup or specific exposure profile

Visual Sample Plan (VSP) Models and Code Verification

VSP is an easy to use, visual and graphic software tool being developed to select the right number and location of environmental samples so that the results of statistical tests performed to provide input to environmental decisions have the required confidence and performance. It is a significant help for implementing the 6th and 7th steps of the Data Quality Objectives (DQO) planning process ("Specify Tolerable Limits on Decision Errors" and "Optimize the Design for Obtaining Data," respectively)