When should travelers begin malaria prophylaxis?

Travelers should start on chloroquine 1 to 2 weeks before entering an area without chloroquine resistance (strength of recommendation [SOR]: C, based on expert opinion). In areas with chloroquine-resistant Plasmodium falciparum, travelers will need to take atovaquone/proguanil, doxycycline, or primaquine 1 day before entering the area, or mefloquine 2 to 7 weeks before travel (SOR: B, based on prospective patient-oriented outcomes and expert opinion). Before prescribing medications, determine malaria risk and sensitivity of Plasmodium species by country at wwwn.cdc.gov/travel/yellowBookCh5- MalariaYellowFeverTable.aspx (SOR: C, based on patient-oriented expert opinion)

BIOECONOMIC MODELING OF THE GULF SHRIMP FISHERY: AN APPLICATION TO GALVESTON BAY AND ADJACENT OFFSHORE AREAS

Resource /Energy Economics and Policy,

Does birth weight predict childhood obesity?

A birth weight greater than 4,000 g is associated with an increased risk of obesity in both childhood and adolescence (strength of recommendation [SOR]: B, systematic review and multiple cohort studies)

Heavy ion induced Single Event Phenomena (SEP) data for semiconductor devices from engineering testing

The accumulation of JPL data on Single Event Phenomena (SEP), from 1979 to August 1986, is presented in full report format. It is expected that every two years a supplement report will be issued for the follow-on period. This data for 135 devices expands on the abbreviated test data presented as part of Refs. (1) and (3) by including figures of Single Event Upset (SEU) cross sections as a function of beam Linear Energy Transfer (LET) when available. It also includes some of the data complied in the JPL computer in RADATA and the SPACERAD data bank. This volume encompasses bipolar and MOS (CMOS and MHNOS) device data as two broad categories for both upsets (bit-flips) and latchup. It also includes comments on less well known phenomena, such as transient upsets and permanent damage modes

Automated Data for DevSecOps Programs

Symposium PresentationApproved for public release; distribution is unlimited

Lupus Anticoagulunt Associated with Transient Severe Factor X Deficiency: A Report of Two Patients Presenting with Major Bleeding Complications

Abstract Acquired factor X (FX) deficiency is rare, but has been reported in diverse disease states, including systemic amyloidosis and respiratory infections. FX deficiency associated with lupus anticoagulant (LA) and a bleeding diathesis has not been previously reported. We report two patients both of whom presented with a severe bleeding diathesis after a preceding respiratory infection due to isolated FX deficiency associated with a LA. The FX deficiency and LA were transient. We conclude that patients with LA may rarely present with severe acquired FX deficiency. This may be another mechanism whereby patients with antiphospholipid antibodies present with bleeding complications

Automated Data for DevSecOps Programs

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumAutomation in DevSecOps (DSO) transforms the practice of building, deploying, and managing software intensive programs. Although this automation supports continuous delivery and rapid builds, the persistent manual collection of information delays (by weeks) the release of program status metrics and the decisions they are intended to inform. Emerging DSO metrics (e.g., deployment rates, lead times) provide insight into how software development is progressing but fall short of replacing program control metrics for assessing progress (e.g., burn rates against spend targets, integration capability tar-get dates, and schedule for the minimum viable capability release). By instrumenting the (potentially in-teracting) DSO pipelines and supporting environments, the continuous measurement of status, identifica-tion of emerging risks, and probabilistic projections are possible and practical. In this paper, we discuss our research on the information modeling, measurement, metrics, and indicators necessary to establish a continuous program control capability that can keep pace with DSO management needs. We discuss the importance of interactive visualization dashboards for addressing program information needs. We also identify and address the gaps and barriers in the current state of the practice. Finally, we recommend future research needs based on our initial findings.Approved for public release; distribution is unlimited

Automated Data for DevSecOps Programs

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumAutomation in DevSecOps (DSO) transforms the practice of building, deploying, and managing software intensive programs. Although this automation supports continuous delivery and rapid builds, the persistent manual collection of information delays (by weeks) the release of program status metrics and the decisions they are intended to inform. Emerging DSO metrics (e.g., deployment rates, lead times) provide insight into how software development is progressing but fall short of replacing program control metrics for assessing progress (e.g., burn rates against spend targets, integration capability tar-get dates, and schedule for the minimum viable capability release). By instrumenting the (potentially in-teracting) DSO pipelines and supporting environments, the continuous measurement of status, identifica-tion of emerging risks, and probabilistic projections are possible and practical. In this paper, we discuss our research on the information modeling, measurement, metrics, and indicators necessary to establish a continuous program control capability that can keep pace with DSO management needs. We discuss the importance of interactive visualization dashboards for addressing program information needs. We also identify and address the gaps and barriers in the current state of the practice. Finally, we recommend future research needs based on our initial findings.Approved for public release; distribution is unlimited

Structured decision making as a conceptual framework to identify thresholds for conservation and management

Threshold and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objectives (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included i the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision threshold are determined by the above-listed components of a structured decision thresholds inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structured decision processes that include all three kinds of thresholds

Structured decision making as a conceptual framework to identify thresholds for conservation and management

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changing in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of he decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (systems models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision threshold inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structures decision process that include all three kinds of thresholds