PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Direct healthcare costs of hip, vertebral, and non-hip, non-vertebral fractures.

Limited data exist regarding the cost of non-hip, non-vertebral (NHNV) fractures. Although NHNV fractures may be less expensive than hip and vertebral fractures, they have a higher incidence rate. The objective of this study was to quantify first-year healthcare costs of hip, vertebral, and NHNV fractures. This was a claims-based retrospective analysis using a case-control design among patients with commercial insurance and Medicare employer-based supplemental coverage. Patients were \u3e or =50 years old with a closed hip, vertebral, or NHNV fracture between 7/1/2001 and 12/31/2004, and continuous enrollment 6 months prior to and 12 months after the index fracture. Adjusted mean first-year healthcare costs associated with these fractures were determined. Six cohorts were identified. Patients 50-64 years: NHNV (n=27,424), vertebral (n=3386) and hip (n=2423); patients \u3e or =65 years: NHNV (n=40,960), vertebral (n=11,751) and hip (n=21,504). The ratio of NHNV to hip fractures was 11:1 in the 50-64 cohort and 2:1 in the \u3e or =65 cohort. Adjusted mean first-year costs associated with hip, vertebral, and NHNV fractures were $26,545,$14,977, and $9183 for the 50-64 age cohort, and$15,196, $6701, and$6106 for patients \u3e or =65 years. After taking prevalence rate into account, the proportion of the total fracture costs accounted for by NHNV, hip, and vertebral fractures were 66%, 21% and 13% for the 50-64 age cohort, and 36%, 52% and 12% for the \u3e or =65 age cohort. Limitations included the exclusion of the uninsured and those covered by Medicaid or military-based insurance programs. The results of this study demonstrate that osteoporotic fractures are associated with significant costs. Although NHNV fractures have a lower per-patient cost than hip or vertebral fractures, their total first-year cost is greater for those 50-64 because of their higher prevalence

“Leveling Up” In Your Favorite Video Game Math Modeling Activity (Middle School)

In this activity, students will write linear equations that represent their situations and determine the intersection of their lines. The students will also graph their equations and discuss potential flaws

Medication utilization patterns among type 2 diabetes patients initiating Exenatide BID or insulin glargine: a retrospective database study

BACKGROUND: Type 2 diabetes is a common and costly illness, associated with significant morbidity and mortality. Despite this, there is relatively little information on the ‘real-world’ medication utilization patterns for patients with type 2 diabetes initiating exenatide BID or glargine. The objective of this study was to evaluate the ‘real-world’ medication utilization patterns in patients with type 2 diabetes treated with exenatide BID (exenatide) versus insulin glargine (glargine). METHODS: Adult patients( ≥18 years of age) with type 2 diabetes who were new initiators of exenatide or glargine from October 1, 2006 through March 31, 2008 with continuous enrollment for the 12 months pre- and 18 months post-index period were selected from the MarketScan® Commercial and Medicare Databases. To control for selection bias, propensity score matching was used to complete a 1:1 match of glargine to exenatide patients. Key study outcomes (including the likelihood of overall treatment modification, discontinuation, switching, or intensification) were analyzed using survival analysis. RESULTS: A total of 9,197 exenatide- and 4,499 glargine-treated patients were selected. Propensity score matching resulted in 3,774 matched pairs with a mean age of 57 years and a mean Deyo Charlson Comorbidity Index score of 1.6; 54% of patients were males. The 18-month treatment intensification rates were 15.9% and 26.0% (p < 0.0001) and the discontinuation rates were 38.3% and 40.0% (p = 0.14) for exenatide and glargine, respectively. Alternatively, 14.9% of exenatide-treated patients switched therapies, compared to 10.0% of glargine-treated patients (p < 0.0001). Overall, glargine-treated patients were more likely to modify their treatment [hazard ratio (HR) = 1.33, p < 0.0001] with shorter mean time on treatment until modification (123 vs. 159 days, p < 0.0001). Compared to exenatide-treated patients, glargine-treated patients were more likely to discontinue [hazard ratio (HR) = 1.25, p < 0.0001] or intensify therapy (HR = 1.72, p < 0.0001) but less likely to switch (HR = 0.71, p < 0.0001) the index therapy. CONCLUSIONS: Patients treated for type 2 diabetes with exenatide BID or insulin glargine differ in their adherence to therapy. Exenatide-treated patients were less likely to discontinue or modify treatment but more likely to switch therapy compared to glargine-treated patients

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (\u3cem\u3eZea mays\u3c/em\u3e) as an Exemplar

Elemental data are commonly used to infer plant quality as a resource to herbivores. However, the ubiquity of carbon in biomolecules, the presence of nitrogen-containing plant defensive compounds, and variation in species-specific correlations between nitrogen and plant protein content all limit the accuracy of these inferences. Additionally, research focused on plant and/or herbivore physiology require a level of accuracy that is not achieved using generalized correlations. The methods presented here offer researchers a clear and rapid protocol for directly measuring plant soluble proteins and digestible carbohydrates, the two plant macronutrients most closely tied to animal physiological performance. The protocols combine well characterized colorimetric assays with optimized plant-specific digestion steps to provide precise and reproducible results. Our analyses of different sweet corn tissues show that these assays have the sensitivity to detect variation in plant soluble protein and digestible carbohydrate content across multiple spatial scales. These include between-plant differences across growing regions and plant species or varieties, as well as within-plant differences in tissue type and even positional differences within the same tissue. Combining soluble protein and digestible carbohydrate content with elemental data also has the potential to provide new opportunities in plant biology to connect plant mineral nutrition with plant physiological processes. These analyses also help generate the soluble protein and digestible carbohydrate data needed to study nutritional ecology, plant-herbivore interactions and food-web dynamics, which will in turn enhance physiology and ecological research