The thickness effect, which is caused by inevitable leakage radiation accompanying the desired radiation, can cause significant decreases of extended-x-ray-absorption-fine-structure (EXAFSj amplitude when the sample is thick enough. The effect is illustrated by measurements of the E-edge EXAFS on a series of copper foils of varying thicknesses. Significant distortions in EXAFS amplitudes occur when deox &.1.5, where dpo is the E-edge step in the absorption coefficient and x is the sample thickness. Therefore, the optimum total sample thickness of p~= 2.6 as determined by statistical considerations will introduce errors in EXAFS amplitudes in concentrated samples due to the thickness efFect. The measurements presented here determine the most accurate values of EXAFS amplitude for copper metal which agree well with theory as corrected for the many-body overlap effect.

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Right heart catheterization data from clinical records of heart transplant patients are used to identify patient-specific models of the cardiovascular system. r These patient-specific cardiovascular models represent a snapshot of cardiovascular function at a given post-transplant recovery time point. r This approach is used to describe cardiac function in 10 heart transplant patients, five of which had multiple right heart catheterizations allowing an assessment of cardiac function over time. r These patient-specific models are used to predict cardiovascular function in the form of right and left ventricular pressure-volume loops and ventricular power, an important metric in the clinical assessment of cardiac function. r Outcomes for the longitudinally tracked patients show that our approach was able to identify the one patient from the group of five that exhibited post-transplant cardiovascular complications.

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen’s semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the “Pandit-Hinch-Niederer” (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

A209OBJECTIVES: Decisions on palliative chemotherapy (CT) for advanced gastric cancer require trade-offs between potential benefits and risks for patients. Healthcare providers and payers agreed that patient preferences should be considered. We conducted a CBC study in patients with mGC or mGEJ-Ca from Germany to evaluate their preferences when trading-off between treatment tolerability, quality of life and survival benefit. METHODS: German oncologists were contacted to identify patients with mGC or mGEJ-Ca who had completed ≥ 2 cycles of palliative CT (ongoing or completed). The primary objective was the quantitative evaluation of patient preferences for palliative CT in this population by CBC analysis. The CBC matrix, developed based on 6 in-depth qualitative interviews, spanned the 3 attributes ability to self-care as a key component for quality of life, treatment toxicity and survival benefit (3-4 factor levels each, 15 iterations). A minimum of 50 participants was needed. Eligible consenting patients completed the 45min standardized CBC-survey, choosing systematically among profiles. CBC models were estimated by mixed-logit regression (MLR) and hierarchical Bayes analysis (HB). Estimates of importance for each attribute and factor-level were calculated. RESULTS: Overall, 55 patients participated in the survey (78% male, median age 63yrs, 82% currently receiving CT). Patients considered low treatment toxicity as most important (45% relative importance, MLR analysis), followed by ability to self-care (32%) and an additional survival benefit of up to 3 months (3 months 23%, 2 months 18%, 1 month 11%). The MLR analysis showed high validity (certainty 37.9%, chi square p< 0.01, root likelihood 0.505). The HB analysis yielded similar results. CONCLUSIONS: Patient preferences related to palliative CT of gastric cancer can appropriately be assessed by CBC analysis. Though patients' varied experiences with chemotherapy may have impacted specific responses, across the population of patients with mGC or mGEJ-Ca improved treatment tolerability and quality of life were ranked highest.