CondiDiag1.0: A flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM)

Numerical models used in weather and climate prediction take into account a comprehensive set of atmospheric processes such as the resolved and unresolved fluid dynamics, radiative transfer, cloud and aerosol life cycles, and mass or energy exchanges with the Earth's surface. In order to identify model deficiencies and improve predictive skills, it is important to obtain process-level understanding of the interactions between different processes. Conditional sampling and budget analysis are powerful tools for process-oriented model evaluation, but they often require tedious ad hoc coding and large amounts of instantaneous model output, resulting in inefficient use of human and computing resources. This paper presents an online diagnostic tool that addresses this challenge by monitoring model variables in a generic manner as they evolve within the time integration cycle. The tool is convenient to use. It allows users to select sampling conditions and specify monitored variables at run time. Both the evolving values of the model variables and their increments caused by different atmospheric processes can be monitored and archived. Online calculation of vertical integrals is also supported. Multiple sampling conditions can be monitored in a single simulation in combination with unconditional sampling. The paper explains in detail the design and implementation of the tool in the Energy Exascale Earth System Model (E3SM) version 1. The usage is demonstrated through three examples: a global budget analysis of dust aerosol mass concentration, a composite analysis of sea salt emission and its dependency on surface wind speed, and a conditionally sampled relative humidity budget. The tool is expected to be easily portable to closely related atmospheric models that use the same or similar data structures and time integration methods.

Optimized procedures for testing plasma metanephrines in patients on hemodialysis

Diagnosis of pheochromocytomas and paragangliomas in patients receiving hemodialysis is troublesome. The aim of the study was to establish optimal conditions for blood sampling for mass spectrometric measurements of normetanephrine, metanephrine and 3-methoxytyramine in patients on hemodialysis and specific reference intervals for plasma metanephrines under the most optimal sampling conditions. Blood was sampled before and near the end of dialysis, including different sampling sites in 170 patients on hemodialysis. Plasma normetanephrine concentrations were lower (P < 0.0001) and metanephrine concentrations higher (P < 0.0001) in shunt than in venous blood, with no differences for 3-methoxytyramine. Normetanephrine, metanephrine and 3-methoxytyramine concentrations in shunt and venous blood were lower (P < 0.0001) near the end than before hemodialysis. Upper cut-offs for normetanephrine were 34% lower when the blood was drawn from the shunt and near the end of hemodialysis compared to blood drawn before hemodialysis. This study establishes optimal sampling conditions using blood from the dialysis shunt near the end of hemodialysis with optimal reference intervals for plasma metanephrines for the diagnosis of pheochromocytomas/paragangliomas among patients on hemodialysis.

Optimized procedures for diagnostic testing for pheochromocytoma and paraganglioma in patients on hemodialysis

Background Diagnosis of pheochromocytomas and paragangliomas in patients receiving hemodialysis is troublesome. Aim To establish optimal conditions for blood sampling for mass spectrometric measurements of normetanephrine, metanephrine and 3-methoxytyramine in patients on hemodialysis and specific reference intervals for plasma metanephrines under the most optimal sampling conditions. Methods Blood was sampled before and near the end of dialysis, including different sampling sites in 170 patients on hemodialysis. Results Plasma normetanephrine concentrations were lower (P < 0.0001) and metanephrine concentrations higher (P < 0.0001) in shunt than in venous blood, with no differences for 3-methyxytyramine. Normetanephrine, metanephrine and 3-methoxytyramine concentrations in shunt and venous blood were lower (P < 0.0001) near the end than before hemodialysis. Upper cut-offs for normetanephrine were 34% lower when the blood was drawn from the shunt and near the end of hemodialysis compared to blood drawn before hemodialysis. Conclusion This study establishes optimal sampling conditions using blood from the dialysis shunt near the end of hemodialysis with optimal reference intervals for plasma metanephrines for the diagnosis of pheochromocytomas/paragangliomas among patients on hemodialysis.

Optimized sample preparation for fecal volatile organic compound analysis by gas chromatography–mass spectrometry

Introduction Headspace gas chromatography–mass spectrometry (HS-GC–MS) is widely considered the gold standard of quantitative fecal VOC analysis. However, guidelines providing general recommendations for bioanalytical method application in research and clinical setting are lacking. Objectives To propose an evidence-based research protocol for fecal VOC analysis by HS-GC–MS, based on extensive testing of instrumental and sampling conditions on detection and quantification limits, linearity, accuracy and repeatability of VOC outcome. Methods The influence of the following variables were assessed: addition of different salt solutions, injection temperature, injection speed, injection volume, septum use, use of calibration curves and fecal sample mass. Ultimately, the optimal sample preparation was assessed using fecal samples from healthy preterm infants. Fecal VOC analysis in this specific population has potential as diagnostic biomarkers, but available amount of feces is limited here, so optimization of VOC extraction is of importance. Results We demonstrated that addition of lithium chloride enhanced the release of polar compounds (e.g. small alcohols) into the headspace. Second, a linear relationship between injection volume, speed and temperature, and fecal sample mass on the abundance of VOC was demonstrated. Furthermore, the use of a septum preserved 90% of the non-polar compounds. By application of optimal instrumental and sampling conditions, a maximum of 320 unique compounds consisting of 14 different chemical classes could be detected. Conclusions These findings may contribute to standardized analysis of fecal VOC by HS-GC–MS, facilitating future application of fecal VOC in clinical practice.