Key Sources Of Variability Research Articles

Multi-scale evaluation of diesel commuter rail fuel use, emissions, and eco-driving

• Diesel commuter rail fuel use and emission rates (FUERs) are quantified. • Key sources of variability in diesel commuter rail FUERs are quantified. • Diesel commuter rail FUERs are 7 to 8 times greater for hotspots than non-hotspots. • Eco-driving can reduce system-wide diesel commuter rail FUERs by 10% annually. • Mesoscopic eco-driving can mitigate or worsen FUERs hotspots. Diesel commuter rail emissions affect populations near rail corridors. An approach is demonstrated to quantify mesoscale and microscale diesel commuter rail fuel use and emission rates (FUERs) of CO 2 , CO, NO x , particulate matter , and total hydrocarbons based on two U.S. systems. A speed trajectory simulator, an energy model, and an emissions model were calibrated, evaluated, and applied. FUERs and potential reductions from eco-driving were quantified based on simulated trajectories. Hotspots were defined as sections with ≥ 90 th percentile of section-average FUERs by species. A few key variables explain 74–80% of variability in mesoscopic and microscopic FUERs. On average, FUERs are 7–8 times greater for hotspots than non-hotspots. Eco-driving was estimated to reduce segment-average FUERs by 3–33% and eliminate 2–11% hotspots. However, mesoscale oriented eco-driving can, at some locations, increase microscopic FUERs. The approach is adaptable to other diesel commuter rail systems.

Reproducibility of interferon gamma (IFN-γ) release Assays. A systematic review.

Interferon gamma (IFN-γ) release assays for latent tuberculosis infection result in a larger-than-expected number of conversions and reversions in occupational screening programs, and reproducibility of test results is a concern. Knowledge of the relative contribution and extent of the individual sources of variability (immunological, preanalytical, or analytical) could help optimize testing protocols. We performed a systematic review of studies published by October 2013 on all potential sources of variability of commercial IFN-γ release assays (QuantiFERON-TB Gold In-Tube and T-SPOT.TB). The included studies assessed test variability under identical conditions and under different conditions (the latter both overall and stratified by individual sources of variability). Linear mixed effects models were used to estimate within-subject SD. We identified a total of 26 articles, including 7 studies analyzing variability under the same conditions, 10 studies analyzing variability with repeat testing over time under different conditions, and 19 studies reporting individual sources of variability. Most data were on QuantiFERON (only three studies on T-SPOT.TB). A considerable number of conversions and reversions were seen around the manufacturer-recommended cut-point. The estimated range of variability of IFN-γ response in QuantiFERON under identical conditions was ±0.47 IU/ml (coefficient of variation, 13%) and ±0.26 IU/ml (30%) for individuals with an initial IFN-γ response in the borderline range (0.25-0.80 IU/ml). The estimated range of variability in noncontrolled settings was substantially larger (±1.4 IU/ml; 60%). Blood volume inoculated into QuantiFERON tubes and preanalytic delay were identified as key sources of variability. This systematic review shows substantial variability with repeat IFN-γ release assays testing even under identical conditions, suggesting that reversions and conversions around the existing cut-point should be interpreted with caution.

Comprehensive Assessment and Standardization of Solid Phase Multiplex-Bead Arrays for the Detection of Antibodies to HLA-Drilling Down on Key Sources of Variation

To the Editor: We appreciate the opportunity to respond to the letter by Dr. Maillard and Dr. Mariat (1Maillard N Mariat C. Solid-phase bead-based assays limitations are not restricted to interlaboratory variability.Am J Transplant. 2013; 13: 3049Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). They raise three questions regarding our recent article focusing on inter-laboratory standardization of solid phase multiplex-bead arrays to detect antibodies to HLA within the framework of the Clinical Trials in Organ Transplantation (2Reed EF, Rao P, Zhang Z, et al. Comprehensive assessment and standardization of solid phase multiplex-bead arrays for the detection of antibodies to HLA. Am J Transplant 13: 1859–1870.Google Scholar). Our article provides insights into the key sources of variability in commercially available solid phase HLA antibody testing kits. Importantly, our study demonstrates that standardization of reagents and protocols significantly reduces assay variance. Utilization of a global normalization algorithm further reduced median fluorescence intensity (MFI) variations in the protocol, thereby improving comparison of data across laboratories. Dr. Maillard and Dr. Mariat comment that our study did not address the prozone effect. Although they raise an important point, we do not believe the prozone effect impacted our estimates of inter-laboratory variance, since all participants adopted a standardized protocol, used the same reagents and tested a constant volume of alloantisera. We do agree with Dr. Maillard and Dr. Mariat that adding dithiothreitol, running dilutions or employing other methods to explore potential prozone/interfering factors is worthy of systematic investigation. Dr. Maillard and Dr. Mariat correctly point out that the %CV decreases within higher MFI strata. Although they indicate this finding is presented in the Bland–Altman plots (2, figure 5), it is actually illustrated in figure 3 of our article, which shows the variation among seven centers across distinct MFI strata. Dr. Maillard and Dr. Mariat suggest that the sudden amelioration in %CV within higher MFI strata is due to saturation of the beads with antibodies. However, as we clearly showed, the decline in %CV begins at 1000 MFI, well below a saturation dosage (<10 000 MFI), which indicates saturation is not the primary reason to explain this result. Their third point questions the impact of intra-laboratory variability on results and whether the improvement in %CV was due to a reduction in variance within an individual laboratory or between laboratories. Since it is standard of care that clinical laboratories utilize a standard operating procedure for HLA antibody testing, we expect the major cause of assay variance is lot-to-lot differences in test kits. Although we did not specifically address intra-laboratory variability in our report, each data point shown in the Bland–Altman plot (2, figure 5) can be converted into a pseudo “intra-laboratory” %CV [i.e. |ΔMFI|/(2×avgMFI)] representing the variation when a lab repeats the test of same sample and bead across two lots of single antigen kits. The median intra-laboratory %CV was 19%, and boxplots demonstrate a decline with increasing MFI range within each center and overall (Figure 1). On average, the intra-laboratory %CV was less than our reported inter-laboratory %CV (∼25%). Nonetheless, we acknowledge that other sources of variation in the aspects of the assay can certainly contribute to intra-laboratory variability and that each laboratory needs to address these concerns. We anticipate that both inter- and intra-laboratory variance will decrease with the implementation of standardized testing protocols and the increasing availability of uniform lots of reagents. This research was performed as part of an American Recovery and Reinvestment (ARRA) funded project under Award Number U0163594 (to P. Heeger), from the National Institute of Allergy and Infectious Diseases. The work was carried out by members of the Clinical Trials in Organ Transplantation (CTOT) and Clinical Trials in Organ Transplantation in Children (CTOT-C) consortia. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

Sources of variability in greenhouse gas and energy balances for biofuel production: a systematic review

AbstractAcross the energy sector, alternatives to fossil fuels are being developed, in response to the dual drivers of climate change and energy security. For transport, biofuels have the greatest potential to replace fossil fuels in the short‐to medium term. However, the ecological benefits of biofuels and the role that their deployment can play in mitigating climate change are being called into question. Life Cycle Assessment (LCA) is a widely used approach that enables the energy and greenhouse gas (GHG) balance of biofuel production to be calculated. Concerns have nevertheless been raised that published data show widely varying and sometimes contradictory results. This review describes a systematic review of GHG emissions and energy balance data from 44 LCA studies of first‐ and second‐generation biofuels. The information collated was used to identify the dominant sources of GHG emissions and energy requirements in biofuel production and the key sources of variability in published LCA data. Our analysis revealed three distinct sources of variation: (1) ‘real’ variability in parameters e.g. cultivation; (2) ‘methodological’ variability due to the implementation of the LCA method; and (3) ‘uncertainty’ due to parameters rarely included and poorly quantified. There is global interest in developing a sustainability assessment protocol for biofuels. Confidence in the results of such an assessment can only be assured if these areas of uncertainty and variability are addressed. A more defined methodology is necessary in order to allow effective and accurate comparison of results. It is also essential that areas of uncertainty such as impacts on soil carbon stocks and fluxes are included in LCA assessments, and that further research is conducted to enable a robust calculation of impacts under different land‐use change scenarios. Without the inclusion of these parameters, we cannot be certain that biofuels are really delivering GHG savings compared with fossil fuels.

Improving patient flow at an outpatient clinic: study of sources of variability and improvement factors

This paper focuses on analyzing and improving patient flow at an outpatient clinic of the Indiana University Medical Group. A structured process analysis and improvement approach was used to identify sources of variability and improvement factors. A process map, that matched the flow process at the clinic, was developed and validated. Key sources of variability that had potential to contribute to congestion in flow were identified. Data on task times were collected by observing the process with stopwatch or from historical records. A simulation model corresponding to the process map was developed, and the output was validated. Several ideas to modify clinic operations were tested on the validated simulation model. The overall result was an improvement in both the mean and the standard deviation of patient wait time, as well as higher utilization of physicians' time. The clinic has implemented several of our recommendations and experienced significant improvements.

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

ABSTRACT This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.

Recommended Practice Regarding Selection of Sensitivity Analysis Methods Applied to Microbial Food Safety Process Risk Models

ABSTRACT A guideline is presented for selection of sensitivity analysis methods applied to microbial food safety process risk (MFSPR) models. The guideline provides useful boundaries and principles for selecting sensitivity analysis methods for MSFPR models. Although the guideline is predicated on a specific branch of risk assessment models related to food-borne diseases, the principles and recommendations provided are typically generally applicable to other types of risk models. Applicable situations include: prioritizing potential critical control points; identifying key sources of variability and uncertainty; and refinement, verification, and validation of a model. Based on the objective of the analysis, characteristics of the model under study, amount of detail expected from sensitivity analysis, and characteristics of the sensitivity analysis method, recommendations for selection of sensitivity analysis methods are provided. A decision framework for method selection is introduced. The decision framework can substantially facilitate the process of selecting a sensitivity analysis method.