Accuracy profile validation of a new analytical method for propane measurement using headspace-gas chromatography-mass spectrometry

Propane can be responsible for several types of lethal intoxication and explosions. Quantifying it would be very helpful to determine in some cases the cause of death. Some gas chromatography-mass spectrometry (GC-MS) methods of propane measurements do already exist. The main drawback of these GC-MS methods described in the literature is the absence of a specific propane internal standard necessary for accurate quantitative analysis. The main outcome of the following study was to provide an innovative Headspace-GC-MS method (HS-GC-MS) applicable to the routine determination of propane concentration in forensic toxicology laboratories. To date, no stable isotope of propane is commercially available. The development of an in situ generation of standards is thus presented. An internal-labeled standard gas (C3DH7) is generated in situ by the stoichiometric formation of propane by the reaction of deuterated water (D2O) with Grignard reagent propylmagnesium chloride (C3H7MgCl). The method aims to use this internal standard to quantify propane concentrations and, therefore, to obtain precise measurements. Consequently, a complete validation with an accuracy profile according to two different guidelines, the French Society of Pharmaceutical Sciences and Techniques (SFSTP) and the Gesellschaft für toxikologische und Forensische Chemie (GTFCh), is presented.

Full activation of the T cell receptor requires both clustering and conformational changes at CD3.

T cell receptor (TCR-CD3) triggering involves both receptor clustering and conformational changes at the cytoplasmic tails of the CD3 subunits. The mechanism by which TCRalphabeta ligand binding confers conformational changes to CD3 is unknown. By using well-defined ligands, we showed that induction of the conformational change requires both multivalent engagement and the mobility restriction of the TCR-CD3 imposed by the plasma membrane. The conformational change is elicited by cooperative rearrangements of two TCR-CD3 complexes and does not require accompanying changes in the structure of the TCRalphabeta ectodomains. This conformational change at CD3 reverts upon ligand dissociation and is required for T cell activation. Thus, our permissive geometry model provides a molecular mechanism that rationalizes how the information of ligand binding to TCRalphabeta is transmitted to the CD3 subunits and to the intracellular signaling machinery.

Multicentric carpotarsal osteolysis is caused by mutations clustering in the amino-terminal transcriptional activation domain of MAFB.

Multicentric carpotarsal osteolysis (MCTO) is a rare skeletal dysplasia characterized by aggressive osteolysis, particularly affecting the carpal and tarsal bones, and is frequently associated with progressive renal failure. Using exome capture and next-generation sequencing in five unrelated simplex cases of MCTO, we identified previously unreported missense mutations clustering within a 51 base pair region of the single exon of MAFB, validated by Sanger sequencing. A further six unrelated simplex cases with MCTO were also heterozygous for previously unreported mutations within this same region, as were affected members of two families with autosomal-dominant MCTO. MAFB encodes a transcription factor that negatively regulates RANKL-induced osteoclastogenesis and is essential for normal renal development. Identification of this gene paves the way for development of novel therapeutic approaches for this crippling disease and provides insight into normal bone and kidney development.

Improving the Accuracy of Camber Predictions for Precast Pretensioned Concrete Beams, TR-625, 2015

The discrepancies between the designed and measured camber of precast pretensioned concrete beams (PPCBs) observed by the Iowa DOT have created challenges in the field during bridge construction, causing construction delays and additional costs. This study was undertaken to systematically identify the potential sources of discrepancies between the designed and measured camber from release to time of erection and improve the accuracy of camber estimations in order to minimize the associated problems in the field. To successfully accomplish the project objectives, engineering properties, including creep and shrinkage, of three normal concrete and four high-performance concrete mix designs were characterized. In parallel, another task focused on identifying the instantaneous camber and the variables affecting the instantaneous camber and evaluated the corresponding impact of this factor using more than 100 PPCBs. Using a combination of finite element analyses and the time-step method, the long-term camber was estimated for 66 PPCBs, with due consideration given to creep and shrinkage of concrete, changes in support location and prestress force, and the thermal effects. Utilizing the outcomes of the project, suitable long-term camber multipliers were developed that account for the time-dependent behavior, including the thermal effects. It is shown that by using the recommended practice for the camber measurements together with the proposed multipliers, the accuracy of camber prediction will be greatly improved. Consequently, it is expected that future bridge projects in Iowa can minimize construction challenges resulting from large discrepancies between the designed and actual camber of PPCBs during construction.

Effect of data acquisition accuracy on timing of stand harvests and expected net present value

Abstract

Predictive accuracy of original and recalibrated Framingham risk score in the Swiss population.

OBJECTIVE: To compare the predictive accuracy of the original and recalibrated Framingham risk function on current morbidity from coronary heart disease (CHD) and mortality data from the Swiss population. METHODS: Data from the CoLaus study, a cross-sectional, population-based study conducted between 2003 and 2006 on 5,773 participants aged 35-74 without CHD were used to recalibrate the Framingham risk function. The predicted number of events from each risk function were compared with those issued from local MONICA incidence rates and official mortality data from Switzerland. RESULTS: With the original risk function, 57.3%, 21.2%, 16.4% and 5.1% of men and 94.9%, 3.8%, 1.2% and 0.1% of women were at very low (<6%), low (6-10%), intermediate (10-20%) and high (>20%) risk, respectively. With the recalibrated risk function, the corresponding values were 84.7%, 10.3%, 4.3% and 0.6% in men and 99.5%, 0.4%, 0.0% and 0.1% in women, respectively. The number of CHD events over 10 years predicted by the original Framingham risk function was 2-3 fold higher than predicted by mortality+case fatality or by MONICA incidence rates (men: 191 vs. 92 and 51 events, respectively). The recalibrated risk function provided more reasonable estimates, albeit slightly overestimated (92 events, 5-95th percentile: 26-223 events); sensitivity analyses showed that the magnitude of the overestimation was between 0.4 and 2.2 in men, and 0.7 and 3.3 in women. CONCLUSION: The recalibrated Framingham risk function provides a reasonable alternative to assess CHD risk in men, but not in women.

Clustering of HCV coinfections on HIV phylogeny indicates domestic and sexual transmission of HCV.

BACKGROUND: HCV coinfection remains a major cause of morbidity and mortality among HIV-infected individuals and its incidence has increased dramatically in HIV-infected men who have sex with men(MSM). METHODS: Hepatitis C virus (HCV) coinfection in the Swiss HIV Cohort Study(SHCS) was studied by combining clinical data with HIV-1 pol-sequences from the SHCS Drug Resistance Database(DRDB). We inferred maximum-likelihood phylogenetic trees, determined Swiss HIV-transmission pairs as monophyletic patient pairs, and then considered the distribution of HCV on those pairs. RESULTS: Among the 9748 patients in the SHCS-DRDB with known HCV status, 2768(28%) were HCV-positive. Focusing on subtype B(7644 patients), we identified 1555 potential HIV-1 transmission pairs. There, we found that, even after controlling for transmission group, calendar year, age and sex, the odds for an HCV coinfection were increased by an odds ratio (OR) of 3.2 [95% confidence interval (CI) 2.2, 4.7) if a patient clustered with another HCV-positive case. This strong association persisted if transmission groups of intravenous drug users (IDUs), MSMs and heterosexuals (HETs) were considered separately(in all cases OR>2). Finally we found that HCV incidence was increased by a hazard ratio of 2.1 (1.1, 3.8) for individuals paired with an HCV-positive partner. CONCLUSIONS: Patients whose HIV virus is closely related to the HIV virus of HIV/HCV-coinfected patients have a higher risk for carrying or acquiring HCV themselves. This indicates the occurrence of domestic and sexual HCV transmission and allows the identification of patients with a high HCV-infection risk.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

Towards a diffusion image processing validation and accuracy prediction framework

Validation is the main bottleneck preventing theadoption of many medical image processing algorithms inthe clinical practice. In the classical approach,a-posteriori analysis is performed based on someobjective metrics. In this work, a different approachbased on Petri Nets (PN) is proposed. The basic ideaconsists in predicting the accuracy that will result froma given processing based on the characterization of thesources of inaccuracy of the system. Here we propose aproof of concept in the scenario of a diffusion imaginganalysis pipeline. A PN is built after the detection ofthe possible sources of inaccuracy. By integrating thefirst qualitative insights based on the PN withquantitative measures, it is possible to optimize the PNitself, to predict the inaccuracy of the system in adifferent setting. Results show that the proposed modelprovides a good prediction performance and suggests theoptimal processing approach.

Aggregation invariance in general clustering approaches

General clustering deals with weighted objects and fuzzy memberships. We investigate the group- or object-aggregation-invariance properties possessed by the relevant functionals (effective number of groups or objects, centroids, dispersion, mutual object-group information, etc.). The classical squared Euclidean case can be generalized to non-Euclidean distances, as well as to non-linear transformations of the memberships, yielding the c-means clustering algorithm as well as two presumably new procedures, the convex and pairwise convex clustering. Cluster stability and aggregation-invariance of the optimal memberships associated to the various clustering schemes are examined as well.

Pre-stimulus beta oscillations within left posterior sylvian regions impact auditory temporal order judgment accuracy.

Both neural and behavioral responses to stimuli are influenced by the state of the brain immediately preceding their presentation, notably by pre-stimulus oscillatory activity. Using frequency analysis of high-density electroencephalogram coupled with source estimations, the present study investigated the role of pre-stimulus oscillatory activity in auditory spatial temporal order judgments (TOJ). Oscillations within the beta range (i.e. 18-23Hz) were significantly stronger before accurate than inaccurate TOJ trials. Distributed source estimations identified bilateral posterior sylvian regions as the principal contributors to pre-stimulus beta oscillations. Activity within the left posterior sylvian region was significantly stronger before accurate than inaccurate TOJ trials. We discuss our results in terms of a modulation of sensory gating mechanisms mediated by beta activity.

Accuracy profile validation of a new analytical method for butane measurement using headspace-gas chromatography-mass spectrometry.

The aim of our study was to provide an innovative HS-GC/MS method applicable to the routine determination of butane concentration in forensic toxicology laboratories. The main drawback of the GC/MS methods discussed in literature concerning butane measurement was the absence of a specific butane internal standard necessary to perform quantification. Because no stable isotope of butane is commercially available, it is essential to develop a new approach by an in situ generation of standards. To avoid the manipulation of a stable isotope-labelled gas, we have chosen to generate in situ an internal labelled standard gas (C(4)H(9)D) following the basis of the stoichiometric formation of butane by the reaction of deuterated water (D(2)O) with Grignard reagent butylmagnesium chloride (C(4)H(9)MgCl). This method allows a precise measurement of butane concentration and therefore, a full validation by accuracy profile was presented.

Accuracy of indirect estimation of power output from uphill performance in cycling.

PURPOSE: To use measurement by cycling power meters (Pmes) to evaluate the accuracy of commonly used models for estimating uphill cycling power (Pest). Experiments were designed to explore the influence of wind speed and steepness of climb on accuracy of Pest. The authors hypothesized that the random error in Pest would be largely influenced by the windy conditions, the bias would be diminished in steeper climbs, and windy conditions would induce larger bias in Pest. METHODS: Sixteen well-trained cyclists performed 15 uphill-cycling trials (range: length 1.3-6.3 km, slope 4.4-10.7%) in a random order. Trials included different riding position in a group (lead or follow) and different wind speeds. Pmes was quantified using a power meter, and Pest was calculated with a methodology used by journalists reporting on the Tour de France. RESULTS: Overall, the difference between Pmes and Pest was -0.95% (95%CI: -10.4%, +8.5%) for all trials and 0.24% (-6.1%, +6.6%) in conditions without wind (<2 m/s). The relationship between percent slope and the error between Pest and Pmes were considered trivial. CONCLUSIONS: Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease as well as common cellular and molecular pathways. Disease-specific signatures were leveraged to build a multi-disease classifier, which predicted the diagnosis of publicly and prospectively collected expression profiles with 93% accuracy. In one sample, the molecular classifier differed from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinical presentation evolved. Finally, integration of IFN-regulated gene programs with the skin database revealed a significant inverse correlation between IFN-β and IFN-γ programs across all conditions. Our study provides an integrative approach to the study of gene signatures from multiple skin conditions, elucidating mechanisms of disease pathogenesis. In addition, these studies provide a framework for developing tools for personalized medicine toward the precise prediction, prevention, and treatment of disease on an individual level.