Evaluation of Portable Multi-Gas Analyzers for use by Safety Personnel

During confined space entry operations as well as Shuttle-safing operations, United Space Alliance (USA)/National Aeronautics and Space Administration (NASA) safety personnel use a variety of portable instrumentation to monitor for hazardous levels of compounds such as nitrogen dioxide (N%), monomethylhydrazine (NMM), FREON 21, ammonia (NH3), oxygen (O2), and combustibles (as hydrogen (H2)). Except for O2 and H2, each compound is monitored using a single analyzer. In many cases these analyzers are 5 to 10 years old and require frequent maintenance. In addition, they are cumbersome to carry and tend to make the job of personnel monitoring physically taxing. As part of an effort to upgrade the sensor technology background information was requested from a total of 27 manufacturers of portable multi-gas instruments. A set of criteria was established to determine which vendors would be selected for laboratory evaluation. These criteria were based on requests made by USA/NASA Safety personnel in order to meet requirements within their respective areas for confined-space and Shuttle-safing operations. Each of the 27 manufacturers of multi-gas analyzers was sent a copy of the criteria and asked to fill in the appropriate information pertaining to their instrumentation. Based on the results of the sensor criteria worksheets, a total of 9 vendors out of 27 surveyed manufacturers were chosen for evaluation. Each vendor included in the final evaluation process was requested to configure each of two analyzers with NO2, NH3, O2, and combustible sensors. A set of lab tests was designed in order to determine which of the multi-gas instruments under evaluation was best suited for use in both shuttle and confined space operations. These tests included linearity/repeatability, zero/span drift response/recovery, humidity, interference, and maintenance. At the conclusion of lab testing three vendors were selected for additional field testing. Based on the results of both the lab and field evaluations a single vendor was recommended for use by NASA/IJSA Safety personnel. Vendor selection criteria, as well as the results from both laboratory and field testing of the multi-gas analyzers, are presented as part of this paper

PREDICTION OF ANKLE JOINT TORQUES USING ARTIFICIAL NEURAL NETWORKS

Major ankle sprains in sports are thought to be due to high levels of ankle torsion. The purpose of this study was to develop a method for measuring in vivo ankle torques developed by athletes. Motion capture, force plate, and insole pressure measurements were used to develop generalized regression neural networks to predict maximum ankle torque and rate of ankle torque based on insole pressures. It was found that network prediction accuracy depended on the number of subjects used for training, as well as the method of pressure sensor grouping. Further work will be performed to determine optimal subject and pressure sensor groupings

hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework

Summary: Haplotypes carry important information that can direct investigators towards underlying susceptibility variants, and hence multiple tagging single nucleotide polymorphisms (tSNPs) are usually studied in candidate gene association studies. However, it is often unknown which SNPs should be included in haplotype analyses, or which tests should be performed for maximum power. We have developed a program, hapConstructor, which automatically builds multi-locus SNP sets to test for association in a case-control framework. The multi-SNP sets considered need not be contiguous; they are built based on significance. An important feature is that the missing data imputation is carried out based on the full data, for maximal information and consistency. HapConstructor is implemented in a Monte Carlo framework and naturally extends to allow for significance testing and false discovery rates that account for the construction process and to related individuals. HapConstructor is a useful tool for exploring multi-locus associations in candidate genes and regions

Disease acceptance and adherence to imatinib in Taiwanese chronic myeloid leukaemia outpatients

Background The launch of imatinib has turned chronic myeloid leukaemia (CML) into a chronic illness due to the dramatic improvement in survival. Several recent studies have demonstrated that poor adherence to imatinib may hamper the therapeutic outcomes and result in increased medical expenditures, whilst research on exploring the reasons for non-adherence to imatinib is still limited. Objective This study aimed to explore the experience of patients as they journey through their CML treatments and associated imatinib utilisation in order to understand the perceptions, attitudes and concerns that may influence adherence to imatinib treatment. Setting This study was conducted at oncology outpatient clinics in a medical centre in southern Taiwan. Methods CML patients who regularly attended the oncology outpatient clinics to receive imatinib treatment from October 2011 to March 2012 were invited to participate in the study. Semi-structured face-to-face interviews were used to explore patients’ experiences and views of their treatment, their current CML status and CML-related health conditions, their concerns about imatinib treatment and imatinib-taking behaviours. Patient interviews were recorded, transcribed verbatim and thematically analysed using the constant comparison approach. Main outcome measure Themes related to patients’ views of the disease and health conditions, worries and concerns influencing imatinib utilisation behaviours are reported. Results Forty-two CML patients participated in the interviews. The emerging themes included: acceptance of current disease and health status, misconceptions about disease progression, factors associated with adherence to imatinib, concerns and management of adverse drug effects. Participants regarded CML as a chronic disease but had misconceptions about disease progression, therapeutic monitoring, resistance to imatinib and symptoms of side effects. Participants were generally adherent to imatinib and favoured long-term prescriptions to avoid regular outpatient visits for medication refills. Experiencing adverse effect was the main reason influencing adherence and led to polypharmacy. Most participants altered medicine-taking behaviours to maintain long-term use of imatinib. Conclusion Taiwanese CML patients are adherent to imatinib but report changing their medication-taking behaviour due to adverse drug effects and associated polypharmacy. Patients’ misconceptions of the disease and medication suggests that it is necessary to improve communication between patients and healthcare professionals. Routinely providing updated information as part of the patient counselling process should be considered as a means of improving this communication

Whole genome association mapping by incompatibilities and local perfect phylogenies

BACKGROUND: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed. RESULTS: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set – the ΔF508 mutation for cystic fibrosis – where the susceptibility variant is already known – and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene. CONCLUSION: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours

Chronic T cell receptor stimulation unmasks NK receptor signaling in peripheral T cell lymphomas via epigenetic reprogramming.

Peripheral T cell lymphomas (PTCLs) represent a significant unmet medical need with dismal clinical outcomes. The T cell receptor (TCR) is emerging as a key driver of T lymphocyte transformation. However, the role of chronic TCR activation in lymphomagenesis and in lymphoma cell survival is still poorly understood. Using a mouse model, we report that chronic TCR stimulation drove T cell lymphomagenesis, whereas TCR signaling did not contribute to PTCL survival. The combination of kinome, transcriptome, and epigenome analyses of mouse PTCLs revealed a NK cell-like reprogramming of PTCL cells with expression of NK receptors (NKRs) and downstream signaling molecules such as Tyrobp and SYK. Activating NKRs were functional in PTCLs and dependent on SYK activity. In vivo blockade of NKR signaling prolonged mouse survival, demonstrating the addiction of PTCLs to NKRs and downstream SYK/mTOR activity for their survival. We studied a large collection of human primary samples and identified several PTCLs recapitulating the phenotype described in this model by their expression of SYK and the NKR, suggesting a similar mechanism of lymphomagenesis and establishing a rationale for clinical studies targeting such molecules

Global haplotype partitioning for maximal associated SNP pairs

<p>Abstract</p> <p>Background</p> <p>Global partitioning based on pairwise associations of SNPs has not previously been used to define haplotype blocks within genomes. Here, we define an association index based on LD between SNP pairs. We use the Fisher's exact test to assess the statistical significance of the LD estimator. By this test, each SNP pair is characterized as associated, independent, or not-statistically-significant. We set limits on the maximum acceptable proportion of independent pairs within all blocks and search for the partitioning with maximal proportion of associated SNP pairs. Essentially, this model is reduced to a constrained optimization problem, the solution of which is obtained by iterating a dynamic programming algorithm.</p> <p>Results</p> <p>In comparison with other methods, our algorithm reports blocks of larger average size. Nevertheless, the haplotype diversity within the blocks is captured by a small number of tagSNPs. Resampling HapMap haplotypes under a block-based model of recombination showed that our algorithm is robust in reproducing the same partitioning for recombinant samples. Our algorithm performed better than previously reported models in a case-control association study aimed at mapping a single locus trait, based on simulation results that were evaluated by a block-based statistical test. Compared to methods of haplotype block partitioning, we performed best on detection of recombination hotspots.</p> <p>Conclusion</p> <p>Our proposed method divides chromosomes into the regions within which allelic associations of SNP pairs are maximized. This approach presents a native design for dimension reduction in genome-wide association studies. Our results show that the pairwise allelic association of SNPs can describe various features of genomic variation, in particular recombination hotspots.</p