Detection of Carbon Monoxide Using Polymer-Composite Films with a Porphyrin-Functionalized Polypyrrole

Post-fire air constituents that are of interest to NASA include CO and some acid gases (HCl and HCN). CO is an important analyte to be able to sense in human habitats since it is a marker for both prefire detection and post-fire cleanup. The need exists for a sensor that can be incorporated into an existing sensing array architecture. The CO sensor needs to be a low-power chemiresistor that operates at room temperature; the sensor fabrication techniques must be compatible with ceramic substrates. Early work on the JPL ElectronicNose indicated that some of the existing polymer-carbon black sensors might be suitable. In addition, the CO sensor based on polypyrrole functionalized with iron porphyrin was demonstrated to be a promising sensor that could meet the requirements. First, pyrrole was polymerized in a ferric chloride/iron porphyrin solution in methanol. The iron porphyrin is 5, 10, 15, 20-tetraphenyl-21H, 23Hporphine iron (III) chloride. This creates a polypyrrole that is functionalized with the porphyrin. After synthesis, the polymer is dried in an oven. Sensors were made from the functionalized polypyrrole by binding it with a small amount of polyethylene oxide (600 MW). This composite made films that were too resistive to be measured in the device. Subsequently, carbon black was added to the composite to bring the sensing film resistivity within a measurable range. A suspension was created in methanol using the functionalized polypyrrole (90% by weight), polyethylene oxide (600,000 MW, 5% by weight), and carbon black (5% by weight). The sensing films were then deposited, like the polymer-carbon black sensors. After deposition, the substrates were dried in a vacuum oven for four hours at 60 C. These sensors showed good response to CO at concentrations over 100 ppm. While the sensor is based on a functionalized pyrrole, the actual composite is more robust and flexible. A polymer binder was added to help keep the sensor material from delaminating from the electrodes, and carbon was added to improve the conductivity of the material

System for detecting and estimating concentrations of gas or liquid analytes

A sensor system for detecting and estimating concentrations of various gas or liquid analytes. In an embodiment, the resistances of a set of sensors are measured to provide a set of responses over time where the resistances are indicative of gas or liquid sorption, depending upon the sensors. A concentration vector for the analytes is estimated by satisfying a criterion of goodness using the set of responses. Other embodiments are described and claimed

Co-polymer films for sensors

Embodiments include a sensor comprising a co-polymer, the co-polymer comprising a first monomer and a second monomer. For some embodiments, the first monomer is poly-4-vinyl pyridine, and the second monomer is poly-4-vinyl pyridinium propylamine chloride. For some embodiments, the first monomer is polystyrene and the second monomer is poly-2-vinyl pyridinium propylamine chloride. For some embodiments, the first monomer is poly-4-vinyl pyridine, and the second monomer is poly-4-vinyl pyridinium benzylamine chloride. Other embodiments are described and claimed

Co-polymer Films for Sensors

Embodiments include a sensor comprising a co-polymer, the co-polymer comprising a first monomer and a second monomer. For some embodiments, the first monomer is poly-4-vinyl pyridine, and the second monomer is poly-4-vinyl pyridinium propylamine chloride. For some embodiments, the first monomer is polystyrene and the second monomer is poly-2-vinyl pyridinium propylamine chloride. For some embodiments, the first monomer is poly-4-vinyl pyridine, and the second monomer is poly-4-vinyl pyridinium benzylamine chloride. Other embodiments are described and claimed

Graphical modeling of binary data using the LASSO: a simulation study

Background: Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Buhlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models. Methods: We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index. We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer. Results: Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms. Conclusions: Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes

Individualized markers optimize class prediction of microarray data

BACKGROUND: Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample. RESULTS: Instead of using the same features for the classification of all samples, the proposed technique starts by creating a pool of informative gene-features. For each sample, the method selects a subset of these features whose expression profiles are most likely to accurately predict the sample's category. Different subsets are utilized for different samples and the outcomes are combined in a hierarchical framework for the classification of all samples. Moreover, this approach can innately identify subgroups of samples within a given class which share common feature sets thus highlighting the effect of individuality on gene expression. CONCLUSION: In addition to high classification accuracy, the proposed method offers a more individualized approach for the identification of biological markers, which may help in better understanding the molecular background of a disease and emphasize the need for more flexible medical interventions

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development

Faster Array Training and Rapid Analysis for a Sensor Array Intended for an Event Monitor in Air

Environmental monitoring, in particular, air monitoring, is a critical need for human space flight. Both monitoring and life support systems have needs for closed loop process feedback and quality control for environmental factors. Monitoring protects the air environment and water supply for the astronaut crew and different sensors help ensure that the habitat falls within acceptable limits, and that the life support system is functioning properly and efficiently. The longer the flight duration and the farther the destination, the more critical it becomes to have carefully monitored and automated control systems for life support. There is an acknowledged need for an event monitor which samples the air continuously and provides near real-time information on changes in the air. Past experiments with the JPL ENose have demonstrated a lifetime of the sensor array, with the software, of around 18 months. We are working on a sensor array and new algorithms that will incorporate transient sensor responses in the analysis. Preliminary work has already showed more rapid quantification and identification of analytes and the potential for faster training time of the array. We will look at some of the factors that contribute to demonstrating faster training time for the array. Faster training will decrease the integrated sensor exposure to training analytes, which will also help extend sensor lifetime