High-pressure apparatus for monitoring solid-liquid phase transitions.

This work presents a new technique for evaluating the solid-liquid phase transformations in complex diesel fuel blends and diesel surrogates under high-pressure conditions intended to simulate those occurring in vehicle fuel injectors. A high-pressure apparatus based on a visual identification of freezing and thawing has been designed and built to monitor phase behavior and determine the crystallization temperature of complex fuels to predict wax precipitation. The proposed methodology was validated using pure substances-n-hexadecane (C16H34), cyclohexane (C6H12), and a mixture of 0.5848 mol fraction n-hexadecane in cyclohexane. The crystallization temperatures of these compounds were measured from atmospheric pressure to 400 MPa for temperatures varying from 290 K to 363 K and compared to those reported in the literature. The standard error of the estimated temperatures for the experimental data obtained in this work, based on a given pressure, was compared to data from the literature. This methodology will be extended to investigate the properties of more complex fuel mixtures.

Evaluation of PCR Systems for Field Screening of Bacillus anthracis.

There is little published data on the performance of hand-portable polymerase chain reaction (PCR) systems that can be used by first responders to determine if a suspicious powder contains a potential biothreat agent. We evaluated 5 commercially available hand-portable PCR instruments for detection of Bacillus anthracis. We used a cost-effective, statistically based test plan to evaluate systems at performance levels ranging from 0.85-0.95 lower confidence bound (LCB) of the probability of detection (POD) at confidence levels of 80% to 95%. We assessed specificity using purified genomic DNA from 13 B. anthracis strains and 18 Bacillus near neighbors, potential interference with 22 suspicious powders that are commonly encountered in the field by first responders during suspected biothreat incidents, and the potential for PCR inhibition when B. anthracis spores were spiked into these powders. Our results indicate that 3 of the 5 systems achieved 0.95 LCB of the probability of detection with 95% confidence levels at test concentrations of 2,000 genome equivalents/mL (GE/mL), which is comparable to 2,000 spores/mL. This is more than sufficient sensitivity for screening visible suspicious powders. These systems exhibited no false-positive results or PCR inhibition with common suspicious powders and reliably detected B. anthracis spores spiked into these powders, though some issues with assay controls were observed. Our testing approach enables efficient performance testing using a statistically rigorous and cost-effective test plan to generate performance data that allow users to make informed decisions regarding the purchase and use of field biodetection equipment.

Evaluation of Immunoassays and General Biological Indicator Tests for Field Screening of Bacillus anthracis and Ricin.

There is little published data on the performance of biological indicator tests and immunoassays that could be used by first responders to determine if a suspicious powder contains a potential biothreat agent. We evaluated a range of biological indicator tests, including 3 protein tests, 2 ATP tests, 1 DNA test, and 1 FTIR spectroscopy instrument for their ability to screen suspicious powders for Bacillus anthracis (B. anthracis) spores and ricin. We also evaluated 12 immunoassays (mostly lateral flow immunoassays) for their ability to screen for B. anthracis and ricin. We used a cost-effective, statistically based test plan that allows instruments to be evaluated at performance levels ranging from 0.85 to 0.95 lower confidence bound of the probability of detection at confidence levels of 80% to 95%. We also assessed interference with 22 common suspicious powders encountered in the field. The detection reproducibility for the biological indicators was evaluated at 108 B. anthracis spores and 62.5 µg ricin, and the immunoassay detection reproducibility was evaluated at 107 spores/mL (B. anthracis) and 0.1 µg/mL (ricin). Seven out of 12 immunoassays met our most stringent criteria for B. anthracis detection, while 9 out of 12 met our most stringent test criteria for ricin detection. Most of the immunoassays also detected ricin in 3 different crude castor seed preparations. Our testing results varied across products and sample preparations, indicating the importance of reviewing performance data for specific instruments and sample types of interest for the application in order to make informed decisions regarding the selection of biodetection equipment for field use.

Evaluating and modeling the effects of surface sampling factors on the recovery of organic chemical attribution signatures using the accelerated diffusion sampler and solvent extraction.

In this study, an experimental design matrix was created and executed to test the effects of various real-world factors on the ability of (1) the accelerated diffusion sampler with solid phase micro-extraction (ADS-SPME) and (2) solvent extraction to capture organic chemical attribution signatures (CAS) from dimethyl methylphosphonate (DMMP) spiked onto painted wall board (PWB) surfaces. The DMMP CAS organic impurities sampled by ADS-SPME and solvent extraction were analyzed by gas chromatography/mass spectrometry (GC/MS). The number of detected DMMP CAS impurities and their respective GC/MS peak areas were determined as a function of DMMP stock, DMMP spiked volume, exposure time, SPME sampling time, and ADS headspace pressure. Based on the statistical analysis of experimental results, several general conclusions are made: (1) the amount of CAS impurity detected using ADS-SPME and GC/MS was most influenced by spiked volume, stock, and ADS headspace pressure, (2) reduced ADS headspace pressure increased the amount of detected CAS impurity, as measured by GC/MS peak area, by up to a factor of 1.7-1.9 compared to ADS at ambient headspace pressure, (3) the ADS had no measurable effect on the number of detected DMMP impurities, that is, ADS (with and without reduced pressure) had no practical effect on the DMMP impurity profile collected from spiked PWB, and (4) solvent extraction out performed ADS-SPME in terms of consistently capturing all or most of the targeted DMMP impurities from spiked PWB.

Cells Respond to Distinct Nanoparticle Properties with Multiple Strategies As Revealed by Single-Cell RNA-Seq.

The impact of distinct nanoparticle (NP) properties on cellular response and ultimately human health is unclear. This gap is partially due to experimental difficulties in achieving uniform NP loads in the studied cells, creating heterogeneous populations with some cells "overloaded" while other cells are loaded with few or no NPs. Yet gene expression studies have been conducted in the population as a whole, identifying generic responses, while missing unique responses due to signal averaging across many cells, each carrying different loads. Here, we applied single-cell RNA-Seq to alveolar epithelial cells carrying defined loads of aminated or carboxylated quantum dots (QDs), showing higher or lower toxicity, respectively. Interestingly, cells carrying lower loads responded with multiple strategies, mostly with up-regulated processes, which were nonetheless coherent and unique to each QD type. In contrast, cells carrying higher loads responded more uniformly, with mostly down-regulated processes that were shared across QD types. Strategies unique to aminated QDs showed strong up-regulation of stress responses, coupled in some cases with regulation of cell cycle, protein synthesis, and organelle activities. In contrast, strategies unique to carboxylated QDs showed up-regulation of DNA repair and RNA activities and decreased regulation of cell division, coupled in some cases with up-regulation of stress responses and ATP-related functions. Together, our studies suggest scenarios where higher NP loads lock cells into uniform responses, mostly shutdown of cellular processes, whereas lower loads allow for unique responses to each NP type that are more diversified proactive defenses or repairs of the NP insults.

Organic Chemical Attribution Signatures for the Sourcing of a Mustard Agent and Its Starting Materials.

Chemical attribution signatures (CAS) are being investigated for the sourcing of chemical warfare (CW) agents and their starting materials that may be implicated in chemical attacks or CW proliferation. The work reported here demonstrates for the first time trace impurities from the synthesis of tris(2-chloroethyl)amine (HN3) that point to the reagent and the specific reagent stocks used in the synthesis of this CW agent. Thirty batches of HN3 were synthesized using different combinations of commercial stocks of triethanolamine (TEA), thionyl chloride, chloroform, and acetone. The HN3 batches and reagent stocks were then analyzed for impurities by gas chromatography/mass spectrometry. All the reagent stocks had impurity profiles that differentiated them from one another. This was demonstrated by building classification models with partial least-squares discriminant analysis (PLSDA) and obtaining average stock classification errors of 2.4, 2.8, 2.8, and 11% by cross-validation for chloroform (7 stocks), thionyl chloride (3 stocks), acetone (7 stocks), and TEA (3 stocks), respectively, and 0% for a validation set of chloroform samples. In addition, some reagent impurities indicative of reagent type were found in the HN3 batches that were originally present in the reagent stocks and presumably not altered during synthesis. More intriguing, impurities in HN3 batches that were apparently produced by side reactions of impurities unique to specific TEA and chloroform stocks, and thus indicative of their use, were observed.

Soil Respiration and Bacterial Structure and Function after 17 Years of a Reciprocal Soil Transplant Experiment.

The effects of climate change on soil organic matter-its structure, microbial community, carbon storage, and respiration response-remain uncertain and widely debated. In addition, the effects of climate changes on ecosystem structure and function are often modulated or delayed, meaning that short-term experiments are not sufficient to characterize ecosystem responses. This study capitalized on a long-term reciprocal soil transplant experiment to examine the response of dryland soils to climate change. The two transplant sites were separated by 500 m of elevation on the same mountain slope in eastern Washington state, USA, and had similar plant species and soil types. We resampled the original 1994 soil transplants and controls, measuring CO2 production, temperature response, enzyme activity, and bacterial community structure after 17 years. Over a laboratory incubation of 100 days, reciprocally transplanted soils respired roughly equal cumulative amounts of carbon as non-transplanted controls from the same site. Soils transplanted from the hot, dry, lower site to the cooler and wetter (difference of -5°C monthly maximum air temperature, +50 mm yr-1 precipitation) upper site exhibited almost no respiratory response to temperature (Q10 of 1.1), but soils originally from the upper, cooler site had generally higher respiration rates. The bacterial community structure of transplants did not differ significantly from that of untransplanted controls, however. Slight differences in local climate between the upper and lower Rattlesnake locations, simulated with environmental control chambers during the incubation, thus prompted significant differences in microbial activity, with no observed change to bacterial structure. These results support the idea that environmental shifts can influence soil C through metabolic changes, and suggest that microbial populations responsible for soil heterotrophic respiration may be constrained in surprising ways, even as shorter- and longer-term soil microbial dynamics may be significantly different under changing climate.

Comparative hazard analysis and toxicological modeling of diverse nanomaterials using the embryonic zebrafish (EZ) metric of toxicity.

The integration of rapid assays, large datasets, informatics, and modeling can overcome current barriers in understanding nanomaterial structure-toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here, we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality in developing embryonic zebrafish, were established at realistic exposure levels and used to develop a hazard ranking of diverse nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both the core composition and outermost surface chemistry of nanomaterials. The resulting clusters guided the development of a surface chemistry-based model of gold nanoparticle toxicity. Our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. Research should continue to focus on methodologies for determining nanomaterial hazard based on multiple sub-lethal responses following realistic, low-dose exposures, thus increasing the availability of quantitative measures of nanomaterial hazard to support the development of nanoparticle structure-activity relationships.

Optimizing detection of RDX vapors using designed experiments for remote sensing.

This paper presents results of designed experiments performed to study the effect of four factors on the detection of RDX vapors from desorption into an atmospheric flow tube mass spectrometer (AFT-MS). The experiments initially included four independent factors: gas flow rate, desorption current, solvent evaporation time and RDX mass. The values of three detection responses, peak height, peak width, and peak area were recorded but only the peak height response was analyzed. Results from the first block of experiments indicated that solvent evaporation time was not statistically significant at the 95% confidence level. A second round of experiments was designed and executed using flow rate, current, and RDX mass as factors and the results were used to create a model to predict conditions resulting in maximum peak height. Those conditions were confirmed experimentally and used to obtain data for a calibration model. The calibration model represented RDX amounts ranging from 1 to 25 pg desorbed into an air flow of 7 L min(-1). Air samples from a shipping container that held 2 closed explosive storage magazines were collected on metal filaments for varying amounts for time ranging from 5 to 90 minutes. RDX was detected from all of the filaments sampled by desorption into the AFT-MS. From the calibration model, RDX vapor concentrations within the shipping container were calculated to be in the range of 1 to 50 parts-per-quadrillion (ppqv) from data collected on 2 separate days.

Origin of parameter degeneracy and molecular shape relationships in geometric-flow calculations of solvation free energies.

Implicit solvent models are important tools for calculating solvation free energies for chemical and biophysical studies since they require fewer computational resources but can achieve accuracy comparable to that of explicit-solvent models. In past papers, geometric flow-based solvation models have been established for solvation analysis of small and large compounds. In the present work, the use of realistic experiment-based parameter choices for the geometric flow models is studied. We find that the experimental parameters of solvent internal pressure p = 172 MPa and surface tension γ = 72 mN/m produce solvation free energies within 1 RT of the global minimum root-mean-squared deviation from experimental data over the expanded set. Our results demonstrate that experimental values can be used for geometric flow solvent model parameters, thus eliminating the need for additional parameterization. We also examine the correlations between optimal values of p and γ which are strongly anti-correlated. Geometric analysis of the small molecule test set shows that these results are inter-connected with an approximately linear relationship between area and volume in the range of molecular sizes spanned by the data set. In spite of this considerable degeneracy between the surface tension and pressure terms in the model, both terms are important for the broader applicability of the model.

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size.

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.

Comparison of probability and likelihood models for peptide identification from tandem mass spectrometry data.

We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.

Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data.

MOTIVATION: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.