Use of High-resolution Metabolomics to Assess Changes in Metabolic Pathways Associated with Acute Exposure to Air Pollutants.

INTRODUCTION: Many studies have examined the positive effects of physical activity on mortality and morbidity, as well as the negative impact of exposure to air pollutants. Few, however, have investigated the complex interaction between physical activity and air pollution exposure. One promising method for analyzing this interaction is using metabolomics to identify the resulting metabolites produced from each exposure. This study will provide insight into the modification by physical activity of health biomarkers associated with acute exposure to air pollutants. AIM: The aim of this study is to identify novel biomarkers of pollutant exposures using metabolomics analyses. Specifically, this study will examine differences in exposures to air pollutants (black carbon, ozone, particulate matter, particle number concentration) and corresponding metabolic changes in subjects before and after outdoor physical activity. METHODS: Sixty-five saliva samples for metabolomics analysis were collected from a sample of 57 individuals in Atlanta, GA from June through July 2016. The ambient outdoor concentrations of PM2.5 (PM), ozone (O3), black carbon (BC), and particle number concentration (PNC) were measured throughout each 2-hr sampling event. Each participant’s physical activity levels were monitored as well. Each pollutant (above) was analyzed per individual via four separate metrics: ambient outdoor concentration, exposure, cumulative inhaled dose, and maximum one-minute dose. Samples were processed via ultra-high resolution liquid chromatography-mass spectrometry. Various mixed regression models used pollutant metrics to predict changes in metabolic feature intensity from pre- to post-exposure. Metabolite identification and pathway analysis for each significant feature was calculated using Mummichog version 1.0.3 with a level of significance of .05. RESULTS: All pollutant metrics were significantly associated with changes in metabolic features. PNC max showed the highest number of significant associations with 172 significantly associated metabolic features. Additionally, Mummichog analysis yielded significant pathway predictions for all feature metrics, with a total of 48 significant metabolic pathway predictions DISCUSSION: We identified 48 metabolic pathways that showed significant correlation with air pollution exposure. However, the lack of consistency in pathway predictions suggests that acute pollution exposure may take more than 2 hours to have a measurable effect on the salivary metabolome. Our research suggests that while high-resolution metabolomics shows potential for reliably identifying biomarkers of pollutant-related stress, there is still much room for further development in this field

A comparison of X-band polarization parameters with in-situ microphysical measurements in the comma head of two winter cyclones

Since the advent of dual-polarization radar, methods of classifying hydrometeors by type from measured polarization variables have been developed. However, the deterministic approach of existing hydrometeor classification algorithms of assigning only one dominant habit to each volume does not properly consider the distribution of habits present in that volume. During the Profiling of Winter Storms (PLOWS) field campaign the NSF/NCAR C-130 aircraft, equipped with in-situ microphysical probes, made multiple passes through the comma head of two cyclones as the Mobile Alabama X-band (MAX) dual-polarization radar performed range-height indicator scans in the same plane as the C-130 flight track. On 14-15 February and 21-22 February 2010, 579 and 202 coincident data points, respectively, were identified when the plane was within 10 s (~1 km) of a radar gate. Using the axis ratio (α), sphericity (β), maximum dimension D, and projected area A of the in-situ imaged crystals, the habit of each particle was identified. For all particles that occurred for times within different binned intervals of radar reflectivity (ZHH) and of differential reflectivity (ZDR), the reflectivity-weighted contribution of each habit, and the frequency distributions of α and β were determined. Habits with less circular shapes (bullet rosettes and aggregates) had greater contributions to the reflectivity compared to other habits when ZHH > 7 dBZ and ZDR > 2 dB. The presence of bullet rosettes and aggregates for similar ZHH and ZDR supports previous studies that bullet rosettes are the favored crystal species for aggregate formation. While irregular particles made up 40% of the observed shapes, only 55% of the ZHH-ZDR bins had irregular particles contribute over 40% of the reflectivity. Additionally, over 88% of the bins did not have a single habit contribute over 75% to the reflectivity. These findings show the general lack of dominance of a given habit for a particular ZHH and ZDR, and suggest that determining the probability of specific habits in radar volumes may be more suitable than the deterministic methods currently used

The University of Washington Ice-Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning

Mixed-phase Southern Ocean clouds are challenging to simulate, and their representation in climate models is an important control on climate sensitivity. In particular, the amount of supercooled water and frozen mass that they contain in the present climate is a predictor of their planetary feedback in a warming climate. The recent Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) vastly increased the amount of in situ data available from mixed-phase Southern Ocean clouds useful for model evaluation. Bulk measurements distinguishing liquid and ice water content are not available from SOCRATES, so single-particle phase classifications from the Two-Dimensional Stereo (2D-S) probe are invaluable for quantifying mixed-phase cloud properties. Motivated by the presence of large biases in existing phase discrimination algorithms, we develop a novel technique for single-particle phase classification of binary 2D-S images using a random forest algorithm, which we refer to as the University of Washington Ice-Liquid Discriminator (UWILD). UWILD uses 14 parameters computed from binary image data, as well as particle inter-arrival time, to predict phase. We use liquid-only and ice-dominated time periods within the SOCRATES dataset as training and testing data. This novel approach to model training avoids major pitfalls associated with using manually labeled data, including reduced model generalizability and high labor costs. We find that UWILD is well calibrated and has an overall accuracy of 95 % compared to 72 % and 79 % for two existing phase classification algorithms that we compare it with. UWILD improves classifications of small ice crystals and large liquid drops in particular and has more flexibility than the other algorithms to identify both liquid-dominated and ice-dominated regions within the SOCRATES dataset. UWILD misclassifies a small percentage of large liquid drops as ice. Such misclassified particles are typically associated with model confidence below 75 % and can easily be filtered out of the dataset. UWILD phase classifications show that particles with area-equivalent diameter (Deq) \u3c 0.17 mm are mostly liquid at all temperatures sampled, down to -40 °. Larger particles (Deq\u3e0.17 mm) are predominantly frozen at all temperatures below 0 °. Between 0 and 5 °, there are roughly equal numbers of frozen and liquid mid-sized particles (0.170.33 mm) are mostly frozen. We also use UWILD\u27s phase classifications to estimate sub-1 Hz phase heterogeneity, and we show examples of meter-scale cloud phase heterogeneity in the SOCRATES dataset

Chronic Liver Disease in Humans Causes Expansion and Differentiation of Liver Lymphatic Endothelial Cells

Liver lymphatic vessels support liver function by draining interstitial fluid, cholesterol, fat, and immune cells for surveillance in the liver draining lymph node. Chronic liver disease is associated with increased inflammation and immune cell infiltrate. However, it is currently unknown if or how lymphatic vessels respond to increased inflammation and immune cell infiltrate in the liver during chronic disease. Here we demonstrate that lymphatic vessel abundance increases in patients with chronic liver disease and is associated with areas of fibrosis and immune cell infiltration. Using single-cell mRNA sequencing and multi-spectral immunofluorescence analysis we identified liver lymphatic endothelial cells and found that chronic liver disease results in lymphatic endothelial cells (LECs) that are in active cell cycle with increased expression of CCL21. Additionally, we found that LECs from patients with NASH adopt a transcriptional program associated with increased IL13 signaling. Moreover, we found that oxidized low density lipoprotein, associated with NASH pathogenesis, induced the transcription and protein production of IL13 in LECs both in vitro and in a mouse model. Finally, we show that oxidized low density lipoprotein reduced the transcription of PROX1 and decreased lymphatic stability. Together these data indicate that LECs are active participants in the liver, expanding in an attempt to maintain tissue homeostasis. However, when inflammatory signals, such as oxidized low density lipoprotein are increased, as in NASH, lymphatic function declines and liver homeostasis is impeded

Processing of Ice Cloud In-Situ Data Collected by Bulk Water, Scattering, and Imaging Probes: Fundamentals, Uncertainties and Efforts towards Consistency

In-situ observations of cloud properties made by airborne probes play a critical role in ice cloud research through their role in process studies, parameterization development, and evaluation of simulations and remote sensing retrievals. To determine how cloud properties vary with environmental conditions, in-situ data collected during different field projects processed by different groups must be used. However, due to the diverse algorithms and codes that are used to process measurements, it can be challenging to compare the results. Therefore it is vital to understand both the limitations of specific probes and uncertainties introduced by processing algorithms. Since there is currently no universally accepted framework regarding how in-situ measurements should be processed, there is a need for a general reference that describes the most commonly applied algorithms along with their strengths and weaknesses. Methods used to process data from bulk water probes, single particle light scattering spectrometers and cloud imaging probes are reviewed herein, with emphasis on measurements of the ice phase. Particular attention is paid to how uncertainties, caveats and assumptions in processing algorithms affect derived products since there is currently no consensus on the optimal way of analyzing data. Recommendations for improving the analysis and interpretation of in-situ data include the following: establishment of a common reference library of individual processing algorithms; better documentation of assumptions used in these algorithms; development and maintenance of sustainable community software for processing in-situ observations; and more studies that compare different algorithms with the same benchmark data sets

Analysis of the field artillery battalion organization using a Markov chain.

This thesis develops a model for comparing Marine Corps field artillery battalion organizations. It specifically examines the 3X8 and 4X6 direct support battalions. The status of the battalions are represented as continuous time, finite state, semi-Markov chains. The primary measure of effectiveness (MOE) for comparing the two structures is the long-run expectation of the number of guns in position. A set of APL programs manipulates the transition probability matrices and mean sojourn times. It then returns the long-run equilibrium probabilities and mean recurrence times for the states. Sensitivity analysis is conducted to explore the effects of changes in the transition probabilities and sojourn times.http://archive.org/details/analysisoffielda00finlMajor, United States Marine CorpsApproved for public release; distribution is unlimited

joefinlon/UIOPS: Support for Hawkeye-2DS

<ul> <li>Supports binary, particle, and PSD processing of the SPEC Hawkeye 2DS (tested on IMPACTS)</li> <li>Minor code enhancements with rare data format issues</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/joefinlon/UIOPS/compare/v3.3.6...v3.4</p&gt

Retrieval of snowflake microphysical properties from multifrequency radar observations

We have developed an algorithm that retrieves the size, number concentration and density of falling snow from multifrequency radar observations. This work builds on previous studies that have indicated that three-frequency radars can provide information on snow density, potentially improving the accuracy of snow parameter estimates. The algorithm is based on a Bayesian framework, using lookup tables mapping the measurement space to the state space, which allows fast and robust retrieval. In the forward model, we calculate the radar reflectivities using recently published snow scattering databases. We demonstrate the algorithm using multifrequency airborne radar observations from the OLYMPEX-RADEX field campaign, comparing the retrieval results to hydrometeor identification using ground-based polarimetric radar and also to collocated in situ observations made using another aircraft. Using these data, we examine how the availability of multiple frequencies affects the retrieval accuracy, and we test the sensitivity of the algorithm to the prior assumptions. The results suggest that multifrequency radars are substantially better than single-frequency radars at retrieving snow microphysical properties. Meanwhile, triple-frequency radars can retrieve wider ranges of snow density than dual-frequency radars and better locate regions of highdensity snow such as graupel, although these benefits are relatively modest compared to the difference in retrieval performance between dual- and single-frequency radars. We also examine the sensitivity of the retrieval results to the fixed a priori assumptions in the algorithm, showing that the multi-frequency method can reliably retrieve snowflake size, while the retrieved number concentration and density are affected significantly by the assumptions.Peer reviewe