Oxygenated organic functional groups and their sources in single and submicron organic particles in MILAGRO 2006 campaign

Fourier Transform Infrared (FTIR) and X-ray Fluorescence (XRF) were used to measure organic functional groups and elements of submicron particles collected during MILAGRO in March 2006 on three platforms: the Mexico City urban area (SIMAT), the high altitude site at 4010 m (Altzomoni), and the NCAR C130 aircraft. Scanning Transmission X-ray Microscopy (STXM) and Near-Edge X-ray Absorption Fine Structure (NEXAFS) were applied to single particle organic functional group abundance analysis of particles simultaneously collected at SIMAT and C130. Correlations of elemental concentrations showed different groups of source-related elements at SIMAT, Altzomoni, and C130, suggesting different processes affecting the air masses sampled at the three platforms. Cluster analysis resulted in seven distinct clusters of FTIR spectra, with the last three clusters consisting of spectra collected almost exclusively on the C130 platform, reflecting the variety of sources contributing to C130 samples. Positive Matrix Factorization (PMF) of STXM-NEXAFS spectra identified three main factors representing soot, secondary, and biomass burning type spectra. PMF of FTIR spectra resulted in two fossil fuel combustion factors and one biomass burning factor, the former representative of source regions to the northeast and southwest of SIMAT. Alkane, carboxylic acid, amine, and alcohol functional groups were mainly associated with combustion related sources, while non-acid carbonyl groups were likely from biomass burning events. The majority of OM and O/C was attributed to combustion sources, although no distinction between direct emissions and atmospherically processed OM could be identified

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid-infrared spectroscopy

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade-offs, but the cost of model parsimony and the potential for underfitting on achievable prediction errors are difficult to anticipate. We propose a metric that penalizes growing model variance against decreasing bias as additional latent variables are added. The magnitude of the penalty is scaled by a user-defined parameter that is formulated to provide a constraint on the fractional increase in root mean square error of cross-validation (RMSECV) when selecting a parsimonious model over the conventional minimum RMSECV solution. We evaluate this approach for quantification of four organic functional groups using 238 laboratory standards and 750 complex atmospheric organic aerosol mixtures with mid-infrared spectroscopy. Parametric variation of this penalty demonstrates that increase in prediction errors due to underfitting is bounded by the magnitude of the penalty for samples similar to laboratory standards used for model training and validation. Imposing an ensemble of penalties corresponding to a 0-30% allowable increase in RMSECV through sum of ranking differences leads to the selection of a model that increases the actual RMSECV up to 20% for laboratory standards but achieves an 85% reduction in the mean error in predicted concentrations for environmental mixtures. Partial least squares models developed with laboratory mixtures can provide useful predictions in complex environmental samples, but may benefit from protection against overfitting. (C) 2015 The Authors. Journal of Chemometrics published by John Wiley & Sons Ltd

Predicting ambient aerosol thermal-optical reflectance (TOR) measurements from infrared spectra: organic carbon

Organic carbon (OC) can constitute 50% or more of the mass of atmospheric particulate matter. Typically, organic carbon is measured from a quartz fiber filter that has been exposed to a volume of ambient air and analyzed using thermal methods such as thermal-optical reflectance (TOR). Here, methods are presented that show the feasibility of using Fourier transform infrared (FT-IR) absorbance spectra from polytetrafluoroethylene (PTFE or Teflon) filters to accurately predict TOR OC. This work marks an initial step in proposing a method that can reduce the operating costs of large air quality monitoring networks with an inexpensive, non-destructive analysis technique using routinely collected PTFE filter samples which, in addition to OC concentrations, can concurrently provide information regarding the composition of organic aerosol. This feasibility study suggests that the minimum detection limit and errors (or uncertainty) of FT-IR predictions are on par with TOR OC such that evaluation of long-term trends and epidemiological studies would not be significantly impacted. To develop and test the method, FT-IR absorbance spectra are obtained from 794 samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011. Partial least-squares regression is used to calibrate sample FT-IR absorbance spectra to TOR OC. The FTIR spectra are divided into calibration and test sets by sampling site and date. The calibration produces precise and accurate TOR OC predictions of the test set samples by FT-IR as indicated by high coefficient of variation (R-2; 0.96), low bias (0.02 mu g m(-3), the nominal IMPROVE sample volume is 32.8 m(3)), low error (0.08 mu g m(-3)) and low normalized error (11%). These performance metrics can be achieved with various degrees of spectral pretreatment (e.g., including or excluding substrate contributions to the absorbances) and are comparable in precision to collocated TOR measurements. FT-IR spectra are also divided into calibration and test sets by OC mass and by OM / OC ratio, which reflects the organic composition of the particulate matter and is obtained from organic functional group composition; these divisions also leads to precise and accurate OC predictions. Low OC concentrations have higher bias and normalized error due to TOR analytical errors and artifact-correction errors, not due to the range of OC mass of the samples in the calibration set. However, samples with low OC mass can be used to predict samples with high OC mass, indicating that the calibration is linear. Using samples in the calibration set that have different OM / OC or ammonium / OC distributions than the test set leads to only a modest increase in bias and normalized error in the predicted samples. We conclude that FT-IR analysis with partial least-squares regression is a robust method for accurately predicting TOR OC in IMPROVE network samples - providing complementary information to the organic functional group composition and organic aerosol mass estimated previously from the same set of sample spectra (Ruthenburg et al., 2014)

An open platform for Aerosol InfraRed Spectroscopy analysis – AIRSpec

AIRSpec is a platform consisting of several chemometric packages developed for analysis of Fourier transform infrared (FTIR) spectra of atmospheric aerosols. The packages are accessible through a browser-based interface, which also generates the necessary input files based on user interactions for provenance management and subsequent use with a command-line interface. The current implementation includes the task of baseline correction, organic functional group (FG) analysis, and multivariate calibration for any analyte with absorption in the mid-infrared. The baseline correction package uses smoothing splines to correct the drift of the baseline of ambient aerosol spectra given the variability in both environmental mixture composition and substrates. The FG analysis is performed by fitting individual Gaussian line shapes for alcohol (aCOH), carboxylic acid (COOH), alkane (aCH), carbonyl (CO), primary amine (aNH2), and ammonium (ammNH) for each spectrum. The multivariate calibration model uses the spectra to estimate the concentration of relevant target variables (e.g., organic or elemental carbon) measured with different reference instruments. In each of these analyses, AIRSpec receives spectra and user choices on parameters for model computation; input files with parameters that can later be used with a command-line interface for batch computation are returned together with diagnostic figures and tables in text format. AIRSpec is built using the open-source software consisting of R and Shiny and is released under the GNU Public License v3. Users can download, modify, and extend the package, or access its functionality through the web application (http://airspec.epfl.ch, last access: 3 April 2019) hosted at the École polytechnique fédérale de Lausanne (EPFL). AIRSpec provides a unified framework by which different chemometric techniques can be shared and accessed, and its underlying suite of packages provides the basic functionality for extending the platform with new types of analyses. For example, basic functionality includes operations for populating and accessing spectra residing in in-memory arrays or relational databases, input and output of spectra and results of computation, and user interface development. Moreover, AIRSpec facilitates the exploratory work, can be used by FTIR spectra acquired with different methods, and can be extended easily with new chemometric packages when they become available. Therefore AIRSpec provides a framework for centralizing and disseminating such algorithms. This paper describes the modular architecture and provides examples of the implemented packages using the spectra of aerosol samples collected on PM2.5 polytetrafluoroethylene (Teflon) filters.</p

Analysis of functional groups in atmospheric aerosols by infrared spectroscopy: systematic intercomparison of calibration methods for US measurement network samples

Peak fitting (PF) and partial least squares (PLS) regression have been independently developed for estimation of functional groups (FGs) from Fourier transform infrared (FTIR) spectra of ambient aerosol collected on Teflon filters. PF is a model that quantifies the functional group composition of the ambient samples by fitting individual Gaussian line shapes to the aerosol spectra. PLS is a data-driven, statistical model calibrated to laboratory standards of relevant compounds and then extrapolated to ambient spectra. In this work, we compare the FG quantification using the most widely used implementations of PF and PLS, including their model parameters, and also perform a comparison when the underlying laboratory standards and spectral processing are harmonized. We evaluate the quantification of organic FGs (alcohol COH, carboxylic COOH, alkane CH, carbonyl CO) and ammonium, using external measurements (organic carbon (OC) measured by thermal optical reflectance (TOR) and ammonium by balance of sulfate and nitrate measured by ion chromatography). We evaluate our predictions using 794 samples collected in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network (USA) in 2011 and 238 laboratory standards from Ruthenburg et al. (2014) (available at https://doi.org/10.1016/j.atmosenv.2013.12.034). Each model shows different biases. Overall, estimates of OC by FTIR show high correlation with TOR OC. However, PLS applied to unprocessed (raw spectra) appears to underpredict oxygenated functional groups in rural samples, while other models appear to underestimate aliphatic CH bonds and OC in urban samples. It is possible to adjust model parameters (absorption coefficients for PF and number of latent variables for PLS) within limits consistent with calibration data to reduce these biases, but this analysis reveals that further progress in parameter selection is required. In addition, we find that the influence of scattering and anomalous transmittance of infrared in coarse particle samples can lead to predictions of OC by FTIR which are inconsistent with TOR OC. We also find through several means that most of the quantified carbonyl is likely associated with carboxylic groups rather than ketones or esters. In evaluating state-of-the-art methods for FG abundance by FTIR, we suggest directions for future research.</p

Tolerance to Self: Which Cells Kill

Immune self tolerance involves the deletion in the thymus of developing T cells that have the ability to recognize self-antigens, but by which cells? New evidence argues that cortical epithelial cells can induce deletion of self-reactive T cells

An automated baseline correction protocol for infrared spectra of atmospheric aerosols collected on polytetrafluoroethylene (Teflon) filters

A growing body of research on statistical applications for characterization of atmospheric aerosol Fourier transform infrared (FT-IR) samples collected on polytetrafluoroethylene (PTFE) filters (e.g., Russell et al., 2011; Ruthenburg et al., 2014) and a rising interest in analyzing FT-IR samples collected by air quality monitoring networks call for an automated PTFE baseline correction solution. The existing polynomial technique (Takahama et al., 2013) is not scalable to a project with a large number of aerosol samples because it contains many parameters and requires expert intervention. Therefore, the question of how to develop an automated method for baseline correcting hundreds to thousands of ambient aerosol spectra given the variability in both environmental mixture composition and PTFE baselines remains. This study approaches the question by detailing the statistical protocol, which allows for the precise definition of analyte and background subregions, applies nonparametric smoothing splines to reproduce sample-specific PTFE variations, and integrates performance metrics from atmospheric aerosol and blank samples alike in the smoothing parameter selection. Referencing 794 atmospheric aerosol samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011, we start by identifying key FT-IR signal characteristics, such as non-negative absorbance or analyte segment transformation, to capture sample-specific transitions between background and analyte. While referring to qualitative properties of PTFE background, the goal of smoothing splines interpolation is to learn the baseline structure in the background region to predict the baseline structure in the analyte region. We then validate the model by comparing smoothing splines baseline-corrected spectra with uncorrected and polynomial baseline (PB)-corrected equivalents via three statistical applications: (1) clustering analysis, (2) functional group quantification, and (3) thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) predictions. The discrepancy rate for a four-cluster solution is 10 %. For all functional groups but carboxylic COH the discrepancy is = 0.94 %, bias <= 0.01 mu g m(-3), and error <= 0.04 mu g m(-3)) are on a par with those obtained from uncorrected and PB-corrected spectra. The proposed protocol leads to visually and analytically similar estimates as those generated by the polynomial method. More importantly, the automated solution allows us and future users to evaluate its analytical reproducibility while minimizing reducible user bias. We anticipate the protocol will enable FT-IR researchers and data analysts to quickly and reliably analyze a large amount of data and connect them to a variety of available statistical learning methods to be applied to analyte absorbances isolated in atmospheric aerosol samples

Immunology and Inflammation

In the thymus, the thymic epithelium provides a microenvironment essential for the development of functionally competent and self-tolerant T cells. Previous findings showed that modulation of Wnt/β-catenin signaling in mouse thymic epithelial cells (TECs) disrupts embryonic thymus organogenesis. However, the role of β-catenin in TECs for postnatal T-cell development remains to be elucidated. Here, we analyzed gain-of-function (GOF) and loss-of-function (LOF) of β-catenin highly specific in mouse TECs. We found that GOF of β-catenin in TECs results in severe thymic dysplasia and T-cell deficiency beginning from the embryonic period. By contrast, LOF of β-catenin in TECs reduces the number of cortical TECs and thymocytes modestly and only postnatally. These results indicate that fine-tuning of β-catenin expression within a permissive range is required for TECs to generate an optimal microenvironment to support postnatal T-cell development