dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Miller et al. , 2018). In this study, we implement a type of model selection known as the Bayesian information criterion (BIC)  (Schwarz et al. , 1978) , and various forms of the BIC have been implemented in numerous recent atmospheric inverse modeling studies (e. g. ,  Gourdji et al. , 2012; Miller et al. , 2013 Miller et al. , , 2018;; Fang and Michalak, 2015; Miller and Michalak, 2020). Using the BIC, we score different combinations of environmental driver datasets that could be included in X based on how well each combination helps reproduce either the real or synthetic OCO-2 observations (z, Eq. 1). We specifically use an implementation of the BIC from  Miller et al. (2018)  and  Miller and Michalak (2020)  that is designed to be computationally efficient for very large satellite datasets.
The BIC scores in this implementation are calculated using the following equation: BIC = L + p ln n * , ( 2 ) where L is the log likelihood of a particular combination of environmental driver datasets (i. e. , columns of X), p is the number of environmental driver datasets in a particular combination, and n * is the effective number of independent observations. This last variable accounts for the fact that not all atmospheric observations are independent, and the modeldata residuals can exhibit spatially and temporally correlated errors  (Miller et al. , 2018). For all simulations here, we use an estimate of n * for the v9 OCO-2 observations from  Miller and Michalak (2020). The first component of Eq. 2 (L) rewards combinations that are a better fit to the OCO-2 obhttps://doi. org/10. 5194/acp-21-6663-2021 Atmos.
Chem. Phys. , 21, 6663-6680, 2021 servations (z), whereas the second component of Eq. (  2 ) (p ln n * ) penalizes models with a greater number of columns to prevent overfitting. The best combination of environmental drivers for X is the combination that receives the lowest BIC score. Miller et al. (2018)  describes this implementation of the BIC in greater detail, including the specific setup and equations. Note that we run model selection for the OCO-2 data and rerun model selection for each set of synthetic OCO-2 datasets generated using each TBM. As a result, we sometimes select different environmental driver datasets for the analysis using different TBMs. This setup parallels that of  Huntzinger et al. (2011)  and  Fang and Michalak (2015). Furthermore, we use the same set of environmental driver datasets in each year of the study period (e.
g. , 2015-2018), a setup that parallels existing GIM studies that use multiple years of atmospheric observations (e. g. ,  Shiga et al. , 2018). We estimate different regression coefficients (β) for each year of the study period, but the actual environmental driver datasets included in the regression do not change from one year to the next. An environmental driver dataset is either selected to be included for all years in a specific biome (based on the BIC scores) or it is not used in any year of the analysis.
Statistical model for estimating the coefficients (β) Once we have chosen a set of environmental driver datasets using model selection, we estimate the coefficients (β) that relate the real or synthetic OCO-2 observations to these environmental datasets (e. g. ,  Gourdji et al. , 2012; Fang and Michalak, 2015) : β = (h(X) T -1 h(X)) -1 h(X) T -1 z, (3) where (n×n) is a covariance matrix that describes modeldata residuals (discussed at the end of this section). Furthermore, the uncertainties in these estimated coefficients can also be estimated using a linear equation (e. g. ,  Gourdji et al. , 2008; Fang and Michalak, 2015) : V β = (h(X) T -1 h(X)) -1 , (4) where V β is a p × p covariance matrix. We test out two different formulations for the covariance matrix to evaluate the sensitivity of the results to the assumptions made about the covariance matrix parameters.
In one set of simulations, we model as a diagonal matrix. The diagonal values characterize model-data errors ( ), estimated for the version 9 retrievals from the recent OCO-2 model inter-comparison project (e. g. ,  Crowell et al. , 2019). The values have an average standard deviation of 0. 98 ppm and range from 0. 29 to 4. 8 ppm. In a second set of simulations, we use a more complex and more complete formulation of : = h(h(Q) T ) + R (e. g. ,  Fang and Michalak, 2015) , where R (n × n) characterizes the model-data errors (described above), and Q (m × m) is a covariance matrix that describes ζ (the patterns in the fluxes that cannot be described by the environmental driver datasets). This formulation is more complete because it fully accounts for the residuals between z and Xβ. However, it is extremely computationally intensive to estimate the coefficients (β) using this complex formulation of.
Statistical model for estimating CO 2 fluxes using OCO-2 observations To complement the analysis described above, we take an additional step for the real OCO-2 observations of estimating ζ , patterns in the fluxes that cannot be described by the environmental driver datasets, also known as the stochastic component of the fluxes (Eq. 1). This step thereby creates a complete estimate of CO 2 fluxes using OCO-2 observations. This additional step accomplishes two goals. First, the fluxes in ζ can reveal flux anomalies or patterns that are too complex to quantify using a linear combination of environmental variables and/or can indicate the strengths and shortfalls of the regression. Second, by estimating all components of the CO 2 fluxes (Xβ and ζ ), we can better evaluate our inferences using OCO-2 against independent, ground-based observations of CO 2.
This column includes anthropogenic emissions from the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC)  (Oda et al. , 2018) , ocean fluxes from NASA Estimating the Circulation and Climate of the Ocean (ECCO) Darwin  (Carroll et al. , 2020) , and biomass burning fluxes from the Global Fire Emissions Database (GFED)  (Randerson et al. , 2018). We estimate a single coefficient or scaling factor (β) for this column. These fluxes are input into the regression at a 4 • latitude by 5 • longitude spatial resolution to match that of GEOS-Chem. The Supplement Sect. S2 contains greater discussion of these CO 2 sources.
Results of model selection The model selection results highlight the strengths and limitations of using current OCO-2 observations to estimate relationships with environmental driver datasets. We use model selection based on the BIC to determine a set of environmental driver datasets to include in the analysis using OCO-2 observations and using the TBMs. We only select 10 environmental driver datasets when we run model selection on the OCO-2 observations -both when we use environmental driver datasets from the CRUJRA and MERRA-2 products. We are generally able to identify at least one or two key environmental relationships in each biome using total column CO 2 observations (shown on the x axis of Fig. 3 ). With that said, we are only able to quantify relationships with these few, salient environmental variables.
More detailed environmental relationships within each biome are difficult to discern. Note that we select a similar number of environmental driver datasets when using synthetic OCO-2 observations that are generated from each of the TBMs. We select anywhere between 8 and 13 environmental driver datasets (an average of 10 datasets) in the analysis using each of the TBMs. This result indicates consistency between the analysis using real OCO-2 observations and the analysis using synthetic OCO-2 observations generated using CO 2 fluxes from each of the 15 different TBMs. Overall, we have difficulty detecting the unique contributions of many environmental driver datasets to variability in the OCO-2 observations. This issue is highlighted by an examination of colinearity in the regression model. In a regression, we cannot estimate different coefficients (β) for two predictor variables (i.
55 can indicate the presence of colinearity (e. g. ,  Ratner, 2012). We find substantial colinearity in the regression analysis (Fig. 2 ). This colinearity likely plays an important role in the model selection results, in addition to errors in the OCO-2 observations and errors in the GEOS-Chem model; it represents an important but potentially overlooked chal-lenge in relating satellite-based CO 2 observations to patterns in environmental drivers. The environmental driver datasets are passed through the GEOS-Chem model (h()) and interpolated the locations of OCO-2 observations as part of the regression (h(X), Eqs. 1 and 3). In other words, these driver datasets are input into GEOS-Chem in place of a traditional CO 2 flux estimate. This step is necessary so that the environmental driver datasets can be directly compared against patterns in the OCO-2 observations.
The driver datasets (i. e. , columns of X) that we use in the regression are generally unique from one another (i. e. , have unique spatial and temporal patterns). However, the differences among many driver datasets disappear once those datasets have been passed through GEOS-Chem. Figure  2  displays the correlation coefficients (r) among environmental driver datasets from MERRA-2 for temperate forests, both before (Fig. 2a ) and after (Fig. 2b ) those driver datasets have been passed through GEOS-Chem and interpolated to the OCO-2 observations. The correlation coefficients increase substantially from the former to the latter case. This colinearity is independent of errors in the OCO-2 observations and indicates a hard limit on the number of relationships with environmental driver datasets (i.
e. , coefficients) that we can quantify in the regression. In other words, model selection results are at least partially limited by the limited sensitivity of OCO-2 observations to variations in these environmental driver datasets -either due to atmospheric smoothing and/or due to limitations in the availability of OCO-2 observations in some regions of the globe. This limitation is in addition to the uncertainties due to errors in the OCO-2 observations and the GEOS-Chem model, which also have a critical impact on inferences about CO 2 fluxes using OCO-2 observations (e. g. ,  Chevallier et al. , 2007 Chevallier et al. , , 2014;; Miller et al. , 2018).
Environmental relationships inferred using observations from OCO-2 We are able to quantify the relationships between OCO-2 observations and several key environmental driver datasets. Figures  3  and 4  display the estimated coefficients from the regression analysis using observations from OCO-2. Across extratropical biomes, PAR is the most commonly selected variable. This result reflects the fact that light availability is a key factor that drives CO 2 flux variability in mid-to-high latitudes (e. g. ,  Fang and Michalak, 2015; Baldocchi et al. , 2017). As expected, the estimated coefficients for PAR are negative, indicating that an increase (or decrease) in PAR in the model is associated with a decrease (or increase) in NEE and an increase (or decrease) in carbon uptake. Note that in this study negative values for NEE refer to CO 2 uptake while positive values refer to net CO 2 release to the atmosphere.
By contrast, precipitation and scaled temperature are the most commonly selected environmental driver datasets across tropical biomes. The magnitude of the coefficients for each of these two variables is similar in most biomes, indi-cating that patterns in both have similarly important associations with patterns in CO 2 fluxes. Specifically, a negative coefficient assigned to scaled temperature indicates that an increase in air temperature is associated with increased carbon uptake when air temperatures are cool and reduced carbon uptake when air temperatures are hot; the scaled temperature function has the shape of an upside-down parabola, and temperature thus has a different association with CO 2 fluxes depending upon whether the air temperature is above or below the optimal temperature for photosynthesis (e.
g. , Fig. S1 ). Indeed, high temperatures in the tropics often exceed the optimal temperature for photosynthesis (e. g. ,  Baldocchi et al. , 2017) , which reduces carbon uptake (e. g. ,  Doughty and Goulden, 2008). Furthermore, negative coefficients for precipitation indicate that an increase in precipitation is associated with an increase in carbon uptake, which is in line with current knowledge that water availability facilitates photosynthesis across seasonal to annual temporal scales, especially in arid or semiarid regions (e. g. ,  Gatti et al. , 2014; Jung et al. , 2017). In addition to this regression analysis, we use a GIM to estimate the stochastic component of the fluxes (ζ , Eq. 1)patterns in the fluxes that are implied by the OCO-2 observations but do not match any existing environmental driver dataset.
To this end, Fig. 5  shows the mean contribution of each environmental driver variable and the stochastic component to the GIM across years 2015-2018 using MERRA-2 for the environmental driver datasets. The magnitude of the stochastic component in this plot is small relative to the contribution of different environmental variables and relative to the contribution of anthropogenic sources. Furthermore, the stochastic component contains very diffuse spatial patterns, and these very broad patterns do not imply any clear deficiency in the other components of the GIM. For example, the regression component of the GIM (X β) accounts for 89. 6 % of the variance in the estimated fluxes, and the stochastic component conversely accounts for only 10. 4 % of the flux variance. Furthermore, the regression component, when passed through the GEOS-Chem model, matches OCO-2 observations nearly as well as the full posterior flux estimate (Figs.
S2  and S13 ). This result shows that a limited number of environmental driver datasets can adeptly reproduce broad patterns in CO 2 fluxes across continental and global spatial scales but reinforces the conclusion that current OCO-2 observations are not sufficient to disentangle more complex environmental relationships. In all of the simulations using OCO-2 observations, we estimate a scaling factor (β) for anthropogenic, biomass burning, and ocean fluxes of near one, indicating that these source types have a magnitude that is broadly consistent with atmospheric observations. Specifically, the estimated scaling factor estimated ranges from 0. 97 to 1. 05, depending upon the year and simulation. Note, however, that we estimate a single scaling factor for all of these source types combined and are unable to confidently constrain separate scaling factors for Figure  3.
The coefficients from the analysis using OCO-2 (red) are broadly within the range of the estimates in TBMs (blue). We further calculate the coefficient of variation (CV) of coefficients for each environmental driver within the TBMs (b), and we find that the largest CVs are from the coefficients for precipitation. each source, a topic discussed in greater detail in the Supplement Sect. S2. Note that the inferences described here are also broadly consistent with independent, ground-based atmospheric observations. We specifically model atmospheric CO 2 using fluxes estimated from the GIM and compare against regular aircraft observations, campaign data from the Atmospheric Tomography Mission (ATom;  Wofsy et al. , 2018) , and observations from the Total Carbon Column Observing Network (TCCON;  Wunch et al.
Furthermore, the coefficients shown for the TBMs in Figs. 3  and 4  are calculated using environmental driver datasets from CRUJRA. Figure  S14  displays the results for the TBMs using environmental driver data from MERRA-2, and the results look similar to those using CRUJRA. We specifically find large differences between the analysis using OCO-2 and the TBMs for relationships with precipitation. The relationships between precipitation are arguably more uncertain within the TBMs than the relationships with other environmental variables (Fig. 3a ) and are more uncertain in tropical biomes than temperate ones. This statement is particularly apparent when we examine the coefficient of variation for each relationship (Fig. 3b ). The coefficient of variation is a measure of the uncertainty relative to the magnitude of the mean, and Fig.
OCO-2 observations are sparse across high latitudes both due to the lack of sunlight in winter and due to frequent cloud cover in many highlatitude regions. We also only select PAR in boreal forests in simulations using 2 of the 15 TBMs. This result also reflects the limited availability of OCO-2 observations over highlatitude regions; for the analysis here, we create synthetic OCO-2 observations using each TBM and apply model selection to each of these synthetic OCO-2 datasets. Hence, the sparsity of OCO-2 observations not only affects the model selection results using real OCO-2 observations but also affects the analysis shown in Figs. 3 and 4 using the TBMs. The fact that PAR is selected for so few TBMs is not a reflec-tion on the important role of PAR across the boreal forest in many TBMs.
Conclusions In this study, we use 4 years of observations from OCO-2 and a top-down statistical framework to evaluate the relationships between patterns in atmospheric CO 2 observations and patterns in environmental driver datasets that are commonly used in modeling the global carbon cycle. We are able to quantify a limited number of these environmental relationships using observations from OCO-2. In spite of these limitations, we are still able to identify relationships with a small number of salient environmental driver datasets, and stateof-the-art TBMs do not show consensus on some of these key relationships, indicating an opportunity to inform these relationships using atmospheric CO 2 observations. We subsequently compare inferences using OCO-2 against inferences from 15 state-of-the-art TBMs that have model outputs available for the same set of years.
Acknowledgements. We thank  Kim Mueller  and  Anna Michalak  for their feedback on the research;  David Baker  for his help with the XCO 2 data;  Colm Sweeny  and  Kathryn McKain  for their help with aircraft datasets from the  NOAA/ESRL Global Greenhouse Gas Reference  Network;  John Miller ,  Luciana Gatti ,  Wouter Peters , and  Manuel Gloor  for their help with the aircraft data from the  INPE ObsPack data product ;  Steven Wofsy ,  Kathryn McKain ,  Colm Sweeny , and  Róisín Commane  for their help with ATom aircraft datasets; and  Debra Wunch  for her help with the TCCON datasets. Daven Henze 's work is supported by  NOAA  grant no. NA16OAR4310113. Daniel Goll 's work is supported by the  ANR CLAND Convergence Institute. The data analysis and inverse modeling were performed on the  NASA Pleiades Supercomputer.
controls (p = 0. 005), with a positive correlation to respective plasma concentrations (p = 0. 032) and were able to detect chronic HF with 63% sensitivity 63% and 92% specificity. Endothelin salivary levels have also been shown to reflect symptom severity by NYHA class  [39]. TNF-α has been studied extensively in periodontal inflammations and oral cancers and has been identified in detectable concentrations in human saliva with values correlating well to plasma values  [52, 53] , indicating that salivary TNF-α could potentially serve as a HF biomarker, providing that supporting data emerged  [54] [55] [56] [57] [58]. Mass spectrometry was used to analyze the saliva proteome in HF patients (n = 75) and healthy controls (n = 36), as reported by Zhang et al. Of the 728 proteins detected, only the protein S100-A7 (S10A7) was significantly different between saliva from NYHA III/IV HF patients compared to healthy controls.
Most of the publications on saliva biomarkers in HF have been conducted during the last decade (11 out of 15 studies). The biomarkers have been tested in HF patients and compared to controls and/or different NYHA class patients. The age of participating patients ranged from 39 to 88 years old. Out of the 15 publications, seven included patients with HFrEF only, five with both HFpEF and HFrEF patients (without proving data separately for each phenotype), while three studies did not provide that information. Eight publications included patients with functional class NYHA I, 10 publications included NYHA II, 11 included NYHA III and five included NYHA IV patients. Most patients belonged to NYHA III group, while in four studies the functional class of HF patients was not mentioned. In most of the studies included in this review, biomarker levels between HF patients and controls were compared, and differences were found to be statistically significant, with one exception.
We have performed a comprehensive comparison between three commercially available chromatography columns; Accucore Amide HILIC (Thermo-Fisher Scientific, Waltham, MA, USA), LUNA-NH2 (Phenomenex, Torrance, CA, USA) or SeQuant ZIC-pHILIC (Millipore-Sigma, St. Louis, MI, USA), and optimized extraction method for mammalian cells, tissues, and physiological fluids. Our protocol and data provide guidance for optimization of the LC-MS method to allow for tailored metabolite profiling of redox metabolites in parallel to other metabolites of interest.
Discussion Here we report the optimization of an LC-MS detection method of redox metabolites suitable for parallel assessment of global metabolism from mammalian cells, tissues, and body fluids. This method will be of interest for researchers from a wide variety of biological fields. We describe optimized detection of chemical standards of the redox metabolites, as well as optimized detection of the endogenous metabolites in mammalian cells and in mouse tissues-liver, kidney, CSF, and plasma. Further, we measured the levels of redox metabolites in the cancer cell line K562 following pharmacological perturbation of the cellular redox state and demonstrate our ability to assess relevant metabolites in cells exposed to mild oxidative stress. Our data suggest that optimal detection of several redox metabolites can be achieved by an extraction solution containing a mild antioxidant (Table  3 ) combined with Ellman's reagent for the derivatization of GSH.
The pH of this solution is in the acidic range and compares well with previously reported optimized conditions for the detection of NADH and NADPH  [28]. Importantly, this method does not compromise detection of the majority of polar metabolites. Users may still wish to use the other buffers presented here depending on their interest in certain metabolites and according to the data presented here (Tables  1, S3  and S4 ). The comparison we present here between three different commercially available HILIC columns provides a comprehensive dataset that can help users decide on the optimal column for their needs (Figure  S3a  and Table  S2 ). Although we achieved the broadest coverage using ZIC-pHILIC chromatography, we recognize that our comparison is not exhaustive and that further conditions or set of small molecules can be found that showcase the superiority of one column over another.
These applications require precise measurements of NH3 line spectroscopic parameters performed with appropriate experimental setup and the best line models. Furthermore, the NH3 absorption in Jupiter's atmosphere, especially, has been shown to be highly sensitive to the choice of spectral absorption line shape model  [9]. Several experimental and theoretical studies of the line-widths and line shifts in infrared fundamental bands of NH3 perturbed by several foreign gases were published. However spectroscopy of this molecule is still requested, especially for the overtones and combination bands with the aims to improve spectroscopic databases  [10] [11] [12]  which are largely used for fundamental, planetary, and industrial applications. Among the previous studies, Fabian et al. measured O2, N2, and Air broadenings for 176 rovibrational lines of NH3 in the ν2 band using a high-resolution Fourier Transform Spectrometer (FTS)  [13].