dataesr/scientific-paragraphs-categorization huggingface dataset:
The systemic Gal-9 levels correlated with baseline disease activity but remained unaffected during csDMARD and anti-TNFα treatments, despite decreasing disease activity. The neutralization of Gal-9 decreased the secretion of MCP-1 and IL-6 in arthritis synovial cultures, dominated by FLSs. Collectively, these observations support that Gal-9 is implicated in the RA pathogenetic pathway by regulating FLS activities within the rheumatoid synovial membrane. Targeting Gal-9 by neutralizing antibodies may emerge as a useful therapeutic adjunct in patients who do not respond adequately to current intensive synovitis-suppressive strategies. Institutional Review Board Statement: All human samples were obtained after informed written consent according to the Declaration of Helsinki and approved by the Local Ethics Committee (The Central Denmark Region committee on health research ethics, project number 20121329) and the Danish Data Protection Agency.
Acknowledgments: We owe a debt of gratitude to  Karin Skovgård Sørensen (Dept. of Biomedicine, Aarhus University ) for technical assistance concerning the ELISA data and the  FACS Core Facility  ( Aarhus University, Denmark ) for technical assistant regarding Flow cytometry. We greatly acknowledge the assistance of medical doctors and nurses at the  Department of Rheumatology, Aarhus University Hospital  for helping to collect the patient samples. We also acknowledge the generous grants from  Aarhus University Research Foundation  and the  Danish Rheumatism Association. Graphical abstract: Created with BioRender. com,License was granted to permit sublicensed for use in journal publications, agreement nr:  WX24VRNF8E.
Volatile organic compounds (VOCs)   are key precursors of particulate matter and tropospheric ozone. Although the terrestrial biosphere is by far the largest source of VOCs into the atmosphere, the emissions of biogenic VOCs remain poorly constrained at regional scale. In this work, we derive top-down biogenic emissions over Europe using weekly-averaged TROPOMI formaldehyde (HCHO) data from 2018 to 2021. The systematic bias of the TROPOMI HCHO columns is characterized and corrected for based on comparisons with FTIR data at seven European stations. The top-down fluxes of biogenic, pyrogenic, and anthropogenic VOC sources are optimized using an inversion framework based on the MAGRITTEv1. 1 chemistry transport model and its adjoint. The inversion leads to strongly increased isoprene emissions with respect to the MEGAN-MOHYCAN inventory over the model domain (from 8.
Additionally, HCHO is also relatively quickly destroyed through photolysis and reaction with OH, and has a lifetime of approximately 5 hours  (Stavrakou et al. , 2015). Transport processes are relatively unimportant for its measured distribution because of its short lifetime and of the rapid chemical processing leading to HCHO production (except at night, during winter, or in NO x -poor environments, see  Marais et al. (2012) ). Consequently, HCHO is an excellent tracer and its total columns can be directly linked to the emissions of reactive NMVOCs. Since the advent of satellite HCHO data products, many studies have been conducted on the derivation of top-down biogenic emissions in an inversion framework. This includes studies performed using satellite HCHO data from GOME (e. g. Palmer et al.
The inversion of VOC emissions is sensitive to constraints from satellite observations. Consequently, it is important to carefully validate the satellite measurements using ground-based and aircraft measurements. Several validation studies have shown that OMI and TROPOMI HCHO columns are too low over source regions with respect to validation datasets  (Zhu et al. , 2020; Vigouroux et al. , 2020; De Smedt et al. , 2021). This bias was not accounted for in previous HCHO emission inversion studies. The origin of the bias is unknown, although some known difficulties in the HCHO retrieval include aerosol effects, uncertainties in the treatment of clouds, spectral interferences with BrO and ozone, and a coarse albedo climatology  (Vigouroux et al. , 2020). The bias can be corrected for based on a linear regression of the ground-based and satellite vertical columns.
In this work, we derive a bias correction based on ground-based FTIR measurements at stations located in the European domain. We describe the different types of data (ground-based and satellite) used in this work in Sect. 2. Section 3 describes the setup of the inversion, as well as the chemistry transport model MAGRITTEv1. 1 around which the inversion framework is built. A detailed characterization of the biases of the TROPOMI HCHO data product is presented in Sect. 4 based on FTIR and MAX-DOAS data over Europe. Based on this analysis, a procedure for correcting the biases is proposed and used in the emission inversions. The inversion results are shown in Sect. 5 including a comparison of the top-down model with ground-based data. In Sect. 6, we provide an analysis of the emission uncertainties by means of sensitivity inversions.
HCHO Data In this section, we provide an overview of the measurement datasets used in this work. TROPOMI HCHO column data are used to constrain the VOC emissions in the model (Sect. 2. 1). We perform a careful validation of the satellite data using groundbased FTIR vertical columns from a range of stations across Europe (Sect. 2. 2). We further validate the top-down results by including European MAX-DOAS stations in our ground-based remote sensing dataset (Sect. 2. 3). Finally, in situ HCHO concentration data are also used to evaluate the model before and after inversion (Sect. 2. 4). The full list of ground-based stations used in this work is shown in Table  1  and Fig. 1.
To build a coherent dataset useful for satellite validation or model evaluation, the retrieval settings of HCHO have been harmonized within the FTIR community  (Vigouroux et al. , 2018). This dataset has been used for the validation of TROPOMI  (Vigouroux et al. , 2020, updated  in quarterly reports at https://mpc-vdaf. tropomi. eu, last access: 21 August 2023), GOME-2 (https://acsaf. org/valreps. php, last access: 21 August 2023) or more recently of OMPS  (Kwon et al. , 2023). Currently 28 stations provide HCHO column data, among which seven are in the European domain (Table  1 ; Fig. 1 ). The details of the harmonized retrieval settings are documented in  Vigouroux et al. (2018). In summary, the fitted spectral signatures lie in the 3. 6 µm region and belong to the ν 1 and ν 5 bands. The spectroscopic database is a compilation from G.
Toon, the atm16 line list (http://mark4sun. jpl. nasa. gov/toon/linelist/linelist. html, last access: 21 August 2023), which corresponds to HITRAN 2012  (Rothman et al. , 2013)  for HCHO and is optimized for each of the interfering species. The Tikhonov regularization  (Tikhonov, 1963)  is used for constraining/optimizing the retrievals of low-resolution vertical HCHO profiles. The degrees of freedom of signal (DOFS, trace of the averaging kernel matrix) amount to only 1 to 1. 4 for the stations used in this study, implying that only total column information can be retrieved. The averaging kernel shape shows that the sensitivity is highest in the free troposphere (although still present at the surface). The FTIR dataset is well characterized in terms of uncertainty budget, which is calculated following  Rodgers (2000).
, 2019) , which is based on a parameterization of the retrieval profile shape and a Monte-Carlo approach for the inversion, and MMF (Mexican MAX-DOAS Fit,  Friedrich et al. , 2019) , an optimal estimation-based algorithm using the radiative transfer code   VLIDORT v2. 7 (Spurr et al. , 2008)  as forward model. In this work, analogously to the official NO 2 product, only HCHO MMF data consistent wth MAPA HCHO are retained. Both inversion algorithms have been extensively tested and validated using synthetic  (Frieß et al. , 2019)  and real data  (Tirpitz et al. , 2021; Karagkiozidis et al. , 2022). Vigouroux et al. (2009)  quantified the uncertainties of individual MAX-DOAS measurements of HCHO VCDs. The systematic uncertainties mainly originate from the HCHO cross-section and amounts to 9% of the HCHO VCD.
In situ Additional data to evaluate our model is provided by ground-based in situ measurement stations. We use data from the EMEP network  (Tørseth et al. , 2012) , available at the EBAS database (https://ebas. nilu. no, last access: 21 August 2023). The list of stations with available HCHO concentration measurements is provided in Table  1. All in situ stations use high-performance liquid chromatography (HPLC) to derive HCHO concentrations at daily or three-daily intervals. For the majority of in situ stations in Table  1 , measurements were taken before the time span covered in this study (i. e. , before 2018). For those stations, we compute a climatological average of the measurements by averaging the data per month across the different years. However, caution is required when comparing a climatological average to a recent model, since HCHO concentrations are sensitive to meteorology and land-use changes.
5 • (latitude, longitude). This simulation provides the boundary Anthropogenic VOC emissions are obtained from the CAMS-GLOB-ANT inventory  (Granier et al. , 2019, version 4. 3) , biomass burning emissions from the Quick Fire Emissions Dataset (QFED) version 2. 4 (Darmenov and da Silva, 2015) combined with emission factors from  Andreae (2019) , and biogenic emissions of isoprene, monoterpenes, and methanol are obtained from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) coupled to the multi-layer canopy environment model  (MOHYCAN, Müller et al. , 2008; Guenther et al. , 2012; Stavrakou et al. , 2018). The anthropogenic VOC emissions display a slow declining trend, from 16. 8 Tg VOC in 2018 to 16. 6 Tg VOC in 2021. The annual biomass burning fluxes over the European domain amount to 1.
Validation of TROPOMI HCHO over Europe Previous studies have shown that satellite HCHO data are biased low for high HCHO columns and high for low HCHO columns compared to aircraft  (Zhu et al. , 2020)  and ground-based data  (Vigouroux et al. , 2020; De Smedt et al. , 2021). A careful validation of the satellite product is therefore required in order to prevent the propagation of these biases into the derived top-down emissions. The nature of these biases is unknown at this point. To rule out a possible geographical dependence of the bias, we focus on stations in the European domain to derive the bias correction. We use the FTIR station data described in Sect. 2. 2 and perform the validation using daily spatio-temporal collocation criteria, following the approach of  Vigouroux et al. (2020).
More specifically, we average TROPOMI scenes within 20 km of the station location. Additionally, we select groundbased observations acquired within three hours of the satellite overpass (i. e. from 10:30 to 16:30 LT) in order to minimize the effect of diurnal variations. These criteria provide an optimal balance between data averaging and collocation (see  Vigouroux et al. , 2020; De Smedt et al. , 2021). Days with no valid TROPOMI measurements or no valid ground-based measurement are excluded. For the validation, we only select ground-based and TROPOMI data from May to September, since we use data during this period as constraints for the inversion. Furthermore, we do not consider MAX-DOAS data for TROPOMI validation due to the difference in vertical sensitivities between TROPOMI and MAX-DOAS.
Finally, a correction to the total column is applied to account for topographical elevation variations of the TROPOMI footprint around the station. The smoothed columns can then be directly compared to the satellite columns. In the comparisons, we used weekly averages, since weekly-averaged TROPOMI HCHO columns are used as top-down constraints in the inversion. A comparison between TROPOMI HCHO columns and the smoothed FTIR columns is illustrated in the scatter plot of Fig. 3. The linear regression is performed using the Passing-Bablok estimator  (Passing and Bablok, 1983, 1984; Bablok et al. , 1988)  which is commonly used in the comparison between measurements of different methods by virtue of its symmetrical properties and its robustness in the presence of outliers. The comparison of the collocated data shows that TROPOMI HCHO columns are biased with respect to FTIR measurements.
The FTIR data suggest that the high TROPOMI HCHO columns are too low and the low TROPOMI columns are too high with respect to the FTIR measurements. This agrees well with previous FTIR-based bias corrections over the global domain  (Vigouroux et al. , 2020). We note that for low TROPOMI HCHO columns, i. e. below 3 × 10 15 molec. cm -2 , the regression line falls below a large majority of FTIR data, indicating that it is not suitable in that range. The statistical uncertainty of the linear fit calculated and shown inset in Fig. 3  is clearly underestimated, given the substantial column differences between the different stations (see also monthly means for individual stations in Fig. S1  of the supplement). We note that stations in northern Europe (Kiruna and Sodankylä) show no significant bias with respect to TROPOMI, while stations at mid-latitudes (Germany and France) show a larger bias.
The linear scaling of the bias with respect to the vertical column suggests a large bias in southern Europe. However, due to the lack of ground-based data available in southern Europe, we cannot confirm or infirm this claim at this point. An accurate bias correction is particularly important for southern Europe since the highest biogenic emissions are released from the warmer southern European ecosystems. An additional source of uncertainty originates in data averaging. Averaging the HCHO data inherently introduces a bias in the fit towards weeks with fewer data. We also find that averaging over longer periods (e. g. monthly) of the satellite data generally results in larger slopes and smaller intercepts in the fit compared to the weekly-based validation. Furthermore, different assumptions in the retrieval of ground-based and satellite HCHO columns might also skew the data set, since negative HCHO columns are absent in the FTIR dataset, whereas they are present in the TROPOMI dataset.
In our inversion scheme, we apply the bias correction based on FTIR data (Fig. 3 ), given by [TROPOMI] BC = 1. 48 × [TROPOMI] -1. 40 × 10 15 molec. cm -2 , (6) and 4b, the FTIR-based correction brings small changes to the HCHO levels in northern Europe, but induces substantial increases (30 to 40% over emission sources) in southern Europe. 5 Inversion results.
At St. Petersburg, the TROPOMI HCHO columns are generally very low, leading to a decrease in the modeled HCHO columns after the inversion, yet FTIR data suggest higher HCHO columns. The reduction of emissions that follows from the inversion in this region (around 60 • N, see Fig. 6 ) is therefore not corroborated by the FTIR data at St. Petersburg. The comparison of the model columns with the MAX-DOAS measurements of stations listed in Table  1  is shown in Fig. 9. For all stations (except Athens), the optimized HCHO columns are not improved with respect to the a priori, and the average absolute difference is increased at several locations. The MAX-DOAS data show a worsening of the optimized model over Germany and the Benelux, whereas the FTIR data suggest an improvement of the model over Germany and France.
4b  for the standard inversion scenario (S0). Because the bias correction leads to much higher HCHO columns over strongly emitting regions (∼ 40% increase), the S1 run has much lower emissions across Europe for all emission categories. Since the effect of the bias correction is strongest in southern Europe, the biogenic emissions are most affected, with the S1 scenario having three times lower isoprene emissions compared to the standard run (6. 7 Tg yr -1 versus 18. 3 Tg yr -1 for S0). The S1 results highlight the strong impact of TROPOMI observations on the top-down emissions, and therefore the importance of a comprehensive and homogeneous validation dataset. Next, we evaluate the effect of different a priori inventories on the inversion results. In scenario S2, we substitute MEGAN-MOHYCAN biogenic emissions with the CAMS-GLOB-BIOv3.
9 to 14. 0 Tg yr -1 ), with the main reduction occurring over Germany. Comparison of the TROPOMI HCHO columns and ground-based FTIR HCHO columns shows that TROPOMI HCHO columns are generally too low, especially for high columns. We derived a linear relationship characterizing the bias against FTIR data at European stations (excluding mountain sites), which agrees with the bias correction reported in previous validation studies using global FTIR and MAX-DOAS networks  (Vigouroux et al. , 2020; De Smedt et al. , 2021). Additionally, we used To evaluate the robustness of the inversion results, we have performed a range of sensitivity inversions. As expected, the topdown emissions are very sensitive to the observed HCHO columns, and therefore also to the bias correction of the TROPOMI data. This highlights the importance of comprehensive and extended validation studies, using network remote-sensing data and/or airborne validation campaigns.
We note a scarcity of validation data in southern Europe, although at the time of writing, there are plans for FTIR measurement sites in Italy and Cyprus. Additionally, we find that the choice of a priori biogenic emissions plays an important role due to large differences between available bottom-up emission inventories. Other modelrelated effects on the inversion induce differences of up to 10% on the final annual total emissions. Finally, the inversion using weekly averages was found to provide better results than the monthly inversion run, except in the northernmost part of the continent. Especially regions with variable meteorology are better treated by the weekly inversion due to the strong fluctuations in the HCHO columns. As the HCHO data quality continues to improve with the advent of more advanced satellite missions, the observational constraints on the inversion become more tight allowing for higher spatiotemporal resolution of the inversion studies.
Data availability. The TROPOMI HCHO dataset is a Copernicus operational product and is available at https://doi. org/10. 5270/S5P-tjlxfd2. The FTIR and MAX-DOAS datasets can be requested by contacting the PIs of each station. In situ data from the EMEP network is available at the EBAS data portal (https://ebas. nilu. no). The top-down biogenic and biomass burning emission datasets generated in this study are available at SEEDS project data portal (https://www. seedsproject. eu/data) and at the BIRA-IASB emission portal (https://emissions. aeronomie. be). The MEGAN-MOHYCAN bottom-up inventory is also available at https://emissions. aeronomie. be.
Statistical Analysis Our goal was to detect an odds ratio of greater than or equal to 0. 91 when comparing above vs recommended GWG between prepandemic and pandemic pregnancies (80% power). From prepandemic estimates,  1  we sought 6000 prepandemic and 12 000 pandemic deliveries (α =. 05). Participants with complete data were included in the analysis. The analysis included participants of American Indian, Asian, Black, Hispanic, or White race; participants with missing data on parity (6 participants), education (53 participants), or race (or reported as other, 542 participants) were.
Cohort Data from four study sites from the PREVENT-Dementia cohort were used (West London, Edinburgh, Cambridge and Oxford). Participants were recruited from multiple sources, with the recruitment goal being a sample of 50% people with and 50% without family history of parental dementia and an age range between 40-59 years old. An additional inclusion criterion for the study was the ability to complete written and verbal assessments in English. Participants were identified from sources including local dementia register databases, via the Join Dementia Research register (https://www. joindementiaresearch. nihr. ac. uk/), or by registering their interest through the PREVENT-Dementia website (https://preventdementia. co. uk/) and public presentations and engagement sessions as well as word of mouth.
GM and WM PV maps were registered to the ASL space using the inverted transform and spline interpolation. All registrations were visually assessed and in case of imperfections, linear registration (FLIRT) was used as an alternative method. The ASL data were visually inspected for potential artifacts. Participants with extremely low mean GM CBF <20 ml/100g/min were excluded from CBF analysis since this is an abnormally low value for a healthy participant  47  and was attributed to acquisition/labeling imperfections. The ASL technique is single time-point, hence there is a possibility for intravascular signal contamination. A voxel-wise threshold of 120 ml/100g/min was applied for the CBF region-of-interest (ROI) analysis. PVC is an essential step to ensure that neurovascular deficits are not masked by underlying atrophy, hence the analysis was run with PVC in native ASL space.
Statistical analysis Demographic variable and ROI analysis was conducted using MATLAB 2021 b (MathWorks Inc, Natick, Massachusetts, USA). Demographic factors were compared between APOE4 carriers (carriers of at least one APOE4 copy; including e2/e4) and noncarriers using the Wilcoxon rank sum test for continuous variables and the v 2 test for categorical variables. CBF and CoV data from the nine examined ROIs were harmonized between sites and different bolus durations (only one site had mixed boli) using the COMBAT harmonization algorithm  50  with age, sex, years of education, dementia family history and APOE4 status as modulators. In case where participants were missing modulator variables they were excluded from the analysis; this applied to four participants for the CBF analysis who were missing information on APOE4 genotype and five for the CoV analysis, one was missing information on education and the rest for APOE4.
Results Demographic characteristics of the sample are shown on Table  1. Overall data from 514 participants were used for the CoV analysis and 375 for the CBF analysis. The APOE4 carrier group was significantly younger compared to the non-carriers and a higher number had hyperlipidemia. GM CBF was not associated with age (t ¼ 1. 53, p ¼ 0. 13) or sex (t female ¼ 1. 42, p ¼ 0. 16). On the contrary, there was a significant positive association between age and GM CoV (t ¼ 2. 98, p < 0. 01) and females tended to have a lower GM CoV (t female ¼ À6. 97, p < 0. 01). CAIDE was not associated with total GM CBF but it was associated with GM CoV (q ¼ 0. 16, p < 0. 01). There were no differences in the normalized GM or WM volumes between the groups. Datasets were excluded from the CBF analysis if they were of bad quality, if they were affected by artifacts or incidental findings were present (63 datasets) or if the mean GM CBF following PVC was lower than 20 ml/100g/min (121 datasets).
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by project grants from the  UK Alzheimer's Society  (grant numbers  178  and  264 ), the  US Alzheimer's Association  (grant number  TriBEKa-17-519007 ), philanthropic donations and supported by the  NIHR Cambridge Biomedical Research Centre  ( BRC-1215-20014 ) and the  NIHR Biomedical Research Centre at Imperial College London. LS is supported by the  Cambridge NIHR Biomedical Research Centre (BRC)  and  Alzheimer's Research UK  ( ARUK-SRF2017B-1 ). IK is supported by the  Oxford Health NIHR BRC ,  MRC Dementias Platform UK  grant and personal  NIHR  fellowships.