dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS Many thanks to  Chris Monk ,  David Whiting , all at Microsoft AI-for-Earth,  Alan Bi ,  Lucy Hatcher ,  Andy Cooke ,  Vicky Robinson ,  Annabelle LePage ,  Sarah Escott ,  Babs Pearson ,  Abbie Keeper ,  Colin Prosser ,  Richard Alexander ,  Howard van Rooijen ,  Daveron Smith ,  Rebecca Jackson-Pitt  and  Mary Vagiou. This work was funded by  Natural England and Microsoft AI-for-Earth , with a set of additional images contributed by the  POLLEN  project ( 2016-00108486 ) of the  Centre-Val de Loire.
DATA AVAILABILITY STATEMENT model (. th Torchvision format) are available from the Dryad Digital Repository https://doi. org/10. 5061/dryad. 63xsj3v34 (Hicks et al. , 2021a). The Python and R codes are available in Zenodo https://doi. org/10. 5281/zenodo. 5347794 (Hicks et al. , 2021b).
( ii) false positives as those model-detected floral units where there are either none in the image or where a floral unit of a different taxon is present; (iii) false negatives as floral units in the image which are not detected by the model; and (iv) true negatives as an incalculable high number. A confusion matrix was constructed from the 50 test images (Figure S. 4 in the Supporting Information) which illustrates performance per taxon on this dataset. Key performance metrics for the final model are as follows: Precision of floral units = True Positive∕(True Positive + False Positive) = 926∕ (926 + 101) = 0. 90 Recall of floral units = True Positive∕ (True Positive + False Negative) = 926∕ (926 + 152) = 0. 86 Mean F1 score = 2 × (Precision × Recall)∕ (Precision + Recall) = 2 × (0. 90166 × 0.
Data The NASA ORACLES project utilized the NASA P-3 aircraft for the 2016, 2017, and 2018 deployments, while the ER-2 aircraft was additionally deployed in 2016 to record high-altitude measurements  (Redemann et al. , 2021). Both aircraft were equipped with instrumentation to sample clouds and aerosols. The P-3 payload included both remote sensing and in situ instruments, while the ER-2 aircraft carried only remote sensing instrumentation. To determine heating rates, we use a combination of measurements taken from the Solar Spectral Flux Radiometer (SSFR), 4STAR, and HSRL-2. SSFR is a system consisting of two pairs of spectrometers that measure the upwelling (nadir) and downwelling (zenith) irradiance between 350 and 2100 nm. The zenith light collector is mounted on an active leveling platform (ALP), which keeps the light collector level with the true horizon during flight.
The stacked legs bracket the aerosol layer above and below, at the bottom of the layer (BOL) and at the top of the layer (TOL). For ORACLES, the BOL leg was located just below the aerosol layer and just above the cloud layer. The column AOD (spectral), ozone, and water vapor are measured by 4STAR, and spectral scene albedo is measured by SSFR. The TOL leg is above the aerosol layer and the cloud, from which HSRL-2 measures profiles of extinction at 532 nm. In addition to the BOL and TOL legs, several other legs were flown within the wall, for example below and within the cloud and within the aerosol layer. A full radiation wall often required over an hour to complete, and a square spiral was often included at one end of the radiation wall. Since the radiation walls provide broad spatial coverage and allow us to sample varying cloud albedos and aerosol extinction profiles for the same aerosol plume, we use the radiation wall data to understand the impact of scene variability (i.
SSA and g spectra are taken from the SSFR retrievals presented in  Cochrane et al. (2021)  and assumed to be vertically homogenous, since the retrieved values represent the entire layer. The spectral albedo measured by SSFR just above the cloud top defines the "surface" beneath the aerosol layer at the altitude of the cloud top. Aerosol extinction is either taken from HSRL-2 (for radiation walls) or derived from the 4STAR AOD profile (for spirals) and detailed in Sect. 2. 3 and 2. 4. Atmospheric gases are defined by the standard tropical atmosphere included in the libRadtran package  (Anderson et al. , 1986). The water vapor and ozone profiles, however, are modified to reflect the specific conditions encountered during the time of the measurements. Specifically, due to the consistently high water vapor observed in the biomass burning plume (e.
For (1), we used the collection of measured spectra along the BOL leg, regardless of whether the BOL and TOL legs were colocated. From that, we created five representative albedo spectra for the heating rate calculations: the minimum, maximum, mean, and mean ± 1 standard deviation of the data collection. For part 2, we pursued a similar approach with the HSRL-2 extinction profiles from the TOL leg, which provide the vertical distribution of the aerosol. We paired this with the 4STAR-derived AOD from the bottom of the spiral (or from the leg-averaged BOL retrievals if a spiral was not available) as the basis for spectral extrapolation, which maintains spectral consistency across all heating rate calculations, as well as with the aerosol property retrievals. It should also be noted that for the two cases with no useable spiral, the aerosol optical properties were set to the ORA-CLES mean SSA and g spectra for the 2016 and 2017 deployments (Table  3  of  Cochrane et al.
, 2021) , and the water vapor profiles (and 4STAR column water vapor for scaling) are obtained from the closest spiral of the day regardless of whether there was a valid aerosol retrieval. For each radiation wall, we calculate aerosol heating rates for extinction profiles from HSRL-2 along TOL, approximately one profile every 5 s (interpolated from the 10 s resolution reported in the HSRL-2 data product), which is approximately every 0. 005 • of latitude or longitude. We extend the 532 nm extinction measured by HSRL-2 to the entire spectrum using the representative 4STAR AOD spectra in the following manner. 1. Integrate HSRL-2 extinction β ext to get AOD at 532 nm: AOD HSRL 532 (x) = TOL BOL β ext (z, x) dz, ( 2 ) where x is the location and z is the height. 2. Compare AOD HSRL 532 (x) (from TOL) with AOD 4STAR.
https://doi. org/10. 5067/Suborbital/ORACLES/P3/2016_V1 (OR-ACLES Science Team, 2017) and https://doi. org/10. 5067/Suborbital/ORACLES/P3/2017_V1 ( OR-ACLES Science Team ,  2019 ). Acknowledgements. We thank the ORACLES deployment support teams and the science team for a successful and productive mission. We thank  Warren Gore  of  NASA AMES  for his support during the ORACLES mission. Thank you to each of the instrument teams who provided data and expertise on using them. Financial support. This research has been supported by the  Earth Sciences Division  (grant no. NNX15AF62G ). Marc Mallet  is supported by the  AErosols, RadiatiOn and CLOuds in southern Africa (AEROCLO-sA)  project funded by the  French National Research Agency  (grant no. ANR-15-CE01-0014-01 ), the  French national programs LEFE/INSU  and PNTS, the  French National Agency for Space Studies (CNES) , the  European Union 's  7th Framework Programme  ( FP7 ;  2014-2018 ) (EUFAR2; grant no.
ABSTRACT: Matrix remodeling is a salient feature of idiopathic pulmonary fibrosis (IPF). Targeting cells driving matrix remodeling could be a promising avenue for IPF treatment. Analysis of transcriptomic database identified the mesenchymal transcription factor PRRX1 as upregulated in IPF. PRRX1, strongly expressed by lung fibroblasts, was regulated by a TGF-b/PGE2 balance in vitro in control and IPF fibroblasts, while IPF fibroblast-derived matrix increased PRRX1 expression in a PDGFR dependent manner in control ones. PRRX1 inhibition decreased fibroblast proliferation by downregulating the expression of S phase cyclins. PRRX1 inhibition also impacted TGF-β driven myofibroblastic differentiation by inhibiting SMAD2/3 phosphorylation through phosphatase PPM1A upregulation and TGFBR2 downregulation, leading to TGF-β response global decrease.
Thus, targeting master transcription factors preferentially expressed in fibroblast could be a promising avenue for IPF treatment. Using an in silico approach, we screened publicly available transcript microarray databases for expression of mesenchyme-associated transcription factors in control and IPF lung samples. We identified the "Paired Related Homeobox Protein-1" (PRRX1) gene as a potential candidate for transcriptional regulation differently modulated in IPF compared to control lungs. The PRRX1 mRNA generates by alternative splicing two proteins, PRRX1a (216 aa) and PRRX1b (245 aa) that differ at their C-terminal parts. Functional in vitro studies suggested that PRRX1a promoted transcriptional activation whereas PRRX1b may act rather as a transcriptional repressor  3. Prrx1 is implicated in the regulation of mesenchymal cell fate during embryonic development.
RESULTS: Identification of PRRX1 isoforms as mesenchymal transcription factors associated with IPF. Since mesenchymal cells are thought to be one of the major effector cells during fibrosis  1, 2  , we sought to identify mesenchymal transcription factors associated with IPF in patients. We screened three curated publicly available transcript microarray databases from NCBI GEO (GDS1252, GDS4279, GDS3951) for transcription factor expression in IPF and control whole lung samples. Among the 210 common genes upregulated at the mRNA level in all three IPF lung datasets compared to their respective control ones (Figure  1a  and supplemental Table  S1 ), 12 genes were annotated as transcription factors (Figure  1a ) after gene ontology analysis. One of these transcription factors, PRRX1 appeared as an appealing candidate since this gene was previously associated with mesenchymal cell fate during embryogenesis  4  and is required for proper lung development  5.
In addition, PRRX1 mRNA was upregulated in a fourth transcriptome dataset comparing "rapid" and "slow" progressor subgroups of IPF patients  13. None of those transcriptome datasets discriminated PRRX1 isoforms, namely PRRX1a and PRRX1b. First, we confirmed that both PRRX1 isoforms were upregulated in Human IPF lungs at the mRNA and protein levels (Figure  1a-c ). Immunoblot revealed that PRRX1a protein (210 aa) was the main PRRX1 isoform expressed in control and IPF lungs. We also investigated PRRX1 expression pattern by immunohistochemistry in control and IPF Human lung tissue sections (the antibody recognized both PRRX1 isoforms, see Figure  1d ). Additional lineage markers were also investigated such as Vimentin (mesenchyme marker), ACTA2 (myofibroblast / smooth muscle marker) and CD45 (hematopoietic lineage) as shown in supplemental Figure  S1.
We confirmed this observation in control and IPF fibroblasts at mRNA and protein levels (Figure  5d  and supplemental Figure  S8 ). We performed a ChIP assay to investigate a possible interaction of PRRX1 TFs with TGFBR2 gene promoter regions. However, we detected no enrichment in PRRX1 TF binding in TGFBR2 promoter regions by ChIP in primary NHLF (data not shown). TGFBR2 is part of the receptor complex with TGFBR1 controlling TGF-β/SMAD signaling cascade by promoting SMAD2 and SMAD3 phosphorylation upon TGF-b1 stimulation. We assayed SMAD2 and SMAD3 phosphorylation in control and IPF fibroblasts treated with PRRX1 siRNA, compared to cells transfected with control siRNA, in presence or absence of TGF-β1 (30min stimulation) (Figure  5e ). In control and IPF fibroblasts, PRRX1 knock down strongly inhibited TGF-β1-induced SMAD2 and SMAD3 phosphorylation (Figure  5e ).
However, the role of PRRX1 TFs in IPF, a disease associated with major perturbations in mesenchymal compartment was not known. We first identified PRRX1 as potentially upregulated TF in IPF lung after analysis of public transcriptomic database comparing control and IPF lungs  [32] [33] [34]. PRRX1 also appeared in the upregulated gene hit list of a subgroup of IPF patients, displaying an accelerated clinical course in another transcriptomic study  13. We confirmed that the mRNA levels of PRRX1 TFs isoforms were actually increased in IPF lung patients as well as in primary fibroblast isolated from IPF lung compared to control ones. This increase was confirmed at the protein level in IPF whole lung extracts compared to control ones. The upregulation of PRRX1 protein levels in fibrotic lungs may therefore reflect the accumulation of PRRX1 positive cells in IPF.
Acknowledgements: We thank  Olivier Thibaudeau  and  Laure Wingertsmann  ( Morphology Platform, Inserm U1152 of X. Bichat Medical School, Paris ) for their efficient collaboration and the  Flow Cytometry Platform of Inserm   U1149  ( CRI, X. Bichat Medical School, Paris ) as well. We also thank  Dr. J. W. Duitman  ( Academic Medical Center, Amsterdam, The Netherlands ) for his technical help. This work was supported by the  ANR  ( JCJC ANR-16-CE14-0018 ), by grants from the  Chancellery of Paris Universities  ( Poix Legacy ), and by the  "Association pour la fibrose pulmonaire idiopathique Pierre ENJALRAN". E. Marchal-Duval  was supported by  "Ecole Doctorale Bio-SPC " (Grant 2015) ; A. Froidure an  European Respiratory Society (ERS)  and the  European Molecular Biology Organization  ( EMBO) -ERS-LTRF 2015 -4476  and by a fellowship from the  Belgian Society for Pulmonology (BVP-SBP) ;  M Homps-Legrand  by the  Fond de dotation "Recherche en Santé Respiratoire " (Grant  2018 ).
Our results have important implications for predicting the response of permafrost peatland C to rapidly increasing temperatures and permafrost thaw by suggesting that previously frozen C stores in many regions may be more stable than previously thought and proposes that peat type and humification are important factors controlling decomposition following permafrost thaw  (Keuper et al. , 2020). Despite warmer temperatures, more labile litter inputs, and no constraint on enzymes due to oxygen unavailability, there is no evidence of a rapid pulse of increased potential enzyme activity of previously frozen peat in the initial decades following permafrost thaw. While warmer temperatures throughout the young bog peat profile may lead to somewhat higher in situ rates of enzymatic degradation indicated by the potential enzyme activities we measured, our data show that microbial activity is not enhanced due to the availability of an intrinsically more labile, previously frozen substrate.
Data This study was approved by the CHU of Montpellier's Institutional Review Board and is registered at ClinicalTrials. gov (n°NCT04653844). The data originates from variant-specific RT-PCR tests performed in France between February 06 and August 21, 2021 on SARS-CoV-2 positive samples  [15, 13]. The multiplex assays use probes targeting variant-specific mutations (see Table  1 ), as well as a region in the N virus gene for control purposes. We use the Ct of the later probe in our analyses. Given the specificity of each assay used, different formatting steps were used. In particular, the initial IDS1 test designed to distinguish between the historical lineages and the Alpha VOC may increase the proportion of the latter VOC for high Ct values. Further details are shown in the flow diagram in Figure  1  and can be found in earlier studies  [8, 15, 13].
Linear mixed models We used a linear model to study the potential link between the Ct value of the screening test and the number of virus copies obtained using ddPCR, using virus lineage and test assay as a co-factor. The results of the statistical model were used to convert Ct values from all assays into a number of virus copies. We analysed the longitudinal data of number of virus copies (or Ct values for IDS1) with linear mixed models and used the R package lme4  [24]  to fit the restricted maximum likelihood parameters to the data. The response variable was the number of virus copies (or the Ct value for IDS1), and we included two random effects on the individual and on the region of sampling. To select which additional effects to include in the linear mixed model, we compared models with all possible parameters combinations listed in Table  2.
Historical lineages vs. the Alpha variant After formatting the database for the IDS1 assay (Figure  1 ), we identified 12,536 suitable samples (Table  3 ). Due to the limited number of observations, we removed patients infected lineages consistent with the Beta/Eta/Gamma VOCs. To analyze the virus load kinetics, we inferred linear mixed models and selected the one with the lowest AIC. The estimated values of the model are shown in Table  4. The linear mixed model revealed significant differences in Ct values dynamics between the historical lineages and the Alpha VOC (Figure  3 A ). First, compared to historical lineages infections, the peak Ct appeared to decrease with age for infections caused by the Alpha VOC (Table  4 ). Overall, for the French age structure, the peak Ct was significantly lower (-0.
Alpha vs. Delta variant We then analysed the data which involved Ct values from 3 other assays that we were able to convert into virus copy numbers (see the Methods and Figure  2 ). In the following, we use a log base 2 relationship with time since peak of viral genome copies to compare the orders of magnitude with the previous results. Infections consistent with viruses from Beta/Gamma/Eta lineages were too rare to be analysed and removed them from the dataset. Overall, we were able to compare infections caused by the Alpha or the Delta variant by analyzing the remaining 2,515 samples (Table  3 ). Compared to the previous dataset, patients were slightly younger (median age 35 vs 41) and the follow-up duration was shorter (median 6 vs 8 days). This latter difference was corrected by the variable taking into account the delay between first and second test.
Discussion Understanding the within-host kinetics of SARS-CoV-2 infections yielded original insights on infection virulence  [17] , or efficiency of screening strategies  [18]. We analyzed a large national dataset of longitudinal RT-PCR Ct values to test the hypothesis that epidemic rebounds associated with SARS-CoV-2 variants could be linked to specificites in their within-host kinetics. A linear mixed model indicated that infections caused by SARS-CoV-2 Alpha variant have higher virus loads, with a significant age-dependence. Furthermore, the temporal decrease in virus load was found to be slower when infections were caused by Alpha instead of the historical lineages. The results are consistent with results from a different cohort in France  [32]. To further investigate the consequences of these variations in within-host kinetics at the epidemiological level, we assumed a linear correlation between Ct value and daily infectiousness, which is consistent with an earlier study  [33]  and confirmed by a modelling approach  [34].
The ensemble data product, SeaFlux (Gregor & Fay (2021), doi. org/10. 5281/zenodo. 4133802, https://github. com/luke-gregor/SeaFlux), accounts for the diversity of the underlying mapping methodologies. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations that can result in an average error of 15% in global mean flux estimates. We address differences in spatial coverage of the surface ocean CO2 between the mapping products which ultimately yields an increase in CO2 uptake of up to 19% for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of an appropriately scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product.
By applying a consistent flux calculation methodology to each pCO2 product, we minimize the methodological divergence of fluxes within the ensemble. SeaFlux provides a more consistent approach specifically targeting the most commonly used pCO2 data products to deliver an end product for consistent intercomparisons within assessment studies such as the Global Carbon Budget  (Friedlingstein et al. 2020; Hauck et al. 2020). By first addressing differences in spatial coverage between the observation-based products we are able to better present a true global pCO2 estimate for each product. This SeaFlux package also provides a means to normalize the gas transfer velocity to a consistent 14 C inventory. By calculating fluxes using multiple scaled gas transfer velocities for different wind products, we present a methodologically consistent database of air-sea CO2 fluxes.
Methods The SeaFlux method is based on six observation-based pCO2 products and spans years 1988-2018 (Table  1 ). These six include three neural network derived products (MPI-SOMFFN, CMEMS-FFNN, NIES-FNN), a mixed layer scheme product (JENA-MLS), a multiple linear regression (JMA-MLR), and a machine learning ensemble (CSIR-ML6). These select products are included as they have been regularly updated to extend their time period and incorporate additional data that comes with each annual release of the SOCAT database. All of these methods provide full three-dimensional fields (latitude, longitude, time) of the sea surface partial pressure of CO2 (pCO2) and the air-sea CO2 flux. In their original form each product may utilize different choices for the inputs to Equation 1 (Table  A1 ). In this work recompute the fluxes using the following inputs to the bulk parameterization approach Equation 1: kw is the gas transfer velocity (further discussed in Sect.
SeaFlux air-sea CO2 flux calculation Following Equation 1, CO2 flux is calculated individually for each of the six observation-based products with each available wind product (CCMPv2, ERA5, JRA55) as discussed in Sect. 2. 2 (Table  4 ). Since we account for spatial coverage differences via our filling method (Sect. 2. 1), taking a global mean flux for each of the data products is now straight forward. one example (other products in Figure  A2 ). The three wind products show very consistent fluxes throughout the time series, however the importance of appropriate scaling of the gas exchange coefficient (a) is evident by the significant differences between global mean fluxes calculated with unscaled and scaled a value (Figure  4 ). It is clear that the impact of applying the appropriate gas exchange coefficient through proper scaling has a larger impact on the resulting flux time series than solely the choice of wind product.
Issues not addressed by SeaFlux While the SeaFlux data set allows us to standardize much of the calculation of air-sea carbon flux, the community is still working towards consensus on other issues that impact this estimate. One source of uncertainty has been raised by  Watson et al. (2020)  who contend that a correction should be applied to pCO 2 observations to account for the vertical temperature gradient between the ship water intake depth and the surface skin layer where gas exchange actually takes place. A further correction should be applied when calculating fluxes to account for the "cool skin" effect caused by evaporation  (Woolf et al. 2016; Watson et al. 2020). Applying these corrections results in an increasing CO2 sink by up to 0. 9 PgC yr -1  (Watson et al. 2020 ). Here, we do not take such corrections into account for two reasons.
Firstly, the skin temperature correction to pCO2 needs to be applied directly to the measurements and not the final interpolated pCO2 from the data products. Hence, it is up to the developers of the SOCAT dataset and the developers of the pCO2 mapping products to decide on the inclusion of this correction. It would then be up to the developers of the data products to update their mapped products. Secondly, the cool skin correction would be equally applied to all methods and would not contribute to the inconsistencies that we are trying to address here. As the ocean carbon community moves towards consensus on such issues, the SeaFlux product will be updated to include revised protocols. To compare these estimates of contemporary air-sea net flux (Fnet) from surface ocean pCO 2 with estimates of the anthropogenic carbon flux (Fant) from interior data  (Mikaloff Fletcher et al.