dataesr/scientific-paragraphs-categorization huggingface dataset:
KEGG pathway annotation and enrichment We assigned KEGG pathway annotations to each gene using the KEGG pathway database (https://www. genome. jp/kegg/pathway. html) API as implemented in CLUSTERPROFILER 3. 14. 3  [60]. Gene names were converted to NCBI ENTREZ identifiers using the Bioconductor org. Dm. eg. db_3. 10. 0 database. We first examined the fold-change distributions of genes within each of the five main KEGG classes (Cellular Processes, Environmental Information Processing, Genetic Information Processing, Metabolism and Organismal Systems) and compared medians using the Kruskal-Wallis test. Second, we calculated the root-sum-square of fold changes within each KEGG class (regulation score). Third, we tested for significant functional overrepresentation of individual KEGG pathways against the D.
2 cm -1 ); this, however, is sufficient, as additional spectral shift and squeeze factors are determined in the later retrieval for both versions. We also use a noise model to correct the uncertainties of the GOSAT and GOSAT-2 spectra estimated during pre-processing and consider possible forward model uncertainties in the retrieval. This noise model is the same as in v1. 0, but we recomputed the parameters for all fitting windows based on an input data set consisting of 1 d per month in 2019 for both GOSAT and GOSAT-2. The resulting parameters are, however, similar for v1. 0 and v3. 0.
Basic post-processing filters In contrast to v1. 0, the basic filtering does not involve filtering based on external information, e. g. by using predescribed limits of scattering parameters or product uncertainties. This is no longer done as these fixed limits removed https://doi. org/10. 5194/amt-15-3401-2022 Atmos. Meas. Tech. , 15, 3401-3437, 2022 too many possibly valid data points, especially in the case of GOSAT-2. Therefore, the basic filtering now only includes the filtering for good convergence (χ 2 smaller than 2) and a maximum residual-to-signal ratio (RSR) as a function of the noise-to-signal ratio (NSR). This is done in the same way as for v1. 0 (see  Noël et al. , 2021)  but with the updated noise model parameters mentioned above. This part of the basic filtering is common for all products.
For GOSAT, the RSR filters for all fitting windows (1-6) are applied to all data products. In the case of GOSAT-2, for consistency, we also apply only the RSR filters for windows 1-6 to those products that are also available from GOSAT (i. e. XCO 2 , methane and water vapour products). For the other two GOSAT-2 products, i. e. XCO and XN 2 O, we only apply RSR filters from the NIR (windows 1 and 2, which contain the majority of the information related to scattering) and those windows where these gases are retrieved, namely window 8 for XCO and window 7 for XN 2 O. This is to avoid inadvertently filtering out a valid XCO 2 measurement due to, for example, a bad XN 2 O fit (or vice versa). In addition to this, we apply a filter on a maximum SZA of 75 • , because we cannot expect good data products at low solar illumination.
Quality filtering The quality filtering is product-specific, but it follows the same strategy for each target gas. In general, we perform independent filtering for water and land surfaces. The final data product contains only the filtered data. The filtering out of low-quality data was done in v1. 0 by a random forest filter. However, as explained in  Noël et al. (2021) , the performance of this method was not ideal as it filtered out fewer data than expected, i. e. less data were filtered out than were marked as "bad" during the training of the random forest filter. Therefore, we replaced this filtering for v3. 0 with a filter procedure that has already been successfully used in OCO-2 retrievals; details can be found in  Reuter et al. (2017a). This procedure is based on a minimisation of the local variance.
This is done by computing, for a subset of the data, the variance of the difference between the retrieved quantity and its median on a 15 • ×15 • grid. Based on this subset, we check which variables from a given list of the candidate variables perform best in reducing the local variance when removing data corresponding to the highest or lowest 1 % of each variable. This action defines a new upper or lower limit for this variable. We repeat this until a prescribed amount of data are removed. The output of this procedure is a list of "best" variables and their new filter limits. This subset has been generated from data of 2019 for GOSAT and GOSAT-2, to which the basic quality filter as described above has been applied. Note that -in contrast to v1. 0 -this subset no longer depends on the reference database used in the bias correction.
Bias correction After filtering data as described above, we apply a bias correction to XCO 2 and the XCH 4 full-physics and proxy products. The overall procedure is the same as described in detail in  Noël et al. (2021). The bias correction is based on a random forest regression using, as for v1. 0, the 10 most relevant parameters and a random forest database as input. These have been determined as described in  Noël et al. (2021)     quality and instrument gain. The final choice of bias correction parameters and their relevance is shown in Fig. A7  for GOSAT and Fig. A8  for GOSAT-2 (see Appendix B). We also perform a correction of the retrieved XCO 2 and XCH 4 uncertainties ( X retr ) via a linear function: X = a c + b c X retr. (2) X is the corrected uncertainty with X being either XCO 2 or XCH 4.
Results All GOSAT data (from 2009) and GOSAT-2 data (from 2019) until the end of 2020 have been processed. Figs. 1  and 2  show the final number of valid FOCAL data as a function of time for the different products. The numbers are different for each product because of the individual filtering (see above). For comparison, the numbers for the v1. 0 XCO 2 products are also shown. Fig. 1a  compares the number of yearly GOSAT FOCAL XCO 2 data with other available GOSAT data products from SRON, the University of Leicester (UoL), NIES and the NASA ACOS v9 product. A similar comparison is shown in Fig. 1b  for XCH 4 full-physics and proxy products. The resulting amount of data for the GOSAT FO-CAL water vapour products is shown in Fig. 2a. The yield of valid FOCAL products was improved in v3. 0 compared to v1.
0. The number of valid FOCAL XCO 2 and methane results exceeds those of all other GOSAT data sets. Note that the increase in data yield from v1. 0 to v3. 0 is actually larger over water (about 60 % for 2019) than over land surfaces (about 30 % for 2019). The main reason for this increase is the improved post-processing quality filtering procedure and -especially for water vapour -also relaxations in the latitudinal and solar zenith angle filtering during preprocessing. In general, the number of GOSAT data increases for all products with time, with typically more data after 2015. As discussed in  Noël et al. (2021) , this is related to optimised GOSAT operations especially resulting in more data over water. In principle, GOSAT-2 should provide more valid data than GOSAT, because GOSAT-2 uses an "intelligent pointing" procedure to avoid cloudy scenes.
However, although the total number of GOSAT-2 FOCAL products (see Fig. 2b ) was also improved, it is still lower than for GOSAT. This is because a larger fraction of data are already removed during the basic filtering due to larger residuals/less convergence. This hints at possible issues with the radiometric calibration or an incomplete instrument model used by FOCAL, neglecting important instrument features, e. g. currently unconsidered effects of remaining polarisation sensitivities of the instrument.
Global maps For each of the different data products, an example map comprising a mean for April 2019, gridded to 5  been omitted to avoid outliers. The spatial patterns of XCO 2 , methane, water vapour and δD look very similar for GOSAT and GOSAT-2. GOSAT-2 data show in general fewer gaps over the oceans but with smaller latitudinal coverage. The latter is due to the currently applied RSR filtering for GOSAT-2, which especially removes data over water surfaces. Note that over the year the spatial range of valid data varies according to solar illumination conditions. The XCO 2 data show higher values in the Northern Hemisphere than in the Southern Hemisphere as expected during springtime. This is because plants absorb more XCO 2 during growing season (i. e. hemispheric summer and autumn). For methane, the known source regions in the USA, Africa and Asia are clearly visible, as well as the inter-hemispheric gradient.
The spatial coverage of the proxy product is much larger than for the full-physics product, especially at higher latitudes. This is due to the relaxed filtering for the proxy product. Water vapour (XH 2 O) also shows the expected behaviour: large values in the tropics and lower values at higher latitudes. The observed spatial distribution of δD is in line with the maps shown in  Frankenberg et al. (2013). All δD values are in the expected range (about 0 to -300 ‰); they also decrease from the tropics to higher northern and southern latitudes. This is because water vapour generated in the tropics by strong evaporation is transported to higher latitudes, during which the heavier HDO decreases more rapidly via precipitation than H 2 O. For GOSAT-2, there are also data for carbon monoxide (XCO) and XN 2 O.
Time series Time series of all GOSAT FOCAL data products for different latitudinal regions are depicted in Fig. 9. These plots show the expected temporal behaviour: a seasonal cycle is visible in all data sets; amplitudes and/or phase differ for northern and southern latitudes with usually more variability in the north. The GOSAT FOCAL XCO 2 results are shown in Fig. 9a. The overall increase of XCO 2 from around 380 ppm in 2009 to about 415 ppm in 2020 is clearly visible, as well as an overlaying seasonal variation, which is most pronounced in the Northern Hemisphere with a minimum in summer due to vegetational growth. In the Southern Hemisphere, the seasonality of XCO 2 is shifted by 6 months but much lower since there are less land masses than in the north. The global variation is very similar to the tropical one.
This is probhttps://doi. org/10. 5194/amt-15-3401-2022 Atmos. Meas. Tech. , 15, 3401-3437, 2022 ably related to the increased number of GOSAT data (especially over ocean) after 2015 (see Fig. 1 ), which changes the sampling. Taking this into account, no clear trend is visible in the GOSAT water vapour data from 2009 to 2020, although there is some indication for a slow increase with time. This is in line with results from other data sets (see e. g. Borger et al. , 2022, and references therein). Average values of δD vary between about -180 ‰ and -120 ‰. As for water vapour, seasonal variations are small in the global average, but year-to-year variations in the seasonal cycle are larger for δD. Especially note that the peaks in July 2012 in the Southern Hemisphere and in December 2018 in the Northern Hemisphere are due to very few data in these regions in these months.
Both databases feature a height grid with 20 layers. The height gridding is done in a way that each layer consists of the same number of dry-air particles so that the column average can simply be computed by averaging the mole-fraction profile. When reading the climatology database, SLIM allows for either nearest neighbour or trilinear interpolation in longitude, latitude and day of year. Additionally, SLIM is able to convert the height gridding to the one that is used, e. g. for the FOCAL OCO-2 XCO 2 retrieval using five height layers for CO 2. First, we computed the global mean XGAS (XCO 2 or XCH 4 ) from the corresponding model for each 1 January (00:00 UTC) in the covered time period. In the next step, we went through all model time steps of the analysed period and subtracted the global mean XGAS, assuming linear growth within the years.
Finally, we created the climatology databases by incrementally computing the average and standard deviation of the gases mole fraction of all growthcorrected model time steps falling into the 10 d temporal grid cells of the database. In this way, the created databases basically consist of growth-removed seasonal cycle anomalies. In addition to the created 4D data fields, the database contains a table of annual growth rates obtained from NOAA (https://gml. noaa. gov/ccgg/trends/gr. html, last access: 3 July 2021). Currently, the implemented table covers the time periods 1959-2020 for CO 2 and 1984-2020 for CH 4 , but it can be extended if needed to improve the quality of SLIM estimates in years before or after these periods. Fig. A1  shows the NOAA annual mean growth rates for CO 2 and CH 4 computed from global marine surface data as stored in the database.
As visible in the figure, the NOAA growth rate agrees well with the growth computed from the model data as described above. In the following, we describe how SLIM uses its database to estimate the CO 2 or CH 4 atmospheric dry-air mole fraction for a given longitude, latitude and time. The database has been generated as follows. First, SLIM computes an estimate of the global average mole fraction by linear interpolation in the accumulated growth rates database. Note that extrapolation to dates outside of the spanned period is done by assuming a 10-year average growth rate (dashed lines in Fig. A1 ). This global average is added to the mole fraction anomaly interpolated from the corresponding 4D database field for the given longitude, latitude and day of year. Figure  A2  shows examples of a global XCO 2 and XCH 4 map as read from the models (panels c and d) and in panels (a) and (b) for the corresponding maps of SLIM XGAS values.
of CO 2 mole fraction data of NOAA's CarbonTracker model version CT2019B Jacobson et al. (2020). It has the same 3 • × 2 • spatial resolution as the used global CarbonTracker model fields. Temporally, it covers 1 year sampled in 36 time steps, corresponding to a grid resolution of about 10 d. The climatology database of SLIMCH4 v2021 has been derived from 13 years (2000-2012) of TM5-4DVAR CH 4 mole fraction data (Bergamaschi et al. , 2013) with a spatial resolution of 6 •.
Acknowledgements. GOSAT and GOSAT-2 spectral data have been provided by  JAXA  and  NIES. CarbonTracker CT2019B and CT-NRT. v2020-1results were provided by  NOAA ESRL, Boulder, Colorado, USA , from the website at http://carbontracker. noaa. gov(last access:  2 June 2022 ). ABSCO cross sections for CO 2 were provided by  NASA  and the ACOS/OCO-2 team. GMTED2010 topography data were provided by the  U. S. Geological Survey (USGS)  and the  National Geospatial-Intelligence Agency (NGA). We thank the  European Centre for Medium-Range Weather Forecasts (ECMWF)  for providing us with analysed meteorological fields (ERA5 data). The GOSAT ACOS v9 Level 2 XCO 2 product from the  NASA/OCO -2 team has been obtained from https: //oco2. gesdisc. eosdis. nasa. gov/data/GOSAT_TANSO_Level2/ACOS_L2_Lite_FP.
9r/,https://doi. org/10. 5067/VWSABTO7ZII4(last access:  16 October 2020 ). The UoL and SRON GOSAT data products have been obtained from the  Copernicus Climate Data Store  (https://cds. climate. copernicus. eu/,last assess: 15 October 2020). GOSAT Level 2 data from  NIES  have been provided by the GOSAT Data Archive Service (GDAS; https://data2. gosat. nies. go. jp/, last access:  17 January 2022 ). RJP is funded via the  UK National Centre for Earth Observation  ( NE/N018079/1 ). This research used the  ALICE High Perfor-mance Computing Facility  at the  University of Leicester  for the UoL GOSAT retrievals. The Paris TCCON site has received funding from  Sorbonne Université , the  French research centre CNRS , the  French space agency CNES  and  Région Île-de-France. The Réunion station is operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-Inwire and the ministerial decree for ICOS  ( FR/35/IC1  to  FR/35/IC6 ) as well as local activities supported by  LACy/UMR8105 -Université de La Réunion.
The TCCON stations at Rikubetsu, Tsukuba and Burgos are supported in part by the  GOSAT  series project. Local support for Burgos is provided by the  Energy Development Corporation  (EDC, Philippines). The Eureka measurements were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  by the  Canadian Network for the Detection of Atmospheric Change (CANDAC) , primarily supported by the  Natural Sciences and Engineering Research Council of Canada, Environment and Climate Change Canada , and the  Canadian Space Agency. The Anmyeondo TCCON station is funded by the  Korea   Meteorological Administration research and development programme "Development of Monitoring and Analysis Techniques for Atmospheric Composition in Korea " under grant  KMA2018-00522. The  TCCON Nicosia  site has received support from the  European Unions '  Horizon 2020 research and innovation programme  under grant agreement no.
Methods A multicentre retrospective cohort study was performed using 2011-2017 data from a French regional trauma registry (RESUVal) that includes prehospital, and in-hospital data on trauma patients. All adults assessed by a physician-led mobile medical team with major trauma (Injury Severity Score [ISS] ≥ 16) were included. Major trauma patients transported directly to a level-I trauma centre were considered as correctly triaged. Multivariate logistic regression was used to identify factors associated with undertriage. Results 7,110 trauma patients were screened, of whom 2,591 had an ISS ≥ 16. Median age was 42 (IQR 27-59) years old, 75. 0% were male and 12. 4% (n = 320) were undertriaged. In-hospital mortality was 18. 3% among undertriaged patients vs 16. 2% among correct-triaged patients (p = 0.
Data Collection Prehospital, Emergency Department (ED) and Intensive Care Unit (ICU) data is collected by the physician in charge of the patient. Research technicians provide continuous monitoring of the completeness and correctness of the RESUVal. They also collect patient outcomes at hospital discharge. Data management was performed by a data-manager-statistician (CC) and a PhD methodologist (LF), both are full-time employees of the RESUVAL. MMT physicians were asked to ll out a standardized case report form for any trauma patient with at least one of the Vittel criteria corresponding to effective or supposed seriously injured patients. A research assistant reviewed patients' medical records if the case report form was incomplete.
Study Data The following prehospital variables were prospectively recorded: age, sex, type and cause of trauma, rst physiological parameters measured by the MMT (systolic blood pressure, heart rate, peripheral oxygen saturation (SpO2), respiratory rate, shock index, Glasgow coma scale (GCS) and capillary haemoglobin concentration (HemoCue® Hb 201 + system)). Data regarding prehospital procedures and management was also recorded: endotracheal intubation, thoracostomy, chest tube insertion, pelvic binder placement, Focused Assessment with Sonography for Trauma (FAST). The following scores were calculated after anatomical and physiological assessments were completed: Abbreviated Injury Scale (AIS) score based on the 1998 version, ISS, as well as the mechanism, GCS score and Age, arterial Pressure score (MGAP) score.
Multiple Imputation Candidate covariates were identi ed based on a literature review and univariate analysis; age, sex, season, year of admission, type of transport, time on site, transportation time, GCS, trauma mechanism, type of accident (weapon, road, fall, other), shock index, endotracheal intubation, FAST. In our cohort, the candidate covariates were complete for 61% (n = 1,583). To gain statistical power and reduce bias, we performed multiple imputation by generating a total of 100 datasets over 20 iterations. Regressions were calculated separately on each dataset, and then combined into a single result using Rubin's rule  12. Auxiliary variables were added to optimize the imputation process  13  : centre level, cause of injury (gunshot/stab wounds, suicide, accident), head trauma, abdominal trauma, thoracic trauma, limb trauma, pelvis trauma, spinal trauma, prehospital administration of catecholamines, prehospital endotracheal intubation, admission to the ICU and AIS by body zone.
Multivariate Logistic Regressions We performed two multivariate logistic regressions. The rst one to estimate undertriage determinants as the primary outcome based on 2,591 patients. The results were evaluated on each imputed dataset and pooled using the Rubin's rule. The second logistic regression, to estimate the effect of undertriage on mortality as secondary outcome, considering in-hospital mortality, was based on the 1,724 patients admitted or deceased before admission to an ICU, to estimate the effect of undertriage on mortality as secondary outcome, considering in-hospital mortality. This logistic regression was performed on the 91% of the patients (1,570/1,724) without missing data (covariates and outcome). The results are presented as odds ratios (ORs) with 95% con dence intervals (CIs) and p-values.
Hence, a patient not requiring neuro-or cardiothoracic surgery could be properly managed in a level-II centre. The study does, however, have some limitations. Its retrospective design means that it is not immune to data collection mistakes. The exact locations of the on-scene traumas were not available in the database, and we could not therefore evaluate the distance to the nearest trauma centre, which may have played a signi cant role in the choice of destination. Furthermore, triage may have been in uenced by other elements that were not collected (i. e. , chronic conditions, trauma centre overcrowding, a decision of withdrawing treatments, futility or family/patient wishes). Even if our de nition of undertriage is validated by the ACSCOT  5  , it may also be argued whether ISS is the most accurate tool for assessing whether a patient required level-1 trauma care compared to a clinical de nition based on the need for critical intervention (i.
Acknowledgements: We thank  Julie Freyssenge , health geographer PhD, for providing the map of trauma centre and  Philip Robinson  ( DRCI, Hospices Civils de Lyon ) for help in manuscript preparation. We thank the local investigators of the trauma registry:  Antoine Blum  ( SMUR 26 ,  Saint Vallier ),  Bastien Anette  ( SMUR 69 ,  Villefranche-sur-Saone ),  Djamel Bekka  ( SMUR 07 , Privas),  Eric Cesareo  ( SMUR 69 , Lyon),  Marine.
Availability of data and materials: Data are available upon reasonable request at c. claustre@resuval. fr Competing interests: Authors do not declare any con ict of interest for this original article Funding: Authors did not receive any funding for this work. The trauma registry is funded by the Regional Agency for health (ARS, Agence Régionale de Santé Auvergne Rhône-Alpes) and coordinated by the RESCUe-RESUVal. This research did not receive any speci c grant.
| ME THODS We selected patients from the French national health data system,  12  which covers the entire French population with any health care in the previous year (ie, 66 million people). We excluded twins and foreigners due to identification issues. We identified a history of comorbidities  13  from 2015 to 2019. We selected patients aged ≥40 years because the risk of severe COVID-19 in younger patients was lower and because treatment with aspirin for primary cardiovascular prevention could involve specific populations. Because our aim was to study aspirin use for primary cardiovascular prevention, patients with known cardiovascular comorbidities were excluded. We considered the age, sex, and region of residence of the patients as demographic variables. Age was defined as a categorical variable by 5-year age group.