dataesr/scientific-paragraphs-categorization huggingface dataset:
335 Month to month variations in emissions are much more prominent, as illustrated in Fig. 5. Seasonal cycles are driven largely by temperature and associated wet/dry changes. The large contribution of F Fert to Western/Eastern European(EUR) emissions is also very evident, with largest F Fert emissions near the start of the growing season. Finally, Fig. 6  illustrates the diurnal variations contained within the CAMS-GLOB-SOIL dataset for locations in Brazil, France and Australia. These factors are derived from the temperature-dependent 3-hourly F biome biome values, but as a good 340 first approximation they can probably be applied to the other components also. The main difference between the French and.
365 CAMS system, including both the IFS model and EMEP MSC-W, and there are risks of both double-counting or omission of emissions when mixing CAMS-GLOB-SOIL with these other data sets. As will be shown below, many of the emissions derive their methods from the EMEP/EEA Emission Inventory Guidebook chapter on crop production and agricultural soils, so we present this first (Sect. 6. 1), then for each emission data-set we present the status of soil-NO emissions, and a recommendation on how these data should be used with CAMS-GLOB-SOIL. emissions due to agricultural activities using the EMEP/EEA Emissions Inventory Guidebook  (Hutchings et al. , 2019). The Guidebook provides methods for calculating soil-NO data from fertilizer and other inputs. Table  S1  presents the main sources for which soil NO emissions ares covered by the Guidebook, and Table  S2  presents the 375 nationally submitted emissions following this system (data provided by Sabine Schindlbacher, EMEP CEIP, 2021).
WebDab/EMEP emissions WebDab (https://www. ceip. at/webdab-emission-database) is the emission database of EMEP, and contains all emission data 380 officially submitted to the Secretariat of the LRTAP Convention by the Parties to the Convention. When the detailed national emissions from webDab are compiled, gap-filled, and processed for use by the EMEP MSC-W, the WebDab 3D emissions noted above are included in the GNFR 1  category 'L' (emissions from agriculture 'other', which excludes livestock). Figure  7  clearly shows that the 'Fert' component of CAMS-GLOB-SOIL is remarkably similar to the sum (3D tot ) of the WebDab '3D' categories components in most European countries. CAMS-GLOB-SOIL suggests much higher NO emissions.
5°degrees). For the European runs anthropogenic emissions are from the CAMS-REG dataset  (Kuenen et al. , 2021; Granier et al. , 2019) , and for global runs from CAMS-GLOB-ANT v5. 1  (Granier et al. , 2019) , but with 'ags' emissions omitted to avoid double-counting as discussed in Sect. 6. The CAMS-GLOB-ANT dataset is based on the EDGARv5 annual emissions for the years 2000-2015 to which the monthly temporal profiles from CAMS-GLOB-TEMPO v2. 1  (Guevara et al. , 2021)  have 445 been applied. For 8 species including NO, ship emissions are from CAMS-GLOB-SHIP v2. 1  (Jalkanen et al. , 2012; Johansson et al. , 2017). Emissions from aircraft are from CAMS-GLOB-AIR, and lightning and biogenic VOC are from EMEP model defaults  (Simpson et al. , 2012 (Simpson et al. , , 2017 (Simpson et al. , , 2020b)).
485 For the CAMS-GLOB-SOIL datasets, we have here aimed at pragmatic solutions rather than sophistication, in order to set up a transparent initial framework, and to avoid over-parameterising a model in which many of the underlying datasets (e. g. on agricultural inputs, or soil characteristics) are necessarily uncertain. For example, the implementation of pulsing as done here (essentially, as 15% of the biome-related emissions where some moisture criteria was met) is deliberately simple, and reflects difficulties in even identifying the timing of pulses, let alone the magnitude. There are also some puzzling differences 490 in the emission rates assigned to different land-cover by SL11, e. g. that the rates for mixed forest are lower than those of any deciduous or coniferous forest (cf Table  2 ).
These data are available through the Copernicus Atmosphere Data Store (ADS) system, (https://doi. org/10. 24380/kz2r-fe18, last access June 2021, Simpson 2021a) or through the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad. aeris-data. fr/, last access June 2021). For review purposes, ECCAD has set up an anony-.
90 (0. 89) � HNO + 3 NO -3 in air, µg(N) m -3 34 0. 42 0. 42 (0. 37) -1 (-14) 0. 08 (0. 10) 0. 94 (0. 94) 0. 97 (0. 94) NO -3 conc. in precip. , mg(N)/l 42 0. 31 0. 27 (0. 23) -12 (-24) 0. 24 (0. 25) 0. 44 (0. 43) 0. 56 (0. 53) 8 Conclusions We have presented a dataset of global soil NO emissions, CAMS-GLOB-SOIL v2. 2, which comprises gridded monthly data, also with 3-hourly weight factors, suitable for atmospheric chemistry modelling. Data are provided globally at 0. 5°×0. 5°d 475 egrees horizontal resolution, and with monthly time resolution over the period 2000-2018. Emissions are provided as total Notes: Statistics given are number of stations (Ns), observed and modelled values, bias, root mean square error (RMSE), correlation coefficient (R 2 ) and index of agreement (IOA).
Through the ESWW, a set of three medals was created. 12 Each of them are under the umbrella of an Academy of Sciences, namely Norway (Birkeland medal), Russia (Chizhevsky medal), and Belgium (Baron Marcel Nicolet medal). They are now recognised worldwide and receive nominations globally. These ESA and EU-COST efforts have certainly played a major role in promoting Space Weather in Europe along with various other endeavours. The discipline plays an increasingly important role in the EU/EC Research programmes. The results of different research programs are available on Community Research and Development Information Service (CORDIS 13  ). Searching "Space Weather" on CORDIS yields only 14 hits for Framework Program 6 (FP6), but already 214 hits for FP7, and even 311 in the Horizon 2020 (H2020) programme (values quoted are from December 2020).
The same search for the term "solar physics" yields 10, 46, and 237 hits, for the FP6, FP7 and H2020 programmes, respectively. Limiting the search to "Projects" only, the term "Space Weather" yields 6 hits for FP6, 82 for FP7, and 45 for the almost-finished H2020 programme. Similarly, for "solar physics", one obtains 5, 10, and 18 hits for the programmes FP6, FP7, and H2020, respectively. 3 What are the new challenges that Space Weather is facing? 3. 1 Challenges specific to the discipline 3. 1. 1 What happens in the post-integration phase? The era extending from the first ESA initiatives in 1995 to the end of the COST ES803 action in 2012 can be qualified as a phase of construction and integration. Then, Space Weather in Europe evolved toward a multiplication of organisations resulting in a dispersion of forces.
Other reports have implicated NF-κB signaling in the regulation of energy metabolism, metabolic stress adaptation, and EMT in both models of inflammation and cancer, including CRC  (18, (22) (23) (24) (25) (26) (27) (28). In keeping with these findings, several clinical studies have identified a correlation between higher NF-κB activity and shorter overall survival (OS), as well as more advanced disease stages, in patients with CRC  (29) (30) (31) (32). However, the paucity of the molecular clinical investigations has limited the mechanistic understanding of the pathogenic relationship of NF-κB signaling with tumor phenotype and clinical evolution in CRC. Consequently, precisely how NF-κB activation drives aggressive disease progression in patients with CRC remains poorly understood. gene signatures in each CMS subtype from patients with CRC (n = 296) in The Cancer Genome Atlas (TCGA) data set.
Shown in the box plots are the median values (horizontal lines), 25th to 75th percentiles (box outlines), and highest and lowest values within 1. 5 times the interquartile range (vertical lines). Notches denote the 95% confidence interval of the medians. Samples from each CMS subtype were compared with other CRC samples by using 2-tailed Student's t test. ***P < 0. 001. Statistical significance for multiple comparisons was calculated using the Kruskal-Wallis test (P < 2. 2 × 10 -16 ). (C) Hierarchical clustering of the 1795 metabolic genes present in the Reactome database from the patients in A and B, arranged according to CMS subtype. The z scores of gene expression are depicted as a gradient from azure (low expression) to orange (high expression). Yellow, CMS1; blue, CMS2; red, CMS3; green, CMS4.
To understand how NF-κB-dependent gene transcription regulated TAG catabolism, we analyzed the gene expression profile of RelA-deficient and control MEFs, at baseline and under GL, by using RNA sequencing. Among the 1792 metabolic genes listed in the Reactome database, we isolated the top 7 genes that were most markedly downregulated by RelA deficiency at all time points investigated (Figure  3A , Supplemental Figure  3A , and Supplemental Table  2 ). Notably, one of the genes in this group encoded carboxylesterase 1d (Ces1d), the murine ortholog of human CES1 (also known as triacylglycerol hydrolase), a TAG and CE lipase predominantly expressed in liver, adipose tissue, intestine, and lung  (40). We confirmed by quantitative reverse transcription PCR (qRT-PCR) that Ces1d transcripts were strongly upregulated by GL in control MEFs, but only weakly in RelA-deficient MEFs (Supplemental Figure  3B ), indicating that Table  1 ).
Interestingly, the analysis of CRC data sets demonstrated that the obesity gene signature and percentage of overweight patients were both significantly increased in the mesenchymal CMS4 subtype compared with other CRC subtypes (Figure  5 , F and G and Supplemental Figure  5, E  and F ). Notably, CES1 expression was correspondingly upregulated in the aggressive CMS4 subtype, in keeping with our findings of a correlation between CES1 expression and worse prognosis in CRC (Figure  5H  and Supplemental Figure  5G ). Of note, CES1 expression was also upregulated in the canonical CMS2 CRC subtype (discussed below). Thus, our analyses of human data sets (33) demonstrated a clear stratification of CES1 expression with clinically relevant core molecular subtypes of CRC, obesity-related inflammation, and patient outcome.
Yet surprisingly little is known about how altered lipid metabolism contributes to the aggressive evolution of CRC in obese patients. We find that, despite the remarkable genetic and phenotypic heterogeneity of CRC (57), CES1 expression markedly increases in the metastasis-prone mesenchymal CMS4 subtype and portends worse clinical outcomes in overweight/obese patients. It was previously suggested that cancer cells use fatty acids released by TME-based adipocytes and stromal cells to support rapid tumor growth  (17, 58). Our data do not survival during GL, as shown by the marked toxicity produced by CES1 deficiency in starved CRC cells (Figure  6C ). Similar results were obtained using 2 independent CES1-targeting hairpins (CES1 #2 and CES1 #3; Supplemental Figure  6, D  and E ). These data suggested that CES1 is the dominant effector of NF-κB-dependent metabolic homeostasis in CRC cells during GL, because RelA knockdown had no effect on CRC cell survival under energy stress conditions, unless it resulted in a decrease of CES1 expression (Figure  6A  and Supplemental Figure  6C ).
Methods A detailed discussion of the Methods is reported in the Supplementary Material. Data availability. mRNAseq data set generated in the current study (Figure  3A ) is included in the Supplementary Material files as Supplemental Table  2  and has been submitted to the European Nucleotide Archive under accession number PRJEB32382. The Metabolic Profiling data sets (general platform and lipid platform) generated in the current study (Figure  1, D  and E ; Figure  2, A, ; Figure  4, A, B,  and D ; Figure  6B ; Supplemental Figure  2A ; and Supplemental Figure  4A ) are included in the Supplementary Material files as Supplemental Table  1. The lipid profiling data set generated from patients with CRC is included in the supplementary material as Supplemental Table  1. Data from the analysis of The Cancer Genome Atlas (TCGA) CRC (COADRED) program and the French National Cartes d'iden-tité des Tumeurs (FNCIT) program were classified according to the CMS algorithms described by the Colorectal Cancer Subtyping Consortium (CRCSC) and downloaded using the Synapse browser (ID: syn2623706).
Additional information is available in the section "CRC patients and human CRC data sets" of the Supplemental Methods. Study approval. A prospective, observational study of patients at serial stages along the CRC disease pathway was conducted between January 2015 and January 2017. Ethical approval for this study was provided by the Research Ethics Committee and Health Research Authority (REC reference: 14/EE/0024). Informed written consent was obtained from all the subjects involved in the studies. Mouse experiments were conducted under the authority of UK Home Office PPL P57F16A53. The PPL was approved by the Imperial College Ethical Review Process and the UK Home Office.
Figure1. NF-κB is activated in the mesenchymal CMS4 CRC subtype and regulates lipid metabolism upon starvation. Box plots showing the median z scores of the NF-κB-activation (A) and inflammatory (B) gene signatures in each CMS subtype from patients with CRC (n = 296) in The Cancer Genome Atlas (TCGA) data set. Shown in the box plots are the median values (horizontal lines), 25th to 75th percentiles (box outlines), and highest and lowest values within 1. 5 times the interquartile range (vertical lines). Notches denote the 95% confidence interval of the medians. Samples from each CMS subtype were compared with other CRC samples by using 2-tailed Student's t test. ***P < 0. 001. Statistical significance for multiple comparisons was calculated using the Kruskal-Wallis test (P < 2. 2 × 10 -16 ).
(C) Hierarchical clustering of the 1795 metabolic genes present in the Reactome database from the patients in A and B, arranged according to CMS subtype. The z scores of gene expression are depicted as a gradient from azure (low expression) to orange (high expression). Yellow, CMS1; blue, CMS2; red, CMS3; green, CMS4. (D) Metabolite set enrichment analysis of significantly deregulated biochemicals (q < 0. 05) in MEFs expressing RelA-specific (RelA) relative to nonspecific (ns) shRNAs (n = 5), cultured under normal conditions (0) or for the indicated times under GL. P values were corrected using the Benjamini-Hochberg false-discovery rate (FDR) procedure. Orange, overenrichment (up); azure, underenrichment (down). (E) Heatmap showing the log 2 fold change of metabolic species in the indicated lipid classes in RelA-deficient relative to control MEFs treated as in D.
Acknowledgments We thank  Steven Ley ,  Jacques Behmoaras ,  Navdeep Chandel ,  Richard Lehner ,  David Carling , and  Antonio Iavarone  for critical comments on the manuscript. We also thank  Enrico De Smaele  for the Tween lentiviral vector. This work was supported in part by  Cancer Research UK program  grant  A15115 ,  Medical Research Council (MRC) Biomedical Catalyst  grant  MR/L005069/1 ,  Bloodwise  project grant  15003 , and  NIHR Imperial Biomedical Research Centre (BRC)   ITMAT Push for Impact Award   P81135  to  G Franzoso. Infrastructure support for this research was provided in part by the  NIHR Imperial BRC. LG, ADV, and GC acknowledge the  Università degli Studi di Perugia and MIUR  for financial support to the project  AMIS , through the program " Dipartimenti di Eccellenza 2018-2022 ".
The colors of C-cluster members are further investigated by looking at color variations as a function of asteroid diameter. We observe that the visible slope becomes bluer with decreasing asteroids diameter, and a transition seems to be present around 20 km. We discuss the origin of this variation and, if not related to a bias in the dataset -analysis, we conclude that it is related to the surface texture of the objects, smaller objects being covered by rocks, while larger objects are covered by a particulate surface. The blueing is interpreted by an increased contribution of the first reflection in the case of rock-dominated surfaces, which can scatter light in a Rayleigh-like manner. We do not have unambiguous evidence of space weathering within the C-cluster based on this analysis, however the generally bluer nature of C-cluster objects compared to CM chondrites could be to some extent related to space weathering.
7 µm metal-OH absorption in subsurface material compared to typical surface spectra  (Kitazato et al. , 2021). Still, unambiguous evidence of space weathering effects on C-type asteroids in the visible range are absent at present, and while lunar-style space-weathering products have been identified in ordinary chondrite regolith breccias  (Noble et al. , 2010)  they have not been observed so far in carbonaceous chondrite regolith breccias, to the best of our knowledge. In the present work, we investigate the variability of colors of C-type main-belt asteroids based on the Sloan Digital Sky Survey dataset (SDSS) with the intent to compare to laboratory spectra of carbonaceous chondrites, discuss the C-type carbonaceous chondrites relations, and search for evidences of space-weathering. The data processing of the SDSS dataset is in essence identical to that presented in DeMeo and  Carry (2013).
C-complex asteroid are then extracted from a cluster analysis combining colors and albedo. This work builds on a recently developed approach to assess the impact of observation geometry on meteorites spectra  (Beck et al. , 2021). We show that C-type asteroids are overall bluer than carbonaceous chondrites, which may suggest that space-weathering is generally affecting the surface of C-type main-belt asteroids. However, we find that large C-type asteroids (D>20 km) are redder than small C-type asteroids (D<20 km). This difference could be related to a sampling artifact of the SDSS dataset, biased toward smaller objects. If not, we suggest that this overall blue nature of smaller C-type is related to the presence of coarser-grained material.
Colors and albedo This work uses data from the 4th release of the Sloan Digital Sky Survey (SDSS). The data processing was done following the procedure developed in DeMeo and  Carry (2013). Chiefly, SDSS offers a color filter survey of about 400 000 Solar System moving objects. The colors used for this work are g' (0. 4686 µm), r' (0. 6166 µm), i' (0. 7480 µm) and z' (0. 8932 µm) while the u' band was excluded for the reason described in DeMeo and  Carry (2013). We used color specific magnitude thresholds as defined in DeMeo and  Carry (2013)  to exclude the faintest objects from the catalogue. Objects with too high photometric uncertainties were also excluded from the dataset. For each object, relative reflectance (normalized to the g' filter) is calculated using: 𝑅𝑓 = 10 !. # * %&' ! (' " )(&' !,⨀ (' ",⨀ )* Where  + and  +,⨀ are the magnitudes in a filter for the object and for the Sun respectively.
Laboratory data and phase angle effects Laboratory spectra were taken from the RELAB database, which provides today the most extensive suite of reflectance spectra of extra-terrestrial materials. The Tagish Lake spectra were measured at IPAG on the same lithologies described in  Gilmour et al. (2019). One of the difficulties of comparing laboratory spectra of meteorite to asteroid observation is the impact of observation geometry, in particular in the case of spectral slope. This work builds on the recent study of  Beck et al. (2021a)  that investigated the effect of observation geometry on reflectance spectra of a series of about 70 different meteorites. This showed that for dark objects reflectance and slope could be significantly impacted, but that these effects can be accounted for. Building on that work, for each reflectance spectra used here and measured at g = 30°, the same spectra could be calculated for g = 0°  (Beck et al.
10 % of the mass of the main-belt, and 3. 55 wt. % when Pallas is removed. When the cluster analysis was applied to the dataset studied here, there was no cluster identified with negative gri slope value and low-albedo, which would correspond to B-type asteroids. Rather, this analysis suggests that there is a continuum of objects between B-and C-type rather than two distinct categories. This is in agreement with the somehow arbitrary definition of the B-type, as being objects bluer than the Sun. In order to assess the potential "separability" of a C-cluster from a B-cluster, we need to address whether the slope variability found within the C-cluster is true, or related to observational uncertainty and biases. First, the slope variability found is significant, and can hardly be explained by observation uncertainty or phase angle effects.
This shows that both z-i and gri slope decrease with decreasing diameter. In the case of gri slope two regions seem to be present with a slope change around 20 km diameter objects. In line with the observations discussed here,  Vernazza et al. (2016)  observed a spectral evolution with asteroid diameter in a spectral survey of about 70 Ch/Cgh objects (sub-type of the C-complex). The smaller object analyzed in  Vernazza et al. (2016)  tend to be bluer than larger ones, as well as to present fainter phyllosilicate absorption bands. Because the SDSS dataset (and the asteroid belt) contains much less large asteroid than small asteroids, one needs to verify that the difference in gri slope observed is not due to a number-related bias. Considering large (>20 km) vs small (<20 km) objects, the average values of gri slope seems so be statistically different if the error on the mean (standard deviation divided by the square root of the number) is considered (2.
27 %/100 nm ± 0. 10, n=470; 1. 43 %/100 nm ± 0. 03, n=6272). Nevertheless, it cannot be strictly ruled out that some bias exists in the dataset or analysis that would produce the gri slope vs diameter trend. We tested phase angle as a possible source of bias, but the range of phase angle are similar between large and small objects, and no correlation was found between phase angle and gri slope. Because large and small objects are on average of different magnitude, the difference may be caused by a magnitude dependent bias. But at the moment we do not have evidence for such a bias. To summarize 5. 1 and 5. 2, there is a continuous variability of slope within the Ccluster, and smaller members seem to be slightly bluer than larger members. We will suppose that this variation is true, and discuss in the following sections several possible causes for this variability.
Grain size effect and Rayleigh-like scattering by grain surfaces Grain size are known to have a significant impact on reflectance spectra of carbonaceous chondrites, and more generally texture (powder, pellet, chip) can affect spectral slope and the depth of absorption features  (Ross et al. , 1969; Johnson and Fanale, 1973, Cloutis et al. , 2018). In order to assess the impact of sample texture, we used reflectance spectra obtained on small fragments of fresh CM chondrites, which were subsequently ground into powders. These data obtained on the Aguas Zarcas CM fall are presented in  Beck et al. (2021b) , and revealed that CM fragments are much bluer than powders, and show fainter absorption features. The reason for this reddening after grinding may be due to the comminution of red-sloped phases that may become more visible when finely mixed with other minerals  (Ross et al.