dataesr/scientific-paragraphs-categorization huggingface dataset:
This may explain the resistance to treatment often observed, in particular to treatments that were effective during the first years of application  [54]. These genes, whose mean expression does not vary significantly but whose dispersion of expression increases in cancer, form de facto a new space for the discovery of potential biomarkers. Specificity of the overrepresented functions among DD+ genes. Although DD genes are the main focus of interest in our approach, we also identified biological processes enriched among upregulated (S4 File) and downregulated genes (S5 File) in tumors with respect to healthy samples. Among all the previously discussed catabolic GO terms enriched among DD + genes, "mRNA catabolic process" and "protein targeting" are not found to be enriched among DE genes for any dataset while the others are found to be enriched among upregulated genes for less datasets and less significantly (S5 File).
Surprisingly, we found that this test performed similarly or, in some conditions, even better than MDSeq and DiPhiSeq. Yet, the authors of MDSeq claimed that their method is more powerful than Levene's test based on simulated datasets where no difference in means and large differences in dipersions were introduced. We hypothesized that our more realistic simulations with moderate to high values of mean fold change, the sensitivity of MDSeq to differences in mean expression and the more conservative FDR-controlling procedure applied to MDSeq outcomes explain our results. Nevertheless, despite a lower sensitivity, the application of these methods to TCGA datasets revealed that MDSeq specifically identified more DD+ genes than Levene's test. Specific features. An advantage of MDSeq and GAMLSS over the other evaluated methods is the inclusion of any additional covariate in their statistical models to prevent some sources of bias from confounding the comparison of interest.
Similar to most differential expression analysis methods based on the NB distribution  [36, 59] , MDSeq implements a GLM that may take into account classical sources of bias, such as batch effects, in the detection of DD genes (see, for example, LMAN2 expression in the lung adenocarcinoma dataset in S7 Fig) and therefore appears to better handle technical biases. On the contrary, DiPhiSeq and DiffDist do not allow the inclusion of any additional covariate in their respective statistical models. In the case of DiPhiSeq, this limitation is partly mitigated by the use of a Tukey's biweight function that removes any aberrant value regardless of its source, either biological or technical. Moreover, MDSeq also implements a zero-inflated model for which the goal is to control the statistical bias that may be introduced by an excess of null values in the analysis of gene expression data, which is particularly relevant for the analysis of single-cell expression data.
Single-cell RNA sequencing technologies have emerged in the last few years, enabling the study of gene expression variability at the cellular level. The application of differential dispersion identification methods to this type of data is promising for a wide range of biological contexts. For example, in the context of cancer, the population of cells composing a tumor may exhibit a high level of gene expression variability, potentially leading to therapeutic failures  [64]. However, analyzing the data generated with these techniques faces new methodological issues. Technical null values, or dropouts, are much more present in single-cell RNAseq data than in bulk RNA-seq data due to the limited amount of mRNA material available at the cellular level  [65]. Besides, the high number of samples of single-cell RNA-seq datasets with respect to bulk RNA-seq datasets and the usually lower numbers of expressed genes observed make the read count matrices much more sparse  [65]  and computation time longer.
With the emergence of comprehensive RNA-seq datasets, composed of either single-cell or bulk samples in a variety of biological contexts, we believe that the changes in dispersion in gene expression between samples from different conditions of biological interest should now be taken into account. In the classical differential mean expression analysis, it would provide a more realistic model of the data and, in the explicit goal of identifying genes with a differential variance in their expression, it may contribute to gaining new insights into biological processes and eventually to discovering new biomarkers and therapeutic avenues.
RNA-seq dataset simulation We simulated RNA-seq count datasets using the compcodeR R package  [66]. The simulated datasets are composed of 10 000 genes and two sets of samples of equal size corresponding to biological conditions of interest. Read counts for gene i and sample j are generated by random sampling using a negative binomial distribution: Y ij � N Bðm irðjÞ ; � irðjÞ Þ, where μ iρ(j) and ϕ iρ(j) are the mean and dispersion parameters, respectively, and ρ(j) denotes the condition of sample j (ρ(j) 2 {1; 2}). The mean μ i1 and dispersion ϕ i1 values for the first condition are estimated by pairs from real datasets  [67, 68]. The mean and dispersion parameters for the second condition were generated by selecting a fraction of the genes to be subjected to a fold change in mean or dispersion.
e. FC m;min i ¼ 1:5, c m i was drawn from an exponential distribution of parameter λ = 1 and d m i was equally likely to be 1 or -1. Since the evaluation of differential mean expression detection performance is not the main goal of our approach, we simulated non-DE genes in a more realistic way than having the same mean expression value for both conditions. Instead, we allowed slight fold changes by random sampling using uniform distributions: FC m i � Uð1; FC m;min i Þ, where the maximum value corresponds to the minimum value of fold change for highly DE genes. These genes are therefore called lowly DE genes rather than non-DE genes. • Moderated mean expression fold changes: Since the purpose of these datasets is only to assess differential dispersion detection performance for lowly DE genes, the distinction between DE and non-DE genes is not required.
For all the genes of these datasets, mean fold changes were introduced using uniform distributions: FC m i � Uð1; FC max i Þ, where FC max i 2 f1:1; 1:2; 1:3; 1:4; 1:5g. For all the simulated datasets, the same fractions of DD genes (or non-DD genes) among highly DE genes and lowly DE genes (for the first set of simulations) were ensured, as well as the same fractions of DD genes with an increase in dispersion in the second condition (DD+) among upregulated genes (DE+) and downregulated genes (DE-) in the second condition. Thus, simulated datasets are composed of 50% DD genes and 50% non-DD genes and 50% highly DE genes and 50% lowly DE genes for the first set of simulations. For the sake of realism, we introduced outliers with very high counts in all the simulated datasets since Li et al.
Differential dispersion detection Levene's test  [47]  was applied to the normalized read counts after log 2 -transformation and the addition of a pseudocount of 1 to avoid null values. Genes with a p-value lower than 0. 05 after the correction by the Benjamini-Hochberg procedure  [72]  were considered as differentially dispersed. The outlier removal function was applied with the minimum sample size lowered to 1 before applying the MDSeq  [39]  main function to all the simulated and TCGA datasets. Batch effects were handled when analyzing TCGA datasets by supplying the sequencing runs that generated the RNA-seq samples, when available, as a covariate in the GLM for both the identification of DD genes and the identification of DE genes. A fold change threshold of 1 was used to identify both DE genes and DD genes.
The default values were used for the other parameters for both the outlier removal and MDSeq functions. The p-values for both differential mean and differential dispersion statistical tests were corrected by the Benjamini-Yekutieli procedure  [73]  to control the FDR as recommended by the authors of MDSeq. DiPhiSeq  [40]  was applied to all the simulated and TCGA datasets with the default values for all the parameters, in particular the c parameter of Tukey's biweight function set to 4 for both the mean and the dispersion estimation since the authors of DiPhiSeq found that this value enables robust parameter estimations  [40]. The p-values for both differential mean and differential dispersion statistical tests were corrected by the Benjamini-Hochberg procedure  [72]  to control the FDR as recommended by the authors of DiPhiSeq.
We applied the approach proposed by de Jong et al. [41]  of using a GAMLSS to identify DD genes by transposing the analysis method presented in the following GitHub repository: https://github. com/Vityay/ExpVarQuant. DiffDist  [42]  was applied to all the simulated and TCGA datasets with the default values for all the parameters. The tail probabilities for both differential mean and differential dispersion statistical were corrected by the Benjamini-Hochberg procedure  [72]  to control the FDR as recommended by the authors of DiffDist.
Performance evaluation The performances of differential dispersion detection methods were evaluated based on the fold changes of dispersion of expression introduced in the simulated datasets. The genes that were simulated to be DD are the positive group for the differential dispersion performance evaluation. The genes that were simulated to be non-DD are the corresponding negative group. For all the evaluated methods, a p-value for the differential dispersion or differential variance statistical test lower than 0. 05 after the application of the appropriate FDR-controlling procedure was used to define a positive detection. The comparisons with the positive group enabled us to count true positive and false positive results for differential dispersion. Similarly, true negative and false negative results were identified thanks to a corrected p-value of differential dispersion statistical test higher than 0.
Gene Ontology term enrichment analysis across datasets For each TCGA dataset, genes of interest, e. g. DD+ genes among non-DE genes, were identified and Gene Ontology (GO) term enrichment analysis was performed using the Biological Processes (BP) ontology. Enriched GO terms were identified thanks to a hypergeometric test p-value after FDR control lower than 0. 05 using the enrichGO function of the clusterProfiler R package  [74]. The list of enriched GO terms was then reduced by gathering terms whose semantic similarity exceeded a threshold value. To do so, clusters of closely related GO terms were generated through the relevance method  [75]  to compute semantic similarity between GO terms. A high similarity threshold (0. 8) was used to gather only closely related GO terms into clusters. The GO term whose p-value is the lowest among a cluster was then chosen to represent the entire cluster.
To facilitate comparisons across datasets, closely related GO terms were searched for among the previously simplified enriched GO term lists originating from each dataset. The similarity of all GO term pairs was calculated with the relevance method. These similarity values were then used to perform hierarchical clustering and gather closely related GO terms by using a conservative threshold value (0. 8). For each cluster of closely related GO terms, the hierarchical structure of the BP ontology was used to identify a generic term common to all the GO terms. This common generic GO term was subsequently used as the representative term for the entire cluster, and its enrichment p-value was retrieved for each TCGA dataset containing an enriched GO term in the cluster.
, 2015). CAMS delivers as well a global reanalysis, which provides consistent multiannual global datasets of atmospheric composition  (Innes et al. , 2019). The forecasts and reanalysis 3 provide three-dimensional fields of aerosols, chemical species and greenhouse gases. In order to provide these forecasts and reanalysis, the CAMS chemistry-transport models require up-to-date surface emissions with constant updates. None of the currently available inventories provide the emissions required by the CAMS modeling system: either they cover a too short period, or they don't provide emissions for the most recent years, as required for global forecasts. Therefore, a new dataset called CAMS-GLOB-ANT has been developed during the past few years, which includes the emissions for air pollutants and greenhouse gases for the most recent years, at the spatial and temporal resolution required by the models.
This paper describes the existing datasets (Section 2) used in the methodology to develop CAMS-GLOB-ANT (Section 3), the comparison of the dataset with other inventories (Section 4) together with information on studies done using the CAMS-GLOB-ANT dataset. Section 5 includes information on how to get access to the dataset and make on-line evaluation of the emissions. It should be noted that the emissions of PM10 and PM2. 5 are not provided in the CAMS-GLOB-ANT dataset: the CAMS-GLOB-ANT version 5. 3 emissions are already used in the CAMS models or other models such as MUSICA  (Tang et al. , 2023) ,  CESM (Gaubert et al. , 2021; Bouarar et al. , 2021) , IFS  (Huijnen, 2022)  where the mass concentrations of PMs are calculated within the models using the specific chemical and physical schemes included in each of these models: such models therefore just require the emissions of species such as SO2, NH3, BC and OC.
Datasets used in the development of CAMS-GLOB-ANT The version of the CAMS-GLOB-ANT global anthropogenic emissions described in this paper is version 5. 3, which is based on the Emissions Database for Global Atmospheric Research (EDGAR, version 5), the emissions provided by the Community Emissions Data System (CEDS, version of April 2021), the temporal profiles from the CAMS-GLOB-TEMPO dataset and the ship emissions from the CAMS-GLOB-SHIP dataset. Each of these inventories is described in the following paragraphs.
EDGAR emissions EDGAR, the Emissions Database for Global Atmospheric Research (https://edgar. jrc. ec. europa. eu/, last access July 2023), is developed at the Joint Research Center (JRC) in Italy. EDGAR provides emissions as national totals and gridmaps at 0. 1 x 0. 1degree resolution at the global level, with yearly and monthly averages. The emissions are calculated using a technology-based emission factor approach for the different countries, and for 27 different sectors: the list of the sectors included in EDGARv5 is shown in Table  S1. The methodology used for the EDGAR emissions is described in  Crippa et al. (2018; 2021)  and online at: https://edgar. jrc. ec. europa. eu/methodology. We have used here version 5 of EDGAR, which provides for the 1970-2015 period, emissions for greenhouse gases (CO2, CH4 and N2O) as well as for air pollutants, i.
e. CO, NOx, NMVOCs, NH3, SO2, BC, OC and particulate matter (PM2. 5 and PM10). The national totals and gridded emissions for the different years and sectors are publicly available at: https://edgar. jrc. ec. europa. eu/dataset_ap50 (last access, July 2023). The emissions for 25 speciated volatile organic compounds are also provided by EDGAR up to the year 2012 (https://edgar. jrc. ec. europa. eu/dataset_ap432_VOC_spec; last access July 2023), following the methodology described in  Huang et al. (2017). S1.
CAMS-GLOB-SHIP emissions For the ship emissions, we have used the CAMS-GLOB-SHIP, version 3. 1, dataset. These emissions are generated by the Ship Traffic Emission Assessment Mode (STEAM3,  Johansson, 2017)  for SOx and NOx in the Baltic Sea, North Sea and North America as well as Chinese domestic ECAs and vessel type specific rules for ships operating in European seas. Global Sulphur cap (limit on the sulphur content in the fuel oil used on board ships) became effective in Jan 1 st 2020, which decreased SOx and PM emissions, but ECAs were unaffected by the cap because ECAs have had similar rules in effect for several years, already. The CAMS-GLOB-SHIP emissions are provided for several chemical compounds, CO2, CO, NOx, NMVOCs, BC and OC, SO2 and SO4 as well as ash. The CAMS-GLOB-SHIP emissions are available from the ECCAD (Emissions of atmospheric Compounds and Compilation of Ancillary Data) database, at https://ecad-aeris-data.
CAMS-GLOB-TEMPO temporal profiles The CAMS-GLOB-ANT monthly variability is implemented using monthly averaged profiles from the CAMS-GLOB-TEMPO dataset, described in  Guevara et al. (2021) : the daily, weekly and monthly temporal profiles are available on the ECCAD database. These temporal profiles consider the primary atmospheric pollutants, NOx, SO2, NMVOCs, NH3, CO and the greenhouse gases CO2 and CH4, for the following categories: energy, industry, road transportation, residential and agriculture (livestock, soil and agricultural waste burning), which have a direct correspondence with the sector classification considered in CAMS-GLOB-ANT (Table  S1 ). For the sectors not considered in CAMS-GLOB-TEMPO, the EDGAR version 5 monthly profiles are used. The temporal profiles are available at a 0.
Definition of the sectors CAMS-GLOB-ANT version 5 provides anthropogenic emissions for 17 sectors and for 36 species: CO2 (divided into short cycle and excluding the short cycle), CH4, N2O, NOx, NMVOCs, SO2, BC, OC, NH3 and 25 speciated volatile organic compounds. The emissions are provided for the sectors indicated in Table  S1 , which corresponds sometimes to the grouping of EDGAR sectors. The dataset starts in 2000, as requested in the CAMS reanalysis which also starts in 2000. In order to calculate the emissions for the full 2000-2023 period for these sectors, a harmonization and grouping of the sectors of the EDGARv5 and CEDS datasets was performed. The sectors used in CAMS-GLOB-ANT are detailed in Table  S1 , and the corresponding sectors in the EDGARv5 and CEDS inventories are shown in table S1.
The sectors have been chosen, so that they represent the main anthropogenic activities corresponding to power generation, industrial activities, fuel operations, different modes of transportation, residential, agriculture activities and waste management. They have also been chosen, so that they are compatible with the definition of the sectors of the European dataset CAMS-REG  (Kuenen et al. , 2022). The sectors considered for the emissions of each of the 25 speciated volatile organic compounds considered in the inventory are indicated in Table  S2.
Extrapolation of the emissions to the most recent years In order to obtain emissions that can be used in global forecasts and long-term reanalyses, it is necessary to extrapolate the emissions to the most recent years and months. The EDGARv5 emissions are available until the year 2015 and the CEDS emissions are available up to 2019. We have used EDGARv5 emissions as a basis for the CAMS emissions, as their spatial resolution is 0. 1x0. 1 degree, which is the resolution of the emissions required by the CAMS global forecasts. The CEDS emissions are used for the extrapolation of the emissions. For this extrapolation, we have first grouped the CEDS emissions for each species, in order to obtain the totals emitted for each country for the sectors included in the CAMS-GLOB-ANT dataset, following Table  S1.
Aircraft emissions To be consistent with the VOCs speciation of the surface anthropogenic emissions, the emissions of speciated VOCs emissions from aircraft in CAMS-GLOB-AIR are based on the annual sector-specific gridmaps of speciated VOCs described by  Huang et al. (2017). We used the EDGAR aircraft emissions for landing/taking off and cruise altitudes. We calculated the ratios of the emissions of each individual VOCs to the total NMVOCs species for each of these two altitude levels in the EDGAR dataset, at a 0. 1x0. 1degree horizontal resolution. The ratios for the landing/taking off level were then applied to the first two levels (0. 305 km and 0. 915 km) to the CAMS-GLOB-AIR NMVOCs species to get the aircraft emissions of each individual VOC. The ratios for the cruise altitude were applied to all the other levels indicated in Table  S2  to obtain the emissions for the rest of the altitudes considered.
Analysis of the CAMS-GLOB-ANT emissions and comparisons with other datasets This section will analyze the changes in the emissions of CO, NOx, NMVOCs, SO2, NH3, BC and OC for the regions for which regional inventories are available, i. e. Western Europe, Central Europe, the USA, China and India. The countries in each of the Western and Central Europe regions are indicated in Table  S6. The changes in the total emissions for each of these regions from 2000 to 2012 and from 2012 to 2023 are shown in Figure  8. This figure shows significant differences between the trends in all species between the two periods. These changes will be discussed in more details in the following sections. Comparisons will be performed using both regional inventories when.
SO2 emissions from power plants, which were still rather important in 2000 have been strongly regulated and have decreased from 2. 5 T SO2/yr to 0. 8 Tg SO2/yr in 2023. However, the emissions of SO2 from industrial activities, the 2 nd largest contributor to the SO2 emissions have decreased by about 30% of the full period, which explains the decrease in the emissions reported in Table  1. Figure  10  shows a comparison of the CAMS-GLOB-ANT_v5. 3 emissions with other inventories, i. e. the global inventories indicated at the beginning of Section 5, as well as with version 5. 1 of the regional CAMS-REG dataset  (Kuenen et al. , 2022). For CO, all the inventories show similar values after 2009, except for MACCity and ECLIPSE, which show lower emissions after 2010: these two inventories are based on older scenarios after 2000-2005.
For NOx, the trends are similar among the inventories, with downward trends for the whole period. MACCity and ECLIPSE, based on scenarios, provide higher NOx emissions in the most recent years. CAMS-GLOB-ANT and CAMS-REG show rather similar values: these two datasets use very different methodologies, CAMS-REG being based on values reported by countries at the EMEP organization  (Wankmüller, 2019) , while CAMS-GLOB-ANT is based on EDGARv5 and CEDS. For SO2, the CAMS-GLOB-ANT emissions, which follow the EDGARv5 and the trends in the CEDS emissions are higher than the regional emissions at the end of the period, and slightly lower at the beginning of the period. Rather similar patterns are seen in the change in the emissions of pollutants and greenhouse gases in Central Europe (Table  3  and Figure S2  considered period, mostly due to an increase in residential combustion and to road transportation to a lesser extent.
United States of America Changes in the US emissions of several species for the same years as considered in Table  1  are given in Table  4. The emissions of all species have decreased significantly, except for CH4 and NH3. The increase in the CH4 is related to oil and gas operations, as well as from livestock agriculture practices. For NH3, the constant increase is due to the use of fertilizers and livestock operations. The differences shown in Figure  11  between the inventories are larger for the NMVOCs species, particularly in the early 2010s, with larger emissions from the EPA dataset and the lowest values in the old MACCity emissions and in EDGAR6. These differences are related to the road transportation sector and to a lesser extent to the solvents sector.