dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 2 global emission database for PM 2. 5 and PM 10 and CAMS v4. 1 for organic carbon (OC), BC, and volatile organic compounds (VOCs). However, the results presented in this article will only cover the species included in the PAPILA inventory. For ENE, TRO, and RES, monthly emission patterns were defined to breakdown total annual emissions into monthly fluxes (see Fig. A2 ). Emissions from other categories were evenly distributed throughout the year. The RES monthly cycle was established from the reports on natural gas consumption reported in national statistics (ENARGAS, 2021) for residential and commercial activities in Buenos Aires. This profile shows a maximum during winter linked to the increase in residential heating. Similarly, the TRO monthly cycle was defined from the total fossil fuel consumption from the road transport reported in the statistics for the entire country (Secretaría de Energía, 2021).
For ENE, the same source of data was used to obtain monthly fossil fuel sales for thermal power plants in Buenos Aires Province. Weekly cycles taken from PREP-CHEM  (Freitas et al. , 2011)  were applied to the resulting total monthly emission fluxes. The diurnal cycles were adapted from those reported by  Wang et al. (2010) , focusing on reproducing Buenos Aires traffic patterns observed in the two monitoring stations: Parque Centenario and Córdoba. With this approach, the best configuration obtained with the simulations includes 3-hourly emission patterns: one related to diesel vehicle emissions, defined using PM observations, and the other associated with gasoline car emissions in winter and in summer, defined with the measured concentrations of CO and NO x.
Model evaluation The highest-resolution model outputs using these two emission inventories were evaluated against CO and NO x groundbased observations from the available monitoring stations in Buenos Aires (see locations of the sites in Fig. A2 ). Air pollutant data from the Environmental Protection Agency of Buenos Aires (APRA) include hourly measurements of NO x and CO at two sites, Córdoba (34. 60 • S, 58. 39 • W) and Parque Centenario (34. 61 • S, 58. 44 • W). Córdoba's site is located in a commercial area with high vehicular flow and very low incidence of stationary sources while Parque Centenario is located in a residential area next to an arboreal space with medium vehicular flow and also very low incidence of stationary sources. As the air quality database is at an hourly resolution, the model was also sampled every hour.
Results and discussion The spatial distribution of 2015 annual emissions of CO, NO x , NMVOCs, NH 3 , and SO 2 is shown in Fig. 4  for the sum of all categories. In addition, this figure includes maps with the differences between PAPILA and CAMS datasets for each species, depicting the differences in terms of intensity and location of emission sources. For comparative purposes, emissions from agricultural fires have been subtracted from the sum of all categories in the CAMS database. In what follows results are presented firstly by species, highlighting the most relevant aspects of the 2015 emission (Sect. 3. 1). Then, surface concentrations of CO and NO x obtained from the use of the PAPILA dataset as input information in a chemical transport model in Buenos Aires, compared to those obtained using CAMS, are presented (Sect.
Carbon monoxide Local estimates of CO for Argentina and Colombia presented lower CO annual emissions than those in CAMS; the largest differences occurred under the TRO category. Although the local estimates for TRO in Chile also showed significantly smaller levels, this difference was masked in the total CO national estimates by the larger emissions from the residen- tial category, even after having omitted CO emissions from fuel combustion other than firewood. Lower CO PAPILA emissions in Argentina (-39 %) and Colombia (-54 %) were compensated for by larger PAPILA emissions in Chile (58 %). According to the CAMS dataset these three countries are only responsible for 25 % of the total SA emissions, and hence the impact of the changes introduced in this work on total SA is very limited. This same situation occurs with the other species, for which it is found that these three countries are only responsible for 20 %-25 % in the case of NO x , NMVOCs, and NH 3 and 31 % for SO 2 (Table  2 ).
However, when analyzing the impact on these three countries together, and even when these countries compensate for each other, a difference with CO CAMS emissions of -19 % is observed. At the urban level, in the Buenos Aires domain PAPILA emission estimates were 12 % higher than those from CAMS; this difference was mainly associated with higher local emissions from TRO and ENE + IND, even with lower emissions from RES. In the same way, Mendoza and B. Blanca exhibited lower CO total levels, mainly associated with differences in TRO and to a lesser extent in RES. In B. Blanca, this difference masked the larger emissions by a factor of 5 in the local estimates of ENE + IND with respect to the global dataset. By downscaling the B. Blanca urban domain, we identified the absence of emissions from shipping activities in the global inventory.
In Antofagasta, although total emissions levels from both datasets were similar, there were substantial differences in the contributions by category: emissions from ENE + IND are almost 7 times larger in PAPILA than in CAMS, and while RES and TRO emissions are negligible in the local estimates, according to global estimates they contribute to almost 90 % of the domain's emissions. On the contrary, in Osorno local estimates for the sum of all categories were 7 times larger than those in CAMS, emissions coming almost entirely from residential firewood burning.
As seen in Fig. 3 , all urban domains showed higher local emissions, except Antofagasta with a barely noticeable difference. In B. Blanca, the most relevant differences were the larger emissions in ENE + IND and SHP, a category for which CAMS reported zero emissions while according to the local data it represented 12 % of the domain's emissions, despite the omission of the international port as a source of emission. Relevant larger emissions existed for TRO, ENE, and RES in Buenos Aires and Mendoza, together with significantly smaller emissions from IND. The larger emissions from other categories in Buenos Aires and B. Blanca are mainly attributable to SHP, and although the impact of other categories on the total budget in these domains was negligible, local estimates showed considerably higher levels than CAMS for emissions from agricultural activities and FEF, a category for which the global database attributed zero emissions in the three urban domains.
Local estimates showed important differences in total emissions for the three Argentinean domains (around 60-70 % lower than CAMS), with IND being the main contributor. Smaller emissions from FEF were observed in B. Blanca and Mendoza while TRO contributed to these differences in the first domain and counteracted them in the second. In Buenos Aires emissions from FEF and TRO were considerably larger than those in CAMS. Even when estimates from RES in PAPILA were around 80 % lower than those of CAMS, they exhibited less of an impact on the differences between the two datasets and on the total emissions in each domain. Local estimates for Santiago and Antofagasta were significantly smaller (around 80 %) than the global ones; the difference is mainly attributable to the adopted local information on locations and emission shares for ENE + IND.
Ammonia Similarly to NMVOCs, the only two countries with local data on NH 3 are Chile and Argentina. At the national level, the inclusion of local information is reflected in differences of -7 % of NH 3 emissions in Chile (only attributable to RES) and -40 % in Argentina, where smaller emissions from AGS were mainly responsible for that difference, partially offset by larger emissions from AGL. These two categories repre-sent the main sources of NH 3 emissions in the country with a contribution of 72 % from soils and 24 % from livestock, according to the local estimates. Smaller emissions in local estimates of Argentina and Chile were reflected in a difference with CAMS of -30 % of the emissions of both countries together. Although the impact of emissions from urban domains on the total levels of each country was negligible (around 1 %), big differences were found at the category level between the two datasets.
Case study: model evaluation and results Table  3  summarizes the overall model performance of PA-PILA and CAMS-based results for hourly CO and NO x concentrations for the Buenos Aires case study. For the winter period, PAPILA-based results had lower normalized mean error than CAMS-based results; the negative bias was larger for the CAMS emission run, exceeding more than 12 % in all cases for both CO and NO x except for NO x in Parque Centenario. FAC2 was also better in PAPILA simulation. Differences in the concentrations resulting from both runs were consistent with those exhibited between the inventories. In terms of CO emissions, PAPILA dataset emissions were 12 % higher than CAMS, with road transportation being mainly responsible followed by industry and residential sources. On the other hand, NO x emissions were 46 % higher in PAPILA, with significant discrepancies in emissions mainly from TRO followed by ENE and IND.
PAPILA-CAMS main differences Our results show relevant differences between the PAPILA and CAMS datasets, in terms of both emission levels and their spatial distribution. They also served to exemplify the quality of the PAPILA-based simulated surface mixing ratios in the city where concentrations were analyzed. The reasons behind the observed differences are diverse and are mainly linked to the activity data and to the methodologies applied to estimate and spatially distribute the emissions. Given the limited availability of local data, the emission factors for the compounds covered in this work were mostly based on default values in both the global and local datasets considered. In general, it is observed that CAMS-GLOB-ANT v4. 1 applies linear trends to the emissions of the aggregated categories, based on the global estimates of EDGAR v4.
Another relevant aspect of national and therefore international statistics is the lack of reliable information on firewood consumption, widely used in rural areas of SA and even in some urban areas, such as the cold regions of Chile. This fact also impacts the correct representation of the replacement of firewood by LPG or natural gas that has taken place in Argentina in the last decade, due to higher production of non-conventional shale gas in the Vaca Muerta basin  (El Pais, 2015)  and the resulting reduction in fossil fuel prices. Additional differences between local and global datasets are related to different resolutions of the population distribution maps used as proxies for the spatial distribution of the emissions from some categories. Local inventories use population density information based on higher-resolution maps than those used by the global ones.
, 2018). This approach may not be the most appropriate for many countries in the region, where changes in economic policies and even the occurrence of economic crises are frequent, affecting not only consumption patterns but also the relocation of activities dependent on regional economies. In addition, substantial differences have been observed in terms of the location of the IND sources in both inventories, probably attributable to the drivers used by the global databases for this purpose, with a very low presence of sources throughout the Argentine territory in CAMS and a striking abundance of sources distributed over the central and northern region in Chile, which however do not reflect the heterogeneous share of emissions shown by the local distribution. Although the representativeness of agricultural emissions in global databases continues to improve over time, there are many aspects that global methodologies have so far failed to replicate.
In this sense, the incorporation of local information has given the inventories a greater capacity to reflect the specific agricultural practices of the region, such as the predominant use of grazing for cattle farming, the use of large proportions of urea for the fertilization of crops, and economic, natural, and technological changes that have occurred during the last decades  (Castesana et al. , 2018 (Castesana et al. , , 2020)). Lastly, and with the aim of contributing some aspects that may improve both global inventories and the dataset presented here, we list some information gaps identified when using CAMS-GLOB-ANT v4. 1 as a base inventory for the region: (i) emissions from domestic and international civil aviation are not included; (ii) navigation activities are not reflected by downscaling the port city B.
, 2019). For its part, the item (v) brings to light the problems around waste management in the region, and consequently around the representativeness of the associated emissions. In SA, waste is often disposed of in open dumpsites, and, particularly regarding CO, NO x , and SO 2 emissions, uncontrolled open burning occurs, and these practices are not reflected in local or global emission maps (UNEP, 2018). All these gaps in the base inventory were addressed in the PAPILA dataset, except FEF emissions and NMVOC emissions from agricultural activities in Argentina.
Data availability Gridded maps with all inventories per species and year are available as netCDF4 (Network Common Data Format) files for the regional domain (34 • N-58 • S, 32-120 • W) at a resolution of 0. 1 • × 0. 1 • (PAPILA dataset) and can be accessed through the open-access data repository https://doi. org/10. 17632/btf2mz4fhf. 3, under a CC-BY 4 license  (Castesana et al. , 2021). The dataset includes all necessary attributes to be easily used in air quality simulations, such as molecular weight, projection, and units. In addition, temporal profiles applied in the inventory evaluation for the metropolitan area of Buenos Aires, Argentina, are available in Appendix A of this work.
Conclusions This work presents the results of the first joint effort of South American countries to generate regional maps of emissions inventories. With a mosaic approach, we developed a new anthropogenic emission inventory for South America, named PAPILA, harmonizing inventories of Argentina, Chile, and Colombia in a uniform format. To such an end, locally developed emission inventories of these countries have been supplemented with the existing data of the global dataset CAMS-GLOB-ANT v4. 1 to fill the gaps. The PA-PILA dataset consists in annual emission gridded maps of reactive gases, namely CO, NO x , NMVOCs, NH 3 , and SO 2 , for the period 2014-2016 with a spatial resolution of 0. 1 • × 0. 1 • over a domain defined by 32-120 • W and 34 • N- 58 • S. This study introduces significant improvements to global inventories mainly observed in the spatial distribution of emissions, in a better representation of the interannual variability of emissions strongly related with short-term variations and technological changes, and in an improvement of the representation of emissions from informal activities, such as the use of wood for heating and cooking.
However, CAMS emissions in these three countries represent only 31 % of the total SA emissions for SO 2 and between 20 % and 25 % for the rest of the species. Therefore, further studies are necessary to complement this work with local data from other countries with significant emission levels, such as Brazil, Peru, and Venezuela. A complete analysis of the performance of the inventory developed in this work could be provided by comparing model simulations with surface and satellite observations in the whole domain where local data have been integrated into the global dataset. Since South America is a wide region with significant differences in terms of latitudinal extension, topography, and vegetation patterns, a high-resolution model would be needed to capture the boundary layer process and surface energy budget of each area where the main changes have been introduced.
As a first step of this verification exercise, we have presented a case study focused on the metropolitan area of Buenos Aires, using the WRF-Chem regional model. Although there is room for improvements that may be linked to both emissions and other processes, the CO and NO x modeling results showed a better performance with the PAPILA dataset over CAMS in reproducing surface observations in this big urban area. Nevertheless, further modeling studies with PAPILA and CAMS datasets are needed over other key areas of Argentina, Chile, and Colombia. This work highlights the strengths and weaknesses not only of global inventories but also of local ones. Although the latter improve the representativeness of the estimates, the groups that generate information on emissions in the region do not necessarily have the same objectives: some are mainly oriented towards the generation of input information for models and others towards mitigation measures that respond to air pollution concerns of their region.
In this sense, it is worth mentioning that although the resources in the region often limit their growth, the capacities of the groups are growing, which is partly reflected in the development of local databases published in this same special issue  (Puliafito et al. , 2021; Álamos et al. , 2021; Osses et al. , 2021). In addition to individual advances, we want to emphasize the role of the PAPILA project and the EMISA initiative, which promote collaboration between groups in the region, enhancing efforts aimed at the development of appropriate and consistent surface emission inventories. In this context, we trust that this work will be a starting point for the development of comprehensive emission inventories for South America enriched with local information. To this end, the first step will be to join the efforts of other countries in this endeavor, encouraging those with inventory capabilities to broaden their focus beyond cities by building national emission maps.
This work was conducted within the framework of the Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) project. We greatly acknowledge ECCAD (https://eccad3. sedoo. fr/, last access: 22 June 2021) for the archiving and distribution of the datasets CNEA-3iA-GEIA (from Argentina), CR2-MMA (from Chile), and CAMS-GLOB-ANT v4. 1 used in this work. The authors also wish to thank the Environmental Protection Agency of Buenos Aires (APRA) for sharing the air quality data for this study. Financial support. This research has been supported by the European Union H2020 program PAPILA (GA 777544), the Agencia Nacional de Promoción Científica y Tecnológica, Fondo para la Investigación Científica y Tecnológica(PICT-O 2016-4802 and PICT  2016-3590), and the Centro Científico y Tecnológico de Valparaíso (ANID PIA/APOYO AFB180002).
Acknowledgements. This work was conducted within the framework of the Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) project. We greatly acknowledge ECCAD (https://eccad3. sedoo. fr/, last access: 22 June 2021) for the archiving and distribution of the datasets CNEA-3iA-GEIA (from Argentina), CR2-MMA (from Chile), and CAMS-GLOB-ANT v4. 1 used in this work. The authors also wish to thank the Environmental Protection Agency of Buenos Aires (APRA) for sharing the air quality data for this study. Financial support. This research has been supported by the European Union H2020 program PAPILA (GA 777544), the Agencia Nacional de Promoción Científica y Tecnológica, Fondo para la Investigación Científica y Tecnológica(PICT-O 2016-4802 and PICT  2016-3590), and the Centro Científico y Tecnológico de Valparaíso (ANID PIA/APOYO AFB180002).
Delayed motor milestone presumably consecutive to joint instability was observed in seven individuals; four of them also had mild (I-5, I-6, and I-8) or severe (I-7) intellectual disability. Predisposition to allergic or inflammatory diseases, which was previously documented in LDS,  18, 19  was evidenced in six individuals (I-5, I-6, I-7, I-8, I-10, and I-12), some of whom also displayed immunological parameters consistent with impaired TGF-b signaling, including hyperIgE, hyperIgG, hypoIgA, and hypereosinophilia. As detailed in Table  2 , 11 variants were identified in the 12 individuals: seven as homozygous and four as compound heterozygous in IPO8 (GenBank: NM_006390. 3). This gene (25 exons, 24 introns) encodes a 1,037 amino acid protein with the b-importin N-terminal domain (22-102 aa) and a CSE1-like domain (202-441 aa) containing a RanGTPase-binding motif characteristic of b-importins (Figures  2A  and 2B ).
The impact of variants on protein level was evaluated by immunoblot analysis of protein lysates from primary cells, either fibroblasts or Epstein-Barr virus-immortalized B cell lines (EBV-B cells). A striking reduction of IPO8 level was observed in the four individuals who could be tested, including I-2 and I-9, who carried homozygous missense variants (Figure  2C ). The overlap of clinical features between all the affected individuals and the rarity or absence of the identified IPO8 variants in population databases, including LOF variants and three missense variants impairing protein level and/or with predicted damaging effect on protein function, strongly supported IPO8 deficiency as disease causing in all the 12 individuals. We next assessed how ipo8 disruption might affect development by using a zebrafish model and focused attention on early dorso-ventral patterning defects that are a telltale sign of altered TGF-b/BMP signaling, as well as on skeletal and cardiovascular defects that were the main clinical hallmarks in the cohort.
Data and code availability The IPO8 variants were submitted to ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/) (GenBank: NM_006390. 3; accession numbers SCV001571677, SCV001571678, SCV001571679, SCV001571680, SCV001571681, SCV001571682, SCV001571683, SCV001571684, SCV001571685, SCV001571686, and SCV001571687). The WES datasets supporting this study have not been deposited in a public repository because of ethical restriction but are available from the corresponding author on request. Figure  5. pSMAD nuclear translocation defects in ipo8 À/À embryos highlight impaired TGF-b/BMP signaling (A) Maximal projections of confocal Z stacks of WT and ipo8 À/À embryos labeled for pSmad1/5/9 (green) and DAPI (blue) showing well-defined ventral to dorsal gradient of pSmad5 in WT gastrulating zebrafish embryos (left panel) but not in the ipo8 À/À embryos (right panel).
Acknowledgments We are grateful to the families who participated in this work. We thank  Nicole Van Bergen ,  MCRI , for performing the confirmatory Sanger sequencing in individual 10. This work was supported by the  Agence Nationale de la Recherche  under '' Investissements d'avenir'' program  ( ANR-10-IAHU-01 ), institutional grants from  INSERM , the European grant  ERC-2013-AdG-339407-IMMUNO-BIOTA , and grants from  Fondation Princesse Grace  and '' Fondation Maladies Rares '' (to N. C. B. ); by institutional grants from the  CNRS  and the  University of Angers  (to D. B. ); by intramural funding (fortu ¨ne) at the  University of Tu ¨bingen  ( 2545-1-0 ) and the  Ministry of Science, Research and Art Baden-Wu ¨rttemberg  (to B. V. ); and by the  Programme Investissements d'Avenir IHU FORe-SIGHT  ( ANR-18-IAHU-01 ) (to F.