dataesr/scientific-paragraphs-categorization huggingface dataset:
S5B ). We used the program Dtrios in Dsuite to calculate the D statistics of all possible combinations of species trios as well as species group trios. P. hamadryas was used as the outgroup (O) in all analyses. The resulting P values were further adjusted by Benjamini and Hochberg (BH) correction for multiple testing bias via the p. adjust function in R. Using a significance threshold (α = 0. 05) that was BH-corrected for a total of 221 comparisons (the maximum number of trios involving a given pair), z scores of >2. 1 were considered significant. We conservatively display only z scores of >3 as a significant signal of introgression. Results from Dtrios were further processed using the Fbranch function to generate a matrix of z scores, and a heatmap of the matrix was visualized using the dtools.
py script, which is provided within the Dsuite package. Following the logic of Zhang et al. (33) , we computed the D statistics in sliding windows to test phylogenetic hypotheses, as the genomic region least affected by introgression should reflect the true relationship. For example, if the phylogenetic relationship of T1 were true, then the genomic region least affected by gene flow should be dominated by T1 topology. In this scenario, we set P1 as the sinica group, P2 as the fascicularis group, and P3 as the silenus group. The sylvanus group was treated as the outgroup (O) because it was always located outside the other three Asian species groups (Fig. 1B ). The same was true for T2. We used the Python script ABBABABAwindows. py from  (32)  to compute the D statistics with window size set to 50 kb, and at least 100 biallelic SNPs were allowed per window (-w 50,000 -m 100).
After applying these filtering strategies, a total of 1663 windows were obtained on the autosomes and 103 on the X chromosome, respectively. For each window sequence, we used the MCMCTREE program in PAML v. 4. 9e package (82) to estimate the relative divergence time for the tree imputed from that window. Two well-justified and widely used fossil record-based calibration points were used to calibrate evolutionary time  (16, 17) : (i) the split between African (M. sylvanus) and Asian macaques: 4. 5 to 6. 5 Ma and (ii) the split between Macacina and Papionina: 5. 8 to 8. 0 Ma. The prior of the overall substitution rate was estimated using the BASEML program in PAML (82), assuming a mean split of the most recent common ancestor of Macacina and Papionina at 7 Ma, based on the GTR + Γ substitution model (rgene gamma = 1, 7.
We annotated all SNP variants using SnpEff software (version 5. 0c)  (84)  and recorded the number of fixed nonsynonymous mutations and fixed 1-kb upstream mutations in each gene using in-house Perl scripts. PSGs were further filtered using the following criteria: (i) the Yates' corrected P value should be significant (<0. 01) in the hybrid group and one of the parental groups, but not significant (>0. 05) in another parental group; (ii) the number of fixed nonsynonymous mutations together with 1-kb upstream mutations was within the top 2. 5% of gene; and (iii) the topology of the gene tree should be consistent with the relationship being tested (built with the following parameters: iqtree -s SNP_data. fasta -st DNA -nt AUTO -ntmax 5 -mem 5G -bb 1000 -bnni -o Pham -m MFP+ASC -quiet -redo).
Acknowledgments: We thank  W. Wang ,  X. Ren , and  X. Li  for their early assistance with this project. Funding: This work was funded by the  National Key Research and Development Program of China  (nos. 2021YFF0702700  and  2022YFF0710901 ), the  National Natural Science Foundation of China  (no. 31822048  to D. -D. W. and no. 32270500  to B. -L. Z. ),  Yunnan Applied Basic Research Projects  (no. 2019FI010  to D. -D. W. ),  CAS Light of West China Program  (xbzgzdsys-202213) and the  Animal Branch of the Germplasm Bank of Wild Species of Chinese Academy of Science  (the  Large Research Infrastructure Funding ). This work was also partially supported by a  Villum Investigator Grant  to G. Z. (no. 25900 ) and the  National Natural Science Foundation of China  to Y. -T. Z. ( U1802284 ). T.
Ligand construction and molecular docking simulations Ligand drawing was carried out employing ACD/ChemSketch (Freeware) 2020. 1. 2  31  generating in this software a previous 3D pre-optimization to each ligand. mol file, conversion of the. mol file to a Z-matrix was conducted with GaussView 5. 0  32  setting an AM1 semiempirical method to develop a complete energy and geometrical minimization, and a correct proton assignation to each atom that requires it, employing the Gaussian 09 33 quantic-chemical package, the resulting output files were employed as. pdb input files to molecular docking simulations. Molecular docking simulations were conducted with AutoDock 4. 2. 6  34  software employing GPER and Bcl-2 3D models previous generated and validated by our research group and published elsewhere  [26] [27] [28] [29] [30]  , setting the follow parameters prior to docking simulations: polar hydrogens were adequately placed over the atoms capable of establishing polar contacts in the ligand/receptor complexes, and Kollman charges were assigned to the whole proteins.
MD simulations The GPER-ligand-membrane and Bcl2-ligand systems were submitted to 100 ns (ns) MD simulations using the Amber 16 package  40. Ligand forcefields were constructed considering the Generalized Amber Force Field (GAFF) and the AM1-BCC method. Topology for the GPER-ligand-membrane systems were constructed with a Leap module using ff14SB  41  , Lipid14 42 and GAFF  43. Topology for the Bcl2-ligand systems were constructed with a Leap module using ff14SB  41  , and GAFF  43. MD simulations were performed under periodic boundary conditions using an NPT ensemble at 310 K and 1 bar pressure. Long-range electrostatic interactions were determined with the PME  44  , considering a 10 Å cutoff for van der Waals interactions. Bond lengths were restricted at the equilibrium using the SHAKE algorithm  45.
to the soil mineralogy map, mainly due to the different assumptions made in each soil dataset to extrapolate a handful of soil measurements to arid and semi-arid regions worldwide. For the least abundant or more difficultto-determine minerals, such as iron oxides, uncertainties in soil mineralogy yield differences in annual mean aerosol mass fractions of up to ∼ 100 %. Although BFT restores coarse aggregates including phyllosilicates that usually break during soil analysis, we still identify an overestimation of coarse quartz mass fractions (above 2 µm in diameter). In a dedicated experiment, we estimate the fraction of dust with undetermined composition as given by a soil map, which makes up ∼ 10 % of the emitted dust mass at the global scale and can be regionally larger. Changes in the underlying soil mineralogy impact our estimates of climate-relevant variables, particularly affecting the regional variability of the single-scattering albedo at solar wavelengths or the total iron deposited over oceans.
The atmospheric chemistry model MONARCH is a fully coupled atmosphere chemistry model developed at the Earth Sciences Department of the Barcelona Supercomputing Center (BSC). The model's atmospheric dynamics rely on the Non-hydrostatic Multiscale Model on the B-grid (NMMB)  (Janjic and Gall, 2012) , which can be applied from regional to global spatial scales. MONARCH has also inherited from the NMMB the ability to use different representations for relevant physics processes (e. g. , the land surface model or the cloud microphysics representation). MONARCH includes an in-line gas-phase chemistry module  (Jorba et al. , 2012; Badia and Jorba, 2015; Badia et al. , 2017)  and advanced representation of natural and anthropogenic aerosols, i. e. , dust  (Pérez et al. , 2011; Haustein et al. , 2012; Klose et al.
We define mineral-dependent mass densities according to literature data (Table  1 ). All minerals are considered to be externally mixed, except for iron oxides. A large part of the emitted flux of iron oxides is considered to be internally mixed with other minerals, e. g. , in the form of accretions in phyllosilicates, in line with observational evidence and previous modeling studies  (Kandler et al. , 2009; Perlwitz et al. , 2015a). In MONARCH, we define two different types of tracers for the iron oxides. One set of tracers carries the masses of the iron oxides that constitute accretions in other minerals, which make up the largest fraction and are allowed to be up to 5 % of the masses of the other minerals at emission  (Perlwitz et al. , 2015a). Given that their total emitted mass is low compared to the sum of all the minerals, we assume that these accretions do not change the mass density of the host particles, and we assign them the density of the most abundant phyllosilicate, i.
C1999-SMA considers 28 arid and semi-arid soil types identified by the FAO (FAO-  UNESCO, 1974)  and mapped in the FAO Digital Soil Map of the World (DSMW) (FAO-UNESCO, 1995) with a horizontal resolution of 0. 083 • (∼ 10 km at mid-latitudes). J2014-SMA relies on the FAO74 (FAO-UNESCO, 1974) and FAO90 (FAO-UNESCO, 1997) soil classifications mapped in the Harmonized World Soil Database (HWSD) version 1. 1 (FAO/IIASA/ISRIC/ISSCAS/JRC, 2009) with a spatial resolution of 0. 0083 • (∼ 1 km at mid-latitudes). Combining both FAO74 and FAO90, there is a total of 211 different soil units with potential mineralogical content according to J2014-SMA. In both C1999-SMA and J2014-SMA, a mean mineralogy is assigned to each soil type or unit by compiling available descriptions from the literature, and it is subsequently extrapolated geographically to any location that shares the same soil classification, hence providing a spatially resolved map of soil mineral fractions.
While Claquin et al. (1999)  report the mineral fractions normalized to sum to 100 %, i. e. , assume that soil mineralogy fundamentally consists only of the eight identified minerals,  Journet et al. (2014)  provide the mean mineralogy as derived from the soil records, with total mass fractions of known minerals ranging from 42. 4 % to 102. 8 % (without considering inferred calcite fractions and other extensions). That is, J2014-SMA allows the possibility of emitted minerals that are unidentified and not included in our Table  1. MONARCH can currently use C1999-SMA normalized and two variations for J2014-SMA: normalized and non-normalized, which are both based on the extended mineralogy data referred to as CASE2 by  Journet et al. (2014). Appendix A describes these datasets (Sect. A1) and summarizes the revision we made (Sect.
Deriving the emitted PSD of dust minerals from the soil composition The mineralogy of the soils provided in the SMAs has been inferred in many cases via techniques that alter the original soil size distribution, i. e. , through wet sieving. This dispersal is particularly relevant for the phyllosilicates, which are usually found in the form of aggregates in soils and are detected in the atmosphere at higher proportions at coarser (silt) sizes than those reported in the soil maps  (Perlwitz et al. , 2015a; Pérez García-Pando et al. , 2016). In these recent studies, it was also shown that BFT  (Kok, 2011)  represents a practical framework to describe the emitted PSD in terms of its constituent minerals as it builds upon the dispersed soil PSD, which underpins the soil mineralogical datasets. BFT is derived by analogy to the fragmentation of brittle materials such as glass spheres.
Experiment definitions We aim to assess the sensitivity of the modeled mineralogy and its potential climate impacts to the choice of a specific soil mineralogy. To that end, we design three different experiments (Table  2 ) that rely on the same model configuration and use the C1999-SMA, J2014-SMA normalized and J2014-SMA non-normalized soil datasets described above (Sect. 2. 3. 1) and referred to as C1999, J2014 and J2014NN, respectively. The configuration of MONARCH for the atmospheric physics is summarized in Sect. 2. 1, and the specific setup for the dust cycle is summarized in Sect. 2. 2. The simulations are performed at the global scale with a regular horizontal grid of 1 • × 1. 4 • (latitude × longitude) resolution and 48 vertical hybrid pressure-sigma layers up to 10 hPa. They cover the period from 2006 to 2010 after 1 year used as spinup (  2005 ) for the soil humidity and mineral initial conditions.
The dust evaluation is complemented by comparison with the dust deposition fluxes compiled by  Albani et al. (2014)  for the present climate, which cover 110 locations (Fig. 2a ) and also give information on the fraction below 10 µm in diameter. For the comparison, we compute the 5-year annual mean of modeled surface concentration and deposition fluxes of dust. For those measurements where the mass below 10 µm in diameter is reported, we estimate the equivalent size range from the model. Spatially, the model values of the nearest-neighbor cell to the observation location are taken. An overview of the model performance against each dataset is given through the estimates of the normalized mean bias (nMB), normalized root mean square error (nRMSE) and correlation (r). The definition of these metrics and their ranges of uncertainty can be found in Sect.
S2). It has to be noted that the evaluation metrics using this filter are very close to those obtained using the full dataset (not shown), which suggests a small contribution of the anthropogenic sources in the studies selected by  Perlwitz et al. (2015b). The sampling periods range from specific days to multiannual periods and span the late 1960s to the 2000s. We consider the interannual variability of the mineral fractions to be relatively low  (Perlwitz et al. , 2015b). We assume that close to dust sources the mineralogy is mainly affected by the soil composition, which does not undergo substantial changes in the considered time period. During transport the mixing of dust plumes of different origins is expected to homogenize the dust composition, thus buffering the effect of an interannual change in each source strength.
0042 (as higher values would indicate the presence of absorbing black and brown carbon, following  Schuster et al. , 2016). These criteria are applied to the available AERONET data for the 2006-2010 period, and monthly means over that period are computed only when 30 or more retrievals are available. Model monthly SSAs are then interpolated at the selected AERONET stations (Fig. 2a ) and masked for unavailable months for the comparison with AERONET.
In spite of the higher resolution of the original  Journet et al. (2014)  dataset, the variability in quartz and feldspars is masked by the effect of the multiple assumptions required to fill in the silt fraction data in soils, which on many occasions involves averaging over the composition of multiple soil units. If we consider J2014NN, at the global scale, the total undetermined mineral fraction at emission adds up to 9. 8 %, a non-negligible fraction. This assumption leads to relative differences in the emitted mineral mass that range from 3. 8 % for hematite to 25. 8 % for gypsum compared to J2014 (Table 4). For the Sahel, Australia or northeastern Africa, the unknown fraction is lower than at the global scale (≈ 7 % to 8 %), while in Central and East Asia or North America it is higher (≈ 14 % to 16 %).
Modeled mineralogy in the atmosphere and at deposition At the global scale, coarse minerals such as quartz, feldspars or even calcite preferentially reduce their abundance once in the atmosphere with respect to emission (Table  4 ). In contrast, the mass fractions of finer minerals (i. e. , illite, kaolinite, smectite or vermiculite) increase compared to their emitted fractions. Both quartz and feldspar have lifetimes around 2. 7 or 2. 8 d depending on the experiment, which is lower than those of dust with mean composition. The atmospheric lifetimes of calcite differ between C1999, 2. 7 d, and J2014 (J2014NN), 3. 0 d, which is attributed to the different size distributions of this mineral in the two soil datasets. Overall, clay minerals remain longer in the atmosphere than homogeneous dust (with lifetimes above 3.
However, the size apportionment in C1999-SMA excluded feldspars from the finer soil fractions, which results in a poor correlation with observations in size and space. Calcite levels are overall better reproduced with J2014-SMA, with large underestimations in C1999. Both maps represent qualitatively known variations of calcite in northern African soils (with higher mass fractions over the Sahara than in the Sahel). The available observations of iron oxides are scarce and do not allow for a comprehensive assessment; however, the iron oxide apportionment provided in J2014-SMA seems to be in better agreement with recent evidence than the C1999-SMA database. The majority of observations suggest that goethite is more abundant than hematite in soils and that the latter is usually confined to the smaller sizes, whereas goethite appears in both the fine and coarse fractions.
From the original analyses found in the literature, they are able to provide mineralogical information for approximately 80 % of the soil types identified as arid or semi-arid. They complete the mean mineralogy for the missing soil types by applying empirical relationships, e. g. , inferring the apportionment of calcite in the clay and silt soil sizes or the quartz and feldspar ratio from soil texture information and associating the soil hematite content with information about soil redness. Nickovic et al. (2012)  extend the original dataset of  Claquin et al. (1999) , adding three new soil types (Yermosols, Haplic Yermosols and Xerosols) that also have a presence in arid and semi-arid regions, and attribute the same amount (wt %) of hematite to the clay fraction as reported in  Claquin et al.
(1999)  for the silt size faction. The horizontal resolution of the FAO DSMW and thus of the C1999-SMA dataset is 0. 083 • (∼ 10 km at mid-latitudes). Note that the increased resolution available in  Nickovic et al. (2012)  results from the remapping of the FAO data onto a higher-resolution grid (0. 0083 • , ∼ 1 km at mid-latitudes) to be compatible with soil texture data at that higher resolution, so that the higher-resolution spatial variations are rarely the result of abundant mineral measurements. Journet et al. (2014)  relate soil mineral composition to the soil unit attribute rather than the soil type used by C1999-SMA, also relying on the FAO soil classification system but updating the information source to the Harmonized World Soil Database (HWSD) version 1. 1 (FAO/IIASA/IS-RIC/ISSCAS/JRC, 2009), a raster with a native spatial resolution of 0.
(1999). To complete the missing information, they apply similar approaches to  Claquin et al. (1999) , again relying on soil texture data, soil color for iron oxides and in-lab analyses (e. g. , for the apportionment of iron oxides in hematite and goethite). Information on chlorite, mica, quartz and feldspars https://doi. org/10. 5194/acp-23-8623-2023 Atmos. Chem. Phys. , 23, 8623-8657, 2023 Underlying soil classes FAO74 as mapped in DSMW, 25 (28) * soil types FAO74 and FAO90 as mapped in HWSD, 211 soil units * Originally,  Claquin et al. (1999)  reported the mean mineralogy for 25 soil types, and  Nickovic et al. (2012)  added 3 more types while extending the same hematite fraction as in silt to the clay size category and referring to both as total iron oxides. in the silt sizes is assigned for many soil units as an average of the mass fractions of the major soil groups or the full silt dataset.
(2014)  and the actual maps published in their work. Some of the differences in the clay fraction arise from a typo in the article for the Acrisols Haplic composition (Table  A2 ), which influences mainly the mineralogy of soils over the Amazon region or central Africa. Also, in their analyses, the mass fractions of calcite and goethite in the silt sizes for Fluvisols, Calcisols Haplic, Andosols and Luvisols Orthic are derived from extensions of the original dataset. In this revision process, we decided to keep the original values reported in the Supplement of the article  (Journet et al. , 2014) , which are derived from the compilation of soil descriptions. The HWSDv1. 2 raster provides high-resolution information on the geographical distribution of the FAO90 and FAO74 soil units by code.
Correspondence between those numerical codes and the actual soil units is provided in the HWSDv1. 2 database. During the revision, we detected a discrepancy in the HWSDv1. 2 database, in which the FAO74 codes for the Fluvisols units differ depending on the source of information (i. e. , the HWSD_DATA compared to the D_SYMBOL74 data sheets). After correspondence with the database developers, we confirmed that the correct assignations for those soil units are FAO74 code 147, Fluvisols Eutric (Je), code 148, Fluvisols Calcaric (Jc), and code 149, Fluvisols Dystric (Jd). Figures S6 to S11 show the distribution of mineral fractions in the clay and silt sizes for C1999-SMA and J2014-SMA (normalized) after this revision process, considering the dominant soil types at a 0. 5 • × 0. 5 • resolution.
Acknowledgements. We thank  Akinori Ito  and the anonymous reviewer who, with their comments, contributed to improving this paper's quality. The authors are grateful to the  AERONET  and  RSMAS PI(s)  and their staff for establishing and maintaining the sites and data used in this investigation. PM 10 measurements in Banizoumbou (Niger), Cinzana (Mali) and  M'Bour  (Senegal) were performed in the framework of the French National Observatory Service INDAAF (Interhttps://doi. org/10. 5194/acp-23-8623-2023 Atmos. Chem. Phys. , 23, 8623-8657, 2023 national Network to study Deposition and Atmospheric composition in AFrica; https://indaaf. obs-mip. fr/, last access: 6 December 2022) piloted by LISA and LAERO and supported by the  INSU/C-NRS , the  IRD , the  Observatoire Midi-Pyrénées  and the  Observatoire des Sciences de l'Univers EFLUVE.
We also thank the  Harmonized World Soil Database  developers from the  FAO ,  IIASA, ISRIC-World Soil Information , the  Institute of Soil Science, the Chinese Academy of Sciences (ISSCAS)  and the  Joint Research Centre of the European Commission (JRC)  for openly publishing their data and for answering our queries. Part of the data processing and analysis in this work has used different R (R Core Team, 2021) packages, climate data operators (CDOs, Schulzweida, 2022), NetCDF operators (NCOs, Zender, 2008) and the  NCAR  command language (NCL,  UCAR /NCAR/CISL/VETS, 2019). This work was funded by the  European Research Council (ERC)  under the  Horizon 2020 research and innovation program  through the  ERC Consolidator Grant FRAGMENT  (grant agreement no. 773051 ), the  AXA Research Fund  through the  AXA Chair   on Sand and Dust Storms  at the  Barcelona Supercomputing Center (BSC) , the  Spanish Ministerio de Economía y Competitividad  through the  NUTRIENT  project ( CGL2017-88911-R ), the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no.
Ron L. Miller  and  Vincenzo Obiso  received support from the  NASA Modeling, Analysis and Prediction program  ( NNG14HH42I ), as well as  Jan Perlwitz  (grant no. 80NSSC19K0056 ), who was also supported by the  NASA   Atmospheric Composition Modeling and Analysis program  (grant no. 80NSSC19K0984 ). Vincenzo Obiso  was supported by the  NASA   Postdoctoral Program  at the  NASA Goddard Institute for Space Studies  administered by  Oak Ridge Associated Universities  under contract with  NASA  ( 80HQTR21CA005 ). Jerónimo Escribano ,  Martina Klose  and  Matt Dawson  received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  agreement nos. H2020-MSCA-COFUND-2016-754433 ,  H2020-MSCA-IF-2017-789630  and  H2020-MSCA-IF-2016-747048 , respectively.