dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein Identification For protein identification, the MS/MS data were interpreted using a local Mascot server with MASCOT 2. 6. 2 algorithm (Matrix Science, London, UK) against an inhouse database containing all Mus musculus and Rattus norvegicus entries from UniPro-tKB/SwissProt (version 2019_10, 50,313 sequences) and the corresponding 50,313 reverse entries. Spectra were searched with a mass tolerance of 10 ppm for MS and 0. 07 Da for MS/MS data, allowing a maximum of one trypsin missed cleavage. Trypsin was specified as an enzyme. Acetylation of protein n-termini, carbamidomethylation of cysteine residues, and oxidation of methionine residues were specified as variable modifications. Identification results were imported into Proline software version 2. 1 (http://proline. profiproteomics.
fr/) for validation. Peptide Spectrum Matches (PSM) with pretty rank equal to one were retained. False Discovery Rate was then optimized to be below 1% at PSM level using Mascot Adjusted E-value and below 1% at Protein Level using Mascot Mudpit score. Mass spectrometry data are available via ProteomeXchange  [46]  with the identifier PXD03002, associated with the doi: 10. 6019/PXD030002.
Label Free Quantification Peptide Abundances were extracted using the Proline software version 2. 1 (http: //proline. profiproteomics. fr/) with a m/z tolerance set at 10 ppm. Alignment of the LC-MS runs was performed using Loess smoothing. Cross Assignment was performed within groups only. Protein Abundances were computed by sum of peptide abundances (normalized using the median).
Reduced Glutathione Measurement The reduced glutathione was assessed by using monochlorobimane at 70 µM final concentration, with exclusion of the dead cells from the analysis, as described previously  [53]. This experiment was carried out with a FacsMelody flow cytometer and the FacsChorus software (v1. 3. 3. BD Biosciences, Le Pont de Claix, France) for data analysis.
Surface Markers The cell surface labelling was used as a marker of cellular status. Cells were seeded into six-well plates and exposed to SAS for 10 days, as described above. Cells were harvested, washed with PBS, and fixed for 30 min with paraformaldehyde 4% at room temperature. Cells were washed again and stored at 4 • C until the cell surface labelling. The fixed cells were collected and dispersed in a 96-well plate with 300,000 cells per well. After a step of washing and centrifugation (1 min at 800× g), the cells were incubated 10 min at room temperature with PBS-fluo (PBS 1X containing 0. 16% sodium azide and 3% of FBS) and Fc-block (purchased from eBiosciences, dilution 1/50). The cells were centrifuged and then incubated  20   (FITC: excitation 488 nm emission 525 nm, APC: excitation 633 nm emission 760 nm, PE: excitation 488 nm, emission 575 nm.
Cytokines and Nitric Oxide Production Cells were grown into six-well plates and exposed to the indicated dose of SAS for 10 days at 37 • C. The cells were also primed or not with LPS (100 ng/mL) for the last 24 h of culture. For nitric oxide production, 5 mM arginine monohydrochloride were added for the last 24 h of culture, in order to provide unlimiting substrate for the NO synthase. The supernatants are collected and centrifuged to eliminate non-adherent cells. For nitric oxide, the nitrite concentration of the supernatants was measured with the Griess reagent at 540 nm. For cytokines, the experiments were carried out with the Cytometric Bead Array Mouse Inflammation Kit (BD Biosciences) and analyzed with FCAP Array software (3. 0, BD Biosciences, Le Pont de Claix, France). This Flex-set kit allows measuring Interleukin-6 (IL-6) and TNFα protein levels in a single sample.
Toxic Effects of Repeated Exposure to Synthetic Amorphous Silica First, we checked that the repeated treatment with either form of SAS was not toxic to the cells. Indeed, the dose was determined from a previous work with fumed silica  [24] , in which an acute LD20 dose was determined to be 20 µg/mL for 24 h. Thus, we fractionated the dose in 10 subdoses of 2 µg/mL each. As shown in Figure  1 , this treatment had no visible toxicity on macrophages.
Silica Uptake Silica uptake was measured after the 10 days exposure period and also after a 24 h exposure to the same cumulative dose (1 × 20 µg/mL vs. 10 × 2 µg/mL/day). The results, shown in Table  1 , indicate a significant uptake of silica in exposed macrophages. Interestingly, the uptake was more pronounced for precipitated silica than for fumed silica in the case of the 10 days exposure. This may be linked to the different aggregate sizes observed for the two forms of SAS after being in contact with proteins, as is the case for complete culture medium (Figure  S1 ). Moreover, the uptake was much lower in the case of the 10 days exposure than in the 24 h exposure. This phenomenon has also been described for silver nanoparticles  [29] , but not in the case of a short-term (4 days) repeated exposure to colloidal silica  [50].
Global Analysis of the Proteomic Results When the raw proteomic data were filtered for proteins identified and quantified by at least two independent peptides, 2322 proteins were selected. As a first step in the exploitation of the proteomic data, a global analysis was performed. The purpose of such an analysis is to use all the protein abundance data (Supplementary Table  S1 ) to determine the intensity of the global effects of the repeated exposures to precipitated and fumed silica. The results, shown in Figure  2 , indicated that precipitated silica had an overall greater effect on macrophages than fumed silica. The first two axes accounted for 35 and 10% of the variance, respectively. In order to take all the information into account, non-metric multidimensional scaling was used. The results, shown in Figure  S4 , confirmed the results of the principal correspondence analysis, and thus that precipitate silica induced more proteome changes than fumed silica.
Of all these proteins, 209 were modulated by both treatments, leaving 78 proteins modulated only in response to fumed silica and 605 modulated only in response to precipitated silica. Both lists were used to perform pathways analyses by the DAVID software (update 2021, Frederick National Laboratory for Cancer Research, Frederick, MD USA). The results of the pathway analyses are detailed in Tables  S4  and S5 , respectively. Some highlighted pathways indicated a global stress response (e. g. , carbon metabolism, splicing, translation), which is expected for any cellular stress, while other pathways (e. g. , lysosome, mitochondrion, phagosome, antigen presentation, immunity) were more specific. The main pathways highlighted by the DAVID software are summarized in Figure  3. Precipitated synthetic amorphous silica is responsible for more protein changes in cell metabolism, function and binding than fumed SAS.
Mitochondrial Proteins Seventy-six mitochondrial proteins were found to be modulated by the pathway analysis software (Table  S6 ). Of these, 32 were decreased upon treatment with precipitated silica while 44 were increased, with a median fold change (in both directions) of ±21%. Furthermore, 14 of these proteins were also modulated in response to exposure to Precipitated synthetic amorphous silica is responsible for more protein changes in cell metabolism, function and binding than fumed SAS. Both these specific pathways and the proteins highlighted in them by the software were selected for detailed analysis.
These numbers indicated rather minor, adaptive changes. For example, the 27 kDa subunit of the MICOS complex (accession number Q78IK4), implied in mitochondrial architecture, was decreased by 10% upon treatment with both forms of SAS. However, the 13, 19, 25, and 60 kDa subunits were also detected in the proteomic screen and did not show any significant change in response to either treatment. This further suggested small amplitude and adaptive changes. To test this hypothesis, we measured the mitochondrial transmembrane potential. The results, shown in Figure  4A, B.
Regarding CD204, a slight but significant increase in its surface expression was observed after exposure to precipitated silica but not to fumed silica. As scavenger receptors are known to be the receptors of silica  [59] [60] [61] , we checked whether this slight overexpression could be linked to a saturation of the silica internalization process after 10 days of continuous exposure. To this purpose, we treated macrophages for nine days with the SAS of interest, and exposed them to fluorescent colloidal Regarding CD204, a slight but significant increase in its surface expression was observed after exposure to precipitated silica but not to fumed silica. As scavenger receptors are known to be the receptors of silica  [59] [60] [61] , we checked whether this slight overexpression could be linked to a saturation of the silica internalization process after 10 days of continuous exposure.
To this purpose, we treated macrophages for nine days with the SAS of interest, and exposed them to fluorescent colloidal amorphous silica for the last day. The results, shown in Figure  7 , indicated that silica-treated cells internalized more fluorescent colloidal silica than untreated cells, which correlated with the increased expression of CD204 and MARCO. 2022, 12, x treated cells internalized more fluorescent colloid related with the increased expression of CD204 an As proteomics cannot easily test secreted pr low steady state levels in cells, we decided to test thus tested the secretion of NO, TNFα, and IL-6 synthetic amorphous silica, with and without shown in Figure  8 , indicated a slight but significan after treatment with SAS. In the case of TNFα, a m after treatment with fumed silica compared to pr observed after LPS stimulation and was just tran As proteomics cannot easily test secreted proteins because of their high efflux and low steady state levels in cells, we decided to test some pro-inflammatory cytokines.
The dose that we have applied (2 µg/mL daily) has also been used in the literature  [31]  and considered as a realistic dose, in line with the typical exposure at the workplace or by consumers. Contrasting with this publication, we focused on the amorphous silica "as present and marketed", i. e. , without forced disaggregation, compared two types of commercial SAS (precipitated and fumed) with similar descriptors, and used proteomics to get a wider description of the macrophages responses to silica. Although the basic parameters of the two SAS compared in this study were very similar (specific surface area of ca. 200 m 2 /g, primary particle size of ca. 20 nm, aggregate size in the 200-500 nm range), the biological responses, as revealed by proteomics, were significantly different. The precipitated silica induced a much stronger response, as indicated by the number of proteins showing a significant change in their amounts (938 for precipitated silica, 342 for fumed silica).
However, we could not detect silicon release in the cell culture medium after exposure to SAS (Figure  S5 ), which may be simply linked to a lack of sensitivity of the technique compared to the low amounts of silicon to be detected. Pathway analysis of the proteomic-detected changes revealed that very different pathways were altered. Some were ubiquitous, in the sense that they are part of the homeostasis of many different cell types. Pathways such as "endoplasmic reticulum", "mitochondria"," cell redox homeostasis", or "lysosome" belong to this class. As this may indicate a major dysfunction of the cells after this repeated exposure to silica, validation experiments were carried out. They indicated no major cellular dysfunction, which was in line with the low toxicity of amorphous silica upon repeated exposure  [31, 50].
5-fold) than after acute exposure (2. 5-fold). Such a lower increase after a time fractioned dose has also been observed for colloidal silica  [50]. The stronger difference was, however, observed for IL-6, which remained undetectable after repeated exposure to both forms of synthetic amorphous silica (this study) while it was clearly detectable (and induced) after an acute exposure to fumed silica  [24]. Besides this intrinsic response of the cells to SAS, we also investigated whether the treatment with SAS would modulate the physiological response of macrophages to a bacterial challenge, mimicked by an exposure to LPS. To this purpose, the cells were first exposed repeatedly to SAS and finally to LPS. Our results showed an increased proinflammatory response in cells exposed to SAS, suggesting a hyper-reactive state and an exacerbated response.
Of note, although the effects detected by proteomics were significantly higher for precipitated silica than for fumed silica, the functional effects, e. g. , the effects on surface markers and cytokine production, were similar for both types of silica. This phenomenon may be linked to the common responses detected by proteomics for both types of SAS. Infrastructure (ProFI) project (grant ANR-10-INBS-08-03) and the EM facilities at the Grenoble Instruct-ERIC Center (ISBG; UMS 3518 CNRS CEAUGA-EMBL) with support from the French Infrastructure for Integrated Structural Biology (FRISBI; ANR-10-INSB-05-02) and GRAL, a project of the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003) within the Grenoble Partnership for Structural Biology.
Acknowledgments: The authors thank the microscopy facility MuLife of  IRIG/DBSCI , funded by  CEA Nanobio and labex Gral , for equipment access and use. The authors also thank  Guy Schoehn  for the direction of the  Electron Microscopy facility  and  Julien Pérard  for the maintenance of the ICP-AES instrument and his assistance for the ICP-AES experiments.
Funding: This work was funded by grants from the  Agence Nationale de Sécurité Environnementale et Sanitaire (ANSES, SILIMMUN  grant  PNREST-2015-32 ) and from the  Agence Nationale de la Recherche  ( ANR ,  Paipito  grant  ANR-16-CE34-0011 ). This work used the flow cytometry facility supported by  GRAL , a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ), as well as the platforms of the French Proteomic.
| Data collection For coding the location of females in the video recordings of the trials we used the software BORIS  (Friard et al. , 2016). To determine whether females prefer a previous partner or rather a novel male, we analysed the relative duration a female spent close to each male without taking into account the time spent in the central section (hereafter "proportion of paired time"). The proportion of paired time was calculated by dividing the time spent near a particular male (previous partner or novel male) by the total time spent near either male within a trial. Additionally, we coded for each male whether the female chose him (1) or not (0) during the test. We coded a male as chosen if the female spent more than half of the proportion of paired time with him. We used the bioacoustics software RAVEN PRO 1.
| Statistical analyses We conducted all statistical analyses in R v3. 6. 0 (R Core Team, 2020), using the integrated development environment RStudio v1. 3. 1093 (RStudio Team, 2019). First, we verified that we used males of similar size by comparing the size of the previous partner and novel male using an independent two samples t-test. We checked data for normal distribution of each group with a Shapiro-Wilk test (size of the previous partner: statistic =. 944, p-value =. 223; size of the novel male: statistic =. 948, p-value =. 266) and for homogeneity of variance with Levene's test (df1 = 1, df2 = 44, statistic =. 609, p-value =. 439). We also checked for a potential directional bias in females. We checked for data normality (Shapiro-Wilk test: statistic =. 822, p-value <. 001) and used a Wilcoxon one sample test comparing the proportion of paired time (whether it was with the previous or novel male) spent on the left side to.
740, p-value <. 001). Then, we analysed the female's preference for a male based on familiarity by using a Wilcoxon one sample test to compare the paired proportion of time spent close to the novel male to. 5. Again, we used a non-parametric test because the response variable did not follow a normal distribution (Shapiro-Wilk test: statistic =. 811, p-value =. 001). We investigated the influence of different characteristics of the advertisement and courtship call on female choice behaviour. We built three generalized linear mixed effect models (GLMMs) following a binomial distribution with the function "glmer" in the lme4 package  (Bates et al. , 2016). We used the choice of the female (if the female spent most of the time with the male -1-or not -0-) as a response variable in all three models.
This is consistent with simulations of a serial founder effects model. Tests for selection reveal that some of these Beringian variants were likely driven to high frequency by adaptive processes, and bioinformatic analyses suggest possible phenotypic pathways that were under selection during the Beringian Isolation period. Specifically, pathways related to cardiac processes and melanocyte function appear to be enriched for selected Beringian variants. The Beringian migration marks one of the most striking events in modern human history. Genetic and archaeological data confirm that a small population consisting of a few thousand people entered the Beringian corridor from Siberia at the advent of the Last Glacial Maximum (LGM), approximately 30 thousand years ago (kya)  [1] [2] [3] [4] [5]. The Beringian ecology provided a refuge for this migrant population as the LGM intensified 6.
The migrant population was thus isolated for upwards of 15 ky in a Beringian refugium (c) until glacial retreat exposed coastal and interior routes into North America (d). To create panel (a), we drew the outline of continents using the R package maptools version 1. 1-2 (https:// cran. r-proje ct. org/ web/ packa ges/ mapto ols/ index. html) and then added the labels and paths of migrations as overlays in Adobe Illustrator. We generated panels (b-d) by adding shading and overlays to portions enlarged from panel (a). in descendants of the out-of-Africa (OOA) migration, represented by a total of 15 populations living in Europe, South Asia, and East Asia. Surprisingly few GSPs were found in Europeans (0, GSPs), South Asians (46, GSPs), and East Asians (449, GSPs). The GSPs in African populations were composed of a mix of ancestral (71%) and derived alleles (29%).
The vast majority of GSPs in out-of-Africa descendants and Beringian descendants were derived alleles (99% and 99. 9%, respectively). With only two exceptions, the group specific polymorphisms have not reached fixation. Both exceptions occur in the descendants of the Beringian migrants. Simulation results. We used coalescent simulations to verify that the observed pattern of derived GSP alleles is consistent with the reduction in heterozygosity that was seen in short tandem repeat and single nucleotide polymorphism data sets that support the serial founder effects model for genetic diversity in contemporary human populations  24, 32. Figure  3  shows the probability density for the age of a derived allele with frequency p ≥ 0. 3 for each of four geographic regions. The allele age distribution for an African population illustrates the great antiquity of human polymorphism Fig.
Functional annotation of Beringian GSPs. The Beringian GSPs located in genic versus inter-genic regions of the genome are presented on the first line of Table  1. The second line presents the same categorization of SNPs in the Random SNP Set. These data make it clear that Beringian GSPs are over-represented in the protein coding sequences relative to the Random SNP Set χ 2 = 212. 01, d. f. = 2, p < 0. 001). Non-synonymous GSPs in protein coding sequences affected a number of different protein classes and biological pathways according to a Panther 33 analysis (Fig. 5 ). Eleven genes that code for metabolite proteins contained non-synonymous GSPs (Fig. 5a ). Additional categories containing non-synonymous GSPs include protein modifying enzymes, transcription regulators, and regulatory proteins.
Gene Chr. Selected SNPs  of Random SNPs in coding sequence (38%). Second, in Beringian GSPs that do occur in coding sequence, the ratio of non-synonymous to synonymous nucleotide substitutions ( ω = 0. 69 ), is substantially higher than the corresponding ratio in the Random SNP set ( ω = 0. 22). We have used the iHS statistic to identify a set GSPs that are candidates for positive selection. These comprise a small fraction of Beringian GSPs 141/20,424 = 0. 0069). The relevant phenotypes that favored survival and reproduction of individuals cannot be directly inferred from the nucleotide sequence data alone. Therefore, we have used bioinformatic analyses to gain provisional insights into potential phenotypes influenced by these alleles. Our analyses were performed by parsing the candidate loci according to three criteria, individual SNPs with outlying iHS scores, specific genes that harbor a disproportionate number of putatively selected GSPs, and gene ontology classes enriched for putatively selected GSPs.
Methods Genomic data. We analyze whole autosomes from the Thousand Genomes Project Phase 3 (TGPP3) sample to identify SNPs originating in the ancient Beringians (Table  S1 )  36. Briefly, the total TGPP3 data consist of 84. 7 million single nucleotide polymorphisms (SNPs) determined from next-generation sequencing of 2,504 individuals. Each individual was sequenced for the whole genome using mean depth = 7. 4× enhanced by sequencing targeted exomes at mean depth = 65. 7×. The TGPP3 sample includes populations from five geographic regions: Africa (five populations, total N = 504 ), Europe (five populations, total N = 503 ), South Asia (5 populations, total N = 489 ), East Asia (5 populations, total N = 504 ) and the Americas (6 populations, total N = 504 ). We use data from all TGPP3 populations residing outside of the Americas and three populations with substantial Native American ancestry that currently reside in the Americas.
Coalescent simulations. We used coalescent simulations to verify that the observed pattern of derived GSP alleles is consistent with the loss of variation from which the serial founder effects model was inferred  24, 32. Demographic parameters for these simulations were estimated by fitting a tree to data from the CEPH-HGDP short tandem repeat (STR) data set  39. We began by choosing a subset of 27 populations from the CEPH-HGDP dataset: San and Kxoe from South Africa; Mandenka, Brong, Igala, Yoruba, and Luhya from Central Africa; Russian, Tuscan, Orcadian, Basque, and French from Western Europe; Punjabi, Tamil, Bengali, Gujarati, and Telugu from South Asia; Cambodian, Dai, Han, North Chinese Han, and Japanese from East and South-East Asia; and Pima, Maya, Mixtec, Embera, and Cabecar who are Native Americans.
The San and Kxoe served to root the tree for the remaining 25 populations  32, 40. The non-South African populations from Europe and Asia were chosen to match the populations in the thousand genomes project data as closely as possible. Native American populations were included to estimate demographic parameters for the Beringian Isolation period. Next we used 619 microsatellite loci  39  to compute Nei's minimum genetic distances between all pairs of populations  41. We re-scaled these distances by multiplying by 2. Using the re-scaled genetic distances, we built a neighbor-joining tree for the 27 populations. We rooted the neighbor-joining topology on the branch between the San-Kxoe and the remaining African and non-African populations, and then fitted branch lengths using the maximum-likelihood method proposed by Cavall-Sforza and Piazza  [42] [43] [44].