dataesr/scientific-paragraphs-categorization huggingface dataset:
First, we measured the diffusion coefficient of endogenous PrP C using five different POM antibodies [POM-x: POM1/19 (single epitopes within the C-terminal half/globular domain), POM2/POM11 (FT-binders with ≥2 epitopes as described above), or POM3: epitope in the central part/hydrophobic core (HC)] without prior exposure to the same POM-x antibodies (Fig. 7A ). Synapses were labeled using FM4-64 dye, and single-molecule trajectories were obtained and analyzed both in and out of synapses. Compared to the extrasynaptic sites, PrP C diffusion was generally slower at the synapses, which is likely due to the crowded environment. Similar diffusion coefficient values of PrP C were obtained with all POM antibodies (Fig. 7B ; each data point represents an average value measured for hundreds of QDs; also refer to table  S2 ).
DISCUSSION A soluble form of PrP, most likely representing sPrP, has been described decades ago  (27). However, convenient and reliable discrimination between sPrP and the usually much higher (and thus masking)   S2. Downloaded from https://www. science. org at EMBL Bibliothek on January 04, 2022 amounts of membrane-associated fl-PrP C in biological samples has only been achieved recently, fueled by the generation of antibodies specifically recognizing sPrP  (26, 63). Thus, knowledge on potential functions of sPrP is mainly based on data obtained using recPrP. Although recPrP lacks glycosylation and may thus differ structurally from both PrP C and physiologically released sPrP, data obtained using recPrP imply physiological functions in synapse formation  (64) , neurite outgrowth guidance  (65) , differentiation, immune signaling, and intercellular communication  (66) (67) (68).
SDS-PAGE and Western blotting (of compound-treated HEK293 cells) A 30-l aliquot of either medium or lysate for each sample was heated at 95°C for 10 min and loaded on two separate SDS-PAGE gels (Any kD Mini-PROTEAN TGX Stain-Free Protein Gels; Bio-Rad). Proteins were electrophoretically transferred to polyvinylidene fluoride membranes, which were then blocked for 20 min in 5% (w/v) nonfat dry milk in tris-buffered saline (TBS) containing 0. 05% Tween 20. After incubation with appropriate primary [D18 (1:5000) and sPrP G228 (1:3000)] and secondary antibodies, signals were revealed using enhanced chemiluminescence (Luminata, Bio-Rad) and visualized by a Bio-Rad XRS ChemiDoc image scanner (Bio-Rad). Values were obtained by densitometric quantification of PrP bands using the Image Lab 5. 2. 1 software (ChemiDoc, Bio-Rad).
After three washes with wash solution [0. 1 M PB (pH 7. 4) containing 0. 25% Triton X-100], sections were incubated for 90 min with secondary antibody in solution, washed again three times in wash solution containing 4′,6-diamidino-2-phenylindole (DAPI), and finally brought on glass slides and embedded in Mowiol/DABCO (1,4-diazabicyclo-[2. 2. 2]-octane). Images were acquired with the Olympus FV1000D Laser Scanning Confocal Microscope (model: FV10-292-115) with a 60× lens (UPLSAPO) and processed with the FV10-ASW 4. 2 Viewer Software (Olympus, Germany).
The resulting images are shown in a pseudocolor scale. For staining of endogenous PrP, hippocampal cultures at div19 were treated with 1 g of POM2 antibody in 500 l of culturing medium for 30 min and, immediately afterward, fixed in 4% PFA/4% sucrose for 10 min at room temperature. Immunocytochemistry was performed as in (87) using POM2 antibody (1:250) followed by a secondary antimouse Alexa Fluor 488 antibody. Actin staining was obtained by using phalloidin-647N (1:100 dilution) in PBS and incubation for 4 hours at room temperature. Images were acquired at a Leica TCS Downloaded from https://www. science. org at EMBL Bibliothek on January 04, 2022 SP5 microscope (Leica Microsystems), controlled by Leica Application Suite Advanced Fluorescence software. Excitation of fluorophores was done with an argon 488-nm laser and a HeNe 633-nm laser, and signals were detected on HyD detectors.
SAXS-Sample preparation, data acquisition, and analysis The recombinant mouse PrP 23-230 (recPrP) was obtained following a previously reported protocol  (91). After lyophilization, recPrP was dissolved in a buffer containing 150 mM NaCl and 10 mM Hepes (pH 7. 5). The recPrP solution was subjected to centrifugation at 10,000g at 4°C, and the supernatant was used for downstream preparation after the concentration determination. The 6D11 antibody (in PBS) was measured in a concentration series (0. 75, 1, 1. 5, and 2 mg/ml) in the same buffer. RecPrP and 6D11 IgG were mixed in a 2:1 molar ratio to obtain the concentration series for the recPrP/6D11antibody complex (i. e. , 0. 3, 0. 5, 1. 5, and 1. 8 mg/ml). After mixing recPrP and 6D11, the mixtures were incubated for 1 hour on ice to achieve maximum recPrP-to-6D11 binding.
Synchrotron SAXS data were acquired on the EMBL beamline P12 (92) at the Petra III storage ring of DESY (Hamburg, Germany). The data were obtained using an automatic sample changer. To avoid radiation damage, the samples were exposed under a continuous flow in a quartz capillary (0. 9 or 1. 7 mm diameter). The parameters of data collection are shown in table  S1. The scattered intensity was calibrated to absolute units using the scattering of water at 293 K. Initial data processing and analysis was carried out using PRIMUS  (93) , and overall parameters were calculated using ATSAS suite  (94). A previously described SAXS dataset and corresponding CORAL model of the murine recPrP data were used from Small Angle Scattering Biological Data Bank (SASBDB; www. sasbdb. org; accession code: SASDHV9).
Acknowledgments: We thank the  Mouse Pathology Core Facility (K. Hartmann) , the  FACS Sorting Core Unit , and the  Microscopy Imaging Facility (UMIF)  (all at  UKE Hamburg ) for excellent technical support;  C. Giudici  and  M. Willem  ( LMU Munich ) for providing N2a PrP -/-cells; and  G. Spagnolli  ( University of Trento ) for the design of fig. S8A. Molecular graphics and analyses were performed with  UCSF Chimera , developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco , with support from  NIH   P41-GM103311. Funding: The authors are thankful for financial research support by the  CJD Foundation Inc. , the  Alzheimer Forschung Initiative e. V. (AFI) , the  Werner Otto Stiftung  (all to H. C. A. ), the  Deutsche Forschungsgemeinschaft [DFG ;.
5 kHz for  13  C hard pulses, and 50 kHz for both 1 H and 13 C crosspolarization (CP) spin lock. The key acquisition parameters of all 1D and 2D experiments are summarized in Table  S3. Furthermore, all the assigned peaks will be deposited to the Complex Carbohydrate Magnetic Resonance Database (CCMRD),  82  and the Bruker Topspin data set is freely available upon request. To probe polysaccharides with different dynamics, four 1D  13  C spectra were acquired using different schemes for creating initial magnetization. 13 C direct polarization (DP) spectra were acquired using either a 30 s or a 2 s recycle delay for quantitative detection of all molecules or selective highlight of mobile molecules, respectively. For rigid molecules, CP was used with a 1 ms contact time. Refocused Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) experiments with two delays of 1.
72 ms followed by two delays of 1. 15 ms were employed to probe mobile species. To assign glycan signals, 2D  13  C DP refocused J-INAD-EQUATE  83, 84  spectrum was collected using a recycle delay of 2 s. 13  C CP refocused J-INADEQUATE was also employed to detect rigid molecules, using the CP parameters described above for 1D analogs. INADEQUATE acquisition and processing parameters have been described before,  84, 85  with each delay during the polarization transfer set to 2. 4 ms. To directly compare INADEQUATEa double quantum (DQ) correlation experimentwith the single quantum (SQ) spectra described below, the INADEQUATE spectrum was sheared  86  using the "ptilt1" command available on Bruker TopSpin software. The resonance assignments of 37 carbohydrates subforms are displayed in Table  S4 , and compared with literature values in Table  S5.
The assignment of more than 35 spin systems (Tables  S4  and S5 ) was facilitated by comparison with the glycan chemical shifts indexed in the Complex Carbohydrate Magnetic Resonance Database. 82 Arabinose signals were readily assigned due to the C 1 chemical shift close to 110 ppm (A1 1 , A1 2 , A1 3 , and A1 4 ), as well as rhamnose which C 5 signal appears at ca. 20 ppm (R5 x , with x from 1 to 8) (Figure  3a , insets). Galactose, on the other hand, was identified by comparison with resonance patterns (C 1 to C 5 ring in galactofuranose or C 6 in galactopyranose) determined on simpler model compounds. Glucose in starch was easily assigned as it is predominant in this sample, and by comparing its signals to those reported in other cells. 90 Since 1D results defined starch as the major rigid molecule in this cell (Figure  2 ), its assignment was also confirmed with a CP-based refocused J-INADEQUATE spectrum (Figure  S4 ).
These collection methods are of major interest in identifying biomarker candidates in OSD such as DED, using powerful "omics" technologies. Indeed, tear proteomics is increasingly employed for such investigations  [16, 17]. In the last two decades, new insights have been achieved in tear proteomics studies using various systems such as Q-TOF or Orbitrap  [18] [19] [20]. The first major proteomics dataset for tears was created by de Souza et al. in 2006, with the identification of 491 proteins, and in the last decade, several new proteomics approaches have been used, creating large datasets of the tear proteome by using various forms of advanced mass spectrometry (MS)  [16, 18, [20] [21] [22]. These studies, revealing the datasets for the healthy tear proteome are summarized in Table  1. Interest in MS continues to grow, and new technologies are emerging for TF analysis  [27].
These more efficient techniques have promoted the reliable identification of large numbers of proteins from TF samples  [28] [29] [30]. Thus, MS has been implemented in several studies exploring the modulation or dysregulation of proteins in OSD  [15, 18, [31] [32] [33] [34]. Nano-scale liquid chromatography (nano-LC), coupled to tandem MS (MS/MS), provides the improved chromatographic separation of peptides, higher sensitivity, and extended dynamic ranges to identify a large number of proteins  [35, 36]. Therefore, nano-LC-MS/MS has become the first choice for proteomic analysis of small samples such as TF  [37]. The timsTOF Pro mass spectrometer (Bruker Daltonics, Bremen, Germany), which combines the sensitivity of trapped ion-mobility spectrometry (TIMS) and a fast quadrupole time-offlight (Q-TOF), has been providing a large dataset for exploring the proteome of several biological fluids  [38, 39].
The 20 most abundant proteins identified with the highest mean MS/MS spectral count in W, B, and R are shown in Tables  S2-S4. In total, 1582 proteins were identified by summing the 3 batches with no pre-fractionation during sample preparation. The raw data was represented in Table  S1 , and the MS proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD030334. Moreover, we compared these results with those from four proteomics studies that used advanced MS technologies in healthy human tear proteome  [19] [20] [21] 25]  in order to enrich the SEP dataset. Among the 1183 proteins identified in our study, 266 were detected in study (A), 972 were detected in study (B), 487 were detected in study (C), and 543 were detected in study (D) (Figure  S1 ).
However, the ScS approach remains the most common method due to its advantages over the other methods  [14]. In order to use this powerful MS technology, we sought to optimize sample preparation. Thus, we investigated the various sections of the ScS, considering the whole strip (W) and its two parts, the bulb (B), and the rest of the ScS (R). The upper section (B), in contact with the conjunctiva, has the feature of cell adhesion lacking in the remaining part (R). The healthy SEP was investigated using different parts of the ScS with the timsTOF Pro to enrich the protein dataset while revealing the differences among protein profiles in W, B, and R. Indeed, processing B and R separately increased the total NIPs, even though the protein quantity of each batch (W, B, and R) was initially normalized before the MS/MS analysis.
The composition of the protein content in W should be more varied and complex than in B and R, as it includes proteins from the totality of the strip. However, new and specific proteins for each piece of the strip were identified, when the strip was cut into two pieces. Thus, factors such as the ScS area and the mechanical action during the protein elution step seem to participate in the protein composition as well as the extraction yield. This suggests that the complexity of the protein composition and the pre-analytical phases of sample preparation greatly influence the nano-LC-MS/MS data analysis. As compared to other tear proteome studies  [19] [20] [21] , the large number of proteins identified in this study has increased our knowledge and contributed to enriching the tear proteome dataset to better decipher all the functions of tears.
This might also have facilitated the detection of proteins due to their lower quantities in B and R. Eluting these two regions separately and pooling them before one-run MS/MS analysis could improve protein identification. Interestingly, as previously reported, enzymes represented the largest group of proteins in the SEP. The composition of enzymes in the healthy SEP revealed that hydrolases, oxidoreductases, and transferases formed the main enzyme families  [21]. In our study, the number of identified enzyme families enriched the proteome dataset, where the percentage of identified hydrolases increased from 41% to 47. 5% and the percentage of oxidoreductases increased from 15% to 22. 1% compared to the results of this study. Nevertheless, the percentage of transferases decreased from 27% to 16.
The enrichment of the SEP dataset also allows the description of the multiple signaling pathways in healthy human tears. Six major signaling pathways have been reported for the OS, and proteins involved in these pathways have been determined. These pathways involve the following: (1) apoptosis; (2) complement; (3) IFN signaling; (4) MMPs; (5) cell junction; and (6) lipid metabolism. In apoptosis pathways, more proteins were identified than in other pathways. Apoptosis, as a response to physiological stimuli to maintain homeostatic mechanisms during cellular development, aging, and injuries, is highly significant in OS changes  [73, 74]. Proteasome 20S and 26S subunits (PSMA/B and C/D, respectively) and proteasome subunit activator 3 (PSME3) constitute almost half of the proteins from this pathway.
The methodology used in this study enriched the dataset of complement cascades by adding C1R, C1QC, C6, C4BPA, C7, C8A, CFD, CFH, and CFI complement factors. The interferon-signaling pathway is also an important cellular response in OSD, which supports further investigations to understand the regulation of these molecular effectors  [60, [77] [78] [79]. Proteins identified in interferon signaling pathways, such as eukaryotic translation-initiation factors (EIF4A-1, -2, and -3, EIF4G1, and EIF4E), important regulators of mRNA translation  [80] , were identified in R and W, but not in B. Additionally, the human leukocyte antigen (HLA) class I proteins (HLA-A/B/C) could be identified in each region of the strip, except for HLA-E, which is unique to R. Regarding the extracellular matrix (ECM) and cell junction, two pathways were identified according to the identified proteins in each section of the ScS.
Nano-LC-MS/MS Data Analysis Raw MS/MS data were analyzed with Bruker Compass Data Analysis (version 5. 1) and further processed with MaxQuant software (version 1. 6. 8. 0) for protein identification  [91]. The search parameters were set as follows: mass tolerances of 25 ppm for MS1 and 40 ppm for MS2, carbamidomethyl as fixed modification, acetyl (protein N-terminal), and oxidation (on M residue) as variable modifications; up to 2 missed cleavages were allowed. Protein identifications were performed against the Uniprot Homo sapiens database. The Uniprot homo sapiens database version used was UP000005640_9606 in 2019 (https://www. uniprot. org/, accessed on 2 August 2019) with 20,672 entries on 2 August 2019, while the p-values of peptides and proteins were adjusted to obtain the corresponding FDRs of <1%, with a minimum of 1 peptide per protein.
Functional Annotation and Signaling Pathway Network Analysis All identified proteins from the three groups (W, B, and R) were examined by comparing gene ontology (GO) terms. The proportions of the associated proteins in each group were obtained from the PANTHER Classification System (http://www. pantherdb. org/, accessed on 3 May 2021)  [93]. Among 1582 identified proteins, Panther software could classify 1562 proteins according to their functional classification. The Reactome database was used for signaling pathway analysis (https://reactome. org/, accessed on 16 March 2021)  [94]. As an example of the powerful abilities of this new-generation MS device, the timsTOF Pro, to separate different molecules with the same mass-to-charge ratio (m/z), Figure  5  presents how the peptide characterization was assessed by sequencing two of the targeted peptides possessing almost the same mass-leukocyte elastase inhibitor and immunoglobulin kappa variable 2-24 (644.
Conclusions By merging three samples corresponding to different sections of the ScS, 1582 proteins were identified in this study. Enzymes constituted the largest protein group among the identified proteins. Investigating two portions of the ScS separately enabled the identification of a number of additional proteins. Eluting sections B and R separately before analysis with this robust, highly sensitive MS device, the timsTOF Pro, might offer a holistic view of the main proteins present on the OS, from both cellular and soluble components, akin to combining a conjunctival imprint and a tear sample. The timsTOF Pro might bring a new dimension to the profiling of the tear proteome in OSD due to its unique sensitivity, enabling in-depth proteomic analysis from a small tear sample. The dataset created will contribute to the tear proteome as well as to the description and comparison of multiple signaling pathways associated with the OS.
, M. A. A. , F. B. -B. , C. B. , R. M. , C. P. , S. C. , R. M. and I. K. ; visualization, K. K. , M. A. A. , F. B. -B. , C. B. , R. M. , S. C. , C. P. , R. M. and I. K. ; supervision, K. K. , M. A. A. , F. B. -B. and C. B. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by Sorbonne Université and the Institut National de la Santé et de la Recherche Médicale, ANR, LabEx LIFESENSES (ANR-10-LABX-65), and IHU FOReSIGHT (ANR-18-IAHU-01). M. A. A. was funded by a H2020-MSCA-ETN program (IT-DED 3 ; grant agreement: 765608).
Cyclostratigraphy We constructed time series of the Caijia section by measuring XRF scanning and color at 0. 5 m resolution resulting in 515 samples in total. The samples were carefully ground, assembled in 32 mm cups, covered by a 4 µm polypropylene film and measured using an ITRAX XRF core scanner. XRF intensities (in counts per second) were obtained for the 13 elements Al, Si, S, Cl, K, Ca, Ti, V, Mn, Fe, Rb, Sr, and Zr. All statistical analyses were performed on element intensity records after log-ratio or centered-log-ratio transformation. We used principal component (PC) analysis as implemented in the ItraXelerate software  (Weltje et al. , 2015)  to examine the covariance between the individual elements in our data set. The results are presented in a biplot of PC1 and 2, which reveals three groups of elements (Figure  S7 ).
Palynology Productive palynological (pollen and spore) samples were collected from gypsum beds and laminated mudrocks in the Tiefo, Xiejia and Caijia sections. Samples collected from red beds in these sections were barren of palynomorphs. The samples were prepared by Palynological Laboratory Services (Anglesey, UK) using standard palynological techniques (HCL, HF, sieving at 10 µm) and examined at the University of Amsterdam, The Netherlands. Ten samples were productive; nine from the Mahalagou Formation, but only one from the Honggou Formation. These samples were added to the previously studied palynological record of the Xining Basin  (Bosboom et al. , 2014; Dupont-Nivet, Hoorn, & Konert, 2008; Hoorn et al. , 2012; Page et al. , 2019  and Figure  S8 ). Samples were published as supplementary data for a recent review of Asian steppe ecology (Barbolini et al.
(2005); (9) Lanzhou Basin: Y. Zhang et al. (2014); age reinterpreted by W. Wang et al. (2016); (10) Xining Basin: Licht et al. (2014); (11) Xorkol Basin (Li et al. , 2018). The Xining Basin is indicated with a star. (b) Geological map of the Xining Basin based on BGMRQP (1965), QBGMR (1985), and Dai et al. (2006) showing the locations of the sections in the Xining Basin. Datasets generated in this study are indicated in red. (c) Lithostratigraphy listing the formations of the Xining Basin adapted from Horton et al. (2004) and updated following Fang et al. (2019). Stratigraphic intervals covered by the datasets of this study are indicated on the right.
Acknowledgments This work was funded by the  ERC consolidator  grant  MAGIC 649081  to GDN. We thank  Meimei Xiao ,  Aijun Sun ,  Zhipeng Wu ,  Shengcheng Liu  and for their help in the field and logistical support. We also thank  Stefan Liening  and  Caroline Zorn  for their help in the SedLab. We are grateful to  Hans van Hateren  for providing his Matlab script to calculate the class-wise R 2 of the end-members. We thank  Peter Selkin ,  Daniel E. Ibarra  and five anonymous reviewers for their comments and feedback. Open Access funding enabled and organized by Projekt DEAL.
Pathway-and gene-centric approaches uncover a peculiar transcriptome landscape in Peruvian HCC Transcriptome profiling was performed in a subset of 39 HCC and non-tumor liver (NTL) matched pair tissues www. oncotarget. com (HCC/NTLs) for whom high-quality RNA was available [RNA integrity number (RIN)  > 7]. We first followed a pathway-centric approach by using the previously described Sample Enrichment Score (SES) data mining method for scoring transcriptome hallmarks in single samples  [26]. Data validation was carried out with random gene sets and dataset authentication by gender (Supplementary Figure  1 ). The SES method was applied to Peruvian HCC/ Surprisingly, AYA vs. MOA (i and ii) did not exhibit noticeable differences, whereas a differential enrichment of 118 gene sets was found in HCC vs.
Gene expression signature characterizes Peruvian HCC as a distinct molecular subtype After batch-effect removal, gene expression data of the Peruvian patients were compared to HCC/NTL microarray datasets of 195 patients from other continental origins with clinical and demographic information (Supplementary Table  2 ). Out of the initial 118 gene sets, 56 were specific to Peruvian patients, pointing at a drastic loss of hepatocyte identity, including more extensive alterations in retinoids, carbohydrates, fatty, and bile acids metabolisms, or in xenobiotics-modifying enzymes (Supplementary Figure  4  and Supplementary Table  3 ). Downregulation of estrogens and somatotropin signaling pathways were also more pronounced in Peruvian tumors. Meanwhile, cell proliferation in Peruvian HCC was more dependent on activation of Hippo/YAP1 and c-Met/HGF proliferation pathways, DNA methylation and repair, and the activity of chromatin modifiers such as that of Polycomb Repressive Complex 2 (PRC2).