dataesr/scientific-paragraphs-categorization huggingface dataset:
In detail, only the last 96 nucleotides of the first exon are coding. The whole highly conserved signal peptide (18 AA, MKCLLLALGLALMCGIQA) of the mature protein (162 AA) is encoded by the nucleotides 643 to 696 of exon 1 and the translation stop codon TAA is created by nucleotides 15 to 17 of exon 6. The AA sequence (from 16 to 150 of the mature protein) reveals the structurally conserved regions typical of a lipocalin core (Supplemental Figure  S1 ; https://dataverse. harvard. edu/dataset. xhtml?persistentId=doi:10. 7910/ DVN/DLFGKD,  Cosenza, 2023). The deduced CDS (coding sequence) length of donkey BLG I gene is 543 bp long, with an average CG (Guanine-Cytosine) content value of 59. 3% and with all the splice junctions following the 5′GT/3′AG splice rule. This structure is comparable to that described by  Wodas et al.
Intronic and Exonic Polymorphisms Detection The analysis and the alignment of the BLG I intronic sequences of the 2 donkeys used in this study have highlighted a discrete genetic variability. In detail, 17 polymorphic sites (6 transversions, 10 transitions, 1 deletion/insertion) were found between the 2 sequenced animals (Supplemental Figure  S1 ; Table  1 ). None of the intronic polymorphisms is apparently located in the regulatory regions (splicing donor/acceptor site, enhancer/silencer) and, as a consequence, they likely do not affect the gene expression. In particular, taking as reference the sequence of the wild type (GenBank accession no. ON886232), 2 polymorphisms (g. 1203-1204delTGGAGC and g. 1339C > T) are detected within a mammalian long-terminal repeat retrotransposon (MaLR) located between the nt 161 and 443 of the first intron.
Asp28 > Asn in the mature protein (Supplemental Figures  S1  and S2 , https://dataverse. harvard. edu/dataset. xhtml?persistentId=doi:10. 7910/ DVN/DLFGKD, Cosenza, 2023; Table  1 ). Instead, comparing the sequences analyzed in this study with those recorded in GenBank for the donkey BLG I gene, it is possible to identify other genetic variability. In particular, 10 single intronic polymorphisms (7 transitions, 1 transversion, 2 indel mutations), 2 exonic polymorphisms (g. 458A > T and g. 1863C > T), and 2 repeats as mono-(g. 4934C [6] ) and tetranucleotide (g. 5524CCTC[2]; Table  1 ). Auzino et al. (2022)  postulated that the increase of about 1,915 Da of the new β-LG I variant can be justified by the fact that the protein entering the secretion pathway in the endoplasmic reticulum may include a N-terminal sequence extension, corresponding to the canonical signal peptide (β-LG I precursor), with an additional Ala residue (AMKCLLLALGLALMC-GIQA).
Regulatory Elements and Polymorphisms Detection at the Promoter of the Gene The analysis of the gene promoter and its regulatory regions may provide an answer to this hypothesis. In fact, variations in regulatory regions are known to affect the structure and expression of milk protein  (Martin et al. , 2002; Szymanowska et al. , 2003; Cosenza et al. , 2007 Cosenza et al. , , 2016 Cosenza et al. , , 2018)). Therefore, in the present study, we decided to characterize and compare the proximal gene promoter and 5′ untranslated region (UTR; about 900 nt) of the 2 investigated alleles. AliBaba v2. 1 program was used to identify the potential binding sites of TF that could affect the gene expression revealed that the TATA box (TATATA) is located between the nucleotides -71/-66. In addition, we identified the following TF: CACCC elements flanked by clusters of overlapping specificity protein 1 sites; activating protein 2; transcription factor IID; CCAAT/enhancer-binding protein α; retinoic acid receptors subtypes α, β; Yin Yang transcription factor-1, nuclear factor I, and milk protein binding factor, enhancer box (E-box; Supplemental Figure  S1 ).
05 (Table  2 ). Using Haploview software version 4. 2, only 3 different allelic combinations (out of the 4 expected) were observed: haplotypes 1 (920-922TGT/1871G), 2 The marker (M) used is the 2-log DNA ladder (0. 1-10 kb; Biolabs) 1a, "g. 1871G" allele specific primer; 2a, "g. 1871A" allele specific primer 1b, "g. 920-922CAC" allele specific primer; 2b, "g. 920-922TGT" allele specific primer. (920-922CAC/1871G), and 3 (920-922TGT/1871A). The first haplotype was the most represented with a frequency of 0. 651, followed by the haplotypes 2 (0. 306) and 3 (0. 043). Because only the SNP g. 1871A > G is associated with the different observed phenotypes, likely this mutation is involved in gene regulation processes.
Allele Nomenclature and Phylogenetic Relationship Among the Markers Considering all 5 AA changes (gene markers in exons 2, 4, and 5) as revealed by the database analysis, the literature, the newly determined in the present study and the 3 observed haplotypes, it is possible to deduce at least 5 different alleles (BLG I A, B, B1, C, D) responsible for 4 potential β-LG I translations (Figure  2 ). In detail, the variant A and B have already been well characterized at the protein level respectively by  Godovac-Zimmermann et al. (1988)  for a Sardinian donkey (Italy) and by  Herrouin et al. (2000)  for French donkeys of the "Commune" breed. The 2 sequences show a homology of 98. 1% as a consequence of 3 AA substitutions: p. Ser36 > Glu, p. Thr97 > Ser and p. Ile150 > Val. Taken together, these 3 AA substitutions explained the mass difference of about 14 Da observed for the entire protein  (18,514.
25 vs. 18,528. 23 Da; Herrouin et al. , 2000). The BLG I B1 (haplotype 1) is a silent allele because it differs from the BLG I B (haplotype 2) for the substitution g. 920-922CAC > TGT in the 5′ untranslated region. The presence of CAC 920-922 should represent the ancestral condition of the gene as constitutive of BLG I and BLG II sequences in mares (GenBank accession no. PJAA01000026. 1, region: 38874907 to 38880546, complement; AF107201. 1) and zebras (GenBank accession no. JAKJSB010000001. 1, region: 96008038 to 96002141; HM012800. 1). The BLG I B1 allele, wild type allele characterized in the present study, is considered as the ancestral form according to nucleotide and AA sequences available for equidae. No cDNA or gene sequence is available for the variant B  (Herrouin et al. , 2000).
Therefore, currently it is not possible to establish the link with the variant B1. The further allele that we named BLG I C derived by the subsequent comparison between the nucleotide sequences of this study and that of an African ass reared in the Copenhagen zoo (Denmark; PSZQ01002145. 1, region: 1252372 to 1258209). This allele is characterized by the SNP g. 1863C > T located at the 32th nucleotide of exon 2 (Table  1 ) responsible for the AA substitutions p. Ala25 > Val in the mature protein  (deduced Mr: 18, 542. 30 Da). The allele C carries the same haplotype 1 of the allele B; therefore, it is likely derived by the latter. The p. Val25 was found also in the sequences of monomeric β-LG as reported for donkey β-LG II (GenBank acc nos. HM012800. 1; HM012799. 1) and equine β-LG II (GenBank acc nos.
AF107201. 1; NM_001082494. 1), while for all mammalian species the presence of alanine occurs at this position both in β-LG I and β-LG II. Finally, the BLG I D allele corresponds to the variant described in the present study characterized by the Asn in position 28 (haplotype 3). Among the 5 alleles, the BLG I D is of real novel identification because this amino acid substitution was never reported earlier in databases. In fact, all monomeric and dimeric β-LG sequences are characterized by Asp at this position and, therefore, it has to be considered as the ancestral condition of these proteins. However, the substitution p. Asp28 > Asn increases the protein mass of a single Da and does not justify the difference in the molecular weight  (18, 514. 25 vs. 20, 428. 5 Da)  observed in the SDS-PAGE of the present study and by  Auzino et al.
ACKNOWLEDGMENTS The authors gratefully acknowledge financial support provided by the  Italian Ministry of Agricultural and Forestry Policies  ( SELMOL  project). The author contributions included the following:  G. Cosenza  and  P. Martin  conceived and designed the experiments; Auzino,  D. Gallo  and  G. Garro  performed the experiments;  G. Cosenza ,  A. Pauciullo  and  P. Martin  analyzed the data;  G. Garro , and  R. Ciampolini  contributed reagents, materials, and analysis tools;  A. Pauciullo  and  R. Ciampolini  provided milk and DNA samples;  G. Cosenza ,  A. Pauciullo , and  P. Martin  wrote the paper;  G. Cosenza ,  A. Pauciullo , and  P. Martin  revised the article critically for important intellectual content;  G. Cosenza ,  A. Pauciullo ,  P. Martin ,  G. Garro ,  D. Gallo ,  R.
Data description RNAs from a single genotype of Douglas-fir, extracted from three distinct wood zones (outer sapwood, inner sapwood and transition zone) at four vegetative seasons to generate an extensive RNA-seq dataset used to apprehend the in-wood dynamic and seasonality of heartwood formation in this hardwood model species. Previously published data collected on somatic embryos of the same genotype could be merged with the present dataset to upgrade grade the Douglas-fir reference transcriptome.
The major objective of our work is to characterise heartwood formation at the transcriptomic level. Hence, we report here an update of the Douglas transcriptome project marked by the production of almost 400 million sequence reads focussed on wood and heartwood formation. This data complements the datasets describing the differentially expressed genes involved in somatic embryogenesis  [10, 11]. Moreover, by combining both of these data (wood and somatic embryogenesis related cDNAs) with the data set already published on Douglas-fir seedlings  [12] [13] [14] [15] [16] [17] [18] , we propose to construct a new version of Douglas-fir reference transcriptome for the scientific community.
Data description A total of eight trees have been felled for this study, one in January and July, and three in April and October. In the field, wood slices were cut with a chainsaw and immediately frozen in liquid nitrogen. Total RNA was then extracted from freshly lyophilized disks. Three wood zones (growth rings) were harvested on each disk: the OSW that corresponds to the first ring under the cambial zone, the TZ, just located before heartwood, and the ISW representing the median ring between OSW and TZ. The rings of interest were isolated from the wood disc with a chisel, cut into fine bits and grinded with bench knife mill (4 × 5 s interspersed with a pause of 30 s) before being crushed into powder (granulometry of about 200 μm) by a ball milling machine (Labman Automation LTD, UK; Phenobois platform mill, INRAE Orléans) according to automated grinding sequences consisting of 6 grinding phases of 30 s interspersed with 50 s of pause.
Reads with a length lower than 50 bp were also discarded, the links of the FastQC files of the different tissues are given in Table 1  [20] [21] [22]. Transcriptome de novo assembly. Digital normalization and transcriptome de novo assembly was conducted using the Trinity 2. 0. 6 software with a default k-mer size of 25. Trinity is composed of three different modules: Inchworm, Chrysalis and Butterfly. Inchworm builds a k-mer dictionary from the reads, which will lead to the construction of contigs. Chrysalis connects all overlapped contigs into components using a de Bruijn graph approach. In a final step, Butterfly simplifies all the generated graphs to report full-length transcripts and their alternatively spliced form. DeconSeq standalone version 0. 4. 3 was used to detect and remove sequence contaminations from the assembled transcriptome, using bacterial, fungal, plant, virus and other databases.
Availability of data and materials The RNA-seq sequences are available in the National Center for Biotechnology Information (NCBI) with the BioProject accession number https:// ident ifiers. org/ ncbi/ biopr oject: PRJNA 945886. The other data accessed on the Recherche Data Gouv repository under doi:https:// doi. org/ 10. 57745/ TZPTXE including  [9, [20] [21] [22].
Note that users received instructions in video, audio, and written form, to ensure their understanding and seamless completion of the tasks. The audio (especially for feedback sounds) was spatialized using the Stea-mAudio plugin. The SRapinal SDK was used for eye-tracking and facilitating the pupillometry, as well as the fixation duration over the text of the cybersickness questionnaire (i. e. , reading ability). Finally, the randomization of the experimental blocks within and between participants, extraction of the data into a CSV file, and the experimental design and control were attained using bmlTUX SDK  [2].
An HTC Vive Pro Eye was used, which embeds an eye-tracker with a binocular gaze data output frequency of 120Hz (i. e. , refresh rate), a tracking accuracy of 0. 5°-1. 1°, a 5-point calibration, and a 110°trackable field of view. The participants then provided their demographic data: age, sex, gender, education, dominant eye, VR experience, computing experience, and gaming experience, by responding to a questionnaire. VR/computing/gaming ex-perience were calculated by adding scores from two questions (6-item Likert Scale) for each one. The first question was regarding the participants' ability (e. g. , 5 -highly skilled) to operate a VR/computer/game, and the second one was regarding the frequency of operating them (e. g. , 4 -once a week). After the demographics, participants were immersed in VR.
RESULTS All analyses were performed using R  [40]. As the variables violated the normality assumption, we used the bestNormalize R package  [38]  to transform and centralize the data. The distribution of the data was then normal. Furthermore, the a f ex (ANOVA analyses;  [47] ), the ggplot2 (plots;  [60] ), the emmeans (post-hoc comparisons;  [30] ), and the lme4 (regression analyses;  [1] ) R packages were used for performing all the analyses. Two-way repeated measures (RM) ANOVA analyses were conducted to examine the existence of interactions between the order of ride and the type of music, as well as their main effects on cybersickness. Mixed regression model analyses were performed to investigate the effect of cybersickness on cognition, motor skills, reading ability, and pupil size. Mixed model analyses were also performed to explore the effects of demographics (age, gender, education, and VR/computing/gaming experience) on cybersickness.
The chips were washed three times with PBS-Tw. Chips were then fixed and directly incubated with ExoView Tetraspanin labeling antibodies that consist of anti-CD81 Alexa Fluor555, anti-CD63 Alexa Fluor647, and anti-CD9 Alexa Fluor488. The antibodies were diluted at 1:5000 in PBS-Tw with 2% BSA. The chips were incubated with 250 µL of the labeling solution for 2 h, washed in ultrapure water, and dried. The chips were then imaged with the ExoView R100 reader using the ExoScan 2. 5. 5 acquisition software. The data were analyzed using ExoViewer 2. 5. 0 with fluorescence thresholds set to detect a maximum of 20 events on the isotype control spots. In Vitro Differentiation of THP-1 Monocytes into Macrophages: THP-1 Dual monocyte cell line (InvivoGen, San Diego, CA, USA), dual reporter cell line which can investigate simultaneous NF-κB signal transduction pathway and IFN pathway via secreted alkaline phosphatase (SEAP) and Lucia luciferase activity, respectively, was used.
Acknowledgements N. O. P. and K. C. contributed equally to this work. The authors gratefully acknowledge the financial support of the  ANR (Agence Nationale de la Recherche ) for the project CycLys-18- CE09-0015-01  and the  French National Research Program for Environmental and Occupational Health of the Anses (Agence nationale de sécurité sanitaire de  l'alimentation, de l'environnement et du travail) ( 2018/1/007 ). This work was supported by the  IdEx Université Paris Cité ,  ANR-18-IDEX-0001  (IVETh plateform); by the  Region Ile de France  under the convention SESAME 2019 -IVETh ( EX047011 ) (IVETh plateform); the convention SESAME E1845 for the JEOL ARM 200F electron microscope installed at the  Université Paris-Cité , by the  Region Ile de France and Banque pour l'Investissement (BPI)  under the convention Accompagnement et transformation des filières projet de recherche et développement Sésame Filières PIA  N° DOS0154423/00 ,  DOS0154424/00 ,  DOS0154426/00 , and  DOS0154427/00  (IVETh platform); and by  Agence Nationale de la Recherche  through the  program France 2023  " Intégrateur Biothérapie Bioproduction " ( ANR-22-AIBB-000 , IVETh integrateur).
This work had benefited from the facilities and expertise of  MIMA2 -Université Paris-Saclay ,  INRAE ,  AgroParisTech , GABI, 78350, Jouy-en-Josas, France (MIMA2: https://doi. org/10. 15454/1. 5572348210007727E12 ). This work was partly supported by the  Interdisciplinary Thematic Institute SysChem via the IdEx Unistra  ( ANR-10-IDEX-0002 ) within the  program Investissement d'Avenir. The authors wish to acknowledge the  Centre National de la Recherche Scientifique (CNRS)  and the  International Center for Frontier Research in Chemistry (icFRC). The authors wish to thank  Shi Guo  for helping with the XPS characterization;  Cathy Royer  from the  "Plateforme Imagerie In Vitro de l'ITI Neurostra ,"  CNRS UAR 3156 ,  University of Strasbourg (Strasbourg, France ) for TEM analyses;  Jean Philippe Herbeuval  for providing the THP-1 dual cells and fruitful discussions;  Catherine Cailleau  from  IGPS  for technical assistance in animal experiments; and  Claudie Delomenie  from IPSIT for transcriptomic experiments.
Based on this dataset, we identified 232 RRGs enriched in LPLCs (Table  S1 ; STAR Methods). RRGs significantly distinguished LPLCs from other subpopulations, as LPLCs showed the highest module score of RRGs (Figure  1F ). RRGs represented key features of reprogramming, such as genes related to liver progenitors, fetal liver functions, epithelial cell differentiation, cell proliferation and adhesion (Figure  S1G ; Table  S1 ). Notably, we validated that RRGs were enriched in hepatoblasts by analyzing the expression of E12. 5 fetal livers (Figure  1G ), confirming that hepatocyte reprogramming re-activated genes expressed during liver embryogenesis. To infer potential trigger signals for LPLC formation, we deciphered the dynamics of pathway activation during reprogramming by single-sample gene set enrichment analysis (Table  S2 ; STAR Methods).
Each circle denoted the number in a field of view. (J and K) Macrophage depletion and post-injury recovery. Serum levels of ALP and TBA were measured (J; n = 6). Immunohistochemistry staining of Ki67 and the ratios of Ki67 + hepatocytes were shown (K; n = 3). Scale bars: 100 mm. Data were presented as mean ± SD; n. s. , not significant; *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001, t test. See also Figure  S2. contrast, liver function pathways remained relatively high in cluster 4 (Figure  S2E ). These data support that LPLC formation is impeded by macrophage depletion. We then analyzed the spatial correlation between macrophages and LPLCs. F4/80 staining showed that macrophages were significantly enriched in the periportal regions (Figures  2H  and 2I ), where LPLCs were formed (Figure  1B ).
Serum levels of ALP and TBA were measured (G). Co-staining of Ki67 and HNF4a and the number of Ki67 + hepatocytes were shown (H). gp130 f/f , n = 3; gp130 Dli , n = 5. (I-L) Schematic view showed DDC injury and recovery in Stat3 f/f mice treated with AAV-GFP/Cre (I). H&E staining and ductal reaction area per periportal field of view were shown (J; n = 3). Serum levels of ALP and TBA were measured (K; n = 5). Co-staining of Ki67 and HNF4a and the number of Ki67 + hepatocytes were shown (L; n = 3). Scale bars: 100 mm. Data were presented as mean ± SD; *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001, t test. See also Figure  S5. 6] [27] [28] [29] [30]  Because RRGs were also highly expressed in hepatoblasts during liver embryogenesis (Figure  1G ), we investigated whether STAT3-mediated RRG regulation was a development-related or, instead, an injury-specific mechanism.
Primary antibodies were detected using Opal TM 4-Color Manual IHC Kit (AKOYA, NEL810001KT) on paraffin sections according to the manufacturer's protocol. Sections were stained with DAPI (4',6-Diamidino-2-Phenylindole) and mounted with fluorescence mounting medium (Dako). Images were taken using an Olympus BX51 microscope or a confocal microscopy Leica TCS SP8 WLL. Images in different channels were merged by ImageJ software. To describe the distribution and density of LPLCs and macrophages (KCs and MoMFs), fluorescent signals of LPLCs (SOX9 + HNF4a + ), macrophages (F4/80 + ), KCs (CLEC4F + F4/80 + ) and MoMFs (CLEC4F -F4/80 + ) were converted to digital signals using the 'spot' function in Imaris software. Position coordinates of each spot were analyzed by Two-Dimensional Kernel Density Estimation, and plotted via the 'ggplot2' package in R to quantify the cell distribution and density.
The cells were analyzed using cytoflex LX or BD LSR Fortessa. Cells were sorted using BD Aria III. Data were generated using FlowJo V10 (https://www. flowjo. com/). To measure intracellular cytokine IL-6, 0. 5-5 3 10 6 non-parenchymal cells were incubated for 2-4 h at 37 C in DMEM with 10% FCS and 1 3 brefeldin A (BioLegend). After incubation, cells were stained extracellularly as above. Intracellular cytokine staining was performed by fixing and permeabilizing extracellularly stained cells according to the manufacturer's instructions using the Intracellular Fixation & Permeabilization Buffer (Thermo Fisher Scientific, 88-8824-00). The cells were analyzed using cytoflex LX or BD LSR Fortessa. Data were generated using FlowJo. To analyze the direct effects of DDC on KCs, freshly isolated KCs from normal livers were treated with DDC at 10 mg/mL or 100 mg/mL in vitro.
Schematic cartoons in Figures Feature quantification for scRNA-seq data For 10X dataset, sequencing reads were aligned to the mouse reference genome (mm10) and estimated cell-containing partitions and associated UMI using the Cell Ranger (v6. 0. 1) single-cell software suite. For Smart-seq2 dataset, nine pair-end libraries, each containing sequencing reads from dozens of single cells, were constructed. For each library, the template switch oligo (TSO) sequences were trimmed by cutadapt (v1. 14),  48 and the low-quality bases at 3' end of reads were trimmed by trimmomatic (v0. 36). 49 After that, demultiplex was done by fastx toolkit (v0. 0. 13) (http://hannonlab. cshl. edu/fastx_toolkit). Since we introduced UMI in our Smart-seq2 protocol, we then tagged the UMI sequence to the read name of each sequence by umi_tools.
Quality control of scRNA-Seq data To further acquire more informative datasets, strict criteria were applied to select high-quality cells. The Seurat (v4. 0. 1) 53,78 R package was employed to perform scRNA-Seq data analysis. For macrophage dataset, cells with total gene detected > 1,500 and mitochondrial (MT) gene content < 5% were selected. For hepatocyte, cells with total UMI > 4,000, number of detected gene > 800, and MT gene proportion < 10% in 10X dataset and cells with total UMI > 100,000 and MT gene content < 10% in Smart-seq2 dataset were selected for the following analysis. After filtering, datasets of different batches were normalized independently with SCTransform. The reciprocal PCA (RPCA)-based integration and graph-based clustering were performed to identify non-hepatocyte contaminants.