dataesr/scientific-paragraphs-categorization huggingface dataset:
This program builds subfamily-and genome-specific LRR hidden Markov model (HMM) profiles, detects LRR-CR proteins that contain LRR motifs and accurately locates LRR motifs within these proteins. We ran the LRRPROFILER pipeline in parallel on the three rice predicted proteomes and found that they greatly differed in terms of the number of LRR-CR genes and their structural annotations. We therefore performed a manual curation of the whole Nipponbare LRR-CR repertoire annotation. To do so, for gene models that diverged between the three annotations, we looked for the reasons of divergence and decided, when appropriate, to supplement the gene models with sequence fragments undoubtedly derived from LRR-CR-encoding genes. In turn, we provided objective information, i. e. whether the gene models were canonical or non-canonical.
Among these 1058 genes, eight (one LRR-RLK, three LRR-RLP and four UC) were located at loci for which none of the three publicly available annotations detected a gene. The LRR-RLK was a canonical full-length sequence on the forward strand of chromosome 2 (from 6 831 702 to 6 834 761). Note that this sequence is actually present in GenBank under accession number EAZ22278. 1 and is located on the reverse strand in a non-coding region of the Os02g0222500 gene. The other seven are non-canonical truncated genes. In addition, for seven of these 1058 Coherent structure ? -start? -splicing sites? -stop? ORF? domains? Missed sequence ? (b) Schematic representation of an example of inconsistency between gene models from publicly available annotations and how the curation was performed. The gene is an LRR-RLK located on chromosome 1 of the Nipponbare genome.
The curation took advantage of the three annotations. It retried a cDNA sequence that overlapped the complete former coding sequence in the two successive correct reading frames via the identification of the indel mutation. The identification of the indel mutation was clear cut as the gene was tagged as 'non-canonical' with the presence of a frameshift, but it allowed a complete protein sequence to be deduced and used for sequence comparison and alignment. © 2021 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2021), doi: 10. 1111/tpj. 15456 validated genes, the LRRPROFILER pipeline did not detect any further LRR motifs in the predicted protein. LRR motifs were initially detected for these genes, but at the threshold limit when using HMM profiles built on the basis of the initial data set (for details, see the LRRPROFILER pipeline section in the Experimental procedures).
These 11 genes were therefore kept in the final data set. The gene subfamilies for these 11 loci were determined based on other functional domains and a homology search against other LRR-CR protein sequences. Finally, the LRR-CR gene set from Kitaake was composed of 360 LRR-RLKs, 140 LRR-RLPs, 510 NLRs and 54 UCs (Data S3; Figure  5 ). These numbers were very similar to those obtained for Nipponbare, i. e. 350 LRR-RLKs, 147 LRR-RLPs, 503 NLRs and 58 UCs. We then tagged all of these Kitaake LRR-CR gene models as either canonical or non-canonical. We obtained 742 (69. 7%) canonical genes and 322 (30. 3%) non-canonical genes. Again, the proportions of canonical and noncanonical genes per subfamily for Kitaake were very similar to those obtained for Nipponbare (Figure  5 ). A notable result is that our strategy enabled us to identify 114 LRR-CR genes (48 of which were canonical) that were not present in the publicly available annotation of the Kitaake genome: 17 LRR-RLKs, 24 LRR-RLPs, 50 NLRs and 23 UCs.
Comparison of LRR-CR allelic pairs between Nipponbare and Kitaake Nipponbare and Kitaake are two varieties of the same subspecies: O. sativa ssp. japonica. As such, for the majority of the genes found in Nipponbare, an allele (i. e. a version of the same gene located at the same chromosomal location) was expected to be found in Kitaake. By using SYNMAP  (Lyons and Freeling, 2008) , we identified 1002 allelic pairs (representing 90. 5% of the total number of loci) between Nipponbare and Kitaake (Data S4). In addition, we noticed that for three NLR gene pairs located close to each other on chromosome 9 in the Nipponbare genome, three consecutive genes on chromosome 3 of the Kitaake genome were found with 100% identity with regards to their predicted coding sequence. The intergenic sequences of these two regions also had a high level of identity (99% over 40.
Our study highlighted that investment in a combination of technologies would guarantee highquality assemblies and annotations, especially when the discovery of allelic diversity is targeted  (Zhou et al. , 2020). The tools and curated data sets that we generated in this study are available from: https://rice-genome-hub. southgreen. fr/content/geloc (data) and https://github. com/ cgottin/LRRprofiler (tools). Note that we focused on developing a website where stop codons and frameshifts are easily identified. We believe that evolutionary studies and allele discovery initiatives for LRR-CRs would be more accurate and reliable when using our manually curated comprehensive annotations for these genes. Moreover, we feel that this comprehensive annotation approach should be widely adopted by the community in the light of the major potential benefits it provides.
Genomes and annotation files Reference genomic sequences of Nipponbare  (Kawahara et al. , 2013)  and Kitaake  (Jain et al. , 2019)  O. sativa ssp. japonica cultivars were downloaded from the Rice Annotation Project Database (RAP-DB) website (https://rapdb. dna. affrc. go. jp) and the Phytozome website (https://phytozome. jgi. doe. gov/pz/portal. html). The general feature format (GFF) and fasta files with coding DNA sequences (CDSs) and protein sequences for Nipponbare were downloaded for three different annotation projects: (i) the MSU 7. 0 annotation was downloaded from the Rice Genome Annotation Project FTP server (http://rice. plantbiology. msu. edu); (ii) the IRGSP annotation files were downloaded from the RAP-DB website (https://rapdb. dna. affrc. go. jp); and (iii) the NCBI annotation (release 102) annotated by the NCBI Eukaryotic Genome Annotation Pipeline was downloaded from the NCBI website (https://www.
LRRPROFILER implementation The LRRPROFILER pipeline was implemented in two steps (Figure  S1 ). The first step involved the iterative refinement of LRR HMM profiles specific to a gene subfamily (LRR-RLK or NLR) and proteome (Figure  S1 ; inspired by  Ng et al. , 2011). Only LRR-RLK and NLR were considered for profile refinement because they contain a specific domain (i. e. kinase and NB-ARC domains, respectively), thereby allowing the clear identification of the subfamily to which they belong. A set of candidate protein sequences was identified from a given proteome to refine the specific LRR profiles. This set was composed of either LRR-RLKs identified with iTAK  (Zheng et al. , 2016)  or NLRs identified with the PF00931 Pfam NB-ARC profile. A first round of LRR motif detection was performed in either of the candidate protein sets using HMMSEARCH (HMMER; Eddy, 2011) with the SM00370 LRR profile from the SMART database.
Six publicly available LRR HMM profiles from the SMART database, i. e. SM00364 (LRR_BAC), SM00365 (LRR_SD22), SM00367 (LRR_CC), SM00368 (LRR_RI), SM00369 (LRR_TYP) and SM00370 (LRR), in addition to the newly built LRR profiles obtained in the first step were used to detect LRR motifs in the complete proteome under consideration using HMMSEARCH. An annotation of the LRR domains, containing the start and end positions of each LRR motif, was part of the output. The annotation of each protein was then supplemented using publicly available profiles for other functional domains: TIR (PF01582), TIR_2 (PF13676), Malectin (PF11721), Malectin-like (PF12819), RPW8 (PF05659), Cys-Pairs  (Dievart and Clark, 2003; Dufayard et al. , 2017) , F-box (PF00646) and FBD (PF08387). NB-ARC and kinase domain annotations were retrieved from the first step, whereas transmembrane domains (TMs) were detected with TMHMM 2.
The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2021), doi: 10. 1111/tpj. 15456 BLASTp search with default parameters against the other gene sets (LRR-RLP, LRR-RLK, NLR and F-box) to estimate their probable membership of one of these gene subfamilies. F-box proteins were removed from our data sets and not considered further in the analyses. We ended up with four gene sets: LRR-RLP, LRR-RLK, NLR and UC. At the end of the construction phase, the complete LRRPROFILER pipeline was tested on the manually reviewed A. thaliana protein data set downloaded from the Swiss-Prot section (https://www. uni prot. org) (Data S1; Figure  S2 ; Methods S1; Table  S1 )  (Boutet et al. , 2007)  of the UniProt databank (The UniProt Consortium, 2019).
This problem arises because it uses the same reading frame to translate the whole genomic sequence (the six reading frames are tested, but each resulting translation just uses one of them). To overcome this issue, intron junctions are then corrected with a PYTHON script that looks for canonical splicing sites in a range of two nucleotides before and after the current junctions. Finally, gene models highly divergent from the Nipponbare query sequence, with multiple premature stop codons or without start or terminal stop codons, and overlapping frameshifts are tagged to be checked manually.
DATA AVAILABILITY STATEMENT All of the data files (gff and fasta files) are available from the dedicated website (https://rice-genome-hub. southgreen. fr/content/geloc) and from the open data repository Zenodo (https://doi. org/10. 5281/zenodo. 5110015). A new identifier was allocated to each LRR-CR gene unraveled by this procedure. These identifiers use the <OSJnip_ChrXX_00000000> or <OSJkit_ChrXX_00000000> pattern for Nipponbare and Kitaake loci, respectively, with XX being the chromosome number followed by the start codon position of the coding sequence (CDS) on the chromosome (Data S2 and S3). According to the Multiple Alignment of Coding Sequences (MACSE) convention  (Ranwez et al. , 2018; Ranwez et al. , 2011) , indels causing frameshift mutations have been pinpointed by the presence of one or two '!' characters in the nucleotide sequences of non-canonical genes and are available in an additional specific data set.
The athletes had been members of their respective teams for 2. 35 years (SD = 2. 37) and had a mean experience of 8. 44 years (SD = 5. 03) in their sport. They spent in average 5. 75 h a week with their respective coach (SD = 3. 23) during training practices and games. Snijders and Bosker (2012)  suggest that 10 groups is appropriate to perform multilevel analysis. In sport, multilevel analysis with less than 20 groups were conducted successfully  (Murray, Coffee, & Eklund, 2020). We calculated sample size for our multilevel models using the samplesize_mixed function in the sjstats package  (Ludecke, 2019). We fixed the power at. 8, to determine an effect size of 0. 25 with a degree of freedom of 39 and 60 (respectively for the indirect relationship and the moderation), with 44 coaches, a sample size of 4 individuals per cluster and a minimum of 186 individuals were recommended for the indirect relationship and a minimum of 5 individuals per cluster and a total of 205 individuals.
Moreover, performance would be at the level 3 (team level) but we used it at the coach-level because not all teams had several coaches meaning that without variance inside the level the condition of application of a third level is not met. In our model (Fig. 1 ), slopes and intercepts are fixed. Mplus software  (Muthén & Muthén, 1998 -2018) , TYPE = TWOLEVEL, CLUSTER and MLR estimator were used. MLR estimation was used to provide adjusted χ 2 and standard errors that account for non-normality. Model fit was assessed using a chi-square goodness-of-fit index, root mean square error of approximation (RMSEA), Tucker-Lewis index (TLI), Comparative fit index (CFI) and both within and between levels' Square root mean residual (SRMR). A minimum cut-off of 0. 90 for CFI and TLI, a maximum cut-off of 0.
Dynamic light scattering The absence of protein aggregates was verified for mAb solutions by dynamic light scattering (DLS) on a Malvern Zetasizer Nano instrument. mAbA was measured at 30 g. L -1 , mAbB at 0. 7 g. L -1 and mAbC at 3 g. L -1. The data were acquired using the ZSExplorer size protocol (30 acquisitions, total time 50. 4 s) and analysed with ZSExplorer software (general purpose model, adaptive correlation mode). From the size distribution by intensity, the hydrodynamic diameter was deduced based on the Stokes-Einstein equation and was 10 nm as expected for mAbA, mAbB and mAbC with low polydispersity index values (< 0. 13).
Acknowledgements We thank  Mikhail Anikin  for excellent technical expertise with XPS acquisition on the equipment of the " Consortium des Moyens Technologiques Communs " (CMTC). GL was supported by an  ANRT   Cifre PhD grant  ( 2017/0735 ). This work was supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ).
8b ). Notably, pulmonary hypertension was ameliorated, accompanied by less right heart hypertrophy in INHBA-ECKO mice compared to INHBA-flox mice after chronic exposure to hypoxia (Fig. 6a, b ). Consistently, vascular remodeling was ameliorated in INHBA-ECKO mice compared to that in INHBA-flox mice (Fig. 6c-e ). These data further support the critical role of EC-derived INHBA/ActA in the progression of PAH. INHBA/ActA is overproduced in pulmonary ECs isolated from patients with idiopathic pulmonary hypertension. In order to assess whether INHBA/ActA-mediated angiocrine is involved in the pathogenesis of PAH in clinical settings, the expression and production of INHBA/ActA were examined using ECs isolated from the lungs of patients with iPAH or normal control subjects. The INHBA mRNA expression levels were significantly higher in iPAH-ECs than in control ECs (Fig.
e, f Immunocytochemistry for GFP-tagged BMPRII (in green color) and lysosomes (in magenta color) in PAECs pretreated with either PitStop (e) or bafilomycin A (f), followed by treatment with either vehicle, BMP-4, or ActA. Bars: 20 μm. Similar results were obtained in four independent experiments. g Representative immunoblots and densitometric analysis for serine/ threonine-phosphorylated BMPRII in PAECs treated with vehicle, BMP-4, or ActA (n = 3 biologically independent cells in each group). ***P < 0. 001; **P < 0. 01; *P < 0. 05. Exact P values are shown in the Source data file. Data are presented as the mean ± SEM. Two-sided Student's t-test was used to analyze the differences between the vehicle and ActA-treated groups at each time point (a). One-way ANOVA with Tukey's post hoc test for multiple comparisons was used to analyze the differences between each study group in all of the western blot quantitation procedures (c, d, and g).
After washing, signals were scanned using an Agilent DNA microarray scanner, and the data were analyzed using Feature Extraction ver. 10. 7. 1. 1 (Agilent Technologies). INHBA and BMPRII plasmid construction and retrovirus production. Human INHBA DNA was prepared with RT-PCR using Pfx polymerase (Invitrogen), followed by subcloning into a pCR-Blunt II-TOPO vector (Invitrogen). The INHBA insert was then cut out, and subcloned into a pMSCVneo vector (Clontech). In order to prepare the expression construct for GFP-tagged BMPRII, DNA for whole-length human BMPRII was prepared by RT-PCR using Phusion High-Fidelity PCR Master Mix with HF Buffer (New England Biolabs), followed by subcloning into a pCR-Blunt II-TOPO vector (Invitrogen). The BMPRII insert was then cut out, and subcloned into a pAcGFP1-N1 plasmid (Clontech).
****P < 0. 0001; **P < 0. 01; *P < 0. 05; #, not significant (P > 0. 05). Exact P values are shown in the Source data file. Data are presented as the mean ± SEM. Two-sided Student's t-test was used to analyze the differences between the WT and VEcad-INHBA-Tg groups in the distal PA count (d), BMPRII and pSMAD1/5 western blot quantitation (f and g), and Id1 mRNA expression levels (h). One-way ANOVA with Tukey's post hoc test for multiple comparisons was used to analyze the differences between each study group in the RVSP and Fulton index measurements (a and b). Two-way ANOVA with Tukey's post hoc test for multiple comparisons was used to analyze the differences between each study group in the PA muscularization (e). Apoptosis assay. In order to assess the PAEC apoptosis vulnerability, cells were seeded at 10,000 cells/well on 96-well plates and cultured overnight to allow adherence.
Fulton index measurement. After organ harvest and overnight incubation in 4% PFA at 4 °C, the hearts of mice were dissected and weighed before and after the separation of the right ventricular wall from the left ventricle and septum. The data are presented as a ratio of the right ventricle to the left ventricle + septum weight. Quantitative real-time PCR. RNAs were extracted from tissues or cells using RNAiso Plus (TAKARA), and then purified using a NucleoSpin RNA Clean-Up kit (Macherey-Nagel). cDNA synthesis was then performed from ~1 μg of total RNA using a PrimeScript RT Reagent Kit with gDNA eraser (TAKARA). The amount of RNA used was always identical among samples. DNase digestion step was included in the cDNA synthesis protocol. Quantitative real-time PCR was performed using LightCycler96 (Roche Science) with FastStart SYBR Green Master (Roche Applied Science).
Images were captured using a Keyence BZ-X800 fluorescence microscopy (Keyence). Data are presented as the number of distal pulmonary arteries per 100 alveoli. Immunofluorescence staining. Antibodies used for immunostaining are shown in Supplementary Table  1. After deparaffinization, the sections were treated with an antigen unmasking solution (Vector Laboratories) prior to incubation with antivon Willebrand factor (Abcam) and FITC-labeled anti-actin, α-smooth muscle (Sigma-Aldrich) antibodies at 4 °C overnight. Subsequently, the sections were washed and incubated with Cy3 fluorescence-labeled goat anti-rabbit secondary antibodies (Abcam), followed by mounting with VECTASHIELD mounting medium with DAPI (Vector Laboratories). Images were captured with a Keyence BZ-X800 fluorescence microscopy (Keyence).
For some immunostaining experiments, images were obtained using an LSM700 laser confocal microscope (Zeiss). In order to assess distal pulmonary artery muscularization, three randomly selected fields of view (×40 magnification) were captured, and the total number of distal pulmonary arteries (diameter; <50 µm) was determined for each group. A distal pulmonary artery was considered to be partially muscularized when 25-75% of the total diameter of the artery (determined by the vWF-positive cells) was α-SMApositive, and be considered fully muscularized when 75-100% of total artery diameter was α-SMA-positive. The muscularized distal pulmonary arteries were then counted and divided by the total number of distal pulmonary arteries in one image. Data are presented as percentages of muscularized pulmonary arteries.
i Tube-formation assay in PAECs treated with conditioned medium derived from iPAH-ECs or control ECs in the presence or absence of Follistatin (100 ng/mL) (n = 3 biologically independent values in each group). Bars: 200 μm. ****P < 0. 0001; ***P < 0. 001; **P < 0. 01; *P < 0. 05; #, not significant (P > 0. 05). Exact P values are shown in the Source data file. Data are presented as the mean ± SEM. Twosided Student's t-test was used to analyze the differences between the groups in the distal PA count (d and e). One-way ANOVA with Tukey's post hoc test for multiple comparisons was used to analyze the differences between each study group in the RVSP, Fulton index measurements, and tube-formation assay (a, b, and i). Mann-Whitney U test (two-sided) was used to analyze the differences between the groups (f-h).
Levels of mRNA encoding INHBA were measured by real-time quantitative PCR using TaqMan gene expression assay (Applied Biosystems). To evaluate the effects of hypoxia on IHNBA expression, human pulmonary ECs were grown to 70-80% of confluence in T-25 culture flasks and then exposed to hypoxia (0. 1% O 2 ) for 24 h in presence of media containing 0. 3% FCS. Conditioned medium was used to assess ActA secretion and human pulmonary ECs were collected to determine levels of IHNBA mRNA. Statistical analysis. All data are presented as the mean ± standard error of the mean (SEM). Differences between two groups were analyzed using two-tailed Student's t-test or Mann-Whitney U test when appropriate. Differences between three or more groups were analyzed with one-way or two-way ANOVA with Tukey's post hoc test or Fisher's exact test.
We aimed to evaluate the feasibility and accuracy of machine learning-based automated dynamic quantification of left ventricular (LV) and left atrial (LA) volumes in an unselected population. We enrolled 600 unselected patients (12% in atrial fibrillation) clinically referred for transthoracic echocardiography (2DTTE), who also underwent 3D echocardiography (3DE) imaging. LV ejection fraction (EF), LV, and LA volumes were obtained from 2D images; 3D images were analyzed using dynamic heart model (DHM) software (Philips) resulting in LV and LA volume-time curves. A subgroup of 140 patients also underwent cardiac magnetic resonance (CMR) imaging. Average time of analysis, feasibility, and image quality were recorded, and results were compared between 2DTTE, DHM, and CMR. The use of DHM was feasible in 522/600 cases (87%).
When feasible, the boundary position was considered accurate in 335/522 patients (64%), while major (n = 38) or minor (n = 149) border corrections were needed. The overall time required for DHM datasets was approximately 40 seconds. As expected, DHM LV volumes were larger than 2D ones (end-diastolic volume: 173 ± 64 vs. 142 ± 58 mL, respectively), while no differences were found for LV EF and LA volumes (EF: 55% ± 12 vs. 56% ± 14; LA volume 89 ± 36 vs. 89 ± 38 mL, respectively). The comparison between DHM and CMR values showed a high correlation for LV volumes (r = 0. 70 and r = 0. 82, p < 0. 001 for end-diastolic and end-systolic volume, respectively) and an excellent correlation for EF (r = 0. 82, p < 0. 001) and LA volumes. The DHM software is feasible, accurate, and quick in a large series of unselected patients, including those with suboptimal 2D images or in atrial fibrillation.
However, this recent methodology has been reported only in small groups of selected patients who have good-quality images. To the best of our knowledge, this is the first study performing this 3D analysis in a large, unselected population. The aims of our study were: (a) to evaluate the feasibility of the new DHM software in a large group of unselected patients with a wide range of acoustic windows, irrespective of heart rhythm; (b) to evaluate the relationship between image quality and accuracy of the detection of LV and LA boundaries; (c) to compare 2D echocardiography and DHMderived LV and LA volumes and function and in a subgroup of cases to cardiac magnetic resonance (CMR) reference values.