dataesr/scientific-paragraphs-categorization huggingface dataset:
Cause-specific post-discharge outcomes 5, 20, 21, 28  In ambulatory participants of the ESC HF Long-Term Registry, 1-year mortality in HFrEF (8. 8%) was, from the clinical perspective, not that much higher than in HFmrEF (7. 6%) and HFpEF (6. 3%), although the difference was statistically significant due to the large number of observations. 5 n our analysis of the AHF cohort of the ESC HF Long-Term Registry, o1ne-year mortality in HFrEF (20%) was considerably higher than in HFmrEF (15%) and HFpEF (16%), and, not surprisingly, over twice as high as in ambulatory patients. Data on post-discharge CV and non-CV risk in HFrEF versus HFmrEF versus HFpEF are lacking. 7] [28]  In our analysis, post-discharge non-CV mortality and non-CV hospitalizations were higher in HFpEF, while CV mortality and CV hospitalizations were higher in HFrEF.
Limitations In this observational registry, there were missing data on both admission, in-hospital, and post-discharge characteristics. To reduce bias due to data missing not at random and thus increase generalizability, we used multiple imputation. The EF captured in AHF was not adjudicated and may have been temporarily affected by co-existing conditions such as rapid AF. There was no information on the exact timing of echocardiography (including EF assessment) during hospitalization, and EF was obtained per local protocol and routine (there was no core laboratory). The   S3. COPD, chronic obstructive pulmonary disease; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; NYHA, New York Heart Association; SBP, systolic blood pressure; TIA, transient ischaemic attack.
Patients admitted to internal medicine or geriatric departments may be expected to be older, with more comorbidities and more often with HFpEF compared to HF patients admitted to cardiology departments. Exclusion of such patients from the registry might have led to an underrepresentation of older and more diseased patients (mostly with HFpEF), with a possible impact on prognosis (mostly in the HFpEF group) and the observed differences in outcomes between HFpEF, HFmrEF and HFrEF. Thus, the extent to which the findings from this study can be generalized to other populations is unclear. In all studies, there is a balance between detailed and adjudicated data (e. g. adjudication lacking in the present analysis), such as in a trial (which provides internal validity) and sample size and generalizability to larger populations (e.
Acknowledgements The Executive Committee of the study had full access to the data and takes complete responsibility for the integrity and the accuracy of the data analysis. The authors would like to thank the  Oversight Committee , the  Registry Executive and Steering Committees of the EURObservational Research Programme (EORP). Data collection was conducted by the EORP department from the  ESC  by  Emanuela Fiorucci  as Project Officer,  Gérard Gracia  and  Maryna Andarala  as  Data Manager. Statistical Team was supervised by  Cécile Laroche. Overall activities were coordinated and supervised by  Doctor Aldo P. Maggioni  (EORP Scientific Coordinator). All investigators are listed in the online supplementary Appendix  S1.
A subset of TEs is highly expressed during early in planta stages of infection To build a high-quality resource that can facilitate future in-depth analyses, within the consortium we combined several robust, independently generated RNAseq datasets from all three isolates that include major soybean infection-stages and in vitro germination (Fig. 3, A  and B ). Altogether, eleven different stages are captured with seven having overlap of two or more isolates, representing a total of 72 different transcriptome data sets (Fig. 3C ). These data were used to support the prediction of gene models with the de novo annotation pipeline of JGI MycoCosm  (28). Those proteins secreted by the pathogen that impact the outcome of an interaction between host and pathogen are called effectors and are of particular interest  (29, 30).
GMC oxidoreductase exhibits important auxiliary activity 3 (AA3_2) according to the Carbohydrate-Active enzymes (CAZy) database  (67)  and is required for the induction of asexual development in Aspergillus nidulans  (68). An extensive approach was used for the global annotation of CAZyme genes in P. pachyrhizi genomes and after comparison with other fungal genomes, we also found clear expansions in glycoside hydrolases (GH) family 18 and glycosyltransferases (GT) family 1 (table  S23 ). GH18 chitinases are required for fungal cell wall degradation and remodelling, as well as multiple other physiological processes including nutrient uptake and pathogenicity  (69, 70). The Phakopsoraceae to which P. pachyrhizi belongs represents a new family branch in the order Pucciniales  (71). With three P.
TE identification and classification To annotate the TEs in the P. pachyrhizi genome, we used the two pipelines of the REPET package (https://urgi. versailles. inra. fr/Tools/REPET (94, 95) ). We identified the de novo TE consensus library from a subset of 300 Mbp of the longest contigs of each P. pachyrhizi isolates MT2006, K8108 and UFV02 following the large genomes repeats annotation recommendations  (96). The subset strategy is based on the rationale that high abundance of TEs renders copy identification feasible in a subset of the whole genome. TE consensus libraries were built independently for each isolate, using the TEdenovo pipeline with default parameters. Briefly, repeats were detected based on similarity approach using Blaster (95), then clustered combining Grouper  (95) , Piler  (97) , Recon (98) and aligned using MAP  (99)  to generate consensus sequences of repeats.
Insertion age of LTR-retrotransposons Full-length LTR-retrotransposons were identified from the genomes set assemblies using LTRharvest with default parameters, this tool belongs to the GenomeTools genome analysis software v1. 6. 1  (104). LTRs annotated as Gypsy or Copia were used for molecular dating, selection was based on a BLASTX against Repbase v20. 11  (105). 3' and 5' LTR sequences were extracted and aligned with mafft v7. 471  (106) , alignments were used to calculate Kimura's 2P distances  (107). The insertion age was determined using the formula T= K / 2r, with K the distance between the 2 LTRs and r the fungal substitution rate of 1. 05 × 10-9 nucleotides per site per year  (21, 22).
Molecular dating and Phylogenetic analysis The phylogenetic tree was generated after alignment of 408 conserved orthologous markers identified from at least 13 out of 17 genomes using PHYling (https://github. com/stajichlab/PHYling_unified). The sequences were aligned and concatenated into a super-alignment with 408 partitions. The phylogenetic tree was built with RAxML-NG (v0. 9. 0) using a partitioned analysis and 200 bootstraps replicates. Molecular dating was established with mcmctree from PAML v4. 8. Calibration points were extracted to Puccinalies  (64)  and Sordariomycetes-Leotiomycetes  (108). The 95% highest posterior density (HPD) values calibrated to the node.
Genome annotation The gene predications and annotations was performed in the genomes of the three isolates in parallel using the JGI Annotation Pipeline  (28). TE masking was done during the JGI procedure, which detects and masks repeats and TEs. Later on, the extensive TE classification performed with REPET was imported and visualized as a supplementary track onto the genome portals. RNAseq data from each isolate (see section below) was used as intrinsic support information for the gene callers from the JGI pipeline. The gene prediction procedure identifies a series of gene models at each gene locus and proposes a best gene model which allows to define a filtered gene catalog. Translated proteins deduced from gene models are further used for functional annotation according to international reference databases.
Quality assessment of the whole-genome assemblies The whole-genome assemblies of P. pachyrhizi were evaluated using two different approaches. First, we used BUSCO version 5. 0  (110) , to assess the genic content based on near-universal single-copy orthologs with basidiomycetes_odb9 database including 1335 gene models. Second, K-mer's from different assemblies were compared using KAT version 2. 4. 1  (111). Genome heterozygosity was estimated using GenomeScope 2. 0  (112).
For downstream analyses requiring a single haplotype representation, hard masking was applied to remove overlapped regions from the haplotigs using BEDtools v2. 27. 0 (103). To identify genes correspondence among the three isolates, we used Liftoff software  (116). The genome assembly of each isolate was used as a reference to map the other two isolates' gene catalogue with >95% coverage and identity of >95%. The correspondence was established based on the gene annotation coordinates of each reference genome and the mapping coordinates from liftoff results (table  S26 ).
Read mapping, variant calling and SNP effect prediction Illumina paired-end reads of the three isolates were trimmed with Trimmomatic v0. 36  (117)  to remove adapters, barcodes, and low-quality sequences with the following parameters: illuminaclip = TruSeq3-PE-2. fa:2:30:10, slidingwindow = 4:20, minlen = 36. Then, sequence data from all three isolates were aligned to the reference assembly of P. pachyrhizi UFV02 v2. 1 using BWA version 0. 7. 17 with the BWA-mem algorithm  (92)  , with the options -M -R. Alignment files were converted to BAM files using SAMtools v1. 9  (118) , and duplicated reads were removed using the Picard package (https://broadinstitute. github. io/picard/). The GATK v3. 8. 1 software  (119)  was used to identify and realign poorly aligned reads around InDels using Realigner Target Creator and Indel Realigner tools, creating a merged bam file for all the three isolates.
The high quality reads were filtered for any possible contamination among the fungi reads using Kraken2 software and parameter --unclassified-out for soybean genome and any possible contaminant species  (123). After all filtering steps, reads from each library were mapped against the three isolates assemblies using STAR v2. 7. 6a  (124). Parameters for mapping were (--outSAMtype BAM SortedByCoordinate, --outFilterMultimapNmax 100, --outFilterMismatchNmax 2, --outSAMattrIHstart 0, --winAnchorMultimapNmax 200, and --outWigType bedGraph). After mapping, duplicated reads were removed using Picard v. 2. 23. 2. HTseq was used to count reads and DEseq2 to determine differential expression using spore germinated condition (spore -stage 1) in each transcriptome experimental design as the calibrator.
To validate gene annotation dedup-BAM files were analysed using StringTie v2. 1. 2 (125) and the gtf files obtained were merged (-m 600 -c 5) for genes and (-m 200 -c 5) for TE (TE). The final gtf file was compared with each of the genome annotation file per isolate using gffcompare (126) software to validate the annotate genes and TEs. We detected 18,132, 19,467, and 22,347 genes presenting transcriptional evidence in K8108, MT2006 and UFV genomes respectively, demonstrating high sensitivity (> 93. 9%) and precision in a locus level (> 75. 4%) in all three isolates (fig. S20  and table S27 ). For functional annotation, genes were considered expressed when each transcriptome reads were mapped against its respective reference genome considering the criteria of TPM (Transcripts Per Kilobase Million) values > 0 in at least two biological replicates.
Prediction and annotation of secreted proteins To predict classically secreted proteins, we initially searched for proteins containing a classic signal peptide and no transmembrane signal using SignalP (versions 3 and 5)  (34) , TMHMM  (131)  and Phobius (132) programs. For the identification of additional secreted proteins without a classic peptide signal and no transmembrane signal (non-classically secreted), we used EffectorP (versions 1 and 2)  (31, 32)  and TMHMM programs. In both approaches, we kept the proteins having a TM in the N-term region. The proteins selected by both approaches were analysed by PS-SCAN program  (133)  to remove putative endoplasmatic reticulum proteins. All programs were performed considering default parameters. The secreted proteins predicted in the previous step were annotated using Blast  (134) , RPSBlast, PredGPI  (135) , InterProScan  (136)  and hmmsearch (137) programs.
Similarity searches using Blast program were performed against the NCBI non-redundant (nr), FunSecKb  (138) , Phi-base  (139)  and LED  (140)  databases, applying an e-value of 10 -5. To search for domains in sequences, we used the programs RPSBlast and hmmsearch against the Conserved Domain Database (CDD)  (141)  and PFAM database (142) respectively, using an e-value of 10 -5 in both cases. Ortholog mapping was done through similarity searches with the hmmsearch program against profile HMMs obtained from eggNOG database  (143). To predict the location of the predicted proteins in plant, ApoplastP  (144) , Localizer  (145) , targetP  (146) , WoLFPSORT  (147)  and DeepLoc (148) programs were performed using default parameters. To assign a final location for each protein, the following criteria were considered: if at least two programs found the same result, that result was considered as a predicted location.
Otherwise, the term "Not classified" was assigned to the protein. To identify the motifs [Y/F/W]xC in the sequences, we used a proprietary script developed in Perl language. A summary of the prediction and annotation pipelines for the secreted proteins are illustrated in figs. S21 and S22. For the prediction of putative effector proteins, we used the list of predicted secreted proteins containing a classical signal peptide. For the prediction of candidate effector proteins in each genome, we defined three different approaches. In the first one, sequences predicted as "Extracellular" or "Not Classified" by the location programs and with no annotation were selected as candidates to effector proteins. With this approach, we obtained 618, 531 and 598 candidates to effector proteins in K8108, MT2006 and UFV02 respectively.
In the second approach, we selected proteins which contain PFAM domains present in effector proteins  (149). Applying this criterion, we selected 142, 128 and 55 candidates in K8108, MT2006 and UFV02, respectively. Finally, in the third approach, we ran EffectorP program to classify the effector candidates, and we obtained 802, 851 and 899 candidates in K8108, MT2006 and UFV02 genome respectively (tables S6 to S8).
(  43 ) Compute the radiated acoustic pressure using the Rayleigh-Sommerfeld integral Eq. (  54 ) 43: end for implemented in VocalTractLab 2. 3 (www. vocaltractlab. de). The geometries provided by this software have been fitted on MRI scans of a male subject  [8]. The vowels /a/, /i/ and /u/ were selected as they represent well the variety of vocal tract shapes for vowels. To ensure the best geometrical accuracy possible, the maximal number of segments provided by the segmentation algorithm implemented in VocalTractLab 2. 3 was used. This results in 129 segments whose length ranges between 0. 55 mm and 1. 6 mm. The corresponding contours were exported in text files so that they could be used with Matlab and GID 3  to generate finite element meshes. During this process, the consecutive contours were linked by straight segments.
The work of  Rémi Blandin  and  Peter Birkholz  was supported by the  German Research Foundation (DFG)  under Grant  BI 1639/7-1. The work of  Marc Arnela  was supported by the  Ministry of Science and Innovation-Agencia Estatal de Investigación  under  Project FEMVoQ  ( PID2020-120441GB-I00/AEI/10. 13039/501100011033 ).
Acknowledging drugs' distribution involves partially considering certain parameters adducing VDss (volume of distribution at steady state), blood-brain barrier permeability, and central nervous system permeability (Table  4  and Figure  2 ). Overall, the compounds are considered to have a low-to-moderate VDss that can be translated into a good distribution in the plasma. Eventually, the phytocompounds did not show the ability to penetrate the central nervous system. It is generally relevant to highlight the predicted activity of a molecule and its interactions along with CYP isozymes to anticipate drug metabolism or toxicity  [55]. In this study, trimethoxyflavone came out to be a substrate to CYP3A4, while other molecules are non-substrates to CYP2D6 nor CYP3A4. In contrast, inhibition of both CYP2D6 and CYP3A4 was predicted for trimethoxyflavone, whereas myricetin only inhibits CYP3A4 and therefore enhances the risk for adverse effects to occur, including DDI (drug-drug interactions) (Table  5 ).
Assuming that binding energy decreases as compound affinity increases, the obtained results, in the form of binding affinity values, may indicate an increased or decreased affinity of the researched molecule toward the specified target in comparison to a native ligand (a known inhibitor). Their molecular docking interactions have been explored with particular enzymatic proteins, such as lipoxygenase-3 (PDB ID: 1N8Q)  [70] , cytochrome P450 (PDB ID: 1OG5)  [71] , NADPH oxidase (PDB ID: 2CDU)  [72] , and bovine serum albumin (PDB ID: 4JK4) [73], all of which have been identified as target receptors for antioxidant chemicals and are acknowledged. The docking scores were presented in a heat-map-style table with a red, white, and green color scheme, ranging from the lowest energy values, highlighted in red (most often corresponding to the native ligand's docking score), to the highest, highlighted in green (Figure  3 ).
6 kcal/mol (of the native ligand adenosine-5′-diphosphate). All the identified compounds in AHEE were found to operate as natural bovine serum albumin (BSA, PDB ID: 4JK4) protein inhibitors, as shown by their interaction scores. Furthermore, myricetin, catechin gallate, and chlorogenic acid were the three most effective ligands, in comparison with the natural ligand of BSA, 3. 5-diiodosalicylic acid (-5. 3 kcal/mol), at -8. 5, -8. 4, and -8. 3 kcal/mol, respectively. Binding interactions with both NADPH oxidase protein and BSA are found in Figures  S4  and S5. The natural ligands' ability to bind to amino acid residues may be related to their antioxidant activity. Thus, it can be said that the examined compounds have strong antioxidant activity according to an examination of common residues and free binding affinity values near the docked ligands and natural inhibitors, as well as being stated in the literature  [75, 76].
In Silico Prediction of the Cytotoxic Potential of AHEE Each of the nine identified compounds' possible targets were predicted using the SwissTarget Prediction tool  [77]. The SMILES strings of the compounds were inputted to the database, and prediction results were reported in Section 3. 4. 2. Molecular docking was utilized after choosing three targets (that had the best likelihood of being a particular tar- It is possible that AHEE compounds have little ability to inhibit NADPH oxidase protein since none of the docked molecules exhibited free binding energies greater or equal to 8. 6 kcal/mol (of the native ligand adenosine-5 -diphosphate). All the identified compounds in AHEE were found to operate as natural bovine serum albumin (BSA, PDB ID: 4JK4) protein inhibitors, as shown by their interaction scores.
In Silico Prediction of the Cytotoxic Potential of AHEE Each of the nine identified compounds' possible targets were predicted using the SwissTarget Prediction tool  [77]. The SMILES strings of the compounds were inputted to the database, and prediction results were reported in Section 3. 4. 2. Molecular docking was utilized after choosing three targets (that had the best likelihood of being a particular target according to interaction occurrence probability) for each molecule. Carbonic anhydrases enzymes (CA, EC 4. 2. 1. 1) catalyze reversible carbon dioxide hydration to bicarbonate and protons, which is a very straightforward yet crucial physiological event. In mammals, at least 16 different α-CA isoforms have been discovered. These isozymes have highly distinct catalytic characteristics as well as a very different distribution in various tissues and organs  [78].
Recent studies show that these CAs have been linked to chemoresistance, tumor cell motility and invasion, and the maintenance of cancer cell stemness  [79, 80]. Our prediction results showed that the majority of the identified components are possible inhibitors for three cytosolic isoforms CA I, II, and VII. Our docking analysis for the first isoform of CA I (PDB ID: 1AZM) revealed three possible inhibitors with a docking score higher than the native ligand (furosemide, -6. 8 kcal/mol), namely, myrcetin, catechin gallate, and chlorogenic acid with -8. 6, -8. 4, and -7. 4 kcal/mol, respectively (Figure  4 ). For the second isozyme CA II (PDB ID: 12CA), all the tested molecules were found to be strong inhibitors (in comparison with the native ligand, herein, tetrazole, which has a docking score of -4.
2 kcal/mol), with the highest affinity found with chlorogenic acid (-7. 2 kcal/mol), followed by caffeic acid, gallic acid, trans-ferulic acid, and syringic acid with binding free energy values of -6. 4, -5. 6, -5. 5, and -5. 4 kcal/mol, respectively. The third isozyme, which was CA VII (PDB ID: 3MDZ), found abundantly in the central nervous system, characterized by its high CO 2 hydration activity and linked to various illnesses such as epilepsy, oxidative stress, and cancer  [81, 82] , was used as a target for six molecules from AHEE. The results showed that three molecules out of six (chlorogenic acid, arbutin, and caffeic acid, with -8. 4, -6. 5, and -6. 4, respectively) had an affinity greater than that of the native ligand (acetazolamide, -6. 3 kcal/mol) (Figure  4 ). Human milk xanthine oxidoreductase (XOR) is a form of xanthine oxidoreductase, a homodimer of ≈300 kDa, that ensures the catabolism of hypoxanthine and xanthine to uric acid  [83].
In low-XOR-expressing cancer cells, the amount of XOR expression may be linked to a worse prognosis due to the inflammatory response brought on by the tissue damage caused by tumor growth. Because it can catalyze the metabolic activation of chemicals that cause cancer, xanthine oxidoreductase (XOR) has been linked to oncogenesis either directly or indirectly by reactive oxygen and nitrogen species that are produced by XOR  [84]. The 3D crystal structure of XOR (PDB ID: 2CKJ) was used a as target of myricetin, and the docking score of this molecule (-8. 6 kcal/mol) was found to be lower than that of oxypurinol (-6. 2 kcal/mol) (Figure  4 ). These results are demonstrated by a previous study by  Zhang et al. (2016)   [85] , providing experimental data on the role of myricetin in inhibiting the activity of XOR.
Myricetin was found to be a potent inhibitor (with -9. 6 kcal/mol in comparison with the another inhibitor at -5. 2 kcal/mol) for human DNA topoisomerases IIa, crucial DNA topoisomerases that have crucial roles in chromosome segregation and chromatine condensation, having been proven to be therapeutic targets of anticancer drugs  [86, 87]. Catechin gallate was found to be a specific ligand for two cancer-implicated targets, human phosphogluconate dehydrogenase (PGD, PDB ID: 2JKV) and human antiapoptotic protein BCL-2 (PDB ID: 1G5M), with docking scores of -9. 2 and -7. 5 kcal/mol, respectively, in comparison with their respective native ligands (-6. 6 and -5. 3 kcal/mol) (Figure  4 ). The first target PGD catalyzes the conversion of 6-phosphogluconate (6-PG) into ribulose 5-phosphate (R-5-P) in the third phase of the pentose phosphate pathway (PPP), resulting in the production of nicotinamide adenine dinucleotide phosphate (NADPH)  [88].