dataesr/scientific-paragraphs-categorization huggingface dataset:
3 is nonetheless still being used by multiple research groups to segment hippocampal subfields  (Duan et al. , 2020; Izzo, Andreassen, Westlye, & van der Meer, 2020; Takaishi et al. , 2020). The hippocampal subfield segmentation module in FreeSurfer 6. 0  (Iglesias et al. , 2015)  uses an ex vivo atlas derived from 15 ex vivo Box 1: Terminology Accuracy: Accuracy is the agreement of a measurement relative to an external standard. Reliability: Reliability is the internal consistency of a measure, which can be evaluated by the agreement between repeated measures (e. g. , between raters or multiple assessments). This is similar to precision. Construct validity or validity: Construct validity is the degree to which a test measures what it purports to be measuring. In the case of hippocampal subfield segmentation, construct validity refers to the degree to which the segmentation protocol correctly reflects known hippocampal subfield anatomy.
0 segmentation of 1 mm 3 isotropic T 1 -weighted images to manual segmentation of highresolution proton density-weighted images with respect to the ability to capture subfield volume associations with MCI and AD, similar to a previous comparison of Freesurfer 5. 3 with manual segmentations (de Flores, La  Joie, Landeau, et al. , 2015). The hippocampal subfield volumes generated by FreeSurfer 6. 0 failed to reveal significant ADrelated differences in the expected subfield, CA1, which is the first region in the hippocampus to accrue neurofibrillary tangle pathology  (Braak & Braak, 1991). In contrast, smaller CA1 volume in β-amyloid positive MCI patients compared to controls was found in the manually segmented proton density dataset in the same subjects, in line with multiple in vivo MRI studies (reviewed by de Flores, La  Joie, & Chetelat, 2015).
| Validation of other automatic methods against manual segmentation In this section, we discuss two methods that developed an approach to compare automatic segmentations of hippocampal subfields against manual segmentations on 1 mm 3 isotropic images. Two studies  (Caldairou et al. , 2016; Pipitone et al. , 2014)  down-sampled manual subfield segmentations obtained on high resolution (Caldairou: 0. 6 × 0. 6 × 0. 6 mm 3 ; Pipitone: 0. 3 × 0. 3 × 0. 3 mm 3 ) T 1 and T 2weighted images to 1 mm 3 isotropic resolution  (Caldairou:  1x1x1 mm 3 ; Pipitone: 0. 9x0. 9x0. 9 mm 3 ) T 1 -weighted MRI scans from the same subjects. The down-sampled data from the same subjects served to evaluate the performance of the automatic segmentation algorithms. Although the Dice Similarity Coefficient (DSC) values presented by  Caldairou et al.
, 2019). Specifically, the T 2 -weighted MRI was up-sampled to isotropic resolution and rigid registration in the "Greedy" tool (https://github. com/pyushkevich/greedy) was performed using the mutual information image similarity metric with multi-resolution optimization (×4, ×2, and ×1 subsampling). The T 2 -weighted MRI image was further resampled to 0. 2 × 0. 2 × 0. 2 mm 3 resolution and the T 1weighted MRI and its segmentation were resliced into the 0. 2 × 0. 2 × 0. 2 mm 3 up-sampled T 2 -weighted MRI space, thus preserving as much of the detail in the segmentation as possible. The quality of image registration was verified in ITK-SNAP. Moreover, grids are displayed so the registration quality can be checked. To facilitate the comparison of the two different methods, we collapsed the labels for the different subiculum and cornu ammonis (CA) regions into one label (red) and the dentate gyrus (DG) and granular cell layer into another label (blue).
See Figure  S1  for a comparison of the manual and automatic segmentation. These results indicate that FreeSurfer 6. 0 hippocampal subfield volumes measured on standard T 1 -weighted images do not capture the expected pattern of atrophy over the course of AD, while an appropriate manual segmentation performed on high resolution images in the same subjects does. Note that this comparison does not allow any conclusions about the application of FreeSurfer 6. 0 package to high resolution T 2 -weighted images. T A B L E 1 Differences in hippocampal subfield volumes in older adults, patients with MCI and patients with AD using automatic FreeSurfer 6. 0 segmentation of T 1 -weighted MRI and manual segmentation of proton density (PD)-weighted MRI. Bold font indicates significance after Bonferroni correction (0.
The inability to disambiguate the two factors driving the metric make it flawed for evaluating FS-T 1 anatomical validity. Relating to this point, the use of the FreeSurfer subfield package has in the past been justified by high test-retest reliability, but test-retest reliability does not speak to construct validity. In contrast, high testretest reliability likely shows the strength of the prior. Note that these concerns also hold for FreeSurfer version 7. 0 and any similar future versions.
| CONCLUSION The interest in MRI-based hippocampal subfield research has significantly increased in recent years due to availability of public-domain datasets, such as the ADNI  (Weiner et al. , 2017)  and human connectome datasets  (Van Essen et al. , 2013) , as well as by publicly available automatic tools, such as FreeSurfer  (Iglesias et al. , 2015; Van Leemput et al. , 2009) , ASHS  (Yushkevich et al. , 2015) , and MAGeT  (Pipitone et al. , 2014; Winterburn et al. , 2013). Although these developments enable large-scale subfield analyses, it is important to remain cautious regarding the increasing application of automatic segmentation methods to inappropriate data sets (e. g. , 1 mm 3 isotropic T 1weighted MRI scans) for several reasons. First, the resolution of these images is insufficient for visualizing the inner structures of the hippocampus, particularly the SRLM, that are crucial for either manual or automatic subfield segmentation.
ACKNOWLEDGMENTS The authors thank  David Wolk  for sharing screenshots of  MRI  scans included in the current paper. The study was supported by  Fondation Plan Alzheimer  (Alzheimer Plan  2008-2012 ) (GC),  Programme Hospitalier de Recherche Clinique  ( PHRCN 2011-A01493-38  and  PHRCN 2012 12-006-0347 ) (GC),  Agence Nationale de la Recherche  ( LONGVIE 2007 ) (GC),  Region Basse-Normandie ;  Association France Alzheimer  et maladies apparentees AAP 2013 (GC), the  Fondation Philippe Chatrier (RDF) , a  Discovery Grant  from the  Natural Sciences and Engineering Research Council of Canada (NSERC ;  RGPIN-2017-06178 ) (RKO), a grant from the  Canadian Institutes of Health  ( PJT-162292 ) (RKO) and new investigator grant from the  Alzheimer Society of Canada (RKO) ,  National Institute of Health  grants  R01 AG055121  (LW),  R01 EB020062  (LW),  R01 AG034613  (CELS),  R01 AG056014  (PY),  R01 AG011230  (NR) and the donors of Alzheimer's  Disease Research, a program  of the  BrightFocus Foundation (LEMW).
RNAseq to generate genomic resources With no fully sequenced genome for mimosoids available when we started this study, we generated transcriptome data for four mimosoid genera to select nuclear markers for targeted enrichment. For the species Albizia julibrissin Durazz. , Entada abyssinica Steud. ex A. Rich. , and Microlobius foetidus (Jacq. ) M. Sousa & G. Andrade, seedlings were grown at the Botanic Garden of the University of Zurich, and RNA was extracted from young leaves and shoot tips, as well as roots (A. julibrissin), using the RNeasy Plant Mini kit (Qiagen, Venlo, The Netherlands). Libraries for sequencing were produced using the TruSeq RNA Library Prep kit (Illumina, San Diego, California, USA) and sequenced 3-plex on an Illumina HiSeq-2000 sequencer, at the Functional Genomics Center in Zurich.
Raw data were cleaned with prinseq-lite  (Schmieder and Edwards, 2011) , and transcriptomes assembled using Trinity  (Grabherr et al. , 2011; Haas et al. , 2013)  with default settings. In addition, transcriptome data for three species of Inga were generated at the Royal Botanic Garden Edinburgh using similar sequencing and assembly methods (described in  Nicholls et al. , 2015). Since the comparisons among transcriptomes (see below) are effectively carried out at genus level, we generated a more comprehensive transcriptome for the genus Inga by combining the separate assemblies for the three Inga species into a nonredundant set of transcripts. This was done by running BLAST searches of the largest Inga transcriptome assembly against the second-largest assembly and adding all transcripts without a significant hit (e-value cutoff 1e-10) in the latter.
Selecting putative single-copy genes From the four transcriptome data sets, putative single-or low-copy nuclear genes were selected, using a procedure inspired by  Wu et al. (2006)  (Fig. 2 ). This procedure was recently used by  Couvreur et al. (2019)  and  Ojeda et al. (2019) , but because it was first designed for the mimosoid bait set, it is described in more detail here. First, for each of the four transcriptome data sets, TransDecoder (https://github. com/Trans Decod er/Trans Decoder) was used to predict open reading frames (ORFs) and translate those to protein sequences, using default settings. Highly similar proteins were removed to reduce redundancy (i. e. , keeping only one protein sequence per gene and removing multiple alleles and isoforms) with CD-HIT  (Li and Godzik, 2006). This was repeated with four cutoff values (90%, 95%, 97%, and 99% identity), to avoid either clustering paralogous sequences with relatively low divergence or keeping alleles and isoforms with relatively high divergence.
This means that the following steps were each repeated four times, and from each repetition only the putative orthologs that were more divergent among and less divergent within taxa were kept. In other words, for each repetition, sequences with higher identity among taxa than the cutoff values were removed. For each transcriptome, we performed a BLAST search of the CD-HIT output against itself ("self-BLAST") with an e-value cutoff of 1e-10, and sequences with multiple hits within the same transcriptome were removed to eliminate gene families. Next, a reciprocal best hit (RBH) algorithm was implemented in a custom Python script (available from https://github. com/erikk oenen/ mimob aits/), to compare the four transcriptome data sets after removing redundancy and gene families. This is an extension of the RBH triangulation method of  Wu et al.
Bait design For bait design, the sequences of the Albizia julibrissin transcriptome were used, because the genus Albizia and allies are the focus of an ongoing project in Zurich, and this will increase successful capture for these taxa. Intron-exon boundaries were predicted for all transcripts in the four ortholog sets (RBH4, RBH3, SSC, and MSC), by running BLAST searches against a custom database combining the Arabidopsis thaliana  (Lamesch et al. , 2011) , Medicago truncatula Gaertn. (Young et al. , 2011) , and Glycine max (L. ) Merr. (Schmutz et al. , 2010)  genomes. For the genome database, gene models including introns were used, and the coordinates to which our transcripts aligned were used to partition sequences for each predicted exon to avoid designing baits spanning intron-exon boundaries.
Assembly of sequence data and aligned matrices After demultiplexing, raw reads were processed with Trimmomatic  (Bolger et al. , 2014)  to remove adapter sequence artifacts and trim or remove low-quality reads (using the settings MAXINFO:40:0. 1 LEADING:20 TRAILING:20), and PEAR  (Zhang et al. , 2013)  to merge overlapping read pairs (after removing adapter artifacts but before trimming). Resulting fastq files of quality-filtered merged, paired, and unpaired reads were used in a de novo assembly for each accession using the SPAdes assembler  (Bankevich et al. , 2012). From the resulting scaffolds, we extracted all ORFs of ≥300 bp long between two stop codons with getorf (using the option -find 2) from the Emboss software suite. We reduced redundancy in the set of ORFs found for each accession with cd-hit, using an identity cutoff of 0.
99. For all ORFs from each accession, a BLAST search was carried out against the target sequences and for each target a multifasta file was created. Each ORF for each accession was added to the target multifasta file for which it received the best BLAST hit under an e-value cutoff of 1e-10, resulting in multifasta files for each target with potentially multiple sequences per accession included, which we refer to hereafter as "clusters. " Numbers of reads on target were estimated by mapping the untrimmed reads to the bait sequences with BLAT  (Kent, 2002) , using a minimum sequence identity threshold of 70%. Numbers of recovered loci were estimated with BLASTX, using protein sequences for the 964 targeted genes as the database and the SPAdes contigs as the query sequences, with an e-value cutoff of 1e-10.
Using the fasta_to_tree. py script of  Yang and Smith (2014) , each cluster was aligned with MAFFT, sites with excessive missing data were removed (with a minimum column occupancy of 0. 3), and a tree was inferred for each cluster with RAxML. We used other scripts of  Yang and Smith (2014)  to trim outlier long tips (with relative and absolute cutoffs of 0. 1 and 0. 3, respectively), mask monophyletic and paraphyletic clusters belonging to the same taxon, and cut deep paralogs (cutting internal branches >0. 3 and keeping subtrees of ≥25 accessions). From the resulting trimmed subtrees, new multifasta files were created for a second round of tree inference, trimming, and masking. However, for the second round we used MACSE  (Ranwez et al. , 2018) , instead of MAFFT, to obtain more accurate alignments and TreeShrink (with quantile of trees to remove set to q = 0.
1;  Mai and Mirarab, 2018)  to trim tips, instead of relative and absolute cutoffs. Finally, after cutting deep paralogs again, we extracted all non-overlapping subclusters with ≥25 accessions using the maximum inclusion (MI) method of  Yang and Smith (2014). Besides analyzing the targeted nuclear genes, we also extracted off-target reads with a BLAST hit against a reference set of chloroplast genomes of Inga leiocalycina Benth. (Dugas et al. , 2015; GenBank accession KT428296) , Leucaena trichandra Urb. (Dugas et al. , 2015 ; GenBank accession KT428297), and Erythrophleum fordii Oliv. (Huang et al. , 2018 ; GenBank accession MG644609), assembled chloroplast sequences for all accessions, and extracted the coding sequences gene by gene using a custom Python script with BLAST searches, confirming that sequence data for the chloroplast genome can be efficiently extracted and analyzed from off-target reads in hybrid capture experiments as shown by  Weitemier et al.
(2014). The clpP gene was discarded because it shows accelerated evolution  (Williams et al. , 2015; Dugas et al. , 2015)  and yields a tree that strongly conflicts with those inferred using the other chloroplast genes. The accD gene has been lost from the chloroplast genome in several papilionoids, is highly variable in others  (Magee et al. , 2010) , and is difficult to align across mimosoids, so we also removed this gene for phylogenetic analysis. The remaining 72 plastid genes were aligned with MACSE and concatenated with the pxcat program of the phyx package  (Brown et al. , 2017).
Transcriptome sequencing, gene selection, and bait design Transcriptome sequencing statistics are in Table  1 ; data are available on the National Center for Biotechnology Information (NCBI) databases under BioProjects PRJEB8722 and PRJNA574148; FASTQ files with raw read data are available on the Sequence Read Archive (SRA), under accession nos. SRX6901075 (Albizia julibrissin), SRX6901076 (Entada abyssinica), ERX719658 (Inga spectabilis (Vahl) Willd. ), ERX719681 (Inga umbellifera (Vahl) Steud. ex DC. ), ERX719690 (Inga sapindoides Willd. ), and SRX6901077 (Microlobius foetidus); assembled transcripts are available on Dryad (for the Inga spp. , https://doi. org/10. 5061/dryad. r9c12) and through the Transcriptome Shotgun Assembly (TSA) database, accession nos. GHWM00000000 (Albizia julibrissin), GHWN00000000 (Entada abyssinica), and GHWO00000000 (Microlobius foetidus).
Results from the gene selection procedure for the RBH4 gene set are summarized in Figure  2. After running the pipeline with four different similarity cutoffs in CD-HIT, we found 433 RBH4 and 334 RBH3 target genes. We recovered 320 MSC and 19 SSC genes, of which 134 and eight genes, respectively, were already included in the RBH sets. Combining all gene sets we obtained a total of 964 low-copy nuclear genes for enrichment. The complete coding sequences from the Albizia julibrissin transcriptome for these targeted genes are in Appendix S1. The bait design included 24,856 probes at 3× tiling. Target sequences and baits are also available at https://github. com/erikk oenen/ mimob aits/.
Targeted sequencing and data assembly Sequencing and de novo assembly statistics for targeted sequencing for all accessions are presented in Appendix 1, including full species names with taxonomic authorities, and sequence reads have been submitted to the European Nucleotide Archive (study no. PRJEB38138). Accessions were enriched and sequenced in three separate batches, with different levels of multiplexing, which explains some of the variation observed in numbers of total reads and reads on target. Total reads per accession varied from 1,360,502 to 70,271,424. For the largest batch of samples, the enrichment was less efficient, with number of reads on target between 3. 81% and 17. 77%, while for the two smaller batches it varied between 69. 00% and 85. 27%. The percentage of reads on target is particularly low for taxa most distantly related to Albizia julibrissin on which the bait sequences are based.
Gene and species tree inference Gene trees for 148 of the genes had relatively high root-to-tip variances (>0. 01; Fig. 4E ). This marked branch length variation suggests they are not suitable for phylogenetic reconstruction, and inspection of these gene trees made it clear (based on our understanding of mimosoid phylogeny) that many of the inferred relationships were spurious. Apart from genuine variation in substitution rates, it is also likely that missing data (e. g. , complete exons missing in some unrelated taxa) could lead to such relationships being inferred. These gene trees were discarded and not analyzed further. After excluding these loci, the remaining 1767 were aligned, giving a total aligned length of 861,525 bp, with 450,375 alignment patterns and 62. 12% missing data. A second concatenated alignment for only those loci with at least half of the accessions included (510 genes/ exons) has a total aligned length of 254,250 bp, or 84,750 amino acids with 176,713 or 73,179 alignment patterns, respectively, and 34.
Outlook for Caesalpinioideae phylogenomics In this study, we developed a gene set for targeted enrichment via hybrid capture in the mimosoid clade. The resulting bait design (available at https://github. com/erikk oenen/ mimob aits/) can be used for phylogenomic studies across mimosoids and beyond. Further work in our lab has shown the utility of this gene set across the whole of Caesalpinioideae (J. J. Ringelberg and E. J. M. Koenen et al. , unpublished data)  and at species level within the genus Albizia (E. J. M. Koenen et al. , unpublished data). As taxon sampling is increased across the Caesalpinioideae and more studies are carried out using this gene set on individual genera, eventually a large and densely sampled phylogeny for the subfamily can be inferred and used for taxonomic reclassification and to study the evolution of this prominent tropical woody plant clade.
ACKNOWLEDGMENTS The authors thank  R. Stalder ,  M. Meierhofer , and  M. Knabe  for greenhouse assistance; the K, L, NY, and WAG herbaria for provision of leaf samples;  P. Ribeiro ,  M. Morim , and  F. Bonadeu in Brazil  and the staff of the  Kew Madagascar Conservation Centre and the Botanical and Zoological Garden of Tsimbazaza, Madagascar , for support during fieldwork; the  Functional Genomics Centre Zurich (FGCZ) , especially  Catherine Aquino , for lab support and high-throughput sequencing; the  S3IT of the University of Zurich  for the use of the ScienceCloud computational infrastructure and  P. -A. Christin ,  A. Liston , and two anonymous reviewers for constructive feedback that greatly improved the manuscript.
DATA AVAILABILITY Transcriptome data are available on the National Center for Biotechnology Information (NCBI) databases under BioProjects PRJEB8722 and PRJNA574148; FASTQ files with raw read data are available on the Sequence Read Archive (SRA), under accession nos. SRX6901075 (Albizia julibrissin), SRX6901076 (Entada abyssinica), ERX719658 (Inga spectabilis (Vahl) Willd. ), ERX719681 (Inga umbellifera (Vahl) Steud. ex DC. ), ERX719690 (Inga sapindoides Willd. ), and SRX6901077 (Microlobius foetidus); assembled transcripts are available on Dryad (for the Inga spp. , https://doi. org/10. 5061/dryad. r9c12) and through the Transcriptome Shotgun Assembly (TSA) database, accession nos. GHWM00000000 (Albizia julibrissin), GHWN00000000 (Entada abyssinica), and GHWO00000000 (Microlobius foetidus).
Hybrid capture sequence reads are deposited in the European Nucleotide Archive (https:// www. ebi. ac. uk/ena) under study no. PRJEB38138. Targeted sequences, alignments, and gene trees are included as Appendices S1-S7 and are made available in TreeBASE (http://purl. org/phylo/ treeb ase/phylo ws/study/ TB2:S26316). The bait design and scripts used in this study are available at https://github. com/erikk oenen/ mimob aits/.
FUNDING INFORMATION This work was supported by the Swiss National Science Foundation (grants 31003A_135522 and 31003A_182453 to C. E. H. ), the U. K. Natural Environment Research Council (Grant NE/I027797/1 to R. T. P. ), the Claraz Schenkung Foundation, and the Department of Systematic and Evolutionary Botany, University of Zurich. Field trips in Brazil were partially funded by FAPESB (PTX0004 and APP0096 to L. P. d. Q. ), CNPq (480530/2012-2 and PROTAX 440487 to J. R. I and L. P. d. Q. ), and Energia Sustentável do Brasil. The use of DNA from Brazilian plant species is authorized by SISGEN n° R4CAAB3 and n° R0AAA9E.
Patients This study was performed in the infectious diseases department and intensive care unit (ICU) at Rennes University Hospital. The study design was approved by our ethic committee (CHU Rennes, n°35RC20_9795_HARMONICOV, ClinicalTrials. gov Identifier: NCT04373200) and informed consent was obtained from patients in accordance with the Declaration of Helsinki. Peripheral blood was collected in tubes containing lithium heparin from COVID-19 neg ARDS pos , COVID-19 pos ARDS pos , and COVID-19 pos ARDS neg patients. Peripheral blood samples were drawn at D0 and D7. PBMC were isolated from whole blood using ficoll before cryopreservation. All patients provided written informed consent. The following data were recorded: gender, age, preexisting chronic kidney disease and acute kidney failure during the ICU stay  (44) , preexisting chronic heart failure  (45) , Body Mass Index (BMI), SAPS II at admission  (46) , duration of mechanical ventilation, length of hospital stay, and outcome (alive or dead) on day 7, day 30 and day 90.
Pre-processing After acquisition, intrafile signal drift was normalized and. fcs files were obtained using CyTOF software. To diminish batch effects, all files were normalized on EQ Beads (Fluidigm Sciences) using the premessa R package (https://github. com/ParkerICI/premessa). Files were then uploaded to the Cytobank cloud-based platform (Cytobank, Inc. ). Data were first arcsinh-transformed using a cofactor of 5. For all files, live single cells were selected by applying a gate on DNA1 vs. DNA2 followed by a gate on DNA1 vs. Cisplatin, then beads were removed by applying a gate on the beads channel (Ce140Di) vs. DNA. 1 Normalized, transformed and gated values were exported as FCS files.