dataesr/scientific-paragraphs-categorization huggingface dataset:
The phase is not typically used for anatomical evaluation. The issue with noise is more fundamental as it is treated as ground truth in our quantitative evaluation, and any deviations from it influence our ranking. This is counterbalanced by using radiologist evaluation for declaring the challenge winner. In planning for the challenge, we were unable to build consensus on an alternative ground truth calculation technique, but this topic could be re-examined in future challenges. For the quantitative evaluation metric, we chose to use SSIM  [45] , with a script showing the script used for evaluation in the fastMRI repository at https://github. com/facebookresearch/fastMRI. SSIM has several parameters. We investigated adjusting these parameters prior to challenge launch, but found that they generally did not alter the ranking of methods evaluated in our quality control phase, and so as a result we used the default parameters in scikit-image  [49].
For the qualitative assessment phase, a board-certified neuroradiologist selected six (two T1 post-contrast, two T2, and two FLAIR) cases from the challenge data set in each of the three tracks. Cases were specifically selected to represent a broad range of neuroimaging pathologies from intracranial tumors and strokes to normal and age-related changes. The selection process favored cases with more subtle pathologies for the 4X track and more obvious pathologies for the 8X track with the objective that this might yield better granularity for separating methods in the 4X track. Selected cases included both intraaxial and extraaxial tumors, strokes, microvascular ischemia, white matter lesions, edema, surgical cavities, as well as postsurgical changes and hardware including craniotomies and ventricular shunts.
The Philips data set was constructed from images of volunteers. Therefore small agerelated imaging changes were used for ranking in place of pathology. Six radiologists with 9-16 years of experience (two of whom are radiology division chiefs) were asked to evaluate the 18 selected image volumes for each team, basing their overall ranking on the quality of the depiction of the pathology using the ground truth as a reference. Radiologists came from a wide set of institutions, including the Mayo Clinic, Baylor College of Medicine, NYU Langone Health, the University of Pittsburgh Medical Center, Stanford University, and the University of California, Los Angeles. None of these institutions had finalist submissions. All radiologists looked at all images in the selected cases during the qualitative evaluation phase, and results were averaged.
Optimization took approximately 7 days on four NVIDIA V100 GPUs. Code is not publicly available. ATB: The ATB model, called "Joint-ICNet"  [51] , was a 10-iteration unrolled algorithm with CNNs replacing regularization terms in a fashion similar to other recent methods  [5] ,  [14] ,  [8]. Joint-ICNet used the U-Net  [41]  at each convolutional layer with the dual-domain processing previously introduced in KIKI-net  [8]. Joint-ICNet used a zero-filled reconstruction as the initial estimate and and coil sensitivities were calculated by refining a rough central k-space estimate with a U-Net  [41] ,  [14]. ATB optimized Joint-ICNet using Adam  [50]  over 50 epochs using a batch size of one at a learning rate of 10 -4 with SSIM as the loss function. Training took approximately 10 days using 8 NVIDIA TITAN GPUs.
Code is not publicly available. MRRecon: The MRRecon model, called "Momentum DIHN," unrolled the Nesterov momentum algorithm with CNN-based regularization for 12 cascades, 6 pre-cascades, and 0 or 1 post-cascade. The CNN module is a "Deep, Iterative, Hierarchical Network" (DIHN) that extends the Down-Up network  [52]  with a hierarchical block design, facilitating memory efficiency over a standard U-Net  [41]. Momentum DIHN used a zero-filled image as the initial estimate and ESPIRiT for calculating coil sensitivities. To improve transfer track performance, models with several hyperparameters were ensembled to generate the final images. MRRecon optimized Momentum DIHN using Adam  [50]  for less than 5 epochs using a batch size of one at a learning rate of 10 -4 with a compound L1/MS-SSIM loss function  [42].
Training took approximately 14 days on an NVIDIA V100 GPU. Code is not publicly available. Neurospin: The Neurospin model, called XPDNet  [53] ,  [54] , is a modular neural network unrolling the Chambolle-Pock algorithm  [55]  for 25 iterations. The model was inspired by the primal-only version of the Primal-Dual net  [56] , replacing the vanilla CNN with a multi-level wavelet CNN  [57]. XPDNet used a zero-filled image as the initial estimate and calculated coil sensitivities using a rough central k-space estimate refined by a U-Net  [41] ,  [14]. Neurospin optimized XPDNet using Rectified Adam  [58]  over 100 epochs using a batch size of one at a learning rate of 10 -4 with a compound L1/MS-SSIM loss function  [42]  (98% MS-SSIM weight). Training took approximately 7 days on an NVIDIA V100 GPU.
Code is available at https://github. com/zaccharieramzi/fastmri-reproducible-benchmark. ResoNNance: ResoNNance used a Recurrent Inference Machine (RIM) that has been previously described  [59] ,  [60] ,  [44] ,  [31]. Coil sensitivities were calculating using the center of k-spaced followed by U-Net refinement  [41]. RIM used ESPIRiT as the model input calculated from the BART toolbox  [61]. ResoNNance optimized RIM using Adam  [50]  over 90 epochs using a batch size of one at a learning rate of 10 -3 with an SSIM loss function. Separate models were trained for every field strength (1. 5 T, 3 T) and contrast (FLAIR, T1/T1PRE/T1POST, and T2). Code for the RIM, data loaders, and documentation can be found through the DIRECT repository at https://github. com/directgroup/direct.
D. Feedback from Participants We asked participants for feedback regarding challenge organization and future organization. Participants were generally enthusiastic about being able to participate in the challenge. We received positive feedback on our communication via the fastMRI GitHub repository at https://github. com/facebookresearch/fastMRI and the forum associated with the web site at https://fastmri. org. We also received positive feedback around the challenge's realism in focusing on multi-coil data, as well as the challenge's generalizability initiative in focusing on the Transfer track. Still, participants felt the realism could be improved in other areas. In particular, the sampling mask used for the challenge used pseudo-regular sampling in order to achieve exact 4X and 8X sampling rates.
ACKNOWLEDGMENT We would like to thank all research groups that participated in the challenge. We thank the scientific advisors that gave feedback during challenge organization. We thank Girish M. Fatterpekar, Nael Kambiz, Amy L. Kotsenas, Christie Mary Lincoln, Joseph Mettenburg, and Max Wintermark for judging the submissions and the contribution of their clinical expertise and time. We would also like to thank Tullie Murrell, Mark Tygert, and C. Lawrence Zitnick for many valuable contributions to the launch and execution of this challenge. We give special thanks to Philips North America and their clinical partner sites for the data they contributed for use as part of the Transfer track. The NYU authors thank NIH grants P41EB017183, R01EB024532, and R21EB027241 for financial support. The ResoNNance team's submission was conducted under the STAIRS project under the TKI-PPP program.
Chloroplast genome size in Ficus is consistently around 160 kb (kilobase), while that of mitochondria varies from 430 kb to 711 kb (Supplementary Table  2 ). Using the nuclear genomic level dataset, coalescent-based Astral species trees  40  were inferred for Ficus based on four types of nuclear genomic sliding windows generated with partition strategies of non-overlapping windows of size 500, 100, and 50 kb, and a SNPs-fixed window of 1000 SNPs (see "Methods" and Supplementary Table  3  for details). The four Astral species trees corresponding to each window size generated similar Ficus topologies with high bootstrap values at almost all nodes (Fig. 1 , Supplementary Fig. 1A-D , and Appendix 1). All four Astral species trees supported the monophyletic subgenus Urostigma (monoecious, pantropical, with both active and passive pollination, Supplementary Note 2) as sister to all other Ficus species.
05 (18. 83-28. 06) Mya, section Adenosperma colonized Australasia (Supplementary Fig. 3 ). However, using the same nuclear genome dataset, analyses using a Bayesian concordance approach (BCA)  42  suggest an alternative topology for the deepest node of Ficus (Supplementary Fig. 1E  and Appendix 2). The primary concordance tree (PCT tree, inferred with the program BUCKy 43 ) instead suggests section Pharmacosycea is best supported as sister to all other figs  39  (concordance factor (CF) = 0. 406, meaning that 40. 6% of genomic windows support this topology  44  ), compared to any other single clade (Supplementary Table  5 ). Apart from the placement of the basal section, the remaining topological structure of the PCT tree is similar to those of the Astral trees (Supplementary Fig. 1 ). Divergence times and ancient biogeography inferred based on the PCT tree suggest that Ficus originated in either Eurasia or the Neotropics roughly 63.
Meanwhile, our study also provides evidence of hybridization at the deepest nodes (50-80 Mya) of a species-rich group. Such demonstrations are rare, perhaps because of what were previously methodological limitations  55, 56. The Ficus hybridization events inferred here also help reconcile the previously encountered inconsistent phylogenetic reconstructions of Ficus that have been based on different morphological or molecular datasets, and analyzed with different methods that nonetheless assume separate gene pools  20, 57. Accordingly, this study highlights the utility of using multiple genomic compartments and multiple alternative analytical methods to distinguish between alternative phylogenetic hypotheses, and to corroborate inferences. Pollinator host-switches could be a direct mechanism underlying Ficus hybridization events.
5% BUSCO completeness (submitted to GSA database (http://gsa. big. ac. cn/) under accession number GWHABKV00000000, Supplementary Note 1) using BWA-v0. 7. 17 (ref. 75 ) with default parameters. The mapped reads were sorted, and duplicated reads were removed using SAMtools-v1. 9 (ref. 76 ). The Realigner Target Creator and Indel Realigner programs from the Genome Analysis Toolkit (GATK-v4) package  77  were used for global realignment of reads around indels from the sorted BAM files. To identify a high-quality SNP dataset, we applied the GATK pipeline following the suggested best practices workflow (https://gatk. broadinstitute. org/hc/en-us/ articles/360036194592-Getting-started-with-GATK4). The GATK best practices included a number of steps to ensure variant accuracy. To remove erroneous mismatches around small indels, IndelRealigner was applied to process the alignment of BAM files.
HaplotypeCaller was used to call variants for each sample and the program GenotypeGVCFs was used to merge all of the individual GVCF (Genomic Variant Call Format) files. The merged GVCF file was subject to quality control so that the minimum quality of each SNP was 30. To avoid false positives of read mapping in repetitive sequences, sequencing depth of each high-quality SNP ranged from 2 to 200. A total of 4,473,644 SNPs were used to construct three nonoverlapping size-fixed windows, with window sizes of 50, 100, and 500 kb. A second, more conservative SNPs calling strategy was also used. In this strategy, samtools/bcftools was applied to call SNPs using the same data with default parameters. SNPs that were present in both the GATK and the samtools/bcftools datasets were used for further analysis.
In addition, we allowed each SNP site with a maximum missing rate of 40% across all samples tested. This ensured that the two outgroup species (M. alba and A. toxicaria) supplied abundant information for inference of the phylogenetic tree. A total of 389,834 SNPs in non-repeat regions were extracted from the VCF data, using a home-made PERL script (https://github. com/tangerzhang/VCFplayer/blob/master/vcf2fasta. pl). Based on the second SNPs dataset (using samtools/bcftools), we used a sliding-window approach and constructed a SNPs-fixed window with window size of 1000 SNPs and a step size of 1100 SNPs, meaning a space of 100 SNPs between two successive windows. To maintain abundant information within each window in the above four windows datasets, we removed windows with fewer than 50 SNPs, and generated 351, 764, 3697, and 3459 windows for the 1000SNP, 500-kb, 100-kb, and 50-kb datasets, respectively (Supplementary Table  3 ).
The physical location of each window in chromosomes of the reference genome was labeled (Source Data 1). All four datasets were used to infer species trees with ASTRAL-II (ref. 40 ); the 1000SNP dataset was also used for BUCKy-v1. 4. 4 analysis  43  ; and the 500-kb datasets were further used for PhyloNetworks analysis  45. Both chloroplast and mitochondrial genomes were de novo assembled with the recommended pipeline of the GetOrganelle toolkit  78. Chloroplast genomes of all 17 species and mitochondrial genomes of 14 species were assembled in circular sequences. For three species whose mitochondrial-genome assemblies were not circular, their assembly graphs were checked using Bandage program  79  , and nontarget contigs were manually cleaned. The cleaned assembly graphs of circular genomes or contigs were exported to FASTA sequences using the GetOrganelle toolkit (Supplementary Table  2  and Appendix 5).
SNPs in chloroplast and mitochondrial genomes from the same sequencing raw data were identified using the GATK pipeline as mentioned above. In total, we identified 5486 SNPs in chloroplast genomes and 4649 SNPs in mitochondrial genomes (Supplementary Table  2 ). All sampled species are inferred as diploid species based on ploidy estimation with nQuire program  80  (Supplementary Table  9 ). Reconstruction of Ficus evolutionary history. We inferred Ficus phylogenies based on the nuclear, chloroplast, and mitochondrial genomes of the target figs with multiple methods separately. We first inferred the Astral species trees under a multi-species coalescent model with the four genomic-windows datasets, using the ASTRAL-II program  40. For each of the four windows datasets, gene trees of all windows were inferred using the RAxML-v8 program  81  with GTR+GAMMA substitution model and 1000 bootstrap replications.
The Astral species tree was inferred with greedy consensus  40  of the 1000 bootstrapped replicate trees of each gene tree, and both support values (showing the percentage of bootstrap replicates that contain a given branch) and branch length in coalescent units were inferred (Supplementary Fig. 1 ). We also inferred the PCT tree of Ficus under the BCA-based  42  BUCKy 43 with the 1000SNP dataset. Whereas the coalescent-based Astral tree assumes that ILS is the sole reason for incongruence in gene trees, the BCA method makes no such assumption and allows both ILS and hybridization as causes of incongruence. The PCT tree with the highest CF value was reconstructed to summarize the main vertical phylogenetic signal (i. e. , the best-supported tree branching pattern) of all gene trees based on all single windows.
The CF value gives the proportion of genomic windows (or genes) supporting a given clade  44. In brief, we first ran Bayesian analysis of each individual gene (window) with MrBayes-v. 3. 2. 5 (ref. 82 ) to obtain a posterior distribution of gene trees. For each gene tree inference, under the GTR+GAMMA substitution model, two Markov chain Monte Carlo simulations were run with one cold chain and three heated chains for chain lengths of 1,000,000 (10% burn-ins) and sampling every 100 generations. Finally, we used BUCKy to estimate the PCT tree and CF values of different splits. Ficus divergence time was estimated using the Bayesian inference in the MCMCTREE program of PAML 4. 9a  83  , based on the topology of the Astral Ficus species tree using the 100-kb dataset, which was for the most part corroborated by the other Astral trees (Supplementary Fig.
1 ). MCMCTREE was used to estimate the branch length with datasets of 10 partitions of the whole aligned genome  84  (Appendix 3). To decide the rate prior, we first ran BASEML to estimate the overall substitution rate under a strict molecular clock with the root age (Crown Moraceae) set to 93 Mya 85 for a rough estimation. We set rgene_gamma = 1, 2, with average 0. 5 × 10 -8 substitutions per site per year. We then estimated the divergence times and 95% credible interval for each node with approximate likelihood principle  86  under HKY85 model, and with independent rates model (clock = 2). Seven fossil calibrations following contemporary accepted standards on Moraceae and Ficus  20, 85  (Supplementary Table  4 ) were placed on the phylogeny using soft bounds  87. The MCMC analyses ran 200,000,000 iterations with sampling every 10,000 cycles after a burn-in of 200,000.
The analyses were run twice to ensure chain convergence with the effective sample size (ESS) larger than 350, using Tracer v. 1. 6 (http://beast. bio. ed. ac. uk/Tracer). To investigate the general biogeographical history of Ficus, we further reconstructed the ancestral geographic distribution of Ficus lineages using the R package "BioGeoBEARS"  88. We categorized current species distributions into four states, Eurasia, Afrotropics, Australasia, and Neotropics, following previous studies  20. We inferred the ancestral distribution probabilities of Ficus lineages based on the present distribution states of the 15 fig species, and the Astral species tree with fossil calibration (Supplementary Fig. 2 ). Maximum number of regions (of the four listed above) in which a lineage could be distributed was set as two.
The method is based on the relative abundance of two allelic configurations called "ABBA" or "BABA". Given four taxa, P1, P2, P3, and an outgroup O, with the relationship "[(P1, P2), P3], O", ABBA patterns are SNPs where P1 retains the outgroup allele and P2 and P3 share the derived allele. Similarly, BABA patterns are SNPs where P1 and P3 share the derived allele and P2 retains the outgroup allele. Under the null hypothesis of no gene flow, the ABBA and BABA patterns should be equally frequent via ILS and the value of D should be equal to zero. A significantly positive D value indicates gene flow between P2 and P3, whereas a significantly negative D value indicates gene flow between P1 and P3 (ref. 47 ). Here, an improved D-statistic  46  method calculated with the program doAbbababa2 implemented in ANGSD  92  was used.
Briefly, BAM files that were used for variant detection of all 17 species sampled were recruited for this analysis. All samples underwent error correction and ABBA-BABA D-statistics analysis was conducted at different taxonomic/phylogenetic levels according to the instructions on the ANGSD website (http://www. popgen. dk/angsd/index. php/ Abbababa2). With these capacities, the improved D-statistics can obtain robust results even when sequencing depth is very low (1-10X)  46. To detect hybridization events throughout Ficus evolution, we conducted ABBA-BABA D-statistics for four-taxon groups hierarchically, from the species tips to the main clades following the topology of the Astral species tree. When Dstatistics analysis was conducted in four-taxon groups at higher taxonomic/ phylogenetic levels, all species within each taxon (section, subgenus, or main clade) were treated as sample replications (Supplementary Data 1).
Only monophyletic higher-level clades or subgenera in Astral species trees were used (Supplementary Fig. 1 ). A non-Ficus species (Morus alba) was taken as outgroup in all tested fourtaxon groups finally shown. The doAbbababa2 program could calculate the Dstatistics for all potential four-taxon combinations of species or groups. This means that for each species or clade, one could potentially infer multiple hybridization events with other taxa if the species was included in multiple four-taxon groups. However, only those four-taxon groups that followed the phylogenetic topology of the Astral species tree were considered. In a few cases, results of some alternative four-taxon groups were also kept when the phylogenetic topology among taxa was unstable (Supplementary Data 1). In addition, we inferred Ficus hybridization events using PhyloNetworks  45  , which infers phylogenetic networks with maximum pseudolikelihood from gene trees or quartet CF value.
It allows both vertical inheritance under the coalescent model, and horizontal inheritance (e. g. hybridization) with reticulation nodes in the network. Following the online pipeline (https://github. com/crsl4/PhyloNetworks. jl/wiki), PhyloNetworks analyses were conducted with the 500-kb windows dataset. Ficus networks were inferred based on the ML gene trees generated with the 500-kb windows dataset. We ran SNaQ to search for the best network with the maximum hybrid node number (hmax) increasing from zero to 15, each hmax being repeated ten times. The optimal number of hybridization events was chosen when the negative log pseudolikelihood (-logplik) score of an additional hmax did not significantly decrease. Furthermore, we also inferred Ficus hybridization events based on the analysis of results under BUCKy on inference of species trees based on the 1000SNP dataset (Appendix 2).
Event-based cophylogenetic reconciliation analyses were conducted using the program JANE 4. 0 (ref. 49 ) to infer phylogenetic incongruences based on five types of evolutionary events: co-divergence/cospeciation, duplication, associate switches, associate losses, and failure to diverge. Incongruence between nuclear and organellar genomes, especially the associate (host-)switch events, indicates organellar genome exchange among Ficus taxa with different nuclear genomes, indirectly supporting the occurrence of hybridization between Ficus species. Hostswitch events inferred between fig and pollinator phylogenies indicate pollinator host switches among Ficus taxa. Considering the close biological relationship between the fig/pollinator wasp interaction and Ficus hybridization, detection of corresponding host switches in both organellar genome and pollinator wasps between the same pair of Ficus taxa would strongly suggest that pollinator hostswitches generate Ficus hybridization.
Acknowledgements We thank  Bruno DeMadeiros  and  Egbert Leigh  for useful comments, the  Xishuangbanna National Nature Reserve , the  Xishuangbanna Station for Tropical Rainforest Ecosystem Studies , and the  Gardening and Horticulture Department of XTBG, CAS , for allowing sample collection;  Simon Segar  and  Daniel Souto-Vilarós  for collecting the F. microdictya sample; the  Public Technology Service Center of XTBG, CAS , for the use of its DNA extraction platform; and  Richard Corlett  and  XTBG  for organizing the "Scientific Paper Writing: XTBG Advanced Workshop", during which the manuscript was significantly improved. This work was supported by the  National Natural Science Foundation of China  (grant numbers:  31870359 ,  U1402264 , and  31400329 ), the  Yunnan Natural Science Foundation  (grant number:  2014FB185 ),  Youth Innovation Promotion Association of CAS  (grant number:  2020392 ), and the " International Partner Program " of  CAS  (to E.
Data availability All data needed to evaluate the conclusions in the paper are presented in the paper, and its Supplementary information files. Reporting summary for this article is available as a Supplementary information file. All raw resequencing data produced for this project have been deposited at the Genbank with the BioProject number as PRJNA684963, and the Genome Sequence Archive BIG Data Center (http://bigd. big. ac. cn/gsa) under the accession number of CRA001150 separately. All other raw results related to all figures or tables are supplied as Appendix files, which are available at Dryad (https://doi. org/ 10. 5061/dryad. zcrjdfn7m)  94. Source data are provided with this paper.