dataesr/scientific-paragraphs-categorization huggingface dataset:
In future photo-z simulations, variations in the test and training sets could be established that correspond to different simulations (e. g. , building a deep training set from the DDFs)-but we consider this out of the scope of the present analysis. We evaluate the photo-z results with two statistics: precision and outlier fraction. To calculate the precision, we first reject catastrophic outliers with |z truez phot | > 1. 5 (this is a nonstandard definition, chosen for this simulated data set, and used also in  Graham et al. 2020 ). Then we calculate the robust standard deviation in the photo-z error, Δz 1+z = (z truez phot )/ (1 + z phot ), by using the value of the interquartile range as an FWHM and dividing by 1. 349 to convert to standard deviation (by definition, σ = FWHM/1. 349). The outlier fraction is the number of galaxies with a photo-z error greater than three times the standard deviation or greater than three times 0.
2012). We also compute the average cadence (in day -1 ), i. e. , the number of g, r, i, or z visits in a fiducial rest-frame interval. The quantities above are determined for each pixel and each night (identified by its Modified Julian Date) and may be used to build full sky maps giving, as a function of the position on the sky (1) the density of SNe, (2) the median maximum redshift (over the observed area), and (3) the median cadence. This metric is the most precise to assess observing strategies, but also the most intricate to implement. A lot of effort has been put to design algorithms combining speed, reliability, and accuracy. The codebase is accessible through Github 47  in the Metric Analysis Framework. We note that while we expect the quality cuts described here will ensure accurate classification of SNe Ia and separate them from other classes of transients, we do not yet have a transient metric to ensure this is the case and leave this important step to future work (see Section 7.
S/N rate and Redshift Limit While the metric in Section 5. 1. 1 is our most accurate metric, we developed two proxy metrics that have also been incorporated in MAF where straightforward, fast, and easyto-run metrics are preferred. These two metrics are quite simple (they do not require the use of a light-curve fitter) and just need templates of SN light curves as input. They are estimated for each band, thus providing tools for further comparison of observing strategy performance. They are sensitive to two key points of observing strategies: the median cadence, and the internight gap 48  variations. The codebase is accessible through Github. 49 hese two metrics rely on the S/N of the light curves (per band), which may be written as: ( ) ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ å s = f S ( ) ( ) | d á ñ = D å - f T f 5 S N.
A follow-up strategy must also be specified for the calculation of σ γ since Rubin/LSST will not generate all of the information needed for this measurement, e. g. , redshift, SN classification. Here, we adopt a maximum survey redshift of z = 0. 2 and follow-up that gives 0. 08 mag magnitude dispersion per SN. The minimum redshift is z = 0. 01, number densities are based on 65% of the SNe Ia rates of  Dilday et al. (2010) , and = k h 0. 1 max Mpc -1 , b = 1. 2, ΛCDM cosmology with W = 0. 3 M 0 , and overdensity power spectra for the given cosmology as calculated by CAMB  (Lewis & Bridle 2002). The code used for the calculations are available in Github. 50.
The fit function depends on the median internight gap between any filter, t gap , and is measured in days, which is an important parameter because we assume a detection of the LSNe Ia in  Huber et al. (2019)  after the third data point exceeds the 5σ point-source depth in any filter. The internight gap (t gap ), survey area Ω WFD , and cumulative season length t csl can be calculated via MAF 51  and a Python script 52  where we only take observations into account that have a 5σ point-source depth greater than  22. 7, 24. 1, 23. 7, 23. 1, 22. 2, and 21. 4  for the filters u, g, r, i, z, and y, respectively. These cuts are motivated by  Huber et al. (2019)  and are important to restrict visits to the WFD, where the metric from Equation (9) is valid. The results are summarized in Figure  6 , where we see that in principle a larger area helps, but only if the cadence is not reduced significantly.
The metrics developed in this paper have been invaluable in gaining insights about observing strategy and lay a useful foundation for ongoing work on this challenging optimization problem. T. A. acknowledges support from  Proyecto Fondecyt N 1190335  and the  ANID-Millennium Science Initiative Program -ICN12_009  awarded to the  Millennium Institute of Astrophysics MAS. H. Aw. also acknowledges support by the  Rutgers Discovery Informatics Institute  ( RDI2 ) Fellowship of Excellence in Computational and Data Science, Rutgers  University & Bevier Dissertation Completion Fellowship, Leinweber Postdoctoral Research Fellowship , and  DOE  grant  DE-SC009193. H. Aw. also thanks the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC, NSF  Cybertraining Grant # 1829740 , the  Brinson Foundation , and the  Moore Foundation , as participation in the program has benefited this work.
This material is based upon work supported by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics Cosmic Frontier Research program  under Award Nos. DE-SC0011636  and  DE-SC0010008 , which supported E. G. and  H. Aw. S. G. G. acknowledges support from the  University of Rochester  and the  DOE Office of High Energy Physics  under grant No. DE-SC0008475. M. L. G. acknowledges support from the  DIRAC Institute  in the  Department of Astronomy at the University of Washington. The  DIRAC Institute  is supported through generous gifts from the  Charles and Lisa Simonyi Fund for Arts and Sciences , and the  Washington Research Foundation. S. H. and S. H. S. thank the  European Research Council (ERC)  for support under the  European Unionʼs Horizon 2020 research and innovation program  (LENSNOVA: grant agreement No.
S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. C. S. , R. B. , and H. V. P. 's work was partially supported by the research environment grant " Gravitational Radiation and Electromagnetic Astrophysical Transients (GREAT) " funded by the  Swedish Research Council (VR)  under Dnr  2016-06012  and the research project grant " Understanding the Dynamic Universe " funded by the  Knut and Alice Wallenberg Foundation  under Dnr  KAW 2018. 0067. T. P. acknowledges the financial support from the  Slovenian Research Agency  (grants  I0-0033 ,  P1-0031 ,  J1-8136 , and  Z1-1853 ). S. H. S. thanks the  Max Planck Society  for support through the  Max Planck Research Group. The DESC acknowledges ongoing support from the  Institut National de Physique Nucléaire et de Physique des Particules in France ; the  Science & Technology Facilities Council  in the United Kingdom; and the  Department of Energy , the  National Science Foundation , and the  LSST Corporation in the United States.
DESC uses resources of the  IN2P3 Computing Center (CC-IN2P3-Lyon/Villeurbanne-France)  funded by the  Centre National de la Recherche Scientifique ; the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231 ;  STFC DiRAC HPC Facilities , funded by  UK BIS National  E-infrastructure capital grants; and the UK particle physics grid, supported by the  GridPP Collaboration. This work was performed in part under  DOE  Contract  DE-AC02-76SF00515. This paper has undergone internal review in the  LSST Dark Energy Science Collaboration. The authors thank the internal reviewers, who were  Ariel Goobar ,  Joshua Meyers , and  Samuel Schmidt. The authors also thank  Jean-Charles Cuillande  and  Christopher Frohmaier  for providing the footprint maps for Euclid and 4MOST, respectively.
Together, the detected RNA processing signature across diverse SE and RI events at weak splices sites suggest impaired splicing fidelity from loss of THOC6. To investigate the role of RNA misprocessing in ID pathology, we intersected our AS events with the genes that are known to cause syndromic ID, deposited in the SysID database (SysIDdb). 152 genes with significant AS events included or excluded in >10% of transcripts in hNPCS were detected in nonsense and missense affected genotypes (Figure  4G ). 185 AS genes in THOC6 W100*/W100* and 105 AS genes in THOC6 E188K/E188K hNPCs are known genes causative for syndromic ID represented in the SysIDdb (Figure  4G ). Aberrantly spliced ID genes were identified in both THOC6 affected genotypes, consistent with a role for THOC6 in ID. 37 ID genes (1.
These shared findings suggest selective conservation of mRNP processing mechanisms by THOC6 in mouse and human forebrain. To assess the correlation between THOC6 mRNP processing defects and expression, differential expression analysis of Thoc6 fs/fs forebrain mRNA sequencing data was performed compared to Thoc6 +/+ controls. Of note, Thoc6 mRNA is downregulated (two-fold, p=<0. 0001), consistent with NMD (Figures  6E  and S6F ). In this model, 5x more genes were upregulated (144 genes) than downregulated (  27 genes). Nevertheless, downregulated genes may covey important pathology. First, downregulated genes functionally converge on neurogenesis, proliferation, and differentiation pathways (Figure  6F ). Upregulated genes are implicated in the hypoxic response, HIF-1 signaling pathway, and glycolysis-biological categories indicative of increased apoptosis in affected cells (Figure  6F ).
To assess the balance of multipotency and differentiation EdU, KI67, and DCX co-labeled cells per NR were quantified. A significant increase in cells costained with the proliferation marker KI67 and EdU per affected NR were detected at day 28ND (p = <0. 0001, two-tailed t test), indicating affected NPCs remain mitotically active longer than control NPCs (Figure  7F ). This finding paired with elevated mRNA and protein expression of OCT4 in affected hNPCs (data not shown) supports retention of multipotency model. Consistent with this finding, we observed a significant reduction in the fraction of EdU cells co-labeled with the migrating neuron marker doublecortin (DCX) in affected NRs (p = <0. 0001, two-tailed t test) (Figure  7F ). Paired with the prolonged proliferation dynamics, this suggests a disruption to the differentiation timeline in affected organoids.
, 2012). Average depth of coverage was calculated across all targeted regions. The data were filtered and annotated from the canonical THOC6 transcript (ENST00000326266. 8 and ENSP00000326531. 8) using in-house bioinformatics software  (Müller et al. , 2017). Variants were also filtered against public databases including the 1000 Genomes Project phase 311, Exome Aggregation Consortium (ExAC) v. 0. 3. 1, Genome Aggregate Database (gnomAD), National Heart, Lung, and Blood Institute Exome Sequencing Project Exome Variant Server (ESP6500SI-V2). Those with a minor allele frequency >3. 3% were excluded. Additionally, variants flagged as low quality or putative false positives (Phred quality score 14; 15 < 20, low quality by depth <20) were excluded from the analysis. Variants in genes known to be associated with ID were selected and prioritized based on predicted pathogenicity.
Glass covers were mounted onto all slides with Prolong Gold (Molecular Probes S36972) and incubated for 24 hours at room temperature prior to imaging. Imaging was performed with a Nikon A1ss inverted confocal microscope using NIS-Elements Advanced Research software. Image analysis was performed using Fiji (ImageJ) software  (Schindelin et al. , 2012). For oligo-dT FISH, Z-series images were taken every 0. 2 μm across entire width of cells for each genotype using same laser intensity settings and collapsed by max intensity using Z project tool in Fiji for quantification of nuclear and cytoplasmic fractions of polyA intensity by automated quantitation with CellProfiler (v4. 2. 1). Hoechst signal was used to segment nuclei and the oligo-dT signal to segment cell body. Three differentiation replicates per genotype.
1. 2 (Team, 2018). ERCC spike-in counts were used to estimate size factors for each sample for DESEq2 analysis. Genes were considered dysregulated if FDR < 0. 05 and fold-change > 2 or < -2. Volcano and PCA plots were made using ggplot2 and pcaExplorer packages in R. Alternative splicing analysis was performed on sorted BAM files using rMATS v4. 1. 2 (Park et al. , 2013)  with the following parameters: '-t paired --readLength 150 -variable-read-length --nthread 4'  (Shen et al. , 2014). AS events were called if FDR < 0. 05 and ΔPSI > 10%. Events with less than 5 average reads were filtered out using the MASER package in R  (Veiga, 2022). To calculate splice site strength at 5' and 3' splice sites in AS transcripts identified by rMATS, maximum entropy modeling was carried out using MaxEntScan  (Yeo and Burge, 2004).
qRT-PCR and mRNA half-live analysis Reverse transcription for cDNA synthesis was performed using 1 μg of total RNA with Superscript III first-strand synthesis kit (Invitrogen 18080051) according to manufacturer's instructions. For validation of AS events, standard PCR was performed as described above. Abundances of THOC6, FOS, GAPDH, MEG3, MEG8, ESRG, and NEAT1 mRNA was determined by quantitative real-time PCR (qPCR) using the Applied Biosystems 7500 system with 7500 Software v2. 3 and Radiant Green 2X qPCR Mix Lo-ROX 2X qPCR Mix (Alkali Scientific inc. , QS1020) according to manufacturer's instructions. Cycler parameters used: cDNA activation (1 cycle at 95°C for 2 minutes), denaturation (40 cycles 95°C for 5 seconds) and annealing/extension (40 cycles at 60°C for 30 seconds). The ΔΔCt method was used to analyze data with GAPDH as a reference gene.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical significance of all quantifications from microscopy images, western blot images, gel images, and qRT-PCR abundances was tested using a student's two-tailed t-test and data was plotted using GraphPad Prism (v9. 3. 1) as mean ±SEM or mean ±SD, as specified in figure legends. Simple linear regression was performed in qRT-PCR standard curve analysis, organoid growth curves, and intron retention analysis. Statistical significance of gene overlaps were tested using Fisher's exact test via GeneOverlap package function testGeneOverlap() in R. Benjamini-Hochberg multiple hypothesis corrections were performed in pathway enrichment analyses. (A) Differentiation protocol to derive human neural progenitor cells from affected and unaffected ESC/iPSCs. (B) Combined rMATS summary results for AS events in THOC6 W100*/W100* and THOC6 E188K/E188K hNPCs relative to THOC6 W100*/+ control hNPCs.
Blue, higher inclusion in unaffected. (C) Significant splice site strength score differences at mis-spliced events in affected hNPCs based on maximum entropy model. (D) transcript number per AS gene and (E) AS event length in THOC6 W100*/W100 and THOC6 E188K/E188K vs. THOC6 W100*/+ NPCs. (F) RT-PCR AS validations of SE (ABAC1, POU2F2) and RI (MAPK15) events in three additional biological replicates of hNPCs per genotype with quantified mis-spliced ratios. Data shown as mean ±SEM. P-values (C-F), two-tailed unpaired t test. ****, p = < 0. 0001. (G) Venn diagram of overlap of THOC6 W100*/W100* and THOC6 E188K/E188K AS genes and all syndromic intellectual disability genes included in the SysID database. Overlap significance tested by Fisher's exact test. ASG, alternatively spliced genes. Metascape analysis on combined significant mis-spliced events (FDR <0.
ACKNOWLEDGEMENTS The authors would like to especially thank all the family members who participated in this study and gave permission to use genetic and clinical information. We thank  Stephanie Moon  for guidance on mRNA stability assay,  Sumin Kim  for reagents and protocols for oligo-dT FISH, and the  University of Michigan Advanced Genomics, Transgenic Animal Model , and  Microscopy Cores  for sequencing and equipment. This research was supported by  NSF   Doctoral Dissertation Research Improvement Grant   1919671 :  F055138 ,  Wenner-Gren Foundation for Anthropological Research   N02820 :.
Animals and fungi have radically distinct morphologies, yet both evolved within the same eukaryotic supergroup: Opisthokonta 1,2. Here we reconstructed the trajectory of genetic changes that accompanied the origin of Metazoa and Fungi since the divergence of Opisthokonta with a dataset that includes four novel genomes from crucial positions in the Opisthokonta phylogeny. We show that animals arose only after the accumulation of genes functionally important for their multicellularity, a tendency that began in the pre-metazoan ancestors and later accelerated in the metazoan root. By contrast, the pre-fungal ancestors experienced net losses of most functional categories, including those gained in the path to Metazoa. On a broad-scale functional level, fungal genomes contain a higher proportion of metabolic genes and diverged less from the last common ancestor of Opisthokonta than did the gene repertoires of Metazoa.
This relationship has stood the test of time, and now animals and fungi are members of Holozoa and Holomycota, respectively, which are the two major divisions of the eukaryotic supergroup Opisthokonta  1. Pinpointing how animals and fungi evolved to be so different requires a detailed reconstruction of the evolutionary changes leading up to the two lineages. This demands not only genomic data from diverse animals and fungi but also from the protist opisthokont groups that branch between them (Fig. 1d ), which are underrepresented in genomic databases  7.
Four new genomes of protist opisthokonts The closest known groups to Metazoa within Holozoa are Choanoflagellatea, Filasterea and Teretosporea (Fig. 1d ). Within Holomycota, the closest known groups to Fungi (here defined as the least inclusive clade including Chytridiomycota and Blastocladiomycota based on the absence of phagotrophy in all the members of this clade  8  ) are Opisthosporidia (a paraphyletic group  9, 10  , which in our genomic dataset is represented by Rozella allomycis and Mitosporodium daphniae-RM clade) and Nucleariidae (Fig. 1d ). To improve the limited genome sampling for the protist opisthokont groups  7  , we sequenced, assembled and annotated the genomes of three filastereans (Ministeria vibrans  11  , Pigoraptor vietnamica  12  and Pigoraptor chileana  12  ) and one nucleariid (Parvularia atlantis  13  ) from metagenomic data produced from cultures of these species (Supplementary Information 1).
1b ). The main characteristic of the genetic turnover that occurred in the path to extant Fungi was a specialization towards metabolism (Fig. 2d ), whereas animal genomes specialized towards other functional categories (Fig. 2a ). In agreement with this, the metazoan root experienced a net loss of metabolic genes (Extended Data Fig. 5d ), despite this node presenting an overall net gain of gene content (Fig. 1b ), whereas the fungal root experienced net metabolic gene gains (Extended Data Fig. 5c ). (Note that an additional supplementary analysis with a dataset that includes transcriptomic data from the aphelid Paraphelidium tribonemae  9  , which is the closest known group to Fungi, suggests that half of the net gene gains originally detected at the fungal root, including the metabolic ones, could have also predated the origin of Fungi; see Supplementary Fig.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-05110-4. Fig. 4  | The large genetic differences between modern animals and fungi are the outcome of two contrasting trajectories of genetic changes that preceded the origin of both groups. These divergent trajectories started immediately after the split of their last common ancestor (Opisthokonta) into Holozoa and Holomycota and continued during the emergence and diversification of Metazoa and Fungi.
Methodological pipeline for genomic data acquisition We sequenced a series of culture lines, each including one of the four species of interest (M. vibrans, P. atlantis, P. vietnamica and P. chileana). The cultures of M. vibrans and P. atlantis (formerly Nuclearia sp. ) were bought in ATCC (M. vibrans Tong. ATCC 50519 and Nuclearia sp. ATCC 50694, respectively). The cultures of P. vietnamica (formerly Opistho-1) and P. chileana (formerly Opistho-2) descend from the environmental isolates (P. vietnamica from a Freshwater Lake, Vietnam; and P. chileana from freshwater temporary water body, Chile) used in ref. 12. As expected, the starting cultures included an uncertain fraction of contaminant species. In particular, the cultures of M. vibrans and P. atlantis included an uncertain diversity of bacterial contamination, whereas the cultures of each Pigoraptor species also included contamination from the eukaryote Parabodo caudatus.
Clustering sequences into orthogroups A dataset of 1,463,920 protein sequences from 83 eukaryotic species, 59 from Opisthokonta (including the four genomes produced) and 24 from other eukaryotic groups, was constructed (draft_euk_db; see Supplementary Table  4 ). Protein sequences were aligned all-against-all using BLASTp 36 v2. 5 [-seg yes, -soft_masking true, -evalue 1e-3]. On the basis of the alignments, proteins were clustered into orthogroups (OGs) with OrthoFinder 37 v2. 7 [-I 2]. We treat OGs as proxies of gene families. The OGs produced by OrthoFinder were processed with the MAPBOS pipeline to fix protein domain heterogeneity problems that would compromise downstream analyses (see Supplementary Information 2 for a discussion of this issue, and for an explanation of the algorithm that we developed to correct it).
Species tree reconstruction Ancestral gene contents were inferred by means of a gene tree-species tree reconciliation software. We thus needed to reconstruct a phylogenetic tree for every gene family and a species tree of the whole eukaryotic supergroup Opisthokonta. The results from the species tree reconstruction analyses are available in Supplementary Information 3. We first selected 342 OGs present in >77% of draft_euk_db taxa and with no more than an average of 1. 16 copies per taxa. We measured alignment instability of the 342 OGs using COS. pl and msa_set_score v2. 02, which are based on the Heads-or-Tails approach  38, 39  , keeping only those OGs with >0. 70 mean column score (MCs). We manually curated the 69 OGs that survived to this filter by performing individual phylogenies for each one, using MAFFT 40 v7.
123b [-einsi] for sequence alignment, trimAl 41 v1. 4. rev15 [-gappyout] for alignment trimming and IQ-TREE 42 v1. 6. 7 for maximum-likelihood (ML) phylogenetic inference, using ModelFinder 43 for model selection. Three of these 69 OGs were discarded because the topology was strongly in disagreement with the expected species topology. For the remaining 66 OGs (hereafter referred to as the MCs70 dataset), we removed sequences whose branching pattern suggested that they were most likely misclassified as OG members. In addition, to keep only one sequence per taxon in every OG, for inparalogue cases, we kept the least divergent sequence according to branch length. We removed a total of 630 sequences from the MCs70 dataset, including likely misclassified OG members but also contaminant sequences.