dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 8  displays the triangles of the cortical surface mesh intersected by each bundle atlas for each hemisphere along with the percentage of intersected triangles. It can be seen that Atlas1 and Atlas2 present a lower coverage of the cortical surface. Furthermore, our atlas presents a better coverage of the cortical gyri, in both external and internal regions of the cortex. While AtlasA and Atlas3 cover quite well the external cortical regions, the internal regions of the cortex appear less connected. In any case, a low percentage coverage (less than 45%) is observed in all atlases, due to a lower intersection of the triangles with the sulci walls. We also computed the intersection percentage between the bundles of AtlasA and the bundles from the previous atlases. As mentioned above, the previous atlas bundles were calculated with different methods and databases, resulting in bundles of different sizes.
Finally, the third clustering stage aims to regroup centroids into a small number of clusters for each connection using QuickBundles with the MDF distance, an algorithm that has also been successfully used in numerous methods  ( Cousineau et al. , 2017; Garyfallidis et al. , 2018; Zhang et al. , 2018b ). The method was applied to probabilistic tractography from 100 subjects of the HCP dataset, obtaining a multi-subject atlas composed of 525 bundles. We compared our atlas to previous atlases to analyze the differences between them. We conclude that our atlas features better cortical surface coverage and finds 97 bundles (58 in the left hemisphere and 39 in the right hemisphere) not described in previous whole-brain atlases. The increase in coverage can be mainly attributed to the use of probabilistic rather than deterministic tractography.
There is also an effect of the registration method used, where nonlinear normalization yields to more reproducible clusters than affine normalization  ( Román et al. , 2017 ). Based on the segmentation of the bundles of our atlas in new subjects, we find medium to high reproducibility of the atlas bundles. As expected, the highest reproducibility was found in the HCP subjects based on the same tractography used for the creation of the atlas (but applied to subjects not used for the atlas creation), where 509 bundles were segmented in 100% of the subjects. However, the reproducibility decreases to 279 bundles found in all the subjects, for the same HCP data but using deterministic tractography. This value is a somewhat lower for the HARDI database (ARCHI), where only 209 bundles were segmented in all the subjects.
Also, several methods have been proposed to filter out implausible fibers. Recently, an interesting work, integrated to the convex optimization modeling for microstructure-informed tractography (COMMIT)  ( Daducci et al. , 2014 ) , uses the assumption that streamlines are grouped in bundles  ( Schiavi et al. , 2020 ) , thus increasing the accuracy of the estimated brain networks. This framework or an adapted one could be used to filter out implausible short white matter fibers and hence reduce the complexity of processing large databases. Our proposed method has its own limitations that should be overcome in the future. First, the elimination of fibers present in a DWM atlas allowed us to eliminate a non-negligible number of centroids belonging to known medium and long fascicles. However, in the future, we could analyze whether this stage eliminates centroids that may represent short association fibers that could be added to the atlas.
An alternative could be to only select the most stable bundles. Hence, along with our whole atlas, we will share folders with the reproducible bundles for the three tested tractogragram databases. We implemented an example of application, where we analyzed the sensitivity of AtlasA and previous atlases for detecting white matter asymmetry. A large number of left lateralized and right lateralized bundles with significant differences of nQA was found especially for atlases with more bundles (Atlas3 and AtlasA). Some differences were found in the detection of bundles with low values of asymmetry, but in general Atlas3 and our atlas presented a better brain coverage. However, bundles from Atlas3 are in general bigger and more dispersed than our atlas, presenting a greater overlap between them.
Some previous works have performed some comparisons using dMRI. Oishi et al. (2011)  identified 10 SWM bundles in the macaque also present in humans, connecting different brain regions such as the precentral and postcentral gyri and the superior frontal and precentral regions. Also, other works have identified the Vertical Occipital Fascicle in macaque  ( Takemura et al. , 2019 )  and vervet monkey  ( Takemura et al. , 2020 ). Zakszewski et al. (2014)  created a white matter parcellation including 7 SWM ROIs, such as superior and middle temporal gyri. Future studies could create more detailed atlases of superficial WM fibers in primates and compare them with existing human SWM bundles atlases. In conclusion, the proposed method successfully created a new SWM bundle atlas with a large number of bundles and high brain coverage, based on probabilistic data from HCP database, contributing to a better description of the short association fibers in healthy subjects.
Acknowledgements HCP Data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. The authors acknowledge the financial support of  ANID , Chile:  FONDECYT 1221665 ;  CONICYT-PCHA/Doctorado Nacional/2016-21160991 ;  ANILLO ACT210053 ;  FONDECYT 1220960 ; and  BASAL Centers: National Center for Artificial Intelligence  (CENIA,  FB210017 );  Advanced Center for Electrical and Electronic Engineering  ( AC3E ,  FB0008 );  Center for Biotechnology and Bioengineering (CeBiB ,  FB0001 ). It has also been funded by the  Human Brain Project , funded from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement No.
Data and code availability All data used in this project is from the Human Connectome Project (HCP) ( www. humanconnectome. org ). This data is publicly available to researchers who agree to the data use terms ( www. humanconnectome. org/study/hcp-young-adult/data-useterms ). All HCP data may be downloaded through the ConnectomeDB (db. humanconnectome. org). ARCHI database is available upon request to Cyril Poupon at the email cyril. poupon@gmail. com.
Highlights Ubiquitin regulatory X (UBX) domaincontaining adaptor proteins guide and control the AAA-ATPase cell division control protein 48 (CDC48) to mediate an astonishing variety of fast-response mechanisms. Recent structural and functional studies have clarified the biological roles of many UBX proteins from animals and fungi, but most plant UBX domain-containing (PUX) proteins remain poorly understood. Despite their evolutionary distance, many domains and their combinations are conserved across eukaryotic UBX proteins, suggesting that they form functionally coherent 'work packages'. The artificial intelligence (AI)-based program AlphaFold is now able to accurately predict protein 3D structures and connections between structural units. The combination of a cross-kingdom comparison and AlphaFold analysis reveals conserved motifs and higherorder features for PUX proteins that collectively provide testable hypotheses to efficiently guide future experimental research.
(PAE) plots can reveal contacts between residues distant in the primary sequence  [6]  (Box 2). Therefore, AlphaFold can be used to identify connections between protein building blocks and reveal secondary structure elements located in disordered protein regions. If these connections and structural elements are conserved across orthologs, they may indicate functionally coherent modules and novel interaction motifs, respectively. The AlphaFold team already made predictions for the proteomes of most model organisms, including Arabidopsis, rice, yeast, and human, and ultimately aims to cover all sequences in the UniProt database  [7]. These predictions are freely available at uniprot. org and alphafold. ebi. ac. uk where interactive web interfaces allow their analysis without requiring in-depth knowledge of structural biology.
Box 3. Novel motifs and modules predicted by AF Applying comparative AF analysis to PUX proteins suggests conserved sequence motifs of currently unknown functions as shown in Table  I. Our AF analysis also suggests structural units formed by several joint building blocks and/or motifs, as shown in Table  II. Figure  I  shows the structure and sequence of these motifs and the predicted conformation of the joint hairpin-helix-SHP-SEP module. The alignments were produced by T-Coffee  [36] , and displayed by ESPript 3. 0  [37]. Groups IVa and IVb contain both a short helix and a predicted second SHP motif upstream of their SEP domain  [16, 18]. For both subgroups, AlphaFold suggests that this helix folds back onto the SEP domain and that the helix-SHP-SEP fragment in these orthologs is, in fact, a single structural unit (Boxes 2 and 3).
This 'FGG' helix is situated between the most Cterminal UIM motif and the UBX domain. A BLAST search identifies the FGG helix in group Va orthologs from other plants, but not in other AtPUX paralogs, suggesting that it is specific for the function of group Va members. Conversely, group Vb proteins contain a UAS domain of unknown biological function. AtPUX7, 8, 9, 13, and ScUbx5 also contain a predicted PPII-EP flap, which emerges from a long helix in AtPUX8 and 13. The sequences of these helices conform with a coiled-coil (CC) pattern, suggesting they mediate self-association. HsUBXN7 also contains a motif that is structurally reminiscent of the PPII-EP flap, but where the EP motif is replaced by a DV sequence (residues 407-408, corresponding to residues D38 and V39 in PDB id 1WJ4). Group VI: plant-specific UBA-UAS-UBX proteins with unknown function We grouped AtPUX14, 15, and 16 which contain UBA, UAS, and UBX domains in group VI.
Experimentally clarifying the biological significance of features identified herein will help in exploring the mechanisms underlying the (auto)regulation of PUX proteins, including post-translational modifications and the formation of homo-or heteromultimers. AlphaFold predicts that PUX proteins comprise multiple interaction modules connected through long, flexible linkers; this is a typical characteristic of proteins involved in biomolecular condensates through liquid-liquid phase separation (LLPS). PUX proteins have not yet been linked directly to LLPS; however, LLPS is involved in the autophagic clearance of nonfunctional CDC48 by nitrogen starvation in plants  [30] , and animal orthologs of the UBA domain can produce LLPS in p97-containing nuclear foci  [31]. An enticing possibility is thus that PUX proteins promote the formation of biomolecular condensates.
Acknowledgments This research was supported by the  King Abdullah University of Science and Technology (KAUST)  through the baseline fund and award  URF/1/4039-01  from the  Office of Sponsored Research (OSR). We thank  U. S. Farook Hameed  for his contributions to the initial stages of this project, and we thank  Ł. Jaremko ,  S. Al-Babili ,  H. Hirt ,  B. Zahodnik-Huntington , and  A. Sandholu  for their constructive comments on the manuscript.
Compilation of the gene age dataset and cancer omics datasets To analyze the roles of PSGs in tumors, we first compiled a genome-wide gene age dataset spanning the most ancient UC genes to the youngest PSGs. Specifically, two strategies for dating gene origination have been developed: protein-family-based methods, also known as phylostratigraphy  [22, 23, [47] [48] [49] , and synteny-based methods  [36, 37, 40, [50] [51] [52] [53]. The former assigns the age of the founder gene to all homologous genes of the same gene family and thus does not differentiate between the child duplicates and the parental copy (Fig. 1a ). In contrast, the latter distinguishes different duplicates on the basis of synteny (gene order) and parsimoniously infers the corresponding age. Thus, for genes harbored by the human genome, PSGs identified by the former strategy indicate that these genes form primate-specific protein families (absent in non-primate species), while those genes identified with the latter strategy represent primate-specific loci.
We compiled a single gene age dataset by merging these two dating strategies given their complementary nature ("Methods"). Since synteny degenerates faster than protein sequence during evolution, the synteny-based pipeline has been applied to date human genes within vertebrate evolution  [40] , while protein-level homology is detectable over a much longer period, thus, phylostratigraphy has been implemented to date ancient genes  [22, 23]. We thus used the age data of groups 9-14 after vertebrate split (Fig. 1b ; "Methods") generated via our previous synteny-based pipeline  [40]. Genes predating the vertebrate split were classified into eight groups in a relatively new phylostratigraphy dataset  [23]. We further divided all groups into 5236 UC genes, 7767 EM genes, 2704 mammal-specific (MM) genes, and 423 PSGs (Fig.
1b , Additional file 1: Fig. S1a ; Additional file 2: Table  S1 ) and considered that UC and EM genes could serve as two controls in subsequent analyses given their respective roles in tumor promotion or repression  [19] [20] [21] [22] [23] [24]. Notably, the dataset of PSGs shows the well-known transcriptional and evolutionary features of young genes, i. e. , narrow expression  [35, 54, 55]  and fast evolution  [56, 57]. First, almost half (190/423) of the PSGs showed tissue-biased expression (e. g. , testis bias, Additional file 1: Fig. S1b ; Methods), while this proportion declined in the three older age groups (43%-9%, Fig. 1b ; Additional file 2: Table  S1 ). Actually, due to pervasive incomplete duplication or relocation  [58, 59] , duplicate PSGs generally show narrower expression than their corresponding parental copies (Additional file 1: Fig.
PSGs are generally upregulated in tumors We identified the pan-cancer upregulation patterns of PSGs by analyzing RNAsequencing (RNA-seq) data with paralogous similarity taken into account c Count of the pan-cancer TCGA datasets used in this study. Only RNA-sequencing transcriptome datasets (marked with "*") were reprocessed, while all other datasets were directly retrieved from previous publications or dedicated websites ("Methods"). Numbers refer to the counts of tumor samples, while numbers in parentheses refer to the counts of normal controls. SCNA refers to somatic copy number alteration ("Methods"). We found that the median log 2 transformed fold changes of PSGs between tumor and normal samples were significantly higher than 0 in 10 of 13 tumor types (Fig. 2a , Additional file 2: Table  S1 ).
S4g ). Among the 55 uPSGs with unique single guide RNAs (sgRNAs), 10 B-PSGs and 5 T-PSGs showed dependency for at least three cell lines in one cancer type (Fig. 4d , Additional file 1: Fig. S4h ; Methods). The importance of these 15 PSGs was demonstrated by the median number  (23)  of cell lines that were dependent on these genes. This number was analogous to that of uUC genes  (20) , which is subsequently higher than the number of downregulated EM genes (1, Fig. 4e ). Certainly, the breadth of essentiality was uneven; some genes, such as the top two genes including DDX11 (also named ChlR1) and ZNF273, showed a common dependency  [72] , whereas other genes, such as TBC1D29, showed relatively narrower essentiality (Fig. 4f  ). Notably, both DDX11 and ZNF273 were identified by the MSigDB based correlation method and the 666-gene list to play cell cycle-related roles, again suggesting the importance of the cell cycle program in tumors.
We reanalyzed a transcriptome dataset profiling embryonic development through the adult stages of seven organs  [90]. To generate a global picture of gene expression in cerebrum (forebrain) development, we assigned genes toward one particular stage according to their biased expression (Additional file 1: Fig. S7a , Additional file 2: Table  S7 ; Methods). We first reproduced previous observations  [40, 57]  (Fig. 6 ): (1) PSGs are disproportionately upregulated in the mid-fetal stages; (2) they are depleted in some postnatal samples in which genes involved in neuron ensheathment or synaptic transmission are disproportionately expressed. We then found that similar to the genome background the highest proportion of PSGs showed maximum expression in the embryonic stage (27. 2%, Fig. 6 ). For this earliest stage, genes involved in the cell cycle are highly overrepresented with corresponding Gene Ontology (GO) terms (DNA replication/repair, cell division) contributing three out of the top four enriched terms (Additional file 2: Table  S8 ).
Consistently, we found the cell cycle-related uPSGs (e. g. , DDX11) tended to be expressed in this stage regardless of whether the MSigDB correlation-based dataset (39. 0%, one-sided binomial test P = 0. 067) or the 666-gene list (80. 0%, P = 0. 0008) was used (Additional file 2: Table  S4 ). Notably, the heightened activity of cell cycle genes was most pronounced in the cerebrum while the activity is significantly lower in other organs including cerebellum (Additional file 1: Fig. S7b ). Consistent with the positive selection signal of DDX11 (Fig. 5e ), we found that the embryonic stage was the only outlier with overrepresentation of genes with positive selection of their coding sequence or promoter regions (47 or 42. 3%, 122 or 36. 3%, Fig. 6 ; Additional file 2: Table  S9 ; "Methods"). Consistent with the overall functional bias of embryonic genes, these 47 or 122 genes are also disproportionately related to the cell cycle (Additional file 2: Table  S8 ).
Compilation of a unified gene age dataset The gene age dataset was extracted from the synteny-based GenTree database [based on Ensembl v73,  [40] ] and further complemented with phylostratigraphy data generated in  [23]. Although multiple phylostratigraphy datasets were generated in human  [19] [20] [21] [22] [23] , we chose  [23]  considering the relatively recent release time and data availability, and so as to reproduce the patterns (upregulation of UC genes and downregulation of EM genes) in  [23]. As introduced in the Results, GenTree takes advantage of genome-wide synteny and performs gene-level dating parsimoniously along the vertebrate phylogenetic tree, while phylostratigraphy performs family-level dating and can be used to infer the age of ancient genes when synteny become degenerated  [40].
We excluded genes for which GenTree and phylostratigraphy produced conflicting age data. Genes from the phylostratigraphy dataset that could not be mapped to Ensembl v73 due to database version differences and genes that lacked age information in GenTree due to difficulty in inferring synteny [e. g. , genes situated in transposon-rich regions or assembly gaps in the outgroup species,  [40] ] were also discarded. We further divided the remaining genes postdating the vertebrate split in GenTree into 6 clades with at least 200 genes for each group (age group 9-14, Fig. 1b ). For genes assigned to vertebrate common ancestors (the oldest age group in GenTree), we divided them into group 1-8 based on the phylostratigraphy analysis (Additional file 1: Fig. S1a ). In addition, for synteny-defined PSGs, we also analyzed their age distribution in the phylostratigraphy dataset  [23]  in order to test whether PSGs potentially involved in the cell cycle represent duplicates derived from gene families with UC genes as the founding members.
Note that our synteny-based dating strategy is conservative in terms of identification of young genes. That is, we previously performed extensive manual curation and found that we tend to assign genes to older ages and generated smaller young gene dataset compared to other synteny-based work in humans or flies  [40, 53]. For each PSG, we downloaded our previously generated origination mechanism information  [40]. In brief, we classified them as duplicate (with a paralog available) and potential de novo genes (without a paralog). The parental copy is defined as the most similar paralog with an age older than the focal PSG. The K a /K s values were also from the previous work  [40].
TCGA multi-omics data retrieval We downloaded multiple datasets generated by the TCGA project with standard pipelines  [46]. Specifically, we retrieved paired-end RNA-seq data from the Genomic Data Commons (GDC) data portal  [120]  to increase the chance of unique mapping and thus the accuracy of paralog quantification. We focused on primary tumor sample from 13 solid cancer types [marked by TCGA barcodes "01", https:// docs. gdc. cancer. gov/ Encyc loped ia/ pages/ TCGA_ Barco de] and required tumor sample number not lower than 150 and normal sample number not lower than 15. We downloaded DNA methylation and SCNA data from the Broad Firehose data browser (https:// gdac. broad insti tute. org/). We retrieved progression-free interval (PFI) information from  [121] , which was presumably more updated than those originally generated by TCGA.
TCGA and normal adult tissue RNA-seq transcriptome data analysis We quantified gene expression via the mapping-free software kallisto version (v) 0. 43. 1 which shows rapid speeds and decent performance on highly similar paralogs  [97, 124, 125]. Note that almost all (>99%) TCGA RNA-seq samples were from different patients with one sample from one individual. For the remaining cases, more than one sample could be from one patient and we randomly selected one sample for downstream analyses to keep consistency with other samples. Since our analyses depended on Ensembl v73  [126]  based gene age data compiled in the GenTree database  [40] , we herein used GENCODE gene annotation v18, which corresponds to Ensembl v73. We summed the estimated counts and transcripts per million mapped (TPM) values across transcripts of the same gene.
We repeated the analyses with only the high purity samples (Additional file 1: Fig. S2a-c ). Note that we used upregulation to enrich PSGs more likely involved in tumors since genes beneficial for tumors (e. g. , UC genes) tend to be upregulated in tumors. In contrast, other PSGs especially those downregulated PSGs may repress tumors as did by genes emerging in early metazoan evolution (EM genes). In addition, we downloaded RNA-seq data from Human Protein Atlas (HPA), which spanned 26 adult tissues with at least three replicates for each tissue. Based on HPA transcriptome data across 26 tissues, we adopted the τ index to measure tissue bias of gene expression profiles  [128]  and took 0. 8 as the cutoff to define tissue-biased genes, since 0. 8 represents a border value in the distribution of τ values.
Filtering protein-coding gene models To further increase the quality of the GENCODE v18 or Ensembl v73 coding gene annotation, we implemented multiple filters: (1) we retained gene models with the biotype tag "protein coding" and excluded transcripts shorter than 150 bp; (2) we removed genes sharing a sequence identity higher than 97. 5% with the closely related paralog, since such a cutoff ensures an average of two or more unique nucleotides for each gene in TCGA paired-end RNA-seq data (length of ~50 bp); (3) based on HPA data, we removed genes with low expression [TPM < 0. 2  [129] ] across 26 adult tissues; and (4) we further discarded unexpressed genes [those with no reads mapped in more than 20% of samples within a cancer type  [130] ] across all 13 cancer types. With these filters, 18195 genes were retained for subsequent analyses.