dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast to previous methods which analyzed changes at a unique anatomical scale (i. e. , cortical cortex, whole brain structures, hippocampus, or hippocampal subfields,. ), we proposed combining whole brain structures parcellation with a representation of hippocampal subfields. This combination has resulted in performances competitive with state-of-the-art methods. Finally, the comparison with state-of-the-art approaches using similar ADNI1 subset has shown that our multi-scale graph-based grading method obtains competitive results for both AD detection and prediction. The high performances of the methods proposed in  [7]  and  [44]  have to be moderated. First, it is unclear how  [7]  obtains high accuracy for AD detection since misdiagnosis of AD in ADNI dataset is around 4%. Second, the excellent result of  [44]  for AD prediction mainly takes advantage of unbalanced results, after the accuracy is corrected (see Table  5 ), this approach obtains lower prediction performance.
7. AcknowledgementThis work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie,Alzheimer's Biogen; Bristol-Myes Squibb Company; CereSpir, Inc.
Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List. pdf. Preprint submitted to NeuroImageJuly 16,.
We explored the Protein Data Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions and compared it to the supposedly binding-induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software.
11, 12 Several software systems have tried to implement accurate ssRNA-protein docking, including: 1. ATTRACT  13  uses a fragment-based approach, with the need of some knowledge about some protein-RNA contacts. 2. RNP-denovo,  14  based on Rosetta,  15  performs folding and docking of the RNA on the protein simultaneously, but requiring the exact coordinates of few nucleotides. 3. RNA-lim  16  models a rough coarse-grained RNA structure (one non-oriented bead per nucleotide) restrained by a set of known binding sites on the protein surface. While all these methods advertise a prediction precision from 2 to 10 Å of RMSD between predicted and experimental ssRNA location, none of them was tested yet on ssDNA-protein docking. To our knowledge, no benchmark is available for ssDNA-protein docking.
Moreover, while it is possible to query ssDNA-protein complexes with the Nucleic Acid Database  17  (NDB), it seems to find none after 2013, thus limiting the scope of a NDB-derived benchmark. In turn, docking algorithms need experimental ground truth to validate and compare methods. Thus, docking benchmarks based on experimentally resolved structures of complexes are needed. Such docking benchmarks exist for protein-protein,  18  membrane proteinprotein,  19  protein-RNA,  20, 21  and dsDNA-protein  22  complexes. Also, while some works studied ssDNA-protein interactions from few structures in the Protein Data Bank (PDB),  23  none seems to be as exhaustive as possible, with a primary goal to improve ssDNA-protein docking. Here, we present an ssDNA-protein docking benchmark based on structures extracted from the PDB that contains 91 sequenceidentity groups of bound-unbound protein chains, created to evaluate ssDNA-protein docking.
Due to the high flexibility of unstructured ssNA, it is not relevant to use their unbound forms in the context of macromolecular docking. This is also the reason why the docking programs presented earlier do not require a known unbound ssRNA structure. In consequence, the main aim of this dataset is to provide bound and unbound structures of the proteins but only bound structures of ssDNA, from ssDNA-protein complexes. When possible, we provide several structures for both bound and unbound states, allowing to differentiate binding-specific from binding-independent conformational changes. Docking experiments were performed to show a use case for this benchmark. It underlines the relevance of using several bound structures as ground truth and to tolerate a minimum conformational deviation from ground truth when evaluating docking results.
| RCSB PDB query We identified the structures containing simultaneously DNA (nonhybrid) and proteins by querying the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB)  24  using their search (https:// search. rcsb. org) and data (https://data. rcsb. org) application programming interface (APIs). Another query was performed to extract the PDB ID of all structures containing only proteins without DNA. These two PDB ID lists were compared to the weekly 100% sequence identity clustering ("clusterNumber100") of protein chains in the PDB (ftp://resources. rcsb. org/sequence/clusters/) to extract identity groups containing chains being part of both DNA-protein and protein-only structures.
| Structure alignment, processing, and identification of interacting ssDNA For each structure containing DNA and protein, the asymmetric units and all biological assemblies (if any) were downloaded from the PDB. 24 We only kept "ATOM" records describing atoms belonging to proteins or NAs. Nucleic residues involved in double strands are located with 3DNA (script find_pair  25  ) in the asymmetric unit and in each biological assembly. A DNA residue is considered singlestranded only if it is not found as double-stranded in any of these F I G U R E 1 Benchmark building summary structures. Biological assemblies allow the identification of cases where a double strand is formed by the repetition of the asymmetric unit (such as PDB ID:3HZI), while the asymmetric unit eases the processing if the assembly is constituted by the repetition of chains having the same identifier (also like in PDB ID:3HZI).
Then, VMDpython (Humphrey et al. , 1996, https://github. com/Eigenstate/vmdpython) was used to compute distances between ssDNA nucleotides and protein residues; ssDNA nucleotides are bound if they are found at less than 5 Å from any protein residue. This bound ssDNA list is processed to only keep the protein chains interacting with a DNA chain of at least four consecutive bound and single-stranded nucleotides. For multi-framed asymmetric units (often encountered with NMR models), only the first frame is used. In this case, we assumed a limited variation of conformation between frames, with no impact on DNA 2D state. Bound protein chains were then subgrouped by interaction interface: interaction interfaces with ssDNA are computed with VMD-python; two bound chains in a sequence identity cluster are grouped if they share at least one interacting residues.
This can lead to one sequence cluster being split into several if distinct interfaces are found. From the RCSB PDB sequence clustering, we retrieved protein unbound chains belonging to structures without DNA that have 100% of sequence identity with previously identified bound chains. Unbound chains were compared to their bound counterpart to identify any structure with missing residues at the interacting interface. If one is found, it is removed from the unbound chains list. In each sequence identity cluster, bound and unbound chains were rigidly superimposed and global RMSD computed with FATCAT 2. 0 standalone software. 25 FATCAT was used because of its ability to superimpose structures with some minor differences between sequences (like missing loop or mutation).
| Structures superposition, RMSD calculation, and clustering The final benchmark is reported in Table  S1  (and RMSD tables in Table  S2 ), in which chains with an RMSD lower than 0. 2 Å are grouped under a single representative chain. This was done to limit the bias from structures containing several times the same chains. These nonredundant datasets are further analyzed. The redundant benchmark and RMSD tables can be found as Tables  S3  and S4 , respectively. The sequence identity between clusters is reported in Table  S5. Interacting ssDNA fragments are reported in Table  S6. We called "fragment" each interacting ssDNA region, while "sequence" refers to unique ssDNA sequence among all clusters. Thus, a DNA chain can have several ssDNA fragments if separated by non-interacting or double-stranded DNA, and several fragments can have the same sequence.
| Docking and docking evaluation Docking experiments were performed using ATTRACT  26  without explicit restraints. ATTRACT docking being rigid, docking was performed using a library of multiple tri-nucleotides (reported in Tables  2  and 3 ) built using protNAff (https://github. com/isaureCdB/ProtNAff). Tri-nucleotides libraries were used as an exhaustive (at 1 Å of heavyatoms RMSD) ensemble of ligand conformations. For each docking, a random selection of 1 000 000 initial poses was performed, a pose corresponding to one fragment conformation from the library at one random starting position on sphere of 35 Å radius around the protein with one random orientation. The fragments position was optimized by gradient descent minimization of the protein-DNA energy in ATTRACT coarse-grained force field.
35 sides, 30 fragments (corresponding to 24 different sequences) contain non-canonical nucleotides. While such fragments may not be suitable for general purpose docking methods, we keep them to leave the choice to the end user, as some non-canonical residues have a canonical counterpart. Finally, 116 interacting fragments (56 sequences) have the minimum required size that we chose for our dataset (4 nts), and 258 (111 sequences) are 6 nts or shorter. Overall, the mean size is 5. 6 and 5. 5 nts for sequences and fragments, respectively. Only 22 sequences (40 fragments) have more than 7 nts, which may limit the interest of this benchmark for long sequence docking.
| Docking To demonstrate a use case, we performed two docking experiments of three ssDNA fragments on two unbound protein structures from our benchmark, using ATTRACT  26  docking software. Results are summarized in Tables  2  and 3. Clusters #4 and 8. 1 were chosen, with four bound references used. ATTRACT was chosen for its common use in literature and its ability to process DNA and to perform ensemble docking. In role in DNA metabolism, and their interactions with proteins are potential targets for therapeutic inhibitors. For instance, the transmission of anti-microbial resistance genes among bacteria could be fought by targeting the excision or transportation of integrative conjugative elements (ICEs) ssDNA. 5 Such projects require the knowledge of the 3D structure of such assemblies for rational drug design.
The low number of retrieved sequences (148) may reflect the general low interest for ssDNA-bound protein structures, and a dataset like the one presented here is an important step in their studies. To our knowledge, this work is the first attempt to aggregate as exhaustively as possible the ssDNA-protein structures available in the PDB. With 98 groups of multi-conformational bound/unbound proteins, this benchmark is an essential first step to understand the mechanisms involved in ssDNA-protein interactions and develop mature ssDNA docking protocols.
RESULTS We performed whole-genome resequencing of 180 specimens of cichlid fishes from Lake Tanganyika and its surroundings (132 A. burtoni and 24 of each of C. horei and H. stappersii) and included available sequence data of 24 individuals of P. philander  (26). We Downloaded from https://www. science. org at IFREMER-Centre de Documentation de la mer on November 25, 2021 examined six A. burtoni lake-stream population pairs and one population pair for each of the three additional species (C. horei, H. stappersii, and P. philander), totaling nine lake-stream population pairs (see Materials and Methods, Fig. 1 , fig. S1 , and tables S1 and S2). Each population pair consisted of one lake and one stream population, whereby the lake population was sampled from a lake habitat close to the stream's estuary.
Overall, the directions of phenotypic ( P ) and genetic ( G_outlier ) differentiations were significantly positively correlated (Mantel test, P = 0. 0075; Pearson correlation coefficient, R 2 = 0. 27; Fig. 5C ), whereas their magnitudes were not (Mantel test L P and L G_outlier , P = 0. 26; linear regression model L P and L G_outlier , P = 0. 55; Fig. 5, D  and E ). In contrast, none of the non-outlier genetic vectors were correlated to phenotypic vectors ( P versus  G and L P versus L G : Mantel tests, P = 0. 34; P = 0. 35; L P and L G : linear regression, P = 0. 93; fig. S5, A to C ). It has recently been proposed to examine the vectors of divergence using a multivariate approach as a complementary set of analyses to investigate (non-)parallelism and convergence  (34). For each dataset (phenotype, genotype outlier, and genotype non-outlier), we thus calculated the eigen decomposition of the respective C matrix (m lake-stream systems × n traits).
Parallelism is higher when ancestral populations are more similar We then examined whether the degree of similarity in the ancestral lake populations correlates with the extent of morphological and genetic parallelisms. We found a significant positive correlation in both datasets, with a stronger effect in the genetic compared to the morphological data (Mantel tests:  G_outlier versus F ST , P = 0. 0039;  P versus D M , P = 0. 045; R 2 = 0. 46 and 0. 17, respectively) (Fig. 5,  H  and K ). This indicates that phenotypic and genetic parallelisms are higher when ancestral populations are phenotypically and genetically more similar. The genetic non-outliers (that is, the neutral markers) did not reveal such a correlation (Mantel test:  G versus F ST , P = 0. 48; fig. S5F ). We also found that the proportion of standing genetic variation between ancestral populations was negatively correlated with  P and  G_outlier (Mantel tests, P = 0.
6A ). For example, the population pair from the Kalambo River involving the upstream population (A. burtoni Kalambo 2, D M = 5. 6) was morphologically more distinct than the between-species comparisons in the Rusizi River (A. burtoni versus H. stappersii, D M = 4. 1). In agreement with the levels of genomic differentiation (d XY = 3. 6 × 10 -3 ), morphological differentiation was high in the P. philander population pair (D M = 6. 0). We then calculated the among-lineage covariance matrices of mean trait values D river and D lake for each dataset (phenotype, genotype outlier, and genotype non-outlier) to investigate the levels of convergence and divergence in lake-stream differentiation. Following the definition of De Lisle and Bolnick  (34) , less variance in D river compared to D lake is indicative of convergent evolution, while more variance in D river compared to D lake is indicative of divergent evolution.
We found divergence at the genomic level in both "outlier" and "non-outlier" datasets since trace subtraction [tr(D river ) -tr(D lake )] was positive in both cases. In contrast, we found convergence at the phenotypic level since the result of trace subtraction was negative. In this latter case, two traits encompassed more than 99% of the total variance, namely, centroid size (66%) and total length (33%). This might be explained by the fact that the different traits and landmarks have different units accounting differently for the total amount of variance (e. g. , variation in total length is measured in millimeters that ranges from 42 to 73, while Table  2. d XY values between divergent populations and species used in this study. d XY values are presented as a 10 -3 factor for readability.
We found that for both phenotypic and genomic outlier datasets, there was one significant axis of parallel evolutionary change, in which seven of nine lake-stream systems evolved in parallel. These seven lakestream systems were the same in the phenotypic and in the genomic outlier datasets. That is, all A. burtoni and C. horei lake-stream systems have a common major axis of phenotypic and genomic parallel evolution, while H. stappersii and P. philander evolved in an antiparallel manner with regard to the seven other lake-stream systems (Fig. 5, G  and J ). The traits underlying parallel phenotypic evolution were related to mouth position, eye size, and slenderness of the fish body, all of which have previously been suggested to contribute to adaptive divergence in A. burtoni  (20). We show here that these phenotypic traits evolved in parallel not only in all A.
Body shape convergence between sympatric species pairs Last, we investigated levels of convergence or divergence among the nine lake-stream population pairs. While we found divergence at the genomic levels (for both outlier and non-outlier datasets), we found convergence at the morphological level (Fig. 6B ). We found body shape convergence in both species pairs coinhabiting the same river systems, namely, A. burtoni and H. stappersii in the Rusizi River and A. burtoni and C. horei in the Lufubu River (Fig. 6B ) (also note that the sympatric population pairs show rather similar F ST values across species; Fig. 3 ). This highlights that local selection regimes constrain body shape evolution in cichlids. This has previously been shown in Midas cichlids (Amphilophus sp. ), where the body shapes of two syntopic species were more similar to one other than the body shape average of the first species from different localities  (53).
Filtered GATK and SAMtools datasets were then combined using bcftools isec. The final VCF file contained variants present in both datasets. Genotypes were then imputed and phased per scaffold using beagle (v. 4. 0). In total, the final VCF file contained 26,704,097 variants. Chomonomer (v. 1. 05; http://catchenlab. life. illinois. edu/chromonomer/) was used to place the 3555 M. zebra scaffolds in 22 linkage groups using two linkage maps  (57). For BayPass selection analyses and allele frequency calculations (see below), we excluded indels and nonbiallelic sites, resulting in a VCF file containing 20,343,366 SNPs.
Genetic structure and phylogenetic relationships Population genetic structure was examined using PCAs implemented in the smartPCA module of Eigensoft (v. 6. 1. 1). To reconstruct a whole-genome nuclear phylogeny, a sequence corresponding to the first haplotype of each scaffold was extracted using bcftools consensus --haplotype 1 of BCFtools v1. 5 (https://github. com/samtools/ bcftools). Individual whole-genome sequences were then concatenated and a maximum likelihood (ML) analysis was performed in RAxML (v. 8. 2. 11) using the GTRGAMMA sequence evolution model and 20 fast bootstrap replicates. The option -f a was used to report the best-scoring ML tree with branch lengths. KBC4, a putative A. burtoni individual sampled in the Rusizi River, did not cluster with other A. burtoni individuals in the phylogeny (labeled "hybrid" Downloaded from https://www.
Demographic modeling For each of the nine lake-stream population pairs, demographic simulations based on the joint site frequency spectrum (SFS) were performed to estimate the most likely model of population divergence and the best values of demographic parameters (effective population sizes, divergence times, and migration rates). Simulated SFS were obtained using diffusion approximation implemented in ∂a∂I (v. 1. 7. 0)  (59). A modified version of the program including additional predefined models and the calculation of the Akaike information criterion (AIC) for model selection was used for the simulations  (60). Eight demographic models of population divergence were tested (fig. S3A ): Bottle-Growth (BG), Strict Isolation (SI), Isolation with Migration (IM), Ancient Migration (AM), Secondary Contact (SC), and versions of these models including two categories of migration rates (IM2M, AM2M, and SC2M).
Regions of differentiation For each lake-stream system, genome-wide F ST (Hudson's estimator of F ST ), | lake - stream | (absolute value of the difference in nucleotide diversity between the lake and the river populations), and d XY (absolute divergence) were calculated in 10-kb nonoverlapping sliding windows using evo (https://github. com/millanek/evo). We defined as window of differentiation each window that was contained in the overlap of the top 5% values of these three metrics. Adjacent differentiation windows and windows separated by 10 kb were then merged in differentiation regions. To test whether the differentiation regions of each system are affected by chromosome center-biased differentiation  (30) , each region was placed either in the "center" or in the "periphery" categories.
GO annotation and enrichment analyses To infer whether candidate genes in differentiation regions were enriched for a particular function, all genes included in differentiation regions of each lake-stream system were extracted. In addition, genes including overlapping SNPs between the southern A. burtoni, the northern A. burtoni, and H. stappersii core outliers were reported, as well as genes including overlapping SNPs between A. burtoni core outliers and SNPs significantly correlated with lake versus stream environment and morphology. All candidate genes were blasted (blastx) against the NR database (version 12. 10. 2017) using BLAST+ v. 2. 6. 0, and the first 50 hits were reported. To obtain a reference gene set, all M. zebra genes were blasted against NR in the same way. GO and InterProScan annotations were retrieved from Blast2GO PRO (v.
Morphometric analyses Geometric morphometrics was used to compare adult body shape between populations. Three juvenile individuals of P. philander from Mbulu creek whose genomes had been sequenced were excluded from the morphological analyses. In total, the photographs of 468 individuals (table  S2 ) were used for geometric morphometric analyses (289 A. burtoni, 81 H. stappersii, 67 C. horei, and 31 P. philander). Using tpsDig2 (v. 2. 26), we placed 17 homologous landmarks (fig. S2A ) on the lateral image of each fish. The tps file with x and y coordinates was used as an input for the program MorphoJ (v. 1. 06d) and superimposed with a Procrustes generalized least squares fit algorithm to remove all nonshape variation (i. e. , size, position, and orientation). In addition, the data were corrected for allometric size effects using the residuals of the regression of shape on centroid size for further analyses.
We calculated nine L P (phenotype), nine L G (genotype), nine L G_outlier values, all lake-stream pairwise comparisons L P ,  P , L G ,  G, L G_outlier , and  G_outlier. We used Mantel tests (mantel. test function in the ape R package v. 5. 3; 9999 permutations) to test the correlation between  P and  G / G_outlier , L P and L G /L G_outlier , and linear regression models (lm function in R) to test the correlation between L P and L G / L G_outlier.