dataesr/scientific-paragraphs-categorization huggingface dataset:
Data preprocessing All image processing and statistical analysis scripts used in this study are available at https://github. com/sheyma/stroke_preprop. T1-weighted structural images were preprocessed using FreeSurfer's recon-all pipeline (v6. 0. 0,  (Dale et al. , 1999) ). The pipeline generated segmentations for grey matter, white matter and cerebrospinal fluid. Individual grey matter masks were registered to standard MNI space (3 mm 3 ). Preprocessing of functional images included: i) removal of the first 5 EPI volumes to avoid signal saturation, ii) slice timing and motion correction (Nipype v0. 14. 0,  (Gorgolewski et al. , 2011; Roche, 2011) ), iii) CompCor denoising approach for time series at the voxel-level (Nilearn v0. 4. 0,  (Behzadi et al. , 2007) ), iv) temporal normalization, v) band-pass filtering in the range of 0.
Lesion delineation Lesions were manually delineated by identifying areas of localized hyperintensity on day 0 DWI images using the ITK-SNAP software (v3. 4. 0,  (Yushkevich et al. , 2006) ). Delineations were done by the first author (Ş. B. ) and checked by a researcher with 6 years' experience in stroke imaging (A. K. ). All lesion masks were normalized to MNI152 (3 mm 3 ) space (ANTs, nearest-neighbor interpolation). Individual lesion masks were smoothed in the atlas space using FSL's dilation tool with 3 × 3 × 3 kernel, extending the mask by one voxel-size (v5. 0. 9,  (Jenkinson et al. , 2012) ).
Mapping stroke lesions onto connectivity gradients To map heterogeneous lesions across our sample of patients, individualized lesion masks were delineated and projected onto a standard MNI brain (Fig. 3A ), as well as onto the first three connectivity gradients (Fig. 3B ). Lesions were heterogeneous in both location and size (mean volume = 4. 11 cm 3 , SD = 2. 80 cm 3 ), and distributed in subcortical (n = 13), cortical (n = 14), and brainstem (n = 1) regions. For further details on individual lesion location and affected vascular territories, see Supplementary Table  1. Projecting lesion locations onto the connectivity gradients enabled us to assess which portions of connectivity space were affected by the stroke. The template connectivity space was based on a decomposition of voxelwise functional connectivity data from healthy controls.
Funding sources This work was supported by the  German Federal Ministry of Education and Research  via  the grant center for Stroke Research Berlin  ( 01EO0801  and  01EO01301 ), the  European Union   Seventh Framework Program  [FP7/2007-2013] under grant agreement no. 278276  ( WAKE-UP ) for J. B. F, and the  Einstein Foundation Berlin  (Grant No. EDP-2016-318 ) for S. O. C.
General genomic features of D. aquatica 174/2 type strain The D. aquatica 174/2 T genome consists in a single circular chromosome of 4,501,560 base pairs in size, with a GC content of 54. 6%. The sequence was submitted to European Nucleotide Archive (accession GCA_900095885). This genome contains a total of 4,202 genes including 4,080 protein-coding DNA sequences (CDS), 22 ribosomal RNA-coding genes organized into seven operons, 76 tRNA-coding genes, and 23 non-coding RNA genes identified by sequence similarity with known functional RNAs entries in the RFAM database  (Daub et al. , 2015)  (Figure  1 ). These features are typical of Dickeya species (Supplementary Table  S2 ). The origin of chromosome replication (oriC position 1-8 bp) was predicted between mnmG/gidA and mioC as observed in Escherichia coli  (Wolanski et al.
Deciphering the early evolution of Dickeya The massive release of Dickeya genomic sequences (7 complete genomes and 41 draft genomes) in public databases (Supplementary Table  S3 ), in addition to the complete genome of D. aquatica 174/2 T reported in this study, provided an interesting resource to explore the evolutionary history of this genus. We investigated the phylogeny of Dickeya using 51 ribosomal proteins (rprots), as these were shown to be well suited to study the systematics of prokaryotes, especially Proteobacteria  (Yutin et al. , 2012; Ramulu et al. , 2014) , on the one hand, and 1,341 core protein families (core-pf), on the other hand. The maximum likelihood rprots tree, rooted with Pectobacterium (Pectobacteriaceae) and Serratia (Yersiniaceae), recovered the monophyly of Dickeya (Bootstrap Value (BV) = 100%, Supplementary Figure  S1 ).
2% vs 52. 6 -56. 9% in Dickeya) (Supplementary Table  S3 ), and the lower number of protein families shared between ATCC 39006 and Dickeya (60. 5%) compared to between Dickeya species (64. 7% -89. 6%, Supplementary Table  S5 ), suggested that strain ATCC 39006 does not belong to Dickeya and represents rather a close but distinct lineage, possibly a new genus, that we propose to call Prodigiosinella, with strain ATCC 39006 being reclassified as Prodigiosinella confusarubida. By using leBIBI QBPP phylogenetic SSU rRNA-based positioning tool  (Flandrois et al. , 2015) , we detected two Erwinia sp. Strains: MK01 (SSU accession number at the NCBI: AY690711. 1) and MK09 (SSU accession number at the NCBI: AY690717. 1), isolated from the rhizosphere of Phragmites communis, that were very closely related to ATCC 39006 and could belong to P.
e. being present in a single proteome) (Figure  7A  and Supplementary Table  S8 ). The number of strain specific protein families ranged from 336 in D. aquatica 174/2 T and 304 in D. gs. Poaceaephila, down to zero in Dickeya solani strains MK16, PPO9134 and RNS0773B (Supplementary Table  S8 ). To determine the origin of these strain specific protein families, we used them as seeds to query with BLASTP (e-value cut-off 10 -4 ) a local database gathering 3,104 complete prokaryotic proteomes, including the 49 Dickeya and P. confusarubida ATCC 39006 proteomes. Results indicated that 1,051 (30. 4%) Dickeya strain specific protein families displayed best hit in one of the other 48 Dickeya strains, meaning that those sequences were wrongly considered as strain specific because they did not satisfy the coverage and identity parameters used to delineate the protein families.
This was not surprising because some protein families could be fast-evolving, meaning that applying uniform parameters can fail to delineate correctly these protein families and lead to an overestimation of the strain specific protein families. In contrast, 1,625 (47. 1%) Dickeya strain specific protein families displayed best hits in non-Dickeya proteomes, meaning that the corresponding genes were likely acquired by HGT from non-Dickeya donors. The taxonomic distribution of the corresponding sequences pinpointed Proteobacteria (especially Enterobacteriales), and to a less extent Firmicutes as major donors (Supplementary Table  S8 B-C ). Yet, the contribution of these two phyla is likely overestimated due to their overrepresentation in sequence databases compared to other lineages. Accordingly, these results should be interpreted as general trends but additional data would be required to precisely estimate the real contribution of Firmicutes and Proteobacteria.
However, their cell walls are thin and cannot be distinguished as primary or secondary cell walls. Furthermore, they lack xyloglucans, which are common in most land plants  (Sarkar et al. , 2009). Intriguingly, D. aquatica lacks the xylanases and xylose degradation pathways and we hypothetize that this could reflect an evolutionary adaptation to charophyte hosts. The comparison of D. aquatica 174/2 and Dickeya strain proteomes available in public databases showed that the Dickeya genus displays a remarkable diversity featuring many unique protein families and emphasizing that our knowledge of this genus is still limited. The real size of the panproteome is an open question encouraging further exploratory studies on these bacteria, the success of which will depend on the collection of new strains isolated from various environments and the sequencing of the genomes of newly identified representatives of the genus.
sfu. ca/islandviewer) (Dhillon et al. , 2015)  for the GI regions, CRISPRDetect (http://brownlabtools. otago. ac. nz/CRISPRDetect/predict_crispr_array. html) (Biswas et al. , 2016)  for CRISPR arrays, whereas putative prophage sequences were identified by PHAST and PHASTER analysis (http://phast. wishartlab. com/) (Zhou et al. , 2011b; Arndt et al. , 2016). The genome sequence has been submitted to EMBL database under accession number GCA_900095885 (http://www. ebi. ac. uk/).
Proteome database construction We built a local database (DickeyaDB) gathering the proteomes of Serratia sp. ATCC 39006, 48 Dickeya available at the NCBI (by january 2017), and D. aquatica type strain 174/2 (Supplementary Table  S3 ). A second database (prokaDB) containing 3,104 proteomes of prokaryotes, including the 50 proteomes of DickeyaDB, was also built.
Assembly of Dickeya and Serratia ATCC 39006 protein families Dickeya and Serratia ATCC 39006 protein families were assembled with SILIX version 1. 2. 9  (Miele et al. , 2011). More precisely, pairwise comparisons of protein sequences contained in DickeyaDB were performed using the BLASTP program version 2. 2. 26 with default parameters  (Altschul et al. , 1997). Proteins in a pair providing HSP (High-scoring Segment Pairs) with identity over 60% and covering at least 80% of the protein lengths were gathered in the same family. This led to the assembly of 12,660 protein families, among which 1,493 were present in at least one copy in all DickeyaDB proteomes. In contrast, considering the 49 Dickeya proteomes without taking into account Serratia ATCC 39006 led to the assembly of 11,566 protein families, among which 1,604 were present at least in one copy in all Dickeya and Serratia ATCC 39006 proteomes, and 1,420 in exactly one copy.
Inferrence of reference phylogenies of Dickeya Reference phylogenies of Dickeya were inferred using ribosomal proteins (rprots) on the one hand and core protein families (core-pf) on the other hand. The rprots phylogenetic tree was rooted with sequences from Serratia ATCC 39006, together with three Pectobacterium species (Pectobacterium carotovorum PC1, Pectobacterium atrosepticum SCRI1043, and Pectobacterium wasabia WPP163) and five additional Serratia species (Serratia marcescens FGI94, Serratia fonticola DSMZ4576, Serratia liquefaciens ATCC27592, Serratia proteamaculans 568, and Serratia plymuthica AS9), while the core-pf phylogenetic tree was rooted with Serratia ATCC 39006 (see results). Rprots sequences were extracted from the DickeyaDB using the engine of the riboDB database  (Jauffrit et al.
1. 0 program  (Sievers et al. , 2011)  and trimmed using GBLOCKS  (Castresana, 2000)  with parameters set to a minimal trimming. The trimmed multiple alignments corresponding to 1,341 core-pf on the one hand and the 51 rprots on the other hand have been combined using Seaview-4. 5. 4  (Gouy et al. , 2010)  to build the core-pf and the rprots supermatrices containing 414,696 and 6,295 amino acid positions, respectively. Maximum likelihood phylogenies of these supermatrices have been inferred with IQ-TREE-1. 5. 3  (Nguyen et al. , 2015)  with a C60 profile mixture of the Le and Gascuel evolutionary model  (Le and Gascuel 2008 ) and a gamma distribution with four site categories (G4) to model the heterogeneity of evolutionary rates across sites, as proposed by the model testing tool  (Kalyaanamoorthy et al.
Phylogenetic analysis of single markers 947 SSU rRNA and 125 LSU rRNA complete sequences from Dickeya, Serratia (including strain ATCC 39006), and Pectobacterium available in public databases were retrieved and aligned with MAFFT v7. 222. The resulting multiple alignments were trimmed using BMGE-1. 1 (default parameters). The phylogeny of the 947 SSU rRNA sequences was inferred using FastTree-2. 1. 9 (Price et al. , 2010) , with the GTR + gamma + cat 4 model, while the LSU rRNA tree was inferred with IQ-TREE with the TIM3+F+I+G4 model according to the BIC criterion, as suggested by the propose model tool available in IQ-TREE. Homologues of proteins of interest were identified in the prokaDB with BLASTP. The first 75 HSP hits with evalue smaller than 10 -4 were kept. The retrieved sequences, together with the seed were aligned using MAFFT v7.
Identification of the D. fangzhongdai phylogenetic cluster To determine whether some sequenced strains are related to D. fangzhongdai, we used all the D. fangzhongdai genes available in public databases SSU-rDNA (KT992690. 1), dnaX (KT992713. 1), fusA (KT992697. 1), purA (KT992705. 1), recA (KT992693. 1), gapA (KT992701. 1) and rplB (KT992709. 1) genes, we extracted the corresponding genes from the DickeyaDB database and then used these sequences as input for phylogenetic analyses using leBIBI QBPP phylogenetic positioning tool  (Flandrois et al. , 2015). We found that the strains B16, MK7 and S1 were systematically affiliated with D. fangzhongdai.
Acknowledgements This work was supported by the  Investissement d'Avenir grant  ( ANR-10-BINF-01-01 ), by the  ANR   Combicontrol grant  ( ANR-15-CE21-0003-01 ), by a grant from the  FR BioEnviS  and using the computing facilities of the  Computing Cluster of the LBBE/PRABI. We would like to thank  Georgi Muskhelishvili  for critical reading the manuscript and  Frederic Jauffrit  as well as  Mailys Dumet  for providing the extraction of ribosomal proteins.
Introduction Insights from Whole Genome Sequencing (WGS) provided a classification of the Mycobacterium tuberculosis complex (MTBC) into seven main human-adapted lineages (Lineages 1-7) and has demonstrated a larger genetic diversity than expected  (Coscolla and Gagneux, 2014). PolyTB  (Coll et al. , 2014b) , a WGS web-database, is a powerful tool to investigate deep phylogenetic correlation among MTBC lineages based on 1,627 M. tuberculosis isolates, containing 74,039 (63% non-synonymous) high quality SNPs  (Coll et al. , 2014a). These markers are linked to spoligotypes, among which 62 allow to define a barcode typing system. Spoligotyping patterns, based on CRISPR-locus (Direct Repeat region) polymorphism, has previously revealed strong phylogeographical specificity  (Brudey et al. , 2006).
The latest database release (SITVIT2) contains 111,635 isolates from 169 countries with a total of 9,658 patterns  (Couvin et al. , 2018). The East-African-Indian (EAI) lineage was defined by a combination of characteristic spoligotype and VNTR patterns and was later encompassed within Lineage 1 (L1)  (Sola et al. , 2001) , as it is considered an "ancient" lineage, phylogeographically restricted to East-Africa, South-Asia, South-East-Asia and the Philippines  (Palittapongarnpim et al. , 2018). L1 is likely to be among the most genetically diverse lineages and some of its sublineages have been associated to spoligotyping-patterns as shown in TB-MINER and SITVIT2, whereas others are not monophyletic  (Azé et al. , 2015; Couvin et al. , 2018; Palittapongarnpim et al. , 2018). The issue of TB origin in Americas is a subject of interest for already many years.
Acknowledgements We are thankful to the  Brazilian program  " Ciência sem Fronteiras" (Science without  border) by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  for investment in this project. M. Jan van Gils ,  Luminex Corp. Austin , TX is acknowledged for technical support as well as the  Beamedex® SAS company (Orsay, France , for coupled-bead supply.
Funding sources This work was supported by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  [ 207071/2014-4 ]; the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES)  [ Finance Code 001 ], by the  Centre National de la Recherche Scientifique  through the financial support of the  Infection Genetics Emerging Pathogen Evolution (IGEPE)  research team and by the  Beamedex® SAS company.
Machine learning algorithm predicts nigrostriatal degeneration We performed an exploratory approach and aimed to distinguish relevant variables allowing accurate prediction of neurodegeneration (i. e. , to operate a feature selection). Overall, we investigated a large number of variables tapping on behavioral, histological, biochemical, transcriptional, and biophysical approaches (Fig. 3A ) applied to several brain areas (n = 40; Fig. 3B ), totalizing 180 variables measured for each individual (fig. S5A  for variable abbreviation nomenclature, table  S1  for exhaustive list of variables, and table S2 for all raw data). We first extracted from this dataset every variable that actually quantified neurodegeneration (i. e. , dopaminergic markers such as TH or dopamine transporter evaluated by immunohistochemistry), ending up with 163 variables per animal.
3C ) for LB-injected (Fig. 3D ) and noLB-injected (Fig. 3E ) animals independently. To avoid possible overfitting, we used several methods in combination. First, we performed cross-validation by splitting the dataset into two parts: a training and a testing set of data. Eighty percent of the data were randomly selected to train the networks (and in-dependently for each network), while the 20% remaining were used to evaluate the networks. Then, to evaluate the robustness of the quality of prediction for a given set, we repeated this cross-validation step 50 times for every set of three input variables (each network was trained and tested using a different partition of the dataset; total number of networks, 35,428,050). Last, we generated random data.
of 18 and used them as input for the MLP. As expected, performances were significantly lower compared to our actual dataset (fig. S5, B  and C ). Overall, this unique approach allowed us to rank input variables according to their explanatory power and therefore to extract the strongest predictors of neurodegeneration for each experimental group. Despite similar levels of nigrostriatal degeneration between LB-and noLB-injected animals (Fig. 2B ), the algorithm allowed us to identify differential variable sorting patterns (Fig. 3, D  and E ).
S8,  C  and D ). Here, overexpression of -syn did not trigger accumulation of Zn 2+ in the SNpc [despite inducing dopaminergic neurodegeneration  (27) ], suggesting that this phenomenon is specific to seeding experiment paradigms. This assertion could be confirmed in other seeding-based models such as striatal preformed fibril injection  (8, 10)  or more refined models using specific types of fibrils or oligomers  (28). Last, we analyzed a publicly available cortical proteomic database of healthy individual and patients with PD. We observed that several Zn 2+ transporters were elevated in the brains of patients with PD, thus suggesting a zinc dyshomeostasis in patients (fig. S8F ). Plasma membrane transporters such as the zinc transporter 1 (ZnT1), the Zrt-/Irt-like protein 6 (ZIP6), and the ZIP10 showed increased levels (fig.
DISCUSSION In the present study, we report that injection of distinct -syn assemblies derived from patients with PD in nonhuman primates leads to dopaminergic degeneration through discrete mechanisms. Applying a machine learning method, we gained insight into unique signatures of degeneration induced by injection of two distinct -syn pathogenic assemblies (i. e. , those contained in the LB and noLB fractions derived from idiopathic PD patients' brains). To do so, we built a large dataset with 180 variables obtained from behavioral, histological, biochemical, transcriptional, and biophysical approaches applied to several brain areas for each individual. By using a distributed MLP algorithm that we developed for the purpose of this study, we identified characteristics that give insight into the strongest predictors of neurodegeneration for each experimental group.
Instead of the classification algorithms (the algorithm learns to identify in which category a sample belongs) that were mostly used in recent applications of machine learning in biology  (43) , we chose, in this study, to predict continuous and biologically relevant variables using MLPs. Our choice was justified by the limited sample size that is often a constraint of experimental biology. Although it might have been possible to use other feature selection methods, the use of MLPs with a distributed architecture allowed us to avoid overfitting issues and to develop a method particularly well suited for low-sample size datasets  (15). As both LB-and noLB-injected monkeys displayed similar levels of degeneration, they were indistinguishable using that end point. Instead of using a clustering analysis or a classification method, hence making the a priori assumption that these groups were different, we preferred to submit the two experimental groups to the MLP independently.
This observation might suggest that Zn 2+ dyshomeostasis is a feature of disease not triggered in the context of human mutant -syn overexpression that is associated with fast progressing pathology  (27). Then, to expand our results to human pathology, we analyzed a publicly available proteomic dataset of human samples. According to that analysis, patients with PD displayed increased levels of plasma membrane Zn 2+ transporters, hence suggesting a Zn dyshomeostasis in patients. In the context of PD, Zn 2+ dyshomeostasis has been associated with autophagy/lysosomal dysfunction in the context of PARK9 mutations  (45). Further studies are now needed to fully unravel this connection. Together, our findings show that primate dopaminergic neurons are sensitive to both small, mostly soluble, -syn extracts and larger, aggregated, -syn extracts derived from patients with PD.
Access to data and machine learning code for replicability and further use by the community The entire raw dataset is made available to the readers (table  S2 ). Authors chose not to provide representative examples of each procedure for the sake of space and because the entire dataset is fully disclosed. Further information and requests, for examples, should be directed to and will be fulfilled by the corresponding contacts. Hyperlink to the machine learning code (http://doi. org/10. 5281/zenodo. 1240558) is provided (https://zenodo. org/record/1240558#. XC8pqy17Su4).
Immunoreactivity was visualized by chemiluminescence (GE Healthcare). The amount of the respective protein in each fraction was determined by the Image Studio Lite software after acquisition of chemiluminescent signals with a ChemiDoc imager (Bio-Rad). Profiles obtained by immunoblot were normalized and plotted with SEM using the Prism software. S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 13 of.
987] collected the data live on the two time points of the study: at 1 and 24 months after surgery. The observer was standing 1 m away from the outdoor cages. We focused on behavioral profiles rather than single items and used two repertoires: One reports the interaction with the environment, and one describes the position within the environment, according to published protocols  (52) (53) (54). We investigated the percentages of occurrence of each item with regard to the total number of scans to obtain mean behavioral and postural time budgets, body orientation, and location profiles.
The extent of the lesion in the striatum was quantified by optical density. Sections were scanned in an Epson Expression 10000XL high-resolution scanner, and images were used in ImageJ open-source software to compare the gray level in each region of interest, i. e. , caudate nucleus and putamen. TH-positive SNpc cells were counted by stereology blind with regard to the experimental condition using a Leica DM6000 B motorized microscope coupled with the Mercator software (ExploraNova, France). The SN was delineated for each slide, and probes for stereological counting were applied to the map obtained (size of probes was 100 × 80 m spaced by 600 × 400 m). Each TH-positive cell with its nucleus included in the probe was counted. The optical fractionator method was lastly used to estimate the total number of TH-positive cells in the SNpc of each monkey hemisphere.