dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA was extracted from root tissue using the QIAGEN RNeasy kit (Qiagen). mRNA was purified with oligo-dT beads (Invitrogen), and RNA-seq libraries made using the NEBNext Ultra Library Prep Kit (NEB). Libraries were sequenced the Illumina HiSeq 2500 v4 platform using 1 × 50 or 1 × 75 single end chemistry. RNAseq reads were aligned as described for the protoplast samples and DE genes were identified using DESeq2 67. Time-based network inference and AUPR validation. The time-based DFG network inference  48  predicted GRN was generated using the N-treatment timeseries data as described in Supplementary Methods. We used a pruning approach to filter this network for high-confidence edges  1. The validated TF-target edges from TARGET were used to perform an AUPR analysis and identify a precision threshold of 0.
32 (Fig. 6  and Table  2 ). This TF-target edge cut-off was chosen to minimize false positives, while recovering as many true positives as possible. The resulting pruned DFG inferred network was visualized (Supplementary Fig. 12 ) using Cytoscape  71. Precision, recall, and F-score were calculated for the edges in the pruned network to generate Supplementary Data 10. Precision ¼ True Positives= True Positives þ False Positives ð Þ : ð3Þ Recall ¼ True Positives= True Positives þ False Negatives ð Þ : ð4Þ F À score ¼ 2 Ã Precision Ã Recall ð Þ = Precision þ Recall ð Þ : ð5Þ Reporting summary. Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  Dr. Daniel Tranchina ,  Dr. Carol Huang , and  Dr. Manpreet Katari  for their input and discussions regarding various analyses, and  Dr. Laurie Leonelli  for her very critical reading of the manuscript. This work was supported by  NIH  Grant  GM032877  to G. M. C. ,  NIH NIGMS  Fellowship  F32GM116347  to M. D. B. , a  Plant Genomics Grant  from the  Zegar Family Foundation  ( A160051 ) to G. M. C. , and in part by the  Laboratoire International Associé (LIA-CoopNet ) funded by the  Centre National de Recherche Scientifique (CNRS)  to G. K. and G. M. C.
Data availability All raw sequencing data from this project have been deposited in the Gene Expression Omnibus (GEO) database accession GSE117857 and GSE128209. Data supporting the findings of this work are available within the paper and its Supplementary Information files. A reporting summary for this Article is available as a Supplementary Information file. The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. The source data underlying Figs. 3a, 3b, 5a, 5b, and 6, as well as Supplementary Figs. 2, 4b-d , 5, 6, 8, 9, 11a, and 11b are provided as a Source Data file.
Hard ticks are widely distributed across temperate regions, show strong variation in host associations, and are potential vectors of a diversity of medically important zoonoses, such as Lyme disease. to address unresolved issues with respect to the evolutionary relationships among certain species or genera, we produced novel RNA-Seq data sets for nine different Ixodes species. We combined this new data with 18 data sets obtained from public databases, both for Ixodes and non-Ixodes hard tick species, using soft ticks as an outgroup. We assembled transcriptomes (for 27 species in total), predicted coding sequences and identified single copy orthologues (SCO). Using Maximum-likelihood and Bayesian frameworks, we reconstructed a hard tick phylogeny for the nuclear genome. We also obtained a mitochondrial DnA-based phylogeny using published genome sequences and mitochondrial sequences derived from the new transcriptomes.
A recent study  26  was the first to propose a transcriptome-based phylogeny of ticks, but included only a limited number of species and focused on soft ticks. Overall, most RNA-Seq data concern Metastriata and soft ticks, whereas few species of Prostriata (I. scapularis, I. ricinus, I. persulcatus and I. holocyclus) have been sequenced for their transcriptomes. To obtain a broader and more robust phylogenetic scenario for tick evolution, there was thus a need to produce more transcriptomes for Ixodes species. In order to clarify the phylogenetic relationship among hard tick species, we produced new RNA-Seq data for nine species of the Ixodes genus -this list includes I. holocyclus for which another research group recently and independently sequenced a transcriptome  27. We also used previously published RNA-Seq data sets for other ticks, including Metastriata and soft tick species, for a total of 27 different species in our phylogenetic analysis.
hexagonus) and 517,072 contigs (I. scapularis), with an average of 30,425 contigs per species (Table  2 ). We predicted between 4613 peptides for I. hexagonus and 289,763 peptides for I. scapularis, with an average of 96,268 predicted peptides per species. A compression step reduced the number of predicted peptides by 61. 3% on average. Completeness of these reduced assemblies was assessed with the BUSCO approach. This metric ranged between 4. 86% for I. hexagonus and 77. 53% for I. uriae with an average completeness of 51. 56% (Supplementary Fig. S1 ). We stress that completeness metrics vary with the version of the BUSCO lineages data sets: we indeed have used the BUSCO Arthropod v1 data set (with 2675 genes) as it was more complete (and, we assumed, as it had more power to compare assemblies); with the more restrictive Arthropod v2 data set (1066 genes), completeness reached 95.
For example, we assume that a quality issue explains the very low completeness of the I. hexagonus data set. Identification of orthologous genes. The optimal clustering parameters with SiLix were an identity of 75% and an overlap of 75% (SCO100, Supplementary Fig. S3 ). Other parameters (partial overlap and minimum length) did not have an impact on the number of SCO100. These parameters were set to a partial overlap of at least 200 amino acid positions and an overlap superior to 90%. For these parameters, SiLiX predicted 30 SCO100 (single copy genes shared by all 27 species), 502 SCO75 (present at least in 75% of the species), and 952 SCO50 (present at least in 50% of the species) -Fig. 1.
With all methods, there was a clear separation within the Ixodes genus between the Australasian lineage on one side (represented by I. holocyclus and I. uriae) and a second clade comprising all other species. The distance between Australasian species and other Ixodes species (0. 270) was only slightly shorter than distances between the Metastriata and any Ixodes species (Table  3 ). Within the Metastriata, Haemaphysalis flava was found to be the sister group of all other Metastriata (supported in all analyses). We also found that Hyalomma excavatum was embedded in the Rhipicephalinae subfamily (represented here by two genera, Dermacentor and Rhipicephalus). While the different Rhipicephalus species formed a robust clade, the position of R. pulchellus was not robust among methods, nor among data sets.
We extended the same approach to nuclear genes, selecting the SCO100 data set (n = 30 single copy orthologues present in the 27 species) to check if this obervation also applied to the nuclear coding genes. This time, most of the comparisons were not significant, with the exception of comparisons including Rhipicephalus microplus, suggesting an acceleration of evolutionary rates restricted to one or a few species of the genus Rhipicephalus, but not in Metastriata as a whole. Finally, we compared evolutionary rates of the two species of Rhipicephalus that form the Boophilus subgenus with other species of Rhipicephalus: we found that mitochondrial genes and nuclear genes showed significantly more substitutions in Boophilus species (Supplementary Table  S4 ) -by factors ~1. 5 and 1. 8 for mitochondrial and nuclear genes, respectively.
After mRNA isolation using a polyA Magnetic Isolation Module, libraries were prepared using the TruSeq stranded mRNA preparation kit, and sequencing was performed on one lane of a Hiseq3000 at the Get-Plage platform (Toulouse) producing strand oriented read pairs (2 × 150 bp). We also included previously published sequences for three additional Ixodes species (I. ricinus, I. persulcatus and I. scapularis) as well as 15 non-Ixodes species of ticks in our analysis (Supplementary Table  S1 ): while sequences corresponding to different technologies have been published, we chose to analyze only RNA-Seq projects performed with the Illumina technology for homogeneity purposes. This type of data was also the most common and typically yield a high output and high sequence accuracy. The non-Ixodes species were chosen to reflect the diversity of Metastriata genera (12 species of five genera included) and the outgroup (three species of the Ornithodoros genus).
Reads quality check. Newly obtained sequences as well as reads obtained from NCBI (SRA division) were cleaned using the same automated approach. For all species, a first round of cleaning was performed using Trimmomatic-0. 36  59  : this step removed adapters and trimmed sequences by quality, with a minimum sequence length of 36 bp. Cleaned reads were checked for quality with FastQC. If necessary, a second round of cleaning by Trimmomatic was performed until no sign of adapters could be detected from the FastQC report. De novo transcriptome assembly. Resulting reads for each of the 27 species were de novo assembled with Trinity (v2. 2. 0)  60. For data sets with strand-oriented sequencing, the corresponding option was applied in the assembly process. Coding sequences were retrieved using Transdecoder.
longOrfs (3. 0. 1)  60. To suppress redundancy resulting from alternative splicing, we proceeded in two steps. First, coding sequences were clusterized by CD-HIT (v4. 6) with a 95% of identity threshold at the amino acid level  61  and only the corresponding contigs were kept. Then we extracted the longest contig by component of the Transcriptome-DeBruijn Graph (T-DBG) which further eliminated potential alternative transcripts. Several statistics were computed at each step of the pipeline to monitor the quality of our assembly and predicted gene sets. We measured completeness using BUSCO (v2 with Arthropod database v1, n = 2675 conserved elements)  62  as well as general assembly statistics (using homemade R scripts). These novel scripts are available at http://www. github. com/npchar/Transcriptome-Assembly-Statistics/.
The usual minimum criteria for satisfactory convergence are an effective size greater than 50 and a maximum discrepancy of 0. 3. Super-networks were constructed using a mixture of gene trees, based on a quartet approach as implemented in the SuperQ software  74. For the super-network approach, SCO gene trees were independently inferred with IQ-TREE  68  under the best model of substitution  70  with 100 classic non-parametric bootstraps. The best model among those tested was chosen according to the Bayesian Information Criterion (BIC). The resulting trees were divided in quartets (subtrees of 4 taxa) and assembled together with SuperQ 74 to produce a planar supernetwork with the Gurobi Optimizer. An advantage of this method is to scale input trees in order to keep information of branch lengths.
Supernetworks were reconstructed for the three different gene occupancy levels (100%, 75%, and 50%) for three data sets: (i) nucleotide level alignments including all codon positions, (ii) nucleotide level alignments including only the first and second codon positions, and (iii) amino-acid level alignments. Finally, genetic distance among among genuses were calculated: we used the patristic distance, defined as the sum of branch lengths between taxa and calculated with the R function distTips.
Mitochondrial genes-based phylogeny. A specific analysis was also performed on mitochondrial (mt) genes, in order to build a mtDNA-based phylogeny and use it as an additional phylogenetic data set. For each species included in our study, we checked for the presence of a published mitogenome and used it if available. For species with several mitogenomes, corresponding to different lineages, we used the accessions with highest identity to mt genes reconstructed from our transcriptome assemblies (for example the sequence KF197132 for I. ricinus, and KJ133594 for O. moubata). A list of the sequences used is given in Supplementary Table  S2 : this list includes four species not studied in the nuclear genes-based phylogeny (because no transcriptome was available for them), I. rubicundus, I. pavlovskyi, I.
We evaluated this phenomenon quantitatively by counting reads mapping to sequences of the different species in each library: we found that the level of cross-mapping was always less than 0. 1%, the corresponding assembled sequences being usually only partial. We thus performed a phylogenetic analysis at the amino acid level for nine mt sequences (atp6, cox1, cox2, cox3, cytb, nad1, nad2, nad4, nad5), amounting to 89% of the cumulated length of mitochondrial protein coding genes. For each of these genes, we obtained complete, or nearly complete sequences in all species (see Results). For each gene, ORF sequences were translated and aligned using ClustalW; the nine mt gene sequences were concatenated (forming a data set hereafter referred to as mtAA9, which comprises 3,188 amino acid positions).
Acknowledgements We thank  Olivier Lambert  and  Philippe Gourlay  from the  Centre Vétérinaire de la Faune Sauvage et des Ecosystèmes des Pays de la Loire (Oniris, Nantes, France ), for providing biological material (I. frontalis and I. hexagonus). We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées (Bioinfo Genotoul)  and to the  Bordeaux Bioinformatics Center (CbiB)  for providing support and computing resources. Funding for I. acuminatus was provided by the  ANR  grant  OSCAR  ( ANR-13-BSV7-0018 ) to O. P. Funding for the transcriptome sequencing of I. uriae was provided by the  ANR  grant  ESPEVEC  ( ANR-13-BSV7-0018 ) and  French Polar Institute  (IPEV, programme n°333 PARASITO-ARCTIQUE) to K. D. M. D. H. was funded by the  Marie Sklodowska-Curie Actions  ( EU-Horizon 2020 ,  Individual Global Fellowship , project n°  799609 ) and the  Fund for Scientific Research -Flanders (FWO).
The tract connected the ventral tegmental area and the prefrontal cortex and authors referred to it as the superolateral branch of the medial forebrain bundle. Other invasive therapies, such as cingulotomy and capsulotomy also aimed at disrupting connectivity from frontal regions by lesioning white matter bundles  28. It could recently be shown that such tract-or network-based concepts may be used to predict clinical improvements across DBS centers and surgeons for the case of Parkinson's Disease  29. Based on modern neuroimaging methods and high-resolution connectomic datasets, connectivity of DBS electrodes to specific cortical regions was associated with stronger therapeutic effects in various diseases treated with this surgical procedure  [29] [30] [31] [32] [33]. For the case of OCD, Baldermann and colleagues recently demonstrated that structural connectivity from DBS electrodes to medial and lateral prefrontal cortices were associated with stronger symptom alleviation  30.
This tract was then used to predict outcome in two completely independent test-cohorts of patients that underwent surgery in a third and fourth center (Madrid & London; Figure  4 , bottom). While the surgical target of the Madrid cohort was the Nucleus Accumbens, electrode placement is comparable to the ALIC / Cologne cohort (Figure  1 ). Here, improvements were taken for each contact pair during a three-month interval, leading to 32 data points (Figure  4 , bottom left, active contact pair color coded). In the London cohort, patients had received two electrodes to each target (four in total) and scores were summed up across targets. In both test-cohorts, stimulation overlap with the tract target significantly correlated with empirical improvement (Madrid: R = 0. 50 at p < 0. 001, London: R = 0.
Discussion We analyzed data from four cohorts of OCD patients with different DBS targets using a connectomic approach. Strikingly, the same optimal tract target emerged when separately analyzing either of a large ALIC-DBS and STN-DBS cohort, alone. Among other connections, this bundle connected the dorsal anterior cingulate and ventrolateral prefrontal cortices to the anteriomedial STN. When the tract was calculated on each cohort alone, it could be used to cross-predict clinical improvement in the other cohort. Furthermore, clinical outcomes in two independent test cohorts from a third and fourth center could be significantly predicted based on overlap with the tract. Finally, literature-based stimulation sites for OCD seemed to cluster close to the identified tract. Indeed, their spatial overlap with the tract correlated with reported clinical improvement across studies.
Based on databases of clinical improvements along affected symptom axes, a mix of networks that would be modulated to alleviate each patient's specific symptom could be identified. Such concepts are still mostly speculation but may be investigated by future studies. This said, we must emphasize that the present study investigated data on a group level and utilized connectivity from individuals without OCD. As mentioned by others in the very context, we could not agree more that surgical decision making for DBS should not be based on such aggregated normative data, alone  49. Further studies are required to determine whether individual patient connectivity or generic connectome data (or both) could assist with optimizations in surgical targeting or DBS programming by determining crossing sites of symptom networks for specific patients.
In DBS, the technique has been applied before and -as in the present study -has led to models that could be used to predict improvement in out-of-sample data  29, 30, 80. In addition to the practical advantage of being applicable in cases where patient-specific data is lacking, normative data also has the theoretical advantage of much better data quality. In the present case, a connectome dataset was derived from a high N of 985 subjects scanned under research conditions by one of the best methodological groups in the world  81. It may be logistically challenging to acquire data of such quality in a clinical routine setting (e. g. pre-operatively) in individual patients but will be feasible in specialized centers. Tractography based DBS targets pointed to coordinates that were sometimes >2 mm when repeating analyses on test-retest scans of the same subject  82.
The normalization strategy that was applied was found to automatically segment the STN as precisely as manual expert segmentations  85  and each step of the pipeline was carefully assessed and corrected if needed by a team with long-standing expertise in this area  86, 87. Besides, both post-operative CT (thirty-three patients) and post-operative MRI (seven-teen patients) were used for electrode localization in the current dataset. Although studies have reported similar agreement between the results based on the two modalities, this might still lead to slight inconsistencies in the data. A larger dataset acquired with a homogeneous protocol would be ideal to validate our results in the future. Importantly, given the correlative nature of the study, our findings may not differentiate between local and global effects.
DBS Lead Localization and VTA Estimation DBS electrodes were localized using Lead-DBS software (http://www. lead-dbs. org) as described in  86   Variance" preset as implemented in Lead-DBS). This specific method was top performer for subcortical image registrations in a recent comparative study that involved >10,000 nonlinear warps and a variety of normalization techniques  85. Both coregistrations and normalizations were visually reviewed and refined if needed. DBS electrodes were then localized using Lead-DBS and warped into MNI space. In the Grenoble, Cologne and Madrid groups, Volumes of Tissue Activated (VTA) were estimated using a finite element method (FEM) as described in  83. Briefly, a volume conductor model was constructed based on a four-compartment mesh that included gray matter, white matter, electrode contacts and insulated parts.
Gray matter was defined by the CIT-168  40  and DISTAL  41  atlases for the ALIC-/NAcc and STN-cohorts, respectively. These atlases were specifically adapted or created for use within the Lead-DBS pipeline. The electric field (E-field) distribution was then simulated using an adaptation of the FieldTrip-SimBio pipeline  90  that was integrated into Lead-DBS (https://www. mrt. uni-jena. de/simbio/; http://fieldtriptoolbox. org/) and thresholded at a level of 0. 2 V/m 83. For the London test cohort, we chose to use the original VTAs of the published study by Tyagi et al. 16. These had instead been processed using Medtronic SureTune™️ software and transferred into MNI space within the original study. The reason we chose to use the original VTAs were twofold. First, it would demonstrate generalizability of our findings (i.
Connectivity Analysis Structural connectivity between VTAs and all other brain areas was calculated based on a normative connectome as similarly done in previous work  29, 30, 41, 44, 80, 83. Specifically, a wholebrain connectome based on state-of-the-art multi-shell diffusion-weighted imaging data from 985 subjects of the Human Connectome Project (HCP) 1200 subjects data release  81  was calculated in each patient using Lead-Connectome. Whole-brain fiber tracts were then normalized into standard space using a multispectral warp based on T1-weighted, T2weighted, and diffusion-weighted acquisitions using ANTs (using the same "Effective Low Variance" preset implemented in Lead-DBS). In each subject, a total of 6,000 fibers were sampled and aggregated to a joint dataset in standard space, resulting in a set of 6,000,000 fibers across 985 HCP subjects.
Acknowledgements We would like to thank  Kristy Kultas-Ilinsky  and  Igor Ilinsky ,  Rudolf Nieuwenhuys  and  Suzanne Haber  for counseling regarding the anatomical validity of tractography results presented in this study. We would like to thank  Cyril Pernet ,  Wolf-Julian Neumann ,  Roxanne Lofredi  for consultation and help on the manuscript. This work was supported by the  German Research Foundation (Deutsche Forschungsgemeinschaft, Emmy Noether Stipend 410169619  to AH,  SPP 0141  to AAK and  KFO 219  to JK). Data were provided in part 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.
83d83. Briefly, postoperative CT and MRI scans were linearly coregistered to preoperative T1 images using Advanced Normalization Tools (ANTs, http://stnava. github. io/ANTs/) 88. Subcortical refinement was applied (as a module in Lead- DBS) to correct for brain shift that may have occurred during surgery. Images were then normalized into ICBM 2009b Nonlinear Asymmetric ("MNI") template 42 space using the SyN approach implemented in ANTs 89 , with an additional subcortical refinement stage to attain a most precise subcortical alignment between patient and template space ("Effective Low.