dataesr/scientific-paragraphs-categorization huggingface dataset:
Deduced from the existing knowledge on these regions' physiological functions, we may suppose lesions in these regions more likely underlie (higher) cognitive, than, for example, basic motor functions. This predilection of specific anatomical regions within the left hemisphere could also explain differences that arose in earlier lesion studies as these studies stratified patients only based on left-or right-sided lesion locations without appreciating any greater spatial detail of lesions. 29, 30, 32   ogether, a sex-specific lateralization effect, comparable to the considerable extent and spatial distribution of the one that we detected in two independent datasets, has neither been described in previous lesion studies, nor studies focused on sex discrepancies in functional cerebral asymmetries in healthy adults.
These differences were robustly replicated in a second independent, international multisite lesion dataset and could not be explained by sex differences in lesion volume or more severe stroke in women in general. Altogether, these findings may enhance acute stroke care by motivating more sex-sensitive treatment decisions, for example female and male-specific cutoffs of salvageable tissue for the administration of thrombolysis and thrombectomy. Thereby, our findings hold promise to represent a crucial step towards individualized medicine.
Replication analysis We repeated analyses in a completely independent dataset of 503 ischemic stroke patients (age: 65. 0 (14. 6), sex: 40. 6% female, NIHSS: 5. 48 (5. 35)), acquired within the framework of the multi-site MRI-GENIE study,  26  to test the robustness of our findings (c. f. supplementary materials for details). territory of the middle cerebral artery. There were neither significant region-wise differences between men and women, nor between left and right hemisphere (Supplementary Tables  1  and 2 ). Some lesion atoms did not comprise any substantial contributions from subcortical brain regions and are shown in transparent. of the right hemisphere had the highest weights, implying a high relevance in explaining stroke severity, in both men and women. In view of seven relevant lesion atoms in women (lesion atoms: 1, 3, 5, 6, 7, 8, 10), yet only four of such relevant lesion atoms in men (lesion atoms: 2,3,7,8), lesion patterns were more wide-spread in women compared to men.
Code availability Preprocessing of MRI images was conducted in a Matlab 2019b framework (The Mathworks, Natick, MA, USA), the packages Statistical Parametric Mapping (SPM12; http://www. fil. ion. ucl. ac. uk/spm/) and the ancillary package SPM_Superres (https://github. com/brudfors/spm_superres). Further analyses were implemented in Python 3. 7 (primarily packages: nilearn 69 and pymc3  70  ). Full code for reproducibility and reuse is available here: www. github. com/TO_BE_ADDED.
Brazil To investigate the origins of the newly generated genomes we downloaded all DENV2 nucleotide sequences longer than 1400 nucelotides (nt) from GenBank  (16)  that had a known date (year, and month and day when available) and location (country, and city when available; n = 1630 as of 20 June 2019). We aligned these sequences using MAFFT automatic settings  (18)  and manually edited them with AliView v1. 19. (19) We subsequently constructed an initial maximum likelihood phylogeny to help identify the genotypes of strains that have historically circulated (collected before 2019) and are currently circulating (collected in 2019) in Brazil. For this genotype assessment, we constructed phylogenies using FastTree v. 2 with gamma-distributed among site rate heterogeneity and a general time reversible nucleotide substitution model.
(20) We observed that all sequences from the Americas (including the newly generated sequences) grouped together in a well-supported monophyletic clade. Therefore, we next constructed a dataset comprising only sequences collected in the Americas (n = 670). To reduce sampling bias towards a high number of samples from well-sampled countries, we removed duplicate sequences (same day and location) from Nicaragua and Peru, yielding a final dataset of 436 genomes (including 66 genomes collected in Brazil between 1990 to 2013). Maximum likelihood phylogenies of the American DENV2 genomes (n = 670 and n = 436) were generated using PhyML  (20)  available through Seaview v. 4. 6. 1,using gamma-distributed among site rate heterogeneity and a general time reversible nucleotide substitution model. (21) oot-to-tip divergence and temporal signal was evaluated using TempEst  (21)  [Supplementary data (Fig.
Data availability -XML files and datasets analysed in this study are available in the GitHub repository (https://github. com/CADDECentre/DENV2-PILOT). New sequences have been deposited in GenBank under accession numbers 959467 -959485 [Supplementary data (Table  III )]. Raw sequence data have been deposited in SRA database from NCBI under numberPRJNA606238. OGP and NRF performed interpretation of the results and writing of the manuscript. All authors read and approved the manuscript. The authors declare no conflict of interests.
The somatic sequencing data presented in this study were from lung tumor tissue DNA and germline sequencing data were obtained from the corresponding white blood cell genomic DNA. EGFR Mutation Status and ALK Rearrangement Examination: Genomic DNA or RNA was extracted from tissue samples according to the manufacturer's protocol. The extracted RNA would be reversed transcripted to cDNA for latter polymerase chain reaction (PCR) amplification and examination. EGFR mutations in exons 18-21 and EML4-ALK rearrangement were detected by AmoyDx EGFR 29 Mutations Detection Kit (Amoy, Xiamen, China) and AmoyDx EML4-ALK Fusion Gene Detection Kit (Amoy, Xiamen, China) separately according to the protocols. Patients with 19del, L858R, T790M, 20ins, G719X (X = A, C, or S), S768I, or L861Q would be classified as EGFR mutated.
Patients with positive EML4-ALK fusion were classified as ALK rearranged. The cutoff ΔCt values for 19del, L858R, T790M, 20ins, G719X, S768I, and L861Q were 13, 14, 12. 5, 13, 13, 12, and 12, respectively. And if any FAM Ct value of EA Fusion Gene Reaction Mix < 30, the sample would be determined as EML4-ALK fusion positive. Sample Preparation for Targeted Next Generation Sequencing (NGS) and Whole-Exome Sequencing (WES) Data Processing: DNA was extracted from tissue samples using the QIAamp DNA Tissue Kit (QIAGEN, Valencia, CA) following the manufacturer's instructions. For blood samples, 10 mL of blood was collected in EDTA tubes and centrifuged at 1600× g for 10 min (4 °C) within 2 h of collection. The peripheral blood lymphocyte (PBL) debris was stored at -20 °C for later use. The supernatants were further centrifuged at 10 000× g for 10 min (4 °C), and plasma was harvested and stored at -80 °C for later use.
Seven to eight PCR cycles, depending on the amount of DNA input, were performed on Pre-capture ligation mediated PCR (Pre-LM-PCR) Oligos (Kapa Biosystems, Inc. ) in 50 µL reactions. The DNA sequencing was then performed on the Illumina Novaseq 6000 system according to the manufacturer's instructions at an average depth of 500×. For somatic mutation detection, the Genome Analysis Toolkit (GATK, Version 4. 1. 7. 0) best practice workflow was followed for somatic short variant discovery. Sequencing data were aligned to the hg19 genome (GRch37) using Burrows-Wheeler Aligner (BWA, Version: 0. 7. 17-r1198) with default settings. Duplicated reads were subsequently marked and removed using the GATK Picard tool. After the base quality score recalibration using BaseRecalibrator and ApplyBQSR functions of GATK, SNVs and INDELs were called from tumor and matched-normal pairs using Mutect2 from GATK.
Statistics, Data Analysis, Calculation of Somatic TMB, and Molecular and TMB Subtyping: Statistical analysis was performed and figures were plotted with GraphPad Prism 5. 0 software (GraphPad Software, Inc. , La Jolla, CA). Student's t-test or nonparametric test was performed when two groups were compared, while ANOVA and post hoc tests were performed when three or more groups were compared. Chi-square test and Fisher test were performed when rate or percentage was compared for significance. And the Benjamini-Hochberg procedure was used to correct P values for multiple hypotheses testing when appropriate. Figures were made with PRISM 5. 0 or the R software (https://www. r-project. org/). * represents P < 0. 05 (significant), ** represents P < 0. 01 (highly significant), and *** represents P < 0.
001 (very highly significant). TMB was calculated by dividing the total number of tissue nonsynonymous SNV and INDEL variations (allele frequency > 5%) by the full length of the WES panel. Molecular subtyping and TMB subtyping were performed based on modified methods from previous reports  [14, 15]  and figures were made with the R software tools (https://github. com/raerose01/ deconstructSigs; https://bioconductor. org/packages/release/bioc/html/ ConsensusClusterPlus. html).
Acknowledgements Y. H. and L. S. contributed equally to this work. This study was supported in part by a grant of  Young Talents in Shanghai, National Natural Science Foundation of China  ( 81802255 ),  Young Talents in Shanghai (2019 QNBJ), "Dream Tutor" Outstanding Young Talents Program  (fkyq1901), Clinical Research Project of  Shanghai Pulmonary Hospital  ( fk18005 ),  Key Discipline  in 2019 (oncology), Project of  Shanghai Municipal Science and Technology Commission (Project of Municipal Science and Technology Commission) , scientific research project of  Shanghai Pulmonary Hospital  ( fkcx1903 ),  Shanghai Municipal Commission of Health and Family Planning  ( 2017YQ050 ),  Innovation Training Project  of  SITP of Tongji University , and key projects of leading talent (19411950300).
Microscopy was performed using a Nikon Eclipse 80i microscope with a PlanApo 100×, 1. 40 NA objective and a Cascade 512B camera (Roper Scientific). Images were acquired using Metavue software (Universal Imaging). RnA extraction and reverse transcription. RNA was extracted using Direct-zol RNA Miniprep Kits from Zymo Research. Purified RNA was reverse transcribed for qPCR using ProtoScript II First-Strand cDNA Transcription kit (NEB) with random hexamers to prime the reaction.
Antibiotic (streptomycin 25-150 μg/ mL, kanamycin 25-150 μg/mL, or vancomycin 50-200 μg/mL) was added at a range of concentrations (10-500x MIC) to produce concentration-independent killing. The block was incubated with shaking, and at each time point, a sample was withdrawn and a ten-fold dilution series was performed in a 96-well plate using fresh PYE medium. These plates were allowed to regrow with incubation and shaking for 7 days. Regrowth in a well indicates that at least 1 surviving cell was present at the inoculation of that well. Survival was measured relative to the time zero CFU dilution plate. Survival was plotted against time to produce biphasic killing curves indicative of persistence. Reported data points are the mean and standard deviation of two biological replicates, each with six technical replicates.
For MS2, the resolution was 17,500 at m/z 400, auto gain control was 2e5 with a maximum injection time of 120 ms, the isolation window was m/z = 3, the normalised collision energy was 30, the underfill ratio was 3%, the intensity threshold was 2. 5e4, and the charge state was 2, 3. Data processing and phosphopeptide validation. Identifications were performed using the C. crescentus NA1000 genome database (https://www. ncbi. nlm. nih. gov/nuccore/CP001340) supplemented by the contaminants database (trypsin, keratins, etc. ). To increase confidence in phosphopeptide identification and add a significant number of phosphopeptides, two search engines were used in combination: MaxQuant (version 1. 2. 2. 5, www. maxquant. org) and X!Tandem (version 2011. 12. 01. 1,www. thegpm. org) with X!TandemPipeline v3.
3. 0, a bioinformatic tool developed by the PAPPSO platform (http://pappso. inra. fr/bioinfo/xtandempipeline/). The parameters for database searches were: one possible miscleavage, Cys carboxyamidomethylation set as a static modification, and Met oxidation and phosphorylation of tyrosine, serine and threonine residues set as variable modifications. Precursor mass and fragment mass tolerance were 10 ppm and 0. 02 Da, respectively. Identified proteins were filtered and grouped using the X! TandemPipeline. Data filtering was achieved according to a peptide E value smaller than 0. 05. The false discovery rate (FDR) at the peptide level was assessed from searches against a decoy database (using the reversed amino acid sequence for each protein). For MaxQuant, peptides composed of at least six amino acids were accepted, whereas those with a posterior error probability score >0.
Acknowledgements The authors thank  Japna Kalra  and  Vicki Deng  for lab assistance and the  Ryan lab  for insightful discussions. Funding for this work was provided by  National Science Foundation  award  1615287  to K. R. R and the  Swedish Research Council VR  and the  Novo Nordisk Foundation  (grant  NNF10CC1016517 ) to IM. Proteomics analyses performed on the PAPPSO platform were supported by  INRAE  ( French National Research Institute for Agriculture, Food , and Environment, http://www. inrae. fr), the  Ile-de-France Regional Council  (https://www. iledefrance. fr/education-recherche), Infrastructures en Biologie Santé et Agronomie  (https://www. ibisa. net)and the  Centre National de la Recherche Scientifique  (http://www. cnrs. fr).
Steered Molecular Dynamics (SMD) Simulations The SMD simulations of ligand pulling from the orthosteric site were performed for the most probable directions where the crevices between transmembrane helices were the widest and also for the additional direction towards the extracellular side (Figure  2 ). To obtain the statistically valid results at least three repeats of SMD simulation were performed for each pulling direction. Ligands were pulled out using those ligand atoms that were located closest to the selected direction of pulling-it means that they were different for each direction. This strategy prevented making a rotation of the ligand in the binding site, which could possibly jam that direction. Most of the receptor structures were inactive structures (crystallized with antagonists or inverse agonists) and only one structure (CB 1 receptor PDB ID: 5XR8) was active.
Steered Molecular Dynamics (SMD) Simulations The SMD simulations of ligand pulling from the orthosteric site were performed for the most probable directions where the crevices between transmembrane helices were the widest and also for the additional direction towards the extracellular side (Figure  2 ). To obtain the statistically valid results at least three repeats of SMD simulation were performed for each pulling direction. Ligands were pulled out using those ligand atoms that were located closest to the selected direction of pulling-it means that they were different for each direction. This strategy prevented making a rotation of the ligand in the binding site, which could possibly jam that direction. Most of the receptor structures were inactive structures (crystallized with antagonists or inverse agonists) and only one structure (CB1 receptor PDB ID: 5XR8) was active.
SuMD Simulations of Two Agonists Entering Inactive CB1 Receptor Structure For the supervised molecular dynamics (SuMD) simulations of ligand entrance into the orthosteric binding site of the CB1 cannabinoid receptor, we used a refined crystal structure (PDB ID: 5U09) of that receptor, with four N-terminal residues (I96, Q97, C98 and G99) added and a C98-C107 disulfide bond recreated. The remodeled fragment (called by us EC0-extracellular loop 0) is essential for the ligand binding process, since some of the residue sidechains (F102 and M103) are reaching deep into the orthosteric binding site to form van der Waals interactions with orthosterically bound ligands (Figure  6  and Figure  7 ), while the other residues of this region are cushioning the surface of the ligand binding channel. We tested both ends of the ligands (hydrophilic and hydrophobic) to enter the receptor, as we have done for the same agonists, Δ 9 -THC and anandamide, in our simulations of a homology model of CB1  [6].
SuMD Simulations of Two Agonists Entering Inactive CB 1 Receptor Structure For the supervised molecular dynamics (SuMD) simulations of ligand entrance into the orthosteric binding site of the CB 1 cannabinoid receptor, we used a refined crystal structure (PDB ID: 5U09) of that receptor, with four N-terminal residues (I96, Q97, C98 and G99) added and a C98-C107 disulfide bond recreated. The remodeled fragment (called by us EC0-extracellular loop 0) is essential for the ligand binding process, since some of the residue sidechains (F102 and M103) are reaching deep into the orthosteric binding site to form van der Waals interactions with orthosterically bound ligands (Figures  6  and 7 ), while the other residues of this region are cushioning the surface of the ligand binding channel. We tested both ends of the ligands (hydrophilic and hydrophobic) to enter the receptor, as we have done for the same agonists, ∆ 9 -THC and anandamide, in our simulations of a homology model of CB 1  [6].
Comparison of SuMD Simulations to Earlier Data In our previous paper on SMD/SuMD of the CB1 receptor  [6] , we used the homology model of the inactive CB1 receptor built with two crystal structures as templates: S1P1 receptor (PDB ID: 3V2Y) and LPA1 receptor (PDB ID: 4Z34). We simulated the exit/entrance of Δ 9 -THC and anandamide entrance into the CB1 receptor homology model and we concluded that residues F174 and F177 are involved in a gating mechanism, allowing only some ligands to go in while rejecting the others. In contrast to what we had observed that time, during current SuMD simulations based on CB1 the crystal structures that we observed had no gating mechanism. Therefore, the first stage of the ligand (Δ 9 -THC or AEA) entrance into the CB1 receptor occurs spontaneously even with the supervision algorithm.
Comparison of SuMD Simulations to Earlier Data In our previous paper on SMD/SuMD of the CB 1 receptor  [6] , we used the homology model of the inactive CB1 receptor built with two crystal structures as templates: S1P 1 receptor (PDB ID: 3V2Y) and LPA 1 receptor (PDB ID: 4Z34). We simulated the exit/entrance of ∆ 9 -THC and anandamide entrance into the CB 1 receptor homology model and we concluded that residues F174 and F177 are involved in a gating mechanism, allowing only some ligands to go in while rejecting the others. In contrast to what we had observed that time, during current SuMD simulations based on CB 1 the crystal structures that we observed had no gating mechanism. Therefore, the first stage of the ligand (∆ 9 -THC or AEA) entrance into the CB 1 receptor occurs spontaneously even with the supervision algorithm.
To obtain statistically valid results we performed the above calculations for different crystal structures of the same receptor (Table  1 ). The average distance between ligand THC in the SuMD final position and the crystal ligands is 3. 4 Å on average, while for AEA ligand it is 3. 1 Å. It indicates that these ligands are close to their putative final positions inside the CB 1 receptor. For comparison, a distance between ligands in two different crystal structures of CB1, 8D0 (PDB id: 5xr8) and 7DY (PDB id: 5u09), is a larger value, 5. 3 Å, indicating that the ligand poses obtained from SuMD are inside the binding site. Similar results were obtained for LPA 1 receptor, where the average distance between the crystallized ligands and the ligand poses obtained from SuMD was 3. 5 Å. However, for the S1P 1 receptor ligand, S1P, the distance is much larger (arround 8.
Obtaining the Receptor Structures In order to identify the most probable ligand entrance pathway into CB 1 , S1P 1 and LPA 1 receptors orthosteric binding sites, we performed at least three repeats of steered molecular dynamics (SMD) and supervised molecular dynamics (SuMD) simulations using various ligands. All receptor structures used for the simulations had been obtained from appropriate crystal structures of human receptors for CB 1 cannabinoid receptor (PDB IDs: 5TGZ  [16] , 5U09  [17]  and 5XR8  [18] ), for the S1P 1 receptor (PDB ID: 3V2Y  [12] ), and for the LPA 1 receptor (PDB ID: 4Z34  [14] ). All extracellular loops are present in the crystal structures of all investigated receptors without any missing fragments. Also, these receptor structures contain essential fragments of N-termini.
Residues from the rebuilt fragment participate in ligand binding in the orthosteric site so they are important for our calculations. For two other receptors, S1P 1 and LPA 1, their lacking parts in the N-terminus were small (15 residues for S1P 1 and 19 residues for LPA 1 ) and they were rebuilt in whole using Rosetta-Membrane  [19]  employing an ab initio approach (1000 models of each N-termini were generated and scored). Smaller fragments lacking in the crystal structure were refined using the YASARA v. 19. 9. 25 program  [20]. For labeling the residues, the classical (Ballesteros-Weinstein) numbering scheme  [21]  was employed.
Ligand Docking and Preparations for SMD and SuMD Simulations For the CB 1 cannabinoid receptor, the SMD simulations of ligand pulling from the orthosteric binding site were performed for ligands found in crystal structures of that receptor: with antagonist AM6538 (PDB ID: 5TGZ, resolution 2. 8 Å), with antagonist taranabant (PDB ID: 5U09, resolution 2. 6 Å), and with agonist AM841 (PDB ID: 5XR8, resolution 2. 95 Å). For the S1P 1 receptor, the SMD simulations of ligand pulling were performed for two agonists of that receptor, sphingosine-1-phosphate (S1P) and fingolimod-(S)-phosphate ((S)-FTY720-P), which were docked to the orthosteric binding site of the S1P 1 receptor crystal structure (PDB ID: 3V2Y, resolution 2. 8 Å) instead of antagonist sphingolipid mimic present in the crystal structure.
For the LPA 1 receptor, the similar SMD simulations were performed for the endogenous orthosteric agonist of that receptor, lysophosphatidic acid, docked to the orthosteric binding site of the LPA 1 crystal structure (PDB ID: 4Z34, resolution 3. 0 Å) instead of antagonist ONO9780307, which is present in the crystal structure. To build ligands and perform docking, the Maestro and Glide programs from the Schrödinger Suite release 2019-1 were employed. In all docking experiments we used Glide, also from the Schrödinger suite, using flexible ligands and a rigid receptor grid, which is a default method in Glide. We employed standard precision (SP) for Glide docking and for each ligand we obtained 20-30 poses. A 3D-grid was generated around all orthosteric ligands in the crystal structures of lipid receptors.
2. 11 simulation package with CUDA support and a CHARMM36 force field. The CHARMM-GUI service (http: //www. charmm-gui. org/) [23] [24] [25]  was used for NAMD input files generation. All receptor structures containing appropriate ligands were placed in a hydrated 1-palmitoyl-2-oleoyl-3-phosphocholine (POPC) bilayer containing 20% (n/n) of cholesterol and in every case, a short equilibration about 1 ns was carried out prior to the main simulation.
The energy (work) required for each ligand exit along each pathway was calculated by numerical integration of the force-distance data (Equation). × (z i+1 -z i ) Additionally, we employed the modified supervised molecular dynamics (SuMD)  [26]  method to simulate the ligand binding events for the lipid receptors. This is a special supervision tabu-like algorithm, monitoring the distance between the ligand and the receptor binding site, which can restart simulation from the checkpoint if that distance increases too much. All details of the SuMD algorithm that were used are described in our previous paper on the CB 1 receptor  [6]. For SuMD simulations, we used only agonists to enter the inactive structures of lipid receptors: anandamide and ∆ 9 -THC for the CB 1 receptor (refined crystal structure PDB ID: 5U09), LPA for the LPA 1 receptor (refined crystal structure PDB ID: 4Z34), S1P and (S)-FTY720-P entrance to the S1P 1 receptor (refined crystal structure PDB ID: 3V2Y).