dataesr/scientific-paragraphs-categorization huggingface dataset:
Other pharmacological comparators Two studies compared MPH with medications not primarily indicated for ADHD. In an open-label RCT,  Quinn and Rapoport (1975)  investigated anxiety in a sample of boys after a year's treatment with MPH, the antidepressant imipramine, or placebo and found no significant differences in anxiety between any treatment. 9 We coded this result as Unclear. Using a large nationwide database,  Steinhausen and Bisgaard (2014)  investigated SUD as an outcome following treatment with either MPH, antipsychotic treatment, antidepressant treatment or mixed treatment, and found a benefit for MPH but the opposite for.
g. risk elevation or reduction of neuropsychiatric disorders in adulthood). These could include long-term effects of relatively short treatment durations, as well as effects that persist after cessation of treatment. The evidence map highlights the potential confound between neuropsychiatric and behavioural outcomes of long-term treatment and neuropsychiatric symptoms that may increase the probability of longterm treatment (neuropsychiatric and behavioural treatment propensity factors). Many of the studies included were of patients with comorbid symptoms that were also listed as our potential adverse outcomes of interest; moreover, these comorbid symptoms may themselves sometimes be adverse outcomes resulting from the stresses of living with ADHD. This confound underscores the importance of self-controlled case-series approaches using large databases e.
g. Man et al. (2017 Man et al. ( , 2016)) , and for large long-term prospective studies. However, the evidence map also highlights the importance of single case-level studies, study discontinuation data, and details from retrospective chart reviews. While large studies can provide confidence that a treatment is generally beneficial, and/or AEs generally mild and infrequent, these individual-level studies underscore the need for recommendations for caution in specific cases, even for neuropsychiatric outcomes for which evidence indicates overall lowering of long-term risk by long-term MPH treatment. Further research is needed into the predictors of serious, if rare, adverse outcomes, for example in the presence of particular comorbid disorders, such as seizures, psychotic symptoms or tics. Again, studies using large databases, particularly those using self-controlled case series methodology may shed light on these risks.
demonstration under grant agreement no  260576. The research was also supported by the  NIHR Nottingham Biomedical Research Centre  and  NIHR MindTech MedTech Co-operative. The views represented are the views of the authors alone and do not necessarily represent the views of the  Department of Health in England ,  NHS , or the  National Institute for Health Research. Dr. Sara Carucci  has collaboration within projects from the  European Union  ( 7th Framework Program ) and collaboration as subinvestigator in sponsored clinical trials by  Shire Pharmaceutical Company, Lundbeck ,  Otsuka  and  Janssen Cilag. Travel support from  Shire Pharmaceutical Company and Fidia Farmaceutici. Prof. Marina Dankaerts  is a member of the European ADHD Guideline Group (EAGG) and holds grants from the  European Union   FP7 programme.
Prof. Edmund Sonuga-Barke 's financial declarations are: Speaker fees, consultancy, research funding and conference support from  Shire Pharma. Speaker  fees from  American University of Beirut, Janssen Cilag, Consultancy from Neurotech solutions, Copenhagen University and Berhanderling, Skolerne, KU Leuven. Book royalties from  OUP  and  Jessica Kingsley. Financial support received from  Arrhus Univeristy and Ghent University  for visiting Professorship. Grants awarded from  MRC ,  ESRC ,  Wellcome Trust ,  Solent NHS Trust ,  European Union ,  Child Health Research Foundation New Zealand ,  NIHR ,  Nuffield Foundation ,  Fonds Wetenschappelijk Onderzoek-Vlaanderen  (FWO), MQ -Transforming Mental health. Editor-in-Chief JCPPsupported by a buy-out of time to  University of Southampton  and personal Honorarium.
Non-financial declarations are: Member of the  European ADHD Guidelines Group. Prof. Ian Wong  reports grants from  European Union   FP7 programme , during the conduct of the study; grants from  Shire , grants from  Janssen-Cilag , grants from  Eli-Lily , grants from  Pfizer , outside the submitted work; and  Prof Wong  was a member of the  National Institute for Health and Clinical Excellence (NICE) ADHD Guideline Group  and the  British Association for Psychopharmacology ADHD guideline group  and acted as an advisor to Shire. Prof. Chris Hollis  reports grants from  European Union   FP7 programme ,  H2020 ,  National Institute of Health Research (NIHR) and Medical Research Council (MRC)  during the conduct of the study; He is a member of the  European ADHD Guideline Group (EAGG)  and  NICE ADHD Guideline Committee.
He is a member of the  NICE ADHD Guideline Committee. Prof. Edmund Sonuga-Barke 's financial declarations are: Speaker fees, consultancy, research funding and conference support from  Shire Pharma. Speaker  fees from  American University of Beirut, Janssen Cilag, Consultancy from Neurotech solutions, Copenhagen University and Berhanderling, Skolerne, KU Leuven. Book royalties from  OUP  and  Jessica Kingsley. Financial support received from  Arrhus Univeristy and Ghent University  for visiting Professorship. Grants awarded from  MRC ,  ESRC ,  Wellcome Trust ,  Solent NHS Trust ,  European Union ,  Child Health Research Foundation New Zealand ,  NIHR ,  Nuffield Foundation ,  Fonds Wetenschappelijk Onderzoek-Vlaanderen  (FWO), MQ -Transforming Mental health. Editor-in-Chief JCPPsupported by a buy-out of time to  University of Southampton  and personal Honorarium.
He  received royalities from  Oxford University Press  and  Giunti OS. The present work is unrelated to the above grants and relationships. Prof. Chris Hollis  reports grants from  European Union   FP7 programme ,  H2020 ,  National Institute of Health Research (NIHR) and Medical Research Council (MRC)  during the conduct of the study; He is a member of the  European ADHD Guideline Group (EAGG)  and  NICE ADHD Guideline Committee. Prof. Kerstin Konrad  reports grants from  European Union   FP7.
Peptide binding kinetics and peptide release was analyzed using peptide and protein concentrations of 30 nM and 50 M, respectively, where ATP (0. 4 mM) was added after overnight incubation at 4 °C to measure release. To distinguish nonspecific binding, we used BSA with the same concentration as analyzed protein. All reactions were carried out in a total volume of 12 l in 384-well black Nunc Plates (Thermo Fischer Scientific). Fluorescence polarization was measured at 21 °C using an Infinite M1000 Pro (Tecan) with excitation and emission wavelengths of 470 and 520 nm, respectively. Data were analyzed using GraphPad Prism version 5. 03 for Windows (GraphPad Software, San Diego, CA). Small-angle X-ray Scattering (SAXS)-The SAXS data sets were collected using the BioSAXS-1000, Rigaku at CEITEC (Brno, Czech Republic).
Data were collected at 277 K with focused (confocal Max-Flux SAXS optic, Rigaku) Cu K␣ X-ray (1. 54 Å). Sample to detector (PILATUS 100K, Dectris) distance was 0. 48 m covering a scattering vector (q ϭ 4sin()/) range from 0. 009 to 0. 6 Å Ϫ1. Size exclusion buffer was used for the blank measurement. Hsp70-T204A protein samples were measured at 2. 5 and 1. 5 mg/ml. Six separate twodimensional images were collected for buffer and sample (10 min exposure per image). Radial averaging, data reduction and buffer subtractions were performed using SAXSLab3. 0. 0r1, Rigaku. Six individual scattering curves (10 min exposures) were compared with check the radiation damage and averaged. Integral structural parameters (supplemental Table  S1 ) were determined using PRIMUS/qt ATSAS v. 2. 8. 3  (31). For molecular weight estimations, an ab initio model was reconstructed using DAMMIF ATSAS v.
2. 8. 3, where the annealing mode was set to "fast," whereas all other parameters were kept at default. The ab initio modeling for superimposition with atomic models was performed by DAMMIN ATSAS v. 2. 8. 3, where the computation mode was set to "slow," point symmetry of the particle to "P2" and all other parameters were kept at default. Evaluation of solution scattering and fitting to experimental scattering curves was performed using CRYSOL ATSAS v. 2. 8. 3, where automatic constant subtraction was allowed; other parameters were kept at default. Superimposition of the atomic and ab initio models was performed by SUPCOMB ATSAS v. 2. 8. 3. UCSF Chimera was used for graphical representation  (32). Surface Plasmon Resonance (SPR)-SPR was carried out on a Biacore T200 system (GE Healthcare). Biotinylated Hsp40 (EZ-Link™ Thermo Fischer Scientific) was immobilized on Series S Sensor chip SA (GE Healthcare) for about 460 response units.
All measurements were performed at 21 °C in SPR buffer (25 mM Hepes, pH 7. 5, 100 mM KAc, 2 mM MgCl 2 , 0. 005% Tween 20). Each Hsp70 protein (1 M) was pre-incubated with or without 0. 2 mM ATP and injected for 20 min at 10 l/min. The first channel on the sensor was coated with biotin and used as a background control. The data were plotted after subtracting this background control. Analytical Size Exclusion Chromatography (SEC)-Separations by SEC were carried out using Superdex 200 Increase 10/300 GL (GE Healthcare) pre-equilibrated with 25 mM Hepes, pH 7. 5, 150 mM KAc, 2 mM MgCl 2. 75 l of Hsp70 proteins (40 M) pre-incubated with/ without 0. 2 mM ATP for 20 min at 21 °C was injected and isocratically eluted at 0. 3 ml/min. In experiments testing the influence of Hsp40 on Hsp70 dimers, Hsp40 (10 M) was added at the end of the Hsp70 incubation with ATP and incubated for 5 min.
Although low collisional activation led to better transmission of dimeric molecules, higher collisional activation provided better resolution and can be used for MW estimation. Other parameters were: sampling cone voltage 50V, source offset 20V, trap gas 4 ml/min and source temperature 30 °C. Data were analyzed in MassLynx 4. 1. Molecular Modeling-The homology model of dimeric human stress-inducible Hsp70 was built using Modeler 9. 17  (33). The D and F chains of BiP (PDB code 5E84) with sequence identity 64% with human stress-inducible Hsp70, were used as template. Experimental Design and Rationale for Hydrogen/Deuterium Exchange (HDX) Mass Spectrometry-Hsp70 and its point mutants N540A, E543A and N540A-E543A were pre-incubated in the presence or absence of 5-fold molar excess of ATP for 30 min in 50 mM Hepes, pH 7.
5, 0. 1 M KAc, 2 mM MgCl 2. Deuterium labeling was initiated by 5-fold dilution into D 2 O-based 50 mM Hepes, pD 7. 5, 0. 1 M KAc, 2 mM MgCl 2. After 20 s to 2 h at 21 °C, exchange was quenched by 8-fold dilution in 1 M glycine, pH 2. 3. Each sample was injected into LC-system and processed as described  (34)  with minor modifications: desalting lasted 3 min, analytical column used was ZORBAX 300SB-C18, 0. 5 ϫ 35 mm, 3. 5 m (Agilent Technologies) with flow rate 20 l. Gradient was shortened to 7 min, finishing at 25% solvent B. MS used 15 T FT-ICR (SolariX XR, Bruker Daltonics, Bremen, Germany). Data were acquired with ftmsControl 2. 1. 0 and HyStar 4. 0, processed in DataAnalysis 4. 1 and analysis of deuterated samples used an in-house program Deutex (unpublished). Peptides arising from pepsin digestion were identified in a separate LC-MS/MS run, where the gradient elution was performed for 35 min.
Each full MS scan was followed by six MS/MS scans of the top most intense ions. Mascot generic files were generated in Data Analysis 4. 1 using two peak-picking algorithms -FTMS and SNAP. These data were searched by MASCOT 2. 5 against a database containing Hsp70 WT and mutant sequences and against the pepsin sequence. Search parameters were: no enzyme specificity, no modification considered, precursor tolerance 3 ppm (although most hits were below 1 ppm), fragment ion tolerance 0. 05-Da. All assignments were checked manually. Based on replicate measurements (wild-type in triplicate, mutants in duplicate, all independent preparations), the average standard deviation of deuterium determination was Ͻ 0. 17-Da. Differences greater than 0. 35-Da in deuterium incorporation between samples were considered significant  (35).
5 mg/ml, 2 ϭ 1. 2; 2. 5 mg/ml, 2 ϭ 1. 1, Fig. 4A  and 4B ), supporting the proposed human Hsp70 ATP-dependent dimer model (Fig. 4C ) over the crystal structures of DnaK and BiP dimers (PDB: 4b9q (chains AC), 5e84 (chains DF)). Taken together, these analyses suggest that the ATPbound monomers of inducible human Hsp70 protein assemble in solution as an antiparallel dimer in a similar orientation as DnaK and BiP dimers in their crystal structures  (11) (12) (13). Hsp70 ATP-dependent Dimers Assemble Through Conserved Interfaces-To pinpoint the structural rearrangements introduced to the monomeric (Apo form) and dimeric (ATPbound) Hsp70 states by N540A, E543A and N540A-E543A mutations, we compared the HDX rate of these mutants with the WT protein (Fig. 5  and supplemental Fig. S3 ), an approach that has been applied previously to study protein homodimerization  (42) (43) (44).
5B ) and dimer destabilization because of elimination of the conserved N540 and E543A residues mediating the intermolecular NBD-SBD␣ contacts (Fig. 3A )  (12). Group (iii) is represented by 487-510 peptide (supplemental Fig. S3A ). The deuteration profile of this peptide probably reflects N540A-FIG. 5. Hsp70 ATP-dependent dimers assemble through NBD-NBD and NBD-SBD␣ interfaces. A, Deuteration level (exchanged deuterons, D) differences between Hsp70 WT and N540A, E543A and N540A-E543A peptides in nucleotide-free state (Apo) measured after 120 s (for other incubation times see supplemental Fig. S3B ) incubation in deuterated buffer. The graph is labeled as in Fig. 3E. Red dots indicate peptides highlighted in part B and C of this figure. B, Crystal structure of human SBD (PDB code 4po2). SBD␣ (lid) helices and the lid-SBD␤ positioning ionic/polar contacts are indicated.
SBD␤ is labeled in marine and SBD␣ in green. Regions covered by peptides highlighted by red dots in A are red. C, Deuteration kinetics of indicated peptides from Apo and ATP-bound Hsp70 proteins. D, Deuteration level (exchanged deuterons, D) differences between Hsp70 WT and N540A, E543A and N540A-E543A in ATP-bound state measured after 20 s (for other incubation times see supplemental Fig. S3B ) incubation in deuterated buffer. The graph is labeled similarly as in Fig. 3E. Red dots indicate peptides highlighted (red/pink) on the atomic structures in E, E. 48 -68, 268 -282, 294 -302, 519 -529 and 530 -542  peptides highlighted in D were projected onto the homology-based Hsp70 ATP-bound dimer atomic model (see Experimental Procedures) and the corresponding peptides (see supplemental Fig. S4C ) in DnaK were projected onto the crystal structure of DnaK ATP-bound dimer (PDB code 4jne).
DATA AVAILABILITY The mass spectrometry proteomics data have been deposited to the  ProteomeXchange Consortium  via the PRIDE (https://www. ebi. ac. uk/pride/archive/) partner repository with the dataset identifier PXD010069. Small angle scattering datasets, atomic model and fits have been deposited to the  Small Angle Scattering Biological Data Bank  (www. sasbdb. org) as entry  SASDDN6. * This work was mainly supported by  Czech Science Foundation  ( 16-20860S ), additional support was provided by the  Ministry of Education, Youth and Sports of the Czech Republic (MEYS CR ,  LO1413 ,  LQ1604  and  LO1509 ), by the  Ministry of Health of the Czech Republic  -conceptual development of research organization (MMCI,  00209805 ) and  EU CZ. 1. 05/1. 1. 00/02. 0109and OPPK CZ. 2. 16/ 3. 1. 00/24023.
CIISB research infrastructure project  LM2015043  funded by  MEYS CR  is gratefully acknowledged for the financial support of the measurements at the CF X-ray Diffraction and  Bio-SAXS ,  CEITEC  and at the  Center of Molecular Structure, BioCeV. Part of the work was carried out with the support of  Core Facility Biomolecular Interactions and Crystallization of CEITEC -Central European Institute of Technology  under CEITEC -open access project  LM2011020  funded by  MEYS CR  under the activity "Projects of major infrastructures for research, development and innovations". □ S This article contains supplemental Figures and Tables. ‡ ‡ Current address:  Heinrich Pette Institute -Leibniz Institute for Experimental Virology, Martinistra␤e 52, 20251 Hamburg, Germany. § § To  whom correspondence may be addressed.
The abbreviations used are: ER, endoplasmic reticulum; ADP, adenosine diphosphate; ATP, adenosine triphosphate; Bag-1, Bcl2associated athanogene 1; Chip, carboxyl terminus of Hsp70-interacting protein; ESI-MS, electrospray ionization mass spectrometry; GST, glutathione S-transferase; HDX, hydrogen/deuterium exchange; Hsc, heat shock cognate; Hsp, heat shock protein; NBD, nucleotide-binding domain; NEF, nucleotide exchange factor; PDB, protein data bank; SAXS, small-angle X-ray scattering; SBD, substrate-binding domain; SBP, streptavidin-binding peptide; SEC, size exclusion chromatography; SPR, surface plasmon resonance; TEV, tobacco etch virus; Tomm34, translocase of outer mitochondrial membrane 34; TPR, tetratricopeptide repeat; WT, wild type.
Predicted regulatory effects (PRE) of MS-associated variants. We integrated genetic association signals from the latest genetic analysis of MS  17  with cell specific information on regulatory elements available from the ENCODE and Epigenomics Roadmap projects (REP) to identify cell-specific networks likely affected by the susceptibility variants (Fig. 1a , Supplementary Fig. 1 ). We included all genome-wide (GW) significant single nucleotide polymorphisms (SNPs) together with their proxies selected at differing LD thresholds (r 2 > 0. 5 was used for the main analysis). All regulatory information retrieved for each MS-associated region was compiled for each cell type in a single master table (Supplementary Data 1). This catalogue contains all of the available regulatory features that potentially modulate the expression of each of the genes mapping to each associated region in a cell-specific manner.
We then used the genotype-level data from one of the GWAS datasets (UCSF, Supplementary Data 31), composed of 2370 patients and 412 controls, to compute cell-specific risk scores for each individual using the same pipeline used for the populationlevel data. Rather than considering all 200 associations, this personalized approach takes into account the specific risk alleles present in each individual and thus enables exploration of subject heterogeneity in a biological context and in a cell specific manner. Hierarchical clustering of subjects and heatmap visualizations of the PRE of genes under regions 9, 49, and 53 (r 2 > 0. 5) for all cell types in this subset of cases and controls are shown as an example (Fig. 4 ). The heterogeneity across individuals in the PRE of a given gene can be readily seen for all cell types.
The smaller but not negligible contribution of CNS pathways to MS risk is in agreement with our previous analysis  17  , which identified the monocyte/macrophage/microglia axis as a key player in directing the autoimmune process to the CNS. However, an important caveat, particularly affecting results for this compartment, must be taken into account: for this analysis, the CNS group was composed of a heterogeneous ensemble of purified primary cells, established cell lines and dissected specimens from specific anatomical regions as available in the ENCODE and REP datasets. Although all derived from CNS tissue, it is highly likely that different regulatory mechanisms are at play in each cell type (in some cases resulting in drastically different expression patterns), thus somehow confounding the overall CNS signature computed here.
Thus, the detected effect of MS-associations which map to the CNS could represent the lower boundary of a more widespread phenomenon. A more detailed CNS-specific data set containing genome-wide regulatory element information might be needed to address this question in larger detail. In the last few years, several post-GWAS pathway approaches such as DEPICT  37  , FUMA  38  and PASCAL  39  have been proposed and utilized to interpret and integrate summary statistics into a biologically meaningful model. While sharing some of the basic characteristics of previous approaches, our method features a set of unique properties, most notably the introduction of datadriven regulatory effects of associated variants (and those in LD), the ability to create cell-specific networks, and the computing of individual disease burden maps (Supplementary Table  1 ).
(44 regions contained multiple independent effects). Similarly, the 416 SR effects were distributed in 354 unique regions (62 regions contained multiple independent effects) and the 3695 NR effects were distributed among 1,883 unique regions (1812 regions contained multiple independent affects). Three sets of SNPs were created for each region according to their r 2 with their corresponding main effect (r 2 ≥ 0. 8, r 2 ≥ 0. 5, and r 2 ≥ 0. 1). A python tool was written to automatically fetch data from RegulomeDB for these SNPs in all three lists (totaling 538,826 SNPs). Similarly, data for SNPs in different levels of LD (r 2 > 0. 8, r 2 > 0. 5, and r 2 > 0. 1) with each primary effect were also retrieved using chromosomal positions. The main analysis was performed using r 2 ≥ 0. 5 whereas the other sets were only used for the sensitivity analysis.
In total, 538,826 SNPs were included in the analysis. To investigate the effect of SNPs across different cell types and to assess which gene has most potential of being regulated across various cell types, we grouped the cell types present in ENCODE (Supplementary Table  4 ) and REP (Supplementary Table  5 ) into four major cell types (buckets). Specifically, these were B cells, T cells, CNS (central nervous system), and M (monocytes). T cell subsets (Th1, Th2, Th17, and Treg) were also analyzed as a separate group. We also built a dataset from lung (L, a cell type/tissue not considered to play a major role in MS susceptibility) as a control. Cancer cell lines were excluded for this analysis. Regulatory elements were grouped into two major classes: PEX (promoter/ enhancer/activator) and R (repressors/inactivators).
Cell or tissue of origin was recorded for each regulatory feature, and cell-specific information was grouped into three main cell types (B cells, T cells, monocytes) and one tissue (CNS) that are of interest in MS. In total, 25 brain regions were considered (15 from ENCODE and 10 from REP). In addition, T cell subsets deemed relevant in the pathogenesis of MS (Th1, Th2, Th17, and Treg) were also analyzed separately. Primary cells and cell lines from a tissue not known to be involved in MS (lung, L) were included as control. In addition, eQTL data for T cells and monocytes from the IMMVAR project  47  were integrated into the PRE computations. The HTML data from the scrapped output was parsed to populate data present in regulomeDB tables. A master table was then compiled with each field of regulomeDB data for all 538,826 SNPs.
All computations were performed in parallel using the 7400-core QB3 computer cluster at UCSF. Protein interaction network-based pathway analysis (PINBPA). An experimentally determined human protein interactome consisting of 15,783 nodes and 455,321 edges was used for this part of the analysis  21. We loaded the network into Cytoscape  48  and created cell-specific sub-networks using gene expression values from elsewhere. Specifically, we filtered interactions realized only by gene products expressed in a given cell type, by using RNAseq expression profiles from Kitsak et al. 49. Thus, for the T cell interactome, we only retrieved interactions between proteins known to be expressed by any T cell subset present in Kitsak et al. In the case of CNS, while gene expression data is sufficiently granular (profiles for different brain cell types and regions exist), epigenomic data for CNS cells/tissues in ENCODE or REP is very sparse, thus we decided to merge all data into a single CNS category.