dataesr/scientific-paragraphs-categorization huggingface dataset:
evry. inra. fr/CATdb/; projects "AU15-01_ Iron-FIT" and "AU13-06_FIT"). Microarray data from this article were deposited at Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/), accession no. GSE65934 and GSE80281. The RNA preparations were also used for differential gene expression via RT-qPCR of selected genes identified in the microarray analysis (Additional file 2: Figure  S2 ).
Construction of the virtual dataset For the construction of the virtual dataset we were interested in qualitative data, and for easier comparison with other experiments the expression data of previous publications as well as our own data were transformed so that in each comparison up-regulation was represented by the value 1 and down-regulation by the value -1. Below-threshold or insignificant regulation was given the value 0. We used our data and the provided expression data from 10 previous publications  [11, 13, 19, 20, 24, 25, [63] [64] [65] 75]. From time course experiments we used the 24 h  [24]  or the 48 h and 72 h data  [64] , respectively. The reconstructed data from two publications  [11, 20]  must be considered incomplete since they only contain iron deficiencyinduced genes. One dataset could not be reconstructed from the available supplementary information  [75].
Together, 14 transcriptomic analyses from 9 studies have been taken into account (Additional file 3: Dataset 5). From the transformed expression change values of the comparison WT -Fe vs. WT +Fe we counted how often each gene was found regulated in any of the analyses irrespective of the direction of regulation. We abbreviated this value as "ABS" (absolute occurrence). Then we added the expression change values. We named the result "SUM" (sum of regulation values). If a gene was always or mostly regulated in one direction this resulted in a positive or negative value of SUM. We set the SUM threshold to ≥ 2 or ≤ -2 to ensure that a gene has been regulated at least twice more into one direction than into the other direction. Then we divided the absolute value of SUM by ABS to measure how often a contradictory regulation has been observed with the respective gene.
We abbreviated this ratio as (ratio between SUM and ABS). To ensure that the gene was regulated at least twice as often in one direction than into the other direction the threshold for RAT was set to ≥ 0. 5. Only genes that matched the SUM and RAT thresholds were considered predominantly regulated into the respective direction under iron deficiency. All the other genes were considered regulated by other factors and removed from the dataset. To be able to rank the genes according to their uniformity of differential expression we introduced the "VIRT" value (virtual expected expression change) by dividing SUM (including the positive or negative sign) by the total number of analyses and multiplying the result with RAT. The total number of analyses was set to 14 for up-regulated genes (SUM > 2) since 14 analyses were used.
For down-regulated genes (SUM < -2) we set the total number of analyses to 12 since 2 of the 14 analyses  [11, 20]  only contained genes that were induced under iron deficiency. The sign of VIRT indicates the direction of regulation and its absolute value roughly represents the probability to find the gene regulated into this direction. Genes that were found regulated in both directions got lower absolute VIRT values than genes that were found regulated in only one direction. We set the threshold of VIRT to be ≥ 0. 25 or ≤ -0. 25. Finally, the VIRT value was used to rank the genes in the virtual dataset according to their probability of regulation in the comparison WT -Fe vs. WT +Fe (Additional file 3: Dataset 6). in seedlings. For comparison the respective seedling expression patterns have been added to the right.
(XLSX 197 kb) Additional file 6: Figure  S4. Co-expression network built from the genes induced under -Fe in the virtual dataset: Regulon 1: contains members of the FIT target network  [13]. Regulon 2: consists of members of the PYE-BTS regulon  [13]. Regulon 3: is largely composed of genes involved in phenylpropanoid metabolism. Regulon 4: mainly comprises genes that participate in the pentose phosphate pathway, glycolysis and gluconeogenesis. Regulon 5: is mostly composed of genes that are involved in RNA processing and translation. Regulon 6: contains mitochondrial proteins. Regulon 7: is heterogeneous but contains comparably many chaperons. Regulon 8: is enriched in genes involved in amino acid metabolism. Regulon 9: is also heterogeneous but enriched in genes that participate in plant-pathogen interaction.
Regulon 10: shows no enrichment of molecular functions. Regulon 11: mainly contains genes that participate in purine, lipid and aromatic compound metabolism. Regulon 12: is composed of genes involved in the response to low sulfur. Regulon 13 shows no enrichment of molecular functions. The network has been created with the String version 10 protein interaction database  [27]. The confidence was set to 'medium' (0. 400) and no genes were added. The 437 genes induced under -Fe in the virtual dataset were used as input. Singlet nodes have been removed and only networks with 4 or more nodes are shown. The resulting network image contains 169 genes (Additional file 5: Table  S7 ). (TIF 8234 kb).
Availability of data and materials The transcriptomic data used in this study are publicly available at CatDB (http://urgv. evry. inra. fr/CATdb/) under the project names "AU15-01_Iron-FIT" and "AU13-06_FIT" and at Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) under the accession numbers GSE65934 and GSE80281. Further relevant data are available in the manuscript and the supporting files.
MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, United Kingdom.
Knee joints were cleaned of loose tissue and incubated with 5-chloromethylfluorescein diacetate (CMFDa) and propidium iodide (pi) (one hour; both 10 µM; invitrogen, paisley, United Kingdom) to label living or dead cells, respectively. 19, 20 Fluorescently-labelled chondrocytes were imaged using a Zeiss axioskop lSM510 (Carl Zeiss, Cambridge, United Kingdom) at low magnification (×10 dry; Na = 0. 3) for chondrocyte density and viability, or with a high magnification dipping-water lens (DW; ×40 objective lens; Na = 1. 2) for chondrocyte morphology and volume. 19, 21 artilage histology. Joints were decalcified and paraffinembedded, sectioned at 5 µm intervals and stained with haematoxylin and eosin (H&E) and toluidine blue using standard protocols. 22 For each joint, cartilage thickness was assessed in three non-consecutive full-depth histological sections, obtained 200 µm apart using image J software (National institutes of Health, Bethesda, Maryland), and the measurements averaged.
However, we do not know if this had a permanent effect and influenced overall joint function, and cartilage properties in particular. While it is possible that these tissues may be capable of some repair and replacement of dead cells, this would not be the case for articular cartilage which has poor repair potential. 14, 15, 27 However, the animals were monitored throughout, and no abnormal gait, external joint swelling or inflammation was observed. The volume of in situ chondrocytes following in vivo cartilage drying. Shamoperated (control) data or that from joints dried following eight weeks of recovery, were obtained from confocal laster scanning microscopy (ClSM) images of 5-chloromethylfluorescein diacetate (CMFDa)-labelled chondrocytes as described (see Materials and Methods). Chondrocyte volumes shown are means ± 95% confidence intervals from cells visualised in Figure  4 , with measurements performed on sham controls (three joints; 870 cells) and week 8 samples (four joints; 1702 cells).
Long-term haematopoietic repopulation was assessed in peripheral blood between 14 and 16 weeks after transplantation. Peripheral blood was collected by bleeding the tail vein into 500 ml of 5 mM EDTA/PBS, and erythrocytes were depleted using PharM Lyse (BD). Cells were stained with anti-CD16/32 (Fc-block), anti-CD45. 1-APC (cloneA20) and anti-CD45. 2-PE (clone 104) monoclonal antibodies (eBioscience), and analysed using a FACSCalibur. Data were analysed using the FlowJo software (TreeStar). Recipients of the chimeric reaggregates were analysed for both CD45. 2 and aGFP donor markers. Mice exhibiting 45% of donor chimerism were considered to be repopulated with dHSCs. Different groups of repopulated mice were compared using Mann-Whitney U statistical tests (*Po0. 05; **Po0. 01; ***Po0. 005).
7-Aminoactinomycin D viability staining solution was used to exclude dead cells, and gates were set using appropriate fluorescence minus one controls. Sorting was performed on a FACSAriaII using the FACSDiva software. Quantitative reverse transcription-PCR. Total RNA was isolated from AoV, AoD, UGRs (using at least 4 e. e. of tissue for each sample) and OP9 with Qiagen RNeasy microkit or minikit (Qiagen), and complementary (cDNA) was prepared using SuperScriptVILO cDNA Synthesis (Invitrogen). The qRT-PCR was performed using the LightCycler 480 SYBR Green I MasterMix (Roche). For gene expression on a small number of cells, total RNA was isolated from 200 cells directly FACS-sorted into a PCR tube containing 10 ml of Â 2 Reaction Mix (CellsDirect, Invitrogen) and 0. 2 ml RNase inhibitor (SUPERase-In Ambion AM2694).
cDNA preamplification of the genes of interest was achieved by adding directly Superscript III/Taq mix (CellsDirect) and primers (10 mM each) to the cell lysate (PCR programme: 50 °C for 15 min; 95 °C for 2 min; 18 cycles of: 95 °C for 15 s; 60 °C for 4 min). A universal probe library strategy with Lightcycler 480 probes mastermix kit (Roche) was used to perform qRT-PCR on these samples. Duplicate reactions were set-up with diluted preamplified cDNA. PCR primers were designed using the Roche software. All experiments were performed in biological duplicates or more. Expression values were normalized against the TATA-binding protein, and standard error of the mean (s. e. m. ) were calculated and plotted using the Prism6 software (GraphPad). Primer sequences can be found in Supplementary Table  Shh doxycycline-inducible OP9 cell line (doxOP9-Shh).
3, Pharmingen), goat anti-CD45 (1:100, clone AF 114, R&D systems) and rat anti-cKit (1:100, clone 2B8, Biolegend), followed by incubation with anti-goat Alexa fluor 488 (1:100, Invitrogen), anti-goat NL577 (1:100, R&D systems), anti-rabbit Alexa fluor 488 (1:100, Invitrogen) and anti-rat Alexa fluor 647 (1:100, Invitrogen). Images were acquired with an inverted confocal microscope (SP8) and processed using FiJi software.
Acknowledgements We thank  J. Verth ,  C. Manson  and  R. McInnis  for assistance with mouse maintenance and breeding,  C. Watt. ,  C. Flockhart ,  C. Forrest  and  A. Dyer  for irradiations,  S. Monard ,  O. Rodriguez ,  F. Rossi  and  C. Cryer  for cell sorting and  B. Vernay  for assistance with confocal microscopy. We thank  S. Morrison  for providing SCF-GFP reporter mice;  V. Ribes  and  J. Briscoe  for GBS-GFP reporter mice;  H. Niwa  for the plasmid pPBhCMV1-cHA-IRES-VenuspA and  Dr Stephen Gould ,  Genentech  for providing Shh antagonist HhAntag. We thank  M. Cohen-Tannoudji ,  J. Davis ,  J. Easterbrook ,  A. McGarvey ,  M. Gehring ,  S. Gordon-Keylock ,  B. Gottgens ,  S. Lowell ,  J. Pimanda ,  A. Tsakiridis ,  L. Forrester ,  P. Travers  and  V. Wilson  for helpful comments. This work was supported by  LLR ,  BBSRC ,  MRC ,  Wellcome Trust  and the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme FP7 /2007-2013/ under REA grant agreement  n°317250.
, 2011; Adams et al. , 2013). Progress in software has made MX accessible to many noncrystallographers by providing automated and semi-automated pipelines that sometimes approach a 'one-click' experience for structure determination and initial model building  (Wlodawer et al. , 2013). This has allowed MX to play an increasingly pivotal role in various biomedical and biological research areas  (Berman et al. , 2013; Giege, 2013; Richardson & Richardson, 2014) , and results in a constantly growing flux of new structures deposited into the Protein Data Bank (PDB;  Berman et al. , 2000). The accelerating expansion of the PDB has led to the development of various new tools for structural bioinformatics and data mining, which has resulted in new methods for structure validation that have recently been standardized and applied to the whole repository  (Read et al.
1. It contains 83 042 conformations and is a very dense conformer library, which in most cases is too complex for direct use in side-chain conformation modeling. This data set was further clustered using hierarchical agglomerative clustering using the r. m. s. d. of all side-chain atoms (excluding C ) as a distance metric, and the maximum linkage criterion as implemented in the hcluster Python package  (Eads, 2008). A medoid (the conformation with the smallest sum of r. m. s. d. values between itself and all other conformations in a cluster, weighted by the number of occurrences of each conformation) was selected as a representative conformation for a given cluster. A library that is referred to as clX contains representative conformations, each of which represents a cluster of conformations with a maximum pairwise r.
Preparation of training and test sets for side-chain modeling Two sets of models of protein structures were generated: the first as a training set to optimize tunable energy-function parameters and the second as a test set to estimate the accuracy of Fitmunk. The primary data set from which these sets were drawn includes models of structures for which it was possible to recalculate R factors close to the reported values (as described in x2. 2), that share no more than 40% identity, that were deposited between 2007 and 2013 and that have a reported resolution between 0. 9 and 3. 5 A ˚. From this primary set, 150 structures per resolution bin were randomly selected. From all models, all ligand, ion and water atoms were removed and the models were then subjected to five cycles of REFMAC refinement with local automatic NCS and without TLS.
Model building Fitmunk was initially developed to build amino-acid side chains during automated building of macromolecular models to electron density, and the configurable parameters of the program were optimized for structures that are in the late stages of refinement. However, during program development our group started to routinely use Fitmunk even during the initial stages of model building and refinement. To date, the program has been tested on 65 different proteins (115 total different data sets), improving the model refinement of serum albumins  (Majorek et al. , 2012) , histone code reader proteins  (Niedzialkowska et al. , 2012) , complexes of antibodies with allergens  (Chruszcz et al. , 2012)  and several enzymes  (Porebski et al. , 2012; Shabalin et al. , 2012). Fitmunk has also been used for the structure determination of many other protein structures from several structural genomics centers, including CSGID, MCSG and NYSGRC, as well as for the rerefinement of previously determined structures in the PDB.
However, the performance of the program was not systematically evaluated in all of these cases. Fig. 3  shows how Fitmunk improved the model-to-data correspondence, the model quality and the model completeness as evaluated using the 115 selected structures cited above. Manual inspection of the models showed that Fitmunk significantly improved different aspects of these models (Fig. 4 ): it was able to select better conformations and better rotamers with a similar fit, to resolve cascade errors and to introduce conformational changes related to differences in crystal contacts or bound ligands in models used for molecular replacement (MR). We also investigated how refinement with Fitmunk affected R and R free , the MolProbity percentile clashscore and the overall MolProbity percentile score.
003 (16 models). The main strengths of Fitmunk are observed when analyzing model quality. Even for nearly final structures, for which Fitmunk did not affect the R factors significantly, application of the side-chain-fitting algorithm considerably improved the geometry by reducing the clashscore and the number of bad rotamers. In the initial data set only 60% of the structures had a clashscore in the 90th percentile and only 20% had a MolProbity score in the 90th percentile when compared with structures of similar resolution. After refinement with Fitmunk 84% of structures had a clashscore and 46% had a MolProbity score in the respective 90th percentiles (Fig. 3 ). To systematically assess the broad applicability of Fitmunk, we tested the program on a large (1100) subset of structures deposited in the PDB with a fraction of their amino-acid sidechain conformations randomly changed or removed.
These artificial test sets cover a broad range of different stages of model building and refinement, and vary significantly in the type of protein and diffraction data quality. Additionally, by systematically increasing the number of model perturbations and by decreasing the model completeness we were able to simulate how Fitmunk will behave in iterative refinement scenarios. Firstly, we observed that refinement using Fitmunk was able to match or improve the R free of almost 50% of structures from the fully refined test set (TS). Most of the remaining structures had an R free that was at most 0. 01 higher than the unrefined value. For the same set, Fitmunk was able to improve the MolProbity percentile score (which in the case of Fitmunk refinement is mostly improved by the selection of better rotamers and changes in the number of steric clashes); more than 80% of the structures had improved MolProbity percentile scores.
If Fitmunk selects a better fitting side-chain conformation than that present in the original deposition it means that the selection of the conformer may be suboptimal. This can be used for validation of conformer selection. We observe that in our test data set about 5% of the residues may have a suboptimal conformation (Fig. 7 ). Changes that are introduced to many neighboring residues may indicate more serious problems that can affect interpretation of the model. For example, one of the significant cases involved an RNA-binding protein that forms different oligomeric states in solution  (Wu et al. , 2012). The authors of the deposition (PDB entry 4emh) wrongly modeled the conformations of side chains in different parts of the protein that were involved in forming the interfaces (Fig. 8 ).
Conclusions We have developed a new algorithm for amino-acid side-chain conformation modeling based on DEE theory, which is specifically tailored for applications in MX. The resulting implementation of the algorithm, in the form of the program Fitmunk, has been tested and evaluated both on newly collected data as well as on artificial test sets based on previously deposited structures. Evaluation of the program demonstrated that the DEE-based algorithm is both robust and effective, and can improve protein structures during the early and middle stages of model building and refinement. Specifically, Fitmunk was able to improve the fit of side chains, select better rotamers, resolve various cascading errors and significantly reduce the number of steric clashes, even in models already deposited in the PDB.
P MRS 31 P-MRS was performed using a clinical 2. 9T MRI system (Tim-Trio, Siemens Medical Solutions, Erlangen, Germany) operating at 49. 9 MHz for 31 P resonance. 31 P MRS data were acquired with a dual tuned 31 P-1 H surface coil with linear polarization (Rapid biomedical GmbH, Rimpar, Germany) positioned under the calf at its maximum diameter. The 31 P single-loop coil diameter was 125 mm surrounding a 110 mm 1 H coil loop. The centering of the coil around the leg was.
AGING (Albany NY) confirmed by T 1 weighted 1 H localizing images and the coil was repositioned if the majority of the gastrocnemius muscle was not within this range. For all subjects a similar ratio between the volumes of gastrocnemius / soleus muscles was maintained within the coil. After a three-plane scout proton image, advanced localized volume shimming was performed. Before each experiment, two fully relaxed spectra were acquired at rest with 3 averages per spectrum and a repetition time of 30 s. Then, MRS data acquisition was performed throughout the rest-exercise-recovery protocol using a FID (free-induction-decay) pulse sequence with a 2. 56 ms adiabatic-half-passage excitation radiofrequency pulse and the following parameters: repetition time = 2 s; receiver bandwidth = 5 kHz; 1024 data points; and 3 averages per spectrum).
Saturation factors were quantified by the comparison between fully relaxed (TR = 30 s) and partially relaxed spectra (TR = 2s). As previously described  [57] , relative concentrations of PCr, inorganic phosphate Pi, and ATP were obtained by a time-domain fitting routine using the Advanced Method for Accurate, Robust and Efficient Spectral (AMARES) fitting algorithm  [58]  incorporated into the CSIAPO software  [49]. Intracellular pH was calculated from the chemical shift difference between the Pi and PCr signals. The free cytosolic [ADP] was calculated from [PCr] and pH using the creatine kinase equilibrium constant (K CK = 1. 66 × 10 9 M -1 ) and assuming that phosphocreatine represents 85% of total creatine content  [59]. The resting concentrations were calculated.
Methods and materials: This was a cross-sectional study which was conducted in Kashani Hospital affiliated with Isfahan University of Medical Sciences, Iran, in 2013. 210 adult patients who suffered from multiple sclerosis were selected and completed the Demographic Characteristics Questionnaire, Eating Disorder Examination Questionnaire (EDE-Q) and Anxiety Disorder Inventory. SPSS Version 16 software was used to conduct statistical tests including t-test, ANOVA and Pearson correlation.
Data Analysis Data was analysed by using SPSS statistical software version 16. For statistical analysis, descriptive statistics (absolute and relative frequency, mean and standard deviation) and inferential statistics (independent t-test, analysis of variance and Pearson) to investigate the relationship between gender, marital status, income, level of education, job, duration of disease with social anxiety and eating disorder score were used. P ≤ 0. 05 was considered as statistical significant level.