dataesr/scientific-paragraphs-categorization huggingface dataset:
It should also be noted that our power calculations require a fixed level of statistical significance be achieved for model inclusion of SNP predictors, however, the inclusion requirements for many machine learning approaches are not as strict and, as such, these methods may be better powered  23. Open challenge study design. The open challenge was designed to assess genetic contribution to prediction of anti-TNF response in RA patients using whole-genome SNP data derived from anti-TNF-treated RA patients (Fig. 1a , Supplementary Table  1 )  6, 27. The question of anti-TNF treatment response was addressed in two ways. The primary endpoint used in the challenge was the classification of response to anti-TNF therapy as defined by EULAR-response criteria  16  (Classification subchallenge). Participants were also invited to directly predict DDAS28 as a continuous measure (Quantitative subchallenge).
In total, 242 individuals representing 30 countries and 4 continents registered to participate in this challenge. Challenge participants were invited to train models using a data set containing whole-genome SNP data, age, sex, anti-TNF therapy, concomitant methotrexate treatment and baseline DAS28 in a subset of 2,031 individuals (Fig. 1b , Supplementary Table  1  and see the 'Methods' section)  6. SNP data were provided as imputed (HapMap phase 2) genotype probabilities and dosages, as well as directly assayed variants for participant use. Participants were provided with a leaderboard with real-time feedback, which evaluated the performance of their predictions in the remaining 675 individuals. To reduce the potential for overfitting or reverse-engineering of treatment outcomes from the leaderboard, each team was limited to 100 leaderboard submissions.
7 and 35. 7%, respectively), likely due to differences in inclusion criteria in the two cohorts, although demographic data were similar between the two (Supplementary Table  1 ). Similar methods were used to quality control (QC) and impute genotypes in both cohorts (see the 'Methods' section for details). Participants remained blinded to outcomes from both the leaderboard and test data sets throughout the experiment. Harmonized data from all cohorts are publicly available as a resource for use by the research community (doi:10. 7303/syn3280809). Performance across predictive modelling methodologies. For the classification subchallenge, 27 final submissions were received from 15 teams and these were scored using both AUROC and AUPR. Overall rank for each submission was determined as the average of the AUROC rank and the AUPR rank among all valid submissions.
6152 and 0. 6237, respectively. Both of the top-performing submissions were generated using Gaussian Process Regression (GPR)  29  models. Team Guanlab selected SNP predictors based on analysis of the training data and previous analyses described in the literature and, using those features, applied a GPR model to predict non-response classification directly. Team SBI_Lab selected SNP predictors based entirely on analysis of the training data, applied a GPR model to predict DDAS28, and then refactored these predictions into classification weights. The code and provenance for the winning algorithms have been catalogued and made available for reuse through the challenge website (see 'Team Guanlab' and 'Team SBI_Lab' in the Supplementary Notes for more details). For the quantitative subchallenge, 28 final models from 17 teams were received and performance was evaluated based on the correlation between predicted and observed DDAS28 (observed range: r ¼ 0.
Methods Data sets. Two separate data sets were provided to participants to train and test the predictive models, respectively (Supplementary Table  1 ). In the case of the test data, only predictor variables were released, and the teams remained blinded to the response variables. The training data consisted of a previously published collection of anti-TNF-treated patients (n ¼ 2,706) of European ancestry, compiled from 13 collections  6  , of which the response variables from 675 patients were held-out as a leaderboard test set. All patients met 1987 ACR criteria for RA, or were diagnosed by a board-certified rheumatologist and were required to have at least moderate DAS28 (ref. 15 at baseline (DAS2843. 2). Available clinical and demographic data included DAS28 at baseline and at least one time point after treatment, sex, age, anti-TNF drug name and methotrexate use.
Follow-up DAS28 was measured 3-12 months after initiating anti-TNF therapy, though precise duration of treatment was not available. Genotypes for each sample were analysed for quality control (QC) for sample and marker missingness, Hardy-Weinberg disequlibrium, relatedness and population outliers, and imputed to HapMap Phase 2 (release 22) as previously described  6. We note that although this data set does not represent the full spectrum of patient information that may be utilized within a clinical setting to inform treatment-including synovial tissue and novel soluble biomarkers like MRP8/14 levels  4, 39, 40  , it did present sufficient data to explicitly assess the contribution of genetics to prediction. The final test set was derived from a subset of patients enroled in the CORRONA CERTAIN study  27.
In contrast, prediction in certolizumab-treated patients was similar to prediction in the remaining three drugs and so these data were included in the final test set. Two ancillary data sets were made available for participant use. The first measured TNFa protein level in HapMap cell lines  44. The second included blood RNA-seq data and genotypes for 60 RA patients from the Arthritis Foundation-sponsored Arthritis Internet Registry, 30 of whom displayed high inflammatory levels and 30 of whom displayed low inflammatory levels. Inflammatory levels were assessed using blood concentrations of C-reactive protein (CRP), and elevated disease was defined as CRP40. 8 mg dl À 1 , while low disease activity was defined as CRPo0. 1 mg dl À 1. In addition to CRP levels, rheumatoid factor antibody levels and cyclic citrullinated peptide levels were also assayed.
Genotypes were assayed on the Illumina HumanOmniExpressExome array. Power calculations. For combinations of a range of risk allele frequency, P ¼ (0. 01, 0. 02, 0. 03, y, 0. 99), and relative risk, l ¼ (1. 1, 1. 11, 1. 12, y, 2. 4), we computed the number, n, of such loci required to explain a heritability of 0. 18, as estimated for this trait, (equation (3) in the study by Wray et al. 8  ), and the power assuming a multiplicative model using the GeneticsDesign Bioconductor package  45  , given a trait prevalence, K ¼ 0. 217, as estimated from the discovery cohort. The expected heritability explained was estimated as the median power over all combinations of p and l for which n rounded to a given value. The AUROC corresponding to various proportions of heritability explained was computed using equation (3) in the study by Wray et al.
The average of the rank of the AUPR and AUROC was used to rank solutions. For the quantitative subchallenge, teams were asked to submit predicted DDAS28, and the Pearson's correlation between the predicted and actual DDAS28 was used to score submissions. Competitive phase of the challenge. The challenge was open to all individuals who agreed to the DREAM terms of use and obtained access to the challenge data by certifying their compliance with the data terms of use. The training and ancillary data were released for use on 10 February 2014. The leaderboards opened on 5 March, at which time participants were able to test their models in real-time against a held-out portion of the training data set. The prediction variables of the test data set were released to participants on 8 May and submission queues for final submissions were open between 21 May and 4 June.
Only the final two submissions per team per subchallenge were scored. Participants who did not have enough computational resources in their home institutions were offered the option to use an IBM z-Enterprise cloud, with two virtual machines running Linux servers, one with 20 processors, 242 GB memory, 9 TB storage space and the other with 12 processors, 128 GB memory and 1 TB of storage space. Cloud users could access the Challenge data directly through the IBM system. Evaluation of submissions. Predictions were evaluated using two data sets: 675 individuals from the training cohort (leaderboard test set) and all individuals from the CORRONA CERTAIN data (final test set). In both cases, response variables were withheld from participants. Participants were allowed 100 submissions to the classification subchallenge leaderboard and unlimited submissions to the quantitative subchallenge leaderboard throughout the competitive phase of the competition, and were provided near-instant results.
Acknowledgements G. P. is partially supported by  NIH  grant#  R01GM114434  and an  IBM  faculty award. E. S. is funded by  NIH   R01GM105857. The Corrona CERTAIN study is sponsored by  Corrona, LLC  with support from the  Agency for Healthcare Research and Quality  ( R01HS018517 ). The majority of funding for the planning and implementation of CERTAIN was derived from  Genentech , with additional support for substudies from  Eli Lilly ,  Momenta Pharmaceuticals  and  Pfizer. CERTAIN investigators also receive support from the  National Institute of Health  ( JRC AR053351 ,  JDG AR054 412 ).
Isotopic measurements Here, we have analyzed the isotopic composition of the first (2 to 4) meters of snow at three localities: Vostok, S2 and Dome C (Fig. 1 ). At Vostok, we can compare new data from the snow pit obtained for this study to a snow pit previously analyzed in δD, δ 18 O and δ 17 O  (Winkler et al. , 2013)  that was dated to 1951 at 3. 46 m. In the following, this snow pit will be called Vostok_winkler. For the different snow pits, the snow was sampled every 3 cm from the top to the bottom. The new δ 18 O, δD and 17 O-excess measurements presented here were performed following the analytical methods of Sect. 2. 2 with a MAT253, while the data from Vos-tok_winkler were measured on a Delta V.
Acknowledgements. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 306045. We are indebted to  Michel Fily  and  Laurent Arnaud  who organized the VANISH expedition and provided samples for this study. We would like to thank the  ANR VANISH  ( ANR-07-VULN-013 ) for their support, as well as the IPEV programme SUNITEDC (1011). We also acknowledge the contribution of the  PRIDE  project funded by  PNRA-MIUR  and of the Russian Antarctic Expeditions. The snow samples from the transects including Syowa-Dome F were sampled by the  Japanese Antarctic Research Expedition  and the  National Institute of Polar Research (NIPR) ,  Tokyo , in the framework of the joint Japanese-Swedish IPY traverse.
Data source Data from the 2011-2014 Japan National Health and Wellness Survey (NHWS) were analyzed in this study. The data for all 4 years were combined to maximize the sample size. As respondents could participate in multiple survey years, most recent data of respondents were retained for analyses. In particular, all unique respondents from the 2011-2014 Japan NHWS (n=115,271) were initially included in the analyses (Figure  1 ). The recruitment of panel members in the Japan NHWS strived to approximate the distribution of adults in the Japanese population. More details on the Japan NHWS participant recruitment procedures and sampling framework have been reported in another study. 13 The survey was approved by Essex Institutional Review Board, Inc. (Lebanon, NJ, USA), and informed consent was obtained from all respondents prior to participation in the present study.
Acknowledgements This work was supported by the  European Commission  through the  Graphene Flagship  ( GA-604391 ), the  FP7-NMP-2012-SMALL-6 "SACS" project  ( GA-310651 ), the  ERC Advanced Grant MaGIC  ( GA-247365 ), the  Agence Nationale de la Recherche  through the  LabEx CSC  ( ANR-10-LABX-0026_CSC ), the  International Center for Frontier Research in Chemistry (icFRC). Dr. Stéphane Berciaud  and  Dr. Marco Gobbi  are thanked for the assitance with the Raman measurement. Dr. Cécilia Menard-Moyon  and  Dr. Alberto Bianco  are acknowledged for the preliminary study on TGA measurement.
To date, a full-length three-dimensional Lon structure has not been determined, and therefore its complex mechanisms of action are only partially understood. Although several LonA X-ray crystal structures have been reported to date, some of them representing a substantial portion of the molecule  16, [20] [21] [22] [23] [24] [25]  , none of them have shown the complete oligomeric protein complex. The most complete structural study reported the determination of two sub-structures of B. subtilis Lon (BsLon), which covered part of its N-terminal domain (PDB ID:3M65) and both its ATPase and proteolytic domains (PDB ID: 3M6A)  20  (Fig. 1a ). The region linking these two domains was not determined, however, the amino acid sequence analysis suggested that it was likely to be formed by coiled-coils.
This linker region seems to be crucial: an E. coli Lon mutated in or lacking this region exhibits severely decreased ATPase activity and disruption to both its substrate translocation and degradation abilities  26, 27. Recently, four X-ray crystal structures of M. taiwanensis LonA have been resolved, including the structure of the AP-domain bound to three ADP molecules (PDB ID: 4YPL). Interestingly, the BsLon AP-domain crystallized as an open, helically arranged hexamer when bound to six molecules of ADP, but the MtaLon's AP-domain adopted a planar conformation. Unfortunately, electron microscopy (EM) studies of Lon and its bacterial and yeast counterparts FtsH and m-AAA proteases have likewise failed to provide a clear picture of the overall organization of the protease  [16] [17] [18] [28] [29] [30] [31] [32].
It could be fitted without steric clashes with six subunits from the crystal structure of B. subtilis Lon (PDB ID 3M6A, chain A) containing the proteolytic and the ATP-binding domains (AP-domain). The "head" therefore contains the catalytic chamber of the protein complex, which has a 20 Å diameter opening at its C-terminal side, but no opening at its N-terminal side (Fig. 2b , cross-section 3). The fitted 3M6A subunits revealed that this closure corresponds to hLon's large ATPase domain (Fig. 3 ) containing the aromatic-hydrophobic motif (Ar-Φ ) in the axial pore loops (RTYVG), which are characteristic for ATP unfoldases  33  and form an entry gate to the catalytic chamber in structures of compartmental proteases  33, 34  , T. onnurineus LonB  35  , and in the recently acquired structure of MtaLon's AP-domain  25.
Interestingly, the MtaLon 4YPL crystal structure adopted a planar conformation as well, even if it contains three ADP-bound and three nucleotide-free monomers, whereas our hLon S855A was incubated with an excess of ATP-analogue AMP-PNP. The "head" narrows into a tight trimeric neck, 93 Å across and ~10 Å long, where the six Lon monomers join into three pairs (Fig. 2a ,b, cross-section 4), forming a narrow, ~15 Å channel (Fig. 2b , cross-section 3, Fig. 3a ). The COILS  36  program predicted that the residues expected to occupy this region, 396-423, are likely to possess a coiled-coil arrangement (Fig. 1b ). Three pairs of legs emerge from this neck in a trimer-of-dimers arrangement, giving rise to signs of a 3-fold symmetry in cross-sections through the N-terminal domains (Fig. 2b ). They consist of two touching tiered-up globular densities, which could be fitted using residues 1-219 of the E.
coli Lon N-terminal domain  21  (PDB ID: 3LJC, Fig. 1a ). This trimer-of-dimers configuration corresponds to the arrangements of the AP-domains of the ADP-bound MtaLon and T. onnurineus LonB  25, 35  , which arose because of the presence of low-and high-affinity ATPase sites  12, 37, 38. The resolution of the reconstructed AMP-PNP incubated hLon S855A structure was however not high enough to resolve the structure of the coiled-coil region and assign individual N-terminal domains to their corresponding AP-domains (Supplementary Fig. S1 ), indeed, the local resolution map of the reconstructed structure shows that the N-terminal domains are its most flexible part (Supplementary Fig. S1 ). Interestingly, a similar pair-wise arrangement of the N-terminal helices has also been observed in the hexameric cryo-EM structure of the 26S proteasome ATPase domain  39  , and re-projections of the reconstructed hLon structure (Supplementary Figs  S1  and S2 ) give rise to the "leggy" and "leg-less" S.
1a ), is predicted to be predominantly α -helical. We found that this shortened hLon still forms a multimeric complex (Fig. 4a ) and preserves a small degree of the ATPase and peptidase activities of the full-length protein, but that it has almost no proteolytic activity (Fig. 4 ). During the cryo-EM image analysis of this protein, we encountered a large amount of structural heterogeneity show the data for the basal and β -casein-stimulated peptidase activity, respectively. The red line corresponds to the wt and blue to the Δ 270 hLon mutant. It can be seen that the Δ 270 mutant basal activity is about one-third of the wild-type, while its stimulated activity is closer to one half. The S855A mutant has no detectable peptidase activity. (e) Protease activity of wild type hLon and its S855A and Δ 270 mutants.
0, 150 mM NaCl, 5 mM MgCl 2 ) and 3 μ l of the dilution were applied to freshly glow-discharged Quantifoil 2/2 grids (EMS, Hatfield, PA, USA). The grids were vitrified by plunge-freezing into liquid ethane in a Vitrobot machine (FEI, Hillsboro, OR, USA) after 2 s blotting (blotting force 2) at 4 °C and 100% humidity. Data acquisition. Datasets of both hLon S855A samples were acquired with a FEI Polara TEM (FEI, Hillsboro, OR, USA) at an operating voltage of 100 kV with a FEI Falcon I 4 k × 4 K direct detection camera at IGBMC Strasbourg, France. Since the overall organization of the Lon protease has been intriguing so far, we made use of the markedly better performance of the Falcon I DED at voltages lower than 200 kV in the low-resolution range of 0-~0. 25 × Nyquist frequency  48. Images were automatically recorded by the EPU software at a nominal magnification of 59000× , yielding a final image pixel size of 1.
8 Å. At this magnification, the 0. 25 × Nyquist frequency corresponds to the resolution of ~14. 4 Å. Image underfocus was set in EPU to vary between -1. 2 μ m and -2. 6 μ m, the total electron dose used to acquire a single image was ~10 electrons/Å 2. In total, 1074 images of ADP-incubated hLon S855A and 575 images of AMP-PNP-incubated hLon S855A were recorded. The hLonΔ 270 dataset was recorded with a C s -corrected FEI Titan Krios TEM at an accelerating voltage of 300 kV at IGBMC Strasbourg, France, on a Falcon II 4 K × 4 K direct-detection camera operated in movie mode. The nominal acquisition magnification was 47000× , resulting in a final image pixel size of 1. 42 Å. Seven of the recorded frames (frames 2-8) covering an accumulated electron dose of ~20 electrons/Å 2 were saved for further processing.
The EPU-driven image underfocus varied between -1. 3 μ m and -2. 4 μ m; in total 2980 stacks of 7 frames were recorded. Image processing. After CTF and ice quality inspection, 498 images of the ADP-incubated hLon S855A, 458 images of the AMP-PNP incubated hLon S855A, and 2739 images of the hLonΔ 270 specimens were selected for further processing. The hLonΔ 270 frames were aligned and summed with the GPU-based dosefgpu_driftcorr  49  program prior to further analysis. Image CTFs were estimated by CTFFIND3  50  , the resolution of our micrographs by the GCTF's EPA procedure  51  , which revealed that reliable signal can be extracted from these micrographs up to a resolution, on average, of 6. 74 Å for the S855A-AMP-PNP sample, 8. 1 Å for the S855A-ADP sample, and 4. 85 Å for the frame-aligned hLonΔ 270 specimen.
The value of the C s coefficient of spherical aberration was set to 0. 1 in the processing of the hLonΔ 270 dataset. Particles were picked semi-automatically using the e2boxer program from EMAN2  52. Altogether, 38100 hLon S855A ADP-incubated particles, 97500 hLon S855A AMP-PNP-incubated particles, and 39800 hLonΔ 270 particles were extracted. The box size was set to 224 pixels for the hLon S855A samples, and to 192 pixels for the hLonΔ 270 specimen. The collected particle sets were fully processed in Relion 1. 3  53. After three runs of 2D classification with 300 classes, 73000 hLon S855A AMP-PNP-incubated particles, 32500 hLon S855A ADP-incubated particles, and 12450 hLonΔ 270 particles were selected for subsequent 3D classification. Inspection of the resulting 2D class averages of full-length specimens revealed top, side, and tilted views (Supplementary Figs  S1-S3 ).
Top views were identified according to oligomeric features  31  , while side and tilted views according to hints of the N-terminal domains. In order to create an initial model for the 3D classification of both full-length hLon S855A datasets, the identified side-view class average of the hLon S855A AMP-PNP-incubated specimen was low-pass filtered to 120 Å and extended to a rotationally symmetric 3D model by rotation of its 2D Fourier transform about its long axis, followed by inverse 3D Fourier transformation. The rotation of the 2D Fourier spectrum was performed by bilinear interpolation between neighboring points of Fourier profiles at each Z-level of the Fourier-transformed side view. All these computations were performed in Matlab (Mathworks, Inc. , Natick, MA, USA). In Relion, these initial 3D models were again low-pass filtered to 120 Å and subjected to grey-scale invariant cross-correlation in the first iteration of the 3D classifications.
All 3D classification runs were performed with the initial starting model, and the number of classes in each 3D classification run was selected so that at least one "empty", i. e. very sparsely populated class, resulted. The first 3D classification of the hLon S855A AMP-PNP-incubated dataset was performed with 5 classes; 30000 particles from the most populated class were then passed through another classification run with 3 classes. From this 2 nd run, 23170 particles were selected for final 3D refinement. 3D classification of the hLon S855A ADP-incubated dataset was performed in 3 runs with 6, 3, and 6 classes, respectively. After the 3 rd run, 10809 particles were available for 3D refinement. In each 3D classification run on the full-length Lon datasets, only those particles contributing to classes with well-formed N-terminal domains were selected for further 3D processing.
The 3D maps of the major classes acquired from the final 3D classification run were low-pass filtered to 40 Å and used as initial models for 3D refinement, giving an 18. 3 Å resolution hLon S855A-AMP-PNP structure, and 22. 4 Å hLon S855A-ADP structure. The reported resolutions were estimated using Relion's gold-standard Fourier Shell Correlation at the level of 0. 143. Both full-length hLon S855A structures were then masked, resulting in a resolution of 15 Å for the AMP-PNP-incubated structure and 21 Å for the ADP-incubated structure. Local resolution maps were computed using ResMap  54. The handedness of all reconstructions was adjusted to match the orientation of the B. subtilis Lon hexamer  20  (PDB ID 3M6A). In order to check the influence of the starting model on the resulting quaternary structure of the full-length protein, we processed the 23170 hLon S855A AMP-PNP-incubated particles against a synthetic soft-edged cylinder with a length of 220 Å and a diameter of 140 Å.