dataesr/scientific-paragraphs-categorization huggingface dataset:
MJED validation First, we compared the computation time between the MJED and the classical FEM method, referred to as "Standard FEM", implemented in SOFA 1. The results are given in Figure  4. We measured the time elapsed for the computation of the nodal forces and the stiffness matrices averaged over 100 iterations. For a more detailed differentiation between force and stiffness assembly computation, the reader is referred to  [41]. We simulated the deformation of a cube with 20 700 tetrahedra and 4300 nodes. For all modes implemented the proposed strategy is definitely more efficient than the standard FEM, up to five times as fast for St Venant Kirchhoff material. Second, the accuracy of the MJED computation was compared against the Open Source software FEBio (version 1. 1. 7) 2 where several elastic and hyperelastic materials are implemented and against analytical solutions for simple cube model.
Liver Simulations To describe the influence of each component in the complete model several simulations were performed where the hyperelastic material used was a fifth-order Arruda-Boyce. The liver mesh was segmented from a CT image and meshed with the GHS3D software (1240 vertices and 5000 tetrahedra). An Euler implicit time integration scheme is used and the linear equations are solved with a conjugated gradient algorithm. As boundary conditions, several nodes of the liver are fixed along the vena cava and suspensive ligament. The liver deforms under the action of gravity such as to overpass the linearity limit of the material. All computations were performed on a laptop PC with a Intel Core Duo processor at 2. 80 GHz. (i) Influence of the viscous component Adding viscosity to hyperelasticity increases the amplitude of the oscillations as the material becomes less stiff.
6). The LPS antigens used in this study were purified from Bp strains RR2808 (ΔwcbB; Bp LPS) and RR4744 (ΔwcbBΔoacA; Bm-like LPS) as previously described  32, 45. The coated plates were blocked at room temperature for 30 min with StartingBlock T20 (TBS) Blocking Buffer (SB; Pierce) and then incubated for 1 h at 37 °C with the various mAbs diluted 1/2000 in Tris-buffered saline + 0. 05% Tween 20 (TBS-T)+10% SB. To facilitate detection, the plates were incubated for 1 h at 37 °C with 1/2000 dilutions of goat anti-mouse IgM or IgG-horse radish peroxidase (HRP) conjugates (Southern Biotech). The plates were then developed with TMB substrate (KPL) and read at 620 nm. The data were plotted and analyzed using GraphPad Prism 5 (GraphPad Software Inc. ). SPR experiments. SPR analysis of binding between mAb 4C7 and synthetic oligosaccharides was performed using a Biacore X-100 instrument (GE Healthcare).
0. 1, GE Healthcare). STD-NMR experiments. NMR experiments were performed with a Bruker 600 MHz DRX instrument equipped with a cryo probe at 283, 298, and 310 K. All the samples were dissolved in deuterated phosphate buffer (pH 7. 4) and spectra were calibrated with internal sodium [D 4 ](trimethylsilyl)propionate (10 μm) at 0. 0 ppm for 1 H NMR. The ligand resonances were assigned by using standard NMR experiments. Samples for STD-NMR contained an mAb/ligand molar ratio from 1:50 to 1:100 and the antibody concentration was 12 μM. STD-NMR experiments were carried out with 32k data points and zero filled to 64k data point prior processing. A total of 4000 scans were recorded. Selective on-resonance irradiation of antibody resonances was performed at 8 ppm; the off-resonance frequency was set at 100 ppm.
The antibody saturation was achieved by using a pulse train of Gaussian shaped pulses of 50 ms duration and 1 ms interpulse delay with an irradiation power of 50 Hz. The saturation time was set at 2 s and a relaxation delay of 4 s was used. A T1ρ filter (50 db spin-lock pulse) and water suppression using excitation sculpting were applied. STD-NMR spectra of ligands in the absence of the antibody and spectra with antibody alone were acquired to obtain reference experiments. The STD effects were calculated by (I 0 -I sat )/I 0 , where (I 0 -I sat ) is the intensity of the signal in the STD-NMR spectrum and I 0 is the peak intensity of the unsaturated reference spectrum (off-resonance). The STD signal with the highest intensity was set to 100%, and others were normalized to this. Data acquisition and processing were performed with TOPSPIN 3.
Protein concentration was determined by BCA Assay (Pierce). The conjugates were further analysed by MALDI-TOF-MS. The results were acquired on a TOF/TOF 5800 System (AB SCIEX) using a linear positive mode. To improve ionization, the conjugated samples were dried and reconstituted with 50 mM ammonium bicarbonate buffer. The conjugates were mixed with 2,4,6-trihydroxyacetophenone, which was used as the matrix for the MALDI analysis. The resulting data were externally calibrated using bovine serum albumin. The disaccharide-based conjugates were named SOC-6 and SOC-7, respectively. Glycoconjugates OC-4744 (RR4744 OAg+CRM197) and OC-2808 (RR2808 OAg +CRM197) were synthesized essentially as previously described  33. The OAgs were purified from Bp RR2808 and RR4744 LPS as previously described  32, 45.
Acknowledgements This work was supported by a grant from the  Agence Nationale de la Recherche Programme Jeunes Chercheuses Jeunes Chercheurs  ( ANR-JCJC-12-JS07-0003-01 ), the  Natural Sciences and Engineering Research Council of Canada (NSERC ,  RGPIN-2016-04950 ), and the  Department of Energy -funded ( DE-FG02-93ER-20097 ) Center for Plant and Microbial Complex Carbohydrates. N. C. is supported by the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health  ( U01AI115520 ). M. M. thanks the  Wroclaw Center of Biotechnology for PDoc funding  within the framework of the  Leading National Research Center (KNOW). The authors acknowledge the  Unité de Chimie des Biomolécules at Institut Pasteur, Paris  for kindly sharing their H-Cube instrument and  Rosemary Roberts  for technical assistance.
X-ray crystallography Low-temperature (110 K) X-ray diffraction experiments were performed with an Oxford XCalibur diffractometer operating with Mo-K a or Cu-K a radiations. The data collections and reductions were performed using the CrysAlisPro soware [CrysAlisPro, Oxford Diffraction Ltd. 2009-2012]. Absorption corrections were performed analytically using multifaceted crystal models. 22 Structure solutions and renements were performed within WinGX soware  23  with programs SIR92 and SHELXL-97. 24 Figures were made with the help of Mercury. 25 CDC 1439676-1439686 contain the supplementary crystallographic data.
Theoretical calculations Density functional theory calculations on CP C1a were performed using the Gaussian09 program package. 26 The uB97XD functional was used together with the cc-pVTZ basis set, the latter being completed with a pseudo-potential for silver atoms. Topological analyses of the electron density were performed using the AIMAll soware. 27 Periodic DFT calculations were performed on CP C4 within Castep soware 28 using plane wave basis and built-in ultraso pseudopotentials. The PBE functional was used,  29  as completed with the semiempirical dispersion correction of Grimme. 30.
HPLC An Agilent Technologies (Waldbronn, Germany) 1100 Series HPLC system (high-pressure binary gradient system equipped with a diode-array detector operating at multiple wave-lengths, a programmable autosampler with a 20 mL loop, and a thermostatted column compartment) was employed. Data acquisition and analyses were carried out with Agilent Technologies ChemStation Version B. 04. 03 chromatographic data soware. The UV absorbance is reported as milliabsorbance units (mAU). Lux Cellulose-2 (Phenomenex, USA) (cellulose tris-3-chloro-4methylphenylcarbamate; 5 mm) was used as chiral column (250 Â 4. 6 mm). HPLC grade n-hexane and 2-propanol were purchased from Sigma-Aldrich (Tauirchen, Germany). Analyses were performed in isocratic mode at 22 C. The ow rate (FR) was set at 0. 8 mL min À1. The enantiomer elution order (EEO) was determined by injecting enantiomers of known absolute conguration.
Acknowledgements CNRS,  Université de Lorraine and Université de Strasbourg  are acknowledged for nancial support. The  Service Commun de Diffraction X of Université de Lorraine  is thanked for providing access to crystallographic facilities. This work was granted access to the  HPC resources of CINES/CEA CCRT/IDRIS  under the allocation x2016087449 made by  GENCI. Viviana Marolda  is thanked for her technical assistance with the in vitro assays.
The method included a combination of a disease (i. e. , heart failure), an intervention (i. e. , statins) as well as the aforementioned filter. Also, we added an outcome (i. e. , sudden cardiac death and/or mortality) to limit the research output. No language restrictions were applied and a bimonthly alert was set up for an automatic update during the study. We also checked the references of included studies for potential additional studies, searched clinicaltrials. gov and tried to contact authors for additional or missing data.
Selection criteria and data abstraction We included randomized clinical trials (RCTs) that evaluated statins efficacy in heart failure patients and contained at least one outcome of interest. No exclusion was based on treatment duration or follow-up period, language or intention-to-treat analysis. Risk of bias was determined by the Cochrane risk bias assessment tool  [27]. Two reviewers (MA&HHL) independently collected data and were checked by a third reviewer (MF) while discrepancies were resolved via discussion and consensus. Abstracted data included eligibility criteria, baseline characteristics, study design (including treatment and control arms), follow-up duration, and outcomes. Pre-specified outcomes of interest included sudden cardiac death (SCD), all-cause mortality and hospitalization for worsening heart failure (HWHF) and were analyzed according to an intention-to-treat principle when possible.
Study selection Electronic databases and manual searches resulted in 1335 studies after removal of duplicates. After screening of titles and abstracts and examination of selected full-texts, we ended up with 17 systematic reviews/meta-analyses  [9, 11, [18] [19] [20] [21] [22] [23] [24] [32] [33] [34] [35] [36] [37] [38] [39]  and 24 randomized controlled trials (RCTs)  [12, 13, [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51].
Risk of bias and quality of studies We assessed studies' risk of bias by means of the Cochrane bias tool  [27]. We noticed that the intention-to-treat principle was not followed in all trials, among which 10 had either high or unclear risk (  a concern for all outcomes and this resulted in moderate evidence. Due to missing data or absence of events, only 8 studies for SCD, 13 studies for all-cause mortality and 12 studies for HWHF were analyzed.
Ongoing studies and perspectives An ongoing study [https://clinicaltrials. gov/ct2/show/NCT01554592] is expected to resolve the issue of statin withdrawal in CHF patients. Future RCTs are still needed to: (i) compare lipophilic statins with hydrophilic statins. (ii) verify any class effect for statins and (iii) if any subpopulation of heart failure might benefit from statins in order to improve survival and reduce morbidity.
Most experts' confidence intervals were asymmetric, and this requires special consideration in aggregating estimates  (O'Leary et al. , 2015). For each expert and for each parameter, we fit an appropriate statistical distribution to the three elicited values depending on the nature of the demographic parameter. We used a beta distribution for rates that are naturally constrained between zero and one (e. g. , survival). We used a log-normal distribution for parameters that were bounded by zero and infinity (e. g. , clutch size). Distributions were fit using the rriskDistributions package  (Belgorodski et al. , 2015)  of the open-source computing language R (RCoreTeam, 2016). We then used the fitted distribution for each expert and for each parameter to generate 10,000 samples of the parameter in question.
Fecundity was defined as the product of clutch size and egg survival divided by 2, as we assumed a 1:1 sex ratio in the population. We emphasize that this model has no spatial aspect and thus assumes there is a single population in Miami-Dade County. The life cycle diagram and matrix model are provided in Fig. 1. The matrix model was parameterized using the set of 50,000 demographic parameters described above. To understand the population level consequences of the tegu life cycle we used the popdemo package of R  (Stott et al. , 2014)  to calculate the finite growth rate (λ), stable age distribution, damping ratio (d), and elasticities (e) of demographic rates for each of the 50,000 matrices. We chose elasticities rather than sensitivities because proportional (rather than additive) perturbations were more in line with the type of management intervention we examined (i.
e. , the proportion of an age class removed per year). The time of year when trapping occurs affects the age classes available for capture and this can be used to target specific age classes. We examined control strategies for reducing tegu population size by examining a range of removal rates targeted at certain age classes. As a basis for comparison, strategy A consisted of varying removal rates targeted at all age classes equally (here and elsewhere we assume that trap mortality is additive to other sources of mortality), such that the projection matrix with control was: A a ¼ 0 0 1 -h ð ÞFS 2 1-h ð ÞFS 3 1-h ð ÞS h 0 0 0 0 1 -h ð ÞS 1 0 0 0 0 1 -h ð ÞS 2 1-h ð ÞS 3 2 6 6 4 3 7 7 5 We used a coauthor's (FM) empirical data on catch per trap day (ω = 0. 034), total trap days in a season (δ = 192; February-October), and an approximate cost per trap per season (θ = $1000USD).
Acknowledgments We thank all those that attended the tegu-control workshop in August 2015, as well as  Tylan Dean  and the  National Park Service  for sponsoring that event. In addition to many of the coauthors, we thank  Kyle Allen ,  Bryan Falk ,  Michelle McEachern Collier ,  Lindsey Garner , and  Mike Rochford  for participating in the expert elicitation. Funding for this research was provided by the  U. S. Geological Survey ,  Greater Everglades Priority Ecosystem Science program. Any use of trade, product, or firm names in this article is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Data were analyzed using image analysis tools from the FMRIB Software Library (FSL v4. 0) (http://www. fmrib. ox. ac. uk/fsl). Before statistical analysis, motion correction using MCFLIRT  (Jenkinson M, 2001) , non-brain removal using BET (Brain Extraction Tool)  [25] , spatial smoothing (Gaussian kernel of 5 mm full width at half maximum [FWHM]), intensity normalization, and non-linear high pass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma= 75. 0 s) were applied. Registration of fMRI data to structural T1-W images and standard space (MNI152 brain) was carried out using FLIRT (FMRIB's Linear Image Registration Tool)  [24, 26]. No structurally distorted brains were present that affected registration and spatial normalization to standard space.
We included a relatively high number of DOC patients, which were studied for a long period of time. In fact, at present, there are three other longitudinal neuroimaging studies of recovery from DOC which have provided insights into the pathogenic mechanisms, reporting two VS patients  [9, 32]  and one MCS patient  [33]. We enrolled here a similar number of DOC patients as a previous one  [31] , but, in addition, the present study differs because is the first that combined fMRI and clinical data to evaluate the cerebral and clinical changes of VS patients from the time of VS through the recovery. In general, the findings of fMRI studies suggest a number of potential clinical and rehabilitative applications. Although clinical examination at bedside remains the mainstay for diagnosis, fMRI brain activation patterns may provide an adjunctive diagnostic role when behavioural findings are very limited or ambiguous.
General cognitive function is a prominent human trait associated with many important life outcomes  1, 2  , including longevity  3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants  4, 5, 6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N=280,360; age range = 16 to 102). We found 9,714 genome-wide significant SNPs (P<5 x 10 -8 ) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI.
General cognitive function, unlike height for example, is not measured the same way in all samples. Here, this was mitigated by applying a consistent method of extracting a general cognitive function component from cognitive test data in the cohorts of the CHARGE and COGENT consortia; all individuals were of European ancestry (Supplementary Materials). Cohorts' participants were required to have scores from at least three cognitive tests, each of which tested a different cognitive domain. Each cohort applied the same data reduction technique (principal component analysis) to extract a general cognitive component. Scores from the first unrotated principal component were used as the general cognitive function phenotype. Using a general cognitive function phenotype in a genetically informative design is supported by the observation that the well-established positive manifold of cognitive tests is best presented by a highly heritable higher-order latent general cognitive function phenotype that mediates genetic and environmental covariances among cognitive tests  4, 8, 13.
Genome-wide association analyses Genotype-phenotype association analyses were performed within each cohort, using an additive model, on imputed SNP dosage scores. Adjustments for age, sex, and population stratification, if required, were included in the model. Cohort-specific covariates-for example, site or familial relationships-were also fitted as required. Cohort specific quality control procedures, imputation methods, and covariates are described in Supplementary Table  S2. Quality control of the cohort-level summary statistics was performed using the EasyQC software  36  , which implemented the exclusion of SNPs with imputation quality < 0. 6 and minor allele count < 25.
Meta-analysis A meta-analysis of the 56 CHARGE-COGENT cohorts was performed using the METAL package with an inverse variance weighted model implemented and single genomic control applied (http://www. sph. umich. edu/csg/abecasis/Metal). The two UK Biobank groups, VNR Assessment Centre and VNR Web-Based, were also meta-analysed using the same method. An inverse-variance weighted meta-analysis of the CHARGE-COGENT and UK Biobank summary results was then performed.
Genetic correlations Genetic correlations were estimated using two methods, bivariate GCTA-GREML  41  and LDSC  40. Bivariate GCTA was used to calculate genetic correlations between phenotypes and cohorts where the genotyping data were available. This method was used to calculate the genetic correlations between different cohorts for the general cognitive function phenotype. It was also employed to investigate the genetic contribution to the stability of UK Biobank's participants' verbal-numerical reasoning test scores in the assessment centre and then in web-based, online testing. In cases where only GWA summary results were available, LDSC was used to estimate genetic correlations between two traits-for example, general cognitive function and longevity-in order to estimate the degree of overlap between polygenic architecture of the traits.
Genetic correlations were estimated between general cognitive function and reaction time and a number of health outcomes. The mapping of eQTLs was performed using each independent significant SNP and those in LD with it. eQTL information was obtained from the following databases: GTEx (http://www. gtexportal. org/home/), BRAINEAC (http://www. braineac. org/), Blood eQTL Browser (http://genenetwork. nl/bloodeqtlbrowser/), and BIOS QTL browser (http://genenetwork. nl/biosqtlbrowser/).
Gene property analysis In order to indicate the role of particular tissue types that influence differences in general cognitive function and reaction time, a gene property analysis was conducted using MAGMA. The goal of this analysis was to determine if, in 30 broad tissue types and 53 specific tissues, tissue-specific differential expression levels were predictive of the association of a gene with general cognitive function and reaction time. Tissue types were taken from the GTEx v6 RNAseq database  54  with expression values being log2 transformed with a pseudocount of 1 after winsorising at 50, with the average expression value being taken from each tissue. Multiple testing was controlled for using a Bonferroni correction. not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.
Statistical analysis Statistical analysis was performed using Excel software (Version 16. 0. 7030. 1021) with the Add-In Real Statistics Resource Pack (Release 4. 3). We tested for normal distributions using the D' Agostino Pearson Omnibus and the + (NA) - - -0/10 Plasmapheresis 3/58 (1/3) 2/24 (0/2) 0/17 (0/0) - - - + (-) - - - - - - - -1/10 (1/1) Noninvasive ventilation 3/58 0/24 2/17 + - - - - + - - - - - -0/10 In bold, statistically significant differences between SLONM with and without monoclonal gammopathy are indicated HIV-NM HIV-associated nemaline myopathy, m male, f female, y years, + symptom/feature present, NA not available,symptom/feature not present, IgG immunoglobulin G, CK serum creatine kinase, N normal value (men <190 U/l, women <170 U/l), dil. dilated cardiomyopathy, VL musculus vastus lateralis, TA musculus tibialis anterior, D musculus deltoideus, GM musculus gluteus medius, QF musculus quadriceps femoris, BF musculus biceps femoris, SC musculus sternocleidomastoideus, unsp.
Targeted next-generation sequencing (NGS) gene panel In 6 SLONM patients without MGUS, targeted NGS was performed using a focused panel of 283 genes (CeGat, Tübingen, Germany). After obtaining written informed consent from each patient, peripheral blood samples were taken and DNA was extracted following standard procedures. The gene panel contained 73 genes that have so far been associated with congenital or distal myopathies, including genes known to cause congenital nemaline myopathy. Of these 73 genes, all variants were further considered. In addition, 210 genes related to diverse myopathies were included, in which only variants that were either marked as "known pathogenic" in the Human Genome Mutation Database (HGMD®), produced a STOP codon, resulted in a frameshift or were located in an essential splice site, were taken into further consideration.
neurological assessment A detailed clinical assessment was done by CH on day 1 (baseline score, before stimulation 1), day 2 (before stimulation 2), and day 3 (after stimulation 2). Each clinical assessment was videorecorded, and one movement-disorder expert (BD) blindly rated the patients before and after each session, using a rating scale that was specifically designed for FMDs (11) (Data Sheet 1 in Supplementary Material), yielding an "FMD score" ranging from 7 (normal) to 41 (most severe). The percentage of improvement was calculated as follows: ((pretreatment score) -(posttreatment score))/((baseline score) -7). No other treatment was provided during the hospital stay, and the patients' medication was kept stable during the study. Physiotherapy was not provided during the protocol, but it was systematically prescribed at discharge.