dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION We have used the basin calculator ArcTem (TemisFlow software) developed at IFPEN to perform 2D simulations in a complex geological setting and to improve fault modelling. The area chosen for this study is a 2D crosssection located in the onshore area of the Niger Delta. The sedimentary layers are crossed by several growth faults which are thought to play a crucial role in over-pressure building and petroleum migration. A simplified section with 24 layers and 11 normal faults was successfully restored and meshed and several ArcTem pressure simulations were performed for different fault config- urations. All the simulations run to the end, even with abrupt fault permeability changes, showing the improved stability of the calculator compared to classic basin calculator where fault modelling is often a source of numerical instabilities.
Software The numerical examples are implemented in python using numpy and the operator discretization library (ODL). 38 The ray tracing for the PET forward operator is performed using CUDA-based NiftyPET. 39 ta The clinical data are from a 70 year old cognitive normal subject, scanned as part of the "Insight 46"-a neuroscience sub-study of the MRC National Survey of Health and Development. 40 The PET data is accompanied by the T1-, T2-weighted MRI, all acquired simultaneously on a Siemens Biograph mMR PET-MRI scanner. The PET data was acquired dynamically for 60 mins using the amyloid tracer 18F-florbetapir tracer with activity of 409 MBq at the time of injection. The subject's -map was synthesized from the T1-and T2-weighted images for better accuracy. 41 The MRI was co-registered to the PET image using NiftyReg software.
ACKNOWLEDGMENTS M. J. E. and C. -B. S. acknowledge support from  Leverhulme Trust  project " Breaking the non-convexity barrier ",  EPSRC  grant " EP/M00483X/1 ",  EPSRC  centre " EP/N014588/1 ", the  Cantab Capital Institute  for the  Math-ematics of Information , and from  CHiPS  ( Horizon 2020 RISE  project grant). Moreover, C. -B. S. is thankful for support by the  Alan Turing Institute. P. M. was supported by the  Medical Research Council  ( MR/N025792/1 ) and  AMYPAD (European Commission  project  ID :  ID115952 ,  H2020-EU. 3. 1. 7. -Innovative Medicines Initiative 2 ). A. C. benefited from a support of the  ANR , " EANOI "  Project   I1148 / ANR-12-IS01-0003  (joint with FWF). Part of this work was done while he was hosted in  Churchill College  and  DAMTP, Centre for Mathematical Sciences, University of Cambridge , thanks to a support of the  French Embassy in the UK  and the  Cantab Capital Institute for Mathematics of Information.
Patients We retrospectively analyzed data of 82 adult patients who had received ECLS in our university hospital for refractory CA or CS between January 2012 and December 2013. Indications and timing for ECLS were determined by practitioners (intensivists, cardiologists, and cardiac surgeons) in charge of the patient. ECLS contraindications were standard, including recent cerebral hemorrhage and other contraindications to anticoagulation, patients older than 75 years old, high comorbidities with chronic organ dysfunction (e. g. , cirrhosis, emphysema, and respiratory failure) or advanced disease with a life expectancy of <5 years (e. g. , terminal malignancy), and patients who had previously signed "do-not-resuscitate" orders. [10] l patients included for refractory CS displayed the following signs: signs and symptoms of organ hypoperfusion (cool peripheries, oliguria, confusion, etc.
Data collection Detailed pre-ECLS data including age, gender, coronary risk factors, prior heart disease, etiology of CA or CS, initial biological data, and Simplified Acute Physiology Score (SAPS) II  [12]  at the time of ECLS initiation were retrospectively collected. In-hospital ECLS-associated complications were noted, such as acute limb ischemia, infection, hemorrhage, pulmonary edema, acute renal injury requiring dialysis, and ECLS dysfunction. The main outcome variable was 3-month survival, but we also analyzed 30-day survival rates and long-term survival. Other outcome measures included duration of Intensive Care Unit (ICU) stay and total hospitalization. To assess long-term clinical condition, patients still living in March 2014 were contacted by telephone and evaluated according to the activities of daily living (ADL) and the New York Heart Association (NYHA) dyspnea scores.
Many prediction models for severe acute respiratory failure requiring ECMO have been published and externally validated over recent years;  [30, 31]  predictive scores for mortality prognostic on ECLS for cardiac failure are less frequent and less robust. [23, 29, 32] Schmidt et al. recently published the SAVE score based on a large pre-ECLS assessment data extracted from the Extracorporeal Life Support Organization registry. [25] owever, scores, especially the SAVE score, are complex leading to limited clinical application. Some authors suggest that simpler scores would be equally effective and easier to apply. [26] l of these observations reinforce the need for early recognition of patients rapidly progressing to refractory cardiac failure and who need immediate transfer to a medical-surgical center experienced in mechanical circulatory support to prevent multiorgan failure and futile indications as previously suggested.
Results The 235 U(n,γ ) reaction rate is extracted by conditions on m cr and E sum , which maximize the signal to background ratio without compromising the statistics. The best results were obtained for 2< m cr <6 and 2. 5< E sum (MeV) <7. 0. Dead time corrections have been applied following a procedure similar to the methodology described in  [16]. The ratio of the experimental resonance integrals to the evaluated values from the ENDF/B-VII. 1 and the latest CIELO release (ib18o23g6cnu5ef1) are shown in Fig. 2  and Fig. 3. The evaluated libraries were accurately broadened, processing the resonance parameters with SAMMY8 code  [17]  and the experimental resolution. The black line represents the statistical uncertainty and the blue and the red lines represent the systematic uncertainties due to the subtraction of the beam and the prompt-fission background components, respectively.
Summary and conclusions The 235 U neutron capture cross section has been measured at the n TOF facility relative to the well known 235 U neutron-induced fission cross section. The two critical parameters for the normalization have been determined: (i) the TAC detection efficiency for 235 U(n,γ ) events and (ii) the fission detection efficiency. Our capture cross section data is, on average, ∼ 6% larger than the ENDF/B-VII. 1 evaluation in the neutron energy range from 0. 2 to 20 eV. Larger discrepancies are observed with the latest CIELO library. The main sources of systematic uncertainties have been carefully determined: (i) the systematic effects associated with the fission γ -ray cascades; (ii) the beam background, and (iii) the dead time corrections. In the investigated neutron energy range component (i) is important.
The fermentation dynamics was monitored in batch microcosms and the biodegradation mechanisms were analyzed at the protein level for Tissue and Whatman Paper through label-free quantitative shotgun proteomics. The in-depth characterization of the three materials was moreover conducted to elaborate precise hypotheses regarding the origin of the differences in metabolic end-product concentrations and in protein levels. The present work is, to our knowledge, the first global shotgun proteomic study of R. cellulolyticum. The obtained data provide evidence that Tissue is degraded the fastest by R. cellulolyticum and is associated to a distinct metabolic pattern compared to both other materials. When comparing Tissue and Whatman Paper, the data show a clear influence of the substrates, even though they are rather similar, on protein levels from the cellulolysis machinery and the central carbon metabolism.
DRI was used as concentration detector and UV as control. The chromatographic solvent was THF, injected concentrations were 1 mg/mL (injection volume 100 microliter), and the dn/dC of cellulose tricarbanilate in THF was taken at a predetermined value of 0. 165. Data were treated by the OmniSec™ (4. 5. 6. version) program (Malvern Co. ). The lignocellulose sugar content of the samples was determined by the commercial facility of Celignis Analytical (http://www. celignis. com, Analysis Package P7, substrate hydrolysis followed by ion chromatography). Van Soest fractionation of the substrates was performed by the commercial facility of INRA Transfert Environnement as in  [25]  (https://www6. montpellier. inra. fr/it-e).
Peptide separation was realized with an Ultimate 3000 RSLCnano system (Dionex, Voisins le Bretonneux, France) using a long gradient and a C18 column (Pepmap100, 0. 075 x 50 cm, 100 Å, 2 μm, Thermo) during 188 min to enhance the resolution and the sensitivity of peptide detection by mass spectrometry. A detailed protocol is provided S1 File. Protein database search and label free quantification. The data processing pipeline was designed using the TOPPAS software  [27] , part of the OpenMS project  [28]. X!Tandem (directly provided in the OpenMS archive) was used to perform database searches in batch mode. Peak lists were created by an OpenMS dedicated tool with an additional processing step. Indeed, a precursor recalculation was computed for each tandem spectrum to improve the number of matches between a spectrum and a peptide sequence.
While proteins were digested with trypsin, the analysis program allowed for 2 missed trypsin cleavage sites. Cysteine carbamidomethylation was set as a fixed modification; methionine oxidation and protein N-terminal acetylation were set as variable modifications for all X!Tandem searches. The mass tolerances in MS and MS/MS were set to 10 ppm and 0. 6 Da respectively. Data were searched against a target/decoy concatenated database to obtain a false discovery rate (FDR) value at the peptide level. All identifications were validated with a final peptide FDR of 5% and a calculated protein FDR of 1%. For the relative quantification based on eXtracted Ion Chromatogram (XIC), peaks were detected in each sample using the « PeakPickerCentroid » algorithm (OpenMS software). Validated identification data were matched with detected peaks.
Peaks which were assigned to the same peptide sequence in different samples were used as anchors for retention time alignment between those samples. A Protein Abundance Index (PAI) was calculated and defined as the average of XIC area values from the three most intense peptides identified for a given protein. Statistical analyses of the label-free quantification proteome datasets. All statistical analyses were performed using the programming language R on log 10 -transformed and quantile normalized PAI. The substrate, incubation time and substrate-by-incubation time interaction effects were assessed by Tobit models with a censoring threshold equal to the minimum non-zero value for a given protein. With y Ã ijkl the normalized log 10 PAI of protein l, for the substrate S i , incubation time T j and replicate k, the model used for each protein is the following: y ijkl ¼ min i 0 j 0 k 0 : y Ã i 0 j 0 k 0 l >0 ðy Ã i 0 j 0 k 0 l Þ if y Ã ijkl ¼ 0 and 9k 0 : y Ã ijk 0 l 6 ¼ 0 y Ã ijkl otherwise * with y Ã ijkl ¼ m l þ a il S i þ b jl T j þ g ijl ðS; TÞ ij þ ε ijkl and ε ijkl $ N ð0; s 2 l Þ Tobit regressions were computed using the Markov chain Monte Carlo algorithm from the R package MCMCpack  [29].
In silico predictions of protein sub-cellular localization Prediction of sub-cellular localization was obtained from LocateP database (www. cmbi. ru. nl/ locatep-db/cgi-bin/locatepdb. py) and by analyzing R. cellulolyticum protein sequences with the SurfG+ program 1. 02  [33]  with default parameter values with a local Galaxy instance (migale. jouy. inra. fr/galaxy/). The protein sequences were moreover analyzed with the last available version of specific tools: SignalP 4. 1 server (www. cbs. dtu. dk/services/SignalP/), LipoP 1. 0 server (www. cbs. dtu. dk/services/LipoP/), SecretomeP 2. 0 server (www. cbs. dtu. dk/services/ SecretomeP/), TMHMM server v. 2. 0 (www. cbs. dtu. dk/services/TMHMM/), selecting the "Gram-positive" option whenever available. The results were manually examined and confronted to the knowledge on R.
cellulolyticum proteins, in particular on cellulosomal proteins and cellulases  [16, 21]. For ABC transporters, the subfamily name was retrieved from the Archaeal and Bacterial ABC Systems database (ABCdb database www-abcdb. biotoul. fr/) and for peptidases, the Peptidase database identity (MEROPS ID) was indicated (merops. sanger. ac. uk/). The mass spectrometry proteomic data are available via ProteomeXchange, identifier PXD001051 and DOI 10. 6019/PXD001051.
In total, 151 proteins showing significantly different levels (Q-value < = 0. 01) were identified and validated (S1 Dataset), including 132 with an effect in the pellets exclusively, 16 with an effect in the supernatants exclusively and 3 with an effect in both. They besides included 20 cellulosomal components (providing information about substrate hydrolysis mechanisms), 8 enzymes from the central carbon catabolism (providing information about the response to intracellular carbon fluxes) and 44 extracytoplasmic proteins (providing information about substrate transport and other specific functions). Carbohydrate-active enzymes (CAZymes) and cellulosomal proteins. Considering their essential role in substrate deconstruction and catabolism, proteins related to cellulolysis and CAZymes were specifically examined.
R. cellulolyticum cellulosomes include structural subunits and numerous different catalytic subunits  [16, 17, 20]  encoded by 65 distinct genes  [21]. Their average protein composition is highly modulated according to the nature of the carbohydrate growth substrate  [16, 21]. R. cellulolyticum genome encodes up to 149 CAZymes according to  [21]  and only a subset of them are extracellular components involved in lignocellulose deconstruction since CAZymes participate to a variety of other biological processes. For the present study, a total of 153 proteins from R. cellulolyticum were considered (S1 Dataset) corresponding to its CAZymes and to its known non-CAZyme cellulosomal structural subunits. Among these 153 proteins, 103 were quantified in at least one sample and 28 showed significantly different levels when comparing growth on Tissue and Whatman Paper (Tables  2 3 4 ).
Hemicelluloses from Tissue were very likely at least partly fermented during the incubations since 3 intracellular proteins involved in xylose or xylose oligomer catabolism (encoded by Ccel_0203, Ccel_3438, Table  4 , and Ccel_3429) had higher levels in Tissue incubations, suggesting a higher xylodextrin intracellular influx therein. It is however unclear whether the higher abundance of xylose in Tissue specifically induced the higher expression of certain xylanases. Indeed, cellulosomal proteins encoded by the "xyl-doc" cluster (14 genes more specifically oriented towards hemicellulolysis  [16] ) did not show any significant differences, although 8 of them were quantified in the whole dataset (S1 Dataset). The cellulosomal endoglucanase Ccel_0429, presenting higher levels in Tissue incubations, could have participated to hemicellulose deconstruction since it exerts xyloglucan depolymerization as a secondary activity  [39] ; however its regulation mechanisms are unknown.
c) According to  [16, 21]   Predicted or characterized (in bold) activities or protein names according to  [16]  and to UniprotKB database. doi:10. 1371/journal. pone. 0170524. t002 genome). Among these endoglucanases, 10 had significantly lower levels in the Tissue incubations, particularly enzymes from the GH5 and GH9 families (Table  2 ). This result is consistent with the presence of shorter cellulose chains in this Tissue as well as its lower CI. In the closely related Ruminiclostridium thermocellum  [40]  and Ruminiclostridium clariflavum  [41] , it has indeed been shown that the levels GH9 endoglucanases, which are also very common in their cellulosomes, are influenced by the substrate's nature, with increased levels in the presence of crystalline cellulose. Overall, CAZyme expression appears to be influenced here more by substrate structure than by its carbohydrate composition, although the present experiments do not provide a fully formal proof.
Central carbon metabolism. To determine whether the level of sugar influx affected the expression of enzymes from the central carbon metabolism, enzymes from the glucose and  The column titles are identical to those from Table  2. doi:10. 1371/journal. pone. 0170524. t003 xylose catabolic pathways of R. cellulolyticum (Fig 2 and S1 Dataset) were specifically examined. Among them, 23 were successfully quantified in at least one of the pellet samples and statistical models could be adjusted for 18 of them. Finally, 8 proteins showed statistically significant effects: the glucose-6-phosphate isomerase (product from Ccel_1445, pgi), the ATPdependent 6-phosphofructokinase (Ccel_2612, pfkA), the xylose isomerase (Ccel_3429, xylA), the glyceraldehyde-3-phosphate dehydrogenase (Ccel_2275), the phosphoglycerate kinase (Ccel_2260), the phosphoglycerate mutase (Ccel_0619), the 2,3-bisphosphoglycerate-independent phosphoglycerate mutase (Ccel_2259, gpmI) and the phosphopyruvate hydratase, also known as enolase (Ccel_2254, eno).
Predicting the subcellular localization of proteins is a complex issue  [43]. To identify extracytoplasmic proteins in the present dataset, the 115 proteins showing at least one statistically significant effect when comparing growth on Tissue and Whatman Paper (other than those already described in the result section on CAZymes and cellulosomal proteins) were specifically analyzed in silico. Among the 115 manually examined proteins, 44 had a predicted extracytoplasmic localization and the detailed results are shown in S3 Table. For 36 of these 44 proteins, the prediction can be considered as very robust since at least both SurfG+ and Loca-teP indicate an extracytoplasmic localization (with however sometimes distinct predicted subcellular localizations). Noticeably, the 44 selected proteins include 8 components of ATP-binding Cassette (ABC) transporters (Table  5 ) and 4 of them, encoded by Ccel_1987, Ccel_2997, Ccel_0998 and Ccel_1133, are likely involved in sugar transport based on blastp analyses against the manually curated part of ABCdb (www-abcdb.
cellulolyticum. For instance, the ABC transporter specificities are poorly described for this species. Strikingly, among the 16 proteins of unknown function present in the dataset of the 151 significant proteins, 13 correspond to predicted extracytoplasmic proteins (S3 Table ). Better characterizing transporters and other extracytoplasmic proteins from R. cellulolyticum thus appears of great more or decreasing less in Tissue incubations than in Whatman Paper incubations (substrate-by-time interaction effect). The statistical models (see Materials and Methods) take into account the replicates and their variability. Protein names and EC numbers are indicated in grey. * indicate effects not significant when adjusting for multiple comparisons (Q-values > 0. 01) but still supporting the overall trend (p-values < = 0.
strate and the effect of time and substrate interaction. Illustrative examples of such effects are provided in S6 Fig. Proteins were extracted from the pellets and supernatants separately at incubation times 46h and 70h (S7 Fig, panel A) and analyzed by LC-MS/MS. At the selected time points, fermentation products were just starting to significantly accumulate (Fig 1A-1C) and the biomass was actively growing (Fig 1F), limiting the possible effects of inhibitors accumulating in batch microcosms. A total of 1194 proteins were quantified by the XIC approach (S1 Dataset). A good reproducibility was obtained (S7 Fig, panel B) and the identified functions were consistent with cellulose fermentation (S8 Fig, S2 Table.
c) Subcellular localization predicted by SurfG+. PSE: potentially surface exposed; MB: membrane. d) Subcellular localization from LocateP database. Lipid anch. : Lipid anchored; Memb: Multitransmembrane;. N-ter anch. : N-terminally anchored (No cleavage site). doi:10. 1371/journal. pone. 0170524. t005.
Functional profiles for all proteins encoded in R. cellulolyticum genome and for the proteins showing significantly different levels in the presence of Tissue compared to Whatman Paper. A selection of 32 Gene Ontology (GO) terms is shown, corresponding to the categories with highest percentages of annotations and to the most enriched or depleted categories when comparing the dataset of proteins with significantly different levels (after removal of categories with less than 3 proteins with significantly different levels) and all genome-encoded proteins. The GO terms are shown from the most enriched to the most depleted, from top to bottom. R. cellulolyticum genome encodes 3290 proteins, of which 2081 have GO annotations in UniprotKB, corresponding to a total of 3674 GO annotations. 151 proteins showed significantly different levels, of which 116 have GO annotations in UniprotKB, corresponding to a total of 385 annotations.
Numeric values and additional details are shown in S2 Table. (TIF) S1 Dataset. Summary of the quantitative results obtained for each protein of R. cellulolyticum. (XLSX) S1 Table. Carbon mass distributions in the microcosms at the initial and final incubation time points. The carbon masses are in mg. The average values (± one standard deviation where relevant) are shown. Cellulosic material: carbon mass in the substrate (contained either in Tissue, Whatman Paper or Cotton)-DOC: organic carbon mass in the liquid phase (Dissolved Organic Carbon)-DIC: inorganic carbon mass in the liquid phase (Dissolved Inorganic Carbon)-CO2 gas: carbon mass in CO2 in the headspace-Sampled: carbon mass removed from the microcosms through sampling of the liquid phase-Total: total carbon mass in the microcosms at the initial incubation time point-Degradation yield: estimated percentage of degraded carbon from the substrate.
a) BP: Biological Process-CC: Cellular Component-MF: Molecular Function. b) For each GO term, percentage of the GO term annotations among the significant proteins. c) For each GO term, percentage of the GO term annotations among all proteins encoded in R. cellulolyticum genome. d) Ratios of both percentages. The GO terms are presented by decreasing ratio values. See legend from S7 Fig for additional details. (XLSX) S3 Table. Extracytoplasmic proteins with at least one statistically significant effect when comparing incubations on Tissue and Whatman Paper. The proteins are listed according to the observed effects, to the subcellular localization predicted by SurfG+ and according to their function. a) The Gene IDs (Ccel_) are given according to UniprotKB database. b) The Protein names are given according to UniprotKB database.
e) Subcellular localization predicted by SurfG+. PSE: potentially surface exposed; EXT: extracellular milieu; CYTO: cytoplasm; MB: membrane. f) Subcellular localization from LocateP database. Lipid anch. : Lipid anchored; Released: Secretory (released) (with cleavage site); Memb: Multi-transmembrane;. N-ter anch. : N-terminally anchored (No cleavage site); Intracell. : Intracellular. g) SignalP predictions concerning the presence of a signal peptide. Y: yes; N: no. h) SecretomeP predictions concerning the secretion by a non-classical pathway (without signal peptide). Y: yes; N: no. i) LipoP predictions concerning lipoproteins and signal peptides. SpII: lipoprotein signal peptide (signal peptidase II); SpI: signal peptide (signal peptidase I); TMH: n-terminal transmembrane helix (this is generally not a very reliable prediction according to LipoP website); CYT: cytoplasmic (all others).