dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Universitede Bordeaux , the  Agence Nationale de la for BSF grown in CMM_Glyc/GlcNAc. Recherche (ANR)  through grants  ACETOTRYP  of the  ANR-BLANC-2010 call and GLYCONOV of the "Generique " call, the Laboratoire.
Acknowledgements This work was funded by the  Australian Research Council  through a  Discovery Early Career Research Award  (Project #:  DE130101458 ). G. W. G. would like to thank the  ARC  for their support. G. W. G. also thanks  Lubris Biopharma  for supporting this research through a gift of their recombinant lubricin materials. G. W. G. would also like to thank  Dr. Noelene Quinsey  and the  Monash Protein Production Unit, Clayton, Monash University, Victoria  for their assistance with the purification of the native bovine lubricin protein and  MC Herd (Corio, VIC Australia)  for allowing access to their facilities for the collection of raw synovial fluid.
Data and statistics In this review, we aimed to express all energy density values in kJ g -1 DW for species comparison and in kJ g -1 WW for use in ecological studies. When possible, the energy density values obtained from the literature were recalculated to kJ g -1 DW and/or kJ g -1 WW using given energy densities, species weights or water contents reported in the references concerned. Energy density values, determined by proximate composition, were calculated by the original authors using a factor of 36. 40 kJ g -1 AFDW for the conversion of the lipid fraction, unless stated otherwise. Protein values represent actual measurements derived from true protein content analysis. When crude protein measurements were used in the original paper, this is specified. We also calculated energy densities from references reporting only proximate composition values (usually given in %WW) using the above mentioned conversion factors.
When the carbohydrate fraction was not given in the source, we assumed it to be the remainder of 100% minus the other fractions (water content, lipids, carbohydrate, protein, ash, and where relevant, chitin). The lengths of fish reported in this review are given in standard length (SL), measured from the most forward part of the head to the end of the vertebrae. Some lengths are given in total length (TL), which is measured from the most forward part of the head to the end of the caudal fin. Previously unpublished data obtained during two expeditions have been included in this review. Individual zooplankton and nekton species were collected on board the RV Polarstern in the Weddell Sea (PS81: August-October 2013) and in the Lazarev Sea (PS89: December/January 2014/2015), using Rectangular Midwater Trawls (RMT) and Surface and Under-Ice Trawls (SUIT).
Benzoic acid (29. 62 kJ g -1 DW) was added to samples that were too small to obtain a minimum sample weight of 0. 5 g. Some jelly fish body parts did not combust in the bomb calorimeter, most likely due to high ash contents (> 75%DW). These tissues were then measured again using a sample consisting of half tissue, half benzoic acid. The AFDW of the jelly fish was obtained by drying a homogenized sample to constant mass at 60 °C, followed by 6 h incineration at 500 °C. In datasets with a sufficient sample size, energy densities were compared using two-way ANOVA followed by a non-parametric Tukey's HSD post hoc test. Linear relationships between DW and energy content were established using ln-transformed data (Van de  Putte et al. 2006). Linear relationships between water content and wet weight energy density were also investigated.
Slopes and intercept of regression models were compared using ANCOVA  (Hartman and Brandt 1995). All analyses were performed with R version 3. 3. 1 (R Core Team 2015). Seasons listed within the tables are defined as stated by the authors, or as summer for December to February, autumn for March to May, winter for June to August and spring for September to November. It should be kept in mind that environmental conditions may vary within a month depending on region. All data used in this review, including the previously unpublished data, are available as part of the SCAR Southern Ocean Diet and Energetics Database, which is a compilation of diet and energetics data from Southern Ocean studies. More information on use and contributing can be found at https ://www. scar. org/data-produ cts/south ern-ocean -diet-energ etics /.
Note that one literature source can contain multiple energy density records, for instance of different species or developmental stages, and that, therefore, one point does not represent one literature source. b Overview map of energy density records, including several fronts. One point on the map represents one source. Therefore, a single point can include multiple measurements on a single species or measurements of multiple species from a single group. Approximate locations were derived from the source material. The map was made using Quantarctica from the Norwegian Polar Institute  (Matsuoka et al. 2018). Mean front positions were taken from  Solokov and Rintoul (2009). Previously unpublished data are included In terms of energetic measurements, fish are the most studied organisms in the Southern Ocean.
2014). The neritic species composition differs between the continental areas, SIZ and around the (sub-)Antarctic islands  (Kock 1992). In some species, the larval stages have a different (vertical) distribution pattern than adult individuals of the same species (e. g. Hubold 1990 ). The availability of previously unpublished data and data of individual fish kindly provided by colleague researchers, allows for a more detailed description and analysis of the energetic density of the nototheniid Pleuragramma antarctica, the myctophids Electrona antarctica, Gymnoscopelus braueri and the bathylagiid Bathylagus antarcticus.
Therefore, as recommended by  Van de Putte et al. (2006) , it is useful to separate energy densities in age or size classes, using distinct energy densities for each age group. In particular, in trophodynamic studies and research on prey utilization of species, as predators are known to often feed on a particular prey size  (Van Franeker et al. 2001). However, again more data are needed to see if size/energy density relationships show a general trend rather than an incidental occurrence, and if differences are found, to be able to characterize the size classes between which differences occur. The available data on Gymnoscopelus braueri show an example where there is a (negative) relationship in one dataset, but none in the other  (Tierney et al. 2002; Van de Putte et al. 2006). Furthermore, it is currently unclear how energy density in young fish is allocated because not all small specimens show increased energy density with decreasing water content, as would be expected  (Tierney et al.
Acknowledgements We are grateful to the captains and crew of RV Polarstern during expeditions  PS81  and  PS89 , and to  Michiel van Dorssen ,  Martina Vortkamp ,  André Meijboom ,  Giulia Castellani ,  Julia Ehrlich ,  Bram Fey  and  Carmen David  for their contribution to sample collection during these expeditions. We thank  Evgeny Pakhomov  and  Brian Hunt  for providing fish and jelly fish samples during PS81. Thanks to  Sander Holthuijsen ,  Felipe Oliveira Ribas  and  Henk van der Veer  ( Netherland Institute for Sea Research ) for the use of their bomb calorimeter and their help with the analysis. Thanks to  Megan Tierney  and  Jaime Färber-Lorda  for providing individual data. We thank the Editor  Dr A. Atkinson  and the reviewers for their comments on a previous version of the manuscript.
Sample size The sample size was calculated based on the primary outcome, namely the occurrence of de novo CKD at 3 years. Assuming an attrition bias of 10% and a competing risk of death of 20%, we calculated that 1200 patients fulfilling the inclusion criteria should be included according to the following distribution: 700 AKI patients who would fully recover kidney function at ICU discharge, 150 with impaired kidney function at ICU discharge and 350 with preexisting CKD. According to epidemiological data provided by the multicenter prospective observational study CUB-REA  [10]  and assuming an inclusion rate of 50% of the patients fulfilling the inclusion criteria, we estimated that the inclusion pace could be 5 patients/center/month leading to study completion within 24 months.
Pre-planned ancillary analysis: 10-year follow-up We plan to extend patient follow-up up to 10 years through record linkage between the PREDICT database and the following datasets: • The national Renal Epidemiology and Information Network (REIN) registry to identify ESRD events (dialysis and kidney transplantation)  [13]  • The national death registry (RNIPP, Registre national d'identité des personnes physiques) to assess vital status and identify cause(s) of death. • The national health insurance information system (SNIIR-AM) to collect data about patient use of health resources. Patient consent for this ancillary study will be requested at inclusion in the main study.
Data collection, monitoring and data analysis During the visits, data will be collected in an electronic case report form (eCRF) using CleanWEB software (TéléMédecine ® , Boulogne, France). This data management system allows direct data entry. Data entry will be monitored by an independent researcher according to a predefined monitoring plan. Patient confidentiality will be ensured by using identification numbers. Patient baseline characteristics will be compared between AKIN categories at inclusion using analysis of variance or Mann-Whitney test and Pearson's Chisquare test for continuous and categorical data, respectively. Factors associated with main outcome will be evaluated using a competing risk survival analysis. Factors associated with the main outcome in univariate analysis with a p value lower than 0.
Sequence analysis to identify putative retroelement insertions was performed using the TEBreak bioinformatic pipeline (https://github. com/adamewing/tebreak) and intersected with known nonreference genome TE insertions found in a wide array of mouse strains  (Nellaker et al. 2012 ). Among the 10 animals examined, we bioinformatically identified 748 polymorphic nonreference insertions, including 154 events comprising 42 SINE (11 B1 and 31 B2), three LTR (all ETn), and 109 L1 (48 A, 19 G F , and 42 T F ) insertions not found in the parental FVB or 129 mouse strains, or other sequenced strains (Supplemental Table  S2 ). Of the 154 insertions, 141 (91. 6%) were found in multiple animals, demonstrating that the vast majority of polymorphic insertions we found were vertically inherited, rather than occurring de novo in the animals analyzed here.
Human ICC WGS analysis Reads were aligned to the human genome reference sequence (hg19 build) with BWA-MEM  (Li 2013) , duplicates were marked with Picard MarkDuplicates (http://broadinstitute. github. io/ picard), and indel realignment was carried out with GATK  (DePristo et al. 2011). Quality assessment via FastQC identified no issues with sequence quality or content, duplication, overrepresented sequences or k-mers. Point mutations (SNVs and indels) were detected with MuTect 2  (Cibulskis et al. 2013) , and structural rearrangements were detected using Manta  (Chen et al. 2016). CNV profiles were derived from normalized tumor vs. normal read depth in 150-kbp windows using a Python script deposited on GitHub (https://gist. github. com/adamewing/70c8819f6f1eec 0ffbdafa6210f0f677) and also available in Supplemental Materials.
TE insertion detection WGS-and RC-seq-aligned BAMs (mouse: mm10 build, human: hg19 build) were processed with TEBreak (https://github. com/ adamewing/tebreak and Supplemental Materials) to detect nonreference transposable element insertions. Further post-filtering on detected insertions was performed as follows: Reads supporting TE insertions must align to TE consensus sequences at ≥90% identity and to the reference genome at ≥99% identity. TE insertion calls must be supported by at least eight split reads and eight discordant read pairs for mouse and at least four split reads and four discordant read pairs for human. At least 50% of all split reads and discordant reads surrounding an insertion call must be attributable (i. e. , remappable) to that insertion. For SINEs, insertions detected with 5 ′ truncations of more than 1 bp, or with 3 ′ truncations, or presenting low complexity TSDs (e.
Relative expression levels of KHDRBS2 were calculated using three technical replicates and normalized to TBP. Statistical analysis was performed with Prism5 (GraphPad Software), applying a two-way ANOVA followed by Tukey's post hoc analysis. Pr. Denis Castaing, Pr. Antonio Sa Cunha, and Pr. Eric Vibert, who are surgeons in the Centre Hépatobiliaire, Villejuif, and the Tissue Biobank Group (AP-HP and Université Paris-Sud) for providing human specimens. We thank members of the Faulkner laboratory for helpful advice and discussion. This study was supported by CSL Centenary Fellowship and National Health and Medical Research Council (NHMRC) Project Grant (GNT1042449, GNT1045991, GNT1067983, GNT1068789, and GNT1106206) funding to G. J. F. , the EU FP7 under grant agreement No. 259743 underpinning the MODHEP consortium to J.
Data access RC-seq, mRC-seq, and WGS data from this study have been submitted to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena/) under project accession number PRJEB18756. Sanger trace files from this study have been submitted to the NCBI Trace Archive (http://www. ncbi. nlm. nih. gov/Traces/home/ index. cgi) with TI numbers 2344290281-2344290314.
, 2011)  showing the product of local order parameter and overall-tumbling correlation time constant, S 2 $t c , for apo (black) and Ggc1-bound TIM9$10 (orange). In the lower panel, the apo data have been multiplied by an overall factor (1. 9) minimizing the offset of the two datasets. Error margins were determined from the spectral noise level, used in fitting the 3Q relaxation buildup curves; see STAR Methods. (legend continued on next page) Precursor Proteins Are Bound as a Dynamic Ensemble with Residual Secondary Structure Having shown the essential role of the precursor protein binding motif in vivo and in vitro, we derived a complete description of the structure and dynamics of apo TIM9$10 and of the precursor protein-chaperone complex at atomic resolution. We first studied the flexibility (picosecond-to-nanosecond motions) of apo TIM9$10 using backbone 15 N and ALV 13 CH 3 nuclear spin relaxation measurements and molecular dynamics (MD) simulation.
Data-Guided Molecular Dynamic Simulations Reveal a Dynamic Ensemble We derived an ensemble description of this highly dynamic complex by integrating the information from all NMR and SAXS data into molecular dynamics simulations. Briefly, we constructed different conformations of the TIM9$10/Ggc1 complex by placing two TIM hexamers into the SAXS-envelope and placing the preprotein using the NMR data as restraints and performed coarse-grained molecular dynamics simulations to sample a large conformational space (Video S1). The conformational distribution was subsequently reweighted to match the SAXS data using an ensemble refinement method (Figures  S6F-S6I ). In the ensemble of structures (Figure  7E ) the precursor protein is wrapped into the clamp-like binding sites of two hexameric modules, makes extensive contacts to the hydrophobic motif, and dynamically samples multiple conformations.
Analytical ultracentrifugation Sedimentation velocity experiments were performed on the Biophysical Platform AUC-PAOL at IBS Grenoble, on an analytical ultracentrifuge XLI, with a rotor Anti-50 (Beckman Coulter, Palo Alto, USA) and double-sector cells of 12 mm and 1. 5 mm optical path length equipped with Sapphire windows (Nanolytics, Potsdam, Germany). Samples were measured in NMR buffer at 10 C at three different concentrations from 0. 2 to 0. 002 mM, and interference and absorption at 277 nm wavelength were recorded. The data were processed by Redate software v 0. 2. 1. The analysis was done with the SEDFIT software, version 14. 7 g and Gussi 1. 1. 0a6.
To obtain TIM [ 2 H]9[ 2 H, 13 C, 15 N]10, the protocol was repeated with [ 2 H, 13 C, 15 N] His-tagged Tim10 and [ 2 H] Tim9. The following experiments were recorded: 2D 15 N-1 H-BEST-TROSY HSQC, 3D BEST-TROSY HNCO, 3D BEST-TROSY HNcaCO, 3D BEST-TROSY HNCA, 3D BEST-TROSY HNcoCA, 3D BEST-TROSYHNcocaCB and 3D BEST-TROSY HNcaCB  (Favier and Brutscher, 2011). Methyl groups Ala b , Leu proS and Val proS were assigned using 3D 1 H-1 H-NOESY-13 C-HSQC and 4D 15 N-HSQC-NOESY-13 C-HSQC with 300ms mixing time. The assignment of the Ala b 13 C resonance was independently confirmed from the HNCACB experiment. Data were processed in NMRPipe  (Delaglio et al. , 1995)  and analyzed with CCPNMR  (Vranken et al. , 2005). Resonance assignments of backbone and ALV methyl sites were deposited in the BioMagResBank under accession number BMRB: 27052.
H intermolecular cross-relaxation experiments for measuring transferred NOEs To detect the contact surface of Ggc1 with TIM9$10, we probed the effect of 1 H spin saturation transfer, i. e. , a NOE-type transfer, from unlabeled Ggc1 to a deuterated 13 CH 3 -ALV-labeled TIM9$10. The principle of this measurement is identical to previously proposed HET-SOFAST experiments  (Schanda et al. , 2006) , except that we apply this concept here to methyls, rather than backbone amides. The effect of saturation transfer, i. e. , cross-relaxation from the protons of Ggc1 to TIM9$10, is monitored by measuring the intensity of TIM9$10 methyl peaks in two experiments. The first dataset (I ref ) is a standard 1 H-13 C SOFAST experiment, using a recycle delay of 0. 2 s. In a second experiment (I sat ), an inversion pulse is applied at the beginning of the recycle delay, which inverts protons of Ggc1.
5 mM MgCl 2 , pH 7. 25). The mixture was incubated for 10 min on ice before UV-irradiation for 30 min at 4 C using a Blak-Ray B-100 AP UV lamp at a distance of 10 cm from the samples. After UV-illumination, the samples were mixed with 4x Laemmli buffer, incubated at 95 C for 5 min and analyzed by SDS-PAGE and silver staining. Small-angle X-ray scattering data collection and analysis SAXS data were collected at ESRF BM29 beam line  (Pernot et al. , 2013)  with a Pilatus 1M detector (Dectris) at the distance of 2. 872 m from the 1. 8 mm diameter flow-through capillary. Data on TIM9$10 were collected in a batch mode. The scattering of pure water was used to calibrate the intensity to absolute units. The X-ray energy was 12. 5 keV and the accessible q-range 0. 032 nm -1 to 4. 9 nm -1. The incoming flux at the sample position was in the order of 10 12 photons/s in 700x700 mm 2.
All images were automatically azimuthally averaged with pyFAI  (Ashiotis et al. , 2015). SAXS data of pure TIM9$10 was collected at 1, 2. 5 and 5 mg/mL using the BioSAXS sample changer  (Round et al. , 2015). Ten frames of 1 s were collected for each concentration. Exposures with radiation damage were discarded, the remaining frames averaged and the background was subtracted by an online processing pipeline  (Brennich et al. , 2016). Data from the three concentrations were merged following standard procedures to create an idealized scattering curve of TIM9$10, using Primus from the ATSAS package  (Petoukhov et al. , 2012). The pair distribution function p(r) was calculated using GNOM  (Svergun, 1992). Online purification of the TIM9$10/Ggc1 complex using a NiNTA column was performed with a high pressure liquid chromatography (HPLC) system (Shimadzu, France), as described in reference  (Brennich et al.
, 2017). The HPLC system was directly coupled to the flow-through capillary of SAXS exposure unit. The buffer used in this experiment was Buffer A. The flow rate for all online experiments was 0. 2 mL/min. Data collection was performed continuously throughout the chromatography run at a frame rate of 1 Hz. The stoichiometry of the individual peaks in the chromatogram was determined from molecular mass estimates based on the correlated and Porod volumes  (Petoukhov et al. , 2012)  and by calculating the ratios of maximum forward scattering and maximum absorbance at 280 nm for each peak and comparing to the theoretical values of different stoichiometries. For creating background corrected scattering curves for the TIM9$10:Ggc1 2:1 complex peak, measurements of buffer with the lowest (200 mM) and the highest (300 mM) possible imidazole concentration, as well as interpolated curves between these two extremes, were subtracted individually from each frame.
The resulting curves were independent of protein concentration, as shown with procedures outlined by  Hynson et al. (2015). Subtraction of the highest imidazole concentration provided a stable signal throughout the center of the peak. 50 frames from the middle of the peak were averaged to create a single SAXS curve for the TIM9$10:Ggc1 2:1 complex. For all subsequent analyses, the robustness of the main findings (general shape and dimensions) was confirmed by analogous treatment of curve resulting from the subtraction of the lowest possible imidazole concentration. The pair distribution function p(r) was calculated using GNOM. SAXS data along with their MD-derived models (described below) are shown in Figure  S6  and the experimental and fit parameters are reported in Table  S4. SAXS data reported in this study have been deposited in the SASBDB data bank (https://www.
sasbdb. org) with accession numbers SASDEF2 (TIM9$10) and SASDEG2 (TIM9$10/Ggc1). Molecular dynamics simulations (a) All-atom MD simulation of apo-TIM9$10 We used the crystal structure (PDB ID 3DXR  [Webb et al. , 2006] ) as the initial model of the TIM9$10 hexamer. Missing residues were added using MODELLER9. 18 (Webb and Sali, 2014). The protein was solvated in a cubic box of TIP3P water molecules with sides of 10. 6 nm and periodic boundary conditions. The Amber ff99SB*-ILDN force field  (Lindorff-Larsen et al. , 2010)  was used for the simulations. The temperature and pressure were kept constant at 298 K using the v-rescale thermostat with a 1 ps coupling constant and at 1. 0 bar using the Parrinello-Rahman barostat with a 2 ps time coupling constant, respectively. We employed the virtual sites for hydrogen atoms with a single LINCS iteration (expansion order 6) and constrained the bonds involving hydrogen atoms using the LINCS algorithm, allowing simulations to be performed with an integration time step of 5 fs.
One bead (C A ) is located at the position of the C a atom. The other bead (C B ) is located at the center of mass of the side chain atoms. The basic idea was to combine a structurebased model for the folded chaperone with a more transferable model, which also includes non-native hydrophobic and electrostatic forces, to describe the interactions between the chaperone and carrier protein, and within the carrier protein. More specifically, the energy function of the coarse-grained model can be expressed by: V CG = V SB + V charge + V hydrophobic + V restraints ; where V SB is the structure-based potential to describe the local dynamics of the TIM9$10 hexamers around its crystal structure (PDB ID 3DXR). The V SB term can be divided into local (bonded) and non-local terms. The non-local part of V SB can be further partitioned into two components consisting of an attractive term, which is a function of the native contact map to stabilize the secondary and tertiary structures, and a repulsive term to provide the excluded volume.