dataesr/scientific-paragraphs-categorization huggingface dataset:
Edited by Norma M. Allewell This work was supported in part by the  Intramural Research Program  of the  National Institutes of Health, NCI, Center for Cancer Research ,  Centre National de la Recherche Scientifique , and  Aix-Marseille Université (AMU). This is the fourth article in the  JBC Reviews series "Molecular chaperones and protein quality control. "The authors declare that they have no conflicts of interest with the contents of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 1 Supported by  Agence Nationale de la Recherche  Grant  ANR-16-CE11-0002-01.
Raw data analysis Raw data collected during nanoLC-MS/MS analyses were processed and converted into �. mgf peak list format with Proteome Discoverer 1. 4 (Thermo Fisher Scientific, Waltham, USA). MS/MS data were interpreted using the search engine Mascot (version 2. 4. 0, Matrix Science, London, UK) installed on a local server. Searches were performed with a tolerance on mass measurement of 0. 2 Da for precursor and 0. 2 Da for fragment ions against a composite target decoy database (16118 total entries) built with the Rattus norvegicus Swissprot database (TaxID 10116-September 25, 2019-7941 entries) fused with the sequences of recombinant trypsin and a list of classical contaminants (118 entries). Cysteine carbamidomethylation, methionine oxidation, protein N-terminal acetylation and cysteine propionamidation were searched as variable modifications.
The nebulizer temperature was set to 14˚C. The response range was set to 100 pA full scale. The analysis was performed using a multistep gradient with (A) 0. 1% trifluoroacetic acid in ultrapure water and (B) 0. 1% trifluoroacetic acid in methanol as the mobile phase. Gradient elution started at 99. 5% (A) and 0. 5% (B) for 6 min followed by an increase to 100% (B) at 21 min and then a reduction to 99. 5% (A) and 0. 5% (B) at 25 min for 3 min. The flow rate of the mobile phase was set at 0. 6 mL. min -1 during the 28 min analysis time, and the injection volume was 5 μl of sample. Data processing was carried out with Chromeleon 7. 2 software (Dionex, Sunnyvale, USA), and the peak area (pA. min -1 ) corresponding to glutathione-odorant conjugate was integrated. To determine the enzymatic glutathione conjugation of odorants, HPLC quantifications of the odorant conjugates were systematically performed in the presence and absence of recombinant GSTM2, two conditions representing the total conjugation (enzymatic and non-enzymatic) and the non-enzymatic conjugation, respectively.
Simulation Results Simulation is carried out under the environment Matlab/Simulink for a 200 days period using PID and synergetic controllers. The system is simulated using Equations (  1 )-(  3 ), (  5 ), (  6 ), (  22 ) and (  27 ), process parameters, given in Section 2. 2, and PID parameters, given in Section 3. 2. It is evident, as can be noted from Figure  2 , that good tracking occurs under both controllers, using the parameters of the PID mentioned above given in Reference  [5]  and T = 0. 01 for the synergetic approach. It is observed that PID control provides a faster response than the proposed approach at transitions and in the event of reference variations. Besides PID control presents elevated peaks at transitions. Overall synergetic performance, such as response time and steady-state error are improved compared to PID.
Simulation Results Simulation is carried out under the environment Matlab/Simulink for a 200 days period using PID and synergetic controllers. The system is simulated using Equations (1-3), (  5 ), (  6 ),  (22) , and (27), process parameters, given in Section 2. 2, and PID parameters, given in Section 3. 2. It is evident, as can be noted from Figure  2 , that good tracking occurs under both controllers, using the parameters of the PID mentioned above given in Reference  [5]  and T=0. 01 for the synergetic approach. It is observed that PID control provides a faster response than the proposed approach at transitions and in the event of reference variations. Besides PID control presents elevated peaks at On the other hand, PID control signal requires much more dilution rate D at transitions time than when synergetic control is used as can be seen in Figure  3c.
High-resolution respirometry A 1. 5 × 10 6 cells/mL pellet was resuspended in the same warm (37°C) medium. High-resolution respirometry was carried out using an Oxygraph-2k instrument (Oroboros Instruments GmbH, Innsbruck, Austria). Oxygraph sensors were calibrated once a day before the experiment. Experiments were performed, according to the standard protocol described above. Initially, basal cell respiration (on endogenous substrates) was measured, followed by the addition of 1 μM oligomycin, then maximal OCR was determined by titrating with 0. 0275 μM of FCCP, followed by the addition of 1 μM rotenone plus antimycin A. Respirometry data were calculated using the DatLab 5. 0 software and normalized to the cell count in the chamber.
Occurrence data We used two sets of occurrence data at the species and at the gene pool levels. At the species level, a total of 606 georeferenced occurrence data  (E. ivorense, n = 150, E. suaveolens, n = 206 and E. africanum, n = 250, fig. 1A)  were available from herbarium databases [Conservatoire et Jardin botaniques de la Ville de Genève (CJB) and the South African National Biodiversity Institute (SANBI)]. These samples cover the whole range of each species. At the gene pool level, 148 georeferenced occurrence data were provided from a genetic dataset  (Duminil et al. 2013)  for the two rainforest species, E. suaveolens and E. ivorense in the Upper and Lower Guinean Subcentres. The genetic samples covered only a restricted part of the species range. Duminil et al. (2013)   1B ). Before modelling, we trimmed the occurrences data so that only one point was kept per grid cell of 1 km 2.
Species distribution modelling To model the environmental niche of the species and gene pools, we used the MaxEnt algorithm  (Phillips et al. 2006)  within the R environment (R version 3. 2. 3, R Core Team, Vienna, Austria). To reduce the influence of bias in the spatial sampling of occurrence data we used the weighted-target group approach  (Anderson 2003). The weighted-target group approach consists in selecting background data from points that reflect a sample selection bias similar to that of the occurrence data. We used as background, occurrence data from herbarium and genetic samples of species others than Erythrophleum. Data from herbarium samples were downloaded from the GBIF database (http://www. gbif. fr) and included all the records available for Angiosperms in Africa. Data from genetic samples  (Hardy et al.
2013)  were kindly provided by the "Plant population genetics and community diversity in tropical rainforests" group (Université Libre de Bruxelles, Belgium). We conducted species-and gene pool-specific tuning for the three species and for the five gene pools studied using the R package 'ENMeval'  (Muscarella et al. 2014). We built SDMs with regularization multipliers ranging from 0. 5 to 3 (increment 0. 5) and from 4 to 6 (increment 1) and with six different feature-class combinations (L, LQ, H, LQH, LQHP, LQHPT; where L corresponds to linear, Q to quadratic, H to hinge, P to product and T to threshold). The Akaike Information Criterion (AIC) was used for model selection. The performance of the selected models was evaluated using four criteria, the Minimum Training Presence omission rate (OR mtp ), the True Skill Statistic (TSS,  Allouche et al.
Quantifying niche overlap and conservatism We quantified and tested the niche conservatism or divergence among pairs of related species and gene pools. Pairs have been constructed by minimizing the phylogenetic and the geographic distances. Niche overlap among pairs was estimated using the Schoener's D statistic  (Schoener 1968 , Warren et al. 2008)  with the 'PhyloClim' package  (Heibl & Calenge 2011). The value of D ranges between 0, when two species/gene pools have no niche overlap, and 1, when the two species/gene pools share the same environmental space. To evaluate niche conservatism between related species and gene pools, two randomization tests were run: the niche identity test and the niche similarity test (the latter is also called "background test",  Warren et al. 2008). For the identity tests, 100 pseudoreplicates were created from the pooled localities for each pair of species/gene pool and D values were calculated for each of the pseudoreplicate models.
ACKNOWLEDGEMENTS We thank the curators and directors of the herbaria CJB and SANBI and the " Plant population genetics and community diversity in tropical rainforests" group  ( Université Libre de Bruxelles, Belgium ) who kindly provided the occurrence data. Further thanks are due to  Ghislain Vieilledent (France), Olivier Hardy (Belgium) and Dakis-Yaoba Ouédraogo (Belgium)  who provided helpful comments on preliminary versions. The manuscript has been kindly edited by  Prof. Mike Swaine  ( United Kingdom ). Financial support of the  NGO Nature+ (Belgium)  is gratefully acknowledged. This research was supported by  Gembloux Ago-Bio Tech, University of Liege.
DIAGURO case-control study Recruitment was conducted in the urological department of the Protestant Clinic (Lyon, France) during 2016-2017. Clinical cases included patients with post-surgery histological confirmation of primary or recurrent UC (BC or/and UTUCC (ureter or renal pelvis)) at any stage and grade. Controls were patients with urological pathological conditions other than UC or undergoing colonoscopy. Inclusion and exclusion criteria are summarized in the appendix (p1). Clinical and epidemiological data were collected. Institutional review boards of the International Agency for Research on Cancer and the French Ministry of Scientific Research approved the study protocol (IARC Ethics Committee. , Project No. 15-23 and CCTIRS No 15. 514bis). Written informed consent was obtained for all participants.
Acknowledegments The authors thank the  French Cancer League  and the  French Foster Research in Molecular Biology  for their financial support. The funders had no role in study design, data collection, data analysis, interpretation or writing of the report. The authors would like to thank all patients participating in the case-control studies,  Thierry Degoul , director of the Protestant clinic for his support to the  DIAGURO  project and the clinical research team members who supported the sample collection (From the  Urology department of the Protestant Clinic of Lyon, France ;  Aurélie Couyotopoulo ,  Emilie Laurent ,  Emilie Morcillo ,  Noemie Tarride ,  Estelle Maillet ,  Véronique Richard ,  Audrey Franchi ,  Odette Jaume ,  Rachel Maynard ). We also thank  René Lattes  for precious support in fund raising,  Jean-Damien Combes  for legal advice,  Hélène Renard  for technical assistance in sample collection,  Nolwenn Saunier  and  Isabelle Rondy  for administrative support and  Katarzyna Szymanska  for scientific editing.
Funding sources The study was supported by the  French Cancer League  for laboratory consumables, data collection and analysis and by the  French Foster Research in Molecular Biology  for purchase of equipment. SM was supported by the  French Association for Research on Molecular Biology. OL, PF and TMD were supported by the  French Cancer League. The work reported in this paper was undertaken during the tenure of PHA's and MIH's postdoctoral fellowships from the  International Agency for Research on Cancer , partially supported by the  European Commission FP7 Marie Curie Actions -People -Co -funding of regional, national and international programmes (COFUND). The corresponding author declares that she had full access to all data in the study and had final responsibility for the decision to submit for publication.
Within the His_Kinase_A clan four different families currently classify HKs according to DHp domain sequences: i) HisKA (PFAM family PF00512), ii) HisKA_2 (PF07568), iii) HisKA_3 (PF07730) and iv) HWE_HK (PF07536). Additionally, HK can be classified according to their CA domain: HATPase_c (PF02518), HATPase_c2 (PF13581) and HATPase_c5 (PF14501), the latter including examples of HKs with novel secondary structure elements  [31]  compared to the most populated family, the HATPase_c. HisKA (covering ~80% of all DHp-containing HKs) and HisKA_3 (~15%) DHp domains, as all DHp sequences, are predicted to contain two antiparallel α-helices forming a helical hairpin that, when dimerized, form a 4-helical bundle. Very few examples of monomeric HKs have been reported, corresponding exclusively to members of the peculiar HisKA_2/HWE_HK group  [32].
80 mm 3 ) were in 1. 9 M malonate pD 5. 9 with 40 mg/ml protein concentration (drop volume 50 µl; protein-to-buffer ratio of 1:1). For the crystal of the S52P/ tafamidis complex (~0. 11 mm 3 ), a protein concentration of 20 mg/ml and tafamidis concentration of 678 μM in 10% D-DMSO (protein-to-ligand molar ratio of 1:2) were used (drop volume 40 µl; protein-to-buffer ratio of 1:1). For neutron data collection, the crystals were mounted in quartz capillaries and surrounded by a small amount of mother liquor from the crystallization well. The capillaries were sealed tightly using wax to eliminate the diffusion of gas and atmospheric water. Neutron data collection and processing. Details of the neutron data collection for WT-TTR have been reported by Haupt et al  49. Neutron quasi-Laue diffraction data from the crystals of the S52P mutant and the S52P/tafamidis complex were collected at room temperature using the LADI-III diffractometer  50  at the Institut Laue-Langevin (ILL), Grenoble.
For the S52P mutant, a neutron wavelength range (Δλ/λ = 30%) of 2. 9-3. 9 Å was used, with data extending to 1. 8 Å resolution. As is typical for a Laue experiment, the crystal was held stationary at different φ (vertical rotation axis) settings for each exposure. A total of 26 images (exposure time of 12 h per image) were collected from two different crystal orientations. For the S52P/ tafamidis complex, a neutron wavelength range of 2. 7-3. 6 Å was used with data extending to 2. 0 Å resolution. In total, 13 images were collected (exposure time of 8 h per image) from three different crystal orientations. The neutron diffraction images were indexed and integrated using the LAUE suite program LAUEGEN  51. The program LSCALE 52 was used to determine the wavelength-normalisation curve using the intensity of symmetry-equivalent reflections measured at different wavelengths and to apply wavelength-normalisation calculations to the observed data.
The data were then merged in SCALA  53. Relevant data collection statistics are summarised in Supplementary Table  2. Neutron monochromatic diffraction data for the T119M mutant crystal were collected at room temperature using the instrument BIODIFF  54  operated by the Forschungsreaktor München II (FRM II) and the Jülich Centre for Neutron Science (JCNS) at Garching. For data collection, the wavelength was set at 2. 67 Å (Δλ/λ = 2. 9%). A total of 202 frames were recorded with a rotation range of 0. 35°and an exposure time of 53 min per frame. The diffraction data were indexed, integrated and scaled using HKL2000 55 to a resolution of 1. 85 Å. The output file of HKL2000 in SCA format was converted into MTZ format by using scalepack2mtz program in the CCP4 suite  56. Relevant data collection statistics are summarised in Supplementary Table  2.
X-ray data collection and processing. X-ray diffraction data of the crystals of all three variants were recorded on beamline ID30B 57 at the European Synchrotron Research Facility (ESRF), Grenoble using a heavily attenuated X-ray beam (1. 96% for S52P; 19. 17% for T119M; 9. 83% for S52P/tafamidis) of wavelength 0. 9763 Å. For S52P and S52P/tafamidis complex, the same crystals used for data collection at LADI-III was used; whereas for T119M, a crystal from the same crystallization drop as the one used at BIODIFF was used. Data were recorded at room temperature on capillary-mounted crystals. Data were processed to the same maximum resolution of the corresponding neutron data with XDS 58 , scaled and merged with SCALA  53  , and converted to structure factors using TRUNCATE in the CCP4 suite  56.
Relevant data collection statistics are summarised in Supplementary Table  2. Joint neutron and X-ray structure refinement. The PDB structures 5CLX (refined X-ray models of TTR S52P; data collection at 100 K) was used as starting models for joint X-ray and neutron refinement against the datasets of S52P and S52P/tafamidis; and 5CM1 (refined X-ray models of TTR T119M; data collection at 100 K) was used for the datasets of T119M. The phenix. refine program  59  in the PHENIX package  60  was used for refinement. The preparation of the starting model, the refinement settings and workflow, and the modelling of solvent molecules were as detailed in Haupt et al. 41. Using the ReadySet option in PHENIX  60  , exchangeable hydrogen and deuterium atoms were placed at appropriate sites of the protein models with deuteriums elsewhere.
The relative abundance of the tetramers was calculated from the peak area of the 13 + to 15 + charge states and expressed as a percentage of the total area of the peaks assigned to the tetrameric TTR. For the experiment of mixing HS52P and DS52P in the presence of tafamidis, tafamidis was in 0. 5% DMSO at a TTR:tafamidis molar ratio of 1:1. In this case, the relative abundance was calculated from the peak area of the 12 + to 14 + charge states. The presence of DMSO resulted in broader peaks on the MS spectra and less charge on the protein particles. The MS data were fitted by Bayesian regression using a first order kinetics model. Native MS data fitting and parameter estimation. Bayesian regression of the native MS data was performed in python using the PyMC3 library  64. The following one-phase exponential decay model was used: yðtÞ ¼ c þ ðy 0 À cÞe Àkt ð1Þ where t is the time (in minutes), y(t) is the relative abundance of each species (in percentage), y 0 is the abundance at t = 0, c is the abundance at t = ∞ and k the rate constant (in inverse minutes); c < y 0 for dissociating species, and c > y 0 for associating species.
8 for atomistic simulations. MD trajectories were analysed and Q calculated with scripts written in python and using the mdtraj library  73. Partial Least-Squares Functional Mode Analysis (PLS-FMA)  34, 35  was performed on the main chain of the proteins and using the fraction of native contacts as the functional property of interest. In particular, we analysed with PLS-FMA the trajectories of the tetramer simulations, using the fraction of native contacts of each monomeric unit as the measure of unfolding. About 75% of simulation snapshots were used for training and 25% for validation. The model was built using 20 PLS components, which resulted in a Pearson correlation coefficient between the data and the model ≥ 0. 97 for both the training and the validation sets, for both force fields. We refer to the ensemble weighted maximally correlated mode (ewMCM) as described Krivobokova et al.
35  simply as the PLS-FMA mode. Free energy calculations of protein mutation. Alchemical free energy calculations were performed using Gromacs 2016  65  and a custom version of Gromacs 4. 6 that implements the soft-core potential proposed by Gapsys et al. 74. The input hybrid topologies were generated using the pmx python tool  75. The starting structures for the calculations were the WT-TTR (PDB-ID 4PVM), S52P-TTR (PDB-ID 5NFW) and T119M-TTR (PDB-ID 5NFE) protonated at pH 7 with pdb2gmx. The dimeric and monomeric starting structures were obtained from the same neutron structures; for the dimers, chains A and B were used, and for the monomers chain A was used. The unfolded proteins were modelled as capped tripeptides, where the central amino acid was the one being mutated, and the other two were the adjacent amino acids in protein sequence.
Grand canonical monte carlo calculations. The simulation package ProtoMS 3. 3 was used for Grand Canonical Monte Carlo (GCMC) calculations and data analysis  29, 78. The starting tetramer structures for the calculations were the WT-TTR (PDB-ID 4PVM), S52P-TTR (PDB-ID 5NFW), S52P-TTR/tafamidis (PDB-ID 6FFT) and T119M-TTR (PDB-ID 5NFE) with protonation states as resolved experimentally. Proteins were modelled with the Amber ff14SB force field  79  , water with the TIP3P model, and tafamidis with the GAFF/AM1-BCC force field  66, 80. For the calculation of the stability of W17, a GC box was created around the water molecule by adding 2 Å padding in all three dimensions and used for both the calculations in S52P-TTR and S52P-TTR/tafamidis. For the calculation of the monomer-monomer interface hydration free energy, the grand canonical box was defined so to encompass the interface containing the conserved water molecules of interest; the same box was used for all proteins, which had been previously aligned.
Acknowledgements V. T. F. acknowledges support from the  EPSRC  under grant numbers  GR/R99393/01  and  EP/C015452/1  which funded the creation of the  Deuteration Laboratory (D-Lab) in the Life Sciences group  of the ILL. The authors thank the  ILL  for the provision of beamtime (proposals  8-01-428 ,  8-01-440 ,  8-01-463 ) on the LADI-III diffractometer. This work used the  MS platform of the Grenoble Instruct Centre  (ISBG:  UMS 3518 CNRS-CEA-UJF-EMBL ) with the support from  FRISBI  (grant number  ANR-10-INSB-05-02 ) and GRAL (grant number  ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB). A. W. Y. was supported by a PhD studentship from  ILL  and  Keele University. M. A. is supported by a  Research Fellowship of the Alexander von Humboldt Foundation. M.
Funding: This work was supported by the  Singapore Ministry of Education  ( MOE) Tier 1 grant  ( RG152/17 ) and  MOE   Tier 2 grant  ( MOE2015-T2-1-028 ) to G. C, MOE Tier 1 ( RG33/15 ) to X. R. , and  Fondation pour la Recherche Médicale  ( DBF20161136768 ),  Labex ARCANE , and  CBH-EUR-GS  ( ANR-17-EURE-0003 ) to J. -L. D.
e. regular drinkers), also information on alcohol consumption quantity was assessed (N = 348 039). Occasional drinkers, defined as drinking less than once per week  (Stockwell et al. , 2016) , were excluded from our analyses, since the nature of our data prevented an accurate assessment of their weekly alcohol consumption quantity. In addition, occasional drinkers are known to underestimate their personal alcohol consumption  (Stockwell et al. , 2014; Stockwell et al. , 2016). The assessment of alcohol consumption quantity was made using the average weekly alcohol intake for five general alcohol beverage classes: 1568 (red wine) 1578 (champagne plus white wine), 1598 (spirits), 1558 (beer plus cider), and 1608 (fortified wine). The following item was used 'In an average WEEK, how many glasses of -class of alcohol-would you drink?' The total units of alcohol were calculated by multiplying the number of glasses with a factor depending on the class of alcohol, a procedure similar to the one used by  Clarke et al.
Acknowledgements. This work was conducted using the  UK Biobank Resource  (application number  25331 ). We thank  Scott Wood  and  John Pearson  from  QIMR Berghofer  for IT support. The  Substance Use Disorders Working Group of the Psychiatric Genomics Consortium (PGC-SUD)  is supported by funds from  NIDA  and  NIMH  to MH109532 and, previously, with analyst support from  NIAAA  to U01AA008401 (COGA). We gratefully acknowledge our contributing studies and the participants in those studies without whom this effort would not be possible. This work is supported by a project grant ( 1123248 ) from the  Australian National Health and Medical Research Council (NHMRC). ATM and EMD are supported by the  Foundation Volksbond Rotterdam , FV is supported by the  Investissement d'Avenir program  managed by the  ANR  under reference  ANR-11-IDEX-0004-02.