dataesr/scientific-paragraphs-categorization huggingface dataset:
Affinity and thermal stability determination MST method was used to determine binding affinities for TMABAL, BAL, and NAD +. HsALDH9A1 was labeled with the His-Tag Labeling Kit RED-tris-NTA (100 nM dye + 200 nM His-tagged protein) for 30 min. The labeled proteins were adjusted to 50 nM with 50 mM Tris/HCl buffer at pH 7. 5 supplemented with 0. 05% Tween. A series of sixteen 1:1 ligand dilutions was prepared using the identical buffer. Measurements were done on a Monolith NT. 115 instrument (NanoTemper Technologies). Data of three independently pipetted measurements were analyzed. Thermostability was measured by nano differential scanning fluorimetry on a Prometheus NT. 48 instrument (Nan-oTemper Technologies) with a back-reflection aggregation detection at a range from 20 to 95 • C and with a heating rate of 1 • C.
min -1. Protein unfolding was followed by tryptophan fluorescence intensity at 330 and 350 nm in various buffers covering pH range of 7. 0-9. 0 in the presence or absence of 100 mM NaCl and 5% (v/v) glycerol. The melting temperature (T m ) was determined by detecting the maximum of the first derivative of the fluorescence ratios (F 350 /F 330 ) after fitting experimental data with a polynomial function. Data were measured in triplicate. Effect of presence of coenzyme, substrate, or product was measured using Tycho NT. 6 instrument with a heating rate of 30 • C. min -1.
5 and 1 mM NAD +. Saturation curves for substrates were measured using 1 mM NAD +. Kinetic constants were determined using GraphPad Prism 5. 0 data analysis software (www. graphpad. com) by fitting data to the Michaelis-Menten equation. When substrate inhibition was observed, data were analyzed by nonlinear regression using Michaelis-Menten equation that accounts for partial substrate inhibition: v = V max. [S]/(K m + [S]. (1+[S]/K i )), where v is the determined initial velocity, V max is the maximal velocity, [S] is the concentration of the substrate, K m is the substrate concentration at half-maximal velocity, K i is the substrate inhibition constant. Saturation curve for NAD + was measured using 0. 1 mM TMABAL, which is a sub-saturating concentration providing the maximal experimentally attainable activity and is not affected by a substrate inhibition.
Crystallization and structure determination Crystallization conditions were screened using Qiagen kits (Valencia) using HsALDH9A1 purified at 28 mg. ml -1 in 50 mM Tris/HCl, pH 7. 5 and 150 mM NaCl. Crystals (P2 1 2 1 2 space group) were obtained in hanging drops by mixing equal volumes of protein solution containing 50 mM NAD + and a precipitant solution containing 12% (w/v) PEG 4000, 0. 1 M sodium citrate pH 5. 6, and 2. 5% isopropanol. The two other crystal forms (P2 1 and C2 space groups) were obtained by mixing equal volumes of protein solution containing 10 mM TMABA and 50 mM NAD + and a precipitant solution of 14% PEG 4000 in 0. 1 M sodium citrate pH 5. 6. The cryoprotectant solution comprised 22% PEG 400 in addition to the precipitant solution. Crystals were flash-frozen in liquid nitrogen and diffraction data were collected at 100 K on the Proxima 1 beamline at SOLEIL synchrotron (Saint-Aubin, France).
Diffraction intensities were integrated with the program XDS  [34]  and data quality was assessed using the correlation coefficient CC 1/2  [35]. The crystal structures were determined by performing molecular replacement with Phaser  [36] , using the dimer of cod liver ALDH9 (PDB ID: 1A4S) as a search model  [19]. Model was refined with NCS restraints and TLS using Buster 2. 10  [37]. Electron density maps were evaluated using COOT  [38].
Acknowledgements The research leading to these results was performed within the  ARISE2  and  EUROVOLC  project (www. ARISEproject. eu,https://eurovolc. eu)and received funding from the  European Community 's  Horizon 2020 program  (grant agreement  653980  and  731070 ). The operation of the ETN array, and infrasound based early warning procedure of eruptive activity at Etna volcano, is performed by  UNIFI for the Italian Civil Protection  and received funding under the  DPC-DEVNET  project.
Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful biophysical technique being increasingly applied to a wide variety of problems. As the HDX-MS community continues to grow, adoption of best practices in data collection, analysis, presentation and interpretation will greatly enhance the accessibility of this technique to nonspecialists. Here we provide recommendations arising from community discussions emerging out of the first International Conference on Hydrogen-Exchange Mass Spectrometry (IC-HDX; 2017). It is meant to represent both a consensus viewpoint and an opportunity to stimulate further additions and refinements as the field advances.
Of particular relevance in industry, HDX-MS also excels at epitope mapping and the characterization of biotherapeutics  12, 13. Measurement of protein HDX dates back to the 1950s  14, 15  , with much of the work in the 1970-1980s using NMR spectroscopy as the method of detection  16. Mass spectrometry has been the method of choice for detecting HDX since the 1990s  17, 18  , because it can accommodate large proteins (>100 kDa), accept low concentrations (less than micromolar) and tolerate complex sample matrices  19. Additional advances in liquid chromatography (LC)-MS technology and automation have greatly increased the user base for HDX-MS. Data analysis software has progressed markedly  [20] [21] [22]  , increasing processing speeds by several orders of magnitude over the past ten years.
The analysis of large multi-subunit proteins no longer takes weeks or months. A reduced barrier to entry has accompanied these developments, resulting in an explosion in the amount of HDX-MS data being generated. It is not surprising that the number and diversity of studies that use some variant of the HDX-MS method is increasing year over year  [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33]  (Fig. 1 ). Producing a deuterium-labeled sample is simple and requires no specialized equipment or chemicals, other than D 2 O. However, the labeling reaction is highly sensitive to experimental conditions, such as pH and temperature fluctuations, among other factors  34. It requires a number of controls and a great amount of care to ensure that the reaction is conducted in a manner that does not bias the results or lead to imprecise measurements.
If properly performed, the HDX-MS method is very reproducible  7, 35, 36  , but repeated HDX reactions must be conducted to assess the degree of variability caused by external factors. When due care and attention is taken to fully account for all aspects of sample production  37  , HDX-MS provides reliable insights into protein structure-function properties. HDX-MS data complement other structural biology techniques, such as nuclear magnetic resonance (NMR) spectroscopy  38  , electron microscopy  30, 39  , native mass spectrometry  40, 41  , molecular dynamics simulation  42  and X-ray crystallography  43, 44  , and are often presented alongside these techniques to nonspecialist reviewers. Likewise, scientists who primarily focus on these complementary techniques may, given the attractiveness and accessibility of HDX-MS, choose to embark on HDX-MS experiments themselves.
By altering the pH of the labelling solution, de Vera et al. 33  observed interactions of disordered protein domain on a millisecond timescale by HDX-MS. of interpretation, as the exchange mechanism is complex and important experimental information is all too often missing. Incomplete information compromises the ability to interpret published findings of others. To remedy this situation with several community-driven objectives, we aim to (1) develop consensus-based minimum recommendations for conducting HDX-MS experiments, along with the minimum information required for publication of HDX-MS data; (2) provide a resource for reviewers of manuscripts containing HDX-MS data, allowing them to assess the quality of presented data, and to assist editorial staff in establishing acceptable publication criteria; and (3) position the HDX-MS community for the creation of a standardized and curatable data format that allows for archiving datasets for meta-analysis and protein modeling.
Reproducibility. To produce quality studies, it is necessary to ensure that, given the same materials, instrumentation and protocol, the same observations would be made for any given experiment. This requirement impacts sample production, data acquisition and analysis. Transparency. Data need to be more readily accessible to the community. These recommendations aim to create conduits to raw data, enabling further meta-analysis, providing increased confidence in reported observations and supporting ongoing software development efforts. If the recommendations are followed, we believe the end result will be an overall increase in the quality of published data and more meaningful scientific insights gleaned from the findings. These recommendations may require revision, enhancement and additions in the future as HDX-MS technology advances and evolves.
1. 7. Biological replicates of the experiment should be conducted where possible. This would require additional preparations of the protein. The repeats ensure that the variability in exchange measurements that can be ascribed to post-translational modifications/differences in protein expression/purification or variable stoichiometry in reconstituted protein complexes is quantified. Biological replicates are especially important for proteins that require extensive sample preparation before HDX-MS (for example, nucleotide loading in monomeric GTPases52). 1. 8. The LC-MS system used to collect the data should be made explicit in the methods section of the manuscript, along with pertinent instrument settings (for example, the LC gradient and flow rates, reversed-phase columns used, MS ion source parameters and so on).
By doing so, one can mitigate the effects of any drifts in instrument parameters, day-to-day variability (in case data are collected over multiple days), temperature fluctuations or any other parameter that might affect the HDX-MS experiment. 2. Data analysis and data presentation guidelines 2. 1. When conducting peptide identification using MS/MS, the spectral search database should include the sequence of all major proteins present in the sample and introduced during workup, to prevent false peptide identifications. This is especially advisable for complex sample types. The list should minimally include the proteases used and any major protein contaminants. The composition of the database used for searching should be reported. 2. 2. Peptide identification criteria should be included in the text, based on the search tool used (that is, scoring cut-off and its statistical basis).
The name of the search software (and version) should be provided, with parameters appropriate for the mass spectrometer used. 2. 3. For quality control purposes, the output of automated HDX-MS computational routines should be supported by an inspection of the raw data, including spectral assignments and isotopologue detection. A summary of the HDX data should be reported in a table (for example, Table  1 ) with the following information for each protein included within the study: (1) HDX reaction details, for example, pH and temperature; (2) HDX time course, for example, what time points where analyzed; (3) number of peptides analyzed (that is, the total number of peptides for which the deuterium content has been analyzed in each dataset); (4) sequence coverage, expressed as a percentage of amides covered by the peptides for which deuterium content has been measured (rather than all peptides identified in the non-deuterated experiment); (5) average peptide length and redundancy; (6) a quantitative measure of the repeatability of deuterium measurement, for instance, the average (mean, median, root-mean-square) standard deviation from replicate (technical or biological) measurements of the deuterium content of all or representative peptides from one or more time points; and (7) threshold for significant differences in HDX (a threshold value interpreted as representing a significant difference in HDX between examined protein states based on the quantitative measure of repeatability.
Appropriate statistical analyses should be applied to all reports of differences between states. Furthermore, HDX information from multiple charge states and overlapping peptides should be used to add certainty to any conclusions. 2. 5. A common data presentation format for HDX-MS data involves color-mapping the time-resolved, peptide-resolution data onto three-dimensional, static, atomic-resolution structures. Inappropriate mapping can result in a loss of information. When mapping HDX data onto structures, scientists should explicitly state their mapping methodology and at which time points data are depicted, and this approach should be based on a quantitative and statistical argument that is applied to the entire dataset. Such an encompassing method avoids selective presentation and permits a balanced assessment of biologically relevant findings.
In cases where a difference in HDX is mapped, regions should be carefully indicated for which there is no sequence coverage. Furthermore, regions that exhibit 'no significant difference' should not be interpreted as 'no change'. Rather, it means no detectable difference within the kinetic regime had been detected. 2  and 3 ; either can be used depending on the output format of the HDX-MS data analysis software in use. We include these downloadable template spreadsheets to encourage the community to include such data in their reporting and to do so using this standardized and readily accessible format. The HDX data table greatly simplifies access to the acquired data for other scientists and will enable any downstream data processing, including use of the data for computational modeling. The minimum requirements are peptide sequence (start and numbering and sequence), peptide monoisotopic mass (uncharged), chromatographic retention time, mean deuterium uptake (shift in average/centroid mass, without any correction for backexchange) and the standard deviation, for each labeling time, with a clear indication of the number (n) and nature of replicates (technical/biological) used to determine this value.
We recommend including the following sentence in methods section of all papers: "To allow access to the HDX data of this study, the HDX data summary table (Table  X ) and the HDX data table (Table  Y ) are included in the supporting information as per consensus guidelines (with a reference to this manuscript). " We also recommend that raw HDX-MS data be made available for reanalysis to interested parties through personal file sharing whenever possible, until a standardized structure for raw data and an open data server for annotated HDX-MS data have been agreed upon or established by the community. The development of a standardized approach for reporting of HDX data has major advantages in increasing the quality of published HDX-MS data. First, the current disparity in published manuscripts as to what data are presented, and how these data are presented, can make reviewing and assessing HDX-MS experiments challenging.
Second, certain key statistics and metrics that may affect the validity of the publication's conclusions are sometimes reported in supplementary data or not mentioned at all. Third, there are enormous unexplored opportunities associated with metaanalyses of archived HDX-MS data. Access to large sets of raw data could provide insights into the mechanism underlying the HDX process-a phenomenon still not completely understood-and furthermore improve the utility of HDX-MS data for computational modeling of protein structures. Finally, a shared database would avoid unnecessary repetition of experiments. We believe that our recommendation to include both 'HDX summary' and 'HDX data' tables as described above would greatly facilitate upload and storage of HDX-MS data in a central community-accessible database.
Acknowledgements There are numerous people who contributed to this document, and we thank the community as a whole for helping to sculpt the manuscript. We note the many participants in the discussions held at the  IC-HDX conference (Gothenburg, Sweden ,  2017 ) who helped form the initial draft. We are grateful for the comments from academics, vendors, commercial scientists, and sponsors who participated in these discussions. We thank  I. Møller  and  E. Trabjerg  for assistance with administrative tasks and data formatting. This study was in part facilitated and funded by the  EU   COST Action BM1403 (Native Mass Spectrometry and Related Methods for Structural Biology ).
Recruitment Patients are expected to be included during a 2-year period starting in august 2017. 2016-2017: Protocol, approvals from ethics committee (CPP Sud-Est I) and the French Medicine Agency (Agence Nationale de Sécurité du Médicament et des produits de santé, ANSM); trial tool development (online case report form (CRF) and randomisation system). 2017-2019: Inclusion of patients. 2019: cleaning and closure of the database, data analyses, writing of the manuscript and submission for publication.
blinding This is an open-label, unblinded trial for the patient and the physician in charge, related to the nature of the intervention (GA with endotracheal intubation or CS). Assessor blinded evaluation of the primary outcome will be performed since the assessor and statistician will be masked to the subjects' assignment group. MEthods: dAtA CollECtIon, MAnAgEMEnt And AnAlysIs data collection and management At each participating centre, data will be collected and entered into the web-based electronic CRF (eCRF) (CSOnline, Clinsight) by trial or clinical trained personal (clinical research associate), blinded to the allocation group, under the supervision of the trial site investigators. From the eCRF, the trial database will be created. Paper CRF will be used in case of technical problems with the eCRF.
Statistical analysis A predefined statistical analysis plan will be followed (online supplementary file 2). All analyses will be conducted with Stata software (V. 13, StataCorp) and R (http:// cran. r-project. org/) before the breaking of randomisation code, in line with the International Conference on Harmonization Good Clinical Practice guidelines. A two-sided p value of less than 0. 05 will be considered for statistical significance. Primary analysis will be done in mITT. Then, a per-protocol analysis will also be done to take into account protocol deviations notably crossover from CS to GA. Patients who withdraw consent will not be included in these analyses. Continuous variables will be presented as mean and SD or as median and quartiles otherwise. Normality will be assessed using the Shapiro-Wilk test and homoscedasticity will be assessed using the Fisher-Snedecor test.
According to clinical relevance and to European Medicines Agency and CONSORT recommendations, post hoc analyses will be proposed after the study of subgroup ×randomisation group interaction in regression models (for repeated data or not). Missing values will be notified and analysed. A sensitivity analysis will be performed and the nature of missing data will be studied (missing at random or not). If the frequency is >5%, additional analyses will be performed using the multiple imputation method. 34 thods: MonItorIng data monitoring Before the start of the study, anaesthetic, neurological and radiological medical and paramedical teams are trained at each site for the study protocol by study coordinators. Physicians are in charge of patient screening and inclusion. Patients admitted for stroke treated by endovascular mechanical thrombectomy and not included in the study will be recorded anonymously at each centre into a screening log.
Data will be collected in a web-based eCRF by trial personnel. Each centre will only have access to site-specific data. Each patient will receive a unique trial identification number. Only the investigators and research team will have access to any protected health information of study participants and any study data. Data monitoring and quality control will be conducted in each centre after the first 10 inclusions then after the next 20 inclusions and at the end of the study by official representatives of the study promoter (Department of Clinical Research and Innovation, Clermont-Ferrand University Hospital). Data will be handled according to the French law. All originals records (including consent forms, reports of SUSARs and relevant correspondences) will be archived at trial sites for 15 years.
The clean trial database file will be anonymised and maintained for 15 years. Only the principal investigators and the statistician will have access to the final dataset. harms Every adverse events that could be related to the trial will be reported to the trial coordinating centre. According to the French law, all suspected serious adverse events will be reported to the ANSM. The DSMB will also be informed. DSMB is independent from the trial investigators and will perform an ongoing review of safety parameters and study conduct. DSMB members are two independent physicians in anaesthesia/critical care medicine and neurology, and a biostatistician that have skills and expertise in anaesthesia, clinical neuroscience and clinical research. The DSMB will be responsible for safeguarding the interests of trial participants, assessing the safety of the interventions during the trial and for monitoring the overall conduct of the trial.
DSMB could also formulate recommendations related to the recruitment/retention of participants, their management, improving adherence to protocol-specified regimens, and the procedures for data management and quality control. No formal criteria are set to stop the study. However, recommendations for pausing or stopping the study could be made by DSMB in case of SARs and SUSAR. The scientific committee will be responsible for promptly reviewing the DSMB recommendations and to decide whether to continue, hold or stop the study, and to determine whether amendments to the protocol are needed.
Open access dIsCussIon We recently observed the 'thrombectomy revolution' in anterior circulation AIS. 35 Emergency interventional procedures in frail stroke patients often require skills from anaesthesia providers since immobility is needed and severe intraprocedural complications may occur (eg, coma, agitation or aspiration pneumonia). Taking into account the increasing volume of procedures and the potential effect of the anaesthetic strategy on outcome with discrepancy in literature, it appears essential to provide a multicentre randomised controlled trial to enhance external validity as suggested by recent recommandations. 15 oncurrent ongoing trials with day 90 mRS as a primary outcome are planning to recruit 635 patients to demonstrate non-inferiority between CS and GA,  36  350 patients to demonstrate superiority of CS versus GA (NCT02822144) or 260 patients to demonstrate superiority of GA versus CS (NCT03263117).
Some limitations could be opposed to the AMETIS trial protocol. First, no specific anaesthetic protocol will be used. We choose this strategy in a pragmatic way since no data demonstrate that a drug is better than another even if modulation of CBF could be variable. However, the protocol requires strict objectives for systolic blood pressure and 'normal' blood carbon dioxide tension in GA group. 37 38 Drugs and dose will be monitored. Second, no maximal time delay from stroke onset or maximal/ minimal NIHSS values are recommended in order to adhere to a pragmatic investigator-based approach. This strategy complies with recent trials and recommendations: patient selection for thrombectomy is made on angioCT or MRI scans with eventual mismatch evaluation especially when delay is >6 hours and for wake-up strokes.