dataesr/scientific-paragraphs-categorization huggingface dataset:
2, 3 However, the field of EVT is developing very quickly and quality of care is improving with, for example, shorter treatment times. 4 Patients treated in clinical practice are less selected and may therefore differ from those included in randomized trials. 5 urthermore, it is unclear if MR PREDICTS is reflecting practice in other health care systems and countries. In the present study, we therefore aim to externally validate and update the MR PREDICTS decision tool with data from multiple international trials and a Dutch national registry, which reflects daily clinical practice.
MR CLEAN Registry The MR CLEAN Registry is a prospective, observational study, which included all patients treated with EVT in the Netherlands. Registration started in March 2014, directly after the final inclusion in the MR CLEAN trial. There was no upper age limit and no minimal ASPECTS or collateral score required. All data were centrally collected and checked for completeness and consistency. An imaging assessment committee assessed imaging characteristics without knowledge of outcome or detailed clinical characteristics and an adverse event committee scored the safety parameters. Functional outcome at 3 months was systematically assessed by experienced and trained research nurses. The central medical ethics committee of the Erasmus MC University Medical Center, Rotterdam, the Netherlands, evaluated the study protocol and granted permission to carry out the study as a registry (MEC-2014-235).
Statistical Analyses MR PREDICTS is a multivariable ordinal logistic regression model that predicts the modified Rankin Scale (mRS) score at 90 days after stroke. The rationale behind the methods of this model has been described previously. 1  External validation was performed using the coefficients and intercept of the original model. Definitions of predictor variables were used as described in the MR PREDICTS paper. 2  As primary outcome, we used the probability of functional independence, defined as mRS score 0 to 2, which was derived from the ordinal model. We used individual patient data and did not account for potential clustering of patients within the HERMES dataset in the 6 constituting trials. After the first validation, the model was updated based on the full HERMES dataset, which includes the MR CLEAN trial.
0] [11]  We also used likelihoodratio tests to assess the removal of nonsignificant variables from the model. The model coefficients were refitted with an adjustment factor for the derivation versus validation cohort. A second validation of this updated model was performed with data from the MR CLEAN Registry. Model performance was evaluated according to discrimination (ie, the ability to distinguish between patients with a low and high probability of a good outcome) and calibration (ie, the level of agreement between predicted and observed outcome). Discriminative ability was quantified with Harrell's concordance (C) statistic, which varies between 0. 5 for a noninformative model and 1 for a perfectly discriminating model. 12 We calculated the C-statistic for the prediction of functional independence (mRS score 0-2) and for the full ordinal analysis.
Model Updating The addition of baseline glucose as nonlinear predictor using restricted cubic splines significantly improved model fit (P=0. 01) in the full HERMES dataset (including MR CLEAN). The odds ratio of glucose was 0. 97 (95% CI, 0. 90-1. 04) for each 10 mg/dL under 120 mg/ dL and 0. 98 (0. 96-1. 00) for each 10 mg/dL above 120 mg/dL. No significant effect was found when omitting previous stroke (P=0. 41), and this variable was eliminated from the model. Then, the model coefficients were refitted based on the complete dataset with an adjustment for the derivation versus validation cohort. The predictor effects were similar as in the derivation cohort (Table  2 ). Discriminative ability was moderate to good, with a C-statistic of 0. 80 (0. 78-0. 82) for functional independence and 0.
Final Model The regression equation of the updated model is provided in the Data Supplement. The intercept, which reflects the baseline risk of outcome not explained by the predictor variables, can be adjusted to obtain predictions for a setting or population similar to that of the trial population in the HERMES validation cohort or the patients included in the MR CLEAN Registry. The web application was updated with the new model coefficients for use in clinical practice and research (www. mrpredicts. com).
DISCUSSION We externally validated and updated the MR PREDICTS decision tool with high-quality individual patient data from recent international randomized controlled trials and a large nationwide registry. Predictors included in the final model based on clinical and statistical considerations were age, baseline National Institutes of Health Stroke Scale, systolic blood pressure, baseline glucose, treatment with IV alteplase, diabetes, prestroke mRS, ASPECTS, occlusion location, collateral score on noninvasive imaging and (estimated) time from symptom onset to groin puncture. Treatment effect was modified by onset to treatment time, collateral score and the baseline probability of good outcome. The updated model showed moderate to good discriminative ability and good calibration in data from daily clinical practice.
The good calibration in the MR CLEAN Registry shows that the model predicts well in a broad population of patients treated in routine clinical practice. Predicted treatment benefit seems substantial in most patients and the potential harm of EVT is small. Although we identified a small subgroup of patients with low treatment benefit, we cannot predict definite harm. However, it is important to remark that we were unable to measure the real treatment benefit in the MR CLEAN Registry due to the lack of a control group. We used reperfusion (defined as extended Thrombolysis in Cerebral Infarction 2b-3) as a surrogate for successful treatment, but reperfusion was not achieved in all treated patient. Another important caveat is that the data are based on service characteristics reflecting either the Netherlands or the HERMES trial centers.
This validation study was funded by the Erasmus MC Cost-Effectiveness Research program. The HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) collaboration was supported by an unrestricted grant from Medtronic to the University of Calgary. The MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry was partly funded by TWIN Foundation, Erasmus MC University Medical Center, Maastricht University Medical Center, and Amsterdam UMC, location AMC. Disclosures Dr Brown reports personal fees from University of Calgary during the conduct of the HERMES (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) Collaboration, and personal fees from Medtronic outside the submitted work.
InterpolationKS Multidimensional cubic spline interpolation in x, k t and µ, based on GSL implementation, is used for KS-type TMDs (see Tab. 1). [43]  with HERAPDF2. 0 [44]. (right): comparison of integrated distribution PV17  [45]  with MMHT2014  [46]. The parameterizations of TMDs in TMDlib are explicitly authorized for each distribution by the corresponding authors. A list of presently available TMD sets is given in Tab. 1. No explicit QCD evolution code is included: the parameterizations are as given in the corresponding references. The grids of each selected TMD set are read into memory once (the I/O time depends on the size of the grid). Each TMD set is initialized as a separate instance of the TMD class, which is created for each different TMD set, for example for uncertainty sets, or if several different TMD sets are needed for the calculation.
TMDlib documentation TMDlib is written in C++ , with an interface for access from FORTRAN code. The source code of TMDlib is available from http://tmdlib. hepforge. org/ and can be installed using the standard autotools sequence configure, make, make install, with options to specify the installation path and the location of the LHAPDF PDF library  [42] , and the ROOT data analysis framework library  [62]  (which is used optionally for plotting). If ROOT is not found via root-config, the plotting option is disabled. After installation, TMDlib-config gives access to necessary environment variables.
Installation of TMD grids The TMD grid files are no longer automatically distributed with the code package, but have to be installed separately. A list of available TMD parameterizations is given in Tab. 1. # get help bin/TMDlib-getdata --help # install all data sets bin/TMDlib-getdata all # install a single data (for example: SV19_nnlo) bin/TMDlib-getdata SV19_nnlo.
Acknowledgments N. Abdulov was also supported by the  RFBR  grant  19-32-90096. S. Baranov ,  A. Lipatov  and  M. Malyshev  are grateful the  DESY Directorate  for the support in the framework of Cooperation Agreement between MSU and DESY on phenomenology of the LHC processes and TMD parton densities. V. Bertone is supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  824093. C. Bissolotti  is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 647981 ,  3DSPIN ) and by the  U. S. Department of Energy  contract  DE-AC05-06OR23177  under which  Jefferson Science Associates  operates the  Thomas Jefferson National Accelerator Facility. F. Hautmann  thanks  DESY, Hamburg  and  CERN ,  Theory Group  for hospitality and support.
2019/35/ST2/03531. K. Kutak  acknowledges the support by  Polish National Science Centre  grant no. DEC-2017/27/B/ST2/01985   Q. Wang  and  H. Yang  acknowledge the support by the  Ministry of Science and Technology  under grant No. 2018YFA040390  and by the  National Natural Science Foundation of China  under grant No. 11661141008.
Manufacturing processes such as welding subject the α/β titanium alloy Ti-6Al-4V to a wide range of temperatures and temperature rates, generating microstructure variations in the phases and in the precipitate dimensions. In this study, the metallurgical and numerical modelling of Ti-6Al-4V when subjected to a high energy density welding process was affected by a series of analytical equations coded in Sysweld commercial specialist FE welding software. Numerical predictions were compared with experimental results from laser welding tests on plates with different thicknesses, initial microstructural morphologies, and operating conditions. The evolution of the microstructure was described by using a diffusion-based approach when the material was operating in the α + β field, whilst empirical equations were used for temperatures above the β-transus temperature.
The finite element (FE) method is an example of this, and its increasing use over the past 50 years, helped by increasing computing power, gives users the chance to analyse welding and other processes in considerable detail. Sysweld, developed and owned by ESI-Group, is a finite element (FE) software code that was specifically developed for the simulation of various welding processes. Within this FE code, the default model of microstructural development is based upon the Johnson-Mehl-Avrami routines, which were originally developed by Avrami for steels  [7]  and then developed further to include transformation-time relations  [8]  and microstructure kinetics  [9] , whereby the evolution of the phase proportions is described by phenomenological equations. This capability of the software was more recently extended to aluminium alloys  [10].
Weld Pool Imaging and Hardness Measurements To study the microstructures, a series of microindentations using a hardness machine allowed for the measurement of the hardness of the material at different locations and marking of location references at which to take BEI images using an Oxford Instrument XL30 ESEM EG (Zeiss, Oberkochen, Germany). Images at larger scale were obtained using a Zeiss Axioskop 2 MAT microscope with AxioVision software v 4. 6. 3. 0 (Zeiss, Oberkochen, Germany). The analysed samples were prepared by polishing and etching in a 2% HF and 10% HNO3 solution. Microstructure measurements were carried out following a procedure described in the literature  [19]. The micrographs shown in Figure  3a -c correspond to the weld zones of the equiaxed microstructure plates, while Figure  3d ,e corresponds to the lamellar microstructure plates.
Weld Pool Imaging and Hardness Measurements To study the microstructures, a series of microindentations using a hardness machine allowed for the measurement of the hardness of the material at different locations and marking of location references at which to take BEI images using an Oxford Instrument XL30 ESEM EG (Zeiss, Oberkochen, Germany). Images at larger scale were obtained using a Zeiss Axioskop 2 MAT microscope with AxioVision software v 4. 6. 3. 0 (Zeiss, Oberkochen, Germany). The analysed samples were prepared by polishing and etching in a 2% HF and 10% HNO 3 solution. Microstructure measurements were carried out following a procedure described in the literature  [19]. The micrographs shown in Figure  3a -c correspond to the weld zones of the equiaxed microstructure plates, while Figure  3d ,e corresponds to the lamellar microstructure plates.
Finite Element Modelling The numerical microstructure models developed previously by Villa et al. [16, 17]  were coded into subroutines within the commercial software Sysweld (by ESI-Group) in order to run weld simulations capable of predicting the microstructural evolution of the material during the joining process. This metallurgical framework is based upon physics-based models for temperatures within the α+β field and the Johnson-Mehl-Avrami (JMA) approach, which is used extensively in the literature to simulate phase transformations  [16] [17] [18] 28]  for temperatures above the beta transus temperature. Appropriate element sizes were used, such that smaller brick elements caused no significant changes to the results. The modelled outputs, which were visible in the post-processor at each time step of every integration point, included volume fractions of the (i) equiaxed α phase, (ii) lamellar α phase, (iii) β phase, and (iv) martensitic α' phase, as well as the sizes of the equiaxed α mean radius, lamellar α thickness, α' needle thickness, and β grain radius.
Acknowledgments: The authors would like to acknowledge the financial support received from  ESI-UK  and  ESI-Group  in this work. Additional thanks are given to the technical support team from  ESI-Group  for their assistance with Sysweld, Visual Weld, and Visual Viewer software modelling. Many thanks are given to colleagues at the  School of Metallurgy and Materials, University of Birmingham , for support with the microhardness kit, optical microscopy, and  SEM  facilities.
When we found deviations from the expected behavior in the plots, we manually investigated the u-v data to find the cause of the aberrations. This investigation generated a candidate set of additional flagging commands. Overall, we found that the observatory-provided calibrations yielded calibrated u-v data with few visible pathologies. As described below, our tests suggested that adding additional flagging had negligible impact on the final images. Reflecting this, after the first part of the Large Program, we shifted our manual quality assurance efforts from the u-v data to the imaged data (Section 8). We did not manually inspect the calibrator data for the last part of the Large Program and follow up programs. 4. Inspection of synthesized beam. As an additional check on the u-v coverage of the data after flagging, we created a map of the synthesized dirty beam at the observed CO(2-1) frequency using the CASA tool imager.
We compared the size and axis ratio of the synthesized beam to expectations based on the u-v coverage before flagging in order to further verify that the flagging did not have any pathological impact on the data. 5. Quality across mosaic. Finally, we examined the spatial structure of the noise across each mosaic. In particular, we calculated the rms u-v amplitude noise at each individual mosaic pointing, considering all frequencies in the main CO(2-1) science window at the spectral resolution of ∼ 2. 6 km s -1. We used this test to check for missing data, e. g. , due to flagging or other problems with individual fields. Figure  2  illustrates this check for the 12-m and 7-m data for one galaxy. In this figure, the field-to-field variations in u-v amplitude noise are 0. 3% on average and ∼6% at most for the 7-m data, and 1.
7% on average and ∼9% at most for the 12-m data. These results are typical of the targets that we inspected. The tests described above would often suggest a modest amount of possible additional manual flagging. To assess the science impact of a final round of human flagging on the delivered data, we manually flagged the data for two cases. Then we compared the resulting images to those made using no additional flagging. We chose one galaxy with bright CO emission and one galaxy with faint CO emission for this experiment. Then we manually inspected the u-v data as described above, identified an aggressive set of flagging commands, and applied the flags to the data. Finally, we imaged the data with and without this additional flagging. In these tests, the difference between the original and the manually-flagged data cubes is less than 5% in total flux and 5% in rms noise for both the bright and faint targets.
In addition to inspecting the quality of individual u-v data, we searched for consistency in the overall calibration scale across the full data set. We examined images of the interferometric calibrators and the flux scale solved for by the pipeline. When we plot the derived flux of any specific secondary calibrator as a function of time, we find good overall consistency among the 12-m array, the 7-m array, and the ALMA calibrator database. For the total power data, we find relatively stable gains, expressed as the observatory-provided Jansky-per-Kelvin (Jy/K or Jy-per-K) values, across the whole data set at any given time. We did observe that, for observations taking place on similar dates, there is a ∼ 7% difference in the observatory-provided Jy/K between data delivered before and after the last quarter of 2018 (see Appendix B).
As this is an observatory-derived calibration, we accepted the provided values for "version 4" of the PHANGS-ALMA delivery. However, we note that based on consultation with the observatory future releases are likely to see the overall flux of some galaxies decline by 2-5% (see discussion in Appendix B). Only five galaxies have observations delivered both before and after the date of this transition. In Appendix B we show that for all other galaxies, the total power data show excellent internal consistency with rms variation of about ±3%. The inspection steps described above repeatedly verified that the calibration and flagging delivered by ALMA are science ready, in good agreement with the observatory goals and our previous experience with the telescope. Given the minimal impact of additional flagging, we decided to adopt the observatory-delivered calibration for the PHANGS-ALMA processing.
Staging and continuum subtraction The PHANGS-ALMA pipeline begins by extracting the calibrated data for each galaxy part and spectral line using the CASA task split. We select only the science target, as specified by either the "scan intents" recorded by ALMA, or we manually select a user-provided field or set of fields. If the continuum subtraction requires multiple spectral windows, we select all spectral windows. Otherwise, for line products we select only the spectral windows overlapping the line of interest given the mean recessional velocity and velocity width of the source. After extracting the science target and window of interest, we subtract the continuum using the CASA task uvcontsub. The pipeline is aware of a set of bright lines and the user-provided systemic velocity and velocity width for each target.
The pipeline calculates the spectral footprint of each line in the u-v data and excludes channels that contain line emission when determining the continuum level. For PHANGS-ALMA we excluded the regions around CO lines from continuum determination. These are the only bright spectral lines in our setup. For PHANGS-ALMA, we used a single spectral window (spw) for continuum subtraction, which we carried out for all targets. We fit a polynomial of order zero (i. e. , a constant) and fit the continuum in each individual integration. For CO(2-1) the observations used a spectral window with width ≈ 1,200 km s -1 , and for other lines like C 18 O(2-1) that were covered, the velocity coverage was even larger. This is much larger than the velocity width of any of our targets, leaving wide bandwidth for continuum subtraction.
Given the low fractional bandwidth of a 1,200 km s -1 spectral window and the low signal-to-noise of the continuum near the CO(2-1) line, we found that a zeroth order polynomial did a good job removing the continuum. For cases with brighter continuum, the pipeline can fit polynomials of higher order, with the order set by the user. In this case, the user can specify the fit to span multiple spectral windows. This is useful, e. g. , for ALMA data at Band 7 or above and for VLA data at L-band, where the continuum is strong and the slope is steep. Using multiple spectral windows is also useful when the spectral line of interest covers the entire window, leaving no free bandwidth to fit the continuum. In this case, the pipeline will extract all relevant spectral windows as part of the split call above and run uvcontsub on all of them, combining spectral windows for the fit.
Time binning: Optionally at this stage, we also apply some time binning to the data. This is specified by the user when defining each interferometric configuration (e. g. , "7-m", "12-m", "12-m extended"). This allows the time binning to be defined in a way that avoids time smearing but compresses the data as much as possible. We did not use this option for PHANGS-ALMA, but this is a common step used in VLA 21-cm data processing or processing of ALMA ACA data, especially at Band 3.
Spectral regridding and rebinning After continuum subtraction, for each spectral line of interest we create a line-specific measurement set that combines all data on the common velocity grid to be used for imaging. This operation begins with the continuum-subtracted u-v data. The output spectral grid adopts the "radio" Doppler shift convention, in which |δv| /c = |δν| /ν 0 , and we work mostly in the kinematic Local Standard of Rest (LSRK) frame. The user provides the central velocity and width of the final frequency grid as an input. For PHANGS-ALMA, these were initially estimated from large extragalactic databases like NED and LEDA  (Paturel et al. 2003; Makarov et al. 2014). We then refined them after inspecting a first round of imaging. On average, the careful systemic velocity estimates using PHANGS-ALMA CO data in  Lang et al.
(2020)  differ from the radio velocity estimates in LEDA by ∼ ±5 km s -1 and from the optical velocity estimates by ∼ ±10 km s -1. This is small compared to the overall velocity widths used for PHANGS-ALMA cubes. This width for most cubes is 500-1,000 km s -1 , with larger values for more massive, heavily inclined galaxies and smaller values for face-on and low-mass galaxies. To place the data on the final frequency grid, we first call the CASA task mstransform to place all observations onto a velocity grid with a common starting channel and channel width in the LSRK frame. This step converts from the topocentric frame, and so adjusts for changes in the Earth's motion compared to the LSRK frame. This operation reduces the data to only a moderate velocity range of interest around the line of interest.