dataesr/scientific-paragraphs-categorization huggingface dataset:
The last type of result visible in the dataset concerns the difference in the field of view and resolution between images of Â2:5 and Â5 magnification. For comparison an image of Â2:5 magnification is shown in subfigure 4 and one with Â5 magnification in subfigure 5 of the same blade 7. The overall largest defect is indicated by a white triangle, this defect is outside of the field of view in subfigure 5 and thus is only visible in subfigure 4. Furthermore, defects on the edge of the field of view, like that indicated by a black triangle are truncated by the edge of the image and appear smaller in subfigure 5 than they are (compare to subfigure 4). To compare the measured porosity to that simulated a suitable magnification needs to be selected. The calculated critical defect sizes for the Â2:5 (black symbols) and Â5 magnifications (red symbols) are plotted in Figure  7.
Comparing the population from Â2:5 and Â5  it is evident that the lower magnification resulted in a higher success rate (56 pct of all images were of type 2 and 3 vs. 36 pct for the Â5 magnification) and thus contains a larger dataset. The thresholding of the image seems to thus work better in terms of removing the false detection of eutectic phases on images with a wider field of view (less type 1 errors). Furthermore, the lower magnification contains larger critical defect sizes (largest defect at Â2:5 is 1705 lm vs. 514 lm for Â5). This seems to be due to the larger magnification missing most of the critical defects in its narrower field of view (less type 4 errors). Lastly, the influence of higher resolution on the accuracy of the defect size measurement was checked. The largest critical defects that were successfully detected by both magnifications (blade 10 cut C and D as well as blade 6 cut F) were directly compared.
A. Determining Accurate Porosity Information from 2D Metallographic Cuts Three different methods can be used to analyze the 2D optical microscopy cuts, maximum Feret diameter and area porosity percentage estimated by IMS as well as critical defect size computed by the automated tool. Figure  9  compares the the maximum Feret diameter against the critical defect. The critical defect size was calculated by the 2D Python script on all the micrographs of blades 3 and 10 (black diamond markers) and 6 and 7 (black square markers) with an optical magnification of Â2:5. The maximum Feret diameter was only calculated for the images with the highest critical defect sizes for each blade cut. From cut A to H the maximum values were found in the following blades 7, 10, 3, 7, 7, 6, 6, and 6. For all cuts the critical defect size for these blades is significantly larger than the maximum Feret diameter (2.
B. Determining the Accuracy of the Automated Porosity Evaluation Applicability of the critical defect parameter can be further strengthened through the analysis displayed in Figure  10. If critical defect size is an adequate measure to assess the 2D porosity, then a proportional relationship between it and the measured maximum porosity would be expected. This assumption is based on the findings that higher percentage of porosity translates into larger critical defect sizes. [43]     can be concluded that the critical defect size estimated by the automated Python tool is indeed proportional to the maximum porosity percentage. Based on this finding it is likely that the porosity percentage calculated by the IMS tool in each cut of the blades are proportional to the likelihood of critical defects present in these cuts.
C. Determining the Accuracy of the Simulated Porosity In Figure  11 , the porosity percentage measured by the IMS tool (marked with black crosses for the mean values and black plusses for the median) is compared against the equivalent values, for all the cut planes predicted by ProCAST (red circles markers). The grey bars represent the variation between the minimum and maximum porosity measured. The scatter bands marked by the grey lines thus show the variation in the dataset of each cut aligns with the indicators for a normal distribution. Thus, cuts D, F, G and H have the highest variations while the remaining cuts display lower data deviation in the porosity dataset. The comparison between mean and median values gives an indication if the data could be normal Gaussian distributed (cuts A, E, F, H, I), the data could contain a tail (cuts B, C, G) or might be significantly affected by a few larger pore indications and exhibit a significant tail (cut D).
The ProCAST dataset however assumes a normal porosity distribution and cannot match findings like that displayed in cut D. Whilst ProCAST seems to be able to predict the expected maximum porosity in cuts B, C, H within the data variation band or even close to the average values (cuts E, G, I) a deviation is visible for cuts A, F (and D as discussed above). For these cuts the dataset indicates a normal distribution but the simulation was unable to correctly reflect the experimental observation in these cuts. Having directly applied the processing parameters of the cast blades, the principal source for the deviation in the ProCAST simulation is expected to originate from the description of the ceramic shell and the alloy properties. As outlined in Section II-A, both the shell and the insulation have been modeled assigning to them a constant thickness.
ACKNOWLEDGMENTS The  Bavarian State Ministry for Economic Affairs, Regional Development and Energy (StMWi)  within the framework of the  aeronautical research program BayLu25-II  and  MTU Aero Engines  are acknowledged for financial support. M. Futoma ,  R. Sowa ,  M. Panella  ( MTU Aero Engines ) are acknowledged for the valuable discussions on this work. C. Schwaminger  and  C. Bader  ( MTU Aero Engines ) are acknowledged for providing the metallographic analysis. T. Go¨hler  ( MTU Aero Engines ),  J. Artozoul  and  I. Tiba  ( LAMPA, Arts et Metiers Institute of Technology ) are acknowledged for their valuable discussions and input.
Study population Participants were initially prospectively recruited  (2, 16)  and then asked to come back for a follow up investigation. A total of 20 patients (14 with BAV and 6 with TAV and AS) could be included for follow up aortic 4D flow MRI examinations, resulting in a total of 40 MRI datasets. The diagnosis of AS was based on the aortic orifice area (AOA). Most patients (14/20) reported at least one symptom (palpitations, dizziness, syncope or dyspnea according to the NYHA classification) of AS at follow up (for further information see Supplementary Table  S1 ). A total of 7 patients (1 patient with TAV and 6 patients with BAV) received an aortic valve replacement between baseline (B) and follow up (FU) (operated group = OP), 13 patients did not (= non-operated group = NOP). The enrollment is shown in Figure  1.
Cardiovascular magnetic resonance imaging acquisition All cardiovascular MRI examinations at both time points were performed on a 3 Tesla Scanner (MAGNETOM Verio; Siemens Healthineers GmbH, Erlangen, Germany). 4D flow cardiovascular MRI data were acquired using a sagittal oblique volume covering the thoracic aorta. Prospective ECG gating was used in combination with a respiratory navigator placed on the lung-liver interface to allow data acquisition during free breathing. The following scan parameters were used: echo time = 2. 6 ms, repetition time = 5. 1 ms, bandwidth = 450 Hz/pixel, imaging acceleration using PEAK GRAPPA with a reduction factor of R = 5, net acceleration 4. 17 Additionally, standard steady-state free-precession cine images were acquired for cardiac chamber quantification and for planimetry of the AOA.
Four dimensional flow image analysis First, 4D flow MRI data were corrected for Maxwell terms and eddycurrents (MATLAB, The MathWorks Inc. , United States)  (17). Aliasing correction was applied in all cases as part of a standardized approach. If correction failed, the respective plane was excluded from analysis. Manual segmentation of the aorta was performed (Mimics, Materialise, Belgium). Helical and vortical blood flow patterns in the ascending aorta were semi-quantitatively evaluated using pathline movies and graded as follows: 0 (none), 1 (flow rotations<360°), 2 (>360°) and 3 (>360° with increased flow)  (18). A vortical flow formation was defined as revolving particles around a point within the vessel with a rotation direction deviating by >90° from the main physiological flow direction.
A helical flow formation was considered as a regional fluid circulation around an axis parallel to bulk fluid motion (i. e. , along the longitudinal axis of the vessel), thereby creating a corkscrew-like motion)  (2). Then, 3D blood flow visualization and positioning of 2D crosssectional planes for flow quantification were conducted (EnSight, 3D WSS was calculated for the entire thoracic aortic wall at peak systole using a home built analysis tool (MATLAB, The MathWorks Inc. , United States), as described previously by Potter et al. (19)  and van Ooij et al. (20). Briefly, based on the 3D segmentation of the thoracic aorta, systolic 3D WSS along the entire aortic wall was calculated from 4D flow velocity data. The WSS vector was estimated at the wall based on the 3D spatial velocity gradient perpendicular to the vessel wall.
Statistical analysis Statistical analysis was performed using SPSS version 25 (IBM, Armonk, NY, United States). Parameters were compared between B and FU for all patients as well as between the OP group and the NOP group. Parameters at B and FU were compared using Wilcoxon Signed Rank Test, parameters between groups were compared using Mann-Whitney U Test as the tested cohort was too small to reach normal distribution. Comparisons between parameters in the ascending aorta, the aortic arch and the descending aorta were performed using Friedman Test, for pairwise comparisons Wilcoxon Test was used. Nominally scaled data were compared using Chi Square Test.
Acknowledgments We thank all patients for participation in the study. We also thank our MR technicians  Denise Kleindienst ,  Martina Kohla ,  Kerstin Kretschel , and our study nurses  Annette Köhler  and  Elke Nickel  for their help in organizing and conducting the study scans. 10. 3389/fcvm. 2023. 1071643Frontiers in Cardiovascular Medicine 08 frontiersin. org.
To identify the amino acid substitutions responsible for the loss of recognition by either of the two Fabs, corresponding cells were sorted before bulk sequencing of their human LAMP-1 mutant sequence. NGS data were comprehensively tabulated with the enrichment score for each substitution on each position of human LAMP-1 (Fig. 3 , Supp. Fig. 2  and 3 ). Most substitutions had limited influence on the binding of Fabs, and are therefore not detected in the sorted populations. In contrast, mutations with an enrichment score greater than two (i. e. , frequencies four times higher in the sorted over unsorted populations) are those that most markedly affect the binding of either Fab to human LAMP-1 (bright red, Fig. 3 ). For each position, we determined an index by counting the number of substitutions with an enrichment score higher than two.
32 The Ala mutagenesis technique can provide some answers on the areas of the antigen involved in the interaction, but is far less precise than DMS, which scans the 20 proteinogenic amino acids. In our dataset, we observe that Ala substitutions would not have identified some important positions, such as K151 for mAb B or L310 for mAb A, and of course not A108, which is already an alanine residue. The nature of the antigen can also be a challenge for some methods. Unlike cryoEM and HDX-MS, Alascan and X-ray crystallography are applicable to soluble proteins or protein domains, but these approaches prove to be technically very complex for integral membrane proteins such as G-protein coupled receptors or transporters. These targets can be studied with DMS expressed on the surface of yeast or mammalian cells, opening new possibilities for such challenging targets.
Affinity measurement by biolayer interferometry Binding kinetics were determined using an Octet RED96 instrument (Molecular Devices, San Jose, USA). Anti-hIgG Fc Capture (AHC) Biosensors were loaded with mAb A or mAb B IgG molecules (25 nM) for 60 seconds. After baseline determination using kinetic buffer (phosphate-buffered saline (PBS), bovine serum albumin 0. 1% (w/v) and Tween 20 0. 02% (v/v)), association of human LAMP-1 or cynomolgus LAMP-1 was measured at different concentrations (200 nM to 6. 25 nM) for 300 seconds before dissociation in kinetic buffer. Data of the control without antigen were subtracted from all binding curves and binding kinetics were fitted using a global 1:1 Langmuir binding model.
NGS sequencing and data analysis The Zymoprep Yeast Plasmid Miniprep II kit (Zymo Research, Irvine, USA) was used to extract plasmids from the sorted population. Regions of interest were amplified in a first PCR step and then adapters and barcodes needed for Illumina sequencing were added in a second PCR step. NGS was performed with an Illumina MiSeq (Illumina, San Diego, USA) device (2x300bp, v3 kit 600 cycles) with at least one million reads per population. The first steps of data analysis were performed on the Galaxy platform. Reads were first paired using the Pear function. Then the reads with an unexpected length were eliminated using Filter FASTQ function. The following analysis steps were performed using RStudio software and eliminated sequences containing more than one mutation compared to the parental antigen sequence.
Crystallization The complex between the first luminal domain of LAMP-1 (LAMP-1 29-195 produced in E. coli) and Fab B was concentrated to 12 mg/ml in 10 mM phosphate buffer saline pH 7. Crystallization was done by vapor diffusion using the sitting drop method. Crystals were obtained in 20% (w/v) polyethylene glycol 3350, 200 mM NaF. 25% (v/v) ethylene glycol was included as cryoprotectant prior to freezing. Datasets were collected at beamline ID29 from the synchrotron ESRF (European Synchrotron Radiation Facility) on a Pilatus 6M at wavelength 0. 976251 Å. The crystals belong to the space group C2 and diffracted to 2. 37 Å. Data were processed using autoproc from GlobalPhasing  53  which relies on the XDS  54  and Aimless 55 programs. Final processing statistics are listed in Supp. table  1.
58is structure was obtained from the Protein Data Bank (http://www. rcsb. org/pdb/home/home. do). A model of the variable domain was constructed in Maestro (Schrödinger, Inc. : Portland, OR, 2012). Molecular replacement was carried out using Phaser56of the CCP4 suite57and two complexes LAMP-1/Fab could be constructed in the asymmetric unit. The structure was refined at 2. 37 Å by doing multiple cycles of Buster (Buster-TNT 2. 11. 5, Global Phasing Ltd) followed by manual corrections in COOT58to a final Rfree of 0. 261 and Rfactor 0. 226. Refinement statistics are available in Supp. The Alphafold 2 model of human LAMP-1 is available at: https://alphafold. ebi. ac. uk/entry/P11279. which the author was not personally involved, are appropriately investigated, and resolved and the resolution documented in the literature.
Acknowledgements The authors wish to acknowledge  Fabienne Soubrier ,  Cecile Capdevila ,  Francis Duffieux ,  Alain Dupuy  and  Alexey Rakfor  the key contributions they made to this work. LAMP-1 proteins and domains as well as Fab B were cloned by FS, produced by  CC , and purified by  FD (Sanofi, LMR, France). The authors also thank  Raphaël Sierocki  ( Deeptope SAS ) for help with the NGS data analysis scripts and useful discussions about the epitope mapping processes.
b Film rupture events in AlCu10Mg15 alloy foam determined by analysing X-ray images obtained under normal and microgravity conditions. The insets show two images of the final state after 21 s. The foam produced under microgravity conditions is clearly more homogeneous and has ~29% less film ruptures and therefore fines pores. c Crosssection of an AlSi8Mg4 foam blown with TiH 2 compared with (d) AlCu10Mg15 foam produced without TiH 2 featuring a more homogeneous distribution of smaller pores  37. However, even such experiments may show a large scatter in the measured data. Additional mass transport that affects the measurement was discussed by Müller and Müller-Vogt  47. It was realised that in-situ monitoring is required to further improve the quality of the data by identifying effects that disturb the measurements.
However, X-ray radioscopy is essential to monitor the diffusion process and rule out disturbing effects. For selected binary alloys sounding rocket (MAPHEUS) experiments using the XRISE-M facility  24  were compared with ground-based experiments  54, 55. It was shown that diffusion experiments can be successfully carried out on ground, if a stable density-layering of the liquids is established in the sample. Therefore, a larger database of accurate diffusion coefficients in liquid binary alloys can be established at comparatively low cost. Aboard MAXUS-9, an ultra-high temperature linear-shear cell furnacedeveloped at DLR together with Airbus -was monitored within SSCs XRMON-DIF2 experiment facility to study chemical diffusion in binary Al-Ti alloys and in Si-Ge. Si-Ge data are shown in Fig.
DISCUSSION We have provided an overview of experiments carried out in the framework of the ESA XRMON project in microgravity   63. Comparison with low Si-content interdiffusion experiment (Ge-GeSi2at% hourglass) from MAPHEUS-3 (microgravity) with the diffusion coefficient effectively corresponding to the Si self-diffusion. Also shown is the interdiffusion coefficient (GeSi10at%-GeSi15at% circle) obtained aboard MAXUS-9. The dashed lines (left to right) indicate the liquidus temperatures of GeSi2at% and GeSi12. 5at% with GeSi15at% about 20 K higher. It has to be noted that due to non-linear corrections required for the MAXUS-9 diffusion experiments a systematic error in this data cannot fully be ruled out. Bottom: Interdiffusion coefficient obtained aboard MAXUS-9 compared with Ge and Si self-diffusion coefficients.
ACKNOWLEDGEMENTS The authors are grateful to  ESA  for financial and practical support for this work, particularly through their  Microgravity Applications Promotion (MAP) programme  ( XRMON :  AO-2004-046 ), and their  PRODEX programme. The space hardware and  XRMON  furnaces development was funded through  ESA 's  ELIPS (European Life and Physical Sciences in Space) programme. ESA  support via the  MAP  project  MicroGfoam ,  AO-1999-075 , is also acknowledged. We are also grateful for support from the  French National Space Agency (CNES) , the  German Space Agency  (DLR,  FKZ 50WM1743 ), and  Enterprise Ireland. We are grateful to the support received by the  Swedish Space Corporation ( SSC) Solna  flight hardware development team prior and during the  ESA  funded campaigns  MASER ,  MAXUS  and  ESA -PF.
De nitions and study groups For the purposes of this study, the 'ideal weight' of the patient was de ned as the weight at which the cat's body fat mass was determined to be optimal and was either based on body composition data from dual-energy X-ray absorptiometry (DEXA) or body condition score (see below). In contrast, 'target weight' was de ned as the goal weight set for the period of controlled weight reduction, and this was individualized for each cat (see below). Cats were grouped according to whether they underwent a complete or partial weight reduction protocol. In a complete weight reduction protocol, the primary aim was to return the cat to within 5% of their ideal weight; to achieve this, the chosen target weight for the cat was the same as the ideal weight. In contrast, for a partial weight reduction protocol, the target weight was deliberately set to be greater than the ideal weight (4).
Initial assessment Before the weight reduction protocol was implemented, cats were determined to be healthy based upon physical examination, routine hematology, serum biochemistry and urinalysis. In all cats, BCS was measured  (11)  and, in most, body composition was measured by DEXA as previously described  (2). At this stage, either body composition data or BCS were used to set the ideal weight, as previously described (6,11). Brie y, for body composition data, measurements of lean mass, fat mass and bone mineral content (in grams) were entered into a spreadsheet (Excel for Mac, version 16. 71, Microsoft), and a bespoke mathematical formula was used to model expected body composition after weight loss, with optimal body fat mass assumed to be ~20% for cats  (2, 13, 14). This mathematical formula incorporated information on known changes in body composition from previous studies using the same DEXA machine and software  (2, 15).
Data handling and statistical analyses Data were entered into an electronic spreadsheet (Microsoft Excel® for Mac version 16. 19 ) and checked for errors, whilst an online open-access statistical language and environment (R, version 4. 2. 3)  (17)  was used for all statistical manipulations, and the level of statistical signi cance was set at P <0. 05 for twosided analyses. Complete datasets were available for all variables except for body composition data because this was not performed in 5 cats both before and after weight loss, and a further 6 cats after weight loss only (Supplementary data); therefore, statistical analyses on body composition data were conducted using data from the remaining 48 cats. All ME intakes and are expressed as both kJ and kcal per kg 0. 67 of ideal bodyweight (IBW), whilst weight loss is expressed as a percentage of SBW.
To calculate the mean daily ME intake during the weight reduction period, the total ME intake for the period was divided by its duration. For the weight loss variables and nutritional intake data, datasets were rst tested for normality using the Shapiro-Wilk test and examination of Q-Q plots. Given that many datasets were not normally distributed, a decision was made to report continuous results as median (range) and categorical data as absolute numbers and percentages. Differences in body composition measurements before and after weight reduction were assessed with the Wilcoxon signed-rank test, whilst differences between cats undergoing complete and partial weight reduction were assessed with the Mann-Whitney test. In both cases, nonparametric effect size was determined by calculating rank biserial (and its 95% con dence interval, 95%-CI) using the 'effectsize' package (version 0.
Mechanical Properties The mechanical parameters, including Young's modulus (YM), tensile strength (TS), and elongation at break (%EB) of the film samples were investigated by a tensile analyzer (EZ Test EZ-X Series, Shimadzu Corporation, Kyoto, Japan) with a 500 N load cell connected to an autograph software (Trapezium X, Shimadzu Corporation, Japan). The tests were carried out following the ASTM D882 standard method. The film samples were cut into a square shape of 15 mm × 100 mm. After conditioning at an RH of 54% for one week (saturated solution of Mg(NO 3 ) 2 ), they were mounted in two grips with a specimen's gauge length of 50 mm; then, the tests were performed at a crosshead speed of 10 mm/min. Mechanical parameters were calculated by the computer-recorded force-deformation curves with the following equations:.
Conspiracy Theory Classification Dataset The COCO dataset contains Twitter posts annotated w. r. t. 12 named COVID-19 related conspiracy theories  (Langguth et al. , 2023). Part of this dataset was used in MediaEval FakeNews challenge  (Pogorelov et al. , 2022) , where participants had to detect mentions of 9 conspiracy theories 2  given the tweet texts. In this work, we use the Me-diaEval dataset, on which the best approaches used fine-tuned CT-BERT models  (Müller et al. , 2020; Peskine et al. , 2021; Korenčić et al. , 2022). The test set has not been shared publicly before early 2023, we can safely use it in our experiments as it is not part of the training data of GPT-3 that contains data up to September 2021 3.