dataesr/scientific-paragraphs-categorization huggingface dataset:
nihr. ac. uk). A. Brankin  is funded by the  NDM Prize Studentship  from the  Oxford Medical Research Council Doctoral Training Partnership  and the  Nuffield Dept of Clinical Medicine. D. J. Wilson  is supported by a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  ( 101237/Z/13/B ), and by the  Robertson Foundation. A. S. Walker is an  NIHR Senior Investigator. T. M. Walker  is a  Wellcome Trust   Clinical Career Development Fellow  ( 214560/Z/18/Z ). A. S. Lachapelle is supported by the  Rhodes Trust. R. J. Wilkinson  receives funding from the  Francis Crick Institute , which is supported by the  Wellcome Trust  ( FC0010218 ),  UKRI  ( FC0010218 ) and  CRUK  ( FC0010218 ). T. Cohen  has received grant funding and salary support from the  National Institutes of Health (NIH) ,  Centers for Disease Control and Prevention (CDC) ,  US Agency for International Development (USAID)  and  Bill & Melinda Gates Foundation.
The computational aspects of this research were supported by the  Wellcome Trust   Core Award  ( 203141/Z/16/Z ) and the  NIHR Oxford BRC. Parts of the work were funded by the  German Center of Infection Research (DZIF). The Scottish Mycobacteria Reference Laboratory is funded through  National Services Scotland. The Wadsworth Center contributions were supported in part by cooperative agreement number  U60OE000103  funded by the  CDC  through the  Association of Public Health Laboratories  and  NIH/NIAID  grant  AI-117312. Additional support for sequencing and analysis was contributed by the  Wadsworth Center Applied Genomic Technologies Core Facility  and the  Wadsworth Center Bioinformatics Core. SYNLAB Holding Germany GmbH  for its direct and indirect support of research activities in the  Institute of Microbiology and Laboratory Medicine Gauting.
DKES cannot determine h if Mi in the limit given by ρ i * ∼ ν i * 1 and 1. Finally, to demonstrate the advantages of (5. 25), (5. 33) and (5. 34), we consider the deeply trapped particles. These particles were singled out as problematic for, for example, the first version of the local orbit-averaged code KNOSOS  (Velasco et al. 2020). The first version of KNOSOS was valid only for stellarators close to omnigeneity  (Calvo et al. 2017) , and as a result, the radial displacement of particles is neglected to lowest order in the expansion in closeness to omnigeneity. This approximation is valid for most particles, but fails for deeply and barely trapped particles because these particles cannot move across magnetic field lines within the same flux surface if they do not move simultaneously along ∇r (across flux surfaces).
Funding This work was supported by the  U. S. Department of Energy  under contract number  DE-AC02-09CH11466. The United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. This work has been carried out within the framework of the  EUROfusion  Consortium, funded by the  European Union  via the  Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the  European Commission. Neither the  European Union  nor the European Commission can be held responsible for them. This research was supported in part by Grant  PGC2018-095307-B-I00 ,  Ministerio de Ciencia, Innovación y Universidades, Spain.
e. , we do not attempt to address the different number and quality of data on different days. This combined data set is assumed to provide an ensemble of comparable observations from which we may estimate the desired statistical properties. Station gains impose an important potential additional systematic uncertainty. In most cases, we marginalize over the credible range of station gains (see Section 4. 3). However, there are two important preprocessing steps that we employ to reduce the impact of gain errors within the 2017 EHT observation campaign. First, we employ the network calibration procedure described in M87 * Paper II to set the gain amplitudes at JCMT, SMA, ALMA, and APEX to within 1%. Second, we calibrate the LMT gain amplitudes by fixing the visibility amplitudes on the LMT-SMT baseline to a Gaussian model, with an FWHM of 60 μas, comparable to the secondmoment estimates of M87 * and Sgr A *.
For application to the 2017 EHT Sgr A * campaign, we adopt gain uncertainties consistent with those reported in Paper III. For sites for which network calibration is performed (ALMA, APEX, JCMT, and SMA) we assume a gain amplitude error of 1%; all other sites are assigned a gain amplitude error of 10%. These additional gain fluctuations are assumed to be normally distributed and applied on a per-scan basis. Finally, we normalize the ensemble data by the correlated flux on the intrasite baselines (ALMA-APEX, JCMT-SMA) as an approximation to the light curve.
(2017). The construction of the simulated iNoisy data sets is described in detail in Section 5 of Paper III. However, the apparent spatial correlations are typically confined to smaller scales than those characteristic of the geometric model. The iNoisy data sets that we employed during the validation of the statistical estimators include a crescent, ring, double source, simple disk, elliptical disk, and ring with a 30minute-period hot spot (see Paper III for further details). Each "truth" movie was resampled on each of the 4 days with different thermal noise, complex gain, scattering, and structural fluctuation realizations. Three examples-the crescent, point source, and elliptical disk-are shown in Figure  4. In all cases, the estimated azimuthally averaged mean and variance maps match those associated with the "truth" movies well.
GRMHD Validation Results GRMHD simulations provide a credible proxy for source structure of astronomical sources on horizon scales (Paper V;  Georgiev et al. 2022). While there is a limited range of potential image morphologies within the GRMHD libraries, they do provide a physically motivated and self-consistent set of intensity fluctuations. Importantly, these models exhibit the spatial and temporal correlations that are anticipated in accreting and jet-launching environments. Each "truth" movie was resampled with (u, v)-coverage identical to that of Sgr A * during the 2017 EHT campaign on each of the four observation days with different realizations of thermal noise, complex gain, and scattering. The simulated data sets share many of the features of those associated with the iNoisy sets: the images are complex, contain significant image-domain correlations that translate into corresponding visibility-domain correlations, and contain an underlying noise spectrum that is not known a priori.
The latter fact motivates a comparison to the data-driven nonparametric estimates from Section 4. However, the GRMHD simulation data sets do differ in two important respects. First, they exhibit a much wider array of behaviors, extending from small-scale fluctuations like those in the iNoisy sets to large-scale arcing structures that differ from the iNoisy images. These arcing features induce longtimescale correlations that are not otherwise present in the iNoisy sets. Second, there is no attempt to restrict the simulations to those that reproduce the variability observed in Sgr A *. As a result, the variability present in the GRMHD simulations is typically larger than that exhibited by Sgr A * (Paper V). Three typical GRMHD validation examples are shown in Figure  5. As with the iNoisy validation examples, this example accurately reconstructs the underlying truth variances associated with the full GRMHD movie for 2.
iNoisy Reconstructions The iNoisy data sets used to validate the image reconstructions in Paper III and described at the beginning of Section 4. 4. 1 provide a natural set of validation tests. Despite the artificial nature of the simulated images, there are some salient differences with tests in the preceding subsection. First, the images are more complex, and we apply a more complicated source model-an adaptive bicubic raster model appropriate for image reconstruction (see  Broderick et al. 2020b ; Paper III)-with an associated increase in model complexity. Second, because fluctuations are applied directly to the image, significant correlations are induced among the complex visibilities. Third, the underlying noise spectrum is not known a priori, and therefore the specific quantitative reconstructions of the noise parameters cannot be compared to some truth in the form shown in Figure  9.
GRMHD Simulation Reconstructions Finally, we turn to GRMHD simulations, which exhibit physically motivated stochastic and coherent substructures in the simulated images. The simulated GRMHD data sets described in Section 4. 4. 2 for the April 6 and 7 (u, v)-coverage of the 2017 EHT Sgr A * campaign are used. We perform two sets of validation experiments with the GRMHD simulation data sets, distinguished by the image model employed. In both sets of experiments we employ the same procedure to set the priors on the noise model using the data-driven, nonparametric estimates as that employed during the iNoisy tests. These priors are reflected in the blue shaded regions in Figures  12  and 13. In the first, we reconstruct the simulated data sets with a parameterized ring model appropriate for comparison with the compact ring structures seen in M87 * and Sgr A * : the mG-ring model described in Paper IV and a simple extension of the m-ring model introduced in Section 5.
Combining all four observing days, 2017 April 5, 6, 10, and 11, we recover the interday variability observed across the week. In particular, we find excellent agreement between the data-driven nonparametric estimates and the self-consistently obtained modeled variability noise. Moreover, the nonvariability components in the noise model, i. e. , refractive scattering and fractional systematic error components, return their anticipated values for M87 * : negligible and 1%, respectively. Both of the methods presented here have potential applications well beyond EHT data sets. As demonstrated with M87 * , they provide a natural method for estimating the degree of variability for sources that vary across multiple observation epochs. Moreover, by simultaneously recovering the variability and the mean flux distribution, Bayesian noise reconstruction enables the self-consistent combination of the data from multiple observations.
The Event Horizon Telescope Collaboration thanks the following organizations and programs: The  Academia Sinica ; the  Academyof Finland  (projects  274477 ,  284495 ,  312496 ,  315721 ); the  Agencia Nacional de Investigación y Desarrollo (ANID) ,  Chile  via  NCN19_058  (TITANs) and Fondecyt  1221421 , the  Alexandervon Humboldt Stiftung ; an Alfred P. Sloan Research Fellowship; Allegro, the  European ALMA Regional Centre node in the Netherlands , the  NL astronomy research network NOVA  and the astronomy institutes of the  University of Amsterdam, Leiden University and Radboud University ; the  ALMA North America Development Fund ; the  Black Hole Initiative , which is funded by grants from the  John Templeton Foundation  and the  Gordon and Betty Moore Foundation  (although the opinions expressed in this work are those of the author(s) and do not necessarily reflect the views of these Foundations); Chandra  DD7-18089X  and  TM6-17006X ; the  China Scholarship Council ;  China Postdoctoral Science Foundation  fellowship ( 2020M671266 );  Consejo Nacional de Ciencia y Tecnología  ( CONACYT, Mexico , projects  U0004-246083 ,  U0004-259839 ,  F0003-272050 ,  M0037-279006 ,  F0003-281692 , 104497 ,  275201 ,  263356 ); the  Consejería de Economía, Conocimiento ,  Empresas y Universidad of the Junta de Andalucía  (grant  P18-FR-1769 ), the  Consejo Superior de Investigaciones Científicas  (grant  2019AEP112 ); the  Delaney Family via the Delaney Family John A.
S. Department of Energy  under Contract No. DE-AC05-00OR22725. We also thank the  Center for Computational Astrophysics, National Astronomical Observatory of Japan. The computing cluster of Shanghai VLBI correlator supported by the  Special Fund for Astronomy  from the  Ministry of Finance in China  is acknowledged. APEX is a collaboration between the  Max-Planck-Institut für Radioastronomie (Germany) ,  ESO , and the  Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the  Smithsonian Institution  and the  Academia Sinica. The JCMT is operated by the  East Asian Observatory  on behalf of the NAOJ, ASIAA, and  KASI , as well as the  Ministry of Finance of China, Chinese Academy of Sciences , and the  National Key Research and Development Program  (No.
2017YFA0402700 ) of China and  Natural Science Foundation of China  grant  11873028. Additional funding support for the JCMT is provided by the  Science and Technologies Facility Council (UK)  and participating universities in the UK and  Canada. The LMT is a project operated by the  Instituto Nacionalde Astrófisica, Óptica, y Electrónica (Mexico)  and the  University of Massachusetts at Amherst (USA). The IRAM 30-m telescope on Pico Veleta, Spain is operated by IRAM and supported by  CNRS  ( Centre National de la Recherche Scientifique, France ),  MPG  ( Max-Planck-Gesellschaft, Germany)  and  IGN (Instituto Geográfico Nacional, Spain). The SMT is operated by the  Arizona Radio Observatory , a part of the  Steward Observatory of the University of Arizona , with financial support of operations from the  State of Arizona  and financial support for instrumentation development from the  NSF.
The parameters' values are determined from the flow stress data observed from the experiments. The yield stress of the material at a reference temperature gives the value of parameter A, parameter B is the modulus of strain hardening, n is the strain-hardening exponent, C is the strain rate sensitivity, and m is the thermal softening exponent. T is the current temperature, T melt and T room are the melting and the room temperatures, respectively, while ε0 = 1 is the reference strain rate. The parameters' values are taken mostly from dynamic experimental material tests such as Split Hopkinson Pressure Bar (SHPB)  [16, 31]. These parameters' values are extrapolated, as experimental data are inadequate to represent the deformation behavior of the material. This significantly gives rise to the proposition of numerous parameter sets for the same material  [16, 32].
A two-dimensional (2D) plane strain model with orthogonal cutting assumption was considered for this work. The finite element software Abaqus was used to model the thermo-mechanical chip formation process. In this FE model, the tool is fixed, and the workpiece moves at the prescribed cutting speed. The tool and the workpiece are meshed with quadrilateral elements with reduced integration for a coupled temperature-displacement calculation (CPE4RT). The length of the workpiece is 3 h, where h is the uncut chip thickness. To achieve a better trade-off between the element size and computation time, the area near the cutting zone (near the tool-tip) was modeled with a finer mesh of size 5 µm. In this approach, the initial geometry of the chip must be predefined with respect to the uncut chip thickness (h).
Post-Processing Automation A post-processing automation script has been created. It investigates the output file (. ODB in Abaqus) stored in a specific folder after processing the input file and the completion of the computation. This post-processing is the crucial part, where the information from the specific nodes is examined and mathematical works (such as Root Mean Square calculations of forces and euclidean distance between the chip sides) are carried out to determine the desired results. In the orthogonal cutting simulation, the cutting force, feed force, and chip thickness are usually analyzed and are compared with the experimental results. The cutting force and feed force were evaluated by considering the average value in the steady-state. The information on the coordinate points of the chip was required to calculate the chip thickness.
The chip produced by the simulation was continuous (it has the shape of a curve). The direct measurement of the chip thickness was unlikely due to the shape of the chip. Both sides of the chip were modeled with Bézier curves, and the chip thickness was measured as an average of distance between those curves evaluated on several points. In addition, kinetic energy vs. internal energy information was acquired to check the stability of the ALE model with mass scaling. The post-processing script helps to analyze the result faster and more accurately.
Two main CD8 + T cell populations identified by differential expression of CD39 and GZMK infiltrate CRC tumors We next hypothesized that the accumulation of CD15 high neutrophils might alter the immune landscape of the tumor, in particular CD8 + T cells. CD8 + T cell infiltration is unanimously accepted as a favorable prognostic marker in CRC  1, 2, [43] [44] [45] [46]. However, querying the TCGA database of non-metastatic CRC patients with a cytolytic CD8 + T cell's transcriptional signature did not result into an effective stratification (DFS, p = 0. 12 Supplementary Fig. 3A ), suggesting that other factors might influence CD8 + T cell-mediated responses and the clinical outcome. Therefore, we employed high dimensional flow cytometry to assess the diversity of the CD8 + T cell compartment, looking at markers of memory and effector differentiation (CD45RA, CCR7, CD27, CD28, CD127, CX3CR1, and CD161), activation (OX40, CD25, CD69, and HLA-DR), inhibitory receptors (PD-1 and TIGIT), tissue residency and tumor reactivity (CD103 and CD39) and effector molecules (GZMB and GZMK).
Finally, querying of publicly available CRC datasets on The Cancer Genome Atlas (TCGA) using CD8_T em transcriptional  signature (see Methods for details) showed a better prognosis in patients with lower infiltration of CD8_T em expressing GZMK (Fig. 4G ) and this was specific of the CD8_T em , since none of the other lymphocyte signatures tested resulted into an equivalent stratification (Supplementary Figs 7B-D and Supplementary 8). Importantly, our CD8_T em was effective in stratifying also patients with other types of carcinomas, like the lung adenocarcinoma (LUAD on TCGA, Supplementary Fig. 7E ). Overall, functional and transcriptional characterization of the tumor immune infiltrates led to an effective stratification of patients and predicted the risk of encountering secondary events in CRC patients and, potentially, in other cancer types.
Multiparametric flow cytometry and sorting High-dimensional flow cytometry was performed on T, NAT, and PB  51. Conjugated antibodies used for flow cytometry are shown in Table  2. Briefly, single-cell suspensions were stained fresh or after thawing in pre-warmed RPMI with 10% FBS. Cells were washed in staining buffer (PBS with 2%FBS and 2 mM EDTA) and incubated for 30 min at 4 °C with surface antibody cocktail in staining buffer, washed twice in staining buffer, fixed and permeabilized using Fixation/Permeabilization Solution Kit (BD, Cat # 554714), and stained intracellularly for 1 h at 4 °C with an antibody cocktail in the permeabilization solution. Samples were acquired using a FACSymphony A5 or a FACSCelesta BVYG equipped with FACSDiva software version 8. 0. 1 (all from BD Biosciences).
Compensation Beads (ThermoFisher, Cat# 01-2222-41) were used to prepare single-stained controls for electronic compensation. Dead cells were excluded using Fixable Viability Stain (BD). To evaluate ex vivo cytokines production, CD8 + T cell subsets were sorted to high purity using FACSAria III (BD Biosciences), then stimulated 3 h with PMA (20 ng/mL), ionomycin (1 μg/mL) and GolgiPlug proteintransport inhibitor (brefeldin A, 1:1000). All the FCS files generated were analyzed and visualized with software FlowJo v10 (TreeStar). RPhenograph 102 package was used to analyze the multiparametric data from CRC patients derived T, NAT and PB. CD8 analysis was performed on 3000 events per sample after manual gating isolation of singlet, LD negative, CD45 + CD3 + CD8 + T cells. All samples were concatenated by the "cytof_exprsMerge function".
The number of nearest neighbors identified in the first iteration of the algorithm, K value, was set to 100. UMAP and tSNE representation were generated and visualized using FlowJo version 10 (FlowJo). Underrepresented clusters (<0. 5%) were discarded in subsequent analysis. The balloon plots, performed using the ggplots2 R package, generate a further metaclustering classification, interpolating MFI and frequency of each marker per cluster. MFI were multiplied to derive the integrated MFI (iMFI, rescaled to values from 0 to 100). Euclidean distance and Ward-linkage methods via cytofkit package were applied to generate hierarchical metaclustering of Phenograph generated clusters. For the neutrophils analysis the following gating strategy was applied: Live, CD45 + , CD56 -, HLA-DR -, CD11b + , CD33 dim , CD66b + and then selected the CD15 + and CD15 -populations.
Briefly, 50 µl of samples together with kit standards were added to each well in duplicate and incubated with the diluted Microparticle Cocktail at 4 °C, ON, on a shaker at 850 rpm. Unbound soluble molecules were removed by washing the plate. The Biotin-Antibody Cocktail specific to the analytes of interest was added to each well for 1 h at RT. After washing again, the Streptavidin-Phycoerythrin conjugated was added for 30 min at RT. After the final washing steps, the microparticles are resuspended in kit buffer and read on a Luminex 200 platform. The outputs (pg/mL) were visualized and statistically analyzed in R upon centering and scaling using the scale function in R (SD from mean pg/mL). Data were visualized together with sample annotations using the Complex-Heatmap package. Group comparisons were visualized as boxplots using the ggpubr package and statistically analyzed by applying the non-parametric Wilcoxon signed-rank test on the values, where significance is set at p-value <0.
Briefly, images were pre-processed with denoising and background subtraction, and nuclei were segmented on the DAPI channel with the plugin StarDist 104 , using the built-in model Versatile (fluorescent nuclei) and the intensity parameters, including "mean intensity" and "raw integrated density", of CD8, GZMK and CD66b fluorescence were quantified in a band of thickness 1 μm around each nucleus. Statistical analysis was performed in R. CD8 + cells were identified from the database by setting criteria on the intensity parameters of the specific fluorescence. Particularly, a cell was considered as positive for CD8 fluorescence channel if its "raw integrated density" was above a threshold value, set by the comparison in images of the "raw integrated density" values of cells expressing CD8 fluorescence and the "raw integrated density" of cells expressing only autofluorescence signal.
Only distances below 10 μm were taken into account to select pairs of CD8 + cell-neutrophil that were close to each other. The mean intensity of GZMK fluorescence in CD8 + cells close to neutrophils was compared to the GZMK mean intensity of cells close to neutrophils that were not positive for CD8. In order to evaluate the expression of GZMK of CD8 + cells in relation to their distance from a neutrophil, we performed the computation of lymphocytes and neutrophils distances in Tumor Area. The distances of CD8 + cells from each neutrophil were calculated in each acquired image using an R script and these distances were filtered with a min function in order to consider the «nearest» neutrophil per each CD8 + cell. For the analysis, we considered only distances <10 µm (Touching) and between 10 and 20 µm (Near).