dataesr/scientific-paragraphs-categorization huggingface dataset:
A Companion paper highlights the pre-analytical semi-automated measures put in place for labeling and data generation  [Hasan et al. , co-submission]. This manuscript details the automated analytic workflow developed for the identification and analysis of 24 cell types across four 8-color cytometry panels. Our work follows from a large number of computational approaches that have been developed for automated flow cytometry analysis. Recently, the FlowCAP study evaluated a range of approaches  [13]. In all cases, however, the datasets used by these investigators were of a smaller scale than the ones in our study, in terms of samples studied (FlowCAP: up to 30 samples; here: 115 samples × 4 panels), and the number of events per experiment (FlowCAP: up to approximately 100,000 events; here: on average 300,000 events per FCS file).
Due to these differences, we found that top-ranked FlowCAP approaches were inadequate to address the needs of our data sets. For example, the ADICyt approach  [4]  required more than 6 h for the analysis of a single sample. The FlowMeans software  [5]  was faster, but required manual assignment of cell types to each cluster in every single sample. The recent X-Cyt approach  [3]  was designed explicitly to efficiently address the problem of larger numbers of samples. However, X-Cyt still requires the definition of a "partitioning scheme", a series of mixture models whose sequence and parameters have to be manually configured and calibrated for each cell type of interest in any given analytic panel. To support the analysis of the Milieu Intérieur cohort dataset, we developed a novel high-dimensional data analysis approach, which we refer to as FlowGM, utilizing fast algorithms that enable the standardized analysis of large numbers of samples.
Dataset Four 8-color cytometry panels targeting major leukocyte populations across 115 individuals from different age groups and genders were designed to characterize the major immune cell populations (T cells, B cells, NK cells and monocytes), as well as subpopulations of T cells, dendritic cells (DC) and polymorphonuclear leukocytes (PMN). The standardized procedure of collection and treatment of the whole blood sample is described in  [Hasan et al. , cosubmission]. For each of the four panels, technical replicates performed by five parallel blood samples obtained from three donors ("repeatability" studies from [Hasan et al. , co-submission]) were generated to examine robustness of the experimental and computational protocols.
Identification of the major cell lineages by FlowGM We first applied FlowGM to the lineage panel dataset [Hasan et al. , co-submission]. Cells were stained with the markers CD45, CD3, CD4, CD8β, CD14, CD16, CD19, and CD56. Following the approach of the manual analysis by Hasan et al. , we used forward and side scatter (FSC/SSC) solely to exclude doublets; the remainder of our data analysis is performed on the dimensions of the indicated eight markers. The number of events in the data files ranged from 106,000 to 787,000. After filtering out doublets, FlowGM estimated the optimal number of clusters k to be 36, using the BIC (see Materials and methods, Section 2. 4) on the reference donor (Fig. 2A ). Once k was determined, FlowGM performed EM clustering 100 times, starting with different random initial configurations of k clusters.
The clustering solution with the highest likelihood p(x|θ) constitutes the reference clustering, whose clusters were then manually labeled with the different cell types of interest (i. e. , leukocyte subpopulations). The corresponding cluster centroids are represented as a heat map, with the assigned cell types indicated (Fig. 2B ). Note that only 24 of the 36 clusters corresponded to cell types of interest, and the color coding is chosen independently for each marker to resolve the entire spectrum of expression across these cell types (using the Matlab HeatMap function). For example, as CD45 -cell populations were not of interest in this study, all selected cells were CD45 + and as indicated by the normalization, the lowest and highest levels of CD45 expression were observed in monocytes and T cells, respectively (Figs.
2B, C ). To facilitate the understanding of our findings and permit user cross-validation, FlowGM allows the embedding of cluster IDs and meta-cluster IDs as additional channels (designated "C-ID" and "MC-ID", respectively) into the FCS input file, permitting importation of all data into FlowJo (or other FCS-compatible software). FlowJo visualizations of the labeled FlowGM lineage clusters confirmed our GMM-based assignments (Fig. 3 ). By gating on MC-ID to select one FlowGM meta-cluster, it is possible to view the clustered cells in 2D projections that correspond to manual gating strategies. FlowJo visualizations of all 36 FlowGM clusters are shown in Fig. S1. Backgating is also possible: starting with manual gated data and examining where the captured events cluster in C-ID or MC-ID space (data not depicted).
Together, these observations suggest that the choice of the initial reference clustering may not have a large impact on the resulting outcome. We also investigated the impact of compensation. Routinely, automatic hardware compensation [Hasan et al. , cosubmission] is employed. Here, we compare the results of our approach on the same input data in an uncompensated state; machine-compensated; or machine-compensated and FlowJocorrected. The computational analyses on these three datasets were initialized with the re-estimated parameters from the.
Furthermore, the facility with which results are accessible for human inspection using conventional tools, and the relative simplicity of the FlowGM approach itself imply a high level of accessibility to non-specialists thatwe believewill continue to play an important role in the evolution of the approach. We believe that the FlowGM workflow is applicable to most other flow cytometry datasets, and anticipate that the need for fast, robust, and high-quality analysis of large cytometry datasets will only increase. Adaptations of the method may be required for heterogeneous samples, in which no single reference sample may be representative for all others, or in cases where certain subpopulations may be activated (e. g. , disease populations). We believe that there are relatively straightforward approaches to extend FlowGM to automatically detect cases of inadequate fit, for example, through the introduction of additional reference donors (with recursive iteration of the manual Step 5).
Statistical analysis. Results were analyzed using Systat 10 software for Windows, and all data are reported as mean ± SEM. For the in vivo experiments on chemically-induced rats, the importance of each factor was tested independently of the experimental groups (ANOVA per factor). If a significant difference was found between groups (p<0. 05), each experimental group was compared with the control using Dunnett's test, the difference between control and hemoglobin diets effect on Min mice tumors was analyzed using the Student's t-test. For the in vitro study, the dose-effect of aldehydes was analyzed using one-way analysis of variance (ANOVA). If a significant difference was found between groups (p<0. 05), each experimental group was compared with the control treatment using Dunnett's test. Secondly, the effect of the mutation effect at each concentration of aldehyde was analyzed with the comparison between the Apc+/+ and Apc-/+ cell lines using the Student's t-test.
Photon CSI The RT Image and RT Structure Set files from MD Anderson containing the containing the CT images and contoured volumes were de-identified  [33, 34] , sent to AUBMC via encrypted and password-protected data transfer, and imported into the AUBMC TPS (Panther, version 4. 72, Prowess Inc. , Concord, CA, USA). Three-dimensional radiation therapy treatment fields were planned according to the standard of care for pediatric photon CSI at AUBMC. The photon CSI plan comprised four 6-MV treatment fields: LPA and UPA spinal fields and right and left lateral cranial fields (RL and LL) to be delivered using AUBMC's clinical linear accelerator and multi-leaf collimator (Artiste, Siemens Medical Solutions USA, Inc. , Malvern, PA, USA). Cranial and upper spinal fields were matched by rotating the collimator used in the cranial fields, and no couch rotation was applied.
Approach 2: Volume-Weighted Average In the second approach, we applied the volume-weighted averaging technique developed by Howell et al. [42]. Dose volume histogram (DVH) data were exported from the TPS. For organs and tissues that were entirely within the treatment field, the DVH data alone was used to determine the equivalent dose in organs and tissues, as described in Section 2. 2. For organs that were entirely outside of the treatment field, the mean organ equivalent dose values were determined from our previous study in which thermoluminescent dosimeter (TLD) measurements were made within an anthropomorphic phantom receiving photon CSI at AUBMC  [41]. The mean organ equivalent dose in each out-of-field organ was calculated as an average of the equivalent doses of all TLD measurements within the organ.
For organs and tissues that were partially in the treatment field, as described by Howell et al. , a sensitivity analysis was performed to determine if the DVH data were sufficiently accurate for calculating the mean organ dose. Specifically, if adjusting D v in the voxels outside the 5% isodose surface by ±60% resulted in a greater than ±5% difference in H T , then H T for that organ was estimated based on a volume-weighted average that combined the TPS estimate of the in-field component of dose with a measured out-of-field component of dose. If not, H T for that organ was determined based on the D v values from the TPS.
The 50% isodose surface was converted to a contoured structure using the TPS. Then the DICOM-RT objects for the dose distributions and contoured structures were exported from the TPS to separate RT Dose and RT Structure Set formatted files, respectively. The coordinates and doses of each voxel in the RT Dose file and the coordinates of each vertex of the 50% isodose surface in the RT Structure Set file were read into an in-house code and processed using commercial software (MATLAB R2008b, The MathWorks, Inc. , Natick, MA, USA). For each voxel that was both outside the 50% isodose surface and at a distance greater than 1 cm from the nearest vertex of the 50% isodose surface, the code replaced H v with the model-based estimate from Equation  (5). The resulting position and dose data were converted back into a DICOM RT Dose file, and H T values were calculated as described in Section 2.
Acknowledgments The authors are thankful to  Kathryn Carnes  at  MD Anderson  for editing,  Dan Jackson  at  MD Anderson  for his assistance with computational codes and parallel processing, and  Toufic Eid at AUBMC  for discussions on treatment planning in photon CSI. We thank  Rebecca Howell  and  Dragan Mirkovic  at  MD Anderson  for sharing their helpful clinical and scientific expertise. This work was supported in part by the  Fogarty International Center  award  K01TW008409 , the  National Cancer Institute  award  1R01CA131463-01A1 , and  Northern Illinois University  through a subcontract of  Department of Defense  award  W81XWH-08-1-0205. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.
The various teams used difference image analysis (DIA) to obtain photometry: μFUN used pySIS  (Albrow et al. 2009) , with the exception of the Auckland data, which were re-reduced using DanDIA  (Bramich 2008; Bramich et al. 2013). DanDIA was also used to reduce the RoboNet and the Danish data sets. PLANET data were reduced online with the WISIS pipeline, and final data sets were prepared using pySIS. 61 OGLE  (Udalski & Szymański 2015)  and MOA  (Bond et al. 2001)  used their own DIA code to reduce their frames. All other data sets were reduced using pySIS. A total of 2955 data points from 16 telescopes were used for our analysis, after problematic data points were masked. A summary of each data set is available in Table  1.
In this paper, we follow an alternative method of first adjusting f and e min to force the residuals, normalized by e , i ¢ to follow a Gaussian distribution around the model. If possible, we also aim to obtain a χ 2 /dof ∼ 1. Note that these two methods lead to the same results, except for the OGLE_I data set. For OGLE_I, the distribution without rescaling shows some data points with large residuals. This is not surprising because the OGLE_I data set covers the entire light curve with a large number of points, especially the faint baseline magnitude (I ∼ 17. 8), with a constant exposure time on the order of 100 s. Inspection of the OGLE_I light curve reveals that the uncertainties during high magnification are underestimated, so we adjust the e min parameter. We tried to force χ 2 /dof ∼ 1 for this data set, but this generated large uncertainties for the low magnification part (i.
LSCA_i and LSCB_i : the Twins Paradox We opted to employ the above methodology in order to investigate and understand the systematic trends in the LSCB_i and LSCA_i data sets using the algorithms described in  Bramich & Freudling (2012). 64 These telescopes are twins: both are LCOGT 1 m telescope clones, both supporting Kodak SBIG STX-16803 CCDs at the time of these observations. SDSS-i prescription filters manufactured at the same time were use to observe OGLE-2013-BLG-0446 during the same period of observation, though not precisely synchronously. We first chose to study LSCB_i because this telescope most strongly favors the planetary model (Δχ 2 ∼ 128. 3; see Table  6 ). For LSCB_i, the DanDIA pipeline extracted 4272 light curves from the images in the LSCB_i data set, each with 378 data points (or epochs), which yields a total of 1,614,816 photometric data points.
2 mmag. This can be seen in Figure  4. The overall level of systematic trends in the LSCB_i data set for OGLE-2013-BLG-446 is ∼2. 0 mmag. To conclude, this analysis reveals that the illumination correction is not sufficient to explain the observed systematics. We chose also to study the LSCA_i data set because this telescope observed the target at the same time but does not show any planetary significance (Δχ 2 ∼ 20. 7). We conduct the same study and the results are summarized in Table  4  and Figure  4. The peak-to-peak amplitudes of the magnitude offsets are 10 times bigger than for the LSCB_i data set. It is surprising to see how two similar intruments can lead to such different data quality. A more careful check of the frames clearly shows a problem in the focus for the LSCA_i telescope.
Correction of Systematics For the three studied data sets (LSCA_i, LSCB_i and Auckland_R), we corrected the systematics for the quantity that yielded the largest peak-to-peak amplitude in the magnitude offsets: namely the epoch. Moreover, this quantity is correlated with other quantities (airmass for example) and so the epoch correction should decrease the systematic trends measured for the other paramters listed in Table  4. We checked this and found that for the three data sets, the epoch correction leads to a significant improvement (order of a factor 10) for the systematics of the correlated quantities. This is a first order correction and we wanted to see the impact on the different models we analyzed. We repeated our modeling process with these new data sets using the previous models as starting points.
We however decided to not realize the same study of photometric systematic errors for the Canopus_I data set because there are no obvious deviations in the FSPL residuals and also because enough doubt has already been cast on the planetary model we found. All these points reveal strong doubts about the reality of the planetary signature in OGLE-2013-BLG-0446. Even if we cannot firmly guarantee that the planet is not detected, we prefer to stay conservative and claim that we do not detect a planet in this event.
JSPS,  JSPS23103002 , JSPS24253004  and  JSPS26247023. The M. O. A. project is supported by the grant  JSPS25103508  and  23340064. D. P. B. acknowledges support from  NSF  grants  AST-1009621  and  AST-1211875 , as well as  NASA  grants  NNX12AF54G  and  NNX13AF64G. Work by I. A. B. and P. Y. was supported by the  Marsden Fund of the Royal Society of New Zealand , contract no. MAU1104. The operation of the Danish 1. 54 m telescope is financed by a grant to UGJ from the  Danish Natural Science Research Council. We also acknowledge support from the  Center of Excellence Centre for Star and Planet Formation (StarPlan)  funded by The  Danish National Research Foundation. The MiNDSTEp monitoring campaign is powered by  ARTEMiS  (Automated Terrestrial Exoplanet Microlensing Search; Dominik et al.
2008, AN 329, 248). K. A. A. , M. D. , K. H. , M. H. , C. S. , R. A. S. , and Y. T. are thankful to  Qatar National Research Fund (QNRF) , member of  Qatar Foundation , for support by grant  NPRP 09-476-1-078. C. S. has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 268421. Work by C. H. was supported by the  Creative Research Initiative Program  ( 2009-0081561 ) of  National Research Foundation of Korea. S. H. G. and X. B. W. would like to thank the financial support from  National Natural Science Foundation of China  through grants Nos. 10873031  and  11473066. T. C. H. acknowledges support from the  Korea Research Council of Fundamental Science & Technology (KRCF)  via the  KRCF Young Scientist Research Fellowship Programme  and for financial support from  KASI  travel grant number  2013-9-400-00.
H. K. acknowledges the support from the  European Commission  under the  Marie Curie IEF Programme  in FP7. M. R. acknowledges support from  FONDECYT postdoctoral fellowship No. 3120097. O. W. (FNRS research fellow) and  J. Surdej  acknowledge support from the  Communauté française de Belgique-Actions de recherche concertées-Académie Wallonie-Europe. The HPC (and/or scientific visualization) resources and services used in this work were provided by the  IT Research Computing group in Texas A&M University at Qatar. IT Research Computing is funded by the  Qatar Foundation for Education, Science and Community Development  (http://www. qf. org. qa).
At lower p T (p T < 2 GeV), a similar scaling behavior is observed, although, according to perfect fluid hydrodynamics, v 2 /n q values must be compared at the same transverse kinetic energy per constituent quark (KE T /n q , where KE T = and Λ/Λ, in pPb collisions at a center-of-mass energy per nucleon pair ( √ s NN ) of 5. 02 TeV. With the implementation of a dedicated high-multiplicity trigger, the 2013 pPb data sample gives access to multiplicities comparable to those in semi-peripheral PbPb collisions. Two-particle correlation functions are constructed by associating a K 0 S or Λ/Λ particle with a charged particle (pairs of K 0 S or Λ/Λ particles are not studied due to their limited statistical precision). In the context of hydrodynamic models, Fourier coefficients of dihadron correlations can be factorized into products of single-particle azimuthal anisotropies.
48) and two endcaps that extend to |η| = 3. 0. The HCAL barrel and endcaps are sampling calorimeters composed of brass and scintillator plates, covering |η| < 3. 0. Iron/quartz-fiber forward calorimeters (HF) are placed on each side of the interaction region, covering 2. 9 < |η| < 5. 2. The detailed Monte Carlo (MC) simulation of the CMS detector response is based on GEANT4  [45]. The data sample used in this analysis was collected with the CMS detector during the LHC pPb run in 2013. The total integrated luminosity of the data set is about 35 nb -1  [46]. The beam energies are 4 TeV for protons and 1. 58 TeV per nucleon for lead nuclei, resulting in a center-ofmass energy per nucleon pair of 5. 02 TeV. The direction of the proton beam was initially set up to be clockwise (20 nb -1 ), and was later reversed (15 nb -1 ).
Charged tracks are then reconstructed online at the HLT using the three layers of pixel detectors, and requiring a track origin within a cylindrical region of 30 cm length along the beam and 0. 2 cm radius perpendicular to the beam. For each event, the number of pixel tracks (N online trk ) with |η| < 2. 4 and p T > 0. 4 GeV is counted separately for each vertex. Only tracks with a distance of closest approach of 0. 4 cm or less to one of the vertices are included. The online selection requires N online trk for the vertex with the most tracks to exceed a specific value. Data are taken with thresholds of N online trk > 100, 130 (from events with L1 threshold of 20 GeV), and 160, 190 (from events with L1 threshold of 40 GeV). While all events with N online trk > 190 are accepted, only a fraction of the events from the other thresholds are kept.
3 GeV and |η| < 2. 4. The efficiency is greater than 90% in the |η| < 1 region for p T > 0. 6 GeV. For the event multiplicity range studied in this paper, no dependence of the tracking efficiency on multiplicity is found and the rate of misreconstructed tracks is 1-2%. The entire pPb data set is divided into classes based on the reconstructed track multiplicity, N offline trk , where primary tracks with |η| < 2. 4 and p T > 0. 4 GeV are counted. Details of the multiplicity classification in this analysis, including the fraction of the full multiplicity distribution and the average number of primary tracks before and after correcting for detector effects in each multiplicity range, are provided in Ref. [47]. A subset of semi-peripheral PbPb data collected during the 2011 LHC heavy-ion run with a minimum bias trigger are also reanalyzed in order to directly compare pPb and PbPb systems at the same collision multiplicity.
As illustrated in Fig. 2 , the near-side longrange signal remains nearly constant in ∆η. Therefore, by taking a difference of 1D ∆φ projections between the short-and long-range regions, the near-side jet correlations can be extracted. As shown in the bottom panels of Fig. 3 , due to biases in multiplicity selection toward higher p T jets, a larger jet peak yield is observed for events selected with higher multiplicities. Because charged particles are directly used in determining the multiplicity in the event, this selection bias is much stronger for charged particles than K 0 S and Λ/Λ hadrons. For N offline trk < 35, no near-side correlations are observed in the long-range region for any particle species. The PbPb data show qualitatively the same behavior as the pPb data, and thus are not presented here.
Recently, the v 2 anisotropy harmonics for charged pions, kaons, and protons have been studied using two-particle correlations in pPb collisions  [31] , and are found to be qualitatively consistent with hydrodynamic models  [32, 33]. In this paper, the elliptic (v 2 ) and triangular (v 3 ) flow harmonics of K 0 S and Λ/Λ particles are extracted from the Fourier decomposition of 1D ∆φ correlation functions for the long-range region (|∆η| > 2) in a significantly larger sample of pPb collisions such that the particle species dependence of v n can be investigated in detail. In Fig. 4 , the v sig 2 of K 0 S and Λ/Λ particles are plotted as a function of p T for the three lowest multiplicity ranges in PbPb and pPb collisions. These data were recorded using a minimum bias trigger. The range of the fraction of the full multiplicity distribution that each multiplicity selection corresponds to, as determined in Ref.