dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was partially supported by grant  ERARE11-135  of the  ERA-Net for Research Programs on Rare Diseases Joint Transnational Call 2011 (EURO-CDG ), the  European Union   FP6-Coordination Action EUROGLYCANET  ( LSHM-CT-2005-512131 ), and institutional funding from  Institut National de la Santé et de la Recherche Médical (INSERM). The whole exome study was funded by the  association Connaître les Syndromes Cérébelleux (CLC). The funding bodies played no role in the design, execution and reporting of this study.
Methods Patients. Patients admitted to the Liver Unit of the Hospital Clínic, Barcelona, between January 2007 and December 2009 with clinical, analytical and histological features of AH were included in a prospective fashion. Experiments were performed with approval and under the accordance of the relevant guidelines established by The Ethics Committee of the Hospital Clinic of Barcelona. All patients included gave written informed consent. The inclusion criteria have been previously described  10, 53, 54. Microarray data were deposited in NCBI's Gene Expression Omnibus (GEO; accession number GSE28619). Patients with malignancies or any other potential cause of liver disease were excluded from the study. Liver biopsies were obtained using a transjugular approach. A fragment of liver tissue, serum and plasma were obtained at the time biopsy.
Comparative Microarray Studies. Comparative analysis of microarray data from patients with AH and NASH was performed. Whole transcriptome analysis from these two types of patients was previously performed by our group  11, 30. All microarray studies were performed at the same time using Human Genome U133A plus 2. 0 GeneChip (Affymetrix Hgu133plus, Santa Clara, CA). Differential expression was assessed by using linear models and empirical Bayes moderated t-statistics. Linear Models for Microarray Analysis (LIMMA) R-package software was used for the analysis of gene expression microarray data  56. Group comparisons and determinations of false discovery rates (FDR computation using Benjamini-Hochberg procedure)  57  were performed. Functional analysis of gene expression data was conducted using the R/Bioconductor package GOstats and the GO database (http://www.
Acknowledgements This work was performed in The  Center for Gastrointestinal Biology and Disease (CGIBD) at UNC, Chapel Hill, NC. The authors are grateful to all the patients who took part in this study. They would also like to thank  M. Humphries  for technical help and  E. Stein  and  V. Massey  for critical reading of the manuscript. This work was supported by the  National Institute on Alcohol Abuse and Alcoholism (NIAA) ( 1U01AA021908-01  and  1U01AA020821 ), and by grants from the  Instituto de Salud Carlos III (ISCIII) ( FIS PI041538 , and  FIS PS09/01164  to  R. Bataller  and  J. Caballeria , respectively). P. Sancho-Bru  is supported by  ISCIII ,  Miguel Servet  ( CP11/00071 ) and by  Fondo Europeo de Desarrollo Europeo (FEDER) ,  Union Europea , " Una manera de hacer Europa ". E.
Seki  is supported by  R01AA020172  and  R01DK085252. M. You  is supported by  National Institute on Alcoholism and Alcohol Abuse  (grants  AA015951  and  AA013623 ). G. Odena received a grant from the  Asociacion Espanola para el Estudio del Higado  and is partially funded by  1U01AA020821. J. Altamirano  received a grant from  Fundación Banco Bilbao Vizcaya Argentaria. D. Rodrigo-Torres  received a grant from the  Ministerio de Educacion ,  Cultura y Deporte ,  FPU program. S. Affo  received a grant from  IDIBAPS. O. Morales-Ibanez  received a grant from the  Generalitat de Catalunya for Research Stays Abroad ,  BE-DGR 2012.
ACKNOWLEDGMENTS We thank our colleagues for fruitful discussions and critical reading of the manuscript. N. Dantuma ,  E. Dunleavy ,  S. Jackson ,  L. Jansen ,  J. Lippincott-Schwartz ,  R. Nishi , and H. van Attikum shared reagents. The  ImagoSeine core facility  ( Institut Jacques Monod, France BioImaging ) assisted with.
confocal microscopy. This work was supported by the  European Research Council  ( ERC-2013-StG-336427  ''EpIn''), the  French National Research Agency  ( ANR-12-JSV6-0002-01 , '' Who am I''? LabEx   ANR-11-LABX-0071 ,  ANR-11-IDEX-0005-01 ,  France-BioImaging   ANR-10-INSB-04 ), EDF  Radiobiology program  RB  2014-01 , and the  Fondation ARC. Research in G. A. group is supported by  la Ligue Nationale contre le Cancer (Equipe labellisee) , the  European Commission Network of Excellence EpiGeneSys  ( HEALTH-F4-2010-257082 ),  ERC-2009-AdG_20090506  ' 'Eccentric,' '  ANR-11-LABX-0044_DEEP , and  ANR-10-IDEX-0001-02 PSL*. P. L. 's group is funded by  VIRUCEPTION  ( ANR-13-BSV3-0001-01 ) and the  LabEx DEVweCAN  ( ANR-10-LABX-61 ). S. A. received PhD fellowships from  University Pierre et Marie Curie and La Ligue contre le Cancer.
Patients and methods The study was approved by our local institutional review board (Comité de Protection des Personnes Ile de France-IX) and was conducted in accordance with the Helsinki Declaration. 2. 1. Study population 2. 1. 1. Splenectomized ITP patients (exposed). We first identified all ITP patients seen in the department of internal medicine of Henri-Mondor university hospital, the French national referral center for adult cytopenias, and registered from 1990 in a database. From 827 ITP patients treated in our institution, we selected those with definite primary ITP (n = 678) who underwent splenectomy before 2004. We excluded patients with systemic lupus diagnosed according to American College of Rheumatology criteria  [17]  and patients with malignant hemopathies. For exhaustivity, we also screened the database from the department of pathology of our institution for records from 1990 to 2004 by using the key words "splenectomy" and "ITP.
" Clinical data were retrospectively collected from medical charts; all patients were interviewed by phone by 2 authors (LT, MMa) who used a standardized questionnaire. Information was systematically completed with the help of the general practitioner and/or medical care center if necessary, especially for deceased patients. The number of patients lost to follow-up was reduced by contacting the patient's family, regular physicians, and/or the city records office of the place of birth in case of death. 2. 1. 2. Nonsplenectomized ITP patients (unexposed controls). Splenectomized ITP patients were matched in a 1:1 ratio to nonsplenectomized patients (screened from the database and drawn by lot) with primary ITP on the date of ITP diagnosis ±5 years, age at diagnosis and gender. The same procedures used for splenectomized patients were used to collect data for controls.
Data analysis Data are presented as number and percentages for categorical variables and median with interquartile range (IQR) for continuous variables. Comparison between groups involved Fisher exact test for qualitative variables and nonparametric Mann-Whitney test for continuous variables. Cumulative incidence of VTE, CV, infection, and cancer events was estimated by the Kaplan-Meier method with the rank test for comparing splenectomized and nonsplenectomized patients. We considered VTE, CV, infection, and cancer events that occurred after the date of splenectomy or the theoretical date of splenectomy for controls as corresponding to the date of surgery for the splenectomized matched patient. The Cox proportional hazards model was used for multivariate analysis; hazard ratios (HR) are presented with their 95% confidence intervals (95% CIs).
Survival estimates were determined by the Kaplan-Meier method and compared by log-rank test. Data were standardized by using French mortality tables to estimate the expected mortality of a cohort of same age distribution and gender. The numbers of expected and observed deaths were compared by the Breslow and Day test. We calculated a propensity score to allow for comparison between splenectomized and nonsplenectomized ITP groups. A propensity score takes into account the factors that may have influenced the treatment allocation. The propensity score calculation included the variables age, gender, hemorrhagic event (see below), and number of treatment lines.
In our cohort, most laparotomies had been performed before the 2000s. The number of early VTE and hemorrhagic events differed, although not significantly, between   cases with laparotomy and laparoscopy. However, on long-term follow-up, the frequency of complications did not differ between these 2 groups; the effect of the surgery was probably less important at that time. The main finding of this study is that splenectomy is an independent risk of VTE. Beyond postoperative VTE of the portal vein system, VTE was more common in splenectomized patients than matched controls over the study period. The proportion of VTE in splenectomized patients (16%) was higher than previously reported by Vianelli et al  [11]  (8%) and Boyle et al  [2]  (4. 3%). Of note, Boyle et al used a coding database to identify major VTE events requiring hospitalization, which could have underestimated the number of events.
[32]  The long-term sustained overall response was 52%, but when not considering relapses that required only transient corticosteroids, we found 60% long-term sustained response, which was close to the results of the largest literature review including more than 2600 splenectomized patients with ITP. [1] he main limitation of our study is its retrospective design, with potentially missed data, but a large prospective cohort study with more than 10 years of follow-up is unlikely to ever be conducted. To collect the maximum data and update the followup, all medical charts were closely reviewed by 2 of the authors, and all living patients and their general practitioners, when necessary, were interviewed. Thus, our data were collected in a prospective manner, instead of from an epidemiological, numerical database.
In this study we compare nine optical flow algorithms that locally measure the flow normal to edges according to accuracy and computation cost. In contrast to conventional, frame-based motion flow algorithms, our open-source implementations compute optical flow based on address-events from a neuromorphic Dynamic Vision Sensor (DVS). For this benchmarking we created a dataset of two synthesized and three real samples recorded from a 240 × 180 pixel Dynamic and Active-pixel Vision Sensor (DAVIS). This dataset contains events from the DVS as well as conventional frames to support testing state-of-the-art frame-based methods. We introduce a new source for the ground truth: In the special case that the perceived motion stems solely from a rotation of the vision sensor around its three camera axes, the true optical flow can be estimated using gyro data from the inertial measurement unit integrated with the DAVIS camera.
INTRODUCTION Accurate and fast measurement of optical flow is a necessary requirement for using this flow in vision tasks such as detecting moving obstacles crossing the path of a vehicle, visually guiding aircraft or space vehicle landing, or acquiring structure from motion information about the environment. The progress of optical flow estimation techniques is marked by two major stepping stones: The quantitative evaluation of optical flow algorithms by  Barron et al. (1994)  provided a dataset and error measures that became standard until succeeded by the more challenging Middlebury benchmark  (Baker et al. , 2010). Both helped identify difficult areas in flow estimation and opened the door for the development of dozens of new algorithms. Today, state-of-theart techniques reach a remarkable degree of accuracy (see the evaluation on the Middlebury dataset online 1 ).
1. 3. Another problem of conventional optical flow methods is motion discontinuities at object boundaries, where at least two distinct motions overlap. The datasets offered in this paper do not have motion discontinuities, because the motions are generated by camera rotation. But in the case of DVS cameras, contrast edges at motion discontinuities would generate events exactly at the discontinuities, which  Barranco et al. (2014)  employs to extract the location and motion of 1 Middlebury Optical Flow, http://vision. middlebury. edu/flow/. contours. Furthermore, they combine DVS events with DAVIS intensity frames to reduce computational cost while at the same time increasing performance and stability. Away from strong contrast edges, in highly textured areas, event-based methods struggle because events that are fired during a short period of time at close-by positions are falsely assumed to stem from the same edge.
In our dataset, camera motion is restricted to camera rotation, so all optical flow can by computed using the rate gyro information. A database is created and offered to compare several event-based optical flow algorithms. We introduce a simple smoothing filter to increase accuracy while reducing computation cost. The database as well as all the code is made public; the dataset link is provided together with a detailed description in Section 2. 2 and each algorithm's open-source software implementation is provided in a footnote link. Section 3 presents the benchmarking results, which are discussed in Section 4 and lead us to the conclusion that economical real-time event-driven computation of motion flow is possible, but further development will be required for many practical applications.
Direction Selective Filter We refer to this method by the acronym "DS. " The DS method was developed by T. Delbruck in 2007 as the jAER class DirectionSelectiveFlow (https://sourceforge. net/p/jaer/code/ HEAD/tree/jAER/trunk/src/ch/unizh/ini/jaer/projects/rbodo/ opticalflow/DirectionSelectiveFlow. java) source code. This subclass of AbstractDirectionSelectiveFilter processes pure DVS event streams. Readers are referred to the source code for implementation details. DS was inspired by the organization of V1 direction selective simple cells  (Delbruck, 2008 (Delbruck, , 2012)). In the DS algorithm, each incoming DVS event is preprocessed by an orientation filter called SimpleOrientationFilter 3 in jAER. SimpleOrientationFilter first stores the current event's DVS timestamp in a 2D memory array indexed by pixel address.
e. , as means of determining the correctness of the computed velocities. No velocity is computed if both eigenvalues are smaller than a certain confidence threshold τ , i. e. , λ 1 < τ. If both are greater than τ , the matrix is considered invertible and the velocity can be computed as shown. If λ 1 ≥ τ and λ 2 < τ , we compute v = -I t ∇I ∇I 2. ( 3 ) Inserting Equation (3) back into the gradient constraint Equation (1) asserts the validity of this formula. Note that it is not feasible to use this equation in the first place, sidestepping the Least-Squares fit of the whole neighborhood. That would result in very noisy flow fields, while taking the whole neighborhood into account helps smooth local fluctuations. The appropriate value of τ 6  depends on the dataset; we found that values greater than 1 produce less noisy results.
But because its components describe the change of time with respect to space, the magnitude (and units) does not match. We therefore normalize this gradient vector and then multiply it by the correct length, which is given by the inverse of its magnitude: |g| = √ a 2 + b 2 /c. The velocity vector is thus obtained as v x v y = 1 |g| 2 g = -c a 2 + b 2 a b. ( 9 ) This formulation is robust against vanishing derivatives a or b. As a confidence measure for the rare case that both gradients vanish (flat plane, infinite velocity), we introduce a threshold th3. If both a and b are below that value, the computed velocity is interpreted as unrealistically high and discarded. The optimal value depends on the application; for the present dataset (which does not contain motions at high speed) we chose th3 of about 1e-3; because the units of time are microseconds, speeds in excess of 10 3 pixels/second are not reported.
Inertial Measurement Unit (IMU) We refer to this method of computing optical flow as IMU. It is implemented as the jAER class IMUFlow 12. The DAVIS240C camera  13  we used in our experiments includes an IMU (Inertial Measurement Unit) that provides gyro and accelerometer data, i. e. , the sensors for an electronic vestibular system  (Delbruck et al. , 2014). Here we used only the rate gyro information. The pure camera rotation used in this study allows us to compute the ground truth true optical flow (the motion field) from the IMU gyro rates, as follows. The gyro data is given in rotational angular rates around the three camera axes (see Figure  1 ). The angular rates ẋ, ẏ, ż stand for tilt (up/down), pan (right/left) and roll (camera axis rotation). To obtain total camera rotational angles x, y, z, these angular rates are integrated over time and high-pass filtered to deal with gyro offsets which were sometimes as large as 2 • /s.
As shown in  Delbruck et al. (2014) , the rotation of the camera transforms a DVS event address e = (e x , e y ) according to Equation (  12 ): e ′ = R(e -e 0 + T) + e 0 ( 12 ) where the 2-vector e ′ is the transformed event address, e 0 is the pixel address nearest the center of the IMU, R is the 2 × 2 rollangle image rotation matrix, and the 2-vector T is the translation of the image due to pan and tilt rotations. An illustration of how an event is rotated and translated is given in Figure  2. The DAVIS240C camera has the IMU mounted directly behind and centered under the image sensor pixel array center at a distance of about 3 mm from it, so in practice e 0 is the center of the pixel array. Equation (  12 ) assumes a pinhole lens with no distortion, so that all pixels have the same magnification, which was approximately the case for the lens used in this study, although on the periphery, some slight lens distortion is evident from the data in Figure  4  for the translating sinusoid, where the bars are slightly curved on the corners of the image (see Section 2.
Dataset To evaluate the methods presented above, we created a public dataset 14 consisting of two synthesized samples, two real samples with simple structure, and one real, more complex indoor scene. We have also included DAVIS data and an extracted image of a standard checkerboard pattern useful for camera calibration in the dataset. An illustrated online README document provides a guide to the dataset, examples of using it, and a step-by-step guide to jAER software installation and flow algorithm use. The real samples were recorded from a 240 × 180 pixel DAVIS240c (Dynamic and Active-pixel Vision Sensor,  Brandli et al. , 2014). This vision sensor combines the functionality of a DVS with frame-based absolute intensity read-out 15. Thus, the recorded dataset contains asynchronous events from the DVS as well as conventional synchronous frames to support testing state-ofthe-art frame-based methods.
Synthesized Samples Address-events with timestamp and polarity can be created in Matlab and imported into jAER. The script for creating this dataset is available in the dataset and the script for importing these synthetic events is located in the jAER repository in the matlab folder  16. In the simplest case, such samples are clean signals without occlusion, specularity, shadowing, transparency etc. Furthermore, the ground truth is known from the underlying model. The early benchmarking dataset by  Barron et al. (1994)  contained two such plain samples. Our synthesized samples tie in with them and cannot be compared to the Middlebury artificial samples with respect to complexity. We believe that these elementary datasets are still justified in testing the relatively young branch of event-based flow estimation.
Translating sinusoid The first real sample shows a contrast pattern that varies sinusoidally in horizontal direction. The camera is panned clockwise around its y-axis to create the impression that the sinusoid is shifting to the left. The contours are slightly curved due to lens distortion. The consequence is that the ground truth from the IMU is not perfectly normal everywhere on the contours. This introduces a systematic error of maximum 2. 2 pixels or 8 • at the corners of the image in the flow vectors on the periphery of the image. Such distortions can be corrected by the fully-integrated SingleCameraCalibration camera calibration method, data for which is available in the dataset.
Ground Truth In their concluding remarks,  Barranco et al. (2014)  call for the creation of new benchmarks with ground truth for neuromorphic cameras. There is a variety of approaches to obtaining ground truth. For synthesized sequences, the true motion field can be constructed from geometric principles or is known from the underlying model used to generate the sample. Except for such synthesized datasets, the ground truth is itself the result of measurements and therefore to some degree imperfect. In  Barranco et al. (2015) , the authors mount a camera on a robot and use odometry and depth-measurements to collect the ground truth. In  Baker et al. (2010) , they spray a fine grid of phosphoric substance onto the scene and measure the displacements with UV light. Another possibility to obtain the ground truth for event-based flow evaluation is to interpolate frame-based datasets  (Barranco et al.
, 2014 (Barranco et al. , , 2015) )  like the Middlebury dataset, where the ground truth is provided. We could not make use of this option because the methods evaluated here produce only normal flow, and to get meaningful error statistics the ground truth should also be normal to contours. In  Barranco et al. (2015) , the ground truth (derived from the Middlebury dataset as mentioned above) is projected onto the gradient direction computed by their event-based contour estimation. This way they can compare their measured normal flow to the normal ground truth. As a novel source of ground truth we propose using the flow field obtained from IMU gyro data as explained in Section 2. 1. 4. When using a camera that hosts an inertial measurement unit, the ground truth can be calculated in real time and parallel to any other filters applied to the incoming frames or events.
Optical flow measurements based on visual cues can be compared directly to the IMU flow, which can serve as feedback or initial guess and to discard outliers. It has some obvious limitations, however: the flow field can only be calculated from the IMU, if the camera is rotating about its axes. Also, the scene has to be static; objects moving independently in the scene would be labeled with the same IMU-deduced flow as resting objects. Finally, there is no depth distinction. For these reasons the dataset created here does not contain moving objects or non-rigid motion.
IMU Integration: Verification of Ground Truth Table  1  summarizes three rudimentary tests to check proper functioning of the IMU flow. The horizontal speed of the translating sinusoid and the box scene as well as the angular velocity of the rotating disk was measured and compared to the IMU flow estimate. This manual measurement consisted in tracing a patch of the moving object for a certain distance and calculating the speed by the time of flight. The main uncertainty with this method lay in tracing the exact location and is estimated to be one pixel. Without IMU offset correction, the IMU constantly outputs some non-zero gyro data that results in flow vectors even when the sensor is at rest. These offsets are computed during stationary camera conditions by averaging several hundred samples and then subtracting these offsets from subsequent readings.