dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to the patients and their families for their participation. We thank  J. Malicki  for providing us with the elipsa zebrafish,  G. Pazour ,  J. Nelson ,  L. Pedersen  and  R. Ko ¨ster  for giving us the Flag-MmIFT54, GFP-MAP4 and EB3-GFP constructs, respectively. We greatly acknowledge  N. Goudin  ( Necker cell imaging facility ) for providing expert knowledge on confocal microscopy,  E. Weyher-Stingl  (core facility,  MPI of Biochemistry ) for assistance with the circular dichroism measurements,  H. Voshol  for support in mass spectrometry and  J. Duca  for help in molecular modelling,  C. Bole-Feysot ,  O. Alibeu ,  S. Pruvost  and  M. Parisot  ( Imagine Genomic plateform ) and the  Necker Bioinformatic facility. We thank  S. Berissi  ( Histology facility ),  G.
Pivert  ( Pathology department, Necker hospital ),  O. Pelle  ´(Cell sorting facility),  S. Laurent  and  N. Stemler  ( Cellular Biology, NIBR ), for their excellent technical assistance. The authors are grateful to the following scientists for their support:  L. Klickstein ,  M. Labow ,  M. Schebesta , and  K. Johnson  for providing intellectual input at various stages of the project. Finally, We thank  S. Helliwell  and  S. Kirkland  for their precious help in manuscript correction. This work was supported by grants from the  'Agence Nationale de la Recherche' (ANR)  to S. S. (20100BLAN112202) and investments for the future program- ANR-10-IAHY-01  to S. S. , the  Fondation pour la Recherche Medicale  ( DEQ20130326532  to SS and RR) and ( DEA20120624188  to P. K. ) and the  Fondation ARC  ( EML20110602384 ) for the purchase of the LEICA SP8 confocal microscope.
Registration: http://www. clinicaltrials. gov/ct2/show/NCT01141361 (Medicine 94(18):e838) Abbreviations: ABI = Ankle to brachial index, DS CW = Stop Duration between walking bouts during Community Walking, GPS = Global Positioning System, MCII = Minimal clinically important improvement, MD CW = Measured Distance in Community Walking between two symptom-limited stops. One patient may have multiple MD CW in a 1 hour recording, MWD = Maximal Walking Distance, The absolute maximal distance walked before limb discomfort or pain forces the patient to stop, PAD = Peripheral Arterial disease, PASE = Physical Activity Scale for the Elderly, WIQ = Walking Impairment Questionnaire, WS CW = Walking Speed During Community Walking: Mean walking speed between two stops during community walking.
METHODS The protocol was approved and monitored by the University Hospital of Angers (France) and performed according to the international ethics standards and the Declaration of Helsinki. The ''Post-GPS'' study was approved by the CPP-Ouest-II ethics committee and registered under the reference no. NCT01141361 (ClinicalTrials. gov). Twenty different physicians participated in the study (9 angiologists, 8 vascular surgeons, 2 cardiologists, and 1 rehabilitation physician). The present multi-center study is the second part of previously reported results on the applicability of GPS recording in clinical routine. 11.
GPS Analysis Validation of the use of the GPS technique to analyze walking parameters in healthy and PAD patients has been reported elsewhere. 10, 14 In brief, GPS-derived parameters included the total distance walked, the total duration of the walk (including the stop duration), the number of stops, highest-MD CW , average-WS CW and average-DS CW. A technically satisfactory recording was defined as 11 : a total walk duration >30 minutes, the presence of recorded data in the GPS device, and the presence of identifiable periods of walking (a succession of periods of displacement compatible with walking, that is, with speeds between 1 and 10 km h À1 and with durations of at least 10 seconds). At time-0 (T0) and time-1 (T1), if the recording was not technically satisfactory, a second attempt was performed within a month of the first attempt.
The concordance between GPS changes and WIQ changes in speed or distance were analyzed with linear regression analysis and Pearson's coefficient of correlation. For all statistical tests, a 2tailed probability level of P < 0. 05 was used to indicate statistical significance. SPSS (V15. 0 SPSS Inc. , 2004) was used for all statistical analyses. The study was designed to include 250 patients, assuming that nearly half of the patients would have no limitations while walking at T0, would be lost to follow-up, or would have unsatisfactory recordings. We also expected that at least one-third of the patients would undergo revascularization during the follow-up period.
Population Description Of the 251 included patients, only 172 had limitations confirmed with GPS at T0 (79 exclusions). Among these 172 patients, 57 patients were excluded, mostly because they refused to perform the second test at T1, and another 32 were excluded because they could not be classified in the reference group because they had no revascularization but changes in medical, lifestyle or non-vascular surgical status between T0 and T1 (Figure  2 ). The clinical characteristics and results of the included patients at T0 are presented in Table  2. Note that the ABI was missing for 14 patients, either because ''non-measurable pressure'' or ''incompressible arteries'' were noted or, in 1 case, because of values that were missing from the data file. Of the 83 studied patients, 44 underwent revascularization (24 had angioplasty, and 20 had surgery) between T0 and T1 (Table  3 ).
ACKNOWLEDGMENTS The authors are indebted to  Nafi Ouedraogo (MD, PhD ),  Johann Marchand  (MD),  Yoanna Onillon ,  Thomas Sauvaget ,  Jean-Marie Chre  ´tien ( DRCi-Methodology/Biostatistics/Data Management Unit ) and  Nicolas Hermann  for their technical assistance. Moreover, the authors would like to thank the following physicians (in alphabetic order) for their help:  Drs Philippe Berlie ,  Sonia Dulong ,  Thierry Dutartre ,  Dominique Eveno ,  Vincent Jaquinandi ,  Eric Jouen ,  Yann Jousset ,  Georges Leftheriotis ,  Jean-Pierre Lumineau ,  Patrick Moreau ,  Laurent Muller ,  Jean-Dominique Pegis ,  Arnaud Perrouillet , and  Olivier Regnard.
Acknowledgements We gratefully acknowledge the craftsmanship of P. Regent ( INRA ) for the Heme-PPIX-Screen setup. We thank  M. Malamy  ( Tufts Univ. , USA ) for providing B. fragilis strains, and  C. Poyart  ( Hôpital Cochin, France ) for meconium samples. We thank  C. Morvan  for and  D. Lechardeur  ( MicrobAdapt team ),  M. A. Petit  ( Micalis, INRA ),  C. Wandersman  ( Pasteur Institute, France ),  P. Bouloc  ( Institut Génétique et Moléculaire, Orsay ),  R. Briandet ,  S. Rabot  ( Micalis, INRA ) and MicrobAdapt team colleagues for valuable discussion of this work. The project received funding from the  French National Research Agency   ANR-11-IDEX-0003-02 ;  ALIAS  project. This work is dedicated to the memory of  Cécile Wandersman , whose passion for life and science has been an inspiration.
This result is in line with data from PAI-1-furin gel shift assays (Figure  S4 ) and from experiments with furin-dependent HPIV3 (Figures  S2H  and S2I ), further suggesting that furin is indeed not inhibited by PAI-1. Finally, we examined the cleavage state of HA on HAEC. These cultures produce and secrete intrinsic airway proteases on their apical side; in fact, they have previously enabled identification of several proteases that cleave HA  (Bo ¨ttcher et al. , 2006). Addition of rPAI-1 to HAEC during IAV WSN/33 infection dramatically reduced HA cleavage, whereas addition of a-PAI-1 increased cleavage (Figure  4F ). These findings are in agreement with our previous results demonstrating virus growth kinetics in HAEC (Figure  2F ). Taken together, these data show that PAI-1 inhibits IAV spread by reducing extracellular cleavage maturation of progeny particles and that this mechanism operates in a physiologically relevant model of airway epithelium.
Cells were then challenged, and virus infectivity was analyzed as described in (B). (D) HA cleavage assay with H1 of A/Puerto Rico/8/34 or H2 of A/Japan/305/57 origin in the presence of protease TMPRSS2 and wild-type (SER) or loss-offunction (SER*) PAI-1. (E) HA cleavage assay with H5 of A/Vietnam/1203/2004 origin and wild-type (SER) PAI-1, or GFP as a negative control. (F) HA cleavage assay on HAEC infected with IAV WSN/33 in the presence of either rPAI (1) or a-PAI-1 (2) and buffer (1) or IgG (2). Progeny virus particles from apical washes at 24 hpi were analyzed. (G-I) Wild-type (B6) or Serpine1 À/À mice were infected intranasally with 36 LD 50 of IAV WSN/33 and monitored for weight loss (G) and survival (H). Data are shown as mean ± SEM from n = 32 infected B6 or Serpine1 À/À mice, and n = 3 respective PBS control animals.
ACKNOWLEDGMENTS We would like to thank all Rice lab members but especially  M. Hsu ,  M. Saeed , and  Z. Ozair , for critical reading and helpful comments on the manuscript;  Jeanne Chiaravalli-Giganti  at the  Rockefeller High-Throughput and Spectroscopy Resource Center and Sebastien Monettes at the Tri-Institutional Laboratory of Comparative Pathology  for technical advice and support;  Margaret MacDonald  for VSV strain Indiana;  Adolfo Garcia-Sastre  for the gift of 2G9 a-HA antibody;  Peter Palese  for influenza viruses A/Puerto Rico/8/1934, A/ Vietnam/1203/2004(HALo), A/California/04/2009, and A/sw/Texas/4199-2/ 1998;  D. Steinhauer  and  S. Galloway  for both the gift of pCAGGS-HA plasmids and for technical advice on the cleavage assay;  Gary Whittaker  for the gift of HAT and TMPRSS2 expression plasmids; and  Sharmila Nair  for advice with VSV in vivo experiments.
This work was supported by  National Institutes of Health (NIH)  grant  R01-AI091707  (C. M. R. ),  U54-AI057158  ( I. Lipkin ,  Northeast Biodefense Center , subcontract to C. M. R. ), the  German Research Foundation  and the  Rockefeller Women and Science Fellowship  (M. D. ),  Medical Research Council  Grants  U117597139  (to A. W. and S. C. ),  NIH  grant  8UL1TR000043  from the  National Center for Translational Sciences  (NCATS; J. -L. C. ), and the  St. Giles Foundation  (J. -L. C. ). Additional funding was provided by the  Greenberg Medical Research Institute , the  Starr Foundation , and anonymous donors.
This score was developed and validated with a very high accuracy (AUROC > 0. 94). A score ≤ 3 has been associated with benignity with a sensitivity of 96. 6% and a specificity of 93. 5%. A multicentre European prospective study (the "EURAD study") is currently in recruitment, with the aim of validating the proposed scoring system (clinical trial NCT01738789). The potential impact of    the score on therapeutic management will then be tested in a future trial. In this review, we present the development of perfusion and diffusion analysis to characterize sonographically indeterminate adnexal masses. Using time intensity curve analysis and visual assessment of DW signal, functional criteria help the radiologist to improve lesion characterization especially for benign lesions and should help the clinician to avoid unnecessary surgeries.
Clinical assessment A standardized questionnaire was sent by email to 1500 practitioners. Physician-reported responses were received from 482 practitioners working a geriatric care unit on June 21, 2011. Data collection included factors thought to be associated with quality of INR control as suggested by previous studies  [13] [14] [15] [16] [17] [18] [19]. For each patient, the following information was collected: sex, age, weight, plasma creatinine, time since instauration of anticoagulation by VKA (more than one year, between three months and a year, and less than three months), history of hemorrhagic event, number and types of current co-medications, type of care structure unit. The last two consecutive bioassays of INR were collected to calculate time in therapeutic range. Falls were considered as significant if the patient fell twice or more during the year preceding data collection  [29].
And for patients suffering from chronic kidney failure, current data showed that patients with kidney failure spent significantly less time in therapeutic range  [13, 16, 17]. Two elements can explain our contradictive results. First, all the precedent studies relied on a very different methodology, particularly where the definition of INR stability is concerned. Second, the population included in all the precedent studies was younger than ours, and this means fewer confounding factors-notably, comorbidities. Further prospective studies must be conducted to clarify the relationships among age, gender, chronic kidney failure and quality of INR control. The present study had some limitations. A reporting bias may be included in the dataset. Indeed, the accuracy and completeness of our data were entirely reliant upon physicians' declarations.
Compensation data The empirical analysis draws on a newly created dataset of French Appeal court rulings from 2006 to 2016 bringing together, for the first time, detailed information on compensation amounts decided in court along with a rich set of firm characteristics. From the court rulings, we extract a wide array of variables related to each case, as well as the firm's name and address. Then, using the firm's name and address, we are able to retrieve the firm's unique administrative identifier  (SIREN ) , which allows us to link our compensation dataset to comprehensive, matched employer-employee data as well as to financial variables. This section highlights the key steps in the construction of this dataset and the main features of the data. Appendix E provides additional and technical details.
In other words, it is possible that a judge's appreciation of the case might color not only the amount granted for unfair dismissal but also the other forms of compensation. Possible additional compensations include: moral and financial damages, compensation for unpaid wages, etc. 6 We extract all these variables automatically from the Appeal court rulings using a Python program based on keywords extraction and natural language processing techniques. In order to control the quality of the process, we assessed the accuracy of the results on a manually-filled dataset forming a subsample of about 2,500 observations, selected at random. We find that the correlation between the compensation amount of the manuallyfilled and the automatically-filled datasets is equal to 94%, which is in the upper range of seminal papers using this type of approach  (Baker et al.
(2016) ). Finally, we also retrieve the unique administrative firm identifier known as SIREN, either directly from the text when it is displayed, or, using the firm's name and address, after an automatic search on online companies registries such as societe. com and bodacc. fr. The SIREN identifier, assigned by France's statistical agency to each company, then allows us to merge our rulings compensation dataset with French administrative social security and tax data. In some cases where the company is very small or when the cases were launched a long time ago, we were not able to retrieve the SIREN.
Social security and tax data In order to analyze the impact of judge decisions on firm performance, we combine our novel rulings data with two comprehensive administrative datasets. Because both have been used in the literature we only briefly highlight their main characteristics Matched employer-employee data. We merge the compensation data with social security data thanks to the firm identifier. We use the comprehensive matched employeremployee dataset called DADS Postes Déclarations Administratives de Données Sociales from 2002 to 2015, which reports detailed payroll information about each employee working for a French private firm. This dataset allows us to track the evolution over time of the wage bill and of the number of employees of the firms in our rulings dataset. Tax data. We rely on tax data, FICUS-FARE, that contain the full company accounts, including for instance sales, net income, EBITDA.
Then, we eliminate cases for which we do not have the relevant information about either (b) the president's name and surname, (c) the total amount of compensation, or (d) the monthly wage was either. Finally, we eliminate cases (e) in which the employer belongs to the public sector, and (f) those decided by judges who covered less than 50 cases, our threshold for the calculation of judge fixed-effects. We eventually end up with 37,149 cases and 159 judges. Source: Authors' Appeal court rulings database. Note: Columns (1) to (4) report the R 2 and adjusted R 2 of the regression of the qualification of the dismissal -i. e dummy indicating whether the dismissal was deemed wrongful -on judge bias and case controls (dummy indicating whether the firm has more than 11 workers at the time of the dismissal, Prud'hommes compensation, salary, seniority), while columns (5) to (8) display similar results for the regression of the compensation in monthly salaries.
Columns (1) and (  5 ) display the R 2 when adding fixed effects only, columns (2) and (  6 ) when controlling for the judge's pro-worker bias, columns (3) and (  7 ) when controlling case characteristics, column (4) and (  8 ) when controlling for both case characteristics and judge bias. Court and year fixed effects are included in all regressions. Source: Appeal court rulings database. 2 ) the symmetric growth rate between t -1 and t + 3 of firm's employment; in Column (3) the symmetric growth rate between t -1 and t + 3 of firm's employment in permanent contract -cdi; in Column (4) the symmetric growth rate between t -1 and t + 3 of firm's employment in temporary contract; in Column (5) the change between t -1 and t + 3 in the share of permanent jobs; in Column (6) the symmetric growth rate between t -1 and t + 3 of sales.
The variable of interest is the judge pro-worker bias computed as defined in section 4. 2. Low roa firms denote firms with a return on assets below the median the year before the judgment. Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. The upper part of the table displays coefficient α 1 of equation (  5 ) and the bottom part coefficients β 1 and β 2 of equation (  6 ). Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database. Note: This table displays the estimates of the correlation between the judge bias and indicators of firms performance for the sample of small firms, below 10 employees the year before the judgment.
Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. The upper part of the table displays coefficient α 1 of equation (  5 ) and the bottom part coefficients β 1 and β 2 of equation (  6 ). Standard errors, displayed in parentheses, are clustered at the judge level. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: DADS, FICUS-FARE, SIREN, Appeal court rulings database. Table  18  -Judge pro-worker bias and firm performance 3 years after the judgment in medium-sized firms Note: This table displays the estimates of the correlation between the judge bias and indicators of firms performance for the sample of medium-sized firms, with 10 to 100 employees the year before the judgment.
Covariates include Appeal court fixed effects, year fixed effects, the leave-one-out average industry annual growth rate of sales and an indicator variable for economic dismissals. Table  20  -Second-stage IV estimates of the effects of total compensations for wrongful dismissal on firm performance 3 years after the judgment                   Case-level data are used, therefore the number of observations is the number of different cases for which we are able to compute the pro-worker bias reported in Table  2. The pro-worker bias is computed as defined in Section 4. 2. Source: Appeal court rulings database. A Caps on dismissal compensation in European countries A majority of European countries have set rules that limit the amounts granted by judges in case of unfair dismissal (excluding cases of discrimination or harassment): (1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6) • In Italy, a fixed amount compensating an unfair dismissal was introduced in 2014 by the so-called (Jobs Act) for the new indefinite-duration contract with progressive employment protection, which depends on seniority: from 4 months for less than 2 years of seniority to 24 months for 12 years of seniority.
E Extraction of compensation amounts and other variables of Appeal court rulings This section provides additional details on the construction of our novel database of anonymized Appeal court rulings. We use the universe of Appeal court ruling over ten years. The latter are available and digitized on a systematic basis, contrary, to first instance rulings, which are collected locally at the court level and are not compiled in a common legal database. We use Natural Language Techniques (NLP) to extract the information from close to 145,000 text documents. Each of these rulings is a few pages long, with some spreading over a dozen pages. Extracting information accurately from textual documents that contain many digressions and qualitative arguments is not a straightforward exercise. In order to reduce the complexity of the problem, we exploit the structure of these legal documents, which follow a well-established template.
The data include a wide array of variables related to the case (compensations for wrongful dismissals, worker seniority, wage, Appeal court, city of the Prud'hommes council, whether it was the worker who appealed, etc. ), as well as the firm's name and address. Using the firm's name and address we are able to retrieve the firm identifier (SIREN ), and then link the compensation dataset to matched employer-employee data as well as financial variables. The stages for the construction of this dataset are the following. Extracting wages and tenure requires paying close attention to the wording of rulings as there is substantial heterogeneity in how they are reported. For instance tenure information is sometimes not explicitly stated as a duration but can to be recovered from the mentions of when the employee was hired.
Variable selection and sample attrition Heterogeneity in the writing of the rulings across jurisdictions and over time means that an automatic extraction can generate mistakes and approximations. Therefore we conducted a series of manual checks on a subsample of 2,560 observations, selected at random. The manual dataset creation was undertaken as part of a project of Pierre Cahuc and Stéphane Carcillo, and funded by the Chaire sécurisation des parcours professionnels. To examine the Appeal court rulings published by the Minister of Justice, ten research assistants were hired, each of them being in charge of a given year. These assistants carried out the research with the following key words: 'licenciement sans cause réelle et sérieuse' (unfair dismissal) and 'indemnités' (compensation). Even though the research assistants were asked to select randomly Appeal court rulings within the year, some of them selected only rulings from particular months: the assistants in charge of studying the 2009, 2010 and 2012 years mostly selected court rulings of September and October, and marginally court rulings from November and December.
Each cell corresponds to one regression where the dependent variable is the total compensation for wrongful dismissal. Control variables included in column (2): indicator variable for economic dismissal,wage, seniority. The bottom and top fifth percentiles of judge bias are trimmed to account for the non-linearity of the relation between judge bias and the qualification of dismissal displayed on Figure 12. Court and year x sector fixed effects are used. Standard errors, clustered at the judge level, are in parenthesis. *, **, and *** denote statistical significance at 10, 5 and 1%. Source: Appeal court rulings database.