dataesr/scientific-paragraphs-categorization huggingface dataset:
The PMSI database contains data assessing a person's consumption of care, and information on insurance, healthcare institutions and health professionals. 37 Each record is for a single hospitalisation; thus, multiple records are possible for an individual with recurrent hospitalisations. For this study, we will use the Anonymous Summary Discharge (Résumé de Sortie Anonyme). 37 The files used are those resulting from the validation of the national Technical Agency of Hospitalisation Information (Agence Technique de l'Information sur l'Hospitalisation).
Eligibility criteria for the denominator's definition The extraction of stays with at least one code linked with an EAIRP act (FCCMA code) will be carried out by the data manager of the French School for Advanced Studies in Public Health (Ecole des Hautes Etudes en Santé Publique) based in Rennes, France. These codes will result from the baseline assessment of eligibility criteria (box 1). After the reception of the PMSI database, we will apply a second wave of exclusion directly on stays of patients. Full and detailed study eligibility criteria are listed in box 1.
Identification and definition of IC In the absence of a specific definition of HAIs related to IR, we will adapt the definition of surgical site infection  40  : 'infection occurs within 30 days after the operation if no implant is left in place or within one year if implant is in place and the infection appears to be related to the operation and infection involves any part of the anatomy (eg, organs and spaces) other than the incision which was opened or manipulated during an operation'. The selection of antibiotic prophylaxis (AP) during the IR procedures indicates that AP was largely guided by the existing surgical literature. 41 42 The risks of infection of open surgical procedures differ from those of the IR procedures. 43 In the PMSI database, the IC will be identified by diagnostic codes.
Secondary endpoints Secondary outcomes will be defined as any of the following: ► Conversion to surgical repair. ► Postprocedural complication. ► Total length of intensive care unit stay and Simplified Acute Physiology Score (SAPS II). ► Hospital length of stay. ► Readmission. ► Mortality, up to 3 and 12 months, if the patient dies in the hospital. step 3: study comorbidities A period of 12 months prior to hospitalisation concerning the realisation of EAIRP will be considered in recovering data on patients' comorbidities. 48 Comorbidity may be defined as the total burden of illnesses unrelated to the principal diagnosis. 49 To achieve complete data processing, we will use two comorbidity measurement tools adapted for use with the ICD-10 and nature of administrative data 50 : Charlson score is a weighted index of comorbidities built to predict mortality at 1 year.
step 5: general guidelines for cohort identification The PMSI is distributed as fixed-width American Standard Code for Information Interchang (ASCII)-formatted data files or tables compressed with WinZip. After the delivery of the PMSI database, we will begin the examination of the four tables in the following order: Common Classification of Medical Acts (CCMA), stay, medical unit (MU) and DAS. At this stage those tables are individually clean, and they are supposed to contain non-redundant data (figure  2 ).
The 'Stay' table It includes clinical and resource use information typically available from discharge abstracts. This hospital-level file contains one observation for each hospital included in the PMSI and contains linkage data elements. We will merge the 'CCMA' and 'stays' tables. The unit of analysis will be patient hospitalisation. For hospital, the unit of analysis will be the National Number of Hospital Identification Open access (Fichier d'Identification National des Etablissements Sanitaires et Sociaux).
The 'DAS' table It contains data derived from the ICD-10 classification. We will pool the three tables previously merged with 'DAS' table. For each patient we will select all hospitalisations following the stay for the first EAIRP. One admission per patient will be used in this study. For multiple admissions, only the first one will be considered and analysed. The objective of this stage is the final file creation called 'PMSI_clean'. To this file, we will apply the algorithm of ICs. This database is the ultimate stage of the various steps of cleaning, selection and validation of variables. The 'Cohort' database It contains both the denominator and the numerator. This file represents the basis for statistical analysis (figure  2 ).
statistical analysis Descriptive statistics will be used to summarise the data from people with identified IC and the non-IC population. We will compare demographics, smoking habits, comorbidities, category of admission (scheduled or non-scheduled), discharge status, multivariable-adjusted outcomes of hospital status (private or public), death and IC, as well as the combined outcome of death and IC. The severity of inpatients' health status after EAIRP will be measured (length of hospital stay, type of MU and SAPS II). The categorical variables will be expressed as frequencies (percentage) and the quantitative variables as mean (SD) or median (IQR) for non-normally distributed variables. Dichotomous variables will be analysed with χ 2 test or Fisher's exact test as appropriate. Bivariate analyses according to infection status (yes/no) will be based on binomial regression as appropriate (Pearson's χ 2 test and analysis of variance).
In this context, research publications from the PMSI data have grown rapidly in recent years. The utility of administrative data depends on the measures studied and how well they are captured in administrative and clinical records. Careful study design, appropriate database selection and rigorous analyses allow investigators to answer key clinical questions. All of these steps require a thorough understanding of the individual composition and links between databases (CCMA, Stay, MU and DAS), including strengths and limitations between them. There will be several limitations to the present study.
The future of healthcare surveillance will involve the capture and use of data from existing electronic databases. 19 Knowledge on the database's structure and the scalable follow-up of the variables contained in the PMSI database are highly necessary for a reliable epidemiological assessment. The protocol's use and its personalisation for epidemiological studies must be clearly stated. In conclusion, we propose a protocol of analysis of the French medicoeconomic database to assess the epidemiology of adverse infectious events following therapeutic endoarterial IR. We chose this database because it is the only one to encompass all hospital stays at the national level. While the PMSI was not designed primarily to perform research on epidemiology, based on previous utilisations, the present protocol should allow us to get sound data on the issue.
Acknowledgements We wish to thank  Pr Abousaleh Youssef ,  Dr Oumou Maiga  and  Juliette Misset  for their valuable inputs and for proof-reading the manuscript. We thank the  CPias team in Paris  for their support and reception. Contributors AKM and BM contributed to the study concept and design. FG will carry out the retrieval of PMSI database. AKM, BM, PA and AS developed the methods of the review, including the search strategy. All of the authors were involved in the critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript. Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Embolisation EASF014, EASF004, EDSF005, EDSF006, EDSF012, EDSF015, EDSF003, EDSF008, EDSF016, EDSF004, ECSF008, EDSF014, DFSF002, ECSF004, ECSF006, EBSF003, EBSF004, ECSF002, EESF007, EESF006, EESF004, EDSF011, EESF001, DDSF001, EDSF001, EDSF009, EDSF007, EDSF013, EDSF002, EDSF010, ECSF001, ECSF003, EESF005, EASF007, EASF011. Thrombectomy EBNF001, EBNF002, EDJF001, ENFF001, EDNF003, ENNF001, ECJF001, DGPF001, EEJF001, EDJF002, EENF001, EDNF001, EENF002, ECNF001, ECNF002, EDNF002. EAIRP, endoarterial interventional radiology procedures; FCCMA, French Common Classification of Medical Acts.
Site environmental analysis For this study, sites were considered to be a circle with a 110 m radius centered at the point where GPS coordinates were taken during rodent trapping. All site-specific environmental variables were measured or estimated over the complete circle. The 110 m radius was chosen to correspond with the approximate home range of R. rattus that has been estimated under similar environmental conditions  [26]. As home range data is not available for the other rodent species studied, we used the R. rattus estimate to delineate sites throughout the study. To classify the degree of urbanization and the intensity of anthropogenic influence at each site, the following estimates of land use were considered: 1) Mean forest cover was estimated using QGIS v 2. 14. 0 and previously published forest cover and loss datasets at the Landsat pixel scale.
R-project. org/package=GoodmanKruskal) [35]. This test measures the strength of associations between categorical data, with values ranging from -1 (perfectly negative association) to +1 (perfectly positive association). A multivariate analysis of mixed data was also performed using the package PCAmixdata (version 3. 1) implemented in R  [36]. To examine links between the probability of Leptospira infection of rodents and site-specific environmental variables, a generalized linear (mixed) model (GLMM or GLM) with logit function was created using the lme4 package implemented in R (version 1. 1-15)  [37]. Analyses were performed using two initial models with explanatory variables chosen according to the strength of association identified in the Goodman and Kruskal's tau statistics, and the results of the multivariate analysis.
Appendix. Leptospira DNA sequences. Sequences too short to submit to GenBank are included in FASTA format. The sample ID and PCR assay which yielded each sequence (rpoB, flaB or 16S) is indicated in each sequence name. (FASTA) S2 Appendix. Differences in the prevalence of Leptospira in rodents between urban, developing and rural site locations. (PDF).
Acknowledgments The authors thank  Amy Hahs  ( The University of Melbourne ),  Andrew Alek Tuen  ( Universiti Malaysia Sarawak ),  Andrew Joris Noyen  ( Padawan Municipal Council ),  Basheer Ahmed  ( Kuching North Municipal Council ),  Danielle Levesque  ( Universiti Malaysia Sarawak ),  Dilop Jina  ( Padawan Municipal Council ),  Jean-Bernard Duchemin  (CSIRO),  Lee Trinidad (CSIRO) ,  Nurshilawati Abdul Latip  ( Universiti Malaysia Sarawak ),  Patrick Lai Ganum (Kuching South Municipal Council) ,  Rachel Amos-Ritchie (CSIRO) , and  Samuel Wong  ( Universiti Malaysia Sarawak ).
S1 Appendix. Leptospira DNA sequences. Sequences too short to submit to GenBank are included in FASTA format. The sample ID and PCR assay which yielded each sequence (rpoB, flaB or 16S) is indicated in each sequence name. (FASTA) S2 Appendix. Differences in the prevalence of Leptospira in rodents between urban, developing and rural site locations. (PDF).
IV. SIMULATIONS AND RESULTS In this section, without being specified, experimental parameters will obey following settings. Supposing the signal length N is 200 and sparsity s is 10, the sparse signal a ∈ R N is generated randomly with its non-zero entries drawn from the normal Gaussian distribution. Each element of the measurement matrix ∈ R M ×N , M = 100 with normalized columns is also drawn from the normal Gaussian distribution, and the measurement y ∈ R M is collected via y = a + ε, with ε a Gaussian noise, the standard derivation of which is 0. 016. Moreover, the threshold λ is set to 0. 05, the time parameter τ is set to 0. 1, and simulations are finished by the ODE45 solver in MATLAB. In addition, each element of initialization u(0) is drawn from the random Gaussian distribution with its standard derivation σ.
Dataset Our work includes two datasets. The first dataset includes 400 vehicles in daytime. The second dataset is based on 250 vehicles captured with low light. (c. f. Figure  5 ). Each captured vehicle was individually identified with its car brand, car model and estimated year of manufacturing in order to deal with vehicle design updates over years. Then, the corresponding car manufacturer datasheets were browsed to find the corresponding heights. These heights are considered as the ground truth for each vehicle. The vehicle height measured with our approach is then compared with the ground truth in order to assess the height measurement error of our process. Still, this height provided by car manufacturers can be affected either by the vehicle load or dynamics. The vehicle load will slightly reduce the vehicle height.
The correlation is less significant for daytime samples. A closer look at absolute values of absolute error gives correlation coefficients of 0. 06 and 0. 47 for daytime and low light respectively. As a result, the approximation in the point of interest selection totally mitigates a likely extrinsic calibration error. Still, the low light dataset reveals that the error tends to increase with the vehicle size. This might be caused by a slight error in the estimation of the side plane orientation. The plane might be slightly outward of its real orientation. This might be caused by several factors: camera intrinsic calibration, calibration plane orthogonality with the real orthogonal direction with respect to the road surface and a too strong approximation of the road shape and car shape. Still, this can be mitigated as the MAE is less than 1.
Height of a Driver's Eye Height of driver's height statistics were measured from the daytime dataset (c. f. Figures  1d  and 5a ). They are based on 400 samples. Figure  8  details the heights extracted from our dataset. It displays the raw CDF as well as the corrected CDF given the measurement error obtained from the previous experiments. After correction, the median value is equal to 1. 26 m (4. 13 ft) while the mean value is 1. 27 m (4. 16 ft). The eye distribution is slightly biased toward the right. 1. 18 m (3. 87 ft) corresponds to the 10th percentile and 1. 38 m (4. 52 ft) corresponds to the 90th percentile. The 5th and 95th percentiles are respectively equal to 1. 16 m (3. 8 ft) and 1. 42 m (4. 65 ft). Values are larger than figures found in the literature. The second percentile is equal to 1.
Height of the Rear Taillight Lower Limit The dataset used to obtain the rear taillight heights is the low light dataset. In fact, taillights are easier to select when the lights are on. It includes 250 vehicles Figure  9  shows the raw CDF as well as the corrected CDF given the measurement error obtained. After correction, the median value of taillights is equal to 0. 87 m (2. 85 ft) while the mean value is also 0. 87 m (2. 85 ft). The taillight distribution does not seem biased. 0. 65 m (2. 67 ft) corresponds to the 10th percentile and 1. 07 m (3. 51 ft) corresponds to the 90th percentile. The 5th and 95th percentiles are respectively equal to 0. 53 m (1. 73 ft) and 1. 14 m (3. 74 ft). It can be seen that the taillight height follows the same trend as the height of driver's eye. It might be explained by higher vehicles on the road or taillights located higher on the vehicles.
Antibody ELISAs Antibody titers against CfaEB and dmLT were determined by ELISA. Corning 3700 384-well microtiter plates were coated with 2 µg/mL of CfaEB antigen or 1 µg/mL dmLT, and blocked with PBS (pH 7. 1), 0. 1% Tween-20, and 1% skim milk. 12-point 3-or 4-fold serial dilutions of the serum and IW samples were then carried out. Detection antibodies included IgG1, IgG2a, total IgG, and IgA (all conjugated to horse radish peroxidase [HRP]) (Southern Biotech). The plates were incubated in the HRP substrate 3,3′,5,5′-Tetramethylbenzidine (TMB) for 4 min, and the reaction was stopped using 1 N H 2 SO 4. Finally, optical density (OD) readings were taken at 450 nm, with data reduction at 570 nm using an automated plate-reader (Biotek Synergy 2). The endpoint titers were determined using the dilution at OD = 0.
ACKNOWLEDGEMENTS We thank  Elise De Castro  and  Franck Raynal  ( Sanofi Pasteur ) for their technical assistance. We thank  Steven Poole  and  LCDR Michael G. Prouty (NMRC)  for provision of the CfaEB antigen and ETEC strains,  Heather Wenzel  ( PATH Vaccine Solutions ) for dmLT, and  Chris Fox  (IDRI) manufacture of SLA-SE. We also thank  Marie Garinot ,  Premkumar Dinadayala ,  Sandrine Buisson , and  Jon Heinrichs  ( Sanofi Pasteur) ,  Dan Stinchcomb  and  Corey Casper  (IDRI),  LCDR Michael G. Prouty (NMRC) ,  Capt. Mark S. Riddle  ( Uniformed Services University of Health Sciences ) and  Thomas Wierzba (PATH)  for helpful discussions and critical reading of this manuscript. This project was funded by  Sanofi-Pasteur  and a sub-award from  PATH  to IDRI.
This trial was registered at ClinicalTrials. gov (NCT01910285). The primary outcome was the comparison of the percentage of excellent intubation conditions using the Scandinavian scale. The secondary outcomes were the percentage of patients with a decrease of over 20% in MAP or HR, time to spontaneous respiration, time between the end of surgery and extubation and time to obtain an Aldrete score of 10. The percentage of patients able to get into bed unassisted, with a VAS pain score>3 or with laryngeal pain 15 minutes after arriving in the PACU were also analyzed. Randomization of the patients was performed by the methodologist of the study using STATA software, and was centralized in the Clinical Pharmacology Service. A physician investigator opened the envelope corresponding to the patient's inclusion number and prepared 3 syringes numbered 1, 2 and 3.
Tramadol was administered where the VAS pain score was >3 and morphine was administered if the VAS remained >3 after administration of the tramadol. The times elapsed from the injection of syringe 1 to laryngoscopy, cuff in ation, initiation of spontaneous respiration, surgical incision, end of surgery and tracheal extubation were recorded. Time to obtain an Aldrete score of 10 was recorded. Laryngeal pain, pain score and ability to get into bed unassisted were also recorded. Data monitoring was done by the DRC (direction de la recherche Clinique) of the CHU Toulouse. Access to data was only limited to statistician of the study.
Statistical Analysis Demographic data and scores were abstracted and described through descriptive statistical analysis. A study of the distribution of the values was carried out using a Kolmogorov-Smirnov test, with in parallel, analyses of kurtosis and skewness. Results were expressed as the median and con dence interval CI 95% [ ] for quantitative variables and in numbers and percentages ( ) for qualitative variables. The study population was separated into 2 groups: the remifentanil group and the sufentanil group. Patient characteristics for the 2 groups were compared using: • Non-parametric tests (Mann-Whitney U test) for continuous variables, because of the non-Gaussian distribution of the majority of the variables; • Chi-squared test or Fisher's exact test for qualitative variables.
Sample size calculation The hypothesis of our study is that the proportion of patients with perfect intubation conditions in the sufentanil group is 41% 9 and 83% 12 18 in the remifentanil group. The expected difference is 42%, with an Alpha-risk of 5% and a Beta of 90%, in bilateral hypothesis, the number required for the study is 35 patients per group. The total number to be included in the study is therefore 70 subjects. The study was carried out using MedCalc® statistical software version 15 (Mariakerke, Belgium). A p < 0. 05 was considered statistically signi cant.
Introduction and Motivation Cloud computing and networking are nowadays very popular and real possibilities for enterprises and end-users. In the last few years, the Data Center (DC) industry has played a key role in the development of the IT sector. Indeed, the total capacity of DCs is growing by 5 percent per year. In 2016, for example, approximatively 1710 important DC locations in North America and 1400 in Europe were counted. Providers (e. g. , CenturyLink, Cogent Communications, Equinix, NTT, Tiscali, TELEHOUSE, AT&T, etc. ) offer services in several continents (for more details, cf. http://www. datacentermap. com/). Content Delivery Networks (CDNs) are distributed, server set based systems. They can be composed of dedicated replica servers located at DCs across the network, providing replication of some contents.
Finally, the viability status of all MDDC populations was acquired using the FACS Canto II cytometer (BD Biosciences, USA). For data analysis, Flow Jo VX (Tristar, USA) was used. T-cell proliferation assay -The ability of Mtb-infected MDDCs to stimulate CD4 + T cells was assessed using an autologous co-culture system, as previously described. (14) Briefly, autologous CD4 + T cells (the responders) were freshly isolated and purified from healthy donors positive for the tuberculin test on the day that the co-culture was initiated. A total of 1. 5 × 10 4 5-(and 6)-Carboxyfluorescein diacetate succinimidyl ester (CFSE) stained CD4 + T cells (responder cells) were added to each well in 200 μL complete media on rounded-bottom, 96-well plates (BD, Pharmingen, USA). Autologous MDDCs (the stimulators) were differentiated until day 6 and pulsed for 24 h with LPS (100 ng/mL) (Sigma, Germany).
Statistical analyses -The corresponding statistical analysis is annotated in each figure legend. Data was compared using two-way ANOVA analysis with Bonferroni's post-hoc test for internalisation index, Wilcoxon analysis for CFU, while comparison inside the groups was performed with the Kruskal-Wallis test and Dunn's post-test assuming non-normally distributed data for all the other analysed variables. Statistical analysis was performed with GraphPad Prism software v. 5. 0 (San Diego, CA, USA). P ≤ 0. 05 was considered as the level of statistical significance. Ethical considerations -The present protocol was reviewed and authorised by the National Research Committee of Instituto Mexicano del Seguro Social (The Mexican Institute of Social Security, IMSS), that includes a subcommittee for ethical approval (agreement number R-2014-785-042).
Fondo de Investigación en Salud  from  Instituto Mexicano del Seguro Social, México  (grant nº  FIS/IMSS/PROT/MD15/1489 ),  Agence Nationale de la Recherche, France  (grant n  ANR-15-CE15-0012 ,  MMI-TB ). AGRM and MAVA were supported by  Consejo Nacional de Ciencia y Tecnología, México  ( CONACyT ) during their PhD studies (fellowship numbers  417995  and  584982 ). For an internship at the IPBS in Toulouse, France, AGRM received sponsoring from  CONACyT  ( Beca Mixta ) and from the host laboratories of  Isabelle Maridonneau-Parini  and  Olivier Neyrolles. B Rivas-Santiago  is a scholar of Fundación IMSS, México.
, 2018; Naveau et al. , 2016). Spatial methods often relate the dependence between events at two stations to the distance between these stations. Traditionally, the Euclidean distance has been used to do so, which might not be very relevant in the case of floods since they evolve along a river network. A comparison of different distance measures (Euclidean, river distance, physiographical distance) showed that river distance and distance regarding mean catchment elevation explained Kendall's tau best. We used river distance as a distance measure since it has a hydrological meaning. R-package riverdist  (Tyers, 2017)  was used to compute river distance based on a line shapefile of the river network. A model depicting the behavior of joint events at multiple stations needs to account for both the marginal behavior of the variables and their dependence structure.