dataesr/scientific-paragraphs-categorization huggingface dataset:
EEG DATA We re-analyzed previously-obtained experimental data from subjects that had been given a short propofol anesthetic. The details of the methods can be found in  Johnson et al. (2003). In brief, after obtaining regional ethical committee approval and written informed consent, five healthy subjects (mean age 27. 7 yrs, four males) were studied. They were on no psychoactive drugs and had been starved for at least 6 h prior to the study. They were monitored and managed as per clinical anesthesia, according to the Australia and New Zealand College of Anesthetists best practice guidelines. The induction consisted of an intravenous infusion of propofol at 1500 mg/hr until the subject no longer responded to verbal command. Typically this occurred about 5 min into the infusion. The estimated effect-site concentrations of propofol were calculated using standard population-based pharmacokinetics models.
The EEG was acquired using the Electrical Geodesics 128 channel Ag/AgCl electrode system (Eugene, CO, USA) referenced to Cz. Electrode impedances were below 30 KOhm (100 MOhm input impedance amplifier). The sampling frequency was 250 Hz, with a 0. 1-100 Hz analog band pass filter, and A-D conversion was at 12 bits precision. The EEG data were re-referenced to a grand mean, and band-pass filtered using 3-rd order Butterworth filters 0. 2-45 Hz to eliminate line-noise. An additional Whittaker filter was applied to reduce movement and blink artifacts. The power in each frequency was obtained applying a short-time Fourier transform with a moving window of 60 s and 54 s overlap. The power spectra have been computed 1 min before infusion start (t = 1 min) and 4 min after infusion (t = 5 min). For visualization reasons, these power spectra at different time instances have been smoothed by a running average over frequencies with a 1 Hz window and a 0.
12  data to reconstruct annual mean FSL (Supplementary Fig. 7 ; Supplementary Table  5 ). We tested a set of paleo-ENSO indices that extend before 1854 as covariates in the linear model. The best model included PC1 of the Kuhnert et al. 12   5 ). The paleo-ENSO index alone explained 62% (Po0. 001) of variance in FSL for the calibration period, 1950-1993, and 47% of variance in FSL (Po0. 001) for the verification period, 1924-1949. Given this stability of the relationships, we reconstructed variations in annual mean FSL (reflecting LC strength) back to 1795 (Fig. 4b ; Supplementary Fig. 8 ). Positive reconstructed FSL anomalies occur at major La Nin ˜a events, while negative anomalies occurred during major El Nin ˜o years (based on the ENSO event classification of Gergis and Fowler  23  (Fig.
The proxy data will be made available through NOAA paleoclimate database (www. ncdc. noaa. gov/paleo/). Proxy data treatment. We used the published bimonthly coral d 18 O record of Kuhnert et al. 12  as our reference coral because the presence of seasonal cycles provided the best age control for all cores. We developed two new annually resolved coral core chronologies for Sr/Ca (cores HAB05B and HAB10A) and measured d 18 O in one core (HAB10A). The age model of the new corals was determined by assigning 1 year of growth to a couplet of high-and low-density banding in X-ray-positive prints cross-checked with luminescence banding  33. The high-density bands were equivalent to summer and low density to winter. The coral d 18 O record of Kuhnert et al. 12  was averaged annually (January-December), and then each individual annually resolved coral proxy record was normalized to its variance relative to the 1900-1993 reference period.
We subsequently spliced the two s. e. series together to show the uncertainty in our SST reconstruction over time. Principal components analysis of the Kuhnert et al. 12  bimonthly d 18 O data was carried out in R (R Development Core Team 2011)  37  using singular value decomposition. Time series for the January-February, March-April and May-June bins were individually detrended (linearly) before the analysis. Bins were treated as variables and years as observations. The loadings for PC1 were À 0. 51, À 0. 66 and À 0. 56 for January-February, March-April and May-June, respectively. PC1 accounts for 64% of the variance in the data for these bins. PC1 scores for years 1795-1993 were used in the FSL reconstruction. We used the software package R to compute the linear models and for the calibration-verification exercise of reconstructing FSL.
12  data in combination with the paleo-ENSO index  21  or observed Nino3. 4 index  6. Other paleo-ENSO indices  38, 39  performed well in our linear regression model capturing the interannual and decadal variability in observed FSL, yet did not capture the full magnitude of observed FSL anomalies in our reconstruction. Nevertheless, the stability of interannual and decadal variability using different paleo-ENSO records adds confidence that our FSL reconstruction robustly captures these time scales of variability. We used the Mann-Whitney U-test to test for significant differences (Po0. 05) in mean SST extremes for El Nin ˜o and La Nin ˜a years pre-and post-1980, following the classification of ENSO event years from Gergis and Fowler  23. Instrumental data. FSL data were obtained from the Permanent Service for Mean Sea Level database: http://www.
Acknowledgements We acknowledge the  Australian Institute of Marine Science  coral core sampling campaign conducted using the RV Solander under the leadership of  Dr Tim Cooper  and with assistance from D. D. (UWA). J. Z. was supported by an  Indian Ocean Marine Research Centre UWA/AIMS/CSIRO  collaborative assistant professorial fellowship. A. R. is supported by a grant from the  Australian National Network in Marine Science with the Centre  for  Marine Futures  at  UWA Oceans Institute. M. F. is supported by the  CSIRO   Wealth from Oceans Flagship. M. F. is also supported by the  Australian Climate Change Science Programme (ACCSP). M. T. M. , M. J. L. and D. D. activities are conducted under the auspices of the  ARC Centre of Excellence for Coral Reef Studies  with D. D. being supported by  ARC   DP0986505.
Data Samples were collected from a household-based cohort of 940 participants in 270 households in a single community in semi-rural northern Vietnam as previously described  [20]. None of the participants had ever received influenza immunisation. Participants aged 5 years or older were asked to provide serial blood samples at times when national influenza surveillance data indicated that influenza circulation was minimal. The samples described here were collected over a period of three consecutive influenza seasons, from December 2007 through April 2010. Serological samples were collected between 1st-7th December 2007 (bleed 1), 9th-15th December 2008 (bleed 2), 2nd-4th June 2009 (bleed 3), and on the 3rd April 2010 (bleed 4). This provided three sets of paired samples either side of an influenza transmission season: 548 paired samples for season 1  (2008)  For each season and subtype, analysis was restricted to households with at most 1 individual for whom paired serum samples were missing.
Estimating the proportion of cases attributed to household transmission and the average household transmission probability In order to estimate the proportion of influenza cases that may be attributed to household transmission, we simulate epidemics in households where i) all parameters are drawn from the posterior distribution and ii) all parameters are drawn from the posterior distribution except the within household transmission rate which is assumed to be null. The case counts difference between i) and ii) gives the proportion of cases that may be attributed to household transmission. For each pair of case-household contact in the dataset, we calculate the associated probability of transmission under the assumption that the case was the first or the only infected in the pair and derive the average household transmission probability across all pairs.
7). After correcting for these variables, estimating an effect of subtype on the probability of transmission in the household did not improve the fit (DDIC = 13. 1). We estimated that 26% (95% CI: 21%, 30%) of cases may be attributed to household transmission. Figure  S3  shows the prevalence of infection along with the estimated contribution of household transmission by season and subtype (NB: Figure  2A  captures only partially variations in the prevalence of infection as the distribution of pre-season HI titers vary for each season and subtype and by age group). The fit of the model to the data was adequate (Table  1 ). In a simulation study we found all parameters could be estimated from the data and no important systematic bias was detected (Table  S1 ). Out of 10 simulated datasets and 11 parameters, there was 94% probability that the simulation value was in the 95% CI.
The use of a final-size model based on serology minimizes the under-ascertainment inherent in studies that detect only symptomatic cases. As such we believe these results are the best available assessment of the risk of acquisition of influenza in the household and the community. The earlier analysis of this dataset  [20]  simply reported empirical infection rates by age based on a four-fold or greater increase in HI titers between paired sera, and did not estimate any other transmission parameters nor influences on the probability of transmission. The analysis presented in this manuscript therefore adds substantial new insights including estimates of the probability of transmission from an infected individual to another household member, the proportion of infections acquired in the household and the community, and how the probability of infection is affected by pre-season HI titers, age and household size This study has some limitations.
Here, we disentangled the relative contributions of households and the community in the risk of influenza infection. This was made under the assumption that households were independent of each other and that all individuals of an age group were exposed to the same risk of infection in the community. Although standard in such analyses  [8, 14, 16, 25, 26] , in practice, the risk of infection in the community may have a spatial component, potentially leading to higher transmission rates between households that are close to each other. However, we were unable to test this assumption here since our dataset was not spatially structured. Estimating the effect of space on influenza transmission will be an important step forward. This can for example be done from the analysis of household serological cohort studies in which the spatial location of each household is be documented  [37].
Children with low pre-season HI titres correspond to the reference group. (PDF) Table  S1  Simulation study to investigate the performance of the statistical approach. Ten datasets with a structure similar to that of the original data were simulated with parameter values equal to their posterior mean in the best fitting model. Each dataset was analyzed with our approach. For each parameter, the table gives the simulation value, the mean of point estimates, the average length of 95% CI, the number of times the simulation value is in the 95% CI. (PDF) Text S1 Description of the MCMC algorithm. (PDF).
The research leading to these results received funding from the  Wellcome Trust  (grants  081613/Z/06/Z  and  077078/Z/05/Z ), the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement nu278433-PREDEMICS, the  European Union   FP7 EMPERIE project , the  NIGMS MIDAS initiative , the  Medical Research Council , and the  Bill & Melinda Gates Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The presence or absence of ERM was earlier shown to be significant for success of treatment with ocriplasmin. Hence, if spectral-domain OCT had been used exclusively, the results might be different, as the assessment of ERM is more profound with spectral-domain OCT. 77 t is important to consider that data from a controlled clinical trial evaluating a premarket drug profile may be significantly different from the results that would be observed in clinical practice. For example, in the Phase III studies, the ocriplasmin group was superior to placebo with regard to having complete PVD at day 28. These results were confirmed by B-scan ultrasonograms, and investigators were trained in and followed a strict protocol to obtain reliable, standardized data. Such a protocol is unsuitable for a real-world setting; therefore, the complete PVD rate might be substantially different than the trial outcomes.
2 mg/day. Allocation is performed by a central randomisation service and is stratified by the centre. Patients, healthcare providers, data collectors and outcome adjudicators are blinded to treatment allocation. This trial is primarily funded by the Canadian Institutes of Health Research. With respect to the study schedule, the last of 10 000 patients from 140 centres in 23 countries was randomised in December 2013. The primary outcome is a composite of 30 day all-cause mortality or non-fatal myocardial infarction. The secondary outcome is a composite of 30 day all-cause mortality, non-fatal myocardial infarction and non-fatal stroke. In addition to the primary outcome, POISE-2 is uniquely positioned to determine the effects of perioperative aspirin (and clonidine) on AKI, to consider whether treatment effects on AKI differ in those with preoperative chronic kidney disease (CKD) and preoperative chronic aspirin use, and to investigate the treatment effects on longer term kidney function, based on a single measurement taken 3 months to 1 year after surgery.
Other questions 5. In patients undergoing non-cardiac surgery, does the use of perioperative clonidine at the time of surgery compared with placebo alter the risk of postoperative AKI? Hypothesis: perioperative clonidine versus placebo at the time of surgery will reduce the risk of postoperative AKI (a notion supported by some animal and human studies,  [19] [20] [21] [22] [23] [24]  recognising that an elevated risk of AKI could also manifest from clonidine-induced hypotension). POISE-2 DATA COLLECTION AND ANALYTIC PLAN POISE-2 enrolment began in July 2010, and enrollment in the POISE-2 AKI substudy began in January 2011. To refine the analytic plan, we reviewed POISE-2 data as of April 2013 (when data reconciliation was ongoing and without any knowledge of patient allocation to the assigned treatments).
POISE-2 AKI substudy data collection A preoperative serum creatinine value is measured within 6 weeks of surgery. The date of surgery (and not the date of randomisation) is used to identify the start of follow-up in this protocol; the median (IQR) number of days between the date of randomisation and the date of surgery is 0 (0-0; 95th percentile 1 day). After receipt of grant funding to support this substudy, serial perioperative serum creatinine data began to be recorded in consecutive patients enrolled at AKI substudy participating centres (earliest centre to start this process began in January 2011). All centres were encouraged to record a serum creatinine value postoperative days 1, 2 and 3 in consecutively enrolled patients ( provided the patient was not discharged from hospital). This will help reduce concerns about an ascertainment bias related to AKI (ie, where aspirin vs placebo alters the incidence of another event such as myocardial infarction or bleeding, which in turn influences the likelihood of serum creatinine measurement).
This category includes the procedure of nephrectomy. Because postoperative changes in serum creatinine in the context of partial or complete nephrectomy can occur for reasons other than AKI, in an additional analysis we will exclude this category of surgery to confirm that the results on the remaining sample are no different than the primary results. 0] [21] [22] [23] [24]  The analytic techniques for the assessment of clonidine on AKI are as specified for aspirin, with only minor adjustments (ie, adjustment for the random allocation to aspirin and no prespecified subgroup analysis for chronic aspirin use). If a greater risk of AKI is observed with clonidine versus placebo, then the blood pressure data collected in POISE-2 will be reviewed to determine if this potentially explains the observed effect.
statistics The association of the 24 covariates (Table  1 ) with SLT success was analyzed using univariate logistic analysis and multiple regression analysis. The high collinearity among the covariates hindered the interpretation of traditional multiple logistic regression. To overcome this, variable selection by elastic net approach was first conducted to remove redundant covariates where the estimates of coefficients equaled to zero prior to multiple regression analysis. We performed both the univariate and multivariate regression analyses for the following three datasets separately: 1) both eyes, 2) right eyes only, and 3) left eyes only. We found that the significant variables detected from the right and left eyes were different, signifying that each eye has a unique underlying distribution that was different from the fellow eye.
Acknowledgements. We are indebted to the following (partial) funding sources: the Bavarian grant  THESIS (GAD) , the  German Deutsche Forschungsgemeinschaft  grant  KU2689/2-1  (GAD and UK), a  Research Professorship (LMUexcellent ) of the Bundesexzellenzinitiative and an advanced grant from the  European Research Council-EVOKES (DBD).
Acknowledgments. I am very grateful to  Laurent Lellouch  for many helpful discussions and encouragement. I also thank the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR.
In examining the relationship between the gravitational center of skipjack stocks and ENSO events, Lehodey et al. [11]  used the longitudinal gravitational center of CPUE in the United States purse seine fishery from 1988-1995 as a spatial index of highest fish stock abundance to explore the relationship between the abundance center of free school catch-per-unit-effort and the ENSO cycle. Up until 1996, covering the period of Lehodey's study, more than 80% of sets the purse seine fleet were on freeswimming schools; after 1996 FAD fishing became more common  [15]. In this study, we used the combined catch data from 1991 to 2005 for purse seine fleets operating in the Western and Central Pacific Ocean, which includes sets on free swimming and objectassociated aggregations. Unlike in Lehodey's data, which could be standardized by effort, during this period effort has shifted towards FAD aggregations and there are considerable differences in fleet size, vessel performance, fishing technology and set type preference among these fleets  [15].
Similarly, the seasonal longitudinal gravitational center of catch (G s ) was calculated with seasonal catch data instead of monthly catch data, so catch is summed over season instead. Seasons were three-month periods beginning with January-March 1991. The tuna purse seine catch data used in this study came from the Western and Central Pacific Fisheries Commission (WCPFC) database, which includes monthly average landings by fishing method (floating object-associated or free-swimming school) and by year and month. The data cover the Western and Central Pacific Ocean, aggregated in 5u latitude 65u longitude squares. The SOI indicates the intensity of El Nin ˜o or La Nin ˜a events in the Pacific Ocean. We obtained SOI records from the University of East Anglia Climate Research Center and considered La Nin ˜a events to occur when the SOI exceeded 1.
Dataset Simulation of cohort data -Procedures and scenarios chosen. All the details of the cohort data simulation and the procedures and scenarios chosen are given in Appendix A. We simulated the cohort data referring to an "illnessdeath" model with transition intensities λ 12 (t), λ 13 (t) and λ 23 (t) (Figure  1 ). The parameter of interest HR(t) corresponded to the ratio λ 23 (t) /λ 13 (t). The average HR(t) is obtained from an exact formula involving the averages of λ 13 (t) and λ 23 (t) which are computed through a numerical approximation (transformation of the time from continuous to discrete values) (See the Appendix B). The average HR(t) adjusted for the different covariates was estimated empirically: its estimation was obtained using large size samples to guarantee good precision.
two models which could be compared. Fitting of model LWA a makes it possible to estimate an average HR(t) i. e. HR a (t), while LWA i is assumed to give HR i (t) for each Z profile (Table  1 ). As the exposure effect was considered to change over time for three of the five configurations, its estimation was assessed by time interval specified a posteriori. Matching methods -Creation of censored correlated data from cohort data. For each data set, the two match- ing methods presented in Section 'Methods' were applied. According to Methods 1 and 2, the two subjects in each pair were matched on the three covariates Z k , and the non-exposed subject had to be disease-free for as long as the time from t = 0 to exposure time of the exposed subject. Then from the 2000 subjects simulated in cohort data sets and equally allocated to the 8 Z profiles, a number of pairs smaller or equal to the number of subjects in State 2 (i.
Real data application Data Our data retrospectively included 870 women treated for breast cancer between January 1990 and December 1999, and diagnosed before 35 years of age. Information on patients' status was collected at the end of the year 2004 and the median follow-up was 87 months (range, 7 to 166) at this date. Tumor, treatment and disease evolution analyses are available in the paper of Largillier et al. [9]. One of the goals of the data analysis was to compare disease-free survival between pregnant and non-pregnant patients. The protocol was submitted to the appropriate French authorities supervising individual computerized data files (Commission Nationale Informatique et Liberté [CNIL]), and obtained ethical approval from the Institut Curie ethics committee (Comité des Etudes en Recherche Clinique [CERC]).
The GETNA Working Group, which was responsible for the data conception, design and acquisition, allowed us access to these real data. We created pairs of pregnant and non-pregnant women using the two matching methods. Both women were matched according to their cancer gravity level using: Scarff-Bloom-Richardson grade (SBR, related to cell proliferation level), pathological node involvement and hormonal receptor status (RH). They were also matched on treatment: hormonotherapy prescribed or not to RH+ patients, and chemotherapy administered or not before and/or after surgery. Within each pair, the non-pregnant woman had to be disease-free for as long as the time from diagnosis to pregnancy of the pregnant woman. We sought to estimate the effect of subsequent pregnancy occurring over time on breast cancer evolution.
Results A total of 105 patients were eligible and enrolled in the study. Patient characteristics and fracture types are presented in Table  1. The majority of the subjects were women (74 %), and the mean age at the time of study inclusion was 69 years old (range 28-91 years). However, a total of 38 subjects were excluded due to the following: lost to follow-up (n = 20) or died prior to final follow-up (n = 18). Ultimately, 67 patients were available for final follow-up and data analysis. Thus, the final study population consisted of 51 women and 16 men with a mean age of 68 years old (range 28-86 years) (Table  2 ).