dataesr/scientific-paragraphs-categorization huggingface dataset:
First of all, we use census data to obtain a highly detailed crosssection of demographic characteristics. Variables collected include ages, size of the household (HH), ethnic group, type of occupation, sector of occupation, number of hours worked per week, and other variables that proxy for social status. 9 In addition, we have income figures from the labor force surveys. These figures are collected periodically at a higher level of aggregation than census data are. 10 Hence, following Smith (2004), we first regress this more aggregate measure of income (which is time varying) on our set of demographics, and then use the estimated coefficients to predict the evolution of income at the lowest census level. Finally, we reconstruct the predicted time-varying income at the level of the LE, based on the list of post codes served by each LE contained in the sample (as provided by Ofcom).
Using Ofcom's data set and the third data set, we computed two variables that may affect the profitability of broadband investment. First, we computed the distance of the LE to the backbone of the broadband network. This is an important cost factor for LLU operators, because the farther a LE is to the backbone, the larger the costs to provide broadband. Second, we computed the distance of the premises in the catchment area of each LE. This variable is also crucial for broadband operators relying on the telephone network, because the quality of their service (negatively) depends on the length of the copper lines connecting the LE to the homes of the final users. While the data set is very rich, we acknowledge that we do not have price information. For the purpose of this study this is not a limitation, as we are most interested in entry strategies and the impact of LLU on penetration.
Finally, the bottom panel shows summary statistics on the demographics. The most important variable is income in the LE, which is time varying. Time-invariant control variables are a dummy for urban status, age, occupation, and ethnic group (the latter is not reported in the table). Summarizing, then, our data provide a precise portrait of the wholesale broadband market in the United Kingdom. They contain information at the geographic level required to study the effects of LLU entry, and cover the period during which investments were made. We are not aware of any other data set with this level of detail elsewhere in Europe or the United States. The size of the data set is unusually large for this kind of study, as the core data set assembled by Ofcom comprises 5,587 LEs for 17 quarters, resulting in almost 100,000 observations.
Empirical Model As explained previously, we make use of data on 4,265 LEs, indexed by i, observed over 17 time periods, t. For each LE i and time period t, we observe the total number of broadband lines of the incumbent, of the LLU entrants, and of cable. We also observe market demographics, including income. The basic specification takes the following form: y it D Á i C t C ˇt N it C X it C u it : (2) Here, y it is the relevant performance measure of broadband penetration. We focus on total broadband penetration-that is, total broadband subscribers as a percentage of total telephone lines. Our main interest is in the variable N it , which is either a dummy variable for LLU coverage (0/1) in the LE, or an ordered variable for the number of LLU entrants (0, 1, 2, 3C). We first consider ˇt D ˇ, and subsequently also include an interaction with a time trend to allow for a nonconstant effect of LLU over time.
However, part of it is related to the intrinsic quality that each operator can offer. To measure the difference in quality between operators, we first estimate the following model for the (log of) speed of a test j 27. Tests have been performed in 97. 6% of the LEs. To further assess the representativeness of this sample, we looked at the operators' market shares at the level of the LEs. We found that the market shares as computed from Ofcom's detailed subscriber data set and the currently used speed test data set display a correlation very close to 1. 28. These four operators account for 94% of the entrants' market alone. 29. http://stakeholders. ofcom. org. uk/market-data-research/other/telecoms-research/broadband-speeds. in LE i: ln speed ij D 1 LLU OP ij C 2 Bitstream OP ij C 3 Cable OP ij C ˇxij C v ij : (5) Here, LLU OP ij , Bitstream OP ij and Cable OP ij are dummy variables equal to 1 if the test was run, respectively, on a line served by an LLU entrant, by a Bit-stream entrant, or by the cable operator.
Regarding immunosuppressive therapy, its more controversial data suggest that sirolimus is associated with SOS/VOD after MAC TBI-based allo-SCT when used in combination with MTX. 25  In contrast, preclinical data highlight the detrimental role of CsA, compared with sirolimus on endothelial cells. 17  immunosuppressive therapy effect exerted on epithelial cells probably depends on treatment association and of the conditioning regimen used. Some of the SOS/VOD risk factors are directly linked to patients' and disease characteristics. Older age, impaired Karnofsky status (o 90) and advanced disease (beyond second CR or relapse/ refractory disease) have been reported as SOS/VOD risk factors. 6 n increased risk of SOS/VOD in women has also been reported; however, that was probably related to the use of progestin therapy to prevent gynecological bleeding, as the incidence of SOS/VOD was higher in women who received norethisterone as compared with those who did not.
44 Large randomized control studies are indispensable to properly evaluate heparin use and to enable definitive recommendation for its continuation or abandonment for SOS/VOD prevention. At present, heparin remains used for SOS/VOD prevention in some EBMT centers. Data on the usefulness of ursodeoxycholic acid for SOS/VOD prevention are non-conclusive: some randomized trials suggest that it decreases the incidence of SOS,  [48] [49] [50]  whereas others fail to demonstrate an advantage. 51, 52 However, patients receiving this prophylaxis have less liver toxicity, less acute GVHD and better survival, strongly suggesting the beneficial effect of ursodeoxycholic acid. 52 Furthermore, it has been shown that ursodeoxycholic acid use is associated with a decrease of non-relapse mortality. 53 Finally, a prospective phase III study recently showed a reduced incidence of SOS/VOD in pediatric HSCT patients who received prophylaxis defibrotide (DF).
57 Additional data were obtained via a treatment investigational new drug protocol that included 425 patients with SOS/VOD after HSCT: 284 with severe and 141 with non-severe SOS/VOD. 59 In the former group, the CR rate was 47% and the day +100 OS was 48%. In patients with non-severe SOS/ VOD, these figures were 47% and 69%, respectively. All HSCT children (⩽16 years) had higher CR rates as compared with adults (41 vs 27%; P = 0. 0038) and better survival (60 vs 49%; P = 0. 0203). The overall toxicity of DF was reported to be manageable: 22% of patients experienced at least one adverse event, which primarily consisted of hemorrhage (17%) and hypotension (4%). These studies led to the approval in 2014 of DF for treatment of severe SOS/VOD after HSCT in European countries by the European Medicines Agency (EMA).
Given the high degree of mismatch and the use of CY, an increased incidence of SOS/VOD was expected in this setting; yet, so far, no center reported such an increase. One explanation might be the administration of CY after allo-HSCT, far apart in time from the conditioning. Furthermore, despite the use of haploidentical donors, there is a decreased alloreactivity in these cases, thanks to the use of post-transplant CY, as highlighted by the low incidence of GVHD. 61 Therefore, data are too preliminary to draw definitive conclusion to consider or not haploidentical allo-HSCT as a SOS/VOD risk factor. We must also question the role of the socalled 'sequential' transplant approach, combining both intensive chemotherapy and transplant conditioning within the same procedure. 62, 63 This procedure is increasingly used in high-risk patients such as relapse/refractory AML.
62, 63 So far, no data are available regarding SOS/VOD incidence after sequential transplant. However, an increased incidence of SOS/VOD is possible, given the intensity of the chemotherapy delivered and that patients receiving a sequential approach are often high-risk heavily pretreated patients. 62, 63 U is a well-established SOS/VOD risk factor. In the last decade, reduced intensity and reduced toxicity conditioning regimens have been developed with decreased doses of BU. 41, 64  Furthermore, i. v. BU has largely replaced oral BU and allow dose adjustments thanks to pharmacokinetic monitoring. It raises the question as to whether BU should be always considered as such a risk factor regardless of the dose used, or whether a threshold below which BU should no longer be considered as a risk factor should be defined.
Similarly, a comparable randomized trial in children would be valuable given that the data from the first trial were insufficient for DF approval in SOS/VOD children prophylaxis. Such studies raise also the question of DF administration in out-patients, for whom development of an oral formulation appears to be essential. The optimal dose of DF employed for prophylaxis also remains to be determined. In summary, our future perspectives in the setting of SOS/ VOD are: • More accurate identification of SOS/VOD risk factors; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4. 0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Acknowledgements We thank  Marion Chatelain ,  Samuel Perret ,  Claudy Haussy ,  Florian Ruiz ,  Pauline Pierret ,  Laurine Forge  and  Raphaël Guttierez  for their help at various stages of the experiment. This work was financed by grants from the local government (Ile-de-France:  Sustainable Development Network   R2DS , No. 2012-11 ) and the  ANR  (No. ANR-13-PDOC-0002 ). This study was carried out in strict accordance with the recommendations of the European Convention for the  Protection of Vertebrate Animals  used for Experimental and  Other Scientific Purposes  (revised Appendix A). All experiments and captures were approved by local authorities and the  "Direction Départementale des Services Vétérinaires de Seine-et-Marne " (permit No. 77-05 ).
We have divided the maps into six regions (I-VI) for better comparison. The total number of 1 min averaged records and the median number of samples per 2R E × 2R E bin for each type of map are shown in Table  1. We show the numbers only for oxygen as the difference from those for protons is less than 0. 1%. Additionally we show the median values of the Dst index, the AE index, SW P dyn , and IMF B z for each type of map in Table  1. To minimize the effect of In the same format as Figure  2. skewed distribution, we use median rather than mean values of the fluxes for construction of the maps, see details in  Kronberg et al. [2012]. In Table  1 , one can see that the low (high) SW P dyn and the northward (southward) IMF B z are correlated with lower (higher) AE index. We checked the linear Pearson correlation between these parameters in our database: AE versus SW P dyn is -0.
7. Our observations are in agreement with models by, e. g. ,  Delcourt [2002] ,  Fok et al. [2006] ,  Welling and Ridley [2010] , and  Welling and Ridley [2011]. for supporting the RAPID instrument at MPS under grant 50 OC 1401. E. E. Grigorenko thanks Russian Scientific Foundation (project 14-12-00824) for the financial support. We also thankful to the International Space Science Institute (ISSI) for their hospitability, and, for giving us the opportunity to gather the team on "Heavy ions: their dynamical impact on the magnetosphere" which allowed us productive collaboration. The authors are grateful for the use of the OMNI database for providing the AE, Dst, and solar wind parameters. The Cluster data can be found at CSA Archive: http://www. cosmos. esa. int/web/csa/. Michael Balikhin thanks the reviewers for their assistance in evaluating this paper.
Cometary orbits We have generated streams for 420 Jupiter-family, Halley-type, and Encke-type comets in the JPL Small Body Database (SBDB) as of 1 August 2013, which have perihelion distances q < 3 AU, semimajor axes a < 30 AU and defined total visual magnitudes. Comets that do not fit these parameters either do not come close enough to the Sun to generate dust under our assumption of water-generated dust production inward of 3 AU, fall outside our region of interest, or do not contain the magnitude information we require to calculate the dust production of the comet. We removed a very small number of comets with highly unpredictable orbital behavior, such as exceeding an eccentricity of 1 for a long period of their orbit, which in many cases would limit the ability of the comet to form a trail that would return to the inner solar system.
The final data sets include 362 Jupiter-family comets, 40 Halley-type comets, and 18 Encke-type comets. Particles are emitted from Halley-type comets starting in 1700, and for Jupiter-family and Encke-type comets starting in 1850. These limits reflect that we expect the most recent dust to be most important, as well as the maximum size of the database that we can support. Dust emission in the model ends in 2080. In a few cases, these limits are modified to account for significant changes in the comet orbits, which can throw the comets into or out of the inner solar system. The limits are additionally modified to introduce fragments after their expected creation dates. Orbital data for comets are not available in SPK format (as accessible by NASA NAIF SPICE) from JPL HORIZONS outside the years 1900-2100.
This reflects the level of uncertainty that exists beyond this time period. For a small number (∼20) of major comets we use JPL HORIZONS orbits that are constructed by piecing together different comet "solutions" given by the HORIZONS database. For all other comets the orbits have been computed using the MODUST code  (Rodmann 2006). The gravitational potential of the Sun and planetary perturbations are included as the most important force terms. Non-gravitational forces on the comet nuclei  (Yeomans et al. 2004)  are not well known for most comets and therefore are not included. MODUST uses a Hermite scheme with individual timesteps for integrating the comets' motion. A detailed description of the method and numerical approach is given in  Ertel et al. (2012).
Dust production rate We estimate the water production rate using the total magnitude and use a gas-to-dust ratio to convert this to a dust production rate. We use this method because the [A(θ) f ρ] parameter, which provides a method for estimating dust production information for a comet by correcting for various observational parameters  (A'Hearn et al. 1984) , is only known for a small number of comets. We use the empirical formula for the correlation between visual magnitudes reduced to a geocentric distance of 1 AU m H and water production rates  ]  given by  Jorda et al. (2008) : Q [H 2 O log Q [H 2 O] = 30. 675(0. 007) -0. 2453(0. 0013)m H, where m H is calculated from the JPL Small Body Database using both the total magnitude M 1 and the magnitude heliocentric distance slope parameter K 1.
Particle integration: creation of a dataset of meteoroid streams The emitted particles are individually integrated from their creation time up until 2080. Particle integrations are performed using a Runge-Kutta-Nyström 7(6) integrator with variable step size  (Dormand & Prince 1978). The emitted particles are individually integrated including gravity of the Sun and eight planets, as well as radiation pressure and the Poynting-Robertson effect (including a factor for solar wind drag of 0. 3  Gustafson 1994). Relativistic corrections to solar gravity are included. Planet positions are taken from JPL's Development Ephemeris 430. The integrator is designed to record the particle states at a number of "save points" per orbit (more near perihelion and near close planetary encounters). This creates a database from which the full trajectories of each particle from each comet can be reconstructed between 1980 and 2080.
The particles are integrated at earlier times, but are not saved to limit the size of the database. The database consists of a set of binary files for each comet, containing position and velocity information for integrated particles. The calculations are completed using the Constellation distributed computing platform, in which the computational load is shared over many individual computers. The work is split into packages of particles called work units, which are distributed among participating computers. Constellation 1 is a system in which public users can donate their idle time on their private computers to provide distributed computing capability to aerospace-related science and engineering projects. Distribution and processing of the work units is managed by the Berkeley Open Infrastructure for Network Computing (BOINC) system developed at the University of Berkeley.
Application of IMEX The model provides a database of integrated trajectories for thousands of dust particles that are emitted during each apparition of 420 short-period comets. The dust trajectories can be retrieved from the database on a given date between 1980 and 2080, either for all particles from one comet, or for all particles near a position in the inner solar system. These can be used to study comet trails and meteor showers and to analyze the streams that impact a given location at a particular time. 1 http://aerospaceresearch. net/constellation/ Additionally, when updated observational data are available for an individual comet, the original structure of IMEX can be used to recalculate the stream to provide a more accurate database entry. New comets can also be added to the model in this way.
The orbit was taken from JPL Horizons. The JPL Small Body Database provides a visual total magnitude of 11. 4 and a magnitude slope parameter of 11, which we used to define the dust production rate. We tested three differential size distribution indices α and three velocity models. Two of the differential size distribution indices, α = -3. 5 and α = -4. 1, are the values found respectively by  Kelley et al. (2008)  and  Agarwal et al. (2010)  to best match their data sets. The third, α = -3. 7, is an intermediate value. The first velocity model is that of  Crifo & Rodionov (1997) , which is used for the main IMEX model. We additionally used velocity models derived using these Spitzer observations. They provide ejection velocities that are much lower than those obtained from the model of  Crifo & Rodionov (1997).
We therefore do not expect high-precision accuracy for the general model. However, this test case demonstrates that the model can be efficiently refined for streams of particular interest to encompass higher quality information on individual comets. This study therefore demonstrates that the IMEX model is successful in fitting observational data for cometary streams when the basic cometary parameters -mass distribution and ejection velocity -are already constrained. If no specific comet is identified, such as in the case of a future spacecraft mission, the full IMEX database can be used to determine streams in the vicinity of point in space or time. When the comet of interest is known, IMEX can be used in conjunction with observational data, such as infrared trail images or meteor shower data, to determine refined parameters for the comet.
Figure6demonstrates how the 2004 and 2006 profiles vary for α = -2. 9--4. 1. Including the error bars (which represent 1σ of the background variation), the 2004 profile is matched best by α = -3. 7--4. 1, and the 2006 profile by α = -2. 9--3. 7. An intermediate α = -3. 7 is appropriate for both datasets.
Individual tubes labelled A and B were then sent to the different labs taking part in the study. Animals were randomly assigned either treatment A or B in the separate studies with blinded analysis of outcomes in individual studies being completed by each participating centre. Full datasets were received from each of these centres by the lead lab before the allocation code was broken and group identity revealed. All studies used subcutaneous (s. c. ) injection of IL-1Ra or placebo at 30 and 180 min after MCAo. Assessment of ischaemic injury. Lesion volumes and oedema were measured by histological staining (three studies) or MRI (three studies). All lesion volumes and oedema measured by staining methods were assessed seven days post-occlusion while MRI measurements ranged from 1 to 28 days post-stroke.
Recordings of neurological deficit ranged from 0 to 28 days post-stroke. Two studies used a modified neurological deficit scoring system based on Hunter et al. ,  16  one study used Bederson et al. 17  and another study assessed behaviour based on a modified version of Garcia et al. 18  The majority of neurological scores accumulates points for increasing deficits. However, the neurological deficit scoring system based on Hunter is the opposite and more points are scored for the lack of deficits. In order to incorporate data from studies using the Hunter scoring system, Hunter scores were deducted from total maximum score (22 points) to produce an inverse value that can be incorporated into analysis, i. e. Hunter values showing high scores (lack of deficit) will result in low values representing a score for neurological deficits present.
Data extraction Individual data were sought for each animal from each leading study investigator by a reviewer (RW) unaffiliated in conducting the studies. All included data were unpublished. Data retrieved included species, sex, age; outcomes including functional (e. g. corner test), neurological scores (e. g. Bederson), oedema (staining or MRI), lesion volume (MRI or staining); and vital status (including information on timing and cause of death -surgery, culling due to poor health, spontaneous). Information on treatment was also obtained: time of treatment from occlusion time (hours before/ after), loading and maintenance dose of IL-1Ra. The following study design information was extracted: experimental model -transient, permanent; randomisation -randomised, pseudo-randomised, not randomised; blinding of surgeon to treatment; blinding of outcome assessors to treatment.
Data analysis Data were transferred to the project's coordinating centre in Manchester by email attachment, e. g. Excel file. Study datasets were merged into a single Microsoft Excel sheet using common field names with one row per animal for analysis. Due to the heterogeneity in study design, no single outcome measure was assessed in the same way across the individual experiments. Therefore, based on the dataset available, it was decided to perform a meta-analysis of the individual studies, with lesion volume, oedema, neurological deficit, functional outcome and mortality as measures of outcome. Cochrane Review Manager (version 5. 2) was used to analyse the effect of IL-1Ra treatment compared to vehicle on post-stroke outcomes. To normalise for differences in the absolute lesion volume obtained across individual centres, individual lesion volumes were recorded as cube root transformations.