dataesr/scientific-paragraphs-categorization huggingface dataset:
Databases Post hoc analyses were performed on two databases of patients with early RA: 65 patients from the GUEPARD tight control trial and 130 matched patients from the ESPOIR usual care cohort  [15]. GUEPARD includes patients with RA as defined by the 1987 criteria of the ACR with maximum disease duration of 6 months and DAS28-ESR >5. 1. Patients who met eligibility criteria were randomized to receive MTX monotherapy or a combination of MTX and adalimumab (ADA), with adjustment of the treatment every 3 months to achieve a DAS28-ESR <3. 2. If this target was not met, the following steps were taken sequentially: MTX and ADA (40 mg every other week), MTX and ADA (40 mg/week), MTX and etanercept (25 mg twice a week) and MTX combined with LEF. ESPOIR is a large national multicentre, longitudinal and prospective cohort initiated by the French Society of Rheumatology  [16].
Patients with undifferentiated arthritis or RA, with disease duration <6 months who were both DMARD-and glucocorticoid-naı ¨ve, were recruited. Patients were seen every 6 months during the 2 first years and then every year. The patients were treated with routine care, without a specific target, and followed by their rheumatologists, without a treatment protocol for a target. The approach was determined entirely by the treating rheumatologist, including a decision to initiate biologic agents. These prior studies and compilation of the databases for further analyses were conducted with the approval of the ESPOIR central ethics committee of Montpellier and the GUEPARD central ethics committee of Cochin, Paris. The analysis plan for this study was submitted to the Institutional Review Board (IRB) of the New York University School of Medicine, which deemed the planned analyses of deidentified data exempt from requirement of IRB approval.
A total of 130 patients in the ESPOIR cohort fulfilling the inclusion criteria of the GUEPARD trial and with no missing data at baseline were matched 1:2 using a propensity score. A propensity score is used in statistical analyses of non-randomized data to attempt to match patients to provide an unbiased estimation of group effects by adjusting for specific variables. The main advantage of a propensity score over random assignment is that it avoids the ethical considerations that arise when a potentially beneficial treatment or strategy is denied at random. Non-randomized data are less costly than clinical trials, and a propensity score allows the analysis of existing data. A propensity score is limited, as one controls only for recognized variables, while hidden bias may remain because of other non-recognized variables.
Study RA Core Data Set measures The RA Core Data Set measures include the number of tender joints (TJC) (028); number of swollen joints (SJC) (028); physician global estimate (DOCGL) (VAS 0100); HAQ-FN (03); pain (VAS 0100); patient global estimate (PATGL) (VAS 0100); ESR (mm/h) or CRP (mg/dl); and, in studies of 512 months, radiographic scores according to the modified Sharp/van der Heijde score. Each of these variables was analysed at baseline, and at 6 and 12 months of follow-up. HAQ-FN was recalculated from 03 to 010 (multiplied by  3. 3)   CDAI is a simple sum of TJC (028) + SJC (028) + DOCGL (010) + PATGL (010), for a total score range of 076  [6]. CDAI categories include remission (42. 8), low activity (2. 910. 0), moderate activity (10. 122. 0) and high activity (>22) (Supplementary data Table  S1 , available at Rheumatology Online).
Statistical analysis All statistical analyses were performed using Stata 12. 0 for Windows (StataCorp LP, College Station, TX, USA). Baseline demographic and clinical measures were compared in GUEPARD tight control vs ESPOIR routine care using the Student's t test for variables normally distributed and Wilcoxon rank-sum test for variables with non-normal distribution. Differences between GUEPARD (tight control) vs ESPOIR (routine care) in the responsiveness of each measure to document clinical changes between baseline and 12 months were calculated according to the method of Tugwell et al. [17]. Initially, standardized response means (SRMs) were calculated for each measure, as described by Wells et al. [18]  (see supplementary data Appendix for formulas, available at Rheumatology Online). Relative efficiencies are expressed in relation to the TJC as the referent measure  [17].
A relative efficiency >1 indicates that the measure is more efficient than TJC to detect a difference between results of two treatment strategies. These analyses were performed without adjustment for mean daily dosage of corticosteroids or the duration of biological agent therapy, as in the previous report  [15]. Results were similar at 6 and 12 months, and only 12-month data are presented for analyses of individual measures. Each of the three composite indices-DAS28, CDAI and RAPID3-was calculated at baseline, 6 months and 12 months. Mean changes from baseline to endpoints at 6 months and 12 months were compared in GUEPARD vs ESPOIR using Student's t test, with statistical significance adjusted for eight comparisons for individual measures and three comparisons for indices. The numbers and proportions of patients in four categories-high, moderate and low activity and remission-were computed at baseline, 6 and 12 months according to each index.
Discussion This report presents data consistent with extensive evidence that a treat-to-target strategy has greater efficacy in the treatment of RA than routine care  [14]. To the best knowledge of the authors, this is the first report to compare relative efficiencies of RA Core Data Set measures in two independently recruited patient groups rather than two arms of a clinical trial. The greater efficacy of the GUEPARD tight control treat-to-target strategy vs ESPOIR routine care is seen at higher levels according to patient self-report and global measures compared with joint counts and laboratory tests. These findings also are consistent with evidence of high relative efficiencies of patient report measures compared with joint counts and laboratory tests to distinguish active from control treatments in clinical trials of LEF [9], MTX [9], adalimumab  [10] , abatacept  [11]  and certolizumab  [12].
In these trials, variation in the most efficient measure is seen, although DOCGL, PATGL, pain and HAQ-FN generally  show greater efficiency than TJC, SJC, ESR and CRP. However, each measure is most efficient in a certain trial, underscoring a need for an RA Core Data Set of seven measures. Each RA Core Data Set measure has a similar order of magnitude to detect differences in clinical trials and clinical care. Patient self-report and global measures have greater sensitivity, while joint counts and laboratory tests have greater specificity. RAPID3 is an index that includes only self-report data provided by patients, without a need for formal joint counts from health professionals, scored in 5 s on a multidimensional health assessment questionnaire (MDHAQ) vs 5100 s for DAS28 or CDAI  [13]. RAPID3 yielded similar, but not identical, results compared to DAS28 or CDAI for categories of high, moderate and low activity, and remission in this study and others  [13, 21].
All RAPID3 data are from the same observer-the patient-overcoming the need for the same health professional observer to perform a joint count at each visit. Indeed, patient self-report measures appear as reproducible as physician-performed formal joint counts  [7] , in addition to their similar or greater relative efficiencies. Moreover, completion of a self-report questionnaire by a patient in the waiting area provides relevant information to the clinician before seeing the patient. An international expert committee recommended that a treat-to-target strategy in daily clinical practice should include a validated composite measure of disease activity to guide treatment decisions in routine clinical practice  [22]. Collection of RAPID3 scores in the waiting area at each visit ensures that some quantitative data will be available in addition to narrative descriptions of patient status.
Acknowledgements The authors thank  Nathalie Rincheval  for expert monitoring and data management, and all the investigators who recruited and followed the patients ( F. Berenbaum ,  Paris-Saint Antoine ;  M. C. Boissier ,  Paris-Bobigny ;  A. Cantagrel , Toulouse;  B. Combe , Montpellier;  M. Dougados ,  Paris-Cochin ;  P. Fardelonne  and  P. Boumier , Amiens;  B. Fautrel  and  P. Bourgeois ,  Paris-La Pitie  ´;  R. M. Flipo , Lille;.
Investigators failing to qualify at first assessment were permitted one retest that consisted of correctly scoring 2 of 3 different examples (from different videos) of the descriptor(s) that they had previously incorrectly scored. Video selection. Videos performed according to a standard procedure  7, 8  were selected (by P. K. and B. R. Y. ) from a resource of videos from clinical trials of patients with active UC. 8 ubjects had consented to the anonymized presentation of these procedures (EUDRACT 2006-001310-32). Each video comprised a full-length sigmoidoscopy, edited to remove contact friability test images where present, because this technical test had confused earlier assessment. Also included were recordings from subjects (Oxford LREC 536407Q1605/58ORH) without UC Some spots or streaks of coagulated blood on the surface of the mucosa ahead of the scope that can be washed away Luminal mild (2)  Some free liquid blood in the lumen Luminal moderate or severe (3) Frank blood in the lumen ahead of the endoscope or visible oozing from the mucosa after washing intraluminal blood, or visible oozing from a hemorrhagic mucosa Erosions and ulcers None (0) Normal mucosa, no visible erosions or ulcers Erosions (1)  Tiny ( 5 mm) defects in the mucosa of a white or yellow color with a flat edge Superficial ulcer (2)  Larger (>5 mm) defects in the mucosa, which are discrete fibrin-covered ulcers when compared with erosions but remain superficial Deep ulcer (3)  Deeper excavated defects in the mucosa with a slightly raised edge NOTE.
9 Correlation between the UCEIS and overall severity on the VAS, and all interobserver analyses avoided data from the second read of duplicate videos between investigators, and all those where clinical details were provided. Intraobserver analyses, including those for clinical detail/no clinical detail pairs, only used data from duplicate videos. Secondary objectives. The impact of knowledge of clinical details was evaluated by comparing UCEIS scores and overall severity scores on the VAS within the 50 clinical details/no clinical details pairs. Simple and absolute differences were computed within each pair. t tests were used to test for nonzero mean differences. For comparison purposes, these analyses were repeated for the duplicate pairs in which neither video was presented with clinical details.
(10)  and  (11) ] are in a very good agreement, to within 2%, with the result of numerical integration of Eq. (  7 ). A more rigorous verification of the presented findings may be performed by comparing them with the results of numerical simulations. In our analysis we use the numerical toolkit WIGGLER3D, which includes a three-dimensional particle tracker with electromagnetic pusher, various models of electromagnetic fields, including an optical lattice, and the module for calculation of coherent and incoherent spectra, using the formula given by Eq. (  6 ). We model the dynamics of an electron with b ¼ 100 in an optical lattice formed by two laser beams with amplitudes a 0 ¼ 0:23 directed with the angle ¼10 to electron propagation. The particle is injected into the center of the lattice channel at various initial angles, and it propagates during a time of 6500 0 =c, which provides about N ' 3 betatron oscillations (exact number depends on excursion).
Genes were amplified using PCR with PuReTaq Ready-To-Go beads in a total volume of 25µL including primers (Appendix 1) and DNA. Amplification cycles were specific to genes (Appendix 2). AMPures and CleanSEQ procedures were used for DNA purification and sequencing was performed on ABI PRISM 3730xl machines by Agencourt Biosciences (Beverly, USA). One missing gene fragment of V. basalis was obtained from Genbank database (accession number: AB585949). The analyses are based on 45 morphological characters and multiple nuclear and mitochondrial loci, comprising: 374 sites of 12S, 528 sites of 16S, 2231 sites of 28S (sequenced in 4 fragments), 1442 sites of CO1 (sequenced in 2 fragments), 880 sites of elongation factor 1α (EF1α), and 328 sites of H3. Each of these genes was aligned separately using the MAFFT software with the L-INS-i algorithm  (Katoh et al.
2005). Morphological characters were coded with Winclada V. 1. 00. 08 (Nixon 2002). Phylogenetic analyses were performed in a parsimony framework using TNT  (Goloboff et al. 2008). Analyses were first conducted on the morphological matrix and on the different molecular datasets separately, then on a matrix of all the data combined. Sequence alignments were merged with Winclada, and the final composite molecular matrix contained 5783 aligned nucleotides. Each analysis was performed with the new technology search algorithms including sectorial searches, the parsimony ratchet, tree drifting and tree fusing, default parameters except: 200 ratchet iterations, upweighting percentage 8, downweighting 4; 50 cycles of drift; minimum length hit 25 times. Molecular gaps were treated as missing data. Node supports were computed as GC-values of a symmetric resampling of 1000 replicates (group supported/contradicted values;  Goloboff et al.
Molecular analyses Of the 27 studied species, specimens relatively recently collected were available for 17 species (including the outgroups). Due to low quality DNA templates and the use of non-specific primers, molecular data are not homogeneous across the genus. The monophyly of the genus Vespa was found in most analyses based on single genes and in the analysis of merged alignments (Table  1 , Fig. 3 ). The main species-groups appear monophyletic over most analyses except for the low variation 28S. Vespa basalis remains the basal species in the genus. Two main clades diverge in the remaining species: the monophyletic tropica group is the first clade, while the bicolor group and a clade of V. crabro, V. mocsaryana and V. affinis form the second. The bicolor group sensu  Archer (1994b)  is supported in all molecular analyses, with V.
Acknowledgments We thank  B. Baliff , and  M. Cunningham  from the  University of Vermont  and  F. Jacquet  from the  Muséum National d'Histoire Naturelle  for assistance with obtaining the molecular data. This work benefited from  NSF  grant  DEB-0843505. The first author benefited from an  Annette Kade Fellowship  from the  American Museum of Natural History.
Virtual London model The research reported here relates as mentioned to the entirety of London. A three-dimensional digital model of the metropolis is used which includes all buildings out to the M25 orbital motorway (Figure  1 ). This 'Virtual London' is constructed from building footprint data in Ordnance Survey maps (Mastermap Topography layer) combined with remotely sensed information on heights obtained by light detection and ranging (LiDAR) methods  (Batty & Hudson-Smith, 2005). The LiDAR measurements are taken on a 1 m grid spacing and can therefore represent internal courts and setbacks on upper floors. The resulting vertical dimensions should be somewhat more accurate than in DEMs where heights are estimated from numbers of storeys. In both cases, roofs are taken to be flat, but in Virtual London the heights for buildings with pitched roofs are averaged from all LiDAR values falling within the given footprint polygon.
Virtual London comprises some 3. 2 million blocks, many of which are fragmentary parts of buildings. A small block, for example, might correspond to a lift tower, an entrance porch or a back extension. The Mastermap data were combined with the Generalised Land Use Database  (HM Government, 2013) , making it possible to distinguish domestic from non-domestic buildings, and to categorize the non-domestic buildings as office, retail, warehouse, etc. (There are weaknesses in the data in the categorization of buildings that contain both domestic and non-domestic premises. ) Volumes of blocks are given simply by multiplying footprint polygon areas by heights. For analysis the volumes were grouped into a series of approximately logarithmic size bands: 0-3, 4-10, 11-30, 31-100 m 3 , etc. Virtual London has no information on numbers of storeys.
Electricity and gas consumption data The energy data were drawn from a national database of annual electricity and gas consumption statistics, compiled by the UK Department of Energy and Climate Change (DECC)  (2009). These are aggregated from individual gas and electricity meter point data for all meters in London, and published at two geographical scales: local authority and MSOA. Figure  2  Three extruded polygon buildings are shown in a three-dimensional view with the buildings having semitransparent walls (and casting a shadow). The left-most building is adjacent to the middle building, and part of their shared wall (darker tone) is not exposed to the elements. Since these two buildings di¡er in height and depth, there are other sections of this wall that are exposed. All other walls (including the inner courtyard walls of the right-most building) contribute to the exposed surface area of the building The meters fall into different groups: daily and nondaily for gas, and half-hourly and non-half-hourly for electricity.
Harrow has the highest gas demand at 239 kWh/m 2 , compared with an electricity intensity of 95 kWh/ m 2 : the borough has a number of industrial parks with large sheds along the M40/A40 corridor. As mentioned, the consumption data are not broken down by end uses. Large-scale data on the construction characteristics for non-domestic buildings in the UK are severely limited. Although it is well understood that a number of non-morphological factors will directly affect the amount of energy consumed in a building (e. g. facade materials, proportion of glazing, heating and core services, information technology (IT) equipment, air-conditioning, and activity type), no such database exists that offers comprehensive or even indicative details on these for London. One would, of course, expect that a significant fraction of the electricity use in the boroughs that are dominated by office activity would be accounted for by IT and computing equipment.
All this work was applied to multi-storey buildings where the passive-non-passive distinction may be expected to be significant for energy use. Ratti et al. (2005)  found that the percentage of total building volume made up by passive zones was for 84% for Toulouse, 77% for London and 61% for Berlin. Modelling energy consumption for the nonpassive zones showed that this did not differ greatly between the three cities. However, consumption in passive and non-passive zones combined was somewhat higher in Berlin at 73 kWh/m 2 /year compared with values of 67 and 68 kWh/m 2 /year for the other two cities. 1 This does indeed suggest an effect on energy consumption of depth above 12 m. Whether the results can be compared with those of the present paper is another matter. Actual energy consumption data are used in this paper, while Ratti et al.
Morphological factors'contribution to total energy use A correlation of energy use with the exposed surface areas of buildings has been demonstrated, although the full significance of the correlation cannot be assigned As mentioned above, a number of construction features of the non-domestic building stock cannot be accounted for at the analysis level and these results provide only initial indications of the relationships between these variables and energy use. The authors intend to explore this further in future studies using data that may become available from a number of sources including the Valuation Office Agency Summary Valuation (SMV) database of non-domestic buildings. Initially, a stepwise selection approach was used to determine the predictive strength of variables. This information was then used along with the addition of variables of interest to develop a model with a reasonable statistical fit and significance.
Euclidian distances (D) between two positions were calculated as follows: D = √ ((x2  x1) 2 + (y2  y1) 2 ). In time-lapse series, the first measured length was set to zero and the variation was calculated according to this reference. SPB/centromere and SPB/kinetochore Euclidian distance measurements were done using the MTrackJ plugin in ImageJ, their respective positions being determined in the best focal plane. Fluorescence recovery was corrected for background noise and continuous photobleaching using ImageJ software, and then normalized to the fluorescence measured before bleach  (Vavylonis et al. , 2008). The fluorescence intensity of a region of interest (5 × 4 pixels) was measured on maximal projection of z-stacks. The fluorescence intensity I r was calculated over time as follows: I = (I region of interest  I background ).
Photobleaching was measured on unbleached nMT arrays, and the intensity was corrected using I r = I y × (I unbleached first point / I unbleached point y ), where y is a specific time point over time. The relative position of the nucleolus versus the SPB was determined using a central symmetry axis along the nucleus draw from the SPB. To determine the location of Arp1/Dyn1 toward nuclear membrane, a line scan (i1) of 5-pixel width containing both GFP/RFP signal and background was drawn along the GFP/RFP signal using ImageJ software. A line of 10-pixel width (at the same location) was drawn in order to calculate the intensity of the surrounding background (i2). The real intensity (ir) was calculated as follows: ib = (i2 × 10)  (i1 × 5) and ir = i1  (ib/5). Individual Arp1/Dyn1/Nup2 Gaussian fits were determined and aligned using GFP fits set to 0 on the x axis.
Images were acquired using a CoolSnap HQ camera (Roper Scientific). The microscope, camera, and shutters (Uniblitz) were controlled by SlideBook software 5. 0 (Intelligent Imaging Innovations). Images are, unless specified, 3D maximal projections of z-stacks performed using a 0. 27-µm step. Few microliters of cell culture were deposited onto a glass slide and directly imaged at room temperature. FRAP experiments were done on an inverted microscope (model DMI 6000; Leica) equipped with a spinning disk confocal head (CSU-X1; Yokogawa Corporation of America), a QuantEM camera (Photometrics), and a scanner FRAP system (Roper Scientific). The diode lasers used were at 408 nm and 491 nm. The objective used was an HCX PL APO CS 100× oil 1. 4 NA. The z-stacks were done with an objective scanner (Piezo P721.
Data analysis In Fig. 1  C, a line scan (i1) of 3-pixel width containing both GFP signal and background was drawn along the nMT array using ImageJ software (National Institutes of Health, Bethesda, MD). A line of 6-pixel width (at the same location) was drawn in order to calculate the intensity of the surrounding background (i2). The real intensity (ir) was calculated as follows: ib = (i2 × 6)  (i1 × 3) and ir = i1  (ib/3). We arbitrarily set the highest.
Our methods also allow for intra-individual correlations when samples are obtained from a common set of individuals. While we focus here on comparing and combining information across different tissue types, our framework could be applied equally to comparing and combining across other units, e. g. different experimental platforms, multiple datasets on the same tissue types, or data on individuals from different populations. The remainder of the paper is as follows. After providing a brief overview of our framework, we use simulations to illustrate its power compared to other methods, and then apply it to map eQTLs, and assess heterogeneity among tissues, using data from Fibroblasts, LCLs and T-cells (  [12] ). Consistent with results from  [16] , we show that our joint analysis framework provides a large gain in power compared with a tissue-by-tissue analysis.