dataesr/scientific-paragraphs-categorization huggingface dataset:
Exterior Orientation of the Images Covering the Walls In the next step of data processing, exterior orientation parameters for all cameras were calculated. All images were divided into the same datasets as at the previous stage of relative orientation. To obtain exterior orientation parameters, two methods were applied: TS method (Scenario I) and TLS method (Scenario II). These two approaches were described in detail in the Section 3. 1. Again, the first group of images, which was processed, was the dataset containing images of the walls (the sensor was not included). Exterior orientation of the images was determined with accuracy, the characteristics of which are presented in Figure  8. On the grounds of the above-described tests performed on three test fields it has been observed that at the stage of calculation of relative orientation of images the choice of orientation method is not crucial for obtaining the highest accuracy since the differences observed for different methods are rather subtle.
Exterior Orientation of the Images Covering the Walls In the next step of data processing, exterior orientation parameters for all cameras were calculated. All images were divided into the same datasets as at the previous stage of relative orientation. To obtain exterior orientation parameters, two methods were applied: TS method (Scenario I) and TLS method (Scenario II). These two approaches were described in detail in the Section 3. 1. Again, the first group of images, which was processed, was the dataset containing images of the walls (the sensor was not included). Exterior orientation of the images was determined with accuracy, the characteristics of which are presented in Figure  8. The Figure  8  contains values of RMSE calculated using coordinates of signalized reference points, which were determined with the use of geodetic measurements and TLS data as explained in the Section 3.
On the contrary, the results acquired on test sites II and III are much more varied depending on the scenario. It is a consequence of higher values of measurement errors appeared both in case of geodetic surveys and the TLS measurements. Nevertheless, the skew values are within the measurement accuracy. The last stage of data processing is the computation of the orientation angles of the sensor: ω, ϕ, κ. To calculate these angles, the rigid-body orientation approach described in the Section 2. 5 was applied. Next, the distances between the vertexes of the sensor frame and the wall were measure with the use of a metal ruler, that served as a reference. Based on these distances, the angles ω, ϕ, κ were calculated once more, providing the second set of data for comparison. The analysis of differences occurring between these two sets of angles was performed for all three test sites and all four orientation scenarios.
The measurement accuracy is also influenced by the way the centre of the marked point is determined. On the Test Site I the points were measured from three stations and their coordinates were calculated in the process of geodetic adjustment. On the contrary, on test sites II and III only one instrument station was established, and the points were measured with the polar method. In case of the TLS measurements, the centre of the marker was determined by means of statistical template matching. The use of automatic detection algorithm based on statistical analysis made it possible to eliminate the observer errors, which are a common cause of blunders occurring in classical surveying. In addition, it is worth noting that the amount of time needed to determine the point coordinates with the use of automatic marker detection performed on TLS data is approximately three times shorter than measurements performed with a total station.
Statistical Analysis Mean value ± standard deviations (S. D. ) was evaluated for MVD, EA, Ki-67, expression in G1, G2 and G3 CPIFI subgroups. The significance of differences in terms of MVD, EA, and Ki-67 means between G1 versus (vs. ) G2, G2 vs. G3, and G3 vs. G1 tumor groups was performed by one-way ANOVA test. Correlations among MVD, EA, and Ki-67 were calculated using Pearson's (r) analysis. All statistical analyses were performed with the SPSS statistical software package (SPSS, Inc. , Chicago, IL, USA).
First of all, we performed an evaluation on a wider case series (90 vs. 60 CPIFIs). Secondly, we introduced another tissue parameter as a key element of the discussion, endothelial area, which corresponds to the immunostained vascular area in a microscopic field, that is, the expression of vessels' blood capacity and diameters. Combined data of MVD and EA give different quantitative aspects of angiogenesis. Moreover, we applied the modified Weidner's method for the quantification of MVD (see Sections 2 and 2. 3), while Couto employed another method. Tissue sections stained with anti-CD31 antibody were scanned at 200× magnification to select the areas of highest vascularization. From these areas, three nonoverlapping fields were identified and captured using a digital camera connected to a microscope and saved on a computer.
From this, the aforementioned records from the Northern Hemisphere highlight a major increase in terrestrial productivity during Termination V until mid-MIS 11 through the growth of plants associated with high productivity (Picea, Tree, Shrubs, Arboreal pollen, Quercus robur) in place of steppic species, tundra or frozen soils. This increase is the highest of the last 445 ka when focusing on high latitudes records (ODP site 646 at 58°N 26 , Lake El'gygytgyn at 67°N)  16. At lakes Ohrid  46, 47  and Tenaghi Philippon  15  , that are located around 40°N, while the increase in pollen abundance during Termination V is comparable to those observed during younger ones, the   69. c Reconstructed envelop for the ratio between global biosphere productivity and pre-industrial biosphere productivity as inferred from Δ 17 O of O 2 at age t (interpolation to 200 years and 101 binomial smoothing with Igor software).
Acknowledgements The authors first acknowledge the three anonymous reviewers and the editor for their useful help in improving the manuscript. It is a pleasure to thank  Jean Jouzel ,  Jochen Schmitt ,  Ji-Woong Yang  and  Michael Bender  for useful discussions as well as  Laurent Bopp ,  Masa Kageyama ,  Chiara Guarnieri  and  Jean-Yves Peterschmitt  for their contribution on model reconstructions. We also thank  Gregory Teste  involved in cutting EDC samples. This work benefited from funding from the  ANR program  HUMI17 as well as the  INSU-LEFE-IMAGO program  BIOCOD. M. B. receives an IDEX-IDI PhD grant from  Paris-Saclay. It is a contribution to the  European Project for Ice Coring in Antarctica (EPICA). This work was supported by the  French  state aid managed by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-11-IDEX-0004-17-EURE-0006.
Statistical analyses IBM SPSS 23 was used for statistical analysis. Descriptive statistics were used for most of the questions. In addition, we compared answers provided by pulmonologists and allergists as well as physicians with previous experience with only Omalizumab (OMA) vs. those with experience of Omalizumab plus anti-IL5 molecule (OMA/IL5) using chi-square tests. For the latter, a p value < 0. 05 was considered significant.
We estimated suppression as normalised log 2 -scale growth difference between the wild and reference strain crosses at the restrictive temperature, and considered a TS allele phenotype suppressed, if this value was above 0. 75, i. e. the wild strain segregants had a 1. 68-fold improvement in growth, and if this was unlikely due to chance (false discovery rate = 0. 012, Methods, Figure  2A , Data S2). wild yeast strains with potential suppressor alleles (blue), to produce large segregant populations selected to carry the TS allele. The fitness of the resulting 14,990 populations was measured in two biological and four technical replicates. A subset of 38 candidate suppression events were used in bulk segregant analysis for linkage mapping of causal loci that display selection at restrictive temperature (dark) but not permissive temperature (light).
We sequenced the populations, and compared variant allele frequencies between the two temperatures (Data S4 and S5). We first considered positive controls expected to involve suppression by an additional, wild-type copy of the query gene described above, either generated by the chrVIII-chrXVI translocation (6 samples) or located on an aneuploid chromosome (6 samples). In all 12 cases, we could indeed observe selection for either the translocation or the aneuploidy, and further confirmed that suppression occurred by the presence of a second, wild-type allele of the query gene (Figures  S2C-D  and S3D ). Second, we sequenced meiotic progeny of six crosses that showed weak "suppression" in our screen (suppression score below 0. 6), unrelated to any known translocations or aneuploidies. Four cases showed selection for newly acquired aneuploidies of either the chromosome carrying the query gene or other loci selected in the crossing protocol.
Suppressor gene identification and validation We next sought to identify the causal suppressor genes. As each of the mapped suppressor regions harbors tens of plausible candidate genes, we computationally prioritised them based on their functional connection to the query gene (Data S6, Methods, (van  Leeuwen et al. 2020) ). We also included known general modifiers, such as MKT1 and HAP1, that each affect the expression of thousands of genes  (Fay 2013; Parts et al. 2014; Albert et al. 2014 Albert et al. , 2018)). To test the phenotypic consequence of the candidates, we replaced their open reading frame and ~100-400 bp surrounding region with the wild version in the reference strain background, and tested for suppression of the corresponding TS allele. In total, we tested 50 suppressor gene candidates from 31 mapped loci of various strength (Data S7, Figure  S5 ), and identified causal genes for 17 loci (55%, Data S7, Figures  4A  and S5 ), validating both our mapping strategy, as well as the computational prioritisation.
Yeast strains, plasmids, and growth Yeast strains were grown using standard rich (YPD) or minimal (SD) media. Methyl methanesulfonate (MMS) and hydroxyurea (HU) were obtained from Sigma-Aldrich. For SGA analysis (see below), we used a collection of temperature sensitive mutants of essential genes (MATα xxx-ts::natMX4 can1∆::STE2pr-SpHIS5 lyp1∆ his3∆1 leu2∆0 ura3∆0 met15∆0; (Costanzo et al. 2016)). Four of these strains appeared to have a different TS mutant allele than originally annotated. Because we could not determine where a potential mistake or mix-up had occurred, we assigned new strain IDs to these strains. TSQ2353 (tre2-5008) was renamed as TSQ2884x (tfg1), TSQ1864 (brr2-5019) as TSQ2885x (fas2), TSQ1877 (iki3-5008) as TSQ2886x (epl1), and TSQ1879 (iki3-5010) as TSQ2887x (epl1). For the allele swaps (see section "suppressor candidate validation") we used strains from either the BY4741 deletion mutant collection (MATa xxxΔ::kanMX4 his3Δ1 leu2Δ0 ura3Δ0 met15Δ0; Euroscarf), or the TS-allele-on-plasmid collection (MATa All other yeast strains used in this study are listed in Data S8.
We digested the plasmid using SwaI, and transformed the wild yeast strains with the linearised plasmid. Transformants were isolated on YPD + NAT, and subsequently replica plated onto YPGal media, to induce overexpression of KAR1, which is lethal and thus selects for recombination between the two LEU2-3' sequences. Recombination was confirmed by testing for growth on YPD + G418 media. Second, to partially delete HIS3, we used plasmid p7411 (Data S8), that contains in the following order: a SwaI restriction site, base pair 137 to 310 of the HIS3 gene, pair 495 to +112 of the HIS3 gene, the AgTEF1 promoter driving LEU2 followed by its endogenous terminator, the URA3 gene under control of its own promoter and terminator, and base pair 495 to +707 bp of HIS3. We digested the plasmid using SwaI, and transformed the wild yeast strains with the linearised plasmid.
Transformants were isolated on SD -Ura -Leu, and subsequently replica plated onto media containing 5fluoroorotic acid (SD + 5-FOA), which is toxic to cells expressing URA3 and will thus select for recombination between the two HIS3-3' sequences. Recombination was confirmed by testing for growth on SD -Leu media. Proper deletion of LEU2 and a part of HIS3 was confirmed by PCR. Strain identity was validated by sequencing the barcodes inserted at the ura3∆ locus  (Cubillos, Louis, and Liti 2009). In total, we obtained 10 wild yeast strains with the genotype MATa ho∆::hphMX6 ura3∆::kanMX4 his3∆1 leu2∆0 (Data S8).
2012). For genotype trees, we calculated the distance between strains as the number of called genetic variants that are present in either strain, but not the other one. For phenotype trees, we calculated the distance between strains as 1 minus the Pearson's correlation of their suppression profiles. To test for consistency of suppression within complexes and pathways, we considered multiple functional annotation datasets. The sources for these datasets were: protein complexes (the Complex Portal  (Meldal et al. 2015) , downloaded June 6, 2018), KEGG pathway annotation  (Kanehisa et al. 2016) , coexpression degree (number of gene partners with a coexpression score > 1;  (Huttenhower et al. 2006) , and subcellular localisation  (Huh et al. 2003). For each annotation that groups multiple genes, we calculated both complex average suppression value across TS alleles and wild strains, as well as average correlation of suppression values across wild strains between all possible allele pairs.
Random sporulation assay A total of 102 wild strain x TS allele combinations were selected for confirmation assays (Data S3). Between 3 to 20 different TS alleles were tested for each of the 10 wild strains, for a total of 78 different TS alleles corresponding to 56 different essential genes. The selected crosses spanned a wide range of suppression scores, and included 7 crosses with a negative suppression value. As controls, we crossed each selected TS allele to a reference S288C strain, and each wild strain was crossed to a wild-type S288C reference strain, giving a total of 78 S288C TS allele controls, and 10 S288C x wild strain controls. All 190 strain pairs were crossed and sporulated. Sporulated cells were plated onto two agar plates that selected for haploid MATa spores that carried the TS allele (SD -His/Arg/Lys +CAN/LYP/NAT).
Colonies were scraped from the agar plates, and genomic DNA was isolated from the pools using the Qiagen DNeasy Blood & Tissue kit. Samples were sequenced using Illumina sequencing. For each bulk segregant sequencing sample, we performed read mapping and variant calling under the Varathon framework (https://github. com/yjx1217/Varathon). Briefly, the raw reads were trimmed by trimmomatic v0. 38  (Bolger, Lohse, and Usadel 2014)  and subsequently mapped to the yeast reference genome (SGD R64-1-1) using bwa v0. 7. 17  (Li and Durbin 2009).
For instance, the set of genes that were co-expressed with the query gene, but not in the same complex or pathway, were further ranked by whether they co-localised (highest rank) or not (lowest rank) with the query. The sources for these datasets were: protein complexes (the Complex Portal  (Meldal et al. 2015) , downloaded June 6, 2018), KEGG pathway annotation  (Kanehisa et al. 2016) , co-expression degree (number of gene partners with a co-expression score > 1;  (Huttenhower et al. 2006) , and subcellular localisation  (Huh et al. 2003 ). We manually added suppressor candidate genes with genetic interactions or other known functional connections to the query gene that were not captured by our computational prediction, and also included known general modifier genes MKT1 and HAP1.
Acknowledgements We thank  M. Costanzo ,  J. Hou ,  S. Soyk ,  G. Tan ,  M. Taschner ,  B. Ünlü , and  A. Vjestica  for critical reading of the manuscript, discussions, reagents, and technical assistance. We also thank  B. Andrews  and  C. Boone  whose lab the work was incepted in, and whose vision shaped the direction of this project. L. P. was supported by  Wellcome  ( 206194 ),  IT Centre of Excellence EXCITE  ( TK148 ), and a  Marie Curie International Outgoing Fellowship  ( 328541 ). J. v. L. was supported by the  Swiss National Science Foundation  ( PCEGP3_181242 ). G. L was supported by the  Foundation for Medical Research  ( EQU202003010413 ). C. P. holds a  Ramon y Cajal fellowship  ( RYC-2017-22959 ). The authors declare that they have no conflicts of interest.
(Parts et al. 2011) remaining 19 samples without ploidy issues, and with strong or moderate suppression scores in the screen, we used Selection QTL Mapper (https://github. com/PMBio/sqtl),whichimplements the approach used for bulk segregant analysis mapping described in(Parts et al. 2011). Briefly, this approach first estimates reference allele frequencies in each sample using a probabilistic model that includes allele frequencies as latent variables, sequencing reads as observations, and the recombination rate parameter to couple frequencies at nearby sites. The posterior allele frequency distributions were then combined across biological replicates according to Bayes rule, and used to identify a broad set of QTL regions that had at least 12% frequency change between permissive and restrictive temperatures, and were at least 1kb long, using parameters "af_lenient=0.
8, sd_lenient=3, af_stringent=0. 12, sd_stringent=5, length_cutoff=1000, peak_cutoff=0. 03". A stricter set with allele frequency change of at least 0. 20 was used for all but reproducibility analyses. Sites within 30kb of the TS allele or a SGA selection marker were not considered as QTL candidates. All whole-genome sequencing data are publicly available at NCBI's Sequence Read Archive (http://www. ncbi. nlm. nih. gov/sra), under accession number PRJNA673501. The resulting read alignment was further processed by samtools v1. 9 (Li et al. 2009), picard tools 2. 18. 25 (https://broadinstitute. github. io/picard/), and GATK3 v3. 6 for sorting, duplicate removal, INDEL realignment and indexing. Variant calling was carried out by freebayes v1. 2. 0 (Garrison and Marth 2012) with the customised options "--ploidy 1 --min-alternate- fraction 0 --genotype-qualities".
Design and study selection This was an individual patient data meta-analysis of published randomized controlled trials (RCTs) selected through a systematic literature review. The study protocol was registered in PROSPERO with the number CRD42017057099  [23]  and published elsewhere. [24]  RCTs were included if they tested the efficacy of biological disease-modifying antirheumatic drugs (bDMARDs) on ≥2-year radiographic outcomes, in patients fulfilling the 1987 ACR or the 2010 ACR-EULAR criteria for RA. [25, 26]  Information on the processes of identifying and selecting studies, as well collecting data are reported in the protocol. [24]  Risk of bias assessment of individual studies Studies selected for retrieval were assessed by two independent reviewers (RF and MN) for methodological validity prior to inclusion in this review, using the "Risk of Bias 2" tool.
Data analysis All "primary" analyses were performed with SAS software (v. 9. 3), within the online secure platforms. For each trial we determined the number of patients with GRO in each definition group (4V-remission, 4V-near-remission, 3V remission and non-remission). The rates of true positive (TP) i. e. remission and GRO, true negative (TN) i. e. non-remission and not-GRO, false negative (FN) i. e. non-remission and GRO, and false positive (FP) i. e. remission and not-GRO cases were also determined for all definitions. The percentage of patients with accurate prediction of having and not having GRO were also determined (sum of TP and TN) for the 4V-and 3V-remission. Missing data was not substituted. Similar analyses were performed for the secondary outcomes.
Likelihood of reaching good radiographic and functional outcomes with 4V-remission compared to 3V-remission If the null hypothesis of this study (the chance of GRO in 4V-near-remission group are similar to the 4V-remission group) is not rejected, the current 4V-remission and the proposed 3Vremission can be compared in terms of their positive (LR+) and negative likelihood ratios (LR-) of GRO per remission group. The TP, TN, FN, and FP values were used to synthesize these measures. Similar procedures were performed regarding GFO. All meta-analyses were performed with the OpenMeta[Analyst] software,  [34]  using the DerSimonian-Laird random-effect method  [35]  and the Arcsine transformed proportion. [36]  The STATA software (v. 14) was used only to determine OR adjusted to covariates (sensitivity analyses).
It should be noted that we did not analyse within trial arms and used the data of clinical trials as in observational studies, therefore discarding the effects of randomization. As patients fulfilled inclusion criteria for RCTs, generalizability of our results is limited to patients with high disease activity starting treatment. In 7 out of the 11 RCTs, joint assessments were performed by independent assessors, and the 4 other studies did not use an independent joint assessor. We do not know whether this may have affected the (interpretation of the) results of our study in any way. Finally, some changes to the published protocol for this study need to be disclosed, namely the use of ΔmTSS≤0. 5 units as the primary outcome instead of the ≤0 cut-off, for the reasons outlined in the methods section.
Funding info This manuscript is based on research using data from data contributors AbbVie, Pfizer & UCB that has been made available through Vivli, Inc. Vivli has not contributed to or approved, and is not in any way responsible for, the contents of this publication. This study was also supported by CSDR (ClinicalStudyDataRequest), which has an agreement with Roche Inc. (Project #1808), who provided the authors with access to the data but did not sponsor or influence this analysis. Data was also obtained from the Yale University Open Data Access Project (YODA Ricardo Ferreira was supported by a grant from ARCo -Associação de Reumatologia de Coimbra, a non-profit association of health professionals. Project.
Acknowledgments We would like to acknowledge the invaluable support provided from  Jos van der Velden  ( SAS Portugal ), who assisted us with the use of SAS software and access to the  SAS Clinical Trial Data Transparency Portal. We also acknowledge the support from  Adam LaMana  ( SAS International ) and from the personal from "data sharing" teams from  Pfizer ,  AbbVie ,  Roche ,  UCB  and YODA. We also would like to acknowledge the support of  Eduardo Santos  (Coimbra, Portugal) in performing the metaanalyses.
COMET -NCT00195494 yes yes CAMEO -NCT00654368 no no The Sponsor was Amgen. Data not available. PRIZE -NCT00913458 no no It was a tapering trial (dose reduction) TEMPO -NCT00393471 yes yes PRESERVE -NCT00565409 yes no It was a tapering trial (dose reduction) ERA -NCT00356590 no no The Sponsor was Amgen. Data not available. ORAL START -NCT01039688 no no  Delays  in the process, which was eventually cancelled. ORAL SCAN -NCT00847613 no no  Delays  in the process, which was eventually cancelled.