dataesr/scientific-paragraphs-categorization huggingface dataset:
Vessel measurements in each study were carried out by trained graders, masked to study subjects' characteristics. Image of the right eye was used for measurements and when this was unavailable or un-gradable (due to disease or when at least 6 measurements of arterioles or venules could not be measured), the left eye image was examined. All arterioles and venules crossing through a specified zone 0. 5-1 disc diameter away from the optic disc margin were measured and summarized as the CRAE or CRVE, respectively. Exposure variables. Study participants' demographic characteristics, lifestyle factors and medical history were obtained using a standardized questionnaire in the 3 Singaporean datasets. Age was defined as the age at the time of clinical examination. Cigarette smoking data was categorized into 2 categories as current smokers and former or non-smokers.
Height in meters (m) and weight in kilograms (kg) were measured in all dataset and used to derive body-mass index (BMI). An average of at least two systolic blood pressures (SBP) and diastolic blood pressures (DBP) measurements was taken and hypertension was defined as a history of hypertension or SBP. 140 mmHg or DBP. 90 mmHg. In SP2, fasting glucose and serum lipid concentrations were analyzed using kits from Boehringer Mannheim Systems (Mannheim, Germany) and read on a BM/Hitachi 747 analyzer (Roche Diagnostics, Corp. Indianapolis, USA). Glycosylated haemoglobin (HbA1c) levels and serum lipids were measured in non-fasting blood from SiMES and SINDI participants, using enzymatic methods implemented in the Advia 2400 Chemistry System (Siemens Medical Solutions Diagnostics, Deerfield, IL, USA).
In the SINDI dataset, non-fasting HbA1c and serum lipids were measured with turbidimetric inhibition immonoassays using the Roche-CobasH CE analyser (Roche Diagnostics, Germany), respectively. Diabetes was defined as participants with a history of diabetes mellitus in all datasets or fasting glucose levels $7. 0 mmol/L in SP2 or HbA1c levels $6. 5% in non-fasting blood samples in SiMES and SINDI.
Statistical Analysis GWAS association analysis with serum C-reactive protein. SNP-based trend tests for serum CRP associations were carried out in a chip-wise manner in the 3 Singaporean datasets. Associations between SNPs and CRP level were analysed in an additive model and adjusted for age, sex and population stratification (the first 2 principal components in SiMES and the first 3 principal components in SINDI only). These analyses were performed using the genome association toolset, SNPTEST (version 1. 1. 5) [32]. For analyses of imputed SNPs, SNP information scores were required to be at least 0. 5. Genomic control (GC) correction was applied to association results from each chip, to further control for possible residual inflations. Global genome-wide data from individual study results were subsequently combined (N = 6,692) using the inverse varianceweighted meta-analysis, assuming only a fixed effects model as the first aim was to identify novel loci.
Cochran's Q with a Q p-value cut-off ,0. 1 was used to determine SNPs with between-study heterogeneity  [37]. All meta-analysis procedures were performed using the meta package in STATA (version 8. 2). In silico replication analysis of previously reported candidate SNPs. All recently discovered index serum CRPassociated variants (35 SNPs from 22 loci) from GWAS studies  [7] [8] [9] [10]  were selected for in silico replication among the 3 Singaporean datasets. 4 index SNPs [rs769449, rs3091244, rs1800961 and rs10521222 at the APOE, CRP, hepatocyte nuclear factor 4 (HNF4A) and sal-like 1 (SALL1) gene loci, respectively] were not genotyped or failed QC procedures or were not captured after imputation procedures in all 3 Singaporean datasets (Table  S3  in File S1). At gene loci where multiple index SNPs were identified [APOE, CRP, glucokinase regulator (GCKR), HNF1A, interleukin 6 receptor (IL6R) and LEPR] pair-wise linkage disequilibrium (LD) was calculated using HapMap CHB and GIH panels.
When LD between SNPs were high (r 2. 0. 8 in both HapMap CHB and GIH panels), 1 tagging SNP was selected for in silico replication, prioritised if the SNP was actually genotyped in the Singapore datasets as compared to imputed SNPs (Table  S3  in File S1). To determine significance, consistency of effect direction with previously reported GWAS data was ensured in the Singaporean datasets and as these loci have been well-validated, a p-value threshold ,0. 05 was used. A Binomial test was used to assess overrepresentation of significant associations compared to the expected at a = 0. 05. For all replicating index SNPs, we repeated the metaanalysis procedure using the more conservative random effects model to more accurately estimate effect sizes from the different ethnic groups (East-Asian Chinese and Malays and South-Asian Indian datasets) that had been combined.
Association analysis with retinal vessel traits. Association between serum CRP and retinal vessel traits (CRVE and CRAE) were first assessed in the Singaporean datasets. Next, one CRP variant that was genotyped in all Singaporean datasets and had the strongest association with serum CRP levels in our study was selected for assessment with retinal vessel traits (CRVE and CRAE). Regression analyses of CRVE and CRAE in SP2, SiMES and SINDI were further adjusted for age, sex and population stratification (the first two principal components in SiMES and the first three principal components in SINDI only), using STATA (version 8. 2). Further validation of any significant association was sought from meta-analysis data of European populations that had recently completed a GWAS for retinal vessel traits  [27].
We subsequently calculated predicted magnitude of effect, between the CRP variant and CRVE in the 3 Singaporean datasets. Predicted increase of CRVE per CRP risk allele was obtained by multiplying the observed beta of serum CRP (per CRP risk allele) with the observed beta of CRVE per SD (standard deviation) increase in serum CRP. Predicted and observed effect estimates were subsequently compared for the increase per risk allele. Power to detect the mean predicted increase in CRVE (from SP2, SiMES and SINDI datasets) among datasets was calculated using QUANTO  [41].
CRP, APOE, HNF1A and LEPR Variants were Associated with Serum C-reactive Protein in Singaporean Asian Datasets at Genome-wide Levels of Significance Individual and meta-analysis GWAS results of SiMES, SP2 and SINDI Asian cohorts revealed serum CRP-associated SNPs in or near CRP, APOE, HNF1A and LEPR with genome-wide levels of significance (meta-analysis p-value between 4. 74610 28 and 1. 90610 221 , Table  S4  in File S1). SNPs at these four loci included the same index SNPs reported in recent GWAS studies  [7] [8] [9] [10] , or were in at least moderate LD [r 2. 0. 8 in Hapmap East-Asian (CHB and JPT) and Indian (GIH) panels] with index SNPs (rs11265260 and rs2794520 from CRP, rs2075650 and rs4420638 from APOE, rs7310409, rs1183910 and rs1169310 from HNF1A and rs1892534 from LEPR), corroborating the initial findings at these loci.
Variants at 4 Additional Gene Loci were Associated with Serum C-reactive Protein and were Directionally Consistent in the in silico Assessment of Singaporean Asian Datasets All recently identified serum CRP-associated variants  [7] [8] [9] [10]  that were combined in a fixed effect meta-analysis are indicated in Table  2  (see Table  S5  in File S1 for full table of all index SNPs in individual datasets and after meta-analysis). In addition to the CRP, APOE, HNF1A and LEPR loci, we detected significant associations with serum CRP level for index SNPs at 6 additional loci [IL6R, G protein-coupled receptor, family C, group 6, member A (GPRC6A), GCKR, interleukin 6 (IL6), interleukin 1 family, member 10 (IL1F10) and protein tyrosine phosphatase, non-receptor type 2 (PTPN2)] at more nominal levels of significance (meta-analyses p-values between 5.
09610 25 and 0. 036) (Table  2 ). Forest plots of all significant loci detected among the Singaporean datasets are provided in Figure  S2  in File S1. At 2 variants, rs6901250 at GPRC6A and rs2847281 at PTPN2 (Table  2 ), direction of effects were observed to be opposite in our Singaporean Asian datasets (as compared to the initial European GWAS  [7] ). varLD scores of pair-wise comparisons between different ethnic groups at these two gene regions did not indicate LD differences that were greater than the entire genome (all regional scores,than 0. 5 SD and thus, below the 67 th percentile of LD variations across the entire genome, Figure  S3  in File S1). We also did not detect any contiguous regions with at least moderate LD (r correlation. 0. 6) with these 2 index SNPs that had opposite directions of effect when comparing European (CEU) and Chinese (CHB) or Indian (GIH) reference panels from HapMap, at the GPRC6A and PTPN2 regions (data not shown).
Thus in total, we were able to replicate associations at 12 SNPs from 8 (CRP, APOE, HNF1A, LEPR, IL6R, GCKR, IL6 and IL1F10) of the 22 known previously-reported CRP-associated gene loci with consistent beta directions, among the East-Asian and South-Asian study subjects participants from Singapore (Binomial-test pvalue ,0. 001 for over-representation of significant results, a = 0. 05). Assuming independence, these 12 SNPs explain approximately 4. 05% of serum CRP variance in the Singaporean populations studied (Table  3 ). QQ-plot of meta-analysis p-value Table  2. Association results of index CRP loci  (7) (8) (9) (10)   22 SNPs were genotyped or imputed and passed QC procedures in all 3 datasets and were combined in a meta-analysis (see Table  S5  in File S1 for full results). Significant results (p-value ,0.
69610 224 and 0. 013, Table  3 ). 2 additional index variants, rs10778213 and rs4903031, showed heterogeneity (Q pvalue ,0. 1) in the meta-analysis of all 3 Sinagporean datasets (Table  2 ). For each of these variants, the direction of effect in the SiMES Malay subjects was opposite to that of the Chinese (SP2) and Asian-Indian (SINDI) ethnic groups.
C-reactive Protein Increasing Variant (rs2794520) is not Associated with Retinal Microvascular Caliber We selected one index CRP SNP, rs2794520, that had been directly genotyped in all Singaporean datasets and had the strongest association with serum CRP levels in our meta-analysis (rs2794520, p-value = 8. 88610 216 in both fixed and random-effect models, Tables  2  and 3 ), for assessment with CRVE and CRAE traits. In all the 3 Singaporean Asian datasets, serum CRP was associated with the CRVE phenotype (p-value #6. 56610 24 , Figure  1 ) and the CRP increasing allele (G allele) of rs2794520 was marginally significant for increased CRVE (p-value between 0. 048 and 0. 022) but, this association was not replicated in the larger CHARGE consortium European samples and overall metaanalysis revealed no statistical significance (Table  4 ).
No significant associations were detected for CRAE in any of the datasets (Table  4 ). Power to detect changes of 0. 45 mm in CRVE (mean predicted effect estimates in SP2, SiMES and SINDI datasets, Figure  1 ), assuming a minor allele frequency (MAF) of 40% (mean MAF of rs2794520 in SP2, SiMES and SINDI) (a = 0. 05) was estimated to be low in individual Singaporean datasets and only adequate after meta-analysis of all Singaporean and European datasets (Table  4 ). Observed effect sizes for CRVE per increase in risk allele of rs2794520 were also 3-4 folds larger than expected among the Singaporean datasets (Figure  1 ).
05) in the Singaporean datasets and showed consistent direction of effect estimates. association of CRP variants with CKD  [16]  highlighted the need to assess the role of CRP in microvascular disease, which has not been tested in previous studies. In our study, although serum CRP was associated with CRVE levels, we do not find evidence to support a causal link between serum CRP and changes in retinal vascular caliber. It is noteworthy however, that marginally significant associations were detected between the CRP variant and CRVE in only the Singaporean Asian datasets (SP2 Chinese, SiMES Malays and SINDI Asian-Indians) but not among the European samples assessed. Although this may highlight an ethnic specific role, we were inadequately powered to detect such associations in the smaller Singaporean datasets and moreover, the association levels in the Singaporean datasets were nominal and observed CRVE effect sizes exceeded predicted estimates in all 3 Singaporean datasets, indicating that these associations may be stochastic.
Furthermore, to prevent possible heterogeneity of assessing multiple SNPs, we limited this analysis to one index SNP (rs2794520 from the CRP gene locus), used as a proxy for serum CRP. However, although highly significant, this SNP only explained approximately 0. 45% of the CRP phenotypic variance among the Singaporean datasets. Repeating the analysis using all 12 CRP-associated SNPs that replicated in the Singaporean meta-analysis (Table  2 ), with a gene-score method, did not improve the association between genetic variants and CRVE (data not shown). A weakness of our study was the modest sample size for replication of serum CRP associations in East-Asian and South-Asian populations and it is likely that we lacked sufficient power to detect all true-positives, especially those with weaker effect sizes that were first detected only after large-scale meta-analyses (in European GWAS studies).
Using an effect size of 1. 35 mg/L increase in serum CRP (approximate effect size the GPCR6A and PTPN2 variants in a recent large-scale European GWAS  [7] ), we estimated that a sample size of approximately 60,000 participants would be necessary to be at least 80% powered (a = 0. 05) to detect variants of similar magnitude in our Singaporean Chinese, Malay and Asian-Indian datasets. Furthermore, although we increased power to detect associations by combining datasets in our metaanalyses, the diverse nature of the populations assessed may have contributed to between-study heterogeneity. However, it was reassuring that the same set of serum CRP-associated index SNPs remained statistically significant in the meta-analyses of datasets under the random effects model. In conclusion, our meta-analysis of East-Asian and South-Asian subjects, who constitute the major portion of populations in Asia, wherein the burden of CVD is substantially increasing  [43] [44] , replicates and corroborates at least 8 serum CRP-associated gene loci.
However, we do not find evidence to support a causal link between serum CRP and markers for microvascular caliber. The test allele for rs2794520 was the G allele. Estimated power: based on average predicted effect estimate (0. 45 mm CRVE) from SiMES, SP2 and SINDI datasets (see Figure  1 ) and a minor allele frequency of 0. 40, at a = 0. 05. doi:10. 1371/journal. pone. 0067650. t004.
in Singaporean datasets. Fixed-effect meta-analysis SP2 (N = 2,179) SiMES (N = 2,275) SINDI (N = 2,238) (N = 6,692) Test rsid Gene Chr Position allele TAF* Beta p-value TAF Beta p-value TAF Beta p-value Beta p-value Q p-value rs2075650 APOE 19 50087459 G 0. 089 0. 107 1. 98610 25 0. 122 0. 127 1. 02610 210 0. 125 0. 110 7. 07610 29 0. 116 1. 90610 221 0. 408 rs11265260 CRP 1 157966663 G 0. 167 20. 122 1. 18610 210 0. 113 20. 106 4. 13610 27 0. 106 20. 071 6. 66610 24 20. 101 1. 13610 217 0. 208 rs2794520 CRP 1 157945440 G 0. 435 0. 072 3. 76610 27 0. 491 0. 065 4. 50610 27 0. 653 0. 051 1. 64610 24 0. 063 8. 88610 216 0. 677 rs4420638 APOE 19 50114787 A 0. 103 0. 081 5. 42610 24 0. 159 0. 126 1. 54610 29 0. 101 0. 108 4. 14610 25 0. 107 1. 55610 215 0. 108 rs1183910 HNF1A 12 119905190 G 0.
Clinical data Clinical characteristics were recorded in each patient group. In the RA group, disease activity and functional impairment were evaluated using the Disease Activity Score (DAS28) and the Health Assessment Questionnaire (HAQ), respectively. For patients with AS, clinical activity was evaluated using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and the Bath Ankylosing Spondylitis Functional Index (BASFI). Weight and body mass index (BMI) were evaluated. According to their BMI, patients were defined as underweight (BMI <18. 5 kg/m 2 ), normal weight (18. 5-24. 9 kg/m 2 ), overweight (25-29. 9 kg/m 2 ), or obese (≥30 kg/m 2 ).
HC or AS vs. HC) using the Mann-Whitney test. Qualitative data (gender) were analyzed using the Chi-square test. Spearman's r was used to calculate correlations between markers of disease activity (ESR, CRP, IL-6, and DAS28 or BASDAI) and serum adipokine and ghrelin levels or body composition measurements. A p value < 0. 05 was considered statistically significant for all tests. All analyses were performed using SigmaStat software (SSS Inc. , Chicago, IL, USA).
ACKNOWLEDGMENTS We would like to thank the following financial supports: the  Association Franc Comtoise pour la recherche et l'Enseignement en Rhumatologie , the  Agence Nationale de la Recherche  ( Labex Lip-STIC , ANR-11-LABX-0021 ), and the  Conseil Régional de Franche-Comté  ("soutien au LabEX LipSTiC" 2012, for Lunar CoreScan software). The authors are indebted to  Mrs. Fiona Ecarnot , MSc. , EA3920,  Department of Cardiology, University Hospital Besançon, France  for her help in preparing the manuscript.
Patients All consecutive patients admitted during a three-year period (June 2008 to June 2011) and who received NIV as initial ventilatory support for AHRF were included. AHRF was defined as recent dyspnea with a respiratory rate >25 breaths/minute and/or sternocleidomastoid muscle activation with pulmonary infiltrates on chest X-ray, and a PaCO 2 below or equal to 45 mmHg. We excluded patients who were intubated before ICU admission or intubated upon ICU admission without prior NIV, and patients for whom NIV was used with a "do not intubate" order. However, the outcome for those who were directly intubated for acute respiratory failure without prior NIV, and who met clinical criteria for moderate or severe ARDS was also collected. The study was conducted after the implementation of a nurse-driven NIV protocol which included prospective daily collection of clinical data and ventilatory parameters on a specific NIV monitoring form.
Data collection From the NIV monitoring forms, we analyzed the number and duration of NIV sessions, ventilator settings (pressure support level, positive end-expiratory pressure, FiO 2 ), ventilatory parameters (SpO 2 , respiratory rate, expiratory tidal volume), hemodynamic parameters (heart rate, blood pressure and level of consciousness assessed using the Richmond Agitation-Sedation Scale (RASS)  [20] , with altered consciousness defined as a RASS <0. NIV tolerance and number of leaks were recorded on a 4-point scale, then dichotomized into "acceptable" (scored 2 to 3) or "poor" (scored 0 to 1) tolerance, and "minor" (scored 0 to 1) or "major" (scored 2 to 3) leaks, respectively. Blood gases were routinely measured one hour after initiation of NIV. Clinical data (respiratory rate, SpO 2 , blood pressure, heart rate, Glasgow coma score) and blood gases at admission before NIV initiation were retrospectively collected from the medical chart.
10 were entered in a Cox proportional hazards model with time to intubation as the dependent variable, censoring data at ICU discharge. Among related variables, the most significant or clinically relevant was entered into the model in order to minimize the effect of colinearity. Because it was measured at 24 hours after admission, the general severity score SAPS 2 was not included in this analysis. Variables included in the model are reported with their corresponding hazard ratio (HR) and 95% CI. We considered two-tailed P-values <0. 05 as significant. Statistical analyses were performed using the statistical software package STATA version 10. 1 (Stata Corp. , TX, USA).
Data Three different datasets were used in our study: 1) An IKONOS standard geometrically corrected image with four multispectral bands (Blue, Green, Red and Near-Infrared) acquired on Aug 18, 2005 with 11% Cloud Cover. The IKONOS System is a commercial satellite from GeoEye and offers multispectral imagery at a spatial resolution of 4 meters and panchromatic imagery at 1 meter, a short revisit time (3-5 days off-nadir and 144 days for true-nadir) and a swath of 11 km x 11 km for each single scene; 2) The Digital Terrain Model (DTM) of S. Miguel Island with a spatial resolution of 10 meters, produced by the Military Geographic Institute of Portugal (IGEOE), used for the ortho-rectification of the IKONOS image; 3) A GIS dataset of 525 points collected on the study area, by using a sub-metric GPS device.
The land-cover characterization associated to each one these points was double checked by performing a photo-interpretation of the available IKONOS image (August 2005) and also by overlaying each point to the SPEA/LIFE Priolo Project's vegetation monitoring program survey (A9 Action), continuously developed between 2004 and 2008  [Teodosio et al. , 2009]. Four representative land cover and vegetation classes are identified in this dataset: (1) CC -Cryptomeria japonica (L. fil. ) D. Don ("Japanese cedar") man-planted production forest stands; (2) DD -Bare Soil and Landslide Areas; (3) LL -Native scrubland patches; (4) NN -Pittosporum Woodland (Pittosporum undulatum's pure or largely dominated patches).
Thus, yielding high accuracy with small training sets may be expected, which could be very advantageous, given the costs of training data acquisition in remote sensing. A constraint on their application in remote sensing is their binary nature, requiring multiclass classifications to be based upon a large number of binary analyses  [Brown et al. , 2000; Foody and Mathur, 2004]. Another innovative methodological aspect of our approach is the previous depuration of the dataset of sites providing information for training and validation. To simulate the interactive outlining of training and validation fields, we selected the segments that included photointerpreted sites. In accordance with the segmentation-based classification concept, training and validation segments were preferred to the original single point sites.
The third phase consisted of the field data depuration process, which started with the selection of most suitable data: segments that included photo-interpreted sites were selected. The second step was the calculation of segmentation-based and pixel-based image statistics to create a dataset of inspected test segments. Then, the whole dataset was displayed in reduced space in order to allow an interactive selection of outliers. Field data was tested for within-class consistency in terms of its average spectral properties according to the IKONOS image. After calculating the average Digital Number (DN) values in each spectral band, a Principal Component Analysis (PCA) was performed and the projected values were displayed in bi-plots of Principal Component (PC1, PC2 and PC3). PC1, PC2 and PC3 accounted for 99.
4% of the total variance. The PC plots were used to interactively identify all the polygons which PC values were actually overlapping a different class considering all 3 PCs. Afterwards, an inspection and diagnostic of outlier segments was performed by using the image and aerial photography. An action of "confirmation", "correction" and/or "elimination" of outliers was developed. The selected outliers were geographically located within a GIS environment and the reason for their peculiar values was identified. All remaining cloudy and shadowed segments were removed from the training dataset. Those who had been assigned to a wrong class were corrected. These actions were performed repeatedly until no more outliers were detected. From this point, the statistics of the remaining training and validation segments were arranged in two separated datasets, differing for their diagnostic variable: Dataset A with 4 classes (CC -Cryptomeria japonica; DD -Bare Soil and Landslide Areas; LL -Native scrubland; NN -Pittosporum woodland); and Dataset B with 5 classes (CC; DD; LL; NN and NN2 -resulting from the splitting of NN in two classes).