dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We compared LAI as simulated by 11 Earth-System Models against satellite data and uncoupled models  [73] , for the Northern Hemisphere during the 1986-2005 period. We compared the mean annual LAI, the spatial pattern of LAI and the onset, dormancy and length of the growing season. Our results show that models consistently overestimate the mean value of LAI, although considering the error of the reference data estimated by comparing the satellite data with ground measurements the model-data misfit is significantly reduced. In addition models have an increased growing season, mostly due to a later dormancy, compared to the satellite data. This is consistent with the finding on the uncoupled models. We conclude that validating LAI in each model against satellite observations should be a fundamental step for all modelling groups.
Acknowledgments We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the  US Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank  Ranga Myneni  for his valuable contribution and comments on the development of the paper,  Xuhui Wang  for his help on the methodology, and the four anonymous reviewers who helped to improve the paper. This work was funded by the  European Commission 's  7th Framework Programme  under Grant Agreements number  238366  ( GREENCYCLESII  project) and  282672  ( EMBRACE  project).
spectrophotometry, immunoturbidimetry) and heterogeneous IC assays in high-workload laboratories with a throughput of 3 to 15 million tests per year. One cobas 8000 configuration consists of up to four analytical modules and is built with a fast rack transport unit, an optional ISE unit (cobas ISE module), two high-throughput CC modules (cobas c 702 and cobas c 701 module), a midvolume throughput CC module (cobas c 502 module), and the IC module (cobas e 602 module). Combinations of those modules offer more than 38 configurations with many choices to tailor solutions to individual laboratory needs. The reagent cassette concept is optimized for high-workload laboratories, using concentrated solutions in compact containers. The high volume of produced data is handled by the integrated Data Manager software.
6 s, resulting in an effective cycle time of 1. 8 s. This is combined with parallel pipetting of four reagent probes, two from each of the cooled reagent storage compartments referred to as "reagent disks" with a capacity for 35 reagent cassettes each. Different from the cobas c 701 module, the cobas c 702 module allows continuous reagent cassette loading during operation via a reagent manager with a capacity of 10 reagent cassettes, integrated on top of the MSB. All processes within the analytical system are managed by the control unit software, whereas data and workflow management is handled by the integrated Data Manager software. In addition, the Data Manager provides an interface between the instrument, the Lab Information System (LIS), and the Roche TeleService-Net enabling access to, and routing of, remote information and functionality to and from Roche.
The evaluated cobas 8000 configurations, comparison instruments, and distribution of tests over the participating sites are listed in Table  2. Calibrator and control materials as well as cobas 8000 reagents and auxiliary materials were provided by the manufacturer. No adjustments or method adaptations of the regular routine assays were made for this study. All materials were used according to the recommendations of the manufacturer. Depending on the experiment, either control materials or human specimens (serum or plasma, urine, and EDTA blood) were used. The study was supported by the software program WinCAEv (Windows-based computer-aided evaluation). 6 All experiments were defined using this program, sample and test requests were generated, and data were transferred online from the analyzers to WinCAEv, allowing traceable, reliable, convenient, and fast data validation by the evaluators and by the Roche staff.
Evaluation Protocol The cobas 8000 combines well-established analytical modules from Roche MODULAR ANALYTICS (ISE module; E170) and cobas 6000 platforms (cobas c 502, analytical unit identical to cobas c 501; cobas e 602, analytical unit identical to cobas e 601) with the new analyzers cobas c 701 and cobas c 702. The study performed over five phases was designed to integrate experiments describing important analytical features of the new module in the first phase, whereas the performance of all modules was covered in the following phases, as shown in Table  3. The acceptance criteria for the analytical performance data are listed in Table  4.
System ). Note that each cobas c 701 module reagent disk is coupled with one sample and two reagent probes, thus generating a disk-specific calibration per test allocation. This experiment was performed during study phase I only with the 11 so-called "core assays" (assays processed per reagent disk on cobas c 701 at all sites in phases I to IIII), AST, Ca, CHOL, CREAJ, CRP, GGT, GLUC, UREA, on cobas c 701 and ISE (Na, K, Cl). Sample materials were two serum pools for CRP and pools of the normal and pathological controls for the general chemistry analytes, 21 replicates per run, 3 d. Additional repeatability data were obtained by all sites for all assays allocated to one reagent disk during the reference run of  the routine simulation precision experiments (see the "Reproducibility in a Simulated Routine Run" section).
Functionality Testing under Routine-like Conditions The following experiments are designed to test the overall system functionality under simulated routine conditions 9 using the respective laboratory's request patterns and, in most parts, routine sample leftovers. Method Comparison Download. Routine workloads were replicated and reprocessed in part or total on cobas 8000 using WinCAEv to capture the requests from the routine analyzers via a download file from the LIS. As a rule, 200 to 1200 primary tubes were processed in one run depending on the schedule and sample retrieval process at the individual sites. Data were evaluated using the Passing-Bablok regression analysis  10  and checked for large deviations. In this article, we focus on comparisons of cobas c 701 modules with the same methods used on the routine systems by the various study sites.
Practicability Using a questionnaire with more than 200 questions,  11  the following five main groups of attributes were rated by 11 sites: Installation (installation environment requirements, spatial arrangement, operation and training), Daily Workflow (start up/shut down, sample processing, reagent handling, workflow and timing, data processing), Quality Assurance (monitoring of the various analyzer processes, calibration and QC characteristics, tracing results to reagent/calibrator/controls), and Maintenance, Troubleshooting, and Versatility. The assessment of each attribute was done according to a scale ranging from 1 to 10, where a score of 1 means useless or poor, a score of 10 excellent, and a score of 5 acceptable or comparable with the present laboratory situation. The scores were combined with a weight factor 1 to 3 (low to high importance).
Analytical Performance Precision: Repeatability, Intermediate, within Lab, and between Lab. At the study sites 1 to 10, a total of 3181 repeatability data were obtained, corresponding to 66 801 results, many of them produced during the reference runs of routine simulation precision experiments. The median CVs were 0. 4% for the ISE tests; between 0. 7% and 0. 8% for enzymes, substrates, and electrolytes; between 1% and 2. 4% for protein and urine tests; and 1. 1% for the heterogeneous immunoassays (Table  6 ). In Figure  2a , b, CVs from the 11 core analytes are presented, comparing median Repeatability CVs of data measured on single disks versus Intermediate CVs over multiple reagent disks. For the low control, Intermediate CVs from two ISE units or two modules (four disks) range from 0. 5% (K, Na) over 0.
Sample Carryover. Sample related carryover data are listed in Table  7. No effects were seen from serum to urinary albumin, from urinary to serum creatinine, and from high to low concentrated CK. An increase of serum potassium results exceeding the acceptance limits was observed after processing urine samples on one of the two installed ISE units, at two of the four sites. Neither the sample itself nor the sample probe was contaminated. The effect was caused by splashing during the sample-aliquot dilution process in the respective ISE units mixing vessel. This phenomenon was corrected in the meantime by sample dispensation optimization with appropriate hardware adjustments and a software update (see postmodification results in Table  7 ). Accuracy QC data. Roche quality controls were measured for each assay before every experiment during the whole study period.
More than 81 000 QC results were generated during the first three studies over time periods between 3 and 4 mo each. The precision of these measurements is reported in the "Precision: Repeatability, Intermediate, within Lab, and between Lab" section. Manufacturer target values were recovered within the declared limits. Interlaboratory survey with reference materials. In addition to Roche control material, six reference materials were measured in this study. Data from 13 of the 14 assays tested are shown in Figure  4. Although uric acid was measured in SRM 909b I and II, the results are not included in this article. In a certificate revision for SRM 909B issued in March 2010, the uric acid values are noted only as guidance and are no longer declared. Median recoveries were as expected for all analytes in SRM materials.
Because the 26 high-volume assays were mirrored on both installed cobas c 701 modules, it was possible to complete the workload (9019 requests for 50 methods and 1064 samples) with one module masked in ~4 h. STAT samples were integrated within typical routine workloads at three sites (labs 1, 7, and 12). The time to results are shown in Table  11. During the CC-only workloads processed on two different hardware configurations, the median SPTs for STATs were 13 min and 20 min during combined CC and IC workloads. Method Comparison Download. Key statistical data from all comparisons whenever >50 samples were tested by the study sites are listed in Table  12. Although all sites used different reagent and calibrator lots on their routine instrumentation, we pooled the data from all validated comparisons whenever the same comparison methods were used.
During the group I experiments (see Table  3 ), unstable communication between the Data Manager and the instrument control unit that led to run interruptions was resolved by a software update tested during the third experiment at each site. Similarly, some provoked sample-related data flags were not transmitted correctly to WinCAEv, and this error was corrected with a software update. In lab 2, the root cause of one deviant IgM result was identified as interference due to Teflon debris on the cell walls from a misaligned laundry unit. In lab 9, the source of an erroneous cup sensor alarm indicating that a sample carrier was invalid or missing was finally identified as signal interference caused by friction between the sensor wiring and the rack transport belt. Both issues, not encountered at any other sites, were resolved by adjustment to specification by RD service.
Practicability A questionnaire was used for practicability assessment of the new system compared with that used in the routine labs at the first 11 sites. The outcome (Fig. 9 ) shows that cobas 8000 was rated to meet or exceed the evaluators' expectations in 97% of the total ~2550 completed questions. There was no trend observed for the poorer ratings, but rather individual perceptions on single attributes. Of the few shortcomings reported by more than one site in phases 1 and 2, some were already appropriately modified by software updates before phase 3, such as, for example, a modification that reduced the operator hands-on time for monthly water bath cleaning or implementation of additional QC management features in the Data Manager. The routine instrumentation over about 2 y. A close agreement of results was obtained in the vast majority of assays tested, which is quite remarkable keeping in mind that the samples tested were taken from the daily routine without any preselection and that analysis on cobas 8000 was usually done 1 d later than in the routine.
During the workflow studies, 8 configurations of the possible 38 combinations were used, covering all available analytical modules and ~100 different applications for serum/ plasma, urine, or EDTA blood. To find the optimal solution that meets the individual needs of a routine laboratory in a real-life setting, the cobas 8000 simulator is applied. This not only helps to identify the best fit module combinations but also supports customization within the module configurations to achieve optimal efficiency within the platform, as demonstrated by the results from laboratory 11. Equally as important as the fast sample processing with consequent production of a high number of results within a short time is the ability of a system to efficiently manage the large volume of data. The cobas 8000 Data Manager has a complete package with data validation functionalities including rerun and reflex testing and offers various datafiltering and -sorting functions that support efficient data handling.
Further features that free up staff for more valued responsibilities in the laboratory are the automated maintenance functions that allow the user-definable combination of instrument maintenance task to be executed automatically at defined times or upon manual initiation. With the Data Manager, system monitoring becomes more convenient and at the same time offering more transparency, such as traceability information, sample status, and workflow statistics. The data manager screen is basically a command and control center providing real-time status information from all system components, cobas 8000 modular analyzer instrument, Data Manager, and e-services. Both the Data Manager and control unit software were rated as being well structured, easy to use, and fast.
Acknowledgements. Amongst the contributors to the  Planeterrella  project, JL especially thanks  Philippe Jeanjacquot , System 2. 0 as well as  Olivier Brissaud ,  Philippe Chauvin ,  Karine Briand ,  Herve Lamy ,  Laurent Lamy ,  Baptiste Cecconi ,  Jean-Francois Donati. Several PhD students were and still are a wonderful support, including  Gael Cessateur ,  Helene Menager ,  David Bernard  and of course the enthusiastic, imaginative and always supportive  Guillaume Gronoff. The  Alpes Delegation CNRS , the  UniversiteJoseph Fourier , the  IPAG laboratory  and the PNST administrations are also thanked for their constant support. The Planeterrella has been built at the SERAS / CNRS workshop. The  COST ES 0803 community  has also supported this project enthusiastically. More than 40 students have already worked on the project for their studies.
Genome-wide association study Clinical and GWAS data were compiled for 2,706 individuals of European ancestry from 13 collections as part of an international collaboration. Table  1  shows sample sizes, phenotypes and clinical variables for the four collections that were the units of analysis (additional details are shown in Table  S1 ). Disease activity score based on a 28-joint count (DAS28) were collected at baseline and at one time point after anti-TNF therapy administration (mean 3. 7 months, range 3-12 months). We defined our primary phenotype as a change in DAS28 (DDAS) from baseline (so that greater DDAS corresponded with better response to therapy; overall mean and standard deviation of 2. 161. 3), adjusted for baseline DAS. A secondary phenotype was used based on European League Against Rheumatism (EULAR) response criteria.
Accordingly, we used only baseline DAS as a clinical covariate, as this allowed us to maximize sample size given clinical variable missing data in some cohorts. We performed quality control (QC) filtering and data processing of GWAS data for each of eleven genotyping batches. Genotyping array platforms are described in the Methods. HapMap2 imputation allowed us to test for association at. 2 M SNPs with imputation quality scores. 0. 5. Genotype data were merged across several genotype batches to create four collections for genome-wide association testing. We performed linear regression association tests using baseline DAS and three principal components as covariates, and performed inverse-variance weighted meta-analysis to combine results across the four collections. Quantile-quantile plots with genomic control l GC values are shown in Figure  S1.
Together, our data support a model in which genomic factors related to CD84 expression serve as a predictor of disease activity and response to etanercept therapy among RA patients of European ancestry, but not anti-TNF therapies that act through different biological mechanisms or potentially in RA patients of other genetic ancestries. showed strong evidence of heterogeneity across collections (Cochran's Q P,0. 001). We first analyzed all samples together (n = 2,706), regardless of drug type. We found no clear evidence of association with treatment response measured by DDAS (Figure  1A ). Similar results were obtained using the binary phenotype of EULAR responder versus EULAR non-responder status (Figures  S1  and S2 ). We next separately analyzed patients treated with either etanercept (n = 733), infliximab (n = 894) or adalimumab (n = 1,071) (Figure  1B-1D ), under the hypothesis that different genetic loci affect response to the different drugs based on their mechanism of action or other biochemical properties.
The top SNP rs6427528 was genotyped in the ReAct dataset (Illumina Omni Express genotyping chip), and was well imputed across all other datasets (imputation quality score INFO $0. 94, which is an estimate of genotype accuracy; the range of INFO scores is 0-1, where 1 indicates high confidence). All of these SNPs had minor allele frequencies ranging from 7-10%. The SNP explains 2. 6% variance in response to etanercept treatment. For patients treated with infliximab, we observed a suggestive result on chromosome 10p14 (rs12570744, P = 2610 27 ). No highly significant or suggestive results were observed for the DDAS phenotype in patients treated with adalimumab (P META. 10 25 ). Qualitatively similar results were attained in the analysis of our secondary phenotype, EULAR good responder vs non-responder status (Figures  S1  and S2 ).
eQTL and sequence analysis of the CD84 gene For each SNP with P,10 26 identified by our GWAS (n = 6 independent SNPs), we searched for biological evidence to support a true positive association. We used genome-wide sequence data from the 1000 Genomes Project to search for putative functional variants in LD with the index SNP (defined as SNPs predicted to change protein-sequence or mRNA splicing). We also used genome-wide expression data to search for an expression quantitative trait locus (eQTL) in public databases and in peripheral blood mononuclear cells (PBMCs) in 228 non-RA patients and in 132 RA patients. While we did not identify any variants disrupting protein-coding sequences or mRNA splicing, we did find that the 1q23 SNP associated with response to etanercept therapy was a strong eQTL in PBMCs (Figure  2A  and Figure  3 ).
In an analysis of 679 SNPs for cis-regulated expression of five genes in the region of LD (SLAMF6, CD84, SLAMF1, CD48, and SLAMF7), we found that rs6427528-CD84 (and SNPs in LD with it) was the top eQTL of all results (n = 228 subjects; Figure  2A ). This SNP was specifically associated with CD84 expression, and was not an eQTL for other genes in the region (P. 0. 36 for the other genes). We replicated our eQTL finding in 132 RA patients with both GWAS data and genome-wide expression data. PBMC expression data were available from RA patients in the Brigham RA Sequential Study (BRASS) and Autoimmune Biomarkers Collaborative Network (ABCoN) collections. We observed a significant association between rs6427528 genotype and CD84 expression (linear regression adjusted for cohort P = 0. 004, rank correlation P = 0.
Expression of CD84 as a biomarker of disease activity and treatment response Because the genetic data demonstrates that the allele associated with better response is associated with higher CD84 expression, this suggests that CD84 expression itself may serve as a useful biomarker of disease activity or treatment response. We tested both hypotheses using PBMC expression data from the BRASS and ABCoN collections. First, we tested if CD84 expression is associated with cross-sectional DAS, adjusting for age, gender and cohort (Figure  4 ). We observed a significant inverse association between CD84 expression and cross-sectional DAS in 210 RA patients (beta = 20. 3, P = 0. 02, r 2 = 0. 02). That is, higher CD84 expression was associated with lower DAS, regardless of treatment. Second, we tested CD84 for association with our primary treatment response phenotype, DDAS.
Replication of genetic data in a small, multi-ethnic cohort Since most of the samples available to us as part of our international collaboration were included in our GWAS, few additional samples were available for replication. In addition, the remaining samples available to us were from different ethnic backgrounds. Nonetheless, we sought to replicate the associations of rs6427528 with DDAS in these additional samples. We genotyped 139 etanercept-treated patients from a rheumatoid arthritis registry in Portugal (Reuma. pt) and 151 etanercepttreated patients from two Japanese collections (IORRA, n = 88 patients on etanercept and Kyoto University, n = 63 on etanercept). Replication sample sizes, clinical data and results for these   S4. Based on the observed effect size in the GWAS and observed allele frequency in the replication samples, we had 32% power to replicate this finding in the Portuguese samples and 17% power to replicate this finding in the Asian samples at P,0.
05. The same association analysis as for GWAS was carried out: linear regression assuming an additive genetic model and using DDAS as phenotype, adjusted for baseline DAS. Replication results are shown in Figure  5. While the SNPs fail to replicate in these patient collections at P,0. 05, the direction of effect is the same in the Portuguese and Kyoto replication samples as in our GWAS. In a combined analysis limited to subjects of European-ancestry (GWAS data and Portuguese replication samples), rs6427528 remained highly suggestive (P = 2610 26 ). Including the Japanese subjects, the overall GWAS+replication combined meta-analysis P-value remained suggestive (P = 5610 24 ).
Importantly, we note that power was extremely limited with the small sample sizes for which we had CD84 expression as well as drug response data (n = 31 RA patients treated with etanercept). The CD84 gene is a compelling candidate for immune response, belonging to the CD2 subset of the immunoglobulin superfamily. It has been implicated in T-cell activation and maturation  [20]. CD84 localizes to the surface of CD4+ and CD8+ T cells, and acts as a costimulatory molecule for IFN-gamma secretion  [21]. CD84 is also expressed in B-cells, monocytes and platelets. CD84 has not been previously implicated in genetic studies of RA risk, disease activity, disease severity, or treatment response. A limitation of our study is the small sample size available for replication (n = 290 etanercept-treated patients), and the lack of replication observed for the top CD84 SNP (rs6427528) among patients of Portuguese and Japanese ancestry.