dataesr/scientific-paragraphs-categorization huggingface dataset:
07 and exclusion as a top gene candidate in the current study. For the current study, some of the phenotype data reported by Leme et al  [23]  was utilized, but we extended this data by including AHR measures from male mice. Overall, we included data for 31 strains that had available AHR slope measures in mice of both genders and available genotype data. In addition to utilizing more phenotype data, we used a newer association method (i. e. EMMA) and more complete strain genotypic data than did the previous study. The primary advantage of EMMA vs. HAM is that EMMA takes the known genetic similarity between strains into account to control for population structure effects. The genome-wide genotype data for mouse strains used in the current study were for 281,300 SNPs, rather than 70,000 used in the previous report.
Mouse in silico Association Mapping Of the 33 mouse strains with AHR phenotype data in males and the 36 mouse strains with AHR phenotype data in females, a subset of 31 strains overlapped and had genome-wide genotype data available. This subset of 31 mouse strains with AHR measures in both males and females and with available genotype data was used to conduct association analyses. The association of SNPs with AHR was measured using Efficient Mixed Model Association (EMMA) software  [19] , which uses a linear mixed model with a variance component using a kinship matrix that is based on the genetic similarity between strains to control for population structure effects. The genotype data for the mouse strains was part of the ''Subspecific Origin and Haplotype Diversity in the Laboratory Mouse'' project of the Jackson Laboratory available at http://cgd.
Primary Human Asthma GWAS The primary group of subjects consisted of participants of the EVE consortium, which comprises nine research teams in the USA with genome-wide association data for diverse asthma cohorts  [26]. Briefly, 4,867 European Americans (1,486 cases, 1,539 controls, and 620 trios), 4,644 Latinos (606 cases, 792 controls, and 1,082 trios), and 3,447 African Americans (1,154 cases, 1,054 controls, and 413 trios) recruited through clinics and health systems in the U. S. , Puerto Rico, Mexico, and Barbados were used in a meta-analysis of GWAS of asthma. The previously generated meta-analysis results for. 2 million SNPs, based on genotyped or HapMap Phase 2 imputed data, for all subjects, as well as those limited to European American, African American, and Latino American groups were utilized.
illumina. com/science/icontroldb. ilmn) using the genetic matching (GEM) algorithm  [44]. Genome-wide genotyping of cases was performed on the Illumina 610 quad platform. Genome-wide genotyping of controls was performed on the Illumina HumanHap 550K v3 platform. Genotypes of SNPs of interest that were not captured by the genotype data were inferred using imputation with the Markov Chain Haplotyping software (MaCH)  [45]  based on HapMap Phase 2 Release 22 data  [46]. The ratio of the empirically observed dosage variance to the expected (binomial) dosage variance for imputed SNPs utilized was greater than 0. 3, indicating good quality of imputation. Dosage data was used to compute association statistics with PLINK  [47]. The corresponding genetic inflation factor was 1. 04, demonstrating minimal population stratification.
2) GABRIEL. This European consortium-based GWAS of asthma consisted of 10,365 persons with physician-diagnosed asthma and 16,110 unaffected persons, all of whom were matched for ancestry  [25]. Publicly available results for the random-effects pooled analysis that tested for association using the entire study population with asthma were used [Available at http://www. cng. fr/gabriel/results. html]. 3) Dutch asthma GWAS (DAG). This cohort was comprised of 920 DAG subjects with doctor-diagnosed asthma and documented AHR and 985 controls  [48, 49]. Genotyping was performed using the Hapmap 317K platform or Illumina 370 Duo Chip. Tests of association were performed via logistic regression using PLINK. Human AHR GWAS 1) SHARP. A GWAS of AHR, quantified as the natural log of the dosage of methacholine causing a 20% drop in FEV 1 , was performed with 994 non-Hispanic white asthmatic subjects from the Childhood Asthma Management Program (CAMP)  [50] , and subsets of clinical trials within the Childhood Asthma Research and Education (CARE) network  [51] , and the Asthma Clinical Research Network (ACRN)  [52]  participating in the NHLBI SNP Health Association Resource Asthma Resource project (SHARP)  [53].
Genome-wide SNP genotyping for SHARP subjects was performed by Affymetrix, Inc. (Santa Clara, CA) using the Affymetrix Genome-Wide Human SNP Array 6. 0. Imputation of all SNPs available in HapMap Phase 2 Release 22 CEU data using MaCH  [45]  was performed. The primary GWAS was based on imputed SNPs with minor allele frequency (MAF). 0. 05 and a ratio of empirically observed dosage variance to the expected (binomial) dosage variance greater than 0. 3, indicating good quality of imputation. The association of SNPs with LnPC20 was measured with a linear regression model using dosage data as implemented in PLINK. 2) Dutch asthma GWAS (DAG). This cohort was comprised of 650 DAG subjects with doctor-diagnosed asthma and documented AHR  [48, 49]. All subjects had smoking history and steroid use data available at the time of the AHR test.
Acknowledgments We thank all  CAMP  subjects for their ongoing participation in this study. We acknowledge the CAMP investigators and research team, supported by the  National Heart, Lung and Blood Institute (NHLBI) , for collection of CAMP Genetic Ancillary Study data. All work on data collected from the  CAMP Genetic Ancillary Study  was conducted at the  Channing Laboratory of the Brigham and Women's Hospital  under appropriate CAMP policies and human subject's protections. We acknowledge the following GALA 1 investigators:  Michael A. LeNoir, MD  ( Bay Area Pediatrics, Oakland, CA, USA ),  Harold J. Farber, MD  ( Section of Pulmonology, Baylor College of Medicine, Houston, TX, USA ),  Rajesh Kumar, MD, MPH  ( Division of Allergy and Immunology, Children's Memorial Hospital, Chicago, IL, USA ),  Pedro C.
L. K. W. is funded by the  American Asthma Foundation , the  Fund for Henry Ford Hospital , and by  NIH   R01AI079139  and  R01AI061774  from the  National Institute of Allergy and Infectious Diseases (NIAID). K. C. B. was supported in part by the  Mary Beryl Patch Turnbull Scholar Program. S. J. L. is supported by the  Division of Intramural Research, National Institute of Environmental Health Sciences. D. A. G. was supported by the  Netherlands Asthma Foundation  grant  AF (AF 95. 09 ,  AF 98. 48 ,  AF 3. 2. 02. 51  and  AF 3. 2. 07. 015 ) and a grant from the  University Medical Center Groningen. GHK was supported by a  Ter Meulen Fund  grant from the  Royal Netherlands Academy of Arts and Sciences. D. S. P. was supported by a  Royal Netherlands Academy of Arts and Sciences Honorary Professorship.
Magnetic resonance imaging data acquisition Magnetic resonance acquisitions were performed using a 3 T whole body TRIO 32-channel TIM system (Siemens). Radiofrequency transmission was performed with body coil, and signal was received with a 12-channel receive head coil. The protocol included whole brain high resolution anatomical 3D T 1 -weighted images, neuromelanin-sensitive images (T 1 -weighted), and diffusion imaging. Whole brain 3D T 1weighted scans were acquired using a sagittal MP-RAGE acquisition (inversion time: 900 ms, repetition time/echo time/flip angle: 2300 ms/4. 18 ms/9 , one average, voxel size: 1 Â 1 Â 1 mm 3 ). Neuromelanin-sensitive images were acquired using 2D axial turbo spin echo T 1 -weighted images (repetition time/echo time/flip angle: 900 ms/15 ms/180 , three averages, voxel size: 0.
Voxel-based morphometry From the 3D T 1 -weighted volume, we obtained the normalized and modulated grey matter and white matter probability maps using the VBM8 toolbox (http://dbm. neuro. uni-jena. de/vbm/) in the SPM8 software (http://www. fil. ion. ucl. ac. uk/spm/) running on MATLAB R2010b (The MathWorks, Inc. ). The processing included de-noising  (Manjon et al. , 2010) , partial volume estimation  (Tohka et al. , 2004)  and normalization to the MNI space using DARTEL  (Ashburner and Friston, 2005)  and the software template based on 550 healthy control subjects. The normalized maps were smoothed with 4 mm full-width at half-maximum Gaussian kernel.
Funding The work was supported by  Agence Nationale de la Recherche  ( ANRMNP 2009 , Nucleipark),  DHOS-Inserm  (2010, Nucleipark),  France Parkinson  ( 2008 ),  Ecole Neuroscience de Paris, 'Investissements d'avenir'  [grant number  ANR-10-IAIHU-06 ],  Institut Federatif de Recherche 49  ( IFR 49 ).
HLA transgenic mice (TgM) Murine H-2 class II-deficient, HLA-A02 (A*0201)  [46] , HLA-B07 (B*0702)  [47] , HLA-A24 (A*2402) (Lemonnier et al. unpublished), HLA-DR2 (DRA1*0101/I-Ea; DRB1*1501/I-Eb)  [48] , HLA-DR3 (DRA1*0101; -DRB1*0301)  [49, 50] , and HLA-DR4 (HLA-DRA1*0101; -DRB1*0401)  [51] , animals were bred and maintained in the Johns Hopkins University School of Medicine Animal Facility. Specific pathogen-free colonies were maintained in a helicobacter-negative mice facility. HLA expression of the experimental transgenic mice was evaluated by flow cytometry (data not shown).
Streptavidin-HRP 100-fold diluted was then added and incubated for 1 h at RT. The plate was washed and reactions were developed with 3-amino-9-ethylcarbazole (AEC) substrate (BD Biosciences Pharmingen). Spot development was stopped after 30 min incubation by washing the plate with distilled water. The plate was dried at RT and the spots were counted with the Immunospot Series 3B Analyser ELISPOT reader (Cellular Technologies Ltd) using the program Immunospot software version 3. 0 (Cellular Technologies Ltd). The spot average was normalized and expressed as the number of spot-forming cells (SFC) per 1 million cells. ELISPOT assays were also used to assess human T-cell responses using a protocol similar to the above using a human IFN-c ELISPOT kit (BD Biosciences); phorbol 12-myristate 13acetate (PMA; Sigma) at 250 ng/mL and ionomycin (Sigma) at 250 ng/mL were used as a positive assay control.
Prediction of HLA class I epitopes and determination of HLA class II core region interaction Prediction of HLA class I T-cell epitopes for HLA-A*0201, HLA-A*2402 and HLA-B*0702 molecules was performed by use of the ANN (artificial neural network) algorithm based prediction tool at the Immune Epitope Database (IEDB) and Analysis Resource (http://tools. immuneepitope. org/analyze/html/mhc_ binding. html). The peptide sequences (13-mer to 17-mer) were input to the prediction server and prediction was carried out for all possible lengths (8-mer to 11-mer). Peptide sequences predicted with IC50 below 500 nM were considered for further analysis. For determination of HLA class II core region interaction within the peptides bearing pan-dengue conserved sequences, stabilization matrix method (SMM), artificial neural network (NN) and Sturniolo alignment algorithms  [57, 58, 59]  were used.
Controls included only buffer (blank), protein and pFITC. Each experiment was performed in quadruplicate and reported as the mean with standard deviation. The reported IC50 values were obtained by fitting the mean of 13 data points to a four-parameter logistic equation: Y ~minz max{min ð Þ 1z10 log=C50{x ð Þ B where, max indicates the upper plateau of the curve; min, the lower plateau and B, is the slope factor, which describes the steepness of the curve transition. Diversity analysis HLA-A*0201-restricted DENV3 NS3 399-407 epitope and variants NS3 protein sequences of each DENV serotype (696 for DENV1, 681 DENV2, 585 DENV3, and 71 DENV4; as of Feb. 2009) were collected from the NCBI Entrez Protein Database and aligned, following the method in  Khan et al. , (2008)   [45]. The region of each serotype alignment corresponding to the DENV3 NS3 399-407 epitope sequence (KLNDWDFVV) was extracted and analysed for all the peptides that were variant/different to the epitope sequence by at least one amino acid difference.
Analysis of variants of DENV3 NS3 399-407 in other DENV serotypes Altered peptide ligand is thought to be a mechanism driving memory T-cells toward an aberrant cytokine response associated with disease severity. Intra-and inter-serotype variant analysis of the A02-restricted epitope (DENV3 NS3 399-407 -KLNDWDFVV) were performed to assess the potential of the peptides to act as altered peptide ligands. The DENV3 NS3 399-407 epitope had an incidence of 63% among all analyzed DENV3 sequences collected from the NCBI Entrez Protein Database (see Methods section). The remaining ,37% of the sequences constituted four intra-serotype variants of the epitope (KLNDWDFVV) with only one peptide of incidence more than 1% (RLNDWDFVV; ,36%). In contrast, there were 11 inter-serotype variants, but only four (DENV1: KNNDWDYVV; DENV2: RTNDWDFVV and RANDWDFVV; DENV4: KLTDWDFVV) had an incidence of about 5% or more among all analyzed DENV sequences of each serotype (Table  2 ).
The combination of immunization strategy (with peptide pool), the difference in topology between HLA class I and II molecules and the HLA class I epitope processing mechanism  [68]  might explain the reasons for the paucity of HLA class I epitopes identified in our study and, thus, revealing to be a caveat for identification of HLA class I restricted epitopes. Binding assay analysis was done in order to confirm the HLA affinity suggested by TgM immunogenicity data. HLA affinity correspondence between the two data was observed for 9 HLA class I and 97 HLA class II epitopes. A schema of DENV3 proteome with the location of each confirmed epitope as well as those reported in the literature are illustrated in Figure  8. A number of these epitopes were shown to be quite promiscuous, binding to as many as 14 HLA class II molecules.
Thus, more studies are needed to determine the role of these altered peptide ligands on disease outcome. Nonetheless, the data suggests that the epitopes are naturally processed and can potentially prime naı ¨ve T-cells, which is important for vaccine design. Our epitope screening strategy revealed HLA class I epitopes found only in non-conserved regions of the virus proteome. We analyzed the intra-and inter-serotype variants of the A02restricted epitope NS3 399-407 (KLNDWDFVV) among the sequences deposited in the NCBI Entrez Protein Database  [45]  to assess for the possibility of altered peptide ligands within DENV3 and between the serotypes. Based on the analysis, an intra-serotype variant (RLNDWDFVV) and four inter-serotype variants were identified (Table  2 ). In silico analysis showed that all, but DENV1 variant had HLA-A*0201 binding motif, and, thus, capable of potentially acting as an altered peptide ligand, leading to aberrant memory T-cell responses.
IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach.
Design of IPSIM-Wheat-Eyespot 1. General Approach. IPSIM-Wheat-Eyespot is based on the DEX method, and is implemented with the software DEXi  [35]. DEX is a method for qualitative hierarchical multi-attribute decision modelling and support, based on a breakdown of a complex decision problem into smaller and less complex subproblems, characterised by indicators (or attributes) that are organised hierarchically into a decision tree. These attributes are characterised by their name, a description and a scale. DEXi is generally used to evaluate and analyse decision problems, e. g. [36]. However, the DEX method has been used here in an original way to model complex agroecosystems. IPSIM-Wheat-Eyespot is therefore a hierarchical and qualitative multi-criteria model, allowing the prediction of eyespot injury according to various factors with sometimes opposite effects.
The level of cultivar resistance has been described using the official list provided by the French National Seed Station (Groupe d'Etude et de controle des Varietes et des Semences; http://cat. geves. info/ Page/ListeNationale; accessed November 2012) and published by Arvalis-Institut du vegetal (http://www. arvalisinfos. fr/_plugins/ WMS_BO_Gallery/page/getElementStream. html?id = 13504& prop = file; accessed November 2012). In this list, cultivars are rated for their susceptibility to eyespot on a 0-9 scale, from very susceptible to resistant. For the climate attribute, a three-value scale (''Unfavourable''; ''Favourable''; ''Very favourable'') was defined using climatic models  [27, 43]  and data from the INRA Climatik database. All the scales in Figure  2  are ordered from values detrimental to the crop (i.
e. favourable to the disease) on the left-hand side to values beneficial to the crop on the right-hand side (i. e. unfavourable to the disease). In the DEXi software, this difference is clearly visible because, by convention, values beneficial to the user are coloured in green, detrimental in red, and neutral in black. The scales for the ''tillage after preceding crop'' and ''tillage after pre-preceding crop'' attributes appear in black since their effects on the disease cannot be defined independently from the crop sequence. Initial input attribute values (either quantitative or qualitative) are translated into qualitative appreciation, according to two to three scales defined on the basis of available information in the literature, models or expertise. Sometimes, a two-value scale is enough to represent the value of an indicator (e.
An example of output results obtained for two simulation units is provided in Figure  4  (input basic attributes and calculated aggregated attribute values for the simulation of two systems: an organic and a highinput one). Table  1. Available knowledge in the scientific literature describing the effects of cropping practices and the production situation on the incidence of eyespot on wheat. Evaluation of the Predictive Quality of IPSIM-Wheat-Eyespot 1. Description of the Dataset Used. Data representative of a wide range of climate patterns, soils and cropping practices are needed to assess the predictive quality of the model. A large dataset was therefore developed to assess the predictive quality of IPSIM-Wheat-Eyespot. A national survey was conducted to identify relevant data from various research and development institutes.
The required datasets had to provide information for input attributes of IPSIM-Wheat-Eyespot (description of cropping practices, soil and climate) and its output (eyespot incidence at the ''milky grain'', stage 7: development of fruit on BBCH scale  [39] ). The dataset obtained is summarised in Table  3. It comprises results from multifactorial trials from 1980 to 1994 in 7 contrasting regions in France, which were set up to analyse the effects of various cropping practices on foot and root winter wheat diseases on different soils and with differing climate patterns. Various cultivars were combined with different crop sequences, conventional and reduced tillage, low or high plant densities, early or late sowing dates, low or high N fertilisation, in various areas of production where eyespot epidemics are observed.
Most of these trials were specific studies on foot diseases  [20, 41, 61, 62] , so the experimental conditions were suited to ensure the presence of eyespot (i. e. infected wheat present in the crop sequence and only susceptible cultivars). Other data originated from a regional agronomic diagnosis  [63]  performed in cereal fields from 1987 to 1994 in 19 French regions to analyse the effects of cultural practices on the incidence and severity of foot and root disease complexes  [64]. In this survey, data were collected on 894 cereal fields in a wide range of production situations. For some situations, the pre-preceding crop (3 possible types of crop in the model: ''host'', ''non-host'' and ''risk amplifying'') and the associated tillage after the harvest of this crop (2 possible values in the model) were not observed.
Evaluation of the Quality of Prediction for Final Incidence Classes The high number of observed site-years in the dataset (526) permitted a reliable evaluation of the predictive quality of IPSIM-Wheat-Eyespot. Residuals were distributed around 0 (Figure  5 ), indicating that the predicted values were close to observations. Nearly half (47. 1%) of the simulated classes encompassed the observed values and 80. 4% had at most a difference of one class only. In addition, there are nearly as many negative as positive differences of exactly one class. The Wilcoxon test performed over the 9 class differences (from 24 to +4) proved that the model was significantly biased (simulated final incidence classes lower than observations, p,1. 0 10 210 ). Figure  6  illustrates the distribution of class differences between observed and predicted final eyespot incidences.
The overall predictive quality of IPSIM-Wheat-Eyespot was judged fair, even if slightly biased. The predictive Table  3. Main features of the datasets used for the evaluation of IPSIM-Wheat-Eyespot's predictive quality. quality was good for the lowest class (52% of all the observations in the dataset): 80% of the observed values between 0 and 20% were correctly simulated. The model underestimated final incidences for observations higher than 20%.
Hierarchical Tree of Attributes and Aggregating Tables. The qualitative nature of the DEX method is well suited to the modelling of complex systems for which no high level of precision is required. The DEXi software tool  [36]  offered a suitable environment for the organisation of available knowledge and a rapid development of IPSIM-Wheat-Eyespot. The main breakthrough of the IPSIM platform is to allow the handling of complexity in a simple way  [33]. The work presented in this paper provides a proof of concept for this innovative modelling approach in the field of crop protection for a single disease. A major innovation of this modelling approach is to be able to aggregate attributes of different natures (e. g. cultivar choice, a nominal variable and fertilisation rate, a quantitative variable) to describe the impact of various components of cropping systems and their interactions on eyespot incidence.
This is because fungicide does not always control the disease efficiently  [59]. The main factor influencing the disease is the spring weather (38%). This is consistent with Matusinsky et al. [43]  who showed that the disease was very dependent on the climatic conditions during spring. 3. Predictive Quality of IPSIM-Wheat-Eyespot. The quality of the analysis of the IPSIM-Wheat-Eyespot predictive quality not only depends on the model itself (hierarchical structure of attributes and aggregating tables) but also on the diversity of the data used, which must reflect a wide range of production situations. These data should represent a variety of soil, climate and cropping practices, but also of final incidences. The dataset used in this study satisfied the three former conditions, but did not fully satisfy the latter.