dataesr/scientific-paragraphs-categorization huggingface dataset:
Images of scanned sections were captured at total magnification of 300. For each animal the glomerular sclerosis index (GSI) was derived as the mean of 50 glomeruli. The glomerular score for individual glomeruli was: grade 0-normal glomerulus; grade 1-mesangial proliferation/sclerosis involving less than 25% of the glomerular tuft; grade 2-sclerosis involving more than 25%, but less than 50% of the glomerular tuft; grade 3-sclerosis involving more than 50%, but less than 75% of the glomerular tuft; grade 4-diffuse glomerulosclerosis with total tuft obliteration. The fractional sclerosed area within the glomerular tuft was determined using Image Pro Plus software (Media Cybernetics, Silver Spring, MD). The number of podocytes per glomerulus was determined according to the Animal Models of Diabetic Complications Consortium protocol (www.
Acknowledgements We wish to express our thanks to  Tomohide Yasunaga  ( American Museum of Natural History, USA ),  David R. Horton  and  Tamera Lewis  ( USDA, USA ),  Kazutaka Yamada  ( Tokushima Prefectural Museum, Japan ) and  Petr Kment  ( NMPC, Czech Republic ) for editing the manuscript and insightful comments. The research was supported by the  Islamic Azad University (Yadegar-e-Imam Khomeini (RAH) Shahre Rey , and  Shiraz Branches ) and  Museum Requien (France).
Acknowledgment Research supported by  GIS   VOR 2012 ,  LTDS 2012  and  PRIN 2010-2011  ( 2010BFXRHS_004 ) projects as well as by one PhD fellowship funded by the  French Ministry of National Education, Higher Education and Research. The research also benefited from several invitations of  C. Callari  at  École Centrale de Lyon -LTDS.
Microarrays were scanned using the Agilent Microarray Scanner and Feature Extraction software (v 11. 0. 1. 1) (Agilent Technologies, USA). Data normalization and processing were carried out using the GeneSpring GX software, v. 12. 1 (Agilent Technologies, USA). The scatterplot method was used to visualize variations (or reproducibility) in gene expression between arrays (Supplementary Material, Fig. S1 ).
Primary and secondary antibody incubation was followed by four washes in TBST 0. 1% (6 min, rocking, room temperature). The blots were developed with the enhanced chemiluminescence (ECL) reagent (WesternBright Sirius, Advansta, CA, USA), imaged on an LAS-4000 imaging system (Fujifilm, USA), and quantified using the Multi Gauge software (v. 3. 0, Fujifilm). To correctly assess the difference in histone acetylation and methylation marks, the blots were normalized to total histone H3 or H4.
Statistical Analysis The data are expressed as the means ± SEM. The behavioral data were analyzed by two-way ANOVA for repeated measures followed by a post hoc Newman-Keuls' test using the Statistica v. 10 software. The statistical analysis of molecular data was conducted using the Prism software (GraphPad Software, v. 7. 0). The statistical differences between gene expression levels and Western blot data were assessed by oneway ANOVA with Dunnett's post hoc test; significance was set at p < 0. 05. R software (v. 3. 1. 2) was used to conduct the unpaired Student's t test and one-way ANOVA followed by the Benjamini and Hochberg false discovery rate (FDR) for multiple comparisons for correction of microarray data. To select differentially expressed genes between the AC and YS groups, cutoffs were set as FDR ≤0.
Profiling Gene Expression During Extinction Training Followed Cocaine Self-Administration The influence of extinction training following cocaine self-administration on the transcriptome in the PFC was analyzed using the microarray approach. We found a total of 416 substantially altered transcripts (334 up-and 82 downregulated) in AC rats compared to YS control (Supplementary Material, Table  S1 ). To increase our understanding of the functional role of these genes, we performed Gene Ontology (GO) analysis using the STRING version 10. 0 database. With respect to cellular components, some important groups were identified including the axon part, Golgi apparatus, chromatin, synapse part, and neuron projection (Supplementary Material, Table  2S ). In terms of molecular functions, three major groups were involved in ion binding, catalytic activity, and transcription factor activity (Supplementary Material, Table  2S ).
The simulated event samples The Monte Carlo (MC) generator program used to produce the simulated event samples for the benchmark model is CALCHEP 3. 4. 1  [36]  interfaced with PYTHIA 6. 4. 24  [37]. These samples are divided into five decay channels (µµµµ, µµµe, µµee, µeee, eeee) for different Z boson masses (m Z ) ranging from 250 to 3000 GeV in increments of 250 GeV. The benchmark model assumes that new particles other than Z and ϕ are heavy enough not to affect the production and decay of the Z boson. Signal MC samples are produced with six different values of the ϕ mass (m ϕ ), with m ϕ = 50 GeV used as the reference mass value in the interpretation of the results. An important feature of this analysis is the presence of a "boosted signature" associated with the collimation of the two leptons coming from the same parent particle and resulting from the large difference between m Z and m ϕ.
In addition, samples are generated with m ϕ masses of 5, 10, 20, 30 and 40% of m Z , for which, in most cases, the contribution from the boosted signature is less important. The product of the leading order (LO) signal cross section and branching fraction in each channel varies with m Z (from 250 to 3000 GeV) as follows: µµµµ and eeee from 0. 8 pb to 3. 0 × 10 -6 pb, µµee from 12. 3 pb to 4. 7 × 10 -5 pb, and µµµe and µeee from 3. 1 pb to 1. 2 × 10 -5 pb. The branching fraction of ϕ → is set to 1 and therefore only the leptonic decay channels are considered. These signal MC samples are used to optimize event selection, evaluate signal efficiencies and calculate exclusion limits. The dominant SM background is the production of ZZ decaying into four leptons. The qqinduced ZZ production is generated using the PYTHIA event generator and the gg-induced production using the GG2ZZ program  [38].
Additional backgrounds from diboson production (WW and WZ) are generated with PYTHIA, and from top quark production (tt, tW, and tW) are generated with POWHEG 1. 0  [39]. Other processes, such as ttZ and triboson production (WWγ, WWZ, WZZ, and ZZZ), are generated with MADGRAPH 5. 1. 3. 30 [40]. Simulated event samples are normalized using the integrated luminosity and higher order theoretical cross sections: next-to-next-to-leading order for tt  [41]  and next-to-leading order for ZZ  [42]  and the other backgrounds. The MC samples are generated using the CTEQ6L  [43]  set of parton distribution functions (PDFs) and the PYTHIA Z2* tune  [44, 45]  in order to model the proton structure and the underlying event. The samples are then processed with the full CMS detector simulation software, based on GEANT4  [46, 47] , which includes trigger simulation and event reconstruction.
Event selection The 2012 data set of proton-proton collisions at √ s = 8 TeV, corresponding to an integrated luminosity of 19. 7 fb -1 , is used for the analysis. Data are collected with lepton triggers with various p T thresholds. The trigger used for the muon-enriched channels (µµµµ, µµµe) requires the presence of at least one muon candidate with p T > 40 GeV and |η| < 2. 1. The trigger used for the electron-enriched channels (µeee, eeee) requires two clusters of energy deposits in the ECAL with transverse energy E T > 33 GeV each. For the µµee channel, the trigger requires both an electron and a muon with p T > 22 GeV. In the subsequent analysis, events are required to contain a reconstructed primary vertex (PV) with at least four associated tracks, and its r (z) coordinates are required to be within 2 (24) cm of the nominal interaction point.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR  and  RAEP (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Typical neuroimaging studies analyze associations between physiological or behavioral traits and brain structure or function. Some rely on predicting these scores from neuroimaging data. To explain association between brain features and multiple traits, reduced-rank regression (RRR) models are often used, such as canonical correlation analysis (CCA) and partial least squares (PLS). These methods estimate latent variables, or canonical modes, that maximize the covariations between neuroimaging features and behavioral scores. Here, we investigate theoretically and empirically the extent to which reduced-rank models predict out-of-sample clinical scores from functional connectivity. Experiments on a schizophrenia dataset show that i) significant correlations between canonical modes do not necessarily mean accurate generalization on unseen data, and ii) better accuracy is achieved when taking into account regularized covariance between scores.
A. Connectivity and clinical scores from COBRE The COBRE study aims at characterizing the brain in schizophrenia (cobre. mrn. org). It comprises anatomical and functional MRI data from 160 subjects. Diagnostic information and clinical scores are collected using the Structured Clinical Interview used for DSM Disorders (SCID). Table  I  lists some clinical scores used in our study. rfMRI scans are preprocessed and normalized following the recommendations in  [14]. Spatial preprocessing with SPM12 includes motion correction, fMRI coregistration to T1-MRI, normalization to MNI template followed by spatial smoothing (6mm FWHM). Temporal preprocessing with Nilearn v0. 2. 6  [15]  includes linear detrending and band filtering (0. 01-0. 1Hz). Confounds are then removed from timeseries, they are composed of age, white matter, 12 motion components, and 6 noise components from CompCor  [16].
B. Experiment settings We present two experiments studying the capacity of the reduced-rank models to predict multiple clinical scores from functional connectivity. The experiments evaluate: i) Prediction performance of RRR, PLS, and CCA: The three models are built on COBRE dataset; we compare their accuracy for out-of-sample prediction of continuous clinical scores related to schizophrenia. ii) Sensitivity of RRR to its parameters: We study the impact of the rank k of the RRR, and the impact of the whitening matrix Γ on the prediction accuracy of RRR model, either by taking the identity matrix, or by varying the low-rank approximation l of Γ. All models are assessed with 100 randomized train and test splits. Test sets represent 25% of the whole dataset. Regression accuracies of clinical scores on each test fold is measured with the cross-validated determination coefficient ∆ cv , and Pearson's correlation between true and predicted values.
∆ cv -or r-squared-is a relative distance between the prediction values and the true values: ∆ cv = 1 - |test| k=1 (y k -y k ) 2 |test| k=1 (y k -y test ) 2 , (11) where y k , y k , y test are respectively the true score, the predicted score, and the mean score on the test fold. Since CCA and PLS are not explicit predictive models, we rescale test set predictions : Y test = X test -X train Σ train U train V T train -1 + Y train , (12) where X train and Y train are the columnwise mean matrices of X train and Y train , respectively. Σ train is the columnwise variance matrix of X train. Experiments are implemented in Python using Scikit-Learn v0. 17  [18].
V. CONCLUSION We assessed different reduced-rank models to predict multiple schizophrenia-related scores from functional connectivity features. Results of RRR on COBRE dataset suggest that a low-rank regularization of co-variations between clinical scores in the whitening matrix Γ helps to improve overall prediction accuracy. Also, obtaining a high correlation of the first modes between imaging and outputs in CCA and PLS does not imply that these outputs can be predicted accurately from connectivity with these models. Finally, we advocate to use ∆ CV instead of using Pearson correlation to assess regression accuracy. Unlike correlations, it is sensitive to absolute differences between the prediction values and the true values, which is essential in the perspective of individualized predictions.
Study design and patient population A post hoc analysis of the ACQUIRE (NCT00559585) study was conducted to evaluate the effect of baseline BMI on the pharmacokinetics of and the clinical response to SC or IV abatacept. The study design, ethics approvals, patient population, and inclusion and exclusion criteria have been reported previously  [28]. Briefly, patients with active RA and an inadequate response to methotrexate for ≥ 3 months (≥ 15 mg/ week) were randomly assigned to receive SC abatacept (125-mg weekly fixed dose) or IV abatacept (~10 mg/kg according to body weight range [<60, 60-100, > 100 kg] every 4 weeks). All patients continued methotrexate at the same dose they were receiving at randomization (≥ 15 mg/week). Patients discontinued any other concomitant DMARDs at least 4 weeks prior to randomization; however, they were permitted to continue any concomitant stable low-dose oral corticosteroids (equivalent to ≤ 10 mg/day prednisone)  [28].
Study assessments All data analyses were performed for both separate and pooled administration routes by baseline BMI subgroup: underweight/normal (< 25 kg/m 2 ), overweight (25 to < 30 kg/m 2 ), and obese (≥ 30 kg/m 2 ). Patient demographic data and disease characteristics were assessed by BMI subgroup for those with BMI data available at baseline. The clinical response to abatacept by BMI subgroup at month 6 (day 169) was assessed by determining the proportion of patients in remission for the separate and pooled SC and IV groups using each of the following criteria: DAS28 (CRP) <2. 6, SDAI score ≤ 3. 3, and CDAI score ≤ 2. 8. The proportions of patients who achieved an ACR20, ACR50 (≥ 50% improvement), or ACR70 (≥ 70% improvement) response, and mean change from baseline in Patient Global Assessment (PtGA), tender joint count-28 joints (TJC28), and swollen joint count-28 joints (SJC28) at month 6 were also determined.
Statistical analyses Baseline patient demographic data and clinical characteristics were analyzed descriptively according to BMI subgroup (percentage for categorical variables and mean [standard deviation] for continuous variables). The proportions of patients with clinical response over 6 months were presented as pooled data and by route of administration for each BMI subgroup. The rates, mean values, and mean change from baseline for various measures of clinical response were determined with corresponding 95% confidence intervals (CIs) at several time points over 6 months. The median, mean, and first and third quartiles of abatacept C min were presented as a box plot by route of administration and BMI at month 3 and at month 6.
Analysis population Baseline BMIs were available for 1456/1457 patients: 526/1456 (36%) patients were underweight/normal, 497/ 1456 (34%) were overweight, and 433/1456 (30%) were obese. Baseline demographic data and disease characteristics were similar across BMI subgroups and by route of abatacept administration; hence, the data were also pooled for administration routes (Table  1 ). Data were obtained for all patients who were either overweight or obese at baseline. For those with an underweight/normal BMI at baseline, data were obtained for 262/265 patients who were administered SC abatacept and for 258/261 patients who were administered IV abatacept.
An example of the latter is the software 'Young Adolescents' Nutrition Assessment on Computer' (YANA-C), which allows children to log the food groups and portions they ate the previous day. This software has been further developed and validated for use in various European contexts, and can also be used as a gold standard  [12, 13]. Methods for recording food intake in children under 10 years old are scarce and unavailable in our context  [8]. In Barcelona, we designed 'POIBA-Cómo comemos?' (POIBA-How do we eat? [POIBA-HDWE]) FFQ in the context of the childhood obesity prevention programme 'Prevención de la Obesidad Infantil en Barcelona' (Prevention of Infant Obesity in Barcelona -POIBA Project). The aim of this study was to validate the POIBA-HDWE FFQ using the YANA-C software tool as the gold standard, and to determine whether the questionnaire could be administered to children under 10 years of age.
The questionnaire included information about age, socioeconomic status (Family Affluence Scale-FAS  [18] ), weight (kg) and height (cm) measured objectively by staff from the Public Health Agency of Barcelona. The POIBA-HDWE FFQ can be accessed at: http://www. aspb. cat/poiba/ Since parents can be a proxy for obtaining children's food consumption, we designed a second FFQ to be completed by parents, POIBA-HDOCE. It included 48 questions on the same food items and categories as the POIBA-HDWE, but we grouped pasta, rice and grains together in one question, and did not differentiate between fried potatoes and other forms of potatoes. YANA-C is a European software program for children aged 11-13 years, and was designed to record their food intake during the previous 24 h. It is a selfadministered 24-h recall tool that includes information from six meals: breakfast, mid-morning snack, lunch, mid-afternoon snack, dinner, and evening snack, and can be used as a criterion standard for FFQ validation.
Study variables We collected sociodemographic data such as age and socioeconomic status, measured by the FAS scale. FAS is an individual socioeconomic index that reflects the affluence of a family by asking about the number of holidays per year, the number of computers and cars at home and the availability of a private bedroom. Subjects are classified in categories of low, middle and high socioeconomic status  [18]. The variables obtained from the two FFQ were the children's normal food intake frequency in relation to consumption in the last month, reported by the children and parents. Furthermore, after pooling data from the three administrations of YANA-C, the program provided information on the average number of portions eaten in a week. Some YANA-C food items were combined to homogenize the food groups analysed in all the questionnaires.
We created the descriptive variable, 'Compliance with food recommendations', from POIBA-HDWE according to the Spanish food recommendations  [19] , and generated a category of the same name. The remaining categories were grouped together as 'Noncompliance with food recommendations' but, since some recommendations were given for a food group instead of for individual foods, we combined rice, pasta, bread and grains as 'carbohydrates', and vegetables and fruits as another group. We created a discrete variable, 'Weekly food intake', for POIBA-HDWE and POIBA-HDOCE to obtain data comparable with the gold standard, YANA-C. For both questionnaires, we designed variables by assigning a frequency to each food consumption category, using the week as a reference. 'Never or <1 a month' in the FFQ was considered 0 times per week in the new variable, '1-3 times/month' was 0.
We calculated the mean intake for each food item in POIBA-HDWE and YANA-C by using the discrete variable 'weekly food intake' and by applying a Wilcoxon test to compare means. Finally, we calculated the percentage of agreement and gross misclassification between the two food registers using the categorical variable 'weekly food consumption'. We analysed the internal consistency and reliability of POIBA-HDWE FFQ for the food frequency questions using Cronbach's alpha, which was expected to be 0. 6-0. 7  [21, 22]. Additionally, the validity of the POIBA-HDWE FFQ was evaluated by analysing the sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) with the variable 'Food consumers'. Data were analysed using the STATA statistics/data analysis version 11.
Ethical issues The POIBA study was approved by the 'Parc de Salut Mar' Clinical Research Ethics Committee (CEIC-Parc Salut Mar, Barcelona), and the participants' families gave informed consent for their children to participate. Information was protected in accordance with the 1964 Declaration of Helsinki and amendments, and we observed the Spanish deontological codes and the Spanish data confidentiality law (Ley Orgànica 15/1999 de 13 de Diciembre de Protección de Datos de carácter personal  [LOPD] ).
Statistical analyses All statistical analyses were done in R (version 3. 0. 2, R Development Core Team 2013, https://www. r-project. org/). All datasets and scripts are publically available from Figshare (doi: 10. 4225/03/58ca18ae80d1a). From the nutritional geometry experiment, we estimated the response of each life history trait to the protein, carbohydrate, and caloric content of the food based on the methods described in  [6, 8, 30]. For survival, we fit a generalized linear model with a quasi-binomial distribution, to account for the overdispersion of the data, and with a logit link function. For the remaining traits, we fit linear mixed effect models, with replicate as the random effect. The effect of both the linear and quadratic components of carbohydrate and protein, as well as their cross-product, was included in our models.
To visualize each trait's response on the macronutrient space defined by our panel of diets, we used non-parametric thin plate splines. To compare response shapes between traits and between species, we standardized the dependent variable values to a mean of zero and a unit standard deviation, and compared responses using partial F tests. The two-choice larval assays provided two types of data: number of larvae with blue and/or red colour in their guts (scored by eye), and quantification of each colour in pooled larvae (determined by spectrophotometer absorbance). For the dataset of larval gut colour (scored by eye) we calculated the proportion of larvae in each replicate that ate protein-rich food only, protein-poor food only, both foods, and no food. We then tested for differences for proportion of individuals in these categories between: (1) choice pairs; (2) species; (3) time points; and (4) possible interactions; by fitting a generalized linear model with a quasi-poisson distribution to account for overdispersion of the data.