dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Data were double-entered using the OpenClinica ™  [25]  software for clinical data management. The main analysis was done on the intention to treat (ITT) cohort that included all volunteers who underwent CHMI with PfSPZ Challenge. AEs were analysed as pre-erythrocytic phase adverse events or erythrocytic phase adverse events as specified in the correspondent section below. AEs were categorized as related to PfSPZ inoculation, malaria and anti-malarial treatment. The pre-patent period was calculated in days dividing by 24 the number of hours from time of inoculation until time of first positive TBS and summarized by group using geometric means. Times to first positive TBS were visualized using Kaplan-Meier graphs (Fig. 2 ). All calculations were done with Stata statistical software version 14.
Acknowledgements We thank all the volunteers that participated in our study, the staff at the  Barcelona Centre for International Health Research, Spain , and  Sanaria Inc. in Rochville, USA. Special thanks to  UKT  staff for performing the qPCR assays;  Xavier Carné  for his guidance on regulatory and ethical aspects of the trial;  Anna Pascual ,  Marta Fernandez  and  Mireia Gonzalez  for their dedication and.
Human interactions and group behaviors are often more complex and variated than those displayed by animals or artificial agents. Moreover, human relations have unique aspects of human psychology that are not usually studied in swarm intelligence research. The presented multi-user virtual environment named HuGoS-Humans Go Swarming-is an open-source software tool implemented using the Unity game development platform. HuGoS turns out to be a versatile tool that allows the implementation of a large number of possible scenarios, making it a comprehensive tool for experimentation. The authors show the functionality of HuGoS experimentally via a coordination task performed by anonymous human participants. The performance of the task includes group activities such as dynamic best-of-n collective decision making, messaging or signaling, and stigmergic interactions.
2 ) and Funnel plots show no SNPs with outlier effects (Supplementary Fig. 3 ). In contrast, we find no evidence for causal effects on PCOS for birth weight (P ¼ 0. 22) or age at menarche (P ¼ 0. 23). Other biological mechanisms associated with PCOS. By systematic testing of all GWAS SNPs across all known biological pathways using meta-analysis gene-set enrichment of variant associations (MAGENTA) software, we find one further pathway (ATP-binding cassette transporters) that is enriched for PCOS-associated variants. This pathway includes the genome-wide significant signal at the DNA repair gene RAD50 (rs13164856) and 37 other genes. The PCOS-susceptibility alleles at our six PCOS loci are also consistently associated with higher anti-Mullerian hormone (AMH) concentrations in girls (cumulative score: P ¼ 8.
Methods Discovery phase. Genome-wide SNP data were available on 5,184 women of White European ancestry with self-reported PCOS and 82,759 controls from the 23andMe study (see Supplementary Table  1  and Supplementary Note for details of the 23andMe study). Imputation was performed against the 1000 Genomes reference (March 2012 v3 release), yielding B9 M variants that passed imputation and minor allele frequency criteria. A logistic regression model adjusting for age-and study-specific principal components was performed assuming an additive allelic model including covariates for age and the top five principal components to account for residual population structure. Test statistics were further adjusted for the observed l-value 1. 041. 23andMe participants provided informed consent to take part in this research under a protocol approved by Ethical and Independent Review Services, an accredited institutional review board.
Acknowledgements This work was supported by the  Medical Research Council  ( U106179472 ,  MC_U106179472 ,  U106179471  and  MC_U106179471 ) and the  National Human Genome Research Institute of the National Institutes of Health  (grant number  R44HG006981  to 23andMe). The  UK Medical Research Council and Wellcome Trust  ( 092731 ), together with the  University of Bristol , provide core support for the ALSPAC study. AMH assays in ALSPAC were funded with a grant from the  US National Institute of Health  ( R01 DK077659 ). DAL works in a unit that receives funding from the  University of Bristol  and the  UK Medical Research Council  ( MC_UU_12013/5 ). We thank the customers and employees of 23andMe for making this work possible. We are extremely grateful to all of the families who took part in the participating studies.
Parameter estimates were obtained by refitting models using restricted maximum likelihood, as recommended in Zuur et al. [65]. Response variables (described below) were log 10 transformed prior to analysis when they did not conform to a normal distribution. We assessed the effects of warming on the diversity of phytoplankton communities by submitting phytoplankton taxon richness and Shannon diversity to repeated measures ANOVA, as described above, using diversity estimates for each mesocosm on each of the seven sampling occasions. We also compared the RADs of warmed and ambient phytoplankton communities from each of the sampling months. Species-abundance data were fitted to the Poisson log-normal distribution using maximum-likelihood fitting implemented with the "mle2" function in the "bbmle" package in R statistical software  [64].
Samples with less than three taxa (n = 12) were dropped from the analysis because the data were insufficient for model fitting. We used the two fitted parameters of the Poisson log-normal distribution (μ and σ) as metrics of the average log-taxon abundance and the relative evenness of the distribution of log-abundances among taxa, respectively, and submitted these variables to repeated measures ANOVA to test for the effect of treatment. Abundance and biomass-related response variables submitted to repeated measures ANOVA included total phytoplankton abundance and biomass, mean phytoplankton body mass, mean zooplankton body mass, and total biomass estimates of the key zooplankton functional groups-e. g. , copepods, cladocerans, ostracods-and selected zooplankton genera, which were the main grazers in our experiment (Daphnia, Bosmina, Chydorus, Alona, Diaptomus).
Expected β-diversity for each pairwise combination of ponds on each sampling occasion was then calculated as the mean across 10 4 iterations of the null model. We then tested whether the observed β-diversity differed significantly from that predicted by the simulations from the null model (i. e. , expected β-diversity) for warmed and ambient ponds using the "oecosimu" function in the "vegan" package for R  [64]. We rejected the null hypothesis of no difference between observed and expected β-diversity if the observed β-diversity was outside the 95% confidence interval of the simulated values. Finally, we calculated the rescaled Raup-Crick metric (β RC ) to quantify the relative roles of stochastic and deterministic factors in driving metacommunity assembly in our experiment  [28]. A β RC value of zero would indicate a dominant role for stochastic assembly mechanisms, whereas values approaching -1 indicate that communities are more similar than expected by chance, while those close to 1 suggest communities are more dissimilar than expected by chance alone.
This approach entailed assembling a path model as a set of hierarchical linear mixed effects models, each of which included hypothesized relationships between a response variable and a set of predictors as fixed effects and mesocosm ID as a random effect on the intercept. Each mixed effects model was fitted using the "lme" function in the "nlme" package, and the overall path model combining models for each response variable was fit using the "piecewise-SEM" package in R  [72]. The predictors included temperature as an exogenous variable (i. e. , whose variance arise outside of the model) and GPP, CR, phytoplankton taxon richness, phytoplankton biomass, and zooplankton biomass as endogenous variables (i. e. , those whose variance the model seeks to explain). All variables were natural-log transformed (except for temperature) and standardized using the mean to linearize relationships and reduce correlations between the coefficients.
Material and methods We carried out the petrographic description and modal analyses of 19 spinel-facies ultrama c xenoliths from León hill, 27 from Matilde hill and 20 from Chenque hill. The modal analysis of the xenoliths was carried out on thin section scanned images using the software Rock. Ar  [29]. The number of counts per section varies between 3000 and 7000. Accuracy of the results of the softwarebased modal analysis was cross-checked manually on the optical microscope tted with an integrated stage and an automatic point counter. Analyses of about 20 thin sections did not show signi cant di erence in the estimated percentages of the mineral phases. The major-element chemical composition of olivine, orthopyroxene, clinopyroxene and spinel was determined with a JEOL JXA-8200 electron microprobe in wavelength dispersive mode at Dipartimento di Scienze della Terra, Università di Milano, Italy, using 15 kV accelerating voltage, 15 nA beam current, 1-3 µm beam diameter, 30 s counting time on the peaks and 10 s on the background.
In orthopyroxene the Al O content shows a weak negative correlation with MgO in Matilde samples and no correlation is found in other localities (Figure  6 ). Clinopyroxene is diopside with En. -. , Fs. -. and Wo. -. contents. The Mg# of spinel ranges from 67. 3 (M67) to 71. 0 (M79), while Cr# varies from 37. 3 (M53) to 44. 4 (M67 and M55) (Figure  6 ). Matilde xenoliths have pyroxene with the lowest Al content and spinels with the highest Cr# (Figure  6 ) in our sample dataset. Olivine from Chenque hill has the widest range of variation in Fo content, from 92. 0 mol% in the harzburgite Q101 to 90. 2 mol% in lherzolite Q-EUG. As in León      5 ). The least depleted pyroxene and spinels from Chenque belong to lherzolite Q65. As a whole, olivine has Fo content in the range of variation of previously published data  [10].
Acknowledgement: We thank nancial support of Facultad de Ciencias Exactas y Naturales (Universidad de La Pampa, Argentina -PI 235), CONICET (PIP 2011-956), and CNR-CONICET joint program (years 2013-2014). We appreciate the suggestions about eldwork by Viviana Alric and the help during electron microprobe analyses of Andrea Risplendente. We acknowledge four anonymous reviewers that helped to improve the manuscript. Harzburgite MH: Coarse-grained to porphyroclastic texture. Olivines reach 8 mm in diameter and are kinkbanded. Grains are subhedral with ragged contacts among olivines, frequently are cross-cut by numerous veinlets of altered (serpentinized) glass. Smaller olivines (<1. 5 mm) are mainly subhedral. Orthopyroxene is anhedral to subhedral with curvilinear borders and 6 mm in maximum size.
Background: The clinical course of advanced adrenocortical carcinoma (ACC) is heterogeneous. Our study aimed primarily to refine and make headway in the prognostic stratification of advanced ACC. Patients and methods: Patients with advanced ENSAT ACC (stage III or stage IV) at diagnosis registered between 2000 and 2009 in the ENSAT database were enrolled. The primary end point was overall survival (OS). Parameters of potential prognostic relevance were selected. Univariate and multivariate analyses were carried out: model 1 'before surgery'; model 2 'post-surgery'. Results: Four hundred and forty-four patients with advanced ENSAT ACC (stage III: 210; stage IV: 234) were analyzed. After a median follow-up of 55. 2 months, the median OS was 24 months. A modified ENSAT (mENSAT) classification was validated: stage III (invasion of surrounding tissues/organs or the vena renalis/cava) and stage IVa, IVb, IVc (2, 3 or >3 metastatic organs, including N, respectively).
To achieve this goal, the ENSAT registry, comprising a large number of ACC patients treated in expert centers in four European countries, was utilized. Data collected included: age, sex, modality of tumor diagnosis [defined as : incidental, symptom-related either to the tumor mass (i. e. abdominal pain) or hormone secretions (glucocorticoids, mineralocorticoids, androgens hypersecretion) or other/unknown], ENSAT or UICC TNM classifications (tumor size, invasion of adjacent tissue/organs or vena cava/renalis vein, lymph node or distant metastases), involved organs, tumor grade based on the pathological analysis of the primary tumor [defined as the Weiss score (≤6/>6) or Ki67 percentage (<20%/≥20%)], the R status at first surgery (complete resection:R0; microscopic residual disease:R1; macroscopic residual disease:R2, resection not-known: Rx).
The primary end point was OS, defined as the interval between the date of the diagnosis of stage III or IV ACC and death due to any cause. Surviving patients were censored at the date of the last follow-up. Follow-up data were last updated in November 2012. The median follow-up was estimated by the reverse Kaplan-Meier method (Schemper's method). Survival rates and 95% confidence intervals were estimated by the Kaplan-Meier method. The log-rank test was used to compare survival curves. The parameters significantly associated with OS in the univariate analysis (P < 0. 05) were further tested in the multivariate analysis. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated using Cox's proportional hazards regression model in the multivariate analysis, with the lowest risk group as the reference group.
All tests were two sided. Variables, as well as cut-off thresholds, are presented in supplementary Table  S1 , available at Annals of Oncology online: they were analyzed for their potential prognostic value in the univariate analysis. Two multivariate analyses were carried out: model 1 (before-surgery) analyzed all prognostic parameters available at the time of ACC diagnosis. Model 2 ( post-surgery) analyzed all prognostic parameters including the pathological grade and R status in patients who underwent adrenalectomy. The statistical analysis was conducted using SAS software version 9. 2.
results population characteristics and follow-up data Four hundred and sixty-three cases were reviewed: 15 were excluded because <18 years old and 4 because lost at follow-up. Thus, 444 patients constituted the study population: 210 (47%) were ENSAT stage III and 234 (53%) stage IV. The main population characteristics are summarized in Table  1. Supplementary Table  S2 , available at Annals of Oncology online describes the different treatments received by the patients during the first year of therapy. Patients with stage III underwent significantly more frequently surgery than those with stage IV: no surgery was carried out in 6 ENSAT stage III patients and in 54 ENSAT stage IV patients. The median follow-up of patients was 55. 2 (range 0-139) months. The median OS was 24 (21. 6-27. 7) months.
acknowledgements We thank the following for their help in this work: the  ENSAT ACC working group ,  ENSAT Cancer grant  ( FP7 ), the  French Network for Adrenal Cancer COMETE  for patient recruitment;  Denis Bakir  for technical assistance, the  'Diplôme Universitaire Européen de Recherche Translationnelle & Clinique en Cancérologie' (DUERTECC)  of the  Gustave Roussy Institute and Lorna Saint Ange  for editing.
u. ), and the drying gas temperature was set to 300°C. Mass spectra were recorded from 50 m/z up to 1000 m/z at a resolution of 30 000 (FWHM at m/z 400) acquired in the centroid mode. Following their acquisition by Xcalibur software (Thermo Fisher Scientific), metabolomic fingerprints were deconvoluted to allow the conversion of the three-dimensional raw data (m/z, retention time, ion current) to time-and mass-aligned chromatographic peaks with associated peak areas. Massmatrix File Conversion was used to convert the original Xcalibur data files ( Ã. raw) to a more exchangeable format ( Ã. mzXML). Data processing was then performed using the open-source Workflow4metabolomics. org. CentWave was used for the peak picking. The interval of m/z value was set to 0. 1, the signal to noise ratio threshold was set to 10, the group band-width was set to 10 and the minimum fraction was set to 0.
Gene expression A large number of ESTs available in the databases for Fucus spp. correspond to 3'UTR regions. Therefore, before designing primer sequences for qRT-PCR, we cloned a larger partial cDNA of a pksIII gene from F. vesiculosus in order to confirm the identity of the coding region of this sequence with other pksIII genes, including a biochemically characterized PKSIII enzyme, such as EsiPKS1 from the brown alga Ectocarpus  [3]. The cDNA of F. vesiculosus cloned covered 67. 5% of the total sequence of EsiPKS1 with 1726 bp fragment. The corresponding amino acid sequence was 98. 3% identical to the PKS1 from Ectocarpus (GenBank accession no: CBN76919) and exhibited significant similarities to algal and bacterial pksIII (Figs A-C in S1 File). The expression kinetics of pksIII, ast6, hsp70 and vbpo genes was monitored by qRT-PCR (Fig  2 ).
Acknowledgments This project was initiated through a  Grant-in-Aid for Scientific Research from Groupement d'Intérêt Scientifique (GIS) Europole MER  to  E. A. G and P. P. (Phlorotann'ING)  and we thank warmly  Valérie Stiger-Pouvreau , the project leader of Phlorotann'ING. We thank also  Dr Inken Kruse  for providing access to unpublished EST sequence data from F. vesiculosus. S. G. was also supported by the project  IDEALG  ( ANR-10-BTBR-04 ) " Investissements d'avenir, Biotechnologies-Bioressources ". E. C. was supported by a Ph. D. fellowship from the  Conseil Régional de Bretagne   ARED SYNTHALG n°7010. We also thank  GIS BiogenOuest  for supporting MetaboMER facilities through the  CORSAIRE metabolomics network. We warmly thank  Dr Monique Ras de Moncuit  and  Pramod Kumar Rathor  for linguistic corrections.
This project was initiated through a  Grantin-Aid for Scientific Research from Groupement d'Intérêt Scientifique (GIS) Europole MER  to  E. A. G and P. P. (Phlorotann'ING). S. G. was also supported by the project  IDEALG  ( ANR-10-BTBR-04 ) " Investissements d'avenir, Biotechnologies-Bioressources ". E. C. was supported by a Ph. D. fellowship from the  Conseil Régional de Bretagne ARED SYNTHALG n°7010. to global quantifications currently used in studies of phenolic compounds in brown algae.
Gene expression microarray and microarray data analysis After assessing the RNA quality, liver, muscle, colon and adipose tissue samples with a RNA Integrity Number (RIN) greater than 7. 0 were selected for further analysis (PCB, controls and concentrations). Samples were labeled with Agilent two-color Quick Amp labeling kits using a total RNA input ranging from 600 to 1000 ng. Labeled cRNA was analyzed using the Nanodrop to ensure that the amplification and labeling reactions were sufficient. Labeled cRNA was hybridized on 18 Agilent 4x44K microarrays. Samples were then scanned on an Agilent DNA scanner. Raw data were extracted using Feature Extraction software and analyzed with Gene-Spring. Lastly, raw data from the 3 sample pools for each tissue (liver, colon, muscle and adipose tissue) were processed for statistical analysis (Student's t-test, P < 0.
Real-time RT-PCR Reverse transcription was completed using 1 μg of pooled RNA and a High capacity cDNA reverse transcription kit as specified by the manufacturer (Applied Biosystems, France). PCR experiments were performed using a LightCycler480 System (Roche, France) in 384 well-plates using a liquid handler automated workstation (Biomek 3000 Beckman). The PCR was performed with 0. 4 μM of each primer and EXPRESS SYBR GreenER qPCR Supermix (Invitrogen, France). Cycling conditions including a 2 min UDG activation at 50°C, 2 min activation at 95°C followed by 40 cycles of amplification (15 s denaturation at 95°C, 1 min primer annealing and fragment elongation at 60°C). A melt program was performed at the end of the PCR. The raw fluorescence data were analyzed using LightCycler480 software. Target gene mRNA expression was normalized to β-actin, and data were quantified using the 2-ΔΔCt method.
We can thus not attribute the reconstructed changes to volcanic forcing alone. ; and (c) Yucatan peninsula (Mexico)  18. Open circles represent the raw data from each record. Solid lines represent the 5-point running average applied to the series. The grey circles represent the locations from which the material for the U/Th dates were obtained and the control points of the age models used. The horizontal grey bars represent the uncertainty of each age control point. The difference in alignments of these records depends on their age models (note poor age control of the Yucatan record  18  ) sampling resolution and extent of local and cave environmental overprinting. equatorial Pacific and the long-term variations of tropical Atlantic SSTs, which is governed by the Atlantic Multi-decadal Oscillation (AMO)  26.
Chemical separation procedures followed those described in ref. 47. The age model for the speleothem is based on a parabolic curve fit to the 230 Th dates and the collection date (Fig. 2b ). Error bars are larger for the ages determined with a Finnigan-MAT Element compared with the dating resolution obtained using the Neptune ICP-MS MC (Fig. 2b ). We used AnalySeries (http://www. lsce. ipsl. fr/logiciels/index. php) to regress the U/Th ages with depth. We found that the upper section of GU-Xi-1 grew according to a second-degree polynomial that was parabolic (r ¼ 0. 99). We removed any sampling bias with AnalySeries using the integration method to provide evenly spaced annual samples. We used the resulting polynomial equation to convert each sample depth to calendar ages from the speleothem, which we used for our age model.
Details on the methodology are given in ref. 53. MS2 was used as in-house reference material (powdered Carrara marble, MS2  53  ), with isotope ratios ( 18 O/  16  O and 13 C/ 12 C) reported in standard Delta notation relative to the Vienna Pee Dee Belemnite (% VPDB) standard (see Supplementary data 1). The external analytical precision for d  18  O is better than 0. 06%. Amount effect. The amount effect on stalagmites has been rigorously studied for Mesoamerica and has shown to be valid  15, 16. The comparison between precipitation and d 18 O in Fig. 3  indicates that the speleothem's oxygen isotopic properties capture the low-frequency variations of rainfall outside the cave, with the proportion of heavy isotope inversely proportional to the amount of precipitation. Observational datasets.
The precipitation data are from the University of East Anglia-Climate Research Unit gridded historical Time Series version 3. 21 for 1901-2012 (for more information, see:http://badc. nerc. ac. uk/view/badc. nerc. ac. uk__ATOM__ACTIVITY_0c08abfc-f2d5-11e2-a948-00163e251233). The SST data are from the NOAA National Climate Data Center Extended Reconstructed Sea Surface Temperature analysis  54  version 3b averaged over the summer months between 1901 and 2012 in Fig. 4a , and between1880 and 2006 in Fig. 4c  to fit with the length of the observed precipitation and d 18 O records, respectively. The SLP data are from the NOAA Environmental Research Laboratories Twentieth Century Reanalysis Project 55 version 2, covering the period between 1901 and 2006 in Fig. 4b  and between 1880 and 2006 in Fig.