dataesr/scientific-paragraphs-categorization huggingface dataset:
The overall effect of LCS on appetite ratings was evaluated comparing areas under the curve (AUC), computed by the trapezoidal method, of the 18 daily ratings (15 hourly ratings plus three post-meal ratings) of hunger, fullness and desire-to-eat recorded in the laboratory from 08:00 to 22:00 h under LCS or water conditions. Participants' compliance during the 5-week habituation phase was assessed as the ratio of the number of bottles/cans the participants were required to drink to the number actually drunk. The participants had to return to the laboratory each bottle/can provided, empty or full. Non-inferiority statistical analyses were performed by the independent Clinical Research Organization RCTS, with the SAS software, version 9. 2. For other analyses, the NCSS ® software was used (Statistical System of Windows, Number Cruncher Statistical System ® version 2017, Kaysville, Utah, USA).
Discussion The present RCT protocol was original in many respects. First, the protocol followed the recommendation of the European Food Safety Authority (EFSA) that studies on the impact of beverages and foods on hunger and satiety extend beyond the standard shortterm 3e4 h period. The present study followed appetite ratings as well as energy intakes for more than 15 h under carefully controlled laboratory conditions. Additional data on energy intakes during the following day were obtained through self-report. EFSA now requires long term high-quality data in support of any hunger and satiety claim. Second, the RCT directly compared non caloric non-sparkling LCS lemonade to non-caloric non-sparkling mineral water, as opposed to comparing LCS to sugar sweetened beverages, a more conventional approach(see review in  Miller and Perez (2014)  and  Rogers et al.
Sampling and processing of entomological data An entomological survey was conducted in 2014 and 2015 during the rainy season (from July to November). Every month, mosquitoes were trapped during two consecutive nights (from 6 PM to 6 AM) in each study site using CO2-baited CDC light traps (BioQuip # 2836Q-6VDC, Rancho Dominguez, USA) placed outdoors. This type of trap is used routinely for sampling arbovirus mosquito vectors  [18, 31, 37]. Two traps were set per site at a height of about 1. 5 m from the ground: one close to a natural water point (river, lake or pond); another close to a livestock pen. The distance between the water source and livestock pen varied from 100 to 800 m depending on the site. In the field, the mosquitoes collected were killed by freezing in dry ice, sorted by genus on a chill table, put in 15 or 50 ml centrifuge tubes/cryotubes and transported in dry ice (-80 °C) to the laboratory where they were identified according to sex and species on a chill table (-20 °C) using morphological keys  [38, 39]  and identification software  [40] [41] [42].
Statistical analysis To characterize the different populations of RVF mosquito vectors, ecological variables were used as predictors. Thus for each site, the following indices were calculated: (i) the ecological indices of composition: total (S) and average (Sm) species richness, total (N) and relative (AR%) abundance, frequency of occurrence or constancy (C%)  [8] ; and (ii) the ecological indices of structure: Shannon--Weaver diversity index (H′), maximum diversity index (H ′ max), Simpson's diversity index (1-D) and equitability index (E)  [43] [44] [45] [46]. Since our data were not normally distributed  [47] , non-parametric Kruskal-Wallis  [49] , Mann-Whitney-Wilcoxon tests  [48, 49] , and principal components analysis (PCA) were used to assess differences in species abundances and meteorological variables (temperature, relative humidity and rainfall) between localities and capture points.
The Pearson's correlation test was used to exclude strongly correlated (r ≥ 0. 9) variables from the analysis. A generalized linear mixed model (GLMM)  [50]  was used to assess the effect of climatic variables [temperature (mean of capture day), relative humidity (mean of capture day) and rainfall (mean and max. from 6 to 7 days prior to the capture event)] on mosquito abundances; site and date of capture were considered as random effects  [51]. The collected data set on the mosquitoes' temporal abundances was randomly divided into a training set and a test set. The training set was used to build the model and the test set to validate the best model based on the Akaike information criterion (AIC)  [52]  for each species. A random selection was performed and 2/3 (67%) of the entire data set (see Additional file 1: Table  S1 ) was assigned to the training set and 1/3 (33%) to the test set.
Acknowledgments The authors would like to thank the " Direction des Services Vétérinaires (DSV)" , the breeders for their collaboration and  Mrs. Grace Delobel  for commenting and correcting the manuscript's grammar. This study was partially funded by  EU  grant  FP7-613996  Vmerge and is catalogued by the  VMERGE Steering Committee as Vmerge024  (http://www. vmerge. eu). The content of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission.
To identify paired plasma samples with sufficient tumor purity for exome sequencing, we developed a novel copy number and purity targeted sequencing strategy using a targeted amplicon panel that included approximately 1000 single nucleotide polymorphisms (SNPs) in regions commonly lost in breast cancer (See Materials and Methods, Supplementary figure  3 ) and combined this with digital PCR data for PIK3CA and ESR1 mutations  (13). Using this approach we identified 16 patients treated with palbociclib plus fulvestrant who had high tumor DNA purity in plasma (>10% tumor purity) at day 1 and EOT, with adequate material for exome library preparation (Supplementary table 1). Of these, 9/16 (56. 3%) had a PIK3CA mutation and 6/16 (37. 5%) an ESR1 mutation in day 1 ctDNA. Five patients had matched germline DNA, and a further 3 additional unmatched germline DNA samples were also sequenced to expand the panel of germlines for filtering sequencing noise.
Digital PCR Digital PCR experiments were performed with Taqman probes in 20ul reactions partitioned into 20,000 micelles in an oil and water emulsion using the Bio-Rad® AutoDG® system before undergoing PCR in a G Storm thermocycler. Prior to use, cycling conditions for each Taqman assay was optimized using a thermal gradient and G-blocks from Integrated DNA Technologies (Iowa, USA). Droplets were read on a Bio-Rad® QX200 and concentrations calculated by fitting a Poisson model to the data using Bio-Rad® QuantaSoft version A), H1047R (c. 3140 A > G) and H1047L (c. 3140 A > T) as previously described  (13). For the purposes of exome sequencing, end of treatment samples were tested for a mutation with digital PCR only if a mutation was found in the matched day 1 sample to estimate purity. For purity estimates from digital PCR allele fractions mutations were assumed to be heterozygous with a copy number of 2.
Clonality analysis was performed with PyClone (0. 13. 0)  (17) , the mutational signature analysis with the R package deconstructSigs  (33)  and the chromosomal instability indices as described in Andor et al  (34). For samples without matched germline DNA, PyClone was used to identify and screen germline SNPs from paired analysis. For samples without matched germline DNA putative SNPs were removed from analysis either using a variant allele fraction (VAF) cut off < 0. 4 or by comparing VAF in both EOT and day 1 samples. Copy number profiles were compared using GISTIC  (35). Additional diagrams and fish plots were produced using clonevol  (36). Sequencing data are publically available at the Sequencing Read Archive, using the accession number SRP157645.
Targeted copy number and purity panel A 1729 amplicon custom panel was designed using the Ampliseq designer software (Thermo Fisher Scientific, Waltham, USA) to allow combined copy number and purity assessment. For purity assessment the panel included approximately 100 SNPs with population prevalence over 20% in 8 chromosomal regions that commonly exhibit loss of heterozygosity in breast cancer: 16q24. 3, 17p12, 8p23. 2, 11q23. 3, 22q13. 31, 1p36. 13, 6q27, 3p21. 31. Assessment of tumor purity was validated down to 10% by comparison with validated digital PCR assay allele fraction (Supplementary table  7 ). For robust assessment of loss a similar approach was used to include RB1, CDKN2A (p16) and PTEN with 119, 134, and 128 SNPs for each, respectively. For copy number assessment approximately 20 amplicons were sited in 11 genes commonly amplified in breast cancer and 19 reference genes identified from TCGA  (12)  and METABRIC  (37)  data as being relatively copy number invariant.
Libraries were constructed with 1-3ng input using the IonTorrent Library kit v2. 0 (Thermo Fisher Scientific, Waltham, USA) and sequenced to a target depth of 1000-2000X on the Ion Proton using P1 chips. Reads were aligned using Torrent Suite software. Loss of heterozygosity was estimated using a bespoke pipeline that used a threshold of 5% for the presence of a ß allele based on variance seen in germline material with a minimum of 15 informative SNPs (Supplementary figure  32 , Supplementary table 8) using an approach adopted from previous CLONET analyses  (38, 39). A minimum of 4 amplicons was used. Purity was estimated by assuming a single diploid clone with loss of heterozygosity, with this approach yielding similar estimates of purity to digital PCR (Supplementary table  7 ) and CLONET  (40)  (Supplementary figure  33 ), with reported profiles in the validated set matching the expected distribution from the TCGA (Supplementary figure  34 ).
Copy number was assessed using the OncoCNV (v6. 8) (41) package with calls based on normalized logR values obtained using thresholds of 0. 24 for gain and -0. 18 for loss. These thresholds were established using 3 standard deviations from the mean, derived from sequenced germline samples using the purity panel. To account for inter-gene variability within the panel an additional z test with an α of 0. 05 was performed against the local logR for all the samples. Copy number estimates were adjusted for purity where purity exceeded the minimum threshold of 10%. Primers capturing CCNE2 were only included for 226/324 samples, and so CCNE2 was not assessable in the other 98 samples. RB1 loss was defined as either evidence of heterozygous loss (normalized logR < -0. 18 with or without evidence of LOH) or probable homozygous loss where the adjusted copy number was <1.
The IonTorrent libraries were then sequenced to a target depth of 2,000X on a Proton with P1 chips. The custom libraries were sequenced on an Illumina HiSeq 2500 to target coverage of 15,000X. Sequencing reads were aligned and BAM files generated for the IonTorrent libraries using the Torrent Suite software and with BWA for the Illumina libraries. Sequencing artefact was removed with iDES(42) and manual curation with variants called from pileup only if present in both datasets above an allele fraction of 0. 3% for hotspots, stopgains and frameshifts and 0. 5% for all other calls, with a minimum of 5 alternative reads. VarDict was used to call indels under the same constraints on both platforms and torrent caller was used for IonTorrent libraries and Mutect2 for Illumina libraries. This approach was validated using dilutions of a blend of circulating tumor DNA (Supplementary figure  35 ).
Statistical analyses Kaplan Meier survival analyses for progression free survival were performed with the log rank test using a Cox proportional hazards model to obtain hazard ratios and 95% confidence intervals. Comparison of frequency of particular genomic aberrations in unpaired day 1 samples versus end of treatment was done using Fisher's exact test. Analysis of paired data between day 1 and end of treatment was performed using McNemar's test with a continuity correction where required. Unless stated otherwise all p values were two-sided with an α of 0. 05. Statistical analyses were conducted using R version 3. 4. 3. To address potential under-sampling at the day 1 time point, for the survival analysis comparing patients with and without an acquired mutation mutations were only included as acquired if the variant calls in the Ion Torrent and Illumina libraries passed an additional statistical test.
The proportions of alternative and reference reads between day 1 and end of treatment were compared using Fisher's exact test, with only those calls with p < 0. 05 in both libraries being included in the survival analysis. Of 60 patients with acquired mutations, 53/60 met these criteria and were included in the survival analysis. Survival analyses were conducted using the latest data cut off from the trial (October 2015) in which the median PFS was 4. 6 months for fulvestrant plus placebo and 11. 2 months for palbociclib plus fulvestrant  (15).
The reference was obtained by measuring the empty Harrick cell with identical conditions in the entire range of temperature investigated. Previous to the measurements, the membranes were preconditioned in the Harrick cell by purging N 2 (RH ~ 0. 1%) for 24 h at room T. The relative humidity was recorded using a humidity sensor (Sensiriont EK-H4) at the outlet of the cell. The data collection was performed on N211 and N115 samples in the 30-200 °C T-range with a step of 10 degrees in two successive heatings. The FTIR bands in the 1100-1200 cm -1 range are saturating (not shown). Measurements were performed after ~10 min at each temperature for system stabilization, when no noticeable spectral changes were observed. The samples were examined by SAXS at the beam line BM26-B  27, 28  at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France.
The energy of X-ray source was 12 keV (λ eV0. 1033 nm), and sample to detector distance was 3005 mm. Data was recorded using the Pilatus 1 M detector, with 172 × 172 μm pixel size. The samples were heated at 2 °C/min from 20 °C to 250 °C, cooled at 40 °C/min to 20 °C, heated again at 2 °C/min to 300 °C and finally cooled at 10 °C/min to 20 °C, in a Linkam DSC600 hot stage for the simulatenous SAXS temperature-dependent study. The SAXS patterns were reduced by BUBBLE and by the homemade XRTools software. The scattering Intensity (I) vs scattering vector, q sc4π sin θλ -1 , where λ is the X-ray wavelength and 2θ is the scattering angle. The scattering vector was calibrated using silver behenate. The scattering patterns were also corrected for transmission, normalized upon primary beam fluctuations and background scattering before data integration.
Dynamic mechanical analysis (DMA) was carried out under nitrogen flow in a Netzsch DMA 242 E in tensile mode. Rectangular Nafion samples (15 × 6 mm 2 ) were cut and tested, respecting the machine direction. The measurements were performed from 30-200 °C with 10 °C steps under N2 flowand amplitude of 4 μm and oscillation frequency of 1 Hz. Broadband dielectric spectroscopy (BDS) data were collected in a specially designed air-tight sample holder able to measure the proton conductivity in the range T = 30-200 °C with RH = 0% (dry N 2 purge)  17. Temperature controllers connected to band heaters placed externally around the cylindrical chambers are monitored by thermocouples (type K) inserted inside the metallic walls. The sample holder is capable of controlling both the temperature (from room temperature up to ~200 °C) and the relative humidity (RH, from ~3 to 100%).
Acknowledgements We thank the Brazilian funding agencies  CAPES ,  CNPq , and  FAPESP  (grant number  2013/50151-5 ,  2014/09087-4 ,  2014/50279-4  and  2016/14785-8 ). Thanks are also due to LNLS (18801 and 20160239) and CNEN. FCF is a CNPq fellow. We thank the  ESRF  and the  HZB  for the allocation of synchrotron radiation beamtime.
Introduction In this chapter, we describe some methods to explore the automatic, unconscious processing of asynchronous events that are judged as being simultaneous. We first describe our conceptual framework, which was guided by clinical inquiries on patients with schizophrenia. We then describe how we applied the Simon effect, as a novel method of analyses. The code provided with the manuscript (see book's GitHub repository) will allow the reader to generate his/her own data. The aim of the chapter is to provide all the necessary information for the reader to train and calculate effects like the Simon effect by including some example data files (see Annex). It must be noted at the onset that these calculations are not meant to replace more standardized measures like the evaluation of the threshold.
The results, thus, suggest a bias to press to the side of the first stimulus when patients cannot perceive an asynchrony explicitly. The readers can train themselves calculating the Simon effect with the example data provided with the manuscript (see book's GitHub repository: 'resultsRaw. txt'). Interestingly, for soas as short as 8 ms, the bias to the side of the first stimulus predicts the difficulties of the patients to explicitly detect asynchronies at larger soas. This correlation may suggest that the bias to the side of the 1st stimulus reveals an elementary mechanism at the core of our ability to predict and follow events over time  (Giersch et al. , 2015). Difficulties at processing temporal information in patients with schizophrenia did not come as a surprise. As already emphasized, it has long been reported that the patients' thought flow is disrupted  (Fuchs, 2007; Vogeley & Kupke, 2007).
As it has been described at the beginning of the paper, the analysis of the Simon effect should be performed using multiple ways of testing, e. g. , the priming task to validate the interpretation. Despite the limitations mentioned, these methods of data analysis can be used to complement standard rt and error analyses, and are powerful enough to yield heuristic hypotheses concerning both processing in healthy volunteers and impairments in patients. Appendix: List of the Files Provided with the Manuscript (See Book's GitHub Repository) 'SyncTest' is the Matlab code for the experiment with synchronous vs. asynchronous stimuli. Some explanation on how the experiment is conducted, the instructions, and computer requirements, are provided in the header of the code. Questions can be addressed to the first author of this chapter.
Participants Participants were recruited from the French NutriNet-Santé cohort, an ongoing web-based cohort of adult volunteers, launched in France in 2009  [20]. At baseline and during the follow-up, participants of the cohort are invited to complete a set of questionnaires to collect data on sociodemographic and lifestyle characteristics, dietary intakes, health status, anthropometric measurements, and physical activity. During follow-up, volunteers are also invited to complete additional and optional questionnaires, pertaining to various aspects of dietary behaviours and health determinants. The NutriNet-Santé study is conducted according to the French Institute for Health and Medical Research (IRB Inserm no. 0000388FWA00005831) and the "Commission Nationale de l'Informatique et des Libertés" (CNIL n • 908450/n • 909216).
Final portion sizes therefore ranged between 1 (e. g. , 15 g or 1 biscuit) and 16 (e. g. , 240 g or 16 biscuits). Final portion sizes between the four labelling conditions were then compared using Kruskall-Wallis tests (PROC NPAR1WAY in SAS software). Finally, associations between FoPLs and the final portion size were analysed using mixed ordinal logistic regression (PROC GENMOD in SAS software), with the labelling exposure as fixed effect, and the subject as.
Final portion sizes therefore ranged between 1 (e. g. , 15 g or 1 biscuit) and 16 (e. g. , 240 g or 16 biscuits). Final portion sizes between the four labelling conditions were then compared using Kruskall-Wallis tests (PROC NPAR1WAY in SAS software). Finally, associations between FoPLs and the final portion size were analysed using mixed ordinal logistic regression (PROC GENMOD in SAS software), with the labelling exposure as fixed effect, and the subject as random effect-to take into account intra-individual correlations. The modelled probability was the increase of a portion unit. As randomisation led to a balanced distribution of individual factors across labelling conditions, ordinal logistic regressions were not adjusted. Analyses were performed by food category and then on all food categories combined.
Sensitivity analyses were conducted taking into account the consumption frequency of the tested food categories: When subjects answered never consuming a food category, their responses for the corresponding category were excluded. Interaction between FoPLs and individual characteristics, including sex, age, educational level, monthly income per household unit, occupational activity, self-estimated diet quality, nutrition knowledge level, and body mass index (BMI) were tested. Interaction between FoPLs and food categories was also tested. All tests of significance were two-sided, and a p-value of 0. 001 was considered significant to take into account multiple testing. Analyses were carried out with SAS software (version 9. 4; SAS Institute, Inc. , Cary, NC, USA).
Acknowledgments: The authors thank all the volunteers from the NutriNet-Santé cohort. We extend special thanks to  Nathalie Druesne-Pecollo ,  Younes Esseddik  and  Frédéric Coffinières  for their technical contribution to the  NutriNet-Santé study and particularly the present study , and the communication service of the  Paris 13 University  for his logistical support in the calibrated photographs shooting.
Study design, patient population and treatment This previously reported phase 3 trial (ClinicalTrials. gov identifier: NCT02268994) was a randomized, double-blind, placebo-controlled trial with a 16-week randomized efficacy period and an 8-week extension period among patients with NDD-CKD and IDA  [21]. Eligibility criteria included an estimated glomerular filtration rate (eGFR; Modification of Diet in Renal Disease study formula) <60 mL/min/1. 73 m 2 , hemoglobin 9. 0-11. 5 g/dL inclusive, ferritin 200 ng/mL, TSAT 25%, serum phosphate !3. 5 mg/dL and intolerance of or inadequate response to prior oral iron supplementation. Ferric citrate was started at 3 Â 1 g tablets daily and titrated at Weeks 4, 8 and 12 by an additional 3 tablets daily to achieve a ! 1. 0 g/dL hemoglobin increase from baseline, up to a maximum of 12 tablets daily.
For baseline variables for which treatment interaction terms were retained by stepwise AIC in multivariable models, we conducted confirmatory MMRM analyses. In contrast to multivariable regression at the single 16-week time point, MMRM uses all available data at all time points, which provides better estimates of the trajectory of change in outcome over time and how this is influenced by baseline variable level and by its interaction with treatment. For MMRMs with serum phosphate as the response, we analyzed absolute serum phosphate values over time by treatment and by baseline covariate tertiles in order to evaluate the effect of baseline covariates and their interaction with treatment on clinically relevant absolute serum phosphate concentrations. For MMRMs with cFGF23 and iFGF23 as the response, we analyzed changes from baseline in cFGF23 and iFGF23 by treatment and by continuously distributed baseline covariates.
Further details on MMRM specifications are described in the Supplementary data. Ferric citrate dose-response. An additional MMRM assessed ferric citrate dose-response by estimating serum phosphate !7 days after a stepwise dose increase (from 0 to 3, 3 to 6, 6 to 9 or 9 to 12 g/day) among ferric citrate-treated patients stratified by baseline serum phosphate tertile. Path analyses. To test whether ferric citrate reduced FGF23 indirectly via ferric citrate-mediated changes in serum phosphate and/or TSAT, we employed path analyses using the lavaan package version 0. 5-23. 1097 (R Project). Path analyses using observed data from randomized controlled trials with repeated laboratory measurements afford the opportunity of testing implied temporal causal associations between treatment, changes in these laboratory measures and outcome  [24].