dataesr/scientific-paragraphs-categorization huggingface dataset:
Callose staining. Callose deposit quantification was performed following ref. 39. Briefly, for each treatment 5 leaves from five 5-week-old Arabidopsis plants were analyzed (n = 5). This experiment was performed three independent times. Leaves were stained using 0. 01% (w:v) aniline blue in 150 mM K 2 HPO 4 (pH 9. 5) buffer for 30 min and then de-stained in a lactophenol clearance solution overnight. Leaves were examined by stereofluorescence microscopy with an Azio Zoom V. 16 (Carl Zeiss Inc. , Oberkochen, Germany) and callose spots were quantified using the ImageJ software. All staining experiments were repeated three times and representative images were selected. Hydrogen peroxide detection assays. Hydrogen peroxide (H 2 O 2 ) was assayed with Arabidopsis plant cultivated in vitro or in soil.
The compound separation was performed using a reverse-phase LUNA® Omega 5 µm Polar C18 column (250 × 4. 6 mm). The mobile phases were composed of 0. 1% in water (solvent A) and acetonitrile (solvent B). The applied gradient was as follows: 0-2 min, 10% B; 2-13 min, 10-98% B; 13-23 min, 98% B; 23-25 min, 98-10% B; 25-30 min, 98% B; post-run 1 min. Data acquisition and analysis were carried out by the MassHunter Acquisition® software for QTOF (Version B. 08), MassHunter Qualitative Analysis® (Version B. 07) software, and MassHunter Quantitative Analysis® (Version B. 07) software (Agilent Technologies). Three independent experiments/pools were prepared (n = 3) for each treatment. Detection and quantification of JA and SA were performed at the Plant Observatory-Chemistry and Metabolomics platform (IJPB/INRA, Versailles, France)  39.
Ethylene production was analyzed with the ETD 300 detector (Sensor-Sense, Nijmegen, the Netherlands). A valve control box allowed automated sampling of ethylene production by six plates at a time, sequentially under a stop-and-flow routine where the gas from one plate accumulated during 1 h, and then flushed to the detector for 12 min; the first sampling relative to the first hour is omitted due to technical cell mounting after spraying the samples. The measurements were then performed over a continuous period of 24 h for each plate. Statistics and reproducibility. Data shown in graphs are presented either as the mean with S. D. or as median values from three biological replicates as indicated in the figure captions. Statistical significance was tested by one-way analysis of variance (ANOVA) test to compare treatments with mock (asterisks denote significant difference, p < 0.
Acknowledgements This work was supported by  FNRS-MIS   LUX -project F. 4502. 19 starting grant to D. C. (M. Z. and A. M. ) and PINT-BILAT-M R. M012. 18; also by INNOVIRIS -2019-Bridge-4: Re4Bru (M. A. K. , S. M. ) for productions of LPMOs and FER-2017 for the HPAEC-PAD analytical platform. We thank  Professor Cédric Delporte  of the  Analytical Platform of the Faculty of Pharmacy (APFP) ,  ULB  for the mass spectrometry of camalexin. APFP is supported by  Belgian National Fund for Scientific Research (FRS-FNRS)  and  ULB  funds (FRS No. 3. 4553. 08and T. 0136. 13 ; ULB FER-2007 ;  FER-2013 ;  FER-2019  and Platform funds). We also thank  Dr. Laurent Grumiau  and  Dr. Florence Rodriguez  (ULB, molecular biology platform) for technical assistance during immunoblotting analysis. M. C. is a Research Associate of the  INRAE (France).
The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). This work has benefited from the support of IJPB's Plant Observatory technological platforms and  Dr. G. Mouille  for SA and JA mass spectrometric analysis. We are grateful to  Dr. Marie-Christine Soulié  for providing B. cinerea  B05. 10 strain and  Dr. Herman Höfte  for providing us BAK1 and THE1 loss-of-function lines, all from  IJPB-INRAE-Versailles, FR. Finally , the authors are deeply grateful to the reviewers for providing exceptional meaningful revision suggesting experiments that markedly improved the final output of the work.
Daily time points from March 1 2020 to June 6 2021 were included in this analysis. Cumulative hospitalization data were included for MA even though these data were not available for our previous two analyses  [1, 25]. Details on CT data sources are in the Supplementary Methods; RI and MA data sources are described in Wikle et al  [1]. Vaccination campaigns were added into the dynamic model. Weekly age-structured SARS-CoV-2 vaccination numbers were obtained separately for the three states  [26] [27] [28] [29] , and vaccinated individuals were moved from the susceptible compartment in the dynamical model to the recovered compartment whenever a seronegative individual was vaccinated. The model allows for vaccination of seropositive individuals. In the model, the fraction = + + + (1 -(1 - ) )(1 -) represents the total fraction on non-symptomatic non-hospitalized individuals that are currently antibody negative and virus negative.
Funding M. F. B. , T. N-A. T. are funded by a grant from the  Bill and Melinda Gates Foundation  ( INV-005517 ). F. Y. is supported by the  NIH/NIAID Center of Excellence in Influenza Research and Surveillance  contract  HHS N272201400007C. K. B. was partially supported by the  National Institute of General Medical Sciences  of the  National Institutes of Health  under award number  R35GM133700. W. P. H. is funded by an award from the  NIGMS  ( U54 GM088558 ). E. A. is funded by the  Penn State MRSEC, Center for Nanoscale Science, NSF   DMR-1420620. E. M. H. was partially supported by  NSF   DMS-2015273. F. W. C is supported by  Cooperative Agreement   6NU50CK000524-01  from the  Centers for Disease Control and Prevention , funds from the  COVID-19 Paycheck Protection Program and Health Care Enhancement Act ,  NIH/NICHD  Grant  1DP2HD091799-01 , and the  Pershing Square Foundation.
Overview This project collected data on a new KT program involving psychoeducational video games and tutorials designed to educate Black African and Caribbean youth about the relationship between cannabis and psychosis. Our objective was to explore the extent to which this online KT program could transfer health information to youth-a challenging population to engage in medical literacy-and assess user satisfaction. This pilot project was based on a partnership involving McMaster University and the Free for All Foundation, a community charitable organization in Ontario that serves Black youth and their families. It is a grassroots community-based program that provides support so that Black youth can fulfill their potential. The study used qualitative and quantitative data collected before, during, and after two tutorial sessions facilitated by undergraduate students.
The primary variable was changes in scores on a knowledge test, the psychosis and cannabis test (PCT) quiz. The training was conducted from September to December 2020, and the recruitment, data collection, and analysis occurred between January and April 2021. A group of 10 undergraduate students at McMaster University participated in a research-based thesis course under the supervision of the senior author. Over 4 months, these undergraduate students studied the research relevant to the association between youth cannabis use and psychosis, using the Back to Reality Series as the inspiration for their learning objectives. They were taught how to facilitate tutorial sessions. The undergraduate students were trained in the proper conduct of data collection, transcribing the data, analyzing, coding procedures, generating themes, and writing up the report.
Acknowledgments We would like to thank  Dr David Green , Executive Director, and  Alfia Ansu , Administrative Assistant, as well as all of the staff, participants, and community members supporting this research project at  Free For All Foundation in Brampton, Ontario ,  Canada. This study was funded by the  Michael G. DeGroote Centre for Medicinal Cannabis Research  and the  Department of Psychiatry, McMaster University. The Back to Reality Series video games were funded by the  Canadian Institute for Health Research, Mitacs ,  Ontario Trillium Foundation , AMS-The  Phoenix  Project, and  CARSTAR Canada Research Innovation  fund.
Methods Participants. All 48 participants were recruited online from https://prolific. co. They were required to be fluent readers of English, within a specified age range (18-40 years) and to have not taken part in previous related studies. Demographically, they were located in different countries, with different native languages, variously employed or in full-time study, aged from 18 to 40 years (M = 27. 1 years, SD = 5. 7), and 21 were female. For their participation in the experiment they were paid a flat rate of £3. 75, based on an estimated session time of 30 minutes. Ethics & Data Protection. The research team were unaware of and had no access to the personal identity of the participants. In addition to the implied consent -given that participants were recruited through a voluntary, professional service -a full information sheet and consent form was presented prior to data collection.
05, level of assurance = 0. 95, and desired power of 0. 8. We specified the main within-subject factors from the current experiment -2 (Target) x 5 (Wolf Velocity) -and identified the main effect of Target as the effect of interest. This analysis yielded a minimum sample size of 11 participants, closely approximating our initial choice. Online Protocols. All data for the current study were collected online. Participants were directed to a dedicated URL on the https://maltacogsci. org domain and were taken through a series of webpages that provided instructions, obtained consent and ran the experimental trials. Anonymous data was transferred automatically on a trial-by-trial basis to a secure server for later download and processing. As participation was remote, we could not control the specific laptop/desktop machines that were used, nor the monitor hardware/settings.
Participants needed to complete 15 of those trials in order to finish the experiment. Block order was fixed, as this factor had not been found to qualitatively affect the pattern of foraging results in our previous work (see  Thornton et al. , 2019  for a detailed discussion) and in an online context, having the less demanding task first was useful from a familiarisation standpoint. Data Analysis. Our primary dependent variable was the average number of runs. As noted above, a "run" corresponds to the sequential selection of targets of the same category. With 40 targets divided into 2 categories, the number of runs on a given trial could vary between 2 and 40. We also examined other dependent variables which have proven sensitive measures of foraging behaviour. These included inter-target times (the time elapsed in seconds between two successive target selections) and inter-target distances (the distance in pixels between two successive target selections).
Methods Participants. A total of 72 new participants were recruited online from https://prolific. co. with the group sample size of 3 x 24 designed to match that of Experiment 1. The selection criteria, basic demographics and payment arrangement were the same as in Experiment 1. Here, the mean age was 24. 4 years, (SD = 5. 5), and there were 28 females. The same Ethics, Data Protection and online protocols were applied as in Experiment 1. Equipment and Stimuli. These were the same as Experiment 1, except for the following: The 4 wolf objects now moved by default at the highest velocity used in Experiment 1 (78 pixels/s) and there was no lone wolf. We removed the Velocity and Wolf Behaviour factors from the design as neither had influenced foraging in Experiment 1. Furthermore, in Experiment 2, all of the wolves were programmed to actively avoid the location of the sheep, changing direction and briefly accelerating if they approached within 100 pixels.
For distracted participants, selection was disabled whenever the eyes of the sheep enlarged. They were thus required to pause with the same temporal frequency as the hunted participants. Baseline participants were equally divided into wolf eyes and sheep eyes conditions, but were told to ignore the changes and were able to forage continuously throughout the trial. All participants completed 10 correct feature trials and 10 correct conjunction trials. The number of trials was reduced relative to Experiment 1, to compensate for the longer trial duration caused by the interleaved big-eyes periods. Data Analysis. We used the same set of dependent variables as in Experiment 1 which were analysed using a 3 (Predation:Baseline/Distracted/Hunted) x 2 (Target: Feature/Conjunction) mixed ANOVA. Full tables and analysis results are provided in the OSF supplementary materials.
Methods Participants. A new group of 48 participants were recruited online from https://prolific. co. The selection criteria, basic demographics and payment arrangement were the same as in Experiments 1 & 2. Here, the mean age was 24. 9 years, (SD = 6. 0), and there were 20 females. The sample size was also determined and verified as in Experiments 1 & 2, and the same Ethics, Data Protection and online protocols were applied. Equipment and Stimuli. These were the same as Experiment 1, except for the following: On each trial, the 40 target items now always consisted of 10 high-value yellow disks (2 points) and 30 low-value blue disks (1 point). The distractors were red and green disks. We did not use a Feature/Conjunction manipulation in this experiment. The 4 wolf objects now always moved at the highest velocity used in Experiment 1 (78 pixels/s) and there was always a lone wolf that directly tracked the position of the sheep object.
While it is clear that some compromises have to be made in terms of display environment and task length, we were still easily able to replicate our findings from previous lab studies. We thus echo the more general message from several recent review papers that collecting data online is a real option for vision/attention/cognition researchers (e. g. ,  Bridges et al. , 2020; Chetverikov & Upravitelev, 2016; Miller et al. , 2018; Pronk et al. , 2019). Furthermore, having access to a very large and diverse participant database -particularly ones with high professional and ethical standards such as https://prolific. co --augers well for future studies that want to focus on individual differences, whether in the context of foraging, as here, or beyond.
Discussion Our research hypothesis is that there is a correlation between the evaluation of the elastic properties of the aorta from cardiac MRI and from stretching tests performed ex-vivo on aorta samples collected during ascending aorta replacement. protection authority for France. CNIL is an independent French administrative regulatory body whose mission is to ensure that data privacy law is applied to the collection, storage, and use of personal data. As the database of this study was authorized by the CNIL, we cannot make available.
Ethics The study has been approved by the national ethic committee (Comité de Protection des Personnes) (2018-A02010-55) and recorded on ClinicalTrials. gov (clinical registration number: NCT03817008). The French law does not require written consent for this study. According to ethics committee, all patients will receive written information note on the study. Oral consent will be obtained from the patient, with a written information note (explaining the principle and the course of the study) provided to the patient at the time of consent. An attestation of the patient's oral consent will be signed by the investigator physician and countersigned by the patient. The project is based on a prospective single-center study including 100 patients with ascending aortic aneurysm requiring surgery. We will perform preoperative cine-MRI and biomechanical laboratory stretching tests on aortic samples collected during the cardiac procedure (Fig  1 ).
Statistical analysis The analyzed variables are continuous variables that will be expressed by their mean and standard deviation. We will test the normality of the variables with the Skewness and Kurtosis tests. STATA 14 statistical software (StataCorp, LP, College Station, Texas, and United States) will be used for the analyses. Continuous data will be analysed using a paired t-test or Wilcoxon test when variables will not follow a normal distribution. To study the correlation between MRI measurements and laboratory tests, we will use the parametric tests of correlation. In the absence of normality, we will use Spearman's nonparametric test. Then we will use a univariate and multivariate linear regression model with the dependent variable being the MRI measurement. With a sample size of 100 patients, for an hypothesis H 0 : ρ = 0.
Patient and tumor characteristics Out of 17575 BC patients in our institutional clinical database, 404 patients had synchronous bilateral BC (2. 3%) (FigS1). Patients with sBBC were slightly older at diagnosis (60. 4 versus 58. 2 years, p=0. 001) and had a higher mean BMI (25. 6 versus 24. 7, p<0. 001) than patients with unilateral breast cancer. A lower proportion of grade 3 (20. 9% versus 29. 4%, p<0. 001), tumors of TNBC (7. 7% versus 11. 1%) or HER2-positive (6. 1% versus 11. 4%, p<0. 001) subtype, or with lymphovascular invasion (21. 2% versus 28. 4%, p<0. 001) were observed in bilateral tumors than in unilateral BCs (TableS1 and FigS2). ER and PR staining percentages were lower in unilateral than in bilateral BCs. Out of 313 patients with invasive sBBCs, most of the tumors were luminal (n=538, 87.
TCR sequencing analysis To further investigate the T cell response to NAC and to compare infiltrating TCR repertoires in clonally distinct tumors within single patients, we extracted TCR beta CDR3 sequences from the RNAseq data, and 6708 clonotypes were identified in the whole cohort. Overall, 89. 4% were unique to individual samples (n=5994) (FigS10A), 10. 2% were patient specific but shared by multiple samples (n=682), and only 32 clonotypes (0. 5%) were identified in different patients (FigS10B). 150 clonotypes (2. 2%) were found in VDJdb, a curated database of T-cell receptor sequences of known antigen specificity (FigS10C). The most common antigens for this subset were CMV and Influenza, but the wide majority of clonotypes were unique to a sample. Among 135 clonotypes found both in PT and paired RD, the read count increased significantly after NAC (p=0.
Mutational signature deconvolution The contribution of mutational signatures to individual tumor samples were explored using the signatures deconvoluted by Alexandrov et al. 27  and referenced in the Cosmic database  59. We restricted the analyses to the 13 signatures previously evidenced in BC. Signature activities were estimated using the decompTumor2Sig algorithm  60  in the musicatk (version 1. 0. 0) R package  61. The percentage of mutational signatures was calculated by summing the relative contribution of each signature in PT samples to the whole tumor spectrum, divided by the number of sampled and the result was multiplied by 100. To avoid overrepresentation of the patient #6 to the cohort and because signatures profiles were similar on each side, we averaged the values of the left side on the one hand, and the values of the right side on the other hand.
TCR-sequencing analysis. We applied the MixCR algorithm on RNASeq data of 20 samples to identify and quantify TCR Beta-chain CDR3 sequences. MiXCR (version 2. 1. 5)  67  was used with its default parameters to extract and quantify TCR b-chain CDR3 sequences from RNA-seq fastq files. From the MiXCR output, we obtained for each sample the total number of distinct TCRb clones and the number of reads supporting each clone, and we normalized the result by the total number of reads. We investigated if TCR sequences identified by MixCR were identified in the VDJDB database, a curated database of T-cell receptor sequences of known antigen specificity  68  (Details available at the following URL : https://github. com/antigenomics/vdjdbdb).
Author's contributions FR and ASHP designed and conceptualized the study. LD, MD, EL and ASHP identified the Curie cohort and the patient tumor samples. ASHP and LD managed the clinical database, including performing retrospective chart review. OM, SV, and MA provided patient tumor samples. ML provided pathology support and performed pathological review and TILs scoring with TL and BG. FZ, JA and JH performed literature review. IB, JYP provided scientific advice and expert guidance on tumor biology. JA, CL, CV, and ASHP performed the whole exome sequencing analyses. BS and ASHP performed the RNASeq analyses. WR provided guidance for MixCR analyses. JW provided scientific direction. FR and ASHP allocated funding. All the authors participated in the preparation of the manuscript.
( Paris and Saint Cloud, France) between 2005 and 2015 in the institutional database (CNIL number 1766392 -v1). Patients were treated according to local guidelines. When indicated, chemotherapy was administered in a neoadjuvant or adjuvant setting, endocrine therapy was indicated in the case of positivity for hormone receptor and according to prognostic factors, and patients with HER2-positive tumors received neoadjuvant and/or adjuvant trastuzumab from 2007 onwards. This study was approved by the Breast Cancer Study Group and by the Institutional Review Board of Institut Curie and was conducted in accordance with institutional and ethical rules regarding research on tissue specimens and patients.
b) but more mediumsized ones (>2,5 kbp, Table  3 ). To complement this observation, M6_2 had the most SCGs and rRNA genes out of all extractions and also the most reads above 2. 5 kbp (Table  3 ), despite having very few long reads (only three > 50 kbp, Table  S1. b) and being the second smallest sample in terms of number of reads and total length before the removal of human reads (Table  3 ). We used the Human Oral Microbiome Database (HOMD) to assign taxonomy to the 16S rRNA genes found in our long reads. The top genera included Prevotella, Rothia, Streptococcus, Veillonella, which are commonly found in oral samples  (Mark Welch et al. 2016; Zaura et al. 2009). We observed a comparable taxonomic profile between M4 and M6, with Streptococcus, Veillonella, Prevotella and Actinomyces identified as the most abundant genera (Figure  3 ).
MinION Library Preparation and Sequencing. Upon receipt, and again immediately prior to sequencing, we measured the flowcell pore counts using the Platform QC script (MinKNOW). We stored the flowcells in their original packaging, which we resealed with parafilm and tape, at 4°C until use. We omitted size selection and the optional shearing step to allow us to evaluate the full distribution of fragment sizes produced by each method. We performed the library preparation using the Ligation Sequencing Kit (SQK-LSK108) and Native Barcoding Kit (EXP-NBD103) for genomic DNA, according to the standard 1D Native barcoding protocol provided by the manufacturer (Oxford Nanopore) (version: NBE_9006_v103_rev2_21DEC16) unless indicated. We carried out all reactions at room temperature. We followed the input DNA mass recommendation of 1.
BluePippin Size Selection. We performed size selection by the BluePippin (Sage Science) system with 0. 75% dye-free agarose cassettes and marker S1. We selected fragments > 6 kB in high-pass collection mode (an approach enabling the collection of DNA fragments above a user-defined size). Our 3. 3 µg sample DNA input was less than the recommended 5 µg; however, this input was maximized given sample DNA concentrations and loading volume constraints. Long-read Sequence analysis. We uploaded the raw MinION FAST5 files produced with the MinKNOW software (versions 1. 15. 4 through 3. 1. 19) to our cluster to perform the base-calling and demultiplexing with guppy v2. 3. 1. FastQ files were generated only for reads meeting a minimum quality threshold (quality score of 7). We used minimap2 v2. 14  (Li 2018 ) and samtools v1.