dataesr/scientific-paragraphs-categorization huggingface dataset:
Smoking status at baseline was then considered as the exposure, grouped as current smoker, ex-smoker, and never smokers, with never smokers as the reference group. Models were initially adjusted with age as the timeline, followed by potential confounders BMI (continuous), physical activity (continuous), education (university degree or not), treated hypertension (yes/no), treated diabetes (yes/no), family history of CVD (yes/no), ever use of menopausal hormone therapy (yes/no), age at menopause. We assessed a-priori effect-modification between ABO group and smoking status by including the joint term in the main model. The p-value for this term was used to determine if effect-modification was likely. Data is presented as mean (standard deviation) for continuous variables, median (inter-quartile range) for non-normal data and % for categorical variables.
The strengths of this study include its prospective design, large number of participants and cases, control for several potential confounders (although as the exposure of blood group was genetic, and could be considered essentially randomised this changed little). As certain people with high-cholesterol may choose to treat the condition with lifestyle changes before considering drugs, we considered both self-reports and self-reports validated by treatments. Despite lacking laboratory cholesterol measurements in the E3N study, which is a major limitation, we considered self-reported dyslipidaemia requiring treatment validated against the use of lipid-lowering medications from a large drug-reimbursement database. Similarly, certain data such as BMI and smoking were based on self-reported data, but have been shown to be reliable  29.
Funding The E3N cohort is being carried out with the financial support of the  "Mutuelle Générale de l'Education Nationale" (MGEN) ;  European Community ;  French League against Cancer (LNCC) ;  Gustave Roussy Institute (IGR) ;  French Institute of Health and Medical Research (INSERM). This present study was also supported by the  French Research Agency (Agence Nationale de la Recherche)  via an  "Investissement d' Avenir" grant (investment for the future grant ,  ANR-10-COHO-0006 ). CJM is supported by funding from the  Federation Française de Cardiologie. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
5 • C  (Kahru et al. , 1993). Furthermore, previous remote sensing data indicate an increase in local SST of 4. 5 • C on a 4 d timescale due to the presence of phytoplankton blooms  (Capone et al. , 1998). Recent high-resolution in situ observations in the Indo-West Pacific Ocean highlight large anomalies of temperature of 0. 95 • C in the uppermost skin layer of the ocean when large phytoplankton blooms appear  (Wurl et al. , 2018). However, all these observations are either on a short timescale or in a geographically limited area. To study the larger-scale impact of phytoplankton light ab-Published by Copernicus Publications on behalf of the European Geosciences Union. R. Asselot et al. :  Climate pathways behind phytoplankton-induced atmospheric warming sorption and its relative magnitude, Earth system models are employed.
Ocean biogeochemistry component The biogeochemical module (BIOGEM) represents the transformation and spatial redistribution of biogeochemical tracers  (Ridgwell et al. , 2007). The state variables are inorganic and organic matter. The biological uptake is represented by an implicit biological community: nutrients are directly converted into organic matter via an uptake rate. The biological uptake is limited by light, temperature and nutrient availability. Organic matter is partitioned into dissolved and particulate phases (DOM and POM). For this study, BIO-GEM does not consider a temperature dependency on iron solubility and iron bioavailability. Our model setup includes iron (Fe) and phosphate (PO 4 ) as limiting nutrients. Similar to  Asselot et al. (2021) , we do not consider nitrate here.
Sensitivity F CO 2 Changes analysis (mol m -2 yr -1 ) (%)   well et al. , 2021). The configuration file is named "RA. ECO. ra32lv. FeTDTL. 36x36x32" and can be found in the directory "EcoGENIE_LA/genie-main/configs". The user-configuration files to run the experiments can be found in the directory "EcoGENIE_LA/genieuserconfigs/RA/Asselotetal_BG". Details of the code installation and basic model configuration can be found on a PDF file (https://www. seao2. info/cgenie/docs/muffin. pdf, last access: 20 October 2021). Finally, Sect. 9 of the manual provides tutorials on the ECOGEM ecosystem model.
Data availability. No data sets were used in this article. Author contributions. All authors designed and developed the concept of the study. RA performed the analysis of the model outputs with inputs from IH. RA drafted the initial version of the manuscript in collaboration with IH, FL and PBH. All co-authors read and reviewed the final version of the paper. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. Our special thanks go to  Félix Pellerin ,  Maike Scheffold  and  Laurin Steidle  for their valuable comments on the early version of this manuscript. We thank  David Armstrong McKay ,  Alexandre Pohl  and an anonymous reviewer for their helpful comments. Financial support. This work was supported by the  Center for Earth System Research and Sustainability (CEN) ,  University of Hamburg , and contributes to the Cluster of Excellence " CLICCS -Climate, Climatic Change, and Society ". Review statement. This paper was edited by  Perran Cook  and reviewed by  Alexandre Pohl ,  David Armstrong McKay , and one anonymous referee.
Echocardiography parameters In order to reproduce real-life practice, all eligible patients were consecutively enrolled, regardless echogenicity. We did not use any contrast agents. Echocardiography recordings were prospectively stored in DICOM format and offline analyses were performed by an investigator (C. B. , senior congenital cardiologist) who was blinded to clinical and CPET data. A second analysis was performed on all recordings, by the same investigator (C. B. ) to assess intraobserver reproducibility, and by a second investigator (V. P. , fellow) to assess interobserver reproducibility, with no access to the results of first analysis. Echocardiography analyses for 2D strain parameters were performed using TomTec Arena™ version 40 (TomTec Imaging Systems GmbH, Unterschleissheim, Germany) software, a vendor-independent program that can measure all echocardiographic images stored in DICOM format.
The Tei index was defined as the sum of the isovolumic contraction and the isovolumic relaxation time divided by ejection time  [30]. Systolic excursion velocity (S) and isovolumic acceleration (IVA) were measured using pulsed Doppler Tissue Imaging, with Doppler sample volume placed in the tricuspid annulus of the RV free wall  [19]. Aortic, mitral and pulmonary regurgitation were semi-quantitatively graded. Inferior vena cava (IVC) diameters (maximum and collapse index) were measured using M-mode echocardiography. Speckle tracking echocardiography was used to measure RV longitudinal 2D strain, using two different methods from the two available TomTec Arena™ version 40 software algorithms: one designed for the left ventricle (LV) and the other one designed for the RV. The RV was visualized from the apical 4-chamber view.
Using the LV software algorithm, the RV was divided into 3 free wall segments (basal, mid, apical) and 3 interventricular septum segments (basal, mid, apical) (Fig. 2 ). Using frame rates of 60-80 frames/sec to optimize myocardial deformation analysis, 2D strain analyses were performed for each segment, globally for the RV free wall, globally for the interventricular septum, and globally for the entire RV. As the LV software algorithm is initially dedicated to LV contouring, three markers were manually positioned at the RV extremities (apex, tricuspid annulus extremity towards the RV free wall, and tricuspid annulus extremity towards the septum), in order to "force" automatic RV contouring. Using the RV software algorithm, the RV was only divided into 3 free wall segments (basal, mid, apical) (Fig.
75, P<0. 001), whereas only one value (global free wall) presented a good interobserver reproducibility with the LV speckle tracking software algorithm (Table  3 ). Overall, eight RV echocardiography parameters had a good reproducibility, with ICCs >0. 75 for both intra and interobserver variability: TAPSE, free wall RV longitudinal 2D strain using RV speckle tracking software algorithm (apical, basal, medial and global segments), free wall global RV longitudinal 2D strain using LV speckle tracking software algorithm, peak systolic S wave, and tricuspid dP/dt.
Discussion In this prospective multicentre study, from a cohort of adult patients with a systemic RV, good reproducibility was found in most conventional and 2D strain RV echocardiography parameters. In order to best match the reality of clinical follow-up, our study analysed echocardiography examinations performed by different sonographers, on different ultrasound systems, and used, for the first time in this population, the Tom-Tec-Arena™ vendor-independent software for 2D strain analysis. Among all echocardiography parameters tested in this study, the TAPSE appeared to be the only predictor of physical capacity in systemic RV, with very low intra and inter-observer variability (ICC of 0. 97 and 0. 86, respectively). Indeed, the TAPSE is easily measurable in M-mode from the apical 4-chamber view, even if image quality is mediocre or endocardial border recognition is difficult, as opposed to strain and FAC measures  [6, 32].
98 and 0. 79 for intra and inter-observer variability, respectively). The systemic RV has a function similar to a LV, however its shape remains close to a normal sub-pulmonary RV. Therefore, the RV software algorithm from TomTec-Arena™ appeared to be well adapted to 2D strain analyses in systemic RV. The recent study from Ladouceur et al. on a similar population found good interobserver reproducibility (ICC of 0. 93) of RV global longitudinal 2D strain using LV software algorithm  [21]. However, in this retrospective study, echocardiography examinations were performed by a single examiner, on a single ultrasound system and off-line analyses were performed on a vendor-dependant software (EchoPAC, General Electric Healthcare). Unfortunately, despite this good reproducibility, the association between RV free wall global longitudinal 2D strain (using the RV software algorithm) and peak VO2 in our study did not reach statistical significance (r ¼ 0.
To better understand the underlying mechanism of axial morphogenesis in c-e Background normalized mean intensity of T/Bra:mCherry (c), TLC:mCherry (d), and AR8:mCherry (e) in epithelialized or non-epithelialized embryoids between 72 and 120 h. Data were collected from three biologically independent experiments. For d, adjusted p-values are: Non-epith. +TS (72 h) vs Nonepith. +TS (96 h), p = 0. 0168. f Representative images at 120 h showing T/Bra:mCherry expression in epithelialized embryoids treated with indicated inhibitors between 96 and 120 h. g Percentage of T/Bra:mCherry-positive epithelialized embryoids at 120 h, treated with indicated inhibitors between 72 and 96 h or 96 and 120 h. Background offset was used to set a threshold for T/Bra:mCherry expression. Adjusted p-values are: Control vs Lefty (96-120 h), p = 0.
0061; Control vs Lefty (72-96 h), p = 0. 0003; Control vs Noggin (72-96 h), p = 0. 0033; Noggin (96-120 h) vs Dkk1 (96-120 h), p = 0. 0001; Noggin (96-120 h) vs Lefty (72-96 h), p = 0. 0121; Noggin (96-120 h) vs Dkk1 (96-120 h), p = 0. 0001; Noggin (96-120 h) vs Dkk1 (72-96 h), p = 0. 0006; Dkk1 (96-120 h) vs Lefty (96-120 h), p = 0. 0325. h Timelapse analysis between 78 and 192 h with 2 h interval showing the onset of T/Bra expression. For control, Dkk1, Lefty, Noggin conditions, total number of embryoids analyzed were 23, 18, 15, 20, respectively. Data are collected from a single experiment. Adjusted p-values are: Control vs Dkk1, p = 0. 0002; Noggin vs Dkk1, p = 0. 0007. i, j Quantification of coverage index of T/Bra:mCherry expression (i) and EPI compartment area (j) at 120 h in epithelialized embryoids treated with indicated inhibitors.
We then used the scRNA-seq dataset to systematically assess the effect of the initial epithelial architecture of embryoids on the resulting cell-type diversity. A comparative analysis of epithelialized and non-epithelialized embryoids showed that several tissue types, including cardiac mesoderm, definitive endoderm, and pharyngeal mesoderm derivatives, were almost exclusively found in non-epithelialized embryoids (Fig. 5e , Supplementary Fig. 13a, b ). Moreover, the neural tissues in non-epithelialized embryoids were shown to be composed spinal cord tissue. Conversely, midbrain/hindbrain tissue, brain-specific neural stem cells, and their respective were only detected in epithelialized embryoids (Supplementary Fig. 12e ). These results demonstrate that the epithelial architecture of initial EPI aggregates has a direct impact on embryonic developmental trajectories and promotes neural differentiation in embryoids.
For morphology measurements, brightfield images were thresholded and segmented. Maximum inscribed circle function was used to fit circles in the identified object. Axial length was determined by connecting centers of the fit circles. Elongation index was calculated by dividing axial length to the diameter of the maximum inscribed circle. All image analysis codes are available upon request. Data was analyzed by using Microsoft Excel and GraphPad Prism. RNA isolation and qPCR. RNA was extracted with the RNeasy Micro kit (QIA-GEN), according to manufacturer's instructions and quantified with a spectrophotometer. One microgram of RNA was reverse transcribed with the iScript cDNA Supermix (#1708890). cDNA was diluted 1:5 and amplified by using Power SYBR Green PCR Master Mix. qPCR was run with a 7900HT Fast PCR machine (#4329001), using Power SYBR Green PCR Master Mix (Applied Biosystems), with an annealing temperature of 60 °C.
85 ± 0. 12 and RNA integrity numbers (RIN) of 9. 9 ± 0. 2 (average ± SD), supporting good purity and absence of degradation. TruSeq stranded mRNA LT libraries were prepared according to Illumina protocol 15031047 Rev. E, starting from 300 ng of RNA, quantified by qubit DNA HS, profiled on TapeStation 4200, and sequenced on an Illumina HiSeq 4000 at a targeted depth of 36 Mreads/sample and paired-end read length of 81,8i,8i,81. The reads were trimmed for adapters with bcl2fastq v2. 20. 0, aligned to the mouse genome mm10 with STAR 2. 7. 0e, and a count matrix was assembled using the cellranger v4. 0 curation of ENSEMBL annotations. In the manuscript, "Gene expression" refers to natural logarithm of counts per million for bulk RNA-seq data. The data were collected from four independent experiments.
6 with Seurat 61 v3. 1. Live cells were selected based on detection of more than 2000 genes, and mitochondrial gene content of 1. 5-15%, and counts were normalized as natural logarithm of counts normalized to 10k per cell (referred to as "Gene expression" in the manuscript). A mini-analysis including dimensionality reduction by PCA based on centered and scaled most variable genes and cell-type annotation transfer from an in vivo atlas  62  with scmap  63  was performed on individual datasets for the needs of doublet removal with DoubletFinder v2. 0. 3  64. After data filtering, all datasets were merged, variable genes, scaling and PCA repeated, and batch correction was then done with Harmony v1  65. A first Louvain clustering yielded clusters that were assigned to germ layers. UMAPs  66  were computed with uwot v0.
1. 5, first with 100 cells per cluster and spectral initialization, and then with all cells, initialized on the previous cluster centroids plus noise. The data were then split by germ layers, and a refined Louvain clustering and UMAP view was computed for each subset. Louvain clusters were finally assigned to cell types based on canonical markers. Custom code was used for visualizing cell-type proportions vs day and/or epithelialization, normalizing for heterogeneous dataset sizes to avoid bias. A further closeup was similarly performed on the neurons of the epiblast + subset. Splicing data were estimated with velocyto 67 v0. 17, and RNA velocity was computed with scVelo v0. 2. 1  68  in deterministic mode, python v3. 7. 2 and scanpy v1. 5  69. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank  G. Rossi  and  S. Vianello  for feedback on the manuscript. We thank  Lorenzo Mattolini  for his help with cell culture and imaging as well as his contribution to image analysis. We thank all members of the  Lutolf laboratory  for sharing materials and insightful discussions. We thank  E. Friman  and  D. Suter  for providing the SBr (Sox1:GFP;T/Bra: mCherry) ESC line,  C. Schröter  for providing the TS:GFP cell line,  Alfonso Martinez-Arias  for providing TLC:mCherry and AR8:mCherry ESC lines. We thank  R. Guiet  and  O. Burri  from the  Bioimaging and Optics Platform (BIOP)  at  EPFL  for writing the codes for image analyses. This work was funded by a  Sinergia grant  ( CRSII5_189956 ) from the  Swiss National Science Foundation , the  National Center of Competence in Research (NCCR) Bio-Inspired Materials  and  EPFL.
Data availability The single-cell RNA-sequencing data generated in this study have been deposited in the GEO database under accession code "GSE171209". The bulk RNA-sequencing data used in this study are available on the GEO database under accession code "GSE171211". All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. Source data are provided with this paper.
The photocurrents signals are split into two parts and then conditioned, digitalized and stored by the units in the green box of the scheme: first, their AC components are extracted with a Lock-In Amplifier (LA, HF2LI, Zurich Instruments, CH) by means of an In-Phase/Quadrature (I/Q) demodulation. In order to do so, a phase reference is required and it is provided by feeding the LA with a reference beating signal coming from the SU of the Zeeman Laser. Meanwhile, the DC components are directly obtained by the Nikon C2 Workstation that is receiving also the LA output after the I/Q demodulation. Then, a Dell Workstation, where the Nikon NIS-elements software is installed, has been used to perform the real-time check on the imaging results of the setup. The analysis of the experimental data has been carried out employing custom Matlab (Mathworks, United States) routines.
Data collection At the inclusion visit (at naloxegol start), the following data were reported by the physicians: cancer and OIC characteristics, current cancer and pain therapies and other treatments that could cause constipation, prior and concomitant OIC treatments, patient's bowel movements over the last 7 days and OIC symptoms using the Bowel Function Index (BFI)  [20]. The BFI is a clinician-administered, patient-reported, 3-item questionnaire (ease of defecation, feeling of incomplete bowel evacuation, and personal judgement of constipation) to evaluate OIC in cancer and non-cancer chronic pain patients. Items are scored on numerical 0-100 scales, and the total score in the range of 0-100 corresponds to the mean value; the lower the total score, the lower the symptom burden. A 12-point change in score constitutes a clinically relevant change in constipation  [12].
During the follow-up visit (around 4 weeks after inclusion), additional patient data were collected by physicians: bowel movements over the last 7 days, BFI, changes in opioids and OIC treatments (including naloxegol), the level of satisfaction with naloxegol, and adverse events. At both visits, patients ful lled self-reported questionnaires for their OIC assessing symptoms severity (Patient Assessment of Constipation Symptoms, PAC-SYM)  [21]  and quality of life (Patient Assessment of Constipation Quality of Life, PAC-QOL)  [22]. The PAC-SYM is a 12-item questionnaire divided into 3 symptom subscales (abdominal, rectal, and stool). The PAC-QOL is a 28-item questionnaire divided into 4 subscales (worries and concerns, physical discomfort, psychosocial discomfort, and satisfaction). For both questionnaires, items are scored on 5-point Likert scales, with scores ranging from 0 to 4.
Statistical analysis Descriptive analysis was performed. All tests were two-sided with α risk at 5%. The 95% CI was provided when relevant. Statistical analyses were carried out using SAS® software (SAS Institute, North Carolina, USA), version 9. 4. Patient baseline data, effectiveness of naloxegol (response to treatment and evolution of the constipation), and quality of life analysis were analyzed on the population of included patients who met all the selection criteria and received at least one naloxegol tablet (e cacy population). The analysis of the primary e cacy criterion (response rate in patients with or without concomitant laxatives, based on the data reported by the physicians) was repeated in the subgroups of patients with and without laxatives during study follow-up, and using data from the 28-day diaries completed by patients.
Disposition of patients Of the 133 patients included, 131 patients received at least one dose of naloxegol and were analyzed in the Safety population. Of the 124 patients who ful lled all the selection criteria (E cacy population), 86 patients (69. 4%) completed the study at week 4. Early study termination was mainly due to patient death (n = 11), patient decision (n = 7), lost to follow-up (n = 11), or other reasons [n = 9; side effects (3), lack of e cacy (2), and disease progression  (2) ]. Among the 86 patients who completed the study, 79 were evaluable for the primary criterion (response to naloxegol based on data reported by the physicians). Regarding patient self-reported questionnaires on constipation symptoms (PAC-SYM) and quality of life (PAC-QOL), 110 were analyzed at inclusion and 78 at W4.
Acknowledgements The authors would like to thank all the physicians and patients for their participation in the MovE study. Assistance supported by  Kyowa Kirin  was provided by  Carine Vera  (monitoring),  Milena PRETI  (data management) and  Karine ANDRE  (statistical analysis) from  Statitec ,  MultiHealth Group , and  Dr Marie-Odile BARBAZA  from  Auxesia  (manuscript preparation under the direction of the authors).
In the Unaware and Aware conditions, the computer sought out behavioral patterns in the participant's choice history (e. g. , R-R-P-P-S-S-…), their win/lose/tie patterns (e. g. , win-stay-lose-switch) and used any patterns it could find against them (for details on the Matlab algorithm used see  22, 25  ). Briefly, the algorithm predicted the most likely next play (R, P, or S) given the previous plays and their outcomes (win/loss/tie). The prediction was then based on the conditional probabilities of all the possible next moves given the history of moves and their outcomes. The next play that the algorithm output was the possibility that followed the most likely pattern, or in other words, the one that was least likely by chance (attached Matlab file for the implementation of the computer's playing algorithm).