dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants and data acquisition (Hamburg) Thirty healthy human participants (16 women, mean age 26. 7, range 20-36) participated in the study after informed consent. All included participants were healthy, with no current or previous diagnosis of psychiatric or neurological disorder (full list of exclusion criteria can be found in  Pfeffer et al. , 2021). The study was approved by the Ethics Committee of the Medical Association Hamburg (approval number PV4648). Two participants were excluded from analyses, one due to excessive MEG artifacts, the other due to not completing all recording sessions (see below). Thus, we report results from N = 28 participants of which 15 were women. The present dataset is a subset of a larger dataset that entailed selective pharmacological manipulations  (Pfeffer et al.
, 2018; Pfeffer et al. , 2021). For the present article, only data from the placebo/resting-state condition was analyzed. Each participant completed three experimental sessions (scheduled at least 2 weeks apart), consisting of drug or placebo intake at two time points, a waiting period of 3 hr, and an MEG recording session. During the recordings, participants were seated on a chair inside a magnetically shielded chamber. Each recording session consisted of two resting-state measurements as well as four blocks consisting of two variants of a behavioral task. Each block was 10 min long and followed by a short break of variable duration. Brain activity was recorded using a whole-head CTF 275 MEG system (CTF Systems, Inc, Canada) at a sampling rate of 1200 Hz. Eye movements and pupil diameter were recorded with an MEGcompatible EyeLink 1000 Long Range Mount system (SR Research, Osgoode, ON, Canada) and electrocardiogram as well as vertical, horizontal, and radial EOG was acquired using Ag/AgCl electrodes.
Participants and data acquisition (Münster) Forty right-handed volunteers (21 women, age 25. 1 ± 2. 7 year (mean ± SD), range 21-32 years) participated in the study. All participants reported having no respiratory or neurological disease and gave written informed consent prior to all experimental procedures. The study was approved by the local ethics committee of the University of Münster (approval number 2018-068f-S). Participants were seated upright in a magnetically shielded room while we simultaneously recorded 5 min of MEG and eye tracking data. MEG data was acquired using a 275 channel whole-head system (OMEGA 275, VSM Medtech Ltd. , Vancouver, Canada) at a sampling frequency of 600 Hz. During MEG recordings, head position was continuously tracked online by the CTF acquisition system.
To this end, a set of three coils were placed on the head of the participant. Pupil area of the right eye was recorded at a sampling rate of 1000 Hz using an EyeLink 1000 plus eye tracker (SR Research). During recording, participants were to keep their eyes on a fixation cross centered on a projector screen placed in front of them. To minimize head movement, participants' heads were stabilized with cotton pads inside the MEG helmet. MRI acquisition of anatomical data for source reconstructions (Siemens 3T Prisma with a 20-channel head coil) was conducted in supine position to reduce head movements and gadolinium markers were placed at the nasion as well as left and right distal outer ear canal positions for landmark-based co-registration of MEG and MRI coordinate systems.
Participants and data acquisition (Glasgow) MEG resting-state data were acquired for 24 healthy, right-handed participants (9 women; age 23. 5 ± 5. 5 years (mean ± SD), range 18-39 years). The study was approved by the local ethics committee (University of Glasgow, College of Science and Engineering; approval number 300140078). Participants gave written informed consent prior to testing. Recordings were obtained with a 248-magnetometer whole-head MEG system (MAGNES 3600 WH, 4-D Neuroimaging), set to a sampling rate of 1017 Hz, and an EyeLink 1000 eye tracker (SR Research), sampling the pupil area of the left eye at 1000 Hz, both situated in a magnetically shielded room (Vacuumschmelze). Head position was measured at the start and end of the recording. To this end, a set of five coils were placed on the head of the participant.
Coil positions and head shape were digitized using a FASTRAK stylus (Polhemus Inc, VT). During recordings, participants sat upright and fixated on a green point (measure) projected centrally on a screen with a DLP projector (60 Hz refresh rate) for a minimum of 7 min. Resting-state recordings were part of a study into audio-visual speech processing that was recorded in two sessions. Audio-visual data have been published elsewhere  (Keitel et al. , 2018; Keitel et al. , 2020). In this study, the resting-state block was recorded as the last block of the second session after seven to nine experimental blocks, or more than 65 min into the session. Note that the resting-state data have not been reported in any of the previous publications. Data of two participants were excluded from analysis after screening recordings, one due to strong intermittent environmental artifacts in their MEG recordings and another due to a technical fault in tracking the pupil.
MEG preprocessing (Münster) MEG preprocessing was done in Fieldtrip  (Oostenveld et al. , 2011)  for MATLAB (The MathWorks Inc). First, noisy recording channels were identified by visual inspection and rejected from further analyses. Next, we adapted the synthetic gradiometer order to the third order for better MEG noise balancing (ft_denoise_synthetic). Power line artifacts were removed using a discrete Fourier transform (DFT) filter on the line frequency of 50 Hz and all its harmonics (including spectrum interpolation; dftfilter argument in ft_preprocessing). Next, we applied ICA  (Hyvärinen, 1999)  on the filtered data to capture eye blinks and cardiac artifacts (ft_componentanalysis with 32 extracted components). On average, artifacts were identified in 2. 35 ± 0. 83 components (M ± SD) per participant and removed from the data.
MEG preprocessing (Glasgow) For MEG and eye tracking data preprocessing and analysis, we used MATLAB (The MathWorks Inc), involving in-house MATLAB routines and the FieldTrip toolbox  (Oostenveld et al. , 2011). The MEG signal was resampled to the sampling rate of the eye tracker (1000 Hz) and denoised using in-built MEG reference sensors. We rejected between 2-27 noisy MEG channels (median = 7. 5 ± 4 absolute deviation) by visual inspection using FieldTrip's ft_rejectvisual. Continuous MEG recordings were then screened for noisy segments. High-pass filtered (2 Hz cutoff, fourth-order Butterworth, forward-reverse two-pass) MEG time series were subjected to an ICA  (Hyvärinen, 1999). Prior to the ICA, a principal component analysis (PCA) projected MEG data into a 32-dimensional subspace. For each participant, we visually identified two to four components (median = 3) capturing eye (blinks, movements) and heart-beat artifacts.
Pupil preprocessing (all sites) Pupil area traces were converted to pupil diameter to linearize our measure of pupil size. Using additional MATLAB routines (available from https://github. com/anne-urai/pupil_preprocessing_tutorial, as used in  Urai et al. , 2017) , blinks were identified by an automatic (and visually validated) procedure and linearly interpolated. The number of blinks and, therefore, the percentage of the interpolated data points varied across MEG sites: in total, 17. 9% of the Glasgow pupil time series (standard deviation: 17. 9%; range: 1. 3-39. 8%, with one outlier of 83. 3%), 11. 9% of the Hamburg pupil time series (standard deviation: range; 11. 3%; 0-37. 1%) and 22. 9% of the Münster pupil time series (standard deviation: 14. 9%; range: 0-56. 7%) were interpolated. In a second pass, a similar procedure was used on smaller eye tracker artifacts (spikes, jumps).
Resulting power spectra were also subjected to the spectral parameterization toolbox ('FOOOF';  Donoghue et al. , 2020)  to extract Hippus peak frequency as well as the exponent describing the spectral slope as a means to characterize the aperiodic activity. We used the FOOOF MATLAB wrapper with the default settings, except setting the maximum number of peaks ('max_n_peaks') to 3, the peak detection threshold ('peak_threshold') to 0. 5, and using a peak width range of [0. 1, 0. 5] Hz ('peak_width_limits').
5 s length and a temporal shift of 50 ms. Sensor-level MEG data were analyzed from 0. 25 s prior (effectively from -0. 5 to 0 s) to 1. 75 s following (effectively from 1. 5 to 2 s) the onset of a microsaccade. Microsaccaderelated changes in spectral power were defined as the percentual change in power relative to the pre-microsaccade baseline interval, defined as the time bin centered at t = -0. 25 s. For each time bin, pupil size values were tapered with the same Hann window and the resulting values were summed. Percentual change in pupil size were then computed equivalently to the changes in spectral power.
For each segment, power spectra were computed using MATLAB's 'pwelch' function, ranging from 2 to 128 Hz, with a frequency resolution of 0. 5 Hz. In addition, for each temporal segment, the mean pupil diameter as well as the mean of the pupil derivative were computed after shifting the pupil diameter, but not the pupil-derivative signal forward by 930 ms relative to the MEG time. Note that we opted for a different spectral decomposition method here than the wavelet-based approach (as described above) for practical reasons: Welch's periodogram method yields a direct estimate of the spectrum, which is convenient for parametrization as this analysis focused on the 1/f feature of the power spectrum rather than on power at any individual frequency. To separate aperiodic and periodic components of the power spectra, we used the spectral parameterization toolbox 'FOOOF' for Python 3.
The coefficients for the quadratic and the linear component are denoted with β 2 and β 1 , respectively, and β 0 describes the offset. The polynomial was fitted by minimizing the sum of the squares of the difference between the polynomial model and the power vs. pupil (pupil derivative) functions (as implemented in MATLAB's 'polyfit' function). The p-values shown in Figure  5  and Figure  5 -figure supplement 1 were obtained by testing the obtained beta-weights ( β 2 ) against zero on the group level (two-tailed paired t-test). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements TP and THD thank  Christiane Reissmann  and  Karin Deazle  for assistance with MEG recordings and the acquisition of structural brain scans. CK, AK, GT, and JG thank  Gavin Paterson  and  Frances Crabbe  for expert assistance with MEG recordings and structural brain scans. CK thanks  Linbi Hong ,  Josef Faller , and  Paul Sajda  for comments on the research. DSK and JG thank  Hildegard Deitermann ,  Ute Trompeter , and  Karin Wilken  for assistance with MEG recordings and structural brain scans. All authors thank  Ruud L van den Brink  for helpful comments on the manuscript. Funding TP has been supported by a  Feodor-Lynen fellowship  of the  Alexander-von-Humboldt Foundation. CK and AK have been supported by a  Wellcome Trust   Senior Investigator Grant awarded  to JG (# 098433 ) and GT (# 98434 ).
Data availability The ethics protocol(s) disallow sharing raw and preprocessed MEG and MRI data via a public repository. Data may be shared however within the context of a collaboration. No proposal is needed. However, the results presented in the manuscript are based on three separate datasets, collected independently in three different laboratories. As such, in order to obtain the data, an (informal) email to the authors responsible for the respective data sets is required (Hamburg: Thomas Pfeffer, thms. pfffr@gmail. com; Glasgow: Anne Keitel, a. keitel@dundee. ac. uk; Münster: Daniel Kluger, daniel. kluger@wwu. de). The code and data immediately underlying all main and supplementary figures has been made publicly available. Source data has been uploaded to a public repository (https://osf.
Z. N. A. ® Tissue DNA Kit (Omega Bio-tek, United States) and following the manufacturer protocol. The psbA locus was amplified using primer pairs: psbA-F1/psbA-R2  (Yoon et al. , 2002)  and the thermal profile for the PCR reaction followed  Peña et al. (2015b). The PCR product was purified and sequenced by the SAI-UBM department of the University of Coruña, Spain. Sequences were assembled with the assistance of CodonCode Aligner ® (CodonCode Corporation, USA), adjusted by eye using SeaView version 4  (Gouy et al. , 2010) , and submitted to the Barcode of Life Data Systems (BOLD,  Ratnasingham and Hebert, 2007)  and GenBank. Estimates of genetic distance (uncorrected p-distances) between the eight psbA sequences generated in the present study and 29 publicly available sequences of the rhodolith species Phymatolithon sp.
Data were captured and processed with a computer, using Titrisoft 3. 2 software (SI Analytics, Mainz, Germany). For quality control, a certified reference material of known total alkalinity was used to calibrate the method (CRMs, Batch No. 160; supplied by the Marine Physical Laboratory, Scripps Institution of Oceanography, USA), yielding TA values within 5 mmol kg -1 of the certified value. Similar to photosynthesis, the maximum light calcification rates (max. G Light ) and the initial slope of the Calcification-Irradiance curve (a G ) were determined. Here, the saturation irradiance (E k ) was estimated as the ratio of max. G Light /a G. In a posterior analysis, calcification was fitted against the respective gross photosynthesis for the different light levels and the initial slope of the curves was estimated using linear least-squares regression.
Statistical Analysis Data were tested for normality and heteroscedasticity, using the Shapiro-Wilk and Levene's tests, respectively, and when required, log-transformed to normalize the data (R D , GP max / R D , a, E k , E c , G Light , a G ). Physiological parameters were analyzed using one-way ANOVAs, with species as the between-subjects factor. In cases where significant differences were found, a Tukey's HSD post hoc test was conducted to determine significant groupings. Paired sample t-tests were used to compare the data from the inhibitor studies (before and after inhibitor treatment). To visualize differential inhibitor response profiles among rhodolith species, heat maps were generated using Python 3. 10. 2, along with the Pandas 1. 3. 5  (McKinney, 2011) , Matplotlib 3. 5. 1  (Hunter, 2007)  and Seaborn 0.
11. 2  (Waskom et al. , 2020)  libraries. To visualize multivariate similarities in physiological characteristics across the range of species and locations under varying environmental contexts, RDA (Redundancy Analysis) ordinations, with the 'vegan' R package  (Oksanen et al. , 2020)  and further customization via the 'ggord' R package (  (Beck 2017) , were carried out as a constrained ordination technique. Separate RDAs were implemented for both photosynthesis and calcification responses. Bi-plots were then constructed, displaying each rhodolith species, and for intra-specific comparison of Phymatolithon sp. from the different locations, including predictive variables (light, temperature) as vectors of varying length and direction. The significance of these multivariate configurations was tested by the 'anova.
ACKNOWLEDGMENTS NS thanks intern  Marie Brock  for her help with the alkalinity sample analysis,  Maeve Edwards  ( National University of Galweay ) for her support with the experiments in Ireland, and  Miguel Rodrigues  (DiveSpot) and the  AguaViva team  for their support with the fieldwork at  Armacão de Pera (Portugal) and in Arvoredo (Brazil) , respectively. The authors thank the  Museu da Baleia da Madeira  for the logistical support during the sampling efforts in Canical, the  Instituto das Florestas e Conservacão da Natureza  for house assistance in  Porto Santo Island , and  Estacão de Biologia Marinha do Funchal  for all the logistical support during the  Madeira Island field campaigns. In Canary Islands , rhodolith samples were collected under license n°SGPM/BDM/ AUTSPP/5212019, provided by the  General Sub-Directorate General for the Protection of the Sea (Ministery for the Ecological Transition  and the  Demographic Challenge-Spain ).
FUNDING This research was funded by the  European Union 's  Horizon 2020 research and innovation programs  under the  Marie Sklodowska-Curie Grant  agreement No. 844703 , the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 730984 , ASSEMBLE  Plus project (Transnational Access # 11154 ) and the  FCT -Foundation for Science and Technology through Stimulus of Scientific Employment, Individual Support  ( CEECIND ;  3rd Edition 2020. 01282. CEECIND). This study was also supported by the Portuguese national funds from  FCT -Foundation for Science and Technology  through projects  UIDB/04326/2020 ,  UIDP/ 04326/2020  and  LA/P/0101/2020 , and from the operational programs CRESC Algarve 2020 and  COMPETE 2020  through project  EMBRC. PT ALG-01-0145-FEDER-022121.
A single cell atlas of the human yolk sac We performed droplet-based scRNA-seq profiling of human YS, including both membrane and contents, and integrated with external datasets to yield 169,798 high quality cells from 10 samples spanning 4-8PCW (CS10-CS23), which can be interrogated on our interactive web portal (https://developmental. cellatlas. io/yolk-sac; password: ys2022) (13) (Fig. 1A-C, S1A-C, Table S1-5 ). All datasets used for cell state validation, in vitro iPSC culture and crossspecies comparisons are shown in Fig. 1A (Table S6-7 ). With iterative graph-based leiden clustering and annotation, the integrated YS scRNA-seq dataset yielded 43 cell types, which we grouped into 15 broad categories including haematopoietic cells, endoderm, mesoderm and mesothelium (Fig. 1B-C, S1B-C , Table  S3 -5).
We consistently apply the term HSPC for cells expressing a core HSPC signature e. g. CD34, SPINK2, HLF, without implying long-term repopulating capacity or multilineage potential. With comparison datasets, unless otherwise specified, we adopt published annotations (Table  S6-7 ). We demonstrate the key marker genes for these broad cell categories, validated by platebased scRNA-seq (Fig. Table S8, S5 ) and surface protein expression from CITE-seq analysis of n=2 YS cell suspension (Fig. 1C , S1F-G, Table  9 ). We used surface protein expression data to construct a decision tree that identified combinatorial antibodies deployable for YS cell type purification and functional characterisation (Fig. S2A ). FACS isolation of CD45 -cells with high scatter and Smart-seq2 analysis enabled enrichment and validation of YS endoderm, mesothelium and erythroid cells (Fig.
S3D-E). To facilitate future use of our YS atlas, we employed a low-dimensional logistic regression (LR) framework (see Methods). The trained LR models and weights from trained scVI models are provided via our interactive web portal to enable mapping of scRNA-seq datasets using CellTypist  (14)  and transfer learning with single-cell architectural surgery (scArches)  (15) , respectively. Corresponding cross-tissue projection probability matrices are provided as Supplementary Tables (Table  S11 -18). Using selected marker genes and proteins from our droplet-based datasets (Fig. 1C ), we performed 3D visualisation of YS by light sheet microscopy, demonstrating the CD34 hi PVLAP hi LYVE1 lo vitelline artery and a CD34 lo PVLAP lo LYVE1 hi vitelline vein contiguous with a branching network of CD34 -PVLAP lo LYVE1 hi vessels within the YS (Fig.
It will be fascinating to explore how these processes may impact on tissue homeostasis and disease across the human lifespan. Fig. 1:  A single cell atlas of the human yolk sac (A) Summary of data included in analyses. Squares represent data generated for this study and circles represent published datasets: YS  (9, 11, 45, 53) , AGM  (11) , liver  (9) , fetal BM (54), fetal brain  (44) , fetal skin  (45) , fetal kidney  (55) , fetal gonads  (46) , mouse  (56) , iPSC  (11, 21). Shape colour indicates the assay used to generate data (Table  S6 ). (B) UMAP visualisation of YS scRNA-seq data (n=10 independent biological replicates; k=169,798), with colours representing broad cell states: DC= dendritic cell, Mac= macrophage, MEM= megakaryocyte-erythroid-mast cell lineage, MK= megakaryocyte, pre.
= precursor. Insert shows the same UMAP coloured by YS tissue layer (Table  S5 ). S4 ). S19 ). S3 , 20, 7). All datasets independently scaled to max value=10 and then combined, except YS and matched EL scRNA-seq data which were scaled together. Genes involved in endoderm metabolic function are grouped. (B) Flower plot of the significant pathways upregulated in YS endoderm (pink), embryonic liver (EL) hepatocytes (blue) and conserved across both tissues (green). Lines indicate connected nodes of expression (Table  S21 ). (C) Left and middle: IHC staining of alpha fetoprotein (AFP), albumin (ALB) and alpha-1 antitrypsin (SERPINA1) in 8PCW YS, 8PCW EL and healthy adult liver. Representative images from 1 of n=5 biological independent YS (4-8PCW), 1 of n=3 biologically independent ELs (7-8PCW) and 1 of n=3 biologically independent adult livers.
Scale bar=100µm. Right: MSB-stained 8PCW EL (representative of n=3 biologically independent samples) and 4PCW YS (representative of n=3 biologically independent samples). Nuclei (grey), erythroid (yellow), fibrin (red), and connective tissue (blue). See 'Immunohistochemistry' section in Methods for details regarding pseudo-colouring shown. Scale bars=100um. (D) Dot plot showing the expression level (colour scale) and percent expression (dot size) of haemostasis factors expressed by YS endoderm (main and gastrula data), embryonic, fetal and adult liver hepatocytes, and endoderm from fetal kidney  (55). Grouped by pathway and role. All datasets independently scaled to max value=10 and then combined, except YS and matched EL scRNA-seq data which were scaled together. (E) Matrix heatmap of Milo-generated DEGs in endoderm from YS scRNA-seq including gastrulation datasets (Table  S19 ).
DEGs are grouped by function. Each dataset scaled zero centre=False and YS endoderm standard_scale='var'. (F) Schematic of the relative contribution of YS (orange), EL (blue), BM (purple) to haematopoiesis, coagulation factors and EPO synthesis in the first trimester of human development. (F) Dot plot showing the level (colour scale) and percent expression (dot size) of macrophage and microglia markers within the YS scRNA-seq monocyte and macrophage lineage in comparison to microglia, microglia-like and macrophage cell states in AGM  (11) , fetal skin  (45) , fetal gonads  (46)  and fetal brain  (44). Fetal brain, skin and gonad cell states annotated inhouse using LR output provided in Fig. S8E. Each dataset was scaled independently to a max_value = 10 then combined for plotting (Table  S18 ).
FDG overlaid with PAGA showing monocyte-independent trajectory from iPSC scRNAseq (21) AEC to macrophage prior to D21 (n=5; k=779; left) and monocyte-dependent trajectory to macrophage after D21 (n=7; k=8,553; right). Arrows indicate the trend of trajectory. Coloured by cell state (Table S7, S5). (I) Matrix heatmap showing the pySCENIC-derived regulons associated with the iPSC macrophage trajectories as shown in i for the iPSC scRNA-seq dataset (21).