dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis. Principal component analysis (PCA) followed by orthogonal partial least squares' discriminant analysis (OPLS-DA) was performed using the SIMCA 16 software (version 16. 0. 2, Umetrics, Umea, Sweden) on the dataset. All data were scaled to unit variance. OPLS-DA is a supervised approach to discriminate groups, i. e. , embryonic stage and line, using metabolomic data. Samples identified as being out of the confidence interval in the PCA (outliers) were removed from the analysis. For optimal model classification, variables with low regression coefficients and wide confidence intervals combined with low variable importance in projection (VIP) were iteratively removed. The overall quality of the models was appreciated by the proportion of variance in Y explained by the predictive component of the model (R 2 Y) and the predictive ability of the model (Q 2 ).
Acknowledgements The authors thank the staff of the  Poultry Experimental Facility (INRAE, UE 1295 Pôle d'Expérimentation Avicole de Tours, 37380 Nouzilly, France)  for producing and rearing animals as well as the technicians of the BOA unit ( INRAE, UMR Biologie des Oiseaux et Aviculture, 37380 Nouzilly, France ) for measurements on birds, sampling and laboratory analyses.
, 2021a). We used a mouse pre-B cell line expressing doxycycline (Dox)-inducible C/EBPα WT or C/EBPα R35A constructs fused with the biotin ligase variant TurboID  (Branon et al. , 2018). To avoid confounding interactions this cell line contained a double knockout of Cebpa and Cebpb, genes which are rapidly activated by exogenous C/EBPα  (Bussmann et al. , 2009). After Dox induction for 6 hr and biotin labeling, biotin-labeled proteins were identified by mass spectrometry. A comparison of the high confidence C/EBPα WT and C/EBPα R35A interactome revealed that C/EBPα R35A preferentially interacted with Spi1 (PU. 1), showing a 3. 6-fold increase in biotin label compared to C/EBPα WT. In addition, preferential but non-significant interactions of C/EBPα R35A were observed with Ncor1 and Ebf1 (Figure  3B ; Figure  3B , source data 1), which, however, were not further studied.
1 acts as a coordinator of the B cell to macrophage conversion. A plausible scenario therefore is that C/EBPα removes PU. 1 from B cell GREs and induces its relocation to macrophage GREs. Moreover, through its enhanced interaction with PU. 1, C/EBPα R35A might be more efficient at relocating the factor, explaining the observed acceleration of BMT. To test this hypothesis, we conducted DNA binding experiments by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) for PU. 1 and C/EBPα with primary B cells expressing C/EBPα WT ER and C/EBPα R35A ER 3 hr after E2 induction. The data obtained were then visualized on the UCSC browser in parallel with our ATAC-seq data with cells from 0, 1, 3, 6, 18, and 120 hpi. The main messages of our results are encapsulated by the Cyfip2-Havcr2 locus, a 270 Kb genomic region containing both a B cell-and a macrophage-restricted gene (Figure  5A ).
Conversely, treatment of 4-OHT-induced C/ EBPα WT -expressing cells with TP064 resulted in a robust acceleration of both Mac-1 upregulation and Cd19 silencing while the kinetics of C/EBPα R35A -expressing RAC1 cells were not altered (Figure  7G  and To determine if endogenous Carm1 levels correlate with BMT velocity, we analyzed a single-cell gene expression dataset of primary B cells undergoing BMT, generated in a previous study  (Francesconi et al. , 2019). This showed that cells with the lowest Carm1 levels transitioned to a macrophage-like identity faster than those with higher levels, with the most substantial differences occurring during the early timepoints (Figure  7H ), reflecting the more pronounced acceleration of gene expression within the first hours after induction of C/EBPα R35A (Figure  1F ).
In co-cultures between B cells and feeder cells, non-adherent cells were collected, joined with trypsinized adherent cells, and centrifuged at 300 rcf for 5 min. Cells were re-suspended in 100 μL PBS containing 1 μg/mL of mouse Fc block for 10 min. Conjugated primary antibodies were added to the blocking solution and cells were further incubated at 4°C in the dark for 20 min. Cells were washed with additional 1 mL of PBS and centrifuged at 300 rcf for 5 min. The supernatant was discarded and cells were re-suspended in 500 μL of PBS containing 5 μg/mL of DAPI. Samples were processed in a FACS analyzer (LSR II, BD; Fortessa, BD) with DiVa software and data analyzed using FlowJo software. Antibodies used for cell sorting and flow cytometry are listed in Supplementary file 3.
Analysis of mass spectrometry data Raw data was analyzed using MaxQuant (version 1. 6. 3. 3) against mouse proteome (Uniprot, 2019 version) with isoforms using standard settings. Additional options included fast LFQ, 'match between run', and lysine biotinylation  (Cox et al. , 2014). The data were analyzed using R-statistical software package (version 3. 6. 1). The protein files from MaxQuant were filtered for contaminants, reverse hits and proteins identified only by site. Intensity values were log2 transformed and filtered to allow only proteins with valid values in all four replicates in at least one of the experimental groups. Remaining missing values were imputed from normal distribution with width of 0. 3× standard deviation and downshift of 1. 8 from an observed mean. Significant interactors were defined as >2-fold enriched against TurboID and no-Dox controls and to pass the significance cutoff (adj.
Immunostaining by flow cytometry After antibody staining of cell surface markers, cells were fixed in 4% BSA for 10 min at room temperature in a rotating wheel. Fixation was stopped with two washes in PBS. Cells were permeabilized in 0. 1% PBST at room temperature in a rotating wheel for 10 min. Cells were blocked using 0. 1% PBST with 3% BSA for 30-45 min. Cells were washed twice in PBS. Cells were incubated with primary antibodies and secondary antibodies diluted at the stated concentrations in 0. 1% PBST with 1% BSA for 2 and 1 hr, respectively, with two washes in PBS in between and after. Cells were resuspended in PBS and processed in a FACS analyzer (LSR II, BD; Fortessa, BD) with DiVa software and data analyzed using FlowJo software. Antibodies used for cell surface immunofluorescence and intracellular staining for flow cytometry are listed in Supplementary file 1.
GO analyses Functional analyses by GO were performed with the R package 'g: profiler2' version 0. 2. 0  (Raudvere et al. , 2019). Baloonplots depict all pathways associated with a specific keyword that were found enriched in at least one cluster. Metaplots for each cluster depict the average log2FC values of genes per timepoint and per cluster. Shaded background corresponds to the mean values ± 1. 644854 s. d. Gene expression analysis of signature genes was performed using the individual values of the B cell genes Pax5,  Ebf1, Foxo1, Ikzf1, Rag1, Rag2, Bcl11a, Spib, Ikzf3, CD2, CD19, Igll1, Vpreb1, Vpreb2, Vpreb3, Pou2a1, Blk, CD79a, CD79b, Lef1 and the macrophage genes C1qc, Fcer1g, Sell, Ccr1, Mitf, Tlr2, Csf1r, Trem2, Fam20c, Adam8, Batf2, Fes, Itgam, Ccl3, CD300lf, Tnsf9, Tyrobp, CD14, Ifitm6, Csf3r  , normalized to timepoints o hr for B cell genes and 120 hr for macrophage genes.
Determination of differentially accessible ATAC peaks In order to pinpoint regions of interest, peaks of all timepoints and all experiments were merged using the bedtools suite command bedtools merge. Read counts falling within the merged peak regions were calculated using the Rsubread package and the featurecounts command with the options isPairedEnd = T, strandSpecific = 0, useMetaFeatures = F. For each transdifferentiation experiment, DESeq2 was used in order to compare all timepoints with timepoint 0 hr. Any peak showing a logFC ≥1, an adjusted p-value ≤0. 05, and average counts across timepoints ≥5 was termed as a differentially accessible region and was kept for further analyses. The total number of peaks isolated was 91,830. Variance stabilized counts were calculated for the isolated regions using the DESeq2 command varianceStabilizingTransformation and the options 'blind = T'', fitType=''parametric''.
Variance stabilized counts were averaged across timepoint replicates by raising them at the power of 2, extracting their average and log2 transforming the resulting mean. PCA was applied to this dataset using the R prcomp command, with 'scale = T'. To group peaks, PCA was initially applied and PC1 and PC2 values for the 91,830 regions were used in order to arbitrary cluster peaks into three groups depending on the sign of their PC1 and PC2 values. Values for each of the three groups were visualized using the pheatmap package. Visual examination of the three main groups showed different trends: peaks whose accessibility higher at 120 hr (43,429 peaks), lower at 120 hr (36,380 peaks), and higher at 18 hr (12,021 peaks).
Promoter accessibility analysis Genomic coordinates of mm10 genes were downloaded from the UCSC table browser (RefSeq genes). A single promoter region was assigned to each gene. The region consisted of 1 kb upstream and downstream of the transcription start site of the largest transcript of each gene. Counts for each timepoint and each transdifferentiation experiment were assigned to each promoter as described above. DESeq2 was used in order to identify differentially accessible promoters as described above with the following differences regarding the cutoffs used: -foldChange ≥ 1. 5 and p-value ≤ 0. 05. Variance stabilized counts were extracted for each differentially accessible promoter, a mean value per replicate was extracted, and the values were plotted using pheatmap. Promoters were then grouped into eight clusters.
Two-parameter SMT analysis Quantification and statistical analysis of SMT was performed as previously described  (Lerner et al. , 2020). In brief, TIF stacks SMT movies were analyzed using MATLAB-based SLIMfast script  (Teves et al. , 2016) , a modified version of MTT  (Sergé et al. , 2008) , with a maximal expected diffusion coefficient (DMax) of 3 μm 2 /s. The SLIMfast output. txt files were reorganized by the homemade csv converter. m MATLAB script (available in  Lerner et al. , 2020)  in. csv format for further analysis. The single molecule tracking. csv files (see previous section) were first classified by the homemade SMT_Motion_Classifier. m MATLAB script. Single molecule trajectories (or tracks) with a track duration shorter than five frames were discarded from the analysis. Motion tracks are classified by the script in different groups: tracks with α≤0.
7 were considered as Confined; motion tracks with 0. 7 < α < 1 as Brownian; and motion tracks with α ≥ 1 as Directed. In addition, the motion tracks showing a behavior similar to a levy-flight (presenting mixed Confined and Directed/Brownian behavior) were detected by the presence of a jump superior to the average jump among the track + a jump threshold of 1. 5, and classified as 'Butterfly'. Butterfly motion tracks were segmented into their corresponding Confined and Directed/Brownian sub-trajectories for posterior analysis. As an additional filtering step of confined motions (including confined segments of Butterfly tracks), we defined a jump threshold of 100 nm, to filter out motion tracks with an average frame-to-frame jump size larger than 100 nm. All scripts are publicly available.
Acknowledgements We thank the  Graf laboratory members  for critical discussions, the  Flow Cytometry and Microscopy unit of UPF-CRG  for technical assistance, the  CRG Genomics core facility  for sequencing and  Lars Velten  for feedback on the manuscript. TG was supported by the  Center for Genomic Regulation, Barcelona , the  Spanish Ministry of Economy, Industry and Competitiveness, (Plan Estatal   PID2019-109354GB-100 ),  AGAUR  ( SGR 006713 ) and the  4D-Genome European Research Council Synergy  grant. KSZ was supported by the  NIH  grant  R01GM36477. We have used ChatGPT to improve parts of the text.
Comparison of RACMO2 and MAR melt area with satellite passive microwave melt data To evaluate the RACMO2 and MAR melt simulations, we used the NASA MEaSUREs Greenland Surface Melt Daily 25 km EASE-Grid 2. 0 dataset 51 during 1980-2020. This dataset classifies the ice sheet surface into melt/no melt areas with daily temporal resolution, based on brightness temperature data acquired by three passive microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS). This dataset has in turn been compared to Greenland station observations of melt conditions and compares favorably to other melt retrieval algorithms applied to passive microwave satellite data  52. In order to compare the model and satellite data, we first bilinearly interpolated the RACMO2 and MAR melt data to the 25 km EASE Grid 2.
0 on which the MEaSUREs melt data is provided, and masked out grid cells in the NE Greenland domain that are not classified as ice sheet by all three datasets. Since the passive microwave melt data is provided as binary melt/no melt values with daily temporal resolution, whereas RACMO2 (MAR) outputs meltwater production values with 3-hourly (hourly) resolution, we developed a method to compare the Fig. 7  | Time series of northeast (NE) Greenland summer (JJA) melt attributable to combined atmospheric river (AR) and foehn conditions. Plotted is polar Regional Atmospheric Climate Model version 2. 3p2 (RACMO2) simulated cumulative JJA melt (solid line) attributed to the four possible combinations of 90th percentile atmospheric river (AR 90 ) and foehn conditions: no AR 90 with no foehn; no AR 90 with foehn; AR 90 with no foehn; AR 90 with foehn.
Also plotted is the percentage of time during each JJA season classified into each category (dashed lines). model meltwater production values with the binary satellite values. We summed the RACMO2 & MAR meltwater production values for each day, then tested a range of threshold values of meltwater production (mmWE/day) for classification as a "melt day" in the model datasets. Previous studies have used a threshold of 1 mmWE/day 53 or 8. 25 mmWE/day  54, 55  to classify "melt days" using MAR, so we tested the following meltwater production thresholds: 0. 1, 1, 2, 5, 8. 25, 10 mmWE/day. Using all available values from ice sheet grid cells in the NE Greenland domain during JJA 1980-2020, we produced a confusion matrix for each model melt threshold. The confusion matrix classifies each pixel on each day into one of four categories: "true negative" (model = no melt, MEaSUREs = no melt); "false positive" (model = melt, MEaSUREs = no melt); "false negative" (model = no melt, MEaSUREs = melt); and "true positive" (model = melt; MEaSUREs = melt).
Finally, to evaluate how well RACMO2 and MAR simulate the spatial pattern of NE Greenland melt, we created maps of the overall percentage of JJA days with melt for each dataset and their differences with the MEaSUREs data. The plots for the 1, 2, and 5 mmWE/day thresholds are shown in Supplementary Figs. S9-S11. These maps show that RACMO2 and MAR broadly agree with MEaSUREs on the spatial pattern of mean JJA melt. RACMO2 overestimates the frequency of melt in most areas with a 1 mmWE/day threshold and underestimates melt prevalence with a 5 mmWE/day threshold, with a relatively even spatial distribution of overestimation and underestimation for the 2 mmWE/day threshold. MAR has a more definite spatial pattern to its bias, with an underestimation of melt frequency at higher elevations even for the 1 mmWE/day threshold, and an overestimation of melt frequency at lower elevations even for the 5 mmWE/day threshold.
Ensemble Empirical Mode Decomposition (EEMD) time series analysis The time series of NAO, Greenland Blocking Index (GBI), and melt attributed to the four possible combinations of AR 90 and foehn conditions are fit with trend lines using the nonlinear Ensemble Empirical Mode Decomposition (EEMD) method (ref. 58 ) using the Rlibeemd package for the R programming language  59. Correlations between anomalies in NAO/GBI and melt values are calculated by subtracting the trend fit from each time series. We present only the NAO results in the paper, as the melt time series correlations with GBI are qualitatively similar but with a reversed sign, and we also note that correlations are similar using the actual (non-detrended) values of each variable. National Weather Service Climate Prediction Center: https://www.
cpc. ncep. noaa. gov/products/precip/CWlink/pna/nao. shtml. Daily time series of the GBI calculated using NCEP / NCAR reanalysis data are obtained from the NOAA Earth System Research Laboratories Physical Sciences Laboratory: https://psl. noaa. gov/data/timeseries/daily/GBI/. Near-surface meteorological observations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) dataset  64  are available at https://www. promice. org/PromiceDataPortal/#Automatic weatherstations. The annual mean GrIS velocity is obtained from the MEaSUREs Multi-year Greenland Ice Sheet Velocity Mosaic, Version 1 dataset  65  distributed by the National Snow and Ice Data Center (NSIDC): https://nsidc. org/data/NSIDC-0670/versions/1. MODIS imagery from the NASA Worldview application, part of the NASA Earth Observing System Data and Information System (EOSDIS), can be obtained here: https://worldview.
Acknowledgements K. S. M. acknowledges support from the  Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) mission ,  NASA  grant  80NSSC18K1485. J. D. W. acknowledges support from the  Agence Nationale de la   Recherche  project,  ANR-20-CE01-0013  (ARCA). B. N. was funded by the  NWO VENI  grant  VI. Veni. 192. 019.
Data availability RACMO2 data were published in ref. 3  and additional variables, including 5. 5 km resolution melt output, are available upon request from Brice Noël (b. p. y. noel@uu. nl) or Michael van den Broeke (M. R. vandenBroeke@uu. nl). MAR data  60  are available at https://doi. org/10. 5281/zenodo. 7591112. Passive microwave GrIS daily melt data 51 are available at https://doi. org/10. 5281/zenodo. 7591560. Output from the Mattingly and Wille AR detection algorithms can be downloaded from the ARTMIP 61 Tier 1 database at https://doi. org/10. 5065/ D62R3QFS. The Polar WRF model output 62 for 2014-2018 is available at https://doi. org/10. 17605/OSF. IO/53E6Z. ERA5 63 data are available from the Copernicus Climate Change Service Climate Data Store: https://cds. climate. copernicus. eu/#!/search?text=ERA5&type=dataset.
(2021). The terminal settling velocity of ash particles is calculated for the mean radii within each bin. Spectral nudging  (Miguez-Macho et al. , 2004 ) has been applied above the PBL (>5. 0 km) to horizontal wind components (u and v) for the European Centre for Medium-Range Weather Forecasts Era-interim reanalysis fields. The nudging coefficient equals 0. 0001 s -1. Only wavelengths larger than 450 km are nudged. This setting allows accounting for the realistic phase of QBO and keeps the large-scale motions close to the observations, letting the model develop small-scale disturbances freely. The meteorological initial and boundary conditions for WRF-Chem are also calculated using the ERA-Interim reanalysis product  (Dee et al. , 2011). The ERA-Interim reanalysis data were obtained with a 0.
The radiative feedback is stronger for large eruptions as they can produce a higher initial ash concentration, causing stronger local radiative heating than small eruptions. However, the Raikoke eruption in 2019, which emitted an order of magnitude less ash and SO 2 than Pinatubo, exhibits a strong lofting of volcanic cloud because of unusually high absorptivity of ash  (Khaykin et al. , 2022). Long-term calculations provide an additional constraint on the spatial-temporal evolution of the volcanic sulfate cloud. We calculated the three-month volcanic cloud's evolution using our inverted solutions as initial conditions for ash and SO 2. The spatialtemporal evolution of the simulated AOD agrees with the SAGE/ASAP dataset a month after the eruption. The first month in the simulation is affected by the SO 2 emissions in the troposphere that quickly oxidized, producing averaged over the tropical belt AOD on the order of 0.
To facilitate the development of such models, we have assembled a set of images of the retinal microvascular system from 499 patients. It consists of 814 angiocubes as well as 2005 en face images. Angiocubes were captured with a swept-source OCT-A device of patients with varying overall cardiovascular risk. To the best of our knowledge, our dataset, RASTA, is the only publicly available dataset comprising such a variety of images from healthy and at-risk patients. This dataset will enable the development of generalizable models for screening of cardiovascular diseases from OCT-A retinal images.
The CHA2DS2-VASc clinical score, which is universally known and easy to calculate, is an embolic risk stratification tool originally used to assess the risk of stroke in patients with non-valvular atrial fibrillation  [12]. It has been recently presented as an effective model for evaluating the cardiovascular risk profile regardless of the arrhythmic status of patients  [13] [14] [15] [16] [17] [18] [19]. Several datasets containing images of retinal fundus photographs are publicly available (i. e. MESSIDOR, STARE project, DRIVE, Eophtha, and EyePACS)  [20] [21] [22] [23] [24]. However, SS OCT-A datasets are less widespread  [25]. To the best of our knowledge, the Retinal oct-Angiography and cardiovascular STAtus (RASTA) dataset is the first publicly available dataset that provides systematic cardiovascular data and complete SS OCT-A retinal imaging.
Ethics Approval The RASTA dataset was acquired in the Department of Ophthalmology at the University Hospital of Dijon, France, and consists of actual clinical acquisitions from different registered clinical studies. The RASTA dataset was anonymized and processed in accordance with the rules established by the Ethics Committee of the University Hospital of Dijon. All administrative information included in the metadata has been removed, making it untraceable. Thus, in accordance with the French law it was not necessary to obtain ethical approval.
Data Composition The RASTA dataset is a new publicly available SS OCT-A retinal image dataset consisting of 499 participants for 2005 en face images and 814 angiocubes combined with clinical and demographic characteristics. Information on data accessibility and specifications is provided in Table  1. Each participant was identified by an anonymized ID and was then included in one of three groups according to their cardiovascular risk category as follows: For each participant, we included the images of their corresponding SS OCT-A 6 x 6-mm angiocubes and en face (two-dimensional) images. Angiocubes were identified on the basis of their side only. En face images (Figure  1 ) were identified on the basis of their plexus followed by their side as follows: • Low cardiovascular risk -CHA2DS2-VASc = [0; 1] • Intermediate cardiovascular risk -CHA2DS2-VASc = [2; 3] • High cardiovascular risk -CHA2DS2-VASc = [3; 9] • « sup » for superficial plexus or « deep » for deep plexus or « cc » for choriocapillaris plexus The RASTA dataset is composed of four different single-center studies with the Ophthalmology Department of the University Hospital of Dijon as the principal investigator since 2018 and one multicenter study conducted by 14 investigative health centers since 2021.