dataesr/scientific-paragraphs-categorization huggingface dataset:
Quantitative polymerase chain reaction (qPCR) was performed using 2X Platinum SYBR Green qPCRSuperMix-UDG (ThermoFisher) and MX3005p qPCR System cycler (Stratagene) according to the manufacturer's protocol. Primers of target genes were designed using the Genscript PCR primer design tool (Table  S3 ). ΔΔCT were determined with MxPro v4. 10 software using cyclophilin A as a housekeeping gene. Immunofluorescence. Fixed tissues (transversely frozen heart sections showing aortic valve from E14. 5, E18 and 2-month old mice) or cells were incubated with 1:50 diluted goat anti-mAngptl2 (AF1444, R&D Systems); 1:100 diluted rabbit anti-CD31 (ab28364, Abcam); 1:100 diluted rat anti-CD31 (AF3628, R&D Systems); 1:100 diluted rabbit anti-p21 (ab188224, Abcam); 1:200 diluted rabbit anti-Ki67 (ab15580, Abcam); 1:200 diluted rabbit anti-Vimentin (VP-V683, Vector Laboratories); 1:100 diluted mouse anti-α-SMA (A5228, Sigma-Aldrich); 1:100 diluted rabbit anti-activated Notch1 (ab8925, Abcam); 1:100 diluted rabbit anti-Notch1 (ab52627, Abcam); 1:100 diluted rat anti-Integrin α5β1 (MAB2514, Sigma-Aldrich); 1:100 diluted rabbit anti-PirB (ab284407, Abcam); 1:500 diluted secondary antibodies Alexa fluor-647 anti-rabbit (#A31573, ThermoFisher); Alexa fluor-488 anti-rat (#A21208, ThermoFisher); Alexa fluor-555 anti-mouse (#A31570, ThermoFisher) and Alexa fluor-555 anti-goat (#A21432, Thermo-Fisher).
Gene expression in human valves. Gene expression profiling by Illumina array was downloaded from the Gene Expression Omnibus (GSE45821). Data from 8 fetal semilunar valves (9, 10, 12, 12, 14, 15, 15 and 17 weeks) were compared to 8 adult aortic valves (14, 17, 18, 19, 50, 51, 53, 55 years old). Data from Illumina HumanRef-8 WG-DASL v3. 0 BeadArray were analyzed by using mean intensity and quantile normalization. The limma workflow was used to assess differentially expressed genes (DEG). DEG were considered significant at log2 fold-change > 1. 5 and FDR < 5%. NetworkAnalyst was used to asses DEG. Gene expression data was illustrated by using heatmap in R. Gene expression intensity matrix was analyzed in the R package WGCNA. The network was constructed by using consensus module detection (blockwiseConsensusModules) with default values.
Gene enrichment pathway was analyzed by using the REACTOME dataset in Enrichr. OCT imaging. Detailed method for OCT imaging is available in the Supplementary Methods at the end of the Supplementary Material file. Statistics and reproducibility. All the data are expressed as means ± SEM. Statistical tests were performed with GraphPad Prism 9 Software for macOS according to the normality of distribution: Two-sided Student t tests or Mann-Whitney U tests to assess differences between two groups, and Spearman or Pearson correlation tests to measure association between two variables. A p value < 0. 05 was considered statistically significant. Sample sizes and replicates are described in legends of figures. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Data availability Gene expression profiling by Illumina array that support the findings of this study was downloaded from the Gene Expression Omnibus under accession number GSE45821  57. In situ hybridizations on whole sagittal sections of wild-type mice embryos at E14. 5 that support the findings of this study was downloaded from the public database Eurexpress under accession number euxassay_007716  46. The pBlueScript SK ( + ) vector used in this study is available from Addgene under ID 212205. Source data are available in the Supplementary Data 1-4. Unedited blots/gels for Fig. S2  are available in the Fig. S12. Data supporting the findings of this study are available within the paper and its supplementary information files, or are available from the corresponding author on reasonable request.
Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients ≥ 70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML model.
Data collection We collected demographical, clinical, and neuroradiological data: patients' age and gender, comorbidities, current medications, Charlson Comorbidity Index (CCI), and smoking habit; Glasgow Coma Scale (GCS), pupillary size and light reaction, neurological deficits, and seizures at admission; ICH side, location (including lobar, basal ganglia, brainstem, or posterior cranial fossa) and volume measured by the ABC/2 method, and presence of intraventricular hemorrhage; data about surgical or medical treatment; length of hospital stay; and Glasgow Outcome Scale (GOS) at discharge and 6-month follow-up.
Machine learning model development The architecture adopted for ML pipeline is outlined in Fig. 1. Considering the characteristics of our dataset, sample size, and outcomes, a random forest (RF) model was deemed the most appropriate ML model  [6]. RF is a popular ensemble ML algorithm with easy hyperparameter tuning, lower risk of bias compared to a simple decision tree, elevated generalizability and accuracy, ability to capture non-linear data patterns, and applicability to different data volumes. It works by fitting a number of different decision trees (ensemble): majority "voting"/ "averaging" of all trees' outcomes are used to classify/predict each patient's outcome, thus improving predictive accuracy and controlling over-fitting. Then, the patients' cohort was randomly split into a training set and a hold-out test set following an 80:20 ratio.
Feature selection RF algorithm can achieve reliable performances even when the number of features exceeds that of the dataset instances or when most features are irrelevant to the outcome. Feature selection was performed using Boruta (v. 0. 3)  [26] , an all-relevant feature selection algorithm wrapped around RF allowing to identify and retain only the most predictive patient's variables (input features). Reducing input features shows several advantages: (a) the model's degrees of freedom reduction decreases the over-fitting and improves the generalizability, and (b) the emphasis of the most important variables enhances the model's interpretability.
Software All statistical analyses were performed in Jupyter Notebook, using Python v. 3. 8. 2 whose packages included the following: "Scikit-learn" to develop and train the random forest models, "Numpy" for Excel dataset handling, "imbalancedlearn" to solve class imbalance problem, "Sci-py" to perform univariable statistical association tests, "Statsmodels" to perform multivariable analyses, "Boruta" to perform recursive feature selection, and "LIME" v. 0. 2. 0. 1 to interpret the ML model. The entire source code utilized to develop the RF model is available at https:// github. com/ valer io-mc/ ML-in-ICH.
Capillary electrophoresis analysis The procedure follows protocols earlier developed in our laboratory using an HP 3D capillary electrophoresis (CE) apparatus (Agilent, Waldbronn, Germany)  [19]. Agilent software 3D-CE Chemstation (rev B. 04. 02) was used to pilot the CE. The HP 3D CE was equipped with an on-capillary TraceDec capacitively coupled contactless conductivity detector (C 4 D) (Innovative Sensor Technologies GmbH, Strasshof, Austria). The detection signal was acquired with the Tracemon software (Istech, version 0. 07a). CE analyses were performed in an uncoated fused-silica capillary of 35 cm total length and 50 μm i. d. purchased from Polymicro Technologies (Phoenix, AZ, USA)  [19]. Substrates are used at large excess relative to the enzyme (about 100 times). For this reason, "free or immobilized" myrosinase was used at 0.
Acknowledgements Authors gratefully acknowledge major financial support from  Labex SynOrg  ( ANR-11-LABX-0029 ), the  Centre National de la Recherche Scientifique (CNRS)  and the  University of Orléans. Authors also gratefully acknowledge financial support from  CHemBio  ( FEDER-FSE 2014-2020-EX003677 ),  Techsab  ( FEDER-FSE 2014-2020-EX011313 ), the  RTR Motivhealth  ( 2019-00131403 ) and the  Region Centre Val de Loire  ( ARD2020  Cosmetosciences). B. D. was funded by the  European Union 's  Horizon 2020 research and innovation program  ( Marie Sklodowska-Curie  grant agreement No. 642870 , ETN-Immunoshape). We also thank  Cyril Colas  and  Guillaume Gabant (CNRS) of the Fédération de Recherche  ( FR-2708 ) for the Maldi-TOF and HRMS experiments.
Statistical analyses The values of relative peak area of the diverse VOCs found in each agronomic treatment were subjected to analysis of variance (ANOVA) (Fisher's LSD post hoc test) using INFOSTAT software (FCA, Universidad Nacional de Córdoba, Argentina). The possible existence of correlations between an abundance of Zulla and volatile compounds was also studied by regression analysis. The multivariant statistical analysis performed was principal component analysis (PCA). These last two analyses were performed using Statsoft Statistical, version 7. 0 (Statsoft, Tulsa, OK). Heatmap visualisation of cluster analysis was performed using the MetaboAnalyst 4. 0 (web interface)  (Chong & Xia, 2018).
Results and discussion A total of 160 volatile compounds were detected by a sequential extraction procedure SBSE-HSSE followed by TD-GC-MS analysis in free fractions from Syrah musts. The total number of compounds detected in each harvest was different, ranging from 123 in the 2020 harvest and 156 in the 2019 harvest. Eighty-two of them were positively identified by mass spectrum according to the mass spectral database and standard LRI and 39 tentatively identified (TI) by mass spectrum and LRI according to data found in the literature. Results were expressed as relative peak area values with respect to IS and are shown in Table  1 , classified according to their functional groups. Alcohols, aldehydes, and ketones were the main volatile compounds identified in the three harvests. The highest total content of volatile compounds was observed in musts from 2019 for most of the different cultivation conditions studied.
Thus, all terpenes, acids, acetic acids esters, and methyl esters were correlated with samples from this harvest. However, the highest number of aldehydes was correlated with samples from the 2021 harvest. Therefore, the PCA results showed an important influence of the harvest on the type of cultivation and this influence is higher for musts from organic cultivation. Moreover, we have noted different influences on volatile compounds with different aromatic notes, this will provide musts with different aromatic profiles. To delve into these results, volatile compounds were grouped according to their aromatic notes in green-vegetable-mint, floral, fruity, fatty-waxy, balsamic-chemical, sweet-caramel-coffee, nutty, citric, dairy, off-flavour, spicy-toasted, and cheesy-pungent. Aromatic notes of volatile compounds used were those described by Callejón,  Morales, Troncoso, and Ferreira (2008) , and in several aroma databases (Flavornet and human odor space, 2021; The Good Scents Company Information System, 2021; and The LRI & Odour Database-Odour Data, 2021).
73 in 2019 and R 2 = -0. 81 in 2021), 2-methyl-butanal (R 2 = -0. 94 in 2019 and R 2 = 0. 91 in 2021), limonene (R 2 = -0. 76 in 2019 and R 2 = 0. 72 in 2020), or 1-penten-3-ol (R 2 = 0. 90 in 2019 and R 2 = -0. 79 in 2020). PCA was carried out, a total of 134 volatile compounds were included according to software criterium (variables with a minimum number of cases with values greater than 0, which show variance). The first three principal components (PCs) explained a low percentage of the total variance of the data (68. 8%). In Fig. 4 , it can be seen how the samples are separated according to their harvests; in particular, PC1 separated the musts from the 2019 and 2021 harvests, and PC2 the musts from the 2020 harvest from musts from the other two harvests. A higher number of variables, 61 volatile compounds, were correlated with samples from the 2020 harvest, among them, most alcohols (20) and ketones (13), and the only two acetic acid esters included in the model (methyl and ethyl acetate).
Acknowledgments This research was funded by the  Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA)  through the  Project "Investigación e Innovación Tecnológica en Vitivinicultura" (PR. AVA. AVA2019. 016 ) and the  European Rural Development Fund (ERDF, EU) ; and by the  European Regional Development Fund (FEDER)  and by the  Consejería de Economía, Conocimiento ,  Empresas y Universidad , of  Junta de Andalucía , within the framework of the Andalusian FEDER operational program 2014-2020. Specific objective 1. 2. 3. «Promotion and generation of frontier knowledge and knowledge lead to the challenges of society, development of emerging technologies» within the framework of the reference research project ( FEDER-UPO 1381315 ). FEDER co-financing percentage 80%.
Statistical Analysis Quantitative analyses were carried out using SAS 9. 4 software (Cary, NC, USA). The odds ratios (ORs) and confidence intervals (95% CIs) were estimated using multivariate logistic regression models. The analysis was performed on complete data (less than 5% missing data). We adjusted for potential confounders identified from the literature. Thus, potential confounders included in the multivariate logistic regression model were selected a priori from literature review: age ≥ 35 years at pregnancy, being primiparous, more than four physical and organizational constraints in the second or third trimester (being on call [night work or night and weekend shifts], home visits, prolonged standing [>6 h per day], working >42 h per week or carrying a heavy load [>15 kg]), infection by a teratogenic virus during pregnancy, exposure to at least one chemical agent (cytostatics, anesthetic gases, nitrous oxide), exposure to ionizing radiation, active smoking during pregnancy, alcohol consumption during pregnancy, the presence of a medical or surgical history (including uterine malformation, conization/myomectomy/scarred uterine surgery, cervical cancer, symptomatic/voluminous uterine fibroids, ovarian cyst, breast cancer, hypertension, type 1 diabetes, type 2 diabetes, thromboembolic disease, dysthyroidism, epilepsy, chronic inflammatory disease, autoimmune disease, depression, pelvic trauma, neoplasia), and the presence of an obstetrical history (recourse to assisted reproductive technology for the current pregnancy, stillbirth, voluntary termination of pregnancy, medical termination of pregnancy, prematurity, hypertensive disorders, cesarean section, premature rupture of membranes in the 2nd trimester, abruption placentae).
Immobilized receptors were regenerated between antibody analytes by dipping 3 × 5 s into regeneration buffer (10 mM Glycine, pH 1. 7). Binding sensorgrams were aligned to the beginning of the association step for inter-step correction and Y-aligned to the pre-association baseline step, and following single reference subtraction consisting of immobilized receptor dipped into kinetics buffer, processed sensorgrams were globally fit to a 1:1 binding isotherm on ForteBio HT Analysis Software (version 11. 1. 1. 39) to determine kinetic constants. Two experiments using streptavidin-coated biosensors (Sartorius AG, Item #18-5019) were conducted to account for variability between sets of biosensors. 10E8v4 binding to rhesus PBMCs. To determine bNAb binding to FcγRs on PBMCs, fluorophores were directly conjugated to 10E8v4 (PacBlue), 10E8v4 LALA (APC), and 10E8v4 EFTAE (FITC) using standard antibody labeling kits (Invitrogen).
Cells from four different rhesus macaques were divided into cluster tubes (Corning; 1 × 10 5 cells/tube) and washed twice with staining buffer (1 ml phosphate-buffered saline supplemented with 1% fetal bovine serum (FBS) and 1 mM EDTA). Live/Dead dye (Fixable Blue, Invitrogen) was added according to the manufacturer's directions and incubated for 15 min at 4 °C protected from light. After washing, 10 µg/ml of each bNAb was added to separate cluster tubes containing PBMCs from each animal, along with surface markers CD64:BV605, CD32:PE, and CD16:BV711 then incubated for 30 min as before. Cells were then washed twice and analyzed on a FACSymphony A5 (BD Biosciences). Data were analyzed in FlowJo and reported as the percent of live single lymphocytes binding the bNAb variant of interest normalized to the percent of cells binding unmodified 10E8v4.
Surface markers were then stained using the following panel: CD3:BV510, CD4:BUV395, CD8:BUV805, CD45RA:PE-Cy7, CD95:BV786, CD107a:FITC. Following fixation in 1% PFA (20 min at 4 °C and permeabilization with 0. 5% saponin in staining buffer, cells were stained for intracellular markers as follows: CD69:ACP-H7, IL-2:PE, IL-4:BV421, IL-21:APC, IFNγ:BV711, and TNFα:BV650. Stained cells were analyzed on a FACSymphony A5 (BD Biosciences) and data were analyzed with FlowJo software. Splenocyte infection assays. Cryopreserved macaque splenocytes were washed twice with RPMI, then resuspended at a concentration of 1 × 10 6 cells/ml in RPMI supplemented with 4. 5 g/l D-glucose, 10% heat-inactivated FBS, 1% L-glutamine, 1% penicillin, and 1% streptomycin (cRPMI) plus either 300 U/ml recombinant rhesus IL-4 and 150 U/ml granulocyte-macrophage colony-stimulating factor to enrich macrophage-derived dendritic cells or with 10 µg/ml phytohemagglutinin to expand T cells.
Acknowledgements We thank  Diane Kubitz  and  Jonathan Otsuji  at  The Scripps Research Institute Antibody Production Core facility  for antibody production,  Christina Corbaci  for assistance with figure design and production,  David Evans  for providing the  CD4 + CCR5 + NKR24  and KHYG-1 NK cell lines, and the  NIH/NIAID Vaccine Research Center  for the expression plasmid for 10E8v4 and the anti-10E8 mAb 2D1. We acknowledge the  NIH AIDS Reagent Program  for providing the following reagents: anti-HIV-1 Env mAb b12 ( ARP-2640 ), TZM-bl cells with FcRs ( ARP-11796 ,  ARP-11797 ,  ARP-11798 ), SIV mac239 gag pooled peptides ( ARP-12364 ), and SHIV SF162P3 virus stock ( ARP-6526 ). This work was supported by the following grants:  R01 AI129801  (A. J. H. ),  P51 OD011092 , and  U42 OD023038-03.
A number of technologies have emerged in the last years with different strategies for multiple epitope detection on a single slide. The commercial system PhenoCycler TM (formerly named CODEX® for co-detection by indexing) is a technology that uses DNA-conjugated antibodies with fluorescent nucleotides  6, 7. Associated to a fluorescence microscope, a multiplexed imaging device cycles through sample washing and marker substitution, allowing an important number of markers to be acquired using only a few fluorochromes. This technology solves the problem of spectral overlap and panel composition but requires an image processing software reconstituting the entire data from the raw acquired data. Here we focus on the processing step to process raw images out from the microscope (Figure  1 (A) ) to obtain the full stitched image (Figure  1  (B)), including segmentation based on nuclei staining.
The output of our workflow can then be used in different analysis software to analyse the different populations of cells (Figure  1 (C) ). Our workflow is very similar to the original Codex Processor software provided by the vendor, or to the open source CODEX Toolkit Uploader 6,8. Briefly, this software computationally concatenates and drift-compensates the images using nuclear stain as a reference, removes out-of-focus light using the Microvolution deconvolution algorithm, subtracts the background (using blank imaging cycles without fluorescent oligonucleotides), and creates hyperstacks of all fluorescence channels and imaging cycles. A Codex Segmenter was also made available by the same team to identify the cells incorporating recent advances in the segmentation methods  9. However, as we encountered several issues with the original software provided along with the device, we developed our own processing pipeline, to solve in particular issues regarding the stitching, the intensity normalisation and the segmentation.
Implementation We have developed a new implementation of the processor, in particular adapted to the PhenoCycler TM , but that could be used for any other system relying on cycles of acquisition. In particular our goal was to solve some problems encountered by the original software provided with the PhenoCycler as demonstrated in Figure  2  (C) and Figure  3 (B). Our pipeline is written using the JAVA programming language. Our code is organised around a core library providing main functionalities and consumed by a command line interface (CLI) as well as a GUI (Graphical User Interface). The CLI application exposes each processing step independently whereas the GUI provides an easy interface to start the entire pipeline, with the option to select a subpart of the workflow to be run. The different dependencies and required packages are listed in https://gitlab.
Operation The workflow was run on a laptop with Intel® Core TM i7-7Y75, CPU @ 1. 30 GHz, 16 Gb of RAM, with Windows 10 64-bit. Image processing performed includes deconvolution, extended depth of field, shading correction, background subtraction, cycle registration, stitching and segmentation (Figure  4 ). Deconvolution is included using DeconvolutionLab2  13  and PSFGenerator 14 from EPFL BIG. However this step can be performed using any deconvolution software, and an example implementation with the commercial licensed software Microvolution® 15 is provided as a branch in the code repository. Extended depth of field is provided using EPFL BIG implementation  16. Shading correction is performed by dividing a profile image computed using median intensities. Background correction is performed by subtracting signal from the blank cycles.
Cycle registration is performed using TurboReg 17 from EPFL BIG. Position-wise tiles from reference channel are registered against tiles from reference cycle. Computed translations are then applied to all the remaining channels. Finally, registered tiles are cropped by the greatest computed translation. Stitching is performed using MIST 18 from NIST. Stitched positions are computed using reference channel of the reference cycle and then applied to all remaining channels and cycles. Segmentation of nuclei is performed using Stardist  19, 20. The output directory structure is compatible with the original analysis software provided with the device CODEX® multiplex analysis viewer (MAV). Furthermore FCS files are generated using Flow Cytometry Data Standards 21 and can be analyzed using any other tool.
Use case We provide an example dataset acquired in our lab using Formalin-Fixed Paraffin-Embedded (FFPE) human tonsil and   describe the step-by-step operation using the GUI to call the core functions and the provided dataset, described in Data Availability. Tissue material FFPE tonsil human sample was obtained from the tissue bank of the Nantes university hospital. FFPE tonsil human was sectioned at a thickness of 5 µm and directly adhered onto poly-L-lysine (Sigma Aldrich) coated coverslips (Akoya Biosciences). Tissue coverslips were stored at 4°C until staining.