The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, of 60247 objects classified as galaxies, it includes a CSV file with a collection of information and a set of files for each object, namely JPG image files, FITS and spectra data. This dataset is used to train and explore the astromlp-models collection of deep learning models for galaxies characterisation.

The dataset includes a CSV data file where each row is an object from the SDSS database, and with the following columns (note that some data may not be available for all objects):

• objid: unique SDSS object identifier
• mjd: MJD of observation
• plate: plate identifier
• tile: tile identifier
• fiberid: fiber identifier
• run: run number
• rerun: rerun number
• camcol: camera column
• field: field number
• ra: right ascension
• dec: declination
• class: spectroscopic class (only objetcs with GALAXY are included)
• subclass: spectroscopic subclass
• modelMag_u: better of DeV/Exp magnitude fit for band u
• modelMag_g: better of DeV/Exp magnitude fit for band g
• modelMag_r: better of DeV/Exp magnitude fit for band r
• modelMag_i: better of DeV/Exp magnitude fit for band i
• modelMag_z: better of DeV/Exp magnitude fit for band z
• redshift: final redshift from SDSS data z
• stellarmass: stellar mass extracted from the eBOSS Firefly catalog
• w1mag: WISE W1 "standard" aperture magnitude
• w2mag: WISE W2 "standard" aperture magnitude
• w3mag: WISE W3 "standard" aperture magnitude
• w4mag: WISE W4 "standard" aperture magnitude
• gz2c_f: Galaxy Zoo 2 classification from Willett et al 2013
• gz2c_s: simplified version of Galaxy Zoo 2 classification (labels set)

Besides the CSV file a set of directories are included in the dataset, in each directory you'll find a list of files named after the objid column from the CSV file, with the corresponding data, the following directories tree is available:

sdss-gs/
├── data.csv
├── fits
├── img
├── spectra
└── ssel

Where, each directory contains:

• img: RGB images from the object in JPEG format, 150x150 pixels, generated using the SkyServer DR16 API
• fits: FITS data subsets around the object across the u, g, r, i, z bands; cut is done using the ImageCutter library
• spectra: full best fit spectra data from SDSS between 4000 and 9000 wavelengths
• ssel: best fit spectra data from SDSS for specific selected intervals of wavelengths discussed by Sánchez Almeida 2010

Changelog

• v0.0.3 - Increase number of objects to ~80k.
• v0.0.2 - Increase number of objects to ~60k.
• v0.0.1 - Initial import.

ImageNet Subsets

Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the zip folder contains PBPK model files (for chloroform and DCM) and corresponding scripts to compile the models, generate human equivalent doses, and run sensitivity analysis.

Details of the 10 additional datasets (the top five datasets are on species-habitat interactions; the second five datasets are wider biological datasets).

The zip-file contains the data and code accompanying the paper 'Effects of nutrient enrichment on freshwater macrophyte and invertebrate abundance: A meta-analysis'. Together, these files should allow for the replication of the results.

The 'raw_data' folder contains the 'MA_database.csv' file, which contains the extracted data from all primary studies that are used in the analysis. Furthermore, this folder contains the file 'MA_database_description.txt', which gives a description of each data column in the database.

The 'derived_data' folder contains the files that are produced by the R-scripts in this study and used for data analysis. The 'MA_database_processed.csv' and 'MA_database_processed.RData' files contain the converted raw database that is suitable for analysis. The 'DB_IA_subsets.RData' file contains the 'Individual Abundance' (IA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria. The 'DB_IA_VCV_matrices.RData' contains for all IA data subsets the variance-covariance (VCV) matrices. The 'DB_AM_subsets.RData' file contains the 'Total Abundance' (TA) and 'Mean Abundance' (MA) data subsets based on taxonomic group (invertebrates/macrophytes) and inclusion criteria.

The 'output_data' folder contains maps with the output data for each data subset (i.e. for each metric, taxonomic group and set of inclusion criteria). For each data subset, the map contains random effects selection results ('Results1_REsel_

The 'scripts' folder contains all R-scripts that we used for this study. The 'PrepareData.R' script takes the database as input and adjusts the file so that it can be used for data analysis. The 'PrepareDataIA.R' and 'PrepareDataAM.R' scripts make subsets of the data and prepare the data for the meta-regression analysis and mixed-effects regression analysis, respectively. The regression analyses are performed in the 'SelectModelsIA.R' and 'SelectModelsAM.R' scripts to calculate the regression model results for the IA metric and MA/TA metrics, respectively. These scripts require the 'RandomAndFixedEffects.R' script, containing the random and fixed effects parameter combinations, as well as the 'Functions.R' script. The 'CreateMap.R' script creates a global map with the location of all studies included in the analysis (figure 1 in the paper). The 'CreateForestPlots.R' script creates plots showing the IA data distribution for both taxonomic groups (figure 2 in the paper). The 'CreateHeatMaps.R' script creates heat maps for all metrics and taxonomic groups (figure 3 in the paper, figures S11.1 and S11.2 in the appendix). The 'CalculateStatistics.R' script calculates the descriptive statistics that are reported throughout the paper, and creates the figures that describe the dataset characteristics (figures S3.1 to S3.5 in the appendix). The 'CreateFunnelPlots.R' script creates the funnel plots for both taxonomic groups (figures S6.1 and S6.2 in the appendix) and performs Egger's tests. The 'CreateControlGraphs.R' script creates graphs showing the dependency of the nutrient response to control concentrations for all metrics and taxonomic groups (figures S10.1 and S10.2 in the appendix).

The 'figures' folder contains all figures that are included in this study.

Source Code for the manuscript "Characterizing Variability and Uncertainty for Parameter Subset Selection in PBPK Models" -- This R code generates the results presented in this manuscript; the zip folder contains PBPK model files (for chloroform and DCM) and corresponding scripts to compile the models, generate human equivalent doses, and run sensitivity analysis.

Feature preparation Preprocessing was applied to the data, such as creating dummy variables and performing transformations (centering, scaling, YeoJohnson) using the preProcess() function from the “caret” package in R. The correlation among the variables was examined and no serious multicollinearity problems were found. A stepwise variable selection was performed using a logistic regression model. The final set of variables included: Demographic: age, body mass index, sex, ethnicity, smoking History of disease: heart disease, migraine, insomnia, gastrointestinal disease, COVID-19 history: covid vaccination, rashes, conjunctivitis, shortness of breath, chest pain, cough, runny nose, dysgeusia, muscle and joint pain, fatigue, fever ,COVID-19 reinfection, and ICU admission. These variables were used to train and test various machine-learning models Model selection and training The data was randomly split into 80% training and 20% testing subsets. The “h2o” package in R version 4.3.1 was employed to implement different algorithms. AutoML was first used, which automatically explored a range of models with different configurations. Gradient Boosting Machines (GBM), Random Forest (RF), and Regularized Generalized Linear Model (GLM) were identified as the best-performing models on our data and their parameters were fine-tuned. An ensemble method that stacked different models together was also used, as it could sometimes improve the accuracy. The models were evaluated using the area under the curve (AUC) and C-statistics as diagnostic measures. The model with the highest AUC was selected for further analysis using the confusion matrix, accuracy, sensitivity, specificity, and F1 and F2 scores. The optimal prediction threshold was determined by plotting the sensitivity, specificity, and accuracy and choosing the point of intersection as it balanced the trade-off between the three metrics. The model’s predictions were also plotted, and the quantile ranges were used to classify the model’s prediction as follows: > 1st quantile, > 2nd quantile, > 3rd quartile and < 3rd quartile (very low, low, moderate, high) respectively. Metric Formula C-statistics (TPR + TNR - 1) / 2 Sensitivity/Recall TP / (TP + FN) Specificity TN / (TN + FP) Accuracy (TP + TN) / (TP + TN + FP + FN) F1 score 2 * (precision * recall) / (precision + recall) Model interpretation We used the variable importance plot, which is a measure of how much each variable contributes to the prediction power of a machine learning model. In H2O package, variable importance for GBM and RF is calculated by measuring the decrease in the model's error when a variable is split on. The more a variable's split decreases the error, the more important that variable is considered to be. The error is calculated using the following formula: 𝑆𝐸=𝑀𝑆𝐸∗𝑁=𝑉𝐴𝑅∗𝑁 and then it is scaled between 0 and 1 and plotted. Also, we used The SHAP summary plot which is a graphical tool to visualize the impact of input features on the prediction of a machine learning model. SHAP stands for SHapley Additive exPlanations, a method to calculate the contribution of each feature to the prediction by averaging over all possible subsets of features [28]. SHAP summary plot shows the distribution of the SHAP values for each feature across the data instances. We use the h2o.shap_summary_plot() function in R to generate the SHAP summary plot for our GBM model. We pass the model object and the test data as arguments, and optionally specify the columns (features) we want to include in the plot. The plot shows the SHAP values for each feature on the x-axis, and the features on the y-axis. The color indicates whether the feature value is low (blue) or high (red). The plot also shows the distribution of the feature values as a density plot on the right.

This dataset tracks the updates made on the dataset "NLM LitArch Open Access Subset" as a repository for previous versions of the data and metadata.

The merra2ools dataset has been assembled through the following steps:

The MERRA-2 collections tavg1_2d_flx_Nx (Surface Flux Diagnostics), tavg1_2d_rad_Nx (Radiation Diagnostics), and tavg1_2d_slv_Nx (Single-level atmospheric state variables) downloaded from NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) (https://disc.gsfc.nasa.gov/datasets?project=MERRA-2) using GNU Wget network utility (https://disc.gsfc.nasa.gov/data-access). Every of the three collections consist of daily netCDF-4 files with 3-dimensional variables (lon x lat x hour). 
The following variables obtained from the netCDF-4 files and merged into long-term time-series:



Northward (V) and Eastward (U) wind at 10 and 50 meters (V10M, V50M, U10M, U50M, respectively), and 10-meter air temperature (T10M) from the tavg1_2d_slv_Nx collection;
Incident shortwave land (SWGDN) and Surface albedo (ALBEDO) fro...

The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, of 100077 objects classified as galaxies, it includes a CSV file with a collection of information and a set of files for each object, namely JPG image files, FITS and spectra data. This dataset is used to train and explore the astromlp-models collection of deep learning models for galaxies characterisation.

The dataset includes a CSV data file where each row is an object from the SDSS database, and with the following columns (note that some data may not be available for all objects):

objid: unique SDSS object identifier

mjd: MJD of observation

plate: plate identifier

tile: tile identifier

fiberid: fiber identifier

run: run number

rerun: rerun number

camcol: camera column

field: field number

ra: right ascension

dec: declination

class: spectroscopic class (only objetcs with GALAXY are included)

subclass: spectroscopic subclass

modelMag_u: better of DeV/Exp magnitude fit for band u

modelMag_g: better of DeV/Exp magnitude fit for band g

modelMag_r: better of DeV/Exp magnitude fit for band r

modelMag_i: better of DeV/Exp magnitude fit for band i

modelMag_z: better of DeV/Exp magnitude fit for band z

redshift: final redshift from SDSS data z

stellarmass: stellar mass extracted from the eBOSS Firefly catalog

w1mag: WISE W1 "standard" aperture magnitude

w2mag: WISE W2 "standard" aperture magnitude

w3mag: WISE W3 "standard" aperture magnitude

w4mag: WISE W4 "standard" aperture magnitude

gz2c_f: Galaxy Zoo 2 classification from Willett et al 2013

gz2c_s: simplified version of Galaxy Zoo 2 classification (labels set)

Besides the CSV file a set of directories are included in the dataset, in each directory you'll find a list of files named after the objid column from the CSV file, with the corresponding data, the following directories tree is available:

sdss-gs/ ├── data.csv ├── fits ├── img ├── spectra └── ssel

Where, each directory contains:

img: RGB images from the object in JPEG format, 150x150 pixels, generated using the SkyServer DR16 API

fits: FITS data subsets around the object across the u, g, r, i, z bands; cut is done using the ImageCutter library

spectra: full best fit spectra data from SDSS between 4000 and 9000 wavelengths

ssel: best fit spectra data from SDSS for specific selected intervals of wavelengths discussed by Sánchez Almeida 2010

Changelog

v0.0.4 - Increase number of objects to ~100k.

v0.0.3 - Increase number of objects to ~80k.

v0.0.2 - Increase number of objects to ~60k.

v0.0.1 - Initial import.

This dataset contains the processed RNA sequencing data of purified CD1c-positive conventional type 2 dendritic cells (CD1c+ cDC2s), functional enrichment analysis, manual and automatic gating data of (i.e., flowSOM) flow cytometry, and multiplex cytokine analyses as outlined in Hiddingh et al. 2022 "Transcriptome network analysis implicates CX3CR1-positive type 3 dendritic cells in non-infectious uveitis see preprint on BioRxiv Data are from two cohorts (cohort I, n=36, and cohort II, n=42) of in total 51 patients with non-infectious uveitis (HLA-B27-positive acute anterior uveitis, idiopathic intermediate uveitis, HLA-A29-positive Birdshot Uveitis (Birdshot chorioretinopathy), and 27 sex/age-matched healthy controls without ocular inflammatory disease). All raw sequencing data are available at NCBI SRA under the accession number: GSE195501 (FACS-sorted cohort I). GSE194060 (MACS-sorted cohort II). This dataverseNL dataset contains additional raw, processed, and metadata (see readme file and reproducible R notebooks (R script and Image) used for the analysis in the manuscript: R scripts (markdown + R image) with step-by-step analyses Figure_1.rmd (see "Figure_1.html") Figure_2.rmd (see "Figure_2.html") Figure_3.rmd (see "Figure_3.html") Figure_4.rmd (see "Figure_4.html") Figure_5.rmd(see "Figure_5.html") Processed RNA seq data (including WGCNA) (see folder Uveitis_mDC in files) Experimental data Manual gating data of MACS-sorted fractions cohort I (see here) Manual gating data for CD1c+ cDC2 subsets in PBMCs (see here) Manual gating CD14+ and CD14- CD1c+ cDC2 fractions from Buffy (see here) qPCR data for CX3CR1,CCR5,CCR2,IRF8,TLR7,RUNX3 and CD36 in sorted CD14+ and CD14- CD1c+ DCs (see here) qPCR data (fold change compared to medium) for RUNX3 and CD36 in overnight stimulated cDC2 cultures (see here) Cell phenotypes identified by flowSOM (7x7 grid) using the cDC2-subset flow cytometry panel (see here) IL-23 ELISA concentration in supernatant of overnight LTA-stimulated cDC2 subset cultures (see here) Luminex Multiplex Cytokine analysis of supernatant of overnight LTA-stimulated cDC2 subset cultures (see here) Other transcriptomic data used in the R scripts (above) WT Untreated cDC2 versus cDC2 from Runx3-11cKO miceGSE48590 generated by Dicken et al., PLoS One 2013 WT Untreated cDC2 versus cDC2 from Notch2-11cKO miceGSE119242 generated by Briseño et al., Proc Natl Acad Sci U S A 2018 Sorted CD14+CD5-CD163+ and CD14-CD5-CD163+ cDC2s from SLE and Scleroderma patients GSE136731 generated by Dutertre et al., Immunity 2019 Single-cell RNA-seq of aqueous humor from 4 HLA-B27-positive uveitis patients and control GSE178833 generated by Kasper et al., Elife 2021 Inflammatory [inf-]cDC2sGSE149619 generated by Bosteels et al., Immunity 2020 RNA-seq data from cDC2s generated from murine bone marrow cells in co-culture with stromal OP-9 cell line transduced with or without expression of the Notch ligand Delta-like 1GSE110577 generated by Kirkling et al., Cell Rep 2018

This index was compiled by Miss Mollie Bentley from various records she has used relating to the police. These include: Almanac listings, Colonial Secretary's Office Records, Police Gazettes, various police department occurrence books and letter books, police journals, government gazettes, estimates, York police records etc.\r \r Entry is by name of policeman. Information given varies but is usually about appointments, promotions, retirements, transfers etc.\r \r The Western Australian Biographical Index (WABI) is a highly used resource at the State Library of Western Australia. A recent generous contribution by the Friends of Battye Library (FOBS) has enabled SLWA to have the original handwritten index cards scanned and later transcribed.\r \r The dataset contains: several csv files with data describing card number, card text and url link to image of the original handwritten card.\r \r The transcription was crowd-sourced and we are aware that there are some data quality issues including:\r \r * Some cards are missing\r * Transcripts are crowdsourced so may contain spelling errors and possibly missing information\r * Some cards are crossed out. Some of these are included in the collection and some are not\r * Some of the cards contain relevant information on the back (usually children of the person mentioned). This info should be on the next consecutive card\r * As the information is an index, collected in the 1970s from print material, it is incomplete. It is also unreferenced.\r It is still a very valuable dataset as it contains a wealth of information about early settlers in Western Australia. It is of particular interest to genealogists and historians.

This data product, sourced from the NEON data portal for the purposes of the ACCE DTP tutorial, contains processed individual level data from measurements of woody individuals and shrub groups.

Recently, several neutralizing anti-HIV antibodies have been isolated from memory B cells of HIV-infected individuals. However, despite extensive evidence of B-cell dysfunction in HIV disease, little is known about the cells from which these rare HIV-specific antibodies originate. Accordingly, HIV envelope gp140 and CD4 or co-receptor (CoR) binding site (bs) mutant probes were used to evaluate HIV-specific responses in the peripheral blood B cells of individuals at various stages of infection. In contrast to non-HIV responses, HIV-specific responses against gp140 were enriched within abnormal B cells, namely activated and exhausted memory subsets, which are largely absent in the blood of uninfected individuals. Responses against the CoRbs (a poorly-neutralizing epitope) arose early whereas those against the CD4bs (a well-characterized neutralizing epitope) were delayed and infrequent. Enrichment of the HIV-specific response within resting memory B cells, the predominant subset in uninfected individuals, did occur in certain infected individuals who maintained low levels of plasma viremia and immune activation with or without antiretroviral therapy. These findings were corroborated by transcriptional profiles. Taken together, our findings provide valuable insight into virus-specific B-cell responses in HIV infection and demonstrate that memory B-cell abnormalities may contribute to the ineffectiveness of the antibody response in infected individuals. HIV-specific responses against gp140 were enriched within abnormal B cells, namely activated (AM) and exhausted (tissue-like; TLM) memory subsets, which are largely absent in the blood of uninfected individuals. These responses are highest during the early stage of HIV infection, significantly decreased following the initiation of antiretroviral therapy (ART), and most importantly, enriched in normal resting memory B cells (RM) when HIV viremia and immune activation are controlled either naturally or as a result of ART. These HIV-specific B cells (AM and TLM) and resting memory B cells (RM) were sorted from peripheral blood mononuclear cells (PBMCs) of 6 HIV infected individuals. In addition, gp140-specific IgG+ B cells were sorted from individuals with either a strong (n= 6) or weak (n= 6) pro-resting memory profile. TaqMan gene expression assay was performed on these HIV-specific B cells and B cell subset. The array consisted of 29 genes.

Subset of MedleyDB: Mix audio files and Melody Annotations for the 108 files in the MedleyDB multitrack dataset containing melody.

There are 3 types of melody annotations released:

melody1: "The f0 curve of the predominant melodic line drawn from a single source"

melody2: "The f0 curve of the predominant melodic line drawn from multiple sources"

melody3: "The f0 curves of all melodic lines drawn from multiple sources"

For further details, refer to the MedleyDB website.

Further Annotation and Metadata files are version controlled and are available in the MedleyDB github repository: Metadata can be found here, Annotations can be found here.

For detailed information about the dataset, please visit MedleyDB's website.

If you make use of MedleyDB for academic purposes, please cite the following publication:

R. Bittner, J. Salamon, M. Tierney, M. Mauch, C. Cannam and J. P. Bello, "MedleyDB: A Multitrack Dataset for Annotation-Intensive MIR Research", in 15th International Society for Music Information Retrieval Conference, Taipei, Taiwan, Oct. 2014.

REVS models for analysis of great tit wing length giving R2 and delta AIC values.

Data from the IFLS, merged across waves, most outcomes taken from wave 5. Includes birth order, family structure, Big 5 Personality, intelligence tests, and risk lotteries

Table of variables

This table contains variable names, labels, and number of missing values. See the complete codebook for more.

[truncated]

Note

This dataset was automatically described using the codebook R package (version 0.8.2).

Atypical B cells are a population of activated B cells that are commonly enriched in individuals with chronic immune activation, but are also part of a normal immune response to infection or vaccination. Prior studies to determine the function of these cells have yielded conflicting results, possibly due to functional heterogeneity among this B cell population. To better define the role(s) of atypical B cells in the host adaptive immune response, we performed single-cell sequencing of transcriptomes, cell surface markers, and B cell receptors in individuals with chronic Plasmodium falciparum exposure, a condition known to lead to accumulation of circulating atypical B cells. Our studies identified three previously uncharacterized populations of atypical B cells with distinct transcriptional and functional profiles, that separate into two differentiation pathways. We identified a set of cell surface markers to distinguish these atypical B cell subsets and confirmed their presence in malaria-experienced children and adults using flow cytometry. Plasmodium falciparum-specific cells were present in equal proportions within each of these atypical B cell populations, indicating that all three subsets develop in response to antigen stimulation. However, we observed marked differences among the three subsets in their ability to produce IgG upon T-cell-dependent activation. Collectively, our findings help explain the conflicting observations in prior studies on the functions of atypical B cells and provide a better understanding of their role in the adaptive immune response in chronic inflammatory conditions. Single cell sequencing analysis of B cells from 2 children at two time points following a malaria episode: 3 weeks after malaria (TP1) and 6 months later (TP2). For each sample, three libraries were constructed on the 10x Genomics platform: gene expression (GEX), cell surface marker expression (ADT), and antibody heavy and light chain variable regions (V(D)J).

The Dutch CELEX data is derived from R.H. Baayen, R. Piepenbrock & L. Gulikers, The CELEX Lexical Database (CD-ROM), Release 2, Dutch Version 3.1, Linguistic Data Consortium, University of Pennsylvania, Philadelphia, PA, 1995.Apart from orthographic features, the CELEX database comprises representations of the phonological, morphological, syntactic and frequency properties of lemmata. For the Dutch data, frequencies have been disambiguated on the basis of the 42.4m Dutch Instituut voor Nederlandse Lexicologie text corpora.To make for greater compatibility with other operating systems, the databases have not been tailored to fit any particular database management program. Instead, the information is presented in a series of plain ASCII files, which can be queried with tools such as AWK and ICON. Unique identity numbers allow the linking of information from different files.This database can be divided into different subsets:· orthography: with or without diacritics, with or without word division positions, alternative spellings, number of letters/syllables;· phonology: phonetic transcriptions with syllable boundaries or primary and secondary stress markers, consonant-vowel patterns, number of phonemes/syllables, alternative pronunciations, frequency per phonetic syllable within words;· morphology: division into stems and affixes, flat or hierarchical representations, stems and their inflections;· syntax: word class, subcategorisations per word class;· frequency of the entries: disambiguated for homographic lemmata.

The IL-17A inhibitor secukinumab is efficacious for the treatment of psoriasis. To better understand its mechanism of action, we investigated its impact on psoriatic lesions from 15 moderate-to-severe plaque psoriasis patients undergoing secukinumab treatment. We characterized the longitudinal transcriptomic changes of whole lesional skin tissue as well as cutaneous CD4+ and CD8+ T effector cells and CD4+ T regulatory cells across 12 weeks of treatment. Secukinumab was clinically effective and reduced disease-associated overexpression of IL17A, IL17F, IL23A, IL23R, and IFNG in whole tissue as soon as 2 weeks after initiation of treatment. IL17A overexpression in T cell subsets, primarily CD8+ T cells, was also reduced. While secukinumab treatment resolved 89-97% of psoriasis-associated expression differences in bulk tissue and T cell subsets by week 12 of treatment, we observed expression differences involved in interferon signaling and metallothionein synthesis that remained unresolved at this time point as well as potential treatment-associated expression differences involved in IL-15 signaling. These changes were accompanied by shifts in broader immune cell composition based on deconvolution of RNA-seq data. In conclusion, our study reveals several phenotypic and cellular changes within the lesion that underlie clinical improvement from secukinumab. We biopsied lesional skin from psoriatic patients before secukinumab treatment and at 2, 4, and 12 weeks of treatment for comparison with skin biopsies from 13 healthy subjects. We performed RNA-seq on bulk skin tissue as well as on CD8+ T cells, CD4 T effectors, and CD4+ Tregs sorted from these biopsies