dataesr/scientific-paragraphs-categorization huggingface dataset:
S1D ), but coincided with disease severity, particularly marked in ICU-admitted patients (SI Appendix, Fig. S1E ), and in lymphopenic cases (SI Appendix, Fig. S1F ). No dependence of these phenotypes was found on any particular treatment, in particular glucocorticoids (Dataset S1). FoxP3 expression was not directly correlated with blood levels of the inflammatory marker C-reactive protein (CRP), but essentially all FoxP3 hi patients had elevated CRP (SI Appendix, Fig. S1G ). Therefore, severe COVID-19 entails a striking induction of FoxP3 expression in Tregs. We examined the expression of several markers and transcription factors (TF) to further assess Treg evolution during COVID-19. CD45RA, which marks naïve Tregs, was reduced in patients, especially in those with heightened Tregs and FoxP3 (SI Appendix, Fig.
We surmise that the presence of Tbet + Tregs is related to the control of Th1 antiviral response among effector cells. Accentuated Treg Transcriptome in Severe COVID-19 Patients. To decipher the functional consequences of FoxP3 overexpression in Tregs from severe COVID-19 patients, we generated 86 RNAsequencing (RNA-seq) transcriptome profiles, passing quality thresholds, of blood Treg (CD4 + CD25 hi CD127 lo ) or Tconv (CD4 + CD25 -). Donors largely coincided with those analyzed above, and we opted to profile purified populations rather than single-cell (higher throughput, lower processing/computational costs) after magnetic purification (SI Appendix, Fig. S2A ). The datasets matched profiles from flow-purified Tregs, with the usual differential expression of "Treg signature" genes (SI Appendix, Fig.
S2 D  and E ). Thus, Treg traits observed in the flow cytometry data were confirmed by the transcriptomic signature of these Tregs, which tends toward a supersuppressive phenotype in severe COVID-19 patients. A Distinct COVID-19 Disease Treg Signature Correlates with Severity. We then asked more generally what changes, beyond Treg signature and effector transcripts, characterized Tregs in severe COVID-19 patients, relative to HD (Fig. 3A  and Dataset S2) (hereafter "Severe COVID19 Treg Signature," SCTS); only a minority of these transcripts belong to the classic Treg signature analyzed above. An SCTS index computed from this gene set was high in all severe patients, but also persisted in muted fashion in many recovered patients (Fig. 3B ), correlating with FOXP3 expression (SI Appendix, Fig.
S3C ). PRDM1 FOXP3 MAF TBX21 IKZF4 BACH1 XBP1 IRF4 RUNX3 JAZF1 STAT3 RORA RELA STAT6 ZNF335 NCOR1 SP4 HIF1A ZBTB38 IKZF2 RUNX2 RUNX1 ZEB1 NFATC2 FOXO4 GATA3 FOXO1 ZC3H7A CIC PRDM2 NFATC1 ZNF831 SATB1 MXD1 HIVEP2 ELF1 LEF1 ZNF532 ZNF480 HIVEP1 FOXP1 RORC FOXO3 REL BACH2 NR4A3 ID3 TCF7 TGIF1 NR4A2 EOMES NR4A1 -1. Focusing on cytokines produced by Tregs, IL10 was only modestly induced, but CD70 (CD27L) and IL32 dominated (Fig. 3E ). The latter was intriguing in this context of the cytokine storm that occurs in these severe patients, since it is mainly a proinflammatory mediator, in positive feedback with interleukin (IL)-6 and IL-1β (  21 ). To answer the common question (whether transcriptional changes reflect a shift in subset balance or are all shared by all Tregs), we reanalyzed a single-cell RNA-seq peripheral blood mononuclear cell (PBMC) dataset from COVID-19 patients  (22) , drilling down on identifiable Treg cells (SI Appendix, Fig.
More unexpected was the strong down-regulation of NR4A1, normally an indicator of T cell receptor (TCR) signaling, which may indicate a decoupling of TCR-delivered signals. Gene enrichment analyses using a curated database of transcriptome variation in CD4 + T cells brought forth 229 datasets with significant overlap to the SCTS (SI Appendix, Fig. S4A ). Besides the expected interferon-related gene sets related to M2, many were related to Tconv and Treg activation, mostly overlapping with M4 and M5. Most intriguing were overlaps with datasets from tumorinfiltrating Tregs (TITR). Indeed, the expression of a signature that distinguishes colorectal TITR from normal colon tissue (13) was strikingly biased in Tregs from severe COVID-19 patients (Fig. 4A ). The same bias was found with gene sets that distinguish breast and lung TITRs from blood Tregs (11, 12) (Fig.
These observations raise two key questions. First, how do the perturbations arise? They are not due to viral infection of the Tregs (no viral RNA reads to speak of in these datasets). They seem unconnected to therapy, as none of the therapies administered to these patients correlate with the Treg traits. More likely, the phenotype is induced by the immunologic milieu in these patients, and this uniquely in Tregs since Tconvs are far less branded. TCRmediated stimulation seems unlikely, given the widespread effect on Tregs in the single-cell data, which likely transcends clonotypic specificity, and the strong loss of Nur77 (NR4A1). Our results suggest that a combination of factors is at play, foremost IL-6 and IL-18 (but other factors may contribute as well), each contributing a particular aspect of the perturbed Treg phenotype.
Materials and Methods Patients Samples and Clinical Data Collection. Peripheral blood samples from 57 adults infected with SARS-CoV-2 prospectively collected in Massachusetts General Hospital (MGH) were included in this study (Dataset S1). Samples from 11 adult HD were also collected. COVID-19 patients were split into three different groups, depending on the disease severity or the time-course postinfection. All participants provided written informed consent in accordance with protocols approved by the Partners Institutional Review Board and the MGH Human Subjects Institutional Review Board. De-identified Treg analysis was performed per approved Human Subjects Institutional Review Board protocol 15-0504-04. PBMC Isolation. Five to 10 mL of whole blood was collected in K2 EDTA tubes and processed within a few hours.
Antibody details can be found in SI Appendix, Supplementary Materials and Methods. Data were recorded on a FACSymphony flow cytometer (BD Biosciences) and analyzed using FlowJo 10 software. RNA-Seq. For ex vivo blood Tregs, RNA-seq was performed in two different batches, including samples coming from two to seven different experimental batches (different experiment dates in Dataset S1). After magnetic isolation and using the postisolation flow cytometry data, samples with purity > 65% and expected number of cells >800 were selected for RNA-seq. For cultured Tregs after 24 h of treatment, cells were sorted as DAPI-CD4 + CD25 hi CD127 lo on a Moflo Astrios Cell Sorter (Beckman Coulter). One thousand cells were double-sorted directly into 5 μL of lysis buffer (TCL Buffer [Qiagen] supplemented with 1% 2-Mercaptoethanol).
From this Treg cluster, cell averages were calculated across all genes of the Severe COVID-19 (SCTS) up signature, and colorcoded on the Treg UMAP space for Fig. 3H. Statistics. Unless specified otherwise, data are presented as mean ± SD and tests of associations for different variables between COVID-19 patients and HD were computed 1) using the nonparametric Mann-Whitney test or 2) using randomization test: random values were generated [nrorm() in R] from the mean and SD of log-transformed values in HD controls, testing the frequency of draws that led to a number of observations > 95th quantile of HD values that was equal or greater to the number of such observations in each patient group. Correlation coefficients were from Pearson correlation. Significance of signature overlaps into our dataset was assessed by Fisher's exact test when computing one signature at a time, or by a hypergeometric test with Benjamini-Hochberg correction when using the large curated CD4 + T cell signatures database.
ACKNOWLEDGMENTS. We thank  Dr. C. Blish  for discussion and access to unpublished data; the  Broad Genetic Platform  for profiling in difficult times;  Dr. M. Haigis  as well as  A. Ringel ,  C. Yao , and  G. Notarangelo  for discussion and sharing of reagents; and  Dr. G. Hotamisligil  as well as  E. Tsaousidou  and  B. Garfinkel  for sharing of their hypoxic chamber. This work was funded by grants from the  Massachusetts Consortium for Pathogen Readiness (MassCPR) ,  R01 AI150686  (to C. B. ), the  JPB Foundation  (to C. B. and D. M. ), and  R24 AI072073  to the  ImmGen Consortium. The  Massachusetts General Hospital (MGH) / MassCPR  COVID biorepository was supported by a gift from  Ms. Enid Schwartz , by the  Mark and Lisa Schwartz Foundation , the  MassCPR  and the  Ragon Institute of MGH, Massachusetts Institute of Technology, and Harvard.
Moreover, a hybrid model was proposed by Laribi et al. [16]  for Sheet Molding Compound (SMC) material. The latter combined a micromechanical approach, based on the Mori-Tanaka scheme, with a phenomenological model in order to investigate fibre/matrix interface decohesion under fatigue loading. Nevertheless, approaches presented in  [14, 16]  require a large experimental database to provide more realistic predictions, representing the main drawback of these models. Besides, some limitations, such as sample preparation, testing procedure and measurement at the fibre scale, related to the use of the reverse engineering method to identify parameters at the fibre/matrix interface, reduce the scope of applications of the model presented in  [15]. Hence, current micromechanical models are not suited to dealing with complex debonding phenomena.
To do so, we consider DEM based on a hybrid particulatelattice model in which cohesive elements are used to model the contin-uous and heterogeneous medium  [32]. For information purposes, all DEM simulations are carried out using MULTICOR 3D++ code based on C++ programming and developed in LTI laboratory  [33]. The main benefit of such a model compared to a full lattice approach is that it also allows binary collisions between particles to be handled which occur during interfacial debonding. In addition, we consider cohesive beam elements which provide more realistic crack patterns than spring ones under several mechanical loadings  [46]. Furthermore, we handle random particulate systems which, combined with a fine discretization, allow us to address anisotropy issues. Using this paradigm, an equivalent continuous domain is established for which mechanical properties related to the beam elements are determined using a calibration process  [33, 35, 36].
It should be noted that the model proposed in this work is applicable for aligned short-fibre reinforced composites, with the desired geometric parameters and fibre volume fraction. Effective elastic properties of PA6/GF30 are evaluated and compared to analytical, numerical predictions and experimental data as a validation means. The interfacial debonding of SFRC during bending tests and under mixed mode is well simulated. Thus, tensile and shear behaviours on failure mechanisms of PA6/GF30 are highlighted. Results show that the proposed DEM model allows a realistic modelling of interfacial debonding and provides useful information, in particular in terms of energy and force/displacement curve, at the fibre/matrix interface scale (Fig. 1 ). Following these guidelines, the present work is organised as follows.
Design of desorption experiments During this study, the effect of three independent experimental factors and their possible interactions on MB desorption yield (Y , %) from ROS was assessed by using the two-level full factorial design approach. The parameters followed include: the solution pH (X 1 ), the NaCl aqueous concentration (X 2 ; Mole) and the MB-loaded-ROS dose in the desorbing solution (X 3 ; g•L -1 ) (Table  1 ). The variation range of these parameters (Table  1 ) was fixed on the basis of preliminary experiments. A set of 24 runs on the basis of three replicates for each experiment were performed according to the model provided by the Minitab V. 17 software (Centre County, Pennsylvania). The full factorial design is a statistical method that uses outputs from an appropriate set of experiments to determine the polynomial regression equation that suitably describes the possible interactions between the operating parameters  [38].
X-ray diffraction analysis Powder X-ray diffraction (XRD) was used to identify the crystalline phases in the raw sawdust as well as for the MB-loaded-ROS before and after desorption experiments. It was carried out on a Panalytical X'Pert powder diffractometer equipped with a copper anode (Malvern Panalytical, Malvern, UK). Diffractogram analysis and phase identification were achieved by using the Panalytical X'Pert HighScore software and the International Center for Diffraction Data (ICDD) database, respectively.
Nighttime Light The concept of using Transfer Learning to estimate poverty from satellite images was proposed in  [Jean et al. 2016]. In this work, the authors used nighttime light values (NLV), which are known, since many years, to be a good proxy for poverty evaluation (see for example,  [Elvidge et al. , 1997] ,  [Doll et al. , 2000]  or  [Chen and Nordhaus, 2011] ). NLV data set is provided by the Earth Observation Group 7  and the corresponding images are extracted from Google Static Map 8  , at 2. 5 meters of resolution. The authors introduce a CNN architecture to classify daytime satellite images by NLV. Three classes are used for low, medium, and high NLV  [Jean et al. , 2016, Supplementary Materials]. The CNN is based on a VGG-F architecture  [Wozniak et al. , 2018]  and is pre-trained on the ImageNet dataset.
Land Use In  [Ayush et al. , 2020] , the authors assume that land use, and specifically the manufactured objects observed in a satellite image emphasize the wealthiness of an area. The authors trained a CNN on a land use detection and classification task, to extract a feature vector related to poverty. As proxy data, they used the xView data set 9  consisting of very high-resolution images (0. 3 m) annotated with bounding boxes defined over 10 main classes (building, fixed-wing aircraft, passenger vehicle, truck, railway vehicle, maritime vessel, engineering vehicle, helipad, vehicle lot, construction site) and 60 sub-classes. As the size of the objects in a satellite image may vary (e. g. , a truck is much smaller than a building), the network used YoloV3, with DarkNet53 backbone architecture  [Redmon and Farhadi, 2018] , which allows the detection at 3 different scales.
Notice that afterward, the Earth Observation Group has provided 500m resolution world maps. Unfortunately, there is no reliable time reference mentioned and associated with Google Static Map images so we assume that 2013 NLV data are roughly consistent with Google Static Map images. Finally, 71,000 daytime images were randomly selected from the NLV world map in Africa. Proxy data for Land Use feature extractor: The xView dataset image resolution (0. 3 m) is much higher than GSM one (2. 5 m), which prevents us from using the object detection methodology based on xView dataset. We then adapted the idea and use a global land use classifier which outputs a single value instead of a set of feature vectors corresponding to each detected object. We used land use labels provided in the Eurosat data set  [Helber et al.
, 2018]. It consists of almost 30,000 satellite images of European places, labeled into 10 classes: sea lake, river, residential, permanent crop, pasture, industrial, highway, herbaceous vegetation, forest, and annual crop. The authors provided a GeoTIFF data set 13 , including the locations of all images, we simply used these locations and downloaded the corresponding Google Static Map API images. Proxy data for Contrastive feature extractor: Since it is an unsupervised method, no proxy data is required. Therefore, we used the poverty indicator location to generate 4,000 × 4,000, images with 2. 5 meters resolution. Then, for each image, we randomly sample 100 anchor, neighbor, and distant image triplets, with the same size, area coverage, and channel as introduced earlier. We set the neighborhood size to 400 pixels, so Light world map: https://www.
ngdc. noaa. gov/eog/dmsp/ downloadV4composites. html. 13 GeoTIFF data set provided by  [Helber et al. , 2018] : https://github. com/phelber/ EuroSAT. the central pixel of each neighbor image (resp. distant image) is less (resp. more) than 400 pixels away from the anchor image. The overall process generates 77,000 triplets. Poverty indicators: We used the poverty indicators from LSMS surveys 14. We focus on the 2016 Malawi survey and consider the natural logarithm of the consumption expenditures as the poverty indicator. Note that the surveys are designed in two steps. The country is divided into clusters, and then, several households are surveyed within each cluster. In the resulting survey, only the clusters coordinates are reported (and randomly displaced for anonymization purposes, as explained earlier).
Experiments and Analysis Performances of feature extractors: The CNNs are used to extract features from images. Therefore, their respective accuracy values are not used to determine whether or not they are good classifiers. However, the accuracy metric can be used as an indirect measure to indicate the degree of consistency between the features generated by the CNNs, according to the classification problem (only for Nighttime Light and Land Use approaches). Nighttime Light reaches 64% of accuracy on the validation data set, whereas Land Use reaches 97%. As mentioned earlier, instead of the accuracy measure, we use the R 2 score for evaluating Contrastive's performance on the poverty prediction task. Ensembling Averaged 0. 41± 0. 08 0. 47± 0. 09 0. 494± 0. 07 0. 487± 0. 07 0. 422± 0. 05 0. 378±0.
, 2012 (Nabat et al. , , 2015)). The RCM median bias map is consistent with the GCM median bias map except for some specific areas. Indeed, the RCM ensemble degrades the behavior with respect to the GCM ensemble above some mountain ranges (Alps, Turkish mountains, southwest Norway, and Iceland) but improves it near the northern coast of Europe and the United Kingdom. The southwestern negative bias is also more pronounced in the RCM ensemble. The explanation of the RCM strong positive biases over the mountains remains unclear and may be also due to limitations of the HELIOSAT reference data set. Overall, there is no obvious improvement in the Euro-CORDEX ensemble compared to the driving CMIP5 ensemble for the bias in annual-mean surface solar downward radiation. Looking at individual RCM biases, we conclude that, at the first order, biases in surface solar downward radiation in RCM simulations are related to the choice of the RCMs.
8/16 (SC), 6. 5/20 (BI), 9. 1/27 (IP), and 2. 0/ 6. 8 (EA). Compared to RX1d, the increases in R99a are slightly smaller: 5. 3/16 (AL), 3. 0/17 (SC), 3. 3/16 (BI), 4. 6/23 (IP), and 1. 0/6. 1 (EA). These numbers indicate that the substantial model overestimation of heavy precipitation indices when assessed with E-OBS data can be reduced by resorting to high-resolution precipitation data sets, which have a denser station network, especially in topographically complex regions. The issue of undercatch gauge correction in observations also needs to be considered when interpreting the biases in precipitation-based indices. Drought frequency, as measured by a dry spell frequency exceeding 6 months (DF6), exhibits a median dry bias in northern Europe (too many spells) and a wet bias (too few spells) in southern Europe.
Data Availability Statement Climate simulations were obtained from the Earth System Grid Federation portals (e. g. , https://esgf-node. ipsl. upmc. fr/projects/esgf-ipsl/) and from the DMI servers (http://prudence. dmi. dk/data/temp/ PRINCIPLES/). E-OBS data sets were obtained from the KNMI server (https://eca. knmi. nl/). ERA5 re-analyses were obtained from the ECMWF server (http://www. ecmwf. int). High precipitation data sets were obtained from several public servers and are described in  Fantini et al. (2018)  (https://www. meteoswiss. admin. ch/home/search. subpage. html/en/data/products/2015/alpine-precipitation. html, ftp://ftp. met. no/projects/klimagrid, https://github. com/ukcp-data/UKCP18_CVs, http://www. aemet. es/en/datos_abiertos/AEMET_OpenData, and http://www. carpatclim-eu.
Experimental data The simulated surface conditions are first compared with the LGM sea-surface temperatures reconstructed by  MARGO Project Members (2009). Thanks to the use of multiple proxies (diatoms, radiolaria, dinoflagellates, foraminifera, Mg/Ca, and alkenones), this dataset, combining 696 individual records, provides a synthesis of our knowledge of the LGM ocean surface temperature. However, it should be noted that most proxies are calibrated against summer SST  (Esper and Gersonde, 2014; Cortese and Prebble, 2015)  or annual SST  (Sikes et al. , 1997; Prahl et al. , 2000). Only planktonic foraminifera allow for the estimation of winter SST  (Howard and Prell, 1992)  but their growth is hampered, and restricted to a couple of species, south of the polar front  (Bé and Hutson, 1977).
We thus extrapolated the modern relationship between summer sea-ice extent and SST, whereby summer sea ice lies south of the 0 • C isotherm  (Nicol et al. , 2000)  to the LGM. Caution is therefore needed when using the results, as this summer contour is not well constrained. We then estimated the sea-ice extent inferred from this data compilation: we imported these contours on a 360×360 points grid (of 1 • × 0. 5 • in longitude and latitude), computed the surface area contained within (summing the weighted area of each grid cell on a perfect sphere) and subtracted an estimated surface of the Antarctic continent (i. e. , land and grounded ice sheet areal extent) at the LGM. Results are discussed in Sect. 3. 3. We estimated a glacial Antarctica of 16. 8 × 10 6 km 2 by computing the total area of the continent and of the continental shelves (up to -1000 m) on a highresolution (16×16 km) modern topographic dataset  (Fretwell et al.
The use of the PMIP2 (ICE-5G) ice sheet reconstruction -with a higher elevation -causes an overall colder climate compared to PMIP4 but differences in simulated surface conditions and deep ocean circulation remain relatively small. In contrast, the modeling choices made in sensitivity tests can cause much larger differences (e. g. , between "Cold P2" and "Warm P2", or "P4-I" and "P4-I brines", or "P4-I" and "P4-I hosing"). In particular, the differences between "Cold P2" and "Warm P2" suggest that, while iLOVECLIM generally simulates a more modest global SAT anomaly than other PMIP4 models  (Kageyama et al. , 2021) , modeling choices related to the glacial temperature profiles used in the radiative code can induce a very significant change. Moreover, thanks to the use of proxy data to evaluate our simulations, this inverse methodology approach is useful to highlight systematic biases in the simulated surface conditions of the Southern Ocean.
-Two additional pre-processing steps are required to generate the necessary input files (one containing the fraction of ocean seen by the T21 grid cells and another containing the interpolation points between the CLIO and the T21 grids). -In order to be able to quickly equilibrate the model when running a simulation with a different bathymetry than its restart, the initialization code of iLOVECLIM has been modified to generate realistic values of the tracer content of new oceanic grid cells. To achieve this, the initialization of all the restart variables in new ocean grid cells is done by averaging the values in neighboring oceanic grid cells when necessary. The conservation of the total content of conservative variables (salt, carbon, etc. ) is ensured. necting more than two basins at the same time.