dataesr/scientific-paragraphs-categorization huggingface dataset:
More details on the electrodes generation based on the stochastic procedure can be found in Section S3, in the Supporting Information. The generated electrode mesostructures are then analysed in terms of their liquidphase and solid-phase  (through TauFactor  [45] ), percentage of current collector (CC) surface covered by AM or CBD phase (%CC-AM or %CC-CBD, respectively) and percentage of AM surface covered by the electrolyte, referred thereafter as active surface (Figure  2C ). Herein, all the void space in the electrode mesostructure is considered to be filled with electrolyte. [46]  Due to the stochastic nature of the generated electrode mesostructures, this procedure is repeated ten times for each calendering condition and the average values constitute the dataset used by the ML algorithm (4400 structures hence 440 data points).
The aim of the latter is to build electrode and process variables-electrode properties interdependencies "maps", in order to ease their identification and understanding. For the development of ML model (Figure  2D  and E ) the Sure Independent Screening and Sparsifying Operator (SISSO)  [47]  was chosen, using training and test sets constituted of randomly selected 80% and 20% of the dataset, respectively. Ouyang et al. [47]  recently developed the SISSO algorithm, which relies on identifying descriptors defined as nonlinear combinations of the original model inputs upon billions of possibilities, where the chosen ones minimize the model error. This data-driven approach relies on training procedures onto the dataset to identify these descriptors, which ranks it as a ML algorithm. SISSO has two main advantages when compared with other ML methods: (i) it outputs a mathematical equation describing the relations between input(s) and output(s), and (ii) it gives reliable results even with relatively small training datasets.
(i) allows to analyse the physical meaning of the model developed and to generalize it to other similar case studies, while (ii) reduces the time and cost needed to build the dataset. Further details of the implementation of the SISSO algorithm, including all the equations obtained, are reported in Section S4 in the Supporting Information. The predictive accuracy of the SISSO models was assessed by using its test set through regression plots (true values vs. predicted values) for all the electrode properties examined, as reported in Section S5 together with the training and test sets selection procedure. These plots allow to compare the predictions obtained by the ML model with the values coming from the test dataset. Moreover, a second dataset composed of 440 data points (not considered in both training and test sets) was used to further assess the accuracy of the developed SISSO model.
The prediction accuracy of the latter was always higher than 97%, except for %CC-AM, whose prediction accuracy is of 76%, which is understandable considering the dimension of AM particles (3-10 μm) and the relatively small surface area of the CC (50  50 μm 2 ). Therefore, even slight changes in the number of AM particles in contact with the CC leads to high standard deviations of the %CC-AM average values, affecting the model accuracy. The   analysed values ranged between 40% and 50%, the AM amount, between 93% and 96% and the calender pressure was varied between 20 and 170 MPa. The training+test datasets consisted in 440 data points, including all the electrode properties for different calendering conditions (fully reported as text file in the Supporting Information), while the second dataset used to further test the SISSO model contained other 440 data points not considered in both training and test sets.
Each data point comes from the average properties of 10 generated electrode mesostructures. Consequently, 8800 electrode mesostructures were generated to build these datasets. Regarding the computational cost of the proposed procedure, the generation of the electrodes mesostructures and the analysis of their properties took approximately 7 days by using an Intel ® Core™ i7-8700 CPU @ 3. 2 GHz with 32 GB of RAM. Training the SISSO models took approximately 24 hours by using 9 processors Intel® Xeon® CPU E5-2680 v4 @ 2. 40 GHz, 128 GB of RAM.
Acknowledgements The authors acknowledge the European Union's  Horizon 2020 research and innovation programme  for the funding support through the  European Research Council  (grant agreement  772873 , " ARTISTIC " project). A. A. F. acknowledges the  Institut Universitaire de France  for the support. M. D. and T. L. acknowledge  Dr. Oier Arcelus  from  LRCS-Amiens (France)  for the fruitful discussions. The authors acknowledge  Dr. Garima Shukla  from  LRCS-Amiens (France)  for providing the experimental manufacturing schema reported in Figure 1A.
Microarray Data Analysis Microarray hybridization (see Supplemental Materials and methods) and analysis were performed on purified R5-PD-1 dim and GC-Tfh isolated from 3 tonsils and 3 FL LN samples, and from 3 TSCs and 3 FRCLs. Differentially expressed genes were identified using a moderated t-test carried out with Chipster software (adjusted p-value <. 05, log2 fold change >2). A preranked Gene Set Enrichment Analysis (GSEA) was performed to evaluate the enrichment of Notch and integrin pathways in T-cell subsets. Moreover, GSEA preRanked was used to explore REACTOME, KEGG, PID, and BIOCARTA pathway databases. Pearson correlation matrix with pairwise complete observation was performed using R cor function on nonredundant transcript normalized values from TSCs, FRCLs, R5-PD-1 dim , and GC-Tfh microarrays, and was visualized using R heatmap function.
In addition, TSCs and FRCLs similarly enhanced the proliferation of R5-PD1 dim and GC-Tfh (Figure  1B ). FRCLs and TSCs displayed thus similar capacities to sustain the growth of R5-PD1 dim and GC-Tfh. In order to decipher the specific impact of FRCLs on follicular CD4 + T cells, we then compared their gene expression profile (GEP) with those of TSCs. Unsupervised Pearson correlation performed on the top 20% most variable transcripts adequately segregated TSCs and FRCLs (Figure  1C ). We then focused on genes overexpressed in FRCLs (Supplemental Table  3 ). Unexpectedly, pathway enrichment analysis using REACTOME database revealed a strong enrichment of FCRL signature for Notch-1 and Noctch-2 signaling. Moreover, several genes known to be involved in adhesion and antigen presentation to T cells were found in this FRCL signature and could impact CD4 + T-cell behavior.
To have a more accurate phenotype of FL LN CD4 + T-cell subsets, expression of typical Tfh genes was evaluated by quantitative RT-PCR. Even though FL R5-PD1 dim expressed lower levels of BCL6 and IL21 compared to tonsil R5-PD1 dim , the expression of those two genes was lower than in paired FL GC-Tfh (Figure  2G ), and higher than in paired FL non-Tfh (data not shown). This finding allowed us to hypothesize that R5-PD1 dim could correspond to Tfh precursors. Like FL GC-Tfh, FL R5-PD1 dim expressed LTA and LTB, as well as high levels of TNFA (Supplemental Figure  3 ), all known to be involved in FRC differentiation  (41) , suggesting that these FL R5-PD1 dim could participate, as well as FL GC-Tfh  (16)  to the FRC network expansion found in FL LNs. Finally, we highlighted an IFN-g hi IL-4 hi IL-17 low phenotype in FL R5-PD1 dim besides the already described specific IFN-g hi IL-4 hi IL-2 hi IL-17 low profile of FL GC-Tfh (Figure  2H )  (11, 13).
This study enhances our current understanding of the multipartite cell interactions occurring in the FL cell niche and encourages their deeper characterization in order to develop targeted therapeutic agents. FIGURE 2 | Characterization of R5-PD1 dim infiltrating FL LN as an IL-4-secreting subset. (A) PD1 and CXCR5 expression of viable FL LN CD4 + CD25 -T cells as evaluated by flow cytometry. Shown is one example of staining. 1: non Tfh; 2: R5-PD1 dim ; 3: GC-Tfh. (B) Frequencies of R5-PD1 dim and GC-Tfh among viable CD4 + CD25 -T cells in tonsils (Tons) and FL LN (FL). (C) Pearson correlation matrix of microarray data obtained from GC-Tfh and R5-PD1 dim subsets isolated from tonsils and FL LN. (D-F) Gene expression profile of R5-PD1 dim isolated from FL LN (FL) and tonsils (Tons) were compared, and GSEA enrichment plots for PID Notch pathway (D), Kegg cell adhesion molecules (E), and Biocarta integrin pathway (F) were drawn.
ACKNOWLEDGMENTS The authors are indebted to the  Centre de Ressources Biologiques (CRB)-Santé ( BB-0033-00056 , http://www. crbsante-rennes. com) of  Rennes hospital  for its support in the processing of biological samples, the  Clinique La Sagesse  and  Christophe Ruaux  for providing tonsil samples, the  UMS   CNRS 3480/US INSERM 018  BIOSIT for cell sorting core facility. The authors thank  Violaine Alunni  from the  IGBMC Microarray and Sequencing platform  ( Illkirch Graffenstaden, France ) for Affymetrix GeneChip analysis.
1C ), and mutations were observed in both NTD and RBD. Interestingly, an insertion (YLTPGD) is present only in the NTD of SARS-CoV-2, RaTG13, and Guangxi pangolin coronaviruses, but absent in related bat coronaviruses (in Guangdong pangolin viruses the region is not fully covered by sequencing) (Fig. 1D ). This motif forms a conformational cluster at the exposed NTD regions of the spike trimer and may contribute to determine host range. However, as mentioned above, within the RBD, SARS-CoV-2 shows higher identity with pangolin viruses belonging to the Guangdong lineage than to RaTG13 (Fig. 1B  and D ). Structural analysis of the binding interface between SARS-CoV-2 RBD and human ACE2 shows a strong network of polar contacts (PDB id: 6m0j)  (Lan et al. , 2020). SARS-CoV-2 RBD mediates these polar interactions through Lys417, Gly446, Tyr449, Asn487, Gln493, Gln498, Thr500, Asn501, Gly502 and Tyr505 with ACE2 (Fig.
(D) Sequence alignments showing absence of the YLTPGD insert in bat sarbecoviruses, and the sequence of the RBD region involved in the interaction with ACE2. (E) The position of YLTPGD inserts forming conformational clusters (red spheres) at the NTD of SARS-CoV-2 spike protein is shown (left). The ribbon structure of the spike protein-ACE2 interaction surface is represented to show polar interactions (right). Polar interactions were analyzed using PyMol using PDB id: 6m0j  (Lan et al. , 2020). (F) Alignment of the region carrying the polybasic amino acid insertion (red) at the S1/S2 cleavage site. GenBank/GISAID accessions for the sequences included in trees are: NC_045512. 2 (SARS-CoV-2), MN996532. 1(RaTG13), EPI_ISL_412977 (RmYN02), MT084071. 1 (MP789 or Guangdong 1), EPI_ISL_410544 (Guangdong P2S), MT040334.
Information on infection with SARS-CoV-2 is being added to health data e. g. inclusion in the UK Biobank with its 500,000 volunteers (www. ukbiobank. ac. uk). The scale of the current pandemic is stimulating free access to human genetic studies as they progress and data sharing for combined analyses, with multiple, diverse partners from academia and industry, e. g. The COVID19 Host Genetics Initiative (www. covid19hg. org). Hopefully, useful information will be produced at a rate not previously seen for a transmissible disease.
Acknowledgements We thank  Michel Tibayrenc  (editor-in-chief  Infection, Genetics and Evolution)  for launching the project of this article, and for inspiring its conception. Tung Phan acknowledges support from the  Division of Clinical Microbiology, University of Pittsburgh Medical Center, USA. Manuela Sironi  was supported by the  Italian Ministry of Health  (" Ricerca Corrente 2019-2020 "). Fernando González-Candelas  was supported by project  BFU2017-89594R  from  MICIN (Spanish Government ).
Acknowledgements The article is part of the scientific activities of the  Global Tuberculosis Network  (GTN);  GREPI (Groupe de Recherche et d'Enseignement en Pneumo-Infectiologie ), a working group from  SPLF  ( Société de Pneumologie de Langue Francaise) ;  SEPAR (Sociedad Española de Neumología and Cirugía Torácica) ;  Moscow Society of Phtisiology  and of the  WHO Collaborating Centre for Tuberculosis and Lung Diseases, Tradate ,  ITA-80 ,  2017---2020-GBM/RC/LDA ). The following clinicians participate in the project:.
Sample selection This paper investigates the M&A risk reporting of US firms that acquired or merged with firms between 2005 and 2008. From 2005 onward, the Securities and Exchange Commission (  2005 ) requires all firms to include Item 1A in their 10-K filings to report "speculative or risky" factors (Item 503 (c) of Regulation S-K). The sample period ends in the year of the financial crisis. Table  1  presents the sample selection. I included all the transactions between 2005 and 2008 that are listed in the Bureau van Dijk ZEPHYR database as an acquisition or a merger. I verified that the acquirer firms owned more than 50% of the target firms' shares after the completion of the M&A. I further restricted the sample to M&As for which the acquirer firm is a publicly listed US firm. I excluded M&As with acquirer firms from the financial services (SIC codes 6000-6999), for which the risk reporting is subject to more specific regulations  (Dobler et al.
, 2011; Elshandidy et al. , 2015). To ensure that the acquirer firms provide accounting information about the M&A (i. e. , purchase price allocation), I focused on M&As with a minimum deal value of USD 250 million. Thus, the initial sample includes 705 M&As. [Insert Table  1 here]  I selected 300 M&As, using simple random sampling  (Sekaran & Bougie, 2013). Of [Insert Table  2  here] The annual report, which includes the purchase price allocation in the notes to the financial statements, is a primary source of M&A-related information for shareholders  (Ott et al. , 2014; Shalev, 2009). Managers report on the M&A risk factors in Item 1A of the 10-K filing (analysis unit). I obtained the historical 10-K filings from the Securities and Exchange Commission's EDGAR database. Together with a research assistant, I manually collected Item 1A of all the 10-K filings.
g. , the differences in the general risk level during the sample period). [Insert Table  3  here] Table  3  summarizes the definitions and measurements of all the variables. All the control variables were measured in the fiscal year prior to the M&A. I manually collected the data on M&A risk reporting, M&A accounting (i. e. , the purchase price allocation), and corporate governance from the acquirer firm's 10-K filings. All the other variables are based on data from the databases DATASTREAM, WORLDSCOPE, and LEXISNEXIS. Table  4  provides descriptive statistics for all the variables. The mean of RISK_QUAN is 0. 010, which means that the acquirer firms mention one M&A risk factor per 100 words in Item 1A of the 10-K filing. The mean of RISK_QUAL, which can range from 0 (for poor 12 Alternative proxies (i.
The sample comprises the randomly selected M&A transactions of US-GAAP acquirer firms completed between January 1 st , 2005, and December 31 st , 2008. The M&A transactions were retrieved from the ZEPHYR database on June 30 th , 2009. Final purchase price allocations have to be published no later than one year after the completion of the M&A transaction in the notes to the financial statements. Risk reports commenting on the risks of M&A transactions are published in Item 1A of the 10-K filing.
number of M&A transactions with listed acquirer firms from the United States 20,205 The sample comprises the randomly selected M&A transactions of US-GAAP acquirer firms completed between January 1 st , 2005, and December 31 st , 2008. The M&A transactions were retrieved from the ZEPHYR database on June 30 th , 2009. Final purchase price allocations have to be published no later than one year after the completion of the M&A transaction in the notes to the financial statements. Risk reports commenting on the risks of M&A transactions are published in Item 1A of the 10-K filing. Time period Not completed between 01/01/2005 and 31/12/2008 -9,646 Deal value (incl. estimates) Below minimum deal value of USD 250 million -9,664 Industry Finance, insurance and real estate M&A transactions (SIC 60-69) -190 Total number of M&A transactions selected 705 Random sample of M&A transactions considered 300 Business combination Not according to SFAS 141 -25 Materiality Immaterial business combination -35 Data availability Missing data -9 Total number of M&A transactions analyzed in the descriptive analysis 231 thereof purchase price allocations to be analyzed 231 thereof risk reports commenting on the risks of M&A transactions 216 Notes:.
, 2016)  is timely. Over the last decades, numerous technical developments have led to an increased number of meteorological measurements. In this study, precipitation over almost the entire Antarctic continent is analyzed at a climatological timescale using a large-scale snowfall dataset that is independent from climate models, allowing objective evaluation. The reference for snowfall rate used here is the map produced by  Palerme et al. (2014)  based on the CloudSat satellite radar, which provided the first 4-year surface snowfall climatology for Antarctica. It has recently been followed by its complete three-dimensional version  (Lemonnier et al. , 2020). We use these satellite observations to assess the Antarctic precipitation rates simulated by the CMIP6 models in various setups, at continental and regional spatial scales, and at the annual and seasonal timescales.
In this work, we use data from the 2007-2010 three-dimensional Antarctic climatology  (Lemonnier et al. , 2020)  yielding the vertical distribution of the snowfall rate with a resolution of 1 • latitude and 2 • longitude -optimizing the agreement with in situ observations  (Souverijns et al. , 2018; Palerme et al. , 2014). Recently the need to take into consideration the effect of soil echoes has been highlighted  (Palerme et al. , 2019) , because it affects the measurement of CPR in particular in the areas of complex topography, such as mountains and fjords. Some abnormal values are ignored in this dataset, but do not highly impact averages. Here we consider the radar information at the level of 1200 m above ground level to assess the surface snowfall rate.
ERA5 reanalyses ERA5 (Copernicus Climate Change Service , C3S) is the latest global reanalysis of the atmosphere made by the European Centre for Medium-Range Weather Forecasts (ECMWF) based on historical observation data since 1979 with the Integrated Forecasting System (IFS) model and its data assimilation system. Outputs from these reanalyses have high horizontal and vertical spatial resolutions (30 km, 137 vertical levels). In this work, the monthly averages of the ERA5 reanalyses are used for the 40 years from 1979 to 2018.
As we will show below (see Sect. Over this period, there is a slight positive mean precipitation trend in the CMIP ensembles (strongest, about 2 % per decade, in the CMIP5 and CMIP6 historical simulations), but the variations induced by this trend over the model periods are substantially weaker than the absolute differences between the model means and the CloudSat observational average. Therefore, and because our results are not particularly sensitive to the choice of model years, CMIP output is averaged over the entire respective simulation period for comparison with CloudSat. A more detailed statistical analysis using the Welch t test, presented in Appendix D, demonstrates that the greatest confidence is attached to the ice shelves, the peninsula, and the east coast (Ross, Filchner, Peninsula, and LowEast regions) when comparing the snowfall means from CloudSat to ERA5 and the CMIP (both 5 and 6) datasets.
However, some uncertainties remain in areas of complex topography, due to sublimation of snow below the first level of CloudSat, which is likely to influence the total snowfall amount taken into account here. In the interior of the Antarctic continent, the comparison has to be treated with caution as the snowfall means from CloudSat, ERA5, and the CMIP datasets are significantly different. This may be mainly due to the CPR of CloudSat that underestimates snowfall means, as a major part of it comes from microphysical processes occurring below the first CloudSat level in this region. Therefore, the comparison is focused on the differences between CMIP5 and CMIP6, while the CloudSat results are kept for information purposes only as the single source of observation over these areas. In addition, the test points out that there is no major reduction of the reliability of the comparison between CloudSat and the CMIP experiments when the whole temporal coverage is considered (instead of a 4-year time series).
Discussion and conclusion The CloudSat precipitation climatology provides the possibility to evaluate climate models and reanalyses against model-independent satellite-derived data. By comparing ERA5 reanalysis output from multiple random 4-year periods against output for the 4-year observational period  (2007) (2008) (2009) (2010)  and the satellite-derived data, we have shown that on regional scales, a 4-year period is long enough to draw robust conclusions about misfits between the models and the satellite dataset. The main results of this short study are as follows. 1. All CMIP model ensemble medians and ERA5 overestimate the continental mean precipitation rates. 2. The positive biases are particularly strong in the plateau regions. 3. There is no measurable improvement, in terms of continental and regional mean precipitation rates and their seasonality, from CMIP5 to CMIP6.
Acknowledgements. This paper is a contribution to the  CALVA  project supported by the  French Polar Institute IPEV. This research was carried out in the framework and with support of the  French space agency CNES/CNRS GDR EECLAT. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP5  and  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. Financial support. This research was carried out in the framework and with support of the  French space agency CNES/CNRS GDR EECLAT. Review statement. This paper was edited by  Alexander Robinson  and reviewed by  John King  and one anonymous referee.
Data availability. All datasets are open access and have been extracted from official public repositories. Data processed from the CloudSat radar  (Lemonnier et al. , 2020)  that have been involved in this research are available at https://doi. org/10. 1594/PANGAEA. 909434 (Lemonnier et al. ,  2019b). The ERA5 dataset used in this work have been generated using Copernicus Climate Change Service Information (Copernicus Climate Change Service , C3S). The CMIP5 datasets have been also generated using C3S. The CMIP6 have been obtained on the Earth System Grid Federation (ESGF) portal (https://esgfnode. llnl. gov/, last access: 9 December 2019,  Cinquini et al. , 2014). Only basic processing has been carried out (temporal and spatial averages). Author contributions. This research was designed by all authors.
Key Points • Two methods, joint modeling and quality-adjusted effect sizes are feasible to calculate the net clinical benefit. • Both methods showed that adjusting for the impact of treatment on health-related quality of life resulted in theoretically reduced survival benefits. In clinical trials including glioma patients, data on healthrelated quality of life (HRQoL) are regularly collected in addition to traditional outcomes such as radiological tumor response, overall survival (OS), and progression-free survival (PFS). Incorporation of survival and quality-of-life outcomes into one expression seems important to properly evaluate treatment benefits. Indeed, a certain treatment strategy may prolong survival but may also be accompanied with considerable toxicity and subsequently a decreased level of functioning in patients.