dataesr/scientific-paragraphs-categorization huggingface dataset:
trost@newcastle. ac. uk Prof Quentin M. Anstee, PhD, FRCP Translational and Clinical Research Institute, The Medical School, Newcastle University, 4th Floor, William Leech Building, Framlington Place, Newcastle-upon-Tyne, NE2 4HH, United Kingdom Email: quentin. anstee@newcastle. ac. uk J o u r n a l P r e -p r o o f Key words: macrophages, immunometabolism, NASH, inflammation Word counts: 6,098 (including abstract, references, table, figure legends) Number of figures and tables: 6 figures and 1 table Conflict of interest: The authors have no potential conflicts (financial, professional or personal) directly relevant to the manuscript. Funding: This study has been supported by the EPoS (Elucidating Pathways of Steatohepatitis) consortium funded by the Horizon 2020 Framework Program of the European Union under Grant Agreement 634413, the LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis) consortium funded by the Innovative Medicines Initiative (IMI2) Program of the European Union under Grant Agreement 777377, which receives funding from the EU Horizon 2020 programme and EFPIA, and the Newcastle NIHR Biomedical Research Centre (to QMA), the Newcastle University start-up funding and the Wellcome Trust Investigator Award (215542/Z/19/Z) (to MT), Knut och Alice Wallenberg Foundation Wallenberg Centre for molecular and translational medicine, University of Gothenburg, Sweden and Åke Wirbergs Research funding #M18-0121 (to AH), Cancerfonfen # 19 0352 Pj (2020-2022) (to AH), the Belgian Federal Science Policy Office (Interuniversity Attraction Poles Program) grant Network P7/83-HEPRO2 (to TR), Rosetrees Trust (to NML), Flemish Cancer Society Kom op tegen Kanker, and Belgian Cancer Society Stichting tegen Kanker (to J.
(2005) , which we subsequently refer to as Pr05. Unlike other data sets, Pr05 also covers warm deserts other than the Sahara. Pr05 employs observations of the backscattering coefficient from the European Remote Sensing Satellite (ERS) scatterometer, calibrated on quality in situ and geomorphological z 0m,b estimates, to derive monthly mean z 0m,b in arid and semi-arid regions for an equal-area grid of 0. 25 • resolution at the Equator. To derive a spatially continuous input field for CLM, we collect the monthly data from all grid cells in Pr05 that fall within a focal grid cell in our simulations. We use the 25th percentile of the corresponding monthly data that fall within the focal grid cell as a temporally constant input for our simulations assuming that the temporal evolution in Pr05 results purely from the seasonality of vegetation (which is represented by the vegetation patches described in the previous section).
Experiment design We present results from two sets of simulations: (1) land-only simulations using CLM version 5. 1 forced by the GSWP3 reanalysis data  (Dirmeyer et al. , 2006; Kim, 2014)  and (2) land-atmosphere simulations with CESM version 2. 1. 2. For each simulation, we conduct a 50-year spinup followed by a 15-year analysis period using a near present-day climatological configuration. The vegetation phenology is prescribed from satellite observations in all simulations (SP mode in CLM). The different patches of vegetation are placed on separated soil columns to suppress lateral exchange of energy and water among them  (Schultz et al. , 2017; Meier et al. , 2018)  and biomass heat storage was activated to enable removal of the stability cap of the Monin-Obukhov stability parameter  (Swenson et al.
, 2019; Meier et al. , 2019). We additionally implement a new history file averaging flag that enables retrieval of model output at a specified local solar time. This allows us to determine the model state at a specific local solar time, for example at the time of MODIS LST observations at 01:30 and 13:30, without archiving data at all model time steps. For each configuration we conduct one control simulation with the existing representation of z 0 in CLM and a simulation in which we apply the updates for z 0 previously described. For the CLM simulations, we use the component configuration set "I2000Clm51Sp". These simulations are run at 0. 5 • resolution. For the atmospheric forcing we cycle through the GSWP3 data of 1998-2012. The resulting simulations are called CLM-CTL and CLM-Z0 subsequently.
Reference data sets We consult two observation-based data sets to assess the impact of the imposed modifications in CLM-Z0 and CESM-Z0 on model performance in terms of LST. First, we use observations of the MODIS system, which is installed on the low-Earth-orbit satellites, Terra and Aqua, to evaluate diurnal variations of the LST at grid cell level. These instruments provide LST estimates at a resolution of 1 km at approxi-mately 01:30 and 13:30 local solar time at the Equator, based on the longwave radiation emitted by the land surface. We employ data from 2003-2012 of the product MYD11C3 version 6  (Wan et al. , 2015) , which has a native resolution of 0. 05 •. From these data, we compute a multi-year monthly climatology as described in  Meier et al. (2019)  at 0. 5 • resolution. For comparison to the CESM simulations, we regrid this climatology to 0.
9 • × 1. 25 • with first-order conservative remapping of the Climate Data Operators (CDO) library. We output the LST in the model simulations at 01:30/13:30 and use only model output from 2003-2012 for a consistent comparison with MODIS. Further, we apply a cloud masking to the model output as described below. In addition to comparing LST directly at grid cell level, we also evaluate the local LST difference between bare soil and vegetation. To extract such information from the MODIS LST observations, we repeat the space-for-time substitution approach as in  Duveiller et al. (2018)  to relate the LST to the land cover type. For the LST, we again employ monthly MYD11C3 data both at daytime (13:30) and during nighttime (01:30). The land cover fractions are based on the ESA Climate Change Initiative Land Cover project  (ESA, 2017).
Note the non-linear color scale. Panels (c) and (f) show the profile of area-weighted mean wind speed difference in DJF (blue) and JJA (red) in regions 1 (c) and 2 (f), which are marked in panel (b). The line depicts the median wind speed difference across all seasonal means and shading shows the range between the 10th and 90th percentiles. Height is calculated assuming a surface pressure of 1013. 2 hPa, a surface air temperature of 288. 15 K, and a constant lapse rate of 6. 5 K km -1. Data from the last 30 years of the spinup period and data from the analysis period (15 years) were used for this figure. ues for z 0m,b , z 0m,s , and z 0m,i are reduced from 1. 0 × 10 -2 to 8. 4 × 10 -4 m, from 2. 4 × 10 -3 to 7. 8 × 10 -4 m, and from 1. 0 × 10 -2 to 2. 3 × 10 -3 m, respectively. Alternatively, the spatially explicit z 0m,b input field from  Prigent et al.
Acknowledgements. The  CESM  project is supported primarily by the  National Science Foundation. We thank all the scientists, software engineers, and administrators who contributed to the development of CESM2. We thank  Wim Thiery  for his valuable advice. All graphics were created with MATLAB R2020b. Financial support. This research has been supported by the  Swiss National Science Foundation (SNSF)  through the  CLIMPULSE  project (http://p3. snf. ch/Project-172715, last access:  23 February 2022 ; grant no. 200021_172715 ). Review statement. This paper was edited by  Jatin Kala  and reviewed by two anonymous referees.
Code and data availability. The CLM code, the CESM code, Du18, and the estimated climatology of the incoming shortwave radiation at the land surface in GSWP3 under clear-sky conditions are available at https://doi. org/10. 3929/ethz-b-000503165 (Meier et  al. , 2021). MYD11C3 can be downloaded from https://lpdaac. usgs. gov/products/myd11c3v006/ (last access: 23 February 2022; Wan et al. , 2015) and Land Cover CCI from http://maps. elie. ucl. ac. be/CCI/ viewer/download. php (last access: 23 February 2022; ESA, 2017). For the data from Hu et al. (2020), contact Xiaolong Hu, and for the data from Prigent et al. (2005), Catherine Prigent. Any model output is available upon request from Ronny Meier.
Here we present a multi-omics approach to fill the knowledge gap and to provide a comprehensive overview on the longitudinal metabolic changes over the course of RA progression. We utilized the collagen-induced arthritis (CIA) mouse model, which resembles the dynamic pathological features of the acute phase of RA  [14, 15]. We combined bulk RNA-seq and metabolomics on CIA mouse paws with metabolomics on CIA plasma, and back-translated our findings with clinical data from different RA disease stages. In addition, the longitudinal plasma metabolomics dataset offers a resource for mining early RA biomarkers. Finally, we identified SIRT1 axis in macrophages as a potential driver of early RA disease stages.
Compared to the plasma metabolomics with >100 significant metabolites at the early time point at day two, the paw transcriptomics had only a handful of significant genes at this time point. Later, a sharp increase of significant differentially expressed genes occurred at week three. Furthermore, the majority of expression changes were upregulated genes (Fig. 3c ). To decipher the molecular mechanism in different stages of arthritis development, we performed gene set enrichment analysis against the biological process gene sets from Gene Ontology database  [26]  (Fig. 3d ). The gene sets of TCA cycle, OXPHOS and respiratory electron transport chain were depleted in CIA samples. In line with the knowledge that disruption in OXPHOS can generate reactive oxygen species (ROS)  [27] , gene sets of nitric oxide biosynthetic process and ROS metabolic process were enriched in CIA samples.
Multi-omics factor analysis identified factors correlating with arthritis severity Combined analysis of omics datasets has dramatically increased both, our understanding and the validation of pathways and targets. Therefore, we assembled the transcriptome data from paws and the metabolomics data from plasma from in total 171 overlapping mice and run multi-omics factor analysis (MOFA). MOFA is an unsupervised method which infers a set of low-dimensional data representations, factors, which drive the major variations among the data  [31]  (Fig. 5a ). MOFA identified 6 factors (Fig. 5b ), based on the criterion that a factor should account for > 2% of the variance in at least one omics dataset. Among these, Factors 1 and 2 were mainly active in the transcriptome data. In contrast, Factor 3 was mainly specific to metabolomics data.
Metabolic dysregulation confirmed by transcriptome changes in various stages of RA patient disease progression To validate the findings on CIA paws in a context of human RA, we looked into a published RNA-seq dataset (GSE89408)  [36]  on synovium biopsies from healthy humans (n=27) and patients with arthralgia (n=10), undifferentiated arthritis (UA, n=6) and early RA (n=57). The pathogenesis of human RA (Fig. 7a ) originates from asymptomatic autoimmunity, which manifests into arthralgia with nonspecific joint symptoms and further progresses to UA with clinical synovitis. Subsequently UA advances into early stage of RA, which is defined as within 12 months of diagnosis without prior treatment. PCA on human synovium transcriptome revealed clear separation between the healthy group and the clinically symptomatic groups (Fig.
ACOD1 encodes an itaconate-producing enzyme, shifting TCA cycle towards itaconate production in pro-inflammatory macrophages  [43]. ACOD1, was also significantly upregulated in CIA paws and LPS-stimulated human macrophages (Fig. 8a-b ). ACOD1 expression in human synovium RNA-seq dataset was too low and therefore it was filtered out before differential gene expression analysis (Fig. 8c ).
Discussion Understanding the pathogenesis of RA is essential for development of new therapeutics. However, few studies have systemically examined the early metabolic changes in arthritis. Here, we utilized RNA-seq and metabolomics to provide a timeresolved comprehensive overview on the arthritis progression in CIA mice. We then used an unsupervised multi-omics factor analysis method to identify the important metabolic processes in etiology of RA development. Furthermore, we validated the major findings of the CIA study with two human datasets, including LPS-stimulated macrophages and synovium biopsies of RA patients. The onset of clinical RA is preceded by a phase of asymptomatic autoimmunity where innate immunity plays a key role in disease progression  [2, 44]. In human RA synovium, neutrophils and macrophages are the highly abundant cell types  [45] [46] [47]  and the number of synovial macrophages may predict joint destruction  [48].
This highlights the translational relevance of our longitudinal study and hopefully encourages the validation of some novel described metabolites like gamma-glutamyl amino acids in the future. Direct comparison of mouse and human datasets in the current study unveiled a shared pattern of enhanced glycolysis, depleted OXPHOS and increased ROS synthesis in arthritis development. Glycolysis provides activated cells with ATP and precursors for biosynthesis via influx into PPP  [5]. Inhibition of glycolysis can reduce disease severity of RA mouse models  [52, 53]. The upstream regulator analyses on all three transcriptome datasets (Fig. 3e, 6d, 7f ) predicted that the key metabolic sensor, SIRT1, was inhibited, while its downstream target NFκB and pro-inflammatory cytokines were activated. This is consistent with the experimental evidence on SIRT1-knockout macrophages, which exhibit hyperacetylated NFkB, higher NFkB-mediated gene transcription and subsequently increased pro-inflammatory cytokine production  [30].
Our result further underlines the role of SIRT1 and macrophage metabolism in the development of arthritis. This study also provides a resource that can be mined for mechanistic investigation of metabolic dysregulation that underlies arthritis development in CIA mouse model. We took the large heterogeneity in arthritis phenotype of these mice into consideration and used a large sample number for the CIA mice. Although validation of the CIA RNA-seq study findings with two human datasets does not yield complete match in terms of magnitude of gene expression changes, this is largely because of the differences in the species, tissue types and sequencing protocols. Also, we acknowledge that singlecell RNA-seq on mouse synovium would have provided more comprehensive gene expression at individual cell level, but this approach would have been technically challenging to apply to the tiny size of mouse synovium.
The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2. 1 x 150 mm, 1. 7 µm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10. 8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1000 m/z. Raw data files are archived and extracted as described below. Several types of controls were analyzed in concert with the experimental samples: a pooled matrix sample generated by taking a small volume of each experimental sample (or alternatively, use of a pool of well-characterized human plasma) served as a technical replicate throughout the data set; extracted water samples served as process blanks; and a cocktail of QC standards that were carefully chosen not to interfere with the measurement of endogenous compounds were spiked into every analyzed sample, allowed instrument performance monitoring and aided chromatographic alignment.
UPLC-MS/MS data extraction and compound identification Raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library +/-10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum.
Additional mass spectral entries have been created for structurally unnamed biochemicals, which have been identified by virtue of their recurrent nature (both chromatographic and mass spectral). Library matches for each compound were checked for each sample and corrected if necessary. This study generated an untargeted metabolomics dataset that comprises a total of 929 biochemicals, 815 compounds of known identity and 114 compounds of unknown structural identity. Peaks were quantified using area-under-the-curve. For studies spanning multiple days, a data normalization step was performed to correct variation resulting from instrument inter-day tuning differences. Essentially, each compound was corrected in run-day blocks by registering the medians to equal one (1. 00) and normalizing each data point proportionately.
Statistical analysis on untargeted metabolomics of mouse plasma Following log transformation, two-way ANOVA using base R package (v4. 0. 0)  [57]  was performed to identify metabolites exhibiting significant differences between treatment groups (CIA vs. controls) and time points. All reported significant results on untargeted metabolomics have adjusted p-value cutoff < 0. 05 after using False Discovery Rate (FDR) method to correct for multiple testing. PCA was performed using the corresponding function in MetaboAnalystR 3. 0  [58].
3. 0. Images were acquired in the positive ion mode in the m/z range of 100 to 2,000 using 2Ώ and 2M magnitude mode with a resolving power of ~400,000 at m/z 400 and a free induction decay of 0. 8 ms using FlexImaging 5. 0 (Bruker Daltonik) by applying 250 laser shots per measurement position at a lateral resolution of 50 µm. To ensure high mass accuracy an internal lock mass calibration was performed on known DHB ion clusters (m/z 273. 03936). For ion image visualization and statical evaluation, raw data were loaded and processed using SCiLS Lab MVS (Version 9. 01. 12514, Bruker Daltonik). Normalization of signal intensities were performed using the total ion count (TIC) normalization and a global peak assignment was performed at a frequency of 1 % for data reduction. Reduced data were exported into imzml data format using SCiLS Lab and a peak picking based on the SPUTNIK R package  [60]  was applied to filter spatial relevant m/z information.
The spatial filtered m/z list was mapped against Sanofi's internal biomarker database at a mass accuracy of 1. 2 ppm (1. 2 ppm reflects mean ± 2 SD of technical variability over the entire mass range). The intensities values of assigned m/z biomarker candidates were exported from SCiLS Lab as Arb. U and used for statistical evaluation. Two-way ANOVA with Tukey multiple pairwise-comparisons using base R package (v4. 0. 0)  [57]  was applied to calculate adjusted p-values. PCA was performed using the corresponding function in MetaboAnalystR 3. 0  [58].
R96 gene model. Paired reads were counted at the gene level. For the public RNA-seq dataset on human synovial biopsies (GSE89408), raw counts at the gene level were extracted from HumanDisease_B38_GC33 of DiseaseLand (Qiagen). Briefly, DiseaseLand contains curated publicly available RNA-seq and expression array data, where all samples have been processed through the same pipeline to allow for cross project comparison. For HumanDisease_B38_GC33, alignment of FASTQ files was done using OSA to Human genome B38. The average sequencing depth is 34. 1 million reads per human synovial sample.
Statistical analysis on RNA-seq data The statistical test on RNA-seq raw count data was performed by DESeq2  [67]. Depending on the respective study design and sequencing depth, genes that have >1 CPM (counts per million) in at least 20% of the mouse samples, at least 50% of the human macrophage samples, or at least 5% of the human synovium samples were included for analysis. For data on mouse samples, a time series differential analysis was done by the likelihood ratio test (LRT) in DESeq2, whereas for data on human macrophages and synovium (GSE89408), Wald test was used. The p-values attained by hypothesis testing in DESeq2 were corrected for multiple testing using the FDR method. Significant differentially expressed genes were defined by cutoffs of FDR < 0. 05 and fold change > 1. 5 in up or down direction.
Gene set enrichment analysis Gene set enrichment analysis was performed with the up-to-date biological process gene sets from the Gene Ontology database  [26]  using fgsea  [68]. Genes were ranked in a decreasing order according to their fold changes, or to their loadings in a MOFA factor, and this ranking was used as the input to fgsea. By default, 1,000 permutations were done to calculate statistical significance. FDR-corrected p-value below 0. 05 was considered as significant for a gene set.
Acknowledgements We thank members of the Type 1/17 Immunology Cluster in Sanofi for technical support on the mouse experiments and  Constanze Holz  ( Sanofi DMPK-Metabolism/tMSI ) for technical support on the tissue MALDI-MS imaging. We are grateful to  Annick Peleraux  ( Sanofi Biomarker Statistics ) and  Stephanie Pierson  ( Sanofi Pasteur/Digital R&D/Bioinformatics ) for implementing the RASflow pipeline on.
Background: ESMO COVID-19 and CAncer REgistry (ESMO-CoCARE) is an international collaborative registry-based, cohort study gathering real-world data from Europe, Asia/Oceania and Africa on the natural history, management and outcomes of patients with cancer infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Patients and methods: ESMO-CoCARE captures information on patients with solid/haematological malignancies, diagnosed with coronavirus disease 2019 (COVID-19). Data collected since June 2020 include demographics, comorbidities, laboratory measurements, cancer characteristics, COVID-19 clinical features, management and outcome. Parameters influencing COVID-19 severity/recovery were investigated as well as factors associated with overall survival (OS) upon SARS-CoV-2 infection.
16 It became, therefore, essential to gather larger and more robust datasets powered to study the effects of COVID-19 in different subgroups of patients with cancer (i. e. histology, staging, treatments) from various geographic areas. The ESMO COVID-19 and CAncer REgistry (ESMO-CoCARE) was initiated to meet this goal and was designed as a large, observational multicentre, transnational database, including centres from Europe, Africa and Asia/Oceania to study the effects of COVID-19 in patients with hematologic or solid tumours. Herein, we present the first ESMO-CoCARE results with data collected until May 2021. We report on risk factors for severity and mortality from COVID-19 in patients with cancer integrating data from centres in Europe, Asia/Oceania and Africa, and we independently validate observations from similar registries, in an effort to contribute to a better understanding of COVID-19 disease in people with cancer, informing clinicians and regulatory bodies on optimal management.