dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design PRE-ONCOVID-19 is a retrospective study of cancer patients presenting to the Comprehensive Cancer Center of Lyon (Centre Leon Berard, CLB) with a suspicion of COVID-19 from March 1st to April 15th 2020. Patient cases were collected using a web-based tool, enabling the collection of clinical information integrated to the electronic patient record, after Institutional Review Board approval. Patients not agreeing for the use of their clinical data for an academic study were excluded, according to the national and European laws. The study was approved by the Institutional review board of the Centre Leon Berard on 12 March of 2020.
Data collected in this study The following data were collected retrospectively: demographic characteristics (age, weight, body mass index, gender,. ), cancer characteristics (histotypes, stage, relapse), the clinical presentation at the time of COVID-19 suspicion (Karnofsky performance status [KPS], fever, dyspnoea, cough, diarrhoea, O2 requirement, central nervous system (CNS) symptoms and vascular symptoms), presence of characteristic COVID-19 images on CT scan when performed, a selected set of biological analysis at the time of the infectious event (CRP, lymphocyte counts,. ), previous cancer treatments in the last month, patient outcome (survival) and co-morbidities (chronic obstructive pulmonary disease (COPD), hypertension and diabetes) in the electronic patient records. As benchmark, the comorbidities reported in the population of 43,171 cancer patients in the CLB since 01/01/2015 are COPD: 2541 (5.
Survival of RT-PCRepositive and RT-PCRenegative cancer patients Fig. 1  shows the survival of SARS-COV-2 RT-PCRepositive and RT-PCRenegative patients in the observation period between March 1st and April 25th. Eight of the 55 RT-PCRepositive patients and 22 of the 147 RT-PCRenegative patients had died at the time of the analysis. With a median follow-up of 25 days, the death rates of RT-PCRÀpositive and RT-PCRÀnegative patients 25 days after the first symptoms were 21% and 10%, respectively, and were not significantly different. Both the RT-PCRÀnegative and RT-PCRÀpositive groups had therefore a high death rate. None of the 302 patients of this series have received azithromycine, chloroquine, lopinavir/ritonavir or remdesivir. One patient each received GNS561 (a chloroquin analogue) and tocilizumab as part of an ongoing randomised clinical trial as standard treatment (Immunoncovid-20, NCT04333914).
Serological test also lack sensitivity, in particular in cancer patients  [22]. The objective of the present PRE-ONCOVID-19 study was to analyse the characteristics, symptoms and outcome of the exhaustive population of cancer patients presenting with clinical or radiological symptoms of COVID-19 in this comprehensive cancer centre, from March 1 st to April 25 th. This study served as the first step to build the prospective multicentre national prospective study open since April 2020 (ONCOVID-19, NCT04363632) investigating in a multicentric setting and in more details the presentation and outcome of cancer patient with documented or suspected COVID-19. The results obtained in the PRE-ONCOVID-19 study show that only a minority, 18. 2% (55/302) cancer patients presenting with clinical symptoms of COVID-19, had demonstrated SARS-COV-2 with RT-PCR performed on nasopharyngeal samples.
Dedicated clinical trials for this patient population are ongoing, testing immunotherapies, chloroquine analogues or anti-IL-6 (Immunoncovid-20, NCT04333914). In conclusion, this retrospective series of cancer patients presenting with suspicion of COVID-19 indicates that the death rate at 30 days after diagnosis is high both in patients with and without documented SARS-COV-2 on RT-PCR, the latter group representing 80% of patients. This subgroup of cancer patients presenting with COVID-19 symptoms without documented SARS-COV-2 gathers also 73%, of the observed deaths at 30 days. Specific therapeutic procedures suggested to improve COVID-19 patient survival, e. g. anti-IL-6 Ab  [23, 25, 27, 28] , chloroquine analogues  [29] , remdesivir  [30] , should be investigated also in this SARS-COV-2enegative cancer patient population presenting with severe symptoms suggestive of COVID-19.
Funding  NetSARC (INCA & DGOS)  and  RREPS (INCA & DGOS) ,  RESOS (INCA & DGOS)  and  LYRICAN (INCA-DGOS-INSERM 12563 ),  Institut Convergence PLASCAN  ( 17-CONV-0002 ),  Association DAM's, Ensemble contre Le GIST ,  Eurosarc  ( FP7-278 , 742 ), la  Fondation ARC, Infosarcome ,  InterSARC (INCA) ,  LabEx DEvweCAN  ( ANR-10-LABX-0061 ),  Ligue de L'Ain  contre le Cancer,  La Ligue  contre le Cancer,  EURACAN  ( EC 739 , 521 ) funded this study.
While the main causes of the temporal gravity variations observed by the GRACE space mission result from water mass redistributions occurring at the surface of the Earth in response to climatic and anthropogenic forcings (e. g. , changes in land hydrology, in ocean mass, in mass of glaciers and ice sheets), solid Earth's mass redistributions are also recorded by these observations. This is the case, in particular, for the Glacial Isostatic Adjustment (GIA) or the viscous response of the mantle to the last deglaciation. However, it is only recently showed that the gravity data also contain the signature of flows inside the outer core and their effects on the core-mantle boundary (CMB)  [1] [2]. Detecting deep Earth's processes in GRACE observations offers an exciting opportunity to provide additional insight on the dynamics of the core-mantle interface.
New data continue to be added to the GRACE record since the launch of the GRACE Follow-On (FO) mission in 2018. GRACE and GRACE FO also measure solid Earth's processes such as co-seismic and post-seismic crustal deformations [e. g. , 9-10], as well as large-scale readjustment of the Earth's mantle to the last deglaciation (the so-called Glacial Isostatic Adjustment -GIA) [e. g. , 11]. GRACE has also the capability of detecting lithospheric deformations and associated gravity changes due to on-going climate-related land ice melt  [12] [13]. While hundreds of scientific articles have been published during the past few years on the GRACEbased superficial mass redistributions, mass redistributions occurring deeper in the Earth have been the object of only a few investigations  [1] [2]. These studies also motivated the GRACEFUL project.
Gravity field data analysis We analysed monthly time series of GRACE gravity fields, over the period January 2003 to December 2015, and extracted the deep solid Earth's signal supposed to be correlated to the core magnetic field, after correcting for well-known other processes causing regional gravity variations. The latter include atmospheric loading, hydrological changes in river basins on land, glaciers and ice sheet mass changes, ocean mass changes due to land ice melt and ocean circulation changes, and GIA  [18]. To extract the time-variable deep Earth's signal from the GRACE data, we used the most-up-to-date knowledge about each contribution (from the atmosphere, oceans, ice sheets and glaciers, terrestrial waters. ) and removed the corresponding signal from the GRACE fields over their geographical areas of interest.
GRACE fields We use the ensemble mean of GRACE gravity field dataset processed by  [19]   4  , available publicly  5. This dataset consists of an average of five different GRACE solutions. They are based on spherical harmonics solutions provided by five different processing centers: CSR (Center for Space Research at Texas University), GFZ (GeoForschungsZentrum), JPL (Jet Propulsion Laboratory), TUG (Technical University of Graz), and GRGS (Groupe de Recherche de Géodésie Spatiale). The "Release 5" solutions are used for the CSR, GFZ and JPL data sets. "Release ITSG 2016" and "Release 3. 3" are used for the TUG and GRGS solutions, respectively. For all GRACE solutions, the spherical harmonic coefficients up to degree 60 were used for the conversion to gridded mass anomalies. The post-processing applied by  [19]  includes: (i) the addition of independent estimates of the geocenter motion and Earth's oblateness as these quantities are either not or poorly observed by GRACE, (ii) a filtering for correlated errors characterized by north-south stripes, and (iii) a correction for the GIA.
Hydrological component The signature of terrestrial waters is the dominant signal in GRACE data over land, in addition to ice mass change of glaciers. Terrestrial waters include surface waters (rivers, lakes, wetlands and man-made reservoirs), canopy, soil moisture, underground waters, and snow pack. Mass variations of these components are mainly driven by climate change and variability as well as human activities such as water extraction in aquifers for crop irrigation and domestic use, and reservoir construction on rivers. Multi-decadal trends of continental water mass are dominated by decreases due to groundwater depletion and by increases due to the construction of new reservoirs. Inter-annual variations are mainly driven by natural climate variability. Associated terrestrial water storage changes are routinely estimated from GRACE [e.
Ocean mass change The dominant signal for ocean mass change is the one due to the present day land ice melt. It is routinely measured by GRACE [e. g. , 29]. The best alternative to obtain independent data is the use of gridded sea level fields from satellite altimetry corrected for ocean thermal expansion. Six different altimetry data sets are available, including the most recently improved one in the context of the ESA Climate Change Initiative project  [30]. Several ocean thermal expansion data sets are also available from XBT (Expendable Bathythermograph) and Argo (international program) profiling float measurements (temperature, salinity, currents, and bio-optical properties)  [31]. These data sets (altimetry and thermal expansion) are described in detail in  [29].
GIA and other static factors GIA induces important regional trends on GRACE solutions (e. g. ,  [11] [12] ). To correct GRACE data for the GIA, global models are used. These models depend on the deglaciation history and mantle viscosity profile. In  [19]  three different solutions are considered to estimate model discrepancies and their impact on GRACE solutions. The ensemble mean GRACE solution considered here is corrected for the ICE6G-C GIA model  [32].
At the position of each observatory, series of both fields are computed as monthly means. Considering that the core processes are large-scale phenomena, the common variability of the magnetic and gravity fields is investigated at global scale. We choose to apply, as in  [1] , the singular value decomposition (SVD) technique, which is generally used to retrieve common variability modes from two data sets  [36]. This can be seen as a generalization of the Empirical Orthogonal Function decomposition, which isolates common variability modes in a set of time series. Applying this method allows us to retrieve this common variability for all the VMGOs, by decomposing the two sets of time series into a number of modes of common variation. Each mode consists of a spatial pattern and a time series for each dataset.
Discussion The promising results on this topic lead the authors to develop the GRACEFUL project, recently selected by the ERC in the context of its SYNERGY program. The project aims at building on the well-known correlation between the magnetic field and LoD variations in addition to the above-mentioned correlation between magnetic and gravity fields. We will refine the latter correlation by improving the GRACE data analysis using all available data sets for correcting the near surface signals and estimating associated uncertainties. We will also use the most up-to-date LoD data at decadal timescale to infer the flow patterns inside the core. The core flow generated by the Earth's rotation itself will be studied in conjunction with the flow induced by the magnetic field. The effect of the CMB topography and the interactions between the core and the mantle at the CMB, and of an inner core will be taken into account to quantify induced gravity changes.
DIA-based strategies enable unbiased measurement of peptide precursor ions (MS1 spectra) as well as peptide fragment ions (MS2 spectra) and leverage mass spectrometry technologies to generate accurate and reproducible peptide measurements towards maximizing proteome quantitation. DIA-based strategies focusing on quantitation of intact peptide ion abundances extracted from retention-time aligned MS1 spectra, including accurate mass and time tag (AMT), the hybrid data acquisition and processing strategy pSMART, and hyper-reaction monitoring techniques, have demonstrated excellent analytical reproducibility  14, 19  and quantitative accuracy  17. As these techniques afford reliable peptide quantitation and increased proteome coverage compared to DDA techniques, they are well-positioned to support high-throughput clinical proteomic analyses for precision medicine applications/ workflows that are often challenged by limited amounts of input material and large numbers of samples per study cohort.
To achieve reproducible and stable quantitative data sets and to facilitate harmonized implementation, standardization of DIA methods will be necessary. Toward this goal, a recent publication described the application of DIA methods to establish digital proteome maps of human tissue samples with the goal of creating prospective, digital proteome biobanks of clinical biospecimens supporting real-time and retrospective data analyses  20. Moreover, optimized synthetic  21  and internal  22  peptide standards have been developed to facilitate peptide retention-time alignment procedures and support facile comparison of DIA datasets generated at different analytical sites. Further, efforts to benchmark software platforms  23  and statistical methods  24  for DIA data analysis have been described, as has the generation of comprehensive peptide spectral libraries  25.
Recently, performance benchmarks for DIA data acquisition, specifically the application of the so-called SWATH DIA-MS approach, were used in analyses of a complex cell line standard in 11 laboratories  26. This study revealed consistent quantitation of more than 4000 proteins from HEK293 cells across all laboratories. To continue to expand the implementation of DIA workflows and integrate them into routine clinical sample analyses, the incorporation of benchmarked standards and standardized QC routines are necessary to maximize the accessibility of downstream data and empower team-driven science initiatives. Here we report on the performance of a high-throughput (e. g. 100 proteins quantified/min of analysis time), streamlined, and QCbenchmarked HRMS1-DIA workflow implemented in a continuous operational mode for seven consecutive days in eleven internationally-distributed labs followed by centralized data processing.
A quality control (QC) system was developed to monitor the entire workflow performance, promptly identify decrements in instrument performance, and guide to trigger troubleshooting when necessary, ensuring high levels of data quality are maintained to achieve the throughput necessary for large clinical cohort studies. Quantitative performance was evaluated with a well-established label-free quantitation sample set at each laboratory through the entire study, enabling distributed and longitudinal acquisition of data that can be compared and normalized during big data analysis. Controlled samples  23  included E. coli, yeast, and human cell line peptide digests were combined at fixed ratios to mimic biological samples and provided proof-ofconcept feasibility and performance of this streamlined workflow.
Results Implementation of a QC-benchmarked, HRMS1-DIA workflow. This report details the analytical performance and reproducibility of a standardized and QC-benchmarked HRMS1-DIA workflow intended to achieve quantitative proteotype analysis studies of large cohorts across several centers and in turn to support precision medicine projects. To address the needs of a robust and high-throughput workflow compatible with largecohort studies, a 60-minute capillary flow LC gradient using 1. 2 µL min -1 analytical flow rate was applied in all the analyses. Primary DIA data acquisition was performed on either the Easy-nLC 1200 or the Ultimate 3000 RSLC liquid chromatography systems coupled online with Q Exactive HF mass spectrometers (Thermo Fisher). The HRMS1-DIA method used here features an original structure, involving multiple MS1 scans interspersed with 18 DIA MS/MS scans per scan cycle (in total 54 DIA MS/MS scans) (Fig.
1a ). Quantification was based on precursor ion signals measured through high-resolution full MS scans with 120k resolution setting; the MS2 scans with 30k resolution setting was utilized for peptide identification only. High-resolution MS1based peptide quantitation strategies, such as in conventional DDA or pSMART implementation of DIA, have demonstrated excellent quantitative performance  17, 27  and improved quantitation precision and dynamic range. In the HRMS1-DIA method    1 ). During the study, a QC standard was analyzed in three technical replicates on days 1, 3, 5, and 7. If the QC criteria were met, sample A and sample B data sets acquired on the same day were analyzed. If the QC standard analysis did not pass QC criteria, either instrument setup maintenance or troubleshooting were undertaken.
To systematically evaluate the reproducibility of the QC-benchmarked HRMS1-DIA workflow, spectral libraries were centrally constructed from the DDA analysis of high pH reverse phase fractions. The same capillary LC configuration and mobile phase gradient conditions were utilized for the HRMS1-DIA analysis and the DDA analysis. Spectronaut software (Biognosys) was applied for both individual onsite data analysis and central data analysis with the centrally prepared spectral libraries. Our workflow represents a two-step procedure. First, the performance of the LC-MS platform operated with the HRMS1-DIA acquisition method was assessed to detect drifts from the predefined performance baseline and to trigger corrective measures to be taken if needed. A QC standard was analyzed using rigorous metrics to support system suitability testing; the QC standard was a commercially available peptide digest derived from the HeLa human cervical cancer cell line.
Second, following successful platform qualification, the actual quantitative measurements of samples of interest were performed using the same acquisition method. The baseline for the system suitability test was generated from the analyses of the QC standard performed by four reference laboratories in continuous operation mode over several days with LC-MS platforms operating at different levels of performance (Supplementary Table  1 ). These data enabled establishment of reference metrics and associated acceptance criteria for platform qualification from three replicate analyses of the QC standard. Reference metrics included median LC elution peak width, number of MS1 and MS2 data points across the LC elution peak, total precursor ions and protein groups identified, and interinjection median CV on the precursor ion signals (Table  1 ).
E. coli Yeast the high efficacy of the standardization approach. The highest deviation from theoretical values was observed in data collected by Lab 1, where the QC analysis passed the acceptance criteria and showed no evidence of reduced chromatographic or mass spectrometric performance. Therefore, the relatively higher deviation observed in data collected by Lab 1 compared to the other labs may have resulted from the samples themselves, and therefore from process error related to sample preparation at this lab. The analytical quantitative precision was excellent for the 10 participating laboratories, as illustrated by median CV values that were typically below 5% for the human and yeast proteins and below 10% for the E. coli proteins (Fig. 5 , lower panel). Again, the slightly higher value obtained for the E.
Data were centrally processed and protein-level abundances were determined across disease histotypes as described above. A total of 5712 unique protein groups were identified from all tumor samples and sites (Supplementary Data 3) and 3808 ± 343 (9. 0% RSD) were co-quantified in individual patient tumor samples that further exhibited high quantitative correlation across analytical sites (Spearman Rho = 0. 62 ± 0. 08, p < 0. 0001). Differential analyses revealed 394 significantly altered proteins (LIMMA adjusted P value < 0. 01) between OCCC and HGSOC patient tissues (Supplementary Data 4; Fig. 6b ) and these features exhibited high quantitative correlation in individual patient samples across analytical sites (Spearman Rho = 0. 92 ± 0. 02) and disease histotypes (Spearman Rho = 0. 63 ± 0. 04) (Fig.
Discussion A standardized analytical approach to support the creation of digital proteogenomic biobanks of clinical biospecimens for prospective and retrospective data analyses has been designed. To realize this goal, developing a standardized and QC-benchmarked workflow was necessary. The approach developed here for generation of harmonized datasets will maximize the consumption and (re)usability of downstream data sets and empower teamdriven science initiatives and population-level insights between the proteome and human disease. Our multi-site, international study defined a QC-driven, HRMS1-DIA workflow with excellent analytical reproducibility and label-free quantitative performance providing deep global proteotype profiling across diverse sample types, including a pilot analyses of clinically-derived, archival cancer tissue samples.
This workflow defines and implements QC benchmark expectations that can be monitored in real-time during primary DIA data production to ensure that data quality standards are achieved and that can be leveraged during downstream data processing to assess and track analytical variability and bias in cohort-level data. The presented HRMS1-DIA workflow is a standardized, quantitative method that is driven by defined QC expectations and that exhibits stable, highly reproducible, and scalable performance to support both basic discovery proteomics research and population-scale clinical sample analyses in a high-throughput manner.
Each biological sample was analyzed by HRMS1-DIA as three technical replicates. Quality control standards were evaluated before, between, and after the archival cancer tissue samples. The data acquisition was performed in a 24/7 operation mode. b Unsupervised cluster analysis of 394 proteins that were significantly altered (LIMMA adjusted p value < 0. 01) between OCCC (red) and HGSOC (blue) tissues. c Correlation plot of proteins significantly altered across analytical sites and disease histotypes. d Correlation of proteins identified as altered in OCCC and HGSOC tissues in this study with a gene signature identified by Hughes et al. 32. We co-quantified 18 significant protein alterations (LIMMA p value < 0. 05) with this historic 112 gene signature stratifying OCCC and HGSOC ovarian cancers.
coli protein digests. Each fraction was evaporated to dryness and resuspended by adding 10 µL or 30 µL for human and for yeast or E. coli, respectively, of an aqueous solution containing 0. 1% (v/v) formic acid supplemented with the iRT kit (Biognosys) at a ratio 1:10 v/v. To generate the cancer tissue-specific spectral library, 30 µg from each patient sample digest were combined (for a total of 120 µg) and fractionated by high pH reversed-phase liquid chromatography into 96 fractions using a linear gradient of ACN (0. 69% per minute) as described above. Concatenated fractions were pooled into 36 fractions, lyophilized, and resuspended in 0. 1% formic acid for analysis. Spectral library generation by data-dependent analysis. HRMS1-DIA data sets were acquired on the Q Exactive HF MS instrument platform at the eleven laboratories.