dataesr/scientific-paragraphs-categorization huggingface dataset:
MS/MS experiments were conducted using Data Dependent Acquisition mode with 3 precursor ions in the m/z range 150-1600; ions were excluded after 6 s. The analysis included 2 segments: in the first one, from 0 to 8 min, collision energies (CE) of 40 eV for monocharged ions and 30 eV for doubly charged ions were applied. In the second one, from 8 min to the end of the analysis, collision energies of 75 eV for monocharged ions and 40 eV for doubly charged ions were applied. CE values were adapted to the kinds of compounds: before 8 min, the molecules belong to the tannin family, which is mostly composed of C-O bonds, weaker than the C-C bonds of the terpenes, which elutes after 8 min. CE values for doubly charged ions are lower than for single-charged ones due to the proximity of both charges.
Venn Diagrams A Venn diagram shows the logical relation between sets; it illustrates sample relationships by comparing common ions between them. InteractiVenn software (http: //www. interactivenn. net/index2. html, accessed for the last figures on 30 September 2021) was used to build the Venn diagrams  [46]. After analyses of the pooled extracts, sets were built using a bucket associating for each detected compound its retention time and the m/z of the most abundant ion. One set is represented by a circle, the overlap between two circles or sets is the ions they have in common, whereas the others are ions that are specific to a set.
Molecular Network Design Molecular networks are visual representations of the chemical space present in tandem mass spectrometry (MS/MS) experiments by comparing mass spectra in pairs to map an extract. Therefore, considering that ions with closed structures will give similar neutral losses or fragment ions, each cluster associates molecules with a similar fragmentation pathway that possess high structural similarities and are likely to belong to the same chemical family. The bucket table was built using MetaboScape software version 4. 0 (Bruker). The T-ReX 3D algorithm detected precursor ions with an intensity threshold of 10,000. a. u. and associated retention time, m/z, and area for each analyte. The mascot generic format (mgf) file and a quantification table were exported to the GNPS platform (http://gnps.
ucsd. edu, accessed for the last treatments on 7 and 8 September 2021) using feature-based molecular networking (FBMN) in order to build the molecular network  [29, 47]. Optimized parameters used for molecular network design were: mass tolerance 0. 02 Da for parent and fragment mass, min pair cos 0. 75, network TopK 10, maximum connected component size 100, minimum matched fragment ions 6, minimum cluster size 2, yes run MSCluster. Quantitative molecular networks were visualized using Cytoscape software version 3. 8. 1 (https://cytoscape. org, accessed for the last figures on 7 and 8 September 2021). Within the network, one node corresponds to one MS/MS spectrum. Nodes are represented by pie charts. The proportion of each color in the nodes correlates with the relative abundance of the ion in each sample.
Acknowledgments: We are thankful to  Thomas Giordanengo  from the  Tonnellerie Radoux  for the many discussions that helped to carry out this study and for the cooperage sampling of oak woods. We are also grateful for  Brigitte Musch  and  Bénédicte Le Guerroué  from the  CGAF-ONF laboratory UMR BioForA (Ardon, France)  for the genetic assignment of species. We thank  Claire Quinones  and  Xavier Mandret  from the  ONF Centre-Ouest-Nouvelle-Aquitaine territorial direction (Boigny-sur-Bionne, France ) for the forest sampling of oak woods.
The authors gratefully acknowledge major financial support from the  region Centre-Val de Loire  through the  "Chêne et vin" program  which made this study possible, and also the projects Valbiocosm ( FEDER-FSE 2014-2020-EX003202 ),  CHemBio  ( FEDER-FSE 2014-2020-EX003677 ),  Techsab  ( FEDER-FSE 2014-2020-EX011313 ), the  RTR Motivhealth  ( 2019-00131403 ) and the  Labex  programs  SYNORG  ( ANR-11-LABX-0029 ), and  IRON  ( ANR-11-LABX-0018-01 ) for their financial support of  ICOA ,  UMR 7311 ,  University of Orléans ,  CNRS. Institutional Review Board Statement: Not applicable.
Introduction COVID-19 is an acute respiratory illness caused by SARS-CoV-2. 1  Between Dec 1, 2019, and June 10, 2021, this illness affected more than 175 million individuals worldwide, according to Worldometer. By Jan 31, 2021, the UK alone had recorded 38 163 patients requiring hospitalisation for COVID-19 (Our World in Data). In such circumstances, health-care infrastructures suffer extraordinary and overwhelming demand for resources and require fast, drastic rationing, which can easily result in poor outcomes (eg, long-term morbidity and death). 2  The efficient allocation of resources is essential to manage the pandemic's long-term effects, not only for the treatment of patients with COVID-19, but also for those For Worldometer see https:// www. worldometers. info/ coronavirus/ For Our World in Data see https://ourworldindata.
Indeed, SARS-CoV-2 might not be detected by conventional PCR tests using samples obtained via upper respiratory tract swabs because viral presence in the upper respiratory tract decreases during the first week after symptom onset. 4 Thus, as a complementary approach, identifying the combination of symptoms that enables early prediction of COVID-19 is essential. ] [8] [9]  The COVID-19 Symptom Study app 6 collects selfreported, longitudinal information about the symptom profiles of patients with COVID-19 (confirmed by a PCR test). Machine learning approaches have been developed by use of the information collected from the COVID-19 Symptom Study app 6 to create diagnostic models and leverage the added value of big data to study new diseases such as COVID-19. 7, 10 Despite some promising results in identifying the main symptoms of COVID-19 and their correlation to outcomes, most proposed models only use the information available at the time of maximum symptom intensity (defined as the peak of symptoms), and, hence, are not as conducive for early diagnosis.
Data sources The data used in this study are part of a prospective dataset acquired by use of the COVID-19 Symptom Study app: a mobile health application developed by ZOE (London, UK) in collaboration with scientists from King's College London (London, UK) and Massachusetts General Hospital (Boston, MA, USA). 6 The data source and study design have been previously described in detail in a validation study, which provided the basis for ethical approval of this study. Briefly, the COVID-19 Symptom Study app collected self-reported data on 19 symptoms and COVID-19 PCR test results.
Added value of this study This study represents a novel effort to identify and refer individuals for COVID-19 testing, enabling a more efficient allocation of medical resources during crucial stages of a pandemic. Our results suggest that a hierarchical Gaussian process model is effective in predicting SARS-CoV-2 infection when earlier symptoms are considered. Furthermore, this study used a uniquely large dataset comprising a prospective, community-based cohort. The longitudinal information provided by the participants also allowed the temporal assessment of the different symptoms collected.
Implications of all the available evidence Our findings show the value of artificial intelligence in modelling COVID-19 symptoms and in the timely detection of SARS-CoV-2 infections. Such a model would enable the prompt self-isolation of individuals with suspected COVID-19 and referral for urgent testing, enabling better allocation of medical resources during an evolving pandemic, particularly during chaotic periods. For more on the data source and study design see https:// clinicaltrials. gov/ct2/show/ NCT04331509 (appendix p 1). For the purposes of this research, we used 18 of these self-reported symptoms, excluding red welts on the face and lips due to possible confounding assessments for this symptom, and the study population was limited to participants in the UK. From the data acquired by the mobile app, we extracted a subset of longitudinal information for 3 days of self-reported symptoms after symptom onset.
Symptoms encoding and description are available in the appendix (pp 1-2). All samples with invalid data entries or incomplete information were excluded from this study. The resulting dataset included a total of 182 991 samples (participants) who reported symptoms between April 29, 2020, and Oct 15, 2020 (the training set), and an additional population comprising 15 049 participants who recorded their symptoms between Oct 16, 2020, and Nov 30, 2020 (the test set). All participants were labelled according to their self-reported PCR test results as being either positive or negative for SARS-CoV-2 infection, which was then used as the diagnostic criteria for both model training and evaluation. To compensate for dataset imbalance in the number of positive versus negative cases during training, we implemented a bootstrapping scheme to increase the proportion of SARS-CoV-2-positive participants in the training set from the initial 2% to 5%.
Data analysis and models definitions We used three approaches to analyse the self-reported symptoms. The three approaches differ according to the number of symptoms considered as relevant to diagnose COVID-19 and in their temporal encoding. The first method mimics the standard approach implemented by the UK's NHS for triage and test referencing. 11 This method, hereafter referred to as the NHS algorithm, considers all individuals reporting either cough, fever, or loss of smell in any of the days in a time window (varying from 1 day to 3 days in our study) as potentially being infected with SARS-CoV-2. The second approach identifies patients with COVID-19 on the basis of a symptomatic profile accumulated over time of a subset of symptoms (ie, loss of smell, persistent cough, fatigue, and skipped meals), which were previously validated to detect COVID-19,  7  and demographic information by use of a logistic regression model proposed by Menni and colleagues.
7 Further details regarding model definitions and implementation are available in the appendix (p 5). For the third approach, we designed a Bayesian framework to model the relationship between selfreported symptoms, comor bidities (eg, diabetes, kidney disease, heart disease, and lung disease), and an individual's COVID-19 status. 18 self-reported symp toms were used by this model. We also included demographic information as covariates in the model, namely age, sex, body-mass index (BMI), occupation according to SARS-CoV-2 exposure (health-care workers vs non-health-care workers), and the risk of contact with infected patients for health-care workers during the COVID-19 pandemic; further details can be found in the appendix (pp 5-6). Bayesian frameworks, such as the Gaussian process, are particularly useful to study SARS-CoV-2 infection, as they allow robust modelling even in highly uncertain or incomplete datasets, such as medical datasets.
12 Unlike the previous two models, the proposed model, defined as a hierarchical Gaussian process model (appendix pp 5-6), encodes both the correlation between symptoms across participants and the correlation between timepoints (temporal evolu tions), considering the prevalence ratio of COVID-19 associated with the geographical area where participants reside. 13 Furthermore, our hierarchical Gaussian process model encodes the ordering of symptoms as a time-series sequence across the different participants, and within participants across timepoints (appendix pp 5-6). The hierarchical Gaussian process model was optimised by use of the open-source software GPflow, version 2. 2,  14  and TensorFlow, version 2. 0. Both the logistic regression model and the NHS algorithm were developed by use of Python, version 3.
Results We included 182 991 participants in the training set and 15 049 participants in the test set. The two populations had similar symptom distributions (appendix p 2) and demographics (table 1), and were completely independent. A detailed description of the population stratified into age and BMI subgroups is available in the appendix (pp 3-4). The ability to predict the COVID-19 test result after a maximum of 3 days of self-reported symptoms was assessed via the sensitivity, specificity, and AUC of the three models (table 2). The model was trained with data from April 29, 2020, to Oct 15, 2020, and tested with data from Oct 16, 2020, to Nov 30, 2020, and appeared robust to changes in the underlying pandemic periods (table  2 ). The hierarchical Gaussian process model showed the highest prediction AUC, which increased with the number of days of self-reported symptoms (0•73 [95% CI 0•73-0•74] for 1 day, 0•79 [0•78-0•79] for 2 days, and 0•80 [0•80-0•81] for 3 days; appendix p 7).
Discussion The early detection of COVID-19 is helpful for resource allocation during the pandemic. Here, we have proposed a Bayesian approach to identify individuals with probable COVID-19 on the basis of self-reported symptoms during 1, 2, and 3 days after symptom onset and demographic information. Using a unique, prospective dataset, we evaluated the proposed method by analysing its ability to predict COVID-19 in the early stages of infection, identify sets of symptoms that can be used to characterise early signs of infection in subgroups of the population, and consider the certainty of estimations for the model predictions to be used to direct people for  testing, self-isolation, or both. This early diagnosis could then lead to better allocation of medical resources when the health-care system is severely strained by the pandemic.
The proposed approach was compared with the methods currently used by the NHS and by related studies. 7, 9, 20 Our model was effective in the identification of COVID-19 after 3 days of symptoms (AUC 0•80), with a mean sensitivity of 0•73 (SD 0•05) and a mean specificity of 0•72 (SD 0•02). Nevertheless, the model was hampered by the bias of data acquisition and requires further validation with external datasets. When compared with the other state-of-the-art diagnostic algorithms,  7, 9  our proposed approach showed significantly better predictive accuracy and sensitivity. By analysing predictive AUC, sensitivity, and specificity in subgroups, we conclude that our model can be particularly relevant in the detection of early signs of COVID-19 for certain groups of the population, such as older patients.
Acknowledgments ZOE provided in-kind support for all aspects of building, running, and supporting the app and service to all users worldwide. This work was supported by the  Wellcome Engineering and Physical Sciences Research Council Centre for Medical Engineering at King's College London  ( WT 203148/Z/16/Z ) and the  UK Department of Health  via the  National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Award to Guy's and St Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust. Investigators also received support from the  Medical Research Council , the  British Heart Foundation , the  Alzheimer's Society , the  EU , the  NIHR , the  COVID-19 Driver Relief Fund , and the  NIHR-funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust , in partnership with King's  College London.
SO was supported by the  French Government  through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency  (reference number  ANR-19-P3IA-0002 ). ATC was supported by a  Stuart and Suzanne Steele Massachusetts General Hospital Research Scholar Award. LHN, DAD, and ATC were supported by the  Massachusetts Consortium on Pathogen Readiness. This research was funded in part by the  Wellcome Trust  ( 215010/Z/18/Z ).
Data sharing Data collected in the COVID-19 Symptom Study smartphone app are being shared with other health researchers through the NHS-funded Health Data Research UK and Secure Anonymised Information Linkage consortium, housed in the UK Secure Research Platform (Swansea, UK). Anonymised data are available to be shared with researchers (with no date restrictions), according to their protocols, in the public interest (https://web. www. healthdatagateway. org/dataset/594cfe55-96e3-45ff-874c-2c0006eeb881). The code for the hierarchical Gaussian process model is freely available at https://gitlab. com/KCL-BMEIS/covid-zoe/ prediction-gp.
The ongoing NASA's Juno mission puts new constraints on the internal dynamics of Jupiter. Data gathered by its onboard magnetometer reveal a dipole-dominated surface magnetic field accompanied by strong localised magnetic flux patches. The gravity measurements indicate that the fierce surface zonal jets extend several thousands of kilometers below the cloud level before rapidly decaying below 0. 94 -0. 96 , being the mean Jovian radius at the one bar level. Jupiter's internal structure can be grossly decomposed in two parts: (i) an outer layer filled with a mixture of molecular hydrogen and helium where the zonal flows are thought to be driven; (ii) an inner region where hydrogen becomes metallic and dynamo action is expected to sustain the magnetic field. Several internal models however suggest a more intricate structure with a thin intermediate region in which helium would segregate from hydrogen, forming a compositionally-stratified layer.
Numerical methods The dynamo model presented in this study has been computed using the open-source MHD code MagIC (freely available at https://github. com/magic-sph/magic, see  Wicht, 2002). MagIC has been tested and validated against several anelastic benchmarks  (Jones et al. , 2011). The set of equations (7-10) complemented by the boundary conditions (15-16) is solved in spherical coordinates by expanding the velocity and the magnetic fields into poloidal and toroidal potentials: ̃ = × ( × ) + × , = × ( × ) + ×. ( 17 ) The quantities , , , , ′ and ′ are expanded in spherical harmonics up to a degree max in the angular directions and in Chebyshev polynomials up to the degree in the radial direction. For the latter, a Chebyshev collocation method is employed using the Gauss-Lobatto interval with grid points defined by = cos ( -1) -1 , ∈ [1, ].
Symbol Λ Elsasser 2 ∕ 0 Ω (10 1 -10 2 ) 8. 52 ∕ Energy ratio 2 ∕ 0 2 (10 2 -10 3 ) 3. 50 ℎ Ohmic fraction  ∕( +  ) 1 0. 78 Axial-dipole fraction 2 =1, =0 ( )∕ 2 , ≤12 ( ) 0. 75 0. 95 The equations are advanced in time using an implicitexplicit Crank-Nicolson Adams-Bashforth second order scheme, which handles the nonlinear terms and the Coriolis force explicitly and the remaining terms implicitly  (Glatzmaier, 1984). Because of the stable stratification, the advection of the background entropy gradient, d ̃ ∕d , that enters Eq. (  10 ) is also handled implicitly to avoid severe time step restrictions when the Brunt-Väisälä frequency exceeds the rotation rate (see  Brown, Vasil and Zweibel, 2012). MagIC uses the open-source library SHTns (freely available at https://bitbucket. org/nschaeff/shtns, see  Schaeffer, 2013)  for the spherical harmonic transforms.
Acknowledgements We thank  Dave Stevenson  and an anonymous reviewer for their useful comments. Numerical computations have been carried out on the S-CAPAD platform at IPGP, on the occigen cluster at  GENCI-CINES  (Grant  A0020410095 ) and on the cobra cluster in Garching. All the figures have been generated using matplotlib (Hunter, 2007) and paraview (https://www. paraview. org). The colormaps come from the cmocean package by  Thyng, Greene, Hetland, Zimmerle and DiMarco  ( 2016 ).
Data source We conducted a single centre retrospective cohort study at the IHU Méditerranée Infection, Marseille (France), which is part of the network of public hospitals in Marseille (AP-HM). All available SARS-CoV-2 genome sequences obtained by our laboratory between March 2020 and April 2021 were reviewed. Patients infected with 20A, 20B, 20C viruses (circulating during the first phase of epidemic), B. 1. 177 or B. 1. 160 variants (circulating during the second phase), or different variants (N501YV) harbouring the N501Y substitution within the spike protein (circulating during the third phase of pandemic), were selected (Fig. 1 ). Additional patients diagnosed with SARS-CoV-2 by real-time reverse-transcription PCR (qPCR) were included for whom viral genotype was determined by partial spike gene sequencing as previous described  (Colson et al.
Demographic and clinical data including comorbidities were retrospectively retrieved from medical files including, notably, the main symptoms, in/outpatient status, transfer to intensive care unit (ICU), and death. At the time of writing, all patients had recovered and been discharged from hospital or had died. Because death could possibly occur long-time post-discharge, mortality was investigated through the national data on COVID-19 related mortality (at least 30 days post-discharge; Institut National de la Statistique et des Etudes Economiques (INSEE), 2021). Patients with missing information were mostly patients whose samples were send to our laboratory by external medical facilities and who were therefore excluded.
Genome assembly was performed using the CLC Genomics workbench v. 7 software by mapping on the SARS-CoV-2 genome GenBank Accession no. NC_045512. 2 (Wuhan-Hu-1 isolate). Recovered genomes were compared to sequences from the GISAID database (https://www. gi said. org/). The phylogenetic analysis was performed using the nextstrain/ncov tool (https://github. com/nextstrain/ncov) that uses the IQ-TREE software  (Minh et al. , 2020)  for phylogenetic tree building then the Auspice software for tree visualisation (https://docs. nextstrain. org/projects/auspice/en/latest/releases/v2. html).
Statistics Statistical analyses were carried out using R (R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2020. URL: https://www. Rpro ject. org/) and Stata version 15. 1 (http://www. stata. com). Qualitative variables were presented by percentage. We used two approaches to conduct the analysis. The first one examined the demographics and clinical profiles of patients infected with different viral mutants or    (Bursac et al. , 2008). The φ coefficient was used to test for multicollinearity among the independent variables. For pairs of variables that were highly correlated (absolute value of correlation coefficient > 0. 7), only one variable was entered into the multivariate model. Multivariate analysis was performed using exact logistic regression.
The overall comparison between the three phases from March 2020 to April 2021 showed that the variants of the second phase (B. 1. 177 (Marseille-2) and B. 1. 160 (Marseille-4) lineages, respectively) were associated with more severe outcomes, leading to higher rates of hospitalisation and death. The appearance of the variants of concern harbouring the N501Y substitution at the end of December 2020 caught the attention of the World Health Organization because of faster spreading than other variants and the original viruses (European Centre for Disease Prevention and Control, 2021; PAHO, 2021;  Lauring and Hodcroft, 2021; Frampton et al. , 2021). However, the association between these variants and severity of COVID-19 disease remains unclear  (Dao et al. , 2021b). In studies that analysed community-based testing datasets, the alpha variant was significantly associated with an increase in hospitalisation and death  (Challen et al.