dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 3. Data processing For ASAP IMS TOF, raw files were directly processed by self-written MATLAB routines, and molecular assignation was performed using the raw formula C6-100H6-200O0-10, double bond equivalent below 25 and H/C ratio between 0. 4 and 2. 8. Nitrogen was removed from the elemental composition, as the manual examination of the spectra revealed a negligible percentage of molecular attributions included a nitrogen atom. A maximum error of 50 ppm was allowed on the m/z values. At least 95% of the total ion intensity was attributed to molecular formulas. The lists of attributed signals were further treated with MATLAB to create a list of molecular formulas common to at least two out of the three replicates. OriginPro (2016) and MATLAB were used for visualization of the data. This filtered list of signals and their elemental composition were compared via multivariate data analysis.
After studying the general ASAP profile and mass spectrometric response, molecular formulas were attributed using the ASAP IM-TOF MS data for the five biomass samples. However, an investigation of isobaric compounds was needed to ensure the reliability of the molecular attribution obtained with the mass resolution of the TOF analyzer. FTICR, with its ultra-high resolution and high mass accuracy, was used for that purpose. A direct insertion ionization source equivalent to ASAP, the DIP APCI source, was used to obtain comparable analytical conditions. Two ion distributions were observed for both ASAP IM-TOF MS and DIP FTICR (Figure A. 1). The distribution in the lower range from m/z 100-300 was more intense with ASAP and the second distribution in the range of m/z 300-450 was more intense with DIP.
Molecular mapping of the biomass samples All ASAP IM-TOF MS data were processed using MATLAB scripts, and a list of molecular attributions for the replicates of each sample was obtained. The Figure A. 5 in the supplementary material shows the Venn diagram for all samples and replicates. Around 1,000 molecular formulas were determined for each biomass sample. Moreover, at least 75% of the molecular formulas for the five samples were common to at least three replicates, pointing out the reproducibility of the ASAP method. The ASAP IM-TOF MS molecular formulas were used in graphical representation for the comparison of the biomass samples. The van Krevelen diagram is a graphical representation that provides information on molecular families present in the studied samples by plotting the O/C and H/C molecular ratios of attributed molecular formulas  [41].
Collision cross section of hardwood and softwood pyrolysis molecular markers The CCS determination was performed on sixty ions from biomass thermo-desorption and pyrolysis products. This number of ions was intended to initiate the creation of an experimental CCS database for wood degradation markers. Half of these ions were found to be characteristic to softwood samples (highest PC1 coefficients). These thirty attributions with the highest PC1 coefficient corresponded to biomass extractables. Most of these extractables were specifically found in softwood. The second half of the sixty chosen ions were found to be characteristic to hardwood samples (lowest PC1 coefficients). These thirty ions corresponded to monomeric and dimeric lignin degradation products. Some of these markers were below the detection limit or at very low abundance for the softwood samples.
Conclusion In this study, we present the direct analysis of solids by high resolution mass spectrometry and ion mobility spectrometry for the chemical description of degradation products from various biomass materials. Previously, DIP FTICR MS, giving information on molecular formula, was developed for the characterization of lignocellulosic biomass  [32]. However, FTICR MS provided only limited structural information. The addition of ion mobility allowed for the observation of isomeric diversity during the biomass pyrolysis process for a wide variety of biomass types and pyrolysis products. The analysis involved the use of an ASAP source that, beneficially, yielded in situ pyrolysis of the samples. Although the IMS data was obtained from a high resolution time of flight instrument, the absence of isobaric interference was validated and confirmed by FTICR MS.
We could show that the ASAP IMS TOF platform is a powerful tool for the description of the pyrolysis product of biomass samples, giving both molecular formulas and isomeric information within minutes. Comprehensive processing of the ASAP IMS TOF data and multivariate data analysis by PCA allowed for the extraction of molecular markers specific to biomass families involving softwoods, hardwoods and miscanthus. The experimental TW CCSN2 of these markers were obtained using ion mobility mass spectrometry. Low TW CCSN2 differences were observed between the biomass samples, showing the isomeric similarities. By using standard molecules, it was demonstrated based on tandem mass spectrometry and ion mobility profiles that various isomers are present in the real biomass. In particular, differences in drift time and increase of peak width (FWHM) in the biomass sample could be observed.
However, the ion mobility resolution from the TWIMS system is limited, leading to poor separation of isomers. In this respect, cyclic ion mobility mass spectrometer with its higher ion mobility resolution could be used to improve the separation of isomers from wood pyrolysis products. Also, the ion mobility analysis procedure and the data processing workflow could be used to differentiate a larger group of biomass species. Future studies will involve the analysis of more standard molecules, including lignin dimers, for the creation of a TW CCSN2 database for wood degradation products. Finally, the calculation of theoretical CCS could be foreseen with different isomers from wood degradation products to confirm in-silico experimental CCS obtained with ASAP IMS TOF.
Acknowledgements The  COBRA laboratory  is financed by the  Labex SynOrg  ( ANR-11-LABX-0029 ) and the  European Regional Development Fund  ( ERDF HN0001343 ). Financial support from the  National Fourier transform ion cyclotron resonance network  ( FR 3624 CNRS ) and the  European Union 's  Horizon 2020 Research Infrastructures program  (grant agreement  731077 ), are gratefully acknowledged.
Those women who had given birth in the past two years and were either visiting the OB/GYN or were admitted in the hospital for gynecology/obstetrics related concerns during the duration of study were also included. All pregnant and postnatal women who were not willing to participate in the study, those patients admitted in Intensive Care Unit, casualty and ventilator and those who left against clinical guidance were excluded. Supporting literatures were collected from authorized international and national journals. Carefully audited data obtained from literature review and scenario at the study site were put together in building a data collection form and a protocol. Questionnaire and leaflets were developed for the study. The Questionnaires were prepared in two forms. A Pre-intervention Questionnaire and a Post-intervention Questionnaire.
The Intervention was provided shortly after the initial pre-intervention data collection. The post intervention phase was also carried out soon after the intervention or within a day of intervention in some cases. The authors are of the opinion that if the duration between intervention and post intervention survey was longer, it could negatively impact the results, as the information would be less retained by the participants, if not aided by the leaflet. All the data collected during the study period were statistically analyzed for formulating the result by using the statistical package for social science [SPSS] 25. 0 for windows version. Mean and standard deviation of age were calculated. Frequency distribution of parameters like age, educational qualification, hospital visits, supplements, comorbidities and drugs were analyzed.
We checked the existence of pre-existing cross-reactive antibodies using sera from 100 PCR-verified COVID-19 negative samples and obtained a specificity of 94%. In the validation exercises, when a test result was not obvious, at least 3 researchers validated the reading. In the rare case of an invalid test (no control line, or wrong location of bands), the test was repeated. A representative set of randomly chosen positive and negative test results are shown in the extended data (Figure  S2 )  27. The kit was also adjudged to have a concordance of 72% with the WHO-recommended Wantai ELISA kit.
1. 2)  33. The data were cleaned by checking for completeness, duplication and consistency. Cleaned data were analyzed with Stata 16 (StataCorp, College Station, Texas, USA) and R/RStudio  34, 35. GraphPad Prism version 8. 0. 0  36  was used for some additional analysis and generation of figures. Descriptive analyses were performed and univariate and multivariate logistic regression models were used to assess the association between seroprevalence and risk factors. Multivariable logistic models for seropositivity were obtained by using a backward stepwise procedure. Demographic variables that were associated with seropositivity at the P<0. 25 level were included. The overall goodness of fit was assessed using the Wald statistic. Unadjusted and adjusted odds ratios with 95% confidence intervals (CI) were computed and presented as parallel dot plots with error bars.
Study sites and participant characteristics A total of 2729 participants were screened for this study (underlying data  37  , extended data, Figure  S1  27 ), including 1305 in Phase I (10 sites: August, 2020), 395 in Phase II (4 sites: October, 2020), 393 in Phase III (2 sites: December, 2020) and 636 in Phase IV (4 sites: February 2021). Altogether, four markets/lorry stations (ML-1 was visited twice), three malls (M1, twice), two research institutes (R, twice), two sets of small towns locations (C1 and C3) and four hospitals (H1 and H2, twice) were screened across all Phases. During Phase III, 200 individuals were also screened from Navrongo and Bolgatanga in the Upper East Region (C1), which had the lowest reported COVID-19 cases in Ghana at the time the study began. Attendees (81) at a free Afrochella concert in Accra were also screened during Phase III (C2), potentially representing a general cross-section of the GAR population.
Acknowledgements With their confirmed permission we are sincerely grateful to other  WACCBIP COVID- 19 Team members (WCT): Kyerewaa Boateng 1 , Sylvester Languon 1,3 , Jerry Quaye 1,3 , Aaron Adom Manu 1 , Eugene Boateng 1,3 , Daniel Lorlorson Okpatah 1,3 , Louisa Baaba Obbeng 1  ,  Oforiwaa Ofori  1,3 , Peggy.
We monitored CO 2 levels to ensure that they remained above 3,000 ppm (equivalent to a partial pressure of 0. 003 atm, an order of magnitude larger than ambient air) for the duration of the experiment using a Perfect Prime C02000 Data Logger. A beaker of water was also placed in the container, which maintained monitored relative humidity (RH) at levels approaching 100 % and always greater than 85 % RH. Temperature was ambient (approx. 20 • C) over the duration of the experiments. To serve as a "control", a fourth core with aggregate composition B was also prepared and placed in a constant humidity chamber, but with only ambient CO 2 levels ("NCB", i. e. non-carbonated B). For DRIFTS and X-ray CT scanning, samples were removed from the carbonation chamber, stacked in a cylindrical mount, and scanned in series; DRIFTS analysis was conducted either directly before or directly after X-ray CT acquisition.
3D volume processing and quantification of phase densities Image processing was accomplished in a series of steps using Mango, a software developed by Applied Mathematics at ANU. First, data volumes taken at later time points (days 3, 10, 28, 108) were registered to the day 0 sample volume, masked to exclude core holder and external void space, and cropped so that all data volumes represented the same physical volume. All the attenuation gray values in the final registered, masked, and cropped tomogram were quantified (Fig. 1 ). The histograms in Fig. 1a  exhibit three peaks corresponding to three density groupings: very low attenuation values are associated with void space; intermediate values are associated with water, hydrated Mg compounds, and HMCs, collectively referred to in this study as "cement"; and high attenuation values indicate high density components (Table  1 ).
At the end of the curing process, subvolumes (approximately 50 % of original sample volume) of carbonated samples were ground, mixed with a corundum spike (Baikalox alumina polishing powder 1 μm), and the resulting powder was mounted on a silicon low-background sample holder and analyzed by X-ray diffraction using a Malvern Panalytical Empyrean X-ray diffractometer. Analyses were undertaken in Bragg-Brentano geometry from 5 to 85 • 2Ɵ at a spacing of 0. 01313 • using a 3 kW X-ray generator and Cu Kα radiation, and a PIXcel 3D detector. Mineral identification was through the SIEMENS software package Diffracplus Eva 10  [41]  and ICDD PDF-2 database, and phase quantification using Rietveld refinement  [42]  with the program HighScore Plus V4. 8. The amorphous (poorly crystalline or extremely fine-grained) component was identified by an elevated background 'hump', and quantified relative to the spike in the internal standard  [43].
Results Results of the experiments are presented in three sections: first, we examine the mineralogy of the reaction products using DRIFTS and endpoint XRD results to help constrain the CT values. Second, we validate the cement CT value rescaling procedure by comparison of temporally-resolved CT and concurrent DRIFTS data for carbonated and non-carbonated samples. We then present the structural and grayscale analysis derived from CT for carbonated samples. Throughout the results and the following discussion, we refer to airfilled regions of the samples as "void" rather than porosity. Porosity is commonly understood to refer to non-solid regions of a porous material, which may be filled with gas or fluid. In these samples, the cement paste (comprised of MgO and water) was initially a fluid, and over time it solidified.
g. , see sample NCB). After day 10, the rescaled attenuation values are relatively more stable than the mineral spectra results; this indicates that while solid and adsorbed phases may be undergoing reactions which change the measurable spectral vibrations (e. g. , brucite converting to HMCs, or mineral phase to amorphous phase and/or surface adsorbed species), the density of the cement phase in carbonated samples is approximately constant. In other words, after day 10 both the XCT and DRIFTS data indicate that the cement phase is not taking up or releasing substantial CO 2 or water. The comparison of cement rescaled intensity values with DRIFTS results demonstrates that the rescaled X-ray intensity is a proxy for cement phase carbonation, in addition to density. Correspondingly, the amorphous HMCs produced in carbonated samples have significantly higher density (and thus, higher rescaled X-ray intensity) than the hydrated non-carbonated "cement" phase produced in the non-carbonated sample NCB.
Acknowledgements U Troitzsch undertook the XRD analysis. A. H. acknowledges financial support via  Australian Research Council (ARC) DE180100082  and the  ANU/UNSW Digicore Research Consortium ; and A. H. and M. S. both acknowledge financial support from  ARC IC180100008. The PANalytical Empyrean X-ray diffractometer and Agilent DRIFTS spectrometer were obtained by  PL King  through  ANU funds (Major Equipment  funds and start-up). The DRIFTS work was partially supported by  ARC  grant  DP200100406  to  PL King.
Radiographic measurements The digital images were processed using OSIRIX ® 5. 8. 2 software (Pixmeo) for reading DICOM images. This software was used to measure distances and angles. A 5-cm long pin was inserted vertically in the skin over the patellar tendon to serve as a length calibration guide. A circle was drawn at the femoral condyles using the OSIRIX ® software. The contours of the circle followed the posterior condyles, by analogy with the circle described by Elias  [9] , which gave the best approximation of the knee's center of rotation. This center of rotation always corresponded to a point located on the Blumensaat line (point A). Next, a second circle was drawn on the tibia: the dense subchondral lines merged with the circle's diameter line and corresponded to the convex shape of the lateral tibial plateau.
The ends of the diameter line corresponded to the ends of the tibial circle (points B, C). The center of the circle was the center of the lateral tibial plateau. Next, the ABC angle was measured between the three points using the OSI-RIX ® software (Fig. 2 ): -Point A: Center of first circle (defining the knee's center of rotation) -Point B: Posterior end of the second circle's diameter -Point C: Anterior end of the second circle's diameter Thus, the ABC angle was open to the front and decreased as the posterior translation increased. To compare the ABC angle to the conventional distance measurement of posterior translation, the method described by Jacobsen was used  [5, 6]. Two lines perpendicular to a line tangent to the medial tibial plateau were traced, one passing through the posterior edge of the medial tibial plateau and the other adjacent to the posterior edge of the medial condyle (Fig.
Statistical analysis The statistical analysis was performed with IBM SPSS ® Statistics software (version 19. 0). Wilcoxon paired tests (non-parametric) were used to compare the distribution of each parameter and analyze the changes in posterior translation in the various experimental conditions. The null hypothesis (H 0 ) was that the distributions of the posterior translation values are equal between the two methods. The significance threshold was set at p < 0. 05. The inter-rater correlation for the angle and distance measurements of posterior tibial translation on the lateral radiographic views was determined with the Kappa correlation coefficient; the inter-rater agreement was expressed as a percentage.
The C++ CONRAD  [4, 5]  code allows us to produce consistent nuclear data, and to compute their sensitivities to nuclear parameters. It is developed at LEPh (CEA-Cadarache) to perform nuclear data evaluation, analysis and integral experiment assimilation. This nuclear evaluation code can produce nuclear data for all the energy range used in neutronics: from a set of nuclear parameters, the code is able to produce cross sections, angular distributions and energy distributions for radiative capture, fission, elastic and inelastic scattering reactions. For the resolved resonance range (R-matrix), CONRAD possesses its own solver. For the continuum, total cross sections are computed with either its own solver or the code ECIS. For partial reactions, CONRAD calls the nuclear data code TALYS  [9] , to compute cross sections, angular and energetic distributions (for the inelastic continuum) at each point of the energy grid asked, or automatically determined.
Sensitivities to optical model parameters As anisotropy of heavy nuclei usually appears at fast neutron energies, it becomes necessary to work on higher energy to address the efficiency of the method for angular distribution contribution with heavy nuclei. At these energies, a new theoretical model has to be used (the optical model), implying different nuclear parameters. In this energy domain, CONRAD uses the TALYS code  [9]  to obtain nuclear data. We tested the method on the PMF001 benchmark, on the optical model nuclear radius r for plutonium-239, its diffuseness a and the depth of the Hartree-Fock mean-field potential well V HF. It should be noticed that the plutonium nuclear data used here do not possess inelastic reactions to restrict the sensitivity contributions to cross section and angular distribution.
Results Data and estimation. Our primary data comes from the CoVIDA project, a sentinel community-based surveillance initiative led by the University of Los Andes that gathered information on an average of 1552 RT-PCR tests per week in Bogota over a 9-month period (see "Methods" Section for a description of the test). We invited an adult population to be tested, most of whom were asymptomatic at the time of taking the test. We over-sampled occupations that were expected to be most exposed to the virus, such as security guards, transportation workers, and health workers. The first half of the sample was randomly drawn from lists of participants obtained through agreements with a number of local partners, mostly composed of large employers. Many of these lists constitute a large share of a given occupation, ensuring that our samples were representative within each occupation.
The second half was a convenience sample based on our "public campaign": a widely disseminated public invitation for free testing. Results are qualitatively similar when restricting to the sample that is randomly drawn from lists (see Supplementary Table  1  and Supplementary Fig. 3 ). We also use administrative data collected by the Health Secretary of Bogota (HSB, Secretaria de Salud de Bogota in Spanish) that covers all the reported cases in Bogota from the beginning of the pandemic on January 23rd, 2020 up until February 14, 2021. In our main estimations, we reweight observations by occupation in order to ensure that our results are representative of the whole population of Bogota. We exclude individuals with symptoms and those who are known to have been in contact with an infected person, resulting in a main sample with 42,164 observations.
This conservative assumption allows us to avoid any bias resulting from these individuals (who typically have higher prevalence rates) being more likely to seek or accept testing  12. We also estimate the daily case incidence using the positivity rate for CoVIDA tests. This calculation converts from prevalence to incidence by assuming that individuals can be tested positive for a mean period of 17 days (a figure based on the estimated sensitivity of the RT-PCR tests used in the study  13  ). For more details on the sample, calculation methods and robustness, see the "Methods" Section. From the tip of the iceberg to its actual size. We use the CoVIDA database to estimate total cases in Bogota (the actual size of the iceberg) and compare it to the number of detected cases from the HSB (the tip of the iceberg).
However, this is not reflected in the CoVIDA data nor in deaths associated to COVID-19 in official data (Fig. 3b, d ). This puzzle seems to be driven by the fact that, between July 2020 and January 2021, the share of cases that were detected grew from 5% to 52% in strata 5 and 6, while it remained relatively stable in the poorest strata (Supplementary Table  3 ). The differences in detection dynamics across strata may be a consequence of the government program PRASS ("Programa de Pruebas, Rastreo y Aislamiento Selectivo Sostenible"), the Colombian adaptation of the Test, Trace and Isolate strategy, which began in 08/12/2020 21. The program decentralized the testing process so that health care providers were responsible for detecting the cases of their affiliated members. Although in Bogota 97% of individuals are affiliated to a health care provider, the quality of service and appointment wait times vary greatly and is highly correlated with income levels.