dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, we show that simulations can be used to identify different combinations of parameters with similarly low mean absolute errors. This can help users to define an effective sampling design by using those combinations of parameters that minimise the sequencing effort. 4. The poolHelper package provides tools for performing simulations with different combinations of parameters (e. g. pool size, depth of coverage, unequal individual contribution) before sampling and generating data, allowing users to define sampling schemes based on simulations. This allows researchers to focus on the best.
Finally, the pooled libraries are sequenced. This step also introduces uncertainties in the analysis due to variation in sequencing depth along the genome, and sequencing errors. The next steps, such as quality control, read alignment and variant calling, are similar to individual-based sequencing. Despite these potential sources of uncertainty (e. g. unequal individual contribution), Pool-seq has been extensively used in a variety of settings  (Begun et al. , 2007; Ferretti et al. , 2013; Prescott et al. , 2015; Zhou et al. , 2011). This has lead to the development of tools such as the R package poolSeq  (Taus et al. , 2017)  and the DIYABC-RF software  (Collin et al. , 2021)  that simulate Pool-seq data, as well as data analysis tools (e. g. Kofler et al. , 2011). Nonetheless, to the best of our knowledge, no tool currently exists that can simultaneously and explicitly account for variation in depth of coverage, unequal contribution and sequencing errors, which are known sources of Pool-seq uncertainty (see Table  S1   Here, we introduce an R package (Team, 2020), poolHelper, to simulate Pool-seq data according to different sampling designs.
Our approach relies on coalescent simulations under neutrality using scrm  (Staab et al. , 2015). The poolHelper package provides tools and functions to simulate Pool-seq datasets, accounting for potential sources of error in the Pool-seq analysis process. Importantly, these errors are modelled by parameters that users can adjust. poolHelper models the unequal contribution resulting from differences in DNA sampling scheme to answer their research questions. poolHelper is comprehensively documented with examples to guide effective use.
K E Y W O R D S experimental design, open source, Pool-seq, R package, simulations concentration and amplification efficiency during DNA extraction and library preparation. Additionally, it accounts for sequencing depth variation across SNPs, sequencing errors, and mapping errors during read alignment. This allows comparing the allele frequencies obtained directly from the simulated individual genotypes with the frequencies obtained from Pool-seq data. Since R is a free and collaborative project, users can use available tools to handle, analyse and visualise genomic datasets. Our goal is to provide a flexible method of simulating Pool-seq data, allowing researchers to design their experiments with a better a priori knowledge of possible errors associated with Pool-seq, thus contributing to the recognition of Pool-seq as a valuable source of data to reconstruct the evolutionary history of populations.
| Main functionality The poolHelper package allows users to compute the mean absolute error of allele frequencies and expected heterozygosity under a variety of conditions. Users can vary the mean depth of coverage and the associated variance, the value of the pooling error and the number of sampled individuals. Additionally, it is possible to evaluate the effect of combinations of parameters, for instance, various mean depths of coverage combined with several pooling error values. Thus, the poolHelper package provides users with a tool to aid in the design of pooled sequencing experiments, by allowing researchers to evaluate the best strategy, in terms of pool sizes or depth of coverage, to obtain accurate estimates of allelic frequencies, while minimising the sampling effort and costs.
DATA AVA I L A B I L I T Y S TAT E M E N T The package poolHelper source code is hosted at https://github. com/ joao-mcarv alho/poolH elper, along with the package tutorials and vignettes. The source code is archived with Zenodo at https://doi. org/10. 5281/zenodo. 7520303 (Carvalho et al. , 2023). poolHelper is.
Details of the pre-flight and on-orbit calibration of OCO-2 Level 1b (L1b) can be found in  Rosenberg et al. (2017) ,  Lee et al. (2017) ,  Crisp et al. (2017), and Marchetti et al. (2019). In general, the instrument calibration and the full-physics retrieval algorithm for OCO-2 have reached relatively mature states. For example, updates to the OCO-2 v10 calibration algorithms used to produce calibrated L1b spectra were limited to an improved treatment of radiometric degradation using lunar calibration observations, a small refinement in the spectral dispersion coefficients, and the identification of additional spectral sample outliers  (Crisp et al. , 2021). Keller et al. (2022)  describe the current state of the calibration for the L1b OCO-3 v10 data products. OCO-3, unlike OCO-2, cannot view the sun from the ISS, making solar calibration impossible  (Rosenberg et al.
More detail is provided in Sect. 2. 1 of the OCO-3 v10. 4 data quality statement  (Chatterjee et al. , 2022). Because of initial difficulties in reducing geolocation errors for OCO-3, plans to perform lunar calibration, intercomparison of L1b radiances with OCO-2, and vicarious calibration using the Railroad Valley Playa were delayed. These are now all underway and will inform the inflight calibration for the next OCO-3 product version. Additional OCO-3 calibration details are contained in the L1b Algorithm Theoretical Basis Document (ATBD)  (Crisp et al. , 2021). 3 The ACOS v10 XCO 2 retrieval pipeline Beginning with the geolocated and calibrated L1b spectra, the ACOS pipeline consists of three distinct steps to produce the final estimates of XCO 2. First, due to the computational demands of the L2FP retrieval algorithm, which requires about 5 min per sounding on a single processor, and the inability to reliably estimate XCO 2 in the presence of clouds and heavy aerosol loadings, a pre-screening step is performed to identify and remove these soundings  (Taylor et al.
Level 2 full-physics retrieval algorithm updates for v10 ACOS v10 is the fourth major reprocessing of the OCO-2 record, which began with the v6 release in December 2014, followed by v7 in  2015 and v8 in 2017 (O'Dell et al. , 2018)). The v9 XCO 2 product, released in 2018, was a postprocessing-only effort to correct XCO 2 errors introduced by a small error in the instrument boresight pointing and geolocation  (Kiel et al. , 2019). Since there were no changes to the L2FP code from v8 to v9, for the remainder of this document the nomenclature "v8/9" will be used to refer to the previous version of the algorithm. For OCO-3, the v10 XCO 2 product is only the second public release. It is a substantial improvement over the first release, vEarly, which employed the ACOS v10 algorithm but had significant instrument calibration and geolocation errors and was quality-filtered and bias-corrected against a very short data record of only a few months  (Taylor et al.
In short, the priors are calculated from a scaling of the NOAA monthly averaged flask values  (Lan et al. , 2022)  measured at the Mauna Loa and American Samoa sites to individual sounding dates and locations. The tropopause altitude is derived from data contained in the 3-hourly GOES-FPIT meteorology, which has a nominal 1 d lag and provides diagnosed potential vorticity, allowing for better representation of latitudinal CO 2 transport in the stratosphere. A previous study using measurements from the Japanese Greenhouse Gases Observing Satellite (GOSAT;  Kuze et al. , 2009)  processed with the ACOS v9 L2FP retrieval showed that a correction to account for the difference in the CO 2 prior from v8/9 to v10 yielded a global mean adjustment in XCO 2 of approximately 0. 2 ppm, with 95 % of changes falling between -0.
Quality filtering and bias correction truth proxies The quality filtering and bias correction procedure requires XCO 2 truth proxies with which to compare the retrieved estimates. The term truth proxies is used to describe sources of data which can be used as an independent estimate of the atmospheric CO 2 abundance. For OCO v10, three truth proxies were used. The first comprised estimates of XCO 2 derived from TCCON measurements. Table  3  provides a list of TCCON stations, locations, operational ranges, and data citations. Although TCCON XCO 2 estimates have relatively high precision and accuracy while providing good temporal coverage at most sites, they are very limited in spatial extent, especially outside the northern midlatitudes. To augment the sparse spatial coverage of TCCON, atmospheric inversion models are used in the OCO XCO 2 quality filtering and bias correction process to provide full global coverage  (O'Dell et al.
, 2018). For ACOS v10, the median XCO 2 was derived from the four-dimensional (4D) CO 2 fields of models that assimilated only in situ CO 2 data. To ensure consistency in the models, for each OCO sounding, only the models with XCO 2 that deviated by less than ±1. 5 ppm from the initial median value were retained. Furthermore, soundings were excluded if more than one of the models had been rejected or if the standard deviation amongst the valid models was > 1 ppm. Tables  4, 5 , and 6 provide information about the suite of models. An asterisk is used in Table 6 to identify the specific models and data versions used in the quality filtering and bias correction procedure: three for OCO-2 and four for OCO-3 v10. Some of the same models were also used in the XCO 2 evaluation, but using a different model data version and a different evaluation period.
3. 2. 4 of the OCO-2 v10 Data User Guide (DUG)  (Osterman et al. , 2020)  and Sect. 5. 1. 2 of the OCO-3 v10. 4 DUG  (Payne et al. , 2022). Training for the quality filtering and bias correction procedure takes place on a quick test set (QTS), which is an intelligently selected subset of approximately 5 % of the full OCO data record that is available at the time of the training. The methodology for the empirical bias correction of the XCO 2 estimates was first described in  Wunch et al. (2011b)  and later in more detail by  O'Dell et al. (2018) , wherein the fundamental equation for OCO is defined as XCO 2,bc (mode, j ) = XCO 2,raw -C P (mode) -C F (j = 1. 8) C 0 (mode). (2) Here, C P is the mode-dependent parametric bias, C F is a mode-and footprint-dependent bias for each of the eight footprints, and C 0 represents a mode-dependent global scaling factor.
The bias correction is very similar for OCO-3 v10, with the exception that the coefficients are slightly different, and over land, the AOD fine term has been replaced with criteria based on the retrieved albedo in the weak CO 2 channel. Table  7  provides a statistical summary of the results from the quality filtering and bias correction for both OCO-2 and OCO-3 for each of the three truth proxies. Results are given separately for land observations (combined nadir and glint) and for ocean-glint observations. The number of soundings contained in each truth proxy dataset that was used in the quality filtering and bias correction procedure is listed, along with the fraction of the total soundings contained in the L2 Lite XCO 2 product that were assigned a good quality flag, i. e. , the throughput.
These same behaviors were observed for most months in 2020. This latitudinally dependent land-ocean bias is an unexpected feature of the dataset that https://doi. org/10. 5194/amt-16-3173-2023 Atmos. Meas. Tech. , 16, 3173-3209, 2023  requires further investigation. The analysis for April and August 2021 (not shown) was qualitatively very similar. Figure  8  shows XCO 2 binned at 10 d by 10 • latitude for the overlapping time period of August 2019 through February 2022 for OCO-2 (top row) and OCO-3 (middle row). Results are shown separately for land (left column) and ocean (right column). Qualitatively, the XCO 2 patterns at this spatiotem-poral resolution look very similar, as expected, with maximum XCO 2 in NH spring just before the biospheric drawdown begins and minimums in NH summer. The plots highlight the secular trend of ≈ 2.
Evaluation of OCO v10 XCO 2 estimates versus truth proxies This section discusses the evaluation of the OCO v10 goodquality-flagged XCO 2 estimates against the truth proxies used in the quality filtering and bias correction procedure. Although there is some circularity in evaluating the satellite data against the same truth proxies used for filtering and bias correction, the multiparameter parametric bias correction is general enough so as not to overfit the OCO data. Furthermore, the truth proxies used for evaluation have been extended in time compared to the datasets used to train the filtering and bias correction. Although it is outside the scope of the current work, OCO-2 data have been validated against a range of other datasets, including in situ, NOAA and Aircore vertical observations  (Rastogi et al.
Such models cannot provide information with variability finer than several hundred kilometers. Rather than ingesting each individual OCO-2 XCO 2 estimate falling inside a model grid box, down-sampling of the data into 10 s averages (10-sec-avg) prior to assimilation into inversion systems has become common  (Crowell et al. , 2019; Peiro et al. , 2022; Byrne et al. , 2023). This provides an even more compact dataset with reduced random sounding-to-sounding errors in the XCO 2 estimates and mitigates the potential impact of correlated errors on ≈ 10 km spatial scales, such as those driven by surface features or the presence of aerosols and clouds  (Massie et al. , 2021; Mauceri et al. , 2023; Massie et al. , 2023). Care is taken to specify an appropriate measurement uncertainty, calculated as a function of the number of soundings within the 10-sec-avg bin, individual uncertainties associated with the soundings, coverage across the grid box, and correlations between their individual errors  (Baker et al.
995, as described in Sect. 3. 2. 3 of the DUG  (Osterman et al. , 2020). For OCO-3 v10, the coastal crossings and model fields were used to determine the ocean-glint global scaling factor of 0. 9961, as described in Sect. 5. 1. 5 of the DUG  (Payne et al. , 2022). Figure  14  shows an analysis of the coastal crossing dataset for the v10 OCO-2 (top row) and OCO-3 (bottom row), containing ≈ 20×10 3 and 0. 5×10 3 crossings, respectively. Figure  14a  and c  show XCO 2 in 5 • latitude bins with 1 standard deviation error bars as thin vertical lines. The mean land-ocean difference tends to be positive (negative) in the southern (northern) extratropical latitudes for both sensors. Figure  14b  and d  show the frequency distributions of XCO 2 for the individual coastal crossings. The means are biased -0.
, 2022)  in gray, and the global monthly mean marine surface values reported by NOAA GML  (Dlugokencky and Tans, 2022)  in blue. Here, a cosine of latitude factor has been applied to the satellite data to weight individual soundings for surface area, i. e. , higher (lower) weighting in the tropics (high latitudes). It is important to distinguish differences in the NOAA and satellite products. NOAA's values are calculated from precisely calibrated surface observations at a few select locations  (Conway et al. , 1994) , while OCO-2 and GOSAT provide full column measurements with much larger random errors (instrument plus retrieval), as well as spatial and temporal sampling biases, but for hundreds to thousands of samples per day. Panel (b) of Fig. A3  shows the monthly calculated AGRs from the OCO-2 v10 (orange) and GOSAT v9 (gray) datasets.
B2 OCO-3 v10 pointing correction Although the first public release of OCO-3 XCO 2 (vEarly) was derived using the v10 L2FP algorithm, the dataset suffered from significant geolocation errors  (Taylor et al. , 2020). Here, an update in the geolocation for v10 is described. Precise geolocation of OCO-3 footprints requires knowledge of (i) the position and attitude of the instrument in space, (ii) the position and control of the PMA, and (iii) the effective alignment of the OCO-3 detectors with respect to the instrument reference. Improvements were made in all three areas for v10, as described in Sect. 2. 2 of the OCO-3 v10. 4 data quality statement  (Chatterjee et al. , 2022). OCO-3 attitude data are now taken primarily from the onboard stellar reference unit (SRU), which was not possible for vEarly due to a systems timing error that yielded large geolocation errors early in the mission.
Acknowledgements. A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). Government sponsorship is acknowledged. The  CSU/CIRA contribution  to this work was supported by  JPL  subcontracts  1439002  ( OCO-2 ) and  1557985  ( OCO-3 ). Aronne Merrelli 's contributions to this work were supported by  JPL  subcontract  1577173. Paul I. Palmer  and  Liang Feng  acknowledge support from the  UK National Centre for Earth Observation  funded by the  National Environment Research Council  ( NE/R016518/1 ). We gratefully acknowledge the use of TCCON data in this work. We thank the two anonymous reviewers for their time to provide very useful comments on the paper.
We also thank the journal editor,  Joanna Joiner , for her willingness to oversee the review process. Financial support. This research has been supported by the  National Aeronautics and Space Administration  (grant no. 80NM0018D0004 ), JPL (subcontracts  1439002  ( OCO-2 ),  1557985  ( OCO-3 ), and  1577173 ), and the  UK National Centre for Earth Observation  funded by the  National Environment Research Council  (grant no. NE/R016518/1 ).
Objectives Infections by SARS-CoV-2 variants of concern (VOCs) might affect children and adolescents differently than earlier viral lineages. We aimed to address five questions about SARS-CoV-2 VOC infections in children and adolescents: (1) symptoms and severity, (2) risk factors for severe disease, (3) the risk of infection, (4) the risk of transmission and (5) longterm consequences following a VOC infection. Design Systematic review. Data sources The COVID-19 Open Access Project database was searched up to 1 March 2022 and PubMed was searched up to 9 May 2022. Eligibility criteria We included observational studies about Alpha, Beta, Gamma, Delta and Omicron VOCs among ≤18-year-olds. We included studies in English, German, French, Greek, Italian, Spanish and Turkish. Data extraction and synthesis Two reviewers extracted and verified the data and assessed the risk of bias.
STRENGTHS AND LIMITATIONS OF THIS STUDY ⇒ This review is based on a wide range of worldwide studies found during repeated systematic searches of different databases, reducing our risk of having missed relevant information. ⇒ We included studies in the various languages the review team spoke, only excluding three studies for language reasons. ⇒ We assessed the included studies' risk of bias at the outcome level to evaluate the quality of the studies included. ⇒ The included studies did not provide information on the specific Omicron subvariants, limiting the interpretation of the respective data. Open access disease caused by the original SARS-CoV-2 lineage were introduced in adults. 4 5  Vaccines were first licensed for 16-18 year-olds in spring 2021 and later for children <16 years old, but vaccination availability and uptake varied by country.
Search strategy We searched: (1) the COVID-19 Open Access Project database, which aggregates COVID-19 research from PubMed, EMBASE, PsycINFO, bioRxiv and medRxiv from 1 January 2020 to 1 March 2022 and (2) PubMed using Medical Subject Headings (MeSH) terms from 1 August 2020 to 7 March 2022. 9 Owing to large numbers of records that did not fulfil the inclusion criteria, we revised the PubMed search strategy, including each VOC, identified by their Greek letter and pangolin lineage terminology and conducted a new search between 8 March and 9 May 2022. We also searched reference lists of systematic reviews and included studies, and we included studies suggested by experts in the field, but not identified by our search. Full search terms are included in the online supplemental note 1.
Eligibility and screening Observational epidemiological studies (case series with more than one case, cross-sectional studies, case-control studies and cohort studies) and modelling studies that were based on the analysis of the original data were eligible for inclusion if data collection occurred during a period when VOCs were predominant in the study setting. The presence of a VOC could be based on genetic sequencing in the study or by assumptions based on the authors' reporting, the dominant lineage at the time of testing and the study location. We did not apply any geographical or language restrictions to the search, but we only read the full text of papers written in languages spoken fluently by the review team (English, German, French, Greek, Italian, Spanish or Turkish). Identified titles and abstracts were screened for potential eligibility by one member from an international team of 10 reviewers, including records about which they were unsure.
Data synthesis We synthesised the information from the included studies descriptively for each question. We tabulated summaries of the data extracted, including the number of studies identified for each VOC, the age groups studied and the outcomes reported. Definitions of disease severity differed between studies. To allow comparisons, we applied the following categorisation: mild or asymptomatic disease (patients were not hospitalised), moderate disease (patients were hospitalised, but did not require intensive care), severe disease (patients were hospitalised and intensive care was necessary). If we did not have sufficient information to apply these categories, we used the authors' definitions and reported these in the text. Where possible, we presented the results separately for different age groups: neonates Open access and infants 0-1 years old; young children 1-4 years old; older children 5-9 years old; younger and older adolescents 10-20 years old.
3% vs 24. 9%,  35  Gamma vs non-VOC: 5. 2% vs 1. 2%  28  ). Severity of Delta variant infection was compared with the pre-Delta period (including periods where previous VOCs where predominant) in eight studies but the reported data were inconsistent, and differences in disease severity could not be assessed. 26-28 36-40  Studies of Omicron variant infection described less severe disease than infection with the Delta variant in children and adolescents <18 years old, based on data from nine studies from South Africa, Qatar and the USA. 29 31 32 41-46  One study from Denmark analysed the symptoms and severity of disease experienced by children and adolescents younger than 17 years old, hospitalised for multiinflammatory syndrome in children during the Delta and non-VOC predominant periods. The range of symptoms was similar in both periods, as was the proportion of children admitted to ICU (Delta, 55% (28/51) vs non-VOC 52% (12/23)).
Open access No studies reported on how symptoms or severity differed between different paediatric age groups or compared children and adolescents infected with a VOC to adults infected with the same variant. Review question 2: risk factors for severe COVID-19 in children and adolescents infected with a SARS-CoV-2 VOC We found one article that used data from the National COVID-19 database in Qatar and compared risk factors for severe COVID-19 in children and adolescents infected with the Omicron and Delta variants. The study showed that children and adolescents <18 years old with ≥1 comorbidity (mostly chronic lung disease) were more likely to have moderate or severe disease than those without comorbidities if they were infected with the Omicron lineage (adjusted OR (aOR) 3. 2, 95% CI 1. 1 to 9.