dataesr/scientific-paragraphs-categorization huggingface dataset:
The subsequent analysis based on a benchmark of 4,896 conserved genes belonging to the Poales order (dataset poales_odb10) revealed that the Flye assembly has 4,557 (93. 10%) completed BUSCO (Benchmarking Universal Single-Copy) genes and only 293 (6%) missing BUSCOs versus 2,765 (56. 50%) and 1,945 (39. 70%) for the FALCON assembly. The Flye assembly after Purge Haplotigs shows 4,304 (87. 90%) completed BUSCOs and 512 (10. 50%) missing BUSCOs (Table  2 ).
22% of the genome. Nonetheless, 34. 34% of TEs are currently unclassified. Among classified repeats, long terminal repeats (LTRs) were the most abundant elements within retrotransposons, whereas Tourist/Harbinger elements were more common amid DNA-transposons. In total, 114,826 sequences were identified as simple repeats and occupy 0. 57% of the genome. In addition, rolling-circles (0. 28% of the genome) and low complexity sequences (0. 11% of the genome) were found (Table  4 ). The subsequent structural annotation of the masked genome revealed 53,535 nuclear genes (Supplementary File 1). On the other hand, the unmasked genome has 154,755 structurally annotated genes and 94,237 of them have BLAST hits in the NCBI non-redundant database. Nonetheless, among the 94,237 genes of the unmasked genome, 12,094 sequences are related to transposable elements.
(South Korea). Nuclear genome assembly and validation. The execution of this work involved using many software tools, whose versions, settings and parameters are described in Supplementary information S2 (available in Supplementary File 6). The de novo assembly of the PacBio data was performed using Flye v. 2. 4  65, 66. The draft assembly was cleaned by running BLASTn v. 2. 10. 0 138 against the NCBI nucleotide database v. 5, and subsequently sending each BLAST hit to the JGI taxonomy server (https:// taxon omy. jgi-psf. org/) with a downstream step of keeping only plant contigs. Thereafter, Qualimap v. 2. 2. 2  139  was used to identify mean coverage for each contig. In the final assembly we kept only contigs with an average coverage of more than 10x. In addition, overrepresented contigs (> 60x) were BLASTed against the NCBI nucleotide database v.
Acknowledgements We would like to express our gratitude to  Eric Johnson  from  SNPsaurus ,  Artem Kasianov  from  Institute for Information Transmission Problems of the Russian Academy of Sciences  (Moscow, Russia) and  Igor A. Shmakov  from  Altai State University (Barnaul, Russia ) for their valuable assistance in the genome assembling. We also thank the iDiv High-Performance Computing cluster for providing computing resources for this paper. Finally, we thank two anonymous reviewers for providing valuable comments on the manuscript. The study was supported by the  Russian Science Foundation  (grant no. 19-74-10067 ). E. B. was supported via the  RSF  (grant no. 19-74-10067 ) and a  DS grant  of the  Jagiellonian University  ( DS/D/WB/IB/2/2019 ). M. N. was supported by the  National Science Centre, Poland  (grant no.
Data availability The raw PacBio reads are available at NCBI Sequence Read Archive  172. The final genome assemblies are deposited into NCBI Assembly database under the following Accession Numbers: nuclear assembly (JAGXJF000000000)  67  ; mitochondrion assembly, contig 1 (MZ161090)  76  , contig 2 (MZ161091)  77  , contig 3 (MZ161093)  78  and contig 4 (MZ161092)  79  ; chloroplast assemblies, haplotype A (MZ146999)  82  and haplotype B (MZ145043)  83. The masked and the unmasked versions of the nuclear genome annotation are presented in the Supplementary File 1 and the Supplementary File 2, respectively. Author contributions.
Both PISA and PIAAC reflect a strong influence of previous adult literacy assessments, such as the International Adult Literacy Survey (IALS) and the Adult Literacy and Lifeskills Survey (ALL), and combine multiple assessment items to derive the underlying unobservable ability of individuals. As a result, although in PISA and PIAAC the relative importance assigned to different cognitive process and context areas differs, the two literacy assessments are very close and, in fact, both PISA 2000 and PIAAC 2012 contain a set of literacy questions that were originally administered in IALS  (Borgonovi et al. , 2017). Contextual features. We use information derived from the PISA system-level data collection to identify the level of standardisation and the level of differentiation of educational systems and data from Education at a Glance to identify the rate of 15-29year-olds who are NEET and tertiary education attainment rates (OECD, 2015b).
Study population and the construction of a pseudo-cohort To study the evolution of gaps in literacy skills between the teenage years and young adulthood, one would ideally use panel data, tracking the same people over time. In the absence of such data for a large number of countries, it is possible to use a synthetic cohort approach. The synthetic cohort approach consists of dividing an initial sample (in our case the sample of 15-year-old students who sat the PISA test in 2000) into groups whose membership is assumed to be fixed over time. The average behaviour of these groups is then tracked over time (in the PIAAC study) and as long as the sample is continually representative of an underlying population that has a fixed composition, estimates from the synthetic panel should be consistent with estimates from an actual panel based on individual-level data.
Analyses include foreign-born individuals who reported having migrated prior to age 10. The threshold was chosen to ensure that individuals would have been part of PISA samples at age 15 and had adequate language proficiency. Previous analyses of PISA data reveal large late-arrival penalties (OECD, 2015a). Although England, Northern Ireland, the Netherlands and the Russian Federation took part in both PISA and PIAAC, they are excluded. The response rate for the UK and the Netherlands in PISA 2000 and PISA 2003 was below the minimum required by the PISA technical standards. The sample for the Russian Federation in PIAAC does not include the population of the Moscow municipal area and therefore does not represent the entire resident population. Only Flanders in Belgium took part in PIAAC, while PISA data refer to results for the Flemish community.
PIAAC used a twoparameter IRT model and a set of 10 plausible values were drawn for each proficiency domain and for each individual. Although each plausible value represents an unbiased estimate of respondents' underlying ability distribution, only by combining plausible values is it possible to correctly account for the underlying level of measurement uncertainty that is inherent in any cognitive test, and reflect such uncertainty in the standard errors associated with any estimate. A second complication when developing estimates using PISA and PIAAC data is that participants are not selected using simple random sampling but, rather, complex sampling designs were employed. We consider PISA and PIAAC plausible values and estimate each model five times for PISA and for PIAAC. We also account for the clustering and representativeness of individuals participating in PISA and PIAAC by estimating each model with replicate weights as well as individual final weights in the two assessments.
They establish scale concordance between two assessments, which we use in our analyses to establish disparities in achievement growth. In this work, all estimates are presented on the PISA scale. A detailed discussion of the linking procedure is presented in  Pokropek & Borgonovi (2020). We examine if there are differences in the evolution of socio-economic disparities in literacy achievement between the teenage years and young adulthood among highachieving students (90th percentile) and among low-achieving students (10th percentile), and compare differences in socio-economic gaps between high and low achievers across education systems. Finally, to examine potential mechanisms leading to SES differentials in achievement growth, we use data from PIAAC for 16-28-year-old participants and estimate differences in the educational attainment and NEET rates of youngsters with at least one parent who obtained tertiary level qualifications and those without.
Source: PIAAC 2012/2015 database. Detailed data from the underlying regression models are available in Table  S8  in the online Supporting Information. Low parental education refers to individuals with no parent who obtained a tertiary degree. High parental education refers to individuals with at least one parent with a tertiary degree. The regression includes PIAAC participants between the ages of 16 and 28. Observed gap refers to the difference in literacy achievement associated with being in the low rather than the high parental education group. Estimates are based on a regression run on a pooled sample of 16-28-year-old PIAAC participants with parental education and country fixed effects as the only controls. Predicted gap refers to the difference in literacy achievement associated with being in the low rather than the high parental education group when further controlling for educational attainment, NEET status, use of skills at home and learning in the workplace (use of skills in the workplace was dropped by the model because of multicollinearity with NEET status  participation in the labour market appear to be the crucial mechanisms that are associated with skill acquisition beyond compulsory schooling.
Such result could be due to the greater variety of learning experiences that occur outside of school settings, a variety that can appeal to the preferences and learning styles of low-ability individuals and the fact that extensive access to such opportunities is only available to those coming from socio-economically advantaged households. In the absence of comparable longitudinal data, our analysis relies on a synthetic cohort approach and concordance scores that allow us to derive comparable measures of literacy achievement across the PISA and PIAAC tests. Lack of longitudinal data means that our inferences only apply at the population level, and we cannot study in depth the mechanisms that shape differentials in achievement growth in different groups. Furthermore, although our study provides the widest comparison to date on SES differences in achievement growth between the age of 15 and 27, the limited number of countries that we could examine means that our analysis of cross-country differences can only identify large effects because we lack power to identify small and medium-size effects.
, 2016). Curves were aligned on the termination of extrusion (no more apical area visible) and normalised with the averaged area on the first five points. Clone area profile (Figures  1D  and 5I ) were obtained by segmenting the group of cells in the clone and calculating the area and the solidity (Area/Convex Area) on Matlab. Categorisation of normal versus abnormal extrusion was based on the dynamics of clone contraction: transient relaxation or accumulation of E-cad at vertices was counted as abnormal extrusion. Large relaxation combined with clear E-cad accumulation at vertices was counted as ''transient holes''.
Signaling dynamics and cross-correlation Measurement of ERK activity ERK dynamics were measured using the mean nuclear intensity of miniCic. Whenever possible, a marker of the nucleus (His3-mIFP) was used to track the nucleus manually (Fiji macro). EKAR FRET signal was obtained after local projection on 6 planes of CFP and YFP signals. CFP and YFP signals were blurred (Gaussian blur, 2 pixel width) and the ratio of YFP/CFP signal was then calculated. Raw YFP-nls signal was thresholded and used as a mask to only keep nuclear FRET signal. For the comparison of miniCic and EKAR, single curves from the same cell were aligned on the peak of the FRET signal (maximum value) and eventually averaged for all the cells. For the measurement of miniCic in neighbours combined with apical area (Figure  3 ), cell contours were tracked using E-cad-GFP (Packing analyser), while nuclei were manually tracked on Matlab using His3-mIFP.
These data sets were then the basis of three statistical analyses (see below). Distribution of distances Distances in time and space between all couples of extrusions were computed. For each data set we plotted the maps of densities of death at a given distance (number of deaths divided by the area of the disc considered) as a function of spatial and temporal distances from each dying cell. From each data set we estimated the effective death rate, i. e. the intensity of the random spatio-temporal process, and used it to simulate the corresponding Poisson process 200 times. For each simulation we performed the same analysis as for the experimental data set, namely calculating maps of death densities, and eventually averaged the 200 maps. Finally, for each movie we calculated the difference between the averaged simulated map and the corresponding experimental map.
Single cell death analysis This pipeline is used to define the probability of death for the first versus second row of cells around a dying cell, in the following 0 h 20 min to 1 h 20 min and 1 h 20 min to 2 h 20 min time periods. First, a sub-area of the movie is duplicated to keep only the space location and timings used in the others spatiotemporal analysis of death distribution. Second, the movies of E-Cad signal is pre-treated with the machine learning based Epyseg software  (Aigouy et al. , 2020)  to improve cell-cell junctions contrast using a pre-trained model. Images where then imported to Tissue Analyser FiJi plugin  (Etournay et al. , 2016). Segmentation is performed on improved images then corrected by hand for the remaining segmentation errors. Death were then automatically tracked using the ''track dying cell'' option, while not keeping cell deaths at the image borders.
Cells identities and boundaries over time were as well extracted from Tissue Analyzer as excel files and imported to Matlab. From the identity (in space and time) of the dying cell, we recover the identities of the cells at one and two cells distances 20 minutes before it dies. We then quantify when these cells die. From these quantifications we calculate the probability of death row 1 and row 2 during 0 h 20 min-1 h 20 min and 1 h 20 min-2 h 20 min time periods after the death of the central cell as well as the 95% confidence interval. The 95% confidence interval around the mean value for these binary variables (alive=0, dead=1) was calculated as 1. 96 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi mvð1Àmv Þ p ffiffi n p where m v is the death probability and n the total number of neighbouring cells in row 1 or row 2.
ACKNOWLEDGMENTS We thank members of  R. L. 's lab  for critical reading of the manuscript, especially  Alexis Matamoro-Vidal  for suggestions on the manuscript organization. We would like to thank  Jakub Voznica  for initiating observations of miniCic in the pupal abdomen during his internship and  Anne Loap  for her contribution to tissue segmentation and caspase dynamics analysis during her internship. We also thank  Virgile Andreani  for help in improving simulations, statistical analysis of the data, and some mathematical expressions. We are grateful for the image analysis center of  Pasteur Institute  and  Jean-Yves Tinevez  for converting the local-projection program to Fiji. We are also grateful to  Magali Suzanne ,  Yohanns Bellaı  ¨che,  Shigeo Hayashi ,  Jared Toettcher , the  Bloomington Drosophila Stock Center , the  Drosophila Genetic Resource Center , and the  Vienna Drosophila Resource Center  for sharing stocks and reagents.
The second interim analysis will inform about the sample size that will be required for part 2, in order to achieve adequate statistical power. Numbers to be randomised (sample size) Number of participants randomized: up to 465, in total -Part 1: 50 (to obtain the proof of concept in COVID-19 patients). -Part 2: 310, potentially increased by 50% (up to 465, based on interim analysis 2) (to confirm the effects of BIO101 observed in part 1). Trial Status: The current protocol Version is V 10. 0, dated on 24. 09. 2020. The recruitment that started on September 1 st 2020 is ongoing and is anticipated to finish for the whole study by March2021. Trial registration: The trial was registered before trial start in trial registries:  EudraCT, No. 2020 -001498-63, registered May 18, 2020; and Clinicaltrials.
gov, identifier NCT04472728, registered July 15, 2020. Full protocol: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. Keywords: COVID-19, SARS-CoV2, randomised controlled trial, Renin Angiotensin System, Mas receptor, angiotensin converting enzyme-2, 20-hydroxyecdysone, BIO101, Inflammation, Acute respiratory Distress Syndrome.
Procedure This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of The University of Chicago (IRB 10-677-B, extended 17. 01. 2012). Informed consent was obtained from all subjects involved in the study with data collection running from 2011 to mid-2012, excluding holiday breaks. Participants are children aged 3 to 9 years old and their caregivers living on the south side of Chicago. The sample is self-defined as African Americans. This study applied a cross-sectional sequence design with the child's sleep as the core measurement, and concomitantly assessed stressors in the child's ecosystem (per caregivers' report). Simple random sampling within the participating community centers was applied. When several children within the family were eligible for the study, the Kish procedure  [42]  for subject selection was used.
Sleep assessment The child's sleep was objectively measured with the Actiwatch-2 (Philips-Respironics. Inc. 2009, version 5) worn on the non-dominant wrist for 24 h. Sleep data recorded during 14-days, allows two weekend nights (Friday and Saturday) assessments. This sleep recording was repeated three times over a study period of three months. The full recording period therefore allows calculation of mean and variability (i. e. , a measure of dispersion relative to the mean) of the standard sleep parameters: Bedtime (BT), Time In Bed (TIB), Risetime (RT), Total Sleep Time (TST), Sleep Onset Latency (SONL), Wake After Sleep Onset (WASO), Sleep Offset Latency (SOFL), Restlessness Index (RESTLESS). Mid-point of sleep (MSF) was calculated, and is an indicator chronotype or biorhythm, and indicative for the tendency of morningnesseveningness.
Statistical analysis Descriptive statistics were performed with Statistica TIBCO Software Inc (TIBCO, 2017) version 13. This is a within-effect design where participants are their own controls. The power of this sample ( n = 24) for two assessment points to detect an effect size (ES) of 0. 15 = 30%; ES of 0. 25 = 65%; ES of 0. 35 = 91% and ES of 0. 45 = 98%. To pursue our associative analyses, we calculated the individual change scores for the stress and sleep variables. They express the change within the individual over the study period. This statistical approach expresses true change (i. e. , when beyond one standard deviation) occurring in the individual. In addition, change scores allow calculation of the number of children for which a significant clinical ( "true ") increase/decrease of variables is noted (here expressed in percentages).
org/10. 5194/acp-2021-13 Preprint. Discussion started: 22 March 2021 c Author(s) 2021. CC BY 4. 0 License. quantify relative contributions of various source categories and regions to the observed C2H6 abundance. The three-dimensional (3D) transport inflow and outflow pathways of C2H6 over the observation site are finally determined by the GEOS-Chem sensitivity simulations and atmospheric circulation pattern. This study can not only enhance the understanding of regional emission, transport, and air clean actions over eastern China, but also contribute to form new reliable remote sensing data in this sparsely-monitored regions for climate change research. The next section describes the retrieval for FTIR tropospheric column-averaged dry-air mole fraction (troDMF) of C2H6, the configuration of GEOS-Chem model simulation, and the GAMs regression approach.
The VMR profiles of CH4 and H2O and column of O3 were also retrieved together with the C2H6 VMR profile for minimizing the atmospheric absorption interference. Spectroscopic absorption parameters of all gases are based on the atm16 line list from the compilation of Geoffrey Toon  (Sun et al. 2020a ). The a priori vertical profiles of temperature H2O, and pressure were interpolated from the National Centers for Environmental Protection (NCEP) reanalysis data and the a priori vertical profiles of other gases were from the statistical averages of the Whole-Atmosphere Community Climate Model version 6 (WACCM) simulations from 1980 to 2020. The diagonal elements of the a priori covariance matrices Sa and the measurement noise covariance matrices Sε were set to standard deviation (SD) of the WACCM simulations and the inverse square of the signal-to-noise ratio (SNR) of each spectrum, respectively  (Franco et al.
Briefly, global anthropogenic emissions were from the Community Emissions Data System (CEDS) inventory which overwrites Asia by the latest Multiresolution Emission Inventory for China (MEIC)  (Hoesly et al. 2018; Li et al. 2017; Lu et al. 2019; Zheng et al. 2018). Global biomass burning and biogenic emissions were from the Global Fire Emissions Database (GFED v4) inventory  (Giglio et al. 2013 ) and the Model of Emissions of Gases and Aerosols from Nature (MEGAN version 2. 1) inventory  (Guenther et al. 2012)  Particular improvements have been done for the latest bottom-up MEIC emission inventory in the accuracy of vehicle emission modelling  (Zheng et al. 2014) , power plant emission calculation  (Liu et al. 2015) , and the non-methane VOCs (NMVOCs) speciation method  (Li et al. 2014). Many studies have verified that the MEIC emission inventory can reasonably represents the anthropogenic emissions over Asia  (Hoesly et al.
0 if it is not correlated with other explanatory variables  (Ma et al. 2020). In this study, we included all the meteorological and emission factors in the GAMs and calculated the VIF for all the influencing factors. The multicollinearity diagnosis concluded that the main causes of multicollinearity are between the HCN and CO, and between the tropopause height and planetary boundary layer height (PBLH). Including either of these two data pairs in the GAMs regression showed significant collinearities with the VIF values of greater than an empirical threshold of 4. 0, indicating an unstable regression  (Lin et al. 2018). After omitting HCN and PBLH in the final GAMs model, the adjusted VIF values of all the variables were less than 4. 0 and the variables uniformly passed the significance tests.
1%), where central, eastern, and northern China dominated the contribution (57. 6%). Seasonal variability in C2H6 transport inflow and outflow over the observation site is largely related to the mid-latitude This study can not only enhance the insights of regional emission, transport, and air clean actions over China, but also contribute to form new reliable remote sensing dataset in this sparselymonitored regions for climate change research. Code and data availability. The new ground-based Fourier transform infrared (FTIR) spectroscopic remote sensing dataset for atmospheric C2H6 over Hefei, eastern China in this study can be accessed from https://doi. org/10. 6084/m9. figshare. 13020545. The MEIC emission inventories used in this study are available from http://meicmodel. org/. Table 1. Error budget and degrees of freedom (DOFS) for signal of randomly selected C2H6 troDMF retrieval at.