dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by the  RNF (RSCF)  grant  15-17-10009  " Evolution of thermokarst lake ecosystems in the context of climate change " (sampling, interpretation,  50% ),  State Task   AAAA-A18-118012390200-5 , RFBR grant  18-05-70087  " Arctic Resources ,"  17-05-00348 ,  19-07-00282 ,  18-45-860002 ,  18-45-703001 , and  18-47-700001 , and the  Swedish Research Council  (grant  2016-05275 ). Chris Benker  is thanked for thorough English editing.
Study Selection We identified 19,421 studies applying the literature search string in four databases (Figure  1 ), of which 11,867 studies remained after removing duplicates. Restricting the search to include only EBC, gave 893 records. We excluded 548 studies based on the title and abstract screening. Finally, we retained 86 studies where 8-isoprostane was analyzed in EBC. After reading these studies thoroughly, all 86 studies were included in the qualitative synthesis including risk of bias and quality assessment  [4, , and 52 studies into the quantitative analysis  [4,15,17-23,25,26,28-31,35,38-40,44,46-55,57,59,60,62-64, 74,75,77,79-82,84-86,88-90,93,95,99]. In the quantitative analysis, ten studies were excluded because no information was provided to estimate variability (GSD). Another 12 studies were excluded because their coefficients of variation (CV) were beyond acceptance limits (greater than 10% and lower than 200%).
Strengths and Limitations This is the first systematic review and meta-analysis on 8-isoprostane in the EBC aiming to estimate references range values for this molecule. Such values are paramount for introduction of 8-isoprostane into clinical and field research and practice, and its further validation as biomarker of oxidative stress damage at pulmonary level. This study was conducted according to the high research standards, as detailed in the standardized research protocol  [110]. It is based on an extensive research in four major databases and we believe it is exhaustive. For every record, the screening, review, and data extraction were double-checked by different reviewers independently, and very few discrepancies were observed, all solved by consensus. Nevertheless, this study has also some limitations which deserve discussion.
Literature Search We conducted literature searches for publications since journal inception and up to 26 March 2019 in the following bibliographic electronic databases: The Cochrane Central Register of controlled Trials (CENTRAL, Cochrane Library), EMBASE, PubMed, and Web of Science. Further details and the complete search strategy (exemplified by the search in PubMED and EMBASE databases) are available as a supplementary digital content. The full search strategy including the search string used can be found in (https://www. doi. org/10. 16909/dataset/17) [110]. We searched for different 8-isoprostane synonyms such as F2-isoprostane, 8-Epi-prostaglandin-F2alpha, and 15-f2t-isop. To be included, articles had to be original research studies, involving healthy adult participants (aged 18+), collecting EBC samples for 8-isoprostane quantification, and published in English or French language.
Our exclusion criteria were as follows: studies without quantitative data for 8-isoprostane, non-human studies, reviews, correspondence, conference papers, expert opinions, and editorials, as well as abstracts without full text. Furthermore, we also excluded studies where EBC collection device failed to meet American Thoracic Society and European Respiratory Society methodological recommendations  [5]. The recommendations were that participants wear a nose-clip and breathe for generally 10 min and the collected samples should be stored at -70 • C. Two reviewers (MH and YS) independently performed a first screening of titles and abstracts retrieved during the searches, using Rayyan software  [114]. Abstracts with insufficient information with regard to the inclusion and exclusion criteria were downloaded in the EndNote software for a full-text screening.
Data Extraction We used the standardized data extraction form developed as part of the study protocol  [111]. When data on several subgroups within a study were available, all subgroup-specific data were extracted. Afterwards, we excluded groups with diseases and groups that were exposed to specific occupational exposures. We kept only the baseline measurement in case the study reported different measurements. We also excluded the studies with identical control groups but we kept the latest and the most complete study. A statistician (PW) crossed-checked all the data extracted for meta-analysis.
Statistical Analysis We aimed to determine the reference range values of 8-isoprosante in EBC of healthy adults. In the meta-analysis, we calculated geometric means (GM) and geometric standard deviations (GSD) because the values of 8-isoprosante were generally log-normally distributed  [115]. The heterogeneity of the original data corresponded to GM and GSD. We followed the instructions of the standard practice in meta-analysis  [117] , and we represented the data as forest plots including the I-square statistics that estimates the percentage of the between-study heterogeneity. When I-square is high, we used mixed model with study ID as a random effect. Our results were provided on the study subgroup level, rather than on the individual level. We used STATA, version 16 software for data management and statistical analyses.
The COVID-19 infection is spreading southbound in Italy, and in Sicily, the number of new cases per day is rapidly growing  [2]. Therefore, the oncology community and administrative or government institutions reacted, preparing to face a possible wide spread as in Northern Italy. In this paper, we report the reactions, the attitudes and countermeasures which have been put in place and possibly implemented by most medical oncology units. We employed data obtained from the time-related analysis of conversations via a pre-existing WhatsApp messengerbased group chat between the medical directors belonging to the Italian College of Medical Oncology Chiefs (CIPOMO-Collegio Italiano dei Primari Oncologi Medici Ospedalieri. www. cipomo. it). The WhatsApp Instant-Messaging system (WM) is a useful and easy tool to rapidly share any information useful to participants in various clinical settings  [11] [12] [13].
Data collection On 27 November, 2017, CIPOMO set up a WhatsApp messenger-based group chat to allow rapid, non-official data exchange between most directors of medical oncology units in Sicily. The Sicilian regional section of the CIPOMO accounts for 14 directors of hospital oncology divisions/departments. Five other units not belonging to the CIPOMO were included in the WhatsApp messenger-based group for a total of 17 units actively participating. The main aim of this paper is to report descriptive time-related analysis of the reactions and attitudes of medical oncology units to the incoming spread of the COVID-19 infection in Southern Italy and, in particular, Sicily, which has a population of 5. 027 million as last assessed in 2018. After previously written e-approval of all participants in respect to the Privacy Acts Law in force in Italy, we retrospectively analysed all conversations recorded in a WhatsApp messenger-based group starting from the first message on COVID-19 sent on 24 February until 28 March, 2020.
The analysis was performed using R: A Language and Environment for Statistical Computing version 3. 6. 3 [25]  with the package Syuzhet: Extract Sentiment and Plot Arcs from Text  [26]. The NRC Word-Emotion Association Lexicon  [27] , available via open access also in the Italian language, which contains 10,170 lexical items coded for Plutchik's classification and implemented in the Syuzhet R package, associates an emotion (or more than one emotion) to each of the 10,170 lexical items. Given a word and emotion X, the NRC Word-Emotion Association Lexicon associates a score (range: 0 to 1) with it. A score of 1 indicates that the word conveys the highest amount of emotion X. A score of 0 indicates that the word conveys the lowest amount of emotion X.
Subsequently, only one person per visit has been allowed, and no caregiver allowed to stay on the wards (fourth action), and finally, in the last two weeks requirement for drugs potentially active against COVID-19, such tocilizumab, was preventively carried out  [31]. Figure  4  shows the distribution of emotions detected through the examination of the raw data and the results of sentiment analysis. The intensity of the scores related to the emotions evaluated is higher for the negative ones (Figure  5 ). The time-trend of the number of oncologists reporting positive emotions was slightly higher than those showing negative emotions. Fear, anger and sadness are dominant, while trust is represented by at least half of the dataset. A significant association between the different weeks and the pattern of emotions (positive or negative) was observed, Chi-square: 8.
Gene Tree Inference We used sets of homolog clusters generated prior to extracting orthologs for species tree inference using the  Yang and Smith (2014)  pipeline, derived from genomes and transcriptomes of representatives of five of the six legume subfamilies and an extensive eudicot outgroup (Supplementary Table  S1  available on Dryad at http://dx. doi. org/10. 5061/dryad. zkh18936s) assembled by  Koenen et al. (2020). We do not include the monospecific subfamily Duparquetioideae for which large-scale nuclear genomic data are presently unavailable. These homolog clusters include multiple sequences per taxon representing paralogs for nonterminal gene duplications; duplications restricted to a terminal taxon are not included. Amino acid sequences of these clusters were aligned with MAFFT v.
Testing Placements of WGDs Using Gene Count Data We used the WGDgc package in R  (Rabier et al. 2014)  to test the placements of WGDs hypothesized by Phyparts and Notung. This probabilistic method models background gene duplication and loss rates using a birth and death process, while adding WGDs on specific branches of the species tree. Birth-death and duplicate gene retention rates for WGDs are estimated with maximum likelihood and the overall likelihood is compared across different configurations of WGDs on the species tree. We extracted gene count data from the rosid gene trees used in the Notung analysis, after removing several transcriptome accessions with relatively high levels of missing data. Furthermore, to use the "oneInBothClades" conditional likelihood option, Eucalyptus grandis and Punica granatum were removed to ensure there are two large clades at the root, the nitrogen-fixing clade of angiosperms (consisting of Cucurbitales, Rosales, Fagales, and Fabales) and a clade consisting of the remaining sampled rosid orders.
Divergence Time Analyses The 20 fossils used to calibrate molecular clock analyses on the species tree are listed in Table  1  and discussed in detail in Supplementary Appendix S1 available on Dryad. Using SortaDate  (Smith et al. 2018) , we analyzed the 1103 gene trees from  Koenen et al. (2020)  to estimate total tree length (a proxy for sequence variation or informativeness), root-to-tip variance (a proxy for clocklikeness) and compatibility of bipartitions with the ML tree inferred using the full data set (the RAxML tree inferred with the LG4X model). We selected the best genes for dating based on arbitrary cutoff values: i) total tree length greater than 5, ii) root-to-tip variance less than 0. 005, and iii) at least 10% of bipartitions compatible with the ML tree. This yielded 36 genes, which were concatenated with an aligned length of 14,462 amino acid sites.
We also used the "pxlstr" program of the Phyx package  (Brown et al. 2017)  to calculate taxonspecific root-to-tip lengths from the ML tree, after pruning Ranunculales, on which the tree was rooted. These values were used to define local clocks. Arabidopsis thaliana, Linum usitatissimum, and Polygala lutea were removed because of much higher root-to-tip lengths relative to their closest relatives. Panax ginseng was also removed because of a low root-to-tip length relative to other sampled asterids, leaving a total of 72 taxa. We used BEAST v. 1. 8. 4  (Drummond et al. 2012 ) with various clock models to estimate divergence times based on the alignment of the selected 36 genes and the 20 fossil calibrations (Supplementary Appendix S1 available on Dryad). Analyses were run with the LG + G model of amino acid substitution using a birthdeath tree prior, and the ML tree to fix the topology.
Fossil calibrations were set as uniform priors between minimum ages specified in Table  1  and a maximum age of 126 Ma (oldest fossil evidence of eudicots) as listed in Supplementary Table  S2  available on Dryad, with the exception of the root node, for which we used a normal prior at 126 Ma with a standard deviation of 1. 0, truncated to minimum and maximum ages of 113 Ma (the Aptian-Albian boundary) and 136 Ma (the oldest crown angiosperm fossil, see  Magallón et al. 2015). We ran analyses under the uncorrelated lognormal (UCLN), strict, random (RLC), and three different fixed local clock (FLC) models (Supplementary Appendix S1 available on Dryad). Analyses sampling from the prior (without data) were run for 100 million generations, the strict clock, FLC3 and FLC6 analyses were run for 25 million generations and all other clock analyses for 50 million generations, confirming convergence with Tracer v1.
7. 1  (Rambaut et al. 2018). For the non-prior analyses, the first 10% of the total number of generations was discarded as burn-in before summarizing median branch lengths and substitution rates with TreeAnnotator from the BEAST package. To infer ages of gene duplication nodes, we made four new subsets of gene trees for time-scaling. The first includes all gene trees for which duplications were mapped on the collapsed legume backbone by Notung, but including only well-sampled taxa (see Supplementary Table  S1  available on Dryad), and all other rosids as outgroup taxa. The other three sets were obtained by taking sequences of all non-legume taxa in the nitrogen-fixing clade of angiosperms as outgroup alongside sequences of selected, well-sampled accessions for each of the subfamilies Caesalpinioideae, Detarioideae, and Papilionoideae, creating separate sets of gene trees for each of these subfamilies.
For the family-wide trees we further tested whether all legume sequences formed a clade to make sure no gene duplications predating the divergence of legumes (e. g. , from the Pentapetalae gamma event) were included. For each subfamily gene tree set, we ran a phyparts analysis and all gene trees with duplications mapping to the crown node of the subfamily were selected. All gene trees in the family-wide and subfamily-specific sets were individually time-scaled using penalized likelihood  (Sanderson 2002)  in the R package ape (function "chronos")  (Paradis et al. 2004; Paradis 2013 ). Based on simulations, it was shown that although the correlated clock model estimates more accurate substitution rates, the strict clock estimates more accurate branch lengths  (Paradis 2013). Since our purpose is to estimate ages, not rates, we used the strict clock in these analyses, and set the smoothing parameter to 1 as done by  Paradis (2013).
The root age was set at 110 Ma for the family-wide gene tree set and to 105 Ma for the subfamily-specific gene tree sets based on crown age estimates for rosids and the nitrogen-fixing clade of angiosperms from time-scaling analyses on the species tree (Supplementary Figs. S6-S13 available on Dryad). After time-calibration, ages of duplication nodes were extracted and histograms and density plots of these were made in R.
RESULTS The removal of sites with >5% missing data and fragmentary sequences from the 9282 homolog clusters generated by  Koenen et al. (2020) , led to the removal of 640 clusters with large amounts of missing data. From trees inferred from the remaining 8642 homologs, we extracted different sets of rooted gene trees for analysis: i) 8038 trees for the Phyparts analyses that include all sampled taxa except Ranunculales which were used for rooting, ii) 8324 trees including only rosid taxa for the Notung and WGDgc analyses, and iii) 4371 pruned trees with only taxa from the nitrogen-fixing clade of angiosperms, including four Caesalpinioideae species and one species from each remaining subfamily, and average BS > 50%, for the GRAMPA analysis. Exemplar gene trees are included in Supplementary Figure  S1  available on Dryad, showing evidence of several gene duplications within legumes.
These also show that due to differential gene loss, the patterns in individual gene trees are not always clear and general patterns can only be inferred from analyzing large numbers of gene trees. Because of the way these homolog sets were assembled, duplications restricted to terminal lineages are not included, therefore testing for WGDs postulated by  Stai et al. (2019)   Dialioideae and within Cercidoideae (excluding Cercis), is not possible with this data set. For time-calibrating the species tree, 36 informative and relatively clocklike genes were selected from the 1103 orthologs of  Koenen et al. (2020). To estimate the timing of gene duplication nodes, we analyzed 863 gene trees extracted from the Notung analysis including taxa from multiple subfamilies and 246, 250, and 272 trees including only Caesalpinioideae, Detarioideae, and Papilionoideae, respectively.
Inferring Phylogenetic Locations of WGDs In the Phyparts analysis, we find significantly elevated numbers of gene duplications at several nodes where WGDs were previously hypothesized to have occurred, including the Salix/Populus clade  (Tuskan et al. 2006 ) and one consistent with the known gamma hexaploidization subtending Pentapetalae  (Jiao et al. 2012 ) (Fig. 1a  and Supplementary Fig. S2  available on Dryad). For Pentapetalae, many homologs show more than one gene duplication at that node, with nearly twice as many duplications  (1901)  as the number of homologs with duplications (1105), as expected for two consecutive rounds of WGD. Some of these duplications may also stem from older events, since missing data and/or gene loss for the three non-Pentapetalae taxa in our data set could mean that we do not find duplicates of older WGDs in these taxa.
Within legumes, high numbers of gene duplications at particular nodes suggest that there were three early WGD events, one located on the stem lineage of the family and one each on the stem lineage of subfamilies Papilionoideae and Detarioideae (Fig. 1a  and Supplementary Fig. S2  available on Dryad). When applying a bootstrap filter to the homolog trees (≥50% support), numbers of duplications are considerably lower, but the pattern is the same (Fig. 1a  and Supplementary Fig. S2  available on Dryad). At the root of the family, the number of gene duplications drops from 1646 to 99 when applying this bootstrap filter, in line with the difficulty of resolving the deepest dichotomies of the legume phylogeny  (Koenen et al. 2020). Notably, for the legume crown node we also find evidence for a significant fraction of homologs showing more than one gene duplication, with 1646 duplications from only 1229 homologs mapping to that node.
This could suggest multiple rounds of WGD (e. g. , Supplementary Fig. S1e ,f available on Dryad), although some of these can be attributed to duplications in both paralog copies of genes duplicated at the Pentapetalae gamma event, and for many others support values across gene trees are low. For other hypothesized WGDs, numbers of homologs with more than one duplication are much lower, suggesting they involved a single round of polyploidization. Using gene tree reconciliation with Notung, we found similar results (Fig. 1b  and Supplementary Figs. S3 and S4 available on Dryad), although here the Pentapetalae node was not included. However, numbers of duplications particular to Detarioideae are higher than in the Phyparts analysis. The opposite is true for Papilionoideae, where Notung finds higher numbers of gene duplications on the node uniting Caesalpinioideae and Papilionoideae, and on several nodes within Papilionoideae relative to the Phyparts results.
2019 ) also represent ancestral legume characters and cannot be placed to subfamily. Therefore, based on fossil evidence, c. 58 Ma can be considered the minimum age of the legume crown node. Molecular age estimates (95% Highest Posterior Density (HPD) intervals) for the crown node range from 65. 47-86. 45 Ma to 73. 46-81. 18 Ma under the UCLN and RLC models, respectively, to minima and maxima between 64. 63 and 68. 85 Ma under various FLC models (Supplementary Table  S3  available on Dryad), the latter suggesting a close association of initial legume diversification with the KPB (Fig. 5 ). Timescaled trees for all clock analyses, annotated with 95% HPD intervals, are in Supplementary Figures  S6-S13  available on Dryad; 95% HPD intervals for selected nodes are listed in Supplementary Table  S3  available on Dryad.
Placement of Eocene fossils of Detarioideae and Cercidoideae within the crown groups of those clades  (Bruneau et al. 2008; Simon et al. 2009; Estrella et al. 2017) , yields older crown age estimates for these clades. However, with these calibrations (alternative prior 1, Supplementary Table  S2  available on Dryad), a >10-fold higher substitution rate along the stem lineages of these two subfamilies relative to the rates within both crown clades is inferred (c. 8. 82×10 -3 vs. 0. 69×10 -3 substitutions site -1 myr -1 , with identical rates estimated independently for Cercidoideae and Detarioideae; Supplementary Fig. S14a  available on Dryad). This rate is also nearly five times higher than the mean rate across the tree as a whole (1. 54×10 -3 substitutions site -1 myr -1 ), while the crown clades of these two subfamilies have estimated rates about half those of the mean.
Analyses with the same clock partitioning but calibrated with Late Eocene Cercis fossils and Mexican amber (Hymenaea) as the oldest crown group evidence for Cercidoideae and Detarioideae, respectively, do not infer such strong substitution rate shifts, with all clock partitions estimated to have substitution rates ranging from 0. 96×10 -3 to 2. 53×10 -3 substitutions site -1 myr -1 (Supplementary Fig. S14b  available on Dryad). Either way, different placements of these fossils have little effect on the crown age estimates for the family in the FLC analyses (Supplementary Table  S3 , Figs. S11, S12, and S15h-j available on Dryad). Age estimates for duplication nodes show that (at least) Caesalpinioideae and Papilionoideae are derived from one or more WGDs that occurred close to the KPB (Fig.
5c  and Supplementary Fig. S16  available on Dryad). The WGD specific to Detarioideae appears to be more recent, in the Eocene (Fig. 5c  and Supplementary Fig. S16  available on Dryad). The duplication nodes corresponding to the legume backbone inferred from the Notung analysis are likely a mixture of Detarioideae WGD duplications and older legume WGDs. This is surprising since it implies that Detarioideae paralogs do not always form sister clades in the gene trees, which could be caused by gene tree estimation errors or an allopolyploid origin for that subfamily. The large spread of ages for the duplication nodes (Fig. 5c ) may be attributed to substitution rate variation across genes, which, in the absence of fossil calibrations, is unaccounted for. However, we note that in the case of allopolyploidy, the estimated ages of duplication nodes reflect the divergence time of the two parental lineages rather than the allopolyploid event itself, thereby overestimating the age of polyploidy.