dataesr/scientific-paragraphs-categorization huggingface dataset:
Reflecting both global air traffic realities and OpenSky's coverage focus, 13 of these airports are in the United States, including the 7 busiest with regards to landings. Several of the major hubs in Europe (Frankfurt, London Heathrow, Paris Charles de Gaulle) and Asia (Kuala Lumpur, Dubai and Delhi) as well as Toronto in Canada make up the remaining 7. Figure  4  shows a time series of airport activity (as measured by departures) in four different regions based on data https://doi. org/10. 5194/essd-13-357-2021 Earth Syst. Sci. Data, 13, 357-366, 2021  from 1 January to 30 April 2020. The impact of the pandemic (or rather the measures to contain it) can be seen clearly in all four. For example, the data show the following: a slow decrease from February in several East Asian airports (even earlier in Hong Kong), -European airports decreasing sharply from early March onward, -America's air traffic dropping later by about 2 weeks, -India stopping all air traffic sharply by mid-March (VABB, VIDP).
Usage notes This dataset may differ from other data sources due to limitations of ADS-B data. On the other hand, there are advantages as it reflects all aircraft types rather than only commercial airlines. It is important to note that ADS-B equipage has been increasing over time as existing aircraft have been retrofitted and older aircraft have been replaced. This effectively means that the number of tracked aircraft in the dataset had been slowly increasing pre-pandemic, reflecting the reality of a dynamic global aviation industry. Further, there are differences in ADS-B equipage across countries' airspaces (depending on their regulatory approach) as well as potentially between aircraft types. For example, small personal aircraft flying locally and below 18 000 feet (5486. 4 m) are often not required to use ADS-B.
Similarly, military aircraft may have exceptions for operational reasons. It is not possible to track and reflect these highly dynamic developments in a static dataset; however, this should be kept in mind for comparative analysis purposes. Finally, as a recommendation for data handling and visualization, Figs. 4 and 5 have been created with the opensource Python package traffic  (Olive, 2019) , which offers dedicated methods for air traffic data and interfaces with OpenSky and other data sources. Further usage notes and tool recommendations are regularly added on the OpenSky website (https://opensky-network. org/community/blog/item/ 6-opensky-covid-19-flight-dataset, last access: 9 February 2021).
Code and data availability The dataset is available under the CC-BY License at Zenodo (see  Olive et al. , 2020 ; https://doi. org/10. 5281/zenodo. 3931948, last access: 9 February 2021). The code to generate and process the data is available in different components. The popular dump1090 package, used as the basis to receive a large majority of crowdsourced information (ca. 80 % in OpenSky), is available in  Foster (2017). Other receiver software may include proprietary and closedsource software such as Radarcape and SBS-3. The OpenSky decoder is available in OpenSky's GitHub repository at https://github. com/openskynetwork/java-adsb (Fuchs and Schäfer, 2020, last access: 9 February 2021). Code concerning data cleaning and processing is documented at https://traffic-viz. github. io/scenarios/covid19.
Conclusions Air traffic and flight data are needed for effective research in many areas of Earth systems science and related fields. We presented an openly accessible, specifically crafted dataset based on crowdsourced data obtained through the OpenSky Network and validated it successfully. From January 2019 to July 2020, the archive includes 41 900 660 flights, from 160 737 aircraft, which were seen to frequent 13 934 airports in 127 countries. As it is updated monthly, this dataset will be growing significantly and will provide deeper insights into flight behaviour before, during and after the COVID-19 pandemic.
Furthermore, it is important to include key minority sub-populations, for example, older adults, racial-ethnic groups, and people with pre-existing medical conditions  [20À22]. This motivated us to take a large-scale approach, whereby individuals who have recovered from COVID-19 infection were compared to concurrently obtained controls whilst accounting for the uneven sociodemographic distribution of virus prevalence and any associated population variability in cognition. More specifically, at the time of writing, we had collected comprehensive cognitive test and questionnaire data from a very large crosssection of the general public, predominantly within the UK, as part of the Great British Intelligence Test -a collaborative project with BBC2 Horizon  [23]. Notably, the online assessment platform had been optimized with NIHR support for remotely delivering cognitive assessments, including for older adults and patients with cognitive and motor deficits, with cross validation against commonly used neuropsychological scales.
Due to the high visibility of the study, the cohort spanned a broad age and demographic range. During May, at the peak of the UK lockdown, we expanded the questionnaire (Table  S1 ) to include questions pertaining to the impact of the pandemic, including suspected or confirmed COVID-19 illness, alongside details of symptom persistence and severity, relevant pre-existing medical conditions, and measures of depression, anxiety and post-traumatic stress  [24, 25]. Here, we analysed data from 81,337 individuals who completed the full extended questionnaire in order to test the hypothesis that those who had recovered from COVID-19 would show objective cognitive deficits when performing tests of attention, working memory, problem solving and emotional processing. We also determined whether the extent and/or nature of cognitive deficit related to severity of respiratory symptoms as gauged by level of medical assistance, positive verification of infection via a biological test, or time since illness onset.
Study promotion We collected data from members of the general public, predominantly from the UK, who completed an extended questionnaire (inclusive of questions pertaining to COVID-19 infection) and series of cognitive tasks via The Great British Intelligence Test, a collaborative citizen science project with BBC2 Horizon that launched in late December 2019. At the beginning of January, articles promoting the study were placed on the Horizon homepage, BBC News homepage and main BBC homepage, and circulated via news meta-apps. They remained in prominent positions within the public eye throughout January. In May, aligned with report of initial results considered of interest to the general public via a BBC2 Horizon documentary, there was a further promotional push. This led to high recruitment in the months of January and May, with lower, but still substantial recruitment between and after these dates.
Data collection The study was promoted as a free way for people to test themselves in order to find out what their greatest personal cognitive strengths were. It comprised a sequence of nine tests from the broader library that is available on our server system based on prior data showing that they can be used to measure distinct aspects of human cognition, spanning planning/reasoning, working memory, attention and emotion processing abilities, in a manner that is sensitive to population variables of interest whilst being robust against the type of device that a person is tested on. In this respect, the battery of tests should not be considered an IQ test in the classic sense,.
Added value of this study We report analyses of a large dataset comprising detailed cognitive assessment and questionnaire data pertaining to COVID-19 infection, from tens of thousands of individuals that span a large cross-section of the general public, around the time of the first wave within the UK. Importantly, as the data were collected in collaboration with BBC2 Horizon, COVID-19 was not mentioned in the promotional material, which mitigates sampling bias. We report that individuals who recovered from COVID-19, including biologically confirmed cases who remained at home and did not receive medical support, perform worse on a range of cognitive tests than would be expected given their detailed age and demographic profiles.
Participants provided informed consent via the study website prior to starting the assessment. All Cognitron tests were programmed in HTML5 with JavaScript by AH and WT. They were hosted on a custom server system (Cognitron) on the Amazon EC2 that can support diverse studies via custom websites. The server system was specifically developed to handle spikey acquisition profiles that are characteristic of main-stream media collaborative studies, fitting the number of server instances in an automated manner to rapid changes in demand. Here, maximum concurrent participants landing on the website information page was ~36,000, with this occurring at the point of the documentary airing on BBC2 in May. After the nine cognitive tests, participants were presented with a detailed questionnaire with items capturing a broad range of sociodemographic, economic, vocational and lifestyle variables.
Statistical methods All processing and analysis steps were conducted in MATLAB by AH with assistance from WT. Visualization was conducted in R (v4. 0. 2) by JMB and AJ. Pre-processing steps were as follows. Participants under 16 or who had not completed the extended questionnaire were removed from the analysis. Each test was designed to produce one primary accuracy-based performance measure (details of test designs are provided below). Values more than 5 standard deviations from the mean were winsorised. Nuisance variables were factored out by applying a generalized linear model and taking the standardized residuals forwards for analysis relative to the variables of interest. This two-step approach was chosen because it leverages the very large data when taking into account broadly applicable nuisance variables such as age whilst ensuring that the model applied to examine effects of interest had minimal possible complexity, thereby reducing any propensity for overfit when contrasting between smaller sub-groups.
Nuisance variables were age, sex, racial-ethnicity, gender, handedness, first language (English vs other), country of residence (UK vs other), education level, vocational status and annual earning. Non-native English speakers who were resident outside of the UK were excluded during article revision based on reviewer feedback. Age in years was taken to the third order in the model to fit precisely the nonlinear age curves that are characteristic of the tests. Only complete datasets were included in the analyses and there was no imputation. The overall summary score, component scores and nine individual test scores with nuisance variables factored out, were taken forwards for analysis with general linear modeling. The first analysis examined differences in scores relative to people who were not ill for those who reported that they believed they had recovered from the COVID-19 illness.
2a -Table  S4 ). People who had been hospitalised showed substantial scaled global performance deficits dependent on whether they were (À0. 47 standard deviations (SDs) N = 44) vs. were not (À0. 26 SDs N = 148) put onto a ventilator. Those who remained at home (i. e. , without inpatient support) showed small statistically significant global performance deficits (assisted at home for respiratory difficulty À0. 13 SD N = 173; no medical assistance but respiratory difficulty À0. 07 SDs N = 3,386; ill without respiratory difficulty À0. 04 SDs N = 8,938). The GLM was re-estimated including confirmation of COVID-19 by biological test as a main effect (Table  S5a ). In proportion with the number of UK confirmed cases when the bulk of data were collected, 386 people reported a positive biological test, including 86% of the hospitalised with ventilator sub-group.
012 (one tailed) estimate = À0. 23SDs). A larger relationship was evident amongst cases who went to hospital but were not put on a ventilator (bio-positive = 22 vs suspected = 126, t=À1. 7923 p = 0. 0375 (one tailed) estimate = À0. 41SDs). A common challenge in studies of COVID-19 is that differences between people who have vs. have not been ill could relate to premorbid differences. To address this issue, a linear model was trained on the broader independent GBIT dataset (N = 269,264) to predict general cognitive performance based on age (to the third order), sex, handedness, ethnicity, first language, country of residence, occupational status and earnings. Predicted and observed general performance correlated substantially r = 0. 53), providing a proxy measure of premorbid intelligence of comparable performance to common explicit tests such as the National Adult Reading Test  [26].
Discussion Our analyses provide converging evidence to support the hypothesis that COVID-19 infection is associated with cognitive deficits that persist into the recovery phase. The observed deficits varied in scale with respiratory symptom severity, related to positive biological verification of having had the virus even amongst milder cases, could not be explained by differences in age, education or other demographic and socioeconomic variables, remained in those who had no other residual symptoms and was of greater scale than common pre-existing conditions that are associated with virus susceptibility and cognitive problems. The scale of the observed deficit was not insubstantial; the 0. 47 SD global composite score reduction for the hospitalized with ventilator sub-group was greater than the average 10-year decline in global performance between the ages of 20 to 70 within this dataset.
AH is supported by the UK Dementia Research Institute Care Research and Technology Centre and Biomedical Research Centre at Imperial College London. WT is supported by the EPSRC Centre for Doctoral Training in Neurotechnology. SRC is funded by a Wellcome Trust Clinical Fellowship 110,049/Z/15/Z. JMB is supported by Medical Research Council (MR/N013700/1). MAM, SCRW and PJH are, in part, supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London Declaration of Competing Interest Dr. Hampshire reports grants from UK Dementia Research Institute, outside the submitted work and is Co-director and owner of H2CD Ltd, and owner and director of Future Cognition Ltd, which support online studies and develop custom cognitive assessment software respectively.
Funding: Funding. AH is supported by the  UK Dementia Research Institute Care Research and Technology Centre  and  Biomedical Research Centre at Imperial College London. WT is supported by the  EPSRC Centre for Doctoral Training in Neurotechnology. SRC is funded by a  Wellcome Trust   Clinical Fellowship   110 , 049/Z/15/ Z. JMB is supported by  Medical Research Council  ( MR/N013700/1 ). MAM, SCRW and PJH are, in part, supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London s).
Finally, our results indicate that the extant Australasian fauna largely results from multiple dispersal events from Southeast Asia rather than in situ diversification. Indeed, we found a clear pattern of decreasing net diversification rate through time, but a strong increase in dispersal events during the Miocene from Southeast Asia (ca. 25% of global dispersal events during this period). Study limitations. Despite the size of our dataset of 2900 species, over 50% of Nymphalidae diversity was not included in our phylogenetic tree. Our dataset corresponds to the molecular information currently available for this group and we included information about missing taxa as much as possible throughout our analyses. Hence, this paper also provides a broad synthesis of the state of Nymphalidae phylogenetics, refining the current taxonomic and geographic sampling and paving the road for future work.
Methods Time-calibrated tree. We inferred a time-calibrated tree of Nymphalidae butterflies using sequence data from the 2866 species for which there was at least one of the following 11 focal gene regions: COI, ArgKin, CAD, DDC, EF1a, GAPDH, IDH, MDH, RpS5, RpS2, wingless (Supplementary Methods 1, Supplementary Data 1). This represents ~45% of the estimated number of species in the family. These sequence data were compiled from published and unpublished studies and subjected to multiple cleaning and verification steps. We generated the final tree using a tree grafting procedure (Supplementary Methods 1, 2, Supplementary Figure  2 ). First, we built a backbone tree relying on a dataset of 789 species of Nymphalidae with least six gene fragments available and and time-calibrated using BEAST 1.
We designed a time-stratified model in which both the adjacency matrices and dispersal matrices varied between time periods (Supplementary Table  3 ). Time was divided into five time periods: 100-80, 80-60, 60-30, 30-10, 10-0 Myr to account for increasing or decreasing connectivity between biogeographic regions through time. Estimation of speciation and extinction rate. We estimated the temporal dynamics of speciation and extinction rates across our phylogeny using BAMM 2. 5  [66] [67] [68]  (Supplementary Methods 4). We accounted for missing species by specifying the sampling fraction at the genus level. The analyzes were run for 50 million generations with four reversible-jump MCMC, sampling parameters every 50,000 generations. The output was then analyzed using the R package BAMMtools  67.
We identified 90 local diversification events, which represent an estimated number of 5373 species (ca. 86% of all nymphalid diversity), and we estimated net diversification, time-variation of the diversification rate (α), and the age of each diversification event. We tested whether (1) any of these parameters differs between regions, and (2) which parameter best explains the extant diversity of these radiations. For each regional diversification event, we fitted a model in which speciation rate was modeled as an exponential function of time, while extinction rate remained constant 70 using the R-package RPANDA 1. 5  71. For each clade, we estimated the extinction parameter (μ) and the two parameters for speciation: speciation rate at present (λ 0 ) and coefficient of time variation (α). Using these parameters, we computed the speciation rate at the crown age (λ crown ), the net diversification at present (netDiv 0 ), and the net diversification rate at the crown age (netDiv crown ).
Acknowledgements This study would not have been possible without the generous support of amateur and professional lepidopterists around the world, too many to name here; thank you to all for providing specimens and information about life histories of the butterflies. F. L. C is supported by an " Investissements d'Avenir " grant managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ). D. J. L. was supported by grants  DEB-1541557  from  NSF  and  WW-227R-17  from the  National Geographic Society. R. I. V. W. was supported by  Leverhulme Trust  emeritus programme. M. E. was supported by an  ATIP grant , a grant from the  Human Frontier Science Program  ( RGP0014/2016 ) and a grant from the  French National Research Agency  ( ANR CLEARWING ANR-16-CE02-0012 ). E. O. A.
Data availability All sequences used in our manuscript are available on Genbank. All Genbank accession codes can be found in Supplementary Data 1. Biogeographic distributions used in our analyzes are also available in Supplementary Data 1. The backbone tree and the complete grafted phylogeny are available at: https://doi. org/10. 5281/zenodo. 5463912. Code for generating the animation of biogeographic history is available at: https://github. com/ evogytis/nymphalidae-animation. Source data are provided with this paper.
Human LMs were cultured for 24 h in absence or presence of LPS (10 ng ml -1 ), poly (I:C) (10 µg ml -1 ) or IL-4 (10 ng ml -1 ). AdipoR1 and AdipoR2 transcript levels were determined by RT-qPCR and normalized against those of a housekeeping gene (HPRT1). Data correspond to the mean ± SEM of 6 independent experiments (*: p < 0. 05). and was associated with a 70% relative reduction in the LPSinduced production of IL-10 (Supplementary Material).
Statistical analyses. Within the USA capture data were used to compare a managed raspberry field and a nearby non-managed wooded area. Within the European countries capture data were pooled from each test location to compare differences between countries within cherry fruits. Attractiveness (# D. suzukii caught) per trap and selectivity (% D. suzukii/total captures) per trap were utilized to compare the lure/trap systems. The average weekly capture and selectivity were used to determine capture trends over time. Trap data were Log(x + 1) transformed to conform the assumptions of normality more closely. Percentage of SWD captured on traps (i. e. , selectivity) were arcsine-square-root transformed to achieve distribution normality. Transformed data were utilized to compare means. Means of capture results were compared using a One-way followed by a Tukey's post hoc test, or a student's t-test to determine significance (α = 0.
5). A Two-way ANOVA followed by a Tukey's post hoc test was utilized to compare the mean SWD capture and selectivity between the raspberry field, wooded area, and period within the USA. Untransformed data are presented in figures. Control dry sticky trap captures were subtracted from the QB dry sticky trap captures to adjust for the captures made by control traps. Statistical analysis was performed using GraphPad Prism (GraphPad Software V9, LLC San Diego, CA, USA).
difficile PG. A clear lytic band with a molecular weight around 46 kDa, corresponding to His 6 -CD1135, was detected (Fig. 1c ), confirming the PG-degrading activity of CD1135. To characterize more precisely Control of CwlA Localization by Phosphorylation this activity and the specificity of the CD1135 enzyme, we incubated the purified protein with PG of the C. difficile 630Derm strain. After incubation, no soluble PG fragment was detected by reverse-phase high-pressure liquid chromatography (RP-HPLC) analysis (data not shown), and the insoluble PG fraction was further digested with mutanolysin to reveal potential cleavages inside the cross-linked PG. The resulting muropeptide profile obtained by RP-HPLC was clearly distinct from the control muropeptide profile of PG digested with mutanolysin alone (Fig.
8 were excluded. Dynamic exclusion was enabled for 20 s and 30 s. Data processing for protein identification and quantification. Raw files were searched MaxQuant software (77), version 1. 5. 3. 8, with Andromeda (78) as a search engine against an internal C. difficile database containing 3,957 proteins  (29) , usual known mass spectrometry contaminants, and reversed sequences of all entries. Andromeda searches were performed choosing trypsin as the specific enzyme with a maximum number of three missed cleavages. Possible modifications included carbamidomethylation (Cys; fixed), oxidation (Met; variable), N-terminal acetylation (variable), and phospho (Ser, Thr, Tyr; variable). The mass tolerance in MS was set to 20 ppm for the first search, 4. 5 ppm for the main search, and 20 ppm for the MS/MS.