dataesr/scientific-paragraphs-categorization huggingface dataset:
Tax records The T1 Family File (T1FF) is an annual administrative database containing the income tax records of all families who file taxes in Canada. T1FF tax files were made available to Statistics Canada from the Canada Revenue Agency and preprocessed for linkage. The tax files include individual social insurance numbers which are used in the record linkage process  [17]  but are excluded from the analytical file. Information from the T1FF also includes a broad range of demographic and tax variables, including: year of birth, sex, marital status, number of children, year of birth of spouse, and province of residence. Income variables that were available for linkage included: wages and salaries, self-employment income, Provincial Parental Insurance Plan (PIPP) premiums, employment insurance premiums and benefits, income from social assistance, disability benefits/tax credit, total income before tax, contributions to a registered pension plan, pension adjustment, donation amount, income from workers' compensation, income from capital gains, investment income, and union dues.
These boys were then assessed annually to age 17, and again at ages 21, 27, and 30. The second study, the QLSKC, is similar to the MLES but assessed 4,648 children, including both boys and girls from low, middle, and higher income neighborhoods, enrolled in kindergarten in Quebec in 1986-1987  [20]. The QLSKC was created to obtain similar data (as the MLES) on children's behavioral outcomes from a random sample of kindergarten children attending French-speaking public schools in the province of Québec over a 2 year period  (1986 and 1987). This strategy was used to obtain a sample of children that was (1) representative of all regions of Quebec, and (2) representative of urban and rural settings. A total of 4,648 students were rated on the Social Behaviour Questionnaire by both parents and teachers.
A subsample of 2,000 students was assessed annually between 6 and 12 years and again at 15, 21, and 30 years. This sample provides a control group for the MLES study. The members of these two cohorts are now in their thirties, and researchers have collected detailed longitudinal data on parent behaviours; child outcomes, behaviours, attitudes, and activities; education outcomes; psychological markers; as well as physical data. These data have been used to construct trajectories of psychological development over time. Results from both the MLES and the QLSKC have been frequently published in peer-reviewed journals.
Derived Record Depository (DRD) In order to link these cohorts with the T1FF, the cohorts were first linked to the Statistics Canada's Derived Record Depository (DRD). The DRD is a national longitudinal data base of individuals derived from a number of Statistics Canada data files and it contains only basic personal identifiers. The purpose of the DRD is to populate a Key Registry through record linkage. It is not a data source for analytical purposes, and it is an evolving data base. New individuals are routinely added and existing records are updated by linking administrative data. For the purpose of the current project, the DRD (version 4) was filtered to include only individuals born between 1977 and 1981 to match the cohort birth years. This included 2,771,101 individuals and 14,504,919 1  records.
Linkage methods and results The principal files for linkage were the combined cohort data (hereafter labeled the 'cohort file') from the 5804 children included in the Montreal Longitudinal Experimental Study (n=1161) and the Quebec Longitudinal Survey of Kindergarten Children (n=4643). Probabilistic record linkage was carried out using G-Link 2  3. 2  [21]. Probabilistic record linkage methodology uses non-unique identifiers (e. g. , name and birth date) to calculate the likelihood that matched records refer to the same entity (e. g. , an individual). This was accomplished in two steps:.
Primary linkage First, the cohort file was cleaned and standardized to prepare for the matching process (e. g. , removing trailing blanks and accents, all characters are set to uppercase, etc. ). An initial quality assessment was also performed in order to ascertain the completeness of the data (i. e. , percent of data available on each matching variable) and likelihood of matching (i. e. , number of distinct values). Efficiency of the record linkage process depends on relatively complete data with a relative high number of distinct values in order to discriminate between cases. Next, a series of twenty-four conditions were created based on surnames (including parents' surnames), given names, date of birth (and its components), province of residence, and the first three characters of postal code to limit the number of potential pairs generated.
Secondary linkage The cohort-DRD linkage keys and the T1PMF-DRD and CCTB-Ident-DRD linkage keys were used to link the cohort to historical tax records (all years of the T1FF between 1992 and 2012). These data provide unique information on the cohort with respect to their administratively-collected income in adulthood. For the purpose of this study, tax records from 1992 through 2012 were linked to the cohort file, although the linkage rate was low between 1992 and 1998 due to the young age of the cohort who were not likely to yet be filing taxes (i. e. , less than 18 years of age). The number of valid cases in the original cohort file with valid tax records was approximately 84%. Other linkages with Canadian tax data has shown similar linkage rates between a cohort and historical tax data  [23].
Coverage analysis Since the Quebec cohorts were not representative of any particular population, comparisons of the linked data set to known estimates could not be made as has been done with previous linkage projects [e. g. , 24]. For example, it was not possible to examine the distribution of the linked sample on certain sociodemographic characteristics (e. g. , income) to the Census of Canada since the cohort was designed as an at-risk sample. However, it was possible to identify potential biases in the results by comparing the distribution of some key variables for the linked dataset, the cohorts, and the non-linked cases. Linkage rates to the DRD by demographics and variables of interest such as sex and birth year are presented in Tables  1 2 3. No distinct trends were noted, other than an effect of time.
Results from analyses of the linked data As a simple example of the value of this linked dataset, we examined the changes in the correlation of childhood behaviour measures in the cohort data and adult earnings over time from the tax data. We calculated the correlation between two childhood behaviour measures (taken in 1986 or 1987) and adult individual earnings at intervals of five years. The childhood behaviour measures were opposition and anxiety [see 19 for a description of the measures and the underlying questionnaire]. Figure  1  demonstrates the evolution of these correlations over time. Both anxiety and opposition were negatively correlated with individual earnings, and after an initial increase in negative correlation, the relationship seems to stabilize. The pattern is quite similar for anxiety and opposition behaviors in early childhood.
Discussion Future research on healthy child development will increasingly rely on linked datasets to capitalize on the advantages of individual-level survey information along with prospective, population-based administrative files. In fact, Canada has several provincial linked datasets that capitalize on the linkage of several administrative data bases (e. g. , Manitoba Population Research Data Repository, Population Data BC, Institute for Clinical Evaluative Sciences data in Ontario). The present study demonstrates one such linkage for child data, which links a large developmental, longitudinal cohort (which includes many child behaviour and other contextual measures) and tax information (T1FF). Using probabilistic linkage methods, Statistics Canada was able to link 84% of valid cases in the original cohort file to valid tax records.
This yields a child cohort dataset with administrative tax outcomes in order to examine long term earnings and related information. This is a significant contribution to child cohort data in that previous studies have relied on self-reported income information which can be limited. An example of the potential use of the data was provided, demonstrating the correlations between two different early child behaviours (anxiety and opposition) and individual earnings and household income. Correlations were modest, although this is roughly equivalent to the relationship between parent and child income demonstrated in several Scandinavian countries  [25]. Anxiety and opposition showed similar patterns of negative correlation to individual earnings over time, but different patterns of correlation to household income: the cor-Figure  1 : Individual earnings and childhood behavior Figure  2 : Household income and childhood behavior relation of household income with anxiety in young adulthood was lower (closer to zero) than opposition, but as individuals aged became more similar  [8, 15].
One possible reason for the different patterns observed between individual earnings and household income is the possibility of belonging to a multiple earner household (through marriage or living with parents). Understanding how and why opposition and anxiety behaviours in early childhood are differently related to income over time (as well as other contextual factors included in the broader cohort data) is an interesting puzzle that can be addressed by a rich dataset such as this, which links multi-dimensional data on economic well-being (individual and household) with multi-dimensional data childhood measures. The correlational analysis presented above would not be possible in the absence of linked administrative data. Furthermore, self-reported income can be subject to higher levels of attrition, non-response bias, and recall errors.
Precursor ions present in strawberry leaves that met all criteria to be classified as ellagitannins under conditions  species in a single data dependent acquisition (DDA). Comparison with recent literature data showed that only 9 non-isomeric ellagitannins in strawberry leaves were provided tentative identifications from previous studies  64, 73. Based on this optimized method we were able to putatively identify 154 non-isomeric ellagitannins from strawberry leaves, which is 17 times higher than the number of ellagitannins reported in the same matrix. Broadening the search to include ellagitannins from any source increased this value to 21 tentative matches. The potential of comprehensive and systematic inclusion of CID spectra for the isomers of each unique species has never been possible before development of this TRT approach.
The tandem HCD-CID method presented is well positioned to be applied across other classes of natural products, where HCD derived reporter ions can serve as triggers for screening a compound class, although the 249/275/301 reporter ions are specific to ellagitannins and not applicable to other compound classes. The paucity of annotated structures of plant-based natural products such as ellagitannins makes accelerated structural assignment of CID spectra challenging  32, 62, 64, 73, 74. In this regard, CID spectral libraries of compounds would first need to be created with existing tentative structure identifications most commonly found in tabulated lists which currently include a few hundred non-isomeric ellagitannins  14, 32, 62, 64, 74. Complementary NMR data could then be acquired with preparatory UHPLC methods to systematically confirm tentative structural assignments.
Strawberry Leaf Extraction. Description of the sample preparation procedure can be found in the Supporting Information. Instrumentation. All analyses were performed using an Ultimate 3000 HPLC (Thermo Scientific, Waltham, MA, USA) coupled to an Orbitrap Fusion (Thermo Scientific) Tribrid mass spectrometer equipped with an electrospray ion source using tune application software 2. 1. 1565. 18 and Xcalibur 4. 0. 27. 13.
UPLC-MS/MS Analysis. All samples subjected to LC-MS/MS analysis were separated on a Waters (Waters Corp. , Milford, MA, USA) Acquity UPLC HSS T3 (150 × 2. 1 mm, 1. 8 µm) column at 30 o C. The following gradient program utilizing water with 0. 1% formic acid as mobile phase A and acetonitrile as mobile phase B was employed: 0 min, 10% B; 2 min, 10% B; 8 min, 30% B; 12. 5 min, 60% B; followed by a 3-minute washing step at 90% B and a subsequent re-equilibration for 7 min at 10% B. The flow rate was set to 0. 22 mL/min and the injection volume chosen was 2 µL. The mass spectrometer was operated in negative ionization mode with a data dependent MS 2 HCD-CID method. The interface conditions were as follows: emitter voltage, -2600 V; vaporizer temperature, 325 o C; ion transfer tube, 325 o C; sheath gas, 55 (arb); aux gas, 10 (arb); and sweep gas, 1 (arb).
We used remote-sensing data to calculate two measures of urban form within each tile: percentage tall vegetation cover and percentage building cover (see Appendix S1). We then applied random sampling, stratified to maximize variation in urban form across five categories for each measure, reflecting the range of values found within the urban limits of the Cranfield triangle: tall vegetation cover (0%-10%; >10%-20%; >20%-30%; >30%-40%; >40%) and built cover (0%-5%; >5%-10%; >10%-15%; >15%-20%; >20%; see Table  S1 ). This provided 25 possible urban forms, with up to five tiles then being randomly selected from each form. We conducted extensive bird surveys in the resulting 106 tiles (less than five tiles were available for seven forms; see Table  S1 ). We did this by identifying up to four sampling points in each tile (mean per tile, 3.
Point counts were conducted for 10-min periods, divided into 2-min intervals. Within each 2-min interval, the number of birds and the radial distance from the observer at which they were seen was recorded in bands of 0-20 m, 20-40 m, 40-60 m and 60-100 m. Birds were recorded independently in each 2-min period. Individuals that moved during a 2-min period were recorded in the distance band in which they were first detected. Data are available from the Centre for Ecology and Hydrology. https://doi. org/10. 5285/c4806e25-5325-4b01-8066-91a8fb55eb41 (Plummer & Siriwardena, 2018). For each individual 10-min point count, we selected the maximum count of each species per distance band from the multiple 2-min intervals. We then combined data from the two visits by selecting the maximum count per band across visits, so providing one maximum overall count per band.
| Human population density, socio-deprivation and green space For each survey tile, we estimated the human population density based on the UK-gridded population map  (Reis et al. , 2016). This dataset is based on the 2011 Census data (Office of National Statistics, 2016) and Centre for Ecology and Hydrology Land Cover Map 2007 and consists of gridded population data with a spatial resolution of 1 km 2 , assigned to the UK National Grid. For each surveyed tile, we scaled the estimated human population relative to the area of the gridded population square that the tile covered (i. e. up to 25%). Where the survey tile covered more than one population square, we weighted our estimate by the proportion of that tile that was covered.
Socio-economic deprivation of human communities has been shown to be negatively correlated with bird abundance and richness  (Fuller et al. , 2008). To arrive at a generalized measure of deprivation for each tile, we used weekly household wages. These were derived from model-based estimates for households (Office of National Statistics; http://www. neighbourhood. statistics. gov. uk/ HTMLDocs/incomeestimates. html). This index estimates income per household per week in pounds sterling, from data identified during the period from April 2007 to April 2008. The household data were averaged across the Middle Layer Super Output Area (MSOA), a geographical hierarchy consisting of 2,000-6,000 households and were the most recent data currently available. We scaled the income relative to the MSOA covered by the tile, where a tile covered multiple MSOAs we weighted by the percentage of tile each MSOA covered.
Avifaunal diversity is strongly associated with green space structure, diversity and complexity in urban areas (e. g. Evans et al. , 2007; Sandström, Angelstam, & Mikusiński, 2006). Therefore, because urban green space consists of large areas of grass as well as shrubs and trees, we used airborne hyperspectral data at 2 m resolution to calculate the proportion of green space in each surveyed tile (instead of vegetation exceeding 0. 7 m in height as used to estimate urban form; see Appendix S1). Green space was defined as the percentage of pixels in the tile at 2 m resolution that had a Normaliszed Difference Vegetation Index (NDVI; a well-established indicator of whether a pixel contains photosynthesising material or not) exceeding a threshold of 0. 2 (i. e. that were vegetated, see Appendix S1).
| Statistical analysis We created two response variables for each of cultural service and disservice birds: abundance and richness per tile. All four resulting response variables were approximately normally distributed. We then built linear mixed models of human population density, weekly household income and green space against each response variable in turn. As population densities often form nonlinear relationships, we repeated each model twice, instead fitting first a quadratic and then a higher order polynomial function to human population density. Of these three models, we selected the one with the lowest AIC, before using the mumIn package  (Bartoń, 2015)  to produce TA B L E 1 The distribution and raw abundance, of survey points in each 500 × 500 m tile, of species providing cultural services and disservices.
DATA ACCE SS I B I LIT Y Airborne lidar and optical data from the four NERC-ARSF flights are available from the Centre for Environmental Data Analysis (CEDA) portal. Users will need to complete a short online registration in order to create a free CEDA account and access the data. DOI: https://doi. org/ 10. 5285/66513a25354543be9ea7587344cbb87a; DOI: https://doi. or g/10. 5285/5732da3b616843b19db7ff69598abf2c; DOI: https://doi. org/10. 5285/c1bd61f95443402d8b34a91eeea903e5; DOI: https:// doi. org/10. 5285/24c097cf77474644a4b3b17286abcd91 (NERC Airborne Research and Survey Facility, 2018a,b,c,d). The bird survey data is under embargo until 31 December 2018, after which it will be available from the Centre for Ecology and Hydrology. https://doi. org/10. 5285/c4806e25-5325-4b01-8066-91a8fb55eb41 (Plummer & Siriwardena, 2018).
The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) are shown next to the branches  (Felsenstein 1992). The evolutionary distances were computed using the Maximum Composite Likelihood method  (Tamura et al. 2004 ) and are in the units of the number of base substitutions per site. The analysis involved 55 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 1159 positions in the final dataset. Evolutionary analyses were conducted in MEGA7  (Kumar et al. 2016). The distribution of MICE or MICE factors is indicated on the right side: full orange dots indicate species for which ICE were previously described in at least one strain; hatched orange dots designate species for which both CDS5 and CDS17 were concomitantly detected; white dots correspond to species for which no MICE has been reported or for which the concomitant occurrence of CDS5 and CDS17 was not detected.
CDS5 and CDS17 were searched by blast analysis using standard parameters and CDS5 and CDS17 amino acid sequences of M. agalactiae (MAGA7030 and MAGA6970 respectively) and M. mycoides subsp. capri GM12 (MMCAP2_0555 and MMCAP2_0567, respectively), all available at the NCBI public database (https://www. ncbi. nlm. nih. gov/). ICEF,  (Calcutt et al. 2002)  and M. agalactiae ICE, ICEA,  (Marenda et al. 2006 ) are very similar, large modular elements of approximately 20-30 kbp (Figure  2 ), occurring as multiple copies in their hostgenomes. Both MICEs are composed of about 20 structural genes with only three (namely cds5, cds12 and cds17) having predicted functional domains common to proteins with DNA mobilityrelated functions in other bacteria (Alvarez-Martinez and Christie 2009). These are (i) a TraG/VirD4 homolog (CDS5, pfam accession number PF02534) that usually couples the relaxome to the conjugal mating pore  (Balzer et al.
1994; Llosa et al. 2002) , (ii) a single stranded DNA binding (SSB) protein (CDS12, pfam accession number PF00436) and (iii) a TraE/VirB4 homolog (pfam accession number PF05309) usually involved in DNA transport  (Komano et al. 2000; Rabel et al. 2003). With the advances in genome sequencing, several other MICEs have since been described which overall organization is relatively similar  (Calcutt et al. 2002; Pinto et al. 2007; Nouvel et al. 2010; Wise et al. 2011; Thiaucourt et al. 2011; Shu et al. 2012)  as illustrated in Figure  2   In other bacteria, the global ICE structure consists of three modules that enable its maintenance, dissemination, and regulation. Little is known regarding the functionality of MICE components but the clustering of seven genes encoding putative proteins with transmembrane domains (in ICEA CDS5 to CDS19) most likely represents the module associated with the conjugative channel (see also below Figure  3 ).
MICEs prevalence among mycoplasmas The The Hominis phylogenetic group also contains species so far deprived of MICE such as the avian pathogen M. synoviae or the mice pathogen M. pulmonis for which only a few sequenced genomes are available in the databases. Finally, no MICE has been detected in the Acholeplasma phylogenetic group. MICEs also vary in their copy number per hostchromosome, with a single strain having multiple copies of the same ICE, or multiple copies of two different ICEs. This is illustrated by the M. fermentans strain M64 which harbours three copies of ICEF-III and three copies of ICE-II  (Shu et al. 2012). MICE multiple occurrences complicate their proper identification in mycoplasma genome by providing repeated sequences that hampers correct assembly. Therefore, detailed MICE analyses in draft genomes should be taken with care and further completed by targeted, sequencing strategies.
Acknowledgements Due to length constraints, many references could not be cited. Our work on the role of MGE and on chromosomal transfer was supported by the  French National Research Agency, ANR , (MycXGene,  ANR09MIE016  coordinated by CC), by the  National Institute for research in Agriculture (INRA) , and by the national veterinary school of Toulouse (ENVT). Funding to CHK was provided by  Academia Sinica (Taipei, Taiwan).
8 Figure 2. 82 Figure 2. Mycoplasma Integrative and Conjugative Element (MICE) backbone and structural organization of major MICE representatives. In MICE backbone, genes highly conserved across MICEs are positioned on the MICE (solid line) and are represented by colored filled arrows; conserved CDS that may be absent or truncated in particular MICEs are represented below the line. Inverted repeats (IR) are indicated by grey triangles. In classical MICE and degenerated vestigial MICE, highly conserved CDS are colored as in MICE backbone. Pseudogenes are represented by arrows filled with hatched color accordingly to MICE backbone. Genbank unique locus tag numbers of the first and the last MICE genes are indicated in blue below the MICE. Genes containing one or several transmembrane domains are indicated with an asterisk.