dataesr/scientific-paragraphs-categorization huggingface dataset:
The second aircraft was a motor glider of type Stemme S10 VT  (Wildmann et al. , 2020). It climbed, motorised, up to about 1. 5km, and after switching off the engine, the glider sunk to an altitude of 1km. Thereafter, the Stemme soared in the waves up to an altitude of 8. 8km (Figure  A1 , left panel). Due to the extremely low air temperature (Figure  A2 , left panel), unfortunately, the data logger switched off, and only wind and temperature data from the first part of the flight are available. Also, the Perlan 2 aircraft encountered very low stratospheric temperatures of around 210 to 220K at the highest flight levels. The strength of the mountain waves can be estimated by the climb rates both aircraft experienced in the lee of the Andes, Figure  A2  (right panel). The climb rate was calculated by central-differencing the 1s global positioning system (GPS) altitude data and filtering them with a 60s window.
(1996, section 3b). This particular choice of a vertical sponge avoids the reflection of gravity waves from the model top. Background and initial fields are obtained from the ERA5 dataset  (Hersbach et al. , 2020). Vertical profiles of V HOR , α H and T are computed in an upstream box covering the areas from 77. 5°W to 82. 5°W and 47. 5°S to 52. 5°S from 0600 to 2100 utc on 11 September 2019, see Figure  13. Simulations were performed using these ambient profiles as initial conditions in the whole domain. The Royal Meteorological Society has partnered with Elsevier to produce a book series covering all aspects of meteorology and related sciences.
Acknowledgments This paper was written when the first author was a guest at the  Finnish Meteorological Institute (FMI) in Sodankylä, Finland. AD thanks  Rigel Kivi  and all the colleagues from  FMI  for their enduring support in the crazy times of March 2020. We thank the staff of El Calafate airport and  Empresa Argentina de Navegación (EANA)  for facilitating the glider flights from the airport in El Calafate and in the Argentinian airspace. We especially thank  Patrick and Sandra  who hosted the motor glider pilots, the film team and  DLR/LMU  staff at the  Brillos Patagónicos cabañas. They allowed us to launch radiosondes from there and provided sustained support for many professional and personal issues. This research was partly funded by the  Deutsche Forschungsgemeinschaft (DFG)  via the project  MS-GWaves  ( GW-TP/ DO 1020/9-1, PACOG/RA 1400/6-1 ).
Data availability statement The Perlan 2 data (Excel data from flight no. 63 and the two radiosonde soundings from El Calafate airport) are downloaded from the Perlan project page http://www. perlanproject. cloud/data. The free accessibility of these data is greatly appreciated. The ALIMA data, as well as the radiosonde from El Calafate, are available from the HALO database (https://halo-db. pa. op. dlr. de). ECMWF ERA5 data are available via the ECMWF web page https://www. ecmwf. int/en/forecasts/ datasets/reanalysis-datasets/era5.
mRNA transcript copy numbers for selected gene targets show that differential mRNA transcript abundance in the brain appears to be partially conserved across species relative to personality type. Secondly, we performed RNA-Seq using whole brains from S. salar and D. labrax scoring positively for both behavioural and molecular assays for proactive behaviour. We further enriched this dataset by incorporating a zebra sh brain transcriptome dataset speci c to the proactive phenotype. Our results indicate that cross-species molecular signatures related to proactive behaviour are functionally conserved where shared functional pathways suggest that evolutionary convergence may be more important than individual mRNAs. Conclusions: Our data supports the proposition that highly polygenic clusters of genes, with small additive effects, likely support the underpinning molecular variation related to the animal personalities in the sh used in this study.
Salar were signi cantly different for all gene transcripts measured (Figure  1a ). Whilst for D. labrax, only GAPDH mRNA copy number was signi cantly different (Figure  1b ). This was further supported by principal component analysis where S. salar individuals could be separated into three clusters with all four detected gene transcripts (Figure  1c ). For D. labrax, GAPDH mRNA transcript copy number contributed 70. 4% to the grouping of individuals with different behavioural phenotypes (Figure  1d ). In order to select individuals for the next set of analyses, global transcriptome, we combined our data for both behavioural screening and absolute rtQPCR assays in both species. Our aim, to produce the most accurate dataset possible for RNA-Seq analyses. Individuals con rmed to be proactive by both behaviourand rtQPCR screening were chosen for subsequent study resulting in 18 proactive individuals for S.
rerio brain transcriptome consisting of 14,102 protein-coding genes (Figure  2a  and Table  S6 ). The whole transcriptomic assemblies of S. salar and D. labrax were mapped to 14,876 and 13,626 protein-coding genes of D. rerio UniProt database respectively (Figure  2a  and Table  S6 ). Overlapping analysis con rmed 9,203 genes are consistently expressed in brains across all three teleost species (Figure  2a ), which consists of 65. 3%, 61. 9% and 67. 5% of the whole transcriptomes from D. rerio, S. salar and D. labrax respectively. Further analysis using conserved protein domains demonstrated that 3,648 Pfam modules are shared across all three species, which comprised 80. 0%, 88. 9% and 91. 8% from D. rerio, S. salar and D. labrax transcriptomes respectively (Figure  2b , Table  S7 ). In addition, comparisons of functional GO annotation demonstrated signi cant similarities across all brain transcriptomes at all three levels, e.
c-i~iii). Spearman correlation analysis for each replicate within each species demonstrated a positive correlation (> 0. 5) for transcripts of "proactive" or "control" groups (Figure  S5  and Table  S8 ). In order to facilitate the species-wise comparisons of proactive related DEGs, sequence annotations of the two non-model species were obtained by mapping against D. rerio UniProt database using BLASTx. A total of 78 cross-species DEGs (at least expressed in two species) were identi ed (Figure  4a  and Table  S9 ). Of which, 55 cross-species DEGs were identi ed in S. salar where 85% of DEGs have very high sequence identities (>60%) with D. rerio, and 95% a high con dence E-value (< 1E-10); For D. labrax, 40 cross-species DEGs were identi ed with 97. 5% of them sharing high sequence identities (>60%) with D.
Functional interactome analysis of risk taking related transcripts across species Aiming to understand the functional signi cance of the correlated transcripts and identify regulatory pathways of proactive behaviour GO network analysis was carried out with all three species datasets (Figure  6  and Table  S10 ) and without the D. rerio dataset (Figure  S7  and Table  S11 ). Overrepresented GO terms for proactive high abundance DEGs speci c GO clusters constituted 22 Groups, of which 6 groups were shared by all three species with the numbers of associated transcripts as follows: PI3K cascade  (31) , phospholipid (PI) metabolism  (41) , RNA localization (9), WNT mediated activation of DVL (3), Oxidative demethylation (2) and role of phospholipids in phagocytosis (38) (Figure  6a , 6b and Table  S10 ).
Tag manager software was used to identify sh leaving to the normoxia tank and those staying (independent of the decreasing oxygen level). Each tank had a separate water inlet and outlet. In the hypoxia tank the inlet was connected to a N 2 gas exchanger (15 mg N 2 L -1 ), which deoxygenated incoming water. Oxygen levels (mgL -1 ) in the tanks were monitored every minute, using an O 2 -monitoring system (Loligo Systems, Tjele, Denmark). Control tests prior to the experiment demonstrated that oxygen depletion was homogenous throughout the water column. Two video cameras were mounted outside the tube on each side of the opening and over water, in order to monitor sh movement. Each test had a duration of approximately 5 hours starting at 8. 30 AM and all tests were conducted in an equal manner.
The concentration of each sample (total RNA) was quanti ed by Nanodrop (ND-1000) and quality visualized under UV light in a 1% agarose gel containing 1 μg/ml ethidium bromide. 2 µg of total RNA was taken from each individual to synthesize cDNA with SuperScript III RNase Transcriptase (Invitrogen) and oligo-dT primer (Promega). Identi cation of salmon and sea bass target mRNAs using the zebra sh AP transcriptome AP-speci c gene lists were derived from our previous study in zebra sh  [14]  using individual zebra sh brain transcriptomes (dataset http://www. ncbi. nlm. nih. gov/geo/; GEO Accession: GSE40615GEO). Brie y, in this study behavioural tests including risk-taking, activity, mirror image stimulation and latency to feeding after con nement were carried out on a population of 280 zebra sh from a wild-type background.
salar were derived from the NCBI (994195 sequences) and UniProt (>9K sequences). B All public resources for sea bass (Dicentrarchus labrax) UniProt (>2. 5K sequences) and NCBI (86801 ESTs) were added to sequence collections for sea bass held in the AquaSea server which forms part of the Aquagenomics website (http://www. aquagenomics. es). Sea bass resources were released in 2015. In order to maximize identi cation of salmon and sea bass mRNAs we applied our best BLAST iterative methodology  [27, 38]   (Goetz et al. , 2010; Mackenzie et al. , 2009). The scripts used consist of a four-step iterative BLAST, combining BLASTx BLASTn BLASTx BLASTn, until a best hit description is assigned, the two rst rounds are based on best description and the two next rounds based on best BLAST scores. If no description is found after the 4 rounds, sequences appear as "no hit found".
Curation of results The lists obtained were then manually ltered to remove redundancy, cross check annotations and curated using the following criteria: 1) Species speci city, 2) contig length, 3) lowest e-value and 4) identity score. We found that BLASTn results were adequate for the analyses. A manual search for functional signi cance was undertaken using both classical literature search methods and also database orientated searches for functionality in model organisms for example GeneCards. This was also supported by network building in the Cytoscape platform aiming to identify interactions between the mRNAs identi ed.
Absolute qRT-PCR All primers for QPCR were design by BatchPrimer3 v1. 0 based on different sources of template genes as: i) for individual separation of mRNA transcript abundance target marker genes were chosen from the proactive-related gene list based on previous study on D. rerio  [14]  primers were designed based on the orthologous sequences from S. salar and D. labrax respectively (Table  S2 ). Target genes were validated for each species using thermal gradient RT-PCR and the products that met the quality criteria were cloned into bacterial plasmids. ii) for the veri cation of RNA-Seq results, sequences of overlapping proactiverelated mRNA transcripts (pp bp1b and edrf1) across all three species were obtained from either the transcriptome assemblies (for S. salar and D. labrax) or from the UniProt database via unique identi ers (D.
Statistical analysis for behavioural and QPCR data Exploratory analysis of the gene expression data in relation to the behavioural data was performed with speci c software (AutoDiscovery®, Butler Scienti cs). The approach evaluates Spearman's Rank correlation coe cients for every pair of numerical variables and one-way ANOVAs for every pair of qualitative numerical variables within the consolidated data set in order to extract the most relevant relationships between the variables (Exploratory Data Analysis or EDA process). This was used as an exploratory analysis without signi cance corrections for false discovery rate (FDR) as the main objective was to suggest potential associations between multiple variables to be further explored. Correlations between all mRNAs, gene expression, tank, sex and weight were also performed.
Data was tested for normality with a Kolmogorov-Smirnoff test and Levene's test for homogeneity of variances. Non-normal data was log 10 (var+1) transformed. Tank effects on sh population total weight and gene copy number were also checked either with paired samples T-test or ANOVA tests. Post-hoc Scheffé or Dunnett test, for non-homogeneous variances, were performed for speci c signi cances. A GLM ANCOVA was used to test for signi cant differences in gene expression between all individuals screened for AP in the populations studied. Dependent variable was Log copy number for each target gene with AP as the xed factor. Tank and weight were used as co-variables. To use the individual mRNA transcript abundance data to screen for AP we rstly analysed individuals using the K-mean cluster method, based on absolute mRNA transcript levels for each individual for both species.
Optimal cluster numbers were chosen and different clusters were named following the cluster groupings. The criteria of classi cation was based on our previous data for zebra sh behavioural phenotypes. As a second approach we applied statistical comparisons between different personalities of both species following one-way ANOVA (individual grouping) or Two-way T-test. Signi cance was reported at P< 0. 05. All analyses were performed with SPSS v19 (IBM®). Graphs were plotted using Prism5® for MacOS X, SPSSv19® or EXCEL® for Mac 2011 (v14. 4. 6). The statistical difference of mean expression levels of gene markers among behavioural groups were calculated by R function "aov" (ANOVA) together with post hoc multiple pairwise comparisons by "TukeyHSD". Signi cantly different groups were reported by LSD.
Total RNA (1 mg) for each pooled RNA sample was used for reverse transcription by SuperScript III RNase Transcriptase (Invitrogen) following the manufacturer's protocol, with the only exception of reduced RNA fragmentation time to maximize obtention of longer reads  [46]. The cDNA libraries were constructed by TruSeq V2 kits (Illumina, CA, USA) and sequenced on a Illumina HiSeq 2500 platform at the Norwegian Sequencing Centre (raw reads: https://www. ncbi. nlm. nih. gov/bioproject/PRJNA543167). The adapters (indexers) in the paired-end raw reads were trimmed out by the quality control tool Trim-Galore (a wrapper tool based on Cutadapt Version 1. 4. 2 and FastQC Version 0. 10. 1) for high throughput sequence data, as set up by default quality threshold of Q20. The FastQC reports from both before-and after-trimming were checked.
Genome guided de novo transcriptomic assembly and completeness estimation To facilitate downstream analyses, the after-trimmed reads across all samples (both proactive and control) were concatenated together into a single dataset for follow-up assembly for S. salar and D. labrax respectively. Trinity was used to generate a de novo transcriptomic assembly. For each species, all 'left' reads were combined into a single le, and the same was applied for the 'right' reads. Trinity parameters were held as default (Version 2. 0. 6) [47]  with the "genome_guided_max_intron" parameter set to 15000 for S. salar and 10000 for D. labrax. The reference genome sequence for S. salar was obtained from NCBI (Accession No. GCA_000233375. 4, assembly ICSASG_v2) and D. labrax reference genome was obtained from the online database (http://seabass.
mpipz. mpg. de). Quantitative assessment of trinity assemblies for both S. salar and D. labrax assemblies were measured based on evolutionarily informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB by BUSCO (v1. 1B1) (python3/3. 5. 0; gcc/5. 2. 0; emboss/6. 5. 7; hmmer/3. 1b1). Additionally, assemblies were inspected by Translate (1. 0. 2) which assigned scores based on alignments of reads to assemblies (Figure  S2  and Table  S4 ). Whole brain transcriptomic annotation and species-wide comparisons For D. rerio, microarray data (Agilent V2, G2519F, 4x44K)  [14]  were re-analyzed as follows: the probe sequences were extracted, and the sequence annotation updated using both BLASTn (2. 2. 26) against RefSeq (GCF_000002035. 5_GRCz10, 54,437 sequences) and BLASTx (2.
2. 26) against UniProt (GRCz10, 59,208 sequences). After background correction (control and low expressed probes removed) and normalization by R (3. 2. 2) package "limma" (3. 24. 15) only probes determined to be positively expressed (N >=4 arrays) in both control and proactive groups were retained for further analysis (Figure  S3 ). To further complement the D. rerio transcriptome information the RNA-Seq dataset (GSE61108) [48] (Figure  S4 ) was parsed into the D. rerio transcriptome increase and improve comparability with the brain transcriptomes of S. salar and D. labrax obtained on the same Illumina platform. To facilitate specieswide transcriptome comparison the gene identi ers from the two non-model species were substituted for zebra sh identi ers based on sequence similarity. For S. salar and D.
labrax, both transcriptome assemblies were compared to the D. rerio UniProt database (GRCz10, 59,208 sequences) using BLASTx (2. 2. 26). The UniProt identi ers of all three species were used for the following comparisons: i) "gene names", "Pfam ID numbers" and "GO (gene ontology) terms" were obtained based on their UniProt identi ers by Biomart (http://www. ensembl. org/biomart) respectively; ii) Venn diagram are used to visualized overlapping and/or unique genes and protein families (Pfam) across three teleost brain transcriptomes. iii) Functional GO terms classi cation was calculated by online software WEGO.
Proactive-related gene identi cation and cross species comparison Gene expression changes between proactive and control groups were compared using two distinct methods for RNA-Seq, e. g. Agilent microarray hybridization and Illumina sequencing. For the rst approach, the intensity value of hybridization for each probe across all D. rerio samples (N=20) was measured by R package "limma". For RNA-Seq the TMM-trimmed (trimmed mean of M values) FPKM (Fragments Per Kilobase of exon per Million reads) value of each transcript was calculated for samples (N = 6) for both S. salar and D. labrax using "edgeR". For D. rerio, the differential expressed genes (DEGs) were calculated and compared, with threshold of FDR < 0. 05 and log 2 "fold change" > 0 (N=10 for each group). For S. salar and D. labrax, the expression abundance of transcripts were estimated as FPKM which were calculated by RSEM  [49]  and normalized by TMM.
Acknowledgements The authors are grateful to  Sebastian Boltana  and  Visila Moiche Vega  for technical assistance. The whole-transcriptome sequencing service was provided by the  Norwegian Sequencing Centre  (www. sequencing. uio. no)hosted by the  University of Oslo  and supported by the  "Functional Genomics" and "Infrastructure" programs of the Research Council of Norway  and the  Southeastern Regional Health Authorities. All downstream computational work was performed using the Abel Supercomputing Cluster, operated by the  University of Oslo  and the  Norwegian metacenter for High Performance Computing (NOTUR) , and operated by the  Department for Research Computing at USIT , the  University of Oslo  IT-department http://www. hpc. uio. no/. XJ is supported by grants from the  Research Council of Norway  (Project No.
Availability of data and material Transcriptomic microarray data: GEO; http://www. ncbi. nlm. nih. gov/geo/; GEO Accession: GSE40615; Sequence Read Archive: SRA; https://www. ncbi. nlm. nih. gov/bioproject/; BioProject Accession: PRJNA543167; (SRA accession SRR9089153-SRR9089164).
IntroduCtIon 2] [3]  Notably, in those aged 10-24 years, neuropsychiatric disorders contribute more than any other cause to the global burden of disease. 2 4 To reduce this burden, earlier identification and enhanced long-term care of those in the early phases of these disorders are key strengths and limitations of this study ► This cohort tracks longitudinally a large number of young people presenting for mental health care and treatment early in the course of common mental disorders. It will provide detailed information about variations in the course of emerging illness over a prolonged, and developmentally sensitive, follow-up period. ► Multiple clinical and functional outcome domains will provide a rich dataset including assessment of social, educational and economic participation, physical health, alcohol and substance use, and deliberate self-harm and suicidal thoughts and behaviours.
Open access The Optymise cohort has been established to provide longitudinal multidimensional outcomes data from a large, transdiagnostic sample of young people, who are typically presenting in the early stages of common mental illness syndromes. Recruitment is based on presentation for care and treatment rather than specific diagnostic criteria or severity thresholds, thus the findings are likely to translate well to other broadly based youth mental health and primary care settings. 0] [31] [32]  These inclusive cohorts are then more appropriate for the study of clinical or neurobiological constructs or dimensions of interest, and are also more likely to inform the development of more meaningful classification systems for common mental disorders. Within the Optymise cohort, selected subgroups have been invited to take part in substudies with more in-depth measures of neuropsychological performance, structural and functional brain imaging, physical health (including anthropometric metabolic and immune function), and sleep-wake and circadian rhythms.
7 All participants received clinician-based case management and relevant psychological, social, and/or medical interventions as part of standard care. This may have also included referral to more specialised mental health services, or hospitalisation, for those whose need exceeded the capacity of the primary care services. All participants (and/or their guardians) gave written informed consent for the use of routinely collected clinical data for research purposes. No personally identifying information is recorded in the proforma in order to protect the privacy of the participants.
data collection Research staff were trained through individual and group training sessions to extract key data from clinical and research files and code inputs according to a specifically designed clinical research proforma (see 'Clinical proforma' below). Clinical files included all available notes and records from standard clinical care, and research files included various assessments as part of participation in substudies (which may include structured or unstructured clinical interviews and the use of symptom rating scales). The proforma records demographic, clinical, and functional information at predetermined time points. The first available clinical assessment at the service is taken as the baseline time point (T1) for each participant and the date of this assessment is used to determine each of the follow-up time points: T2 (3 months), T3 (6 months), T4 (12 months), T5 (2 years), T6 (3 years), T7 (4 years), and T8 (5 years).
If there is no clinical information available for any time point (ie, the participant did not attend the service during that time), then that entry is left missing. A 'time last seen' (TLS) entry is also used to capture clinical information from the most recent presentation to the clinical service, which does not always align with one of the prespecified time points. All clinical and research notes from the preceding time points, up to and including the current time point are used to inform and complete the current proforma entry. The clinical research team responsible for collecting the data consult regularly to resolve ambiguities regarding any of the proforma items and ensure these are dealt with consistently. As of December 2018, phase 1 of data entry has been completed, with 2767 participants included in the cohort and 78 excluded due to insufficient data.