dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY Scripts used for alignment, coverage calculation, and feature quantification of CoLoC-seq data can be found at Zenodo (https://doi. org/10. 5281/zenodo. 6389451). The CoLoC-seq and Mock-CoLoC-seq data presented in this article have been deposited in NCBI Gene Expression Omnibus (GEO) under accession number GSE183167 [link to access the dataset https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE183167]. The mass spectrometry proteomics data have been deposited in open access to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD037383.
Information on flight speeds  (Masden et al. , 2021)  and potential behavioural responses to anthropogenic structures  (Schaub et al. , 2020)  are ultimately essential for accurate collision-risk analysis. All these parameters can be recorded using high-resolution miniaturized Global Positioning System (GPS) devices. The Eurasian curlew Numenius arquata arquata; hereafter curlew is a near-threatened shorebird species due to largescale and long-term population declines across the whole East Atlantic Flyway  (Delany et al. , 2009; van Roomen et al. , 2019; BirdLife International, 2022). We therefore collected the above data at high temporal and spatial resolutions for curlews as a model species, and compiled an extensive tracking dataset using data from four countries collected over four consecutive years.
Study area and telemetry Flight paths, altitudes and timing of migration were investigated across the Baltic Sea and adjacent mainland in an area south of 66°N, north of 53°N, east of 8. 5°E and west of 35°E (rectangle in Fig. 1 ). In a study by  Pederson et al. (2022) , a total of 85 curlews were equipped with solar-powered Global Positioning System (GPS) Global System for Mobile Communications (GSM) data loggers to assess their general migration patterns along the East Atlantic Flyway. For the current study, we selected a subset of n = 51 individuals from this dataset which had recorded data at sufficiently high temporal resolution to allow for accurate altitude measurements  (Poessel et al. , 2018 (Poessel et al. , , P eron et al. , 2020;; Schwemmer et al. , 2021 ; see below) and which at the same time crossed our study area.
These birds were equipped with GPS-GSM data loggers (Ornitela, Lithuania) weighing 10 g (n = 24), 15 g (n = 22) or 20 g (n = 5) respectively. The devices recorded time (UTC), geographical position, speed (m/s), acceleration (m/s 2 ) and flight altitude (metres above sea level). Data transfer was enabled via GSM connection and the data stored in the online portal Movebank  (Kays et al. , 2021). Curlews were tagged from 2018 to 2021 in four different countries: Germany (Wadden Sea; n = 23), France (Atlantic coast; n = 14), Estonia (n = 6) and Finland (n = 8) (Fig. 1 ). Curlews were either caught with mist nets at coastal high tide roosts during winter (Germany and France), or with walk-in traps on nests  (Estonia, Finland). The GPS data loggers used in Germany and Estonia were attached by breast harnesses  (Guillaumet et al.
We therefore scanned the whole dataset for inconsistencies in flight-altitude records. Altitude values with a difference of >500 m between consecutive fixes within 5 min and a high DOP were excluded, accounting for 497 fixes (0. 46% of all recorded data). Due to the known non-systematic error of GPS-based altitude recordings that scatters above and below the true value, tags of curlews flying very low above the water surface may show negative altitude values  (Poessel et al. , 2018; P eron et al. , 2020). In the case of our dataset, 9139 negative values (8. 6% of the overall dataset) were included, ranging from À244 m to À1 m. Based on the median (À11 m), these values were strongly right-skewed and large negative values were the exception. In accordance with P eron et al. (2020), we did not exclude these negative values because this would have significantly reduced low-altitude records, resulting in a bias towards higher flight altitudes.
Home ranges We identified areas with different migration intensities across the Baltic Sea (and adjacent mainland) by computing home ranges (i. e. 25, 50, 75 and 95 utilization distributions (UDs)) of the flight tracks (excluding stop-over positions) using the R package 'adehabitatHR'  (Calenge, 2006). Prior to homerange analyses, we excluded all data from stop-over locations of curlews by exploring each migration track in the GIS, that is only in-flight fixes were included, to ensure that home ranges were only computed for active migration tracks. Differences in some of the GPS tracking schedules led to different fix resolutions (i. e. between 1 and 5 min according to battery charge), and we therefore interpolated tracks between different stop-over sites to 1-min intervals using the R package 'sula'  (Lerma, 2021).
Data analyses and statistics Flight tracks and altitudes were plotted on maps using Arc-GIS (version 10. 3; Environmental Systems Research Institute (ESRI), 2011). All other analyses were carried out using R (Version 3. 5. 3; R Development Core Team, 2021). Each inflight GPS position was assigned as recorded over either sea or land using the R package 'spData'  (Bivand et al. , 2021)  and R package 'sf'  (Pebesma, 2018) , and from morning to evening civil twilight ('day') or from evening to morning civil twilight ('night') using the R package 'suncalc'  (Thieurmel & Elmarhraoui, 2019). We then tested if flight altitude differed between spring and autumn, between day and night and across land and sea using generalized linear mixed models (GLMMs;  Zuur, Leno, & Saveliev, 2017; Korner-Nievergelt et al.
, 2015) , with bird ID as a random factor. The current study aimed to use all the available information to produce a dataset with high temporal and spatial resolution, including even small-scale behaviours, such as sudden adjustments in flight altitude when the birds crossed from sea to land. We therefore did not conduct simple thinning out, pooling or smoothing of the data (as often found in telemetric studies of flight altitudes; e. g. Poessel et al. , 2018) , but instead applied a combination of thinning and resampling to account for spatial and temporal autocorrelation of the high-resolution tracking data. The main aim was to reduce autocorrelation by temporally thinning the data before applying regression methods, to repeat this process several times with the newly generated thinned dataresamples, and eventually to jointly analyse the regression results from all the resamples.
Based on a total of 2000 regression coefficient resamples (comprising the two above-mentioned sources of uncertainties), the final results were calculated based on the appropriate quantiles. The same statistical procedure was applied to test for differences in flight speeds between sea/land, day/ night and autumn/spring. Flight-altitude data were used to compute the relative time that migrating curlews spent at turbine level. The collision risk of birds with OWFs is highest at the rotor level (the tallest turbine currently installed has an overall height of 188 m); however, the rotors may extend down to 20 m above sea level  (Iberdrola, 2021; Vattenfall, 2021). In light of different rotor diameters among turbine types and the measuring inaccuracies of the GPS devices, we presented different scenarios of proportional overlap between flight altitudes and turbines for turbine heights of <150 -<300 m (the latter being the projected height of future turbines; Vattenfall, 2021), both for the entire turbine (i.
e. starting at water level) and for the rotor (i. e. excluding data <20 m). In addition to migration maps (1st and 2nd dimensions) and the altitude distribution (3rd dimension), we also assessed the temporal pattern of curlew migration on three different time scales (4th dimension). We aimed to detect seasonal differences in migration intensity across the Baltic Sea by comparing the proportion of time the birds spent migrating across the sea during each crossing of the study area between autumn and spring, using linear mixed-effect models  (Venables & Ripley, 2002). We also examined seasonal peaks of migration intensity across the Baltic Sea by summing the number of actively migrating curlews for each day, and summing the number of these migration days for every 5 days (pentad). We then computed the relative values on the total of 116 bird-migration days for spring and 137 bird-migration days for autumn for each pentad.
Conflicts between curlew migration patterns and OWFs in the Baltic Sea Using a large, international dataset on GPS-tagged curlews, we found that while curlews migrated across a large band of the Baltic Sea, they had predictable high-use flight areas that overlapped with OWFs. Our flight altitude data showed that curlews were migrating across marine areas that contained OWFs at significantly lower altitudes than across land, suggesting higher collision risks for offshore migrants regardless of time of day. The nature of the broad migration front of curlews across the Baltic Sea and adjacent mainland was previously.
Use of GPS telemetry data in improving collision risk models Telemetry studies have the advantage of collecting individual-based information on migration patterns and allowing their repeatability to be assessed in consecutive years  (Battley, 2006; Schwemmer et al. , 2021) ; however, it must be noted that these studies include some bias, because the migration tracks only reflect the sub-population caught at  the tagging location(s). Nevertheless, this potential bias was reduced in this study by using a unique international dataset of tracking data covering a large geographical scale and 51 tracked individuals with 118 flight tracks from different tagging locations. Our statistical design, based on resampling of temporally thinned data in combination with regression analyses, enabled us to use all the raw data, while if the data were simply pooled or thinned out, information would become blurred or lost respectively.
Acknowledgments Parts of this study were supported by the projects  BIRD-MOVE (FKZ 3515822100 ) and TRACKBIRD (FKZ 3519861400) funded by the  German Federal Agency for Nature Conservation  with funds from the  Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) , by the  French Ministry of Ecology  (project  BirdMan ) and the  Agence Franc ßaise de D eveloppement , (grant QUALIDRIS),  Contrat de Plan Etat-R egion, CNRS  (grant ECONAT), Ligue pour la Protection des Oiseaux as well as by the project ' Applied research for conservation of the Eurasian curlew ' funded by  Estonian Environmental Investment Centre  (grant No. 8172 ). Catching, handling and tagging of curlews complied with European laws. Curlews in Germany were treated according to the permission issued by the  Ministerium f€ ur Energiewende, Landwirschaft ,  Umwelt, Natur und Digitalisierung of the federal state of Schleswig-Holstein  (file numbers V  312-7224.
Circular intronic RNAs (ciRNAs) are still unexplored regarding mechanisms for their emergence. We considered the ATXN2L intron lariat-derived circular RNA (ciRNA-ATXN2L) as an opportunity to conduct a cross-species examination of ciRNA genesis. To this end, we investigated 207 datasets from 4 tissues and from 13 mammalian species. While in eight species, ciRNA-ATXN2L was never detected, in pigs and rabbits, ciRNA-ATXN2L was expressed in all tissues and sometimes at very high levels. Bovine tissues were an intermediate case and in macaques and cats, only ciRNA-ATXN2L traces were detected. The pattern of ciRNA-ATXN2L restricted to only five species is not related to a particular evolution of intronic sequences. To empower our analysis, we considered 221 additional introns including 80 introns where a lariat-derived ciRNA was previously described.
We know that ATXN2L plays a very important role in embryos  [35, 36]. However, the detection of this ciRNA in porcine liver and muscle raised questions  [34] , as the role of ATXN2L in these tissues seems less obvious. For this study, we take advantage of total RNA datasets publicly available to investigate ciRNA-ATXN2L in 13 mammalian species with the aim of understanding its genesis and perhaps finding elements about its possible function. We found this ciRNA in five species and our investigations have shown that this presence is disconnected from the phylogeny of the intronic sequences. To improve the understanding of ciRNA generation, we started by considering 64 introns of similar size (< 180 bp like the ATXN2L intron), of which 18 were able to produce a ciRNA. Because we suspected that intron length was important for categorizing ciRNAs, we analyzed 157 additional introns.
Datasets For the main analysis, 207 datasets (total-RNAseq from males) were retained: 120 datasets from the project PRJEB33381 (10 mammalian species), 6 porcine datasets from PRJNA506525, 6 bovine datasets from PRJNA471564, 18 rabbit datasets from PRJNA475375, 12 porcine datasets from PRJNA720752, 21 goat datasets from PRJCA002010, sheep datasets from PRJNA613135, and the 6 bovine datasets already used in  [34]  from PRJEB34570. All details of these datasets are given in Appendix-A.
Identification of ciRNA-ATXN2L RNA-seq reads were mapped to the genome reference assemblies of the respective species using the rapid splice-aware read mapper Spliced Transcripts Alignment to a Reference (STAR)  [37]. We used the gene annotation provided by Ensembl (Suppl. Doc. 1, Table  S1 ) and selected the single-end alignments mode of STAR (STAR-SE) mapping mates of each pair independently. The detection of circRNAs consists of the identification of reads containing a circular junction and the used approach (CD) has previously been described  [34]. CD identifies reads containing a circular junction within those reads that STAR calls "chimeric reads" (CR) from the tabular file (chimeric. out. junction) provided by STAR: subsequently, these reads will be called "circular chimeric reads" (CCRs).
Only circRNAs characterized by at least five CCRs were retained in the output file provided by CD. This tool also provides a file reporting all statistics of STAR mapping. The source code of CD is available from https://github. com/GenEpi-GenPhySE/circRNA. git. ATXN2L is annotated in all 13 mammalian species considered in this study (Suppl. Doc. 1, Table  S1 ). We identified ciRNA-ATXN2L by examining the appropriate region in the putative circRNA output list provided by CD. We suggest quantifying the presence of ciRNA-ATXN2L as a number of CCRs by millions of uniquely mapped reads. The expression (E) of ciRNA-ATXN2L was classified in five classes: high, moderate, weak, and very weak expression correspond to an "E > 3", "1 < E < 3", "0. 1 < E < 1", and "0. 01 < E < 0. 1", respectively. To evaluate the number of linear transcripts produced by the ATXN2L gene, we suggest considering the number of split reads (SRs) observed at the exon-exon junction concerned by the ciRNA and reported by STAR.
Sequences analyses We chose to name a ciRNA that comprises less than 155 nucleotides (nt): a micro-ciRNA. The threshold of 155 nt was chosen due to restrictions for the RNA secondary structure analyses performed on these micro-ciRNAs (see also below). The large-ciRNAs considered in this study comprise at least 300 nt and less than 1,000 nt. We define a microintron as comprising less than 180 bp and a midi-intron between and 1,100 bp. We called mini-introns the introns with a size intermediate between micro-and midi-introns. Ensembl reference sequences were considered for all loci analyzed in this study  [39]. To examine porcine ciRNAs in addition of ciRNA-ATXN2L, we prioritized dataset SRR8237163, containing a lot of porcine ciRNAs previously identified  [12, 33, 34]. For considering additional bovine ciRNAs, we selected bovine introns already known to be concerned by ciRNA  [34].
Statistical analyses All the statistical analyses were carried out using R (v. 4. 0. 2)  [49]. Due to the small sample sizes, significant differences between circRNA proportions from contingency tables were identified with Fisher's exact test (fisher. test function from R stats package v. 4. 0. 2)  [49]. Significant differences between theoretical and observed frequency were identified with exact binomial test (binom. test function from R stats package v. 4. 0. 2)  [49]. A p-value less than 0. 05 was considered as statistically significant. Hierarchical clustering was performed for the circRNA classification (hclust function from R stats package v. 4. 0. 2) using the ward. D2 agglomeration method from a distance matrix  [50]. Several distance measures were computed, first with the Euclidean method (dist function from R stats package v.
4. 0. 2), then with the Ahmad method adapted for mixed variables (distmix function from R kmed package v. 0. 4. 0)  [51, 52]. The hierarchical clustering figures were performed with the function fviz_dend from the R factoextra package V. 1. 0. 7  [53]. We retained 207 total-RNA-seq datasets publicly available and consequently considered 13 mammalian species (pig, macaque, marmoset, opossum, mouse, rat, dog, horse, cat, rabbit, cattle, goat and sheep). In a first round of 207 analyses, ciRNA-ATXN2L was not detected in any datasets of goat testis, sheep testis, cattle liver and cattle muscle. Accordingly, we tried to improve the detection power of this ciRNA in these species-tissue combinations by merging these 45 datasets by age of animals within species to obtain 12 collective datasets. This strategy increased the power and enabled the detection of ciRNA-ATXN2L in an additional tissue-species combination (bovine liver).
Web tools This study was carried out with datasets produced in eight different laboratories, and we are aware that the circRNA analyses are difficult when merging datasets from different origins  [34]. Consequently, we searched to obtain quality criteria to validate ciRNA-ATXN2L detection especially for datasets in which ciRNA-ATXN2L seems to be missing. Details of these analyses are reported in Suppl. Doc. 2. Thus, in summary we performed 174 (162+12) initial circRNA detections of which 166 were considered for further exploration leading to the consideration of 44,255 million uniquely mapped reads. (For details, see Suppl. Doc. 1, Table  S2 ).
Expression level of ciRNA-ATXN2L We are aware that when analyzing datasets from different origins, it is necessary to be very careful when giving and comparing statistics on ciRNA expression. Statistics about the full analysis are reported in Suppl. Doc. 3 (Figure  S1 ) and we chose retaining only semi-quantitative results. The expression of ciRNA-ATXN2L was classified in five classes (not detected, very low, low, moderate, and high) and are reported in Table  1. From these results, we noticed a large variability between datasets of different origins, even though they were from the same tissue and species (porcine testis from PRJNA506525, PRJEB33381 and PRJNA720752). We suggest retaining the highest expression observed for each species. In summary, we will retain that in pigs and rabbits, ciRNA-ATXN2L could be detected at very high levels compared to cats and macaques showing only low ciRNA-ATXN2L expression and cattle with an intermediate level.
The ciRNA-ATXN2L can constitute a large proportion of the circular transcriptome landscape, since we detected nearly 11,000 and 8,200 reads (CCRs) in pig and rabbit datasets, respectively. For six and four pig and rabbit datasets, ciRNA-ATXN2L-specific CCRs represent more than 2% of total CCRs, the highest proportion being 3. 2% and 5. 20 %, respectively. In the six pig testis datasets from PRJNA506525, ciRNA-ATXN2L is the top-ranked circRNA in terms of CCR number and with a large distance to the second-ranked one. In all three rabbit testis datasets from PRJEB83381, ciRNA-ATXN2L is also the first circRNA ranked in terms of CCR, but this ranking was very tight.
Doc. 7, Table  S4 ). The analysis of the intronic sequences did not explain why a ciRNA derived from intron_C can be detected in only five of the 13 species and why a ciRNA derived from intron_A was never detected. The two introns are classical introns (5 ′ SS-3 ′ SS = GT-AG) in all species. As the comparative analysis of 5 ′ SS and 3 ′ SS was not informative, we will suggest a careful study of the branching region. (see Section 3. 2. 2. ) The ciRNA-ATXN2L detection provided the opportunity to examine the individual sequence of the ATXN2L intron_C specific to those animals considered by the datasets. We examined the nucleotide sequence of reads mapped on the intron to detect a sequence variation from the reference genome. Sequence variations could lead to other forms of Figure  2. Searching the nucleotide convenient for branch point use.
These alternative "A"s were highlighted in yellow. In pigs and cats, it is highly unlikely that this first "tnA" is used to form a lariat around an "A", which would lead to a lariat with a very long 3 ′ tail (69 and 76 nt, respectively). (B2) For these five species, ciRNAs were identified. The analysis of the sequence at the ciRNA circular junction identified the nucleotide used as branch point for lariats leading to the detected ciRNAs. This nucleotide was indicated as bold character and the nucleotide trio was highlighted in cyan. ciRNA-ATXN2L with possible different characteristics. This analysis was performed for the six pigs included in PRJNA506525, the nine rabbits (German Zika) from PRJNA475375, the six bulls from PRJNA471564, and for the macaque and the cat, including one positive dataset for ciRNA-ATXN2L.
Nevertheless, six micro-introns used another trio of nucleotides rather than using the "ctC" present in the usual branching region. This study emphasizes that the nature and environment of the "non-A" available is certainly important for use as a branching point when the canonical branching region does not contain "tnA". Sequence analyses of the 37 micro-ciRNAs and their 23 parent microintrons (ATXN2L intron_C from 5 species, 11 porcine, 3 bovine, and 4 marmoset, further ciRNA+ micro-introns) showed that in the absence of a canonical "A" ("tnA") in the canonical branching region, the recruitment of an alternative nucleotide takes place primarily in the branching region. For the marmoset, because no "tnA" was present in the canonical branching region of ATXN2L intron_C and nevertheless no ciRNA was observed, it would be interesting to increase the number of datasets screened to be sure that indeed a ciRNA-ATXN2L is not present.
Even though all human RBPs were considered, no result was obtained. Even though this study was performed with datasets from multiple origins, it was still possible to compare the number of linear transcripts with those of circular intronic transcripts between datasets simultaneously produced. Considering the six porcine testis samples from PRJNA506525, we observed a ratio ciRNA/mRNA between 4 to 26 for ATXN2L, namely ciRNA-ATXN2L is more abundant than ATXN2L linear transcripts (all details were reported in Suppl. Doc. 15, Figure  S6 ). In rabbit muscle (PRJNA720752), the ratio ciRNA/mRNA varied from 0. 54 to 32 (Chinese Qixin) and from 0. 82 to 20. 5 (German Zika). When we considered only datasets from rabbit muscle from German Zika animals, we noted the ratio ciRNA/mRNA increased with age (Table  S6 ).
According to Li et al. [29] , RNAse H1 is able to degrade ciRNA involved in a competition with the pre-mRNA to form an R-loop with the DNA. Pursuit of this hypothesis would lead us to suggest that ciRNAs with no function in transcription regulation are the least easily degraded. However, among the three porcine testicular datasets from PRJEB33381, the one with highest ciRNA-ATXN2L expression was also the one with the highest expression for the gene RNASEH1 (RSEM evaluation performed for  [38] ). We reported similar observations for the rabbit muscle: these datasets presenting the highest expression of RNASEH1 (linear transcript evaluated by SRs) had the highest ratio ciRNA/mRNA of ATXN2L (Suppl. Doc. 15, Table  S6 ). The ratio ciRNA/ mRNA appeared to increase with age in rabbit muscle. This is similar to the accumulation of exonic circRNA in neurons with age  [67].