dataesr/scientific-paragraphs-categorization huggingface dataset:
Search strings The following search string was built during a scoping exercise in Web of Science, described in the systematic map protocol  [60] , for use in bibliographic databases (see section below): TS=(("protected area$" OR "protected landscape$" OR "protected site$" OR "receptor site$" OR "reintroduction site$" OR "natur* reserve$" OR "national park$" OR "regional park$" OR "national reserve$" OR "biological reserve$" OR "biosphere reserve$" OR "regional reserve$" OR "wilderness area$" OR "natural monument$" OR "management area$" OR sanctuar*) AND TS=("assisted colonization" OR "assisted population migration" OR "assisted migration" OR "assisted gene flow" OR "managed relocation$" OR transloc* OR reintroduc* OR reinforc* OR "assisted range expansion$" OR "assisted long-distance migration$" OR rewilding OR "wild release")).
Web-based search engines A supplementary retrieval of articles was undertaken using these web-based search engines: • Google Scholar (https:// schol ar. google. com/). We used the software programme "Publish or perish (version 6)". Given the conditions of this software (characters limit allowed), we could not use the search string described above and a selection of keywords had to be made (see Additional file 2search strings). The software's use of Boolean characters also differs; as a result, the search string was broken down into 10 separate sub-searches in order to achieve a similar comprehensiveness to WOSCC.
Testing the comprehensiveness of the search results A test list of 40 scientific articles was used to assess the comprehensiveness of the search string on literature databases. The test list was composed of relevant scientific articles including a range of conservation translocation interventions, study populations, and publication years thought to be representative of the literature base and appropriate for answering the primary question. The review team identified these articles prior to the mapping process. Of the 40 articles 38 were retrieved by the search string (either in WOS, Google, or Scopus). Firstly, we checked if the search string retrieved articles in WOSCC. If an article was not retrieved by the search string in WOSCC, we checked for its retrieval in Google. If not retrieved in Google, we checked for its retrieval in Scopus.
Data coding strategy Metadata were coded for all the articles passing the three screening stages. For this coding, the "study unit" was defined as "a unique translocation operation". Thus, if multiple studies were reported within one article they were entered as an independent line in the systematic map database. In addition, one translocation operation was distinguished from another if the populations were different and/or if release site localities were unique geographical locations (cf. "Data mapping method"). The key variables of interest were coded in terms of: (1) bibliographic information (the document type, and type of bibliographic content), (  2 ) study characteristics (the study country, release and capture coordinates, climate zones of capture and release sites, distance (km) between each, source and destination e.
g. captive site to wild release, protected area context and IUCN protection category, the release site's biome, and programme's motive, period of time, costs), (3) population characteristics (the taxonomic group, sample size, age of released individuals), (4) intervention characteristics (whether a reintroduction, supplementation, introduction, or a combination of two), and (5) outcome characteristics (whether related to physiology, feeding, behaviour, space use, reproduction, survival, demography, or genetics). Full details are given in the systematic map protocol, with no deviations (a codebook is provided along with example coding formats). When the data was not sufficiently detailed or simply unknown, we coded as such (using "Unknown" when necessary). The coding stage was performed by three coders (JL, RS and HM) who shared the eligible articles between them and coded all variables for their respective samples.
Due to resource and time limitations, an independent dual coding of the entire database was indeed not feasible. However, in order to guarantee that information was coded and extracted in a consistent and repeatable way, a four-step process was undertaken. • Step 1 "discussion session": discussions were held between the three coders, before the extraction of metadata commenced, to ensure any doubts prior to coding were clarified: JL gave thorough explana-tions regarding coding rules to RS and HM for all the characteristics of the Map. • Step 2 "practice and re-discussion session": immediately following the first discussion session, 5 articles were randomly selected to be practice coded by RS and HM. This coding step was monitored by JL who gave further explanations to the two other coders in cases of doubt.
Indeed, any disagreements were re-discussed. This was a pre-pilot testing phase, purely for practice purposes. • Step 3 "pilot-testing": a pilot extraction phase was undertaken by JL, RS, and HM on an identical sample of 10 articles. This was undertaken to ensure all characteristics were coded consistently by all three reviewers, discussing any potential disagreements. Before starting the actual coding process, this pilottesting step was repeated until no disagreements occurred. We required two tries until all coded characteristics were homogenous. 20 articles were therefore coded in total. • Step 4 "post-coding crosscheck": once coding had been completed, an a posteriori crosscheck was carried out by JL on the entire database to identify any errors and homogenize all coded terminology. All disagreements were discussed until a consensus was reached for the concerned articles.
Data mapping method All the coded metadata for all the studies were included in a systematic map database: a bespoke Microsoft excel spreadsheet. By cross-tabulating the data, summary figures and tables were produced for this Map report to identify knowledge gaps (i. e. , subtopics requiring further primary research) and knowledge clusters (i. e. , subtopics that are sufficiently covered by existing studies to allow for full systematic reviewing). The distribution and frequency of translocation operations was presented with heatmaps, or geographical maps illustrating the number of interventions by country. Based on these results, recommendations were made on priorities for policy makers, practitioners, and research. Because our aim was to not only code the literature base in terms of number of publications (hereafter referred to as "Bibliometric synthesis") but also extract information on specific translocation events within these retained publications (hereafter referred to as "Translocation synthesis"), we underwent a three-step mapping process: i.
Firstly, we permitted a coding of "unclear context" due to the complexity of many publications. If publications did not explicitly describe the name of a protected area in the given translocation event but gave sufficient information to assume our PICO criteria were otherwise respected, during the screening process, these articles were coded but consequently not included in the systematic database. A brief narrative summary is given without tables (cf. "Unclear cases" section, in Review findings). Concerning the latter, these were considered inappropriate and too time consuming to code in the translocation synthesis as the same individuals may be repeatedly transferred back-and-forth between fenced reserves in order to cope with the particular problem of lack of gene flow between enclosed populations.
iii. Concerning the "Translocation synthesis": from all the remaining publications this part of the synthesis involved coding the specific translocation events consistent with the IUCN definitions. Two key variables (method and location) were used as cut-offs to define a study unit  [63] , i. e. a translocation. Therefore, a translocation was coded as a unique data line in the Excel datasheet if: (1) a different species was translocated, and/or (2) the release site was a unique geographical location. If the same species were translocated to clearly different release sites, these were presumed not to be the same individuals under movement thus coded as unique data entries. Time duration was not considered as a cut-off when distinguishing translocations events. Consequently, some translocation interventions, coded as for example, "Reintroduction + supplementation" may have particularly long time periods if the supplementation part of the intervention followed the initial reintroduction several years later.
Literature searches and screening process The ROSES flow diagram for systematic maps illustrates the screening process (Fig. 1 ). (inclusion/exclusion screening process, dates, screeners, reasons for exclusion, etc. ) (sheet "List of all articles"). The list of articles excluded at full-text stage along with reasons for their exclusion can be found more easily in a specific sheet of the Additional file 5 (sheet "List of excluded articles FT"). From the 690 articles accepted as eligible for the review after the three screening stages, i. 42 publications (65 translocations) were coded as "unclear context" not sufficiently describing the role of the protected area in the given translocation event despite these articles meeting our PICO during the screening process. These articles were consequently not included in the systematic database but in a specific Additional file 6 (sheet "Unclear contexts") and described briefly in "Unclear cases" section in Review findings.
ii. 58 linked translocations (35 publications) were detected and eliminated from both parts of the narrative synthesis (see Additional file 7, sheet "Linked data"). iii. Concerning the Bibliometric synthesis [Additional file 7-sheet "4. Full database (  613 )"]: 613 publications in all were coded for the following bibliometric variables: document type, bibliographic content, and programme motivation). Except programme motivation was not coded for 90 reviews (cf. "Data mapping method"). iv. Concerning the "Translocation synthesis": 841 translocations (i. e. , intentional movements of any organism from one site to another) were fully coded in respect to all descriptors [see Additional file 7, sheet "Translocation database (498)"]. These translocation events were extracted from 498 different publications (having not extracted translocation events from 115 publications: 90 review articles and 25 "metapopulation management" articles).
Implications for policy/management Given the map focused on translocations related to protected areas, the results are addressed primarily to their managers. The map database provides an estimate of the 12. The number of translocations and their "direction" of transfer i. e. protected area context by taxonomic kingdom. "To": transfer from a non-protected area (PA) to a PA; "From-To": transfer from a PA to another; "Within same": transfer occurring within the same PA perimeter; "From": transfer from a PA to outside of PA. N. B. , only known direction of movement is presented in the current figure   358 ) where sufficient information was available in the publications to collate geographic coordinates either directly by authors or with detailed enough information given on capture and release sites to be able to retrieve coordinates manually extent of conservation translocations carried out worldwide, in which protected areas are involved, concerning all biodiversity (fauna, flora, fungi).
In this respect, it should enable protected area managers to better perceive their role, in the global network of protected areas, both as suppliers or recipients of translocated organisms. Also, it could aid managers and practitioners in making their own choices by comparing previous experiences, regarding both the species concerned and the precise translocation modalities (number of individuals, etc. ) (cf. Fig. 15  for a 'walkthrough' on how to navigate the database). Thus, at a time where reserve managers may be increasingly questioning whether to take such 'risk-based' actions, a detailed view of such interventions is clearly needed. As a consequence, the systematic map database provides valuable information to support decision making for future translocations. It may also be of interest to institutions and NGOs alike (i.
'Web of science Core Collection' (WOSCC) accessible on the Web of Science platform (Clarivate), using the right access from the MNHN (French National Museum of Natural History). The search covered the entire database (i. e. all the citations indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI and CCREX-PANDED). It returned 2322 citations. • Scopus (Elsevier), using the access from the CNRS (National Centre of Scientific Research). It returned 2563 citations.
• 'Web of science Core Collection' (WOSCC) accessible on the Web of Science platform (Clarivate), using the right access from the MNHN (French National Museum of Natural History). The search covered the entire database (i. e. all the citations indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI and CCREX-PANDED). It returned 2322 citations. • Scopus (Elsevier), using the access from the CNRS (National Centre of Scientific Research). It returned 2563 citations.
Citation Scope of review Nature of synthesis Search databases No. of other literature sources Publication data range of included articles No. of included publications 498 124 69 41 145 1969 to 2020 1979 to 1998 1995 to 2016 1981 to 2018 1986 to 2017 2 databases: WOS, SCOPUS 12 (+2 grey literature calls) 0 databases A search for articles across 12 major journals only 1 database: WOS 0 0 databases (Search in 0 google scholar only) 2 databases: WOS, SCOPUS 1 Systematic map Map-like Map-like Meta-analysis Map-like P: all biodiversity worldwide I: reintroductions, introductions, Sup- plementations C: protected areas P: animals worldwide I: reintroductions, supplementations, introductions P: key-stone species I: reintroductions only P: all biodiversity a I: translocations a C: antipredator training, soft release, or environmental Enrichment P: animals I: introduction, reintroduction, translocations a Our map (translocation synthesis) Fischer J, Lindenmayer DB.
Hydrolysis rates are described by first-order kinetics. Substrate utilization rates are described by the Monod kinetics. is the stoichiometry matrix containing the yield factors ( , ) of each metabolite () for each reaction (), (•) is a vector function with the equations representing transport phenomena (liquid-gas transfer), and  is the vector of the model parameters. The original model has 18 state variables (compartments in Figure. 1) and was implemented in Matlab (the code is accessible at https://doi. org/10. 5281/zenodo. 4047640). An implementation in R software is also available  (Kettle et al. , 2018). In the present work, we incorporated an additional state variable to represent the dynamics of bromoform concentration. The original model was extended to account for the impact of A.
Dynamic prediction of rumen fermentation The extended model developed in the present work to account for the impact of A. taxiformis on the rumen fermentation is freely available at https://doi. org/10. 5281/zenodo. 4090332 with all the detailed information of the model and the experimental data used for model calibration. An open source version in the Scilab software (https://www. scilab. org/) was made available to facilitate reproductibility since Scilab files can be opened with a text editor. The software R (https://www. r-project. org/) was used to plot the figures. Figure  2  shows the dynamic data of fermentation variables for the levels of A. taxiformis at 0. 06% and 0. 5% compared against the model predicted variables. Figure  3  displays the comparison of all observations against model predictions.
Acknowledgements Authors thank  Henk van Lingen  ( Wageningen University, The Netherlands ),  Alberto Atzori  ( University of Sassari, Italy ) and two anonymous reviewers appointed by  Luis Tedeschi  ( Texas A&M University, USA ) for the evaluation of this manuscript by  PCI Animal Science  (https://animsci. peercommunityin. org/). Their reviews have greatly improved this paper. Version 4 of this preprint has been peer-reviewed and recommended by  Peer Community In Animal Science  (https://doi. org/10. 24072/pci. animsci. 100006).
However, manual inspection of the GO biological processes reveals three genes associated with the interferon response (HLA-G, MVB12A, TRIM34; Supplementary Data 3). Besides, analyses of a dataset of interferonstimulated genes (ISGs) across different animal species  21  , shows that 58% (22) of the genes from the set of "induced gene & Epromoter" overlapped with a conserved set of 98 ISGs, as compared with 12% (47 genes) of the "induced gene only" set (Fig. 2b ; P-val. = 0. 00001, Chi-Square test), suggesting that induced Epromoters are preferentially associated with a conserved interferon response. Classical type I interferon response requires binding to the ISRE by the regulatory complex formed by phosphorylated STAT1 and STAT2 TFs with the constitutive IRF9 TF also referred to as the ISGF3 complex  16, 22.
Subsequent activation can be also mediated by the inducible IRF1 TF. To assess the binding of these TFs to the three sets of IFNa-response promoters, we analyzed previously generated ChIP-seq data in IFNa-stimulated K562 cells for STAT1, STAT2, and IRF1 (Supplementary Data 4) and generated ChIP-seq for IRF9. Strikingly, all four TFs preferentially bound to the set of "induced gene & Epromoter" as compared to the other two sets. In particular, the set of promoters associated with "induced gene only" displayed binding levels close to the background (Fig. 2c ). Recruitment of these TFs was specific to IFNa-induced Epromoters as no binding was observed in non-induced Epromoters (Supplementary Fig. 2c ). As expected, the majority of induced Epromoters were bound by the ISGF3 complex and were more likely to be associated with a conserved ISGs (Supplementary Fig.
ISG15 encodes for an IFN-induced ubiquitin-like protein and plays a central role in the host antiviral response  23. HES4 and AGRN are also classified as interferon-stimulated genes (ISG) in the Interferome database  24  , while HES4, encoding for a bHLH (basic helix loop helix) TF, have been suggested to play a role in the IFN response  25, 26. As shown in Fig. 4a , the induced ISG15 Epromoter, recruits the TFs STAT1, STAT2, IRF1, and IRF9 in IFNa-stimulated K562 cells, while the other induced genes located in the same cluster, AGRN, and HES4, do not. The three genes are located in the same TAD and no other region, apart from the ISG15 Epromoter, was found to bind the TFs within the TAD (Supplementary Fig. 6 , upper panel). This suggested that the ISG15 Epromoter might harbor the necessary TFs to induce the transcription of all three genes in an Epromoter-dependent manner.
h Piecharts showing the percentage of promoters from the indicated datasets that contain either none (gray), one (blue) or two or more ISRE-binding sites (red) (merged sites for IRF1-9 and STA1-2 motifs) based on the Jaspar 2020 annotation  74  by default (All ISRE sites) or with a P-value < 1e-4 (High confidence). We observed that some of the IFNa-induced clusters contained more than one gene associated with an Epromoter (Fig. 3d ). One example is provided by the IFIT locus, harboring a family of genes encoding for IFN-induced proteins with tetratricopeptide repeats and have broad-spectrum activity against replication, spread, and disease pathogenesis of a range of human viruses  27. The cluster contains four IFNa-induced genes; IFIT3, IFIT1, and IFIT5 have inducible Epromoters that recruit all four TFs, while IFIT2 does not have an Epromoter and only recruit the IRF1 factor (Supplementary Fig.
Whether the clustered genes were found within the same Topological Associated Domain (TAD) (Y), different TADs (N), or outside any TAD (O) is indicated. can be predicted as having an Epromoter-like function. We used this prediction to explore the function of Epromoters in other inflammatory responses, namely the primary immune response of macrophages. For this purpose, we analyzed a dataset including stimulation of mouse primary macrophages by Lipopolysaccharides (LPS)  29. The dataset consisted of RNA-seq as well as ChIP-seq for the IRF1, IRF8, and STAT2 TFs before and after LPS stimulation for 4 h. Briefly, we identified clusters of coinduced genes (for which the TSS are separated by less than 100 kb). Then, determined how many promoters from the cluster recruit a given TF (Fig. 6a ; see Methods).
9b, c ). We observed that both IL15RA and IL2RA genes were induced by LPS stimulation of in vitro differentiated THP-1 macrophages with similar kinetics and reached their maximum induction after 6 h (Fig. 6d ), in line with the co-regulation observed in mouse macrophages. Next, we examined the 3D chromatin organization of the human IL15RA/ IL2RA locus. CTCF binding and Hi-C data in macrophagedifferentiated THP-1 cells  35  revealed that both genes are in the same TAD within a regulatory subdomain flanked by CTCF (Fig. 6e ). Moreover, analysis of Promoter Capture Hi-C interactions  36  centered on the IL15RA promoter showed that the contact frequency (ChICAGO score) with the IL2RAproximal CTCF site increase from monocyte to macrophage differentiation (M0) to reach the highest contact frequency in LPS stimulated macrophages (M1), but not in Il13-stimulated macrophages (M2) (Fig.
6 ). Experimental validation by CRISPR/Cas9 genome editing at three independent clusters demonstrated that the Epromoters were required for accurate induction of the ISGs within the same cluster. Based on the binding of the ISGF3 complex, we suggest that ISGs within clusters are more likely to be regulated by Epromoters while ISGs outside clusters are more likely to be regulated by typical enhancers. Indeed, we found 21 clusters included at least one Epromoter as defined by the STARR-seq. However, the number of genes regulated by Epromoters might be underestimated by several reasons. On the one hand, we have used a stringent criterion of clustering based on a maximum of 100 kb between the TSS of induced genes. Indeed, analyses of TADs identify three additional clusters containing either induced or constitutive Epromoters (DHX58, TMEM140 and SIGLEC14; Supplemental Dataset 5) that were missed by the 100 kb clustering.
We transfected 50 millions of K562 cells with 1. 25 mg of the CapSTARR-seq promoter library using the Neon Transfection System (Thermo Fisher Scientific; pulse voltage 1450 V, pulse width 10, pulse number 3) and after 18 h we treated the cells with IFNa (50 ng/mL) for 6 h in duplicate. After 24 h, we processed the cells according to the Starr-seq protocol  5, 19. Briefly, transfected (cDNA) and non-transfected (input) libraries were single-end sequenced on an Illumina NextSeq 500 sequencer (Supplementary Data 4). FastQ files were trimmed using sickle with -q 20 option and mapped to the hg19 reference genome using Bowtie2 with default parameters. Sam files were converted using SamTools and bed files were generated with BedTools (v2. 17. 0) "BamToBed" command. Fragment reads were extended to 314 nt, corresponding to the average size of the captured fragments.
Results are summarized in Supplementary Data 1 and 2. RNA-seq. Poly(A) RNA was isolated from three replicates of K562 cells nonstimulated or stimulated with IFNa for 6 h and used for RNA-seq library preparation, following the TruSeq RNA Library Prep Kit v2 (Illumina). Libraries were paired-end sequenced on an Illumina NextSeq 500 sequencer (Supplementary Data 4). Reads were aligned using STAR aligner (v2. 4. 2a) with arguments "out-FilterMismatchNoverLmax" and "outFilterMultimapNmax" set to 0. 08 and 1, respectively. Differential gene expression was performed using DESeq2 (v1. 6. 3) Bioconductor package  65  implemented in the R statistical environment, and transcripts associated with promoters analyzed by CapSTARR-seq were retrieved. Genes with changes in expression with a log 2 fold-change >1 and adjusted P-value <0.