dataesr/scientific-paragraphs-categorization huggingface dataset:
e. surveillance, prevention, management, eradication), (iii) and "Mixed" including mixed cost of damage and control (cases where the reported costs were not clearly distinguishable); (v) Impacted_sector: the activity, societal or market sector that was affected by the cost. Seven sectors are described in the database: agriculture, authorities-stakeholders (official structures allocating efforts to manage biological invasions), environment, fishery, forestry, health, and public and social welfare  (Diagne et al. , 2020a ; see Supplementary Material 1).
Comparison with other taxonomic groups In order to put the costs of invasive alien fish species in a broader taxonomic perspective, we compared the economic costs of invasive alien fish with other invasive vertebrates: birds and mammals. The comparison was based on the total cost and the number of documents reporting costs in the InvaCost database, coupled with the number of invasive alien species per taxon, and the numbers of scientific publications in the field of invasion science. First, total monetary costs and number of entries for birds and mammals were calculated following the same methods and database version as for fishes (as detailed above). Secondly, we estimated the number of publications available for each group using the same search protocol as for the InvaCost database (see  Diagne et al.
, 2020a) , excluding words referring to costs and adding the name of the biotic group (i. e. "fish", "mammal", or "bird"), in order to obtain a comparative approximation of the research effort in invasion ecology for these three taxa. The exact search strings used can be found in Supplementary Material 3. The information considered in this comparison was collected using the Web of Science Core collection. Thirdly, the numbers of alien species for each of the three taxonomic groups mentioned above was estimated using the IUCN Red List database (https://www. iucnredlist. org/). We classified a species as alien according to the IUCN legends of the countries where they occur. If a species is considered as introduced in at least one country, then we consider this species as alien. Finally, we used Pearson's Chi-squared test of independence to assess whether the data for the three taxonomic groups had the same distribution of values (number of alien species, number of cost entries, number of studies reporting invasion costs, and total costs).
Results A total of 384 annualized cost entries for 27 invasive alien species belonging to 18 fish families were available in the database, totalling US$37. 08 billion. The majority of costs was deemed as potential (US$34. 79 billion; n = 88, hereafter the number of cost entries), while observed costs amounted to only US$2. 28 billion (n = 296). Furthermore, the majority of costs (US$25. 31 billion; n = 295) were considered of high reliability, while US$11. 77 billion (n = 89) were considered of low reliability (Supplementary Material 4).
Comparisons across biotic groups Records for alien fishes from the IUCN Red List database (n = 147, hereafter the number of species) were 30% fewer than recorded alien birds (n = 210) and 39% more than recorded alien mammals (n = 106). Conversely, fishes were the taxonomic group with the highest number of scientific publications on alien species (17,864 papers), about twice the number of publications on birds (8,759) and four times the number on mammals (4,880) (Figure  5 ). Nevertheless, invasive alien fish species had the lowest number of unique references reporting costs in the InvaCost database (55) compared to mammals (378) and birds (64). In turn, the total cost of invasive alien fish species (US$37. 08 billion) was much lower than that of mammals (US$ 424. 56 billion), but higher than that of birds (US$7.
, 2019) , the rabbitfish Siganus rivulatus and S. luridus, the pufferfish L. sceleratus in the Mediterranean  (Kalogirou et al. , 2013; Giakoumi, 2014)  and the lionfish P. miles  (Moonsammy et al. , 2011). We think that the low number of entries in the database for marine fish, and for fishes in general, reflect limited knowledge of the costs being incurred, rather than their absence.
Conservative nature of reported costs Considering the biases described above, the cost estimates presented here are likely to be very conservative, as cost data are scarce for most invasive alien fish species and for most regions of the world (see also  Diagne et al. , 2021  for an overview of the reasons for cost underestimation). A limited understanding of the costs of invasive alien fish is likely to hamper effective communication, investments in detection, control, prevention and management, and relegate them to the bottom of the priority list of policy makers and/or resource managers facing budgetary constraints. This is despite the fact that much of the funding used to manage invasive alien fish in North America comes directly from angling licence sales and taxes on fishing gear and boat fuel, and was therefore not reported or tracked in InvaCost.
This is an important driver of limitations inherent in the InvaCost database. Firstly, the monetary costs recorded in InvaCost were largely based on a systematic use of research terms  (Diagne et al. , 2020a) , however, different studies and parties use different terminology to describe invasive alien species. As a result, costs may have been missed in these searches given the pervasive differences in keywords across cost reporting documents. Another similar reason is the fact that some source documents may use the vernacular names that were not considered in the search strings. Additionally, despite the effort to include literature in multiple languages (15 additional non-English languages in InvaCost searches, see  Angulo et al. , 2021) , it has not been possible to cover all languages that may be reporting costs for invasive alien fish globally.
It is important to stress that many of these aforementioned limitations likely make our results substantial underestimates. Considering that InvaCost is a living database meant to be updated on an ongoing basis by authors and future users  (Diagne et al. , 2020a) , we expect that these limitations can be alleviated in the future, yielding improved and more realistic estimates of costs for invasive alien fish and other species. Finally, we note that invasive alien fish species are also known to have economic benefits (especially when they have commercial value) as well as aesthetic and spiritual values  (Gozlan, 2010) , which requires a better understanding of the trade-offs and incentives to introduce new species and/or maintain a long-term sustainable stock of their invasive population. Considering the benefits of invasive alien fish and understanding these trade-offs was beyond the scope of both the InvaCost database and this paper.
Acknowledgement and funding: The authors acknowledge the very helpful input from the anonymous reviewers. Further, the authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  Invacost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. RNC is funded by a  Research Fellowship  from the  Alexander von Humboldt Foundation. CD is funded by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ).
3b ). We then asked whether chromatin accessibility at GAF mitotically retained regions required the presence of GAF. For this, we used ATAC-seq data performed on embryos where GAF levels were significantly reduced  28. From this dataset, we retrieved GAF-bound loci for which accessibility was shown to be dependent on GAF. We found that the vast majority of these GAF-dependent regions (96%) correspond to GAF targets that we identified as mitotically retained (Supplementary Fig. 3d ). Interestingly, targets depending on GAF for their accessibility mostly coincide with TSS and enhancer regions but do not overlap TAD boundaries  51  (Supplementary Fig. 3c ). Importantly, interphase GAF targets or Zelda-only bound targets (not bound by GAF) did not show such a dependency on GAF for their accessibility (Fig.
83 µm) and β a discrete function of K tabulated in ref. 83. F exc (t) is defined as in ref. 80  , slightly modified with respect to the Gaussian illumination, leading to the following equation: F exc t ð Þ ¼ F 1 À 1 À e ÀK K À F 1 e Àk off t ð7Þ with K defined as previously. FRAP curve fitting was done with MatLab 2014b (Mathworks Inc. USA). Fluorescence correlation spectroscopy. Fluorescence correlation spectroscopy (FCS) experiments were performed on a Zeiss LSM780 microscope using a ×40/1. 2 water objective. GFP was excited using the 488 nm line of an Argon laser with a pinhole of 1 airy unit. Intensity fluctuation measured for 10 s were acquired and   84. Multiple measurements per nucleus in multiple nuclei and embryos at 20 °C were used to generate multiple ACF, used to extract parameters.
Sequencing was performed on Illumina HiSeq 4000 on pair-end 75 bp. ChIP-seq analysis. Both reads from ChIP-seq and Input experiments were trimmed for quality using a threshold of 20 and filtered for adapters using Cutadapt (v1. 16). Reads shorter than 30 bp after trimming were removed. Reads were mapped to Drosophila melanogaster genome (dm6 release) using Bowtie2  86. Aligned sequences were processed with the R package PASHA to generate the used wiggle files  87. Pasha elongates in silico the aligned reads using the DNA fragment size estimated from paired reads. Then, the resulting elongated reads were used to calculate the coverage score at each nucleotide in the genome. Wiggle files representing the average enrichment score every 50 bp were generated. In order to normalize the enrichment scores to reads per million, we rescaled the wiggle files using PASHA package.
Gap). The three parameters were then used with an in-house script that realizes peak calling by using the algorithm employed by Thresholding function of IGB. Peaks calling was done with a threshold of 100 for GAF-ChIP-seq and 22 for H4K8ac-ChIP-seq, a minimum run of 50 bp and maximum gap of 200 bp. Interphase-only peaks correspond to peaks from interphase ChIP-seq with no overlap with peaks from mitotic ChIP-seq. Mitotically retained correspond to interphase peaks with an overlap (min 1 base pair) with peaks from mitotic ChIPseq. Mitotic-only peaks correspond to peaks from mitotic ChIP-seq with no overlap with peaks from interphase ChIP-seq. Motif search was done with the MEME ChIP tool (MEME suite 5. 1. 1). Peaks were considered as promoters if overlap with the region defined by 100 bp around TSS.
Peaks were considered as enhancers if overlapping with identified enhancer  53  and/or overlapping with a H3K27ac peak  59. ATAC-seq data are from ref. 54  (GSE83851). Wig files were converted to BigWig using Wig/BedGraph-to-bigWig converter (Galaxy Version 1. 1. 1). ATACseq mean signal was then plotted on regions of interest (mitotically retained peak coordinates and Interphase-only coordinates) using computeMatrix by centering ATAC-seq signal to the center of the regions (and +/-1 kb) followed by plotProfile (Galaxy Version 3. 3. 2. 0. 0). All studied features for the mitotically retained GAF peaks are summarized in Supplementary Data 3. Mitotically retained GAF peaks were subdivided by k-means clustering based on chromatin state (H3K27ac and H3K27me3 ChIP-seq  56  ) and transcriptional status (nc14 RNA-seq  57  ) using deepTools  88.
Peaks were partitioned into three clusters: cluster 1, n = 1073, cluster 2, n = 612 and cluster 3, n = 732. To further characterize mitotically retained clusters we plotted heatmaps using deepTools  88  for publicly available ChIP-seq data for H3K27ac 56 , H3K27me3 56 , Pc  59  , and ATAC-seq  54. GAF-bound loci for which accessibility is dependent on GAF were taken from ref. 28. All whole-genome data and stages are listed in Supplementary Table  1. All ChIP-seq data and bed files are accessible at GEO Accession viewer under the number: GSE180812. Live imaging. Movies of His2Av-mRFP; sfGFP-GAF_CRISPR (related to Supplementary Movies 1 and 2 and Fig. 1b ) were acquired using a Zeiss LSM880 with a confocal microscope in fast Airyscan mode with a Plan-Apochromat ×40/1. 3 oil objective lens. GFP and mRFP were excited using a 488 nm and 561 nm laser, respectively, with the following settings: 256 × 256-pixel images, 15 z-planes 1 μm apart, and zoom ×4, resulting in a time resolution of 9.
5 s per Z-stack. Average intensity profiles were measured for histones, nucleoplasmic GAF, and cytoplasmic GAF from three movies of embryos transitioning from nc13 into nc14. Maximum intensity projected images were used for automatic tracking using a homemade software as in ref. 31. First, detection of nuclei is made using His2Av-mRFP allowing the monitoring of histone intensity fluctuation, then a mask of His2Av-mRFP detected nuclei was projected on the sfGFP-GAF channel allowing the recovery of sfGFP-GAF present on histones. Finally, five ROI in each movie corresponding to cytoplasmic regions were tracked for sfGFP-GAF intensity in the cytoplasm. The laser power is 12 µW for 488 nm laser and 22 µW for 561 nm laser. Measurements are taken with a ×10 objective. Movies of MCP-eGFP-His2Av-mRFP>snail-primary-enhancer_MS2/+ embryos (related to Supplementary Fig.
Briefly, using a custom-made algorithm developed in Python TM and implemented in the MitoTrack software, nuclei were segmented and tracked in 2D, working on the maximum intensity projected stack. In order to detect spots in 3D, we perform a 3D Laplacian of Gaussian filter on the raw data, frame by frame, and threshold the filtered 3D images with a user-defined value. The threshold value is expressed as µ + thr * sigma, where µ is the average and sigma is the standard deviation of the filtered images: thr is the user-defined value and it is common for all the analyses. The thresholded images are then masked with raw data, allowing to retrieve single nuclei intensities. All spots present during mitosis were removed in the successive cycle such that only de novo appearing MS2 punctae were analyzed.
For intensity analysis (related to Supplementary Figs. 6f  and 7c ) the intensity of detected spots was collected for each frame to study the transcriptional intensity behavior throughout nuclear cycle 14. Transcription site intensity is given by the sum of the intensities in all the selected pixels  89. Nuclei coming from inactive and nuclei coming from active were separated for Supplementary Fig. 6f. The background was measured in each movie using FIJI  90  , by taking six different areas outside a transcriptional spot and calculating their mean. Next, intensity values were divided by the mean background of each movie. The updated version of MitoTrack (with intensities measurements) is available at: https://github. com/ant-trullo/MitoTrack_v4_0. qRT-PCR in RNAi embryos. Total RNA from 0 to 2 h AEL RNAi-white or RNAi-GAF driven by nos-Gal4 and mat-alphaTub-Gal4 embryos was extracted with TRIzol following the manufacturer's instructions.
RNA was DNase-treated. In all, 1 μg of RNA extracted from ~300 embryos per replicate was reverse transcribed using SuperScript IV and random primers. Quantitative PCR analyses were performed with the LightCycler480 SYBR Green I Master system (primers used listed in Supplementary Data 1, targeting both isoforms of GAF). RNA levels were calculated using the RpL32 housekeeping gene as a reference and not bound by GAF according to the GAF-ChIP-seq. Each experiment was performed with biological triplicates and technical triplicates. Western blot analysis. Fifty embryos from RNAi-white or RNAi-GAF driven by nos-Gal4 and mat-alphaTub-Gal4 0-2 h AEL embryos were collected and crushed in 100 μl of NuPAGE™ LDS sample buffer and reducing agent. Samples were heated 10 min at 70 °C, and the volume-equivalent of five embryos was loaded per well on a 4-12% Bis-Tris NuPAGE™ Novex™ gel and ran at 180 V.
For DNA-immunoFISH (with GAF antibody), embryos were proceeded as immunostaining protocol from here. Embryos were stained with DAPI at 0. 5 μg/ml, washed in PBT, and mounted between slide and coverslip. Images were acquired using a Zeiss LSM880 with a confocal microscope in Airyscan mode with a Plan-Apochromat ×63/1. 4 oil objective lens with the following settings: zoom ×3. 0, z-planes 0. 3 μm apart, 1024 × 1024 pixels. Distance measurements. To measure the distances between probes (scyl-chrb and chrb-ctrl, or esg-sna and sna-ctrl), we used custom-made software developed in Python TM. This software is available through this link: https://github. com/anttrullo/DNA_FishAnalyzer. All probes channels were treated with a 3D Laplacian of Gaussian filter (with kernel size 1) and then detected in 3D with manual thresholding on the filtered matrices; for each of the detected spots, the center of mass was determined.
Acknowledgements We are grateful to  G. Cavalli ,  B. Schuttengruber ,  F. Juge ,  F. Bantignes ,  E. Bertrand , and  J. Chubb  for sharing antibodies, probes, advise on DNA FISH and/or insightful discussions. We thank members of the  Lagha lab ,  Cyril Esnault ,  Virginia Pimmett , and  Etienne Schwob  for their critical reading of the manuscript. We acknowledge  M. Dejean  and  M. Goussard  for technical assistance. We acknowledge the  Montpellier Ressources Imagerie facility (France-BioImaging ). M. B. is a recipient of an  FRM fellowship. This work was supported by the  ERC SyncDev starting  grant to M. L. M. L. , J. D. , and C. F. are sponsored by  CNRS. O. R. acknowledges support from the  French National Research Agency  ( ANR-17-CE40-0036 , project  SYMBIONT ).
Data availability All relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. All ChIP-seq data and bed files are accessible at GEO Accession viewer under the number GSE180812. The updated version of MitoTrack is available at https://github. com/ant-trullo/MitoTrack_v4_0. The distance measurement software is available through this link: https://github. com/ant-trullo/DNA_FishAnalyzer.
Next, we computed the overall tumour immune cost (averaging the immune component, p p F Q = max ( ) I α α ∈clone MT MT over all tumour clones). Consistently, the immune fitness cost was lower in recurrent LTS tumours compared with in STS tumours (Fig. 4c ). Furthermore, we considered the immune cost only of clones that are new in recurrent tumours, but not present in primary tumours. Recurrent LTS tumours contained both fewer new neoantigens (1% versus 18%; Fig. 4d ) and new clones with markedly lower immune fitness cost (Fig. 4e ) compared with recurrent STS tumours. These observations again suggest that the LTS recurrent tumours had been subject to immunoediting. Finally, we confirmed these results by analysing TCR sequencing data in the available recurrent tumour samples. We quantified the specificity of T cell clonal expansion using the TCR dissimilarity index  18  (Supplementary Methods and Extended Data Fig.
Acknowledgements This work was supported by  NIH   U01 CA224175  (to V. P. B. ), a  Stand Up to Cancer Convergence Award  (to B. D. G. , V. P. B. and M. Ł. ), a  Damon Runyon Clinical Investigator Award  (to V. P. B. ), and the  Avner Pancreatic Cancer Foundation  (to A. J. and A. G. ). M. Ł. is a  Pew Biomedical Scholar. Services of the Integrated Genomics Core were funded by the  NCI Cancer Center Support Grant  ( P30 CA08748 ),  Cycle for Survival , and the  Marie-Josée and Henry R. Kravis Center for Molecular Oncology.
Data availability All raw sequencing data obtained through the Johns Hopkins Hospital medical donation programme have been previously described  19  and are available at the European Genome-Phenome Archive under accession number EGAS00001004097. All other raw sequencing data are available at the NCBI Sequence Read Archive under accession number PRJNA648923. The ICGC data used in this study are available at the ICGC (https://dcc. icgc. org/repositories) under the identifier PACA-AU. The TCGA data used in this study are from TCGA-PAAD dataset available at the NCI Genomic Data Commons (https://gdc. cancer. gov/). Source data are provided with this paper.
Patients and samples Patient samples and retrospective clinical information were either obtained through the databases of the Heidelberg University Department of Neuropathology and the German Cancer Research Center (DKFZ), or directly collected from the respective collaborating national or international institutions in compliance with local regulations. The sample set included CNS tumors enrolled in the INFORM, PTT2. 0, and MNP2. 0 molecular profiling studies  [50, 67].
Histology and immunohistochemistry Detailed histopathological review was retrospectively performed on a subset of the tumors (n = 15) to investigate their histological and immunohistochemical features. Representative hematoxylin and eosin (H&E)-stained sections and immunohistochemical stains from the 15 tumors were digitally scanned on an Aperio slide scanner to assemble a digital pathology library from which histological and immunohistochemical features were reviewed and annotated using ImageScope software (Leica Biosystems). H&E and immunohistochemical staining was performed at the UCSF Histology Laboratory and the Heidelberg University Department of Neuropathology or received from other participating medical centers. Immunohistochemistry was performed on formalin-fixed, paraffin-embedded tissue sections using the following antibodies: glial fibrillary acidic protein (GFAP, DAKO, polyclonal, 1:3000 dilution, no antigen retrieval), oligodendrocyte transcription factor 2 (OLIG2, Immuno Bio Labs, polyclonal, 1:200 dilution, ER1 antigen retrieval), synaptophysin (Cell Marque, polyclonal, 1:100 dilution, ER2 antigen retrieval), neurofilament (Cell Marque, clone 2F11, undiluted, ER1 antigen retrieval), epithelial membrane antigen (EMA, Leica, clone GP1.
Copy number analysis The Integrative Genomics Viewer (IGV) was used to visualize copy number variants (CNVs) of the respective amplified PLAG-family genes  [46]. Amplifications and deletions were visualized across the entire genome by summary plots. Copy number profiles generated by the conumee R-package were segmented with the circular binary segmentation (CBS) algorithm using the default settings of the conumee package for the 450 k and EPIC array  [23]. Resulting segments for each sample were combined to a cohort file using R version 3. 6. 2 [42] , which was then analyzed with the GISTIC_2. 0 method available within the GenePattern cloud tool (https:// cloud. genep attern. org/)  [37, 43]. Human_Hg19. mat was used as reference gene file. Maxspace was set to 10,000. Amplification/deletion thresholds were set to 0.
ChIP-seq analysis Chromatin immunoprecipitation (ChIP) was performed at Active Motif (Carlsbad, CA, USA) using antibodies against H3K27ac (AM#39133, Active Motif), PLAGL1 (HPA055706, Sigma), and PLAGL2 (SAB3500815, Sigma) according to Active Motif's established ChIP protocol, which includes validation of ChIP reactions via quantitative PCR (qPCR). Twenty pooled CNS tumor samples were used as an Input control. Illumina sequencing libraries were prepared from ChIPs and Input at Active Motif using their standard protocol. Libraries were sequenced via paired-end sequencing with a read length of 100 bp or 75 bp at the DKFZ Genomics and Proteomics Core Facility on the Illumina HiSeq 4000 or NextSeq550, respectively. Paired end reads were aligned to hg38 using bowtie2. Alignment SAM files were converted to BAM files using samtools.