dataesr/scientific-paragraphs-categorization huggingface dataset:
Natural hazards cost data collection and filtering Global and regional cost data for natural hazards are available from EM-DAT, CRED/UCLouvain, Brussels, Belgium international disasters database (www. emdat. be) -the global database on natural and technological disasters, which includes geographical, temporal, human and economic data on the occurrence and effects of more than 21,000 disasters in the world at country level, from 1900 to present. Only natural hazard events which fulfil at least one of the defined criteria are reported. These criteria include: 10 or more people dead; 100 or more people affected (e. g. injured, homeless); country of where the event occurs declares a state of emergency or an international assistance appeal. Data were downloaded on 06 April 2022 [query id x0lcUA], using the following filters: Disaster Classification: Natural; Location: Asia, Africa, Americas, Europe, Oceania; and from 1980 to 2019.
As for InvaCost data, we adapted the TDWG World Geographic Scheme for Recording Plant Distributions Standard  (TDWG, 2001)  to reclassify the EM-DAT field "Continent" (Supplementary Material Data S3). Natural hazard cost data for the USA were obtained via Billion-Dollar Weather and Climate Disasters from NOAA National Centers for Environmental Information (NCEI) (2022). These data were downloaded on 06 April 2022 (Supplementary material data S4). We added the USA as a national case study because of its relative data completeness to see if we obtained similar results when using a national database with different inclusion criteria of events. Weather and climate events reported in the NOAA NCEI dataset are those with direct cost (incl. physical damage to residential, commercial and government/municipal buildings, material assets within a building, time element losses, vehicles, boats, offshore energy platforms, public infrastructure and agricultural assets) equal to or above $1bn USD -which account for more than 80% of the damage from all recorded weather and climate events.
Standardizing cost values to USD 2020 To compare costs across years and between databases, we standardised all cost values to US Dollars (USD) 2020 using the consumer price index (CPI) (2010 = 100) data, which are available from the World Bank Open Data (https://data. worldbank. org/ indicator/FP. CPI. TOTL). These data were downloaded using the 'wbstats' R package (version 1. 0. 4)  (Piburn, 2020)  on December 14, 2022. InvaCost cost values are expressed in USD 2017 -the methodology used to convert raw costs in local currencies for a given year to USD 2017 is described in  Diagne et al. (2020). We used the function adjustInflation() (Turbelin, 2022) to adjust the 2017 USD cost estimate values to 2020 USD values. The function draws the 2020 CPI value (118. 6905016) from the World Bank Open Data file to convert 2017 USD costs to 2020 USD costs using the following formula: Cost(2020USD) = (1 + ((CPI 2020 -CPI 2017 )/CPI 2017 )) × Cost(2017USD) Natural hazard costs from the EM-DAT database are expressed in thousands of USD (i) in the value of the year of occurrence and (ii) adjusted for inflation to the current USD value -at the time of our Damage costs of natural disasters in the world, the USA, and biological invasions are from the International Disaster Database EM-DAT (www.
emdat. be), the National Centers for Environmental Information National Oceanic and Atmospheric Administration (NOAA NCEI; www. ncdc. noaa. gov), and InvaCost 4. 1 (www. invacost. fr), respectively. Costs from emerging diseases are not included in biological invasions. Costs in EM-DAT are values of all damages and economic losses directly or indirectly related to the disaster; only natural hazard events which fulfil at least one of the defined criteria are reported (e. g. 10 or more people dead; 100 or more people affected). Weather and climate events reported in the NOAA NCEI dataset are those with direct cost (incl. physical damage to residential, commercial and government/municipal buildings, material assets within a building, time element losses, vehicles, boats, offshore energy platforms, public infrastructure and agricultural assets) equal to or above $1bn USD.
Analyses All analyses were conducted in R and figures were formatted in Adobe Illustrator. The R script and data are available in Supplementary Materials and/or on GitHub (Turbelin, 2022). As the aim of our study is to compare the magnitude of global economic losses from biological invasions to that of natural hazards, we calculated the overall costs by summing all cost estimates provided in the expanded dataset -see Supplementary Material Data S1. The total costs for natural hazards were obtained by summing costs in USD 2020 of all recorded events separately for the global and USA datasets. The rate of change (ROC) between periods 1980-1999 and 2000-2019 was calculated using the following equation: ROC = ((Cost 2000-2019 /Cost 1980-1999 ) -1) × 100.
Consequently, economic costs of biological invasions are also severely underestimated. Despite a likely cost underestimation, both global databases -InvaCost and EM-DAT -can be considered a baseline for global and regional costs of biological invasions and natural hazards, respectively, due to their robust systematic approach in collecting cost information and their 'living' nature, whereby the data are continuously reviewed. Nonetheless, costs should be considered as minimum estimates, and seen in terms of order of magnitude rather than as precise values. Biological invasions are known to be a major driver of biodiversity loss and ecosystem service disruption (IPBES, 2019). Importantly, biological invasions can interact with multiple natural hazards to exacerbate impacts. Indeed, biological invasions can worsen flood, wildfire and drought risks by modifying ecosystem structure and function [e.
Funding This work was supported by the  AXA Research Fund Chair of Invasion Biology of University Paris Saclay  ( AJT ),  BiodivERsA Belmont-Forum  call  2018 on biodiversity scenarios (FC , FE),  French National Research Agency  [ ANR-14-CE02-0021 ] (FC),  BNP-Paribas Foundation Climate Initiative (FC) ,  Leverhulme Trust  [ ECF-2021-001 ] (RNC), and the  Natural Sciences and Engineering Research Council of Canada (AR).
Study population We prospectively enrolled 100 participants referred for a FDG-PET/CT to our university hospital between August 2018 and November 2020 for any indication, except treated oncologic disease, as part of the PACTEP study (Central Arterial Pressure and Positron Emission Tomography; Clinicaltrials. gov site number: NCT03345290)  (Zimmermann et al. 2022). Participants included 25 young adults (18-to-40 years), 25 middle-aged adults (41-to-60 years), and 50 older adults (> 60 years) with the latter group providing a larger subgroup of individuals with increased arterial stiffness and BP. Baseline PWV and brachial BP were measured on the day of the FDG-PET/CT. A baseline PWV was not determined for atrial fibrillation patients.
All together the data substantiates indexed aorta volume, measured in the proximal and more elastic half of the thoracic aorta, as a strong indicator of arterial aging throughout adulthood. We also found aorta calcification of the proximal half of the thoracic aorta to be highly correlated with age and hemodynamic parameters. The significant increase in calcification volume was however only apparent in the older age-group and not in middle-aged participants, contrary to what was observed for aorta volume (Fig. 1 ). The low rates of diabetes in our young and middle-aged groups (only one patient) could also partly explain this last observation, with diabetes being the most potent risk factor for early arteriosclerotic calcification  (Tsai et al. 2019). Results provided by PET with 18 F-sodium fluoride could also be of interest in this setting, given that this tracer provides detection of microcalcifications, which are yet invisible on CT  (Reijrink et al.
Subjects This study included all surviving infants born between April 2008 and January 2013 at a gestational age of less than 33 weeks who were enrolled in the LIFT cohort. Written consent was obtained from the parents of each child before inclusion. The LIFT network includes 24 maternity clinics and three hospitals with neonatal intensive care units (Nantes, Angers, Le Mans), and follows up with vulnerable children up to the age of 7. The aim of this network is to detect early clinical problems and to provide patient-specific care. Standard follow-ups are carried out at 3, 9, 12, 18 and 24 months and at 3, 4, 5 and 7 years. Written consent was obtained for each patient before inclusion in the study and the cohort was registered with the French Data Protection Authority for clinical research (CNIL, No.
The score for each dimension is obtained by adding the scores of the 5 questions exploring the domain, with 10 corresponding to the highest stage of the dimension. By combining the scores for each of the difficulties, a total difficulty score is calculated, scored from 0 to 40, and includes conduct, emotional, peer relationship, and hyperactivity disorders. Thresholds for each score were set so that all children scoring above the 90th percentile were considered "abnormal", all those between the 80th and 90th percentile as "borderline", and all others "normal". Perinatal and socioeconomic data from the LIFT cohort registry were collected for the children included in the study. These data included gestational age, sex, birth weight, the existence of bronchopulmonary dysplasia, the presence of brain lesions (occurrence of an IVH of grade 3 or 4, or periventricular leukomalacia identified with ultrasound monitoring during neonatal hospitalization), the insured status of the Universal Medical Insurance, the relationship status of the parents (couple or not) and the highest professional level of the mother.
Statistical Analysis Results are reported as means and standard deviations for continuous variables and as numbers of subjects and percentages for categorical variables. The significance level was determined by p < 0. 05 for all analyses. An SDQ was "abnormal" if the total difficulty score was between 17 and 40, "borderline" between 14 and 16, and normal between 0 and 13. A GSA score under 48 is associated with a risk of school-based learning difficulties. To investigate the role of neonatal and socioeconomic data on the occurrence of each disorder, we performed a multiple regression. Statistical analyses were performed using SPSS software.
Somatic mutational signatures The mutational somatic signature from single base substitution (SBS) was recovered from COSMIC public database (v 3. 2)  16  with XP-V tumor data, using the package MutationalPatterns  17. Figure  4a  displays the 10% most represented mutational signatures obtained, where the seven first tumor samples presented a highly homogenous signature attribution, with signatures SBS7A and SBS7B (UV exposure) responsible for more than 30% of all signatures found for those samples. In addition, SBS18 (damage by ROS) also appears in these seven tumor samples. However, the other four samples, XP11GO, XP45GO, XP85GO_1, and XP85GO_2, had much lower contributions of these signatures. Interestingly, these samples had significant contributions of SBS29 (tobacco chewing) and SBS24 (aflatoxin exposure), specially XP11GO, XP85GO_1 and XP85GO_2, probably because of the significant levels of C>A mutations.
Three clusters were obtained from the Gene Ontology database  35  (GO): GOTERM_BP_DIRECT, GOTERM_MF_DIRECT, GOTERM_CC_DIRECT, which stands for Biological Process, Molecular Function, and Cellular Component, respectively. Among the most significant biological processes detected in the mutated gene list (Supplementary Table  14 ), extracellular matrix organization was the most significant, with 28 genes (Supplementary Table  15 ), and collagen fibril organization involved a total of 14 genes (Supplementary Table  16 ). The main molecular functions in the mutated gene list are presented in Supplementary Table  17 : extracellular matrix components and calcium ion binding were the most significant. The 22 genes of the extracellular matrix structural constituent playing a role are listed in Supplementary Table  18 ; the calcium ion binding molecular function with most genes participating (44 genes) (Supplementary Table  19 ).
Although these genes are not included in the COSMIC Cancer Gene Census  32  , the results obtained in this work reinforce the possibility that they are not merely passenger genes in skin tumors and may represent novel driver genes. Moreover, 289 genes carrying mutations were classified as cancer genes in the COSMIC Cancer Gene Census 32. Interestingly, approximately similar frequencies of these genes are identified as oncogenes or tumor suppressor genes, and only a few (14%) are described in skin tumors from DNA repair proficient patients (including melanoma). The mutation burden in XP-V tumors is commonly high, so their involvement in tumor formation needs confirmation. The main pathways affected by mutations were identified considering the 301 genes containing five or more mutations. Curiously, extracellular matrix-related pathways were found in most functions detected in KEGG and GO databases.
Calling single nucleotide variations and small Insertions and deletions The FASTQ file for each sample was mapped against the human reference genome (GRCh37) using the BWA-MEM aligner, (version 0. 7. 17, default parameters)  60. Potential PCR duplicated reads were removed using Picard (version 2. 9. patient, variants shared between their germline (saliva) and tumor samples were excluded from the set of somatic mutations. Additionally, variants with minor allele frequency (MAF) < 0. 001 in genome aggregation database (gnomAD  63  ) and in the Brazilian genomic variants (ABraOM 64 ) database was used to classify variants as rare.
sideRETRO also uses a specific set of alignments (discordant alignments and supplementary alignments) which are clustered and processed using an unsupervised machine learning method. As a result, sideRETRO generates a text format file that contains features related to each retroCNVs (e. g. , their parental gene name, genomic position, strandness, genomic context, and genotyping). The results were obtained using sideRETRO's default parameters. A somatic event was defined as a retroCNVs or MEI occurring uniquely in a tumor. MEIs or retroCNVs shared by two or more individuals or found in public datasets were defined as polymorphic/germinative.
Point mutations, mutational spectra, and mutational signature analyses Unique point mutations, simple motif sequences, and exploratory analyses were obtained using WOLAND (https://github. com/tiagoantonio/woland). The mutational signatures and somatic spectra of the 11 tumors were analyzed using the SomaticSignatures 67 , which explore the SNV frequencies together with the adjacent 3' and 5' bases  68  with a non-negative matrix factorization (NMF) algorithm  69. Mutational mechanisms can be characterized by the frequency of tri-nucleotides motifs, and the frequency of the 96 possible motifs across all samples defines the mutational spectrum  68. The sequence motif was built with probability logo (pLogo) (https://plogo. uconn. edu/) using exome sequence as background and removing duplicate from both foreground and background sequences.
Identification of altered pathways of mutated genes All mutated genes obtained from the 11 tumor samples were annotated and identified. Silent mutations were not considered for these analyses. Genes with five or more mutations for all tumors were selected for function annotation search using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources (2021 Update) (https://david. ncifcrf. gov/)  33. The search focused on Kyoto Encyclopedia of Genes and Genomes pathway database (https://www. genome. jp/kegg/)  34  and Gene Ontology Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) (http://geneontology. org/)  35. The whole list of mutated genes was compared to COSMIC Cancer Gene Census (tier 1 and 2) for cancer genes  32.
Acknowledgements This work was supported by  FAPESP (Sao Paulo Research Foundation, SP, Brazil)  [grants # 2019/19435-3 , # 2013/08028-1  and # 2017/24418-5 ] under the  International Collaboration Research  from  FAPESP  and The  Netherlands Organization for Scientific Research (NWO, The Netherlands) ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brasília , DF,  Brazil , grant # 308868/2018-8 );  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brasília, DF, Brazil , financial code 001). This work was also supported by a grant from  L'Oréal Research & Innovation. We are grateful to the support with  NGS  at the core  Facility for Scientific Research -USP (CEFAP-USP/GENIAL) , and Multi-user genomic Section of the  Human Genome & Stem Cell Research Center (HUG-CELL).
2; default parameters; http://broadinstitute. github. io/picard). Patient ID Gender (F/M) Smoker (Y/N) POLH mutation Sample ID Sample Type Sample Origin HPE Age at surgery (y) XP03GO. br F N intron6/ exon8 (het. ) nT-XP03GO nonTumor Saliva T-XP03GO Tumor Neck -BCC 35 37 nT-XP06GO nonTumor Saliva - 12 T-XP06GO_1 Tumor Nose BCC 13 XP06GO. br M N intron 6 (hom. ) T-XP06GO_2 Tumor Nape BCC 13 T-XP06GO_3 Tumor Nose BCC 15 T-XP06GO_4 Tumor Jaw BCC 15 XP11GO. br F Y intron 6 (hom. ) nT-XP11GO nonTumor Saliva T-XP11GO Tumor Ankle -SCC 64 66 XP45GO. br F Y intron 6 (hom. ) nT-XP45GO nonTumor Saliva T-XP45GO Tumor Upper Lip BCC - 46 40 nT-XP52GO nonTumor Saliva - 44 XP52GO. br M Y intron 6 (hom. ) T-XP52GO_1 Tumor Arm BCC 46 T-XP52GO_2 Tumor Arm BCC 48 nT-XP85GO nonTumor Saliva - 77 XP85GO. br* M Y intron 6 (hom.
Our study provides the proof-of-concept that iodine labeling enabled to monitor the hydrogel biodistribution in vivo in rodent brains and knees up to 3 days post-administration of the dual-labeled repair kit. These organs were chosen because neurological and osteoarticular diseases are considered prime applications for stem cells-based therapies. The volume of hydrogel could be estimated thanks to the 3D reconstructions of CT scans. Interestingly, the in vivo fate of gel and cells differed according to the target tissue. Taken together, our data indicate that the iodine-labeled HA hydrogel that we have designed presents stable iodine labeling as well as excellent properties for cell encapsulation and injection. In addition, our gel is suitable for imaging with translational CT, from bench-top micro-CT to cutting-edge spectral CT.
4) under stirring at 95 °C for 10 min. The agarose solution was then poured in an Eppendorf ® tube and the sample was kept at 4 °C for 24 h before the injection tests. The latter were carried out with a TJ-1A syringe pump controller (Aniphy, USA), at a rate of 5 µL/min). Rheology. Dynamic rheological experiments were performed using a strain-controlled rheometer (ARES-RFS from TA Instruments) equipped with two parallel plates. All the dynamic rheological data were checked as a function of strain amplitude to ensure that the measurements were performed in the linear viscoelastic region. The parallel plate on which samples were placed has a diameter of 25 mm. The distance between the plates was 0. 25 mm. A thin layer of low-viscosity silicone oil (50 mPa. s) was applied on the exposed surface of the samples, to prevent water evaporation.
Live/Dead assay -image acquisition. A staining Live/Dead solution was made according to supplier information, by adding 10 μL of the Calcein-AM solution (green, excitation/emission 490/515 nm) and 5 μL of the propidium iodide solution (red, excitation/emission 535/617 nm) in 5 mL DPBS 1X. After 3 or 7 days of cells incubated in the hydrogels, the cell media was removed and the staining solution was then added to the gels with incubation for 15 minutes at 37 °C. Hydrogels containing hASCs were then analyzed under an epifluorescence microscope (ZEISS Axiovert 200M) equipped with a CoolSnap HQ 2 camera using MetaMorph 3. 5 imaging software. Images of cells encapsulated in 3D in the hydrogels were acquired using a Nikon 10X objective. 2D images were captured from 10 random fields of view for each sample.
Fluorescent labeled cells in green (live cells) and red (dead cells) were counted using an image analysis software, ImageJ (National Institute of Mental Health, Bethesda, MD, USA; imagej. nih. gox/ij/). Trypan blue assay. Individual cell-embedded hyaluronic acid hydrogels were treated by addition of Dotarem (aqueous solution of gadoteric acid, Gerbet ® ) diluted in DPBS 1X solution at 37 °C-5 % CO2 for 30 minutes to induce gel-sol transition and pelleted. hASCs were washed two times with a DPBS 1X solution, and then counted using a Trypan Blue solution 0. 4%. A final cell density in the hydrogels was calculated compared to the initial cell density at day 0. Cell proliferation was estimated by the percentage of live and dead cells of each hydrogel at day 3 or day 7. For each hydrogel, triplicate (n=3) cell countings were individually performed at different times and with different hASC batches.
Acknowledgments This project was funded by the  French national research agency  ( ANR18-CE19-003 ,  Breakthru grant ), and partly funded by the  GlycoAlps IDEX UGA  in the framework of the  Investissements d'avenir program  [ ANR-15-IDEX-02 ]). ANR also supported the national infrastructure: " ECELLFRANCE: Development of a national adult mesenchymal stem cell based therapy platform"  ( ANR-11-INSB-005 ). We thank  Caroline Bouillot  and  Salim Si-Mohamed  for performing respectively µCT and SPCCT experiments at Lyon's multimodal imaging platform Cermep. The authors thank the  NMR platform of ICMG (FR2607)  for its support; the  European Synchrotron Radiation Facility  ( ESRF, beamline ID17 ) for allocation of beamtime (MD1333) and their local contact  Herwig Requardt  for help during the experiments.
Acknowledgements. We acknowledge financial support through grants  PRIN-MIUR 2017WSCC32 ,  2020SKSTHZ  and the  INAF GO  Grant  2022  "The revolution is around the corner: JWST will probe globular cluster precursors and  Population III stellar clusters at cosmic dawn" (PI Vanzella ). FC and RP acknowledge funding from  PRIN INAF 1. 05. 01. 85. 01.
The statistical test package in the phyloP program computes divergence from neutrality at the nucleotide level and outputs positive values for constrained sites and negative values for sites under accelerate substitution rates  69. Here we worked with the most recent phyloP scores calculated from multiple alignments of 241 mammalian species including 42 primates and 16 cercopithecidae 2. We used either the human (Hg38) or the rhesus macaque (rheMac10) as reference genomes. According to this analysis, the HERVK CTCF motifs have neutral phyloP scores (Fig. 6A ). The Enh5 cRE is also evolving neutrally in both humans and macaques (Fig. 6B ). In contrast, the JPX TSS show distinct evolutionary behaviours in humans and macaques: the human TSS displays a neutral phyloP score while, in macaques, the negative phyloP score marks accelerated substitution rates and indicates directional selection potentially leading to the emergence of a new function (Fig.
Acknowledgements We thank lab members for critical evaluation of the work leading to this publication. We thank  Gregory Andrews  from  UMass Chan Medical School  for sharing the annotation of human constraint CTCF sites. We thank  Dr. Magali Hennion  for bioinformatics support provided at the  Bioinformatics and Biostatistics Core Facility (BiBs), Paris Epigenetics and Cell Fate Center. We thank the (EPI) 2 Imaging platform -UMR7216 Epigenetic and Cell Fate Centre -for access to instruments and technical advices. We thank the Functional Epigenomics (EpiG) platform hosted at the UMR7216 Epigenetic and Cell Fate, for technical advices and access to instruments. We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of the  France BioImaging infrastructure  ( ANR-10-INBS-04 ) and  GIS-IBiSA , in particular, we thank the Imagoseine cytometry platform and especially  Nicolas Valentin  for technical assistance and advices.