dataesr/scientific-paragraphs-categorization huggingface dataset:
If two flanking genes were similar, pairwise alignment was performed (C-b). And if there was no similarity, the region was not considered a potential GSH (C-c). Step 5) The presence of possible annotated coding or non-coding genes in the predicted GSH locus was evaluated by GDV (Gallus gallus genome assembly bGalGal1. GRCg7b/w) and the UCSC Genome Browser (D). Step 6)  The coordinates of predicted GSH loci were evaluated by Hi-C data to ensure that the insertion site was contained within an individual TAD (E). Step 7)  The expression levels of the genes flanking the potential GSH locus were accessed from Gene Expression Atlas to determine whether these genes are highly-transcribed (F). A sgRNA-binding site in close proximity of the highly-transcribed gene will be selected by a valid online tool (G) THUMPD3, flanking the potential chicken GSH locus, and OVAL and OVALY flanking the a non-GSH locus) was selected and visualized at 5 kb resolution.
Chicken RNAseq data in the Gene Expression Atlas (https:// www. ebi. ac. uk/ gxa/ home) were used to determine the transcript level of the flanking genes (Fig. 1F ) in embryonic and adult stages. In the Gene Expression Atlas, the expression heatmap included four defined cutoff levels: below (0. 5 TPM; transcript per million), low (0. 5 to 10 TPM), medium (11 to 1000 TPM), and high (more than 1000 TPM).
Image Analysis Using ImageJ and Gnuastro Softwares The images captured from each group of heterogeneous cell pools (containing either CMV or ΔCMV promoter) were analyzed by ImageJ Fiji software (version 1. 52p) to calculate the integrity density index. For this purpose, cells were imaged with identical parameters (for images from CMV groups: 10X magnification, 100 µs exposure, 1X analog gain, and for images from ΔCMV groups: 20X magnification, 1 s exposure, 2. 2X analog gain). Also, isogenous cell clones containing ΔCMV promoter were analyzed to calculate the integrity density index. These cells were also imaged with identical parameters (20X magnification, 1 s exposure, 1X analog gain). The integrity density index was further analyzed by GNUastro Linux-based software (version 0. 18) for images taken from isogenous cell lines containing ΔCMV promoter (Additional file 11).
Bioinformatic Analysis for Predication of Genome Safe Harbor Loci in the Chicken Genome A multiomics bioinformatics pipeline was used to predict potential GSH loci in the chicken genome (Gallus gallus domesticus) (Fig. 1 ). Genome data viewer (Fig. 2A ), Hi-C data (Fig. 2B  and Additional file 1), and RNA-seq data (Fig. 2C ) were exploited to predict the potential GSH loci in the chicken genome. Based on two well-known GSH loci, HIPP (so-called H11) and Gt (ROSA) 26Sor (so-called ROSA26) which are validated GSH loci in several organisms including mice, humans, and pigs, we first evaluated the genes surrounding these intergenic loci. HIPP and ROSA26 intergenic loci are surrounded by EIF4ENIF1/DRG1 and THUMPD3/ SETD5 genes, respectively in mouse, human, and pig. Copy number = 10ˆ n -b m , Our survey using the genome data viewer of NCBI revealed that the EIF4ENIF1/DRG1 genomic arrangement in the chicken genome (Fig.
e. , THUMPD3/SETD5/SRGAP3) have been relatively the same as the genes seen around the predicted cROSA locus. A-e The schematic presentation of the non-GSH cOVA locus in the chicken genome. B The coordinates of DRG1/EIF4ENIF1, THUMPD3/SRGAP3, and OVAL genes relative to the location of TADs, extracted from the chicken Hi-C data, and visualized by JUICEBOX online software (adopted from ref  [48] ). C The expression levels of the flanking genes in several tissues and developmental stages, adopted from the Gene Expression Atlas. ⇨: (adopted from ref  [49] ). ➲: (adopted from ref  [50] ). TPM avg. : transcripts per million averages. E: embryonic day. PN: post natal day  At the end of MTH2, correctly-knocked-in heterogenous cell pools for each locus/replicate were evaluated by flow cytometry to estimate the percentage of EGFPpositive cells.
5A-j ) (analyzed by imageJ and GNUastro softwares). However, the comparison of this index between cROSA and cOVA clones showed no significant differences, judged by GNUastro software (Fig. 5A-k ). qPCR results showed that the copy number of EGFP transcripts Fig. 4  Transgene Expression from the weak heterologous promoter is principally locus-dependent. CRISPR-mediated integration of ∆CMV-driven EGFP and promoter-less DsRed2 in chicken predicted GSH loci and non-GSH locus was performed in DF1 cell lines. A Schematic depiction of ∆CMV-EGFP-expressing heterogeneous cell pools at the end of MTH2; B Schematic illustration of CMV and ∆CMV promoter as well as negatively-and positively-regulated transcription factor response elements (TFREs). C-a, D-a, E-a) CRISPR-mediated integration of DsRed2-∆CMV-EGFP in cROSA, cHIPP, and cOVA loci verified by 5'/3' junction PCR, restriction enzyme digestion of the amplicons, and Sanger sequencing; C-b, D-b, E-b) flowcytometry results from EGFP-expressing cROSA, cHIPP, and cOVA cells have been achieved in three individual experiments (each in triplicates).
The integrated density (ID) index was compared using the ImageJ (A-j, B-j) and GNUastro (A-k, B-k) software. The copy number of EGFP transcripts (A-l, B-l), and the expression levels of EGFP (A-m, B-m) were determined in the main experimental groups (i. e. , cROSA and cHIPP) versus the control group (cOVA) at the end of MTH4 and MTH6. ns: non-significant, ***: p < 0. 005, and ****: p < 0. 0001 are statistically significant. Avg. : The average expression of EGFP. N: number. Integrated density by imageJ (ID by Im. J). Integrated density by GNUastro (ID by Gnu). Copy Number (CN) by qPCR. Expression by Western Blotting (Exp. by WB) engineered cells were achieved by either antibiotic selection or limit diluting method, there was no need to apply highly-active gRNA for increasing knock-in efficiency.
30  The use of computer-controlled coils would be convenient to apply a constant-adiabaticity field inversion profile, therefore minimizing the inversion time and maximizing the transfer efficiency. Hyperpolarization [1-13 C]pyruvate by 1 H→ 13 C FIRE-dDNP would allow one to prepare hyperpolarized tracers for in vivo imaging applications in only a few minutes without introducing supplementary demanding or fragile hardware equipment to the dDNP apparatus. under the European Union's Horizon 2020 research and innovation program (ERC grant agreement no. 714519/HP4all and Marie Skłodowska-Curie grant agreement no. 766402/ZULF).
Acknowledgements We acknowledge  Bruker Biospin  for providing the prototype dDNP polarizer and the Fourier 80, in particular  D. Eshchenko ,  R. Melzi ,  M. Rossire ,  M. Sacher , and  J. Kempf  for scientific and technical support on the dDNP polarizer, and  V. Decker ,  B. Knittel , and  F. Decker  and for technical support on the Fourier 80 spectrometer. We additionally acknowledge  C. Jose  and  C. Pages  for use of the ISA Prototype Service, and  S. Martinez  of the  UCBL  mechanical workshop for machining parts of the experimental apparatus. We thank  R. Picazo-Frutos  and  D. Budker  for lending us the magnetic shields and  S. Lambert  for providing advice on coil construction. This research was supported by  ENS-Lyon , the  French CNRS ,  Lyon 1 University , the  European Research Council.
Plastic exposure risk for petrels We analysed 1,736,880 tracked locations for 7137 adults of 77 petrel species (64% of species within Oceanitidae, Hydrobatidae and Procellariidae, excluding the two Macronectes species), from 148 populations in 27 countries and Antarctica, between 1995 and 2020 (mean = 2012). For each population, we calculated monthly 95% utilisation distributions (UDs) that estimate time spent by tracked petrels in 10 km grid cells (i. e. , smoothed density of 12-hourly tracked locations; Fig. 1a ), and combined monthly UDs into seasons (breeding or non-breeding). If data were available from multiple populations of a species, we created species UDs weighted by approximate population size. We calculated a geometric mean of global marine plastic densities estimated by three published models  6, 9, 34  for micro-and macro-plastics (~0.
Nonetheless, studies that compared ingestion for different populations of the same species using the same methods control for these factors, and so can be compared to our exposure risk scores. For example, flesh-footed shearwaters Ardenna carneipes c Summed 95% utilisation distributions for all species, with species weighted equally if year-round tracks were available or by 0. 5 if tracks were only available for the breeding season. If we had data from multiple populations for a species, densities were weighted by approximate population size. d Exposure risk to plastic was calculated by multiplying the density value in each cell for plastics (scaled to sum to 1) by the value for petrels (scaled to sum to 1). For visualisation only, the values are capped at 1% due to extreme values, and all other values are shown on a linear scale.
1 ). We identified priority species for future research in each of these regions (Supplementary Table  4 ). Sample sizes varied substantially among species, from 3 to 960 individuals (median = 35, mean = 93), so additional tracking for some species could be beneficial (Supplementary Data 2). Furthermore, tracking immature birds or adults when deferring breeding could reveal differences in exposure risk  33. Our method could also be applied to global-scale, multi-species tracking datasets  12  for other marine megafauna, such as turtles and marine mammals, for which plastic pollution is also a threat  1. Collecting more data on plastic density, identifying sources, and developing density models to provide better spatial coverage at a higher resolution would aid targeted mitigation strategies, and enable a better understanding of the effects of spatial scale on plastic exposure risk.
Methods In brief, we collated tracking data for petrels and computed gridded utilisation distributions (UDs) at a monthly scale. We then combined gridded distributions of marine plastic density and multiplied them by the petrel UDs to map estimated exposure risk. For each map, we summed the plastic exposure risk values in all cells to provide a score representing relative estimated exposure risk. We combined maps and scores to investigate variation in exposure risk between breeding and non-breeding seasons, among populations and species, and across Exclusive Economic Zones (EEZs) and the high seas. Steps for processing and analysing the data are described in detail below and represented graphically in Supplementary Fig. 3. All data handling was carried out in R  55  and R scripts are provided, along with example data and templates  54.
Petrel tracking data collation and processing We collated tracking data that were collected using Global Positioning System (GPS) loggers, Platform Terminal Transmitters (PTTs) and Global Location Sensor (GLS) loggers deployed on adult petrels (Table  S1 ; Oceanitidae, Hydrobatidae and Procellariidae). We searched for published and unpublished tracking data for all petrel species between March and August 2020, excluding the two giant petrel species Macronectes giganteus and M. halli because our analyses focused on marine areas and they regularly feed on land  56. We obtained data for 77 species (64% of the 121 target species) from the Seabird Tracking Database (www. seabirdtracking. org), ZoaTrack (www. zoatrack. org)  57 , Movebank (www. movebank. org)  58 , and individual researchers (represented by authors of this study or detailed in the Supplementary Acknowledgements).
We collated 1,736,880 tracked locations for 7137 individuals tracked from 27 countries and Antarctica. Datasets varied in terms of number of colonies per species, and numbers of individuals, years, and months tracked per population (Supplementary Data 2) and species (Supplementary Data 3). We standardised tracking datasets to contain the following fields in the same format: latitude, longitude, datetime, species, colony name, colony latitude, colony longitude and device type. For GLS, we removed locations around the equinoxes (March equinox: -21, +7 days; September equinox: -7, +21 days) as they are unreliable  59  , unless latitudes were estimated using additional information such as sea surface temperature prior to our analysis. For GPS and PTT data, we filtered locations for unrealistic speeds (>90 km/h), and visually checked maps and removed locations that were clear outliers.
We removed locations within 5 km of the colony for GPS data or within 15 km of the colony for PTT data, but not for GLS locations due to large location error for these devices. We linearly interpolated and resampled GPS and PTT datasets to the sampling frequency for GLS of two locations per day. We grouped data for each species into 148 breeding populations determined according to jurisdiction, the distance between colonies, and overlap in at-sea distributions based on the tracking data, i. e. , if distributions overlapped substantially (at a 1 × 1°scale) and colonies are in close geographical proximity and in the same country, we considered colonies to belong to the same population.
Density of tracked petrel locations For each population, we pooled all locations for all individuals across all years by month, and then removed months with fewer than five locations. For each month, we reprojected tracked locations onto a Lambert azimuthal equal area projection centred around the geometric mean of all locations. We estimated kernel densities of tracked locations to compute a 95% UD, a common home-range metric, which, because the sampling frequency was standardised, represented the estimated time spent by all tracked petrels in that population within that month. We used the adehabitatHR R package  60  , using a cell size of 10 km 2 and a smoothing factor of 200 km (based on the magnitude of error in estimating locations from GLS  33  ). We trimmed all cells that fell over land (Natural Earth land 1:10 m polygons version 5.
1. 1 downloaded from www. naturalearthdata. com/) because these species do not forage in terrestrial environments and it is extremely rare for them to travel over land, so any locations are most likely due to device error  33. We then reprojected the resulting rasters back to a latitude and longitude projection (WGS84). Of the 148 tracked populations, 108 (61 species) were tracked both in the breeding and non-breeding seasons. For these populations, we collated published information on the timing of breeding at a monthly scale (Supplementary Data 4) for each species or, where possible, each population. We also labelled months as breeding or non-breeding based on the tracking data. Locations were not always available for all months, with March and September often excluded from GLS datasets due to the uncertainty in light-based geolocation around equinoxes.
We first calculated the distance between each location at sea and the breeding colony. For each population, we calculated the mean distance from the colony for each month, and a mean of those monthly means. If the mean for a month was greater than the population-specific mean across all months or if no individuals travelled within 200 km (chosen due to the approximate 200 km error common when using GLS devices) of the colony, this month was classified as non-breeding. To ensure there was only one breeding and one non-breeding season, if the classification of one month differed from the previous and following months, it was reclassified. We used published values except in cases when a month was labelled as breeding, but the tracking data showed that the subset of tracked birds did not attend the colony during that month, in which case, we used the label identified by the distance-to-colony method.
We then summed all cell values and multiplied all scores by 1,000,000 to reduce the number of decimal places to produce a single score for that month (ranging from 0. 0007 to 1091). For comparison, we calculated a theoretical score of 15. 3, which represents what the exposure risk score would be for any species if all global grid cells contained the mean plastic density (i. e. , assuming that plastic was evenly distributed across the world's oceans). We combined monthly grids to produce grids for each population, breeding or non-breeding season (if data were available for non-breeding months) and species. Scores for each population are the mean of all tracked months, and scores for each season are the mean of all months in that season (Supplementary Data 5). We used the mean to allow comparison between species with different numbers of tracked months.
Maps for most populations are in Supplementary Fig. 5. For the 33 species for which we had multiple tracked populations, we searched for published population estimates (Supplementary Data 6). We calculated species-level scores as the mean of scores for each population weighted by the population size and multiplied by 0. 5 if the population was only tracked during the breeding season (Supplementary Data 7). We tested how robust our results were in relation to population size estimates, sampling frequency and tracking year. Population estimates for some species have large uncertainty, so we tested the correlation between species-level scores calculated with and without weighting by population size using Kendall's tau because scores are not normally distributed. They were highly correlated (tau = 0.
83; T = 483, p < 0. 001), so our results are unlikely to be affected by uncertainty in population size estimates. To investigate possible effects of sampling frequency, we reprocessed the tracking data without subsampling all datasets to 12-hourly intervals. We identified 44 populations for which all data were derived from GPS or PTT devices. For each track, we calculated the median interval between successive locations and recorded the maximum median for each population, and if this was less than 6 h, we regularised tracking locations at that frequency (intervals ranging from 1 min to 5 h, median = 1 h, mean = 82 min). We performed kernel density estimation with the higher-frequency datasets using a smaller 50 km smoothing factor  33  for the remaining 39 populations and used them to calculate exposure risk scores for each population.
The scores estimated using the higher and lower resolution data were highly correlated (tau = 0. 90, T = 703, p < 0. 001), so we conclude that 12-hour sampling intervals and 200 km smoothing parameter are sufficient for a study of this scale. Birds were tracked between 1995 and 2020 with a mean tracking year of 2012. Among the 148 populations, 139 (94%) were tracked within 5 years of 2014 (2009-2019), the year for which plastic density was estimated. Given petrels are long-lived and generally faithful to breeding sites  69  and foraging areas during both breeding and nonbreeding seasons  [70] [71] [72] [73]  , we assumed that distributions were unlikely to vary substantially across the study period. Data on long-term trends in plastic ingestion by seabirds have not shown substantial increases during the study period  27, 74, 75.
A subset of 13 populations had been tracked with geolocators for the same set of months across more than three years (Supplementary Fig. 2 ). For these, we calculated an exposure risk score for each year and then tested the effect of population and year using a generalised linear model with a Gamma distribution (due to positive continuous right-skewed response variable). We checked model fit by simulating residuals using the DHARMa R package  76. We recorded the most recent IUCN Red List assessment threat category  77  , where 36 species were Least Concern (LC), 9 Near Threatened (NT), 16 Vulnerable (VU), 10 Endangered (EN) and 6 Critically Endangered (CR). Red List status categories from the year each species was first tracked remained the same for 71 of the 77 species, and we used the most recent assessment for the 6 species for which changes have occurred.
Spatial coverage and research priorities To assess spatial coverage and identify research priorities for tracked species, we compared the distribution of the tracking data for each species with the estimated range maps  77. We assessed whether major populations (>1% of the global population or 200 pairs) of each tracked petrel species were missing from any of 10 major ocean areas (NW/NE/ SW/SE Atlantic, NW/NE/SW/SE Pacific, Indian or Southern Oceans) according to the SeaVoX Salt and Fresh Water Body Gazetteer (https:// www. marineregions. org/). Our tracking data covered all ocean regions within the published estimated ranges of 54 of the 77 species considered (70%). Our data compilation also revealed the main gaps in coverage for the remaining 23 species (Supplementary Table  2 ). To identify research priorities for high exposure risk areas identified in this study, we used range maps to identify species or populations for which tracking data were not included in this study, but range maps indicated they may overlap (Supplementary Table  3 ).
Acknowledgements B. L. C. , C. H. , and A. M. were funded by the  Cambridge Conservation Initiative's Collaborative Fund  sponsored by the  Prince Albert II of Monaco Foundation. E. J. P. was supported by the  Natural Environment Research Council C-CLEAR doctoral training programme  (Grant no. NE/S007164/1). We are grateful to all those who assisted with the collection and curation of tracking data. Further details are provided in the Supplementary Acknowledgements. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Data availability The plastic exposure risk data generated in this study and the plastic density data used in this study are provided in the Supplementary Data files, available at https://github. com/BirdLifeInternational/petrelsplastics and have been deposited in the Zenodo database at https://doi. org/10. 5281/zenodo. 7852143. The seabird tracking data are available under restricted access because the data were collected for other purposes that vary between datasets and revealing the exact locations of sensitive species may put them at risk. Access can be obtained by.
5 scenario. Some models do not have all datasets available for all experiments, and models are excluded when variables are not available. For the idealized warming experiments (4xCO 2 and 1pctCO 2 ), where ensemble members are 1 3 generally not available, we use a single ensemble member, and for the SSP scenarios we use the ensemble-mean results based on three members. We analyze 85 years of simulation for the SSP experiments, 150 years for the idealized experiments, and 200 years for the piControl experiment respectively.
To investigate the causes of discrepancy between changes in the Bjerknes Index and ENSO response, we also analyze changes to atmospheric noise within the models for which wind stress data is available. Atmospheric noise, including westerly wind bursts that occur frequently in the tropical Pacific, is believed to play an important role in sustaining ENSO and especially extreme El Niño events (e. g. Puy et al. 2019; Yu and Fedorov 2020; Fedorov 2002; Fedorov et al. 2015; Hu and Fedorov 2019; Capotondi et al. 2018). We find that on average, correlations between changes in atmospheric noise and ENSO SST amplitude (Fig. 7 ) are significant and higher than those between changes in the mean state and ENSO SST amplitude. This indicates that changes in atmospheric noise could play an important role in ENSO response to global warming and potentially explain the general strengthening of ENSO, even though there are a few outliers in the abrupt-4xCO 2 experiment.