dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, since U2AF1 contains two zinc finger domains (Fig. S5A ), first described as DNA-binding motifs, it is possible that U2AF1 might interact directly with these TFs. To test this hypothesis, we analysed U2AF1 ChIP-seq peak data from the ENCODE Project Consortium (The ENCODE Project Consortium, 2012) for two cancer cell lines, K562 (GSE105685) and HepG2 (GSE106030). A distribution analysis of U2AF1 binding loci relative to the transcription start site (TSS) of U2AF1 target genes showed that ~15% of its binding loci are localized 0-3 kb each side of the TSS (Fig. S5B ). Furthermore, an analysis of the distribution of U2AF1 binding sites on genomic regions shows that ~15-17% of U2AF1 binding sites are located in promoter regions, while 43-45% are in exons and introns (Fig. S5C ). Among the U2AF1 ChIP-seq targets, 14 and 20 genes overlap with upand downregulated genes following U2AF1 down-regulation, respectively (Fig.
3B ). Of the subset of 245 alternatively spliced transcriptional regulators (p-value = 0. 0257), >30% were zinc finger proteins which is consistent with the finding that links the knockdown of these proteins to AS modulation  [40]. To understand the impact of these AS events at the protein level, we investigated whether these events could modulate specific domains that are involved in the transcriptional regulation of gene expression. To do so, we analysed the impact of these AS on the open reading frame (ORF) of transcripts using the database, VastDB  [41]. This database provides complementary information on splicing events previously validated by the computational method, VAST-TOOLS. Of the 245 AS events associated with a transcriptional regulator, 64% are predicted to have an effect at the protein level, among which <26% are predicted to disrupt the ORF upon sequence inclusion and 41% are predicted to create a new alternative protein isoforms which represents about 18% increase as compared to new isoforms of non transcriptional regulators (Fig.
Acknowledgments We thank  M. Irimia  and  A. Gohr  for technical assistance and computing resources. We thank  S. Tuffery-Giraud  and  D. Lleres  for critical comments on the manuscript. We thank  A. Camasses ,  R. Ziane ,  D. Lleres , and  A. Vautrin  for technical assistance and helpful discussions. We thank  Fasteris  for the RNA-sequencing, the  MRI FLOW CYTOMETRY platform  and  M. Boyer  for cell sorting. We thank  Nature Research Editing Service  for reviewing the manuscript. This work has benefited from support by the  Labex EpiGenMed , an  « Investissements d'avenir » program , reference:  ANR-10-LABX-12-01 , to AML. This work was supported by the  FRM Fondation pour la Recherche Médicale , reference: ( FDT20160435249 ) to AML, and by the  Region Languedoc Roussillon (Projet Graine)  to JT.
Funding This work was supported by the  Labex EpiGenMed , an  « Investissements d'avenir » [ ANR-10-LABX-12-01 ] to AML. This work was supported by the  FRM Fondation pour la Recherche Médicale  [ FDT20160435249 ] to AML, and by the  Region Languedoc Roussillon [Projet Graine ] to JT.
Accession Numbers The RNA-seq datasets generated and/or analyzed during the current study are available through https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE119700. We analyzed the retrieved high-throughput RNA-seq open access data for hESCs and differentiated cell lines from GEO repositories (GSM1112847, GSM1112845, GSM1112846, GSM111284, GSM1112835, GSM1112833, GSM1112834, GSM1112837) included in this published article  (Gifford et al. , 2013).
, 2013). Therefore, the application of open science principles remains a challenge. From a political and management perspective, there is a need to combine surveys across international boundaries because commercial species spread and redistribute over multiple management areas, requiring transboundary assessments  (Baudron et al. , 2020; Ramesh et al. , 2019). The lack of such assessments may lead to political disputes over shifting fisheries resources  (Pinsky et al. , 2018; Shackell et al. , 2016; Spijkers & Boonstra, 2017). If the data generated by bottom trawl surveys are available and combined properly-they may allow near-seamless comparisons of species distribution and abundance in time and space. Developing knowledge on marine species responses to climate change is the first step towards developing transboundary and international management plans.
| Global data synthesis To assess the existence and accessibility of fishery-independent data on demersal species in the global ocean, we collected survey metadata (latitude, longitude, and depth if available) for recent and ongoing surveys that included at least 1 year of sampling since 2015 and use otter trawl gear, the most common gear type. We only retained surveys that were performed for 4 years or more between 2001 and 2019 (complete list in Table  S1. 1). Finally, we excluded surveys covering near-shore areas (within 3 miles from the coast) as they primarily target juvenile or coastal fish. Species life cycles are affected by climate change  (Petitgas et al. , 2013) , and surveys providing information on the different life stages of shifting species-such as near-shore surveys-could constitute a complementary global source of survey data to the one presented here.
| Surveys cover productive and fished continental shelves We compared the spatial extent of the surveys with: (a) the area covered by productive continental shelves, (b) the area fished by bottom trawlers, and (c) fisheries catch. We used global depth data (GEBCO https://www. gebco. net/), chlorophyll-a concentration data  (GlobColour GSM, 2005 -2015; Maritorena et al. , 2010) , and bottom trawl fishing data from Global Fishing Watch for the years 2013 to 2016  (Kroodsma et al. , 2018). Data were aggregated on a grid (0. 04° × 0. 04°). High concentrations of chlorophyll-a in continental seas is one of the main factors forming the basis for high fisheries productivity  (Stock et al. , 2017). Therefore, to define productive shelf sites that could be monitored by bottom trawl surveys, we assigned depth and chlorophyll-a thresholds: a cell was considered a productive shelf site if its depth was 30-500 m-the main depth range targeted by bottom trawl surveys-and if its chlorophyll-a concentration was higher than 0.
5 mg/m 3 (alternative thresholds are shown in Supplementary S3). A cell was counted as fished if more than one trawling activity was detected in the period 2013-2016. We then compared cells with the survey convex hulls to compute the global proportion of productive shelves and fished areas covered by surveys. We estimated that the surveys cover 62% of continental productive seas and 54% of coastal bottom trawl fished areas  (Figures S3. 3 and S3. 5 ). To estimate the fisheries productive areas covered by the surveys, we used global marine fisheries landing data for 2000-2014  (Watson, 2017). Landings data were averaged on the time-period in each 30 min spatial grid cell to estimate an average fisheries production estimate (in tons/year). We computed the ratio of total fisheries landings (tons/year) from areas within the survey areas to the total global landings (tons/year).
BOX 1 Applying open science principles to bottom trawl survey data Open Science is broadly defined as "Open data and content that can be freely used, modified, and shared by anyone for any  [ethical]  purpose" (http://opend efini tion. org/), and is more specifically described by six main principles (see  Gallagher et al. , 2020  for a general description). Following is a summary of advances towards Open Science and challenges regarding the use of bottom trawl surveys.
Ensure ethical use of shared information It is crucial that the push towards open science recognizes the value and human side of information. Nations and communities, particularly those that have been historically exploited must be able to benefit from their own data and be able to control their own information to minimize potential abuse. Open science must ensure that open data do not enable an opportunistic fishing company to exploit a nation's or community's resources. While open science can promote transparent science and understanding, it is essential that any use of open data give priority, proper credit, acknowledgement and potentially compensation to those who collected the information, paid for collection and recognize the nation where the data were collected. Access to data from economically stressed nations may require some type of compensation to ensure the data continue to be collected and made available.
Improve the accessibility and availability of surveys ("Open data") An evaluation of the accessibility and availability of surveys is necessary to enhance further open data science. We assessed that 59% of the samples collected are not publicly available to varying degrees (Figure  1 ). The network created here greatly improves the visibility of surveys, by presenting their metadata. Further, the availability of data can only be improved by changing the way we share scientific information, for instance by publishing data  (Costello et al. , 2013)  and ensuring quality-controlled use of data. Several bottom trawl surveys are published online, but the most recent years are not always included, or links to access the data are not always maintained, e. g. , the Norwegian surveys  (Djupevåg, 2018) , the southern Gulf of St Lawrence survey  (Swain et al.
, 2016) , Mauritania  (Kidé et al. , 2015) , southeast Asian surveys  (Garces et al. , 2006). Ensuring online publication of data are updated and maintained is key, as is done for other repositories (e. g. , DATRAS from the International Council for the Exploration of the Sea https://datras. ices. dk/). Existing platforms that enable online data publication, however, may not always allow updating or involve peer-review of the data (e. g. , PANGAEA https://www. panga ea. de/ and DRYAD https://datad ryad. org/stash). To ensure data are available beyond a single report or publication, a dedicated, sustainable, long-term management strategy is required with dedicated personal. The data repositories mentioned above that update and maintain their information all have dedicated programs and resources to ensure the data are available.
Improve the visibility of the expertise on surveys ("Open source" and "Open methods") To ensure appropriate use of the data, it is highly important that survey protocols, reports, and common practices are shared together with the raw survey data. Furthermore, providing example code to clean the data or to combine data from multiple surveys can ensure the appropriate use of data. Such types of open source and open access methods have been developed in recent years, mostly for Europe and North America (for instance https://ocean adapt. rutge rs. edu/; https://james -thors on. github. io// for codes; https://datras. ices. dk/ and  Moriarty et al. 2017  for codes and published reports). However, such documentation and tools need to be available and easy to find beyond Europe and North America, as well as in multiple languages.
5° × 0. 5° grid. We used the modeled native range and considered it as the "true" habitat and species range. The habitat in AquaMaps is based on publicly available global occurrence data and expert judgment on species environmental niches. Even though AquaMaps may sometimes misrepresent species ranges because of poor occurrence data and lack of knowledge for some species  (O'Hara et al. , 2017) , the ranges of the selected commercial species are generally well documented. The preferred habitat data layer of each species for the analysis was defined as all locations from the AquaMaps habitat maps where the probability of occurrence was higher than 0. 5 (alternative thresholds are shown in Supplementary S5). Next, we calculated the percentage of cells from the AquaMaps species habitat area covered by the survey footprints, showing the overlap between the two.
To assess whether species are transboundary, we calculated how many EEZs are covered by each of the 10 species mostly covered by surveys (Figure  2c ), using species' predicted distribution from AquaMaps as the species range. All 10 species are transboundary species and 50% of them are spread over more than 10 EEZs. Importantly, these species are not managed at a species level, but at a population (or stock) level. Climate change is affecting marine   (d-g ) show the number of EEZs overlapped by each stock. Note than stock delineation in some of these stocks (e. g. , stock number 1 for hake) is under current research and may change in the near future life at multiple levels from species, to stock complexes containing populations, to single fished stocks  (Free et al. , 2019). To assess fisheries stocks that may be transboundary, we used the RAM Legacy Stock Boundary Database  (Free et al.
, 2019) , focusing on the available spatial boundaries of 21 Atlantic cod stocks (Gadus morhua) and 9 European hake stocks (Merluccius merluccius) as case studies (Figure  2d -g; see Supplementary S6 for methodological details). F I G U R E 2 Atlantic cod is spread over more than 18 EEZs (Figure  2c ), and its stocks are transboundary as well (14 out of 21 stocks are spread over more than 1 EEZ, Figure  2d, f ). Similarly, European hake range covers 30 EEZs, and all stocks are transboundary (Figure  2e, g ). We note, however, that this database of stock boundaries is under revision, and so these numbers should be taken as illustrative but not definitive. It is critical to improve our knowledge of stock delimitations and population complexity, which differ across systems/species, and can be altered by climate change  (Kerr et al.
| A case study to combine surveys across regions We used arrowtooth flounder (Atheresthes stomias) to illustrate how to combine survey data across multiple regions when tracking and investigating population-scale range shifts in species distribution. Arrowtooth flounder is a widespread and ecologically important predator in the northeast Pacific  (Aydin & Mueter, 2007) , monitored and assessed by 10 distinct but contiguous surveys across the region from the California Current to the Bering Sea between 2001 and 2018, conducted by the United States and Canada (Supplementary S7; Figure  3a ). To predict densities within the entire survey domain, we fit a spatio-temporal Poisson-link deltagamma model  (Thorson, 2017)  to biomass data from each survey using the R-package VAST (Vector Autoregressive Spatio-Temporal,  Thorson, 2019b; Thorson & Barnett, 2017).
, 2020). Our knowledge of species redistribution across surveys will clearly benefit from long-term consistent surveys, when combined and modeled appropriately. Long-term global monitoring datasets are essential to develop transboundary science, and offer opportunities to improve the management and conservation of migrating transboundary species (Box 2). Under global change, migrating species create a potential for economic and political conflicts  (Mendenhall et al. , 2020) , and may lead to species overexploitation or collapse in the case of lack of adaptation and cooperation  (Miller & Munro, 2004; Oremus et al. , 2020; Pershing et al. , 2015; Pinsky et al. , 2018; Vosooghi, 2019). The 1982 United Nations Convention on the Law of the Sea (UNCLOS) provides the legal framework for international obligations towards safeguarding marine resources.
A list of existing surveys, their time coverage and available samples are available here for demersal species (see Table  S1. 1). If surveys are not publicly available, one can use the network of contacts published in this paper and/or establish bilateral/multilateral agreements to gain access to the survey data. Metadata should also ideally include a list of species recorded in the surveys. b. Estimate the change in species density and distribution. Surveys vary in terms of design, gear, catchability, and sampling methods. Multiple surveys can be combined to estimate species density and reconstruct past temporal changes in spatial distribution. Modeling the change in species distributions can be done with multiple models and needs to take into account survey discrepancies  (Dolder et al. , 2018; Ono et al.
, 2019). In addition, citizen science initiatives reporting species observed well outside their typical geographic ranges (e. g. , the Range Extension Database and Mapping project; Redmap  [Pecl et al. , 2019]  https://www. redmap. org. au/ and the European Alien Species Information; EASIN  [Schade et al. , 2019] ; https://easin. jrc. ec. europa. eu/easin), also add valuable evidence of species range shifts in poorly sampled areas. Surveys are highly valuable for following marine species redistribution and biodiversity change, but maintaining surveys in a consistent way through time is a challenge as they are costly. However, ecological time series become more informative the longer their timespan, highlighting the need to maintain long-term monitoring programs  (Hughes et al. , 2017; Schindler & Hilborn, 2015).
ACK N OWLED G EM ENTS We thank all persons who have collected the survey data around the world. We are also grateful to everyone who answered our emails and provided contacts, expertise and guidance on survey existence and status. We thank  Inês Dias Bernardes  for providing the survey metadata from the  Nansen Program. We are grateful to  Christopher Free , who compiled the RAM Legacy Stock Boundary Database and made it publicly available, and to  Daniel van Denderen  for preprocessing the global fisheries landing data. We thank  M. Karp  and  D. Ovando  for providing editorial comments that improved the quality of the manuscript. We thank  Myron Peck  and one anonymous reviewer for their insightful comments on the manuscript. We thank.
Simulation Results and Analyses The EV charging station management strategy simulation is performed with MATLAB/Simulink, in which the process CC/CV with three charging modes is implemented. An integral proportional controller controls the current and voltage of EVs during the EV charging process. The simulated scenario follows the described EV charging station based on DC microgrid and under power limitation. When an EV arrives, the driver chooses the charging mode. Under the power limitation, if the charging station available power is greater than the maximal power demanded by the charging mode chosen by the driver, the EV charges directly. If not, the system offers options for drivers. Five EV chargers are set in the simulation model, according to the priority order, namely from the charger 1 to charger 5, the order from top to bottom in Figure  1.
Recording of EV Charging It is necessary that the EV charging station has the function of recording data in real-time, which will provide a data base for adjustment and future planning of the station. The recording of EV charging station is shown in Figure  19. It can be seen that N1, N2, N3, N4, and N5 are the number of EVs arrived at the station, the number of EVs being charged, the number of EVs fully charged, the number of EVs on waiting, and the number of departing EVs, respectively.
Recording of EV charging It is necessary that the EV charging station has the function of recording data in real-time, which will provide a data base for adjustment and future planning of the station. The recording of EV charging station is shown in Figure  19. It can be seen that N1, N2, N3, N4, and N5 are the number of EVs arrived at the station, the number of EVs being charged, the number of EVs fully charged, the number of EVs on waiting, and the number of departing EVs, respectively. One notes that N1 is the sum of N2, N3, N4, and N5. The equation is expressed by Equation (  12 ): N1 = N2 + N3 + N4 + N5 (12) The results show that most of the EVs arrived at the charging station choose to be charged. When the available power of the charging station is insufficient, the drivers are more inclined to wait or choose other charging modes.
Acknowledgements We thank  Ding Yu Shao  for collaborations at the early stage of this work. We are also grateful to  Jonas Lindert  for the clarifications of the results in ref. [54] and to  Gudrun Heinrich  about the two-loop grid used in Powheg-Box. LBC is supported by the  National Natural Science Foundation of China(NSFC)  under the grants  11747051  and  11805042. HTL is supported by the  Los Alamos National Laboratory LDRD program. The work of  HSS  is supported by the  ILP Labex  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ). The work of JW has been supported by the program for Taishan scholars.
The total runtime was 28. 33 min. For the MeOH fractions, the run started at 80 • C for 1 min and increased by 10 • C min -1 up to 325 • C, holding 1 min at the final temperature. The run was followed by 3 min of post-run at 80 • C for a total runtime of 26. 5 min. For both fractions, a solution with a mix of C8-C20 and C21-C40 alkanes (Fluka Analytical) was also injected for the determination of compound retention index. The identification of fatty acid methyl esters (FAMEs) was confirmed by comparison with a standard mixture (SupelCo 37 FAME mix). For each experiment and fraction, a quality control sample (QC) was prepared with 25 µL of each sample. It was used to monitor MS shift over time and to normalize data according to injection order. The run started with two blank injections, followed by 5 injections of the QC.
GC-MS Agilent data files acquired from GC-MS analyses were exported into mzXML files using MSconvert  (Chambers et al. , 2012). mzXML files were then processed using the eRah package (version 1. 1. 0; Domingo-almenara et al. , 2016)  under R performing preprocessing, peak deconvolution [min. peak. width = 2. 5 (1. 5 for MeOH fractions), min. peak. height = 2,500 (1,500 for MeOH fractions), noise. threshold = 500, avoid. processing. mz = c (73,149,207)], peak alignment [min. spectra. cor = 0. 90, max. time. dist = 5 (3 for MeOH fractions), mz. range = 40:600] and missing compounds recovery (recMissComp function, minimum number of samples was set at 6). The matrix of compounds obtained was then filtered: peaks present in blanks (signal/noise ratio > 10) and those with higher coefficient of variation in pools (CV < 25%) were removed from the dataset.
LC-MS LC-MS raw data files were converted to mzXML files with MSconvert. mzXML files were then processed using the package XCMS for R software (R version 3. 3. 2, XCMS version 3. 2. 0). Optimized parameters for XCMS were used as follows: peak picking [method = "centwave", peakwidth = c(5,20), ppm = 10], retention time correction (method = "obiwarp", plottype = "deviation") with final grouping parameters (bw = 2, mzwid = 0. 015) and filling in missing peaks. Other parameters were set to default values. A matrix of compounds with peak intensity, m/z value and retention time was generated. The latter was filtered according to blanks and QC to remove technical variability using in-house R scripts [1-Filtering the matrix according to peaks present in blanks relative to pools (signal/noise ratio > 10), 2-filtering the matrix according to peaks coefficient of variation (CV) calculated on pool (CV < 20%) and 3-filtering the matrix according to autocorrelation between peaks].
Metabolites were annotated with constructor software (Bruker Compass DataAnalysis 4. 4). Molecular network based on LC-MS/MS spectra were constructed with GNPS (M. Wang et al. , 2016)  using the following settings: precursor ion mass tolerance: 2 Da, fragment ion mass tolerance: 0. 5 Da, min pairs cos: 0. 7, minimum matched fragment ion: 6, node topK: 10 and minimum cluster size: 2. Resulting networks were observed under Cytoscape 3. 5. 0 (Shannon et al. , 2003). Metlin 1 , MassBank, SIRIUS 4. 0  (Böcker and Dührkop, 2016)  and In-Silico MS/MS DataBase (ISDB)  (Allard et al. , 2016)  were also used for putative annotation. Data from LC-MS and GC-MS were normalized by log-transformation before statistical analyses. The relative standard deviations (%RSD = standard deviation/mean * 100) were calculated for each metabolite  (Parsons et al.