dataesr/scientific-paragraphs-categorization huggingface dataset:
These ratios-hsa-miR-21-5p/hsa-miR-199a-5p and hsa-miR-155-5p/hsa-miR-199a-5p-showed great diagnostic utility in AFP-negative HCC cases. Recommendations. Considering hsa-miR-21-5p and/or hsa-miR-155-5p as potential precision therapeutic target(s) for CHCV-G4-related HCC and liver cirrhosis treatment. This will consider sub-classification examination and testing repurposing drugs (potentially targets to be obtained via miRDB), relying on targeting miR21 and miR155 genes, and proofing the mechanism experimentally. Sustainability Plan. Blocking-hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-192-5p, and hsa-miR-199a-5p-target genes based on the findings from gene-gene interaction networks and KEGG curated databases pathways and text-mining, as potential treatment options based on ncRNA, a step toward precision health.
miRs Selection Based on literature search, some miRs were examined by our research group and based on bioinformatics identification of the investigated miR panel, retrieved from miRDB  [40]  (https://mirdb. org/mirdb/index. html) and the human ncRNA gene database, GeneCaRNA  [41]  (https://www. genecards. org/genecarna; Version 5. 13, updated 9 November 2022) as well as the miRTarget  [42]  Link 2. 0 (Released 11 December 2020) (https: //ccb-compute. cs. uni-saarland. de/mirtargetlink2) (miRPathDB v2. 0 https://mpd. bioinf. uni-sb. de/overview. html).
Patients' Data Demographic data, including age, gender, and patients' full histories, were retrieved from the hospital medical records. All patients were asked about family cancer history for recording as well as general clinical examination. For calculating body mass index (BMI) (in kg/m 2 ), calculation was performed according to (https://www. nhlbi. nih. gov/ health/educational/lose_wt/BMI/bmicalc. htm), with normal weight = 18. 5-24. 9 kg/m 2 , overweight = 25-29. 9 kg/m 2 , obesity = BMI of 30 kg/m 2.
Statistical Analysis Data collected were coded and analyzed using the Statistical Package for Social Science software (SPSS, Version 17, Chicago, IL, USA). Qualitative data are presented as frequencies (n) and percentages (%). Data were tested for normality using a Shapiro-Wilk calculator (https://www. statskingdom. com/shapiro-wilk-test-calculator. htmL). Normally, distributed, variables are presented as mean + S. D. and analyzed using two samples' independent Students' t-tests for comparison. For non-normally distributed variables, data are presented as median (interquartile range as 1st-3rd quartiles or 25th-75th quartiles), then Mann-Whitney (U) was conducted to compare between any two independent groups. Student's t-test and the Chi-square χ 2 test were used to compare quantitative and qualitative normally distributed variables between the patients and control groups, respectively.
In order to bridge the gap between observation and explanation, we designed a tool to create an ensemble of models fitting each map derived from variability analysis. The result is an ensemble of PDB models that can be used to describe protein movements, highlight transiently disordered parts of the structure, local rearrangements, helical twists and more. This ensemble of structures is complementary to high resolution structures, describes local motion and can serve as a base to more detailed dynamic studies. This tool has been made available in the Phenix software package  [13]  under the name phenix. varref and referred to as "variability refinement".
Application of phenix. varref to large complexes Variability refinement was tested on a large super-complex to assess the robustness of the software. CryoEM maps derived from variability analysis of EMPIAR-10180 entry were graciously shared by the Cry-oSPARC team for this analysis. This structure of the pre-catalytic yeast spliceosome  [34]  contains 39 protein chains and 5 nucleic acid chains, for a total of 2. 5 MDa. Variability refinement successfully modeled each chain in the map, faithfully following the movement observed in cryoEM maps (Fig. 5A, B ). Each chain undergoes different movement according to the deformation observed in the cryoEM structure, allowing for investigators to observe protein movements within the structure. In addition to being a large complex, this structure is also of modest resolution (7 Å overall), making that variability analysis was calculated at 8 Å resolution.
We also showed that variability refinement can be used for even larger systems like the spliceosome  [34]  that undergo much larger structural changes with movements of large domains, and it will be interesting to test it for other similar systems  [35, 36]. The variability refinement tool is publicly available in Phenix starting with the version dev-4799 for investigators to use. Now that Artificial Intelligence (AI) has undergone a major leap in deciphering the code to predict protein structures from sequence  [37] , the next step is to know how these proteins move. Using variability refinement, the community has now a method to create a new type of learning set for AI to predict protein movements, and steps will be undertaken to make this data publicly available. Along the same lines, the very recent release of another type of movement descriptor by 3DFLEX  [38]  allows for a whole visualization of the movement, not limited to linear interpretation, and reflecting conformational flexibility.
Generation of the map ensemble for the ABC transporter BmrA 3D variability analysis for BmrA is performed in CryoSPARC v3. 3. 3D variability analysis was carried on 327,764 particles used for the consensus high-resolution reconstruction  [33]  (PDB: 7OW8. EMDB: 13095). Variability was computed in simple mode, at 6 Å resolution and C1 symmetry.
Variability refinement of BmrA Maps originating from 3DVA were input to phenix. varref along with the refined structure and a resolution of 6 Å, on a 72-core Linux calculation server (Intel® Xeon® 6240 at 2. 60 GHz). Using 50 cores, the job ran in about 6 h. Some distance restraints were used between ATP atoms and some residues in the neighboring loops.
Variability refinement of the spliceosome Maps originating from 3DVA were generously shared by the cry-oSPARC team, performed on EMPIAR entry 10180. Only the component 0 was used to assess the feasibility of using phenix. varref on such a large super-complex. 3DVA was calculated by the cryoSPARC team using the particles and mask from the EMPIAR entry, using a filter resolution of 8 Å. Finally, maps were displayed with a filter resolution of 6 Å. Using 50 cores (on an Intel® Xeon® 6240 at 2. 60 GHz), the job ran in about 6 days. A/ Variability analysis of EMPIAR-10180 calculated by CryoSPARC, with the two outmost maps displayed; movements range from dark blue to cyan. B/ Variability refinement of the spliceosome, with protein chains colored from dark blue to cyan (following the maps coloring), and DNA chains from red to yellow.
Acknowledgments This work was supported by the  CNRS , and the  French National Research Agency  grant number  ANR-19-CE11-0023-01  to VC and AG. VC wishes to thank  Xavier Robert  for his help running the crystallography calculation cluster. PVA and BKP thanks the  NIH  (grants  R01GM071939 ,  P01GM063210  and  R24GM141254 ) and the  PHENIX Industrial Consortium  for support of the  PHENIX  project, as well as the support by the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. The authors thank the cryoSPARC team for sharing the spliceosome 3DVA maps.
The Southern Ocean is a productive and biodiverse region, but it is also threatened by anthropogenic pressures. Protecting the Southern Ocean should start with well-informed Marine Ecosystem Assessments of the Southern Ocean (MEASO) being performed, a process that will require biodiversity data. In this context, open geospatial biodiversity databases such as OBIS and GBIF provide good avenues, through aggregated geo-referenced taxon locations. However, like most aggregated databases, these might suffer from sampling biases, which may hinder their usability for a MEASO. Here, we assess the quality and distribution of OBIS and GBIF data in the context of a MEASO. We found strong spatial, temporal and taxonomic biases in these data, with several biases likely emerging from the remoteness and inaccessibility of the Southern Ocean (e.
, 2021). Finally, GBIF and OBIS put the emphasis on data with geospatial information, which is not the case of, e. g. , portals dedicated to molecular data such as GenBank. These biodiversity research data infrastructures therefore represent a promising avenue for understanding large-scale diversity patterns and the long-term state of Southern Ocean ecosystems. Whether these promising data have the quality and spatial, temporal and taxonomic coverage to support the calculation of essential variables for a MEASO, however, remains unknown. Assessing the quality and biases of GBIF data in European seas,  (Ramıŕez et al. , 2022)  found severe biases towards waters closer to well-funded institutions (around the United Kingdom and in the North Sea), recent years (with a peak in the 2010s), and the most conspicuous and abundant taxa (e.
Data access Biodiversity data from the Southern Ocean was sourced from GBIF (http://www. gbif. org, interpreted data downloaded on November 10, 2022; GBIF. org, 2022) and OBIS (https://obis. org, downloaded on November 10, 2022) with the R package robis (Provoost and Bosch, 2021) and from the GBIF data portal and R package rgbif  (Chamberlain et al. , 2023). The data was queried using a polygon combining all MEASO regions, obtained through the measoshapes package  (Sumner, 2020). The MEASO regions divide the Southern Ocean into five longitudinal sectors that are further subdivided in three longitudinal zones.
Spatial filters As in Moudrýand Devillers (2020), coordinates were rounded to four decimal places, and occurrences located on land were flagged for further removal, based on shorelines from the Global Self-consistent, Hierarchical, High-resolution Geography database (GSHHG full resolution L1for sub-Antarctic islandsand L6for Antarctica, Version 2. 3. 7 Released June 15, 2017, downloaded from www. soest. hawaii. edu/pwessel/gshhg/). Although shoreline maps can sometimes be inaccurate in the Southern Ocean, no buffer was used to avoid removing too many coastal records. Note that coordinate precision was not taken into account here, which might lead in a few cases to the undue removal of records (e. g. for gridded data, the center point for the grid cell could be on land even if the recorded occurrence was in the water -a problem which might be more prevalent for benthic species).
Temporal filters Temporal filters were applied based on the eventDate and year, month, and day fields. When there was a range of dates in the eventDate field, only the first date was considered. When there was a conflict between information in the eventDate field and data in the year/month/day fields, priority was given to the year/month/day fields. Records with missing or incomplete dates were flagged for further removal. Dates prior to 1773 (first recorded crossing of the Antarctic circle by captain Cook) and after 2022 were also flagged for further removal.
Taxonomic filters Records with no information in the scientificName field were flagged for further removal. For all other records, taxon names present in the scientificName field (or in the species field when there was no match with scientificName) were matched with the World Register of Marine Species (WoRMS; www. marinespecies. org), the reference taxonomic database for marine wildlife. We did so using the worrms package  (Chamberlain, 2020)  and retaining only exact matches. For each taxon that matched, a unique WoRMS identifier (i. e. , aphiaID)favouring accepted names when multiple matches were foundcould be assigned to it, along with the corresponding valid name, that was used in further analyses. Higher level categorisations (e. g. , families for taxa identified at the genus level) imported from WoRMS were also added to the working dataset.
Observation type filters Absence data were flagged for further removal, as not all biodiversity survey datasets include a list of taxa that were not observed at a given time and place (absence data represent a marginal fraction of all data present on GBIF and are absent by default from OBIS downloads). Fossil data were also flagged for further removal, as only extant data was of interest for this study. The remaining data was further separated into 'machine observations' (using the MachineObservation tag in the basisOfRecord fielddefined by Darwin Core as "an output of a machine observation process", e. g. photographs, videos, audio recordings, remote sensing images, telemetry, etc. https:// dwc. tdwg. org/terms/#machineobservation) and 'human observations' (all remaining data). These two sub-datasets were separated for further analysis (see Evaluation of biases section), because occurrences associated with various types of tracking of individual movements (e.
Potential duplicates Following these steps (i. e. , after matching taxonomic names with WoRMS), records that were duplicated between and/or within databases were flagged for further removal. Records that had identical values (including NAs) in the fields decimalLatitude, decimalLongitude (rounded to 4 decimals), year, month, dayor eventDateand WoRMS-matched scientificName were considered as potential duplicates. Potential duplicates could be multiple entries of a species at the same geographic location and time within or between databases or could be records that are duplicated with changes to the occurrenceID field, for instance when data is uploaded again as part of an extended dataset.
Evaluation of biases A working datasetin which all flagged data were removed, and valid taxonomic names were associated using WoRMSwas used for the evaluation of geographical, temporal, and taxonomic biases. Biases were visualised either for the whole working dataset, or for machine or human observations only, for certain abundant phyla, or for other groups of interest (see Analysis of focal groups for details). Temporal biases were explored by calculating the number of records per month of year, per year or per decade. Spatial biases were visualised on a reference square grid, using a Polar Stereographic projection and a 100km x 100km resolution, by plotting the number of records in each grid cell. Spatial coverage for the groups of interest was estimated by creating a 3°x 3°grid covering the whole MEASO region (origin set at 180°W and 90°S, as in  Griffiths et al.
Analysis of focal groups Benthic vs. pelagic species were identified following  (Griffiths et al. , 2014). Information was only retained for taxa with unambiguous matching. Seventeen other focal groups were defined: 1. Birds and mammals, 2. Crustacea, 3. Microzooheterotrophs, 4. Mollusca, 5. Tunicata, 6. Echinodermata, 7. Bacteria, 8. Eukaryote primary producers, 9. Gelatinous zooplankton, 10. Pisces, 11. Plantae, 12. Annelida, 13. Bryozoa, 14. Porifera, 15. Protozoa, 16. Tintinnid ciliates, 17. Macroalgae. These groups were defined by extending the definitions of  Ramıŕez et al. (2022)  and the list of corresponding taxa can be found in Supplementary Table  S1. Finally, records were considered as zooplankton or phytoplankton (to estimate the amount of data in the EOV categoriessee Table  1 ) based on functional groups associated with WoRMS entries, accessed using the worrms package and a modification of a code available from https:// github.
Suitability of the available data for a MEASO Based on definitions provided by GEO BON and GOOS (see Table  1  for details), we identified, for each relevant EBV and EOV, the data necessary for their calculation. We then evaluated how appropriate data from OBIS and GBIF are for this exercise, based on qualitative evaluation of biases as well as quantitative evaluations of the percentage of records containing the required information. Note that our evaluation of the availability of abundance data was focused on the organismQuantity(Type) and individualCount columns, and excluded information that may have been recorded in extension tables (Extended Measurement or Fact), as only occurrence data was included in this analysis. This may underestimate to a certain extent the availability of abundance data.
Data availability and overall quality The search criteria matched 5,993,533 records on OBIS and 5,579,904 on GBIF. Of these, 547,944 records appeared to be situated on land (Figure  1 ). In total, 1,478 records had no scientificName (0. 01% of the data), and 467,966 records had a scientificName with no match in WoRMS (4. 04% of the data). Note that for records from DNA sequence reads, the number without a scientificName match in WoRMS increased substantially to 40. 56%. 5. 04% of records had incomplete date information (year missing for 2. 84% of records, month for 4. 55% and day for 5. 04%). In addition, 5,369 records prior to 1773 (first recorded crossing of the Antarctic circle), and 6 records after 2022, were flagged as erroneous for further removal (e. g. 5,353 records supposedly in year 0).
141,966 records (1. 23% of all data) referred to species absences (all from GBIF; Figure  1 ). 7,002,899 (60. 51% of the total) records were potential duplicates of other records (2,118,495 within OBIS, 1,373,050 within GBIF, and 3,511,354 between the two databases, leaving 4,570,538 unique records in the database. Machine observations accounted for 39. 89% of all records (before quality controls), and human Note that extension tables were not evaluated here.
Taxonomic/ phylogenetic diversity The diversity of species identities, and/or phylogenetic positions, of organisms in ecological assemblages. Geo-referenced presences and phylogenies that can be matched based on taxonomic names. Geo-referenced presences available in OBIS-GBIF, providing biases are taken into account. Phylogenetic information not available in OBIS-GBIF -need for external phylogenetic database(s), e. g. , the "tree-of-life" databases (see http:// tolweb. org/tree/phylogeny. html).
Ecosystem vertical profile The vertical distribution of biomass in ecosystems, above and below the land surface. Depth information related to geo-referenced presences. Depth information only available for 30. 72% of the dataset, skewed towards shallow depths (50. 08% of the records with depth information in the top 10 meters of the ocean). Of these records with depth information, 62. 21% % have a measure of biomass (organismQuantity(Type) fields). (Continued) observations for 60. 11%, with the remaining ~3400 records being unclassified or fossil records. Only 19. 39% of all (i. e. , presence and absence) records contained information on the number of individuals observed (individualCount field), and 26. 27% of all records contained abundance information (i. e. , explicit mention of number of individuals per unit of space and/ or time in the organismQuantity and organismQuantityType fields).
Note that absence records could be considered as carrying abundance information (i. e. , null abundance) but the individualCount and organismQuantity fields were filled in for respectively only 3. 46% and 68. 17% of absence records. An occurrenceID was missing for 953,954 records (8. 24%), probably pre-dating the implementation of occurrenceID as an obligatory field. Records originated from 3,484 individually identified datasets (based on unique value in the datasetName field) and 570 institutions (institutionCode field) -but note that 10,751,553 records had no data in the datasetName field and 5,582,236 records had no data in the institutionCode field. The contributions of individual datasets were highly variable: 2,929 datasets had less than 10 records, while 6 datasets had more than 50,000 records (including "The Unless explicitly mentioned, percentages are expressed as percentages of the working dataset (after quality control and removal of duplicates).
In contrast, although most abundant around research stations (Antarctic Peninsula, sub-Antarctic islands, some areas on the Antarctic coast; Supplementary Figure  2B ), machine observation data covered a larger part of the Southern Ocean and appeared not to be confounded by ship movement (Figure  3B , Supplementary Figures  2B, 4 ). However, in the case of tracking data from central-place foragers (a large part of the dataset), they will likely be biased by sampled colonies.
Taxonomic composition Overall, the data included 23,947 different taxonomic units, based on values from the scientificName field, after matching with the WoRMS taxonomic backbone. These units ranged from (sub-)species to kingdom-level identifications. After matching with the WoRMS taxonomic backbone, we identified 93 individual phyla in the data. The 10 best represented phyla for the whole working dataset (machine and human observations) were  Chordata (3, 146, 356 records), Arthropoda (506, 936), Mollusca (70, 372), Echinodermata (66, 693), Foraminifera (57, 147), Ochrophyta (56, 567), Cnidaria (36, 977), Proteobacteria (28, 092), Bryozoa (25, 822), and Annelida (25, 512). The evolution of the number of records through time varied across phyla, with several phyla increasing until 2001-2010 and then plateauing or decreasing in the last decade, while Proteobacteria, which were absent from the databases until the decade 1980-1989, showing a very marked increase continuing in the last decade, making them to the 3rd currently most-sampled phylum (Supplementary Figure  5 ).
Opportunities arising from open biodiversity databases for a MEASO By providing enormous amounts of geo-referenced data (millions of records), on a large taxonomic diversity (thousands of taxonomic units) and with large spatial extent, OBIS and GBIF represent promising data infrastructures for mapping distributions in the Southern Ocean. These data aggregators are continuously growing, due to increased data collection and incentives to submit data to publicly accessible repositories, while standards for publishing these data are also constantly improving  (Van de Putte et al. , 2021). Several key expeditions and joint efforts can be related to periods with increased data collection visible in Figure  2  e. g. , the Discovery Investigations , the Biological Investigations Of Marine Antarctic Systems and Stocks (BIOMASS) survey  (1980) (1981) (1982) (1983) (1984) (1985) , the Southern Ocean Continuous Plankton Recorder surveys (SO-CPR, 1991-), or the Census of Antarctic Marine Life (2005-2010) (see  Griffiths, 2010  for a more detailed perspective).
The rate of data accumulation has also increased drastically until the last decadeprobably due to a delay between data collection and publication, and increased interest in certain (until recently under-sampled) taxa, suggesting that the utility of the database could keep increasing. This delay is often caused by the fact that samples need to be collected in the field, transported to a research institute and identified by experts who may only have limited time and resources. Here machine based observation can help close this temporal gap between observation and publication of data. Guidance on how to implement this exists for DNA derived data, GPS data (van der Kolk et al. , 2022) and imaging devices  (Mortelmans et al. , 2019)  4. 2 Challenges with using open biodiversity databases for a MEASO However, the strong biases we highlighted in the data, severely limit their direct and widespread use for a MEASO.