dataesr/scientific-paragraphs-categorization huggingface dataset:
The HEADER section is completed only once within a single ADA-M Profile. It comprises 13 items, 3 of which are required (i. e. , values must be provided) whereas the other 10 are all optional (may be left empty). The MAIN BODY section is completed one or more times within a single ADA-M Profile, with multiple copies being needed when more than one different combination of data use criteria have to be stated, or when different data use criteria are to apply for discovery versus access. In such situations the different completed copies of the Main Body are entered sequentially in the structure of the completed Profile document, all under one Header section. The MAIN BODY comprises 42 concepts, only one of which is a required field. All other fields are optional, so long as at least one of the main fields in the "Permissions" or the "Terms" sections are given a value (i.
e. , so the Profile asserts at least one condition of use). All fields without values can be left blank and not reported in the final ADA-M Profile. The MAIN BODY also enables conditionalities and obligations to be specified. Conditionalities arise when a particular use permission (e. g. , use for research on disease X is allowed) is conditional on some other part of the Profile (e. g. , resulting data must be made public). Obligations involve situations where a particular use is not only permitted to occur but is obligated to occur (e. g. , use for research on disease X is a necessity). The MAIN BODY comprises three sub-sections: A permissions section A permissions section covers 26 hierarchically arranged concepts generally relating to laws, institutional policies (Data/Sample Access Policies, Material/Data Transfer Agreement, Data Access Fig.
COMPUTING, VALIDATION RULES, AND THE BROADER COMPATIBILITY OF ADA-M PROFILES When we take into account all the possible permutations of the categories and subcategories available in ADA-M, tens of thousands of unique metadata Profiles can be created, each of which describes different usage parameters. Yet the structure by which this information is recorded remains fixed and interoperable, allowing it to be used by a wide variety of computing platforms and software, and to work (and evolve, as need be) alongside other standardization efforts. Computing Three envisioned basic uses of ADA-M involve computer handling of completed Profiles. First, ADA-M is a means for unambiguously communicating data use conditions from one system to another, for interpretation or display. There are two standard formats currently available for such transfers, a JSON document and a keyvalue pair text document (see Appendix 4 of the Guidance Document, "Document Formats for ADA-M v1.
0 Profile" at https:// www. ga4gh. org/docs/ga4ghtoolkit/regulatoryandethics/ ADAM_GuidanceDocument_15Dec2016_Final_v2. pdf). Second, ADA-M is a means for enabling discovery services to include data use conditions in the range of characteristics they permit a user to query. Beyond adhering to the core syntax, semantics, and validation principles of ADA-M, it remains a matter of local choice what document or databasing format and technology is used for such applications. Third, ADA-M is a means for automated decision making on granting access to data. Given the complexity of some usage requests, and of some use condition Profiles, and also considering the normative issues surrounding some social groups as well as the many unknowns entailed in Big Data analytics,  4  it is unlikely that this task will ever be fully automated.
Accessed 30 May 2018). Broader compatibility ADA-M is designed to be compatible with and work alongside other standardization efforts that cover different aspects of digitization and automation regarding consent and data use conditions, including the Oasis eXtensible Access Control Markup Language,  5  the HL7 Healthcare Privacy and Security Classification System,  6  the FHIR Consent Directives,  7  the Consent Codes specification (for those specific codes, see https://www. ga4gh. org/docs/ga4ghtoolkit/regulatoryandethics/ DataUseBeacon_160209_tab_0. pdf. Accessed 30 May 2018), the Genetic Alliance Platform for Engaging Everyone Responsibly,  8  and the Broad Data Use Oversight System (DUOS). 9 All such initiatives have a certain degree of overlap regarding the actual terminologies used and their definitions, and the ADA-M developers are pleased to be working with a Data Use Ontology 10 project led from the European Bioinformatics Institute to formulate a common ontology for this domain.
CONCLUSION Clearly, it will take much work on many fronts to fully optimize biomedical data and biospecimen sharing. Capture and organization of heterogeneous, yet crucial, regulatory information is an important first step. ADA-M can act as a catalyst that quickens the process by enabling this information to be placed in a standardized and interoperable structure. ADA-M is designed to expand, evolve, and adapt with use. Part of the input for ADA-M's ongoing development will come from considering the types of information users enter into the free-text fields mentioned above. Options for ontologies that might be employed to bring more standardization to this aspect of the metadata model, and how to fit them into the model, are already being deliberated. The ongoing development of support software and the ADA-M reference API will continue, and other valuable areas of development are being considered, such as defining ways to reliably associate ADA-M Profiles with the data entities to which they refer, and creating a standardized data request structure that will correspond to ADA-M.
GA4GH's broader regulatory and ethics efforts, see: https://www. ga4gh. org/ga4ghtoolkit/regulatoryandethics/. Accessed 30 May 2018. For information on ADA-M in the contexts of the IRDiRC's efforts to advance knowledge on the natural history of rare diseases, see: http://www. irdirc. org/draft-version-of-ada-matrix-open-forcomments/. Accessed 30 May 2018. For a comprehensive description of how to use ADA-M, see the Guidance Document at: https://www. ga4gh. org/docs/ga4ghtoolkit/regulatoryandethics/ ADAM_GuidanceDocument_15Dec2016_Final_v2. pdf. Accessed 30 May 2018. For a repository providing a reference API implementation of ADA-M, see: https://github. com/ga4gh/ADA-M. Accessed 30 May 2018. For videos on an overview of how ADA-M operates and on a more detailed user example, see: https://doi.
ACKNOWLEDGEMENTS The ADA Task Team would like to acknowledge  Petra Kauffmann ,  Lilian Lau ,  Mahsa Shabani ,  Susan Wallace , and the contribution of all members of the  Automatable Discovery and Access Task Team  who have been involved in various stages of the work. Additional substantial contributions are acknowledged from  Global Alliance for Genomics and Health  ( GA4GH ), the  Public Population Project in Genomics and Society  ( P3G ),  Intel , and the  International Rare Disease Research Consortium (IRDiRC). This project as a whole was made possible by the support of the  Global Alliance for Genomics and Health  ( GA4GH ) and the  International Rare Disease Research Consortium (IRDiRC) , whose funding supported the initial workshop, and the  Public Population Project in Genomics and Society  ( P3G ) as well as  Intel , who funded the software.
Funding and support for specific authors include: The contributions of A. J. B. were supported by  GCOF  (award # 643439 ), and  EMIF  (award # 115372 ), both  EU  sourced funding. The contributions of E. K. , A. -M. T. , S. O. M. D. , and A. T. were funded by The  Can-SHARE  project, which is supported by  Genome Quebec ,  Genome Canada , the  Government of Canada , the  Ministère de l'Économie, Innovation et Exportation du Québec , and the  Canadian Institutes of Health Research  (fund # 141210 ). The contributions of B. M. K. were made possible by the  Canada Research Chair in Law and Medicine. The contributions of J. P. W. were made possible by a postdoctoral Fellowship at  Harris Manchester College  and related research done at the  Center for Health, Law, and Emerging Technologies (HeLEX) , both at the  University of Oxford.
Movies (5 fps) were recorded for each experiment with a 1920 Â 1080 pixel resolution (1 pixel B 6. 2 mm). 25 sequential frames were then compared and analysed using PIVlab (v. 1. 41, Matlab toolbox application),  55  yielding flow velocities for each interrogation area (the frames were split into a number of interrogation areas of size 64 Â 64 pixels, which were then individually cross-correlated with the previous frame to obtain displacement vectors). Matlab (v. R2017b, MathWorks) was then used to plot the flow velocities, while Adobe Photoshop CC2018 (v. 19. 0, Adobe Systems) was utilized to create masks and add images of the analysed drops on top of the flow velocity figures.
Acknowledgements A. M. acknowledges financial support from the  European Union 's  Horizon 2020 research and innovation framework programme  under the  M. Skłodowska-Curie  grant agreement no. 752896. Z. R. and K. K. were supported by the  Polish National Science Centre through OPUS programme  ( 2015/19/B/ST3/03055 ). The contributions from K. J. M. and F. K. E. were supported by the  Research Council of Norway through its Centres of Excellence funding scheme , project number  262644  (PoreLab). The authors thank  Prof. Eirik Grude Flekkøy  and  Prof. Tomasz Hornowski  for a careful reading of the manuscript and useful feedback.
OBCDs of MEIOB are in orange, OBCD of SPATA22 is in yellow, Cterminal helixes are shown as striped boxes, and MYC-FLAG and 3xHA tags are shown in red and purple, respectively. (B) MEIOB and/or SPATA22 were transfected into HEK293 cells and MEIOB was immunoprecipitated (FLAG (MEIOB) IP) using anti-FLAG antibody. Full-length and truncated forms of MEIOB were expressed alone (lanes 2 to 5) or coexpressed with SPATA22 (lanes 6 to 9). SPATA22, RPA1 and RPA2 immunoprecipitated proteins were detected using anti-HA, anti-RPA1 and anti-RPA2 antibodies, respectively. Tagged full-length MEIOB, OBCD3, OBCD3 and C helix were detected with anti-FLAG and displayed expected sizes of 56, 37, 23 and 54 kDa. The data are representative of three independent experiments. (C) MEIOB and/or SPATA22 were transfected into HEK293 cells and SPATA22 was immunoprecipitated (HA (SPATA22) IP) using anti-HA antibody.
Full-length or truncated forms of SPATA22 were expressed alone (lanes 2 to 5) or coexpressed with MEIOB (lanes 6 to 9). MEIOB, RPA1 and RPA2 immunoprecipitated proteins were detected using anti-FLAG, anti-RPA1 and anti-RPA2 antibodies, respectively. Tagged full-length SPATA22, OBCD, OBCD and C helix were detected with anti-HA and displayed expected sizes of 44, 30, 18 and 41 kDa. (*) represents a nonspecific band or a posttranslational modification of SPATA22 OBCD that displays a molecular weight greater than 30 kDa. (#) MEIOB is retained slightly in a nonspecific manner on the anti-HA column. The data are representative of minimum two independent experiments. (D) MEIOB OBCD3 and SPATA22 OBCD cooperate to interact with RPA. MEIOB and/or SPATA22 were transfected into HEK293 cells, and MEIOB was immunoprecipitated (FLAG (MEIOB) IP) using anti-FLAG antibody.
Full-length SPATA22 and full-length MEIOB expressed alone (lanes 1 and 2) or coexpressed (lane 5). SPATA22 OBCD and MEIOB OBCD3 expressed alone (lanes 3 and 4) or coexpressed (lane 6). MEIOB, SPATA22, RPA1 and RPA2 immunoprecipitated proteins were detected using anti-FLAG, anti-HA, anti-RPA1 and anti-RPA2 antibodies, respectively. The data are representative of two independent experiments. (E) Model of interactions between MEIOB, SPATA22 and RPA. MEIOB and SPATA22 interactions recapitulate interactions between RPA subunits and involve their C-terminal OBCDs. Furthermore, MEIOB and SPATA22 collaborate to interact with RPA. Striped boxes represent OBCDs that are not involved in complex formation.
Acknowledgements We thank members of the  Laboratory of the Development of the Gonads  for discussions and technical support, and are especially grateful to  M. J Guerquin  for data analysis. We thank members of the animal housing facility at iRCM, in particular  V. Neuville  and  C. Joubert. We thank  M. S. Wold  for RPA plasmids and  P. Drevet  for development of the baculovirus system. We are especially grateful to  M. Grelon ,  V. Borde  and  J. Haber  for careful reading and constructive comments on the manuscript. This work was supported by  ANR ,  INSERM ,  Fondation ARC , and  La Ligue  contre le Cancer. J. R. was supported by a  CEA  fellowship and  La Ligue  contre le Cancer. D. J. was supported by a  Human Frontier Science Program  fellowship.
Sampling and analyses For different objectives, during GEOVIDE, suspended particles were collected using different sampling techniques. The main goal of the Niskin sampling was to derive Ba xs concentrations and, thus, carbon remineralisation fluxes in the mesopelagic zone (high resolution in the 100-1000 m layer) at stations where PP data and carbon export fluxes were also determined. The goal of the Go-Flo sampling was, at first, dedicated to the determination of all dissolved and particulate trace elements and their isotopes. Since particulate Ba and Al were determined on samples collected by both sampling techniques, we took the opportunity to compare both datasets in order to assess the quality of our data. 1. Ba xs concentrations measured in samples collected using a standard CTD rosette equipped with 12 L Niskin bottles.
To verify the relationship between Ba xs and barite particles (see Sect. 4. 1), we evaluated the contribution of the barite particles to Ba xs concentrations for the sample collected at 600 m at Station 69 and which has a high mesopelagic Ba xs content (see Sect. 3). Using the FE-SEM, 0. 003 % of the total filter surface was scanned and size and volume of all detected barite particles present in this surface area were assessed. To this aim, each barite particle was pictured using a magnification setting between 12 000 and 15 000×. Images were then analysed with the software ImageJ and, for each barite particle, the longest and shortest axes were measured and pixels were converted to nanometres. Barite particles were then assimilated to ellipses to calculate their volume. Finally, the concentration of pBa of each barite particle was calculated using Eq.
, 2008)  and the Southern Ocean  (Cardinal et al. , 2005; Jacquet et al. , 2008a Jacquet et al. , , b, 2011b Jacquet et al. , , 2015;; Planchon et al. , 2013)  were calculated based on the Ba xs inventories. The insert shows data for the North Atlantic: sites indicated by circles lined in black are from the present study; at sites labelled with # symbols remineralisation was deduced from POC fluxes recorded by moored sediment traps  Honjo et al. , 2008) ; at sites labelled by • remineralisation was obtained from on-board incubations  (Collins et al. , 2015; Giering et al. , 2014) ; sites labelled with * are GEOSECS sites for which we calculated remineralisation from existing Ba xs profiles (Brewer et al. , unpublished results). Data were plotted using the ODV software  (Schlitzer, 2017). oceans.
Acknowledgements. We would like to thank the captain and the crew of the R/V Pourquoi Pas?, as well as  Fabien Perault  and  Emmanuel De Saint Léger  from the  CNRS DT-INSU  for their help during the CTD deployments. Pierre Branellec ,  Floriane Desprez de Gésincourt ,  Michel Hamon ,  Catherine Kermabon ,  Philippe Le Bot ,  Stéphane Leizour , and  Olivier Ménage  are also acknowledged for their technical help during the cruise. We acknowledge  Lorna Foliot ,  Raphaëlle Sauzède ,  Joséphine Ras ,  Hervé Claustre , and  Céline Dimier  for the sampling and analysis of pigments. We would like to thank the co-chief scientists  Pascale Lherminier  and  Géraldine Sarthou. We also express our thanks to  Pascale Lherminier ,  Herlé Mercier ,  Monika Rhein ,  Julie Deshayes , and  Claude Talandier  for providing useful help on the characteristics of the Labrador Sea Water.
Satellite chlorophyll a data and visualisations used in this study were produced with the  Giovanni  and the  Ocean Color (Ocean Biology  Processing Group; OBPG) online data system, developed and maintained by  NASA. This work was funded by the  Flanders Research Foundation  (project  G071512N ), the  Vrije Universiteit Brussel  (strategy research program: project  SRP-2 ), the  French National Research Agency  ( ANR-13-BS06-0014  and  ANR-12-PDOC-0025-01 ), the  French National Center for Scientific Research (CNRS-LEFE-CYBER) ,  IFREMER , and the  "Laboratoire d'Excellence "  Labex-Mer  ( ANR-10-LABX-19 ). For this work,  Maribel I. Garcia-Ibáñez  was supported by the  Centre for Climate Dynamics at the Bjerknes Centre  and by the  Spanish Ministry of Economy and Competitiveness  through the  BOCATS  ( CTM2013-41048-P ) project co-funded by the  Fondo Europeo de Desarrollo Regional   2014-2020  (FEDER).
Quantification of MR imaging biomarkers For all fitting procedures, non-linear least square fits to the signal intensities were performed based on the Levenberg-Marquardt-Algorithm (Matlab "lsqcurvefit") in a pixel-by-pixel manner. For calculation of T1 time, the signal intensities of the inversion-recovery trueFISP sequence were fitted to the equation S TI i ð Þ ¼ S 0 1 À INV Ã e À TI i T1Ã ð1Þ with INV indicating the ratio between the initial signal S 0 and the steady state signal and T 1 being calculated from T 1 ¼ T Ã 1 cos a 2 INV À 1 ð Þ ð2Þ according to a Schmitt et al. [6]  The T2 time was calculated from the RARE sequence by fitting the equation S TE i ð Þ ¼ S 0 ð Þ Ã e À TE i T2 þ N ð3Þ to the signal intensities of the different echo time TE i with N meaning the noise. The apparent diffusion coefficient (ADC) was calculated from the logarithmic signal intensities The MTR values (in %) were calculated from the MT sequences with and without MT prepulse in the following manner: ADC ¼ 1 b ln Sðb 0 Þ SðbÞ MTR ¼ M 0 À M sat M 0 ð5Þ with M 0 meaning the signal intensity without MT prepulse and M sat the signal intensity with MT prepulse.
Definition of a region-of-interest (ROI) Applying custom-written Matlab scripts (MathWorks, Natick, MA, USA) a region-of-interest (ROI) analysis was performed with manually drawn RoIs on anatomical images of the respective sequences and subsequently copied to the parametrical maps. Three independent polygonal ROIs were drawn in the right lobe under avoidance of large vessel structures within the parenchyma and representative ROIs are shown in Fig 2. The image noise was determined in a ROI positioned outside the body in the upper left hand corner in the background of the image and corrected by squared subtraction according to Gudbjartsson  [7]. For the volumetric assessment of liver regeneration, the liver contour was manually retraced on each of the 0. 23mm-thick slices using ImageJ (http://imagej.
Statistical evaluation and image post-processing For descriptive analysis, mean values and standard deviations of the liver remnant volume, the T1 and T2 times, ADC values and MTR were calculated and numeric data are presented as mean ± standard deviation. Statistical evaluation of the acquired parameters between the study groups was performed by two-way analysis of variance (ANOVA) with Bonferroni correction using GraphPad Prism 5 software (GraphPad Software, Inc. , La Jolla, CA, USA). Assessing the correlation between the liver volume and the retrieved MR parameters for the regenerating liver, a Pearson correlation was carried out using SPSS Software vers. 25 (IBM Corporation, Armonk, NY, USA) and GraphPad Prism 5. Generally, a P-value of less than 0. 05 was considered significant. All MR parameters obtained for animals after ePH have been further analyzed by binomial logistic regression accommodating the MR parameters as quantitative, independent and predictive variables and the death of the animal by PLF as dependent dichotomous variable by using SPSS.
Acknowledgments The authors gratefully acknowledge the excellent maintenance of the Bruker MRI facility by  Udo Ungethuem  and the outstanding technical assistance of  Ursula Suess ,  Pia Fuchs  and  Nadja Bain  for the tissue examination by  IHC. This study was funded by the  Clinical Research Priority Program-Molecular Imaging Network Zurich. ML is supported by a professorship grant from the  Swiss National Science Foundation  ( PP00P3_152990 ).
Microscopy All images were acquired with a motorised Zeiss Ax-ioImager Z1 epifluorescence microscope (Carl Zeiss) using an AxioCam Mrm camera (Carl Zeiss), PL Apochromat 100×/1. 40 oil objective and Zeiss filter sets adapted for the fluorochromes used: (25HE (DAPI), 38HE (Alexa 488), 43HE (Alexa 596)). Images were captured as Z-stacks and collapsed Z-stack projections obtained using the Extendedfocus module (projection method) of the Axiovision 4. 6. 2 and further processed with the Zeiss ZEN lite and Adobe Photoshop CS4 software.
Statistical analysis The software GraphPad PRISM 7 was used to perform the statistical analyses and to generate the graphs. To determine whether differences between two groups were statistically significant, groups were compared using the Mann-Whitney test. For multiple comparisons, statistical analyses were conducted by ordinary one-way ANOVA followed by Tukey's multiple comparison post-hoc test with a single pooled variance. A P-value of 0. 05 or less was considered to be statistically significant. *P-value < 0. 05, **P-value < 0. 005, ***P-value < 0. 0001.
ACKNOWLEDGEMENTS We thank  Lieven de Veylder  and  Holger Puchta  for providing trd1 and the double heterozygote TERT/tert RTEL1-1/rtel1-1 mutant seeds, respectively. We thank  Nathalie Grandin  for critical reading of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Author contributions: M. O. , M. E. G. and C. I. W. conceived and designed the experiments. M. O. , C. C. and S. A. performed the experiments. M. O. , M. E. G. and C. I. W. analysed the data. M. O. , M. E. G. and C. I. W. wrote the article.
The relative polarisation between the pump and probe beams was held at magic angle (54. 7 ) by using a l/2 waveplate. The time delay between the pump and probe pulses was controlled using a hollow corner gold retroreflector mounted on a motorised translation stage in the probe beam path; maximum Dt = 2 ns. The setup used in these experiments provides an instrument response function with a full width at half maximum of $80 fs. TAS for each time delay were calculated from the difference in probe pulse intensities passing through sequentially excited and non-excited sample prepared by a mechanical chopper in the pump beam path. To extract the dynamical information from the TAS, a global fitting sequential model (e. g. A t 1 B t 2 C) was employed using the Glotaran software package  [38]. The quality of the fits was evaluated upon inspection of the resulting residuals shown in the SD.
Acknowledgements The authors would like to thank  Dr. W. D. Quan  for assistance with MA deuteration, and the  Warwick Centre for Ultrafast Spectroscopy  (WCUS; go. warwick. ac. uk/wcus) for use of the PerkinElmer Lambda 850 UV/Vis spectrophotometer. N. D. N. R. thanks the  EPSRC  for doctoral funding. N. C. C. F. and M. D. H. thank the  Leverhulme Trust  for postdoctoral funding. M. S. thanks the  EPSRC  for an equipment grant ( EP/N010825 ). Y. P. thanks the  École Normale Supérieure Paris-Saclay  for doctoral funding. V. G. S. thanks the  EPSRC for equipment grants  ( EP/J007153  and  EP/ N010825 ) and the  Royal Society and Leverhulme Trust for a Royal Society Leverhulme Trust Senior Research Fellowship.