dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this contribution we have discussed the status of planetary research data with a focus on planetary maps and map metadata in particular. We consider maps, in contrast to technical plans and charts, as currently belonging to one of the highest levels of research data on their path to becoming information and knowledge at a point by means of human abstraction. Thus, maps can be considered representative of a variety of other information that resides on lower level of abstraction and development. Mapping processes are certainly complex, not only with respect to abstraction, to implementation and to semantics, but also with respect to the publishing process in the Planetary Sciences where a number of actors (users) and their interests, and processes interact. Despite this effort, with a exemptions, little is being done to re-insert such products in the research cycle in a way that makes accessing them for study and refinement purposes efficient (or even possible).
The understanding of that need and requirement is not always a congruent one among funding institutes and researchers on one side and research institutes and universities on the other. The aim of this study was to characterize the relationships between different interest groups (stakeholders) and products as well as processes and to discuss how processes could potentially be optimized and streamlined in order to re-insert planetary maps (and research information) into a healthy and sustainable research cycle. Developments like this could build a thematic bridge to Earth-based RD repositories like EOSC, Earth cube und pangea in order to push the reuse of planetary maps. One way of realizing an approach targeted at improving the re-use of research data in the planetary sciences is by merging the existing alternatives and to integrate the structural benefits from the INSPIRE (or any other SDI) domain (cf.
Terrestrial infrastructures and overarching coordinative platforms, such as INSPIRE, went through such processes and have been sharing their experiences in this process. Technical Implementation. Archiving systems and research data repositories need to be maintained on both national and international levels to provide a service for the community that allows to make efficient use of that data. Efficient use also includes making information available for future decision making. This concept comes close to what is known as data warehouses in the business world. The technical maintenance of data and information synchronized over various national entities is a matter that would not pose a major challenge today. The workflow of collection, review and distribution of information and the required data models, however, are a matter that can be complicated in details but has been dealt with in environments dealing with Earth data and information infrastructures.
Acknowledgement The part of this research done by the author AN was carried out in part under the projects AP3400 Planetenforschung and Big-Data-Platform by the  German Aerospace Center (DLR). AN, APR, BC acknowledge support from the  Europlanet-2024 Research Infrastructure project , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  871149.
1. This value compares with control values of 86. 0 for the broadspectrum kinase inhibitor staurosporine and of 18. 8 for dasatinib, a clinically approved selective inhibitor of tyrosine kinases. To assess whether some of the kinases identified in both approaches were in fact functionally inhibited, we measured IC 50 values against four kinases that were high on both lists and represented by multiple family members (http://www. reactionbiology. com/ webapps/site/kinaseassay. aspx?gclid=EAIaIQobChMI9K2248-V5 wIVyp6zCh2HSwAKEAAYASABEgJCsPD_BwE). Some were affected significantly more strongly than others (Supplementary Table  4 ). These data demonstrate that, although TRULI inhibits Lats kinases, it might also interfere with the activity of other enzymes. In the future, it will be necessary to further explore the potential off-targets of the compound, particularly in a tissue-specific context.
MCF 10A cells were then plated in 40 μL of MCF 10A culture medium to achieve a final concentration of 10 μM for each compound and 0. 25% (vol/vol; 35 mM) DMSO. Negative (100% confluent) and positive (25% confluent) control wells included an identical concentration of the DMSO vehicle. After 24 h incubation, the cells were fixed, washed thrice in PBS, and immunolabeled for Yap. The nuclei were stained with 3 mM 4,6-diamidino-2phenylindole dihydrochloride (DAPI). The plates were imaged automatically with a ×10 objective lens on the ImageXpress XLS wide-field Micro reader (Molecular Devices, Sunnyvale, CA) with MetaXpress software version 4. 1 (Molecular Devices). Using an empty plate to avoid out-of-focus images, we configured a laserbased autofocus routine for automatic well-bottom detection.
We imaged DAPI and Yap conjugated to Alexa Fluor 488 with filter cubes for respectively DAPI (excitation, 350-400 nm; emission 415-480 nm) and FITC (excitation, 460-505 nm; emission, 510-565 nm). The acquisition system was configured to image one site per well. After the fluorescence of DAPI had been used to set the image-based autofocus, the exposure time for each fluorophore was determined with a negativecontrol well in which the fluorescence signal was set to 75% of the camera's maximal intensity. An image segmentation application from MetaXpress software was used to automatically analyze the images. The number of cells was estimated by selecting an average size and intensity level above the local background in the DAPI channel. A built-in translocation module that measured the intensity movement from one compartment to another was configured to determine the nuclear or cytoplasmic status of the Yap.
These measurements were acquired first for the positive and negative control wells to determine an arbitrary threshold for the intensity ratio that characterized nuclear translocation versus cytoplasmic retardation of Yap. The same threshold was then applied to each well treated with a small molecule to identify the substances that increased the number of cells with nuclear Yap without affecting cell survival. Positive controls were not used to normalize data, but to confirm whether the cells were healthy and behaving as expected. Images were stored in the MDCStore database and analyzed using MetaXpress software (Molecular Devices). Output data were uploaded and analyzed using the CDD Vault from Collaborative Drug Discovery (Burlingame, CA). The compounds showing over 10% Yap nuclear translocation and over 5000 cells per field were selected and the corresponding images were checked to verify the phenotype.
Human retinal organoids were produced from these cells  58   after incubation for 5 days in 10 μM TRULI and 10 μM EdU. The organoids used in the immunoblot analysis were 160-178 days of age and were incubated for 24 h in 10 μM TRULI. Immunohistochemistry of murine inner-ear sensory epithelia. Utricles were fixed in 4% formaldehyde (Thermo 28906) for 1 h at room temperature and then blocked for 2 h at room temperature with 3% bovine serum albumin (BSA; Jackson AB 2336846), 3% normal donkey serum (Sigma-Aldrich D9663), and 0. 3% Triton X-100 (Sigma 93443), in Tris-buffered saline solution (Thermo 28358). The primary antisera-goat anti-Sox2 (R&D AF2018, 1:1000), rabbit antimyosin 7A (Proteus 25-6790, 1:100), mouse anti-Yap (SC-101199, 1:500), rabbit anti-Sall2 (HPA004162, 1:200), and mouse anti-Pou4F3 (SC-81980, 1:100)-were reconstituted in blocking solution and applied overnight at 4 °C.
The ratio of the Yap to the Sall2 intensity was calculated for each condition in 680 cells after TRULI treatment and 570 cells for DMSO controls. For quantifying the proliferation of supporting cells, we imaged utricles at ×60, then assembled the images into a composite tiling (Grid Collection Stitching, Fiji). The maximal values in z-stacks were projected to display the entire supporting-cell layer of the sensory epithelium, excluding connective tissue and the surrounding epithelium. Using CellProlifer 60 , we counted the number of EdU-positive nuclei. Alternatively, EdU-and Sox-doubly positive nuclei were counted using Multi-point Tool in ImageJ. Images acquired from neonatal cardiac cultures were analyzed using ImageJ software by thresholding for cardiomyocytes according to endogenous tdTomato fluorescence.
This complex was selected because the sequence identity between Lats1 and Rock1 in the ATPbinding site is 72. 2% and the bound inhibitor shares an azaindole group with TRULI. Although the resolution of the structure is a modest 0. 33 nm (3. 3 Å), the electron density within the ATP-binding site is good and allows the conformation of the ligand and the amino acid side-chains to be determined unambiguously. The protein structure was downloaded from the Protein Databank, selenomethionines were changed to methionines, and missing sidechains were added with Schrodinger's Preparation Wizard. This program was also used to fix the orientations of the asparagine, glutamine, and histidine residues as well as the protonation states of all ionizable residues. Heteroatomic species such as water molecules, buffer solvents.
). A minimum of two biological replicates was collected for each condition, and at least 20 million 150 base-paired-end reads were sequenced for each replicate. Reads were mapped to GRCm38/mm10 genome assembly using STAR  66. Differentially expressed genes were identified by DESeq2 67. Only the protein-coding sequences were considered for FPKM calculation. For data visualization, the ggplot2 package was used (H. Wickham, Springer New York, 2009). Principal-components analysis was performed with pcaExplorer 68 on the 1000 genes whose differential expression was most significant. Adenoviral gene transfer. The AdEasy Adenoviral Vector System (Agilent) was used to create adenoviral vectors containing the full-length coding sequence of murine Atoh1 under the control of a cytomegalovirus promoter.
Acknowledgements This project was made possible by the dedicated personnel of two service facilities. At  Rockefeller University 's High-Throughput Screening Resource Center directed by  Fraser Glickman ,  Carolina Adura Alcaino  helped design assays for enzyme activity. The effort was supported in part by the  Robertson Therapeutic Development Fund. At the Tri-Institutional Therapeutics Discovery Institute (TDI)  supervised by  Peter Meinke ,  Leigh Baxt ,  Stacia Kargman ,  Robert Myers , and  Nigel Liverton  assisted with assay design and  Rui Liang  performed chemical syntheses. TDI is a 501(c)(3) organization that receives funding from its parent institutes (The  Rockefeller University, Memorial Sloan Kettering Cancer Center , and  Weill Cornell Medical College ), from  Takeda Pharmaceutical Company , and from  Mr.
Data availability The datasets generated during the current study have been deposited to NCBI's Gene Expression Omnibus (GEO) database, accession number GSE148528. For each figure, raw data from cell counting, intensity measurements for the immunohistochemistry, and images whole Western blots that include weight markers are provided in the source data file. PDB structure of ROCK1 is available at RCSB PDB 5KKS). Source data are provided with this paper.
Timing: 3-4 days This step describes the Image processing steps before quantitative analysis. 21. Process images with ImageJ using a custom made macro (enclosed in the supplementary file: Data S1_image-process-macro-forBudJ_related_to_step21. ijm) that: a. merges the 5 Z stack projections into one image and keeps the maximum intensity pixels from each stack. b. merges the LED Trans, 488 nm and 561 nm channels and displays them by ''color''. c. concatenates all 61 time points into one file. You can find an example of the image output after using the macro in the supplementary file Data S2_H4_Pos_11_input_images_for_BudJ_related_to_step21. tiff. This tiff file is used as input for BudJ analysis in step 22. We have added the following examples of movies for the red, green and LED trans channels that were generated after the image processing step: Methods video S1:H4den-dra2_red, related to step 21, Methods video S2: H4dendra2 green, related_to_step 21 and Methods video S3: H4dendra2_LED-Trans, related step 21.
QUANTIFICATION AND STATISTICAL ANALYSIS We use custom made Perl scripts to analyze the data generated by BudJ. The scripts are provided in the Data S3_related_to_Quantification_and_Statistical_Analysis. zip package in Supplementary Files. They are executed with the TrIPPed. bat program in Windows 10 (you need to read the Read-me_before_running_TrIPPed. txt file enclosed in the package before running the program). Table  1  shows the raw data generated by BudJ for one H4-dendra2 mother cell (cell 1 in the table) and its two daughters (cells 2 and 3 in the table). Time 0 is the first time point after photo-conversion. Images were taken every 8. 5 minutes. Fluorescence Intensities for each mother cell and its first and second daughter are first analyzed separately and then compiled for all cells.
ACKNOWLEDGMENTS The authors acknowledge the following funding sources:  ANR   2014 GenChroSeg grant  ( Agence Nationale de la Recherche, France)  (to M. R. -L. ) and  Labex EpigenMed fellowship (Laboratoire d'Excellence EpigenMed, University of Montpellier, France)  (to M. A. ). The authors thank  Virginie Georget  and  Leslie Bancel-Valle  ´e ( MRI, Biocampus, Montpellier ) for helping with the microscope setup.
Based on their mode of action, alginate lyases can be classified as endolytic enzymes that cleave long alginate chains by attacking internal positions in the polymer, and exolytic oligoalginate lyases that degrade smaller oligosaccharides starting from their ends. To date, alginate lyases are classified in 12 different polysaccharide lyase (PL) families in the Carbohydrate-Active enZYmes database CAZy  (Lombard et al. 2014) , namely families  PL5,. The latter five families were created only recently  (Helbert et al. 2019; Ji et al. 2019) , showing the unexpectedly large diversity of alginate lyases in nature and consequently the importance of alginate as carbon source. To understand how the diversity of alginate lyase sequences matches diversity of alginate structures, compositions and occurrences, it is important to match the different sequences with structural and biochemical characterizations  (Garron and Henrissat 2019).
The CAZy database currently contains 722 enzymes (as of May 2021) in the PL17 family, all from bacterial origin. This family is subdivided into subfamily 1 that contains endolytic hyaluronan and glucuronan lyases, and subfamily 2 (sf2) that contains exolytic oligoalginate lyases  (Lombard et al. 2010). The first characterized PL17 alginate lyase was AlyII from Pseudomonas sp. OS-ALG-9  (Kraiwattanapong et al. 1999). Since then, 20 alginate lyases have been biochemically characterized in PL17 subfamily 2  (Mathieu et al. 2018) , the majority originating from Gammaproteobacteria (15 enzymes) and a lower number from Bacteroidetes and Alphaproteobacteria (4 and 1 enzyme, respectively). Most of them show a substrate preference for mannuronate motifs compared with guluronate. Only one structure is available in the PL17 family, that of Alg17c from the marine gammaproteobacterium Saccharophagus degradans  (Park et al.
Mutagenesis analysis Three amino-acids were selected for individual point mutations based on their conservation in other alginate lyases (Figure  S1 ) and predicted roles as catalytic residues (Y274 and Y466) or charge neutralizer (H200). The mutations Y274A and Y466A virtually abolished the alginate lyase activity (Figure  S6 , 0. 2 and 0. 4% remaining activity compared with wild-type (WT) AlyA3, respectively). The mutation H200A strongly impaired the activity (13. 9% activity remaining). Crystal structure of AlyA3 and its complex with oligo-mannuronate substrate The crystal structure of AlyA3 was determined at 1. 42-Å resolution (Table  II ), using phases determined by molecular replacement and coordinates of the only other available PL17 alginate lyase (Alg17c, PDB code 4OJZ) as the starting model (Figure  5 ).
However, it is important to point out that in general, alginate-oligosaccharides or alginate block-structures are difficult to obtain as 100% pure, with perfectly defined composition. Consequently, activity observed on polyG or MG blocks of the various PL17 alginate lyases might result from M stretches present as impurities in those substrates. Conversely, the M-blocks we used for the determination of kinetic parameters were contaminated with 13% G motifs (estimated by NMR, data not shown). This might explain why k cat /K m was lower on M-blocks compared with the activity on DP5/6 oligo-mannuronates. The crystal structure of AlyA3 is globally similar to that described for Alg17c (PDB code 4NEI), having the same fold and quaternary arrangement, and structural comparison of this family to PL15 enzymes has been described previously  (Park et al.
Sequence analysis Characterized members of the PL17 family were retrieved from the CAZy database  (Lombard et al. 2014 ) and a literature survey. Their protein sequences were aligned using MAFFT  (Katoh et al. 2019)  with the L-INS-I algorithm and the scoring matrix BLOSUM62, using a structural alignment of Alg17C (PDB accession 4NEI) and AlyA3 (this study) as a constraint. The alignment was visualized using ESPript 3. 0  (Robert and Gouet 2014).
The recorded 1D carbon spectrum (using inverse gated proton decoupling) contains 32 K data points and has a spectral width of 200 ppm, 32 scans with a 30 • flip angle and relaxation delay of 2. 1 s (total recording time of 81 s). After completion of the time-resolved experiment, a 13 C-1 H HSQC spectrum was recorded. All NMR spectra were recorded on a BRUKER AVIIIHD 800-MHz spectrometer (Bruker BioSpin AG, Fällanden, Switzerland) Downloaded from https://academic. oup. com/glycob/advance-article/doi/10. 1093/glycob/cwab058/6302544 by Station Biologique user on 21 September 2021 Structure-function analysis of a new PL17 oligoalginate lyase 13 equipped with 5-mm cryogenic TCI probe. The spectra were recorded, processed and analyzed using TopSpin 3. 6pl7 and TopSpin 4. 0. 7 software (Bruker BioSpin AG).
42-Å resolution for AlyA3 and on Proxima-2 (SOLEIL, St Aubin, France) at 2. 16-Å resolution for AlyA3_Y274A in complex with oligo-mannuronate. All crystals belonged to space group P6 5 with unit cell parameters as given in Table  II , and all structures had two molecules in the crystallographic asymmetric unit. All data sets were treated and converted with the program suite XDS  (Kabsch 2010 ) and scaled using aimless  (Winn et al. 2011). The 3D structure of AlyA3 was solved by the molecular replacement method using PHASER  (McCoy 2007)  with Alg17c from S. degradans (PDB 4OJZ) as initial model. The complexed structure of AlyA3_Y274A was obtained starting from the coordinates of native AlyA3, after deleting all ligands, ions and water molecules. The starting phases were optimized by alternating cycles of model building in COOT  (Emsley and Cowtan 2004)  and refinement using the program REFMAC 5  (Murshudov et al.
Acknowledgements We are grateful to  Nelly Kervarec  from the Mass Spectrometry platform at the  Université de Bretagne Occidentale  for recording the MALDI-TOF mass spectra and to  Dr. Lionel Cladière  for assistance with the calibration of the size exclusion column. We thank  Martin Sevko  and  William Shepard  for their precious local contact support on beamline Proxima2 at  SOLEIL (Paris, France ) and ID29 beamline staff for their help at  ESRF (European Synchrotron Research Facilities, Grenoble, France ). The authors appreciate the access to the CristalO platform ( FR2424 ,  Station Biologique de Roscoff ), which is part of the  Biogenouest core facility network. We thank  Håvard Sletta  from the  Department of Biotechnology and Nanomedicine (SINTEF Industry ) in Trondheim, Norway, who provided purified alginate samples for the NMR experiments.
Funding EU for its support with regards to the  GENIALG Program  ( FP7-NMP , project  604530 , to M. C. and D. J. );  ANR  for its support with regards to the investment expenditure program IDEALG (http://www. idealg. ueb. eu/, grant agreement No. ANR-10-BTBR-04 , to M. C. and T. R. );  ANR  for its support with regards to project  ALGAVOR  ( ANR-18-CE02-0001-01  to F. T. and N. L. D. );  Research Council of Norway  through grants  294946  (The  Norwegian Seaweed Biorefinery Platform ) and  Norwegian NMR platform (NNP)   226244  to L. K. and F. L. A.
Modern analogue technique (MAT) September sea-ice concentrations (SICs) and SSSTs (January to March) are estimated by applying a MAT transfer function to the MIS 5e diatom assemblages. The MAT compares the relative abundances of 33 diatom species in each MIS 5e sample to the abundances of the same species in a modern reference dataset composed of 257 surface sediment samples (modern analogues) from the SO. Modern conditions for each surface sediment sample are interpolated on a 1 • × 1 • grid, with SSSTs from the World Ocean Atlas 2013  (Locarnini et al. , 2013)  and September SIC from the numerical atlas of  Schweitzer (1995). The MAT was implemented using the "bioindic" R package  (Guiot and de Vernal, 2011) , with chord distance used to select the five most similar modern analogues to each MIS 5e assemblage.
A cut-off threshold, above which any analogues are deemed too dissimilar to the MIS 5e sample, is fixed as the first quartile of random distances determined by a Monte Carlo simulation of the reference dataset  (Simpson, 2007). The MAT257-33-5 (based on 257 reference samples, 33 taxa and up to five analogues) utilized in this study is an evolution of the MAT195-33-5 detailed in  Crosta et al. (1998) , with the addition of a further 62 surface sediment samples (Fig. 1 ). The incremental evolutions of this transfer function over the last 20 years have yielded robust SST and sea-ice reconstructions when compared alongside other proxies within the same cores  (Civel-Mazens et al. , 2021; Crosta et al. , 2004; Ghadi et al. , 2020; Nair et al. , 2019; Shemesh et al. , 2002). Quantitative estimates of September SIC and SSSTs are produced for each MIS 5e sample from a distance-weighted average of the climate values associated with the selected analogues.
ANTA91-8 chronology The chronology for core ANTA91-8 was constructed by aligning the magnetic susceptibility (MS) to the LR04 benthic foraminifera δ 18 O stack  (Lisiecki and Raymo, 2005)  using the AnalySeries software  (Paillard et al. , 1996). Increased supply of terrigenous glacigenic detritus from the Antarctic continent to its margin and increased dust input from Patagonia and Australia to the pelagic SO during glacial periods resulted in higher MS values during glacial periods than interglacial periods  (Bareille et al. , 1994; Pugh et al. , 2009; Walter et al. , 2000). Tie points were selected in the MS record at the boundaries of MISs and sub-stages (Fig. 2  and Table 3 ). Ages for the MIS 5 sub-stage boundaries are from  Govin et al. (2009) , and the ages are translated from the LR04 chronology onto the AICC2012 chronology.
, 1998). Our new chronology is corroborated by R. leventerae, which occurs in abundances < 1 % in ANTA91-8 samples between 2. 72 and 3. 14 m b. s. f. (Chadwick and Allen, 2021a). If the sediments in this depth interval were deposited during MIS 6, as suggested by the  Ceccaroni et al. (1998)  and  Brambati et al. (2002)  age models, then the R. leventerae abundances in and the LR04 benthic δ 18 O stack (black) using the AnalySeries software  (Paillard et al. , 1996). Blue squares and connecting lines mark the tie points between records. the corresponding samples should be > 1 %  (Zielinski et al. , 2002).