dataesr/scientific-paragraphs-categorization huggingface dataset:
This panel develops their database from a numbr of sources including advertising and 'word-of-mouth'" terpretation of the results are social desir-ability and political sensitivities. Overall, care was taken in the ordering and fram-ing of questions and discussion prompts to minimise socially desirable responses……. Where possible, questions were not framed in the context of plain packaging, how-ever, it was necessary in some instances to ask participants to recall perceived changes since its implementation…. " Comment: The main question did ask ret-rospectively for self-reported changes since the standardised packaging legislation so social desirability may have influenced re-sponses High risk Comment: Enhanced pictorial warnings were implemented at the same time as stan-dardised packaging so it is difficult to sep-arate the effects.
g. , Operetta High-Content imaging system), as well as open-source tools such as Micropilot  (Conrad et al. , 2011) , the existing software did not meet our requirement in term of flexibility (use of ImageJ collection of plug-ins) or analysis specificity (measurement of chromosome segregation errors). On the concept of basic machine learning method (i. e. , decision tree), we developed automated open-source software called the Mitotic Analysis and Recording System (MAARS). MAARS provides automatic, multiparametric, and quantitative single-cell analysis of mitotic progression. We tested the robustness of this method on several S. pombe mutants and were able to identify unexpected phenotypes in wild-type and mutant cells. Our study illustrates the impressive benefits of using MAARS to analyze quantitatively mitotic fidelity in eukaryotic cells.
MAARS: an automated, robust, and open-source software for high-content analysis of mitosis To analyze quantitatively the mechanisms controlling mitotic fidelity, we developed an automated open-source image acquisition and on-the-fly analysis pipeline named MAARS. We first built the connection between the computer and the microscope using opensource software called Micro-Manager  (Edelstein et al. , 2010). In contrast to all available commercial software, Micro-Manager supports multiple microscopy platforms and is compatible with the image-processing package ImageJ. Thus MAARS can also be installed on a variety of wide-field microscopes. MAARS delivers a graphic interface and controls image acquisitions via Micro-Manager. With its user-friendly window, it is possible to adjust microscope parameters such as exposure time, frequency of image acquisition, type of color channel, and so on.
From prophase to metaphase, the average rate of spindle elongation was similar between mutants and wild-type cells (∼0. 1815 μm/min in wild type; n = 75). In contrast, in anaphase, mod20∆ and rad21-K1 mutants exhibited shifted or non-Gaussian distributions of spindle elongation rates compared with wild type (either lower or higher than wild type), although the average elongation rate was similar to wild type (0. 7936 μm/min in wild type, n = 75; 0. 7950 μm/min in mod20∆, n = 33; and 0. 8241 μm/min in rad21-K1, n = 88). To test whether some of these defects could be attributed to merotelic attachment, we upgraded the MAARS pipeline with a Python extension to automatically detect the presence of lagging kinetochores at anaphase in cells expressing spindle poles and kinetochore markers (see Materials and Methods and Supplemental Figure  S3 ).
We accurately detected anaphase B onset in 81% (n = 75) of analyzed cells. The incorrect detection can be explained by low sample quality (i. e. , one SPB moves out of focus during acquisition or either metaphase or anaphase length is too short). This quantitative analysis reveals some heterogeneity in metaphase length in mal3∆ (Figure  5C , from -20 to 0 min) and in the cohesin (https://github. com/bnoi/MAARS/tree/ master/doc/demos), our software is able to record cell division, specifically find mitotic cells, and extract on-the-fly multiple mitotic parameters. Our results demonstrate that MAARS is practical, time-efficient, and robust software for the multiparametric analysis of mitosis. This software provides statistical analysis at the cell population or single-cell level. Within the entire cell population, MAARS determines the percentage of cells in metaphase, anaphase, or cytokinesis and identifies cell size, spindle pole position, and so on.
The results are displayed as a spots distribution, but it is possible to return to the cell image to confirm unexpected results. MAARS can adapt to different cell types (including the budding yeast by adjusting the solidity parameter) and different fluorescent markers (GFP, red fluorescent protein [RFP], or CFP). By simply adjusting the solidity parameter, it is possible to use MAARS for the analysis of cells displaying abnormal cell shapes, such as a pear or T shape. We tested MAARS with long cells (cdc25-22) and short cells (wee1-50;  Nurse, 1975)  and obtained very similar data qualities, suggesting that our software is insensitive to cell shape variations (unpublished data). In this study, we specifically used MAARS to detect spindle poles and kinetochores, but other types of organelles could be detected by simply adjusting MAARS parameters such as number, size, or shape of spots.
Of importance, MAARS is delivered with the software Micro-Manager and is freely available  (Edelstein et al. , 2010). By characterizing a population of 10,000 cells within 5 min, MAARS provides statistical significance comparable to flow cytometry combined with a spatiotemporal analysis of mitotic events. MAARS can extract several parameters from single cells, such as spindle size, orientation, positioning, and elongation, as well as mitotic phase duration. MAARS was tested in various conditions and challenged with established genetic mutants. Our quantitative single-cell analysis revealed an unexpected heterogeneity in cell size, spindle elongation rate, and spindle positioning in mal3∆ or mod20∆ mutants, which are defective for cytoplasmic microtubule dynamics. Of importance, most of these differences were not detected when average values were measured.
By following single cells during mitotic progression, it will be possible to identify the timing of correction of merotelic attachment. MAARS will thus be a perfect expert system to decipher the mechanisms of merotelic attachment signaling and correction. With future developments, MAARS software will deliver high-content information for other mitotic parameters such as kinetochore congression at the metaphase plate or kinetochore oscillation  (Mary et al. , 2015). A combination analysis of these parameters will create new insights into of mitosis. For example, because mitosis in fission yeast implicates almost 500 proteins, a large-data combinational analysis could unravel new functions for known proteins or assign mitotic functions for unknown proteins. By adapting MAARS's existing segmentation parameters, we will be able to detect and analyze prokaryotic cell division with either single or multiple chromosomes.
Description of MAARS software MAARS is a Micro-Manager plug-in designed to perform on-the-fly analysis and real-time microscope control or postacquisition analysis. MAARS was written using the Java programming language and uses numerous image-processing libraries, including Fiji  (Schindelin et al. , 2012) , TrackMate  (Tinevez et al. , 2017) , LOCI bioformats  (Linkert et al. , 2010), and Micro-Manager (Edelstein et al. , 2010). MAARS is licensed under open-source license BSD (Berkeley Software Distribution License). Documentation and source code are available in a public repository (https://github. com/bnoi/MAARS/releases/tag/v1. 0. 0). The plug-in as a Jar file can be used in ImageJ. We recommend following and kinetochore markers as long as a suitable segmentation algorithm is implemented.
Mitotic cells and phase detection. The spindle is defined by the recognition of the two SPBs in a single cell. Metrics related to the distance between the two SPBs or their position within the cell can precisely the installation instructions (https://github. com/bnoi/ MAARS/blob/master/doc/install_MAARS. md). Several tutorials are also provided (https://github. com/bnoi/MAARS/tree/master/doc/ demos) to illustrate the simplicity and efficiency of MAARS software. Bright-field segmentation. Segmentation of S. pombe was performed using a correlation imaging technique based on bright-field images taken at various focal positions, as previously described  (Julou et al. , 2013). The principle of this correlation imaging technique is that under bright-field illumination, the cell wall of Manual annotation.
ACKNOWLEDGMENTS We thank the  S. pombe community  for supplying strains,  Olivier Gadal  ( Centre de Biologie Integrative, Toulouse ) for supplying a budding yeast strain with SPB markers,  Lucie Chadelle  for the comments on the MAARS tutorial, and  Thomas Mangeat  and  Guillaume Gay  ( DamCB, Marseille ) for helpful discussions. H. M. was supported by the  Fondation pour la Recherche Medicale. J. F. and S. C. were supported by the plan Cancer 2009-2013 Systems Biology. This work was funded by  Agence Nationale de la Recherche Blanc120601 Chromocatch  and the plan Cancer 2009-2013 Systems Biology. Microscopy equipment was funded by the  Centre National de la Recherche Scientifique ,  Agence Nationale de la Recherche Blanc120601  and l' Association de la Recherche sur le Cancer.
Structural comparison between OccJ-octopine and NocT-octopine complexes: a different octopine binding mode. The octopine bound between the two closed lobes of OccJ is very well defined in the electron density maps (Fig. 3c ), and is surrounded by 18 residues defining the ligand binding site of OccJ (Table  3 ). Both structures of OccJ and NocT in complex with octopine (PDB code 5ITP for NocT-octopine,  14  ) superimpose with an average RMSD of 1. 7 Å over all Cα atoms. They share a very similar binding site around the arginine moiety of octopine (Table  3  and Fig. 3c, d ). Indeed, the guanidyl side chain of arginine is wedged between two conserved aromatic residues (Tyr33/39 and Trp71/77 in OccJ/NocT) and points toward the opening of the cleft by making six hydrogen bonds with the conserved side chains of residues Glu30/36 and Gln159/165 and the carbonyl of Ala88/94.
Met117 and Ser207 in NocT interact with the pyruvate carboxylate in contrast to Ser169 that is too far from it. Ser91 in OccJ   once again appears as a major difference in the binding site of the pyruvate moiety of octopine in OccJ compared with NocT, making this residue potentially critical for octopine binding. Indeed, Ser91 in OccJ plays a remarkable role in holding octopine in three parts of the molecule, including both carboxyl groups. Ser91 is around 2 Å from the α-ketoglutarate of nopaline upon superposition with the 1. 75 Å resolution NocT-nopaline structure that we solved in this study (Table  S1 ; Fig. 3f ). This structure is very similar to that published at 2. 3 Å resolution (PDB code 4POX, RMSD of 0. 2 Å for all Cα atoms). As previously mentioned in Lang et al. 14  , Ser91 seems incompatible with a possible binding of nopaline to OccJ due to steric hindrance.
S1 ). Therefore, in contrast to OccJ, a serine at position 97 does not favour high affinity for octopine in NocT. We thus validated that the presence of Gly97 in NocT was essential for the spatial accommodation of the α-ketoglutarate moiety of nopaline. We solved the structure of NocT-G97S in complex with octopine at 2. 35 Å. Both structures of the WT NocT (PDB code 5ITP) and NocT-G97S mutant in complex with octopine are similar with an average RMSD of 0. 25 Å for all Cα atoms. Remarkably, both octopine molecules overlap and adopt a similar conformation, unlike observations in OccJ (Supplementary Fig. S2a ). Both structures, together with that previously reported of NocT-M117N in complex with octopine, (PDB code 5ITO,  14  ) clearly show that each bound octopine adopts a similar conformation, making similar protein interactions with residues at positions 117, 170 and 207.
A single mutation in LAO can transform LAO as an octopine binding protein. OccJ and LAO bind similarly the side chain of the arginine ligand and its carboxyl group (Table  3 ). Nonetheless, the amino group of the arginine ligand is tightly bound in LOA with the side chains of Ser72 (corresponding to Ser91 in OccJ) and Asp161 (Asn202 in OccJ). Importantly, the major ionic interaction is missing in OccJ leading to less efficient binding of the arginine ligand, in line with the micromolar affinity versus nanomolar affinity for LAO. The presence of Asp161 in LAO (Asn202 in OccJ) seems incompatible with an octopine binding ability, due to steric clash upon superposition of OccJ-octopine complex and LAO-arginine complex (PDB code 1LAF, Fig. 4a ). In agreement, LAO cannot bind octopine. To validate this hypothesis, we produced the OccJ-N202D mutant in   1  and Supplementary Fig.
Binding OccJ is present in a few bacteria. Using a threshold set at at least 40% identity, about 524 bacterial OccJ-homologous PBPs were recovered using blastP from NCBI bacterial sequence database and AgrobacterScope genome library (Genescope, France). All redundant sequences were removed, and the sequence of S. enterica LAO was added. The relation tree was built from 41 sequences (Fig. 5 ). Members of the OccJ subgroup sharing >69% sequence identity possess the octopine binding signature Glu30-Tyr33-Trp71-Ser91-Arg 96-Gln159-Asn111-Thr163-Ala164-Asn202. They belong to five octopine-type A. tumefaciens (B6, AF242881, TT111, NCPPB 1641 and Ach5) and two A. vitis (T1/7 and NCPPB 3554) strains and to several strains belonging to soil and plant interacting genera Sinorhizobium, Ensifer, Ochrobactrum and Shinella.
After cell lysis in denaturing buffer containing 50 mM Tris HCl pH 8, 10 mM imidazole and 7 M urea, the supernatant was loaded onto a nickel column equilibrated in the same buffer. Low-affinity binding contaminants were washed from the column with 50 mM Tris HCl pH 8, 20 mM imidazole, and 7 M urea. A buffer of 50 mM Tris HCl pH 8, 20 mM imidazole, and 500 mM NaCl was passed through the column to return the protein to renaturing conditions prior to its elution with 50 mM Tris HCl pH 8, 300 mM imidazole, and 500 mM NaCl. The eluted sample was further purified using a HiLoad 26/60 Superdex 200 prep grade (GE Healthcare) in 50 mM Tris-HCl pH 8 and 150 mM NaCl. The protein fractions are pooled, concentrated and stored at -80 °C. Crystallization and data collection. Crystallization conditions for unliganded OccJ (20 mg/mL) and OccJ-octopine (90 mg/mL protein and 6 mM octopine) were screened using Qiagen kits (Valencia, CA, USA) with a Cartesian nanodrop robot (Genomic solutions).
The conditions are reported in Table  2. NocT and NocT-G97S were co-crystallized as previously described  6. The crystals were manually reproduced in hanging drops experiments by mixing equal volumes of protein solution and precipitant solution. Crystals were transferred to a cryoprotectant solution (mother liquor supplemented with 25% PEG 400) and flash-frozen in liquid nitrogen. X-ray diffraction data sets were collected at 100 K on the Proxima 1 or 2 beamlines (SOLEIL synchrotron, Saint-Aubin, France). Structure determination and refinement. Data processing was performed using the XDS package  30  (Table  2 ). All the structures were determined by molecular replacement with PHASER  31. The crystal structures of both unliganded and liganded OccJ were solved using the coordinates of lobe 1 and lobe 2 of NocT monomer as separated search models (PDB code 4POW).
The structures of the NocT and NocT-G97S mutant complexes were solved using the whole NocT-octopine structure (PDB code 5ITP). Refinement of each structure was performed with BUSTER-2. 10 32 with NCS restraints when the asymmetric unit contains more than one protein molecule. TLS group was assigned for each structure. Inspection of the density maps and manual rebuilding were performed using COOT  33. The three dimensional models of octopine analogues were generated using the ProDRG webserver  34. Refinement details of each structure are shown in Table  2. Molecular graphics images were generated using PyMOL (http://www. pymol. org).
Fluorescence titration measurements. Binding of ligands to OccJ, NocT, LAO and protein mutants was monitored by autofluorescence by exciting the protein at a wavelength of 295 nm and monitoring the quenching of fluorescence emission of tryptophans at 335 nm. All experiments were performed at 20 °C in 96 wells plates (1/2 Area Plate-96F, Perkin Elmer) using Tecan Infinite M1000 reader (Tecan), in 50 mM Tris-HCl pH 8. 0, 150 mM NaCl buffer with a fixed amount of proteins (1 µM) and increasing concentrations of ligand. No ligand exhibited an emission signal at 335 nm. The data were analysed using Origin ® 7 software and fitted to the equa- tion f = ΔFluorescence max * abs(x)/(K D + abs(x)). Isothermal titration microcalorimetry measurements. Isothermal titration microcalorimetry experiments were performed with an ITC200 isothermal titration calorimeter from MicroCal (GE Healthcare).
The experiments were carried out at 20 °C. Protein concentration in the microcalorimeter cell (0. 2 mL) varied from 20 to 150 µM. Nineteen injections of 2 µL of ligand solution (amino acids, opines and octopine derivatives) at concentration ranging from 240 µM to 1. 5 mM were performed at intervals of 180 s while stirring at 500 rpm. The experimental data were fitted to theoretical titration curves with software supplied by MicroCal (ORIGIN ® ). This software uses the relationship between the heat generated by each injection and ΔH (enthalpy change in kcal/ mol), Ka (the association binding constant in M -1 ), n (the number of binding sites), total protein concentration and free and total ligand concentrations  35. Phylogenetic analysis. Sequences were analyzed using blastP algorithm from NCBI (http://blast.
ncbi. nlm. nih. gov/). Alignments of OccJ and related sequences were conducted using the ClustalW software. Relationship trees were constructed using the MEGA software, Version 5. Phylogeny was inferred using the neighbor-joining method. The bootstrap consensus tree inferred from 1,000 replicates was taken to represent the evolutionary history of the analyzed taxa. The evolutionary distances are in units of the number of amino acid substitutions per site.
Crystallographic data and refinement parameters. Values in parenthesis are those for the last shell. CC 1/2 = percentage of correlation between intensities from random half-dataset (P. A. Karplus, K. Diederichs, Science 2012, 336, 1030-1033). which Asn202 was replaced by an aspartate residue. Affinity measurements by different techniques suggest that OccJ-N202D is similar to the WT with respect to binding of octopine and arginine, and demonstrate that the presence of this aspartate in OccJ has no effect on the ligand recognition (Table.
Accession Codes. Coordinates and structure factors have been deposited at the  Protein Data Bank (PDB)  under accession codes 5ORE for OccJ structure, 5ORG for OccJ-octopine structure, 5OT8 for NocT-G97S-octopine structure, 5OTA for NocT-octopinic acid structure, 5OTC for NocT-noroctopinic acid, 5OT9 for NocT-histopine structure and  5OVZ  for the high resolution structure of NocT-nopaline complex.
Acknowledgements AV, JL, MAN, YD, DF and SM were supported by  CNRS  and  ANR-Blanc   SENSOR  ( ANR-12-BSV8-0003-01/02/03 ) and AES by a PhD-grant of the  Paris-Saclay University  (ED 425). This work benefited from the I2BC crystallization and protein-protein interactions facilities, supported by  FRISBI   ANR-10-INSB-05-01. We acknowledge SOLEIL for provision of synchrotron radiation facilities (proposal ID 20130869, 20140774 and 20150780) in using Proxima beamlines. We are grateful to  Ben Ashley  for critical reading of the manuscript.
General locomotor behavior (total distance moved and velocity of movement) was analyzed with EthoVision XT 7. Novel Object Recognition Test. At 8 weeks, the animal's exploratory behavior and its nonspatial learning and memory capacities were assessed with novel object recognition test  [23, 24]. Briefly, the rat (n 5 10/group) was placed in an open apparatus (100 3 100 cm). Each animal's journey was video-recorded and analyzed with the EthoVision XT7 software. The day before the test, animals received a habituation session of 30 minutes in the OF. On the testing day, each rat completed four 3-minute sessions (intertrial interval of 30 minutes). During the first three sessions, the rat was allowed to explore the arena, which contained two identical objects. Total exploration time of the two objects was noted.
Single Photon Emission Computed Tomography Radiotracers. Hexamethylpropyleneamine oxime kit (HMPAO, Cerestab, General Electrics, Velizy-Villacoublay, France, http:// www. gehealthcare. com) was radiolabeled with fresh [ 99m Tc]TcO - 1. 5% sevoflurane and a cerebral SPECT/CT imaging was acquired for 20 minutes (NanoSPECT/CT1 camera, Bioscan Europe Ltd. , Paris, France). SPECT Image Analysis. Images analysis was performed using the 3D-ROI module part of InVivoScope software v2. 0p4 (InviCRO, Boston, https://www. invicro. com). Two volumes of interest (VOI) were drawn over right (ipsilateral) and left (contralateral) cerebral hemisphere for each animal in the axial section. Radioactivity inside each VOI was quantified and corrected by the tissue volume (MBq/mm 3 ). We then calculated the i/c ratios (i/c, %).
Statistical Analysis Values were reported as mean 6 SD unless otherwise indicated. Physiological parameters were analyzed by unpaired t test. TUNEL and immunoassaying data were tested for normality and were compared with unpaired t test with Bonferroni correction for post hoc intergroup comparisons. Behavioral and morphological outcomes were compared between the groups using one-way analysis of variance (ANOVA) followed by post hoc Bonferroni test. Statistical analyses were performed with Prism software v5. 03 (GraphPad Software, La Jolla, CA). A p value <. 05 was considered statistically significant.
Using ECFC from cord blood is more debated. Their use in clinical practice remains tricky since ECFC are allogeneic and take several days to be produced. We have characterized cord blood stem cells of neonates with neonatal asphyxia and compared them with those from healthy newborn infants (NEOCORD, NCT01284673). We observed that neonatal asphyxia seemed to enrich cord blood stem cell content, with a higher quantity of endothelial progenitor cells found in cord blood of asphyxiated neonates (unpublished data). All these arguments confirm our choice to establish and set up a clinical study (NEOSTEM, NCT02881970). Three other studies are ongoing (NCT01506258, NCT02256618, NCT02605018) and the neonatal physician community has placed much hope in these strategies.
D. Genome-wide distribution of selected RNAi inserts determined by ion torrent sequencing, vertical dashes representing the boundaries of each chromosome (with Chromosome 1 at the left). Targets highlighted comprise those with a high enrichment in the selected population and a predicted RNA binding domain. The full dataset is available in S1 Table. https://doi. org/10. 1371/journal. ppat. 1006279. g001. The upper band was somewhat less abundant in stumpy forms compared to slender and procyclic forms, and in procyclic forms the overall abundance of both 57 and 59 kDa bands was greater than in either bloodstream form. Consistent with this, quantitative proteomic analysis has previously indicated that REG9. 1 is 2-  [17]  to 3-fold  [30]  more abundant in procyclic forms vs. stumpy forms. To further explore the function of REG9.