dataesr/scientific-paragraphs-categorization huggingface dataset:
N-(N'-acetylindol-3-yl)methyl-phtalimide X-ray data collection and refinement of the structures 2, 14, and 15. X-ray diffraction data were collected at 295 K for 2, 14 and 15 on SuperNova kappa-geometry diffractometer equipped with copper X-ray source using CrysAlisPro software CrysAlisPRO, Agilent Technologies, Yarnton  49. Absorption was corrected for by multi-scan methods, (empirical absorption correction using spherical harmonics, implemented in SCALE3 ABSPACK scaling algorithm). The structures were solved by direct methods using SHELXS-97  50  , and refined by full-matrix least-squares calculations on F 2 with SHELXL-2014/7  51. Anisotropic displacement parameters were refined for all non-hydrogen atoms. H atoms were positioned geometrically and allowed to ride on their respective parent atoms, with Uiso(H) = 1.
2 Ueq(C) and C-H = 0. 93 Å for aryl, C-H = 0. 97 Å for methylene and C-H = 0. 96 Å for methyl, except for the hydrogen atom bonded to N1 in 14 which was located on a difference Fourier map and allowed to refine freely. The crystal structure of 14 is noncentrosymmetric, and the value of the Flack parameter x = 0. 08(5) indicated  52  the proper assignment of the absolute structure of the crystal. MER-CURY  38  , a computer graphics program, was used to prepare drawings. Crystal data and data collection and refinement details are collected in Table  S1  (supplementary part). Calculation. PM5 semiempirical calculations were performed using the WinMopac 2003 program  39, 53, 54. In silico study. Potential pharmacological activities of the synthesized compounds have been determined based on computer-aided drug discovery approach with in silico Prediction of Activity Spectra for Substances (PASSs) program (Pharma Expert Predictive Services©2011-2013, Version 2.
Data availability Crystallographic dates CCDC 1485335-1485337 contains supplementary crystallographic data for the structure 2, 14 and 15. These data can be obtained free of charge at www. ccdc. cam. ac. uk/conts/retrieving. html [or from the Cambridge Crystallographic Data Centre (CCDC), 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44(0) 1223 336 033; email: deposit@ccdc. cam. ac. uk].
2C ). Many studies have explored the possibilities of using vascular information to register volumes either intra or inter modalities. In clinical neuroscience, an active strong area of research has been to explore brain shift correction during intraoperative surgery in order to correct brain MR images from live Doppler data  [11]. By leveraging the brain vascular footprints and integrating the approach directly in the functional ultrasound experimental workflow, we can improve the quality and standardization of the experimental data. In that regard, our 'on the fly' BPS approach brings functional ultrasound imaging closer to fMRI, where MR images can be used to position acquisition slices and MR contrast templates can be used by software suites such as SPM or FSL to wrap and normalize the datasets to a common space for group-level analysis  [12, 13].
On the fly vascular registration and neuronavigation: During the experiments, a 3D Doppler volume was acquired and then automatically registered using a dedicated prototype software. First, Doppler data was used to automatically perform an affine monomodal registration with the reference Doppler. The chosen registration algorithm is based on Mattes mutual information metric maximization  [21, 22]  to measure how similar the images are and an evolutionary optimizer  [23]  to find at each iteration a set of parameters that produce the best registration result (Mathworks, Natick, Massachusetts, USA). To ensure the reproducibility of registration, all the spatial samples were used to compute the probability density over 50 bins enabling mutual information estimation between the two data sets. The software then automatically applied the geometric transformations from the Allen Atlas space to the 3D volume through the Reference scan to overlay the Allen structures directly on the Doppler scan and to successively acquired scans.
Automatic registration is as accurate as neuroanatomist expert annotation The accuracy of the vascular registration is a critical point in the proposed neuro-navigation approach. We first assessed the accuracy of the ultrafast doppler vascular to vascular volume registration in the same mouse at different time points. Inherent spatial shifts between acquisitions is inevitable during the experiment and the mouse installation in the stereotactic frame. The green-magenta representation illustrates this initial misalignment between the two datasets before registration (Fig. 3A ). The first acquisition (time point t0) is displayed in green) while the second acquisition from the same animal acquired at time point t1 (24h after) is considered to be the moving volume (magenta) that we want to register.
After registration and resampling of the second volume to the first one, a good match is found by the registration algorithm between the two vascular datasets (Fig. 3A ) as illustrated by the dominance of white color. On the same animal, the normalized cross-correlation between the reference and registered volumes shows a strong (value = 0. 9) and sharp (width at half-maximum dx = 0. 65 mm, dy = 1. 34 mm, dz = 0. 64 mm) peak located in the center (Fig. 3C ). As reference, autocorrelation (same volume) yields a full width at half maximum (dx = 0. 5 mm, dy = 1. 15 mm, dz = 0. 55 mm). For registration between different animals and although the details from vascular networks are different, the general vascular architecture still enables a good registration mainly thanks to large vessels (Fig. 3B ) as illustrated by the white areas located in larger vessels in the green-magenta representation.
Then we compared the discrepancies of the landmarks placed by trained experts in different datasets with those automatically predicted by the registration as described in Material and methods. The results are reported in Table  1. First, we compared independently each expert annotation to the BPS registration. Automatic registration had an average shift of 120 ∓ 84 μm (ranging from 78 ∓ 58 to 158 ∓ 94 μm) for the four vascular landmarks compared to the first neuroanatomist expert annotation. The second expert annotated the landmarks with an average 130 ∓ 82 μm (ranging from 105 ∓ 70 to 155 ∓ 90 μm) shift compared to the automatic registration. As a comparison, we compared the two experts' annotations to one another. Their estimations were globally misaligned by 215 ∓ 87 μm (ranging from 184 ∓ 94 to 253 ∓ 101 μm).
Finally, the super resolution vascular images were used here only as a mean to investigate the registration accuracy but they could one day be used as a second step to further refine the registration to tens of micrometer scale, enabling extremely precise neuronavigation in depth in a unique way. The brain vascular GPS approach presented here enables on the fly complex brain navigation without neuroanatomic expertise in the context of functional ultrasound acquisition and data analysis with more accurate positioning and identification of regions of interest. It could help with the standardization of acquisitions in standardized planes to allow the building of large, reproducible and reliable fUS datasets and studies. Beyond mice studies, the BPS proposed here can be generalized to many other animal models including rats, ferrets, marmosets, non human primates as well as to clinical applications such as transcranial imaging  [17] , neurosurgery guidance or neonate functional imaging.
For each pair, the automatic registered data was resampled in the reference dataset space and two neuroanatomists experts were asked to annotate four landmarks within the two datasets. Individual landmark 3D distance shifts between registration prediction and expert annotation were averaged over the 20 estimations, as well as the overall shift. Automatic registration (AR) was compared to individual expert annotation and the two experts annotations were compared to each other. Automatic registration predictions were globally shifted by 120 ∓84 μm related to the first expert annotation and by 130 ∓82 μm related to the second whereas inter-annotator shift was globally estimated to 215 ∓87 μm for inter-animal datasets registration. The same shifts are estimated to respectively 164 ∓78 μm , 220 ∓104 μm and 259 ∓102 μm for inter-animal datasets registration.
Acknowledgments This research work was supported by  Inserm ,  ESPCI Paris ,  CNRS  and the  Inserm ART (Technology Research Accelerator) in "Biomedical Ultrasound" , the  ANR  (funding  18-CE37-0005-01  and  16-CE19-0004 ). This work was also supported by  Iconeus company  under a research collaboration grant  Iconeus/Inserm-ESPCI Paris-CNRS. The authors would like to thank all engineers and scientists at Iconeus for their help and support.
Bacterial Zeta Potential Changes in the surface groups of the bacteria were indirectly measured by determining the surface charge of the fresh and rehydrated freeze-dried Lp. plantarum HAC03 cells with or without the food additives incorporated into the mix. The assay was carried out by mixing the previously rehydrated bacteria with 9 mL of double-distilled water (DDW) at pH 2. 0 to reach a final concentration of 1 × 10 8 CFU/mL. The pH was measured and corrected to pH 2. 0 by adding 0. 1 N HCl or 0. 1 N NaOH, and 800 µL samples were loaded in DTS1070 cuvettes. The samples were measured on a Zetasizer Nano ZEN 3600 after 2 min equilibration time (Malvern Panalytical, Malvern, UK) and the Smoluchowski model was used to convert electrophoretic mobility data to zeta potential values  [34] , which were considered as the average of three reads.
Product Formulation Based on analyses of the initial data generated from evaluations of the effects of 40 different food grade ingredients, seven food additives were selected based on their performance for protecting Lp. plantarum HAC03 freeze-dried cells during the simulated GIT passage, for enhancing bacterial adhesion to mucin, and modulating zeta potential values close to fresh overnight cell cultures. Five preparations (A to E) were formulated (Table  1 , with different proportions of a combination of different food additives. Effectivity of the formulations was first evaluated based on supporting Lp. plantarum HAC03 viability after SSDP, using plate counting and subsequent confirmation in real time by flow cytometry. The formulation with the best outcome was also assessed by in vitro essays for cell adhesion and immune stimulation to determine whether there was an enhancement of adhesion to Caco-2/TC-7 and immune response stimulation in a macrophage cell line, which are probiotic properties frequently compromised by the freeze-drying process.
05% NaN 3 (Sigma Aldrich). The cells were stained by adding 20. 4 µL PI to a final concentration of 30 µg/mL and incubating for 1 min in the dark after gently mixing. Bacterial viability was determined by Flow Cytometer ZE5 and Everest software v 2. 2. 08. 0 (Bio-Rad Laboratories, Hercules, CA, USA). Fresh overnight cultures and freeze-dried Lp. plantarum HAC03, rehydrated only with distilled water, were used as controls.
Statistical Analysis To determine the statistical significance of differences between the treatments, the results of three independent experiments were analyzed. Statistical analysis is described in each figure legend and was determined using GraphPad Prism 6 software (San Diego, CA, USA). One-way ANOVA was used for comparing more than 3 groups. Differences were adjudicated using the post hoc analysis recommended by PRISM. Statistical outliers were determined via the ROUT test (p < 0. 05) and removed. p < 0. 05 was considered to be statistically significant.
Hydrophobicity has also been linked to adhesive properties in bacteria; therefore, it was essential to determine if Lp. plantarum HAC03 hydrophobicity was also affected and The results showed that after the GIT simulation, the viable numbers of Lp. plantarum HAC03 determined by plate counting corresponded to the data obtained by flow cytometry. The viability of freeze-dried cells rehydrated with DW was 6. 17% determined by flow cytometry and 4. 92% by plate counting (Figure  1  and Table  1 ). The flow cytometry results showed a viability of fresh cells and freeze-dried bacteria reactivated with B-active solution of 49. 95% and 37. 87%, respectively, compared to the 27. 34% and 36. 36% determined by plate counting (Figure  1  and Table  1 ). The differences in results between the plate counting and flow cytometry could be related to method sensitivity and to determining the counts with the flow cytometer in real time.
In this way, the relevant data and information sourced from widely scattered sources were assessed to comprehensively illustrate the relative potential of A. marocana in different economic sectors. Feasibility evaluation of A. marocana (Level II evaluation) involved point-scoring of 12 selected attributes, considered either as prerequisites of common interest across various economic sectors (eight attributes), or as unique identity elements or special features (four attributes) to be exploited in terms of product branding and marketing  [18]. The designated readiness timescale evaluation for the sustainable exploitation for A. marocana (Level III evaluation) involved the completion of eight criteria, based on SWOT (Strengths, Weaknesses, Opportunities, Threats) and gap analyses. These evaluations were sourced from previous research or our own  [18]  and are presented herein in detail for A.
Then, the taxonomically identified stored seedlot of A. marocana (Figure  6 ) obtained a unique IPEN (International Plant Exchange Network) accession the EU Regulation 511/2014. Then, the taxonomically identified stored seedlot of A. marocana (Figure  6 ) obtained a unique IPEN (International Plant Exchange Network) accession number MA-1-BBGK-20,428 (identifier and passport data illustrating origin, restrictions of use, storage institution, year of collection, and identity number), and it was photographed using a Ricoh WG-6 camera with an incorporated LED ring light.
Statistical Analysis The experimental design was a completely randomized design with two factors. The factors were the duration of CS and the incubation temperature (3 × 4 factorial design). The GP data was transformed to arc-sine square root values before analysis  [57, 58]. The transformed data, as well as the MGT data, were checked for normality and homogeneity of variances, and they were analyzed using the ANOVA method  [58]  in the frame of the general linear model (GLM), while the comparisons of the means were performed using the R-E-G-WQ test  [59, 60]. All statistical analyses were carried out using SPSS 21. 0 (SPSS, Inc. , Armonk, New York, USA).
15 pC and 0. 22 pC. This event corresponds to a nuclear reaction between a high energy proton or neutron and a silicon nucleus. Besides, both light ions are emitted horizontally in the pixel array. The pixel charge level treatment along the ion track allows the LET profile of the secondary ions to be deduced. It is not possible to deduce the incidental particle characteristics (i. e. nature, energy and direction) precisely. Nevertheless, analyses based on linear energy transfert characteristics and the nuclear neutron/proton databases suggest that a neutron or a proton with significant energy (several hundred MeV) will be able to induce this secondary ion configurations.
Paraffin-embedded bladder biopsy sections were examined by fluorescence in situ hybridization (FISH) specifically targeting the rpoB gene of the M. tuberculosis complex to detect M. bovis BCG. After deparaffinization, tissue sections were treated with Sudan black (Sigma-Aldrich, Saint-Quentin-Fancy, France) to reduce the autofluorescent background  [5]  and the FISH protocol was performed using an oligonucleotide probe specific to the M. tuberculosis complex, as previously described  [6]. Microscopic observation and image acquisition were performed using the 63 × objective of the Zeiss LSM 800 confocal microscope, driven by ZEN software (Zeiss, Marly-le-Roi, France). BCG mycobacteria were detected in the bladder biopsy section, appearing as red fluorescent bacilli, and the cell nuclei were visualized using L-4 0 ,6-diamidino-2-phenylindole (DAPI) (blue-fluorescent signal) (Fig.
Acknowledgements This work was supported by the  French Government  under the  Investissements d'Avenir (Investments for the Future ) programme managed by the  Agence Nationale de la Recherche  ( French National Research Agency (ANR)  (ref. Méditerranée Infection  10-IAHU-03 ). This work was supported by the  Région Le Sud (Provence Alpes Côte d'Azur) and European funding  ( FEDER PA 0000320 PRIMMI ). Remerciements Ces travaux ont été soutenus par le gouvernement français dans le cadre du programme  Investissements d'Avenir géré par l ' Agence Nationale de la Recherche (ANR) , [référence : Méditerranée Infection  10-IAHU-03 ]. Ce travail a été soutenu par la  Région Le Sud  ( Provence Alpes Côte d'Azur) et par un financement européen  [ FEDER PA 0000320 PRIMMI].
These features indicate that SNI1/NSE6 is more susceptible to variation, which may explain identification of this gene as a modifier of meiotic crossover. Our data suggest that the I235V substitution in SNI1 is casual to rQTL4, although it is unclear how this change affects protein functionality as evidenced by altered recombination frequency (Fig. 1 ) and different responses to environmental changes (Fig. 2E ). Previously, variation in REC8, a meiosis-specific component of cohesin, has been implicated as causative for differences in genome-wide recombination rates in even-toed ungulates  (27, 28, 71)  and plants  (32, 33). Our work reveals that the SMC5/6 complex, similar to the cohesion complex (SMC1/SMC3), plays a role in crossover formation. Cohesin is a component of the chromosome axis, which holds sister chromatids together and organizes them into multiple chromatin loops  (72).
Materials and Methods Plant Material. All the lines used in the study are described in SI Appendix, Table  S37. Genotyping of mutant lines was performed using oligonucleotides as described in SI Appendix, Table  S38. Plants were grown in controlled environment chambers at 22 °C with long day 16-/8-h light/dark photoperiods, 60% humidity, and 150-μmol light intensity. Prior to germination seeds were kept for 3 d in the dark at 4 °C to stratify germination. Measurement of Crossovers Using Seed-Based Systems. Pictures of seed were acquired using epifluorescent stereomicroscope Lumar version 12 (Zeiss) equipped with a charge-coupled device (CCD) camera in brightfield, ultraviolet (UV) through a dsRed filter, and UV through a green fluorescent protein filter. The CellProfiler program was used to identify seed boundaries in micrographs and to assign a dsRed and eGFP fluorescence intensity value to each seed object  (37, 82).
Histograms of seed fluorescence were used to classify fluorescent and non-fluorescent seeds for each color. The genetic distance is calculated as cM = 100 × (1-(1-2(N G +N R )/N T ) 1/2 ), where N G is the number of green alone seeds, N R is the number of red alone seeds, and N T is the total number of seeds analyzed. rQTL Mapping. Genomic DNA was extracted using cetrimonium bromide and genotyped using PCR amplification of Col/Ler simple sequence length polymorphism, cleaved amplified polymorphic sequences (CAPS), or derived CAPS markers (SI Appendix, Table  S39 ). R statistical package rQTL was used to performed one-dimensional QTL mapping  (83). The Haley-Knott regression algorithm using 0. 1-cM steps for rQTL4 fine mapping was implemented. To fit models with multiple QTLs, the fitqtl function with Haley-Knott regression was used.
Cytological Analysis. Chromosome spreads and FISH were conducted following the previously described protocol  (84). Immunolocalizations were performed as previously described  (85). The primary antibodies were anti-AtZYP1 (rat; 1/500 dilution), anti-MLH1 (rabbit; 1/500 dilution), anti-ASY1 (rabbit, 1/500), anti-RAD51 (rat; 1/500), and anti-SYN1 (rabbit; 1/500). Secondary antibodies were anti-rat IgG FITC-conjugated (Agrisera) and antirabbit IgG Alexa Fluor 555 conjugated (Molecular Probes) for immunolocalizations to detect ZYP1+MLH1 and ASY1+RAD51 and anti-rat Alexa Fluor 555-conjugated (Molecular Probes) and anti-rabbit FITC-conjugated (Sigma-Aldrich) for the immunolocalization to detect ZYP1+SYN1. For scoring RAD51 and MLH1 foci, blind analyses were applied as a protection against bias. Slides were examined on an Olympus BX61 epifluorescence microscope equipped with a CCD Olympus DP71 camera and analyze using analySIS software (Soft Imaging System).
Fertility Assays. Pollen viability was assessed from 1,500 pollen grains collected from open flowers of three plants per genotype, using Alexander staining  (86). Seed set and silique length were assessed from five fruits, located at positions 6 through 10 of the main stem, in ten plants per genotype. Additional Experimental Procedures. A detailed description of qRT-PCR analysis, DNA extraction, and library preparation for GBS, GBS bioinformatics analysis, Pollen-based measurements of genetic distance, and crossover interference can be found in SI Appendix. Data Availability. All data are publicly available. Sequencing data for sni1 Col×Ct GBS libraries have been deposited under ArrayExpress accession E-MTAB-9413  (87).
ACKNOWLEDGMENTS. We thank  Gregory Copenhaver  ( University of North Carolina ),  Avi Levy  (The Weizmann Institute), and  Scott Poethig  ( University of Pennsylvania ) for FTLs (CTLs);  Erik Wijnker ,  Jose van der Belt , and  Joost Keurentjes  ( University of Wageningen ) for the CCCLC chromosome substitution line;  Xinnian Dong  ( Duke University ) for the sni1-1 mutant line;  Jane Parker  and  Raphael Mercier  ( Max Planck Institute for Plant Breeding Research ) for the Col-eds1-2 and fancm-1 mutant lines, respectively;  Chris Franklin  ( University of Birmingham ) for ZYP1,  MLH1 ,  ASY1 ,  RAD51 , and SYN1 antibodies; and  Ales Pecinka  ( Institute of Experimental Botany, CAS ) for critical reading of the manuscript. This work was supported by the  Polish National Science Center Grants   2016/21/B/NZ2/01757  and  2016/22/E/NZ2/00455  to P.
A. Z. and the  Foundation for Polish Science Grant  ( POIR. 04. 04. 00-00-5C0F/ 17-00 ) to P. A. Z. N. F. -J. is a PhD fellow funded by the trainee teaching staff program of  Spanish Ministry of Education  ( FPU16/02772 ). Work in the Henderson group was supported by grants from the  European Research Council  ( SynthHotSpot Consolidator Award ) and  Biotechnology and Biological Sciences Research Council  Grants  BB/L006847/1  and  BB/M004937/1. M. P. acknowledges the support of the  European Union  ( Marie Curie Innovative Training Networks, Meiotic Control of Recombination in Crops [MEICOM] 765212 , and  European Cooperation in Science and Technology [COST] Action CA 16212 "INDEPTH").
Statistical analysis Statistical analyses were carried out with StatView, version 5. 0, software (Abacus Concepts, Berkeley, CA). Comparison between groups was performed using the χ2 test for categorical data and nonparametric Mann-Whitney U test or Kruskall Wallis test for continuous data. The relationship between 2 continuous variables was evaluated using the nonparametric Spearman rank correlation test. Overall survival was analyzed by Kaplan-Meier method, and the curves were compared using the log-rank test. Multivariate survival analysis was performed using Cox's regression model. P-values <0. 05 were considered significant.