dataesr/scientific-paragraphs-categorization huggingface dataset:
FLAM8 was detected using a rabbit polyclonal serum (1:1,000 dilution) that was kindly provided by Paul McKean (University of Lancaster, UK). GFP/YFP visualization on live trypanosomes was performed as described  (Buisson et al. , 2013). IFT videos were acquired using a Zeiss inverted microscope (Axiovert 200; Jena, Germany) equipped with an oil immersion objective (magnification 100× with numerical aperture 1. 4) and a spinning disk confocal head (CSU22; Yokogawa, Tokyo, Japan). Images were acquired using Volocity software with an electron-multiplying charge-coupled device camera (C-9100; Hamamatsu) operating in streaming mode. A sample of parasites (6 to 8. 10 6 cells/mL) was taken directly from the culture and trapped between slide and coverslip. The samples were kept at ambient temperature and used no longer than 30 min.
Acknowledgements We thank  Brice Rotureau  for help in quantitative analysis of cell motility. We acknowledge  Derrick Robinson  and  Paul McKean  for generous gifts of antibodies, and  Raphaël Demonchy  for preliminary observations of the KIF9B phenotype. We thank  Jean-Yves Tinevez  (Imagopole) and  Gérard Pehau-Arnaudet (Ultrapole)  for training and valuable advises for light and electron microscopy, respectively.
Funding C. F. is supported by fellowships from  French National Ministry for Research and Technology  (doctoral school  CDV515 ) and from  La Fondation pour la Recherche Médicale  ( FDT20150532023 ). This work was funded by  ANR  grants ( 11-BSV8-016  and  14-CE35-0009-01 ) and by  La Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20150734356 ).
Methods This study is based on the correlation of wells (well-logs with a description of the cuttings and a few cores) and outcrops (Fig. S1  in the Supplement). The well-log database consists of: (1) 213 petroleum wells (gamma-ray and few resistivity logs), (2) 50 petroleum coredrills (spontaneous polarization and resistivity), and (3) 114 seismic core-drills (gamma-ray only). Data (1) are available from BEPH ("Bureau Exploration Production des Hydrocarbures", www. beph. net), (  2 ) and (3) are available in the BSS ("Banque du Sous-Sol", http://infoterre. brgm. fr). The few cores that are available come from scientific programmes of the 1960s and 1970s (Chaignes, Montjavoult, Le Ludes, Cuise-la-Motte, Le Tillet, Mont Bernon; Paris University/BRGM projects), mining projects (Provins, Brie 1, 2, 3, 4;  Wyns and Ducreux, 1983) , or BEPH fundings (Ste Colombe, "Craie 700" project;  Mégnien and Hanot, 2000; Fig.
Time lines calibrated in absolute ages In a low-subsidence setting, the time resolution of the dating is of primary importance to quantify the accommodation at high resolution. Palaeogene deposits are dated by biostratigraphy and chemostratigraphy, but only the PETM (Palaeocene-Eocene Thermal Maximum) is dated by chemostratigraphy  (Quesnel et al. , 2011). Biostratigraphic data are based on calcareous nannofossils  (Aubry, 1983; Janin and Bignot, 1993; Bignot et al. , 1994; Steurbaut, 1998), dinocysts (Wezellielacea -Chateauneuf and Gruas-Cavagnetto, 1978) , benthic foraminifera  (Bignot and Neumann, 1991) , charophytes  (Pomerol and Riveline, 1975) , and mammals  (Russel, 1964; Nel et al. , 1999; Smith et al. , 2014). Dinocyst data were re-evaluated within the framework of this study by one of us (J.
Estimation of decompacted thicknesses from lithology and porosity data Lithologies were determined from well-logs, cuttings, cores, and outcrops. Decompaction of the sediments was based on: (1) the sediment porosity at the time of their deposition, and (2) the porosity-depth coefficient, both of which are directly linked to the unit lithology. Here, we use a new compilation of compaction laws  (Dabrio, 1982). This compilation takes envelope surfaces into account, including the global range of porosity versus depth for four main lithologies (sandstone, clay, carbonate, and silt). Here, we only use three lithologies: (1) sand, (2) carbonate, and (3) clay. These envelopes can be used to define the upper and lower compaction curves that are used to calibrate the error induced by the compaction law.
Accommodation space measurement along the S-N transect (Melun-Soissons transect) The accommodation space was measured on eight time lines (T1 to L1), defined above, and 31 wells that compose the S-N transect, crossing two faults: the major Bray Fault and the Belou Fault (Fig. 5 ). The data set is available in Figs. S1  and S7. The observed facies are quite high wave-energy facies and a fair-weather wave base between -20 and -15 m is realistic. Thus, from the shoreline to proximal upper offshore deposits, two palaeodepth hypotheses were tested: between 0 and -20 m and 0 and -30 m. For inner estuarine and protected marine environments (more or less tidal-influenced embayments), well-log correlations show persistent electrofacies over large distances, suggesting relatively flat profiles, and have been ranked between -5 and 0 m.
Conclusions The objective of this study was to use high-resolution 3-D stratigraphic data to discuss the deformation of an intracratonic basin, the Paris Basin, at the time of a major change in subsidence occurring around the Cretaceous-Palaeogene boundary, from the subsiding Cretaceous time to a (very) low subsiding Palaeogene time with low sediment preservation. -An age model integrating biostratigraphic uncertainties, sequence stratigraphic surfaces, high-resolution oxygen isotope curves  (Cramer et al. , 2011) , and Earth orbital solutions for long-term eccentricity  (Laskar et al. , 2011)  was performed at a resolution of 100 Kyrs (Fig. 3  and Table  2 ). -A 3-D stratigraphic database comprising more than 300 well-logs and eight time lines (depth, lithology facies) was built  (Figs. 6, 7, 8, and 9 ).
Acknowledgements. We are grateful to the  BRGM (Bureau de Recherches Géologiques et Minières ) for funding the  Justine Briais Ph. D. thesis. We thank  Chantal Bourdillon  ( ERADATA ) for biostratigraphic dating and  Olivier Dauteuil  for its help. We also thank  Sara Mullin  for the English translation. Finally, we thank the editor  P. Galy , and  Jocelyn Barbarand  and an anonymous reviewer for their constructive comments. Edited by:  P. Albert Galy.
Acknowledgements The authors gratefully acknowledge funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under the grant agreement number  TRANSPOL-264399 , and  Horizon   2020 Framework Programme  under the grant agreement number  H2020-MSCA-ITN-2014 ,  BIOPOL  project. The funding by  Agence Nationale pour la Recherche  ( ANR-11 BSV2 014 03 ,  ANR-14-CE14-0002-02  and  ANR-14-CE16-0004-03 ),  Human Frontier Science Program  grant  RGP0029-2014 , and  European Research Council  advanced grant (project  340485 ) is also acknowledged. HK is funded by  Lundbeckfonden. Some parts of the computations were carried out on the Horseshoe clusters at the  SDU  node for the  Danish Center for Scientific Computing (DCSC). We also acknowledge computer resources allocated to the  PRACE - 2IP  project ( FP7 RI-283493 ) resource ARCHER based on the United Kingdom at https://www.
PATIENTS AND METHODS A descriptive, observational, retrospective, consecutive, uncontrolled multicentre pilot case series was conducted in France from March 2011 to June 2013. All patients received clear, detailed prior information on the treatment and expected risks and benefits. As the data were collected retrospectively and the patients' management was not modified, according to French law (n°2004-806, 9 August 2004), this study did not require research ethics committee approval. It was conducted in accordance with the law on data protection (n°2004-801, 6 August 2004). All consecutive adult patients presenting with clinical or subclinical post-surgical macular oedema, including IG syndrome, treated with intravitreal injections of dexamethasone implant 0. 7 mg (Ozurdex) and followed up for at least 6 months, were included in the study.
Statistical methods BCVA, CSMT, and IOP were described over time using mean ±SD at each time point. Baseline values were at the day of Ozurdex injection and measures were repeated for each patient at months 1, 2, 3, 4, 5, and 6. A subgroup of patients underwent a second injection, and measures were also repeated for each patient at months 1, 2, 3, 4, 5, and 6 after this second injection. Differences in BCVA, CSMT, and IOP at months 1, 2, 3, 4, 5, and 6 compared to baseline values were assessed using univariate analysis of variance (ANOVA), taking into account the dependence of repeated measures over time for each patient. For this, the lme function of the package nlme  17  in the software R  18  was used. A value of p<0. 05 was considered statistically significant.
1B , right). No significant difference could be seen between the WT and the ryhB mutant in the absence of iron chelation. In contrast, the activity of the fusion was diminished by 2-fold when the WT strain was grown in the presence of 250 M DIP but was unaltered in the ryhB mutant (Fig. 1B ). We thus concluded that RyhB represses the activity of P BAD -erpA-lacZ during iron starvation. RyhB base pairs to the erpA mRNA. If RyhB's regulation of erpA expression is direct, base pairing between both erpA and RyhB transcripts is expected. We predicted a large base pairing involving almost all of the first 65 nucleotides (nt) of RyhB and 57 nt of the erpA mRNA using the Mfold program (Fig. 2A ). Interestingly, this base-pairing region encompasses the ribosome binding site (RBS) and the AUG codon of erpA.
A similar prediction had been proposed by Wright et al. using the target prediction program CopraRNA  (22). We introduced a series of five mutations in the erpA-lacZ fusion, named mut1 to mut5, predicted to disrupt the base pairing depicted above (Fig. 2A ). Overexpression of RyhB failed to repress activity of erpA mut2 and erpA mut3 alleles but repressed expression of the 3 other fusion variants (Fig. 2B ). This result indicated that while extensive base pairing can be predicted between the two RNAs, only the region of base pairing close to the translation initiation region seems required for effective regulation. Next, we introduced a mutation in the plasmid carrying the ryhB gene such that it would disrupt base pairing with the WT erpA mRNA but restore complementarity to the erpA mut3 allele.
FUNDING INFORMATION This work, including the efforts of  Pierre Mandin ,  Sylvia Chareyre , and  Frederic J Barras , was funded by  Agence Nationale de la Recherche (ANR)  ( SPV05511 ). This work, including the efforts of  Pierre Mandin ,  Sylvia Chareyre , and  Frederic J Barras , was funded by  Centre National de la Recherche Scientifique (CNRS). This work, including the efforts of  Pierre Mandin ,  Sylvia Chareyre , and  Frederic J Barras , was funded by  Aix-Marseille Université (AMU). The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement no. 294027.
TREE-RING DATASETS We re-analysed tree-ring datasets covering three species and several sites. In all cases, living and dead trees were sampled at the same sites. Each species dataset differed in terms of the (i) number of sites considered; (ii) number of trees sampled; (iii) ratio between living and dead trees; and (iv) distribution of the death events over time (Table  1 ). These datasets cover ecologically contrasting species including Abies alba Mill. , Nothofagus dombeyi (Mirb. ) Oerst. , and Quercus petraea (Matt. ) Liebl. , which were selected to reflect the diversity of sampling strategies observed in the literature (Appendix B). Abies alba is a gymnosperm from European temperate mountain forests. Tree-ring data were taken from the studies of  Bigler et al. (2004) ,  Cailleret et al.
At every site, tree-ring widths had been measured to the nearest 0. 01 mm on increment cores taken at breast height (1. 3 m). When a core did not reach the pith, the total missing width and the number of missing rings were estimated by interpolating the distance to the pith using the curvature of the innermost rings of the sample, which allowed us to estimate tree cambial age (at breast height). DBH data were available for two species only (A. alba and N. dombeyi). Annual DBH values were reconstructed for these species by subtracting DBH measured at the time of coring by the ring width produced earlier ('from outside to inside'; annual DBH values were rescaled in case of negative DBH calculated at age=1).
Accepted Article This article is protected by copyright. All rights reserved. regression models with fixed effects were used for N. dombeyi, as trees were growing at one site and died on the same date (Eq. 1): (1) where Pr(Y i,t ) follows a binomial distribution with Y i,t = 0 indicating that tree i is dead at time t, while Y i,t = 1 indicates that the tree is alive; α 0 is the intercept; and β 0 is the coefficient for the growth variable calculated over a time window of length p (G i,t,p ; p for period). When the tree-ring dataset included multiple sites and/or the mortality events occurred during several years (i. e. , A. alba and Q. petraea), logistic regression models with mixed effects were used. The species-specific growth-mortality relationship was provided by the fixed effects of the model.
e. for each dead tree that died at time t, we sampled only one 'alive' observation from the same site at the same t (Fig. C1 ). The pair was removed from the data set if one of the two trees did not contain a given explanatory variable. The 'alive' observation was sampled from both living and now-dead trees and chosen either randomly (P-R; with replacement) or from a tree that was as similar as possible to the dead tree in terms of cambial age (P-A; note that an 'alive' observation could be used repeatedly if the number of death events was higher than the number of survival events). The P-A approach is often employed in studies to help control for the temporal changes in microenvironmental conditions. We also assessed if the structure and performance of the mortality models was impacted by (i) the status of the trees used to sample the 'alive' observations (e.
2015) , and their AUC was calculated using the package ROCR  (Sing et al. 2013 ). In the case of very large eigenvalues and a perfect model (AUC = 1), the significance of the effect of each growth variable was assessed using Markov Chain Monte Carlo techniques (package MCMCglmm; Hadfield 2010), and Firth's penalized-likelihood logistic regression (package logistf;  Heinze et al. 2013) , respectively.
However, with the increase of p, young trees (or trees for which partial tree-ring data were available) were increasingly excluded from the calibration dataset, inducing a decrease of sample size (Fig. 2 ). The change of AUC with changing p may thus be biased due to differences in the set of trees that were selected in the sample (number and characteristics of the trees). Therefore, we assessed the change of model AUC with p for identical samplings using an iterative process, hereafter called constant sampling approach. We firstly used the trees with at least p max rings and calculated model AUC obtained for every p with this dataset. Then, we used the same approach with the trees with at least p max -1 rings (same or higher number of trees than previously), etc. , down to p min. We ended up with (p max -p min ) different patterns of changes in model AUC over p (Fig.
DEATH As revealed by the high AUC values of the multivariate models, there is a high potential of using tree-rings as a robust indicator of tree mortality probability  (Bigler and Bugmann 2004 ). In the present study, AUC was calculated using the same data as for model calibration and optimized using appropriate algorithms. As our aim was to assess growth-mortality relationships per se, and not to predict mortality probability by applying the models, they were not validated on an independent dataset (such as in  Bigler and Bugmann 2004) , and thus their performance may be exaggerated. Nevertheless, AUC can be quite low, e. g. , for Q. petraea, because growth may not reflect all environmental influences that drive mortality probability  (Das et al. 2008; Kane and Kolb 2010; Ferrenberg et al.
DETERMINATION OF THE BEST-FITTING TIME WINDOW Instead of arbitrarily selecting and comparing different time windows (p), we tested all possible p between 1 (p min ) and 50 (p max ) years using an objective statistical approach that optimized the discriminatory power of each explanatory variable. This procedure allowed for considering longterm changes in growth dynamics and detecting local maxima in model performance. However, with the increase in p max , young trees (or trees whose innermost rings are lacking) were progressively excluded from the calibration dataset. This may induce a change in the structure and performance of the best model if trees of different ages have different growth-mortality relationships and when sample size is low (e. g. , for N. dombeyi). To account for both the effect of increasing p max and the change of the calibration dataset, we suggest that one should follow the constant sampling approach, which may highlight potential developmental shifts in speciesspecific growth-mortality relationships.
of 4 variables with p min = 1 and p max = 50. In order to reduce simulation time, we (i) used Differential-Evolution optimization algorithms (R package DEoptim; Mullen et al. 2011) to detect the combination of best-fitting p that optimizes model AUC while escaping local minima (Storn and Price 1997), (ii) developed one logistic model including all RW-related growth variables (RW, sloRW, SD, A1), and (iii) used three possible p max (20; 35; 50) that would allow us to disentangle the effects of changing calibration dataset and changing the maximum length of the time windows. To assess the relative importance of each growth variable on predicted ,we calculated the proportion of the total variance in explained by the variation in each explanatory variable (i. e. , effect size of the fixed effects) using variance analysis techniques.
Data Processing Part Let x ∈ S and y ∈ K. Then, x ⊕ y ∈ S. Therefore, for any X ∈ S and Y ∈ K, X ⊕ Y ∈ S. As long as the plaintext P ∈ S, the state after adding the whitening key, WK ∈ K, belongs to S. Then, the state is processed by the SubCell operation. Here, we exploit two particular data transitions through the S-box for Midori64; Sb 0 (8) = 8 and Sb 0 (9) = 9. Namely, as long as the input state belongs to S, SubCell is equivalent to the identity mapping. Obviously, we obtain S ← SubCell(S). The subsequent ShuffleCell is a cell-wise permutation, and since all cells in S satisfy S, S ← ShuffleCell(S). The MixColumn operation is slightly more complex. Because the diffusion matrix is a binary matrix, each output cell from MixColumn can be represented as the XOR of three input cells. As long as the input state belongs to S, each of three cells is either 8 or 9.
Data Two series of measurements taken at birth on the hands of human newborns are displayed: a series from babies born pre-term, from 26 to 36 weeks EGA (Estimated Gestational Age), and a series from babies born at term, from 37 to 41 weeks EGA. Data was collected in the Neonatal Unit of the CHRU Jeanne de Flandre (University Hospital) in Lille, France, from January until May 2014. Seven measurement criteria were selected, concerning either lengths, widths or ray of the hand, the palm and the digits. They are recorded with the EGA, the sex and the weight of the individuals, regardless of the sideright hand or left hand (Tables  1  and 2 ).
Acknowledgements The first author is funded by the  British Academy  ( Newton International Fellowship , grant code:  75110 ). We thank all parents who have agreed their child being part of the morphometric data collection. We thank the administrative and medical staff from the neonatal unit of the  CHRU Jeanne de Flandre of Lille, France. We are also grateful to  Dr. S. Bouquillon-Delerive  and  Prof P. Thomas  for their help.
The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques. Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The proposed approach has been tested over the OAEI benchmark dataset and some real mammographic ontologies since this work is a part of CMCU project for Mammographic images analysis for Assistance Diagnostic Breast Cancer. The system performs good results in the terms of precision and recall with respect to other alignment system.
It starts by transforming ontologies into graphs. Then, clustering algorithm is applied to partition the graphs into disjoint clusters. Contrarily to the Falcon -AO system, the aligning process is based only on the partitions' roots to determine similar clusters. The use of limited information about the cluster may result in less alignment quality. To dependently cluster ontologies, TaxoMap  [6]  uses a co-clustering technique, the system determines similar modules through the clustering process. The system provides one-to-many mappings between single concepts. In  [7] , the author proposed a clustering approach based on structural nodes similarity. Therefore, each cluster of the source ontology has to be aligned with only one subset of the target ontology. In  [8] , the approach starts by anchoring, a pair of "look-alike"neighbors concepts to be aligned.
Results on benchmark dataset This section presents the performance evaluation of our method FHCbM applied on the OAEI benchmark data sets and compared with other systems participated in 2010  [18]. We use this version, since the gold standard results of each alignment as well as the performance results of multiple partitioning and nonpartitioning systems are available. The OAEI benchmark data sets include a number of ontologies with varied levels of complexities. The base ontology is test-101 considered as the reference/target ontology while the rest represent the source ontologies. The descriptions of these tests are provided in Table  1 , they are containing mainly three sets: simple tests (1xx), systematic tests (2xx) and real-life ontologies (3xx). Table  1. Description of the benchmark data sets.
Conclusion In this paper we have presented the FHCbM, an ontology alignment system using the ontology knowledge mining. The use of the hierarchical fuzzy clustering has proven to be effective in mapping entities. The significance of using clustering techniques was evaluated and compared with a number of existing alignment systems using the benchmark ontology data sets of the OAEI 2010 and real mammographic ontologies. The evaluation results are highly encouraging. Currently, we are investigating the verification process of FHCbM in order to improve its performance in precision without degrading recall. For this purpose, we are planning to perform the reorganization of ontologies in the pre-alignment step. We also plan to participate in the OAEI campaign in the future. It is worth noting that FHCbM in its current form require the user intervention to initialize the process.
Indeed,  Kindler et al. (2013) , using fMRI, showed widespread changes in superior temporal and inferior frontal regions after rTMS treatment, demonstrating wider effects on a network of regions involved in hallucinatory experiences. This functional approach is in accordance with findings regarding the propagation of the effects of rTMS in the entire functional network of a stimulated region  (Siebner et al. , 2003) , and a randomized controlled trial is currently underway to validate such an approach (https://clinicaltrials. gov/ct2/show/ NCT01373866).
Four food groups were selected as follows: fish (50 g/serving), eggs (50 g/serving), red meat (50 g/serving), and milk (200 mL/serving). Consumption frequencies of milk and egg were standardized into servings per day and into servings per week for fish and red meat. The data about the mean daily intake of nutrients were processed using the professional Nutri Pro 7 program (Nutri Pro 7 software, CERDEN, Brussels, Belgium).