dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Kimmo Tanhuanpää  and  Mika Molin  from the  Light Microscope Unit, University of Helsinki  for excellent technical guidance in design and execution of confocal microscopy and image processing. Eija Koivunen  from the  Department of Biosciences, University of Helsinki  is acknowledged for orientation to the paraffin embedding. Carmen Escopedo-Lucea  and  Marika Häkli  from the  Division of Biopharmaceutics and Pharmacokinetics and Centre for Drug Research, University of Helsinki , respectively, are gratefully thanked for the transport and processing of human liver tissues. Antti Laukkanen  from  UPM New Businesses  and Development is granted for valuable comments on the hydrogel properties. Sanna Toivonen  from the  Molecular Neurology and Biomedicum Stem Cell Center, University of Helsinki  is granted for the design of CycloG and HNF4A primers.
The dogs were kept in open kennels for 24 months. Regular deworming treatments were administered; the use of antiparasitic drugs or insecticides/repellents against sand flies was prohibited to allow maximum natural exposure of the animals to sandfly bites. The study followed the Good Clinical Practice guidelines. All analyses and clinical examinations were performed in a blinded manner by professionals who had access only to the dog identification codes. Treatment group codes were only unblinded after all data had been entered into a data-management system and all decisions regarding the status of each dog had been taken.
Statistical Analysis Analyses were performed with the software SAS V9. 1. The significance threshold was a = 0. 05. The site effect was taken into account in the statistical tests. The status of the vaccinated and control dogs was compared at M24 when the efficacy of the vaccine was calculated using the standard efficacy calculation: (%symptomatic controls -%symptomatic vaccinated)/% symptomatic controls * 100. The status of any dog which had died due to CanL was carried forward to this point. The percentage of non-symptomatic vaccinated animals was reported as the protection rate. Frequencies and percentages of symptomatic dogs were compared between groups using a Cochran-Mantel-Haenszel test to examine the association between symptomatic animals and treatment group while adjusting for the site (or study) effect.
The PCR products were purified using Exosap-IT (GE Healthcare, Buckinghamshire, UK). Sequencing of the purified PCR products was performed by the Institute for Molecular Medicine Finland (FIMM, Helsinki, Finland) laboratory. We compared the obtained sequences to the GenBank nucleotide database (http:// www. ncbi. nlm. nih. gov/genbank/). In September 2013, a small-scale field survey was conducted in Aland to detect again the presence of Ampelomyces mycoparasites in powdery mildew colonies. Mycoparasites were collected from four sample sites (Fig. 1 ), and seven strains were isolated and maintained in culture as described in  Liang et al. (2007). The ITS sequences were determined in these seven newly isolated Aland strains as described above. Design of a molecular detection test for Ampelomyces spp.
As P. plantaginis is haploid, we considered the samples to be coinfected by various mildew strains if two alleles were detected in at least one SNP locus (see details in  Tollenaere et al. 2012). Linking the prevalence of Ampelomyces with its fungal host population dynamics Firstly, we tested whether Ampelomyces prevalence increased with the level of coinfection (as estimated from the SNP genotyping of P. plantaginis samples described above). This analysis was performed on the whole data set (808 samples from 424 plants from 98 populations). Specifically, we modelled for each plant the proportion of leaves with Ampelomyces as a function of the fraction of leaves showing multiple genotypes of the powdery mildew (i. e. whether a unique strain or a mix of various strains was detected using SNP markers).
As the response variable is a proportion, we used a binomial distribution and logit link. To account for variation among populations, we included population identity as a random effect. We used the framework of generalized linear mixed models as implemented in procedure GLIMMIX in SAS 9. 3  (Littell et al. 2006). Secondly, we assessed the impact of the prevalence of Ampelomyces hyperparasite on the overwintering of its fungal host from September 2011 to July 2012 at two spatial scales: a 1-m 2 quadrat and the population level. For both analyses, we used Ampelomyces prevalence and the fraction of leaves with chasmothecia (resting structures) in autumn 2011 as the explanatory variables of overwintering success. We included the latter variable as previous work has shown that the overwintering success of P.
ITS sequencing of Ampelomyces strains from Aland The ITS sequences determined in the four infected dried Plantago leaves bearing Ampelomyces pycnidia, collected in Aland in September 2011, as well as those determined in six of seven strains isolated in the archipelago in 2013, were all identical (GenBank Accession nos: KC494280 and KM066091-KM066097) and approximately 97-98% similar to the ITS sequences of the Ampelomyces strains belonging to the 'Podosphaera (sect. Sphaerotheca) clade' identified by  Park et al. (2010). The seventh strain isolated in 2013 belonged to the same clade but differed by a single SNP from the other Ampelomyces strains isolated in Aland.
Ampelomyces detection within the Aland archipelago When applying the molecular test to the large-scale field sampling (951 samples), a straight shape instead of the expected sigmoid for the qPCR assay (evolution of the fluorescence through time) was obtained for 126 samples (13. 2% of the data set). This suggests possible inhibition of the reaction, and these samples were removed before further analyses. The powdery mildew SNP assay was applied to all the samples. SNP genotyping assay worked for all the samples except 12 (1. 3% of the data set), which were consequently removed from all the analyses as a negative result for such samples may be due to poor DNA quality. A total of 813 samples were finally used in this study. We performed three replicates on each sample and found repeatability of 88.
Acknowledgements We are grateful to  Hanna Susi ,  Elisa Metsovuori ,  Coong Lo  and  Aki Suzuki  for their participation in sample collection and to  Claire Morandin ,  Kalle Trontti  and  Caroline Tatard  for their help with the design of the quantitative PCR assay. We thank  Krista Raveala  and  Anna Huber  for assistance in the laboratory work and  Evgeniy Meyke  for producing the map. The manuscript benefitted from helpful comments from three anonymous reviewers. This work was supported by funding from the  Academy of Finland  (Grant Nos 250444, 136393, 133499),  European Research Council  (PATHEVOL;  281517 ) to A-LL and the  Hungarian Scientific Research Fund (OTKA , Grant  NN 100415 ). G. M. Kov acs acknowledges the support of a J anos  Bolyai Research Scholarship of the Hungarian Academy of Sciences  (MTA).
TRANSCRIPTOME CHANGES CONTROLLED BY EDELFOSINE, A PI-PLC INHIBITOR, RESEMBLE THOSE CONTROLLED BY SA We had previously identified genes whose basal expression is dependent on PI-PLC activity. Briefly, by microarray analysis, we monitored transcript levels in ACSC treated or not treated for 4 h with a PI-PLC inhibitor called edelfosine  (Djafi et al. , 2013). We defined the list of genes altered in response to edelfosine as a "signature. " The Genevestigator interface compares the transcript expression levels from an experiment of interest, the signature, to a subset of other microarray data and returns microarray experiments with similar gene responses. For each comparison, a correlation factor is calculated. From a subset of 734 microarray experiments categorized as dealing with phytohormone responses, the edelfosine-induced transcriptome changes in ACSC correlated most with changes caused by SA or by the gai and penta mutations (Figure  1A ).
As the strongest and most frequent correlations were with the SA response, we performed a second signature similarity search, this time restricting the query database to experiments specifically dealing with SA responses (Figure  1B ). The similarity between the edelfosine response and the SA gene response is found in several independent SA experiments on whole seedlings or leaves and in many Arabidopsis cultivars. We noted that the edelfosine data correlates more with the early SA response (4 h) than with later responses (28 or 52 h).
To continue the analysis, only the genes that were common to a cluster in both the edelfosine and the U73122 analyses were considered. In these stringent clusters A, B, C, and D, there are 105, 39, 97, and 23 genes, respectively (Table  S4 ). As all the data were from microarray experiments, the regulation of expression of some genes was confirmed by quantifying transcripts by qPCR. In fact, cluster A and cluster D gene expression had already been "confirmed" by  Krinke et al. (2007). We therefore focused on two SA inhibited genes representative of the 118 genes in cluster C, the largest non-stringent cluster (Figure  7 ). The SA responsive genes form two pools, one corresponding to genes dependent on PI-PLC substrates for their SA response, and the other corresponding to genes dependent on PI-PLC products for their SA response.
SPECIFIC PROMOTER CIS-ELEMENTS ARE OVER-REPRESENTED IN THE DIFFERENT CLUSTERS The transcriptome responses to SA and to PI-PLC inhibition are rapid. We compared the promoters of the genes in stringent clusters A, B, C, and D by scanning for motifs. Some motifs are over-represented in the genes of the clusters relative to the whole genome dataset (Figure  10 ). Some of the motifs identified in clusters A and B and in clusters C and D are also over-represented when the motif scan compared all SA-induced and SA-repressed genes against the whole genome data set. This is not surprising since these clusters together include most of the SA-sensitive genes of Arabidopsis. In cluster A promoters, the W box is over-represented compared to the whole set of SA induced genes. In cluster B promoters, the T-box motif is over-represented when compared to the whole set of SA-induced genes.
Interestingly,  Chou et al. (2004)  identified a few genes for which inhibition of PI-PLC by U73122 mimics similar experiments retrieved using, as a signature, the response to SA of a list of genes composed of cluster B and C. The similarity search was performed using the Genevestigator signature module. An Euclidean distance-derived similarity score was calculated between our signature and each experiment of a set. A relative similarity score was calculated where a relative similarity score of 1 stands for a similarity between the input signature and an experiment that is the same as the average over all experiments of the set. The analysis was performed using stringent clusters as defined in the main text. Red blocks indicate relative higher transcript levels in the condition written in red at the top of the table (versus that written in green); green blocks indicate higher transcript levels in the conditions written green at the top of the table (versus that written in red) and black blocks indicate no significant difference in transcript levels between both conditions.
If the genes of clusters B E , B W30 , C E , and C W30 were considered in this way, the percentage of B and C cluster genes among SA-induced or SArepressed genes would be even higher. The effect of SA inhibition of PI-PLC on gene expression appears to be a major event in the SA response. What is the significance of the pool of genes in clusters B and C? What is the SA-triggered signaling event controlling expression of this pool? From a lipid signaling point of view it is tempting to consider that these genes are regulated in resting cells by PI-PLC pathway products. However, correlation alone is not proof. For clusters B and C we have only described correlations between different datasets showing that (i) challenging cells with SA induces in vivo PI-PLC inhibition, and (ii) inhibiting PI-PLC by different drugs alters the expression of a set of genes that is also affected when SA was added.
TRANSCRIPTOMIC DATA No new transcriptomic data were generated for this study. The microarray data used for this article are deposited in Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/; accession no. GSE7495, GSE9695, GSE19850 and GSE 35872) and CATdb (http://urgv. evry. inra. fr/CATdb/; Projects: RS05-04, AU07-01, RS09-04, and AU10-12).
MOTIF ANALYSIS Promoters (up to -1000 bp upstream of the transcription start site, without overlaps with other genes, and excluding 5 UTRs) of genes were extracted from the database of The Arabidopsis Information Resource (TAIR;  Rhee et al. , 2003). Sequences were used to search for over-represented motifs ranging from 4 to 10 bp using SIFT software  (Hudson and Quail, 2003). The list of genes used as reference was either the list of promoters from the whole genome (33,062 promoters) or from all SA-induced genes or SA-repressed genes, as indicated. The motifs we defined were then compared with the ones in the PLACE database  (Higo et al. , 1999)  to search for related cis-elements and similar motifs.
A. Consistency protocol The mass-spring cache protocol is quite independent from the top level data consistency protocol: it introduces a distributed shared cache among the neighborhood. From the cache coherence point of view, this distributed cache is used the same way as a regular private local cache. Therefore, we use the same data consistency protocol and we measure the cache hit rate, as well as the protocol traffic, with both 1chance forwarding data sliding and N -chance forwarding mass-spring model. In the following experiments, we refer to the baseline data consistency protocol. This protocol is widely used in multicore processors. One of its popular implementation is the home-based, four-state MESI protocol, standing for Modified, Exclusive, Shared and Invalid. In a home-based protocol, each shared data (or piece of data) is paired with a core (the home-node), that is responsible of keeping trace of the data and its current state.
A CT scan classification based on data from the Traumatic Coma Data Bank was proposed by Marshall et al. [12] , including a MLS >0. 5 cm as one of the main CT criteria for the severity of traumatic brain injury (TBI)  [12, 13]  and a multivariate analysis of a cohort of over 10,000 TBI patients showed that the compression of the third ventricle and a MLS >0. 5 cm were both major predictors of mortality within the first 15 days after injury  [14]. Similarly, Ropper observed that, following stroke, alteration of consciousness was directly proportional to the MLS on CT scan  [15]  and Pullicino et al. found that both MLS (P = 0. 001) and coma (P = 0. 019) were independent predictors of mortality at 15 days following acute stroke  [16]. The mass effect associated with a MLS after ischemic or hemorrhagic stroke is considered one of the major outcome predictors.
Statistical analysis The correlations between TCS and CT scan measures of the MLS, between MLS and ICP, as well as between the measures from the two sonographs were all analysed by linear regression with Pearson's correlation coefficient. The agreement between the methods was studied by the Bland and Altman method with calculation of bias and the limits of agreement  [31]. Finally, the diagnostic performance of TCS to detect a significant MLS (>0. 5 cm calculated with method 2 from the CT scan) was estimated by analysis of the receiver operating characteristic (ROC) curve. Data are expressed as mean (± SD) or median (± range). The agreement between sonography units was assessed by weighted kappa-statistic. Hence, the two ratings were first 'presence of significant MLS' (for example, when MLS assessed on TCS was equal to or above a sonographic cut-off extracted from ROC analysis) and second 'absence of significant MLS' in the other case.
Acknowledgements We are grateful to  Roberto Pittau  for his support of  CutTools , to  Stefan Prestel  for his dedicated help with the NLO matching to Pythia8 and for discussions on related topics, to  Matteo Cacciari  for his help and support with FastJet (core), to  Andreas Papaefstathiou  for his dedicated help with Herwig++, to  Simon Badger ,  Daniel Maitre , and  Markus Schulze  for clarifications concerning bootstrap and D-dimensional unitarity methods, to  Markus Cristinziani  and  Roberto Chierici  for information on top-physics measurements by ATLAS and CMS, to  Adam Alloul ,  Neil Christensen ,  Celine Degrande ,  Claude Duhr  and  Benjamin Fuks  for their collaboration to BSM developments and support of  FeynRules , to  Frank Krauss  for explanations on the use of the MC@NLO method in SHERPA, to  Simon de Visscher  for his help with LO merging, to  Pierre Artoisenet  and  Robbert Rietkerk  for their work on MadSpin, to  Valery Yundin  and  Andreas van Hameren  for their support of PJFry++ and OneLoop, to  Diogo Buarque Franzosi  for his collaboration on the implementation of the complex mass scheme, to  Alexis Kalogeropoulos  for his contributions in developing and testing large-scale event production, to  Kentarou Mawatari  for many fruitful collaborations, to  Stefano Pozzorini ,  Peter Skands  and  Torbjörn Sjöstrand  for useful clarifications, to  Josh Bendavid ,  Vitaliano Ciulli , and  Sanjay Padhi  for the very useful feedback on the use of the code, and to  Michelangelo Mangano  for his support and insight.
In this study, we compare the convergence and the computational efficiency of the above-mentioned optimization methods [non-linear conjugate gradient (nl-CG), l-BFGS, Gauss-Newton (GN) and full Newton (FN)] when they are implemented in efficient frequencydomain FWI with and without random source encoding. A stopping criterion of iterations is designed allowing for a fair comparison of the optimization methods and a fair assessment of the speed-up provided by the random source encoding method. These work flows are first applied on a realistic synthetic experiment inspired by the geology of the Gulf of Mexico. Then, we assess the benefit provided by random source encoding and second-order optimization methods when applied on a 2-D real ocean-bottom-cable (OBC) data set recorded from the Valhall oil field.
The quantities used for comparison: convergence rate, computational efficiency and speed up For each data set we perform the inversion with and without source encoding, to compare the differences in the computational costs and in the number of iterations required to attain a desired relative reduction of the misfit value. We compare these values using four different optimization algorithms. (i) We measure the 'convergence rate' by looking at how φ(m n ) tends to φ(m * ) as a function of the iterations, where φ(m * ) is the value of the misfit evaluated at the optimal model m *. The optimization method requiring the fewest number of iterations n to reach a predefined φ(m * ) means it has the highest convergence rate. (ii) We also measure the 'computational efficiency' through the number of direct problems required to attain a given misfit function reduction.
Experimental protocol We define the relative reduction of the misfit function for each frequency group 1 , the number of supersources K to be used, the maximum number of iterations allowed to solve the truncated system N CG and the values of the hyperparameters λ x and λ z. The convergence and computational efficiency curves are determined for each optimization method, with and without source encoding. A speed up measure can then be deduced for each optimization method. We apply this protocol initially to the BP-2004 salt model using data without noise and observe that without source encoding the convergence rate is notably higher for optimization methods that take into account the effect of the Hessian. On the contrary, when source encoding is applied, the difference in convergence rates is similar for all optimization methods, indicating that including the Hessian information does not improve considerably the descent direction.
We observe that l-BFGS is the most computationally efficient method, solving the fewest number of direct problems. The method with the highest speed up (the method that has the largest difference when used with and without source encoding) is CG. The final velocity models provided by all optimization methods, with and without encoding, are the same. When using the Valhall real data set, the comparison between optimization methods is more difficult because the final value of the misfit value is not the same for all optimization methods. In particular, we observe that l-BFGS terminates the inversion early due to failed line searches. It is not straightforward to determine the most computationally efficient method, or the one with the highest convergence rate because the terminal point of the optimization is not the same.
For the stopping criteria we used in the Valhall real data set application, we obtained speed-ups of around 90 per cent. Besides the computational gain that can be attained, we illustrate in Appendix B that source encoding techniques may provide advantages when minimizing non-convex misfit functions. Indeed, stochastic optimization methods allow for the exploration of regions in the model space that are never accessible in the deterministic case, because stochastic approaches allow the misfit function to increase. We present a numerical test where we inverted nine frequencies in a single frequency group to render the misfit function non-convex, and we found that the final model is more accurate when we use random source encoding relative to the case where we use all the sources independently.
A C K N O W L E D G E M E N T S This study was partly funded by the petroleum SEISCOPE consortium (http://seiscope. oca. eu,http://seiscope2. osug. fr) and the Université de Nice Sophia Antipolis. The linear systems were solved with the MUMPS package, available on http://graal. ens-lyon. fr/MUMPS/index. html and http://mumps. enseeiht. fr. This study was granted access to the high-performance computing facilities of the SIGAMM (Observatoire de la Côte d'Azur) and we gratefully acknowledge these facilities and the sup- port of their staff. We thank BP Norge AS and their Valhall partner Hess Norge AS, for allowing access to the Valhall data set as well as the well-log velocities. We also thank the anonymous reviewer and the associate editor for comments and suggestions to improve the paper. We perform FWI using the BP-2004 salt model adding 25 per cent of mean zero additive Gaussian noise to the data to test the sensibility to the noise.
MATERIALS AND METHODS Cell lines and viruses RD (human rhabdomyosarcoma) cells were purchased from the American Type Culture Collection (ATCC NO CCL-136). The EV71 isolate Fuyang573 (subgenotype C4a, GenBank accession number: HM064456, isolated from a 2008 epidemic sample in Anhui province) was used throughout this study, unless otherwise stated. EV71 isolates SH12-036 (GenBank accession NO KC570452) and SH12-276 (GenBank accession NO KC570453) were isolated from patient samples in Shanghai, in 2012. SEP-4 (2012 Cambodia EV71 isolate, GenBank accession NO KF543271) was provided by the Virology Unit of the Institut Pasteur in Cambodia. Coxsackie virus A16 (strain shzh05-1, GenBank accession NO EU262658) and poliovirus-1 (Sabin, type I oral poliovirus vaccine) were also used to evaluate antiviral potency.