dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to  J. E. Cleaver  for XP30RO cells,  T. Shiomi  for  HCT116 RAD18 À / À cells  and  A. Lehmann  for MRC5-HisPCNA K164R cells. We thank  M. Modesti  and  A. Dejean  for the kind gift of His-SUMO plasmids,  W. Yang  for useful advice on the design of polZ SUMO and  R Fuchs  for the TT-CPD substrate. We thank  C. Garandeau  and  A. Cahn  for technical assistance. We are indebted to  C. Ribeyre  and  A. Constantinou  for their help with the iPOND experiments and to  Y. Lecluse  and  P. Rameau  for cell sorting. We thank  J. H. Guervilly ,  P. H. Gaillard ,  V. Naim  and  S. Aoufouchi  for fruitful discussions. The PLK laboratory is supported by  La Ligue Nationale contre le Cancer  (Equipe labellisee). This work was supported by grant from  INCa  PLBio-2010, INCa-DGOS-INSERM 6043, ARC foundation (grant2014) and Gefluc.
Analytical Assays Glucose, maltose, ethanol and organic acids (lactic, malic, oxalic, propionic, tartaric, citric and acetic acids) were separated and quantified by HPLC (Agilent Technologies, 1200 series, UK) using a column Aminex HPX-87H, 300 mm × 7. 8 mm (Biorad) coupled to a refractometer (Agilent Technologies) and a UV diode array detector (Agilent Technologies). The acquisition and processing of data were carried out using the Chromoleon software. The operating conditions for the determination were as follows: oven temperature: 50 • C; duration of analysis: 35 min; mobile phase: 5 mM sulfuric acid; flow rate of mobile phase: 0. 5 mL/min and injected sample volume: 20 µL. The quantification of the concentration is performed by UV detection at 210 nm. Calibration is carried out from solutions containing the different analytes in the linear ranges of each.
The sample was then transferred to an insert (Supelco, Bellefonte, PA, USA), returned to the vial and closed. Samples were analyzed with a Hewlett Packard (Agilent Technologies) 6890 gas chromatograph equipped with a CTC Combi PAL Autosampler AOC-5000 (Shimadzu, Columbia, MD, USA) and coupled to an HP 5973 mass spectrometry detector (HP, now Agilent Technologies). The instrument was controlled and the data analyzed with HP G1701DA ChemStation software (HP, now Agilent Technologies). The gas chromatograph was fitted with a 30 m × 0. 25 mm Phenomenex fused silica capillary column ZB-WAX, 0. 25 µm film thickness (Agilent Technologies). The carrier gas was helium, linear velocity 36 cm/s, flow rate 1. 0 mL/min in constant flow mode. The initial oven temperature was 40 • C for 3 min. The temperature was increased by 4 • C/min until it reached 220 • C, and was held at this temperature for 20 min.
Statistical Analysis The analysis of variance (ANOVA) and Tukey tests and Levene (t-test) were performed with the STATISTICA software, 99th Edition, to compare variables analyzed on the sweet and fermented worts. Statistical differences with p < 0. 05 were considered significant. The principal component analysis (PCA) was used to compare beer products from the measured variables. PCA allowed grouping the measured variables into new variables called "components" or "factors. " This grouping is based on the correlation of variables. The XLSTAT software (Adinosoft Inc. ) was used for the PCA.
Data were then recorded during 45 seconds at comfortable gait velocity determined during the familiarization period. Dynamic joint angle data as a function of the gait cycle are presented in the form of curves over 100% of the gait cycle (100 points). The gait cycle was divided into the stance phase (0-60%) and the swing phase (60-100%)  [34]. The stance phase comprised the initial contact, loading response (0-10%), mid stance (10-30%), terminal stance (30-50%) and preswing (50-60%)  [34]. In each plane, total range of motion (ROM) and range of motion in abduction/adduction and tibial rotation at each percentage of gait were calculated and analysed separately for stance and swing. Also, in the sagittal plane, peak flexion was measured. In the frontal plane, ROM from initial contact to maximum mid stance (10-30%) angle and the amplitude between the mean mid stance angle and initial contact were calculated.
Statistical Analysis All statistical analysis was performed using SPSS software version 19. 0 (SPSS Inc, Chicago, IL, USA). Continuous variables of kinematics results, ROM, mechanical axis measurement and subjective scores were compared with paired t test for patients with bilateral TKA. The comparison of continuous independent variables of kinematics results of Nexgen, Triathlon and asymptomatic knees was performed using one-way ANOVA. Continuous variables of demographic data of the TKA and asymptomatic groups were compared with independent t test whereas Pearson's chisquared test was used for categorical variables. Statistical significance was set at p = 0. 05. The Pearson correlation coefficient was used to test whether there was an association between the mechanical femorotibial angle and peak stance angle in the frontal plane.
External rotation(+)-Internal rotation(-) Triathlon Nexgen Asymptomatic benefit associated with their introduction. The first objective of the study was to assess if sagittal, frontal and transverse knee kinematic data post TKA are comparable with those of asymptomatic knees. Then compare kynematic data between two knees of the same patient with two different TKA implant designs. And last, to verify if difference in clinical and subjective scores can be observed between the two knees with different TKA implants. Comparison between TKAs and asymptomatic knees revealed that asymptomatic knees had significantly less knee flexion at initial contact (p < 0. 04) and more flexion for most of the swing phase (p between 0. 004 and 0. 04). Asymptomatic knees also had less varus at loading response, during stance phase and during most of swing phase (p between 0.
Extraction of total RNA and real-time PCR Total RNA was isolated from cells using the SV Total RNA Isolation System (Promega, Madison, WI, USA). RNA quantity and purity were assessed with a Nanodrop ND-1000 spectrophotometer (Nyxor Biotech, Paris, France). Total RNA (1 μg) was reverse-transcribed into cDNA using the High-Capacity cDNA kit (Applied Biosystems, Foster City, CA, USA). Realtime quantitative RT-PCR was performed with the fluorescent dye SYBR Green methodology using the SYBR Green PCR Master Mix (Applied Biosystems) and the 7900HT fast real-time PCR system (Applied Biosystems). Primer pairs for each transcript were chosen with IDT software http://eu. idtdna. com/scitools/Applications/RealTimePCR/ and "blasted" with NCBI http://www. ncbi. nlm. nih. gov/BLAST/. Amplification curves were read with SDS 2.
This later point is of relevance when undertaking dynamic studies requiring several sampling time points for the same animal  15. Regarding the potential variation of MSCs during the lactation cycle, advancing lactation has been associated to an increase of MSCs in milk  16. This increment is firstly due to a concentration effect as a result of the reduction of milk yield that occurs after the lactation peak. In addition, rises in MSCs have generally been associated to an increase of polymorphonuclear cells  13, 17. However, it has been demonstrated that advancing lactation has also a stimulatory effect on MECs exfoliation process  12  , thus, there is also an increase of MECs towards late lactation  12. The RNA-Seq profiling dataset was generated on high-quality total RNA on an Illumina HiSeq 2000 platform (Table  2 ).
This approach generated a total of 1,116 million paired-end reads from the transcriptome sequencing of the 30 milk samples. All samples had a suitable level of real quality, a high mapping rate (Table 2, Technical validation) and no contamination was found through the alignment against the Escherichia coli genome. The highly expression of genes codifying for major milk proteins in all the stages of lactation analysed supported that the gene expression profile of MSCs are representative from lactating mammary gland. To the best of our knowledge, this dataset (GE) represents the largest public RNA-Seq longitudinal dataset on sheep lactating mammary gland. In the related work published on Scientific Reports we performed an in depth analysis of these data, providing the first integrated overview on sheep milk gene expression across lactation  18.
Power calculation The online tool Scotty (http://scotty. genetics. utah. edu/) was used in the design of the RNA-Seq experiment. This tool enables the calculation of the optimal sequencing depth and the number of replicates needed per condition to plan RNA-Seq experiments with adequate power to detect differential expression. The power calculations on Scotty (http://scotty. genetics. utah. edu/) require to upload a prototype dataset and to fix several experimental constraints for power optimization. As prototype dataset, we used our own pilot RNA-Seq data obtained from MSCs from four sheep per breed. To estimate the power based on our pilot dataset we set the following parameters: a cost per replicate of 50 US Dollars (USD), a cost per million reads aligned to genes of 150 USD, an alignment rate of the 85%, a maximum of 10 replicates per condition, a read depth between 10 and 40 millions of reads, a maximum cost of the experiment of 100,000 USD, a 50% of differential expressed genes detected with a fold change of 2 and a P-value of 0.
RNA-Seq data analysis The read quality of the RNA-seq libraries was evaluated using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Reads were mapped against the ovine genome assembly v. 3. 1. (Oar_v3. 1) using the STAR aligner (v. 2. 3. 1y)  22. The data was also tested for contamination on the Escherichia coli genome using BWA  23. Cuffquant and Cuffnorm packages from Cufflinks  24  were used to compare gene expression levels within the same sample. Gene abundances were normalized by library and gene length by calculating Fragments Per Kilobase Of Exon Per Million Fragments Mapped (FPKM) using the Ensembl annotated genes (Oar_v3. 1) as a reference. The Cufflinks and Cuffmerge tools from the Cufflinks package  24  were used to create a 'transcripts. gtf' file to be used as reference in our assembly.
The aim of the assembly was producing a new annotation reference including novel genes and transcripts to be used in the downstream differential expression analyses. The Cufflinks option 'g' followed by the available gtf file from the Oar_v3. 1 reference sequence was used to guide the assembly but without excluding new genes. Cuffmerge was used to filter genes with low or no expression from our reference gtf file. To compare the expression levels of genes across samples, raw counts for the genes and transcripts were obtained using SigCufflinks (available at http://www. sigenae. org) using de '-G' option of SigCufflinks to guide the alignment but excluding new genes. SigCufflinks is a modified version of the cufflinks code that provides raw read counts per gene and transcript, by using the sorted bam file from the alignment and the reference gtf file created in the assembly.
Data Records The raw fastq files for the RNA-seq libraries were deposited at the Gene Expression Omnibus (GEO) database under the accession number GSE74825 (Data Citation 1). The processing of all fastq samples is summarized in Tables  1  and 2. The output file from the quantification of transcripts by Sigcufflinks is also deposited in the Gene Expression Omnibus (GEO) under the same accession number GSE74825. It contains all the genes identified in the assembly and the raw counts per gene for each sample.
Technical Validation Power calculations The results for the power estimates achieved in each experiment configuration tested with Scotty (http://scotty. genetics. utah. edu/) are described in Supplementary File 1 and summarized in Fig. 2. The least expensive experiment that has enough power to perform a differential expression analysis according to the settings fixed was sequencing six replicates to a depth of 10 million aligned reads per replicate. The most powerful experiment that matches our criteria was sequencing 10 replicates to a depth of 26. 67 million reads to genes. According to these results, the animals available to perform the experiment and the nature of the lactating mammary gland transcriptome (mostly enriched in transcripts codifying for major milk proteins), we finally decided to sequenced the MSCs RNA samples from eight replicates (four Churra and four Assaf) at each of the lactation time-points selected for the study (with the exception of D120 for which only 6 replicates were sequenced) to an average depth of 35 million reads.
Quality validation and analysis of RNA-seq data A total of 30 RNA libraries were sequenced to a depth between 23-46 million paired-end reads among which about 88. 10% of the reads mapped to unique locations in the ovine genome assembly (Oar_v3. 1) (Table  2 ). No contamination was found in the alignment against the Escherichia coli genome. In order to validate the quality of the RNA-seq libraries as representative from lactating mammary gland, we evaluated the profile of the highly expressed genes identified for our samples. As expected, the genes with the highest FPKM values for both sheep breeds and at the four studied lactation time points are CSN2 (β-casein), CSN3 (κ-casein), ENSOARG00000005099 (LGB, β-lactoglobulin), CSN1S2 (casein-α-S2), CSN1S1 (α-S1-casein) and LALBA (α-lactalbumin) (Fig.
Usage Notes The RNA-Seq fastq files could be aligned using publicly splice-aware software solutions like TopHat2 (ref. 27) or STAR  22. As reference genome we have used the ovine genome assembly (Oar_v3. 1) downloaded from Ensembl database (http://www. ensembl. org/Ovis_aries/Info/Index). Cufflinks package  24  could be used to perform the assembly, quantification and differential expression analysis but also other publicly software combinations could be used for quantification and differential expression analysis: e. g. SigCufflinks (available at http://www. sigenae. org) or HTSeq  28  for quantification, combined with edgeR  25  or DESeq2 (ref. 26) for the differential expression analyses. Based on power estimations (Fig. 2 ) we recommend to use at least 5 replicates per condition to perform differential expression analysis.
Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions.
Introduction Metabolomics is a science which concerns the study of small molecules, called metabolites, and their interactions in the cell. An important problem of metabolomics is the identification of the metabolites present in a sample. Information on metabolites can be obtained using tandem mass spectrometry. This technology allows to obtain a tandem mass spectrum, also called MS/MS spectrum, by fragmenting a compound. A MS/MS spectrum is a plot containing a set of peaks, where each peak corresponds to a fragment. These peaks represent the relative abundance of the different fragments, also called intensity, in function of their mass-to-charge ratio. The identification of the metabolite from its mass spectrum is then needed for a more detailed biological interpretation. In general this step consists in a research of the obtained spectrum in databases of reference spectra, followed by an analysis by experts of the domain.
Computational approaches for interpreting and predicting MS/ MS data of small molecules date back to the 1960s  (Lindsay et al. , 1980). However, the early approaches were hampered by the unavailability of large scale data on molecular structures as well as reference spectra. The introduction of molecular structure databases such as PubChem  (Bolton et al. , 2008)  as well as open mass spectral reference databases  (da Silva et al. , 2015; Horai et al. , 2010)  has in recent years fuelled the development of novel methods. Several novel strategies have been proposed, including simulation of mass spectra from molecular structure  (Allen et al. , 2014 (Allen et al. , , 2015)) , combinatorial fragmentation  (Heinonen et al. , 2008; Hill and Mortishire-Smith, 2005; Ridder et al. , 2013; Wang et al.
They are typically represented as binary vectors, whose values indicate the presence or absence of some molecular properties, e. g. the existence of particular substructures in the metabolite or some physiochemical properties. If two molecules share a large number of molecular properties they are likely to be similar in structure, which is the rationale in using them for metabolite identification. To identify a metabolite, the fingerprint predicted from its tandem mass spectrum is matched against a large molecular database such as PubChem. In  Shen et al. (2014)  and  Du ¨hrkop et al. (2015)  fragmentation trees are computed to model the fragmentation process of the molecules and then used for predicting the molecular fingerprints. The other machine learning approach for metabolite identification, used by CFM-ID  (Allen et al.
Running times We computed the running times of CSI:FingerID and IOKR using the 4138 spectra from GNPS as training set and 625 spectra from the Massbank dataset  (Horai et al. , 2010)  as test set (see Table  4 ). The running times correspond to the times that would have been obtained if we were using a single core. The training times were computed using fixed values for the parameters (regularization and kernel parameters). The computation of the fragmentation trees, input kernels and fingerprints was not taken into account here. The running times for the training and the test steps are shown in Table  4. In this table, we observe a substantial difference between the training times obtained with these two approaches: the IOKR method is approximately 7000 times faster to train. This can be explained by the fact that CSI:FingerID needs to train a SVM classifier for each molecular property, this means 2765 SVMs to train in this experiment.
Detailed evaluation of identification with IOKR We will now analyze more in details the results obtained with our method on the GNPS dataset. We begin by presenting the results obtained for the different input and output kernels introduced in Section 2. Figure  4  contains the percentage of correctly identified structures (i. e. correct structures ranked top over all candidates) obtained with IOKR for the different pairs of input and output kernels. The two last columns correspond to the linear kernel combinations with UNIMKL and ALIGNF. We observe that the two MKL approaches clearly improve the results compared with the single kernels. The best performance is obtained with the UNIMKL approach, which is performing slightly better than ALIGNF. 30. 74% of the metabolites are correctly identified with UNIMKL combined kernel.
Prediction analysis In the following, we detail the performance of the testing metabolites with IOKR in function of the size of their candidate sets. For this, we consider the best pair of kernels: UNIMKL combined kernel in input and Gaussian kernel in output. Figure  6a  shows the distribution of the sizes of candidate sets, and the figure 6b represents the percentage of correctly identified metabolites in top 1, top 10 and above. We observe that the majority of the candidate sets contain <1000 candidates in our dataset. For these candidate sets, 32. 8% of metabolites are identified correctly in the first position (magenta  We found 1203 compounds in the GNPS dataset that can be linked to the ontological classification database ChEBI  (Hastings et al. , 2013). We are interested in evaluating whether there are some classes of compounds we can identify very well and some for which we cannot.
Due to the hierarchical nature of the ontological classification, the classes far away from the root are very specific classes and contain very few compounds while the classes close to the root are very generic classes which contain too many compounds. As a result, we restrict the attention to the classes with shortest paths of length 7 from the root node chemical entity (ChEBI id 24431). For those classes, we count how many compounds in the GNPS dataset belong to them and represent the counts as the size of the points in Figure  7. For each compound, the number of candidates and rank of the correct compound are known, so we plot the median number of candidates associated with the compounds in each class on the x-axis and the proportion of cases for which we have correct compounds with rank 10 on the y-axis.
, 2015) : the metabolites that fall outside the compounds in molecular structure databases. There, we need to design better kernels and preimage algorithms for molecular structures. Finally, it is important to note that the recent breakthroughs in machine learning methodologies for metabolite identification rely heavily on the existence off community efforts building open reference databases such as GNPS and Massbank. At the same time, the reference databases still cover a small fraction of relevant metabolite space. Although machine learning can generalize and extrapolate beyond the training data, as also shown in this article, the scarceness of training data still imposes limits on how accurate models can be built. To really push metabolomics forward, we should widen and make more systematic the community efforts in building and utilizing reference databases.
In section 3 we discuss various constraints from low energy physics on light pseudoscalars, and discuss separately the different scenarios. Benchmark points are presented with the help of the public Fortran code NMSSMTools  [50, 51]. For the different A 1 masses we study the average separation in rapidity of the diphotons and the two leading photons from 3π 0 , which allows to estimate the corresponding acceptances. In the final section 4 we summarize and discuss possible alternative signatures, which could help to distinguish different scenarios if the excess survives the next runs of the LHC.
Viable scenarios with a light NMSSM pseudoscalar As discussed in the introduction we will study scenarios with different values of the mass of a light pseudoscalar, denoted subsequently by M A 1. Constraints on such a light NMSSM pseudoscalar with a mass below ∼ 1 GeV have been discussed previously in  [52] [53] [54] [55] [56] [57] [58] [59]. Strong constraints originate from the mediation of FCNCs. Assuming minimal flavour violation, flavour violating couplings of A 1 still originate from SUSY loops involving stops, sbottoms and charginos and depend on the corresponding masses and trilinear couplings like A t. These contribute notably to B-physics observables like B s → µ + µ -, ∆M d and ∆M s. We have implemented the computation of these and many more B-physics observables and some K-physics observables in the code NMSSMTools  [50, 51]  following the update in  [59]  and checked that, for the scenario presented here, the constraints are satisfied due to the mostly singlet-like nature of A 1 and the relatively heavy SUSY spectrum.
Acknowledgements The authors acknowledge the support of  France Grilles  for providing cloud computing resources on the  French National Grid Infrastructure  and thank  J. Jaeckel  and  M. Spira  for helpful discussions, and notably  F. Domingo  for communication on possible A 1 -η mixings. U. E. acknowledges support from the  European Union Initial Training Networks HiggsTools  ( PITN-GA-2012-316704 ),  INVISIBLES  ( PITN-GA-2011-289442 ), the  ERC  advanced grant  Higgs@LHC , and from the grant  H2020-MSCA-RISE-2014 No. 645722  (NonMinimalHiggs).