dataesr/scientific-paragraphs-categorization huggingface dataset:
Phylogenetic analysis of JAZ genes Multiple alignment was carried out on the total protein sequences of the JAZs of Arabidopsis and Hevea. Alignment with Gblocks software  [55]  led to the isolation of conserved blocks at least 10 amino acids long. The blocks were then used to construct the phylogenetic tree using PhyML software  [56]  which implements a maximum likelihood tree reconstruction method using the LG + gamma model, starting from a BioNJ tree  [57]. A RAP-Green analysis was carried out using the original tree from PhyML to predict duplications  [58]. The final tree was visualized with the Archaeoptheryx program  [59].
The negative control is used as reference for the calculation of transactivation activity as follows: (pJAZ_1405(? 955) + pHbMYC)/(pJAZ_1405 (? 267) + pHbMYC). This method avoids any risk of disturbance due to the gateway cassette present in pMDC32 vector. The primer sequences used for gateway cloning are listed in Additional file 4. Transformation was replicated 6 times independently. After 16 h, GFP expression was quantified by flow cytometry (FACS Calibur II instrument, BD Biosciences) on the Montpellier Rio Imaging (MRI) platform. Data were analysed using Flowing Software version 2. 5. 0.
For example, the dbSNP database (build 138)  [8]  contains about 62 million SNPs and 11 million Indels. Half a million of these observed SNPs are found within an exon and modify a single amino acid. These are known as non-synonymous SNPs (nsSNPs) and are the most frequent cause of Mendelian diseases since they alter the protein's function  [9]. Insertions and/or deletions, commonly known as Indels, are the second most frequent type of human variation  [10]. When an Indel occurs within an exon, it can be either frameshifting (FS) or non-frameshifting (NFS): FS-Indels either modify the complete C-terminal region of the protein or result in premature termination of translation, while NFS-Indels involve multiples of three nucleotides, leading to the insertion/deletion of one or more amino acids  [11].
SM2PH is built around a knowledge base, which provides unified access to diverse information associated with any human protein (pathway, tissue expression, interactions, evolution, etc. ) and facilitates the integrated study of the structural and functional impacts of nsSNPs and their phenotypic effects. The framework also incorporates the MSV3d  [22]  database of known missense variants in all human proteins for which a 3D structure template is available. The human missense variants in MSV3d are mainly retrieved from the dbSNP  [8]  and SwissVar  [23]  databases and are classified into 2 categories: disease-causing variants associated with OMIM  [24]  diseases and Variants of Uncertain Significance (VUS). Each missense variant is then characterized using a large set of structural, functional and evolutionary parameters.
Data set collection The reference set of NFS-Indels was constructed using the same approach as that described in the article presenting the PROVEAN method  [18]. First, NFS-Indels were collected from the UniProtKB/Swiss-Prot database  [27] , and annotated in-house as deleterious, neutral or unknown, based on keywords found in the UniProtKB annotations (e. g. variants described using words such as "inhibit'', ''affect'', etc. were classified as deleterious). Second, this set was enriched with NFS-Indels retrieved from the 1000 Genomes Project database  [28]. Variants with average allele frequencies of >10% were collected and could thus be considered as common, i. e. non pathogenic or neutral, in the human population. All the variants were then mapped to the human protein sequences in the SM2PH knowledge base.
Dataset annotation The annotations used to characterize the NFS-Indel data set were either extracted from the SM2PH knowledge base or calculated using existing tools for protein structural analysis and in-house developed Python scripts. The SM2PH knowledge base contains high quality multiple sequence alignments for all human proteins, which are annotated with structural and functional parameters derived from MACSIMS (Multiple Alignment of Complete Sequences Information Management System). MACSIMS combines knowledge-based methods with complementary ab initio sequence-based predictions to extract valuable information from multiple alignments  [29]. These parameters are described in more detail in  [25]  and on the SM2PH help pages (decrypthon. igbmc. fr/sm2ph/cgi-bin/help). This information was complemented by additional structural parameters, including secondary structures and residue solvent accessible surface area (RSA) calculated using Spine-X  [30].
Indel length We used the definition of a 'small' NFS-Indel given in  [40] , as being in the range of 3-24 base pairs (i. e. 1-8 amino acids). In fact, 62% of the NFS-Indels were < =6 base pairs and longer NFS-Indels become increasing rare, and NFS-Indel of 24 bp (8 amino acids), represent only 3%. We limited our study to NFS-Indels ranging from 3-18 base pairs for which enough data is available. We hypothesize that longer NFS-Indels are more disruptive than shorter ones for the protein structure. The column 'In final method?' indicates whether the parameter is used in the final ILP rule set for prediction of deleterious NFS-Indels. *The numerical values range from -5 to +5 but, in order to reduce computational cost, we have regrouped values higher than ±2 into the semantic category two or more/two or less.
Such combinations are called 'rules'. The process is then repeated until all positive examples are covered by at least one rule. We used the ILP algorithm implemented in Aleph  [26]. Aleph allows the user to set a number of program options, including the minimum number of positive examples (minpos) and the maximum number of negative examples for each rule (noise). Then ILP evaluates each rule based on the difference between the number positive examples covered and the number of negative examples. If the minpos and noise constraints are satisfied the rule is added to the hypothesis space. In the experiments described in the Results section we set minpos = 6 and noise = 0, in order to eliminate false positive predictions. The rules produced by the ILP algorithm can be used to predict the status of unknown objects, i.
e. to predict whether an unknown NFS-Indel is deleterious or neutral. A 1 2 0 1 C 2 2 0 1 D 2 0 -1 0 E 3 0 -1 0 F 5 3 0 1 G 1 1 0 1 H 3 1 1 0 I 4 3 0 1 K 4 0 1 0 L 4 3 0 1 M 4 3 0 1 N 2 0 0 0 P 2 0 0 1 Q 3 1 0 0 R 4 0 1 0 S 1 1 0 0 T 2 1 0 0 V 3 3 0 1 W 5 3 0 0 Y 5 2 0 0 þ Fn = Sensitivity Tp Tp þ Fn Specificity Tn Tn þ Fp = MCC TpÃTn-FpÃFn ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi TpþFp ð ÞTpþFn ð ÞTnþFp ð ÞTpþFn ð Þ p Accuracy: Tp þ Tn Tp þTn þ Fp þ Fn T=True, F=False, p=Positive, n=Negative, NPV=Negative predictive value, MCC=Matthews correlation coefficient. To evaluate the prediction power of our algorithm, we used a 10-fold cross-validation strategy. Specifically, we randomly split the data set into 10 parts: 9 parts were used to train the ILP algorithm and 1 part was used to estimate the prediction performance of the method.
Results The first part of this study was aimed at identifying a comprehensive set of parameters that may usefully characterize the structural or functional consequences of a NFS-Indel in a protein sequence. We started with the existing parameters in the SM2PH knowledge base, originally used to characterize nsSNPs. It is assumed that 83% of the harmful nsSNPs affect protein stability. For this reason, the previously developed nsSNP prediction methods have focused on information about the structure and function of the protein, as well as the conservation and the physico-chemical properties of the change implied by the mutation  [31]. Using the same philosophy, we estimated or retrieved various data from SM2PH related to the sequence, structure and function of the protein and/or of the inserted/ deleted amino acids.
We then identified a number of additional parameters, designed specifically for the characterization of NFS-Indels, such as the probability of disordered regions in the proteins or the local perturbation of physico-chemical properties caused by the mutation. We then used this large set of parameters to develop a machine learning strategy to study the relationships between NFS-Indels and their corresponding phenotypes. The strategy involves (i) the construction of a large data set for training and testing and the comprehensive annotation of the examples in the data set with our diverse set of structural, functional and evolutionary parameters, (ii) the optimization of an efficient Inductive Logic Programming (ILP) method to learn a set of rules that distinguish between positive (deleterious) and negative (neutral) examples and (iii) the exploitation of these rules to extract knowledge about the phenotypic effects of unknown NFS-Indels.
Construction and annotation of the NFS-Indel data set The background knowledge used for the machine learning strategy consists of a large data set of positive and negative examples of NFS-Indels. The construction of a suitable background knowledge base is perhaps the most important step in the development of any prediction method. Our data set consists of 757 disease-causing/deleterious NFS-Indels (108 insertions and 649 deletions) and 757 neutral NFS-Indels (101 insertions and 657 deletions), collected from publically available databases.
Functional annotations: As shown in Figure  2 , disease-causing NFS-Indels are also more likely to occur at known functional sites. The most significant difference is observed for protein domains extracted from the Pfam database. Physico-chemical properties and their local perturbation: The 4 physico-chemical parameters associated with the amino acids in the NFS-Indel, as well as their perturbation in the local environment, are shown in Figures  3, 4 , 5 and 6 (amino acid volume in Figure  3 , hydrophobicity in Figure  4 , polarity in 5 and charge in Figure  6 ) for both average and total scores. The results are similar for the four properties and the two types of scores (average/total): Physico-chemical properties: Disease-causing NFS-Indels tend to be bigger, more hydrophobic, and more apolar than neutral NFS-Indels.
Machine learning method and prediction performance We used a 10-fold cross validation experiment to estimate the accuracy of our method, where the complete data set was randomly divided into 10 parts (nine parts for training, the rest for testing) and the process was repeated 10 times. For the 10 tests, our computational strategy achieves an average prediction accuracy on the test set of 79%, sensitivity of 89%, specificity of 69%, precision of 75%, negative prediction value of 85%, and Matthews Correlation Coefficient (MCC) of 0. 59 (Table  5 ). We then identified the set of rules produced by the ILP algorithm that achieved the highest accuracy (83% on the test set, corresponding to fold 1). This final rule set is composed of 241 human-interpretable rules that represent new knowledge about the molecular perturbations underlying disease-causing NFS-Indels.
The complete list of parameters used in this set are indicated in Table  1. Finally, we compared the performance of KD4i with two recent methods for the prediction of the phenotypic effects of NFS-Indels, namely DDIG-in and SIFT-Indel. DDIG-in uses a Support Vector Machine (SVM) as the learning method, while SIFT-Indel uses a rule set derived from a decision tree algorithm. In order to provide a more direct comparison with the DDIG-in method, we have also trained a SVM on our dataset and parameters. The results are shown in Table  6. We observe that the accuracies obtained with DDIG-in and our SVM implementation are similar, despite the fact that the size of the data set used to train the DDIG-in algorithm is almost 4 times larger than the one used here, since we Figure  7  Comparison of structural parameters for disease-causing and neutral NFS-Indels.
P-value for chi-squared test, where the null hypothesis is that there is no significant difference between the values of the parameter for deleterious and neutral variants. used only publically available data in our experiment. In comparison to SIFT-Indel, the KD4i ILP approach (based on the final rule set) achieves similar accuracy, but with higher sensitivity and lower specificity. The higher sensitivity of KD4i is due in part to the power of the ILP approach, but also to the parameters used in the final set of rules. Indeed, all the tools tested here use different sets of parameters to characterize the NFS-Indels. As shown on Table  7 , the disorder probability and the sequence conservation are important parameters for all three methods. However, the KD4i rule set includes other parameters that are used in only one or the other of the existing methods, such as the solvent accessible surface area (also used in DDIG-in) or location in a Pfam functional domain (also used in SIFT-Indel).
Since we set the noise option in the ILP algorithm to 0 (i. e. the rules cannot cover any negative examples), the precision of the rules in the training set is 100%. Figure  9  shows the precision of the rules calculated for the complete data set and for the test set only. If the test set is representative of the whole data set, the precision values should be similar. As expected, the precision of the rules observed in the test set is generally slightly lower than for the whole set, with less rules achieving 100% precision. The precision should be increased when the size of whole data set is increased and the precision in the test set should tend towards that observed for the whole data set, as was demonstrated in  [45]. The complete list of ranked rules is provided in Additional file 5, together with their coverage and precision statistics, and the top ranking rules are discussed in the following section.
This can be combined with other parameters, such as the location of the NFS-Indel in a conserved segment (core block) of the protein. Other predictors of disease-causing NFS-Indels include the perturbation of local physico-chemical properties, such as hydrophobicity or amino acid volume. In the future, in order to improve the accuracy KD4i, we will address the optimization of the parameters, extend the data set to include more representative NFS-Indels and investigate new parameters that can be used to better characterize the variants. Furthermore, we intend to combine the results of the ILP (deleterious/ neutral) with other machine learning methods (such as SVM or decision trees) into a single 'consensus' prediction. This will hopefully improve the prediction accuracy. Finally, we hope to expand our infrastructure to cover other types of variants, for example those occurring outside the protein coding regions (promoter, 5-UTR, 3-UTR, etc.
Acknowledgements We would like to thank the members of the  BISTRO Bioinformatics Platform in Strasbourg  for their support. This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI) , the  ANR   Investissement d'Avenir project BIP-BIP  and institute funds from the  CNRS, INSERM , and the  Université de Strasbourg.
Mapping of sequence reads, calculations of expression levels and meta-coverage plot Sequencing reads were mapped to the respective reference genomes (see Supplementary Table  S2 ) using bowtie-2 with default 'local-sensitive' mode  (43)  and further processed using samtools  (44). For the small RNA libraries, reads shorter than 18 bp were discarded before mapping. To express the transcript levels for individual genes as shown in Table  1 , we determined the number of reads per kilobase per million reads (RPKM)  (45). Briefly, we counted the number of reads mapped to all annotated transcriptomic features (e. g. mRNA) on the same strand (i. e. sense) and opposite strand (i. e. antisense). Both the sense and antisense read numbers were normalized by length of the feature (in kilobase) and the total number of reads (in millions) mapped to non-structural RNAs in the corresponding library (i.
As previously described  [29, 30] , CD4 + clones were submitted to ex vivo culture for one month prior to HTLV-1 screening and RNA extraction. At this time, infected and uninfected cells remained non-immortalized and required IL-2 for continued growth. Given that in vitro T-cell activation is known to modify AEU  [31, 32] , exon array analysis of untransformed CD4 + clones was carried out with RNAs extracted from unstimulated and phytohemagglutinin (PHA)-stimulated CD4 + T-cells (Additional file 1: Table  S1 ). By this approach that take into account the in vitro cell culture, we assumed that significant changes in exon expression between infected and uninfected clones mainly resulted from the global impact of HTLV-1 infection, irrespective of activation status. The microarray data have been deposited in toto into the Gene Expression Omnibus database and are available under record number GSE52244.
T cell limiting cloning Peripheral blood mononuclear cells (PBMCs) were obtained by Ficoll separation of whole blood of HTLV-1 infected individuals. For T-cell limiting dilution cloning, PBMCs were seeded at 0. 1 cell/well in Terasaki plates after removal of adherent cells. T-lymphocytes were cultured in RPMI 1640 containing penicillin and streptomycin, sodium pyruvate, non-essential amino acids, 2-mercaptoethanol, 10% filtered human AB serum, 100 U/mL recombinant IL-2 (Chiron Corporation), and 75 μM HTLV-1 integrase inhibitor L-731,988  [30]. Tlymphocytes were re-stimulated every 14 days with PHA (1 μg/mL) and fresh feeder cells (5. 10 5 cells/mL) that were composed of lethally irradiated PBMCs from three distinct allogenic HTLV-1-negative donors. Clones were phenotyped by flow cytometry using antibodies against CD4 and CD8 (DakoCytomation) and isotypematched controls on a FACScan system using CellQuest software (Becton Dickinson).
Microarray analysis Affymetrix exon array hybridization was completed by labeling 1 μg of TRIzol-purified total RNA with Affymetrix reagents according to the manufacturer's instructions. Hybridization cocktails containing 5-5. 5 μg cDNA were prepared and hybridized to Affymetrix-GeneChip Human Exon 1. 0 ST arrays (Affymetrix). Affymetrix Expression Console Software was used for quality assessment, while exon array data were normalized using quintile normalization and analyzed using FasterDB annotation (https://fasterdb. lyon. unicancer. fr/) [41]. Background correction and probe selection were performed as described previously  [42]. Statistical analyses were performed using a Student's t-test on the splicing index (SI) that corresponds to comparison of gene-normalized exon intensity values between two experimental conditions  [30, 42].
Acknowledgments The authors would like to thank  Joel Lachuer  and  Nicolas Nazaret  ( ProfilExpert, Lyon, France ) for providing transcriptomic facilities. This work was supported by the  Ligue Nationale Contre le Cancer  ( Comités de l'Ain, de la Drome, de la Saone et Loire, and du Rhône ),  Fondation de France ,  Association Laurette Fugain ,  Association pour la Recherche sur le Cancer (ARC) ,  Association Guillaume Espoir , and the  Agence Nationale pour la Recherche (EPIVIR). M. T. and C. V. were supported by bursaries from the  French Ministry of Higher Education and Science. F. M. was supported by  INSERM  and by the  Hospices Civils de Lyon  (AVIESAN CHRT program 2010). E. W. was supported by  Hospices Civils de Lyon and Lyon I University (France). T. W. was supported in part by a  US NIH Grant  ( CA140474 ), the  American Lebanese-Syrian Associated Charities (USA) , and  St.
After 3-D coregistration, 9 full years of elevation changes in the Mont-Blanc area are depicted (Fig. 8 ). These satellite-derived elevation changes are compared to the mean elevation changes derived from GNSS measurements performed every year around 10 September by LGGE on nine transverse profiles (five on the Mer de Glace and four on the Argentière Glacier, Fig. 8 ). Due to the retreat of the front of the Mer de Glace, the lowest profile, Mottet, has been deglaciated since 2009 and could not be used in our comparison. The 2003-2012 elevation differences derived from satellite data are, on the average, only 0. 3 m higher than those measured in the field (SD = 1. 3 m, N = 8). To evaluate how the two satellite DEMs contribute to the dispersion of the elevation differences (±1. 3 m), we directly extracted the DEM elevations at the location of the GNSS transverse profiles for each DEM separately.
The mean elevation difference between the 2003 SPOT 5 DEM and the 2003 field data is 0. 5 m (SD = 1. 3 m, N = 8), and the mean elevation difference between the Pléiades DEM and the 2012 field measurements is 0. 8 m (SD = 0. 4 m, N = 8). As expected from its higher resolution, the Pléiades DEM is more precise than the SPOT 5 DEM with a SD three times lower. The fact that both satellite DEMs are higher than the GNSS profiles can be explained by glacier thinning between the DEM acquisition dates (around 20 August) and the dates of the field surveys (around 10 September) in late summer when strong ablation (and thus thinning) is still ongoing in the European Alps. For each 50 m altitude interval, the histogram of the elevation changes is computed. The distribution is approximated by a Gaussian curve, which permits the calculation of the mean thickness change as the average of all the values less than 3 SDs from the mode of the Gaussian curve  (Berthier et al.
One or two accurate GCPs seem sufficient to reduce the vertical biases to a few decimetres. There is a slight improvement of the DEM coverage when they are derived from a tri-stereo. We have shown for the Mont-Blanc area that a simple combination of the different DEMs derived from the three images of a tri-stereo can reduce the percentage of data voids and slightly improve precision of the merged DEM. However, because glacier topography is often relatively smooth, a standard stereo coverage with a limited difference in incidence angles (typically a base-to-height ratio of about 0. 35-0. 45) provides a relatively comprehensive and cost-effective coverage of the glacier surfaces. One strong advantage of DEMs derived from Pléiades (and from other optical stereo sensors) compared to DEMs derived from radar images (such as the SRTM and Tandem-X DEMs) is the absence of penetration into snow and ice.
Fungal material The screening was carried out on cultures derived from 27 strains of various Xylariaceae species (LIP), including 12 Hypoxylon species, 7 Annulohypoxylon species, 2 Phylacia species and 1 Biscogniauxia species, which were collected in 2012 in French Guyana. Among these the H. monticulosum strain GYJF12243 (Figure  3 ) and two additional specimens of the same species collected  in 2013 in Thailand (MFLU 13-0356, MFLU 13-0358) were chosen for further evaluation. Cultures (MUCL 54604, MFLUCC 13-0593, MFLUCC 13-0595) were derived from multispore isolation of the respective fruiting bodies as described in  Kuhnert, Fournier et al. (2014). Strains were determined by morphological characters and supported by molecular data of the ITS region (Genbank Acc. No. : KJ810554, KJ810555, KJ810556) according to  Kuhnert, Fournier et al.
Structure elucidation Optical rotations were determined with a PerkinElmer 241 polarimeter, UV-Vis spectra were recorded with a Shimadzu (Kyoto, Japan) UV-Vis spectrophotometer UV-2450, IR spectra were measured with Spectrum 100 FTIR spectrometer (Perkin Elmer). NMR spectra were recorded with a Bruker (Bremen, Germany) Ascend 700 spectrometer with a 5 mm TXI cryoprobe ( 1 H 700 MHz, 13 C 175 MHz). Electrospray ionization mass spectrometry spectra were obtained with an ion trap MS (Amazon, Bruker), high resolution electrospray ionization mass spectrometry spectra were obtained with a time-of-flight MS (Maxis, Bruker) as described by  Pažoutová et al. (2013). For HPLC-based dereplication, data from the cited previous studies on Xylariaceae, recorded by the method described in general by  Bitzer et al.