dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of 59 and 39 Untranslated Regions (UTR) For the analysis of the 59 and 39 UTR, the partial or complete sequences of the two distal regions from all the USUV genomes present in GenBank (Vienna, Meise H, Budapest, Manheim, Italian, South African and Bologna/09) were aligned using ClustalW software and corrected manually. To identify the presence of single nucleotide mutations between the different USUV isolates and the human isolate, the nucleotide sequence of the first 96 and the last 666 nucleotides of the different genomes, corresponding respectively to the 59 and 39 UTR, were used for the analysis. To compare the stability of the deduced secondary structure of the 59 and 39 terminal sequences among the different strains, the free energy (Kcal/ml) of each isolate was calculated by CLC program (CLC RNA Workbench, v4.
Analysis of Synonymous Nucleotide Substitutions Comparative analysis of the ORF of the USUV isolates revealed 41 Synonymous Nucleotide Substitutions (SNS) (Figure  4 ). Interestingly these SNS identified two clusters of genetic variants amongst the USUV isolates, viz. , Italian and Austrian-Hungarian clusters. In particular, the Italian group showed 15 unique SNS distributed throughout the entire coding region (G564A, G1455A, T1503C, G1599A, C2085T, G2664A, A2853C, T2919C, T3201C, T 6909C, G7713A, C8478T, C8817T). Importantly, all SNS in the NS1 gene were unique to the Italian isolates (Figure  4 ). In contrast, the Austrian-Hungarian group showed only 4 unique SNS throughout the entire coding region (G438A, T606C, C8073T and T8994C). On the basis of these specific nucleotide substitutions, a phylogenetic analysis based on the Env region (between nucleotide positions 1180 to 1585) obtained from different USUV partial sequences available in GenBank was performed in order to confirm our previous results.
Analysis of 59 and 39 UTR Regions Comparative analysis of the nucleotide sequences of 59 and 39 untranslated regions (UTR) of Bologna/09 and of the USUV sequences available from GenBank showed several nt substitutions. Within the 96-nt long 59 UTR, one nucleotide substitution was found in the Bologna/09 strain whereas all the other sequences, from mosquito and bird derived USUV isolates did not show this nt variation, as shown in Figure  6. This single nucleotide substitution was observed at nt position 8 (Figure  6 ). Two distinct nucleotide substitutions were observed at nt positions 10 (C) and 14 (T) in the Meise H and Germany strains. These two nt substitutions were not detected in the other strains (Figure  6 ). Computer simulation of the 59-UTR folding derived from Bologna/09 strain, produced a secondary structure model for the USUV genome.
All four structures are built with repeated structural blocks for which good templates exist. Since the majority of contacts for these domains could be derived from the templates, their inclusion could introduce a bias in the evaluation. In practice, differences in the results on the original and the reduced sets were minor for the majority of analyses, and therefore we present here the results only for the original datasets, except for the domain-length dependence analysis, where using the reduced sets is more appropriate. An estimate of the difficulty of individual domains for contact prediction is shown in Fig. S1  of Supplementary Material.
Thus, for every group we generated 12 reduced lists of contacts per predicted domain, whenever possible. The results for all lists of contacts and all contact range categories are available at http:/predictioncenter. org/casp10/rr_results. cgi. In this paper we focus on the results for the L/5 lists of long-range contacts. The numbers of domains predicted on these datasets for each of the participating groups are summarized in Figure  1. Two groups (G334 and G077) submitted just a few predictions for the evaluated domains and one (G246) did none, so we excluded them from the analysis and present the results on the reduced lists for the remaining 23 groups. For every group, the final scores on the reduced datasets are averages of the per-domain scores. Padded contact maps-As contact probability maps generated from submitted predictions are sparse, they are usually unsuitable for many analyses that require complete predictions (i.
e. we need each pair of residues to be predicted either in contact or not). We remediate the "sparseness" problem here by setting the values of the empty cells of contact probability maps to zero ("padded" lists). In other words, pairs of residues that are missing in predictions are considered as non-contacts. Under such assumption, each prediction list classifies every pair of residues within the selected range to one of the four cases: TPcorrectly predicted contact; FP -non-contact predicted as contact; TN -correctly "predicted" non-contact (i. e. , the non-contact not included in the predicted contact list) and FN -contact "predicted" as non-contact (i. e. , the contact missing in the submitted list). We only assessed the groups that submitted predictions for at least 10 domains on the "padded" datasets -these are the same 23 groups as above, plus group G334.
Evaluation procedure In CASP10 we have substantially expanded the set of evaluation tools to assess residueresidue contact predictions. Besides the methods used in the previous CASPs, we introduced several new evaluations providing an alternative point of view on methods' performance. While in previous CASPs the assessors analyzed the results exclusively on the "reduced" datasets, implicitly concentrating on two aspects of contact prediction: (1) how good are methods in identifying the most reliable predicted contacts and (2) how accurate are the methods in predicting contacts with the highest reliability, in this CASP we complemented the assessment with analyses on the full sets of contacts addressing the issue of how accurate are all submitted contact predictions, including those predicted with lower reliability.
Basic scoring functions and group performance on the reduced datasets Since CASP6, predictions in the RR category have been evaluated on the reduced contact lists using two main scores: precision=TP/(TP+FP), and Xd. The detailed description of these scores can be found in the previous CASP contact assessment papers  [40] [41] [42] [43]. Note, that in those papers the measure defined by the formula TP/(TP+FP) was called "accuracy" (Acc); here we have changed its name to "precision" to be consistent with the classic descriptive statistics definition. The precision-based results are discussed in the main text of this paper, while the Xd-based results are shown in the Supplementary material. Based on these two scores, the performance of groups was further compared with two strategies: cumulative z-score ranking (sum of precision-based and Xd-based z-scores) and "head-to-head" comparisons  43.
Evaluation measures for the padded datasets Matthews' correlation coefficient and other binary descriptive statistics measures-For the assessment of the effectiveness of the predictive methods as binary classifiers we used four evaluation measures. The first two are precision and recall, a. k. a. sensitivity: They were already used in previous CASPs, but were shown to be equivalent on the reduced prediction sets  41. On the complete datasets, precision and recall are not inter-dependent any more as the number of predicted contacts is different for different predictions. Based on the formulae, one can notice that each of these measures takes into account only two out of the four parameters of prediction quality (TP, FP, TN and FN) and therefore focuses on the specific aspects of predicting contacts only (ignoring non-contacts).
Therefore, in this paper we consider this measure as the main estimator of binary classifiers on the expanded datasets. Precision-recall curve analysis-In previous rounds of CASP, the probability score assigned to every predicted contact was used in assessment only to select the most reliable contacts (according to the predictors' estimates) for the reduced evaluation datasets. However one can argue that the probability score holds valuable information that can be used both in modeling of the structure and in assessment. For example, it can be used to test the ability of predictors to correctly rank the predicted contacts and select the proper cutoff separating contacts (positive cases) from non-contacts (negative cases). To address these issues we carried out the analysis based on the precision-recall (PR) curves, which are widely used in statistical evaluations of disproportional datasets  [48] [49] [50] [51].
Group performance on the reduced datasets: precision and Xd The results of the analysis of the group performance for long-range contacts in the L/5 contact lists are presented in Figure  3. For each group we show the values of precision and cumulative z-score (sum of precision-based and Xd-based z-scores) averaged over all predicted domains from the "FM" and "FM+TBM_hard" datasets (see Materials for a detailed description of the datasets and evaluation measures). Panel A of Figure  3  demonstrates that the precision of the current prediction methods on FM targets does not exceed 20%. The three best performing groups on the FM targets (G125, G222 and G424) attain precision of 19% and belong to the same family of methods (Multicom, group leader J. Cheng, University of Missouri). Multicom-construct method (G222) was also shown to reach the highest score according to the Xd measure (see Figure  S2  in Supplementary Material), and is ranked first according to the cumulative z-score (Figure  3 , panel B).
The Multicom group is shown to be statistically better than all other predictors on the FM +TBM_hard set of targets (see Table  II  in the main text and Table  S1  in Supplementary Materials) and consistently better than other methods in head-to-head comparisons (Table  III  and Table  S2  in Supplementary Materials). However, it should be mentioned that the method used by group G489 is not conceptually an ab-initio contact prediction method, as it relies on the 3D models submitted by CASP10 servers. The better performance of this group on the FM+TBM_hard dataset can be explained by the method's consensus strategy, which works well on the TBM targets that constitute a substantial fraction of the FM+TBM_hard dataset.
Dependence of group performance on the domain length and the depth of alignment Figure  S1  (Supplementary Materials) shows that the contacts are harder to predict for some domains. The predictive difficulty of a domain is not always directly connected with the availability of templates, and from Figure  S1  it can be seen that in CASP10 the third easiest target (T0739-D2) is in fact an FM domain, while the second hardest (T0668-D1) is a template-based target. This raises the question of which other features, besides template availability, may influence the accuracy of contact prediction. In particular, we investigated the influence of domain length and depth of alignment. Figure  4A  shows the precision of the best ten performing groups as a function of domain length. The CASP10 FM dataset covers a wide range of domain length spanning from 58 to 535 residues.
Two domains are short (under 60 residues), two rather long (over 390 residues) and the remaining twelve are of medium length (80-220 residues). On four of the domains (the shortest two and one from each of the medium and long sub-ranges), the best groups reach a very high precision (over 50%). It should be noticed, though, that the two longest domains in this graph (T0653-D1 and T0695-D1) represent non-globular targets with a repeated topology (see the description of Set 2R in Materials), and this may introduce bias in the analysis. Therefore, we analyzed per-group trends in the results excluding these two domains. Inspection of the graph reveals that the vast majority of groups reach better precision on shorter targets. To analyze the dependence of group performance on the depth of the target alignments, we searched for sequence homologs for each target with PSI-BLAST 57 running 5 iterations against the non-redundant database with parameters "-h 0.
Comparison of CASP10 with previous experiments Establishing progress in contact prediction is not a trivial task as targets, methods and databases change in time. Unfortunately, no methods are available to adequately take all these relevant factors into account. We report here a comparison of the results without attempting to make any claim about the presence of real and measurable progress. Figure  8  shows the results of the top 10 groups in the latest three CASPs on FM domains for the L/5 lists of long-range contacts (CASP10 results for the FM+TBM_hard domains are also included for comparison). On average, the CASP8 predictions (12 domains) have the highest precision -24. 6%, followed by CASP9 (29 domains) -21. 4%, CASP10 (FM +TBM_hard, 28 domains) -21. 4%, and CASP10: (FM, 16 domains) -17.
At the same time, it should be mentioned that the list of CASP targets does mirror the proteins that the biological community considers interesting and worth an effort. The predictions submitted by the best performing groups are statistically indistinguishable on the set of free-modeling domains. When hard template-based targets are added to the dataset, the results of the Multicom group, which uses consensus strategy to extract the contacts from predicted 3D structures, are better than the others. Among the remaining groups, two implementations of the Distill method and ab-initio predictors from the Multicom series of methods quite consistently perform better. Based on the CASP10 data, we show that shorter domains are in general easier targets for contact prediction, and that the difficulty of predicting contacts in domains is not correlated with the depth of target sequence alignment.
10,000 laser shots were accumulated for each spectrum of MS1 and MS2. The 4,700 calibration standard kit Cal Mix (AB Sciex) was used for external calibration. Spectra were analyzed using Data Explorer® software (AB Sciex). The parameters used to reject or exclude outliers were a signal-to-noise ratio threshold of 3%, a centroid of 50, a noise window width of 250, and a threshold (m/z) after signal-to-noise ratio recalculation of 10. Relative quantification of the different N-glycan species was based on the MALDI-TOF-MS spectra of permethylated N-glycans as previously described  (42). For this purpose, the height of the interesting ions was used to calculate their relative intensity as compared with that of all the glycan structures identified. The values presented in Table I correspond to the mean of data obtained from five independent N-glycan preparations and MALDI-TOF analyses.
Monosaccharide Composition of 2-AB-labeled N-glycans by GC-EIMS-The monosaccharide composition of 2-AB-labeled N-glycans was determined via GC-EIMS. Samples were hydrolyzed with 2 M TFA, reduced with NaBD 4 , and peracetylated. The resulting alditol acetates were separated via GC (Hewlett-Packard 6890 series gas chromatographic system) on an HP-5MS capillary column (0. 25 mm inner diameter ϫ 30 m, 0. 25-m film thickness; Hewlett-Packard, Palo Alto, CA) and analyzed via electron ionization using an Autospec mass spectrometer of EBE geometry (Micromass, Manchester, UK) equipped with an Opus 3. 1 data system. Helium was the carrier gas, and the flow rate was 0. 8 ml min Ϫ1. The oven temperature was as follows: 100 °C for 1 min, 100 °C to 160 °C at 10 °C/min, 160 °C to 200 °C at 2 °C/min, 200°C to 300 °C at 15 °C/min, and 300 °C for 1 min.
Dynamic exclusion was enabled with a repeat and exclusion duration of 10 s, a repeat count of 1, a list size of 500, and an exclusion mass width of Ϯ10 ppm. Unassigned charge states and charge states of 1 were rejected. Glycopeptide Identification-For peptide identification, X!Tandem CYCLONE (49) incorporated into the proteomics data-processing pipeline Proteomatic (50) was used. MSA-CID spectra were matched against a target-decoy database composed of JGI4. 3 Augustus 10. 2 gene models merged with mitochondrial and chloroplast protein sequences from NCBI databases BK000554. 2 and NC_001638. 1, respectively. This database was supplemented with the protein sequences of jack bean Con A (UniProtKB: CVJB), Flavobacterium meningosepticum PNGase F (UniProtKB: P21163), and sequences of contaminant proteins from the Common Repository of Adventitious Proteins (version 1.
0, released January 1, 2012). The total number of protein entries in the composite database was 16,940. Decoy protein sequences were generated by randomly shuffling tryptic peptides while retaining the redundancy of non-proteotypic peptides. The maximum number of missed cleavages allowed was two. The mass accuracy was set at 5 ppm for MS1 precursor ions and 0. 5 Da for product ions. X!Tandem analyses were performed several times on spectra files, each time with a slightly modified set of glycosylationrelated variable modifications (see below). The following modifications were used for all X!Tandem analyses: carbamidomethylation of cysteine (static), oxidation of methionine (variable), and deamidation of asparagine (variable). Peptide identifications were statistically validated using Qvality (version 2.
0794 Da); and (2) modification of asparagine by chitobiose (ϩ406. 1587 Da). Conflicting peptide-spectrum matches were filtered on the basis of e-values as described for the identification of 18 Olabeled peptides. However, peptide glycosylations considered as ambiguous were not discarded automatically and were validated through manual inspection of the fragmentation spectra. All the raw MS data have been placed in a public database repository at PeptideAtlas.
3 Augustus 10. 2 gene model database, but conserved domain searches indicated that most proteins are related to protein lysis, cell wall degradation, and carbohydrate binding. Among secreted N-glycosylated proteins of C. reinhardtii cultivated under iron-deficient conditions, FEA1 and FEA2 were identified. These are two related proteins that have been proposed as members of the iron uptake pathway  (59). All glycosylation sites of FEA1/2 were detected independently with both the PNGase F method and IS-CID. In the same fraction, another N-glycosylated protein (MnSOD3) that was previously proposed to play an important role in iron-deficiency responses was also identified  (60). The highest numbers of glycosylation sites were determined for the flagellar proteins PKHD1-1 and FMG1-1/ FMG1-2. PKHD1-1 is a close homolog of human polycystic kidney and hepatic disease 1-like 1 protein (PKHD1L1) (BLASTp E-value ϭ 0.
Acknowledgments-We would like to dedicate this paper to  Dr. Arsenio Villarejo , a friend and co-worker who was taken from us too early on May 2, 2011, and who initiated this research with us. * Collaborative research conducted by the  universities of Rouen (France) ,  Mu ¨nster (Germany) , and  Autonoma of Madrid (Spain)  was supported by the  ANR ,  PLANT-KBBE 2008 program  in the frame of the  ALGALGLYCO  project. The experiments done at  Bioprocessing Technology Institute (BTI, Singapore)  were supported by the  Biomedical Research Council of A*STAR (Agency for Science, Technology and Research). The collaboration between the  University of Rouen (France)  and  BTI (Singapore)  is supported by the  MERLION 2011 initiative , project  Glyco-TOOLS. This work has also been supported by  INSA Rouen ,  CNRS ,  Region Haute-Normandie, EFRD  (No.
31708 ), and  Labex SynOrg  ( ANR-11-LABX-0029 ). □ S This article contains supplemental material. ¶ ¶ To  whom correspondence should be addressed:  Dr. Muriel Bardor ,  Universitede Rouen ,  Laboratoire Glyco-MEV ,  EA 4358 ,  Institut de Recherche et d'Innovation Biomedicale (IRIB) ,  Facultedes Sciences et Techniques, 76821 Mont-Saint-Aignan Cedex, France, Tel. :  33-2-35-14-67-58 , Fax:  33-2-35-14-66 -15 , E-mail: muriel. bardor@univ-rouen. fr; Prof. Michael Hippler ,  Institute of Plant Biology and Biotechnology, Schlossplatz 8, University of Mu ¨nster, D-48143, Germany, Tel. :  49 -251-83-24790 , Fax:  49 -251-83-28371 , E-mail: mhippler@uni-muenster. de. § These authors contributed to this work equally. ** These authors contributed to this work equally. Author contributions: E. M. -R.
Analysis of public gene-array data Studies were retrieved from Array Express Repository (http:// www. ebi. ac. uk/arrayexpress/experiments) by using the keyword ''glioma'' and the following filters: Organism: Homo Sapiens, All arrays, All assays by molecule and Array assays. We retrieved 423 experiments. They were manually curated to include only microarray experiments in patients' samples. We have retained 39 studies, integrating 2083 samples (Table  S1 , Flowchart S1). Whenever a study re-analyzed previous ones it was the only one retained for analysis. In the repositories, meta-data were preprocessed and therefore additional preprocessing was not made. As a first step, a correspondence with the GEO Database (http:// www. ncbi. nlm. nih. gov/gds) was established (it retrieved 338 datasets).
Thirty four studies were common in the two repositories, while 3 and 2 studies were unique in GEO and Array Express respectively. Values for the seven molecules of interest (APRIL/ TNFSF13, BAFF/TNFSF13B, TWEAK/TNFSF12, BAFFR/ TNFRSF13C, TACI/TNFRSF13B, BCMA/TNFRSF17, Fn14/ TNFRSF12) were retrieved using the online tool GEO2R (http:// www. ncbi. nlm. nih. gov/geo/geo2r) whenever possible (37 studies) and manually retrieved in two studies (Flowchart S1). Clinicopathological data (including histological types and/or WHO_Grade classification) were retrieved from sdrf files from the Array Express repository (Table  S1 , Checklist S1). Whenever WHO Grade was not included or was estimated by an earlier WHO classification, tumor samples were annotated according to the latest one (2007 WHO classification)  [3].
If not already contained in metadata files, retrieved data were normalized by Log2-transformation before use. Some studies contain, in addition to disease states, control data, which were also used in the meta-analysis. In all other cases, a pooled mean and variance of non-tumoral values per technology were calculated, as previously described  [58]  and used in the analysis. In cases where metadata were reported as changes over control, a control mean of 0 or 1, as per study, was used with the same number of cases and variance/SD as for tumor samples. In all cases, mean value and SD were calculated and meta-analysis was performed by the use of the MetaXL program (EpiGear International Pty Ltd) in Microsoft's Excel, reporting Cohen's d and standardized mean differences, using random effects analysis  [59].
Finally, standardized means were analyzed with ANOVA and correlation, with the SPSS V 21. 0 program (IBM SPSS Statistics). For the retained studies, in which a significant difference was identified for each of the seven parameters, microarray data available through www. oncomine. org were further analyzed for a significant co-expression of these TNFSF members with other genes. Finally, lower grade gliomas (83 provisional samples) and glioblastomas (206 samples) data from the Cancer Genome Atlas ((http://cancergenome. nih. gov/) were queried, using the cBioPortal for Cancer Genomics (http://www. cbioportal. org/public-portal/) for estimating the impact of these gene alterations on patients' survival.