dataesr/scientific-paragraphs-categorization huggingface dataset:
66  (Katoh et al. , 2005)  using the iterative refinement method and the following settings: Needleman-Wunsch algorithm active, 2 tree rebuilding steps, 1000 iterations and the program's default values for gap opening and gap extension penalties. No further manual manipulation of the alignment was performed. Sequences from Aloe, Haworthia and Gasteria species were retrieved from the GenBank and used in the phylogenetic analysis (Table  2 ).
Phylogenetic analyses Maximum parsimony (MP) and Bayesian inference (BI) methods were used in phylogenetic reconstruction. For each analysis, the cpDNA (trnH-psbA, matK and rbcL) and nrITSI sequence matrices were treated separately first and then combined. Congruence of the cpDNA and ITS1 datasets was assessed using the partition homogeneity (ILD) test  (Farris et al. , 1994)  implemented in PAUP* v. 4. 0b10  (Swofford, 2002)  and using 1000 replicates. Maximum parsimony searches were performed with PAUP*. Gaps were treated as missing characters and cladograms were generated using heuristic searches based on 1000 random sequence additions, TBR branch swapping and MULPARS effective. Bootstrap analyses were done using the same settings with a 1000 replicates. Bayesian inference was performed using MrBayes v.
3. 1. 1  (Huelsenbeck and Ronquist, 2001). The nucleotide substitution model that best fit each sequence matrix was determined with MrModeltest v. 2. 2. (Nylander, 2004)  with model selection based on the Akaike information criterion (AIC). The MCMC run in MrBayes was set to 5 × 10 6 generations with tree sampling every 100th generations. The log-likelihood scores for the sampled tree were analysed to determine the number of generations that could be excluded before stationary has been reached (i. e. the "burn-in" value). Effective sampling size (ESS) values were assessed in Tracer version v1. 5 (http:// tree. bio. ed. ac. uk/software/tracer/) as a further means to determine convergence. The trees generated were viewed in FigTree v. 1. 4. 0 (http:// tree. bio. ed. ac. uk/software/figtree/) to obtain tree topologies and the posterior probability values for their nodes.
NMR fingerprinting The chemical profiling of indigenous Mascarene Aloe was performed by 1 H NMR. The 1 H NMR data were subjected to PCA aiming to highlight the metabolic differences among the species. PCA analysis clearly distinguished Aloe vera from the Mascarene Aloes bases on their sugar profile (3. 5-4. 2 ppm) (Fig. 1 ). Fig. 2  indicates 3 principal clusters: A. macra, A. vera and A. purpurea/A tormentorii group.
Phylogenetic reconstruction Statistics from the MP analysis of individual chloroplast genes, ITS1, combined chloroplast only genes (matK, psbA-trnH and rbcL), and combined chloroplast and ITS datasets are shown in Table  4. Combining the chloroplast genes and ITS yielded a P = 0. 0100 using the PHT test in PAUP, indicating that some of the genomic regions are phylogenetically incongruent. Through a process of elimination, the psbA-trnH gene was discovered causing incongruence in the combined data set. A PHT test of the combined matK, rbcl and ITS1 sequence matrices yielded a P = 0. 2300, indicating congruence and a matrix containing these genomic regions were subsequently used in phylogenetic analyses. Parsimony analysis of the combined dataset yielded 68 most parsimonious trees with tree length = 68 steps, consistency index (CI) = 0.
8971 and retention index (RI) = 0. 9300 (Fig. 4 ). Phylogenetic trees generated from parsimony and Bayesian analysis clustered the Aloe species in a monophyletic group (bootstrap = 100%, posterior probability (PP) = 1) (Fig. 3 ). On this tree the Mascarene Aloes formed  Phylogenetic analysis of the ITS region was conducted to determine the phylogenetic signal of this region in comparison to the chlororplast genes. The MP analysis of the ITS I sequences yielded 39 parsimonious trees with tree length = 40 steps, CI = 0. 900 and RI = 0. 9310. (Fig. 3 ). The topology of the trees generated from parsimony and Bayesian analyses reflected the trees obtained from the combined data matrix (Fig. 4 ).
of PI characters (%) 0 (0) 0 (0) 0 (0) 0 (  0   are less informative compared to the ITS I region to elucidate the close relationships which exist among the Mascarene Aloes. Parsimony analysis of the ITS and combined datasets separately revealed a polytomy at the node that is shared by A. purpurea from Reunion and Mauritius, respectively, and A. macra and A. tormentorii formed a monophyletic group. The non-informative characteristic of the chloroplast DNA rbcL and matK has been previously reported  (Gielly and Taberlet, 1994; Muller et al. , 2006). However, the variable nature of the psbA-trnH intergenic spacer has also been reported previously  (Ramdhani et al. , 2011) , and therefore it was expected that this region would be more variable than the other genes from the chloroplast genome.
A combined cpDNA (psbA-trnH, matk and rbcl) and ITS1 phylogeny was thus used to obtain a finer resolution of the taxonomy of the Mascarene Aloes. In this study the combined dataset including the chloroplast genes and ITS1 yielded a phylogeny that separated most of the Mascarene Aloe species with strong support. The trees generated in this study clustered the Mascarene Aloes in a strongly supported monophyletic group. Within this group, A. tormentorii formed a monophyletic group with A. macra and A. purpurea from Reunion Island being a sister species to this group as supported by the Bayesian posterior probability for the node. Interestingly, A. purpurea from Reunion Island was placed paraphyletic to A. purpurea from Mauritius and basal to A. macra and A. tomentorii. A. purpurea from Reunion Island is morphologically distinct from the A.
The data sets were aligned to the mitochondrial genome and normalized by sequencing depth; a cleavage score (C score) was determined for each nucleotide across the mitochondrial transcriptome for all data sets from coverage profiles of the 5′ nucleotide of each read, as previously described  33. On the basis of these C scores, we identified regions within mitochondrial transcripts protected from endonuclease cleavage by LRPPRC or SLIRP in the control data sets. For each identified region, a footprint score (F score) was calculated based on the central RNase accessibility relative to the flanking regions in both controls (WT; Lrpprc loxP/loxP or Slirp +/+ ) and knockout (KO; Lrpprc loxP/loxP,Cre+ or Slirp -/-) data sets. A significant increase in the F score in the KO relative to the WT data sets enabled us to identify 178 different footprints for LRPPRC that were dispersed throughout mt-RNAs, the majority concentrated on mRNAs and to a lesser degree in rRNAs (Fig.
0 --2. 5 -1. 5 --2. 0 -1. 0 --1. 5 -0. 5 --1. 0 0. 0 --0. 5 An in vivo LRPPRC-binding signature identified by PAR-CLIP. We performed PAR-CLIP on mouse embryonic fibroblast (MEF) cells isolated from Lrpprc-FLAG transgenic mice to identify the direct binding sites of LRPPRC and to compare them with those identified by our footprinting. The incubation of MEF cells with 4-thiouridine (4TU) and its incorporation into de novo transcripts, followed by cross-linking and immunoprecipitation, enabled us to map the precise LRPPRC-binding sites by identifying sites with thymidine (T)-to-cytidine (C) transition rates indicative of cross-linking in the sequenced data set. The specificity of LRPPRC binding was established when compared to a control PAR-CLIP experiment that did not result in specific alignment to the mitochondrial transcriptome (Supplementary Table  1 ).
LRPPRC binding may facilitate the processing of the 16S rRNA from the RNA19 precursor transcript since loss of LRPPRC only decreases the levels of tRNA Leu that is also contained within this precursor transcript  20. To validate the identified in vivo binding sites of this complex, we carried out RNA electrophoretic mobility shift assays (RNA EMSAs) using targets identified in both the footprinting and PAR-CLIP analyses. We show that the mouse LRPPRC-SLIRP protein complex has a high affinity for a region within the mt-Co3 mRNA identified both in the footprinting and PAR-CLIP data sets (Fig. 2b ) and that the recombinant mouse SLIRP alone does not bind RNA (Supplementary Fig. 4 ) as previously identified for human SLIRP  32. Since the LRPPRC-SLIRP complex has high RNA-binding affinity with a large variety of RNA targets, we included a competitor RNA to mimic the physiological conditions in vivo.
2d ), reflecting the preponderance of different sites recognized throughout the mitochondrial transcriptome. Transcriptome-wide consequences of LRPPRC loss. In contrast to the direct LRPPRC-binding sites, which are enriched in mRNAs and almost entirely absent from tRNAs, the footprints defined by comparing RNase accessibility in knockout and wildtype animals are found in all mt-RNAs, suggesting that the binding of LRPPRC affects the RNA structure and accessibility to other RBPs. Therefore to investigate transcriptome-wide consequences of LRPPRC loss we identified footprints in the LRPPRC knockout mice and examined their changes relative to control data sets (Fig. 3a ). Here, we identified regions within mitochondrial transcripts protected from endonuclease cleavage by the absence of LRPPRC in the KO data sets.
We identified 124 footprints with significant reduction of the F score in the knockout relative to the control samples, indicative of either RBP or secondary structure changes that render these specific sites resistant to RNase cleavage in the absence of LRPPRC (Fig. 3a  and Supplementary Data 3). For example, we observe an appearance of a footprint in the mt-Nd5 mRNA in the Lrpprc KO mice that may be a result of a change in secondary RNA structure or caused by another RBP that associates with this mRNA in the absence of LRPPRC (Fig. 3b ). The number of footprints in the KO data set is less than the LRPPRC footprints identified in Fig. 1 , suggesting that LRPPRC has a general RNA chaperone role in regulating RNA maturation, stability and translation. The same analyses in the SLIRP knockout data set failed to identify any significantly enriched footprints, further confirming that SLIRP does not exert any direct effect on RNAs.
The RNase accessibility of each base, i, in the mitochondrial genome was quantified according to its C score, defined as: Cscore i ¼ log 10 max A iþ1 ; T1 iþ1 ; If iþ1 ð Þ þ 1 Untreated iþ1 þ 1 ; where A i+1 , T1 i+1 , If i+1 and untreated i+1 represent the normalized 5′ coverage of the nucleotide immediately downstream of the inspected position in RNase A-, T1-, and If-treated and untreated samples, respectively. The footprint detection algorithm searches for a span of between 8 and 40 nucleotides with an average C score lower than the flanking left and right three nucleotides, the central footprinting region, and calculates its F score: Fscore ¼ 10 C 10 L þ 10 C 10 R ; where C, L and R are the average C score of the central, left flanking and right flanking regions, respectively. We initially searched for footprints in the control data and for each footprint we calculated its F score (control) and the F score of the equivalent region in the knockout sample, F score (experiment) , and the log 2 -transformed fold change of F scores (knockout/control).
The propensity for secondary structure was quantified according to the ratio of double-stranded RNase cleavage over single-stranded, its R score: Rscore i ¼ log 2 A iþ1 þ 1 T1 iþ1 þ 1 Violin plots were generated with the vioplot package  43  , and the log 2 fold change of R scores calculated as: log 2 2 RscoreKO 2 RscoreWT Control footprint analyses of the cytoplasmic 28S rRNA were carried out in an identical manner, except alignment was performed to the GENCODE vM14 reference transcript set combined with the 28S rRNA sequence downloaded from GenBank (NR_003279. 1), and no significant footprints were identified (Supplementary Data 5). PAR-CLIP method and analyses. MEFs from Lrpprc-FLAG transgenic mice  20  were maintained in DMEM medium (Thermo Fisher Scientific) supplemented with GlutaMAX-I, 10% fetal bovine serum, 1% penicillin-streptomycin and 1× MEM non-essential amino acids.
Sequenced reads were aligned against the mouse transcriptome with RSEM v1. 2. 31 (-bowtie2 -strandedness reverse -paired-end) using the GENCODE vM13 transcript set combined with custom mitochondrial rRNA and mRNA sequences with an additional 50 adenine residues added to the 3′-ends to allow mapping of polyA tails. Gene-level counts were imported and analyzed for differential expression with tximport v1. 4. 0 53 and DESeq2 v1. 16. 1. Coverage profiles for each mitochondrial transcript were generated from full fragment BED files with BEDtools v2. 26. 0  41.
Acknowledgements This project was supported by fellowships and project grants from the  National Health and Medical Research Council  ( APP1058442 ,  APP1045677 ,  APP1041582 ,  APP1023460 ,  APP1005030 ,  APP1043978  to A. F. and O. R. ),  Australian Research Council  (to A. F. and O. R. ), the  Cancer Council of Western Australia  (to O. R. ),  Swedish Research Council  ( Rådsprofessor 2015-0418  to N. -G. L. ), the  Knut and Alice Wallenberg Foundation  (to N. -G. L. ),  Australian Genome Research Facility  (AGRF, to A. F. ), and the  Alexander von Humboldt Foundation  (to A. F. , O. R. and N. -G. L. ). SJS was supported by an  Australian Postgraduate Awards (APA) Scholarship. We thank  Dr Yuliya Karpievitch  and  Dr Ganqiang Liu  for advice on biostatistics and analyses,  Judith Ermer  for technical assistance, and  AGRF  for advice and help with library construction.
MATERIALS AND METHODS Patients and tumours. The PAPILLOPHAR study is a prospective cohort of 340 adult patients with OPC recruited from May 2009 to April 2012 in 14 French centres. Patients were followed according to the recommendations of the French Otolaryngology, Head and Neck Surgery Society: every 2 months during the first year and every 3 months during the second year. All patients provided written informed consent. The study was approved by the ethical committee (Comitede Protection des Personnes Ile-de-France III), and French data protection authority (Commission Nationale de l'Informatique et des Libertes). Clinical Trials. gov was NCT00918710. HPV status. High risk HPV DNA and E6/E7 mRNA were detected from frozen biopsies using the INNO-LIPA kit (Innogenetics, Gent, Belgium) and the NucliSENS EasyQ kit (bioMerieux, Marcy-l'Etoile, France), respectively.
RESULTS Among the 340 patients included in the PAPILLOPHAR study, 296 patients with OPC have been investigated for EMT markers. Baseline characteristics of the patients are shown in Table  1. Out of 296 patients, 77 (26. 01%) were HPV DNA and HPV E6/ E7 mRNA positive. Out of them, 28 (36. 36%) were current smokers or recent former smokers (o10 year-cessation). Multivariate analyses (without taking in account EMT markers) confirmed that HPV status is an independent prognostic factor for PFS (HR ¼ 0. 41 (0. 23; 0. 74)), OS (HR ¼ 0. 19 (0. 09; 0. 43)), but not for MFS (P ¼ 0. 15, data not shown). Histology and immunohistochemical findings. (Table  1 ) Diagnosis of basaloid tumour was more frequent in HPV positive than in HPV negative tumours (15,6% vs 5,5%, P ¼ 0. 0052) and lack of differentiation of the OPC was more frequent in HPV positive than in HPV negative tumours (75.
ACKNOWLEDGEMENTS The sponsor was  Assistance Publique-Hopitaux de Paris (Departement de la Recherche Clinique et du Developpement, Ile de France). We thank  Mireille Toy Miou  and  France Guyot-Rousseau  from the  Clinical Research Unit of East of Paris (URC-Est), Saint Antoine University Hospital (Assistance Publique-Hopitaux de Paris and Pierre-et-Marie Curie University-Paris 6 ) for study coordination and logistics,  Judith Leblanc  from the  Clinical Research Center of East of Paris (CRC-Est) ,  Saint Antoine University Hospital (Assistance Publique-Hopitaux de Paris)  for her logistic support and  Silvia Franceschi  for helpful discussion. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4.
Thiol-based redox post-translational modifications have emerged as important mechanisms of signaling and regulation in all organisms and thioredoxin plays a key role by controlling the thioldisulfide status of target proteins. Recent redox proteomic studies revealed hundreds of proteins regulated by glutathionylation and nitrosylation in the unicellular green alga Chlamydomonas reinhardtii while much less is known about the thioredoxin interactome in this organism. By combining qualitative and quantitative proteomic analyses, we have investigated comprehensively the Chlamydomonas thioredoxome and 1188 targets have been identified. They participate in a wide range of metabolic pathways and cellular processes. This study broadens not only the redox regulation to new enzymes involved in well-known thioredoxin-regulated metabolic pathways, but also sheds light on cellular processes for which data supporting redox regulation are scarce (aromatic amino-acid biosynthesis, nuclear transport,.
). Moreover, we characterized 1052 thioredoxin-dependent regulatory sites and showed that these data constitute a valuable resource for future functional studies in Chlamydomonas. By comparing this thioredoxome with proteomic data for glutathionylation and nitrosylation at the protein and cysteine levels, this work confirms the existence of a complex redox regulation network in Chlamydomonas and provides evidence of a tremendous selectivity of redox posttranslational modifications for specific cysteine residues.
EXPERIMENTAL PROCEDURES Detailed descriptions of experimental and analytical approaches are found in SI Materials and Methods. All mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomeexchange. org) via the PRIDE partner repository  (Vizcaino et al. , 2013)  with the data set identifiers PXD006097 (Reviewer account Username: reviewer97461@ebi. ac. uk; Password: pM7i6HgK) and PXD006116 (Reviewer account Username: reviewer69485@ebi. ac. uk; Password: YuEMZT17).
, 2009; Goyer et al. , 2002; Rivera-Madrid et al. , 1995; Stein et al. , 1995). Soluble protein extracts from two independent Chlamydomonas cultures grown under standard laboratory growth conditions in Tris-Acetate-Phosphate medium were subjected to the classical workflow depicted in Figure  1A. For each biological replicate, soluble proteins retained on the column were selectively eluted with DTT and further analyzed in duplicate by nLC-MS/MS without any prior separation on SDS-PAGE or 2D-gels. Moreover, to go deeper into this sub-proteome and identify less-abundant Trx targets, flow-through fractions were kept, concentrated and loaded again onto the column. Raw MS data from analytical replicates were concatenated before identification and the results obtained from successively eluted protein fractions of the same biological replicate were merged.
Therefore, they were finally reintegrated in our Trx-target dataset. For the 24 remaining cysteinelacking proteins, we hypothesized that their presence likely reflects their natural abundance in our soluble proteins extracts rather than a lack of specificity. To confirm this hypothesis, we experimentally characterized the soluble proteome of Chlamydomonas using extracts from cells grown under conditions similar to those employed for identification of Trx targets. The 24 proteins were almost all identified among the most abundant proteins (according to their sequence coverage) (Supplemental Table  2 ). This suggests that, under our experimental conditions, false positive contaminants were limited to a very small number of highly abundant proteins. The affinity chromatography approach allowed us to finally identify 980 putative Trx targets (Figure  2A ) with low contamination (Supplemental Table  1 ).
We also noted that 142 proteins identified as putative Trx targets by affinity chromatography were not identified in the soluble proteome confirming the enrichment capacity of this approach. Among these 980 proteins, we found 54 of the 55 previously identified Trx-targets  (Lemaire et al. , 2004). In fact, the missing CPN20 chaperonin was identified in only one of the two biological replicates and thus was not taken into account in our dataset. Moreover, almost all proteins known to be regulated by Trx in other photosynthetic organisms, but not yet established in Chlamydomonas, were identified by this approach. These include methionine sulfoxide reductases  (Tarrago et al. , 2009; Vieira Dos Santos et al. , 2005) , ACCase  (Hunter and Ohlrogge, 1998; Kozaki et al. , 2001; Sasaki et al. , 1997) , APS reductase  (Bick et al.
, 2001) , DAHP synthase  (Entus et al. , 2002) , glucan water dikinase  (Mikkelsen et al. , 2005) , uricase  (Du et al. , 2010) , magnesium chelatase CHLI subunit  (Ikegami et al. , 2007) , cytosolic NAD-MDH  (Hara et al. , 2006)  or ADP-glucose pyrophosphorylase  (Ballicora et al. , 2000). This suggests that other proteins identified in this dataset are also relevant. Thus, the Trx-affinity strategy combined to modern mass spectrometry is a powerful method to identify proteins forming mixed-disulfides with thioredoxins. However, one major drawback of this approach is that the exact cysteine targeted by Trx within the protein remains undetermined. Therefore, in addition to the Trx-affinity chromatography, we developed a quantitative reductome approach in order to gain further insights into the thioredoxome of Chlamydomonas.
To generate results with high confidence, we calculated logarithmized thresholds based on the global mean ratio and three standard deviations as shown in the following equation: Log 2 (Thresholds) = ± |Global Mean Ratio| ± 3 x Standard Deviation Application of this formula to our experimental dataset revealed that peptides having ICAT H /ICAT L ratios below 0. 46 and above 2. 18 could be considered as having a cysteine whose redox state is significantly reduced (ratio above 2. 18) or oxidized (ratio below 0. 46) by Trx. We have to notice that both distributions failed the Shapiro statistical test indicating that they do not strictly follow a Normal distribution. Thus, a precise false positive rate for these thresholds cannot be statistically extrapolated but was estimated to remain below 1.
5% based on our experimental data (1. 27% for TCEP reduction and 1. 37% for enzymatic reduction). Finally, we decided also to check whether an isotopic effect could be observed in our workflow. We therefore performed, on the same biological sample, two reductome experiments in parallel that were similar in all aspects except that the heavy and light forms of cICAT reagents were interchanged. We performed comprehensive MS analyses (five technical replicates) associated to stringent criteria for MS identification (peptide FDR< 0. 1%) and quantification (peptide quantified at least 3 times over the 5 replicates). Frequency profiles of cICAT ratios from both experiments appeared similar indicating that enzymatically reduced and control protein samples can be indifferently labeled by light or heavy cICAT (Supplemental Figure  3 ).
The quantification results from the two experimental datasets obtained after incubation in the presence or absence of the Trx system (Figure  1B ) were taken into consideration and concatenated. Among peptides identified in our analysis, only around 6% did not contain cysteine in their sequence (data not shown) underlining the specificity of our approach. Moreover, among cysteine-containing peptides, 977 peptides had a significant ICAT H /ICAT L ratio (Supplemental Table  3 ). Actually, only one peptide belonging to the β-subunit of the T-complex protein 1 had inconsistent quantitative data and thus was not included in the final list. Almost all identified cysteines showed a higher reduction level and only 25 cysteines appeared unexpectedly oxidized after the Trx reduction treatment. The appearance of oxidized proteins after Trx reduction was previously observed by Zhang and colleagues using a Cys-TMT approach  (Zhang et al.