dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, a minimum sequence length of six amino-acid residues was required. Results were post-processed to only accept peptide spectrum match (PSMs) with less than 6 ppm from the global identification average. One-hit wonders (i. e. , proteins identified with only one mass spectrum) with the peptide having an XCorr of less than 2. 5 were discarded. This last filter led to FDRs, now at the protein level, to be lower than 1% for all search results. PatternLab's TrendQuest module was used for grouping the proteomic profiles from each protein according the signals provided by the corresponding reporter ions as previously performed  (19). PatternLab's isobaric analyzer module was employed for pinpointing differentially abundant proteins when comparing the profound tissue samples from the first and second surgery as described in our bioinformatics protocol  (16).
The TrendQuest module generated four major protein groups by clustering proteins according to their relative abundance in each of the four samples under consideration (Figure  3 ). We note that not all identifications belong into one of these four groups. The clusters 1, 2, 3, 4 are composed of 377, 103, 392, and 281 protein entries, respectively. A visual assessment in Figure  3  reveals that Cluster #1 relates to proteins with higher abundancy in the external tissue (second surgery), #2 high in the profound tissue (first surgery), #3 high in intermediary tissue (second surgery), and #4, high in the intermediary (second surgery) and slightly less in the external tissue (second surgery). TrendQuest's unsupervised clustering provides a bird's-eye view of the proteomic landscape throughout the four biological conditions.
It is tempting to hypothesize that most changes would occur when comparing the GBM fragments from 1 year apart; yet, our results prove otherwise. We used the PatternLab's isobaric analyzer module to further pursue the differentially abundant proteins between the tissues of the profound regions obtained from different surgeries. We recall that this module employs a peptide-centric approach to assigned paired t-test p-values to each peptide and then converge to a final p-value, at the protein level, through the Stouffer's method. We applied stringency filters to only consider the signal from unique reactome. org generated with our data. The circles and boxes represent small molecules and sets of proteins, respectively. Some protein sets are partially filled with dark green representing the percentage of the proteins we identified in that collection.
The S100-B protein has been used in clinical studies to evaluate the staging malignant melanoma and the treatment success, being considering an independent prognostic factor to predict the survival rate of the patients  (53). The increasing S100-B concentrations indicate tumor progression and a worse prognosis for the patient  (53) ; this is coherent with our results, which the patient presents in the second surgery, a high abundance of this protein. The patient deceased 1 year and 1 month after the second surgery. gene Ontology enrichment analysis PatternLab's Gene Ontology Explore module was employed to statistically pinpoint over-represented gene ontology terms according to the hypergeometric distribution. The completed list of the enriched terms is long, and made available as Table  S10  in Supplementary Material.
Among these terms, protein members of the Ras family are frequently found; some examples are: Rab GDP dissociation inhibitor alpha (P31150), Ras-related protein Rap-1A (P62834), Rho GDP-dissociation inhibitor (J3QQX2), Rho GDP-dissociation inhibitor 1 (P52565), Rho GTPase-activating protein 1 (Q07960), Transforming protein RhoA (P61586), Importin subunit beta-1 (Q14974), Rho GDP-dissociation inhibitor 2 (P52566), Ras-related protein Rap-2c (Q9Y3L5), Ras-related protein Rap-2a (P10114). Some other key proteins related to tumors, identified in our dataset, and pertaining to these terms are: Cofilin-1 (P23528), 14-3-3 protein beta/alpha (P31946), Apolipoprotein A-I, E, C-III, Flotillin-1 (P35241) Radixin (P35241, A7YIJ8).
Secondary antibodies (Amersham) were used at 1:10,000, and signals were revealed by chemiluminescence (ECL, GE Healthcare). Where possible, membranes were stripped and reprobed with indicated antibodies. In the cases where this was not possible (for example, wherein the phospho and total antibodies or antibodies recognizing same proteins were used and significant signal remained on the membrane after striping), same lysates were ran simultaneously on duplicate gels, and probed with phospho and total antibodies. Each experiment was performed at least twice independently and the representative data are shown. X-ray films and/or ECL scans of whole membranes are shown in Supplementary Fig. 10. Although we trust that western blotting should be used for qualitative rather than quantitative measurements, as requested by reviewers, we performed densitometric analysis using ImageJ (W.
S. Rasband, ImageJ; National Institutes of Health, Bethesda, MD). The resulting data were log2 transformed, normalized per replicate and to the mean of the control, and analysed using analysis of variance in R (r-project. org) (Supplementary Fig. 9 ). In vitro kinase assay. For in vitro kinase assay, HEK293E cells were transfected in a 10-cm Petri dish with 7 mg of HA-Raptor using Lipofectamine 2000 (Invitrogen) according to manufacturer's instruction. HA-immunoprecipitation was performed in CHAPS buffer (40 mM HEPES KOH (pH 7. 4), 2 mM EDTA, 10 mM sodium pyrophosphate, 0. 1 M NaCl, 0. 3% CHAPS), washed twice with CHAPS buffer and once in CHAPS buffer supplemented with 0. 4 M NaCl. Beads were then washed twice in kinase buffer (25 mM HEPES KOH (pH 7. 5), 50 mM KCl, 10 mM MgCl 2 ) and resuspended in 40 ml of kinase buffer.
For microarray analysis, fractions corresponding to heavy polysomes (more than three ribsomes) were pooled (Fig. 1a ). RNA was submitted to the Bioinformatics and Expression analysis core facility at Karolinska Institutet. RNA quality was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies) and complementary DNA was generated and hybridized onto the Affymetrix Human Gene 1. 1 ST Array. The oligo package version 1. 30. 0 was used to summarize and normalize expression data using robust multiarray average (rma) in R version 3. 1. 1 (www. r-project. org) and bioconductor version 3. 0. We used updated probe set definitions 66 as these showed improved precision and accuracy  67. We assessed the reproducibility using principal component analysis. Samples clustered according to RNA (cytosolic or polysome associated) and experimental group (control, insulin or insulin þ torin1) indicating good reproducibility.
We obtained mean expression levels for each treatment and RNA combination. These were then used to calculate mean log 2 fold changes between insulin þ torin1 versus insulin and insulin versus control for each RNA separately (polysome associated or cytosolic). Two-sided Wilcoxon rank-sum test was used to compare differences in fold changes between mRNAs whose translation was stimulated by phosphorylation of eIF2a 5 to those that were not using data from polysome-associated mRNA or cytoplasmic mRNA separately. Data are displayed in Fig. 1. Raw and processed data are available at the Gene Expression Omnibus (GSE76766). For reverse transcriptase-quantitative PCRs (RT-qPCRs), RNA was extracted using Trizol according to the manufacturer's instructions. RT-qPCRs were performed using SuperScript III Reverse Transcriptase, followed by Fast SYBR Green Mastermix (both from Invitrogen), according to the manufacturer's instructions.
Acknowledgements This manuscript is dedicated to the memory of Colin Lister. We are grateful to  M. Witcher  (pWPI-GFP),  T. Waldman  (PTEN þ / þ and  PTEN À / À HT116 ),  N. Sonenberg  (pRenilla),  Y. Svitkin  (purified eIF2 a/g) and  D. Kwiatkowski  ( TSC2 WT  and knockout MEFs) for providing invaluable reagents and  B. Fonseca  for the advise on TOS motif search. We thank  J. Young  for help with circular dichroism spectroscopy and  M. Hatzoglou ,  T. Ferreira  and  E. Nuro  for the critical reading of the manuscript. This research was supported by the  Swedish Research Council , the  Swedish Childhood Cancer Foundation , the  Swedish Cancer Society , the  Cancer Society in Stockholm , the  Wallenberg Academy Fellows Program  and STRATCAN grants (O. L. ), and grants from the  Canadian Institutes of Health Research  ( MOP-115195  to I.
melanogaster's midgut (Table  1 ). The genomes of 39 L. plantarum strains were sequenced using Illumina Miseq sequencing technology, while the genomes of 4 strains were sequenced using Ion Torrent PGM (Life Technologies) (Supporting information Table  S1 ). The draft genome sequences of the 43 L. plantarum strains were analysed together with 11 publicly available L. plantarum genomes (Table  1 ) and aligned to the L. plantarum WCFS1 reference genome. To report all the detailed information about the comparative analyses of these genomes, we have created an online database that presents all the results concerning the strain-specific gene   (Molenaar et al. , 2005; Siezen et al. , 2010; Siezen and van Hylckama Vlieg, 2011; Lukjancenko et al. , 2012; Mendes-Soares et al. , 2014). Nevertheless, the pan-genome did not appear to approach saturation with the current strain collection, implying that the genetic repertoire of L.
Most of the sequenced L. plantarum strains appeared to have several plasmids (Table  D4  available at http://igfl. ens-lyon. fr/equipes/f. -leulier-functional-genomics-of-host-intestinal-bacteria-interactions/ l-plantarum/), with the exception of 6 strains (CNW10, NIZO2264, NIZO2855, NIZO2877, NC8 and JDM1). Our comparative genome analysis identified 4137 novel OGs that are not present in the reference genome WCFS1 (Supporting information Table  S2 ). We evaluated the degree of gene content variation among the 54 genomes by analysing the presence/absence of the OGs encoding proteins associated with central cellular processes and/or molecular functions. Metabolic maps of OGs were created using KEGG database (http://www. genome. jp/keg/pathway. html) (http://igfl. ens-lyon. fr/equipes/ f. -leulier-functional-genomics-of-host-intestinal-bacteriainteractions/l-plantarum/).
A detailed analysis of the genes belonging to the conserved and variable regions of the 54 L. plantarum strains is available in the online database (http://igfl. ens-lyon. fr/equipes/f. -leulier-functional-genomicsof-host-intestinal-bacteria-interactions/l-plantarum/). The genes are grouped depending on their metabolic functions and each category presents a detailed description of gene distribution and activity, together with tables summarizing all gene information and their presence/absence among the strains, and the metabolic maps related to each category.
To gain insights into the potential role of such allelic variation in environmental specialization and to reveal the evolutionary relationships of the L. plantarum strains studied, we performed a phylogenetic analysis the 54 strains. A phylogenetic tree of the 16S ribosomal RNA gene was initially generated to have a first glance of nucleotide divergence among the strains (Supporting information Fig. S4 ). The overall mean distance was calculated using the number of differences model in MEGA7 software  (Kumar et al. , 2016). It resulted to be 96. 7%, which is slightly lower than the similarity score used as cutoff for species discrimination  (Reller et al. , 2007). A phylogenetic analysis was also carried out on the entire core genome of the 54 strains (Fig. 2 ) and the average amino-acid identity (AAI) was found to be 82.
Caffrey et al. previously made similar observations based on the analysis of 750 complete bacterial proteomes  (Caffrey et al. , 2012)  but their study also included COGs related to transport and metabolism of carbohydrates and nucleotides. The remaining 20% of COGs were found to be enriched in functional divergence (Fig. 3A ): they include cellular defence mechanisms (V), cell motility (N), secondary metabolites biosynthesis, transport and catabolism (Q) and intracellular trafficking, secretion and vesicular transport (U) (Supporting information Table  S4 ). CAFS software was also used to verify whether each strain exhibited significant functional divergence in a particular COG (Fig. 3B ). No clear habitatdependent pattern was found: the distribution of both impoverished and enriched categories resulted to be related to strain clustering, rather than to the environmental origin of the isolates (Fig.
Therefore, we decided to focus on the variable functional categories and analyse each of them separately to evaluate whether the OG content of a particular functional category may predict the strains' origins. We performed independent hierarchical clustering of 3 of the functional categories that displayed the highest variability in terms of OG presence and absence among the 54 L. plantarum strains (i. e. , EPS, sugar utilization cassettes and secretome) (Fig. 4 ). In addition, we performed independent phylogenetic analyses (Supporting information Figs S5-S7) within the shared gene sequences of each category. EPS/CPS biosynthesis genes. The gene clusters involved in the EPS/CPS biosynthesis vary considerably in size, composition, sequence and gene order across the 54 L. plantarum strains. A detailed description and comparative analysis of these regions is reported in the database we have developed online (http://igfl.
ens-lyon. fr/equipes/ f. -leulier-functional-genomics-of-host-intestinal-bacteriainteractions/l-plantarum/database-for-comparative-genomics-of-54-l-plantarum-strains-7-7). Moreover, strains isolated from the same habitat appear to be scattered across the phylogenetic tree (Supporting information Fig. S5 ), and strains that contain a similar set of EPS/CPSassigned OGs were isolated from different habitats. These results demonstrate that the strain-specific distribution of genes that were assigned to the functional category of EPS/ CPS biosynthesis does not reflect the origin of isolation of L. plantarum. Secretome. The OGs assigned to the secretome were sorted and categorized into functional categories (Table  D11  available at http://igfl. ens-lyon. fr/equipes/f. -leulierfunctional-genomics-of-host-intestinal-bacteria-interactions/l-plantarum/database-for-comparative-genomics-of-54-l-plantarum-strains-7-7).
However, to date, no clear relationship has been deduced between the sugar utilization repertoire of different strains of L. plantarum and their source of isolation. The genomic analysis of the 54 L. plantarum strains confirmed the high plasticity and diversity of the genetic repertoire related to sugar metabolism in this species. The distribution of all OGs involved in sugar metabolism in the 54 L. plantarum genomes is reported in the database that we developed online (Table  D12  available at http://igfl. ens-lyon. fr/equipes/f. -leulier-functional-genomics-of-host-intestinal-bacteria-interactions/l-plantarum/database-for-comparativegenomics-of-54-l-plantarum-strains-7-7). The OGs assigned to carbohydrate utilization are generally organized in 'cassette'-like genomic loci, whose distribution among the strains are hypervariable and largely explain the differences found in their genome size.
ens-lyon. fr/equipes/f. -leulier-functional-genomics-of-host-intestinal-bacteria-interactions/l-plantarum/ database-for-comparative-genomics-of-54-l-plantarumstrains-7-7). Therefore, no clear correlation between sugar metabolism and environmental origin could be found in L. plantarum. Most of the sugar utilization cassettes are spread among the genomes of strains with different origins. Analogously, the genetic divergence of the genes assigned to the sugar utilization cassettes did not segragate the strains according to their origin of isolation (Supporting information Fig. S7 ). Overall, in contrast to the general concept proposed for several LAB species, our study based on 54 L. plantarum strains strongly suggest that both the presence/absence of genetic cassettes involved in sugar utilization and their genetic divergence do not reflect strain adaptation to a particular habitat involving utilizing specific substrates for growth.
Lactobacillus plantarum isolate collection and DNA isolation Forty-three L. plantarum isolates used in this study were obtained from various institutions and universities (Table  1 ). Eleven L. plantarum genomes, including L. plantarum reference strain WCFS1, were obtained from the National Center for Biotechnology Information (NCBI) database  (Geer et al. , 2010). All isolates were grown in standing MRS broth (Difco Laboratories, BD, US) at 378C. Genomic DNA from each isolate was extracted using UltraClean Microbial DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA), following manufacturer's instructions.
Genome sequencing and annotation Genomes of the 38 L. plantarum strains obtained from the NIZO culture collection, together with the WJL_IGFL strain, were sequenced using Illumina Miseq technology, while sequencing of 4 L. plantarum strains (ER, NAB1, NAB2, 19. 1) obtained from different institutions were accomplished using Ion Torrent PGM technology (Life Technologies). The reads of each strain were assembled into contigs using Ray  (Boisvert et al. , 2012). The RAST annotation server  (Aziz et al. , 2008)  was used to find open reading frames (ORFs) that could code for proteins and to provide an automatic annotation of the encoded functions. A RAST annotation was also done for the 5 L. plantarum partial genomes available in the NCBI database to allow for a straightforward comparison of annotations across all genomes.
plantarum strains as input. The trees were built using the Neighbour-Joining method with translated amino-acid sequences  (Saitou and Nei, 1987)  in MEGA v7 software  (Kumar et al. , 2016). Genetic distances were computed using the Kimura two-parameter model  (Kimura, 1980). A bootstrap test was used to test the reliability of trees  (Felsenstein, 1985). Correlation tests between trees have been performed using the R package dendextend and its function cor_corphenetic that gives the cophenetic correlation coefficient for two trees. A dedicated R script has been created to perform a permutation test and obtain a pvalue (<0. 05) indicating if the given coefficient could have been obtained by chance. The analysis of functional divergence across the 54 L. plantarum strains has been conducted using CAFS software  (Caffrey et al.
The enrichment status of each category is then calculated. Finally, a heatmap is generated from the enrichment status of functional categories within strains. Nucleotide sequence accession numbers LUWR00000000, LUWS00000000, LUWT00000000, LUWU00000000, LUWV00000000, LUWW00000000, LUWX00000000, LUWY00000000, LUWZ00000000, LUXA00000000, LUXB00000000, LUXC00000000, LUXD00000000, LUXE00000000, LUXF00000000, LUXG00000000, LUXH00000000, LUXI00000000, LUXJ00000000, LUXK00000000.
Acknowledgements The authors are grateful to  Dr Roland Siezen  for his help during genomes annotations and comparative analysis, to  Dr Tom Delmont  for the help in using Anvi'o software and to  Dr Dali Ma  for proofreading. The work was funded by an  ERC  starting grant ( FP7/2007-2013-N8309704 ). MEM was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement  N8659510. The lab of FL is sponsored by the  EMBO YIP program , the  ATIP/AVENIR program , the fondation FINOVI and the  'Fondation Schlumberger pour l'Education et la Recherche '. The authors declare no competing interests.
All DNA sequences were deposited at DDBJ/ENA/GenBank under the following accession numbers LUXM00000000, LUWN00000000, LUXL00000000, LUXN00000000, LUXO00000000, LTAU00000000, LUWA00000000, LUWB00000000, LUWC00000000, LUWD00000000, LUWE00000000, LUWF00000000, LUWG00000000, LUWH00000000, LUWI00000000, LUWJ00000000, LUWK00000000, LUWL00000000, LUWM00000000, LUWO00000000, LUWP00000000, LUWQ00000000,.
Data were normalized to the average fluorescence of the first 3 probe pulses (f 0 ) before bleaching. Filled black and red circles are means ± SEM. Black and gray horizontal lines denote measurement windows for f 1s and f 5s reported in C. (C) Average fluorescence recovery for 62 MFTs at 1 s (black; f 1s , average 0. 4-1. 7 s) and 5 s (gray; f 5s , average 2. 7-6. 9 s) after bleaching, normalized between f 0 and f b , where f b is the fluorescence just after bleaching (average 0. 02-0. MFT and recorded fluorescence using the confocal spot detection method. The intensity of a brief bleaching pulse was set to produce a modest reduction in fluorescence (~35%) to ensure the bleached volume did not extend significantly beyond the core of the illumination point-spread function of our microscope (iPSF; FWHM xy = 0.
clm. utexas. edu/tools/reconstruct/reconstruct. stm). MFTs from VGLUT1 Venus mice were qualitatively analysed at low and high magnification and compared to those obtained from C57Bl6 mice. All GFP-and VGLUT1-immunopositive MFTs were large, contained clusters of mitochondria and had vesicle densities similar to those found in C57Bl6 mice. The cloud of vesicles surrounding the AZs were also apparent with variable size. The proximity of mitochondria to AZs was also highly variable, similar to that found in C57Bl6 mice (data not shown).
FRAP recordings Parasagittal slices of the cerebellar vermis were prepared  (Nielsen et al. , 2004 ) from VGLUT1 Venus knock-in mice  (P22-33, n = 19). FRAP in the cerebellar slices was performed on a custom spot confocal system (Prairie Technologies, Middleton, Wisconsin; DiGregorio et al. , 2007) at 35 ˚C unless stated otherwise. Data was acquired and analyzed using NeuroMatic (RRID:SCR_004186; http:// www. neuromatic. thinkrandom. com) that runs within the IGOR Pro environment (RRID:SCR_000325; WaveMetrics, Portland, Oregon). Laser light (488 nm) was focussed to a diffraction-limited spot with 100x objective lens (1. 0 NA, Olympus). The bleaching pulse was 28. 5 mW (after the objective) of 0. 5 ms duration and probe pulses were 0. 04 mW of 2 ms duration. The ACSF contained (in mM) 125 NaCl, 2.
Acknowledgements We thank  Nils Brose  for supplying the VGLUT1 Venus knock-in mice,  Stefan Hallermann  for providing electrophysiological data on wild-type mice,  Noemi Holderith  for performing electron tomography and  Diccon Coyle  for providing technical assistance. We would also like to thank  J Ashmore ,  D Attwell ,  T Fernandez-Alfonso ,  F Lanore ,  B Marin ,  A Valera  and  T Younts  for their comments on the manuscript.
3e ), was the one showing the highest (a) Functional analysis of predicted miRNAs targets was performed using Ingenuity software and integrated database. The p-value associated with a biological process or pathway annotation was calculated with the right-tailed Fisher's Exact Test by the Ingenuity Function and Pathways analysis system. Top related pathways were those involved in organismal injury and abnormalities, cancer, gastrointestinal disease, liver hyperplasia and hepatocellular carcinoma. (b) Gene Ontology (GO) terms classification performed by DAVID bioinformatics software v 6. 7. Top enriched processes were those involved in metabolic and cellular processes (26% and 19% respectively), biological process (10%), developmental process (10%) and localization (9%). P-values were obtained with the overrepresentation Fisher Exact Test and corrected by the Benjamini Hochberg post-hoc method, calculated by the EASE method in DAVID.