dataesr/scientific-paragraphs-categorization huggingface dataset:
Mascot searching was carried out on a local Mascot server against gene annotations from ToxoDB version 7. 2 (http://toxodb. org/toxo/). MS/MS ion search was used to search the data output from the LTQ. Database search parameters included: fixed carbamidomethyl modification of cysteine residues; variable oxidation of methionine; a peptide tolerance of ±1. 5 Da; MS/MS tolerance ±0. 8 Da; +1, +2, +3 peptide charge state; and a single missed trypsin cleavage. Instrument was set as ESI-TRAP.
RNA extraction and qRT-PCR analysis The Toxoplasma stage preparation was controlled through the stage-specific markers sag1, bag1 and eno1 by qRT-PCR (Supplemental data 1). These genes were chosen as bradyzoite markers (bag1  (Bohne et al. , 1995)  and eno1  (Holmes et al. , 2010) ) and tachyzoite markers (sag1  (Burg et al. , 1988) ). Total RNAs of each strain were extracted using the RNeasy Ò Mini Kit (Qiagen) according to the manufacturer's instructions. One microgram of total RNA was reverse transcribed to cDNA using the iScript Transcription Supermix (Bio-Rad, USA) according to the manufacturer's instructions. The qRT-PCR was performed using the IQ™ SYBER Ò Supermix kit (Bio-Rad) with an iCycler™ iQ real-time PCR Detection system (Bio-Rad). PCR primers (Invitrogen™ life technologies, France) were designed using ProbeFinder Version: 2.
Statistical analysis The mRNA levels of rop2, mic2, eno2 and imc1 genes were normalized to housekeeping toxoplasma b-tubulin gene using the 2 ÀDCt method. All real time qRT-PCR experiments were performed in duplicate; values are expressed as a median ± interquartile spaces (IQs) of Ct values, and analyzed using a non-parametric exact Wilcoxon-Mann-Whitney test (StatXact software, CytelSudio7). The statistically significant differences level was defined as ⁄ p < 0. 05 and ⁄⁄ p < 0. 01.
Identification of differentially expressed proteins For this study, we used a 2D-DIGE approach to identify proteins associated in natural resistance mechanisms to sulfadiazine (Supplemental data 2). Protein abundance was compared between sulfadiazine sensitive and resistant strains from T. gondii with same genotype: RH versus TgA 103001 (Type I), ME-49 versus TgH 32006 (Type II) and ME-49 versus TgH 32045 (Type II variant). Protein spots which appeared with at least a 1. 3-fold change (p 6 0. 05) between resistant and sensitive strains were used for analysis. Fourteen protein spots were identified in the Type I strain: only one of them, identified as a hypothetical protein, increased in the resistant strain TgA 103001 and 13 increased in the sensitive RH strain (Table  1 ). Eighteen protein spots were in different abundance in Type II strains: 17 of them were over-expressed in the resistant strain TgH 32006 and one in the sensitive strain ME-49 (Table  2 ).
However the mass spectrometry is not able to determine the precursor or the active form of the protein identified. For MIC2 we observed the same results as DIGE for the active form of the protein. Results obtained with TgH 32045 are questionable. In fact, TgH 32045 is a strain which presents genetic recombination and deletion for some genes, e. g. gra7 (GenBank Accession No. DQ465955). So we can hypothesize that antibodies used cannot recognize the protein epitope. GRA7 protein showed an up-regulation in the Type II sensitive strains in comparison to the Type II resistant strains. The two sensitive strains of same genotype have shown the same protein regulation for the protein studied.
Confirmation of differentially expressed proteins at the mRNA level In order to verify the Toxoplasma stage preparation, gene expression levels of sag1 (tachyzoites markers) bag1 and eno1 (bradyzoites markers) were analyzed by qRT-PCR. Our results showed that only sag1 gene expression was highly expressed for all strains studied (Supplemental data 1), this confirms the presence of only tachyzoites in our samples. Then, to confirm the differential expression of proteins common to two of the strain comparisons, gene expression levels of rop2, mic2, eno2 and imc1 were determined by qRT-PCR (Fig. 3 ). For the Type II strains, the results showed that the transcription levels of rop2 (significant at p < 0. 01) (Fig. 3A ) and eno2 (significant at p < 0. 05) (Fig. 3C ) were consistent with the protein levels identified by DIGE.
Acknowledgments We thank  Dr. S. Tomavo  ( University Lille, France ) for his helpful discussion and providing ENO2 and IMC1 antibodies, and we thank  Dr. J. F. Dubremetz  ( University Montpellier, France ) for providing MIC2 and ROP2 antibodies. This research was funded by a grant from  Region Champagne-Ardenne and Roche Laboratory , which was awarded to  C. Doliwa  for completion of a doctorate degree. We thank the  BRC Toxoplasma  for providing strains of this work and the  Proteomics and Functional Genomics Group, University of Liverpool , for their contribution to proteomic instrumentation support.
Statistical analyses Analyses were performed using Minitab 16 statistical software. Data were checked for normality (Anderson-Darling) and replicates were checked for between-experiment variation using oneway ANOVA. Sua 4. 0 cell growth and viability assays were also analyzed using one-way ANOVA. The effects of AMPs on P. berghei ookinetes were assessed by the Wilcoxon signed rank test and oocyst burdens of both rodent and human malaria were compared via the Mann-Whitney U test. Differences in infection prevalence and mosquito sizes (wing length) used in all in vivo studies were assessed using a 2-sample t-test. Longevity studies were analyzed by Kaplan-Meier tests, where survival curves were compared using the Wilcoxon test to weight early failures (immediately after the first feed) more heavily.
Live Cell Dynamic Microscopy A human allo-specific CD4 + CD28 + T cell clone  [30]  (Teff; 2. 10 5 cells) or a human T regulatory cell clone (Treg#1; 2. 10 5 cells) were used in their resting state (two weeks after their last stimulation), stained with the FURA-2 AM probe (0. 5 mM for 30 min; Interchim, Montlucon, France), washed and added to 4. 10 5 human EBV-transformed B lymphoblastoı ¨d cells, as described  [31] , on a coverslip coated with Poly-L-Lysine (0. 001%; Sigma, Saint-Louis, Minnesota). Bright-field and fluorescent images were acquired at 15 second intervals on a Leica microscope (Leica Microsystems, Wetzler, Germany) using the Metafluor image analysis software (Molecular Devices, Sunnyvale, CA). Individual T cell-APC interactions and individual T cell calcium peaks were recorded manually over a 20 min.
incubation period with Metafluor (version 7. 1. 7) and Metamorph (version 7. 5. 6; Roper Scientific, Go ¨ttingen, Germany) software. A calcium peak was recorded when fluorescence levels reached 2-fold the baseline level. T cells were tracked using the ImageJ free software (version 1. 41). Antibodies were all used at 10 mg/ml. Data are presented as mean 6 SD for each condition.
Suppression Assays and Cytokine Release Assays All experiments were performed with PBMCs obtained from healthy donors and the Treg#1 clone. CD4 + T cells were enriched from PBMCs by negative selection using CD4 + T cells isolation Kit II (Miltenyi) and an autoMACS Pro separator (Miltenyi). Enriched CD4 + cells were then stained with antihuman CD4, CD25 and CD127 mAbs at 4uC for 30 min. CD4 + CD25 hi CD127 low regulatory T cells (formerly referred to as natural Treg and now recently proposed to be named thymusderived Treg or tTreg  [32] ) and CD4 + CD25 2 naı ¨ve T cells were sorted (purity routinely above 95%) with a high-speed cell sorter (FACSAria; BD Biosciences) and FACSDiva software (BD Biosciences). Allogeneic mature DC (mDCs) were generated from monocytes as described  [33]. Briefly, monocytes were enriched by elutriation (.
Activated LFA-1 and Adhesion Molecules Analyses and Flow Cytometry Teff and Treg#1 clone (2. 10 5 cells/well for each) were incubated with indicated antibodies (10 mg/ml) during 30 min in HBSS 1X, 5 mM Hepes at 37uC. After washing, they were added to human EBV-transformed B lymphoblastoı ¨d cell lines (pool of cells from 3 donors, 4. 10 5 cells/well) in the presence of fluorescent anti-CD11a, anti-CD18act (MEM-148) or anti-CD18tot (YFC118. 3) mAbs and incubated during 30 min in HBSS 1X, 5 mM Hepes at 37uC. After washing, cells were stained with fluorescent anti-CD3 and anti-CD4 antibodies. Alternatively, cells were stained with fluorescent anti-CD2, anti-CD44, anti-CD49d and anti-Nrp1 antibodies. Samples were acquired on a BD FACSCANTO TM flow cytometer (BD Biosciences) and analyzed with the FlowJo software.
Control of Teff Synapses, Motility, Velocity and Activation by CD80/86 Ligands By using Teff clones and their cognate allogeneic B cells, it was possible to capture several antigen-specific contact events per microscope field. Their antigen-specificity was confirmed by the observation that no contacts formed in the presence of another Bcell line (Raji cells; data not shown). Teff cells in their resting state were incubated with their cognate allogeneic B cells in the presence or absence of blocking reagents for different costimulation pathways, and T cell-APC contacts as well as T cell motility and activation were measured. Cells were added onto poly-L-lysin coated slides that allow mild but free cell movements. To assess the CD28 dependency of T cell motility and synapse formation, we used FR104, a novel anti-CD28 antagonist monovalent antibody that induces immunosuppression by blocking CD28-mediated costimulation with a demonstrated absence of signaling activity  [29, 35].
05; Fig. 4D ), that was mostly visible after a 5 min incubation period (Fig. 4C ). In accordance with data showing that CTLA-4 is essential for Treg suppressive function  [36] , we observed that concomitant blockade of CTLA-4 signals inhibited Treg calcium responses (0. 2360. 02 peaks/min with FR104+ anti-CTLA-4 vs. 0. 6860. 06 peaks/min with FR104 alone). Blockade of CD80/86 resulted in even stronger inhibition of calcium flux than blockade of both CD28 and CTLA-4 (0. 0760. 02 peaks/min or 0. 1160. 02 peaks/min with anti-CD80/86 antibodies or CTLA-4Ig, respectively; Fig. 4D ). Taken together, these data indicate that in contrast with Teff, CD28 blockade in these experiments increased Treg-APC contacts and Treg activation, and that these mechanisms were dependent on CTLA-4. Finally, as with Teff cells, confocal microscopy was performed to determine the formation of IS in Tregs.
This work was funded by the '' Tolerance Restoration In Autoimmune Disease ''  FP7 Health 2011 Program # EC-GA nu281493  of the  European Commission  and supported in part by the Progreffe foundation and by a joint grant from  Effimune  and the  French ANRS , in the framework of a CIFRE convention. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was partly supported by Effimune through a joint grant with the  French ANRS , in the framework of a CIFRE convention. The authors have the following interests. Caroline Mary  and  Nicolas Poirier  are employed by  Effimune S. A. S.
Motivation: RNA molecules specifically enriched in the neuropil of neuronal cells and in particular in dendritic spines are of great interest for neurobiology in virtue of their involvement in synaptic structure and plasticity. The systematic recognition of such molecules is therefore a very important task. High resolution images of RNA in situ hybridization experiments contained in the Allen Brain Atlas (ABA) represent a very rich resource to identify them and have been so far exploited for this task through human-expert analysis. However, software tools that may automatically address the same objective are not very well developed. Results: In this study we describe an automatic method for exploring in situ hybridization data and discover neuropilenriched RNAs in the mouse hippocampus. We called it Hippo-ATESC (Automatic Texture Extraction from the Hippocampal region using Soft Computing).
Bioinformatic validation showed that the Hippo-ATESC is very efficient in the recognition of RNAs which are manually identified by expert curators as neuropil-enriched on the same image series. Moreover, we show that our method can also highlight genes revealed by microdissection-based methods but missed by human visual inspection. We experimentally validated our approach by identifying a non-coding transcript enriched in mouse synaptosomes. The code is freely available on the web at http://ibislab. ce. unipr. it/software/hippo/.
2A ). Afterwards, we  performed a segmentation step in order to identify 13 different hippocampal sub-regions, corresponding to specific sub-sections of the dentate gyrus and of the CA1 and CA3 regions of the Ammon's Horn (Fig. 2B ). From these regions, we extracted 220 textural features from each image. From this large dataset we first extracted a training set composed of three prototypical neuropilenriched genes and 17 negative examples, based on which a genetic algorithm  [22]  selected a subset of 52 significant features (Table  S1 ). Finally, for every gene, we extracted its 52-feature vector and compared it to a reference vector (the average of the three prototype vectors) using the Pearson coefficient as a distance measure  [23].
Model Training and Ranking Procedure A small dataset composed of 20 genes was used to train the system. The idea was to select a feature subset able both to represent well the dendrite-enriched mRNAs and to successfully distinguish them from negative examples. To do so, we used as positive examples (prototypes) three genes which are well known for the dendrite and spine enrichment of their mRNA, namely Camk2a (ID = 12322)  [7, 8] , Map2 (ID = 17756)  [9]  and Arc (ID = 11838)  [12]. Moreover, we used as negative examples 17 genes with different characteristics. In particular, we chose: Camk2b (ID = 12323), Tubb3 (ID = 22152) and Grin1 (ID = 14810), i. e. three genes whose mRNA is specifically expressed in neurons but is not transported in dendrites; Gapdh (ID = 14433), Pgk1 (ID = 18655) and Pfkm (ID = 18642), i.
Features that, in this small dataset, showed a very high correlation between each other (. 0. 99) were considered as being the same, and one of them was removed. After this, a binary genetic algorithm (with population size 50, run for 300 generations, with crossover rate of 0. 8, mutation rate 0. 06, and tournament selection of size 4) was used. Every individual in the population encodes a feature subset and the silhouette index  [30]  is used to evaluate its effectiveness. This index is computed using the selected subset of features and considering the positive examples in one group and the negative examples in another. The silhouette index is a measure of an object's similarity to the others of the same group and dissimilarity from the elements in the other groups. In the following formula, ''a'' identifies the average distance (or dissimilarity) between an element with the data in its own group, while ''b'' is the average dissimilarity between the element and the elements of the other group.
Detection of Neuropil-enriched Transcripts by the Hippo-ATESC Pipeline The Hippo-ATESC pipeline was trained using as positive cases three of the best experimentally characterized neuropil-enriched genes described in the literature (Camk2a, Arc and Map2) as well as 17 negative genes (see the methods section for the details). We then set out to scan parasagittal images contained in the ABA to obtain similarity scores between the texture vectors of all informative probes and a prototype, or reference vector, obtained by averaging the vectors of the prototype genes. In particular, to avoid scoring genes expressed at negligible levels in adult mouse hippocampus, we only considered image series in which the expression level or the expression density reported for the hippocampal region or for the hippocampal formation was above 20, a background level determined on the basis of cell cycle genes which are known to be silent in brain after the end of development.
This work was funded by the  Neuroscience Program of the ''Compagnia di S. Paolo '' to MG, SC and FDC. FDC is also funded by the  Telethon Foundation  and by the  Jerome Lejeune Foundation. Roberto Ugolotti  was funded through a  fellowship from the same program. Pablo Mesejo  was funded by the  European Commission  ( MIBISOC Marie Curie Initial Training Network ,  FP7 PEOPLE-ITN-2008 , GA n. 238819). BB is supported by  INSERM ,  CNRS ,  ANR  ( E-RARE 09 ),  Fondation pour la Recherche Medicale. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Technically, we estimate the ER by assuming a relaxed molecular clock  [9] , when building the phylogeny using Bayesian inference methods  [10]. This allows us to alleviate the limiting assumption that evolutionary rates are constant among lineages of the phylogeny and through time, i. e. the strict molecular clock hypothesis  [8]. Note that we do test that the relaxed molecular clock assumption explains the sequence data better than the strict clock assumption (see the Methods). We used full-length genomes from the Los Alamos National HIV Database. For the WH level, the database contained one adequate dataset, originating from a US patient  [11, 12]. Four additional datasets  [13] , also from US patients, could be analysed but they were all from acute infections (they spanned at most over 11 months) and had lower genome-wide coverage.
For the BH data, many of the sequences we used originated from a study conducted in Boston  [14]. In order to investigate evolutionary rates across the HIV genome, we had to consider specific sub-regions of the genome separately. We proceeded by splitting the genome into segments according to regions of overlaps between reading frames (Figure  1A ). This also allowed us to also investigate the difference in evolutionary rates between overlapping and nonoverlapping regions. Note that other WH and BH datasets were used to investigate evolutionary rates in a specific part of the genome (the pol gene) as discussed in the Results and discussion and in the Methods. Our assumption of a relaxed molecular clock allowed us to estimate ER on internal and on external branches of the virus phylogeny separately  [15].
At the WH level, we know that internal branches correspond to viruses that will have an offspring. For external branches however, this is not always the case. Therefore, we expect some of the viruses sampled to bear more deleterious mutations in their genome. In other words, at the WH level, we can expect the substitution rate on external branches to be higher and closer to the virus mutation rate. At the BH level, we do not expect much differences between ER on internal and external branches because selection has had the time to act. Concerning HIV, it is known that BH substitution rates in the env gene are higher than that in the gag gene  [16]. We are not aware of studies that compare WH and BH evolutionary rates in different genomic regions. Here, we provide a genome-wide overview of molecular rates In panel C, the colour code is red for PIC1362, green for PIC38417, blue for PIC71101, cyan for PIC83747, purple for PIC90770 and black for the BH data (UP-up4).
At the BH level, the coefficient of variation (CoV) of the ER did not strongly vary across the HIV genome: it was always close to 0. 3 (Figure  2B ), which is consistent with earlier results obtained in the C2V5 region only  [4]. At the WH level, in patient PIC1362 the CoV were higher and more variable (Figure  2A ). This discrepancy between WH and BH estimates of CoV is consistent with what has been reported in the hepatitis C genome  [7]. In the four other WH datasets from acute infections, on average the CoV were more similar to that observed at the BH level (Additional file 1: Figure  S3 ), but this was likely caused by the fact that several segments had lower confidence interval very close to 0 (and were ignored in subsequent analyses). The second method, which tests for the temporal signal (i.
Some segments exhibited weak molecular clock signal using both the CoV and the root-to-tip methods and were removed from the statistical analyses. At the BH level, the three (out of 21) segments ignored were REV-ENV, VIF-VPR and VPR-TAT, which altogether represent 2. 3% of the total sequence length considered (see Additional file 2: Table  S2  for the complete list of segments). At the WH level, the segments ignored for PIC1392 were ENV2, GAG, GAG-POL, TAT-REV-ENV and POL-VIF (i. e. 15. 4% of the total sequence length). In the other four WH datasets, we removed segments C2V5, ENV1-3, ENV2, POL, POL-VIF, REV-ENV, TAT, TAT-REV, TAT-REV-ENV, VIF-VPR, VPR and VPR-TAT in PIC38417 (i. e. 42. 4% of the total sequence length), segments ENV1-1, ENV1-3, LTR3, GAG-POL, POL-VIF, TAT-REV, TAT-REV-ENV, VIF-VPR, VPR, VPR-TAT and VPU in PIC71101 (i.
e. 21. 4% of the total sequence length), segments GAG-POL, POL-VIF, TAT, TAT-REV, TAT-REV-ENV, VIF, VIF-VPR, VPR-TAT, VPR and VPU-ENV in PIC83747 (i. e. 11. 2% of the total sequence length) and segments ENV1-4, GAG, GAG-POL, LTR3, POL-VIF, REV-ENV, TAT, TAT-REV-ENV, VIF-VPR and VPU-ENV in PIC90770 (i. e. 14. 3% of the total sequence length). The fact that many of these segments belong to overlapping reading frames is discussed below. Overall, dataset PIC1362 was our WH dataset with the best coverage (see Additional file 1: Figure  S1 ). In the following we use it as our reference dataset to stress some specific points.
Comparing rates on internal and external branches At the WH level, especially in the PIC1362 dataset, we found a mismatch between ER measured on internal and external branches of the phylogeny, with lower rates on the internal branches (Figure  3A ). This is not surprising since, as mentioned earlier, selection has had little time to act and some of the virus sequences sampled could contain deleterious mutations. Note that the ratio between ER on the internal and on the external branches is particularly low in env, which is the only region considered by earlier studies  [4, 6]. On the contrary, at the BH level, this ratio is always close to 1 suggesting that evolutionary rates are more homogeneous on the phylogeny (Figure  3B ). Fitted values for each genomic segment are shown in Additional file 2: Table  S3.
Evolutionary rates Evolutionary rates varied across the HIV genome (Figure  1B ) and were significantly higher in the env region than in the rest of the genome, both at the WH and at the BH levels (Table  1 ). Figure  1C  shows the evolutionary rates for all the datasets pooled in Figure  1B  (see also Figure  S4  in Additional file 1 for more detailed results on all the datasets and Table  S2  in Additional file 2 for the exact ER values in each segment). Furthermore, WH evolutionary rates were significantly higher than BH evolutionary rates. This can be seen in Table  1 , which reveals a 4. 7 fold difference (0. 67 log 10 ) in the env gene and a 4. 6 fold difference (0. 66 log 10 ) elsewhere in the genome for the pooled data. When we focus  on patient PIC1362, this difference in ER in env (shaded area in Figure  4 ) is clearer (5.
75 fold difference in env vs. 3. 98 elsewhere in the genome). We also found a significant difference when we compared the ratio between WH ER and BH ER observed in env to that observed in the rest of the genome (t = 17. 3, df = 2035. 7, p-value < 10 -3 ). We also considered the 'Overlap' factor, i. e. whether or not the segment is an overlapping reading frame. As expected, in the WH dataset, we found lower rates in overlapping regions (factor 1. 3, see Table  2 ). For the BH dataset, the difference was slightly less important (factor 1. 2) and even went in the opposite direction. Many of the segments in overlapping reading frames also exhibited so little molecular clock signal that an ER could not be estimated at all (see the list of segments excluded above), further suggesting that mutations are especially likely to be deleterious in these regions of the viral genome.