dataesr/scientific-paragraphs-categorization huggingface dataset:
Cntn4, -5 and -6 differentially increase the growth of rat primary cortical neurons The automated quantification of neurite outgrowth was performed using the Acapella software as described in Materials and Methods. The sequential steps of analysis are depicted in Fig. 2. When primary neurons were co-cultured with HEK293 cells without Cntn expression, the length of the longest neurite and the numbers of roots and segments significantly increased between 4 and 6 days in vitro (DIV), and then they leveled off between 6 and 7-8 DIV (Fig. 3A ). All neurons grew concomitantly in length and in branching as shown by plotting length versus roots and roots versus segments (Fig. 3B ). In the latter graphs, slope values were calculated as an index of branching complexity. When neurons were co-cultured with HEK293 cells overexpressing the Cntns, increasing effects were observed on both neurite length and branching, but the effects were different depending on the Cntn member and duration of coculture (Fig.
6C ), strongly indicating an identical interaction of human CNTN4, -5 and -6 with PTPRG CA. Fig. 3. Neurite outgrowth dynamics when cultured in the presence of HEK293 cells. Cultures of rat cortical neurons were established from newborn rats (P0-P1) as described in Materials and Methods. Neurons were kept for up to 8 days in culture (DIV). The same densities of HEK293 cells were added 48-72 hours before the end of culture, in all conditions (see Materials and Methods). The development of neuronal trees was followed over time. At the end of co-culture, neurons were fixed and immunostained with an anti-MAP2 antibody. (A) Quantification of neurite outgrowth parameters was performed using Acapella software. It includes the length of the longest neurite per neuron, the total number of roots, and the total number of segments.
Quantification was performed using Acapella software. Numbers of neurons analyzed are indicated in each graph. Scatter dot plots with a line at the median and the interquartile range are represented. Data are expressed as percent of control mean obtained in the absence of Cntn. Statistical analysis was performed using a Student's t-test. **P,0. 01 and ***P,0. 001. At early stages of growth, increasing effects of Cntn4 and -6 were pronounced on the length of the longest neurite (referred to as the length in graph abcissa). By contrast, increasing effects of Cntn5 were more pronounced on the number of roots and segments as an index of arborization. neurons with an anti-MAP2 antibody. The extent of neurite outgrowth depends on the density of cells, and the constituents of culture medium including the growth factors.
The PTPRG binding sites are located in the CA domain of this protein and contact at the second and third immunoglobulin domains of mouse Cntn4  (Bouyain and Watkins, 2010a). We hypothesized that the differential effects of Cntn4, -5 and -6 on neuritogenesis could be partly explained by distinct binding properties that these molecules may have on the hairpin loop of the extracellular PTPRG CA. The crystal structures of chicken and human contactin 2 (TAG-1) have been analyzed  (Freigang et al. , 2000; Mo ¨rtl et al. , 2007). By contrast, the X-ray structures of human CNTN4-6 have not been identified yet. Only the first four Ig-like domains of mouse Cntn4 have been registered in the Protein Data Bank. We therefore built a model of the Ig1-4 domains for each of the human CNTN4, -5 and -6. We then identified the residues present at the interfaces between the CNTN domains and human PTPRG CA.
For example, this plasticity could slightly modulate the binding affinity of human CNTN5 and -6 for PTPRG and thus modify its intracellular phosphatase activity. The residues that participate in the interactions between human PTPRG CA and each human CNTN were found to be identical, and they were also identical to residues participating in the interaction between the murine Ptprg CA and Cntn4 Ig1-4. We then measured the binding of Ptprg-Fc to rat Cntn5 and -6, when overexpressed by HEK293 cells. We did not find any significant modification in the intensity Ptprg binding between the two Cntns. Our data strongly suggest that the differences in neurite outgrowth properties of Cntns that we observed in this study are not the consequence of molecular changes at Ptprg-Cntn interfaces. For Cntn5 and -6, they do not seem to result from changes in PTPRG binding capacities either.
Cell culture procedures HEK293 cells HEK293 cells were cultured in Minimum Essential Medium (MEM) containing 100 units/ml penicillin, 100 mg/ml streptomycin, 2 mM glutamine (Invitrogen TM , Life technologies SAS, Saint-Aubin, France) and 10% fetal calf serum (ref. CVFSVF00-01, Eurobio, Courtaboeuf, France) in a 5% CO 2 incubator at 37˚C. Cells were grown in 100 mm Petri dishes. The day before transfection, cells were trypsinized, suspended in fresh medium and plated in 6-well plates containing glass coverslips. Coverslips were previously coated with 0. 1 mg/ml of poly-L-Lysine (ref. P6282, Sigma-Aldrich Chimie, Saint-Quentin Fallavier, France). Cells were transfected either with rat Cntn4, Cntn5 or with Cntn6 cDNA sequences cloned in pcDNA3. 1 vector (GenBank accession numbers for rat Cntn4, Cntn5 and Cntn6 are U35371, D87212 and D87248, respectively) using the jet PRIME H kit and according to the manufacturer's protocol  Polyplustransfection TM SA, Illkirch, France).
Mouse anti-rat NB2 (3G12) and anti-rat NB3 (2F7) monoclonal antibodies amplified in ascites were used, at a dilution of 1/500  (Toyoshima et al. , 2009b; Sakurai et al. , 2009). These antibodies give a clear single band at approx. 130 kDa. The anti-Cntn4 antibody was purchased from R&D systems (AF2205, R&D Systems Inc. , Minneapolis, USA) and used at a dilution of 1/200. For Cntn5 and Cntn6, the secondary antibody was an Alexa Fluor H 594 Goat Anti-Mouse IgG (H + L) used at a dilution of 1/200 (A11005, Molecular Probes H , Life Technologies). For Cntn4, the secondary antibody was an Alexa Fluor H 488 Donkey Anti-Goat IgG (H + L) used at a dilution of 1/200 (A11055, Molecular Probes H , Life Technologies). Cells were washed 3 times in PBS and rinsed once in milliQ water. Semi-quantitative analysis of fluorescence in permeabilized HEK293 cells was performed using Image J software.
Image acquisition, processing and statistical analysis Fluorescence images were acquired with an inverted microscope Axio Observer. Z1 (Carl Zeiss, Le Pecq, France) equipped with an AxioCam MR3 camera. For neurite detection, ten to fifteen mosaic images were acquired for each condition per experiment with a LD Plan-neofluar 206 objective and a 64HE mPlum reflector (Alexa 594 channel). Total image width and height were of 4041 and 3025 pixels, respectively. Each pixel represented 16 bit gray levels. Quantitative image analysis was performed using the Acapella image analysis, version 2. 3 software (PerkinElmer, Inc. ). A specific script for automated detection of neurons and analysis of their morphology was used. The same threshold parameters were applied in all conditions. Measured parameters extracted from specific module ''neurite detection'' include the length of the longest neurite per neuron, number of roots and total number of segments.
5% BSA for 1 hour at RT. After washing with PBS 5 times, cells were incubated with Alexa Fluor 488-conjugated anti-human IgG (1:3000) for 1 hour at RT. After washing with PBS 5 times, cells were incubated with the 3G12 anti-Cntn5 antibody (1/500) or the 2F7 anti-Cntn6 antibody (1/500) for 1 hour at RT  (Sakurai et al. , 2009; Toyoshima et al. , 2009a; Sakurai et al. , 2010). After washing with PBS 5 times, the cells were incubated with an Alexa Fluor 594-conjugated anti-mouse IgG (1:200) for 1 hour at RT. After five washes with PBS, coverslips were mounted on glass slides. Fluorescent signals were quantified using a specific plugin of the Icy software (de Chaumont et al. , 2012) (http://icy. bioimageanalysis. org). The plugin was based on wavelets detection to visualize the red fluorescence and provide the mean for the red and green fluorescent signals  (Olivo-Marin, 2002).
Homology modeling The FASTA sequences of human CNTN4 Ig1-4 , CNTN5 Ig1-4 , CNTN6 Ig1-4 were retrieved from the UniProtKB server at (http://www. uniprot. org/uniprot) and 2Daligned with the homologous mouse Cntn4 Ig1-4 as reference sequence (3KLD. fasta retrieved from the solved structure at http://www. rcsb. org/pdb/home/home. do) using ClustalW (http://www. ebi. ac. uk/Tools/msa/clustalw2). They were then 3D-aligned using Espript (http://espript. ibcp. fr/ESPript/ESPript) to include the mouse Cntn4 Ig1-4 secondary structure from the PDB entry 3KLD  (Gouet et al. , 1999; Bouyain and Watkins, 2010a). Human CNTN4 Ig1-4 , CNTN5 Ig1-4 , CNTN6 Ig1-4 were homology-modeled by computing both the alignment and the solved X-ray structure of the mouse Cntn4 Ig1-4 used as 3D template (Protein Data Bank ID: 3KLD).
For each CNTN, 50 models were generated using the model-building software Modeller (mod9v7), to satisfy the spatial restraints issued from the alignment with the target protein  (Eswar et al. , 2006). Models with the lowest score function values and best stereochemistry and checked by Molprobity (http://molprobity. biochem. duke. edu) (Chen et al. , 2010)  were then subjected to subsequent refinement by energy minimization using CHARMM implemented in Discovery Studio 2. 5 (DS2. 5; Accelrys, Inc. )  (Brooks et al. , 2009). Similarly, the human PTPRG CA was homology-modeled using the mouse Ptprg CA as reference sequence for alignment and 3D template (from the 3D the complex of mouse Cntn4 Ig1-4 /mouse Ptprg CA ). 50 models were generated; one was selected from its score function before being minimized using CHARMM forcefield.
Acknowledgements This work was supported by the  Institut Pasteur ,  INSERM ,  APHP, ANR  ( ANR-08-MNPS-037-01-SynGen ),  Neuron-ERANET (EUHF-AUTISM) ,  Fondation Orange, Fondation pour la Recherche Medicale, RTRS SanteMentale  ( Fondation FondaMental ) and  Fondation Bettencourt Schueller. O. M. is supported by an  undergraduate fellowship  from the  Neuropole de recherche francilien (NeRF). We are grateful to  Nathalie Lemie  `re for her technical assistance.
This is basically a no-issue in practice (especially on today's large problems), still, it is a common criticism from people who usually have never experimented with neural networks on real problems. As a consequence, neural networks were slightly over-shadowed in the 90s with the appearance of the sup-02001-p. 9 port vector machine and AdaBoost. Today neural networks are, again, becoming more popular partly because of user-friendly program packages (e. g. ,  [26] ), partly due to the computational efficiency of the training algorithm (especially its stochastic version), and partly because of the recent success of unsupervised feature-learning techniques that use deep neural architectures to solve hard computer vision and language processing problems  [13] [14] [15]. Support vector machines also learn generalized linear discriminant functions of the form  (11)  with h (t) (x) = y t K(x t , x), where K(x, x ) is a positive semidefinite kernel function that expresses the similarity between two observations x and x.
The reason is paradoxically the simplicity of AdaBoost: since it is so easy to implement with a little background in programming, no machine learning expert took the effort to provide a turn-key software package easily usable for non-experts. 10 In fact it is not surprising that the only large non-computer-scientist community in which AdaBoost is much more popular than SVM is experimental physics: physicists, especially in high energy physics, are heavy computer-users with considerable programming skills. In the last five years AdaBoost (and other similar ensemble methods) seem to have been taking over the field of large-scale applications. In recent large-scale challenges  [34, 35]  the top entries are dominated by ensemble-based solutions, and SVM is almost non-existent in the most efficient approaches.
Music classification, web page ranking, muon counting In  [43]  we use AdaBoost for telling apart speech from music. The system starts by constructing the spectrogram on a set of recorded audio signals which constitute the observation vectors x in this case. AdaBoost is then run in a feature space inspired by image classification on the spectrogram "images". The output y of the system is binary, that is, y ∈ C = {music, speech}. In  [44]  we stay within the music classification domain but we tackle a more difficult problem: finding the performing artist and the genre of a song based on the audio signal of the first  [30] s of the song. The first module of the classifier pipeline is an elaborate signal processor that collects a vector of features for each  [2] s segment and then aggregates them to constitute an observation vector x with about 800 components per song.
RNA extraction and RT-PCR assays RNA from ascitic fluid and frozen faeces was extracted by using the QIAamp viral RNA Mini Kit (QIAGEN). The screening of coronavirus positive samples was performed by using the previously described primers and protocol targeting the highly conserved 3 0 UTR of the coronavirus genome  (Herrewegh et al. , 1995). Primers amplifying nucleocapsid N gene (forward, 5 0 -AACA AACACACCTGGAAGA-3 0 reverse, 5 0 -GTGTCATCAAACACATCTGT-3 0 ) and ORF3 (forward, 5 0 -CACTAAACTCAAAATGTTGATTC-3 0 reverse, 5 0 -TTAAGGATTAAAAACATATTCTA-3 0 ) were designated on the basis of sequences available in GenBank. RT-PCR assays were carried out using the OneStep RT-PCR Kit (Qiagen). Briefly, the RNA was reverse transcribed at 50 °C for 30 min and then amplified by 40 cycles of 1 min of denaturation at 94 °C, 1 min of primer annealing at 50 °C and 1 min of primer extension at 72 °C.
Bank accession numbers The PCR products were subjected to sequence analysis (Eurofins Company). The partial sequences of the N gene of strains cats-1, -9,  -21, -22, -24, -29, -30, -33, -41, -57, -61, -74, -81, -118, -122, -125, -127, -130, -139, -149, -163, -179, -180, -181, -196, -198  and dog-1 have been registered in GenBank under accession numbers JN687591-JN687617. The ORF3 sequences of strains cats-9, -22, -29, -30, -163 and dog-1 have been registered in GenBank under accession numbers JN714195-JN714200. The partial sequences of the S gene of cats-22, -29, -30, -163 and dog-1 have been registered in the European Nucleotide Archive under accession numbers HG325837-HG325840.
Sequence analysis The ClustalW2 program (www. ebi. ac. uk/Tools/clustalw2) was used for sequence alignment. Phylogenetic tree was constructed by the Neighbour-Joining method using MEGA version 4, supplying a statistical support with bootstrapping over 1000 replicates  (Tamura et al. , 2007). Potential signal peptide and N-glycosylation site were determined respectively with Sigcleave (emboss. bioinformatics. nl/cgi-bin/emboss/sigcleave) and NetNGlyc (www. cbs. dtu. dk/services/NetNGlyc/) programs, respectively.
Sequence analysis of N gene Sequence analysis of a fragment of the N gene was performed on the assumption that this ordinarily conserved gene would allow significant discrimination of the phylogenetic relationships between strains. The sequences obtained were compared by phylogenetic analyses with representative FCoV-I/II, CCoV-I/II and TGEV strains retrieved from GenBank. The N sequences clustered into two main clades (Fig. 1 ). The first clade included FCoV-I/II whereas the second clade is divided into two separate clusters comprising CCoV-I and CCoV-II/TGEV. 20/26 of the feline samples tested fell into the typical FCoV genotype. Half of them were recovered from asymptomatic infections, the others from sick cats but no genetic distinction was evident between N sequences from healthy or diseased cats.
9% and less than 86. 2% with prototype CCoV-I strains (Fig. 2 ). In addition, the sequence from Cat-163, which died from FIP, exhibited a Leucine residue at position 1058, recently described as virulence A neighbour-joining tree was constructed using the MEGA 4. 1 software  (Tamura et al. , 2007). The N gene of human coronavirus 229E (HCoV-229E) serves as an outgroup. A bootstrap analysis of 1000 replicates was performed and values above 90% are indicated on the branches. Horizontal branches are drawn to scale; the scale bar represents 0. 1 nucleotide substitutions per site. The following reference strains were used for phylogenetic tree construction (GenBank accession numbers are reported in parentheses): FCoVII-79-1683 (AB086904), FCoVII-79-1146 (DQ010921), FCoVI-UCD1 (AB086902), FCoVI-Black (EU186072), FCoVI-KU-2 (AB086881), CCoVII-1-71 (JQ404409), CCoVII-BGF10 (AY342160), CCoVII-INSAVC (D13096), CCoVI-23/03 (AY548235), TGEV (AJ271965), HCoV-229E (DQ2439391).
ORF3 sequences from Dog-1 and Cat-22 were 595 bp in length, sequences from Cats-9, -29 and -30 were 568 bp in length and the sequence from Cat-163 was only 541 bp in length, instead of 623 bp for the reference ORF3 sequences deposited in GenBank. By nucleotide alignment with sequences available from GenBank, two deletions, never described before, were identified within the ORF3 gene isolated from cats and Dog-1 (Fig. 3A ). The first deletion, located between nucleotides 262 and 289, preserved the reading frame of ORF3 and resulted in the loss of 9 amino acid (aa) starting at the aa 88 (Fig. 3B ). This deletion was only observed in Cats-9, -29, -30 and -163. The second deletion was common to all analysed ORF3 sequences, even in the ORF3 gene from Dog-1, which lived with Cat-22. It comprised nucleotides 461-482 and introduced an early stop codon at the aa 159 (Fig.
3B ). The ORF3 retrieved from Cat-163 is shortened at the 3 0 end of the gene. Computer analysis predicted that the deleted ORF3 would encode a truncated gp3 protein of 149 or 158 aa, while the gp3 protein comprises 207 aa in CCoV-I isolates studied so far (Fig. 3B ). All of the truncated gp3 proteins retained the 14-aa signal peptide and FCoVI CCoVI FCoVII/CCoVII Fig. 2. Phylogenetic tree based on partial nucleotide sequences of the 3' end of the S gene. A neighbour-joining tree was constructed using the MEGA 4. 1 software  (Tamura et al. , 2007). A bootstrap analysis of 1000 replicates was performed and values above 90% are indicated on the branches. Horizontal branches are drawn to scale; the scale bar represents 0. 05 nucleotide substitutions per site. The following reference strains were used for phylogenetic tree construction (GenBank accession numbers are reported in parentheses): FCoVI-Black (EU186072), FCoVI-UCD1 (AB088222), FCoVI-KU-2 (D32044), CCoVI-23/03 (AY307021), CCoVI-Elmo/02 (AY307020), CCoVI-17.
Acknowledgements We thank  Dr Richardson  and  Dr Zientara  for helpful discussions. We are very grateful to  Dr Flamand  for her critical reading of the manuscript. This work was supported by a grant from the  French National Institute for Agricultural Research (INRA)  and the  Basse-Normandie region for a project entitled ''Recherche de variants interspécifiques de coronavirus (REVISCO) ''. Dr C. Horhogea  was the recipient of European Socrate funding. We are grateful to our colleagues from the  Alfort Veterinary Hospital (CHUVA)  for their collaboration in sample collection.
Methods SO 2 fluxes were measured using a USB2000 ultraviolet spectrometer, and SO 2 column amounts were retrieved by following standard DOAS calibration and analysis procedures  (Kraus, 2006; Platt and Stutz, 2008). The spectral range of the spectrometer is 280-400 nm with a spectral resolution of 0. 5 nm FWHM (full width at half maximum). Light entered the spectrometer through a telescope (8 mrad FOV) and via a fiber optic bundle. The Vispec program (http:// vispect. sourceforge. net/) was used to field-track the volcanic plume. Total integration times of 3 s (exposure time 300 ms, 10 co-added spectra) and 1. 6 s (exposure time 200 ms, 8 coadded spectra) were applied to Papandayan and Bromo respectively. Reference spectra included in the non-linear fit were obtained by convolving high-resolution SO 2  (Bogumil et al.
Gene expression analysis Gene expression profiling was performed as published  [8, 9, 26, 35]. In brief, labeled cRNA was hybridized to U133 2. 0 plus arrays according to the manufacturer's instructions (Affymetrix, Santa Clara, CA, USA). Expression data for myeloma cell samples are deposited in ArrayExpress under the accession numbers E-MTAB-317, E-GEOD-2658 and Gene Expression Omnibus GSE4581 (the latter two for the total therapy 2 (TT2) data and molecular class association, respectively). Quality controls were performed as previously published  [26]. For qRT-PCR, cDNA synthesis was performed from 1 mg of total RNA after Oligo(dT) primed and first strand cDNA-synthesis was performed according to the manufacturer's guidelines (Super Script TM First -Strand Synthesis System for RT-PCR, Invitrogen, Karlsruhe, Germany).
Selection of target genes The classification and nomenclature was used according to the Glycov4 chip of the consortium for functional glycomics (CFG, http://www. functionalglycomics. org) which contains 1259 genes and further adapted as described previously  [37]. We focused in our analysis on a set of glycosylation relevant enzymes and proteins that belong to glycosyltransferases (GT), sulfo-transferases (ST), glycan-degrading enzymes (GD), genes necessary for sugar synthesis and transport (sugar s/t)) resulting in a gene list of 295 genes (Table  S2A -C). Target genes were mapped to the Kyoto encyclopedia of genes and genomes (KEGG) database (http:// www. genome. jp/kegg/pathway), Table  S3. The 295 glycomespecific genes were found to be represented by 630 probe sets. Using the ''presence absence calls with Negative Probesets'' (PANP) algorithm, we identified 247 genes represented on 426 probe sets expressed at least in one sample  [38].
Flow cytometric analysis Flow cytometric analysis of cell surface antigens was performed using a FACS Canto II and the FACSDIVA software (BD). Dead cells were excluded by staining with Via-Probe (BD). Cell surface staining was performed with an anti-human CD55 (clone 555693) and an anti-human CD59 (clone 555763) mouse monoclonal antibody, both conjugated to FITC or with an anti-human CD56 (clone 555518) mouse monoclonal antibody conjugated to APC (all from BD) for 30 min at 4uC. In order to remove the GPIanchored CD55 and CD59 proteins from the cell surface KMS-12-BM and OPM-2 cells (1610 6 cells suspended in 400 ml PBS) were incubated with 40 mL phospholipase C (100 enzymatic units/ mL; Life Technologies, Darmstadt, Germany) for 1 h at 37uC prior to antibody staining.
Statistical analysis Expression data were gcrma-preprocessed  [43]  and log2 transformed. Unspecific filtering was applied selecting transcripts from the consensus list with at least a single present call  [44]  for further analysis. In case multiple probesets mapped to the same gene, the probeset showing highest standard deviation in the training cohort across samples was selected to represent the gene. Using this strategy, we selected 247 genes represented by 247 probe sets. Unsupervised hierarchical clustering was performed using complete linkage and dissimilarity based on Euclidian distances. Cluster reliability was assessed using a bootstrap-based approach, which gives approximately unbiased probabilities. Differentially expressed genes between entities (normal bone marrow plasma cells, human myeloma cell lines, myeloma cells) as well as between myeloma samples with/without cytogenetic abnormalities were identified using the empirical Bayes approach based on the moderated t-statistic as implemented in the Bioconductor package limma  [45].