dataesr/scientific-paragraphs-categorization huggingface dataset:
Using point mutations in both Dlg1 and LGN, we demonstrate that the direct interaction between Dlg1 and LGN plays a key role in LGN cortical recruitment and spindle orientation in vivo. We further show that Dlg1 also controls LGN cortical accumulation and substrate-induced spindle orientation in cells cultured on adhesive micropatterns. Our data reveal a major function for Dlg1 in recruiting LGN to the mitotic cortex and in proper spindle orientation in multiple cellular contexts in vertebrates.
Dlg1 is required for planar spindle orientation in chick neural progenitors We focused on chick Dlg1/SAP97/Dlh: among the four DLG family members found in chick databases, Dlg1 is structurally Dlg1 regulates spindle orientation in vertebrates • Saadaoui et al. toward the 45° orientation mainly during late prometaphase and early metaphase  (Machicoane et al. , 2014). The final orientation is typically reached within 15 min after metaphase onset and maintained until anaphase (Fig. 3 D ). In contrast, we observed that directed rotation toward the 45° position was reduced in Dlg1-depleted cells, with movements of the spindle essentially consisting in oscillations around its initial position (Fig. 3 D ). Hence, directed spindle rotation in the xy plane during metaphase is also compromised by Dlg1 knockdown in HeLa cells cultured on adhesive micropatterns.
Image acquisition Optical sections of fixed samples (en face views from half-embryos or transverse views from cryosections) were obtained on a confocal microscope (SP5; Leica) using 20 and 40× (Plan Neofluar NA 1. 3 oil immersion) objectives and LAS software (Leica). Fiji software  (Schindelin et al. , 2012)  was used for images processing (Gaussian blur) and data analysis (spindle orientation measurement). When necessary, images were subjected to brightness and contrast adjustment to equilibrate channel intensities and background using Photoshop CS4 software (Adobe).
3D measurement of spindle orientation in fixed samples Spindle orientation was measured on en face mounted neural tubes from E3 embryos labeled with an anti--tubulin antibody to reveal spindle poles and with DAPI dye to label chromosomes. Electroporated cells were identified by their expression of a Histone2B-GFP reporter protein (carried by the miRNA plasmid), also revealing the chromosomal plate of dividing cells. In addition, for rescue and dominant-negative experiments (Figs. 4, E  and F ; and S4 C), expression of Myc-tagged expression constructs was revealed by an anti-Myc antibody. En face image stacks (0. 5-µm z interval) were acquired at 40× magnification. Z views and spindle orientation quantification were performed in Fiji software  (Schindelin et al. , 2012)  using custom-designed macros.
Quantification of cortical signals in mitotic cells The profiles of LGN or NuMA signal at the cell cortex of metaphase HeLa cells shown in Fig. 3  D were measured in Fiji software as follows: a 5-pixelwide line (Freehand Line tool) was manually traced following the cell contour in a confocal optical section corresponding to the middle plane of the cell. The start (and finish) point of this circular line was chosen facing the chromosome plate on one side of the cell. Pixel values along the line were calculated using the Plot Profile tool (each value corresponds to the mean value of the 5 pixels on the line width). As the absolute length of the circular line varies from cell to cell as a function of cell diameter, we designed a macro that interpolates plot values to calculate a normalized set of 360 values along the line, where positions 0 and 180 face the equatorial plate and positions 90 and 270 face the spindle poles, as illustrated in Fig.
We recorded 30-µm-thick z stacks (1 µm between individual sections) at 1-min intervals for 4-6 h using MetaMorph software (Molecular Devices) and an electron-multiplying charge-coupled device camera (Evolve; Roper Scientific). 3D tracking of the centrosomes. Based on 4D imaging of mitotic cells expressing a centrosome reporter (PACT domain of pericentrin fused to mKO1;  Konno et al. , 2008) , we implemented a homemade MATLAB routine (MathWorks, Inc. ; zip file 2) to measure the three spatial coordinates x, y, and z of each centrosome during the time course of the division. The routine runs on regions of interest centered on single dividing cells that are manually selected and cropped (x, y, and temporal) beforehand from the full-length acquisition. In brief, our software uncouples tracking in the xy plane and tracking in the z direction, and both operations are performed successively.
Zip file 1 contains a PDF document that explains the procedure for the successive use of two Fiji macros to perform batch measurements of mitotic spindle orientation relative to the apical surface of the tissue and the two Fiji macro files. Zip file 2 contains a PDF file describing the two MATLAB procedures and two folders containing the MATLAB code. Online supplemental material is available at http://www. jcb. org/cgi/content/full/jcb. 201405060/DC1.
Work  in  A. Echard 's laboratory is supported by the  Institut Pasteur , the  Centre National de la Recherche Scientifique , the  FRM (Equipe FRM   DEQ20120323707 ), and the  Fondation ARC  (to M. Machicoane). This work has received support under the program Investissements d'Avenir launched by the  French government  and implemented by the  ANR  (references:  ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02 PSL* Research University ). The authors declare no competing financial interests. Submitted: 26 May 2014 Accepted: 7 August 2014.
Cell culture Mouse Bone Marrow-Derived Macrophages (BMDMs) were isolated and prepared as previously described  [32]. Quantitative real-time PCR (qRT-PCR) BMDMs (10 5 cells/assay) were infected with T. whipplei as previously described  [15]. BMDMs were lysed, and DNA was extracted using a QIAamp DNA MiniKit (Qiagen). PCR was performed using a LightCycler-FastStart DNA Master SYBR Green system (Roche) and was conducted with primers specific for the T. whipplei 16S-23S ribosomal intergenic spacer region (tws3f and tws4r), as described previously  [33]. In each PCR run, a standard curve was generated using serial dilutions ranging from 10 to 10 8 copies of the intergenic spacer region and established by the LightCycler 5. 32 software (LC-Run version 5. 32; Roche) as previously described  [15].
Immunofluorescence and confocal microscopy Infected cells were fixed with 3% paraformaldehyde in phosphate-buffered saline (PBS pH 7. 4) and prepared for immunofluorescent labelling, as previously described  [34]. Coverslips were mounted in Mowiol, and slides were viewed on an inverted Leica TCS SPE confocal laser-scanning microscope (Leica, Heidelberg, Germany). Image acquisition was performed using Leica Confocal Software. The collected images were processed using Adobe Photoshop CS5. The cells were evaluated as follows: 25 fields with at least three cells per field were examined for each experimental condition; in total, approximately 100 cells were examined per experimental condition. Each cell contained at least 3 to 5 phagosomes. In total, more than 400 phagosomes were examined per experimental condition.
The colocalisation analysis was performed using ImageJ software (http://rsb. info. nih. gov/ij) and the JaCoP plugin (http://rsbweb. nih. gov/ij/plugins/track/ jacop. html), as previously described  [35, 36]. The calculation of overlap is based on the calculation of the Manders coefficients M1 and M2, reflecting channel1/channel2 overlap and channel2/ channel1 overlap.
RT-PCR RNA was extracted from infected BMDMs using the QIAamp RNA MiniKit (Qiagen). cDNA was synthesized from 1 mg of total RNA using SuperScript II RNase H reverse transcriptase (Invitrogen). Specific primers for each gene (actin 59tggaatcctgtggcatccatgaaac, actin 39taaaacgcagctcagtaacagtccg, Rab5 59cgggccaaatactggaaata, Rab5 39 aggacttgcttgcctttgaa, Rab7 59gagcggactttctgaccaag, Rab7 39ccggtcattcttgtccagtt) were designed using Primer3Plus. PCR was performed using HotStar Taq polymerase (Qiagen) following manufacturer's recommendations, and PCR products were electrophoresed on 1% agarose gel containing ethidium bromide. Data were acquired with Gel Doc 2000 (BioRad) and gene expression was normalized to the b-actin gene.
This work was supported by the  Agence National de la Recherche  ( ANR ,  ANR-Microbiology 05-MIIM-043-01 ), the  Centre National de la Recherche Scientifique (CNRS)  and POR Campania FSE 2007-2013 (Project  CREME ). NB was a fellow of the  French Research Ministry. AOB was a fellow of the  Scientific Cooperation Foundation 'Infectiopole Sud '. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments We would like to thank  Shouping Huang ,  Sanjeev Kandpal ,  Christy Gianios ,  Jr. and  Amie Holmes  for administrative and/or technical guidance. This work was supported by  ARO  grant # W911NF-09-1-0296  (J. P. W. ) and  NIH  grant # ES016893  (J. P. W. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Health.
Paper outline The rest of our paper is structured as follows: in the next section, we describe 2014 provided data for Task 2, and the additional data used to train the system. Section 3 lists the entire feature set, involving WMT 2013 set as well as a new feature proposed for this year. Baseline system experiments and methods for optimizing it are furthered discussed in Section 4 and Section 5 respectively. Section 6 selects the most outstanding system for submission. The last section summarizes the approach and opens new outlook.
The test set consists of 382 sentences where all the labels accompanying words are hidden. For optimizing parameters of the classifier, we extract last 200 sentences from the training set to form a development (dev) set. Besides, the Spanish -English corpus provided in WMT 2013 (total of 1087 tuples) is also exploited to enrich our WMT 2014 system. Unfortunately, 2013 data can only help us in the binary variant, due to the discrepancy in training labels. Some statistics about each set can be found in Table  1. In addition, additional (MT-independent) resources are used for the feature extraction, including: • Spanish and English Word Language Models (LM) • Spanish and English POS Language Models • Spanish -English 2013 MT system On the contrary, no specific MT setting is provided (e. g. the code to re-run Moses system like WMT 2013), leading to the unavailability of some crucial resources, such as the N-best list and alignment information.
WMT13 features • Source word features: all the source words that align to the target one, represented in BIO 2  format. • Target alignment context features: the combinations of the source word and each word in the window ±2 (two before, two after) of the target word. • Backoff Behaviour: a score assigned to the word according to how many times the target Language Model has to back-off in order to assign a probability to the word sequence, as described in  (Raybaud et al. , 2011). • Part-Of-Speech (POS) features (using TreeTagger 3 toolkit): The target word's POS; the source POS (POS of all source words aligned to it); bigram and trigram sequences between its POS and the POS of previous and following words. • Binary lexical features that indicate whether the word is a: stop word (based on the stop word list for target language), punctuation symbol, proper name or numerical.
• Language Model (LM) features: the "longest target n-gram length" and "longest source n-gram length"(length of the longest sequence created by the current target (source aligned) word and its previous ones in the target (source) LM). For example, with the target word w i : if the sequence w i-2 w i-1 w i appears in the target LM but the sequence w i-3 w i-2 w i-1 w i does not, the n-gram value for w i will be 3. • The word's constituent label and its depth in the tree (or the distance between it and the tree root) obtained from the constituent tree as an output of the Berkeley parser  (Petrov and Klein, 2007)  (trained over a Spanish treebank: AnCora 4 ). • Occurrence in Google Translate hypothesis: we check whether this target word appears in 3 http://www. ims. uni-stuttgart. de/projekte/corplex/TreeTagger/ 4 http://clic.
ub. edu/corpus/en/ancora the sentence generated by Google Translate engine for the same source. • Polysemy Count: the number of senses of each word given its POS can be a reliable indicator for judging if it is the translation of a particular source word. Here, we investigate the polysemy characteristic in both target word and its aligned source word. For source word (English), the number of senses can be counted by applying a Perl extension named Lingua::WordNet 5  , which provides functions for manipulating the WordNet database. For target word (Spanish), we employ BabelNet 6  -a multilingual semantic network that works similarly to WordNet but covers more European languages, including Spanish.
Experimental Classifiers We experiment with the following classifiers: • BL(bin): all features (WMT14+WMT13) trained on train14 only, using binary labels ("OK" and "BAD") • BL(L1): all features trained on train14 only, using level 1 labels ("OK", "Accuracy", and "Fluency") • BL(mult): all features trained on train14 only, using 16 labels • BL+WMT13(bin): all features trained on train14 + {train+dev+test}13, using binary labels. Interestingly, the combination with WMT13 data boosts the baseline prediction capability in both labels: BL+WMT13(bin) outperforms BL(bin) in 1. 10% ( 3. 89%) for OK (BAD) label. Nevertheless, level 1 and multi-class systems maintain only good score for "OK" class. In addition, BL(mult) seems suffer seriously from its class imbalance, as well as the lack of training data for each, resulting in the inability of prediction for several among them (not all are reported in Table  2  ).
In addition, we found that four isolates exhibited <98. 7% nucleotide sequence similarity with Arthrobacter nitroguajacolicus, Jeotgalicoccus psychrophilus, Pseudomonas bauzanensis and Lysinibacillus sphaericus. This value was lower than the 98. 7% threshold required to delineate a new species without carrying out DNA-DNA hybridization  [12]. However, these isolates were considered to be new species and named Arthrobacter saudimassiliensis (GenBank No HG931344), Jeotgalicoccus saudimassiliensis (GenBank No HG931342), Pseudomonas saudimassiliensis (GenBank No HG931341) and Lysinibacillus saudimassiliensis (GenBank No HG931343). A. saudimassiliensis was only isolated from the waste disposal area, J. saudimassiliensis only from Al-Maisim whereas P. saudimassiliensis and L. saudimassiliensis existed in Al-Maisim and Al-Sharaia.
However, the construction of a dendrogram from spectra of unidentified bacteria decreased the need for 16S rRNA gene sequencing. Here, because we tested environmental isolates, the MALDI-TOF database did not contain their spectra, and only 83% of the airborne isolates were correctly identified. Moreover, the presence of a low number of spectra in the database for some species did not allow MALDI-TOF to identify bacteria in the groups with biodiversity within species. In contrast, in a previous study on patient samples, MALDI-TOF MS was much more effective and identified 95% of the bacterial isolates  [14]. The appropriate interpretation of air sampling results remains a challenge  [15]. Previous studies have revealed that numerous bacteria and fungi are present in air  [16, 17]. However, the diversity of the air microbial community is generally underestimated because most environmental microbes are resistant to culture  [18].
MALDI-TOF mass spectrometry We used a MALDI-TOF MS (Bruker Daltonics, Billerica, Mass. , U. S. A. ) to quickly identify bacterial colonies (Figure  3 ). Each isolate was deposited onto a MALDI-TOF target in two spots and each colony was covered with 2 ml of matrix solution (saturated α-cyano-4hydroxycinnamic acid in 50% acetonitrile and 2. 5% trifluoroacetic acid) without other supplements. The bacterial spectra were automatically acquired using flexControl 3. 0 software, and the analysis was carried out with Biotyper 2. 0 software. As previous described, a species was considered to be correctly identified when at least one spectrum presented a score ≥1. 9  [14, 24]. For isolates with a score <1. 9, a dendrogram was made from their spectra by the MALDI Biotyper 3. 0 software. Unidentified isolates that clustered together were categorized as belonging to the same bacterial species, and only one isolate per cluster was used for PCR amplification and sequencing of the 16S rRNA gene.
Molecular assays Total genomic DNA was extracted from isolates using a QIAamp tissue kit (Qiagen, Hilden, Germany). The genomic DNA was stored at 4°C until used for PCR amplification and sequencing of the 16S rRNA gene using the methods previously described  [25]. All the sequences were compared with those available in the GenBank, EMBL, and DJB databases using the gapped BLASTN 2. 0. 5 program through the National Center for Biotechnology Information. A threshold similarity of >98. 7% was used to define a new bacterial species  [12].
RNA interference Small interfering RNA (siRNA) duplexes for rat CERT (Gene accession number XM 345143. 1) S87, S522 and control nontargeting siRNAs (scrambled sequences of S87 and S522 oligonucleotides) described in  [23]  were used. We used Stealth RNAi, the chemically modified synthetic RNAi duplexes that virtually eliminate the induction of non-specific cellular stress response, and that also improve the specific, effective knock-down of gene expression. INS-1 cells plated at 2610 5 cell/cm 2 were maintained for 24 h in RPMI 1640 plus 10% FCS and then transfected in the same medium with a 1:1 (by mol) mix of S87 + S522 (si-CERT) or the non-targeting corresponding sequences (si-CT) using LipofectAMINE 2000 according to the manufacturer's protocol. The final concentration of siRNA-lipofectamine duplex mixture was 100 nM.
The mobile phase consists of solvent A (15 mM ammonium formate (pH 4. 0):MeOH:THF, 5:2:3) and solvent B (15 mM ammonium formate (pH 4. 0):MeOH:THF, 1:2:7). Elution was performed at a flow rate of 0. 30 ml min -1 in a binary gradient mode. The initial composition of mobile phase was 65:35 (A:B), linearly changed to 70:30 (A:B) over 12 min, and maintained this composition over 22 min, changed to initial composition 65:35 (A:B) over 1 min, followed by 7 min of column re-equilibration. Column eluant was directed to waste for the initial 1 min. The HPLC system was coupled online to an Agilent 6460 triple quadrupole mass spectrometer (Agilent Technologies) equipped with a Jet Stream ion source. Data were recorded in positive ionization mode using electrospray ionization with nitrogen as the nebulizing gas.
Effect of palmitate and glucose on ceramide, sphingomyelin and glucosylceramide molecular species Next, we evaluated the effect of palmitate treatment on Cer, SM and GlcCer mass levels, and the levels of their metabolic molecular species by LC/MS/MS. Our data showed that palmitate, in the presence of 5 mM glucose as well as 30 mM glucose alone, did not significantly alter Cer, SM and GlcCer mass levels in INS-1 cells (Fig. 2 ); In contrast, palmitate with high glucose levels (30 mM) promoted an increase in Cer mass levels with a concomitant decrease in the mass levels of SM but not that of GlcCer. Our data show that glucolipotoxic conditions led to an increase in saturated ceramides, the greatest increase being observed in C18:0-Cer and C22:0-Cer (Fig. 2 ). In addition, this condition mostly reduced the levels of saturated SM and, more specifically, of C18:0-SM and C24:0-SM (Fig.
This work was supported by grants from the  University of Milan  PUR to PG, grants from the  Italian Ministry of University and Scientific and Technological Research PRIN  to PV, and grants from  Science Foundation Ireland  ( SFI/06/RFP/GEN034  and  SFI/08/RFP/EOB1087 ) to CK-YN. This project was partly supported by grants from  Centre National de la Recherche Scientifique (CNRS)  and  Agence Nationale de la Recherche  ( ANR-06-JCJC-0040 ) to HLS. NC received a postdoctoral fellowship from the  UniversiteParis Diderot  and the  French Society of Nutrition (SFN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We used the bio-resources from two collaborative projects, BEEM (http://www. beem. utoronto. ca; funded by Genome Canada) and MAMBA (http://mamba. bangor. ac. uk; funded by the EU FP7 program), which are focused on investigating moderate-temperature deep-sea sites in the Mediterranean Sea. We particularly focused here on the salt-saturated deep-sea lakes Medee, Bannock and Kryos  (Daffonchio et al. , 2006; Yakimov et al. , 2013)  and the hadopelagic seawater column at Station Matapan-Vavilov Deep  (Smedile et al. , 2013)  in the Eastern Mediterranean Sea (Fig. 1 ). A 'superficial' marine hydrothermal habitat was used for comparison. The extensive characterization of a set of nine different enzymes isolated from those sites by naïve screens and the determination of five crystal structures provided, to the best of our knowledge, the first experimental evidence linking pressure adaptation to thermal adaptation in salt-saturated habitats, by as yet unknown molecular mechanisms.
0°C or higher than 16. 5°C. Total DNA was extracted, and subsets of 40 024 clones from the four libraries generated in this study harbouring nearly 1. 3 Gbp of community genomes were scored for the ability to hydrolyse α-naphthyl acetate (αNA) and tributyrin, as previously reported  (Reyes-Duarte et al. , 2012) , which is indicative of EST/lipase activity. A total of five unique clones were selected as active. Hydrochemistry of selected deep-sea regions and EST screening statistics are shown in Fig. 1  and Table  S2. The inserts were sequenced, analysed and compared with the sequences available in the National Center for Biotechnology Information (NCBI) non-redundant public database  (Hall, 1999). Five (one per active clone) predicted metagenome sequence (MGS)-encoding ESTs with the α/β hydrolase fold were identified and successfully produced as soluble proteins when expressed in Escherichia coli, and their properties were investigated.
They were named based on the source ID followed by a serial number: MGS-M1 and MGS-M2 (from the Medee basin), MGS-B1 (from the Bannock basin), MGS-K1 (from the Kryos basin), and MGS-MT1 (from the Matapan basin). According to blast searches of the NCBI non-redundant database, the five studied protein sequences were 44-62% similar to homologous proteins in the database (Table  S3A -B). The deduced molecular masses and estimated pI values of these proteins ranged from 31. 6 to 56. 2 kDa and from 4. 4 to 7. 3 respectively. The pairwise amino acid sequence identity ranged from 8. 3% to 21. 8%; MGS-B1 and MGS-MT1 were the most similar enzymes (21. 8% sequence identity), whereas MGS-K1 and MGS-M1 were the most divergent at the sequence level (8. 3% sequence identity). The selected α/β hydrolases contain a classical Ser-Asp-His catalytic triad, but the catalytic elbow and oxyanion hole (i.
Adaptation to high pressure is linked to high thermal resistance in deep-sea brines As pressure is one of the most representative characteristics of deep-sea habitats, the influence of hydrostatic pressure (350 bar) on enzyme performance was further evaluated in high-pressure 2 ml reactors as described in the Experimental procedures, and presumptive links between the site and EST characteristics were evaluated. The data presented in Fig. 4  revealed that the relative percentage of activity at 350 bar compared with that at atmospheric pressure positively correlated with the optimal temperature for activity in the two ESTs from the salt-saturated Lake Medee; thus, the highest adaptation to pressure was obtained for the MGS-M2 EST, which had the highest temperature optima (70°C) compared with MGS-M1 (25°C).