dataesr/scientific-paragraphs-categorization huggingface dataset:
5 D. Mascot output was saved as a comma separated variable (CSV) file. Semiquantitative comparative analysis of calcium-treated (+Ca) and untreated (Ca) data was performed using an in-house algorithm. In brief, the protein ID and Mascot score present in the CSV data from the +Ca and Ca database searches were compared and separated into three classes: proteins uniquely identified in the +Ca search, proteins uniquely identified in the Ca search, and "common" proteins identified in both searches. For each common protein, the log 2 ratio (Mascot ratio) of its Mascot score from +Ca and Ca was calculated. Individual Mascot ratios from all common proteins were then used to calculate a mean Mascot ratio and standard deviation. For each protein Mascot ratio, the number of standard deviations () from the mean ratio was calculated.
TIRF microscopy After transfection, cells were visualized with a TIRF microscope (Marianas and Everest; Intelligent Imaging Innovations, Inc. ) fitted with a 100× oil immersion objective (NA 1. 46; Carl Zeiss) using an electron multiplying charge-coupled device camera (QuantEM 512SC; Photometrics) and Slide-Book software (version 4. 2. 0. 27/28; Intelligent Imaging Innovations, Inc. ) for acquisition. Cells were bathed in buffer A before the addition of 100 µM nicotine, and a time-lapse video using simultaneous acquisition was captured at 1 frame/s for the indicated period. The speed and density of NPY-mCherry-labeled SGs were measured using SlideBook (version 4. 2. 0. 27/28) and ImageJ software. For long-term TIRF imaging, cells were bathed in PSS control buffer and imaged at 1 frame/2 s for 40 min after stimulation with high K + -PSS buffer.
To quantify the density of SGs, the average density of 40 frames per time point was measured using SlideBook (version 4. 2. 0. 27/28) and ImageJ software. Particle tracking of NPY-mCherry-labeled SGs was performed on previously acquired TIRF images using the spottracking function of IMARIS (Version 7. 3. 1; Bitplane AG). SGs were tracked for 3 min after nicotine stimulation. The spot diameter was 0. 48 µm, and vesicles were selected based on local contrast thresholding after background subtraction. Tracking was performed using the built-in autoregressive motion model with maximum displacement of 1 µm per time step and a gap size of three frames. MSD was calculated for each vesicle over incrementing time intervals and then averaged  (Steyer and Almers, 1999). To characterize SG mobility, MSD data were fitted to various diffusion models; best fits were obtained when a confined diffusion model was assumed  (Saxton, 1993).
In this model, the MSD is described as follows and provides a second order approximation of the SG diffusion within a finite space or cage: MSD ∆ ∆ t r A A D r t c c ( ) = -× - ×         2 1 2 2 1 4 exp. A 1 and A 2 are constants determined by the cage geometry with A 1 = 0. 99 and A 2 = 0. 85 for circular cages  (Saxton, 1993; Saxton and Jacobson, 1997). D is the diffusion coefficient of a particle within the cage, and r is the radius of the circular cage in which an infinitesimally small particle is free to diffuse with diffusion coefficient D . Only SGs tracked for >10 consecutive frames were considered. To characterize the MSD of SGs moving toward the plasma membrane, the manual particle-tracking function from SlideBook software was used. These SGs can be identified as they appear in the TIRF plane, and their fluorescence intensity increases as they approach the plasma membrane.
The trajectories of this population of SGs were followed after nicotine stimulation for 12-15 s, and the MSD for each track was calculated and averaged as stated in the previous paragraph. 40-80 trajectories were counted per condition. SGs already tethered in the TIRF plan were not included in this analysis. To examine the possible change in SG movement upon association with GFP-MyoVI-full after stimulation, we used the colocalization module of SlideBook to identify GFP-MyoVI-positive or -negative NPY-mCherry-positive SGs. From the scatter plot obtained, the population of SGs with high fluorescence levels for the mCherry channel only was selected and considered as GFP-MyoVI-negative SGs. In addition, we also selected a population of SGs with high fluorescence levels for both mCherry and GFP.
Z stacks were taken over 10 min under control conditions and after 100 µM nicotine (Sigma-Aldrich) stimulation. Scan speed was 22 s/stack, and pinholes were adjusted to obtain an optical slice of 2 µm. Image analysis was performed using ImageJ software using raw nonsaturated images. A region of interest was drawn around GFP-MyoVIpositive structures, and the mean intensity was measured for both the GFP and mCherry channels for the duration of the video. Both intensities were expressed as a percentage of initial intensity and as means ± SEM of three independent experiments.
Fig. S5  demonstrates that the potentiation of NPY-evoked release mediated by myosin VI SI requires c-Src kinase activity. Table  S1  shows a selection of proteins identified using MALDI-TOF/TOF after pull-down experiment using purified SG. Video 1 (related to Fig. 4 E ) shows that myosin VI SI restricts the movement of SGs near the plasma membrane. Video 2 (related to Fig. 4  F) shows that the myosin VI NI isoform lacks the ability to cage SG movement at the plasma membrane. Online supplemental material is available at http://www. jcb. org/cgi/content/full/jcb. 201204092/DC1. Additional data are available in the JCB DataViewer at http://dx. doi. org/10. 1083/jcb. 201204092. dv. EM was performed at the Australian Microscopy and Microanalysis Facility at the Centre for Microscopy and Microanalysis at the University of Queensland.
We thank  Dr. Alpha Yap  and  Dr. Brett Collins  for gifting plasmids and antibodies and insightful discussions. We also give thanks to  Luke Hammond  for technical support with microscopy experiments and  Rowan Tweedale  for helpful comments on the manuscript. This work was supported by a  National Health and Medical Research Council (NHMRC)  project grant and an  Australian Research Council Linkage Infrastructure, Equipment  and  Facilities grant  ( LE0882864 ). F. A. Meunier is a Senior Research Fellow of the  NHMRC. This work was also funded by the  Wellcome Trust  ( F. Buss ) and the  Medical Research Council  ( J. Kendrick-Jones ). The  Cambridge Institute for Medical Research  is in receipt of a strategic award from the  Wellcome Trust.
The string language of a PLTAG grammar is the set of string yields of its complete prefix trees. As explained in Section 2, incrementality and connectedness are key desiderata for a psycholinguistically motivated parsing model. In order to implement these properties in PLTAG we need to ensure that the parser always constructs well-formed prefix trees. This entails that PLTAG parsing differs in crucial ways from standard non-incremental TAG parsing. The reason for this is that not all operations that could be performed in a non-incremental parsing regime lead to a well-formed prefix tree. The simplest example involves a prefix tree that contains two substitution nodes: A derivation which substitutes a canonical tree into the rightmost substitution node will produce a derived tree in which a substitution node comes before a lexical leaf, and which therefore violates the incrementality requirement.
Psycholinguistic Evaluation Although we have focused on the computational issues of PLTAG parsing so far, a key motivation behind our incremental parser is to develop a more realistic model of human language processing. A treebank-based evaluation as in the previous section does not directly provide evidence of psycholinguistic validity; however, a parser with good coverage and high parsing accuracy is a prerequisite for an evaluation on eye-tracking corpora, which  Keller (2010)  argues are the benchmark for models of human sentence processing. In what follows, we report an evaluation study that uses our PLTAG parser to predict human reading times, and compares its performance on this task to a standard model based on surprisal  (Hale 2001). Surprisal assumes that processing difficulty is associated with expectations built up by the sentence processor: A word that is unexpected given its preceding context is harder to process.
Furthermore, verification cost is able to explain experimental evidence that cannot be accounted for by surprisal alone, such as the relative clause asymmetry  (Demberg and Keller 2009) ; see also Section 7. 4. To summarize, our implementation of the PLTAG incremental parsing algorithm allowed us to carry out a crucial test of Prediction Theory, that is, an evaluation using the naturally occurring reading data that the Dundee eye-tracking corpus provides. Due to the good coverage of the PLTAG parser, Prediction Theory estimates could be calculated for all but 1. 5% of data points. Based on these estimates, we were able to show that Prediction Theory provides a better fit to the Dundee reading time data than a rival theory, namely, lexical and structural surprisal, as estimated using Roark's incremental parser.
Conclusion This article presented a probabilistic parser for PLTAG, a psycholinguistically motivated version of TAG. Our parser is incremental, builds fully connected structures (partial structures contain no unattached nodes), and models prediction, that is, the anticipation of upcoming syntactic material that is an important feature of human sentence processing. We proposed an efficient implementation of our parser based on fringes-data structures that indicate which nodes in a partial tree are available for the application of parsing rules. We trained our parser on a TAG-transformed version of the Penn Treebank and we showed that it achieves broad coverage (98. 09%) and an F-score of 79. 41 (with gold-standard POS tags) for TAG structure recovery. We argued that broad coverage and competitive parsing accuracy are essential properties when it comes to testing a psycholinguistic model on realistic data, including the eye-tracking corpora which have recently become the gold-standard for psycholinguistic evaluation (e.
Acknowledgments Portions of this work have benefited from presentations at the 2008 Architectures and Mechanisms for Sentence Processing Conference and at the 2008  CUNY Sentence Processing Conferences. We are grateful to and  Aravind Joshi ,  Roger Levy ,  Mark Steedman ,  Patrick Sturt , and the four anonymous reviewers for feedback. This research was supported by  EPSRC  research grant  EP/C546830/1 : Prediction in Human Parsing.
Microarray Experimental Design A standard reference design with hybridization of Cy3-and Cy5-labeled RNAs  [15]  was used to compare mRNA transcript levels in chickens immunized with profilin/ISA 70 vs. profilin alone and profilin/ISA 71 vs. profilin alone. Two biological replicates were conducted in each comparison with substitution of Cy3-and Cy5-labeled RNAs to prevent data distortion from sample labeling as previously described  [12]. Labeled RNAs were hybridized to a Chicken Gene Expression Microarray (Agilent Technologies, Santa Clara, CA) containing 43,803 elements using the Gene Expression Hybridization Kit (Agilent Technologies) with constant mixing at 10 rpm for 17 hr at 65uC. After washing, microarray images were scanned, and data extraction and analysis were performed using Feature Extraction software version 10.
Microarray Data Analysis GeneSpring GX10 software (Silicon Genetics, Redwood, CA) was used to qualify and normalize hybridization images, and to perform fold-change analyses as described  [11, 12]. Median signal intensities were qualified by subtracting the median local background and normalized by locally-weighted scatterplot smoothing (LOWESS). Flag information was applied to strain the spots with 100% valid values from each group and one-way analysis of variance (ANOVA) was performed to analyze the significance of differences between treatment groups. To generate signal ratios, signal channel values from the profilin/ISA 70 and profilin/ISA 71 groups were divided by the channel values from the profilin-only group. Modulated mRNA transcripts, defined as those with a cutoff of P,0. 0005, were applied by asymptotic T-test analysis.
Bioinformatic Analysis Differentially expressed transcripts were analyzed by Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA). Each identifier was mapped to its corresponding gene object in the Ingenuity Knowledge Base. Both up-regulated and down-regulated identifiers were defined as value parameters for the analysis. These focus genes were overlaid onto a global molecular network developed from information contained in the Ingenuity Knowledge Base. Biological functional analysis was performed and the canonical pathways from the datasets were mapped with the Ingenuity Pathways Knowledge Base. The Fischer's exact test was used to calculate the probability that each biological function and associated pathways assigned to that dataset was statistically significant.
Spleen Lymphocyte Transcript Levels Following Immunization with Profilin Plus ISA 70 or Profilin Plus ISA 71 Microarray hybridizations were performed using the Agilent Technology Chicken Gene Expression Microarray with total RNAs isolated from spleen lymphocytes of chickens immunized with profilin alone, profilin plus ISA 70, or profilin plus ISA 71 to identify global transcriptome changes in the respective treatment groups. A critical P value of 0. 0005 was employed to compare transcript levels in the profilin/ISA 70 vs. profilin alone, the profilin/ISA 71 vs. profilin alone, and the profilin/ISA 70 vs. profilin/ISA 71 groups. From this dataset, the levels of 509 (288 up-regulated, 221 down-regulated), 296 (157 up-regulated, 139 down-regulated), and 315 (183 up-regulated, 132 down-regulated) mRNAs were identified as differentially altered in the denoted comparisons (Figure  1A ).
Twenty-two altered transcripts were identical in the profilin/ISA 70 vs. profilin alone (11 up-regulated, 11 down-regulated) and the profilin/ISA 71 vs. profilin alone (10 up-regulated, 12 down-regulated) groups (Figure  1B ). Tables  2  and 3  list the 20 most up-regulated and 20 most down-regulated The left ordinate and bars illustrates the statistical significance of each pathway expressed as the -log 10 (P value) calculated using the right-tailed Fisher exact test. The right ordinate and line illustrate the ratio of the number of genes from the dataset that map to the indicated pathway divided by the total number of genes within that particular pathway. doi:10. 1371/journal. pone. 0059786. g002 transcripts for these latter two comparisons in decreasing order of fold-change.
Biological Function and Pathway Analysis of Differentially Regulated Splenocyte Transcripts The differently modified datasets were mapped to the corresponding genes of the human, mouse, and rat genomes using Ingenuity Knowledge Base software. From this analysis, 192 chicken genes were identified and annotated in the profilin/ISA 70 vs. profilin alone comparison, and 112 genes in the profilin/ISA 71 vs. profilin alone comparison. Biological function analysis using the IPA database identified the category ''Disease and Disorder'' as the most significantly associated with the genes identified in both comparisons, with 25 and 24 significantly associated biological functions respectively (Table  4 ). Of these, two biological functions were uniquely associated with the profilin/ISA 70 vs. profilin alone comparison, ''Endocrine System Disorders'' and ''Nutritional Disease'', and one biofunction was exclusively associated with the profilin/ISA 71 vs.
Discussion This study demonstrated that chickens immunized with profilin plus ISA 70 or profilin plus ISA 71 exhibited spleen lymphocyte transcriptome changes, compared with immunization with profilin alone, that were consistent with alteration of levels of mRNAs encoded by genes belonging to the IPA database category ''Disease and Disorder''. Compared with profilin/ISA 70 vs. profilin alone, the profilin/ISA 71 vs. profilin alone comparison was associated with more unique biological functions, and a larger number of genes and pathways associated with these functions, suggesting that vaccination with profilin/ISA 71 may induce a greater protective host response to experimental Eimeria infection. Compared with profilin alone, vaccination with profilin/ISA 71 was correlated with (a) increased body weight gains following E.
00677-0. 0283 Cancer 0. 0114-0. 0296 Infectious Disease 0. 00838-0. 0265 Dermatological Diseases & Condition 0. 0169-0. 0229 Auditory Disease 0. 0142-0. 0283 Hepatic System Disease 0. 0169-0. 0229 Developmental Disorder 0. 0142-0. 0283 Renal and Urological Disease 0. 0194-0. 0251 Ophthalmic Disease 0. 0142-0. 0283 Auditory Disease 0. 0229 Hypersensitivity Response 0. 0102 Connective Tissue Disorders 0. 0229 Antimicrobial Response 0. 0142 Neurological Disease 0. 0229 Reproductive System Disease 0. 0142 Nutritional Disease 0. 0229 Skeletal & Muscular Disorders 0. 0142 Skeletal & Muscular Disorders 0. 0229 1Datasets were analyzed by BioFunction analysis using IPA software. 2 P values were calculated using the right-tailed Fisher exact test and are listed in decreasing order of statistical significance.
The data sample, collected with the D0 detector during 2004-2005 in Run II of the Fermilab Tevatron Collider, corresponds to an integrated luminosity of 0. 7 fb -1. Jets are defined by the Run II midpoint cone jet algorithm  [10]  with a cone radius of R cone = 0. 7. Rapidity is related to the polar scattering angle θ with respect to the beam axis by y = 0. 5 ln [(1 + β cos θ)/(1 -β cos θ)], with β = | p|/E. The inclusive n-jet event sample (for n = 2, 3) is defined by all events in which the n highest p T jets have p T > p T min and |y| < 2. 4. The separations in the plane of rapidity and azimuthal angle φ, R jj = (∆y) 2 + (∆φ) 2 between the n highest p T jets are required to be larger than twice the cone radius (R jj > 2R cone ). The rapidity requirement restricts the jet phase space to the region where jets are well-reconstructed in the D0 detector and the energy calibration is known to 1.
The total non-perturbative correction factors are in the range of 0. 96-0. 99 with uncertainties of less than 1%. The theoretical predictions for the MSTW2008NLO PDF sets are overlaid on the data in Fig. 1. The results for CT10 and NNPDFv2. 1 PDFs (not shown) agree with those obtained for MSTW2008NLO to better than 0. 1% for p T max < 300 GeV, and are always better than 0. 4%. Figure  1  shows good agreement between the theoretical predictions and data. At the lowest p T min value, the ra- tio of data and theory decreases from +8% to -6% with increasing p T max. For p T min = 50 and 70 GeV, the ratio of data to the theoretical predictions is consistent with unity over the entire range of p T max. For p T min = 90 GeV the theoretical predictions are slightly higher than the experimental results, but still consistent within the theoretical uncertainty.
While a dedicated study of the sensitivity of the pythia parameters is beyond the scope of this letter, these R 3/2 data demonstrate the limitations of current pythia tunes and provide strong constraints for future parameter adjustments. In summary, we have presented the first measurement of the ratio R 3/2 of three-jet to two-jet cross sections in hadron-hadron collisions at a center of mass energy of √ s = 1. 96 TeV. The ratio R 3/2 is presented for p T min requirements of 30, 50, 70, and 90 GeV, as a function of the highest jet p T , p T max , in the range of 80-500 GeV. sherpa predicts a slightly different p T max dependence, but it describes the data within approximately -10% to +20%. None of the pythia tunes DW, BW, A, AMBT1, S Global, and Perugia 2011 describe the data. The data are well described by the pQCD predictions at the nextto-leading order in the strong coupling constant α s , corrected for non-perturbative effects.
Molecular dynamics simulations and potential of mean force calculations All molecular dynamics (MD) simulations were carried out using the GROMACS 4. 0. 5 software package  (Hess et al. , 2008)  and the MARTINI force field  (Marrink et al. , 2007; Monticelli et al. , 2008). Non-bonded interactions were calculated within a cutoff of 1. 2 nm using shift functions (shift between 0. 9 and 1. 2 nm for Lennard-Jones interactions, between 0 and 1. 2 nm for electrostatics). These parameters are standard for the MARTINI force field. Prior to potential of mean force calculations, energy minimization of the system was carried out with the steepest descent algorithm. For each system, minimization was followed by a short equilibration run (2. 5 ns). The temperature of the systems was set to 325 K using the Berendsen weak coupling method  (Berendsen et al.
, 1984)  with a coupling time of 0. 1 ps. The pressure was set to 1 bar using a semi-isotropic coupling scheme with lateral and perpendicular pressures coupled separately with coupling time 0. 1 ps and compressibility of 2 × 10 -5 bar -1  (Berendsen et al. , 1984). We used an integration time step of 25 fs in all simulations. The same conditions were used during equilibration and during the umbrella sampling runs. The umbrella sampling algorithm  (Torrie and Valleau, 1977)  was used to carry out the potential of mean force (PMF) calculations. The umbrella sampling windows were generated using the pull code of GROMACS with a force constant of 2200 kJ mol -1 nm -2 , starting from the initial structure where the inter-helical distance was 0. 75 nm (Fig. 1 ). A total of 28 windows (structures) with spacing of 0.
To predict secondary structures of recombinant RNA molecules and estimate their free energies (dG), the Mfold program  [46, 47]  and IDT Sci-Tools OligoAnalyser 3. 1 software  [48]  were used. RNA synthesis and purification. The yeast tRK1 T7transcript was obtained as described  [32]. For small artificial RNAs, PCR amplification of the following oligonucleotides containing a T7 promoter at the 59-end (underlined) was performed: FD-L TAATACGACTCACTATAGCGCAATCGG-TAGCGCCTCTTTACAGTGCTTAGTTCTCGAGCCCCC-TACAGGGCTCTT FD-H TAATACGACTCACTATAGCGCAATCGG-TAGCGCAGTAAGCACTGTAAATGAGCCCCCTA-CAGGGCTCTT HD: TAATACGACTCACTATAGCGCAATCGG-TAGCGCCTCTTTACAGTGCTTAGTTCTC HF: TAATACGACTCACTATAGGTCTTTACAGTGCT-TACTTCTCGAGCCCCCTACAGGGCTCCA RNA transcripts were obtained in vitro using the Ribomax kit (Promega). Following transcription, the DNA template was removed by digestion with RQ1 RNase-Free DNase (Promega).
6 M sorbitol, 10 mM HEPES-KOH (pH 6. 7) and 4 mM EDTA, then resuspended in 100 ml of the same buffer and treated with an equal volume of 0. 2% digitonin (Sigma) solution to disrupt the mitochondrial outer membrane, followed by purification of mitoplasts. The mitoplast pellet was resuspended in the solution containing 100 mM CH 3 COONa, 10 mM MgCl 2 , 1% SDS and 0. 05% diethylpyrocarbonate (DEPC), boiled for 1 min and RNA was extracted at 50uC with water-saturated phenol. RNA was precipitated with ethanol and separated by 12% PAGE containing 8 M urea, followed by quantification with the Typhoon-Trio scanner using the Image Quant-Tools software (GE Healthcare). The amount of the imported RNA was determined by comparison of the band density of the protected full-sized RNA isolated from the mitoplasts after the import assay with an aliquot (2-5%) of the RNA input.
This work was supported by  CNRS  ( Centre National de Recherche Scientifique ),  University of Strasbourg ,  AFM (Association Francaise contre les Myopathies ),  ANR  ( Agence Nationale de la Recherche) ,  FRM  ( Fondation pour la Recherche Medicale ) and  National Program ''Investissment d'Avenir'' (Labex MitoCross ). A. G. was supported by  Pakistan Government's HEC-SFERE PhD  fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.