dataesr/scientific-paragraphs-categorization huggingface dataset:
Request flow estimation In this section, we specify the discussion by estimating the zero-censored request count distribution (or mixture distribution in statistical terms) P[N = j | N > 0], j ≥ 1. For the naive approach, we generate 50 000 IRM traces using the estimated parameters. We then calculate the average empirical distribution of the request per document. The number of generated traces ensures a coefficient of variation lower that 10 -4 for all points of the distribution. As regards the ML approach, using the ĝnp density, we compute the associated zero-censored request distribution using  (1). In Figure  5 , we show the resulting zero-censored request distribution estimated by each method. For comparison, we include the real mixture distribution for the #prt dataset, which can be calculated explicitly.
For the #yt and #vod datasets, we show instead the observed request distribution. Two issues are raised by the naive approach, that are not present in the maximum likelihood estimation: -first, at the head of the distribution, where most of the mass is concentrated, large estimation errors are produced by the naive approach. Such errors produce a mass shift towards the tail of the distribution. On the contrary, the NP estimation matches perfectly the head of the distribution; -second, the naive method over-fits the tail of the distribution. We observe in Figure  5d  that the naive estimate shows a "horizontal branch" at the tail, and differs significantly from the ground-truth that is approximately a straight "diagonal" line. This horizontal branch is in fact a few isolated masses, though they look as a line on the figure.
Indeed, in the #yt dataset, documents receive an average of 7. 3 requests per document, whereas this average decreases to 1. 4 in the #prt trace. It follows that the coefficient of variation of the request count distribution is greater in the #prt trace than in the #yt trace. As expected, the inaccuracy of the naive method is greater for the former than for the latter. Note also that from an operational point of view, the focus is on the miss ratio, which determines the dimensioning requirements upstream of the cache. The inaccuracy of the naive hit ratio prediction for the #yt dataset becomes relatively significant in this context. As shown by the Zipf curve, the knowledge of a relevant parametric family allows us to improve the hit-ratio estimation. The error, however, remains significant with a MARE of 0.
Numerical integration of the high fidelity model Swing equations are numerically integrated in the commercial software MATLAB. A built-in function 'ode15s' has been used in the evaluation of the time dependent ODE represented in Eq. (  4 ), because of the potential stiffness of the problem this is the most effective ODE solver available in MATLAB. It is based on the numerical differentiation formulas (NDF) and optionally use backward differentiation formulas (BDF) and is a multistep solver. A detailed description of this method and its incorporation in MATLAB environment is provided in the article by Shampine and Reichelt  [26].
In the current study, we are using MATLAB based ODE solver 'ode15s', which requires that the nonlinear function to be defined analytically. Recall the Eq. (  2 ) describing the nonlinear function p i = N k=1 |V i ||V k |b ik sin(δ i -δ k ) for i = 1,. , N The nonlinear function here depends upon the original basis δ, in the reduced basis it has to be defined as: p i ( Ũz) = N k=1 |V i ||V k |b ik sin ⎛ ⎝ q j=1 ( Ũij -Ũkj )z j ⎞ ⎠ for i = 1,. , N pl (z) = N i=1 Ũil p i ( Ũz) for l = 1,. , q  (14)  The nonlinear function p(z) in the reduced basis therefore becomes, pl (z) = N i=1 Ũil N k=1 |V i ||V k |b ik sin ⎛ ⎝ q j=1 ( Ũij -Ũkj ⎞ ⎠ z j ) for l = 1,. , q (15) It is evident from the Eq. (  15 ), it requires O(N 2 × q 2 ) operations, which is counterproductive to the reduced order modeling. To save computation costs and truly exploit the benefits of reduced order modeling, it would be necessary to eliminate the N 2 number of operations which is the dimension of original basis.
Video processing, trajectory extraction and analysis. We recorded videos of total duration of 120 s, at a time resolution of 50 ms/frame with Nikon NIS software in the proprietary ND2 format (Fig. 1a ). A filtering pretreatment was done with Fiji software (version 1. 46j; NIH, USA), which consists in subtracting to each frame the median intensity of the whole stack in order to remove static fluorescent spots. For each dynamic fND-containing endosome, we reconstructed the full trajectory using the Spot Detector plugin of ICY image analysis software (GPLv3 open source) developed by the Quantitative Image Analysis team at Institut Pasteur (Paris, France)  33. Using the Track Manager plugin, we exported a text file containing the spot (X,Y) coordinates and the signal amplitude and background (based on a 2D-gaussian fitting of fluorescence spots), and we processed these data with a home-made program written for Igor Pro software (WaveMetrics Inc.
, USA). Detection of "stop" and "go" phases relies on two main steps, namely a coarse "stop" phase detection based on the confinement ratio function, and a refine "go" detection based on three different sequential filters as detailed in Supplementary Fig. 3. Transport readouts for each trajectory are then computed and the program finally makes a statistical report for the entire run set corresponding to one experiment, namely one LabTek well. The IgorPro program is available upon request.
Localization precision. We calibrated the localization precision of our set-up by tracking the position of more than 100 fNDs immobilized on a glass coverslip and imaged over 100 frames. We then analysed the video with the same tracking software parameters as for the intraneuronal trajectories. Movies were background-subtracted using Fiji plugin with a rolling ball of 20 pixels, in order to mimic the effect of a median subtraction. For each fND, the localization precision was measured as the standard deviation of the center positions of the fluorescent spot over time. We then plotted all localization precisions with respect to the signal-to-background ratio (SBR) associated to each fND (Fig. 1c ). SBR was determined as the ratio of the maximum fluorescence intensity of the fND (amplitude minus residual background) over the residual background intensity.
8, and digitalization speed 35 MHz). The slight shift between both channels was measured using 100 nm TetraSpeck fluorescent beads (Ref. T7279, ThermoFisher). For colocalisation study of fixed cultured, z-stack (7 planes with 0. 25 µm step, total of 1. 75 µm displacement) were recorded for each field of view and maximum intensity projections were carried out. As for video microscopy, we used ICY Spot Detector 33 plugin to extract spot coordinates (in pixels), spot maximum intensity (in counts) and spot size (surface in pixels#). Then, a custom program was written with IGOR Pro to count colocalised spots between each half of the image within 1. 5 pixels uncertainty (equivalent to one standard deviation) in x and y directions. To filter out non-specific labelling, we only considered spots composed of 15 pixels (≈350 nm in diameter, compatible with the convolution of the microscope point spread function and 100 nm sized endosomes) and being at least three times brighter than the dimmer spot (values empirically determined).
Sequences of primers and probes are available on request. PCR assays had a final reaction volume of 20 μl, and contained 2 U of Taq polymerase (Applied Biosystems, USA), 10 μM primers and fluorogenic probe. PCR was carried out over 40 cycles of 95°C for 15 s, 60°C for 1 min and 50°C for 1 min. We used the DNA Engine Opticon 2 sequence detection system (Bio-rad, USA), with the Opticon Monitor software for data analysis. For each group, the cDNAs synthesized from total RNA were serially diluted to cover the 0. 19 ng to 50 ng range for specific mRNAs and 0. 98 pg to 250 pg range for 18S rRNAs. These serial dilutions were used to construct standard curves for 18S and for each gene of interest and to calculate the amounts of RNA. Each qPCR signal was normalized with respect to 18S and expressed in arbitrary units.
Acknowledgments We thank  Paul Tchénio  for his comments on the manuscript,  Alain Lombard  for his contribution to data processing,  Joanna Lipecka  for her help with western blotting. This work was supported by a public grant overseen by the  French National research Agency (ANR)  as part of the  « Investissement d'Avenir » program  IDEX  Paris-Saclay   ANR-11-IDEX-0003-02  (S. H. ); the  ANR  and the  Ministry of Science and Technology (Taiwan)  through grant  ANR-2010-INTB-1002  (M. S. , F. T. , H. C. C. , C. C. W. ); the  ANR  through the  Eranet Euronanomed  project  2011-ERNA-006  (F. T. , M. S. , C. S. , A. C. ); the  Fondation Jérôme Lejeune  (M. S. ), the  European Union   FP7-Health "AgedBrainSysBio " grant (M. S. ); grant n°12018592 from  Region Ile-de-France  (F. T. ); CNRS " Programme interdisciplinaire " (F.
Genome Information and Sequencing A draft genome sequence of Paratrypanosoma confusum (strain CUL13-MS,  Flegontov et al. 2013 ) was produced by sequencing total DNA using Illumina MiSeq system with paired-end (insert size 450 bp) and mate-pair (insert size 2-6 kb) libraries. AUGUSTUS 2. 5. 5  (Stanke et al. 2006 ) was used for genome annotation, which was further manually improved. An updated draft genome sequence of Leishmania donovani (BPK282/0cl4, referred to in this manuscript using the internal reference number LdPBQ7IC8 to better distinguish this sequence from the current reference LdBPK282A1) was created using Pacific Biosciences Single Molecule, Real-Time (SMRT) Sequencing (unpublished) with a weighted median length (N50) of 11. 7 kb. Final iterative base and indel corrections were performed with BPK282/ 0cl4 Illumina reads used for the previous assembly  (Downing et al.
Gene Density Analysis Different sets of CDS were collected from public databanks for six euglenozoans, four apicomplexans, one amoebozoan, five fungi, and two metazoa (see the full taxon list in fig. 3 ). In order to gather HSP70 family members of each of these selected taxa, the allele sequence of LmjF. 28. 2770 was used as a query to perform a first BLAST similarity search (blastp) against each CDS sets, and each of the first hits was used as query to perform a PSI-BLAST search. The HSP70 gene density was estimated by the percentage of CDS belonging to the HSP70 family.
Characterization of L. major HSP70 Protein Family Members Members of the HSP70 protein family are characterized by conserved signature domains and sequence elements, including an N-terminal nucleotide binding domain of about 45 kDa (NBD) and a C-terminal substrate binding domain of about 25 kDa (SBD), which both are connected through a protease sensitive linker element  (Liu and Hendrickson 2007). Using reciprocal BLAST and PSI-BLAST searches, we identified a total of 16 genes within the L. major CDS with homology to either the HSP70 domain (accession numbers pfam00012, PTZ00009, and PTZ00186) or the NBD sub-domain (accession numbers cd10233, cd11733, and cd10170) with E-values of 0-3. 6Â10 À4 , and 0-2. 6Â10 À4 , respectively (fig. 1 , left panel and table  1 ). Five family members  (LmjF.
26. 0900, LmjF. 18. 1370, LmjF. 35. 4710, LmjF. 29. 1240, and LmjF. 32. 0190)  showed considerable C-terminal sequence extensions that are conserved across Leishmania species (data not shown), likely conferring highly trypanosomatid-specific functions to these proteins. In addition, unlike most eukaryotic HSP70 proteins, three L. major HSP70 members showed additional domains, including a domain of unknown function (DUF3919) characterized by a conserved YLNG motif (LmjF. 28. 1200), a Hydantoiniase/Oxoprolinase domain (accession number pfam01968) involved in hydrolysis of pyrimidine precursors  (Syldatk et al. 1999; LmjF. 18. 1370) , and a tetratricopeptide (TPR) domain (accession number pfam13424) involved in protein-protein interaction  (Blatch and Lassle 1999; LmjF. 29. 1240 ; see table  1  and fig.
2A ). Based on the presence of a conserved C-terminal EEVD motif and clustering with the canonical human cytosolic HSP70 members HSPA1A (supplementary fig. S1 , Supplementary Material online), we propose to name  LmjF. 28. 2770 , LmjF. 28. 2780 , and LmjF. 26. 1240, respectively, LmjF_HSPA1A, LmjF_HSPA1B, and LmjF_HSPA2 (     Proposed name following the nomenclature assigned by the HUGO Gene Nomenclature Committee (http://www. genenames. org/genefamilies/HSP, last accessed June 18, 2016) and used in the NCBI Entrez Gene database for human HSPs. cd10233, Nucleotide-binding domain of HSPA1-A, -B, -L, HSPA-2, -6, -7, -8, and similar proteins (HSPA1-2_6-8-like_NBD); cl17037, nucleotide-binding domain of the sugar kinase/HSP70/actin superfamily (NBD_sugar-kinase_HSP70_actin); PTZ00009, heat shock 70 kDa protein domain; pfam00012, HSP70 domain; pfam01968, Hydantoinase/oxoprolinase domain; cl00668, Hydantoinase_A Superfamily; pfam13057, Protein domain of unknown function (DUF3919); cl16063, protein domain of unknown function (DUF3919); cd11733, nucleotide-binding domain of human HSPA9, Escherichia coli DnaK, and similar proteins (HSPA9-like_NBD); PTZ00186, heat shock 70 kDa precursor protein domain; cd10228, nucleotide-binding domain of 105/110 kDa heat shock proteins including HSPA4 and similar proteins (HSPA4_like_NDB); pfam02782, FGGY family of carbohydrate kinases, C-terminal domain (FGGY_C); cl17173, AdoMet_MTases superfamily; cd10230, nucleotide-binding domain of human HYOU1 and similar proteins (HYOU1-like_NBD); cd10170, nucleotide-binding domain of the HSP70 family (HSP70_NBD); PTZ00400, DnaKtype molecular chaperone; pfam00515, tetratricopeptide repeat domain TPR_1; cl22897, TPR_1 superfamily; pfam13424, tetratricopeptide repeat domain TPR_12.
pasteur. fr/SynTView/flash/Leishmania/ SynWeb. html, last accessed June 18, 2016), but revealed substantial gene copy number variations for the HSPA1 and HSPA9 gene arrays across the different Leishmania species (fig. 5A  and supplementary table  S3 , Supplementary Material online) and confirms important differences in copy number of these genes to the L. major genome database  (Folgueira et al. 2007 ). To more accurately estimate the number of gene copies, we exploited recently published HTseq data  (Rogers et al. 2011). Read depth analysis uncovered even more dramatic changes in copy number across various Leishmania spp. , with L. major showing the highest rate of gene amplification with estimated 14 and 17 copies for HSPA1 and HSPA9, respectively (fig. 5B ). No CNV was observed for all other HSP70 members, which are all encoded by single copy genes.
compared with other eukaryotes. Even though the quantification of exact copy number variation of duplicated regions remains difficult for current sequencing techniques, our data indicate that the copy number of these genes is significantly underestimated in current public databases, which, for example, may attain up to 14 and 17 gene copies for L. major HSPA1 and HSPA9, respectively. The presence of abundant gene arrays is a main characteristic of the Leishmania genome structure, believed to increase gene expression in the absence of transcriptional control  (Ivens et al. 2005; Peacock et al. 2007; Rogers et al. 2011). Often gene amplification is selected for in response to environmental stress. For example, drug resistance in Leishmania has been correlated to gene amplification  (Downing et al.
Acknowledgments The authors thank  Sophie Veillault  for editorial help,  Simonetta Gribaldo  for discussion, and  Mariette Matondo  from the  Institut Pasteur Proteomics platform  for proteomics data analysis. This work was supported by the  French Government 's  Investissements d'Avenir programme: Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  (Grant no. ANR-10-LABX-62-IBEID  to G. F. S. );  KALADRUG-R (EU/FP7-222895  to J. C. D. ), the  Belgian Development Cooperation  ( FA3  II VL control and  FA3  project  95502  to J. C. D. ), the  Belgian Science Policy Office (TRIT ,  P7/41  to J. C. D. ), the  Flemish Fund for Scientific Research  ( G. 0. B81. 12  to J. C. D. ), the  INBEV-Baillet Latour foundation , and  EWI  (GEMINI and SINGLE grants to ITM SOFI-B to J.
2012) , focusing on the analysis of the detected galactic wind. This is a pilot study which ultimate goal is the exploration of the frequency and physical conditions of such outflows in the complete sample of CALIFA. The paper is organized as fol-lows: we present the main properties of UGC 10043 in §2; the data are described in §3, and the conducted analysis in §4. Section 5 describes the analysis of observations with a scanning Fabry-Perot interferometer at the Russian 6-m telescope; while the main results are presented in §6. Finally, we present the main conclusions and discussion in §7. Throughout the paper, we adopted the standard LCDM cosmology, with parameters H 0 = 70. 4 km s -1 Mpc -1 , Ω m = 0. 268 and Ω Λ = 0. 732.
DATA In our work we are using the IFS observations of UGC10043 from the first and second CALIFA data releases (e. g. Husemann et al. 2013; García-Benito et al. 2015). The CALIFA survey is a recently completed project that comprises three data releases (DR1, DR2 and DR3), the last one delivered in 2016  (Sánchez et al. 2016a). The aim of CALIFA was to obtain spatially resolved spectroscopy of more than 600 galaxies at the Local Universe (0. 005< z < 0. 03) covering a wide range of morphological and stellar masses. The observations cover the optical size of the galaxies up to 2. 5 effective radii using the wide-field Integral Field Unit (IFU) Pmas fiber PAcK (PPAK;  Kelz et al. 2006 ) of the Potsdam Multi-Aperture Spectrophotometer instrument (PMAS;  Roth et al. 2005). The PPAK fiber bundle consists of 331 fibers of 2.
7 diameter each one covering a total hexagonal FoV of 74 × 64 with a filling factor of ∼ 60%. In order to guarantee a complete coverage of the FoV, three dithering pointings were applied to obtain 993 independent spectra for each object. The final spatial resolution is ∼1 kpc at the redshift of the sample. This allows to resolve spatially the spectroscopic properties from the most relevant components of the galaxies (H ii regions, bars, spiral arms, bulges). Each galaxy of the CALIFA sample was observed in two different configurations, one of low resolution (V500, R∼ 850) covering the optical range 3750-7500 Å and another one of intermediate resolution (V1200, R∼ 1650) that covers the blue part of the optical range of the spectra 3700-4800 Å. Along this article we use the data of the V500 set-up for UGC 10043.
The data were reduced using version 1. 5 of the CALIFA pipeline, whose modifications with respect to the one presented in  Sánchez et al. (2012)  and  Husemann et al. (2013) , are described in  García-Benito et al. (2015). The procedure comprises the usual steps in reduction of IFS data, as described in  Sánchez (2006) : bias and dark subtraction, cosmic-ray removal, CCD flat-fielding, spectra tracing and extraction, wavelength and flux calibration, and finally cube reconstruction. The final product of the data reduction is a regular-grid data-cube, with x and y coordinates indicating the right ascension and declination of the target and z a common step in wavelength. The CALIFA pipeline also provides with the propagated error cube, a proper mask cube of bad pixels, and a prescription of how to handle the errors when performing spatial binning (due to covariance between adjacent pixels after image reconstruction).
The operating spectral range around the [N ii]λ6584 emission line was cut by a narrow bandpass filter with a FWHM ≈ 21 Å bandwidth. The interferometer provides a free spectral range between the neighbouring interference orders ∼ 35 Å with a FWHM of the instrumental profile ∼ 1. 7 Å (R∼3860). During the scanning process, we have consecutively obtained 40 interferograms, each of 1800 s exposure, at different distances between the FPI plates, under seeing conditions of 1. 7 -2. 1. The FoV was 6. 1 × 6. 1 with a sampling of 0. 7 per pixel. The data were reduced using algorithms and programs described by  Moiseev & Egorov (2008)  and  Moiseev (2015). Thus, each spaxel in the reduced data cube contains a 40-channel spectrum.
Continuum Subtraction To study the properties of the ionized gas we need to uncouple the continuum from each spectra of the data-cube. There are several routines focused in the analysis of the stellar populations (e. g. STARLIGHT, pPXF, Cid Fernandes et al. 2011a; Cappellari & Emsellem 2004 ). Here we adopted Pipe3D, a pipeline developed to analyse IFS data-cubes  (Sánchez et al. 2016c , S16 hereafter) using the fitting package FIT3D  (Sánchez et al. 2016b, S15 hereafter). Generally the continuum spectra show a wide range of signal to noise ratios (S/N) that are higher in the central regions and gradually decreasing outward from the galactic centre. From simulations tested in S15, we observed that a minimum S/N is required in each spectrum to obtain an accurate model of the continuum and therefore to derive the properties of the ionized gas.
CONCLUSIONS We used the unique capabilities of the CALIFA survey to study the emission of the ionized gas in the edge on galaxy UGC 10043. We detect the presence of a possibly bi-conical extraplanar emission of ionized gas that reaches distances up to 4 kpc over the galaxy disk. We favour, that this extraplanar emission is most probably produced by a galactic wind driven by a weak nuclear starburst, consistent with a low SFR = 0. 35 M yr -1 as measured with Hα. Based on shock models, we find that our data are better described by a fast velocity shock model (v s = 100-400 km s -1 ), and a pre-shock density decreasing towards larger extraplanar regions. These shock velocities are in agreement to first order with the outflow kinematic parameters derived from scanning Fabry-Pérot observations: the line-of-sight velocity dispersion σ < 300 km s -1 , and the de-projected outflow velocity V out < 250 km s -1.
ACKNOWLEDGEMENTS The authors wish to thank the anonymous referee for his/her thorough review, and the valuable suggestions and comments which significantly contributed to improve the quality of the publication. CALIFA is the first legacy survey being performed at Calar Alto. The CALIFA collaboration would like to thank the  IAA-CSIC  and  MPIA-MPG  as major partners of the observatory, and CAHA itself, for the unique access to telescope time and support in manpower and infrastructures. The CALIFA collaboration thanks also the  CAHA  staff for the dedication to this project. We thank  CONACYT-125180 ,  DGAPA(UNAM)-IA100815  and  DGAPA(UNAM)-IN107215  projects and the institutions ( CONACYT  and  DGAPA-UNAM ) for providing financial support for this study. The Fabry-Pérot observations were obtained with the 6-m telescope of the  Special Astrophysical Observatory of the Russian Academy of Sciences , and were carried out with the financial support of the  Ministry of Education and Science of the Russian Federation  (Contract No.
N14. 619. 21. 0004  for the project  RFMEFI61914X0004 ). AVM and DVO are also grateful for the financial support via grant  MD3623. 2015. 2  from the  President of the Russian Federation. L. G. was supported in part by the  US National Science Foundation  under Grant  AST-1311862. RAM acknowledges support by the  Swiss National Science Foundation.
Databases which store and manage long-term scientific information related to life science are used to store huge amount of quantitative attributes. Introduction of a new entity attribute requires modification of the existing data tables and the programs that use these data tables. A feasible solution is increasing the virtual data tables while the number of screens remains the same. The main objective of the present study was to introduce a logic called Joker Tao (JT) which provides universal data storage for cloud-based databases. It means all types of input data can be interpreted as an entity and attribute at the same time, in the same data table.