dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance Evaluation An executable program has been released by the authors of SURF 3. Unfortunately, we were only able to use it partially. So, in order to evaluate and validate our implementation of the SURF algorithm, some of the benchmarks proposed in  [2]  have been reproduced. In these benchmarks, performance curves are obtained from the experimental protocol from  [19]  which provides several image sequences with geometric distortion (along with the ground-truth) and the MATLAB code to compute scores. Two different types of performance measures are evaluated to assess the quality of the feature detection and description: the repeatability score and the precisionrecall rate. Repeatability The repeatability score represents the proportion of nearly identical features detected in two different views of the same scene.
Two keypoints X k and X i are considered similar if the overlap error between the two registered regions is small enough. We denote R i the support region of the interest point X i back-projected in u, which is an elliptic region obtained from the geometric transform T of the disk B s i (x i , y i ). As advocated in  [19] , the radius s i verifies s i σ i = s σ k to take into account the difference of scale between the two interest-points. The overlap score is then defined as overlap score (X k , X i ) = R k ∩ R i R k ∪ R i. A minimum of 50 % overlap score is required to consider that a keypoint X k in u is repeated in u. Simply speaking, the repeatability score is the proportion of keypoints in u that have a similar keypoint in u. For more details, please refer to  [19, Section 4. 1]. We have used two image sequences displayed in Figure  18 : the 'Graffiti' database, where the same (planar) scene is captured under different view angles (from 0 • to 60 • ), and the 'Boat' sequence, with rotations and zoom-out (up to a scale change factor of 3).
MATERIALS AND METHODS The pipeline we used is represented on Figure  1. Hi-C reads from different raw datasets (Supplementary Table  S1  sheet H), of various sizes, experimental approaches and laboratories were aligned using Bowtie 2 in very sensitive mode  (29). We used an iterative alignment procedure similar to the one presented in  (30). To avoid misalignments over repetitive regions, two different strategies were designed (Figure  1A ). The first and simpler approach consisted in applying a stringent mapping quality filtering while discarding ambiguous mapping results. We used a threshold of 40 for the mapping quality, which for instance retained ∼72% of the reads of the human genome datasets. It should be noted that even if a read is overlapping partially with a repeat element referenced in UCSC server it remains possible to map it unambiguously thanks to the differences between the regions neighboring the repeat.
The second strategy was much more stringent, since only the reads that did not overlap with any of the repeat elements, were retained for further analysis  (31). This procedure only kept ∼ 23% of the reads for the human genome datasets. Only inter-chromosomal contacts, defined as a pair of uniquely mapped reads on two different chromosomes, were kept for the analysis in order to avoid artifacts that could arise from the correlated distributions of repeated elements along the chromosomes.
Receiver operating characteristic (ROC) curves To measure the enrichment in old repetitive elements among the repetitive elements that present a significant colocalization, we used a ROC analysis. We took the chronological ordering of the 360 human TE from Giordano et al. (Supplementary Table  S2  of (  35 )) and labeled each TE that presented a significant co-localization according to our analysis. We took the repeats with a significant CS in human embryonic stem cells i. e. Supplementary Table  S1 , sheet A. We then applied the receiver operating characteristic of the R package ROCR with default parameters. The area under curve (AUC) and the P-value were calculated with the R function roc. area. The P-value produced is related to the Mann-Whitney U statistics. True positives correspond to repeats having a significant CS and the false positives correspond to repeats not having a significant CS.
We used a similar approach to show enrichment within the pool of repeats presenting highly significant CS with repeats that are enriched with TFBS. We ordered all the repeat elements according to the P-value of their CS and identified each repeat enriched with TFBS. True positives correspond to repeats enriched with TFBS and false positives to repeats not enriched with TFBS. Enrichment for TFBS were computed exploiting the following datasets: human. r -Supplementary Table  S1  of (  36 ) that provides Hu- man repeat-associated binding sites (RABS) for OCT4, NANOG and CTCF for hESC (human embryonic stem cell). r -Supplementary Table  S3  of (37) that provides DHS- associated repeats (DARs) enriched for TFs ChIP-Seq for hESC. r -Supplementary Table  S4  of (37) that provides DARs en- riched for TFs motifs for hESC.
Several subfamilies of repetitive elements co-localize in hESC nuclei The potential co-localization of specific repetitive elements in hESC was investigated using inter-chromosomal contacts obtained from several Hi-C datasets generated through different protocols and laboratories  (25). The 1395 different repeats subfamilies from the Re-peatMasker table of UCSC were pooled into six families: satellites/low complexity sequences, SINE, LINE, LTR, DNA-transposons and RNA repeats  (31). For each subfamily a CS was computed ('Materials and Methods' section), to assess the tendency for regions of the genome carrying these repeats to make physical contacts in the nuclear space. Importantly, and to avoid potential false positive contacts resulting from misalignment of the reads along the genome, CSs were computed through two independent approaches ('Materials and Methods' section).
The first and simplest approach was to retain all pairs of reads that unambiguously aligned against the genome when using stringent and restrictive mapping parameters (Figure  1A ). The second approach was more stringent, since only unambiguous pairs of reads that did not overlap with any repeat were considered. The significance of each CS was then estimated using null models designed to account for three known potential biases: the sequencing coverage of the Hi-C experiment, the GC content and the distribution of repeats along chromosomes (see Figure  1B  and Supplementary Figure  S2 , 'Materials and Methods' section). For each dataset, a subset of repeat subfamilies exhibited CS significantly higher than expected by chance, with the three null models giving very similar results (Supplementary Table  S1 , sheet B, Spearman correlation > 0.
The spatial association of some repetitive elements is cell type dependent and relies on cell type specific binding factors CSs were computed for two other cell types (human embryonic lung fibroblasts cells (IMR90; 25) and in lymphoblastoid cells (GM12878; 27), giving overall similar results to those obtained with hESC and suggesting that the average large-scale folding of the genome could be influenced by the genome sequence itself rather than by cell-type specific reorganization (see Supplementary Figure  S7  and Supplementary Table  S1 , sheet C and D). Using different datasets on the same cell type, we were also able to show that our results do not depend on read length (see Supplementary Figure  S7C ). Interestingly, despite an overall CSs conservation, some local changes in the inter-chromosomal contact maps generated using different cells types could nevertheless be observed.
In order to investigate whether or not these changes could be associated with specific repetitive elements, we computed the log ratio between the CS of each repeat in hESC and IMR90 (two datasets generated through the same protocol). The distribution of this log ratio is symmetric and centered around zero. We then computed the same distribution for repetitive elements that have been identified as bound by three different transcription factors: CTCF, NANOG and OCT4  (12). We found that the CS of the repeats containing binding sites for both OCT4 and NANOG simultaneously, which are expressed only in hESC, are significantly higher in this cell line compare to IMR90 (Mann-Whitney test, P-value of 0. 015) whereas there was no significant change for CTCF, which is expressed in both cell types (Figure  3C ).
ACKNOWLEDGEMENTS We are thankful to  Jean-Baptiste Boulé ,  Christophe Escudé ,  Annick Lesne ,  Thierry Forné ,  Marie-Claude Marsolier-Kergoat  and  Jean-Marc Victor  and his team for helpful discussions and advices. We thank  Matthew Campbell  for comments on the manuscript and ' Spatial Regulation of Genomes ' team for their support and discussions.
FUNDING  ANR PIRIBIO  [ ANR-09-PIRI-0024 ];  European Research Council  under the  7th Framework Program  ( FP7/2007-2013) / ERC  grant agreement [ 260822  to R. K. ];  UPMC   Convergence program  CVG  1110. Funding for open access charge:  European Research Council  under the  7th Framework Program  ( FP7/2007-2013) / ERC  grant agreement [ 260822  to R. K. ].
2. 3). Figure  2. SPIRE maps at 250, 350, and 500 µm for a lensed source. In this case a single very bright SPIRE source is detected. This source is confirmed at the Jy level and has a redshift of z = 3. 0, measured from multi-line spectroscopy acquired at IRAM using EMIR  (Canameras 2015). The white contour is the Planck IN region, defined in the same way as in Fig. 1. These data come from a 7 ′ × 7 ′ SPIRE scan (see Table  1  and Sect. 2. 3). the sky (Planck Collaboration Int. VII 2013). Secondly, we select all the sources with S/N > 4 at 545 GHz and the colours S 857 /S 545 < 1. 5 (where S ν is the flux density at frequency ν in GHz) and S 217 < S 353. Thirdly, we inspect each source with respect to the NASA/IPAC Extragalactic Database (NED), IRAS maps  (Neugebauer et al. 1984) , and optical maps using ALADIN.
Photometric analysis We first measure the noise in each map at each wavelength (channel) by fitting a Gaussian function to the histogram of the maps and deriving the standard deviation, σ channel , a mixture of confusion noise  (Dole et al. 2003; Nguyen et al. 2010 ) and detector noise. The values are reported in Table  2. Data are sky subtracted and at mean 0, as done in HIPE 10. All pixels are used to plot the noise histogram. Histogram have a Gaussian shape, with a bright pixel tail. The bright pixel tail contribution (larger than 3 7 Herschel public data can be downloaded from the Herschel Science Archive: http://herschel. esac. esa. int/Science_Archive. shtml sigma) to the histogram is nearly 1% in number for each field at 350um. Then we extract the sources using a blind method and by a band-merging procedure described in the following sections.
Classification of the sources We classify the Herschel fields in an automated way and for preliminary analysis into two main categories: (1) overdensities, appearing as clustered sources, and (2) lensed candidates, appearing as a bright single source as a counterpart of the Planck source. To make this classification, we measure within the IN Planck region the flux density and colour of the brightest SPIRE source. Planck Collaboration: high-z sources discovered by Planck and confirmed by Herschel We sort the fields using two criteria: (1) the SPIRE channel at which the source has its maximum flux density; and (2) its flux density at 350 µm. We will use this two-parameter space to classify the sources. This classification provides the first set of information about the sources, although a definitive classification will require follow-up data to confirm the nature of the sources.
, 2014;; Brodwin et al. 2013; Galametz et al. 2013; Rigby et al. 2014; Wylezalek et al. 2013a; Chiang et al. 2014; Cooke et al. 2014; Cucciati et al. 2014 ). However, it is hard to estimate the fraction that may be comprised of random alignments of unrelated clumps (e. g. ,  Chiang et al. 2013) , since the fluctuation field due to clustering is highly non-Gaussian. Ancillary data (particularly redshifts) are needed to confirm the associations, and an ambitious follow-up programme is underway. To further characterize the overdensities discovered by Planck and confirmed by Herschel, we also started to stack them in many ancillary data sets. The first data set is the Planck lensing map  (Planck Collaboration XVII 2014). The goal is to detect a signature of the presence of large gravitational potential, as has been demonstrated already by correlating the CIB fluctuations with the lensing map (Planck Collaboration XVIII 2014).
The second data set is to use the nine frequency maps and the diffuse tSZ map obtained by MILCA  (Hurier et al. 2013). The goal here is to detect a signature of the thermal Sunyaev-Zeldovich (tSZ) effect  (Sunyaev & Zeldovich 1969 , 1972)  and thus the presence of hot gas. These analyses will be published in a subsequent paper. The Planck data thus provide unique and powerful samples to uncover rare populations, exploitable for extragalactic studies, as was shown with our successful follow-up with Herschel of 228 Planck high-z candidates. There are hundreds more Planck high-z candidates (the full catalogue is being finalized and will be published in Planck Collaboration 2015) to be studied and characterized. -if one source is found, we take this source to be the 250 µm counterpart; -if two sources are found, we replace the position of the 350 µm unique source by the two positions of the 250 µm sources (the 350 µm flux density will be measured later) and take the two 250 µm flux densities; -we apply the same procedure for triply or more matched sources: we keep the positions of the 250 µm sources as priors (only one case, outside the Planck beam.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at.
IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). Based in part on observations carried out with the  IRAM Plateau de Bure Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). Based in part on observations made at JCMT with SCUBA-2. The James Clerk Maxwell Telescope is operated by the  Joint Astronomy Centre  on behalf of the  Science and Technology Facilities Council of the United Kingdom , the  Netherlands Organisation for Scientific Research , and the  National Research Council of Canada. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.
We acknowledge the support from the  CNES , the  PNCG  ( Programme National de Cosmologie et Galaxies ),  ANR   HUGE  ( ANR-09-BLAN-0224-HUGE ) and  ANR MULTIVERSE  ( ANR-11-BS56-015 ). We also acknowledge the support from  Région Ile-de-France with DIM-ACAV. We acknowledge the  Integrated Data & Operation Center (IDOC) at Institut d'Astrophysique Spatiale  and.
Introduction Computational modelling in physiology has contributed to many significant breakthroughs, but the models themselves have usually not become working tools for experimentalists nor even for other modellers outside the developer's own group. We provide here a practical implementation of one of the classic and most complete models of body fluid and acid-base regulation, and we give several examples of the use of the model. We give the complete model description in the language of Berkeley Madonna, which is very easy to read and can readily be converted for other numerical solvers. Physiologists and clinicians will find this model easy to use, and this complete example will facilitate extensions in order to simulate related clinical situations or new experimental findings. Inspired by the classic model of blood pressure regulation by  Guyton et al.
Berkeley Madonna Description Berkeley Madonna is a fast, robust, multi-platform solver of systems of ordinary differential-algebraic equations. Compared with other such solvers, it is extremely easy to program (a simple list of the equations in any order), has a very effective user interface for plotting or tabulating the results and varying the parameters (simple ''sliders'' can be easily defined to vary individual model variables or parameters, with instant re-run of the model), and it has proven to be very fast compared to other solvers we have used.
Acknowledgments This work was funded by the following Grants: VPH NoE (EU FP7, Grant 23920) (http://cordis. europa. eu/fp7/ict/); SAPHIR project, Grant ANR-06-BYOS-0007-01, Agence Nationale de la Recherche (http://www. agence-nationale-recherche. fr/en/); and BIMBO project, Grant ANR-09-SYSCOMM-002, Agence Nationale de la Recherche (http://www. agence-nationale-recherche. fr/en/). of physiological saline at a rate of 1000 ml per 5 min. 3. Transient response of the respiratory system to 5 % CO 2 inhalation (Fig. 11  of  Ikeda et al. (1979) ) • Online Resource 04, file: ''ESM_04''-Simulation of the inhalation of 5 % CO 2 in air over 30 min. 4. Glucose tolerance curve with the extracellular potassium concentration (Fig. 12  of  Ikeda et al. (1979) ) • Online Resource 05, file: ''ESM_05''-Simulation during 3 h of a test of glucose metabolism, corresponding to the infusion of glucose at a rate of 1 g/min during 50 min.
Molecular libraries For screening of drugs for repurposing to select candidates for Ebola virus entry inhibitors, 1463 approved and 4975 experimental drugs from DrugBank (http://www. drugbank. ca) were screened. For development of the predictive criterion used in this analysis, the learning set (Dataset 1) encompassing 152 drugs which are selected as inhibitors of Ebola virus infection by in vitro and in vivo screening of 3828 FDA-approved drugs  2, 3  , was established. As control data sets 45,010,644 compounds from PubChem (http://www. ncbi. nlm. nih. gov/pccompound) and 49 Ebola virus entry inhibitors collected by data mining of literature and patents, were used. For screening of literature data the NCBI literature database PubMed (http://www. ncbi. nlm. nih. gov/pubmed) was used. For search of patents and patent applications we used the Free Patent Online browser (http:// www.
Results and discussion Previously, analyses of the EIIP/AQVN distribution of 45,010,644 compounds from the PubChem database (http://www. ncbi. nlm. nih. gov/pccompound) revealed that 92. 5% of presented compounds are homogenously distributed within EIIP and AQVN intervals (0. 00 -0. 11 Ry) and (2. 4 -3. 3), respectively). This domain of the EIIP/AQVN space, encompassing the majority of known chemical compounds, is referred to as the "basic EIIP/AQVN chemical space" (BCS)  6. Analysis of the molecular training set (Dataset 1), encompassing 152 small molecule inhibitors of Ebola virus infection selected by in vitro screening of 3828 FDA approved drugs  2, 3  , show that 79% of these compounds are placed within AQVN and EIIP region (2. 3 -2. 7) and (0. 0829 -0. 0954 Ry), respectively ("Ebola Virus Infection Inhibitors Space", EVIIS).
Consistently, microtubule inhibitors which block this trafficking process could decrease infection without interfering with the direct binding and translocation of the Ebola virus into cells. AQVN and EIIP values of microtubule modulators and transcription inhibitors with reported anti-Ebola virus activity are given in Table  2. As can be seen, all these compounds, which do not directly affect binding and internalization of Ebola virus, are located outside of EVIIS. This additionally confirms the specificity of the EVIS domain. In further analysis we used EVIIS as a filter for virtual screening for candidate Ebola virus infection inhibitors. In Dataset 2 622 approved and 1089 experimental drugs in Dataset 3 selected by EVIIS screening of 6532 drugs from DrugBank are reported. Using cEVIIS, we located 267 approved and 382 experimental drugs.
The EIIP/AQVN domains of other classes of anti-HIV agents are located outside EVIIS. This indicates that some HIV entry inhibitors and HIV protease inhibitors could also be effective drugs against Ebola virus infection. In conclusion, the presented results show that the EIIP/AQVN criterion can be used as an efficient filter in virtual screening of molecular libraries for candidate inhibitors of Ebola virus infection. Approved (Dataset 2) and experimental drugs (Dataset 3), anti-malarial drugs (Table  3 ) and antibiotics (Table  5 ) selected by this criterion This manuscript deals with the analysis of molecules for their activity against Ebola Virus (EBV). in silico They started from a reference library of compounds who have previously demonstrated and/or in vitro in activity against EBV and analyzed these compounds by the determination of their EIIP and AQVN.
vivo With these data, they scanned a larger collection of compounds with unknown activity against EBV and selected possible candidates to test for their activity and/or. This is a straight forward in vitro in vivo manuscript however it may be better structured. The part of the M&M dealing with the EIIP and AQVN is more appropriate to go into the introduction since this is background information of the calculation. For clarity of the manuscript is also better to separate the results section as it is difficult to follow now. Start with the analysis of the compounds with known activity, the two datasets, and then proceed with results from the unknown dataset. Then in the discussion, the different products of interest may be evaluated. This will largely increase the readability. Upon the antibiotics, it would be good to elaborate a bit on how they work on EBV, since common sense tells that antibiotics do not work on viruses.
Data availability The virtual screen for candidate inhibitors of EBOLA virus infection web tool is available at: http://www. biomedconsulting. info/ tools/ebolascreen. php. An archived version can be accessed at: http://www. webcitation. org/6Vxtuojgx 38 F1000Research: Dataset 1. FDA-approved drugs which are active against Ebola virus infection 2,3 , 10. 5256/f1000research. 6110. d42876 39 F1000Research: Dataset 2. Approved and experimental drugs selected as candidate for treatment of EVD, 10. 5256/f1000research. 6110. d42877 40 F1000Research: Dataset 3. Experimental drugs selected as candidate for treatment of EVD, 10. 5256/f1000research. 6110. d42878 41 F1000Research: Dataset 4. Approved antibiotics screened for candidate anti-Ebola drugs, 10. 5256/f1000research. 6110. d42879 42.
Dataset 2. Figure 1. 21 Figure 1. Distribution of compounds according to their average quasivalence number (AQVN) and electron-ion interaction potential (EIIP) values. (A) 45010644 compounds from the PubChem database (http://www. ncbi. nlm. nih. gov/pccompound); (B) FDA-approved drugs which are active against Ebola virus infection (Dataset 1) 2,3 ; (C) Entry inhibitors of Ebola virus (Table1).