dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell Adhesion Assays For analyses of BM-MSC adhesion to endothelial cells, 2610 5  HMEC were seeded in 96-well microplates and cultured for 1 day prior the assay. Coated wells were incubated for 5 minutes with 0. 1 ml of 5610 4 cells/ml of Fast-DiO prelabelled MSCs which were or not pretreated with 1 mg/ml rAMF. Cell suspension was discarded and attached cells were fixed with 2% paraformaldehyde. Cells were counted using fluorescent-field microscopy at 200X: pictures taken from ten representative visual fields were analyzed using CellProfiler software (www. cellprofiler. com) and values were normalized to untreated control.
PCR amplifications were carried out using a cycle of 95uC for 10 minutes and 45 cycles under the following parameters: 95uC for 30 seconds, 58uC for 30 seconds, 72uC for 1 minute. At the end of PCR reaction, the temperature was increased from 60uC to 95uC at a rate of 2uC/min, and the fluorescence was measured every 15 seconds to construct the melting curve. Values were normalized to levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH; used as housekeeping) transcript (forward 59-CATCTCTGCCCCCTCTGCTG-39 and reverse 59-GCCTGCTTCACCACCTTCTTG-39). Data were processed by the DDCt method. The relative amount of the PCR product amplified from untreated cells was set as 1. A nontemplate control (NTC) was run in every assay, and all determinations were performed as triplicates in three separated experiments.
Three-Dimensional Spheroids Ninety-six-well tissue culture plates were coated with 2% agarose in PBS. A total of 3610 3 HuH7 cells, 1610 3 LX-2, 1610 3 HMEC-1 with or without 1610 3 BM-MSCs per spheroid were mixed in complete DMEM to obtain a single multicellular spheroid per well. Seventy-five microliters of supernatant were carefully removed from each well every 2 days and replaced with fresh medium. Viability above 75% was confirmed by Trypan blue exclusion test in all experiments. Spheroid size was evaluated using inverted microscopy at 40X: images were captured and values determined using ImageJ software (National Institute of Health, NIH). Spheroid volume was calculated using the formula p/66 larger diameter 6 (smaller diameter) 2 , and expressed as arbitrary units.
Images represent the radiant efficiency and were analyzed with IVIS Living Image (Caliper Life Sciences) software. Regions of interest (ROI) were automatically drawn around the isolated organs to assess the fluorescence signal emitted. Results were expressed as average radiant efficiency in units of photons/second within the region of interest [p/s/cm 2 / sr]/[mW/cm 2 ] or as total radiant efficiency in units of photons/ second within the region of interest [p/s]/[mW/cm 2 ].
AMF produced by HCC induce migration of MSCs from different sources A 55 KDa soluble AMF form was detected in the conditioned medium of cell culture monolayers (CCM) and SC tumors (TCM) derived from HuH7 cell line and from HC-PT-5 HCC primary cultures (Figure  1A ). AMF levels in TCM were higher when compared to CCM of similar origin. We then asked whether AMF may be able to act as MSC chemoattractant in vitro. With this aim, migration capacity of human MSCs derived from bone marrow (BM-MSCs), perivascular umbilical cord (HUCPVCs) and adipose tissue (AT-MSCs) towards AMF was analyzed using a modified Boyden chamber assay. MSCs isolated from the three sources studied migrated in a dose-dependent manner towards rAMF (Figure  1B-D ). Chemokinesis experiments showed similar number of cells/field that migration toward DMEM (control, data not shown), indicating that AMF plays a role as chemoattractant on MSCs.
Acknowledgements Aleksander A Rebane, the second author, performed exemplary work in accomplishing the complex data analyses herein reported. We thank  Ying Gao  for technical assistance and  Jingshi Shen ,  Daniel Kummel , and  Xinming Zhang  for reading the manuscript. This work was supported by the  NIH  grants  GM093341  to YZ and  DK027044  to JER.
, 2012; Haubold et al. , 2005; Ulitsky et al. , 2006). A common feature of all these methods is, however, that they are based on exact word matches between the input sequences. Database searching is another traditional application of word matching in sequence analysis. Fast local alignment programs such as BLAST  (Altschul et al. , 1990)  originally relied on identifying word matches of a fixed length, so-called seeds. Rapid indexing methods can be used to identify such 'seeds', while, in a second phase of the algorithm, seeds are extended into both directions to find statistically relevant high-scoring segment pairs. In a pioneering paper,  Ma et al. (2002)  proposed to use spaced seeds instead of contiguous-word matches as a first step in homology searching. Here, a fixed pattern or 'mask' of match and don't care positions is defined, and two words of the corresponding length are considered to match if they coincide at the specified match positions, while mismatches are allowed at the don't care positions.
It is then straightforward to define a distance d P ðS i , S j Þ between two sequences S i and S j as the distance between these frequency vectors, using some standard distance metric on vector spaces. This approach can be generalized by considering a whole set P ¼ fP 1 ,. , P m g of patterns instead of a single pattern P [similarly,  Li et al. (2003)  used multiple spaced seeds for database searching]. Here, we define a distance d P as the average of the distances defined by the patterns P 2 P, i. e. we define d P ðS i , S j Þ ¼ 1 m X P2P d P ðS i , S j Þ ð 1Þ We call this extension the multiple-pattern version of our spaced-word approach.
(2009)  for their Feature Frequency Profile (FFP) approach. In addition, we applied the Euclidean distance to the relative-frequency vectors obtained with our multiple-pattern approach. Finally, we applied the NeighbourJoining program  (Saitou and Nei, 1987)  from the PHYLIP package  (Felsenstein, 1989)  to calculate unrooted trees from these distance matrices. As a comparison, we applied three state-of-the-art alignment-free approaches, Average Common Substring (ACS)  (Ulitsky et al. , 2006) , K r  (Haubold et al. , 2005)  and FFP  (Sims et al. , 2009) , as well as a traditional approach to phylogeny reconstruction using Clustal W  (Thompson et al. , 1994)  and Maximum Likelihood  (Felsenstein, 1981). For ACS, we used our own implementation  (Leimeister and Morgenstern, 2014)  because the original software is not publicly available.
For FFP, we used the word length that gave the best results for a given category of benchmark sequences. To evaluate these different methods, we compared for each sequence set the resulting trees with the corresponding reference tree using the Robinson-Foulds (RF) metric  (Robinson and Foulds, 1981). As test data for genomic sequence comparison, we used a set of 27 primate mitochondrial genomes that have been previously used by  Haubold et al. (2009)  to benchmark alignment-free methods. For these sequences, a reliable phylogenetic tree is known that is based on multiple sequence alignment and Maximum Likelihood. Further, we used a set of 13 fully sequenced flowering-plant genomes from the Malvidae clade plus the grape vine genome as outgroup. As a reference tree for these 14 genomes, we used a tree based on a multiple sequence alignment of manually assembled CAP and Arp2/3 protein sequences as published by  Hatje and Kollmar (2012).
To benchmark our approach on protein sequences, we used BAliBASE 3. 0, a standard benchmark database for multiple alignment  (Thompson et al. , 2005). BAliBASE consists of 218 sets of related protein sequences together with reference multiple alignments based on 3D superposition that are considered to be reliable. As BAliBASE contains no information about the underlying phylogenetic trees, we constructed a reference tree for each sequence set by applying the standard Maximum Likelihood method  (Felsenstein, 1981)  to the corresponding reference multiple alignment. In addition to these real-world benchmark sequences, we generated a large number of simulated genomic and protein sequence sets using the program Rose  (Stoye et al. , 1998). This program mimics molecular evolution by producing a set of sequences along an evolutionary tree, starting with a common ancestral sequence at the root.
Standard deviations are shown as error bars. Note that for ' ¼ k, only one single pattern exists so here no standard deviations could be calculated. For small values of ',5100 patterns are possible, so only a single set P exists and standard deviations cannot be calculated for the multiple-pattern approach Fig. 2. Test results on a set of 50 simulated DNA sequences of length 16 000 nt each. Patterns with a weight of k ¼ 8 were used. Experimental conditions, notation and colour coding as in Figure  1. In addition to the various alignment-free methods, a classical approach using multiple alignment and Maximum Likelihood was used length 16 000 nt and a set of 125 simulated protein sequences of length 300 aa. We applied our multiple-pattern method to these datasets using different values of k and ' and pattern sets P of different size m.
, 2012). In database searching, word matches have been replaced by so-called spaced seeds where string matches according to a nonperiodic pattern P of match and don't care positions are used  (Ma et al. , 2002). Motivated by this approach, we proposed to use spaced words instead of the traditionally used contiguous words to estimate distances between sequences and to construct phylogenetic trees. While, under an i. i. d. sequence model, the expected number of occurrences of a spaced word is approximately the same as for the corresponding contiguous word (obtained by removing the don't care positions), spaced-word 5. results sequences; the spaced-word was with a pattern weight of k ¼ 4. Notation as in Figure  1  Fig. 7. Influence of the number of patterns on the results of Spaced words applied to a set of 125 simulated protein sequences of length 300 aa, generated with Rose.
Sets P of patterns generated in Figure  6. Note that for short pattern lengths ', the set of all possible patterns is limited, so for ' ¼ 9 and 14, only small values of m could be tested 6. Influence of the number of patterns on the results of our multiplepattern approach. The program was run on a set of 50 simulated DNA sequences of length 16 000 nt each, generated with Rose. For k ¼ 8, different values of ' and m ¼ 10,20,. ,150, we generated 100 sets P of patterns each, every set P containing m patterns. The quality of the produced trees, measured as the average RF distance of the 100 trees to the respective reference trees, is plotted against m matches at neighbouring sequence positions are less dependent on each other if a non-periodic pattern P is used. In the first version of this spaced-word approach, we used a tree structure to find matching spaced words  (Boden et al.
8. Variation coefficients of distance values calculated with spacedand contiguous-word frequencies using single patterns (upper curve) and multiple patterns (lower curve) on 100 pairs of simulated DNA sequences; see subsection 5. 4 for details. Note that for 0 don't care positions, the spaced-word approach coincides with the contiguous-word approach (and the multi-pattern version coincides with the single-pattern version, as only one single pattern is possible for a given number k of match positions). Thus, for 0 don't care positions, our graphic shows the variation coefficient for the classical contiguous-word approach  phylogenetic distances between DNA or protein sequences based on their spaced-word frequencies. To answer these questions, the statistical properties of spacedword frequencies need to be analysed in detail, as has been done for the hit probabilities of spaced seeds in database searching  (Keich et al.
ACKNOWLEDGEMENTS The authors would like to thank  Susana Vinga ,  Bernhard Haubold ,  Thomas Lingner  and  Anirban Mukhopadhyay  for helpful discussions,  Martin Kollmar  and  Klas Hatje  for help with the plant genomes,  Martin Scho¨neich  for his work on an early version of our program and  Bingyao Zhu  for help with the program evaluation.
Quantification For all platelet quantification data, >50 platelets were analyzed per experiment after confocal z-stack acquisitions. Data are expressed in percentages as means ± SEM obtained from several individual experiments as indicated. Online supplemental material Fig. S1  shows the quantification of experiments presented in Fig. 1 (B  and C ) on the inhibition of microtubule motor proteins. Fig. S2  shows MB coiling induced by different concentrations of ADP after treatment of platelets with the dynein inhibitor, the Tubulin Tracker, or taxol and two time points of time-lapse videos of microtubule tracker-stained platelets with MB coiling induced by calyculin. Fig. S3  shows MB length measurements at successive time points during MB coiling shown in the time-lapse videos Video 5 after ADP activation and kinesin inhibition and Video 6 after ADP activation.
Image acquisition and processing Fluorescent images were acquired and processed as follows for each figure. For Fig. 1  B and Fig. S1  A, a wide-field epifluorescence microscope (BX41; Olympus), objective Plan 100×/1. 25 NA oil, camera (DP70; Olympus), and acquisition software (analySIS; Olympus) were used. For Fig. 1  C, a wide-field epifluorescence microscope (BX41), objective Plan 100×/1. 25 NA oil, camera (DP70), and acquisition software (analySIS) were used. Merges were produced using Photoshop (Adobe). For Carl Zeiss) were used. Image processing was performed using the custom ImageJ macro Marginal Band 3D Analysis (Supplemental material), and the 3D reconstitution was performed with ImageJ 3D Project. For Fig. 2  C, a confocal laser-scanning microscope (LSM 510), objective Plan Apochromat 100×/1.
4 NA oil differential interference contrast III, and acquisition software (LSM 510) were used. Image processing was performed with MetaMorph. For acquisition details, see the legend of Video 4. For Fig. 2  (D  and E ) and Fig. S2  B, a confocal laser-scanning microscope (LSM 710), objective Plan Apochromat 63×/1. 4 NA oil, and acquisition software (Zen 2010) were used. 3D reconstitution and depth coding were performed using the custom ImageJ macro 3D Project Color Depth Coding (Supplemental material). For acquisition details, see the legends of Videos 5, 6, and 10. For Fig. 3  A, a confocal laser-scanning microscope (LSM 710), objective Plan Apochromat 63×/1. 4 NA oil, and acquisition software (Zen 2010) were used. Image processing and 3D reconstitution were performed using the custom ImageJ macro Marginal Band 3D Analysis.
For Fig. 4 (A-F ), a confocal laser-scanning microscope (LSM 710), objective Plan Apochromat 63×/1. 4 NA oil, and acquisition software (Zen 2010) were used. 3D reconstitutions in the top images were produced using ImageJ 3D Project, and skeletonization of MBs in the bottom images were obtained using the custom ImageJ macro Marginal Band 3D Analysis.
3D MB length measurements The length of MBs was measured using the custom ImageJ macro Marginal Band 3D Analysis. In brief, after acquisition of confocal z stacks, the stacks were first interpolated using the processing software ImageJ to obtain isotropic voxels  (Abramoff et al. , 2004). Then, a band pass filter was applied using the difference of two Gaussian filters. With the first filter, the original stack was smoothed by convolving with a 3D Gaussian kernel of standard deviation s1. Using a second filter, the original stack was smoothed with a Gaussian kernel of standard deviation s2, with s2 < s1 (here, we used s1 = 7 or slightly coiled MBs triple stained for acetylated, tyrosinated, and total tubulin as well as the same images reduced to one voxel skeletons. Video 9 (Fig. 4 , D-F) shows a 360° rotation of platelets with strongly coiled MBs triple stained for acetylated, tyrosinated, and total tubulin as well as the same images reduced to one voxel skeletons.
Video 10 (Fig  S2 B ) shows time-lapse videos of MB coiling induced by activation of actomyosin contraction. A source code for a custom ImageJ macro for Marginal Band 3D Analysis, with a toolset for image processing, 3D viewing, skeletonization of MBs, manual skeleton editing, and MB length measurements is provided online as a text file. A source code for a custom ImageJ macro for 3D Project Color Depth Coding, with a tool for depth color coding of 3D confocal time-lapse series and visualization at different view angles is provided online as a text file. Online supplemental material is available at http://www. jcb. org/cgi/content/full/jcb. 201306085/DC1. Additional data are available in the JCB DataViewer at http://dx. doi. org/ 10. 1083/jcb. 201306085. dv. We are grateful to Joel Plumas and Sandrine Fournel for access to buffy coats, Veronique Gerson and Liliane Chollez for blood sampling, Didier Job and Bertrand Fourcade for helpful discussions, and Richard Abbatt and Arthur Forer for critical reading of the manuscript.
Imaging of bioluminescence from animals and organs Images were acquired using the Biospace Lab PhotonIMAGER (Biospace Lab, Paris, France) according to the manufacturer's instructions. Acquisition and analysis were performed using Biospace Lab Photo acquisition 2. 8 and M3 Vision software, respectively. For ex vivo bioluminescent imaging of organs, mice were euthanized by cervical dislocation at designated time points, and the entire intestinal tract was extracted. Image acquisitions were matched to photonic signals integrated for 5 min. BLI signals were quantified from selected regions of interest (ROI) and expressed as photon/second/cm 2 /steradian (ph/s/cm 2 /Sr). For the comparison of signals (a) among the different mice, and (b) among the intestinal tracts of different mice, background photon emission was subtracted and the signals were presented on the same scale.
[14]. Compared with the previous results obtained with the same dataset  [13] , this new analysis profits from a refined energy calibration procedure that improves the expected mass resolution of the signal in the inclusive diphoton sample by approximately 10%  [15]. In addition, the uncertainty on the photon energy resolution is reduced by approximately a factor of two. Furthermore, experimental uncertainties on the integrated luminosity, photon identification, and photon isolation are reduced. Two new categories enriched in t tH events and a dedicated dilepton category that distinguishes ZH from W H production have been added. Finally, the event selection and categorization are tuned to improve the sensitivity of the analysis. The above refinements contribute almost equally to an overall improvement of about 10% in the expected uncertainty on the combined signal strength.
III. DATA AND MONTE CARLO SAMPLES Events from pp collisions were recorded using a diphoton trigger with E T thresholds of  35  GeV and 25 GeV for the leading and sub-leading photon candidates, respectively, in the 8 TeV data and 20 GeV for both photon candidates in the 7 TeV data  [17]. In the high-level trigger, clusters of energy in the EM calorimeter were reconstructed and required to satisfy loose criteria according to expectations for EM showers initiated by photons. This trigger has a signal efficiency above 99% for events fulfilling the final event selection. After application of data quality requirements, the 8 TeV (7 TeV) data sample corresponds to a total integrated luminosity of 20. 3 fb -1 (4. 5 fb -1 ). The instantaneous luminosity is typically about 6 • 10 33 cm -2 s -1 (3 • 10 33 cm -2 s -1 ) in the analyzed 8 TeV (7 TeV) data, resulting in an average number of pp collisions per bunch crossing of about 21  (9)  in the 8 TeV (7 TeV) data.
VII B. For these studies γγ and γ-jet background samples were generated by Sherpa  [74, 75]  and the jet-jet background samples by Pythia8. The normalizations of these samples are determined by measurements of a data sample of preselected diphoton events as described in Sec. VII B. More details about the background control sample used for each category are also given in Sec. VII B. A summary of the event generators and PDF sets for the individual signal and background processes used in this analysis is reported in Table  I  The stable particles, defined as the particles with a lifetime longer than 10 ps, are passed through a full detector simulation  [76]  based on Geant4  [77]. Pileup effects are simulated by overlaying each MC event with a variable number of MC inelastic pp collisions generated using Pythia8, taking into account in-time pileup (collisions in the same bunch crossing as the signal), out-oftime pileup (collisions in other bunch crossings within the time-window of the detector sensitivity), and the LHC bunch train structure.
4 GeV for the 7 TeV data) within a cone of size ∆R = 0. 2 around each photon. The track isolation efficiency is insensitive to out-of-time pileup and its dependence on the in-time pileup is reduced by selecting only tracks consistent with originating from the diphoton production vertex (defined in Sec. V) and not associated with converted photon candidates. A track in the 7 TeV (8 TeV) data is considered to be associated with the diphoton production vertex if the point of closest approach of its extrapolation is within 5 mm (15 mm) of the vertex along the z-axis and within 0. 5 mm (1. 5 mm) of the vertex in the transverse plane. For a given sample purity, a reduction of the dependence of the selection efficiency on the in-time pileup is obtained by combining a looser calorimeter isolation selection with a track isolation requirement.
Photon candidates are required to have a calorimetric isolation less than 6 GeV (5. 5 GeV for the 7 TeV data) and a track isolation less than 2. 6 GeV (2. 2 GeV for the 7 TeV data). The efficiency of the isolation cuts in the simulation is corrected by a small p Tdependent factor extracted from measurements in data performed with a pure sample of photons from radiative Z → eeγ decays and Z → ee events.
The trajectory of each photon is measured using the longitudinal segmentation of the calorimeter and a constraint from the average collision point of the proton beams. For converted photons, the position of the conversion vertex is also used if tracks from the conversion have hits in the silicon detectors. The production vertex selection is studied with Z → ee events in data and simulation by removing the electron tracks from the events and then measuring the efficiency for finding the vertex associated with the Z boson production. The MC simulation is found to accurately describe the efficiency measured in data, as shown in Fig. 2. The efficiency for finding the reconstructed diphoton primary vertex PV in simulated H → γγ events from ggF production within 0. 3 mm (15 mm) of the true vertex is around 85% (93%) over the typical range of the number of collision vertices per event observed in the 8 TeV data.
The efficiency PV increases for large diphoton p T as the hadron system recoiling against the diphoton evolves into one or more jets, which in turn contain additional higher p T tracks. These additional tracks make it more likely to reconstruct the diphoton vertex as a primary vertex. Therefore, by re-weighting the simulated Z → ee events to approximate the harder p T spectrum of the simulated Higgs boson signal, PV is well reproduced. The corresponding efficiencies for the 7 TeV data and MC samples are slightly higher, due to less pileup, and the efficiencies are as consistent as those for the 8 TeV data and MC samples. ∫ = 125 GeV H m (ggF), γ γ → H -reweighted) T p , MC ( ee → Z , MC ee → Z , Data ee → Z ATLAS FIG. 2. Efficiency PV to select a diphoton vertex within 0. 3 mm of the production vertex as a function of the number of primary vertices in the event.