dataesr/scientific-paragraphs-categorization huggingface dataset:
6a ). Similar to IL-3 and Flu, conjugated modes were both represented, whereas opposite modes were mutually exclusive. At 24 h, diversity increased from 6 to 9 modes, with a more balanced representation of positive and negative interactions, and an increase in 'high stabilization' (Supplementary Fig. 6b ). This augmented diversity over time may be due to secondary autocrine and paracrine loops interfering with the exogenous stimuli. Altogether, the pDC data sets revealed 'low stabilization' and 'high stabilization' as the most frequent modes. We performed additional analyses to address whether such behaviours may be due to technological aspects related to the methodology and mRNA read out used for our study. Results suggest that these modes cannot simply be explained by technological limitations due to detection limits or to saturation, respectively (Supplementary Fig.
Contrary to pDC data sets, a majority of negative interactions were observed with a large proportion of emergent negative synergies at 24 h (Fig. 4c ). However, the mutual exclusivity of opposite modes was even more apparent than in the pDC data sets. For example, 'Y inhibits X' almost completely excluded 'X inhibits Y' at both 6 and 24 h (Fig. 4c ). Figure  4f  shows representative genes for each interaction mode observed with the combination MDP þ mLP at 24 h. SERPINB7 (emergent positive synergy) not only may function as an inhibitor of Lys-specific proteases but also influences the maturation of megakaryocytes  27. The costimulatory molecule CD40 (high stabilization) was compatible with a plateau in DC maturation. The Major Histocompatibility Complex (MHC) -related CD1E (negative synergy) participates in the presentation of lipid antigens  26.
This allowed us to identify signalling and biological pathways showing consistently large interaction effects. Among those, we found terms almost exclusively dominated by specific interaction modes. For example, the terms 'DC maturation' (Fig. 5c ) and 'aminoacyl tRNA' (Fig. 5d ) were dominated by 'high stabilization' and 'emergent negative synergy', respectively. Interestingly, 'DC maturation' was partially overlapping with the class 'Th17 in disease', found independently with ingenuity pathway analysis (Fig. 5a ), in particular for the genes TNF, IL1B, IL8 and CCL20. By mapping the hits in these terms onto a database of molecular interactions  31  , we could extract connected networks in both cases (Fig. 5e, f ), which supports the existence of monomodal molecular networks. While the genes contained in these networks are a relatively minor fraction of the modes from which they are extracted (respectively, 21/602 ¼ 3% and 11/679 ¼ 2%), they cover a relevant fraction of the annotation term in which they are embedded (respectively, 21/60 ¼ 35% and 11/40 ¼ 27.
The solution is searched numerically with a MATLAB linear solver. The variables e 0 , e X , e Y and e X þ Y were constrained to the interval 2-16, taken as an approximate of the range of expression values from Affymetrix chips in log2 scale. The method is implemented in the MATLAB code and is available upon request. Classification algorithm. To classify the measured expression levels of the genes into the interaction modes, we developed a MATLAB algorithm consisting of the following steps: (1) For all genes: identification of differentially expressed genes. This is done with a one-way ANOVA (MATLAB function anova1), and subsequent correction of the P values through the function mafdr (Benjamini-Hocheberg) and a significance threshold of 5%. (2) For differentially expressed genes: identification of non-additive genes.
This step is done with a two-way ANOVA (MATLAB function anova2), and subsequent correction of the P values through the function mafdr (Benjamini-Hocheberg) and a significance threshold of 5%. (3) For non-additive genes: match between theoretical and experimental interaction profiles. To obtain the match, we performed the pairwise comparisons in 1-6 in statistical terms by calculating the confidence interval (CI) of the differences of all the group means. Consistent with the theoretical analysis, a comparison between two experimental groups was assigned 0 if the CI contained 0; 1, if the CI was always positive; and À 1, if the CI was always negative. This allowed producing a six-dimensional vector for each gene, which we matched with one of the vectors defined theoretically. If, due to statistical inconsistencies, a non-additive gene could not be classified in any of the theoretical profiles, it was assigned to the class containing its nearest neighbour.
Such step was done using a k-nearest neighbour algorithm (MATLAB function knnclassify) with the correlation distance. We refer to these cases as 'learned interaction profiles'. The above workflow was implemented in a MATLAB function available upon request. Functional interpretation of the interaction modes. Enrichment analysis of the gene lists corresponding to the interaction modes was done with ingenuity pathway analysis. For pathway differential analysis, for each gene i, we calculated the parameter s i ¼ oDe i X þ Y 4 À oDe i X 4 þ oDe i Y 4 , where the symbol o Á 4 denotes the average with respect to multiple donors and i ¼ 1, y, n. The number n is the size of the background, which was chosen as the set of all genes passing the step of independent filtering. The parameter s i measures the deviation from additive integration and can thus be defined as the average interaction strength for gene i.
The background distribution of s i can be modelled by a Gaussian function with average m b , and s. d. s b , where the subscript refers to background. The parameters of the background distribution were estimated by maximizing the maximum Likelihood criterion. Next, we downloaded the Molecular Signature Database (MSigDB, Broad Institute). For each term having at least 10 members, we computed the distribution of the interactions strength. The within-term distributions again modelled as normal functions with parameters m p and s p , and compared with background using the Akaike Information Criterion (AIC) corresponding to the following alternatives: (1) The background distribution and the within-term distribution can be described by the same normal function ( m p ¼ m b ¼ m, s p ¼ s b ¼ s). In this case, we have AIC ¼ (n þ m)(log(2ps 2 ) þ 1) þ 2 Â 2, ( 2 ) The background distribution and the within-term distribution can be described by two normal functions with different average but same s.
PDC gene chip data generation. RNA amplification and labelling was performed according to the protocol recommended by Affymetrix with 100 ng initial totRNA per sample. Samples were hybridized to Affymetrix Human Genome U133 Plus 2. 0 arrays. This pDC Affymetrix data are already submitted to GEO and access can be given to the reviewers. Sample preparation, hybridization, washing, staining and scanning was performed by the Institut Curie Gene Chip core facility. The raw data. CEL files were imported into Partek Genomics Suite software for normalization with the GC-RMA algorithm. For all subsequent analyses, the log2 of expression values were used. Then, independent filtering of the data was performed with each data set independently and consisted of following steps: (1) for genes with multiple probe sets, only the probe set with the highest average value was retained.
(2) Genes not detected in any experimental condition were removed from the data set. In this study, we used exclusively data generated with the HG U133 Plus 2. 0 arrays, for which we defined the detection threshold to be 4. Every replicate of at least one condition had to be 44, for a given gene to be selected for further analysis. This pre-processed data was then imported into MATLAB for further computational analysis. Quantitative reverse transcription PCR. cDNA was synthesized with a mix containing random hexamers, oligo(dT)15 (Promega) and SuperScript II reverse transcriptase (Invitrogen). Transcripts were quantified by real-time quantitative reverse transcription PCR on Light Cycler 480 (Roche) with Applied Biosystems predesigned TaqMan Gene Expression Assays and Absolute qPCR ROXmix (Thermo Fisher Scientific).
All cycle thresholds (Cts) were normalized to the housekeeping gene B2M (beta 2 microglobulin) by subtracting the target gene Ct from the housekeeping gene Ct. In that way, the qPCR data are represented as a raw delta Ct value, which is a log2 relative expression scale. This simple way to calculate relative expression values enables direct comparison of expression differences between two conditions in qPCR and the log2 Affymetrix values. The following gene expression assays were used: ATP5O: Hs00426889_m1; CCL3: Hs00234142_m1; CCL4: Hs99999148_m1; CCL5: Hs00982282_m1; CCND2: Hs00277041_m1; CD86: Hs00199349_m1; CXCL10: Hs00171042_m1; CXCL11: Hs00171138_m1; DUSP6: Hs01044001_m1; EEF1A1: Hs00742749_s1; FOXP1: Hs00212860_m1; GJA1: Hs00748445_m1; ICOSLG: Hs00323621_m1; IFNA2: Hs00265051_s1; IL1B: Hs00174097_m1; IL6: Hs00174131_m1; IL8: Hs00174103_m1; NDUFA1: Hs00244980_m1; TNF: Hs00174128_m1; TNFRSF17: Hs00171292_m1 and B2M: Hs99999907_m1.
Monocytes purification and culture. This data was published by Schenk and colleagues  25  and is accessible through the GEO number: GSE34156. Briefly, whole blood from healthy donors (UCLA I. R. B. no. 92-10-591-31) with informed consent was obtained. PBMCs were isolated using Ficoll (GE Healthcare) gradient centrifugation and monocytes were further purified using negative selection with microbead-coupled IgG (Miltenyi Biotec). Cells were cultured for in RPMI and 10% fetal calf serum (FCS) (Omega Scientific). For activation via TLR2/ 1, the 19-kDa mLP (EMC Microcollections) was used at a final concentration of 1 mg ml À 1 in all experiments. BMP4 was used at 100 ng ml À 1 (R&D Systems).
Acknowledgements We thank  Sebastian Amigorena ,  Olivier Lantz  and  Nicolas Manel  for discussions and critical review of the manuscript,  Zofia Maciorowski  and the  Institut Curie Flow Cytometry facility  for cell sorting, and  David Gentien  and the  Institut Curie transcriptomics platform  for generating the microarray data. This work was supported by grants from the  European Research Council  ( ERC consolidator grant  to V. S. ), European community  FP6 Excellence  grant (V. S. ),  ANR Eurotransbio, Fondation pour la Recherche Medicale ,  Agence Nationale de la Recherche , Labex DCBIOL. R. Z. was supported by a fellowship from  Fondation pour la Recherche Medicale. The following programs supported R. L. M. and M. S. :  NIH   P50 AR063020 ,  R01 AI022553 ,  R01 AR040312  and  R01 AI047868.
1 ) so that the measured velocity was maximal. The diameter of the artery at different points was measured by quantitative coronary angiography (QCA) and the values are shown in Table  1. Measurements were made using a ComboMap ® analysis unit (Volcano Corp. ), a PCbased system with an LCD color touch screen and a remote control operation. The ComboMap ® system provides both pressure and flow rate (fractional flow reserve (FFR) and coronary flow reserve (CFR)) and allows digital storage to the hard drive or archival storage to a CD. A Doppler flow intravascular wire (ComboWire ® with a diameter 0. 3556 mm) was used to measure the instantaneous velocity. The experimental data obtained by in vivo measurements in the coronary artery of all patients were recorded for further analysis. The resulting measured physiological data shows variations in the time period and velocity between each time cycle (Fig.
Signal analysis and flow models For the in vivo measurement part of this work, the critical question relates to the diversity of in vivo velocity signals. Despite this variation, a method that allows us to deduce desired additional flow properties such as flow rate, wall shear rate, wall shear stress and wall shear stress time derivative has to be found. Moreover, as mentioned, the main purpose is to find the best method to quantify the flow properties from in vivo and in vitro measurements for further in vitro tests. The chosen method has to be accurate enough to reasonably describe properties of blood flows. Furthermore, it has to be fast enough in order to take as few computer resources as possible and easy to implement in any existing programming language such as C, Matlab ® or Labview ®.
However, blood viscosity has non-Newtonian character. Therefore, a 2D axisymmetric, non-Newtonian CFD simulation was used to validate the assumption of an approximate Newtonian behavior of blood. Below, briefly, are presented equations that were used to calculate physical quantities and assess the validity of assumptions. For the analytical calculations a script in Matlab ® program was written. In order to check the validity of the first assumption-the laminar behavior of the fluid-the patient's maximum mean velocity was taken into account in order to find the corresponding Reynolds number using as the reference value the pipe diameter and the mean velocity. A maximum Re number of 442 for patient 4 was found and it validated the assumption that the flow has a laminar behavior for the analyzed vessels.
The values for all patients are presented in Table  2. Based on the assumptions described above, the flow momentum equation for direction z (flow direction) in a cylindrical system becomes: where; P = P(z, t) is the pressure, U z = U z (r, t) is the axial velocity along z axis and assuming no flow in direction θ and r. Womersley's solution obtained by the analytical integration of Eq. 2 is based on Fourier series in time and Bessel's function in space. The final equations used to calculate blood properties in a Matlab script are therefore as presented below. The peak velocity, at the centre (r = 0) is given by Fourier series: The U(r,t) velocity profile is given by: where: (2) ∂U z ∂t = - 1 ρ ∂P ∂z + µ ρ ∂ 2 U z ∂r 2 + µ ρ 1 r ∂U z ∂r (3) U(t, r = 0) = ∞ n=-∞ C n e iωnt (4) U(r,t) = ∞ -∞ G n iR 2 µΩ 2 n 1 - J 0 (ζ n ) J 0 (Λ n ) e iωnt The pressure gradient coefficient G n can be calculated based on the Eqs.
Discussion In vivo pulsatile blood velocity data were successfully obtained for five patients using a Doppler flow ComboWire ® (Volcano Corp. ), although there were some difficulties to obtain the Doppler flow signal. For example, placing a wire in a suitable position, i. e. , as centrally as possible in order to get sufficient signal intensity took several minutes, an inconvenience probably related to the difficulty in choosing the best coronary artery/ place to perform an accurate measurement i. e. to measure maximum velocity avoiding any artifact and noises. Patients were selected from those needing coronagraphy according to the ethical board's policy, and we were limited to the use of sites where coronagraphy had to be performed. Within these restrictions, our goal was to find healthy vessels without stenosis and as straight as possible in order to obtain proper measurements.
Nevertheless, some measurements had to be performed close to branches or curves. These practical problems add to the inherent variability between successive human heart beats, perturbed signals due to movements of the patient, etc. Hence, the data obtained were often noisy and additional filtering was needed. After filtering, the five different in vivo signal measurements were analyzed and postprocessed. Results showed that the flow pattern varied widely among patients and confirmed the reported variability of pulsatile flow waveforms observed in the arteries (see, e. g. , Mills et al. [22] , McDonald  [23] ). The differences among patients in the magnitude of the velocities as well as in the shape of the curve could be due to intrinsic differences between patients and/or measurement positions.
This short analysis shows that it is difficult to agree on a constant value and that the patterns of variability in different patients are considerable. Nevertheless our study in 5 patients provides a limited set of preliminary data to plan in vitro experiments and create a database of patient flow patterns. The aim of this project was also to provide bioengineers and physicians with a tool that will be able to mimic blood flows and later automatically calculate desired flow properties, such as WSS and WSSTD. These calculations of the real coronary wall shear stress signals are critical for future development. The challenge will now be to reproduce these signals perfectly in this in vitro system. Meeting this challenge (see above) will also allow two critical observations made in this paper to be exploited: patients can be divided into different groups with different behaviors, which are much more complex than described in previous publications.
Acknowledgments This project was supported by  Gorazdze Cement , member of the Heidelberg Cement Group; we would like to especially acknowledge the help of  Mr Daniel Gauthier. We also tanks  Luc Vanhamme , the Director of Research at the  Belgian National  fund for scientific research. The project was also supported by the  CHU Charleroi Scientific Committee. This research was partially supported by the  EU , 7th  FP  under Grant Agreement No 269966 -Thrombus ,  Foundation for Cardiac Surgery (Brussels, Belgium).
Bridging the Gap: the BioMedBridges Initiative It is clear that life science research infrastructures and e-infrastructures must work closely together to be able to address the new challenges connected to the dramatic increase of data that is being generated. However, looking more closely, it is not trivial to determine specific requirements and how they may be addressed. This arises from the fact that so far the involved actors do not share the same technical language. To some extent, there is great variation concerning data and possible approaches even among different life science disciplines (e. g. , genomic and imaging data). BioMedBridges 10 is addressing this communication gap with a series of targeted workshops bringing e-infrastructure representatives and personnel from the emerging life science research infrastructures on the ESFRI roadmap together.
ELIXIR European countries, companies and funding bodies invest heavily in biological research, seeking solutions to the many serious challenges facing society today 13. This research produces vast amounts of data at a continuously increasing rate; it has been estimated that by 2020 these data will be generated at up to 1 million times the current rate. At the core of ELIXIR's strategy is the recognition that large-scale data management in the life sciences is not limited to a few sites. A European data infrastructure must be able to cope with the aggregation, annotation, and integration of data from thousands of laboratories as well as scaling these data-services to millions of users worldwide. ELIXIR brings together EMBL-EBI and national bioinformatics expertise throughout Europe to create a coordinated infrastructure whose contributors share responsibility for biological data delivery, sustainability and management.
org/10. 5281/zenodo. 13942 12 REPORT: BioMedBridges workshop on e-Infrastructure support for the life sciences -Preparing for the data deluge http://dx. doi. org/10. 5281/zenodo. 13942 13 http://elixir-europe. org/ 14 https://www. elixir-europe. org/about/elixir-programme-2014-2018 Bringing the Tools to Data -Provide Scientists with Personalized and On-Demand Bioinformatics Services on the Cloud Thanks to the continuous improvement of experimental technologies, life science researchers face a deluge of data, the exploitation of which requires large computing resources and appropriate software tools. They simultaneously use many of the bioinformatics tools from the arsenal of thousands available within the international community. Usually, they combine their data with public data that are too large to be moved easily.
Therefore, the computational infrastructure needs to be tightly connected to public biological databases. One important aspect of deploying a cloud for life science is to provide virtual machines (appliances) that encapsulate the many complex bioinformatics pipelines and workflows needed to analyze distributed life science data. At the IFB, we developed several bioinformatics services available as cloud appliances. A cloud appliance is a predefined virtual machine that can be run on a remote cloud infrastructure. As their size is usually in the range of gigabytes, it is more efficient to move the appliance to the location of the terabytes of biological data to be analyzed, instead of moving the data to the appliance. However, this approach requires at least few of the computing resources to be available close to the stored data.
Delivering ICT Infrastructure for Biomedical Research As Biomedical science data volumes grow, local computational resources needed/required to satisfy their processing quickly become insufficient. In addition to computing services and technical support, users need significant storage capacities, and access to large reference data to reflect their findings in the context of the current knowledge. The size of the datasets in biomedical science like the human genetic variation 1000 Genomes, The Cancer Genome Atlas (TCGA) and the Finnish sequencing initiative data are hundreds of terabytes to petabytes in size and grow rapidly. Data capacity challenges form a major research bottleneck. The CSC -IT Center for Science (CSC) Infrastructure provides a Service (IaaS) cloud concept developed in 2011-2013 in collaboration with biomedical research organizations  (Nyrönen et al.
, 2012). The services are part of the construction of the ELIXIR Finland research infrastructure and are included in the national research infrastructure 2014-2020 roadmap. Life science service providers typically analyze and integrate high-throughput data, visualize data, share analysis sessions and save and share tool workflows. Tool environment must evolve with the state-of-the-art. The FIMM environment is packaged into virtual machine images and, in theory, any third party can run and support the data analysis infrastructure. Adding and removing capacity from cloud is straightforward, since the building of the data analysis tool environment supports virtualisation.
Perspectives The current trend for life science ICT infrastructure uptake is to use the most diverse and state-of-the-art computing technologies (Grid, Cloud, and now Mist among others) to access computable storage, memory and compute in each country/resource federation that can provide these services. The computational requirements of most standard workflows continue to rise. A characteristic case is the analysis of NGS data; READemption, a pipeline for the computational evaluation of RNA-Seq data  (Förstner et al. , 2014) , requires from 2/3 VMs in normal use, which can rise up to around 30 VMs at peak performance with more than 20 cores, more than 70 GB of memory, and around 1 TB of storage. Chipster, a user-friendly analysis software for high-throughput data  (Kallio et al. , 2011) , exhibits similar characteristics where each server requires over four virtual CPUs, over 16 GB of RAM and around 500 GB of storage at each site.
e. , Cloud Infrastructure as a Service 15 http://www. egi. eu/how-to/use_the_federated_Cloud. html 16 http://e-grant. egi. eu/ (IaaS), to virtualise the local data analysis infrastructure. In our experience this leads to a better division of work between specialized parties to support life sciences. Collaboration focused e-infrastructure virtualisation technologies seems thus natural, and it can allow life sciences to spend more time on data features of the services and less on operating the IT infrastructure. For example, scalability of interactive visualizations for sequencing data for thousands of users cannot be achieved without expert IT infrastructure supporting the overall service delivery. The challenge that e-Infrastructures face today is how to deliver ICT services supporting high-volume life science data analysis.