dataesr/scientific-paragraphs-categorization huggingface dataset:
We defined a threshold > 2 according to the isotope ratio 13 C/ 12 C (Table  1 ). Above this threshold, we identified 22 host proteins (Table  1 ). Among them, FYN, a previously known TIM-1 binding partner  (Curtiss et al. , 2011) , was detected, validating our approach. We then analyzed the protein-protein interaction network of the identified TIM-1 binding factors using the STRING database (Figure  S6E ). This analysis highlighted an important network of proteins implicated in the regulation of protein ubiquitination, including four E3 Ub ligases (ITCH, RNF149, STUB1, and NEDD4L), receptor trafficking, and endosomal sorting proteins (Table  1 ; Figure  S6E ). We then performed a loss-of-function screening to determine the effect of each factor in DENV (F) Parental, TIM-1 WT, TIM-1Dcyt-, or TIM-1 KKRR-expressing HeLa cells were incubated with DENV2 for 1 hr on ice, shifted to 37 C for 2 hr, and treated with proteinase K for 45 min at 4 C to remove cell surface-bound particles.
Internalized DENV viral RNA was quantified by qRT-PCR. (G) Parental, TIM-1 WT, TIM-1Dcyt-, or TIM-1 KKRR-expressing HeLa cells were incubated with DENV2 AF594 for 1 hr on ice, shifted to 37 C for 30 min, or kept at 4 C as a control. Cell surface-bound particles were eliminated with trypsin treatment. DENV2 AF594-bound particles were quantified using Fiji software. Data shown are mean ± SD of at least three independent experiments (n > 150). (H) Parental, TIM-1 WT, or TIM-1 KKRR HeLa cells were incubated on ice with DENV2 JAM at an MOI of 50. Cells were shifted to 37 C for 60 min and treated or not treated with trypsin for 15 min at 4 C. Cells were then fixed and stained for DENV viral RNA (red). White lines outline the cell membrane. Images are representative of at least three independent experiments (n > 30).
Scale bars, 10 mm. Right: quantification of spot number per cell obtained from FISH images using ImageJ software. (I) Fluorescent DENV2 AF594 particles were added to HeLa TIM-1 GFP, TIM-1Dcyt GFP, or TIM-1 KKRR GFP at 37 C. Cells were imaged by TIRF microscopy, and viral particles (spots) inside the yellow line were quantified over time using Fiji software from the different videos recorded. Yellow lines outline the edge of cells. Dot quantifications are representative of five independent experiments. For (E)-(H), significance was calculated using one-way ANOVA; **p < 0. 001, ***p < 0. 0001. infection. We silenced the expression of each of these factors by RNAi and investigated their effect on DENV infection (Figure  S6F ). This RNAi screen identified UBA52, ITCH, and STAM-1 as TIM-1 partners required for DENV infection (Figure  S6F ).
Live Microscopy and Data Analysis For TIRF microscopy, cells were seeded on MatTek dishes (MatTek) and live cells were imaged with a Zeiss LSM780 Elyra TIRF microscope using ZEN software. For clathrin imaging, TIM-1 GFP cells were transfected for 48 hr with a plasmid expressing CLC-dsRed before imaging. For monitoring DENV2 AF594 internalization, TIM-1 GFP, TIM-1 Dcyt GFP, or TIM-1 KKRR GFP were imaged in the continuous presence of DENV2 AF594 at 37 C. Trajectories and fluorescence intensities of TIM-1 GFP and DENV2 AF594 spots were analyzed by computational analysis with TrackMate  (Tinevez et al. , 2017)  and MATLAB (MathWorks). Quantification of DENV2 AF594 spots was assessed using Fiji software. For live-cell confocal microscopy, cells were seeded on MatTek dishes, and images were acquired for 20 min at 1 image per 2 s on a spinning disk microscope (UltraVIEW VOX, PerkinElmer) in the continuous presence of DENV2 AF594.
Statistical Analysis Graphical representation and statistical analyses were performed using Prism5 software (GraphPad). Unless otherwise stated, results are shown as means ± SD from three independent experiments. Differences were tested for statistical relevance using unpaired two-tailed t test or one-way ANOVA with Tukey post-test. Pierson correlation coefficient (PCC) was calculated using the ImageJ plugin Coloc2 and is represented as the mean ± SEM.
Scale bars, 10 mm. (E) Parental or TIM-1 mutant-expressing HeLa cells were challenged with DENV2 JAM at the indicated MOIs. Infection was quantified 24 hpi by flow cytometry. Data are shown as mean ± SD of three independent experiments. (F) Parental, TIM-1 WT, TIM-1Dcyt-, or TIM-1 KKRR-expressing HeLa cells were incubated with DENV2 for 1 hr on ice, shifted to 37 C for 2 hr, and treated with proteinase K for 45 min at 4 C to remove cell surface-bound particles. Internalized DENV viral RNA was quantified by qRT-PCR. (G) Parental, TIM-1 WT, TIM-1Dcyt-, or TIM-1 KKRR-expressing HeLa cells were incubated with DENV2 AF594 for 1 hr on ice, shifted to 37 C for 30 min, or kept at 4 C as a control. Cell surface-bound particles were eliminated with trypsin treatment. DENV2 AF594-bound particles were quantified using Fiji software.
Data shown are mean ± SD of at least three independent experiments (n > 150). (H) Parental, TIM-1 WT, or TIM-1 KKRR HeLa cells were incubated on ice with DENV2 JAM at an MOI of 50. Cells were shifted to 37 C for 60 min and treated or not treated with trypsin for 15 min at 4 C. Cells were then fixed and stained for DENV viral RNA (red). White lines outline the cell membrane. Images are representative of at least three independent experiments (n > 30). Scale bars, 10 mm. Right: quantification of spot number per cell obtained from FISH images using ImageJ software. (I) Fluorescent DENV2 AF594 particles were added to HeLa TIM-1 GFP, TIM-1Dcyt GFP, or TIM-1 KKRR GFP at 37 C. Cells were imaged by TIRF microscopy, and viral particles (spots) inside the yellow line were quantified over time using Fiji software from the different videos recorded.
ACKNOWLEDGMENTS This study has received funding from the French government's Investissement d'Avenir program, Laboratoire d'Excellence ''Integrative Biology of Emerging Infectious Diseases'' (grant ANR-10-LABX-62-IBEID), and the CHIKV-Viro-Immuno ANR-14-CE14-0015-01 and TIMTAMDEN ANR-14-CE14-0029 projects. O. D and M. L. H are funded by fellowships from the Fondation pour la Recherche Medicale and ANR, respectively. The authors thank Dr. Alessia.
Images are representative of two independent experiments. Scale bars, 10 mm. (E) Parental or TIM-1 mutant-expressing HeLa cells were challenged with DENV2 JAM at the indicated MOIs. Infection was quantified 24 hpi by flow cytometry. Data are shown as mean ± SD of three independent experiments. (F) Parental, TIM-1 WT, TIM-1Dcyt-, or TIM-1 KKRR-expressing HeLa cells were incubated with DENV2 for 1 hr on ice, shifted to 37 C for 2 hr, and treated with proteinase K for 45 min at 4 C to remove cell surface-bound particles. Internalized DENV viral RNA was quantified by qRT-PCR. (G) Parental, TIM-1 WT, TIM-1Dcyt-, or TIM-1 KKRR-expressing HeLa cells were incubated with DENV2 AF594 for 1 hr on ice, shifted to 37 C for 30 min, or kept at 4 C as a control. Cell surface-bound particles were eliminated with trypsin treatment.
DENV2 AF594-bound particles were quantified using Fiji software. Data shown are mean ± SD of at least three independent experiments (n > 150). (H) Parental, TIM-1 WT, or TIM-1 KKRR HeLa cells were incubated on ice with DENV2 JAM at an MOI of 50. Cells were shifted to 37 C for 60 min and treated or not treated with trypsin for 15 min at 4 C. Cells were then fixed and stained for DENV viral RNA (red). White lines outline the cell membrane. Images are representative of at least three independent experiments (n > 30). Scale bars, 10 mm. Right: quantification of spot number per cell obtained from FISH images using ImageJ software. (I) Fluorescent DENV2 AF594 particles were added to HeLa TIM-1 GFP, TIM-1Dcyt GFP, or TIM-1 KKRR GFP at 37 C. Cells were imaged by TIRF microscopy, and viral particles (spots) inside the yellow line were quantified over time using Fiji software from the different videos recorded.
In this work, we propose a novel architecture based on microservices to address interoperability for a multi-cloud environment, called PacificClouds, in order to mitigate vendor lock-in and aid to obtain full cloud advantages. PacificClouds promotes flexibility for user's decisions related to requirements and application architecture, and for choosing the clouds to execute each microservice of the application. Therefore, PacificClouds differentiate from the other works as it possesses features related to the use of the microservice and native cloud application, and mainly due to the flexibility. In Section 4, we present in details PacificClouds. PacificClouds is flexible as it allows the use of several independent cloud providers. The providers can use different technology backgrounds and let software architects utilize several techniques in developing the applications, update the software and the user and application requirements at runtime.
The proposed architecture is lightweight, since each module is responsible only for one business logic. It possesses decentralized governance of the application because each part of the application is independent of one another, i. e. , the application modules possess loosely coupled. The applications are native cloud applications based on microservices. Thus, the applications can be deployed/redeployed/updated at runtime, analyzing the application requirements and the user's SLA. Therefore, software architects do not need to be concerned with the cloud selections, application deployment and what technologies are used in application development. In addition, they can request, in runtime, a new SLA by analyzing the monitoring metrics. According to the context aforementioned, the contributions of this article are given below: • The proposition and description of PacificClouds, a novel architecture, which provides greater ease in the development of native cloud application, and more flexibility in the deployment and execution of an application in multiple clouds environment because of microservice use.
PacificClouds In order to promote greater adoption of cloud computing, we propose PacificClouds to mitigate vendor lock-in. PacificClouds is a novel architecture that addresses the interoperability of distributed application in multiple clouds via microservices. Thus, Pa-cificClouds intends to provide flexibility for the software architect both in making decisions related to ap-plication requirements and the use of the clouds. In addition, PacificClouds can migrate microservice in runtime to better meet application and user needs. In this section, we describe a scenario that shows some of the PacificClouds contributions. Next, we introduce and explain the PacificClouds architecture.
Deployment Service Deployment Service, illustrated in Figure  7 , receives the microservices from AMS through Application Microservices Reception Service (AMRS), besides registering each microservice in McsData. Next, Deployment Service identifies the cloud for each microservice according to deploy plan via Microservice Association Service (MAS); after, it deploys the microservices via Microservice Deployment Service (MD-plS). We can observe that PacificClouds intends to manage the deployment and execution process of the distributed application in multiple clouds to better meet the needs of the application and consequently improve end-user satisfaction with the application performance. Thus, the PacificClouds will be able to help the software architecture in the process of de- signing and developing the application.
DISCUSS In this section, we present an analysis of the works described in Section 5 and the PacificClouds. For this, we offer a summary of the features of all the solutions described in this article, including PacificClouds, related to promoting interoperability in a multi-cloud environment. We consider 10 aspects described in this article, because through them we can observe the level of flexibility of a given solution. In Table  1 , there is the only aspect that all solutions promote the semantic interoperability. Some solutions use the semantic interoperability in the applications portability, while others use it in the interoperability between application modules distributed over multiple clouds, and it includes solutions that use it to treat vertical interoperability between service models.
RASIC treats interoperability in the IaaS service model for multi-cloud  ASMEMA and PacificClouds solutions are flexible because they allow deploying each application module in a different cloud, and they enable one module to use different technologies than another module. Also, it enables one module to be updated independently of other modules. The difference between ASMEMA and PacificClouds is that the former does not do decentralized governance by not treating either interoperability or portability. Therefore, Paci-ficClouds is the only proposed solution that promotes the decentralized governance of the applications, to the best of our knowledge, allowing the application modules to have a loosely coupled, in addition to generating less traffic in the network and therefore allowing greater flexibility.
CONCLUSION We show in this work a novel architecture, called Pa-cificClouds, that supports the interoperability across multiple clouds, allowing the software architect to choose the application requirements and architecture with no need to worry about the application deployment in the clouds. The architecture also allows the use of several independent clouds and different technologies backgrounds. Thus, PacificClouds achieves another goal of flexibility, which helps more cloud computing adoption. The PacificClouds architecture is based on microservices to help achieve their goals. In this manner, PacificClouds provides other contributions: a) lightweight, since each microservice has only one function; b) decentralized governance of the application, because each microservice is independent of one another, which allows loosely coupled; c) the software architect can request at runtime a new SLA by analyzing the monitoring metrics; d) facilitates the use of native cloud application in application development.
One of the contributions of PacificClouds is the flexibility; however, to achieve this, some challenges need to be overcome. One of the more significant challenges is to reach the interoperability of clouds that possess distinct background technological; in this manner, software architects are primarily concerned with application development and the user's SLA. Pa-cificClouds must deploy the microservices that compose applications through a deployment plan, and it must monitor the requirements of the application and user in runtime, beyond the capabilities of the clouds.
The ionised gas structure we report here extends between the galaxies. The sensitivity, field of view, spectral range, and resolution of the MUSE integral field spectrograph allows the mapping of both emission lines fluxes and kinematics. We use this capability here to infer the mass, origin, and sources of ionisation of the extended gas. This paper is structured as follows. In section 2 we introduce our dataset and the data reduction steps. In section 3 we show the ionised gas distribution and kinematics, identify the galaxies embedded in this structure, and extract some of their properties. We present the results of a spectral analysis in section 4. Finally, B. Epinat et al. :  Ionised gas structure of 100 kpc in an over-dense region at z ∼ 0. 7 we provide our interpretation of the observed structure in section 5 and summarise our results in section 6.
Ancillary dataset Because this galaxy group is inside the COSMOS field  (Scoville et al. 2007) , many ancillary data are available and already reduced. They are presented in the last COSMOS catalogue release (COSMOS2015,  Laigle et al. 2016). These datasets include radio data from the VLA, infrared and far-infrared data from Spitzer (MIPS and IRAC) and Herschel (PACS and SPIRE), nearinfrared and optical observations from the HST-ACS (F814W filter), the SDSS, the VIRCAM/VISTA camera (Y, J, H, K S ), the WIRCam/CFHT camera (H and K S bands) and the Mega-Cam/CFHT camera (U and I bands), the Kitt Peak National Observatory (K S band), the HSC/Subaru Y band and Suprime-Cam/Subaru camera (B, V, g, r, i, z filters as well as 14 medium-and narrow-band filters), ultraviolet data from the Galaxy Evolution Explorer (GALEX, near and far ultraviolet), and X-ray from Chandra and XMM.
75 Å). Nevertheless, the doublet is unresolved when the velocity dispersion is large, either due to an intrinsically large dispersion or due 1 https://bitbucket. org/bepinat/camel. git to beam-smearing effects on galaxies. Based on the average line ratio estimated in the regions where the doublet is well resolved in the structure, we decided to constrain the ratio of [O ii]λ3729 over [O ii]λ3727 line fluxes to between 1. 3 and 1. 5 (see section 4. 2). The [O ii]λλ3727,3729 flux map is shown in Figure  2  as contours on top of the HST-ACS image in band F814W and in Figure 3 as an image. Fourteen galaxies in this area are unambiguously identified as galaxy group members from various spectral features (cf. section 3. 2). Eleven of them have a clear detection in  [O ii]. However, the [O ii] doublet is also detected over almost 10 4 kpc 2 , and its emission extends both in between and beyond galaxies.
Galaxies morphology High-resolution morphology was derived for group galaxies in order to constrain the projection parameters of galaxy discs on sky (inclination and position angle of major axis) as well as the bulge and disc sizes and their relative strength. This analysis is necessary to gather information on the geometry of the structure, but also to extract galaxy spectra. We used HST-ACS images obtained in the F814W filter since this dataset has the best spatial resolution among the ancillary data. These images were produced using the MultiDrizzle softwares  (Koekemoer et al. 2007 ) on the COSMOS field  (Scoville et al. 2007). They have a scale of 0. 03"/pixel and a median exposure of 2028 seconds. We followed the same method as in  Contini et al. (2016)  for galaxies in the Hubble Deep Field South (HDFS).
5 < log (τ[yr -1 ]) < 10), and a  Calzetti et al. (2000)  extinction law. We added in quadrature a 0. 05 dex uncertainty to each band in order to derive formal uncertainties on the SED measurements that account for residual calibration uncertainties. In general, systematic uncertainties due to different modelling assumptions can amount to ∼ 0. 2 dex in stellar mass and ∼ 0. 3 dex in SFR (e. g. Conroy 2013 , and references therein), which is the main source of discrepancy when comparing to other SED fitting methods or to other datasets. For the galaxies with a good match between photometric and spectroscopic redshifts, we checked that the COSMOS2015 mass estimates were compatible with the estimates used here and found a good agreement. We note that the fit for CGr30-71 leads to a high χ 2 value.
The ratio is equal to 1. 9 for CGr30-F, which is above the theoretical limit of 1. 5 allowed for star-forming regions with typical temperatures. This may indicate that the electron density is very low and that the temperature in the diffuse gas may be significantly lower than in star-forming regions. In principle, the electron temperature can be determined using the [O iii]λ5007+[O iii]λ4959 [O iii]λ4363 ratio. Using our data for the brightest [O iii]λλ4959,5007 emitters and a 3σ threshold on the [O iii]λ4363 line, we can only infer an upper limit on the electron temperature of T e < 30000 K using the same tstsdas/Temden IRAF task.
If the shock source is limited in both time and space, ionisation would vanish too quickly when the shock front travels. Shock ionisation may last shorter than 10 6 yr after the passage of a shock front  (Allen et al. 2008 ), whereas the typical travel time of a galaxy in the structure might be around ∼ 10 9 yr. This means that the shock source has to be still active. One explanation is that the extended gas is continuously shocked by various galaxies that are currently located inside the structure. Another caveat is that emission before the shock front is expected. At the redshift of COSMOS-Gr30, the shock and its precursor should be unresolved, but models without a precursor match our data better. The whole structure is probably not fully ionised by shocks. In addition, the line ratios available with our MUSE data remain consistent with photoionisation for all regions.
Acknowledgements. This work has been carried out through the support of the  ANR FOGHAR  ( ANR-13-BS05-0010-02 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ), and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ), which are funded by the " Investissements d'avenir" French government program  managed by the  ANR. BE  acknowledges financial support from the  "Programme National de Cosmologie et Galaxies" (PNCG)  funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France. JB acknowledges support by  Fundação para a Ciência e a Tecnologia (FCT)  through national funds ( UID/FIS/04434/2013 ) and Investigador FCT contract  IF/01654/2014/CP1215/CT0003. , and by  FEDER  through  COMPETE2020  ( POCI-01-0145-FEDER-007672 ). RB acknowledges support from the  ERC  advanced grant 339659-MUSICOS. SC gratefully acknowledges support from  Swiss National Science Foundation  grant  PP00P2_163824.
RAM acknowledges support by the  Swiss National Science Foundation. This research Article number, page 20 of 21 B. Epinat et al. : Ionised gas structure of 100 kpc in an over-dense region at z ∼ 0. 7 has made use of pyqz (Dopita et al. 2013), a  Python module  to derive the ionisation parameter and oxygen abundance of H ii regions from their strong emission line ratios hosted at http://http://fpavogt. github. io/pyqz. pyqz relies on statsmodel (Seabold & Perktold 2010) and matplotlib (Hunter 2007).
In our case, we developed the Image Schema Extractor and the Gesture Modeler as an alternative to the Intention and Behavior Planners and we extended the Behavior Realizer in order to take into account Ideational Units in the production of the animations (see Figure  3 ). The system reads an XML-based text file (a Behavior Markup Language BML document as described in  Vilhjálmsson et al. , 2007)  that describes the textual speech of the agent marked with prosodic and Ideational Unit information and produces  the complete animation with the audio using a Text-To-Speech component.
This organization is similar to a class inheritance system. It is important to mention that a word might belong to different synonym sets if it can have multiple meanings. For instance, the word table can mean a piece of furniture or a set of data arranged in rows and columns. Our algorithm works as follows (see Algorithm 1): for each word in the text, we use the Lesk method to disambiguate the meaning of the word and find the most likely synonym set for it using WordNet  (Lesk, 1986). The Lesk algorithm compares the set of neighbors of the word being analyzed, in the current sentence, with its different definitions and chooses the definition (the synonym set) that has the most words in common with the neighbors. Then, we follow the hypernym path up in the hierarchy until we find a synonym set corresponding to our Image Schemas (if none is found, no Image Schema is returned).
All mutation operators have their own rate expressed in mutations. base-pair -. generation -  (mut. bp -. gen -  ). codons). The sequence of codons then constitutes the primary structure of a protein. Importantly, this decoding process introduces degrees of freedom between the genome and the proteome: complex genomes can encode for simple proteomes (e. g. if all genes have the same sequence). On the opposite, complex proteomes can be encoded on small sequences if genes share sequences through e. g. polycistronic mRNA or overlapping genes. These degrees of freedom are similar to the ones real organisms own. Given the primary structure of a protein, Aevol computes its functional contribution. Although mimicking biological processes at the sequence level is feasible, it is -at least to date -impossible to compute the function of a protein from its primary structure in a realistic way.