dataesr/scientific-paragraphs-categorization huggingface dataset:
Of interest, phosphorylated p65 S536 was recruited to the promoter of BMP2 in chromatin immunoprecipitation (ChIP) assay. During NFκB signaling the inhibitor of κB (IκB) is rapidly induced and transits to the nucleus where it competes with p65 for κB sites. This process ensures a negative feedback loop to control the inflammatory response mediated by the NFκB pathway. However, phosphorylation of p65 S536 is not sensitive to the nuclear inhibition provided by IκB. Hence, genes dependent on phosphorylated p65 S536 are less sensitive to negative retro-feedback and experienced sustained expression. Experiments carried out with constitutively active mutant IκB super repressor (IκBα SS32-36AA) showed that LysoPA-induced expression of BMP2 was not sensitive to the repression  (56). These data thus indicate that LysoPA-induced activation of BMP2 is dependent on phosphorylated p65 S536 and is not responsive to the negative feedback regulation provided by IκB.
Sequencing and Bioinformatics Analysis Guarani genome reads obtained from MiSeq sequencer (Illumina) were trimmed using Trimmomatic  (Bolger et al. , 2014) , then assembled into one scaffold through Spades software (default parameters) and manual finishing  (Bankevich et al. , 2012). Coding DNA sequences were predicted using GeneMarkS  (Besemer, 2001) , and functional annotation was carried out using BLASTp searches (Basic Local Alignment Search Tool) against the Non Redundant protein collection of the NCBI, COGs and NCVOGs databases (e-value < 1 × 10 -3 ). Conserved domain analysis, for both annotated genes and ORFans (ORF12 only), has been performed using CD-Search (NCBI database) and InterPro scan (EBI database)  (Marchler-Bauer and Bryant, 2004; Hunter et al. , 2009 ; Supplementary Table  S1 ).
For ORF12, protein homology detection and structure prediction were conducted using HHpred and Phyre2  (Kelley et al. , 2015; Söding et al. , 2005). However, theses analyses failed to provide additional information to what has been found by BLASTp search. Protein sequence comparisons with the other Sputnik-like virophages were made using BLASTp searches against the virophage gene collection in the NCBI database. Nucleotide sequence comparisons were made using BLASTn. Gene synteny and nucleotide divergence between available virophage genomes were analyzed using the Circos software  (Krzywinski et al. , 2009).
Promoter Research The intergenic regions were manually extracted from the Guarani genome. A total of 21 sequences were found in both positive (16 intergenic regions) and negative strands (5 intergenic regions). For comparison, the length of the intergenic region in the positive strand was 3562 bp vs. 15405 bp for the coding region. The search for promoter motif was performed using the Fuzznuc program of the EMBOSS software. The late Mimivirus promoter was searched using the same motif pattern previously described for Mimivirus and Sputnik, with one mismatch allowed  (Poirot et al. , 2010). The early Mimivirus promoter was searched with no mismatch using the following sequence AAAATTGA  (Claverie, 2005). The Berkeley Logo platform 1  was used to create the promoter sequence logo. [AT](8)T[AC]TN(4)[AT](5)[AG]TA[TG]A.
Likewise, the virophage progeny were also observed in the cytoplasm of the infected cells, clustered in a typical manner (Figure  1D ) or scattered around different intracellular compartments and mimivirus particles (Figures  1E, F ). Next, we used real-time PCR to measure the replication of Guarani DNA during its viral cycle. Giant viruses representative of the three lineages of the family Mimiviridae (Mimivirus from lineage A, Moumouvirus and Courdo 11 from lineage B and C, respectively) were used to test the replication efficiency of the virophage. Our data strongly suggests that Guarani genome replication starts at a late stage (at H4 post co-infection approximatively for all Mimiviridae lineages) and continues to occur until the last steps of the giant virus cycle (H16 post co-infection) (Figure  2A ), in contrast to the giant virus DNA replication step which seems to begin at a very early stage (described in Figure  3A  for APMV).
The Impact of the Guarani Virophage on Mimivirus Cycle To determine whether the infection with Guarani could affect the viral fitness of its host, we quantified the DNA replication of Mimivirus with and without Guarani virophage infection, from H0 to H16 p. i. time points. Our data suggest that APMV infected with Guarani replicates more slowly and produces significantly fewer genomic copies than Mimivirus alone during the viral cycle (Figure  3A ). To quantify the virophage inhibition of Mimivirus infectivity, A. castellanii cells were infected with APMV (MOI = 1 TCID 50 ) with or without Guarani virophage infection (MOI = 10). After 48 h of incubation, the titer of infectious particles was determined by end-point dilution, as described above. We observed that Guarani infection caused a decrease in the host virus titer (up to 90% reduction compared to the APMV which is not infected with the virophage, Figure  3B ).
Comparative Analysis With the Sputnik-Like Virophage Genomes Eighteen different ORFs in Guarani genome displayed a very high homology in amino acids to predicted genes from Sputnik virophage (from 92 to 100%) (Table  2 ). Based on BLASTp and conserved domain analysis, some of these genes are predicted to encode a collagen-like protein (ORF8, ORF9), a putative integrase (ORF13), and a putative transposase (ORF20), while many of them encode unidentified proteins (ORF2, ORF3, ORF4, ORF6, ORF7, ORF14, and ORF15). Additionally, some of the identified genes encode virophage conserved core proteins. They include major capsid proteins (ORF22 and ORF1), a DNA packaging ATPase (ORF5), minor virion proteins (ORF10, ORF21), a cysteine protease (ORF11) and a DNA replication protein (ORF16). Similarly, the Zn-ribbon domain, which is also one of the six virophage core genes, was predicted to match V14 in Sputnik genome according to a previous phylogenomic analysis  (Yutin et al.
However, the latter presents an insertion of an adenine at the position 12936 bp of Sputnik genome. This change affects the reading frame and is associated with a reduction of the V17 length in Sputnik (88 amino acids) compared to its counterparts in Guarani and Sputnik 2 (190 and 187 amino acids, respectively). The Guarani ORF12, in contrast, did not show significant similarity to any data available in the NCBI databases. In addition, we failed to predict any conserved domain in ORF12, using CD-Search (NCBI database) and InterPro Scan (EBI database). Protein structure prediction and homology detection with HHpred (Max-Planck-Institute) and Phyr2 (Imperial College London) also failed to characterize the ORF12 product. The G+C content of ORF12 (18% on average) seems to be lower than G+C content of the rest of Guarani genome (26% on average for the complete genome, 26 and 27% on average for ORF10 and ORF13, respectively).
FUNDING This work was supported by the  French Government  under the " Investissements d'avenir" program  managed by the  Agence Nationale de la Recherche (ANR) , (reference: Méditerranée-Infection  10-IAHU-03 ), by  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI. SM was supported by the  Fondation de Coopération Scientifique ,  Méditerranée-Infection (Infectiopole Sud 2016 ). BS and JA belong to a CAPES-COFECUB project. JA is a CNPq researcher.
Following 24 h, 48 h, and 72 h of culture, cells on hydrogel surfaces were washed twice with PBS and treated with a 4% PFA solution in PBS for 1 h. Cells were then stained with 4,6-diamidino-2-phenylindole (DAPI) and imaged with a LEICA microscope. Fluorescence images were transformed into 8-bit digital grayscale images using ImageJ software, followed by the selection of a threshold on gray levels in order to separate lighter appearing cells from the darker background.
As shown in Fig. 5b , the initial cell adhesion is similar for all hydrogels. After 48 h, cell proliferation has occurred but to an extent that decreases with increasing collagen concentration. After 72 h, the cellular activity is the same for all samples except for the 30 mg mL À1 collagen composite. Again, if this detrimental effect was only related to silica, then we could have expected that it would be more marked for the highest silica:collagen ratio. In fact, we observe the opposite trend. We then used fluorescence microscopy and SEM to image the different samples. Selected images for the first series of samples with similar G' but different compositions after 72 h (data after 24 h are provided as Supplementary Fig. S4 ) were shown in Fig. 6. For fluorescence, cell nuclei were stained with DAPI.
Vineyard potential study The first study focused on two different kinds of vineyard: high maturation potential and low maturation potential. Wines were prepared from grapes of the 2015 vintage of the Pirineos Winery, with the Somontano appellation of origin. Samples of each wine were then analysed in September 2016. Five full-production and commercial vineyards were selected: three high-potential vineyards and two low-potential vineyards. The Vitis vinifera grapevine was cv. Grenache, grafted on a 110-Richter rootstock. Vineyard age ranged between 11 and 32 years. The vineyards are close to each other and at a similar altitude (443. 4 ± 46. 2 m). The soils are calcareous and poor in organic matter content. The vineyard blocks (high and low potential) with a priori maximal diversity in quality were selected based on historical data and criteria derived from the commercial system Dyostem® (Vivelys, Villeneuve-lès-Maguelone, France).
In case of EDLC and LIC SHC , U OCV is measured by a continuous 1 C charge starting at U min until U max. Measured parameters for modeling U OCV and R inter characteristics are given in Figure  2. R RC and C RC are determined by using Matlab ® Optimization Toolbox (The MathWorks, Inc. , Natick, MA, USA) in which a nonlinear Levenberg-Marquardt algorithm is used to fit the parameters R RC and C RC in the least square sense to minimize modeled and measured error of U cell. Besides the high-energy versions of LIBs (LFP HE and LCO), also a high-power oriented LFP cell (LFP HP ) is parameterized for the virtual concept study. To analyze energy and power density, each cell is charged by a 1 C constant-current-constant-voltage (CCCV) regime until the upper cut-off voltage U max and a cut-off current of C/20 are reached.
Cell to Cell Analysis (C2C) A Basytec HPS Battery Tester (BaSyTec GmbH, Asselfingen, Germany) is used for the C2C experiments to provide I load (PD: 10 s; D: 0. 5) and to measure the terminal voltage of the HESS U HESS. An Agilent Keysight 3872A (Agilent Technologies, Inc. , Santa Clara, CA, USA) measures the voltage drop over a shunt resistance (1 mΩ) to identify I LIB and I SC. With Ohm´s law, I LIB can be determined directly. Using Equation (  8 ), I SC can be calculated by using measurement data of I LIB and I load with a time resolution of 1 s. As HESS, LFP HE and LCO are connected to all SC technologies under study. In the case of the EDLC, two EDLC have to be connected in serial to gain the voltage ranges of LIBs. The schematics of the test bench and data acquisition are presented in Figure  4a.
Cell to Cell Analysis (C2C) A Basytec HPS Battery Tester (BaSyTec GmbH, Asselfingen, Germany) is used for the C2C experiments to provide Iload (PD: 10 s; D: 0. 5) and to measure the terminal voltage of the HESS UHESS. An Agilent Keysight 3872A (Agilent Technologies, Inc. , Santa Clara, CA, USA) measures the voltage drop over a shunt resistance (1 mΩ) to identify ILIB and ISC. With Ohm´s law, ILIB can be determined directly. Using Equation (  8 ), ISC can be calculated by using measurement data of ILIB and Iload with a time resolution of 1 s. As HESS, LFPHE and LCO are connected to all SC technologies under study. In the case of the EDLC, two EDLC have to be connected in serial to gain the voltage ranges of LIBs. The schematics of the test bench and data acquisition are presented in Figure  4a.
Acknowledgments: The authors thank  Anna Smith  and her research group from  Institute for Applied Materials at Karlsruhe Institute of Technology  for supplying cell data and also the necessary test environment and equipment. We also would like to thank  Manuel Baena  and  Alexis Kalk  from  Institute of Electrical Engineering at Karlsruhe Institute of Technology  for their support in developing and assembling the 48 V prototypes and their BMS.
g. ,  Dai et al. , 2000; Callahan et al. , 2004; Chen et al. , 2004; Hong et al. , 2005; He, 2010; Lei et al. , 2018; Ye et al. , 2018). These studies show no consistent seasonality and estuarine mixing behavior of  [DOC]  and CDOM and no correlation between the two variables except one occasion for the mid-salinity (5-20) section of the estuary  (Callahan et al. , 2004). The lack of seasonality and consistent estuarine mixing behavior of  [DOC]  and CDOM suggests complex processes controlling their transport, production, and loss in the PRE; it could, however, also result in part from the difference in spatiotemporal coverage of the stations sampled by different studies. As previous DOC and CDOM data were collected over a span of 18 and 15 years, respectively, the possibility of interannual variability cannot be ruled out.
6) and validated using residual and split-half analyses as described by  Stedmon and Bro (2008). The parameters obtained from the PARAFAC model were used to calculate an approximate abundance of each component, expressed as F max in Raman units, which corresponds to the maximum fluorescence intensity for a particular sample. Based on analysis of triplicate samples from stations M01, M08, and M10, the uncertainty of F max for each modeled component was < 2 %. PARAFAC modeling identified five distinct FDOM components (C1-C5, Fig. 2 ), which explained 99. 75 % of the variance and thus adequately modeled the different FDOM profiles in the dataset. Based on a comparison with the Open-Fluor database (https://openfluor. lablicate. com/, last access: 7 January 2019), particularly with the PARAFAC spectra published by several well-recognized groups (e.
Miscellaneous aspects Analysis of statistical significance (α = 0. 05) was performed using a one-way ANOVA (analysis of variance) and Student's t test in Microsoft Excel 2010. For the benefit of conciseness, this statistic approach will not be re-described when presenting and discussing the results. The monthly-averaged freshwater discharge rates of the Pearl River for the sampling months were obtained from the Ministry of Water Resources of the People's Republic of China (available online at http://www. mwr. gov. cn/sj/#tjgb, last access: 7 July 2019). For brevity of presenting and discussing data, seasons for a property, where applicable, are added as a superscript to the symbol or abbreviation denoting that property. For example, [DOC] Aug stands for [DOC] in August. Names of the PARAFAC-modeled FDOM components signify their F max as well.
Relationships between DOM variables and salinity Surface and bottom data for each variable in each season form a consistent property-salinity pattern (data not shown) and are thus treated as a single dataset. All quantitative variables displayed sharp decreases at salinity <∼ 5 but remained rather constant  ([DOC]  in May and November) or declined linearly (all other cases) at higher salinities (Figs. 5  and 6 ). Hereafter, the upper part of the estuary showing fast changes of DOM properties is termed the head region, while the area downstream of it is referred to as the main estuary. The salinity demarcating these two regions was often ∼ 5 but could change to some extent with season and the DOM variable of interest (Figs. 5  and 6 ). Results of linear regressions for the main estuary are summarized in Table  S3.
At a 95 % confidence level, both the slopes and intercepts were statistically no different between August and January for  [DOC]  and a 330 and between all three seasons for C h , indicating that the multi-season data on each of these occasions can be combined into a single dataset. The slope for a 330 in November was, however, ≥ 32 % lower than those in August and January. The slope for C p presented significant seasonal variations, with the value in January being 23 % and 89 % higher than those in November and August, respectively. The percent decrease in each variable per unit increase in salinity across the main estuary was calculated using the known regression equations shown in Table  S3. a 330 decreased 2. 1 and 2. 7 times faster than  [DOC]  in August and January, respectively (Table  S4 ).
Notably, chlorophyll a concentration maxima of up to 11. 0 µg L -1 and turbidity maxima of up to 154 mg L -1 were spotted in the mid-estuary and lower estuary during our cruises  (Li et al. , 2017). Nonetheless, there existed no covariations of [DOC], CDOM , and FDOM with chlorophyll a or suspended particulate matter (SPM) (data not shown). This observation, in conjunction with the linear DOM abundance vs. salinity relations, demonstrates that autochthonous production was unlikely a major source of DOM and that adsorption and flocculation were not a major sink of DOM in the main estuary. The short residence time of freshwater likely minimized the influences of these processes. To reinforce the argument that the dynamics of DOM in the main estuary of the PRE was dominated by physical mixing, a principal component analysis (PCA) of the all-cruises dataset was performed in R 3.
5. 2 using the prcomp() function. The dataset includes variables in addition to salinity, such as water temperature, nutrients (nitrate, nitrite, silicate), chlorophyll a, SPM, and freshwater discharge rate. Variables used in the PCA were zero centered and scaled to the unit variance. The first two axes of the PCA explained ∼ 74 % of the variability in the dataset (Fig. 9 ). DOC and a 330 , along with nitrate and silicate, were strongly negatively related to salinity, a typical indication of a conservative mixing behavior. In contrast, DOC and a 330 were not or only weakly linked to chlorophyll a, SPM, water temperature, and the freshwater discharge rate. The completely different behaviors of [DOC] and CDOM with respect to salinity in the main estuary in November (Fig. 3c, f ) led to a decoupling of the two variables.
However, as DOM undergoes marginal processing during its transit through the estuary, the Pearl River delivers protein-rich, labile organic matter to the continental shelf of the South China Sea where it may fuel heterotrophy. Data availability. The authors declare that all the data obtained in this work are available in the main text and the Supplement of this article and from the corresponding authors upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-2751-2019-supplement. Author contributions. GS and HX designed the study. HX and GS interpreted the results and prepared the paper with input from PM. YL performed sample analysis and data processing. YL, GS, FY, and RL participated in field sampling. PM carried out PARAFAC modeling, PCA, and OpenFluor database search.
Acknowledgements. We are grateful to the captain and crews of the cruises for their corporation and to  Zhen Shi ,  Mianrun Chen ,  Qingyang Sun , and  Liuyu Han  for their help during sampling. Comments from reviewers and the editor improved the paper. Huixiang Xie  was holding an adjunct professorship at  Tianjin University of Science & Technology  during this work. Financial support. This research has been supported by the  National Natural Science Foundation of China  (grant nos. 41606098  and  41376081 ) and the  Tianjin Natural Science Foundation  (grant no. 16JCQNJC08000 ). Review statement. This paper was edited by  Silvio Pantoja  and reviewed by  Ding He  and two anonymous referees.
They argued for development of a framework bringing together raptor tissue collections in Europe -including those of natural history museums (NHMs), environmental specimen banks (ESBs) and others (e. g. in research institutions) -in support of contaminant research and monitoring for better chemicals management. They suggested this framework would involve among other things identifying and digitising relevant collections and developing a searchable database of these collections to make them more visible and accessible to ecotoxicologists and competent authorities. Here, we outline progress that has been made in this regard through the launch of two mutually reinforcing initiatives, the COST Action European Raptor Biomonitoring Facility' (ERBFacility 41 ), which launched in October 2017and in which 27 COST member countries are participating, and the EU LIFE project Systematic use of contaminant data from apex predators and their prey in chemicals management (LIFE APEX 42 ), which launched in September 2018.
Taking forward the concept of a distributed ERSpeB will involve further work under the auspices of the COST Action on a number of issues, building on relevant existing networks such as the Consortium of European Taxonomic Facilities and the network of European Bird Curators. These include: prioritising species, tissue matrices and compounds and carrying out proof of concept work; addressing legal, personnel and freezer constraints to expanding raptor tissue sample collections and shipping samples for analysis; adapting NHM standards and protocols (learning from ESBs) for the gathering, processing and storage of samples, to enhance sample quality for contaminant analysis; developing appropriate policy on access to samples to ensure valuable samples required for contaminant monitoring are not consumed for other purposes; circumscribing the data content of the European samples database and how it will be developed, populated and updated to provide real-time information on raptor tissue samples across Europe.
Europe in support of contaminant monitoring for better chemicals management. We outline recent progress in this regard under the COST Action 'European Raptor Biomonitoring Facility' (ERBFacility). A review of 116 collections reveals that many thousands of raptor specimens arrive annually. In most countries, the main recipients are natural history museums, many of which store frozen samples suitable for contaminant monitoring. There is strong interest among collections to collaborate and enhance this tissue resource through development of a 'distributed European Raptor Specimen Bank'. Developing the Bank will involve: prioritising species, tissue matrices and compounds; proof of concept work; addressing legal and other constraints; enhancing standards and protocols for gathering, processing and storing samples; developing policy on access to samples; and developing a samples database.
Ethical Statement Prior to enrolling in the study, participants were informed of the risks and benefits associated with participation before providing written informed consent. Ethical approval was granted by the University of Limerick Education and Health Sciences Research Ethics Committee (2016_12_09 EHS, 2013_01_13 EHS), which conforms to standards set by the Declaration of Helsinki. The study was pre-registered at https://www. clinicaltrials. gov as NCT03297151.
Data collection The first task concerns data collection. This task includes collection in a digital format of data concerning the city such as urban infrastructures and buildings, urban services, population profile. These data could be collected and stored using GIS system. It includes also data from sensors, which are used to monitor urban infrastructures and services as well as the environment. These sensors record and transmit in real time data about urban system functioning. It also includes data from citizens, who can send data and information about their observations and suggestions. The platform collects also data from open-sources such as the weather, transportation, pollution and events related to urban hazards. All these data are stored into the smart city information system and updated regularly.