dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION This paper presents the application of three different velocity estimators to estimate the valve movement activity of an Oyster population for ecological monitoring, i. e. water quality surveillance. The behavior of the population of 16 oysters was normalized and averaged to generate the behavior of the population. This data was then used to estimate the velocities. The three different velocity estimators were: algebraic-based method differentiator, nonhomogeneous higher order sliding mode differentiator and homogeneous finite-time differentiator. Their performance was satisfactory in estimating the velocity of the oyster population. Their output was more or less almost the same. This estimated velocity was then used to monitor the water quality as the valve movement activity depends on this.
Results Exploitation and Statistical Analysis All data for microtitration were obtained in fluorescence or absorbance units and expressed as a percentage of the negative control (culture medium). Each point was tested in three different wells, and experiments were reproduced in triplicate. Data are expressed as means ± standard deviation. The mean values for each test were analyzed by one-way ANOVA followed by the Dunnett test (SigmaStat 2. 0; Chicago, Illinois, USA), and the level of significance was fixed at 0. 05.
We also studied chromatin condensation, an irreversible early phase of apoptosis assessed by the Hoechst 33342 assay, because even no loss of cell viability at 48 h does not mean no apoptosis at 48 h. In Fig. 3    of Aβ-induced condensation of Müller cell chromatin. Finally, we evaluated caspase activation. We focused our attention on caspase 8 and caspase 3. No significant difference was observed between Aβ-treated MIO-M1 compared to negative control (Fig. 4a, b ). Altogether, these data indicated that Aβ first alters the chromatin state of Müller cells without inducing cell death at 48 h. Protective Effects of EPA/DHA Associated in Fish Oil and BBG on Aβ-Cytotoxicity EPA and DHA have antioxidant and antiapoptotic roles in the retina, but their protective effects on Müller cells against Aβ remains undetermined.
Discussion Müller cells have been shown to be implicated in AMD  [47, 48] , and selective ablation of these cells led to photoreceptor apoptosis, blood-retinal barrier breakdown, and retinal neovascularization  [49]. We studied the Aβ effects on MIO-M1 cells to study the impact of this peptide involved in AMD pathogenesis on Müller cells. The lack of necrosis in the cells was determined by no increase in LDH levels and low levels of propidium iodide (data not shown). Apoptosis was revealed by higher levels of YO-PRO-1 and Hoechst 33342 fluorescence signals in Aβ-incubated cells than in control cells. Therefore, we report that oligomerized Aβ induces apoptosis rather necrosis on human retinal Müller cells (MIO-M1). P2X7 receptor is involved in oxidative stress, cell death, and inflammatory processes, all of which have been linked to AMD.
Acknowledgments The authors would like to thank  Adebiopharm ER67 ,  French Ministère de la Recherche  and  Agence Nationale de la Recherche (Paris, France)  for their financial support. This study was a part of the  ANR-BLAN-0204  project coordinated by  INSERM U598 unit  ( Professor Francine Behar-Cohen ,  Doctor Frédéric Mascarelli 's team). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The PdBI data was calibrated with the GILDAS 10 /CLIC software. The bright quasars 3C84, 2200+420 and 3C279 were used to calibrate the radio-frequency bandpass, and two nearby quasars (0420-014 and 0528+134) were regularly observed to calibrate phase and amplitude temporal variations. MWC349 was used to derive the absolute flux scale. In order to recover the extended emission that is filtered out by the PdBI, we observed the same region with the IRAM-30m telescope during ∼ 15 hours of average summer weather in July and October 2013. The GILDAS/CLASS software was used to process the IRAM-30m data and produce the single-dish map, which was then combined with the PdBI observations in GILDAS/MAPPING, in the same way as described in  Guzmán et al. (2013).
Line spectra along the direction of the illuminating star, centered at dy = 0 ′′ , are shown in the upper panel of Fig. 2. To increase the signal-to-noise ratios we have averaged the spectra over 20 ′′ in the dy-direction, i. e. , the region between the two gray lines in Fig. 1. In the bottom panel the integrated line intensity profiles of the hydrocarbons are shown, as well as that of the H 2 line and the PAH emission. The dashed vertical lines mark three characteristic positions in the Horsehead: the Core, corresponding to the peak of the DCO + line emission (RA = 5 h 40 m 55. 61 s , Dec = -2 • 27 ′ 38 ′′ , J2000), and characteristic of the cold UV-shielded gas (marked by the blue 10 See http://www. iram. fr/IRAMFR/GILDAS for more information about the GILDAS softwares  (Pety 2005). cross in Fig 1 ); the PDR, corresponding to the peak of the HCO line emission emission (RA = 5 h 40 m 53.
Acknowledgments The authors would like to thank the past and present members of both branches of the  Laboratory of Human Genetics of Infectious Diseases , and our collaborators on HSE, for the human studies discussed in this article, particularly,  Itai Pessach. They also thank their colleagues for helpful discussions and critical reading of this manuscript. The authors warmly thank their patients, their families, and physicians. The work was funded by the  National Center for Translational Sciences (NCATS)  of the  National Institutes of Health (NIH)  ( 8UL1TR000043 ),  National Institute of Allergy and Infectious Diseases  ( R01AI088364 ),  National Institute of Neurological Disorders and Stroke  ( R01NS072381 ), the  Rockefeller University , the  St. Giles Foundation , the  French National Research Agency (ANR) ,  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Paris Descartes University , the  March of Dimes , the  Thrasher Research Fund , and the  New York Stem Cell Foundation (NYSCF).
For this analysis we employed the R interface to the g:Profiler toolkit (gProfileR-v0. 5 R package from the Comprehensive R Archive Network-CRAN). We focus our study on GO:BP functional evidence of the specific Sp1 signature, genes being sorted by their differential expression. For multiple testing correction the g:GOST native method, g:SCS, was applied. The electronic GO annotations were excluded from the analysis, and we used a 0. 05 p-value threshold to select the significantly enriched GO terms. We also excluded terms containing less than 5 genes. To resume the gene list enrichement results the terms were grouped by a moderate hierarchical filtering (Table  1 , S2 Table ). R-2. 14. 2 and R-3. 0. 1 were used for these analyses.
Sp1 overexpression activates an innate immune transcriptome To identify the early response activated by Sp1 overexpression, a functional analysis of microarrays data previously generated (  [14] ; ArrayExpress accession number E-MEXP-1702) was performed with the g:Profiler method. We first refined the list of genes over-expressed from a fold change (FC)>1. 3 to a FC>1. 5 (1187 genes, FC> = 1. 5; S1 Table ) and this more specific list signature was used for functional enrichment analysis via the g:Profiler toolkit for Biological Process subontology of Gene Ontology (GO:BP)  [28, 29]. g:Profiler presents two major advantages for studies implying long gene lists as ours: a custom multiple testing correction procedure and the analysis of ordered gene lists (see details in Materials and Methods section).
such as metabolism related terms, cell death ("single-organism process") and more surprisingly "innate immune response". Additionnaly, among the 16 more general enriched terms, three are directly connected to the "innate immun response" term: "cellular response to interferonbeta", "positive regulation of type I interferon-mediated signaling pathway", and "type I interferon production". We finally performed a Gene Set Enrichment Analysis (GSEA) (http:// www. broadinstitute. org/gsea; [38, 39] ) on our microarray dataset of deregulated genes by Sp1 (FC> = 1. 5; S1 Table ) and we found that 30% of the first 20 enriched gene sets are related to innate immune response. All these results clearly demonstrated that increased Sp1 level in untransformed cells is associated with the activation of an innate immune transcriptome.
A previous promoter analysis suggested that many up-regulated genes by Sp1 overexpression were not direct transcriptional targets of Sp1  [14]. We used another bioinformatic tool, oPOSSUM (http://www. cisreg. ca/ oPOSSUM/;  [41] ), to look for Sp1 binding sites enrichment in our list of deregulated genes by Sp1 (FC> = 1. 5; S1 Table ) and in a list corresponding to "RIG-I-like receptor signaling pathway" in the KEGG pathway database (http://www. genome. jp/kegg/). No enrichment for Sp1 binding sites was observed in either of the lists. Finally, we tested whether overexpression of Sp1 in human embryonic kidney (HEK) 293T cells may directly induce specific promoter activity of specific genes activated downstream of RIG-I, such as the IFNβ-responsive promoter, the IRF3-dependent interferon-stimulated regulatory element (ISRE) or the NF-kB-dependent promoter.
Acknowledgments We acknowledge  J. Tschopp 's lab for Cardif (MAVS) deficient mice,  D. H. Raulet  and  E. Vivier  for providing us the EL4-Rae cell line and  R. H. Silverman  for MEFs RNAse L knock-out. We thank  F. Martinon  for his help in XBP1 splicing assay,  P. Mangeot  and  M. Tomkowiak  for their contribution in experiments performed on primary MEFs. We acknowledge the contribution of  SFR BioSciences Gerland-Lyon Sud  ( UMS3444/US8 ) facilities:  Gisèle Froment ,  Didier Nègre  and  Caroline Costa  ( AniRA-Analyse génétique et vectorologie-UMS3444 );  Thibault Andrieu  and  Sébastien Dussurgey  ( AniRA-Cytométrie en Flux-UMS3444 );  Olga Azocar  and  Olivier Duc  from the  PLATIM (PLAteau Technique Imagerie/Microscopie)  and the staff of the  Animal House (AniRA-PBES-UMS3444 ). We thank  Christophe Arpin ,  Antoine Marçais  and  Laurent Genestier  for critical readings and discussions.
Acoustic data collection Ethical approval and field permits were not required for this study because no animal samples were taken. We only used positional and acoustic data obtained from existing data sets. All data used in this study come from echosurveys complying with the MEDIAS (Mediterranean International Acoustic Survey) protocol. Acoustic sampling was performed during day time by means of scientific split-beam echosounders working at 38 kHz and calibrated following standard techniques  [30]. Acoustic data were recorded in both areas following a regular sampling design (parallel transects, Fig  1 ) at a constant speed of 8-10 nmi h -1. In the Strait of Sicily, acoustic data were collected during June-July 2002-2010 on board RV "G. Dallaporta". The echosurvey sampling strategy adopted parallel transects characterized by 5 nmi inter-transect distance (Fig  1A ).
The investigated bathymetric range was 20-300 m. Acoustic data were acquired using Simrad EK500 (in 2002 and 2003) and Simrad EK60 (in 2005-2010) scientific split-beam echosounders. In this paper the analysis was limited to specific sub-areas, surveyed by applying a consistent survey design protocol (Fig  1A ). In the North Aegean Sea, acoustic data were collected on board R/V Philia during June 2004-2006 and 2008 in two sub-areas: Thracian Sea and Thermaikos gulf (Fig  1B ). Echosurveys were carried out along predetermined parallel transects with 10 nmi inter-transect distance, by means of a Biosonic DT-X scientific split-beam echosounder. Minimum sampling depth varied from 10 to 20 m depending on the area. The size of the Elementary Distance Sampling Unit (EDSU) was one nautical mile (nmi, 1.
852 km). Midwater pelagic trawl sampling was carried out in order to identify and verify anchovy and sardine echo traces and fish length frequencies. We considered as anchovy/sardine any school or echo assigned either by echo trace classification or based on the catch composition of identification hauls  [31]. Acoustic data analysis was made using Myriax Echoview software in all cases. Anchovy and sardine density (tnmi -2 ) for each EDSU was evaluated by merging the biological and acoustic data, based on the nearest haul method  [31]. Daytime nautical area backscattering coefficients (NASC) assigned to anchovy/sardine were estimated for each ESDU. Further details on acoustic sampling per survey area are given below. Total species biomass in the Strait of Sicily was computed for each year by assigning an area of influence to each EDSU, multiplying the density values of each EDSU by the corresponding area of influence and summing up all resulting values.
This c80 involves density values that allow recovering 80% of total biomass thus describing the spatial structure of the dominant part of fish abundance. EVA2 software  [33]  was used to estimate the autocorrelation range (structural component) and the normalized nugget (i. e. expressed in percentage of variance) of the indicator variogram for each year, sub-area and target species. These variables represent an approximate measure of the average size of fish patches and small-scale roughness, respectively. Spatial indicators. A series of spatial indicators was calculated on density values (tnmi -2 ) for anchovy and sardine in each study sub-area (Table  1 ). An exhaustive description of the derivation of each indicator and the functions used can be found in  [3]. Spatial data are given in the WGS84 coordinate system.
Specifically, PCA was applied to: a) pooled anchovy data from both areas; b) pooled sardine data from both areas; c) pooled species data from the Strait of Sicily and d) pooled species data from the North Aegean Sea. Analysis per both area and species was not possible due to the limited number of available data (years). Standardized fish abundance (abundance divided by the mean in the area) and Pck were added as supplementary variables  [36]. Such a procedure allows identification of possible correlations between supplementary and active variables. R library FactoMineR  [37]  software was used for the analysis, a package for multivariate data analysis with R  [34] , available from the Comprehensive R Archive Network (CRAN) at http://CRAN. R-project. org/. Multiple Regression Analysis. In a subsequent step, multiple regression was applied to relate the standardized abundance of anchovy and sardine with the PCA scores of the most important PCA axes (i.
Spatial indicators Spatial indicators were computed per year, species and sub-area (Figs 6 to 7, data in S2 Table) and revealed differences in the spatial patterns of the two species. Concerning location, both species presented highly consistent CGs in Greek waters (Figs  4  and 5 ) and so did sardine in the Strait of Sicily (Fig  3 ). Specifically, the distance between CGs ranged from 0. 7 nmi -6 nmi in Thermaikos Gulf, 1. 05 nmi to 14. 27 nmi in the Thracian Sea and 1. 3 nmi to 16 nmi in the Strait of Sicily. On the other hand, the location of anchovy in the Strait of Sicily was more variable between years (Fig 2, ranged from 0. 228 nmi to 25 nmi). Both species presented a consistently higher occupation index, expressed as higher positive area (PA), in the Strait of Sicily compared to Greek waters (Figs  6 to 7 ).
The estimated degree of overlap between anchovy and sardine populations could be related to interspecific competition. In the Strait of Sicily, the low GIC values denote reduced overlap between anchovy and sardine. Here, the suitable habitat for small pelagics is limited along the narrow band of the south coast of Sicily and over the Adventure and Maltese Banks. Studies on habitat suitability modelling, using acoustic survey data from the Aegean Sea and the Strait of Sicily along with satellite environmental information  [39] ,  [40] , has shown common environmental preferences for the two species during summer, e. g. <100 m bottom depth and SST<22°C. Thus, both species tend to separate their population centres (sardine is distributed in the narrow band of the central sector of the Strait of Sicily mainly whereas anchovy is distributed over the banks), probably to reduce inter-specific competition and exploit most of the available resources.
It is possible that, over such a long lifetime, a fault goes through phases when it does not propagate laterally but rather accumulates displacement at a constant length (see ''Discussion''). Even so, the fault lengthens overall in one or two directions over its whole history, and this is the overall lengthening that we consider here. We analyze a population of 47 crustal faults worldwide, which have been mapped in prior works and whose direction(s) of long-term propagation has (have) been documented (Table  S1  and references therein). The latter condition is the one that most limits the dataset. S1  and Fig. ES I ). The remaining $ 9% were observed in cross-section, and therefore, we examine these faults along their top-tobottom width (W f , Table  S1  and Fig. ES I ). The faults have a broad range of sizes (L f or W f ) ranging from 10 À5 to 2300 km.
From theoretical models or limited observations, it has been suggested, however, that the length (e. g. ,  Bu ¨rgmann et al. , 1994; de Joussineau et al. , 2007; Kim et al. , 2003; McGrath and Davison, 1995)  and the width (e. g. ,  Davatzes and Aydin, 2003; de Joussineau et al. , 2007, Vermilye and Scholz, 1998)  of splay zones might scale with parent fault lengths or total displacements. The length-to-length, and width-to-length ratios that were suggested are in the range 0. 2-0. 6  (de Joussineau et al. , 2007), and 0. 016-0. 2 (de Joussineau et al. , 2007; Vermilye and Scholz, 1998) , respectively. Therefore, our results are consistent with those from the few prior studies. Yet, since they are based on a much larger dataset spanning a broader range of scales from millimetres to thousands of kilometres, they generalize the prior observations and they demonstrate the generic, self-similar properties of the tip splay networks.
Upon internalization, S. aureus also reprograms gene expression  (8) (9) (10). Bacterial genes involved in capsule synthesis, oxidative stress and virulence are up-regulated following ingestion of the pathogen  (7, 9). After S. aureus uptake by the human lung epithelial cells, bacterial genes involved in cell division and nutrient transport are down-regulated whereas genes involved in iron scavenging and virulence are upregulated. During infection, S. aureus generates many virulence factors whose timing and expression levels are tuned by dozens of regulatory proteins and RNAs (sRNAs). S. aureus expresses around 159 sRNAs  (11, 12)  recently compiled into the SRD database  (13)  with, for the most part, no associated functions. These sRNAs are expressed from the core and variable accessory genome including Pathogenicity Islands and transposons  (14).
Aliquots were thawed just before use. Dilutions of stock suspensions of FITC-labeled bacteria or control bacteria were plated on agar to determine the number of CFU/ml. Coverslips were mounted with Vectashield mounting medium including DAPI (Vector Laboratories, Burlingame, CA). Staining was viewed with an inverted confocal microscope (DMIRE2, Leica) equipped with a 63XNA 1. 4 objective lens using the LS 3D software (Leica). Images were analyzed using the 'MetaMorph' (Molecular Devices, Downington, PA) and Volocity (Perkin Elmer, version 6. 0. 1) software.
Samples preparation, flow cytometry and phagocytosis assays in the human THP1 and macrophages Human THP1 cells (10 6 cells) were incubated with 10 7 bacteria in RPMI, 10% human serum AB (SAB) (Institut Jacques Boy, Reims, France) to stimulate opsonization, for different times, as indicated, at 37 • C, 180 rpm. The extracellular bacteria were lysed by lysostaphin (10 g/ml) for 10 min and the cells were washed with RPMI, 10% SAB and with PBS. At the indicated time points, the THP1 cells containing intracellular S. aureus were lysed for total RNA extraction. The cells were fixed in PBS / 4% paraformaldehyde and analyzed using a FC500 flow cytometer and CXP Analysis Software (Beckman Coulter, Villepinte, France). To quantify bacterial phagocytosis, the human THP1 macrophages were infected with S.
Two micrograms of RNA was used for first strand cDNA synthesis using the High-Capacity cDNA Archive Kit (Applied Biosystems, Foster City, USA) following the manufacturer's protocols. Real-time quantitative PCR was performed with the fluorescent dye SYBR Green methodology using the Real Master Mix (Eppendorf) and the Mastercycler realplex (Eppendorf). Supplementary Table  S3  shows primer pairs for each transcript chosen with the Primer 3 program (http://frodo. wi. mit. edu/cgi-bin/primer3/ primer3 www. cgi). Gene specificity of each pair of primers was checked by comparing their sequences to the GenBank database using the program BLASTN (http://www. ncbi. nlm. nih. gov/BLAST/). Using the comparative Ct method, the amount of target sequence in unknown samples was normalized to Hu or 16S reference.
SprC negatively controls the expression of the major autolysin ATL in S. aureus In a large-scale analysis of the S. aureus transcriptome, we identified a small RNA, SprC (srn 3610, SRD database, (13)), expressed from the genome of a converting phage  (14). We determined the 5 -and 3 -ends of SprC by RACE (Rapid Amplification of cDNA Ends) mapping. They were located, respectively, at positions 1918280 and 1918432 within the S. aureus Newman sequence. In strain Newman, sprC is located in the νSAβ pathogenicity island (PI) that contains several virulence factors  (14). Another S. aureus sRNA, SprD, also situated within a PI, has a positive role on virulence  (15). According to the location of sprC within a PI, we postulated that it could also be involved in staphylococcal pathogenesis. In order to identify molecular targets of SprC, we analyzed whether the sRNA modifies the expression of the extracellular proteins that include virulence factors, as reported  (15).
Thus, the regulation of ATL by SprC is not at the mRNA accumulation level, implying that SprC may prevent atl mRNA translation. The above data prompted us to test for the existence of an interaction between SprC and the atl mRNA that could reduce the mRNA translation. We did electrophoretic mobility shift assays (EMSA) to analyze duplex formation between SprC and a 154 nt-long atl mRNA fragment containing its 5 UTR sequence (34 nt plus 3Gs at the 5 -end), followed by the first 39 codons. SprC formed a specific complex with the atl 151 mRNA in vitro (Figure  2A , left panel), suggesting that the atl mRNA is a direct target of SprC. The estimated Kd between the two RNAs is approximatively 35 M, suggesting that additional ligands might be involved in the recognition process between the two RNAs in vivo.
ACKNOWLEDGEMENT We thank Dr P. Ame-Thomas (UMR U917, Rennes University) for her advices and help in THP1 cell culture and flow cytometry and Dr S. Dutertre (UMS 3480-US018 Biosit Ibisa Plateform of Microscopy, Rennes Imaging Center MRic-Photonics) for her help with the microscopes and data collection. We also thank M1 student S. Raynaud for his help in the ATL purification and cell culture experiments and Dr M. Sassi for the SprC sequence alignments. We also thank the colleagues from the lab for their comments on the manuscript. Conflict of interest statement. None declared.