dataesr/scientific-paragraphs-categorization huggingface dataset:
An Extraordinary Set of Enzymes Made Z. galactanivorans a Bacterial Model for the Use of Algal Sugars Z. galactanivorans Dsij T has been extensively studied for its ability to use a wide array of macroalgal compounds as sole carbon and energy sources, including agars and carrageen ans from red algae, as well as alginate, laminarin, mannitol and fucose-containing sulfated polysaccharides from brown algae. Recently, it was also shown to directly degrade fresh tissues of the kelp Laminaria digitata, corroborating its effi ciency for macroalgal biomass turnover  ( Zhu et al. 2017 ). Annotation of its 5. 5-Mb genome revealed that up to 9% of its gene content could be dedicated to polysaccharide utili zation  (Barbeyron et al. 2016). This includes genes encod ing an impressive number of 142 glycoside hydrolases (GHs) and 17 polysaccharide lyases (PLs), representing 56 different functional carbohydrate active enzyme families (CAZymes), together with 37 carbohydrate-binding modules as described in the CAZy database  (Lombard et al.
3 Schematic view of the multifaceted model organism Z. galactanivorans. The currently available experimental tools are listed, together with selected features that make Z. galactanivorans a useful model to investigate how marine bacteria degrade and colonize macroalgal biomass. The typical organization of polysaccharide utilization loci (PUL) is exemplified by the alginate utilization system. The genetic organization of the multi-loci carrageenan utilization system and alginate utilization system is shown, highlighting the number of proteins that have been characterized biochemically and structurally, as well as deletion mutants analyzed so far. For protein structures, the PDB accession ID is given. (Available on www. rcsb. org/. ) indistinguishable based on in vitro biochemical assays and (ii) the role of a distal TBDT/SusD-like tandem that was absent from the main carrageenolytic locus.
Reverse genetics usually requires a priori knowl edge of the genomic context and has been facilitated in the past 10 years owing to the increasing number of full genome sequences available  ( Zeaiter et al. 2018)  and with the wealth of information provided by oceanic metagenomic datasets  (Rusch et al. 2007; Sunagawa et al. 2015; Biller et al. 2018). Reverse genetics requires first a method to transfer foreign DNA into the target cells and then strategies for genome editing, shuttle vector and promoter design and the choice of selectable and counter-selectable markers. The toolkit can be expanded to include reporter system design to allow selec tion of mutated organisms or to follow gene expression. Gene inactivation is achieved primarily by homologous recombi nation either mediated by plasmids using the endogenous Emerging Marine Model Organisms recombination machinery of the host or, more recently, by using phage recombination systems, also known as recom bineering  (Fels et al.
First, whole-genome analysis of individual strains pro vides a comprehensive view of cell physiology capacities, which is an essential step when establishing a new bacterial model. Additionally, the development of genetic tools relies on thorough and precise information about gene organization and regulation in the target strain raised as a model. Accurate lists of genes, gene annotations and transcriptomic and pro teomic datasets, as well as the existence of computational platforms for data integration and systems-levels analysis, are among the essential criteria to establish bacterial mod els  (Liu and Deutschbauer 2018 ). An increasing quantity of genomic data for isolated strains are now available. These genomes are available in various types of databases (not specifically marine), such as that maintained by the Joint Genome Institute (JGI) Genome Portal (https://genome.
jgi. doe. gov/portal/) or the one maintained by the Genoscope in France (https://mage. genoscope. cns. fr/microscope/home/ index. php). For cyanobacteria, especially marine picocyano bacteria, specific databases that include genome exploration tools are available, such as Cyanobase (http://genome. kazusa. or. jp/cyanobase) and Cyanorak (http://application. sb-roscoff. fr/cyanorak/). In some databanks, one important diffi culty is that many genomes are still incomplete and published as "draft genomes", which can limit their utilization in genetic approaches. The availability of numerous complete and annotated bacterial genomes in databanks facilitates the choice of the genes to knock out when starting targeted mutagen esis approaches, which is an essential step when building an isolated strain as a model of interest.
Relevance of Sustainability in DSSC Research To date, sustainability has played a subordinate role in DSSC research. In order to visualize this statement, data from the Web of Science were evaluated using the VOSviewer tool. VOSviewer is a tool developed by Nees Jan van Eck and Ludo Waltman at Leiden University's Centre for Science and Technology Studies (CWTS). With the software tool bibliometric networks can be constructed and visualized. With the text mining func-tion co-occurrence networks of important terms can be visualized  [28]. The search result for 'dye sensitized solar cell' from the Web of Science returned 24,441 publications (15 March 2021). The titles and abstracts were exported and then evaluated using the VOSviewer tool. The results are shown in Figure  1. Energies 2021, 14, x FOR PEER REVIEW 3 of 13 function co-occurrence networks of important terms can be visualized  [28].
The search result for 'dye sensitized solar cell' from the Web of Science returned 24,441 publications (03/15/2021). The titles and abstracts were exported and then evaluated using the VOSviewer tool. The results are shown in Figure  1. Each point represents a term from the titles and abstracts of the publications in our dataset. The terms of interest are located based on their co-occurrence in the title and abstract. The higher the number of co-occurrences of two terms, the closer they are located on the map. In green, we see publications that focus on the sensitizer, red are publications about the counter electrode and morphology, the blue area focuses on the technology part, sustainability is also located here. In yellow, we see publications that focus on the electrolyte. The overall frequencies of some important terms are: "counter electrode (appears 2769 times)", "sensitizer (appears 2810 times)" or "morphology (appears 1899 times)".
Sustainability, on the other hand, occurs only 35 times and is thus, not visible in Figure  1. Figure  2  shows the same graph but different colors for the average publication date. Blue for older publications and yellow for recent publications. For example, terms such as "scarcity (appears 26 times)" and "energy demand (appears 65 times)" are associated with sustainability and appear in yellow with an average publication date of 2017. 09 (February 2017) for "energy demand" and "scarcity" 2016. 31 (April 2016), indicating that this research field is relatively new and under-researched. The average publication date shows the average date of all publications containing the term, in this case "energy demand". The average publication date of 65 papers containing Each point represents a term from the titles and abstracts of the publications in our dataset.
Blue for older publications and yellow for recent publications. Energies 2021, 14, x FOR PEER REVIEW 3 of 13 function co-occurrence networks of important terms can be visualized  [28]. The search result for 'dye sensitized solar cell' from the Web of Science returned 24,441 publications (03/15/2021). The titles and abstracts were exported and then evaluated using the VOSviewer tool. The results are shown in Figure  1. Each point represents a term from the titles and abstracts of the publications in our dataset. The terms of interest are located based on their co-occurrence in the title and abstract. The higher the number of co-occurrences of two terms, the closer they are located on the map. In green, we see publications that focus on the sensitizer, red are publications about the counter electrode and morphology, the blue area focuses on the technology part, sustainability is also located here.
1. Download abstracts and titles from the web of science with the search results of "dye sensitized solar cell" (24,441 results). 2. Create map based on text data (term map). 3. Read data from bibliographic database files. 4. Select web of science data. 5. Title and abstract. 6. Binary counting (only the presence or absence of a word matters not the number of the occurrences). 7. Minimum number of occurrences  (10).
Results Blood NK cell diversity during SIV infection in the natural versus heterologous host. NK cell effector function is known to increase together with the diversity of the receptor repertoire on NK cells  [23] [24] [25] [26]. We first estimated and compared phenotypic changes in blood NK cells following SIV infection between pathogenic and natural host models. The NK cell gating strategy and animals are describre in, respectively, Supplementary Figs. 1  and 2. Force-directed clustering analysis was performed based on expression data from nine markers of NK cells followed longitudinally (between day 0 and day 240 p. i. ) in 6 MAC and 6 AGM. This generated NK cell cluster profiles that were different between AGM and MAC following SIV infection but consistent between monkeys from a same species (Fig.
i. ). a, b viSNE analysis (Cytobank) on the concatenated flow cytometry data of a six MAC and b six AGM. Cells were stained with 12 markers and measured by flow cytometry. viSNE analysis was run on 80000 live CD45 + CD3 -CD20 -CD14-single cells per sample using all surface markers. Data show expression on all viable single cells from blood, subjected to the tSNE algorithm, at the given time point. The red and blue colors indicate higher and lower mean fluorescence intensity (MFI) of a given marker. c, d Frequency of NK cell subpopulations over time in blood from c six MAC and d six AGM. The arrows indicate the analyzed time points. The full line represents the median, and the interquartile range is indicated by the area between the dotted lines. The color code used to define each of the four subpopulations is the same throughout the manuscript as described in Supplementary Fig.
008 for ca-vRNA and ca-vDNA), while a negative correlation was observed with the NKG2 a/c high CD16 -NK cells (p < 0. 03 for ca-vRNA and ca-vDNA; Supplementary Table  S1 ). No significant negative correlations were measured between NK cell markers and viral load in LN in MAC or AGM (Fig. 3d, e ). The data suggest that viral replication is driving the expansion of NKG2 a/c high CD16 + NK cells in SIVmac infection but of NKG2 a/c low CD16 + NK cells in SIVagm infection. In order to perform an unbiased analysis of NK cell subsets in SLT, we performed a genome-wide transcriptional mapping of the LN NK cells. Four NK cell subsets were sorted from the LN of the same animals and same time points as described above for the blood. The four subsets corresponded to NKG2 a/c low CD16 -, NKG2 a/c high CD16 -, NKG2 a/c low CD16 + , and CXCR5 + NK cells.
P values ≤ 0. 05 were considered statistically significant. The transcriptome similarity among clusters of the blood sample was evaluated by the Euclidean distance. In panel b-d the name of the proteins coding by the transcripts are indicated. Source data been deposited in the Gene Expression Omnibus database; the accession number is GSE140600. Fig. 5a-c ), that the LN NKG2 a/c low CD16 + NK cells expressed high levels of markers known to be associated with NK cell terminal differentiation. Also, the NKG2 a/c high CD16 -NK cells showed again the less differentiated profile and expressed high levels of transcripts coding for NKG2D (KLRK1), NKp46 (NCR1), and CD56 (NCAM-1) similar to the CD56 high NK cell population observed in human SLT  15. Differential expression of NK cell markers at the transcription level were confirmed at the protein level by flow cytometry (Supplementary Fig.
d, e Genome-wide transcriptomes were used to define heatmaps of d markers linked to ITAM/ITIM signaling pathways and e genes strongly upregulated in NKG2 a/c low CD16 + NK cells. The orange and blue colors indicate higher and lower levels of transcripts measured in LN NK cell subsets from three chronically infected monkeys. A p-value adjustment was performed to account for multiple comparisons and control the false positive rate to a chosen level. P values ≤ 0. 05 were considered statistically significant. In panel d, e the gene and the name of the proteins corresponding to the transcripts are indicated. Source data been deposited in the Gene Expression Omnibus database; the accession number is GSE140600. f Spearman's correlation between THEMIS and KLRC1 (NKG2A; r = -0. 58, p = 0. 0025, n = 24) or GRAP2 (r = 0.
The anti-NKG2A antibody used recognizes both NKG2A and NKG2C on simian cells  80. Gating strategy used for NK and CD4 T cells analysis are shown in Supplementary Fig. 1. Flow cytometry acquisitions were done on a LSRFortessa (BD Biosciences). Intracellular staining was performed using BD Cytofix/Cytoperm™. The data were further analyzed using FlowJo 10. 4. 2 software (FlowJo, LLC, Ashland, OR, USA). Multiparametric analyses were performed using SPICE (version 5. 1). t-SNE was performed with the cytobank (Cytobank, Inc. ), using 2000 iterations and a perplexity of 60. Immunofluorescence staining. K562-E*0101 cells were incubated 16 h with biotinylated peptides. Cells were then fixed with PFA 4% and staining was performed using DAPI and Streptavidin coupled with Alexa 568 for 1 h at 37 °C followed by three washing steps.
Cells were analyzed using a Nikon microscope. Appropriate filters for immunofluorescence analysis of labeled cells were used and images were imported into ImageJ software. Fresh pLN tissues were embedded and snap frozen in optimum cold temperature compound (OCT) and 10 µm frozen sections were stained using unconjugated primary antibodies followed by appropriate secondary antibodies conjugated to Alexa 488 (green), Alexa 568 (red; Molecular Probes, Eugene, OR; dilution: 1/2000). The primary Abs used in this study anti-NKG2a (Epitomics (T3308), Ag Retrieval:10 mM citrate buffer (pH 6) 98 °C 20 min, dilution: 1/200); anti CD3 (AbD Serotec (MCA 1477), Ag Retrieval:10 mM citrate buffer (pH 6) 98 °C 20 min; dilution: 1/300), and anti HLA-E (MEM-E/06 (ab3984), Ag Retrieval:1 mM EDTA buffer (pH 8) 98 °C 20 min; dilution: 1/200).
Control cultures were kept at 37 °C for over 16 h without peptides. Cells were then harvested, washed in PBS, and cell-surface expression of HLA-E was determined by incubation with PE-conjugated anti-HLA-E antibody, washed twice with PBS and fixed in 100 μl of Cytofix (BD Biosciences). Cells were acquired using a LSR II (BD Biosciences), and FlowJo software (version 9. 6. 4, Tree Star, Ashland, OR) was used for all analyses. Results were expressed either directly as mean fluorescent intensity (MFI). Peptide competition assay. Peptide-binding assays were performed according to methods described in the literature (36). In brief, K562-E*0101 cells were incubated overnight (18 h) at 37 °C with biotinylated peptides (20 μM) and dilutions of non-biotinylated competitor peptide were added to the culture binding buffer (0.
01% Nonidet P-40, 10 nM Citrate-phosphate buffer and Protease inhibitor cocktail (Roche, Nutley, NJ). Cell surface biotinylated peptides were determined by adding alexa-488 labeled Streptavidin to each sample well. The expression of biotinylated peptides was assessed by flow cytometry. Cells were acquired using a LSR II (BD Biosciences), and FlowJo software (version 9. 6. 4, Tree Star, Ashland, OR) was used for all analyses. Results were expressed either directly as mean fluorescent intensity (MFI). NK cell cytotoxic in vitro assay. NK cell activity was determined through expression of cell surface CD107a  88, 89. NK cells were cultured overnight in the presence or absence of the MHC-class-I-devoid target cell line K-562 at an effector: target ratio of 1:5 for a total of 6-8 h in the presence of anti-human CD107a.
Acknowledgements We are grateful for the excellent help from the veterinarians and staff at the  IDMIT Center (Benoit Delache ,  Jean-Marie Helies ,  Raphaël Ho Tsong Fang  and  Julie Morin ). We thank  M. Mietsch  and  M. Daskalaki  for comprehensive veterinary support, and  S. Heine ,  J. Hampe , and  N. Leuchte  for excellent technical assistance. We also thank all the people from the Illumina Hiseq 2500 platform at the  Institut Pasteur and especially Caroline Proux and Jean-Yves Coppee. The Biomics Platform  ( C2RT ,  Institut Pasteur)  is supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA NH was supported by the  VRI Labex , the  Fondation Jacquelin Beytout and Institut Pasteur , and PR was recipient of a PhD fellowship from the  University Paris Diderot ,  Sorbonne Paris Cité.
C. P. was supported by a  MSDAvenir Postdoctoral Fellowship Grant. FK was supported by the  DFG  ( CRC 1279  and  SPP 1923. We would like to acknowledge grant support from  ANRS , the  Fondation Jacqueline Beytout , and the  Fondation Les Ailes  to M. M. T. , from the  NIH  ( R01AI143457 ) to M. M. T. and R. K. R. and from MSDAvenir to A. S. C. and M. M. T. ; J. L. H. and M. P. were supported by  NIH  grant  R01AI116379  to M. P. and by  ORIP/OD  award  P51OD011132  to  Yerkes National Primate Research Center. J. N. was supported by the  DFG  ( SPP 1937 ,  SFB TR57 ), the  Hector Foundation , and the  DZIF (TTU-HIV). We gratefully acknowledge the support to IDMIT from the  French government: Investments for the Future program for infrastructures (PIA)  through the  ANR-11-INBS-0008  grant as well as from the  PIA  grant  ANR-10-EQPX-02-01  to the  FlowCyTech facility at IDMIT.
, 2014). Cues and marks used to identify individual bull sharks included missing or deformed fins, notches, scratches and coloration patterns  (Brunnschweiler and Baensch, 2011). This information collected is then recorded in a database. For this study, we used the presence-absence data for 91 individual bull sharks (77 females, 14 males; see below) encountered during 3,063 dives taking place on 1,736 diving days between 26 January 2003 and 23 June 2016 (Supplementary Table  1 ), with a mean number of diving days per year of 129. 3 (SD = ± 49. 6). The degree of residency to the feeding site for each individual was quantified by dividing the number of days the individual was observed by the number of days data was collected at the SRMR (site fidelity index SFI). Site fidelity values range from 0 to 1, with values closer to 0 indicating low site fidelity and values closer to 1 indicating high site fidelity.
In this study, out of 125 cataloged bull sharks we included only individuals which were encountered on ≥34 dives which signified the first quartile (Q1 = the median of the lower half of the dataset) (mean ± SD = 151 ± 152; median = 102). Thirty-four individuals did not meet the Q1 threshold, thus the remaining 91 individuals (77 females and 14 males) who qualified were included in analyses. Because of the large number of individuals that were added to the database during the years 2009 and 2010 (Supplementary   1,356 dives). This allowed us to investigate the effect of a significant increase in individuals observed at the feeding site, and determine what if any variations had occurred in the nature of the associations during the discrete time series. To measure the intensity of associations between pairs of individuals we calculated the Simple Ratio Index (SRI), the recommended association index when calibration data are not available  (Hoppitt and Farine, 2018; Mourier and Planes, 2021) , in SOCPROG 2.
Site Fidelity Observation periods were defined as the cumulative number of days (including non-diving days) between the date an individual bull shark was first observed at the SRMR and recorded in the database, and the last day the individual was observed visiting the site. Observation periods within the database ranged from 128 days (ID# 91) to 4,885 days (ID# 2) with a median of 2,194 days (mean ± SD = 2,143 ± 1411) (Supplementary Figure  1 ). Presence data with respect to individual bull sharks varied greatly, with the least site visits recorded being 21 days (ID# 88) and the most site visits recorded being 554 days (ID# 2) (Supplementary Table  1 ). Site fidelity indexes (SFI) ranged from 0. 03 (ID# 1 and ID# 16) to 0. 84 (ID# 89) (mean ± SD = 0. 23 ± 0. 13).
Preferred and/or Avoided Associations Preferred long-term companionships were observed as indicated by significantly higher SD and CV values of the real dataset compared to randomly permuted data for the entire study period, between 2009 and 2010, and from 2011 (Table  3 ). After 2011, avoided associations can be observed as the proportion of nonzero association indices are lower in the real dataset compared to the randomly permuted data, indicating that some individuals avoid others. Social preferences that investigate the active decision to interact or not were measured by GAIs (Table  3 ). Since GAIs gave similar results to SRIs, social preferences were also common and occurred within the same time series  (2009-2010 and from 2011)  than preferred associations. For all time series, the mean of GAI values was positive, indicating that preferred companionships were common.
200 km away, where a shark feeding site was established back in 2015. In the absence of direct observation data collection at each dive site, the joint movement of individuals may be monitored using acoustic telemetry. Brunnschweiler and Barnett (2013)  found that the overall diel patterns in movement are for acoustically tagged bull sharks to use the area around the feeding site in the morning before spreading out over Shark Reef throughout the day and dispersing over a larger coastal area at night. Trophic information suggests that they continue to forage on natural prey  (Abrantes et al. , 2018) , quite possibly in the Navua estuary at night  (Brunnschweiler and Barnett, 2013) , nevertheless we are lacking key information including regarding resting behavior (e. g. , where, when or if they rest in groups).
Marine biodiversity underpins ecosystem health and societal well-being. Preservation of biodiversity hotspots is a global challenge. Molecular tools, like DNA barcoding and metabarcoding, hold great potential for biodiversity monitoring, possibly outperforming more traditional taxonomic methods. However, metabarcoding-based biodiversity assessments are limited by the availability of sequences in barcoding reference databases; a lack thereof results in high percentages of unassigned sequences. In this study, we (i) present the current status of known vs. barcoded marine animal species at a global scale based on online taxonomic and genetic databases (NCBI and BOLD) and (ii) compare the current status with data from ten years ago. Then,.
, 2019; Raupach et al. , 2016; Wangensteen et al. , 2018 ). Yet, substantial gaps remain in public DNA barcoding databases, leading to a high proportion of unassigned sequences in metabarcoding-based studies (e. g. ,  Martin et al. , 2021) , and substantial uncertainty in the estimated number of species living in a surveyed area  (Valentini et al. , 2016). Furthermore, many national and regional initiatives aimed at expanding DNA barcode databases are not providing complete species documentation, including morphology, ecology, and biology. Moreover, databases are usually not fully curated and harmonized by the time funds are exhausted after project completions  (Collins et al. , 2020). Consequently, some databases may have limited accessibility or are taken offline only a few years after their publication, and the obtained sequences are not always uploaded on public barcoding databases (see Supplementary material S1, based on  Radulovici et al.