dataesr/scientific-paragraphs-categorization huggingface dataset:
Wells were then supplemented with 0. 2% glucose (w/v), and/or 100 µM 2-ethynylpyridine and incubated for another 3 days. Data represent changes normalised against the untreated control grown in nitrification medium and are presented as the percentage change in biomass. Error bars represent the SEM (n = 3). Statistical significance, p < 0. 05, is indicated by * Fig. 8  Pre-established (4 days) mixed-species nitrifying biofilms treated with glucose and 2-ethynylpyridine exhibit a shift in community composition. Bacteria were grown in Nitrification medium for 3 days, at which point the media were replenished and the plates were further incubated for 24 h. Wells were then supplemented with 0. 2% glucose (w/v), and/or 100 µM 2ethynylpyridine (2E) and incubated for another 3 days. The community composition was determined by LH-PCR and processed into a similarity table using PRIMER-6 software.
Bioinformatic analysis was conducted by stitching pair-ended reads using the Illumina Paired-End read merger tool. 40 Reads were retained only if they contained the complete sequences of the forward and reverse primers, were ≥340 bp in length and received a q score of >20. Chimeras were removed from the quality trimmed reads using vsearch (v1. 11. 1),  41  using both reference based (www. drive5. com/ uchime/rdp_gold. fa) and de novo approaches. The dereplicated reads were clustered into OTUs at 97% of similarity using vsearch. Taxonomy was assigned to each operational taxonomic unit (OTU) using QIIME (v1. 9,  42  UCLUST method)  43  against the Greengenes database. 44 The table of OTUs was subsampled to 4000 sequences per sample to allow rigorous comparison between samples.
Community comparison The fragment profiles generated from T-REX for varying treatments was analysed using the Plymouth Routines in Multivariate Ecological Research (PRIMER) statistical package (version 6. 1. 15) with the PERMANOVA + module (version 1. 0. 5) (Primer-E, New Zealand). To reduce the impact of low abundance organisms on community analysis, fragments representing less than 1% of each sample were assigned a value of 0 and any fragment sizes then not having representation in any of the samples were removed. 50 Bacterial community structure was characterised using Bray-Curtis dissimilarity computed on fourth root transformed OTU tables. Samples were clustered by average-link clustering and represented as a dendrogram.
Among the 4 cluster families with a cDC phenotype, one family highly expressed CD123, CD86, and CD16. Finally, a family of cell clusters corresponding to the phenotype of pDC (CD14 -, CD11c low , CD123 + , CD4 + , HLADR + ) was defined. A total of 76 cell clusters were associated with the monocyte families, whereas cDC and pDC families These results indicate that monocytes and DCs in the dataset include heterogeneous and discrete cell subpopulations carrying a specific combination of markers that may reflect a divergence in functions or differentiation that could be associated with HIV infection.
Primary HIV-Infection Induces a Significant Loss of Peripheral cDC2 and pDCs Previous reports show the loss of DC subpopulations during early HIV and SIV infection  (10, 11). To assess this process in our dataset, we first analyzed the variations of cell cluster abundances for the cDC2 (CD1c + cDC) and pDC families among the different conditions. The percentage of cells in each condition relative to the number of cells in the CD45 + parent population were compared (Figure  3 ). We observed significant decreases of the percentages of cDC2 (80% of decline, p = 0. 0142) and pDC (69% of decline, p = 0. 00263) in the blood of primary HIV-infected patients compared to the same patients 1 year after effective cART. These significant decreases were also observed for primary HIV-infected patients in comparison to HIV controllers (85% of cDC2 decline, p = 0.
0003 and 66% of pDC decline, p = 0. 0022) and to healthy donors (92% of cDC2 decline, p = 0. 0065 and 74% of pDC decline, p = 0. 0059). Interestingly, our results also demonstrated significantly higher percentages of cDC2 (p = 0. 0302) in the healthy group than in the HIV cART-treated group (Figure  3A ). No significant difference was observed between these two groups for pDCs (Figure  3B ). Concordantly with previous studies, our dataset demonstrates that primary HIVinfection induces a profound decline of peripheral cDC2s and pDCs abundance that seems to be partially recovered under cART, but to a lesser extent for cDC2s.
Automatic Identification of Cell Clusters SPADE algorithm was used to perform automatic identification of cell clusters  (25). We observed large differences in the number of manually gated myeloid cells in each sample, ranging from 2,255 to 35,658 cells. Consequently, we first down-sampled 2,255 cells from each sample to avoid over-representation of samples with high number of cells. These uniformly down-sampled events were up-sampled at the end of the SPADE analysis. The SPADE clusters were generated using the entire and singular dataset (24 samples from 18 individuals, including 6 during primary infection and 1 year later after effective cART). Cells having similar expression of the 23 selected clustering markers (shown in blue in Figure  1 ) were grouped into clusters, regardless of donor origin.
Phenotypic Characterization of Identified Cell Clusters We used the SPADEVizR R-package to perform the phenotypic categorization of the 150 cell clusters  (26). Expression level of each marker was classified into five categories, which were defined based on the range of expression (5th-95th percentile) relative to the myeloid population gate across all samples. The five uniform categories represent negative, low, medium, high, and very high levels of marker expression. The categories of expression were displayed using a color scale, which ranged from white to dark red. The dendrogram, represented in the heatmap, was constructed using Euclidian distance and the complete linkage method. There were 36 clusters that displayed a lymphoid phenotype (based on the negative expression of CD33, CD123, CD14, and LILRB3), which were identified among the total 150 cell clusters and were removed from the SPADE analysis to leave only myeloid cell clusters.
Identification of Differentially Abundant Clusters and Correlating Clusters We used the SPADEVizR R-package to identify differentially abundant clusters (DACs), and correlated clusters (CCs)  (26). DACs identification was based on the percentage of cells in the clusters, relative to total myeloid cells by unpaired Student ttests (absolute fold-change > 2 and p < 0. 05). CCs identification was based on the percentage of cells in clusters relative to the total myeloid cells that correlated with HIV RNA levels (absolute Pearson correlation coefficient > 0. 65 and p < 0. 05).
ETHICS STATEMENT This study and the associated protocol were approved by the Ethics Committee of Cochin Hospital and ethics review committee of Ile de France VII. All subjects gave written informed consent in accordance with the Declaration of Helsinki. CD1c + cDC clusters. The marker expression densities for cluster #39 are shown in blue whereas the marker expression densities for all CD32b + CD1c + cDC clusters are shown in gray. The differences between marker expression densities were quantified using the Kolmogorov-Smirnov distances (KS) using the entire and singular dataset. Table  S1  | Overview of the mass cytometry panel. The metal, antigen, clone, isotype, and supplier are indicated for each antibody. * Not used for cell clustering.
FigureS6| Marker expression densities showing the phenotypic specificity of cluster #39 relative to CD1c + cDC clusters. The expression densities of all clustering markers, as well as CD33 and CX3CR1 are shown for cluster #39 and CD1c + cDC clusters. The marker expression densities for cluster #39 are shown in blue whereas the marker expression densities for all CD1c + cDC clusters are shown in gray. The differences between marker expression densities were quantified using the Kolmogorov-Smirnov distances (KS) using the entire and singular dataset.
ACKNOWLEDGMENTS We thank the  ANRS (French National Agency  for AIDS research)  CO6 PRIMO  and  CO21  CODEX cohort for helpful collaboration and all the medical professionals involved in blood sampling at the  Hôpital Kremlin-Bicêtre. We also thank  Bruno Iannascoli  ( Institut Pasteur ) for the in vitro production of anti-CD32b antibodies and  Oscar Haigh  and  Jean-Louis Palgen  for critical reading of the manuscript.
FUNDING This work was supported by  French government Programme d'Investissements d'Avenir (PIA)  under Grant  ANR-11-INBS-0008  that fund the  Infectious Disease Models and Innovative Therapies (IDMIT, Fontenay-aux-Roses, France) infrastructure and PIA  grant  ANR-10-EQPX-02-01  that funds the FlowCyTech facility. NT held a fellowship from the  ANRS (France Recherche Nord&Sud Sida-HIV Hépatites ).
For the AOC sub-section, two AOCs per wine-growing region were chosen: one very renowned, and another less known and corresponding to a generic name (e. g. for Bordeaux: "Saint Emilion" and "Bordeaux"). The choice of the AOCs and the regions were made from the work of  [20, 21]  on the notoriety of French AOCs in France. The order of the questions was randomized before sending the questionnaire but remained the same for all of them. The answers were collected via the internet. The questionnaires were sent by mail using several databases provided by the "Vignerons Indépendants de France" (VIF), the association of Rural Families and, the School of Agriculture of Angers (ESA). It was also diffused in the "Ethical Consumption" and "60 Million Consumers" forums and personal networks. 3700 complete cumulative responses were obtained for all surveys.
The segmentation of the sample according to wine and environmental involvement was done by a calculation notes' sum. The sample was then divided into three classes, to have three levels of involvement (Low, Medium, and High): V1, V2, V3 (Wine involvement) and E1, E2, E3 (Environmental Involvement). Data processing was performed using flat sorting, cross sorting, Principal Component Analysis (PCA) and Variance Analysis (ANOVA). The analysis of the results systematically includes a verification of the influence of wine and environmental involvement on the ratings of the different questions. These tests were carried out using the statistical software XLSTAT 2014 and Question Data.
Future surface flux estimates, particularly in the deep convective regime associated with the Indo-Pacific warm pool, will benefit tremendously from the scientific and operational achievements made by the autonomous surface platforms. IndOOS observations also provide critical data to evaluate and constrain ocean data assimilation (ODA) or ocean re-analyses products, which are crucial for estimations of decadal and longterm trends. In particular, RAMA and Argo are the backbone datasets for improving the fidelity and consistency of ODA products in the subsurface ocean  (Dombrowsky et al. , 2009; Lee et al. , 2009; Fujii et al. , 2015)   2. IndOOS is also an integral element of GOOS, which contributes observations to the Ocean Reanalysis Intercomparison Project (ORA-IP)  (Balmaseda et al. , 2015)  which carries out intercomparisons and evaluations of a suite of ODA systems.
, 2018). The end-user community for IndOOS also includes scientific and technical experts in other disciplines, including oceanfocused ecologists, economists, geographers, policy analysts, and many others. For this group of end-users, engagement challenges include the need to connect specialized use-cases for ocean observation data (i. e. , ocean state estimation, atmospheric re-analyses, and surface flux estimation) with other fields of analysis (e. g. , ecological assessment, input-output analysis, costbenefit analysis). This also serves to highlight the need for the links with regional and process orientated observing programs. Opportunities also exist to leverage the capability of global data analysis and communication initiatives for the benefit of ocean observation specifically tailored to meeting societal needs in the Indian Ocean region.
For example institutional and data connections with the Blue Planet Initiative of the Group on Earth Observations (BP-GEO) could leverage BP-GEO's aims to address global challenges and improve decision-making through coordination and development of Earth observation efforts among 105 Member governments, supported by several nongovernmental organizations (GEO, 2017). There is a strong need to ensure that Indian Ocean rim countries have access to the data produced through IndOOS, as well as understanding its applications. As highlighted in Section "Capacity Development", a number of workshops have taken place in order to implement this, but these efforts need to be sustained. A possible mechanism for continuing this and for ensuring that IndOOS reaches Government stakeholders and policy makers is to use the Indian Ocean Rim Association 3  and its sub grouping of the Academic Group.
Surface drifters need to be augmented with barometric sensors to support weather and seasonal forecasting. Essential XBT lines such as those monitoring the ITF output or crossing the Arabian Sea upwellings need to be maintained or enhanced, as do western and eastern boundary arrays in the southern subtropical gyre, to quantify mass and heat fluxes and improve predictability of basin-scale heat content change. The Global Sea Level Observing System (GLOSS) network needs to be expanded and data made available. Utilization of new autonomous and/or expendable platforms and new sensor technologies should always be explored for enhancements of IndOOS. It is not just in situ observations that need to be enhanced; satellite missions provide key variables to complement IndOOS and need to be supported to ensure good continuity for EOVS, in particular measurements of surface winds at diurnal timescale, all-weather sea surface temperature, sea surface height, sea surface salinity and ocean color are of highest priority (Essential Tier I).
In situ data provides calibration, validation and extrapolation (e. g. , from surface to vertical) of satellite measurements and help de-alias signals that are not adequately sampled. The focus of the aforementioned observations are beyond the EEZ, however, future observing systems need to consider the coastal zone, as a key region for understanding and monitoring the ocean's influence on, and response to, climate change processes. Coastal dynamics play a substantial role on the open ocean by modulating air-sea fluxes and distributing nutrients and marine plankton, and influencing the monsoon variability and MJO (e. g. , Prasanna  Kumar et al. , 2001; Vialard et al. , 2012). While the coastal zone is not directly covered by IndOOS currently, except for the expansion related to boundary current arrays, future enhancement of IndOOS needs to include these waters (Tier III; pilot projects, exploring IndOOS integration) for the study of coastal currents, coastal heatwaves OMZ, ocean acidification, carbon uptake, land sea interaction through, for example, effluent discharge from terrestrial sources and other critical processes, which infringe on or occur within EEZs and which are especially poorly monitored in the Indian Ocean.
The ecosystem is also influenced directly by human activities such as nutrients in river discharge, increasing aeolian dust flux, ocean acidification, overfishing, and increasing pollutants (e. g. ,  Jickells et al. , 2017). Understanding possible mechanisms of physical, biogeochemical, and ecosystem responses to natural and anthropogenic perturbations is an emergent issue of marine science, with increasing human activity degrading the quality and quantity of marine ecosystem services on which our society is dependent. The main part of this activity consists of in situ observations using research vessels. Data from IndOOS will be utilized as background information to this research activity and, in turn, RAMA sites will be maintained and new technologies tested for IndOOS. (c) Surface fluxes and diurnal mixed layer variability at key tropical sites and improvement of monsoon, MJO, and MISO prediction.
This includes 9 years of Indian Ocean cruises collecting continuous oceanic and meteorological observations, including conductivity, temperature, and depth (CTD) measurements, Global Positioning System radiosonde, and automatic weather station data (Figure  4 ). Observations prior to the monsoon are very important because the air-sea interaction within these periods can be a precursor for monsoon onset. Data from these in situ observations will have an important value for assimilation into ocean re-analyses and for weather and seasonal forecast models. (e) The Agulhas Current System and associated air-sea interaction and upwelling. Measuring the Agulhas Current has been identified as a Tier II future priority in the IndOOS decadal review, largely owing to its dominant role in basin-wide heat and freshwater budgets.
Innovative strategies for integrated observing systems are required, for example combining current meter arrays, gliders, and Current and Pressure Inverted Echosounders with periodic hydrographic sections for important climate variables such as carbon, nutrients, and oxygen. In addition, innovation in real-time data return for subsurface arrays, such as fast telemetry and data pods, is necessary. Future strategies for IndOOS, particularly regarding connection to end users and stakeholders, need to be better linked to regional and national efforts, such as via the GOOS Regional Alliances. A key factor affecting investment in scientific research is the level of national appreciation of the importance of the marine sector to the country's economy and resources. GOOS (and associated Regional Alliances) is in a unique position to be able to make the case to national governments and stakeholders (GOOS strategy) through the IOC of the United Nations Educational, Scientific and Cultural Organization (UNESCO).
To support regional capacity development and involvement in the sustained observations, international partnerships need to be fostered and maintained, in particular with Indian Ocean rim countries. Intergovernmental bodies such as WMO and IOC, as well as international scientific bodies such as CLIVAR and Scientific Commission for Oceanographic Research (SCOR) can be used to help facilitate this. It is essential to improve evaluation mechanisms for what has been achieved to date and consider long term thinking around funding mechanisms. These programs also serve a vital role with regards to performing observations and accessing data held within countries EEZs.
IMPLEMENTATION CHALLENGES FOR IndOOS Sustained observing systems over the Indian Ocean face general challenges as is common to all basin-wide observing systems. However, the Indian Ocean has its own unique challenges due to the physics of the basin, as described, as well as the problems within the Indian Ocean countries that rely heavily on coastal regions for food security. Special focus needs to be placed on access to EEZs for observing system components (such as boundary arrays), and on capacity, resource and coordination development of rim countries that remains a challenge due to human resources, finances, and logistics. International coordination, best practices, and data sharing are also challenges. With strong potential synergies between IndOOS, process studies, and regional observing systems (see section "Key Regional and Process Efforts Connecting With IndOOS") integration and mutual interactions have not developed satisfactorily over the last decade.
Background: Hepatocellular carcinoma (HCC) is the third leading cause of death by malignancy worldwide. HCC has a poor prognosis due to tumor invasiveness and metastasis. There is substantial evidence that the epithelial-to-mesenchymal transition (EMT) plays a central role in cancer metastasis. In a previous study, a possible association between integrator complex 8 (INTS8) and the progression and development of HCC was discovered. However, its role and the molecular mechanisms in HCC are poorly understood. Methods: The PROGgeneV2 platform database and Kaplan-Meier plotter analysis were used to analyze the potential effects of INTS8 in HCC. Moreover, we performed migration, transwell, and metastasis assays to investigate the effects of INTS8 on HCC cells. In addition, relevant signaling pathways were examined by western blot and RT-qPCR assays.
Results: We used the PROGgeneV2 platform database and Kaplan-Meier plotter analysis, which indicated that increased expression of INTS8 is associated with poor overall survival of HCC. Moreover, INTS8 expression was higher in HCC tissues than in adjacent noncancerous tissues. INTS8 depletion reduced the invasion and migration of HCC cell lines. Downregulation of INTS8 in vivo resulted in fewer observed metastatic nodules in lungs. Moreover, INTS8 knockdown also increased the expression of epithelial markers (E-cadherin) and decreased the expression of mesenchymal markers (N-cadherin and vimentin) following the downregulation of SMAD4. In addition, pretreatment with TGF-β1 could partly prevent the decrease in the expression of SMAD4 and EMT markers induced by INTS8 knockdown. Conclusion: Overall, these findings suggest that INTS8 accelerates the EMT in HCC by upregulating the TGF-β signaling pathway.
immunohistochemistry (ihC) IHC staining was performed blindly by a pathologist. We assessed INTS8 expression by IHC of paraffin-embedded specimens. The slides were incubated overnight at 4°C with rabbit anti-INTS8 primary antibody (1:200; Proteintech, Wuhan, China). The following day, secondary antibody was applied for 30 minutes at 37°C and expression was quantified using ImageJ software.
statistical analysis We used SPSS 20. 0 software to perform statistical analysis. All experiments were performed at least in triplicate, and the data are presented as the means ± SDs. We assessed statistical significance using a two-tailed Student's t-test, or, when comparing two groups, a one-way analysis of variance test with Dunnett's test. We considered P-values of 0. 05 or less as statistically significant.
ASC from MM Patients Exhibit a Senescent Phenotype We then analyzed cellular senescence. HD-ASC cultures contained only low levels of senescent cells, as shown by their low SA-β-Galactosidase activity. In contrast, the number of senescent cells was 3-fold higher in MM-ASC cultures (Figure  4A ). Accordingly, we observed increased expression of the cell cycle inhibitors p21 CIP1/WAF1 and p16 INK4 , which are generally associated with senescence, in MM-ASC cultures (Figure  4B ). These results were once again independent of the bone lesions status (Supplementary Figure  S1 ) and the age of the donor or patient (data not shown). Thus, our results showed that MM-ASC-derived osteoblasts display abnormally high levels of cellular senescence markers.
ASC and MSC from MM Patients Present Comparable Defects Finally, we investigated whether the alterations observed in MM-ASC were similar to those found in MM-MSC isolated from the bone marrow. A comparative analysis was carried out using MSC and ASC obtained by bone marrow aspiration or liposuction from the same MM patients. Adipocyte differentiation was functional in both MM-ASC and MM-MSC, as evidenced by Oil-red-O staining and the quantification of the adipogenic transcription factors PPAR (Supplementary Figure  1 ) and the age of the donor or patient (data not shown). Thus, our results showed that MM-ASC-derived osteoblasts display abnormally high levels of cellular senescence markers.
Acknowledgments: We would like to thank  Michael Atlan  ( APHP Hopital Tenon, Sorbonne Université ,  UMR_S 938 ) et  Jérôme Larghero  ( AP-HP, Hopital Saint-Louis, Unité de Therapie cellulaire, Paris, France ) for providing us some adipose tissue-derived and bone marrow-derived stem cells at the beginning of the project.