dataesr/scientific-paragraphs-categorization huggingface dataset:
TD, PN, MMa, MMi, SR, and OD designed the experimental methodology and participated in the analyses. TD, PN, MMa, MMi, and SR contributed to the model development. OD specifically contributed to the satellite portion of this work. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. This work has received funding from the EU-funded Copernicus Atmospheric Monitoring Services 43 "Development of global aerosol aspects" (CAMS_43) project as well as from the EU Horizon 2020 Research and Innovation program under grant agreement no. 101003536 (ESM2025 -Earth System Models for the Future).
Acknowledgements This work was supported by the  National Research Foundation  ( 2020M3E4A1080112 ,  2022R1A2B5B03002560 ),  Institute of Information & Communications Technology Planning & Evaluation (IITP)  grant (No. 2020-0-00841 ) funded by the  Korea government, Samsung Science and Technology Foundation  under Project No. SSTF-BA1602-05 ,  French National Research Agency  through the Project  SCENIC  ( ANR-20-CE09-0005 ), and by the  French Renatech/Renatch + networks.
2a, b ). We also observed increased staining of choleratoxin B, suggestive of increased ganglioside GM1, a component of cholesterolenriched lipid rafts (Supplementary Fig. 2c-e ). To validate the findings on cholesterol accumulation, we performed Gas Chromatography -Mass Spectrometry (GC-MS). T cell Abca1/Abcg1 deficiency increased total cholesterol by 2. 5fold, reflected by increases in free cholesterol and cholesteryl esters (CE) (Supplementary Fig. 2f ). In line with CE accumulation, T cell Abca1/Abcg1 deficiency increased Oil Red O staining, reflecting lipid droplets in para-aortic lymph nodes (LNs) (Supplementary Fig. 2g ), and BODIPY 493/503 staining in Ldlr -/-and Ldlr +/+ T cells (Supplementary Fig. 3a, b ). We also observed BODIPY 493/503 staining reflecting lipid droplets, in Abcg1-deficient T cells, in line with CE accumulation data as shown in  23  , but not in Abca1-deficient T cells or controls (Supplementary Fig.
1b  and Supplementary Fig. 7b-d ), even though TCRβ + CD24 -and TCRβ + CD69 -cells, indicative of excessive negative selection  30  , were decreased compared to Ldlr -/-controls as a percentage of thymocytes (Supplementary Fig. 7e-k ). Also, unlike LckCreAbcg1 fl/fl mice that showed an increase in thymic T reg cells  22  , thymic T reg cells were not different between T-Abc dko Ldlr -/-mice and controls (Supplementary Fig. 7l-o ). We next examined T cell numbers in spleen and paraaortic LNs. Similar to observations in blood, we found that T cell Abca1/Abcg1 deficiency decreased splenic and para-aortic LN CD4 + and CD8 + T cells by ~50-70% (Fig. 2d-g ). Together these data indicate that T cell Abca1/Abcg1 deficiency decreases blood, splenic, and para-aortic LN T cells without affecting thymic DN, DP, CD4 + , or CD8 + SP cells.
8i ). To obtain more insights into the consequences of these processes for atherosclerotic plaques of middle-aged T-Abc dko Ldlr -/-mice, we characterized their atherosclerotic lesions further. Similar to data in young Ldlr -/-mice (Supplementary Fig. 23a, d, e ), T cell Abca1/Abcg1 deficiency did not affect α-SMA staining or fibrous cap thickness in middle-Fig. 7  Effects of T cell Abca1/Abcg1 deficiency on para-aortic LN T cell subsets and atherosclerosis in Ldlr -/-mice. Female Ldlr -/-and T-Abc dko Ldlr -/- mice were fed chow diet for 28 weeks (a-i, k, n-o) or WTD for 10 weeks (j, l-m). a-i Para-aortic LNs were isolated, stained with the indicated antibodies, and analyzed by flow cytometry. Gating strategies are shown in Supplementary Fig. 18. a Representative flow cytometry plots and (b) quantification of CD25 -Foxp3 + (p = 0.
All primer sequences are available in Supplementary Table  3. Filipin staining-flow cytometry. To analyze T cell membrane cholesterol, blood leukocytes (4-5 months old male and female Ldlr -/-and T-Abc dko Ldlr -/-mice, and young (3 months) and aged (24 months) wild-type male and female mice) and thymocytes (4-5 months old male and female Ldlr -/-and T-Abc dko Ldlr -/ mice) were stained with antibodies against TCRβ-APC, CD8-PE, and CD4-FITC, and thymocytes with antibodies against CD4-APC and CD8-PE. Cells were then centrifuged, washed, and stained with 50 μg/mL Filipin III (Sigma-Aldrich) in 10% FCS/PBS for 45 min at room temperature (RT) and then washed twice with PBS. Samples were analyzed by flow cytometry immediately after staining. Filipin III mfi on blood TCRβ + CD4 + and TCRβ + CD8 + cells, and on thymic CD4 + CD8 + , CD4 + , and CD8 + T cells was assessed on LSRII (BD Biosciences), running FACSDiVa software (BD Biosciences).
The data were analyzed using the FlowJo software (FlowJo). Confocal microscopy, BODIPY, and filipin staining. Splenic CD4 + and CD8 + T cells from 4-5 months old male and female control, T-Abc dko , Ldlr -/-, and T-Abc dko Ldlr -/-mice, and total T cells from 4-5 months old male and female T-Abca1 ko Ldlr -/-and T-Abcg1 ko Ldlr -/-mice, and young (3 months) and aged (24 months) wild-type male and female mice were isolated as described above. Cells were seeded on Poly-L-Lysine (PLL)-coated glass coverslips and incubated at 37 °C for 1 h. For lipid droplet imaging, cells were fixed (4% PFA; 15 min; 4 °C), permeabilized for 30 min at RT using CLSM buffer (PBS containing 3% BSA, 10 mM glycine, 0. 1% saponin), and stained for 30 min at RT with BODIPY 493/503 (Invitrogen; 7. 5 μM in CLSM buffer).
After two PBS washes, cells were mounted using VECTASHIELD Antifade Mounting Medium with DAPI (Vector Laboratories). To assess free cholesterol, cells were fixed (3% PFA; 1 h; RT) and stained with filipin (50 μg/mL in 10% FCS in PBS; 2 h; RT). After three PBS washes, cells were mounted using VECTASHIELD Antifade Mounting Medium (Vector Laboratories). All samples were imaged on the Zeiss LSM800 microscope with immersion oil lens (63X,1. 4 N. A) running Zen software (Zeiss). Background signal was removed using Zen software and fluorescence intensity of filipin or BODIPY was analyzed using ImageJ software (NIH). Choleratoxin B staining. For analysis of T cell lipid rafts, blood leukocytes from 4-5 months old male and female Ldlr -/-and T-Abc dko Ldlr -/-mice, and young (3 months) and aged (24 months) wild-type male and female mice were stained with a cocktail of antibodies against TCRβ-PB, CD8-PE, and CD4-APC.
Quantification of the number of lipid droplets on sections of para-aortic LN sections (per field) was performed in a blinded fashion, i. e. the observer was unaware of the genotypes. For atherosclerosis analysis, the average from 6 heart sections (40 μm distance between sections) for each animal was used to determine lesion size. Lesion size was quantified by morphometric analysis and Oil Red O + area was measured using ImageJ software (NIH). Transmission electron microscopy. Splenic CD4 + and CD8 + T cells from 4-5 months old male and female Ldlr -/-and T-Abc dko Ldlr -/-mice were isolated as described above. Cells were seeded on PLL-coated glass coverslips placed in 24well plates and incubated at 37 °C for 1 h. Cells were then fixed with 2% glutaraldehyde (Sigma-Aldrich) in PB (0. 1 M phosphate buffer, pH 7.
Cells were incubated at 37 °C for 30 min, and then BODIPY C10 fluorescence lifetime was assessed by fluorescence-lifetime imaging microscopy (FLIM). All images were collected with a MicroTime 200 microscope (PicoQuant) equipped with an Olympus (100x/1,4) oil immersion objective. Images were acquired using the SymPhoTime 64 software (PicoQuant). Data analysis of the FLIM images was performed using the open-source FLIMfit software  80. Further image loading options included 2 × 2 spatial binning and time bins 1563 (32 ps/bin). After loading FLIM images, the software's default settings were used. BODIPY C10 fluorescence lifetime of a whole cell (with lipid droplets (LDs)) or of the plasma membrane without including LDs was determined by fitting the fluorescence histograms with a mono-exponential decay function, using the tool "Fit Selected Decay".
For analysis of naïve and activated Tbet + cells, LN homogenates were stained with a cocktail of antibodies against TCRβ-PB, CD8-PE, CD44-PECy7, and CD62L-APC, then fixed and permeabilized, and subsequently stained with Tbet-AF488 (1/100 dilution) or its isotype control (1/100 dilution). Naive Tbet + cells were identified as TCRβ + CD4 + Tbet + CD44 -CD62L + and TCRβ + CD8 + Tbet + CD44 -CD62L +. Activated Tbet + cells were identified as TCRβ + CD4 + Tbet + CD44 + CD62L -, TCRβ + CD8 + Tbet + CD44 + CD62L -(T mem/eff ) and TCRβ + CD8 + Tbet + CD44 + CD62L + (T CM ) cells. All samples were analyzed on LSRII (BD Biosciences), running FACSDiVa software (BD Biosciences). The data were analyzed using the FlowJo software (FlowJo). In vitro T cell proliferation assay. Splenic T cells from male and female control, T-Abc dko , Ldlr -/-, and T-Abc dko Ldlr -/-mice (4-5 or 12-13 months old), young (3 months) and aged (24 months) wild-type male and female mice, and total aortic cells from middle-aged (12-13 months) male and female Ldlr -/-and T-Abc dko Ldlr -/-mice were isolated and labeled with 10 μM CFSE-PB (Invitrogen) and stimulated with Mouse T-Activator CD3/CD28 Dynabeads (ratio beads to cells 1:2 for splenic cells and 10:1 for aortic cells) in RPMI 1640 medium supplemented with 10% FCS, 1% pen-strep, and 50 μM β-mercaptoethanol at 37 °C, 5% CO 2.
Splenic T cells from Ldlr -/-and T-Abc dko Ldlr -/-mice were also incubated with 50 μg/mL rHDL for the whole duration of the proliferation assay. Cells were incubated at 37 °C for 72 h (splenic) or 96 h (aortic). Subsequently, beads were removed, T cells were stained using CD4 and CD8 antibodies as described above, and CFSE-PB dilution was assessed by flow cytometry. Using the Proliferation Modeling tool in the FlowJo software, histograms of CFSE dilution in CD4 + and CD8 + T cells were obtained where each cell division was depicted as a separate peak. The number of CD4 + and CD8 + T cells in each division was automatically calculated by the software after designing gates for each peak. To calculate the percentage of CD4 + and CD8 + T cells in each division, the number of CD4 + and CD8 + T cells in one division was divided by the total number of CD4 + and CD8 + T cells in all divisions, respectively.
Cell lysates were separated by SDS-PAGE gel electrophoresis and immunoblotted with primary antibodies against total JNK1/2 (1:1000; Cell Signaling Technology), phosphorylated JNK1/2 (1:1000; Cell Signaling Technology), or gasdermin D (1:1000; a kind gift from Genentech). Heat shock protein 90 (HSP90) (1:1000; Cell Signaling Technology) was used as loading control. Goat anti-mouse (1:2000; Invitrogen; for phosphorylated JNK1/2), goat anti-rabbit (1:2000; Invitrogen; for total JNK1/2 and HSP90), and goat anti-rat (1:1000; Cell Signaling Technology; for gasdermin D) HRP-conjugated secondary antibodies were used. Blots were developed using the Amersham Imager 600 (GE Healthcare) and bands were quantified using ImageJ software (NIH). Uncropped and unprocessed scans of blots are shown in the Source Data file.
In vitro T cell apoptosis assays. Splenic CD4 + and CD8 + T cells from 4-5 months old male and female Ldlr -/-and T-Abc dko Ldlr -/-mice were isolated and labeled using Caspase3/7 Red (APC) Reagent (Sartorius). Cells were cultured as described above, and stimulated with 5 μg/mL plate-bound αCD3 and 100 U/mL IL-2 for 12 h. For Incucyte experiments, images were taken every 2 h. For image analysis, background signal was removed using the Incucyte ZOOM software (Sartorius) and the number of total cells and cleaved caspase3/7 + cells per image were quantified using ImageJ software (NIH) in a blinded fashion, i. e. the observer was unaware of the genotypes. Alternatively, splenic T cells from 4-5 month old male and female control (Ldlr +/+ ), T-Abc dko , T-Abca1 ko Ldlr -/-and T-Abcg1 ko Ldlr -/-mice, middle-aged (12-13 months) male and female Ldlr -/- and T-Abc dko Ldlr -/-mice, and young (3 months) and aged (24 months) wild-type male and female mice were isolated and labeled using caspase3/7 reagent (1/1000 dilution), stimulated as described for the Incucyte assay, harvested, stained with CD4-PB (1/200 dilution) or CD8-PB (1/200 dilution), and analyzed by flow cytometry for cleaved caspase 3/7 mfi in splenic CD4 + or CD8 + T cells.
31 mL/ min in PBS (pH 7. 4). Cholesterol and TG were measured in line by the addition of cholesterol and TG reagents, respectively, at a constant flow rate of 0. 1 mL/min using an additional PU-4080i infusion pump (Jasco). Data acquisition and analysis were performed using ChromNav software (Jasco). Atherosclerotic lesion analysis. Female Ldlr -/-and T-Abc dko Ldlr -/-mice were fed a WTD for 10 weeks (starting at 8-12 weeks of age; n = 16 Ldlr -/-and n = 16 T-Abc dko Ldlr -/-mice) or a chow diet for 28 weeks (n = 18 Ldlr -/-and n = 16.
This information is crucial to guide and to prioritize interventions to address WaSH and WBD related problems among vulnerable populations. In this context, we analyzed data on the incidence of waterborne diseases in Ecuador during the last eight years. We perform a descriptive analysis of the different ethnic Ecuadorian groups from 2011 to 2020 to picture dissimilarities among populations as well as to try to understand the link between burden of diseases, age, gender, and other social determinants of health.
Variables We constructed measurements for disease occurrence using hospital admission rates as a proxy for incidence. In terms of mortality, sex-age and ethnicity speci c rates were computed using in-hospital mortality and death certi cates as numerators and the population at risk as denominators. The lists of diseases used for the analysis was obtained from the INEC databases based on The International Statistical Classi cation of Diseases and Related Health Problems in its 10th version (ICD-10) (Table  2 ). Due to data source incompleteness and underreporting, the proportion of entries with missing data on ethnic identity and educational attainment did not match the cumulative incidence and mortality for sex, age, and place of residence. The data was retrieved as it was documented within the reporting system.
Statistical analysis We performed descriptive statistics based on demographic variables to obtain absolute and relative variations. We also constructed measurements for disease occurrence, including in-hospital mortality (%), incidence and mortality countrywide rates. The analysis of the data was performed using the SPSS statistics software for Mac (IBM Corp. 2014, version 24. 0. Armonk, NY, USA) and R version 3. 6. 2. Figures and graphs were performed in Prism 8 GraphPad Software version 8. 2. 0 (2365 Northside Dr. Suite San Diego, CA 92108). The basic cartography maps were generated using QGIS Development Team 180 2. 8 (Creative Commons Attribution-ShareAlike 3. 0 licence CC BY-SA).
In Ecuador, diseases related to poor hygiene and lack of potable water, clean and safe for health, mainly affect the indigenous population. In a report from the INEC, it was evidenced that the houses with adequate access to clean water did not exceed 80%, while in other groups such as mestizos and white population, it surpassed 97%  [36]. In general, Ecuadorian indigenous groups have reduced access to safe drinking water and appropriate sanitation and wastewater treatment facilities, which in uences greater morbidity and mortality caused by diseases transmitted by the water. All this information is available from the countrywide databases that includes data from indigenous populations that live in Ecuador and have access to health services. In the country however, as well as in other parts of the Amazon rainforest there are indigenous populations living in voluntary isolation that drink water directly from the rivers  [40, 41].
Limitations One of the main limitations we encountered in this study was the quality of the national disease reporting system. As a result, we cannot differentiate the number of people getting sick from the number of events (hospital admissions) that required hospitalization. The same patient could have entered the hospital twice in the same week, but the database cannot differentiate these events. At the same time, we could not purge the database from the presence of garbage codes, which might hinder the accurate analysis of this type of information.
intestinal infections A05 Other bacterial foodborne intoxications, not elsewhere classi ed A06 Amoebiasis A07 Other protozoal intestinal diseases A08 Viral and other speci ed intestinal infections A09 Other gastroenteritis and colitis of infectious and unspeci ed origin helminthiases, not elsewhere classi ed B82 Unspeci ed intestinal parasitism Data Source and Measurement We used the latest estimated projections based on the 2010 National Population Census to compute incidence and mortality sex-age speci c rates with a yearly resolution from 2011-2020. Information by ethnicity was retrieved directly from the variable "ethnic background" from the o cial National Institute of Statistics and Census (INEC) databases.
ICD intestinal infections A05 Other bacterial foodborne intoxications, not elsewhere classi ed A06 Amoebiasis A07 Other protozoal intestinal diseases A08 Viral and other speci ed intestinal infections A09 Other gastroenteritis and colitis of infectious and unspeci ed origin helminthiases, not elsewhere classi ed B82 Unspeci ed intestinal parasitism Data Source and Measurement We used the latest estimated projections based on the 2010 National Population Census to compute incidence and mortality sex-age speci c rates with a yearly resolution from 2011-2020. Information by ethnicity was retrieved directly from the variable "ethnic background" from the o cial National Institute of Statistics and Census (INEC) databases.
Furthermore, anti-CD96 treatment increased the expression of multiple cytokines, including IL2, IL4, IL5, IL13, IL17A, IL17F, IL10, and IL22, consistent with an augmentation of T cell effector function by CD96 (Figure  6B , right). We conducted gene set enrichment analysis using the hallmarks gene sets from the Molecular Signatures Database (30) and pathway analysis using Ingenuity Pathway Analysis (IPA). Multiple hallmarks associated with an activated T cell signature were significantly enriched in the anti-CD96 treatment group. These included gene signatures associated with cell cycle progression, such as Myc, e2F, or G 2 /M checkpoint, consistent with the observed increase in T cell proliferation upon anti-CD96 treatment (Figure  6C ). Signatures related to metabolic reprogramming ("MTORC1 signaling" and "Glycolysis"), effector differentiation ("IFN-γ response"), as well as sustained proliferation and survival ("IL-2/STAT5 signaling" and "TNF-α signaling via NF-κB") were also enriched in the anti-CD96 treatment group.
Agonist anti-CD96 mAb augments the proliferation of TILs. Given that anti-CD96 mAbs were able to costimulate peripheral blood T cells, we asked if this approach could also promote the proliferation of TILs, which are known to exist in various dysfunctional states  (32). Using publicly available data from The Cancer Genome Atlas database through the Tumor Immune Estimation Resource  (33) , we first investigated the effect of CD96 expression on the survival of patients with HPV + or HPV -head and neck squamous cell carcinoma (HNSCC). Interestingly, high CD96 transcript levels correlated with improved.
To assess the ability of anti-CD96 mAbs to inhibit the interaction between CD155 and CD96 and determine IC 50 values, CD96-expressing CHO cells were incubated with titrated amounts of anti-CD96 mAbs and biotinylated human CD155-Fc produced in-house for 30 minutes at 4°C. Cells were washed and CD155-Fc binding was detected using FITC-conjugated streptavidin (BD Biosciences) and flow cytometry analysis. Dose-response curves were obtained from GraphPad Prism software, using a nonlinear regression fit with a variable slope model, and used to determine the IC 50. Antibody VH and VL sequences were cloned into human IgG1 (huG1), N297S human IgG1 (huS1), V12 human IgG1, or human IgG2 (huG2) backbones (MImAbs), and the EC 50 of the various variants was determined as described above. Proliferation and Treg suppression assays.
Quality control analysis of the RNA-Seq data was conducted using FastQC. Sequencing reads were mapped to the hg38 human reference genome using STAR  (37)  and counted using FeatureCount  (38). To identify differentially expressed genes, the R package DESeq2 (version 1. 26. 0) was used with the design formula "~donor + condition. " Genes with a FDR of equal to or less than 0. 05 were considered as significantly different. To generate a matrix of regularized counts for sample visualizations, DESeq2 rlog transformation of the count data was used to stabilize the variance across the mean. To identify functional categories, genes were ranked according to log-fold change (LFC), using apeglm to model the distribution of LFCs  (39). The GSEA_v4. 0. 3 software (UCSD and Broad Institute, ref. 40 ) was used to calculate normalized enrichment scores and FDR values for the 50 Hallmark gene sets.
Subsequent data analysis used IPA (Qiagen). The RNA-Seq data are available from the NCBI Gene Expression Omnibus (GEO) database (GSE193864). Survival plots generation for patients with HNSCC. The Tumor Immune Estimation Resource (33) was used to generate Kaplan-Meier survival curves for HPV + and HPV -patients with HNSCC, based on publicly available data from The Cancer Genome Atlas database. Patients were dichotomized based on CD96 expression levels and log-rank tests were used to compare survival curves and determine P values. Statistics. Statistical analysis was performed using GraphPad Prism (v8. 2. 1) software. Statistical tests applied are indicated throughout and include the log-rank test, 2-tailed paired Student's t test, 1-way ANO-VA, 2-way ANOVA, Friedman's test, Dunn's multiple comparisons test, and 2-tailed Wilcoxon's paired test.
Acknowledgments The authors thank  François Romagné  (MImAbs) and  Christine Penfold  ( University of Southampton ) for antibody production. This work was supported by grants from  Talix Therapeutics and Cancer Research UK  ( A30681 ,  A25778 ). Address correspondence to:  Aymen Al-Shamkhani ,  Centre for Cancer Immunology, School of Cancer Sciences, Faculty of Medicine, University of Southampton , Southampton  SO17 1BJ ,  United Kingdom. Phone:  0044. 23. 81206285 ;Email: aymen@soton. ac. uk. FRD's present address is: GenOway, Lyon, France. SLB's present address is:  Bournemouth University, Fern Barrow, Poole, United Kingdom. XP's present address is:  Emergence Therapeutics, Marseille, France.
Geochemical logging Geochemical logging was performed using the Itrax high-resolution X-ray fluorescence (XRF) core scanner on split cores at the Institute of Geological Sciences, University of Bern (Switzerland). Measurements were taken at 5 mm intervals using an integration time of 20 s at 30 kV and 45 mA. To counter potentially biased measurements linked to the uneven surface of CWC cores, such as the direct measurement of air or of CWC skeletons, a 3-step post treatment of the dataset was carried out. First, X-ray fluorescence values with Argon counts higher than 6000, representing the measurement of air and thereby more porous/cracked media not representative of changes in matrix sediment composition, were removed from the final dataset. Secondly, each individual measurement point was compared to high-resolution core images to assess if the measurement was taken on the matrix sediment or not.