dataesr/scientific-paragraphs-categorization huggingface dataset:
Defining an odds ratio or risk ratio for each element of the canonical pathway and the other pathways will be the next step to understand the imbalance that occurs during NAFLD progression. This study also showed for the first time the role of proteins YWHAZ and YWHAH which belong to the 14-3-3 protein family. Both proteins YWHAZ and YWHAH are implicated in HCC progression and metastasis. Indeed, the YWHAZ gene is well known to be up-regulated in HCC patients but now YWHAZ is identified as an oncogene based on recent research on Cancer Genome Atlas  [60]  and implicated in mitochondrial function  [79]. YWHAH has also been implicated in liver cancer depending on regulated c-myc expression  [63] , in insulin resistance  [62]  and mitochondrial function  [80]. Finally, to make the final connection between genes implicated in NASH progression and hepatocellular carcinoma development, we investigated the genes in two genome datasets including normal, cirrhotic, and HCC patients (GSE14323) and also 353 patients with HCC for which copy number variations and SNP were identified.
Indeed, SCD, FADS1, and FADS2 are dysregulated in NASH as we showed in this study and previously  [19, 20, 81]. Finally, to go further into the understanding of the genes implicated in the canonical pathway described in this study, the next step will be first to test this canonical pathway in a different new cohort of patients with steatosis and NASH associated to different genetic backgrounds and environments in prospective studies. Afterwards, it will be to perform single-cell analysis in different liver biopsies from healthy lean and obese patients, lean and obese patients with NAFL, NASH, cirrhosis, and HCC developed from NASH. The cells that should be analyzed will be at least hepatocytes, cholangiocytes, hepatic stellate cells, endothelial cells, and Kupffer cells. Indeed, in our study we used dataset from liver biopsies reflecting the bulk liver RNA.
Bioinformatics and Statistical Analyses Text-Mining Approach Bioinformatics of gene expression analysis was approached by dimensional reduction of gene expression matrix by text-mining approach. Workflow of this analysis is described in Figure  1. Text-mining based on MeSH, a natural language processing, allowed us to found connections between language terms and gene identifiers in scientific literature such as PubMed database. Co-occurrence quantification by this approach allowed to connect gene databases to scientific literature and to highlighted important scientific relations with small set of molecules  [55]. Mathematical dimensional reduction of transcriptome matrix was focused on significant genes found connected to literature after false discovery rate (FDR) correction in order to minimized false positive discovery and set-up below 5%  [89].
Lipid genes differential expressed between NASH, healthy obese, and NAFL obese liver samples were searched with Significance Analysis for Microarray (SAM) algorithm by implementing FDR threshold under 5 percent  [90]. Unsupervised PCA was performed with "FactoMineR" R-package and group discrimination p-value was estimated with variable correlation to the first principal component axis  [91, 92]. Functional enrichment analysis was performed with the standalone software GO-Elite version 1. 2  [93]  on Gene Ontology-Biological Process (GO-BP) and the Comparative Toxicogenomics Database (CTD). Functional enrichment networks were built with Cytoscape software version 3. 0. [94]  with information collected during functional enrichment: the blue edge represents connections between genes and functions, blue circle nodes represent enriched genes, and octagon nodes represent enriched functions, scale color from yellow to purple in the function nodes is proportional to the Z-scores obtained during the enrichment.
Transcriptome Dataset Analysis Narrowing Text-Mining Discovery Transcriptome dataset of liver samples included HO subjects (n = 24), NAFL obese patients (n = 23), and samples of patients affected by NASH (n = 24)  [61] ; it was downloaded from Gene Expression Omnibus database under accession number GSE61260 (https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE61260). These experiments were performed with Affymetrix technology and microarray version Human Gene 1. 1 ST array. Normalized matrix by robust multi-array average (RMA) algorithm  [97]  was merged on identifier column with corresponding annotation platform GPL11532 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GPL11532) to create an in silico experimental matrix. This matrix comprises 71 patients divided in three groups for which each liver sample hybridized array comprised more than 750,000 unique 25-mer oligonucleotide probes that interrogate more than 28,000 genes.
Then, receiver operating characteristic (ROC) curves with area under the curve (AUC) performed on altered lipid genes were done with "pROC" and "Epi" R-packages  [98, 99]. Boxplots, Kruskal-Wallis test, and Student t-test were performed in R software environment version 3. 4. 3 [92]. The patient sample characteristics from GSE61260 dataset can be found in the paper published by Horvath et al. on the Supplementary Information of the paper  [61]. Briefly, patients were only Caucasians from Germany. RNA was extracted from human liver samples and analyzed as described above. Patients were divided in different groups such as NAFL (n = 23), NASH (n = 24), HO subjects (n = 24) based on the results of the histology analysis performed by a pathologist. Liver samples were obtained percutaneously for patients undergoing liver biopsy for suspected NAFLD or intraoperatively for assessment of liver histology.
Confirmation of NASH Canonical Pathway by Using an Independent Validation Cohort and a Different Transcriptome Technology Analysis An independent transcriptome series was process in order to validate the inflammatory/lipid gene expression profile of the NASH canonical pathway using a published GSE33814  [65]  including 44 human liver tissue surgical samples (normal n = 13; steatosis n = 19; steatohepatitis n = 12) which was process with Illumina HumanWG-6 v3. 0 expression beadchip technology. Normalized dataset was annotated with corresponding Gene Expression Omnibus platform GPL6884. Annotated matrix was restricted to inflammatory lipidic signature by SQL querying and process to perform supervised expression heatmap with "made4" R-package/Bioconductor repository  [101]  and unsupervised PCA with "FactoMineR" R-package  [91].
Genes Implicated in NASH Progression Involved in Progression to Hepatocellular Carcinoma: Liver Cancer Genomic Data To assess the play of the genes implicated in NASH progression, we investigate the gene profile of GSE14323  [68]. Liver tissue samples were obtained from patients waiting for liver transplantation at one of the GR2HCC Centers. Additionally, normal liver and tumor samples were also obtained from the Liver Tissue Cell Distribution System. For each sample, RNA was extracted and hybridized to an Affymetrix GeneChip ([HG-U133A] Affymetrix Human Genome U133A Array) including 19 controls, 41 HCV-cirrhosis and 38 HCV-advanced hepatocellular carcinoma patients. In the second way, we investigated genomic data (copy number variations: CNV and single nucleotide polymorphism: SNP) of liver cancer cohort from The Cancer Genome Atlas (TCGA) consortium  [72]  through cbioportal web application (https://www.
cbioportal. org/) [102, 103]. This dataset contained Tumor Samples with sequencing and CNA data (353 patients/samples). Oncoprint of the CNV and SNP comprised in this dataset for the 30 lipid-related genes were performed. The number of patients who relapsed or progressed based on their cancer genetic profile (i. e. free survival) was assessed over the time using Kaplan-Meier analysis with "survival" R-package and analyzed using stratified log-rank survival test. The research protocol was approved by the respective institutional review boards, and informed consent was obtained in all cases, as published in the original paper  [68].
To compute treatment exposure, we calculated the ratio of the dose intensity measured as a proportion of the theoretical dose intensity. Lantuejoul). 21 The pathologist in charge of analysing the specimens was blinded to treatment and patient response. Expression was categorized according to tumour proportion scores (i. e. , percentage of tumour cells with membranous PD-L1 staining, regardless of intensity). An exploratory cut-off threshold of 25% tumour cells presenting membranous PD-L1 staining was chosen based on data resulting from a post-hoc analysis to ensure there were sufficient numbers of patients in each subset.
It should also be underlined that the safety profiles of nivolumab alone or combined with ipilimumab compared favourably with what has already been posited in the literature for platinum-based chemotherapy, and that AEs observed in our trial were similar in type to those reported for immunotherapy drugs used in other settings and in numerous previous trials. This AE incidence could certainly raise questions regarding the chosen dosing schedule in the combination arm. As mentioned above, we chose what was considered to be the most tolerable of six schemes previously tested in the Checkmate 012 Phase 1 trial  22  , and this scheme being subsequently selected for the NSCLC Phase 3 Checkmate 227 trial  23  and the ongoing first-line Phase 3 Checkmate 743 trial in MPM patients (NCT02899299). It is also paramount to note that most AEs were classed as Grade 1-2, including a substantial number that were purely biological or asymptomatic, rapidly resolving either spontaneously or once treatment was interrupted.
Also, a Phase Ib trial was recently published reporting 20% overall response rate (ORR) (95% CI: 6•8-40•7) and 52% stable disease in 25 patients with PD-L1-expressing MPM (>1% positive tumour cells) treated with the anti-PD-1 antibody pembrolizumab, mostly as second-line treatment, along with a median duration of response (DOR) of 12 months (95% CI: 3•7-not reached), without any safety concerns. Lastly, a single-arm Phase II trial ("NIBIT-MESO-1"; NCT02588131) assessed the combination of 1mg/kg anti-CTLA-4 tremelimumab and 20mg/kg anti-PDL-1 durvalumab, intravenously injected every 4 weeks in four doses and followed by maintenance durvalumab, as first-or second-line treatment in unresectable malignant mesothelioma patients. The authors reported evidence of clinical activity, demonstrated by 27•5% (out of 40 patients) exhibiting immune-related (ir) partial response (median DOR: 16•1 months) and another 65%-patient subset exhibiting ir disease control, resulting in a median ir PFS of 8 months and mOS of 16•6 months.
Acknowledgments Bristol-Myers-Squibb (BMS) (France)  supplied nivolumab/ipilimumab and a research grant to  Intergroupe Francophone de Cancérologie Thoracique (IFCT)  for the MAPS-2 biomarkers studies. We thank the participating patients and their families as well as the study teams involved in the trial, the clinical research assistants, study coordinators and IFCT operations staff, as well as  Dr. G. Cremer  at  Cremer Consulting (Strasbourg, France ) for medical writing support. We thank also the  French National Cancer Institute (INCa)  and the  French League against Cancer.
Nivolumab, Ipilimumab and an unrestricted research grant were provided by Bristol-Myers-Squibb (BMS) (France). The Intergroupe Francophone de Cancérologie Thoracique (IFCT) designed the whole trial, collected and interpreted the data. IFCT investigators and staff participated in study design and data analysis. BMS had no role in designing the research, analyzing the data or writing the report. The corresponding author (AS) and co-principal investigator (GZ) had full access to all study raw data and took final responsibility in the decision to publish the trial data. Role of the funding source L1 tumour expression (28. 8 PharmDX or SP263 Assays) using a non-adjusted Cox model. This IFCT-1501 trial was registered beforehand with the European Union Clinical Trials Register (number 2015- 004475-75) and ClinicalTrials.
Gas Chromatography of Citrus Essential Oils Analysis of BEO and OEO was carried out by Shimadzu system composed of GC-2010 equipped with capillary columns flame ionization detector (GC-FID). The gas chromatograph column is packed with the polyimide-coated fused silica phase. A GC Solution Software (Shimadzu, Milan, Italy) was used for the acquisition of the data. An Agilent 7890 GC apparatus equipped with a split/splitless injection and an FID detector, maintained at 250 • C, was used. As previously reported by  Badri et al. (2018) , a volume of 1 µL BEO/OEO was injected in split mode (1/120) to the HP-1 GC column (50 m × 0. 320 mm × 0. 50 µm). During this procedure, helium, used as a carrier gas, was maintained at constant pressure (3 psi). During the analysis, the oven temperature was modulated as follows: 8 min at 80 • C, increasing to 220 • C at 2 • C/min, from 220 to 310 • C at 10 • C/min and finally held isothermally 10 min at 310 • C  [22].
Essential oil EE% was determined in the following way: 1 mL of a given sample was introduced in Amicon-Ultra filter-0. 5 mL and then centrifuged at 14,000 rpm during 30 min. Supernatant was separated from pellet. The amount of encapsulated essential oil was determined after pellet dissolution in ethanol. Essential oil encapsulation efficiency (EE%) was directly determined by using the established calibration curve (Equations (  2 ) and (  3 )) and the following equation: EE = M Mo × 100 (4) where M (mg) is the amount of essential oil loaded in nanoparticles and Mo (mg) is the initial amount of essential oil added to the organic phase. All the measurements were carried out in triplicate. The UV-obtained data were compared with those obtained from gas chromatography (GC) analysis.
Historical ocean temperature profiles, particularly those outside expert community control such as the Argo Data Management System (ADMS) or outside highly controlled programs such as the World Ocean Circulation Experiment (WOCE) can suffer from inconsistencies in QC that can impact OHC estimation. The International Quality Controlled Oceanographic Database (IQuOD) program  (Domingues and Palmer, 2015)   2  is filling this gap by developing internationally coordinated delayed QC standards which will be implemented in a homogeneous, structured, and fully documented form.
Argo's leadership model must prove capable of spanning scientific generations while preserving its focus, originality, and collaborative nature. Finally, Argo's role as an inter-dependent element of the integrated observing system requires that all elements thrive together. Diversity in sensors and platforms are essential to help build confidence in the OHC record, particularly for tracking the small but persistent global ocean warming signals. With its present dependence on one sensor manufacturer, Argo is highly vulnerable to manufacturing errors. GO-SHIP and OceanSITE records, which are post calibrated and of high quality, are essential points of cross-reference for Argo. Satellite altimeter data are also used to identify and remove suspect Argo data. XBT lines give an insight into scales of variability not resolved by Argo, particularly near the margins of the open oceans.
, 2018). Inter-comparison among three available ocean analyses (EN4, Ishii and IAP dataset) revealed a large spread of OHC change in the WBC and ACC regions: >10 × 10 8 J m -2 even during the Argo era (2005-2012), 2-10 times larger than the open ocean regions  (Wang et al. , 2018). Removal of WBC and ACC regions reduces the spread of global integrated OHC change estimates in the upper 1500 m by 13% during 1976-2012, despite these regions small (∼6%) portion of the global ocean. Within these regions, the rootmean-squared error (RMSE) of OHC tendency could be larger than 100 Wm -2 locally (i. e. , with respect to the local surface and not with respect to the global surface at TOA,  Wang et al. , 2018) , which has the same magnitude as the climatology mean net air-sea heat flux  (Liang and Yu, 2016).
The large error among these regions is partially because the calculation of tendency from a noisy time series exacerbates the noise. Therefore, advanced ocean observing systems in the WBC and ACC regions are required to better resolve mesoscale and sub-mesoscale variability and aid in higher resolution ocean analysis. Complementary to Argo, high-resolution XBT casts, gliders under pre-set routines, as well as mooring networks such as the North Pacific Ocean Circulation Experiment (NPOCE)  (Wang and Hu, 2010)  could reduce the uncertainty of OHC change estimates. It has always been difficult to obtain subsurface ocean temperature measurements in the Arctic, leading to a dearth of historic and recent data  (Zweng et al. , 2018). In 2017, there were subsurface temperature data from only three research cruises north of 66 • N generally available through the World Ocean Database, down from 12 in 2016.
Some semi-enclosed ocean areas such as the Mediterranean and the Gulf of Mexico are sampled by Argo and other systems. Others, such as the Sea of Okhotsk have almost no available data for the last 10 years. Argo extensions and the systematic deployment of gliders can add reliable data collection in marginal seas. The ocean below 2000 m has warmed significantly since the 1990s, accounting for ∼10% of the total ocean heat uptake  (Purkey and Johnson, 2010; Rhein et al. , 2013; Desbruyères et al. , 2016). Recent estimates of the deep OHC change are based on decadal repeats of coarse hydrographic sections  (Talley et al. , 2016)  and the only statistically robust deep OHC trends are basin-wide, decadal averages owing to limited data (Figure  2 ). Nonetheless, a spatially coherent global picture has emerged of an intensified deep warming, originating from deep water formation sites in the Southern Ocean and propagating through the Meridional Overturning Circulation, with a statistically significant contribution to the global ocean heat uptake.
d The range indicates the uncertainty for different surface area integral from simple area integral of objective map of the sparse dataset to weighted integral defined as the simple integral weighted by the ratio of the total area over the "observed" area. e The upper row indicates OHC estimates using optimally analyzed temperatures on a 1 • × 1 • grid, and the uncertainties in OHC is computed following  Ishii and Kimoto (2009). The lower row is for observed temperatures arithmetically averaged in 5 Estimating the uncertainty in OHC changes at interannual time scales is more challenging because of insufficient spatiotemporal coverage. So far, few studies have provided estimates. Estimates of annual OHC changes for 0-2000 m have standard errors of 0. 3-0. 6 Wm -2 over the Argo era, and those errors increase substantially for the pre-Argo time period  (Johnson et al.
Gap Analysis of the Current Measurement Capacity and Ways for Improvement Currently there are about 25 years of satellite altimeter data since 1993 made by a series of altimeter missions. The dataset is long enough to study interannual variations and bidecadal trends in the global OHC. ITOT can be used to analyze 3 years of GeoSat data from 1986-1989 to retrieve OHC changes in the 1980's. In the next 5 years, there will be a few new altimeter missions in operation including Jason classes, Sentinal-3 series, HY-2 series (Haiyang-2,  Dong et al. , 2004) , and GFO-2 (Geosat Follow-On-2,  Benveniste, 2011). The combination of these satellites will maintain the measurement capability of ITOT at the present level. In the next 5-10 years, the two major challenges of ITOT may be addressed, leading to improvements of ITOT's measurement capability.
Radiative Flux Radiative fluxes (irradiances) at the ocean and atmosphere boundary are estimated by radiative transfer models. These models are based on radiative transfer theory and solve an integro-differential equation of radiative transfer typically with a two-or a four-stream approximation. Polarization state is neglected. Primary inputs to the radiative transfer model are height dependent atmospheric temperature and water vapor mixing ratio, cloud and aerosol properties, ocean surface albedo and emissivity. Satellite observation-based estimates generally take temperature and humidity from reanalysis data products. In additions, the vertical profile of particle size and phase can be derived from their observations. Once particle size and phase are derived, wavelength dependent extinction coefficient, single scattering albedo and asymmetry parameter are determined theoretically by assuming cloud particle shape.
g. ,  Wouters et al. , 2014). In 05/2018, the GRACE Follow-On mission was launched to continue the GRACE data record. The measurement system of both missions is similar: two identical satellites orbit the Earth at ∼490 km, and a microwave ranging system tracks minute variations (order if micro-meters) of the separation distance, which is about 220 km. Mass variations at or near the Earth's surface alter the Earth's gravity field, which in turn impacts the relative distance between the twin gravimetry satellites. The observations are used to calculate mass changes to an accuracy of ∼1 cm over an area of 300 × 300 km. Averaged over the global oceans, the measurement uncertainty on a monthly time scale is on the order of 1-2 mm. However, as GRACE(-FO) cannot distinguish between the source of the observed gravity change, the isolation of ocean mass from the observations requires several corrections, each of which have different error characteristics.
1 ± 0. 08 mmyr -1 (5-95% CL,  Purkey and Johnson, 2010; Desbruyères et al. , 2016)  which correspond to 0. 04 ± 0. 04 Wm -2. However, presently the magnitude of measurement uncertainty associated with SL thermo (±0. 5 mmyr -1 , see below) does not allow for the closure of the sea level budget accurately enough to estimate the deep ocean contribution on decadal time scales (The WCRP Global sea level budget group, 2018). From SL thermo and with knowledge of the ocean's expansion efficiency of heat, ε, OHC can be derived (see Equation  2 ). The period over which we present the published datasets, represents the  Argo-era (2006 Argo-era ( -2015)). The global mean SL total shown in Figure  8  (black line) is a multi-product average based on the six time series presented in section "Sea Level. " The shading indicates the monthly measurement error derived using the error budget approach (see section "Sea Level") that considers all possible sources of error in the measurement system.
We calculate the SL total trend using weighted least squares regression. The calculated trend is 3. 3 ± 0. 5 mmyr -1 (5-95% CL) largely in line with earlier estimates, by  Dieng et al. (2017)  and The WCRP Global sea level budget group (2018) over similar time period. The time series represent monthly anomalies (mean annual cycle removed) that have been smoothed by applying a 5-month running average filter. The calculation of decadal trend uncertainty is detailed in section "Sea Level. " SL mass is a multi-product average (Figure  8 , red line) derived from four GRACE-based datasets provided by the Center for Space Research (CSR), the Helmholtz Centre Potsdam, German Research Centre for Geosciences (GFZ), and the Jet Propulsion Laboratory (JPL). The global mean series together with standard errors after  Johnson and Chambers (2013)  and  Chambers et al.