dataesr/scientific-paragraphs-categorization huggingface dataset:
47 (95%CI 0. 27-0. 82)] [9]. Proton therapy in patients with chordomas and chondrosarcomas was associated with improved OS at 5 years, respectively, 100% versus 34. 1% (p = 0. 03) and 75. 0% versus 19. 1% (p = 0. 05) using the US National Cancer Database for the years 2003-2014  [10]. A meta-analysis showed that particle therapy was more effective following surgery for chordoma than conventional RT with higher percentages of survival after 10 years for proton therapy [60% (95% CI, 43%-77%)] and CIRT [45% (95% CI, 36%-55%)]  [11]. Proton therapy is associated with improved OS [HR 0. 47 (95%CI 0. 38-0. 58)] compared to photon RT for patients with primary gliomas in the US National Cancer Database  [12]. Consecutive patients with oesophageal cancer of the University of Texas MD Anderson Cancer Center receiving proton beam therapy (PBT) were compared with patients receiving intensity-modulated radiation therapy (IMRT).
Another European project summarises the data on carbon ion therapy  [20]. Different evidence-based clinical trial strategies can be applied to investigate whether the use of protons over photons is justified: the choice of trial design depends on several factors, such as the primary study objective (efficacy versus prevention), the availability of high quality multivariable normal tissue complication probability (NTCP)-models, financial resources and national reimbursement policies  [21]. We could identify seven ongoing phase 3 clinical trials on clinicaltrial. gov (Table  2 ): • Radiation therapy with protons or photons in treating patients with liver cancer (ClinicalTrials. gov identifier: NCT03186898) • Trial of proton versus carbon ion radiation therapy in patients with low and intermediate grade chondrosarcoma of the skull base (CSP12C) (ClinicalTrials.
gov identifier: NCT01182753) • Trial of proton versus carbon ion radiation therapy in patients with chordoma of the skull base (HIT-1) (ClinicalTrials. gov identifier: NCT01182779) • Comparing proton therapy to photon radiation therapy for oesophageal cancer (ClinicalTrials. gov identifier: NCT03801876) • Comparing photon therapy to proton therapy to treat patients with lung cancer (ClinicalTrials. gov identifier: NCT01993810) • Randomised trial of intensity-modulated PBT (IMPT) versus intensity-modulated photon therapy (IMRT) for the treatment of oropharyngeal cancer of the head and neck (NCT01893307)  [22].
21 (0. 10-0. 33) stereotactic RT 0. 40 (0. 30-0. 55) proton RT 0. 60 (0. 43-0. 77) carbon ion RT 0. 45 (0. 36-0. 55) Jhaveri et al [12] National Cancer Database, Primary gliomas 170 Proton RT Overall survival 0. 66 (0. 53-0. 83) USA 49,405 Photon RT Xi et al [13] MD Anderson Cancer Oesophageal cancer 211 IMRT Overall survival 1. 45 (1. 09-1. 94) Center, USA 132 Proton RT 211 IMRT Progression free 1. 56 (1. 19-2. 05) 132 Proton RT interval Shiraishi et al* [14] MD Anderson Cancer Oesophageal cancer 136 IMRT Grade 4 lymphopenia Center, USA 136 Proton RT 0. 29 (0. 16-0. 52) * propensity-matched study, IMRT: intensity-modulated radiation therapy.
Reservoir planning without the explicit accommodation of evaporation loss leads to errors in capacity estimates. However, whenever evaporation loss is considered, its quantification uses linear approximations of the intrinsically nonlinear height-area-storage (H-A-S) relationship to estimate the reservoir area, leading to bias in capacity estimates. In this work, biases resulting from using various H-A-S models are evaluated. These models include linear and nonlinear functions, either specifically developed for the case-study sites or available in the Global Reservoir and Dam (GRanD) database. All empirically derived approximations used data for two dams in India: the Bhakra on Sutlej River and the Pong on the Beas River, both tributaries of the Indus River. The results showed that linear H-A-S models underestimate the exposed surface area of the Pong reservoir by up to 11.
Examples of such nonlinear functions are the H-A-S relationships developed from the Global Reservoir and Dam (GRanD) database  [11]  that are used in many regional and global water-resource studies. The GRanD models were developed using data from several world reservoirs and are nonlinear in conformity with the intrinsic nonlinear nature of the relationships. Another possibility is to use evaporation as water depth as in, e. g. , the Water Evaluation and Planning (WEAP) tool  [12] , which completely removes the need for the exposed surface area. The aim of this study is to assess the effect of different empirical formulations of H-A-S reservoir relationships on reservoir area and capacity estimates. This was achieved by: (i). empirically fitting linear and nonlinear H-A-S functions to the observed bathymetric area, volume, and height data for the selected reservoirs, as well as extracting existing functions in the GRanD database; (ii).
Data Collection The study required runoff, evaporation, and rainfall data of case-study reservoirs for the implementation of reservoir-planning analysis. Additionally, topographical data in the form of the corresponding H-A-S relationship at the analyzed reservoir sites were also required for developing the empirical functions to directly incorporate the evaporation loss in planning analysis. The study used the data for two reservoirs located on the Beas and Sutlej Rivers, respectively, the main tributaries of the Indus River in India. The two reservoirs are close, both located in the state of Himachal Pradesh, and are the main case-study reservoirs for an ongoing large-scale research program on sustaining water resources in the Indian Himalayas under climate change (SusHi-Wat: Sustainable Himalayan Water Resources in a Changing Climate, Project NE/N016394/1).
The current work contributes to the overall successful implementation of the research project. All the required data were sourced from the Bhakra-Beas Management Board (BBMB), which is responsible for the management and operation of the two reservoirs. For the Bhakra reservoir on Sutlej River, the monthly time series of evaporation rates, direct rainfall, inflow, and releases spanning from January 2000 to December 2006 were collected. For the Pong reservoir on Beas River, the monthly data spanned from January 2001 to December 2010. The time series of the net evaporation depth were calculated as the difference between the time series of the measured evaporation rates and direct precipitation for each reservoir. Although these data are relatively short to assess long-term behavior, they were the only ones available from the BBMB at the time.
Nonlinear Area-Storage Equation Following examples in the literature, e. g. , the GRanD database, the intrinsic nonlinear area-storage relationship was formulated using a power function of form A t = a S t b (4) where A t is the reservoir surface area (10 6 m 2 ) at time t, S t is the corresponding gross (active + dead) storage volume (10 6 m 3 ) at t, and a and b are the parameters of the model. The parameters of Equation (4) were obtained by least-squares regression fitting using the available area-storage data.
GRanD Volume-Area Equation Given the popularity with which the GRanD models are used for regional water-resource assessment across the globe  [16] , it was felt that this study would not be complete without testing its efficacy at the case-study reservoirs. The generalised GRanD volume-area regression model was developed from the Global Reservoir and Dam database using 5824 reservoirs  [11]. Its main purpose is to estimate missing reservoir volumes around the world. Although both volume-area and volume-height-area equations are available in the GRanD database, this study only deployed the volume-area equation to ensure consistency with the other models in the research. The GRanD volume-area model takes the particular form: S t = 30. 68(A t ) 0. 978  (17)  where A t is the reservoir surface area at time t (10 6 m 2 or km 2 ); S t is the corresponding reservoir total storage volume (10 6 m 3 ).
Height-Area-Storage Models of the Reservoirs The calibrated H-A-S models are presented in Table  2  for both reservoirs. As a reminder, all empirical functions apart from the GRanD were calibrated using H-A-S data for the respective reservoir sites. The GRanD model was taken directly from the database and is therefore the same for both reservoirs. The steepness of the slopes for each relationship has an influence on the rate of evaporation loss from the reservoir. As seen in Figure  4a , the area-storage relationship for Pong is steeper than Bhakra, implying that, at the former site, a small change in reservoir storage significantly alters the exposed surface area and, by extension, the evaporation loss from the reservoir. This would mean that the earlier observation regarding the relative spread of both reservoirs, as presented in Table  1 , is not restricted to the top water level alone, but applies throughout the entire area-storage range at the Pong and Bhakra reservoir sites.
Height-Area-Storage Models of the Reservoirs The calibrated H-A-S models are presented in Table  2  for both reservoirs. As a reminder, all empirical functions apart from the GRanD were calibrated using H-A-S data for the respective reservoir sites. The GRanD model was taken directly from the database and is therefore the same for both reservoirs. As the R 2 values indicate, nonlinear functions are better than linear ones, which should be expected from the intrinsically nonlinear nature of the relationship. Using three piecewise linear approximations for the area-storage relationship appears to improve the prediction. As found in the previous independent studies discussed earlier, the performance of the GRanD model is by far inferior to all empirical models calibrated in the current study.
Data Evaluation A list of all the data that will be evaluated on a cyclical basis is provided below. These include quantitative data and themes that will be explored qualitatively with HCPs, patients, and carers to inform the next clinic. Issues for improvement, changes to the clinic process, and the rationale for changes will be recorded in a learning log. The impact of changes will be reviewed following the subsequent clinic visit and from any further feedback from qualitative interviews and focus groups. Fidelity to the original project plans and reasons for the changes made will be reported as part of the project analysis. This list of outcomes is not exhaustive, as the cyclical approach may identify other key areas that are important to monitor. If additional questionnaires or methods of data collection from patients are required, they will be submitted to the ethics committee as an amendment.
Analysis of Quantitative Outcome Data The objective of all analyses is to examine differences in measured variables between time points. The following comparisons will be made: Comparisons between time points will be made using the paired t test or the Wilcoxon matched-pairs test, depending on the distribution of the changes in outcome between time points. Summaries of the measures of monitoring and supported follow-up will be prepared (measures outlined above). Patient satisfaction measures will be summarized descriptively. Numbers and percentages will be used for categorical variables, whereas mean and SD or median and interquartile range will be used for continuous variables. Uncertainty in percentages, means, and medians will be quantified by calculating the appropriate 95% CIs.
Nonalcoholic steatohepatitis (NASH) is becoming a public health problem worldwide. While the number of research studies on NASH progression rises every year, sometime their findings are controversial. To identify the most important and commonly described findings related to NASH progression, we used an original bioinformatics, integrative, text-mining approach that combines PubMed database querying and available gene expression omnibus dataset. We have identified a signature of 25 genes that are commonly found to be dysregulated during steatosis progression to NASH and cancer. These genes are implicated in lipid metabolism, insulin resistance, inflammation, and cancer. They are functionally connected, forming the basis necessary for steatosis progression to NASH and further progression to hepatocellular carcinoma (HCC).
A plethora of publicly available biomedical resources do currently exist and are constantly increasing. In parallel, specialized repositories have been developed, indexing numerous clinical and biomedical tools. Natural Language Processing research in the clinical domain has been active since the 1960s. In addition to maintaining the GenBank®nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank®and other biological data made available through the NCBI website  [49] [50] [51] [52] [53] [54]. Here we used text-mining and data-mining bioinformatic approaches by investigating Medical Subject Headings (MeSH) and gene expression omnibus dataset (NCBI) combined with different databases (e. g. , Kyoto Encyclopedia Gene and Genome/KEGG or InnateDB) to determine specific and global mechanisms involved in NASH progression.
A Subset of Lipid-Related Genes is Differentially Expressed in NASH vs Healthy Obese Human Liver Samples Natural language processing is performed to discover semantic relationships with scientific literature  [51] , and to connect literature to databases like RefSeq gene symbol identifiers  [55]. The most important resource for text-mining applications is currently the PubMed database developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) (https://www. nlm. nih. gov/pubmed) [49, 50, [52] [53] [54] 56]. Text-mining analysis by connecting the PubMed database using MeSH described by the workflow on Figure  1  allowed us to find 320,794 co-occurrence connections between gene symbol and lipid-related terms: "Non-esterified Fatty acids" (29,003connections), "lipids" (193,087 connections), "cholesterol" (13,680 connections), "ceramides" (18,142 connections), "sphingolipids"(51,827 connections), and "phospholipids" (15,055 connections) related to "NASH" (Figure  2A ).
False discovery rate correction performed on genes selected by text-mining allowed us to find 87 lipid-related genes that show significant association in scientific literature (Figure  2B  and Supplementary Datasets Table  S1  online). Mathematical dimensional reduction of matrix in transcriptome analysis, such as using gene set enrichment analysis (GSEA), allowed us to improve discovery by reducing the false positive discovery rate  [57]. We applied text-mining dimensional reduction on normalized gene expression matrix from dataset GSE61260 in order to find differentially expressed lipid-related genes with improved accuracy. Significance Analysis for Microarray (SAM) algorithm was employed on reduced matrix (87 genes × 48 samples) between liver samples from NASH patients (n = 24) and liver samples from healthy obese (HO) subjects.
Twenty-five genes were found to be differentially expressed between samples with an FDR threshold set to less than five percent (FDR < 5%; Supplementary Datasets Table  S2  online). Expression heatmap revealed that 22 of 25 differentially expressed lipid-related genes were over-expressed in NASH liver samples compared to healthy obese liver samples and three were down-regulated (PPARA, PPARGC1A, and CNPB) associated with misclassification error rate of 16. 6% (Figure  2C ). LPL gene encoding for lipoprotein lipase was the most over-expressed gene in NASH with a fold change of +1. 93, followed by chemokine CCL2 (fold change = +1. 61) and the enzyme FADS2 (fatty acid desaturase 2, fold change = +1. 57) as summarized in Supplementary Datasets Table  S2  online. Transcriptome expression matrix was increased by addition of 25 liver samples of patients with NAFL (resulting lipid related matrix dimensions: 87 genes × 73 samples).
05). (C) Expression heatmap of lipid related genes found differential expressed between NASH liver samples (n = 24) and Healthy obese liver samples (n = 24) in transcriptome dataset GSE21260 (D) unsupervised principal component analysis performed with lipid related genes found differentially expressed between NASH and Healthy obese in GSE21260 and impact of their prediction to predicted NAFL samples (n = 23), p-value was calculated by correlation of sample group discrimination on first principal axis.
NASH is Associated with Impaired Function of Genes Implicated in Lipid Metabolism, Insulin-Resistance, Inflammation and Tumorigenesis We further examined the relationship between lipid genes and HCC, and found that differentially expressed lipid-related genes (Supplementary Datasets Table  S2  online) were enriched on the Comparative Toxicogenomics Database which connects genes to disease phenotypes  [58]. As expected, this enrichment confirmed that these genes have a well association with phenotypic manifestations of NASH such as lipid metabolism perturbations linked to fatty liver, hypercholesterolemia, and hypertriglyceridemia. They are also associated with Type 1 and Type 2 diabetes, insulin resistance, and impairment in clinical parameters such as body weight and atherosclerosis (Figure  3A ).
These results suggest that the lipid text-mining approach for analysis of the transcriptome that we developed is well adapted to study the altered gene-expression signature of NASH (Figure  3B ). The 25 differentially expressed lipid-related genes identified earlier (Supplementary Datasets Table  S2  online) were also analyzed by their functionalities using Gene Ontology Biological Process database. As expected, functional enrichment in this database revealed that these genes are mostly implicated in lipid metabolism, especially cholesterol storage (LPL, CD36, and transcription factor SREBF2 were significantly up-regulated), fatty acid oxidation (PPARA and PPARGC1A were down-regulated), long-chain fatty acid import (transporters SLC27A4 and CD36 were up-regulated), and triglyceride biosynthesis (FASN, DGAT1, and LPL were all upregulated) as summarized in Figure  4.
Connecting Lipid Related Genes Altered in NASH to Immunity, Inflammation and Liver Pathogenesis Progression As observed above in functional enrichment performed with Gene Ontology Biological Process, lipid-related genes altered in NASH also showed implication of monocyte/macrophage cells and inflammation (Figure  4 ). Responses to hypoxia, gluconeogenesis, and circadian rhythm were also found to be affected by down-regulation of PPAR genes. So, these results showed that inflammation and progression of liver pathogenesis could be affected by lipid related genes in NASH, as demonstrated above. In order to cross-reference this information, we wanted to verify the role these molecules may play in immunity, inflammation, and liver pathogenesis progression through the PubMed database. Candidate gene prioritization approach allowed to focus on important affected genes with literature relevance  [59].
Connection with NCBI database allowed to select the 10 best lipid-related genes that are important in the liver (Figure  5A  and Supplementary Datasets Table  S3  online). For these 10 liver-related genes, we applied PubMed filtering with secondary terms related to immunity, inflammation, and liver pathogenesis progression such as stroma, hepatocellular carcinoma, liver cancer, cirrhosis, hepatic macrophage, B-lymphocyte, T-lymphocyte, inflammation, and immunomodulation. This analysis showed a good literature prioritization for CD36 and TNF, and also highlighted LIPA gene which is well-known to play a role in liver pathophysiology, (Figure  5B  and Supplementary Datasets Table  S4  online). Among these 10 prioritized genes, individual ROC-curves and expression boxplots (Figure  5C ) were performed with transcriptome matrix for genes that were found also deregulated in NASH liver samples (Supplementary Datasets Table S2 online).
The 14-3-3 Protein Family is the Cornerstone between Dysregulated Lipid Metabolism, Inflammatory and Insulin Pathways during NASH Progression to HCC Thus, with the first analysis, genes implicated in inflammation, lipid metabolism, and progression to cancer were highlighted, and the second analysis showed connection between lipid dysregulation and insulin resistance and inflammation. We then focused on the YWHAZ gene identified in Figure  2A , a member of 14-3-3 protein family, which has been associated with liver cancer  [60]. A total of 399 YWHAZ-protein partners were identified based on InnateDB database. Significance analysis of microarray (SAM) of the 399 protein partners performed on GSE61260  [61]  identified 44 genes that discriminated between NAFL patients and NASH patients as shown by the heatmap (Figure  6A  and Supplementary Datasets Table  S5  online) and by PCA analysis (Figure  6B ).
The 44 genes are up-regulated in the group of NASH patients demonstrating their connection (Figure  6C ) with inflammatory processes, cell proliferation, metabolism, and especially cell-cell adhesion based on GO-BP and KEGG pathway analysis (Figure  6D  and 6E ). One of these 44 genes, YWHAH (Figure  6A  and Supplementary Datasets Table  S5  online) belongs also to the 14-3-3 proteins family and it has also been associated to insulin function  [62]  and liver cancer  [63]. Taken together, these data show that 14-3-3 protein family has an important role in the progression of NAFL to NASH through the dysregulation of metabolism and inflammatory processes associated with cell proliferation and dysregulation in cell-cell adhesion, making the link to the progression to HCC. The main results of these analyses are summarized in the Figure  7  and connect lipid dysregulation, insulin resistance, inflammatory processes, and HCC development based on the 25 genes found after text-mining and in silico analyses.
Confirmation of the NASH Canonical Pathway Using Independent Illumina Gene Expression Beadchip To confirm our findings and demonstrate that the "canonical signature" based on the 25 genes identified after the text-mining approach is applicable to other groups of NAFL and NASH patients, we used these set of genes to a new group of healthy control subjects, NAFL, and NASH patients, for which gene expression analyses were performed using a completely different approach than that used to establish this signature. The gene expression analyses were performed in 44 human liver surgical samples (normal n = 13; steatosis n = 19; steatohepatitis n = 12), which were processed with Illumina HumanWG-6 v3. 0 expression beadchip technology and referenced as GSE33814  [65]. Two genes, PLIN1 and PLIN2 implicated in lipid droplet synthesis, were not annotated in the Illumina beadchip.
Genes Implicated in Progression of NASH are Part of the Progression to Hepatocellular Carcinoma We identified 25 genes implicated in the progressions of steatosis to NASH, particularly genes implicated in lipid metabolism, inflammation processes, and five more genes implicated in cancer development. Thus, to dissect the role of these 30 genes in the progression of NASH to HCC, we used the data provided by the GSE14323  [68]  including biopsies for which gene expression was analyzed from 19 controls, 41 cirrhosis, and 38 HCC using [HG-U133A] Affymetrix Human Genome U133A Array. PCA showed that the three groups of patients separated significantly on the first dimension (p-value = 2. 255952 × 10 -11 ) and second dimension (p-value = 0. 001885) implicating 13 genes (Figure  9A, B ). These 13 genes include genes involved in regulation of fatty acid and cholesterol metabolism such as LPL, VLDLR, LIPA, ANXA2, and PLEK; lipid accumulation (CIDEC, PLIN1); and metabolism (PPARA and BCL2A1); and especially genes implicated in inflammatory and cancer processes such as CCL2, CD36, TUBA1A, and YWHAZ (Figure  9C ).