dataesr/scientific-paragraphs-categorization huggingface dataset:
New metabolic signature to predict patient survival in PDAC To investigate whether a metabolic profile could impact the survival of PDAC patients, we performed an independent component analysis including all the metabolites, followed by a survival analysis using univariate Cox regression based on PaCaOmics patient cohort  (Nicolle et al, 2020)  to assess their prognostic value. ICA5 component significantly associated with overall survival (OS) in our patient cohort. The weights in this component attributed a score for each sample in the dataset and subsequently used for survival analysis by creating low and high score groups (Figure  1C ). Patient survival in the high-ICA5-score group was significantly improved compared with patients in the low-ICA5-score group with log rank p-value < 0. 001 and hazard ratio HR = 2.
68 (95% CI: 1. 5-4. 9) (Figure  1D ). These results demonstrated that the metabolic signature identified here has a significant predictive value in OS of patients with PDAC. This predictive value of the metabolic component was then confirmed using a relative risk (RR) regression model. Results showed that patients with lower ICA5 score have significantly higher RR (log rank p = 0. 03, HR = 0. 71 [95% IC: 0. 52-0. 97]) than patients with higher ICA5 score (Figure  1E ). Clinical features and the ICA5 score of each patient are presented in the Dataset S3. As described above, the most represented metabolites in the overall patient metabolome belonged to the glycerophospholipid class; however glycerolipids contributed most to the metabolomics signature identified here for predicting patient survival.
In Figure  1F , we represented the top 100 metabolites according to their weights (negative or positive contribution) in component ICA5. Triacylglycerols, such as TG30 and TG56 along with several oxidized fatty acids, had a negative association to the component suggesting that these metabolites could be associated with poor prognosis. However, cholesteryl esters (ChoE_11 and ChoE_8) and glycerophospholipids, especially lysophospholipids, were positively associated to the component indicating a possible link with improved prognosis in patients with high ICA5 scores (Figure  1F  and Dataset S4). Therefore, component analysis of metabolomic profiles allows us to idenfity key signature associated with patient survival.
Establishment of drug resistance score To explore the association between the metabolic profiles and drug resistance, we established a new score of resistance based on GR inhibition metrics as described in the methods section. In fact, traditional doseresponse metrics such IC50 and Emax could be confounded by the number of cell divisions and the assay duration which induces bias to biological effect of the drug  (Hafner et al, 2016). Using GR metrics fitted to sigmoidal curves allowed thereby a better quantification of the response to drugs. Dose-response data of 35 primary cell lines derived from pancreatic PDTX samples were used to calculate metrics for five drugs tested in this study including gemcitabine, oxaliplatin, docetaxel, SN-38 and 5-fluorouracil (5FU) (Figure  3A ). For each drug, after performing principal component analysis (PCA), a weighting coefficient was calculated for each metric based on its correlation to the first three dimensions that explain more than 90% of variance in the dataset (Supplementary Figures  S1 to S5 ).
Identification of metabolic markers associated to gemcitabine resistance As described above, the GRM score allowed the ranking of patients according to gemcitabine resistance (from the most sensitive to the most resistant). To investigate the metabolites associated with gemcitabine resistance, we performed a Pearson regression analysis between all the metabolites in the dataset and gemcitabine GRM scores (correlation threshold p < 0. 05). As shown in Figure  4A , the majority of identified metabolites belonged to the glycerophospholipid class, such as diacyl-PC (i. e. DAPC47 and DAPC16) and diacyl-PE (i. e. DAPE09), which positively correlated to the resistant score, whereas several monoacyl-PCs (i. e. MAPC16, MAPC07), monacyl-PE (i. e. MAPE31) and mono-phosphatidylinositols (PI), such as MAPI03, negatively correlated.
Data preprocessing and normalization Raw data were processed using the TargetLynx application manager for MassLynx 4. 1 software (Waters Corp. , Milford, USA). A set of predefined retention time, mass-to-charge ratio pairs, Rt-m/z, corresponding to metabolites included in the analysis are considered. Associated extracted ion chromatograms (mass tolerance window = 0. 05 Da) are then peak-detected and noise-reduced in both the LC and MS domains. A list of chromatographic peak areas is then generated for each sample injection. Normalization factors were calculated for each metabolite by dividing their intensities in each sample by the recorded intensity of an appropriate internal standard in that same sample, following the procedure described by  Martinez-Arranz et al. (Martinez-Arranz et al, 2015).
Establishment of drug resistance score Primary cell cultures were derived from 35 PDTX samples with metabolomics data. These cell cultures were treated with increasing doses of five different drugs: gemcitabine, oxaliplatin, docetaxel, the active metabolite of Irinotecan. Dose-response data were used to calculate the Gowth Rate (GR) inhibition metrics using GRmetrics R package  (Hafner et al, 2016)  and to obtain the fitted curves for each drug. Thus, four metrics were considered to establish the GRM (growth rate multimetrics) score, the new score of resistance to drugs based on the following growth rate inhibition metrics: 1) GR50 correspond to the concentration at which the effect reaches 50% of growth rate; 2) GRmax represent the effect at the highest concentration; 3) GR_AOC, refers the area over the curve and 4) The hill coefficient of the fitted curve (h_GR), indicating how steep the curve is.
Next, we performed a principal component analysis on these metrics using the FactoMineR  (Le et al, 2008)  package with five dimensions on four GR metrics including GR50, GRmax, GR_AOC and hGR. For each drug, a weighting coefficient was calculated for each metric based on its correlation with PCA dimensions and the corresponding percent of variance as follow:  = ∑ (( , ()) × %()).
Data analysis All statistical and bioinformatics analysis were performed using different packages and custom scripts of the R programming language. Data were log2 transformed and centered prior to downstream analysis. Hierarchical clustering analysis was performed using ComplexHeatmap library. Independent component analysis (ICA) was performed using JADE package based on five components. Cox proportional hazard regression model from the "survival" package was used to perform the univariate survival analysis and determine relative risk of death associated to different factors, with a confidence interval of CI = 95%. Each metabolic feature was associated to the PAMG  (Nicolle et al, 2020)  and drug resistance score using a significant Pearson's coefficient of correlation with a p value of correlation test < 0.
Acknowledgements We thank  Fabienne Guillaumond  for critically reading the manuscript. This work is part of the national program  Cartes d'Identité des Tumeurs (CIT)  funded and developed by the  Ligue Nationale Contre le Cancer. This work was supported by  INCa  (Grants number  2018-078  and  2018-079 ),  Canceropole PACA ,  Amidex Foundation  and INSERM.
MEM Algorithm In MSI-PCR, a microsatellite is considered stable if its length distribution is comparable to the reference distribution and unstable if its length distribution corresponds to a mixture model, i. e. , the mixture of several sub-distributions of different mean lengths (generally a sub-distribution corresponding to the length of the stable allele, similar to the reference distribution, and one or more sub-distributions of different mean lengths, corresponding to the unstable alleles). MEM is a Java-based bioinformatics algorithm built on CLC Genomics Workbench 20 (QIAGEN, Hilden, Germany), available at https://github. com/MGPC-Nantes/MEM (accessed on 19 August 2021). MEM attempts to closely replicate the MSI-PCR interpretation method. Since the amplification steps during NGS library preparation induce replication slippage of microsatellite sequences as with MSI-PCR, MEM identifies the stable or unstable nature of each microsatellite by (i) determining the length distribution of microsatellite sequences, without post-analytical bias related to filtering or alignment of the sequencing data, and (ii) determining whether the observed distribution corresponds to a mixture model whose sub-distributions differ from the reference distribution (Figure  1 ; see Appendix A for a detailed description).
Validation on Tumour Samples NGS data from colorectal cancer samples sent to Nantes University Hospital between 1 January and 31 December 2019 for MSI assessment by MSI-PCR in conjunction with testing for KRAS, NRAS and BRAF by NGS were re-analysed retrospectively with the MEM algorithm. The DNA of formalin-fixed paraffin-embedded (FFPE) colorectal cancer samples was extracted using the Maxwell ® RSC RNA FFPE Kit. NGS libraries were produced from these DNA extracts using the QIAseq Targeted DNA Custom Panel (QIAGEN, Hilden, Germany) kit, an amplicon library construction kit based on Anchored Multiplex PCR (AMP) technology. This panel targeted, among others, KRAS, NRAS and BRAF genes and BAT-25, BAT-26, NR-21, NR-24 and NR-27 microsatellites (see Supplementary Table  S1  for the genomic positions of the primers targeting microsatellites).
NGS libraries were prepared according to the supplier's recommendations and then sequenced on a MiSeq sequencer (Illumina, San Diego, CA, USA). The MSI-PCR analysis was carried out by the genetics laboratory of Nantes University Hospital using an in-house kit targeting BAT-25, BAT-26, NR-21, NR-22 and NR-24 microsatellites (see  [29]  for the list of primer sequences). Fragment analysis was performed on a 3500 xL Genetic Analyser capillary sequencer (Applied Biosystems, Waltham, MA, USA) and interpreted in comparison with a control sample stable for the five microsatellites, on GeneMapper software 5, by trained operators who were kept blinded to MEM results. Both MEM algorithm results and MSI-PCR results were collected in an anonymized computer database together with the percentage of cancer cells in the sample, estimated by examining hematoxylin and eosin-stained tissue slides and the tumour expression of MLH1, MSH2, MSH6 and PMS2 evaluated by MMR-IHC, provided by the pathologist.
MEM Algorithm vs. MSI-PCR Comparison A total of 146 colorectal cancer samples were sent to Nantes University Hospital between 1 January and 31 December 2019, to test for KRAS, NRAS or BRAF mutations by NGS and microsatellite instability by MSI-PCR. A total of 28 samples out of 146 (19. 2%) were MSI according to MSI-PCR, and the remaining 118 samples were MSS (Figure  2 ). The percentage of cancer cells in samples exceeded 50% for 117 samples (80. 1%), ranged from 25% to 50% for 28 samples (19. 2%) and from 10% to 25% for a single sample (0. 7%). No sample contained less than 10% cancer cells. NGS data obtained from these samples were re-analysed with the MEM algorithm. MEM conclusions were entirely consistent with those of MSI-PCR (Cohen's kappa = 1; Table  1 ). All MSI samples in MSI-PCR were identified as MSI by MEM (28/28), and all MSS samples in MSI-PCR were identified as MSS with MEM (118/118).
Considering MSI-PCR as gold-standard, MEM had sensitivity, specificity and positive and negative predictive values of 100% for the determination of MSI status. NGS data obtained from these samples were re-analysed with the MEM algorithm. MEM conclusions were entirely consistent with those of MSI-PCR (Cohen's kappa = 1; Table  1 ). All MSI samples in MSI-PCR were identified as MSI by MEM (28/28), and all MSS samples in MSI-PCR were identified as MSS with MEM (118/118). Considering MSI-PCR as gold-standard, MEM had sensitivity, specificity and positive and negative predictive values of 100% for the determination of MSI status. In terms of each individual microsatellite, the MEM algorithm was also entirely consistent with the MSI-PCR results for BAT-25, BAT-26, NR-21 and NR-24 (Cohen's kappa = 1 in each case; Table  2 ).
4, 5 easurable targets relating to incidence and deaths have been proposed to assess progress in HIV, but they have not been synthesised to provide a comprehensive assessment of HIV burden. The original 90-90-90 targets were proposed in 2014,  6  and set out that by 2020, 90% of people living with HIV will know their HIV status, 90% of those who are aware of their HIV-positive status will have initiated antiretroviral therapy (ART), and 90% of those on treatment will be virally suppressed. However, improve ments in these metrics might not necessarily reflect progress and data might be misreported;  7  underreporting presents a problem for any modelling effort that relies on treatment coverage to inform diseaseburden estimates. Several more measurable metrics were agreed upon at a meeting convened by UNAIDS in 2017.
Modelling strategy We grouped countries and territories on the basis of availability of different types of data to use unique modelling strategies that capitalise on the best available data in each country. Group 1 included countries and territories with HIV-prevalence data from antenatal care clinics or representative population-based seroprevalence surveys (ie, 48 countries in total, including several in sub-Saharan Africa and in the Dominican Republic, Haiti, India, Papua New Guinea, and Sudan). Group 2 included the remaining 156 countries, which generally had data on HIV deaths, except for 33 countries with no data on HIV deaths. The groups were further divided on the basis of peak prevalence with or without registration-data completeness (appendix, section 2. 3, figure  S1, p 6 ). To estimate sex-specific HIV burden and trends for countries and territories in group 1, we improved the published version of EPP-ASM.
Simple linear models of observed survey prevalence against year informed our priors for the change of ratios over time and intercepts (appendix, section 2. 5. 2, p 11). India was a modelling exception to other group 1 countries because we sought to use available data from antenatal care sentinel surveillance and from the sample registration system (SRS) on HIV deaths. We used an in-house version of the original estimation and projection package  22  to estimate incidence and prevalence on the basis of data on antenatal care. We then used data on SRS deaths to inform age and sex patterns in incidence and deaths via cohort incidence bias adjustment (CIBA). Spectrum was used for final estimates. For all countries in group 2, we developed CIBA with a modified version of Spectrum to estimate age and sexspecific HIV burden and trends.
CIBA is a demographic cohort model that addresses the general scarcity of reliable data on incidence that could provide a goldstandard incidence to use as input for the Spectrum model. CIBA instead aims to derive a plausible incidence from more-reliable data on HIV deaths. We selected the incidence to input from estimates available as part of public-use files or the incidence estimated as part of a past GBD cycle that best minimised the ultimate bias between data from Spectrum deaths and reported deaths. To create an adjustment factor, we determined the ratio of Spectrum deaths to vital statistics deaths for each year. Age and sex specific incidence cohorts were defined by year of infection. The cohort that would have produced those deaths was scaled by this factor using the cohort-survival estimates derived from Spectrum (appendix, section 2.
Input data Country-specific inputs that are common across the modelling strategies included data on demographic estimates and intervention coverage reported to UNAIDS, such as ART and prevention of mother-to-child transmission. Rates of disease progression and HIV-free mortality (ie, the expected background mortality not caused by HIV), required for both models were also inputs for all countries and territories. 18 Model-specific inputs for countries and territories in group 1 for which EPP-ASM was used included population-representative HIV sero prevalence surveys and antenatal care data that were also adjusted to be population representative. For countries and territories in group 2, we used adjusted vital registration data (appendix, section 2. 4, p 7).
Estimating uncertainty All the models were run 1000 times to create 1000 draws from which 95% uncertainty intervals (UIs) were derived, with the 2•5th and 97•5th percentile draws as the upper and lower bounds. On-ART and off-ART mortality were modelled separately. To propagate uncertainty, one draw was randomly selected each time EPP-ASM and Spectrum were run. We drew treatment inputs from a uniform distribution on each draw to ensure adequate uncertainty was captured. For more on the data and code for GBD 2019 HIV estimation see http://ghdx. healthdata. org/ gbd-2019.
Further, GBD provides two important advantages unavailable from UNAIDS, which include internally consistent estimates with other causes of death and similar estimates across diseases. This study has several limitations. The results of the cohort-incidence bias adjustment approach to incorpor ating vital registration data are sensitive to our initial incidence input to the first stage of Spectrum. Although we aimed to mitigate this problem by choosing the most plausible incidence option that yields deaths closest to more trustworthy vital registration data, there is a general scarcity of reliable data on incidence that could provide a gold-standard incidence input. Future work establishing the most likely incidence in a statistical framework could improve the selection of the incidence input relative to vital registration data and thus improve final burden estimates.
T Bärnighausen  was supported by the  Alexander von Humboldt Foundation  through the  Alexander von Humboldt Professor award , funded by the  German Federal Ministry of Education and Research. S Bazargan-Hejazi  was partly supported by the  National Institute of Health (NIH) National Center for Advancing Translational Science UCLA CTSI  (grant number  UL1TR001881 ). L Degenhardt is supported by an  NHMRC   Senior Principal Research Fellowship  ( 1135991 ) and a  US NIH National Institute on Drug Abuse (NIDA)  grant ( R01DA1104470 ). NDARC, UNSW Sydney , is supported by funding from the  Australian Government Department of Health  under the  Drug and Alcohol Programme. J Eaton  was supported by the  Bill & Melinda Gates Foundation  ( OPP1190661 ),  UNAIDS , the  National Institute of Allergy and Infectious Disease of the NIH  under award numbers  R01AI136664 , and the  MRC Centre for Global Infectious Disease Analysis  (reference  MR/R015600/1 ), jointly funded by the  UK Medical Research Council (MRC)  and the  UK Foreign, Commonwealth, and Development Office (FCDO) , under the  MRC/ FCDO  Concordat agreement and is also part of the EDCTP2 programme supported by the  European Union.
M Kumar  acknowledges grant  FIC/NIH K43 TW010716-04. I Landires  is member of the  Sistema Nacional de Investigación , supported by the  Secretaría Nacional de Ciencia, Tecnología e Innovación ,  Panama. J Lazarus  acknowledges support to ISGlobal from the  Spanish Ministry of Science, Innovation, and Universities  through the  Centro de Excelencia Severo Ochoa   2019-23  programme ( CEX2018-000806-S ), and from the  Government of Catalonia, Spain , through the  CERCA programme. P Mahasha  acknowledges grants, innovation, and product development from the  South African Medical Research Council. P Meylahk 's portion of this research was supported by the  Russian Science Foundation  (under grant project number  20-18-00307 ; the health of nation: the multidimensional analysis of health, health inequality, and health-related quality of life).
M Molokhia is supported by the  National Institute for Health Research Biomedical Research Centre at Guy's and St Thomas' National Health Service Foundation Trust and King's College London. J Nachega  is an infectious disease internist and epidemiologist and Principal Investigator of  NIH/Fogarty International Center  (grant numbers  1R25TW011217-01 ,  1R21TW011706-01 , and  1D43TW010937-01A1 ). A Samy acknowledges support from a fellowship of the  Egyptian Fulbright Mission programme. A Shetty  acknowledges the support given by  Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal. N Taveira 's work is partially funded by Fundação para a  Ciência e Tecnologia, Portugal , and  Aga Khan Development Network ,  Portugal Collaborative Research Network in Portuguese speaking countries in Africa  (project  332821690 ), and by the  LIFE  project ( RIA2016MC-1615 ), and  European and Developing Countries Clinical Trials Partnership.
Diffraction peaks were acquired such that a given number of neutrons was counted and the count time was adapted automatically. These were 800 counts for the iron peaks and 1400 counts for the aluminum peaks. This procedure ensures equal quality of data even at positions close to the surface where the intensity drops due to the smaller sampled gauge volume (SGV). The longitudinal and transverse components were measured in transmission mode while the normal component was obtained in reflection mode (Fig. 3 ). Measurements were performed such that the IGV was scanned across the interface in 0. 1 mm steps, which corresponds approximately to a 15th of its diagonal. This way the volume of the SGV changes successively, thus allowing to resolve the residual stress gradients near the interface. Scans were performed in the sample thickness direction for the three principal axis directions, by orienting the sample accordingly as described above (Figs.
d and d 0 are the corresponding lattice spacings. The measurements were post processed using LAMP software  [30]  by fitting a Gaussian distribution function to the peak profiles. Intensities were normalized by the count time. The obtained intensity profile is a measure for the size of the SGV and therefore for its position. The COG for each step, i. e. the measuring location, is precisely determined using the in-house code "PS-Fit" written in Mathcad. A paper explaining the model used in the program PS-Fit is in preparation  [31]. The same program is used to correct for pseudo peak shift, caused by the fact that the sampled gauge volume is displaced with respect to both the instrument center to which angular calibration is performed and the wavelength distribution within the primary beam.
The guideline-based decision support system (GL-DSS) of the DESIREE project and OncoDoc are two clinical decision support systems applied to the management of breast cancer. In order to evaluate the DESIREE GL-DSS, we decided to reuse a sample of clinical cases previously resolved by the multidisciplinary tumor board (MTB) of the Tenon Hospital (Paris, France) when using OncoDoc. Since we had two different knowledge representation models to represent clinical parameters and decisions, and two formalisms to represent guidelines, we developed a transformation sequence, involving the creation of synthetic patients, the enrichment of DESIREE ontology, and the translation of clinical cases and their decisions, to transform OncoDoc data into the DESIREE representation. Considering MTB decisions as the gold standard, the 84% compliance rate of DESIREE recommendations was rather satisfactory.