dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The RCMIP Phase 1 and 2 data were downloaded from https:// zenodo. org/ record/ 40166 13#. YioYX JYo9PY and https:// zenodo. org/ record/ 46245 66#. YioYd pYo9PY. The ESM data were downloaded from the CMIP6 archive (https:// esgf-node. llnl. gov/ search/ cmip6). The IAM database maintained at International Institute for Applied Systems Analysis (IIASA) is available at https:// tntcat. iiasa. ac. at/ SspDb/ dsd? Action= htmlp age& page= welco me. The global CO 2 concentration data were downloaded from the supplement of  Meinshausen et al. (2020). Processed model outputs, evaluation data, and the Jupyter notebook that were used to make the figures in the article are available at https:// zenodo. org/ recor ds/ 10162 686.
Improvements were also maintained in stiffness and physical function measured using the Western Ontario and McMaster Universities Osteoarthritis Index, pain and fatigue endpoints measuring using the Brief Pain Inventory short-form and Brief Pain Inventory, respectively, and in ambulatory function (6-Minute Walk Test). A post-hoc exploratory analysis exploring outcomes in participants who discontinued burosumab treatment between the studies (n=7) and those who received at least one dose (n=23) indicated that the benefits of burosumab on clinical laboratory tests of efficacy, PROs and ambulatory function may be lost when treatment is interrupted but recover over time when treatment is reinstated. Conclusion Continued treatment with burosumab appears necessary for sustained clinical benefit. Trial registration numbers Phase 3: NCT02526160; open-label extension: NCT03920072.
Study design The phase 3 randomised, double-blind, placebocontrolled efficacy and safety study was conducted at 25 centres in the USA, France, UK, Ireland, Italy, Japan and South Korea. 5 14 16 Eligible participants were randomised 1:1 to receive burosumab or placebo, administered subcutaneously every 4 weeks for 24 weeks (online supplemental figure  1 ). Thereafter, all participants entered a treatment continuation period during which they received open-label burosumab. The final study visit was at week 96 at European centres (participants in the USA continued for up to 53 weeks further; participants in Japan and South Korea transferred to a long-term extension study (NCT04308096)). European participants from the phase 3 study were eligible to enrol onto an open-label extension (online supplemental figure 1); 34 participants from the phase 3 study from nine study centres met the eligibility criteria and were enrolled but 2 participants subsequently withdrew (online supplemental figure  2 ).
Statistical analysis Statistical analyses were conducted in SAS. 9. 4 (SAS Institute, Cary, North Carolina, USA). The population for the primary analysis comprised all participants who enrolled in the phase 3 and open-label extension studies and who recorded at least one measurement after phase 3 baseline in the latter. Continuous data are summarised as mean and SD, and categorical data as n (%). Clinical tests of laboratory efficacy are evaluated at each analytical time point alongside the LLN. PROs and functional endpoints are evaluated as change from phase 3 baseline to each analytical time point through to week 96 in the phase 3 study and in the open-label extension through to week 48. To maintain consistency with the analysis for the phase 3 study previously reported, a generalised estimating equation repeated-measures analysis was also performed.
The analysis did not enable us to determine the  A decrease in scores indicates improvement on the WOMAC, BPI-SF and BFI. An increase in distance on the 6MWT indicates improvement. BPI-SF and BFI data were captured at a single site visit and were not completed as part of a patient diary at weeks 72a and 96a. Fasting serum phosphate p values are for the difference between the groups (end of dosing cycle) at week 0b (tested using Fisher's exact test); 52% of the interim burosumab group but none of no interim burosumab group had values ≥LLN at the start of the open-label extension period (p=0. 01; Fisher's exact test). PROs and 6MWT (tested using the Mann-Whitney U test) p<0. 05 was considered significant. There was no significant difference between the groups at study baseline. Serum phosphate samples from the two studies were measured at different central laboratories, with different LLN values: 0.
In addition, because of restrictions imposed during the COVID-19 pandemic, a small number of serum phosphate results were assessed at local laboratories and some data for the ambulatory assessment (6MWT) are missing at weeks 36b and 48b. Data supporting the conclusion that burosumab is a safe long-term treatment for adults with XLH have been reported for participants receiving burosumab for an average of more than 3 years. 24 The results reported here provide further safety evidence, with no grade 4 or 5 TEAEs reported and no treatment-emergent SAEs. Further safety data are being collected in a postauthorisation registry study of burosumab,  25 26  which will provide important insight into the use of burosumab in a real-world setting. In conclusion, the effect of burosumab treatment in restoring serum phosphate concentrations to within the normal range is maintained with continued treatment beyond 96 weeks, and also maintains benefits in associated clinical laboratory tests of efficacy, PROs and functional measures.
Figure3Effect of burosumab maintenance on PROs and 6MWT. Data available for 31 participants at BL, 28 at week 48b for WOMAC, 27 at week 48b for BPI-SF and BFI and 16 at week 48b for 6MWT. Analysis weeks in the phase 3 study and open-label extension are indicated by 'a' and 'b' suffixes, respectively. A decrease in score indicates improvement on the WOMAC, BPI-SF and BFI. An increase in distance indicates improvement on the 6MWT. BPI-SF and BFI data were captured at a single site visit and were not completed as part of a patient diary at weeks 72a and 96a. *p<0. 05 for LSM change from BL (generalised estimating equation repeated-measures analysis). Six participants (19%) used an assistive device during the 6MWT at the phase 3 baseline, decreasing to three (10%) at phase 3 week 72a, and one from week 12b.
Acknowledgements The authors thank the participants, patients and healthcare professionals who participated in this study. Contributors A-LL, JSW, KB, MKJ, MC-S, MLB, PK, SHR, RK, RHL, RKC and SK were investigators on the study. AN and MN conducted the data analysis with oversight by WS. AN, AJR and AW drafted the initial manuscript content. All authors were involved in critically reviewing and revising the manuscript and providing final approval of the submitted version. PK is responsible for the overall content as guarantor.
RNA sequencing and bioinformatics RNA was isolated using ReliaPrep RNA Cell Miniprep System (Promega). Samples quantity and quality were evaluated on a Fragment analyzer (Advanced analytical). Libraries were generated with TruSeq stranded mRNA Illumina and quantified on a Fragment analyzer by the GeT-PlaGe Genotoul Toulouse-France. Sequencing was performed by Integragen (France) via an Illumina NovaSeq ™ 6000 S2 high throughput sequencer resulting in 30 to 38 million paired end reads (2 × 100 bp) per sample. Reads were cleaned with HTStream (v1. 3. 2) [https:// github. com/ s4hts/ HTStr eam] and aligned with Salmon (v1. 9. 0) (  [16]  using the gcBias option, against the Gencode v40 human transcript database  [17] ). The rest of the analysis was performed in R (v4. 2. 3) [R Core Team (2021).
R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [https:// www. R-proje ct. org/]. Tximport (v1. 26. 1)  [18]  was used to load in the Salmon mapping results. Functions from edgeR (v3. 40. 2)  [19]  were used to filter genes by expression and calculate normalization factors. limma (v3. 54. 2)  [20] was used for the voom With Quality Weights function and then to perform cyclic Loess normalization with the "affy" method. Then we employed sPLS-DA from the mixOmics (v6. 22. 0) to select genes that we able to select genes differential with respect to the DMD and CON group factor. Figures were created with mixOmics plotIndiv, a custom ggplot2 (v3. 4. 2) [H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016] adaptation of mixOmics' plotVar and pheatmap (v1.
0. 12) [https:// cran. r-proje ct. org/ packa ge= pheat map]. Pathway enrichment was performed with EnrichR [21] using the KEGG 2021 Human database  [22]  and Human-Cyc 2016 database  [23]. RNA-Seq data analysis was done by bioinformatic platform in Toulouse, functional annotation clustering and for up-regulated and down-regulated genes with a fold change of ≥ 2 and enrichment score of ≥ 1.
Flow cytometry Phenotypic analysis of hiPSC-fibs was performed using multicolor flow cytometry. Briefly, nonspecific binding of antibodies by Fc receptors was blocked by incubating the cells with mouse serum diluted in FACS buffer (PBS 4% FBS and 2 mM EDTA). Cell surface markers were then stained with antibodies conjugated with fluorochromes (Table  3 ). Viability was assessed by incubation with Live-Dead Yellow (Invitrogen). The following isotype controls mouse IgG1k BV421, mouse IgG1k PE, mouse IgG1k FITC and mouse IgG1k APC were used for compensation setups. Samples were acquired on LSR Fortessa (BD Bioscience). Analysis of flow cytometric data was performed using FlowJo (TreeStar).
The x and y axes correspond to the sample placement of Fig. 2A. Black points were discarded either because the y-position was more significant than the x-position or if that gene had a vip-score of < 1. C: Heatmap of differentially expressed genes selected from Fig. 2B. The row annotation colors are as in Fig. 2B  (Up in DMD (red) and Up in CON (blue)). The column annotation is colored as in Fig. 2A. Normalized RNA expression is row-scaled so that red and blue indicate expression above or below the row mean. EnrichR was used to find significantly enriched pathways with the DMD up-regulated genes, as selected by sPLS-DA, using both D: Kyoto encyclopedia of genes and genomesdatabase (KEGG2021)as well as the E: human metabolic pathways database (HumanCyc 2016). Bar length is indicative of significance (-Log10(P.
Value)). Principal enriched pathways include TCA cycle metabolism, gluconeogenesisand glycolysis (See figure on next page. ) first analyzed fibroblast markers expressed by control and DMD hiPSC-fibs by flow cytometry. All patients' fibroblasts exhibited a fibroblast-like morphology and were negative for CD31 and CD45, regardless of genotype (Data not shown). The control and DMD hiPSCfibs were all positive for CD29 and CD105 whereas about 60 ± 10% expressed Thy1. 1, and 4 ± 1% expressed PDGFRα with no significant differences between the genotypes (Fig. 1B-C ).
RNA expression analysis revealed altered metabolic pathways in DMD hiPSC-fibroblasts To select gene expression altered by the loss of dystrophin expression in DMD and control hiPSC-fibs, we used sPLS-DA (keeping 400 variables on 2 components) from mixOmics in order to select genes with the most importance in separating samples based on DMD/ CON (Component 1) as seen in the individual (sample) plot show in Fig. 2A  (left panel), with DMD samples in black and CON samples in light grey. The results of the mixOmics vip program was used to further select the best genes by filtering with vip scores for component 1 above 1. 0 and requiring that the x position was greater than the y position within the variable (gene) plot shown in Fig. 2A  (right panel). The final set of genes selected as upregulated in DMD (red) or CON (blue) samples were then clustered with ward.
D clustering of the Euclidean distance in a heatmap (Fig. 2B , same red/blue coloring as in Fig. 2A  on rows and black/grey coloring on columns). The heatmap is showing a total of 323 genes, 159 up in DMD hiPSC-fibs patients and 164 up in CON hiPSCfibs patients. The 159 up in DMD genes were used with EnrichR to select significantly enriched pathways upregulated in DMD fibroblasts. The KEGG (Fig. 2C ) and HumanCyc (Fig. 2D ) databases revealed many common DMD up-regulated pathways related to glycolysis, gluconeogenesis and the TCA cycle (Additional file 1: Fig. S2 ). In addition, these results suggest DMD fibroblasts may be suffering hypoxic stress as we found genes to be upregulated from the HIF-1 signaling pathway.
3D ). (See figure on next page. ) Fig. 3  iPSC-derived cardiac fibroblasts from DMD patients exhibit high glycolytic capacity. A: The extracellular acidification rate (ECAR) profile of iPSC-derived cardiac fibroblasts subjected to a glycolysis stress test and the individual parameters for glycolysis, glycolytic capacityand glycolytic reserve. iPSC-derived cardiac fibroblasts were seeded to 80,000 cells/well and incubated for 24 h. ECAR was measured under basal conditions followed by the sequential addition of 10 mM glucose, 0. 5 µM oligomycin and 100 mM 2-deoxyglucose (2-DG). Each data point represents an ECAR measurement obtained from the six biological samples of different batches of iPSC-derived cardiac fibroblasts. Data are expressed as mean ± SEM***p < 0. 001 by non parametric t test.
Acknowledgements The authors thank the  GeT-Santé facility (I2MC ,  Inserm, Génome et Transcriptome, GenoToul, Toulouse, France)  for the advice and technical contribution. We thank the flow cytometry facility ( I2MC-Toulouse ) and  A. Zakaroff  and  E. Riant  for their help and advice, the microscopy facility  R. Flores-Flores  for his excellent assistance and advice. The authors thank  Cecilia Ferrantini  ( University of Florence ),  Alessandra Fornaro  ( Careggi University Hospital, Florence ) and  Prof. Yvan Torrente  ( Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico; University of Milan ) for patient recruitment.
1 mM For expression of the phosphoinositide biosensors, a 1106 bp promoter of ENOD11 was used. Sequences of 2xPH PLC and 2xPH FAPP1 , and of 2xPH PLC mut were designed according to  (Simon et al. , 2014)  and  (Yagisawa et al. , 1998) , respectively, and synthesized by Life Technologies. The sequence data from RPG, VPY, EXO70H4, LIN, NPL, SYMTUB can be found in phytozome (https://phytozome. jgi. doe. gov/) with the following gene ID: RPG (Medtr1g090807), VPY (Medtr6g027840), EXO70H4 (Medtr4g062330), LIN (Medtr1g090320), NPL (Medtr3g086320), SYMTUB (Medtr8g107250). The sequences of 2xPH PLC and 2xPH FAPP1 biosensors can be downloaded at http://www. ens-lyon. fr/RDP/SiCE/PIPline. html.
, 2017)  and incubated for 7-10 days. ClearSee solution was refreshed every two days. Prior to imaging, roots were stained with Calcofluor white for 45 minutes, washed twice with ClearSee and incubated for 1h in the last wash. Infected root hairs were imaged using a confocal microscope and measurement of the infection thread diameter was performed with ImageJ/(Fiji) software  (Schindelin et al. , 2012). Infection thread diameter has been scored on 20-32 independently transformed roots from at least 5 plants.
Intensity-based and FLIM-based image analysis Intensity-based images were analyzed using ImageJ/(Fiji) software  (Schindelin et al. , 2012). FLIM-based images were analyzed using LAS X SP8 Control Software (Leica Microsystems GmbH). Global fitting of the intensity decay profile using n-exponential reconvolution was performed to separate major fluorescent components within each channel and calculate their lifetime. The number of components (n) used for curve fitting was determined according to evaluation of the chi-squared (χ2) value  (Lakowicz, 2006)  and a threshold of 30 photons was applied to generate the final images. Components with lifetime value < 1. 2 ns representing autofluorescent species accumulating in plant cell walls  (Donaldson, 2020; Heskes et al. , 2012)  were subtracted from each channel.
ACKNOWLEDGEMENTS We would like to thank  Carla Brillada ,  Marco Trujillo  and  Franck A. Ditengou  as well as  Eija Schulze  and  Rosula Hinnenberg  and  Pascal Krohn  for their experimental support and technical help, respectively, and the entire  Ott lab  team for the continuous inputs into the project. We would also like to thank  Pierre-Marc Delaux  and  Jean Keller  ( LRSV, Université de Toulouse ) for critically reading the manuscript and providing a comprehensive phylogeny on RPG, respectively, as well as  Leonel Malacrida  ( Advanced Bioimaging Unit, IP Montevideo, Universidad de la República ) for the fruitful  FUNDING Engineering Nitrogen Symbiosis for Africa  (ENSA) project currently supported through a grant to the  University of Cambridge  by the  Bill & Melinda Gates Foundation  ( OPP1172165 ) and  UK government's Department for International Development (DFID) (TO) Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation )  431626755  (TO)  DFG under Germany 's  Excellence Strategy  grant  CIBSS -EXC-2189 -Project ID 39093984  (TO)  China Scholarship Council (CSC)  grants  201708080016  (CS) DFG project number  414136422  (CLSM; TO),  DFG  project number  426849454  (TEM; TO).
Measuring the similarity between materials is essential for estimating their properties and revealing the associated physical mechanisms. However, current methods for measuring the similarity between materials rely on theoretically derived descriptors and parameters fitted from experimental or computational data, which are often insufficient and biased. Furthermore, outliers and data generated by multiple mechanisms are usually included in the dataset, making the data-driven approach challenging and mathematically complicated. To overcome such issues, we apply the Dempster-Shafer theory to develop an evidential regression-based similarity measurement (eRSM) method, which can rationally transform data into evidence. It then combines such evidence to conclude the similarities between materials, considering their physical properties.
To evaluate the eRSM, we used two material datasets, including 3d transition metal-4f rare-earth binary and quaternary high-entropy alloys with target properties, Curie temperature, and magnetization. Based on the information obtained on the similarities between the materials, a clustering technique is applied to learn the cluster structures of the materials that facilitate the interpretation of the mechanism. The unsupervised learning experiments demonstrate that the obtained similarities are applicable to detect anomalies and appropriately identify groups of materials whose properties correlate differently with their compositions. Furthermore, significant improvements in the accuracies of the predictions for the Curie temperature and magnetization of the quaternary alloys are obtained by introducing the similarities, with the reduction in mean absolute errors of 36% and 18%, respectively.
Incidentally, inductive reasoning with inefficient similarity assessment can lead to misidentification of outliers  13  and difficulty in explaining the underlying physicochemical mechanisms of datasets using single models. Therefore, regarding predefined material descriptors, an exhaustive examination of all possible hypotheses about the unknown physicochemical mechanisms is necessary to assess the similarity between the materials. Furthermore, similarity measures are usually context-dependent. Because the context changes, the similarity measure must be modified to adequately capture the phenomena under study. 14, 15 Thus, a quantitative measure of similarity needs to consider the uncertainty arising from the context or the measurement itself, especially in situations where material data are often insufficient and heavily biased.
Further analysis of models describing the subgroups provides valuable information to extract, interpret, and understand physical mechanisms. The Dempster-Shafer theory can be regarded as a generalization of the Bayesian approach for solving the problem of incomplete and insufficient information. Moreover, it is suitable for solving material data problems. 19, 20 The measure of similarity here refers to whether the observed physical properties of the materials under study are described using the same hidden mechanism that has not yet been revealed. In other words, we consider any pair of materials (in the dataset) as similar if their physical properties can be described by the same hidden mechanism; otherwise, the pair of materials is considered dissimilar. We then first generate numerous hypothetical mechanisms by randomly choosing subsets of data instances and constructing regression models for each subset.
Each regression model is considered a source of evidence of the similarities between materials. Thereafter, the Dempster-Shafer theory,  [16] [17] [18]  which has a foundation for modeling and combining the uncertainty of evidence, is applied to integrate the collected pieces of evidence to draw conclusions about the similarities between materials. The eRSM consists of three main steps as follows: 1. Collect sources of evidence: Hypothetical mechanisms are collected from a dataset by applying regression analysis with single or mixture models and are used as sources of evidence to rationalize the similarity states of materials. 2. Model similarity evidence: An appropriate mass function is designed to model the obtained evidence within the framework of the evidence theory. 3. Combine pieces of evidence: Dempster's rule of combination is used to integrate the pieces of the evidence.
The steps of the eRSM are explained in detail in Sec. II. Regarding the framework of the evidence theory, the essential contributions of the eRSM are collecting sources of evidence about the similarities between materials from datasets and designing suitable mass functions to model the pieces of evidence rationally. The effectiveness of obtained similarities using the eRSM for subdividing alloys from datasets into homogenous subgroups is supported by experiments on (1) a dataset of binary alloys with their Curie temperature as a target property (Sec. III B) and (2) two datasets of quaternary alloys with their magnetization (Sec. III C) and Curie temperature (Sec. III D) as the target properties. Further analysis of the detected subgroups to interpret the underlying physical mechanisms is shown in Sec.
II. METHODOLOGY We consider a dataset D consisting of p data instances. We assume that a data instance with index i in D is described by n predefined descriptors and is represented by an n-dimensional numerical vector, x i ¼ x 1 i , x 2 i ,. , x n i À Á [ R n. The target property of the data instance x i is y i [ R. Thereafter, the dataset D ¼ (x 1 , y 1 ), (x 2 , y 2 ). (x p , y p ) È É is represented using a p Â n þ 1 ð Þ ð Þmatrix. In this study, we consider that D may contain pairs of data instances x i and x j , where x i % x j ; however, the value of y i is far from y j.
A. Collecting sources of similarity evidence We perform random subset sampling of the data instances without replacement to collect a large amount of evidence of the similarity between pairs of data instances in D. Considering each sample, we obtain two datasets: the reference dataset, D ref , and the evaluation dataset, D eval (D ref > D eval ¼ ; and D ref < D eval ¼ D). Considering D ref , we can generate a single function or multiple reference functions f r : R n ! R using a Gaussian process (GP)  21  or a mixture of Gaussian processes (MGP),  22  respectively. This study applies GP-or MGP-based models instead of other nonlinear regression models, such as kernel ridge regression,  23  random forest regression,  24  or artificial neural networks  25  because GP or MGP can quantify the uncertainty of its prediction without introducing any other statistical validation.