dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were prepared at relative humidities ranging from 11% to 100% RH, providing values of λ in the range 2 to 20 (Figure  S1  and Tab. S1-S2 †). Nafion and Aquivion samples generated by simulation were initialized in similar hydration ranges. B. Methods 1. SAXS. Measurements were performed at the European Synchrotron Radiation Facility (Grenoble, France) on the BM02-D2AM beam-line, with an incoming X-rays energy of 14 keV. Aquivion membranes (850 g/eq) were first equilibrated for several hours under various relative humidity conditions and then enclosed in hermetic cells (mica windows). The water vapor pressures were imposed by using saturated salt solutions. Standard procedures for background subtraction, data corrections, and radial averaging were applied to obtain the final 1-dim I(Q) scattering spectra (Figure  S2 † ), with Q the exchanged wave-vector.
Data have been treated with the QENSH software (available at: wwwllb. cea. fr/en/Phocea/Page/index. php?id=21) and analyzed following the method we used in Ref. [15]  to clarify the dynamics of protons in Nafion membranes. A large number of data sets recorded on Nafion, in hydration conditions ranging from the dried to the fully swollen states, were best accounted for by employing several quasi-elastic components, due to the existence of protons with different unusual dynamical features when confined in soft charged media. Localized motions were analyzed using a standard single-Lorentzian approach, diffusive motions in terms of the generalized Gaussian model for localized translational motion (GMLTM). [66]  The generalized GMLTM was developed to describe the diffusion of species confined in soft ill-defined environments (e.
A C C E P T E D M A N U S C R I P T The study protocol was approved by the appropriate clinical research ethics committees in each participating country and by the relevant institutional review board at each site. The protocol complies with the Declaration of Helsinki, the International Conference on Harmonisation of Good Clinical Practice, and the local regulations for clinical research (ClinicalTrials. gov identifiers: NCT02322762 for DISCOVER; NCT02226822 for J-DISCOVER). All participating patients provided signed informed consent.
Site and investigator selection Characteristics of physicians and practices involved in the management of patients with type 2 diabetes were assessed in each participating country by combining data from peerreviewed articles, information from reports published by organizations such as the World Health Organization, and insights from national coordinating investigators. Proportions of different types of physicians (primary care physicians, diabetologists, endocrinologists, cardiologists, and other specialists) and practices (primary care centers, specialized diabetes centers, and different types of hospitals), as well as the location of practices (urban vs rural and geographical distribution within a country), were collated. A list of prospective sites that would match these characteristics as much as possible was then established for each country, and all sites were invited to participate in the study.
Data collection using an electronic case report form For all eligible patients who sign the informed consent form, data will be collected at baseline (date of initiation of the second-line therapy) and at 6, 12, 24, and 36 months (Fig. 2A ). In most countries, data for all time points will be collected by investigators using an electronic case report form (eCRF) via a web-based data capture system. Data will be saved immediately to a central database, and all eCRFs will be checked to ensure that they A C C E P T E D M A N U S C R I P T are completed appropriately. Where technically feasible and allowed by local regulations, the information captured in the eCRF will be linked with other sources of health information, such as existing electronic medical records (EMRs) and disease registries, to build a comprehensive data resource for research.
Baseline data will be collected at the routine visit, during which the second-line glucose-lowering therapy will be prescribed. If this is not possible (e. g. because of time constraints), the investigator will arrange for another visit to take place within 2 weeks to collect all necessary information. This will include demographic and anthropometric data, available laboratory test results, medical history of type 2 diabetes, presence of comorbidities, co-medications, level of care, previous glucose-lowering treatment (first-line therapy) and reason for change, second-line glucose-lowering drug class, HbA 1c target, and PROs (Table  2 ). During the 3-year follow-up, data will be captured at 6, 12, 24, and 36 months within a 4-month window ( 2 months) (Fig. 2A ) to increase the probability that the data collection time point coincides with a routine patient visit; the protocol does not mandate any compulsory follow-up visits in order to ensure that the study results reflect routine clinical practice as much as possible.
Data collection from electronic medical records In Denmark, France, Norway, and Sweden, available study variables will be extracted automatically from existing primary care EMR databases. For all patients meeting the inclusion criteria at each participating site, EMR data will be extracted at baseline and yearly thereafter. At the time of de-identified EMR data extraction, a key code will be created that links each patient identification number to their registry data. This key code will be encrypted, password protected, and stored separately from the database used for analysis. Data not routinely captured in EMR databases (e. g. reason for treatment change) will be collected by investigators using an additional questionnaire. These data will then be linked back to the EMR to create a full dataset for each patient.
A C C E P T E D M A N U S C R I P T and Norway, data extracted from EMRs will be merged with data from mandatory national health registries (e. g. information on hospitalizations, prescribed drugs, and cause of death) to create a de-identified study database. A similar approach will be used in Canada. All patients were identified retrospectively, using the IMS Brogan EMR database. Eligible patients were invited to participate and enrolled after providing informed consent. Most data will be extracted from the EMR database for all time points. A short eCRF completed by the patient will also be used to collect additional data not routinely captured in the EMR database (e. g. hypoglycemic events and PROs). The eCRF was not be used at baseline because patients were enrolled retrospectively. This approach will provide a more limited dataset than in other DISCOVER countries; therefore, data from Canada will only be included in the global analysis of the DISCOVER results when appropriate.
In Canada, Denmark, France, Norway, and Sweden, it will also be possible to collect EMR data beyond the 3-year follow-up period, providing insights on longer-term disease management and outcomes. In these five countries, retrospective data will be collected from EMRs and/or national health registries for all patients with type 2 diabetes treated at each primary care site participating in the DISCOVER study (including patients not enrolled in the prospective DISCOVER study) and analyzed separately. This approach will enable a longer observation period in a large cohort and provide important data to assess the external validity of the results from the global DISCOVER study. The possibility of implementing a similar extended follow-up in other DISCOVER countries will be explored.
Statistical analysis Descriptive statistics will be used to summarize demographic variables, patient characteristics, treatment patterns, changes in HbA 1c level, blood glucose, lipid profile, body weight, body mass index, and blood pressure, incidence of complications and hypoglycemic events, PROs, and healthcare resource utilization. Data will be stratified by pharmacological drug class and/or individual agent when clinically relevant. Hierarchical multivariate regression analyses and general linear models will be used to determine potential predictors of treatment choices and clinical outcomes. When applicable, model-based point estimates of odds ratios or risk ratios, as appropriate, corresponding 95% confidence intervals, and p values will be reported. For analyses of clinical outcomes during follow-up, Cox models and mixed models for longitudinal data will be used, adjusted for age and sex, and using country as the indicator variable.
Discussion Although large, international, non-interventional studies of patients with type 2 diabetes have been conducted  (Bradley et al. , 2011; Echtay et al. , 2013; Freemantle et al. , 2012; Khunti et al. , 2012; Kim et al. , 2012; Li et al. , 2011; Liebl et al. , 2012; Mathieu et al. , 2013 ; A C C E P T E D M A N U S C R I P T A C C E P T E D M A N U S C R I P T determinants of treatment decisions, and associations between treatment patterns and several disease-specific outcomes, including glycemic control, microvascular and macrovascular events, hypoglycemic episodes, and PROs. In addition, the large sample size will allow subanalyses to help to identify patient profiles or subgroups that respond well to specific therapeutic approaches. Importantly, the DISCOVER study program will generate real-world data on the management of type 2 diabetes and associated outcomes in countries with alarmingly high and rising prevalences of type 2 diabetes that have been under-studied to date.
Loss to follow-up might also be a source of bias. Patients with poorly controlled diabetes may be more likely to drop out over the 3-year follow-up than those with better outcomes, which could result in an underestimation of negative outcomes. It should also be noted that patient visits are not determined by the protocol; therefore, data collection during follow-up will rely on patients attending regular routine visits (the frequency of which may be country dependent) and on investigators being able to collect all relevant information (e. g. data about hospitalizations or emergency department visits). Finally, in common with all observational studies, confounding should be considered when interpreting the results. However, the available variables collected using the study eCRF will be used in multivariate analyses to minimize confounding when assessing associations between clinical outcomes and patient, physician, and healthcare system characteristics, and treatment patterns.
The DISCOVER study program also has major strengths in addition to the large sample size and the broad geographical representation. First, the naturalistic design of the studies, whereby the chances of protocol-induced findings and protocol-induced healthcare resource use are minimized, will help to ensure external validity of the results. In addition, the same eCRF will be used in all countries, ensuring consistent data collection. This user-friendly eCRF, combined with a relatively small target number of patients (< 20 on average) per investigator, will ensure that the time burden for investigators is minimal and that data are collected completely and accurately for all patients. The long follow-up period is also a key strength of DISCOVER and will allow the study of treatment patterns beyond initiation of second-line therapy.
Conclusion The DISCOVER study program is a global initiative that will provide valuable insights into the treatment of patients with type 2 diabetes in real-world settings and will highlight differences between daily clinical practice and recommendations in current national and international guidelines. In addition, collection of patient-centric health status and healthcare resource utilization data will enable the evaluation of important outcomes beyond glycemic control and clinical events. The DISCOVER study program will also allow benchmarking of clinical practice between and within countries, help to inform future updates to clinical guidelines, and will further improve the care of patients with type 2 diabetes.
existing EMR databases, thus providing the opportunity to follow up some patients for longer than 3 years. Comparison of data extracted from EMRs and data collected with the eCRF may also inform the design of future observational studies. In addition, the DISCOVER study program will allow platforms for future research to be set up in countries with no previous systems for the capture of observational data from patients with type 2 diabetes. It should be noted that, although the DISCOVER study program does not cover the USA, it has been developed in parallel with the recently launched Diabetes Collaborative Registry (DCR) (Arnold et al. , 2016) , which has the potential to become the largest crossspecialty clinical registry to track, evaluate, and eventually improve the quality of diabetes and metabolic care in the USA.
Fig. 1 ). A de novo assembly of the Santhica transcriptome was here performed by merging the reads obtained from the hypocotyl (previously published transcriptome  22  ) with those obtained here for the bast fibres of adult plants: we reasoned that this would enable us to better capture the cell wall-related dynamism in the isolated phloem fibres of the variety under investigation. We nevertheless validated the data by comparing the results obtained after mapping using our de novo assembly with those generated after mapping against the Finola transcriptome 23 (Suppl. Dataset File). As discussed in the next paragraphs, the two mapping strategies gave comparable results. A total of 3268 differentially expressed contigs ranging from 283 to 7095 bps was obtained after data processing (see Material and Methods); of these, 2317 are annotated (Suppl.
Dataset File). The Independent Component Analysis (computed with the FastICA algorithm in MetaGeneAlyse) revealed a good separation of the different stem regions when two components were used (percentage of variance explained: 98. 79%) (Suppl. Fig. 2 ). This indicates that the bast fibres sampled for the analysis were in different developmental stages and therefore characterized by distinct transcriptomic signatures. The RNA-Seq data were validated using targeted RT-qPCR on a subset of 12 genes (Suppl. Dataset File): the calculated coefficient is 0. 9284, which indicates a very good correlation between the RNA-Seq and RT-qPCR data (Suppl. Fig. 3 ). To get insights into the gene expression patterns, data clustering was performed using an arbitrary Pearson correlation coefficient threshold of 0.
75. The analysis resulted in eleven clusters (Fig. 2 ); among these, cluster 1 and 2 account for the highest number of contigs (843 and 703 annotated contigs, respectively; Suppl. Dataset File). The hemp contigs can be assigned to three major expression patterns: progressive decrease in expression from the top to the bottom of the stem (clusters 1, 3, 4), progressive increase in expression along the stem axis (clusters 2, 5, 7) and maximum expression at the internode containing the snap point (clusters 6, 9, 10). Two additional trends were revealed with the clustering analysis: cluster 11 groups contigs showing a tendency towards decreased expression at the snap point, cluster 8 comprises genes showing no major changes along the stem axis. Since the bast fibres undergo progressive cell wall thickening from the top to the bottom of the hemp stem axis, we reasoned that the bulk of cell wall-related information would be obtained by focusing on the above-mentioned three major expression patterns (clusters 1-3-4, clusters 2-5-7, clusters 6-9-10).
Hereafter is a description of the key gene ontologies characterizing each stem region. Gene ontology enrichment analysis of the TOP region. The transcriptomic landscape of the TOP region is dominated by genes belonging to the DNA replication and cell cycle ontologies (Fig. 3 ), a result confirmed also by the analysis performed after mapping using the Finola transcriptome (Suppl. Dataset File). These findings are indicative of active nuclear division and are in agreement with what was previously reported in flax bast fibres  24. Bast fibres are indeed multinucleate and during intrusive growth the number of nuclei increases, as previously documented in flax  8. Compared to the MID and BOT respectively, CDC6 (cell division control 6) was expressed ca. 2 and 10 times more, DMC1 (encoding a meiotic recombination protein) was upregulated 2.
4 and 7 times, MCM2, 4 and 5 (minichromosome maintenance protein 2, 4 and 5) were ca. 3 and 5 times more abundantly expressed, the ORC1A and ORC6 genes (origin recognition complex subunit 1 and 6) were between 2. 5 and 5-7 times more expressed. Additionally, the key gene PCNA2 (proliferating cell nuclear antigen 2) involved in DNA replication  25  was highly expressed at the TOP (ca. 99 RPKM) with respect to the BOT (18 RPKM); finally, DRT100 (coding for a DNA-damage-repair/toleration protein) was expressed ca. 12 times more at the TOP as compared to the BOT (Suppl. Dataset File). The enrichment of genes involved in cell division was accompanied by the over-representation of candidates partaking in the lipid biosynthetic process: several 3-ketoacyl-CoA synthase isoforms (KCS10, 11, 19) were highly expressed at the TOP, together with the β-ketoacyl reductase 1 (KCR1) and the fatty acid desaturases FAD5 and 8.
The enrichment of these genes can be explained by the diffuse (intrusive) growth mechanism of bast fibres 8 : the elongation of fibres is indeed ensured by the growth of the entire surface of the cell. Notably, among the genes belonging to the lipid biosynthetic process, there are also homogentisate phytyltransferases (HPT1), which are involved in the biosynthesis of tocopherols. These are lipid antioxidants protecting against oxidative stress (e. g. light stress  26  ). Genes involved in photosynthetic light reaction were likewise enriched at the TOP, notably CAB1 (chlorophyll A/B binding protein 1), LHCB4. 3 and LHCA5 (light harvesting complex), expressed between 5 and 8 times more as compared to the BOT (Suppl. Dataset File). The TOP region was also characterized by genes involved in the response to pathogens: among them it is worth mentioning here UVI4 (UV-B-Insensitive 4, encoding the protein POLYCHOME), whose overexpression was shown to induce increased resistance to a bacterial pathogen via the activation of disease resistance genes  27.
In our dataset, several pathogenesis-related (PR) genes were also upregulated at the TOP, namely PRB1, PR-1-like, PR4 (Suppl. Dataset File). Interestingly, the Gene Ontology term Enrichment (GOE) analysis highlighted the enrichment of genes involved in secondary metabolism biosynthetic processes too, namely terpenoids, flavonoids, indole-containing compounds and phenylpropanoids (Fig. 3 ). In the terpenoid biosynthetic process there are genes coding for three cytochrome P450s, i. e. CYP76C1 and C2 and CYP82G1: CYP76C1 and C2 are involved in floral linalool metabolism  28  , while CYP82G1 is responsible for the synthesis of homoterpene volatiles in Arabidopsis  29. Hemp trichomes are factories producing several terpenes contributing to the plant peculiar aroma  30  , however these metabolites can also be found in resin ducts.
Since we observed the presence of non-glandular trichomes in our fibre samples, we speculate that these genes may be expressed in "contaminating" resin ducts present in our bast fibre samples. Three genes encoding UDP glucosyltransferases were present in the "flavonoid biosynthetic process" ontology: UGT73C1 and C7 and UGT78D2. The first two belong to group D, whose members are related to stress-inducible response  31  ; notably, UGT73C1 was shown to glucosylate cytokinins (trans-zeatin and dihydrozeatin  32  ) and may therefore play a role in hormone homeostasis. UGT78D2 glucosylates instead flavonols in the C3 position and is strongly co-regulated with flavanone 3-hydroxylase (F3H, participating in the conversion of p-coumaroyl CoA to kaempferol/quercetin  33  ), whose gene was also upregulated at the TOP in our dataset (Suppl.
Dataset File). Two genes encoding cytochrome P450s, i. e. CYP81D1 and D8 and two MYB transcription factors (TFs), MYB34 and MYB122, were present in the indole-containing compound metabolic process: MYB34 and 122 are two of the three TFs reported to control indole glucosinolate biosynthesis  34. These TFs respond to phytohormones in different manners: MYB34 responds to both abscisic acid (ABA) and jasmonate (JA), while MYB122 plays a minor role in indole glucosinolate biosynthesis upon ethylene (ET) and JA signalling  34. Given the role of indole glucosinolates in defence responses upon mechanical damage (e. g. herbivore attack  35  ) and since the phytohormone JA is considered "the wound hormone"  36  , it is tempting to speculate that a mechanism involving the synthesis of JA and the subsequent activation of indole glucosinolates may be involved in the intrusive growth phase of bast fibres.
Among the genes grouped in the "phenylpropanoid biosynthetic process" ontology, there are five contigs annotated as PRR1 (pinoresinol reductase 1), whose expression was upregulated at the TOP (Suppl. Dataset File). In flax, lignomics unveiled a complex monolignol metabolism associated with bast fibre hypolignification, with the accumulation of both aglycone forms and glycosides of (neo)lignans  37  ; hence PRR1 may be involved in a similar rich monolignol metabolism in hemp bast fibres. Additionally, some lignans, as dehydrodiconiferyl alcohol, are known to regulate cell division  38  ; therefore PRR1 may also partake in the synthesis of specific lignans contributing to the regulation of cell division. Recently a transcriptome analysis performed on a wild-type and mutant jute (referred to as deficient lignified phloem fibre) identified the presence of many isoforms (more than other bast fibre-producing plants) belonging to the monolignol and shikimate-aromatic amino acid metabolism  15.
The exact role of these genes remains to be elucidated via functional studies; however their expression pattern strongly points towards an involvement during hemp bast fibre early growth stages. Gene ontology enrichment analysis of the MID region. The transcriptome of the MID region is mainly characterized by processes related to secondary cell wall biogenesis (Fig. 4 ); this result was confirmed with the mapping against the Finola transcriptome (Suppl. Dataset File). These data are of particular relevance because they provide an overview of the key cell wall-related events responsible for the change in mechanical properties of the bast fibres observed at the snap point. In this study the MID region corresponds to the internode containing the snap point (Fig. 1 ); hence the fibres separated are progressively shifting from a stage of elongation to a phase of tertiary cell wall formation.
According to our data, the transcriptome of the bast fibres at the MID region is characterized by genes involved in cell expansion and cell wall loosening, as well as by an active transcriptional dynamics of candidates involved in hemicellulose biosynthesis and transcriptional regulators orchestrating secondary cell wall biogenesis (Suppl. Dataset File). Alpha expansin genes (EXPA8, 10, 11 and 12) and a xyloglucan endotransglycosylase/hydrolase (XTH33) peaked at the MID region (EXPA10 and 11 showed an increase in FC > 5 with respect to the TOP; Suppl. Dataset File); their higher expression is probably due to the phase of elongation characterizing the heterogeneous fibre stages at the MID. The master regulator MYB46 and its downstream target MYB63 were expressed 4. 8 and 1. 4 times more at the MID as compared to the TOP (and 3.