dataesr/scientific-paragraphs-categorization huggingface dataset:
A quality control (QC) of the sequencing data was performed using the program RAVAQ (https://gitlab. com/gmarenne/ravaq). After QC, 184 individuals were included for analysis with 127 presenting an early-onset VTE and 57 a late-onset VTE. Only variants passing all QC steps and with a MAF lower than 1% in the sample were considered in the association tests comparing early and lateonset groups. For these comparisons, rare variants were gathered either by CADD regions or by using the sliding windows procedure implemented in WGScan  (18). Qualifying variants were selected based on CADD scores and using two filtering strategies: a fixed CADD threshold of 15 (as recommended by https://cadd. gs. washington. edu/info, version v1. 4) or the RAVA-FIRST CADD region-specific filtering (applied on adjusted scores).
Our dataset contains less than 200 individuals, suggesting that the gain in computation time of CADD regions compared to sliding window procedures would be even greater in larger WGS datasets. No significant result was found when selecting variants with a CADD score greater than 15 using neither the sliding window strategies nor the CADD regions to gather rare variants, whereas one association reached borderline significance (p = 6. 41•10 -7 ) when using the RAVA-FIRST strategy. Figure  2 : QQ-plot of WSS analyses on VTE data using the three strategies of analysis. Early-onset patients (<50 years old) were compared to late-onset patients (≥50 years old). This association maps to R126442, a CADD region of 21 kb on chromosome 18:66788277-66809402 that contains 31 rare variants after RAVA-FIRST filtering.
1•10 -4 after correction for multiple testing, it is worth noting that the two proteins that exhibited the strongest significance with marginal association at p < 0. 001, procalcitonin tagged by the HPA043700 antibody (p = 7. 2•10 -4 ) and PDPK1 tagged by HPA035199 (p = 7. 5•10 -4 ), have been both proposed to be involved in red blood cell biology  (37, 38). According to ENCODE data, the R1246442 CADD region overlaps "intergenic" and "regulatory" categories with one distant enhancer-like signature. To describe this region further, we looked at TADs positions in https://dna. cs. miami. edu/TADKB/brows. php in HUVEC and HMEC cell lines, two cell types known to be relevant for VTE pathophysiology. We found that the CADD region is included into the topological associated domains (TADs) 18:66450000-68150000.
This reinforces the value of the association signal in this CADD region, although it should be further described and validated using functional experiments. Preliminary investigations that need to be further explored, at both experimental and epidemiological levels, strongly suggest that this region is associated with several inflammatory markers impaired in anaemia of inflammation  (38, 46)  and in platelets, both mechanisms being involved in thrombotic processes  (47). Some limits can be pointed out on our RAVA-FIRST approach. Firstly, the definition of CADD regions relies on the GnomAD population and on the adjusted CADD threshold. We chose to use the whole GnomAD dataset but it could be of interest to select some of the populations to be more specific. It has for example been suggested that different expression patterns could be found between different populations  (48).
Data availability The files containing the positions of CADD regions, the positions of genomic categories and the adjusted CADD scores are available at https://lysine. univ-brest. fr/RAVA-FIRST/. All the functions needed for RAVA-FIRST to annotate, group, filter and analyse rare variants have been implemented in the package R Ravages (https://cran. r-project. org/web/packages/Ravages/, https://github. com/genostats/Ravages) which directly downloads the files from https://lysine. univbrest. fr/RAVA-FIRST/.
Acknowledgements V. E. G. and G. B. are supported by  Centre National de la Recherche Scientifique. NeuroDiderot is affiliated to  C-BRAINS , a field of major innovation and research (DIM) supported by the  Ile-de-France Regional Council. This work was funded by  Inserm, Institut Curie ,  Université Paris Cité , the  PNREST Anses  ( ANSES-21-RF-24 ) and the  ANR  ( ANR-20-CE13-0012 ).
Force field parameters Classical all-atom molecular dynamics (MD) simulations have been performed using the AMBER18 package  56. A single Cytc monomer structure was used (108 residues), taken from Cytochrome c PDB ID 3TYI Saccharomyces cerevisae structure  34. The protonation state of titratable residues was determined via H++ server (http://biophysics. cs. vt. edu/H++)  57  at the experimental pH of 6. 0  34, 35. The heme group was connected to the H18 and M80 residues using the "Metal Center Parameter Builder" (MCPB) module in AmberTools18 58. The atomic charges for the heme were obtained from RESP calculations 59 on a geometry optimized at the DFT-B3LYP/6-31G(d) level of theory, following a previous parametrization in our group 48. A Stuttgart-Dresden SDD pseudopotential was used for the iron center of the heme cofactor.
MD Simulations Protocol MD simulations were carried out using coordinates from a dimeric co-crystallized Cytc-sclx 4 X-ray structure (PDB ID code: 3TYI), from which one Cytc monomer was considered. The ligand sclx 4 was removed and then placed far from the protein (at more than 10 Å between the centers of mass, see Figure  S1 ), to probe molecular association with no bias. The binding of a second sclx 4 molecule (Cytc-2 sclx 4 system) was also simulated with independent trajectories (see Figure  S2 ). All the details about relative positions and distances are reported in Table  S1. Independent replica of 200 ns (see Table  S2 ) were used to ensure ligand diffusion on the protein surface and sample stable binding events. The last 100 ns of the production trajectories were used to capture relevant features of the main binding modes.
MD Simulations Details The systems were initially minimized for 10000 steps (5000 of steepest descent and 5000 of conjugate gradient), and then heated up from 0 K to 300 K (with an integration time step t step of 1. 0 fs) for a total of 30 ps using the Langevin thermostat (γ coll = 1 ps -1 ) in the isothermal-isochoric ensemble (NVT). Equilibration was carried out for 1 ns , using an integration timestep of 2 fs in the isothermal-isobaric (NPT) ensemble (P = 1 atm and T = 300 K). Pressure control in NPT simulations was achieved using the Berendsen barostat. For both systems, different NPT replica production runs (total simulation time reported in Table  S2 ) with different initial velocities have followed, and they have been carried out with the pmemd. cuda module of AMBER18, through the use of graphics processing units (GPUs).
Acknowledgement A. B. is grateful for a post-doctoral fellowship by the  Fundation "Maison de la Chimie ". The authors thank the  SYSPROD  project and  AXELERA Pôle de Compétitivité  for financial support (PSMN Data Center). G. P. is grateful to the  Doctoral School of Chemical Sciences of the University of Strasbourg  ( ED222 ) for financial support. Computational HPC resources from the  Mesocentre of the University of Strasbourg  and the  Mesocentre of the University of Reims, ROMEO , are also gratefully acknowledged.
Labeled cRNA was purified and fragmented before hybridization on Agilent Human Gene Expression 4X44K Arrays (Agilent Technologies, Palo Alto, CA, USA) at 65 • C for 17 h. Raw microarray signals were scanned and extracted using Agilent Feature Extraction Software (Agilent Technologies, Santa Clara, CA, USA). Quality control and data normalization using quantile methods and a background correction were applied on a BioAnalyzer Agilent 2100 (Agilent Technologies, Palo Alto, CA, USA). Raw signals with a fluorescence intensity of less than 100 were excluded for further analysis. For the differential gene expression analysis on microarrays, the values of the induction and inhibition of TWEAK or TNFα cytokines versus control (fold change (FC)) at the different timepoints were selected based on a FC > 1.
5 or < -1. 5, respectively. In case of discordance between FC duplicates, the pointed gene was excluded from the analysis. We analyzed genes that displayed a significant increase or decrease. Consequently, for the biological interpretation, 16,389 genes for TNFα and 16,763 genes for TWEAK were included from the 3 h time point; 14,455 genes for TNFα and 12,577 genes for TWEAK were included from the 12 h time point; 13,017 genes for TNFα and 14,469 genes for TWEAK were included from the 24 h time point. Biological interpretation of the transcriptomic data was performed using Qiagen Ingenuity Pathway Analyses (IPA) following instructions provided by Qiagen (Ingenuity Systems Inc. , Redwood City, CA, USA; www. ingenuity. com, accessed on 20 September 2022). Similarities and differences in canonical pathways were analyzed.
The rationale of using IPA is that it utilizes expertly curated biological interactions and functional annotations from multiple databases. The significance of the association between the data set and the canonical pathway was determined based on two parameters: (i) a ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway and (ii) a p-value calculated using Fischer's exact test determining the probability that the association between the genes in the data set and the canonical pathway is not due to chance alone. The Z-score algorithm was designed to reduce the chance that random data will generate significant predictions. IPA predicts that the canonical pathway is significantly increased when Z-score ≥ 2 and is significantly decreased when Z-score ≤ -2.
2 µg/mL) (Sigma-Aldrich St. Louis, MO, USA), diluted in blocking buffer overnight at 4 • C. After washing, membranes were incubated with a peroxidase-conjugated secondary antibody (Thermo Fisher Scientific, Waltham, MA, USA). Finally, proteins were detected using a chemiluminescence kit (Amersham Biosciences, Buckinghamshire, UK). Membranes were digitized using Nine-Alliance imaging system (UVITEC Cambridge, UK), and optical densities of the bands were quantified using Image J densitometric software (https://imagej. nih. gov/ij, accessed on 20 September 2022). The experiment was performed in triplicate for each condition.
Gene To compare the results of the microarray studies to a second established transcriptomic method, qPCR was performed to assess the mRNA expression levels of 4 selected genes (CLDN5, JAM1, THY1, CXCL12) implicated in leukocyte extravasation in Table  1. Comparison of FC values between microarray and qPCR methods are shown in Table  2. CLDN5 showed the most robust correlation between results obtained by microarray and qPCR, displaying a significant decrease in expression after either TWEAK or TNFα exposure. THY1, CXCL12, and JAM1 showed inconsistent directionality of results between qPCR and microarray data for either TWEAK or TNFα exposure. TWEAK or TNFα (MMP9, CCXCL12, CLDN5, JAM1, PIK3C2B) but only CXCL12 was upregulated by both (FC > 1. 5). Table  1. Differential expression of IPA-identified genes belonging to leukocyte extravasation signaling in hCMEC/D3 cells after 24 h of TWEAK or TNFα treatment.
Red: upregulated genes (FC > 1. 5), blue: downregulated genes (FC < -1. 5). FCs with values between -1 and 1 were not included in IPA-run analysis. To compare the results of the microarray studies to a second established transcriptomic method, qPCR was performed to assess the mRNA expression levels of 4 selected genes (CLDN5, JAM1, THY1, CXCL12) implicated in leukocyte extravasation in Table 1. Comparison of FC values between microarray and qPCR methods are shown in Table  2. CLDN5 showed the most robust correlation between results obtained by microarray and qPCR, displaying a significant decrease in expression after either TWEAK or TNFα exposure. THY1, CXCL12, and JAM1 showed inconsistent directionality of results between qPCR and microarray data for either TWEAK or TNFα exposure.
PIK3C2B, an upstream activator of PRKCZ, decreased its mRNA levels when our in vitro model was exposed to TWEAK as well  [19]. qPCR was used to validate microarray analysis for a set of leukocyte extravasation genes. Although some of our data were in disagreement, this can be expected  [20]. We postulate that the location of the PCR primers and microarray probes could have decreased the correlation of the results, an observation that was previously found when comparing data using these methods  [21]. Due to its consistent decrease across all quantification methods as well as its crucial role in BBB integrity, we chose to look at the protein levels of claudin-5 in hCMEC/D3 cells after TWEAK treatment. Mirroring its mRNA levels, claudin-5 protein expression was significantly reduced by TWEAK incubation (Figure  1 ).
ACKNOWLEDGEMENTS S. A. H. and G. K. D. are registered in the  EMJMD LIVE (Erasmus + Mundus Joint Master Degree Leading International Vaccinology Education ), co-funded by the  EACEA (Education, Audiovisual and Culture Executive Agency , award  2018-1484 ) of the  European Commission. S. A. H. receives a scholarship from the  EACEA  and  G. K. D. receives a scholarship from the  Department of Science and Technology-Science Education Institute (DOST-SEI ) of the Philippines to study in the EMJMD LIVE.
After extensive washes, proteins were eluted in presence of 60 μl of AcTEV protease for 1h at RT. In order to determine the relative amount of Mus81-Eme1 between different samples, 3 μl of each TEV eluate was treated with phosphatase before SDS-PAGE and Western blot analysis in order to collapse the Eme1 signal into a single band of dephosphorylated Eme1. The relative intensity of the dephosphorylated Eme1 band was quantified for each sample using the ImageLab software. Dilution folds were calculated and used to bring the concentration of each sample down to that of the least concentrated sample. 3 μl of each normalized sample was used in nuclease assays with 32 P-labelled DNA substrates as previously described  [28].
Acknowledgments Many thanks to  Paul Russell ,  Nick Boddy  and  Jean-Hugues Guervilly  for their critical and careful reading of the manuscript and for their encouragements. " Milles merci " to  Charly Chahwan  for hinting at the SUMO-related functions of Eme1 a long time ago and for his enthusiastic support. We thank our colleagues of the 3R community at  CRCM  for their support and stimulating discussions. We thank  Samuel Granjeaud  for help on statistical analyses. We are grateful to  Nick Boddy ,  Katsunori Tanaka  and  Benoit Arcangioli  for sharing the nse2-SA, pmt3Δ and pli1Δ mutant strains, respectively.
This work was supported by grants from  Agence Nationale de la Recherche  ( ANR-10BLAN-1512-01 ) and  Institut National du Cancer (INCa- PLBio2016-159  and  INCa-PLBio2019-152 ) awarded to PHLG. CG was a recipient of fellowships awarded by the  Fondation pour la Recherche Medicale (FRM  grant number stimulation of Mus81-Eme1. ity. viability in absence of the Rqh1 BLM helicase, which is related to the BLM helicase defective in highly cancer prone Bloom syndrome patients. ECO20170637468) and the  Fondation ARC  (grant  ARCDOC42020010001276 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Search Strategy We retrieved HTA and/or appraisal reports for nusinersen from reimbursement and HTA agencies in Belgium, Canada, France, England and Wales, Germany, Italy, Ireland, Scotland, Sweden, the Netherlands, and the US. Countries were chosen depending on the availability of public information. In addition, we have aimed to balance our country selection and reported data from countries that have adopted either a Bismarck or Beveridge model, with either a social or private security system and with an even geographical spread. Reports and relevant publications were translated via Google Translate. Information on economic evaluation was incorporated as submitted by Biogen and presented by HTA or reimbursement agencies. Additionally, in jurisdictions of which HTA reports contained no information on reimbursement decisions and MEAs, a literature search on Google Scholar or PubMed was performed to identify publications, either peer-reviewed or grey literature.
Data Extraction We created two data extraction tables that allowed a systematic data extraction from each HTA or appraisal report. Per jurisdiction, we extracted information on the economic evaluation (study design and results of the base case and sensitivity analysis) (see Table  2  and Table 3 ) and the budget impact analysis (see Table  4 ). However, not all jurisdictions are included in each table, as a result of data unavailability. For instance, the jurisdictions England and Wales were not added to Table  4  since budget impact data were not included in the HTA report. Finally, we included information on the overall reimbursement decision, the conditions for reimbursement, and the MEA in the results section.
Ireland The National Centre for Pharmacoeconomics (NCPE) based its assessment report on nusinersen on the economic evaluation as submitted by Biogen. The economic evaluation included two Markov models, 1) for early-onset (EO) (type I) and 2) late-onset (LO) (types II and III) SMA, comparing nusinersen to the standard of care. Life years gained (LYG), patient's qualityadjusted life years (QALY), and caregiver QALYs were included for both models and calculated over a lifetime horizon. Direct medical costs (technology and health state maintenance) were included. Biogen obtained utilities for both EO and LO SMA by deriving Pediatric Quality of Life Inventory (PedsQL) data from LO SMA patients enrolled in the CHERISH (SMA II) trial. PedsQL is a questionnaire developed to measure the health-related QoL in children and adolescents  (Varni et al.
Economic evaluation technique Cost-effectiveness (LYG) + cost-utility analysis (QALY) Cost-utility analysis (QALY) Cost-utility analysis (QALY) + costeffectiveness analysis (LYG) Cost-utility analysis (QALY) + cost-effectiveness analysis (LYG)   Scotland  (SMC, 2018)  Sweden  (NT-council, 2019)  The Netherlands  (Zorginstituut Nederland, 2018; Nederland Zorginstituut, 2019; Bruins, 2019)  enrolled in the CHERISH (SMA II) trial onto the EQ-5D scale the EQ-5D scale on to the EQ-5D scale using a mapping algorithm. For Biogen, these utilities lacked face validity and were not used for basic scenario analysis scale on to the EQ-5D scale using a published algorithm    Base case results including the caregiver perspective were not generated Calculation of utilities: -PedsQL data from the CHERISH (SMA II) trial were converted to EQ-5D using a mapping algorithm by Khan et al.
This resulted in ICERs of €17,142/QALY; €322,858/QALY; and €1,564,889/ QALY for EO type I, LO type II, and LO type III, respectively. These ICERs were most sensitive to utility estimates, although a tornado diagram was not provided. TLV excluded caregiver utilities from its base case reanalysis, reporting ICER ranges from €583,035/QALY to €8,251,567/QALY for EO and €736,298/QALY to €1,297,144/QALY for LO type II SMA (depending on utility values used). TLV did not include LO type III SMA outcomes as these data were obtained from noncontrolled studies. ICERs calculated by TLV were higher as its model was based on different assumptions. It tested, for instance, more realistic assumptions regarding disease progression for nusinersen patients, such as a lower probability of death for patients on nusinersen compared to the standard of treatment.
The report did not include information on budget impact. In order to address long-term uncertainties, Biogen proposed an MEA for a 5-year term and included eligibility and stopping criteria in its draft proposal. Data collection is proposed after 14 months initially and 12 months afterward. The outcomes calculated are survival, ventilation/respiratory events, motor function, and the QoL (for both patients and caregivers). They are collected through the SMART NET registry, including patients who discontinue nusinersen. However, NICE remarked on Biogen's intention to not include comparative data on patients receiving standard of care, which was considered a significant limitation. They also mentioned the lack of outcome collection for patients with type 0 or IV SMA. NICE concluded that nusinersen was not cost-effective.
The Netherlands Zorginstituut Netherland (ZIN) issued its final advice on the reimbursement of nusinersen on February 5, 2018. Its advice was based on several appraisal criteria and reported on efficacy, therapeutic need, cost-effectiveness, and budget impact. The data submitted by Biogen included an economic evaluation based on two Markov models, for EO (type I) and LO (type II and III) SMA, comparing nusinersen to the standard of care. Both models included incremental LYGs and patient QALYs, calculated over a lifetime horizon, set at 40 and 80 years for EO and LO SMA, respectively. The target population of the economic evaluation corresponded to the trial population (ENDEAR and CS3A), which represented a subgroup of both EO (type I) and LO (type II and III) SMA. The scope of costs included direct medical, as well as direct and indirect nonmedical costs.
Nearly all HTA agencies highlighted uncertainties regarding utilities and ICER values. These resulted from a lack of data on QoL, in addition to several shortcomings regarding the methodologies used to obtain utilities, through either PedsQL data mapping or a vignette study. Moreover, in Scotland, Canada, the US, the Netherlands, and England and Wales, Biogen's assumptions on disease progression and hence long-term outcomes and survival rates with nusinersen were considered to be rather optimistic, especially compared to the assumptions made for the comparator group. Only reports from England and Wales, France, and the Netherlands presented a tornado diagram, illustrating the ICERs sensitivity to mostly the vial price and utility estimates. The reports from the Netherlands, England, Wales, and Canada each reported a 0% chance for nusinersen to be cost-effective at the local willingness-to-pay threshold, whereas HAS-CEESP (France) estimated that nusinersen would have an 80% chance of being cost-effective at a threshold of €3.
Study Limitations Our study is not without limitations. First of all, reporting on budget impact was incomplete and nontransparent, which made it difficult to draw conclusions on its use in decisionmaking. Second, we analyzed reimbursement reports from a limited number of jurisdictions, which may have led to selection bias. The inclusion of additional jurisdictions could have led to different conclusions. However, as we selected jurisdictions that were spread throughout the EU, we believe that our findings are generalizable to orphan drugs in general. Third, by using an online translator such as Google Translate, there is a potential risk of misinterpretation of data due to translation errors. Fourth, more thorough information on MEAs could have been retrieved by performing a systematic literature review, including search terms in languages from all respective countries.
Nguengang Wakap, S. , Lambert, D. M. , Olry, A. , Rodwell, C. , Gueydan, C. , Lanneau, V. , et al. (2020). Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database. Eur. J. Hum. Genet. 28 (2), 165-173. doi:10. 1038/s41431-019-0508-0.