dataesr/scientific-paragraphs-categorization huggingface dataset:
Data reductions were done with the LAMP software. 57 N16B was set up using an incident neutron wavelength of 3. 275 Å and with Si(311) analyzer crystals. With this setup, the instrument yields an E resolution at fwhm of ∼2 μeV and an accessible E range of ±56 μeV. The accessible q range was 1. 5-3. 5 Å -1. In addition to the QENS measurements, measurements of the elastic and inelastic intensities upon temperature variation, so-called elastic and inelastic fixed window scans (EFWS and IFWS, respectively),  45  were recorded upon cooling from T = 300 to 2 K. The inelastic intensities were probed at 4 and 10 μeV, respectively. Measurements were taken in the temperature range of 2-150 K with the 2 K spectra used as a resolution function in the data analysis. Data reductions were performed with the Mantid software.
Analysis of the repertoire of Plasmodium sporozoite 6-cys proteins suggests that P36, P52 and B9 are employed by infectious sporozoites only In order to define the repertoire of 6-cys proteins expressed at the sporozoite stage, we first analysed the proteome datasets of P. falciparum  22, 26  , P. vivax  24  , P. yoelii  26  and P. berghei  21  sporozoites. As expected, P36 and P52 were identified by mass spectrometry in sporozoites from all four species. Interestingly, three other 6-cys proteins, P12p, P38 and B9, were consistently identified across the datasets. Among this core of five 6-cys proteins, P12p and P38 have been identified in the surface proteome of P. falciparum sporozoites, P12p being quantitatively enriched on the surface of activated parasites in the presence of bovine serum albumin  27.
falciparum merozoites, where it forms a protein complex that is essential for invasion of erythrocytes  42, 43. B9 is enriched in cysteines, nine being preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted October 26, 2021. ; https://doi. org/10. 1101/2021. 10. 25. 465731 doi: bioRxiv preprint located in the predicted propeller domain that we suppose are involved in the formation of disulphide bonds in a similar manner to CyRPA  44  , to stabilize the protein structure (Fig. 4a ). To explore the structural features of the B9 propeller, we predicted the tertiary structure of PfB9 propeller (covering positions 26 to 386) by homology modelling using CyRPA as a template structure  44  (PDB ID: 5TIH; Supplementary File 1; Supplementary Fig.
In addition, PbDb9 sporozoites do not discharge their rhoptries upon contact with host cells, similar to PbDp36 sporozoites, indicating that both proteins are acting at an early step during invasion. We further provide evidence that B9 interacts with P36 and P52, suggesting that the three proteins participate in an invasion complex that is required for productive invasion of hepatocytes. Comparison of profile hidden Markov models between PfB9 and tertiary structure database identified an N-terminus beta propeller domain structurally similar to CyRPA, a cysteine-rich protein expressed in P. falciparum merozoites, where it forms a protein complex that is essential for invasion of erythrocytes  42, 43. Our data suggest that the propeller domain of B9 directly interacts with both P36 and P52. We speculate that blades 3 and 4 of the propeller, which are the most conserved, could be involved in these interactions.
33 The secondary structure of PfB9 was predicted by hydrophobic cluster analysis  56  and using PSIPRED 4. 0  57. Conserved domains were searched using InterPro 58 and HHpred  59. Glycosylphosphatidylinositol (GPI) anchors were predicted using the NetGPI tool (https://services. healthtech. dtu. dk/service. php?NetGPI)  60. Intrinsic disorder prediction was made using the IUPred2A web server (https://iupred2a. elte. hu/)  61. The homology model of PfB9 propeller (amino acids 26 to 386) was built with the X-ray structure at 2. 4 Å resolution of CyRPA from P. falciparum (PDB ID: 5TIH  44  ) using the Robetta web server 62 (default parameters). The model was refined and energy-minimized using respectively GalaxyRefine  63  and Yasara  64  , then validated using MolProbity  65  and Prosa II 66 (Supplementary Fig.
Evolutionary analysis of B9 and CyRPA The amino acid sequence of PfB9 (PlasmoDB code: PF3D7_0317100) and CyRPA (PF3D7_0423800) were queried against the PlasmoDB database 70 (release 46) and the NCBI non-redundant protein database using blastp searchs (BLOSUM62 scoring matrix). Twenty-three B9 and eighteen CyRPA sequences were retrieved from distinct Plasmodium species. Protein sequence alignments were generated using MAFFT version 7 (default parameters  71  ). Output alignments were visually inspected and manually edited with BioEdit v7. 2. 5. Amino acid positions containing gaps in at least 30% of all sequences were removed. Phylogenetic relationships of B9 and CyRPA preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
ACKNOWLEDGEMENTS We thank  Jean-François Franetich ,  Maurel Tefit  and  Thierry Houpert  for rearing of mosquitoes. The following reagent was obtained through  BEI Resources, NIAID, NIH: Monoclonal Anti-Plasmodium Apical Membrane Antigen 1 ,  Clone 28G2  (produced in vitro), MRA-897A, contributed by  Alan W. Thomas. This work was funded by grants from  the Laboratoire d'Excellence ParaFrap  ( ANR-11-LABX-0024 ), the  Agence Nationale de la Recherche  ( ANR-16-CE15-0004 ) and the  Fondation pour la Recherche Médicale  ( EQU201903007823 ). The authors acknowledge the  Conseil Régional d'Ile-de-France ,  Sorbonne Université , the  National Institute for Health and Medical Research (INSERM)  and the  Biology, Health and Agronomy Infrastructure (IBiSA)  for funding the timsTOF PRO. ML was supported by a 'DIM 1Health' doctoral fellowship awarded by the  Conseil Régional d'Ile-de-France.
Cladistic analysis A heuristic analysis was performed using TNT software  (Goloboff et al. , 2008). The data matrix of  Grande (2010) , modified by  Brito et al. (2017)  in which the Kimmeridgian fossil Nhanulepisosteus mexicanus was included, was employed, with the addition of coding for the new taxon. The analysis was processed using the same parameters as  Brito et al. (2017) , with all 105 characters equally weighted and Semionotus employed as the outgroup. Only one species of each genus of the Obaichthyidae were included due to Obaichthys africanus and Dentilepisosteus kemkemensis being considered data deficient  (Brito et al. , 2017). Bootstrap values and Bremer Decay values were calculated in TNT software (Fig. 13 ).
Acknowledgements Thanks to  Mr Mick Green  of the  Isle of Wight  for his partial preparation of the specimen. Mr Richard Hing  of the  University of Portsmouth  is thanked for photography and laboratory assistance. We are delighted to thank  Ms Emma Barnard  and the photographic team of the  NHMUK  for their help and for supplying images. Thanks to  Mr S everin Morel  and  Prof. François Meunier  ( Paris ) for thin sectioning a tooth. In the field thanks to  Dr Roy Smith ,  Megan Jacobs ,  Mick Oates ,  Rab Smyth. For their amazing hospitality, we thank our friends in Asfla village. Finally, we thank the reviewers,  Lionel Cavin  and  Romain Vullo , for their inciteful and constructive comments which greatly improved the quality of our manuscript, and our Editor who has always been extremely helpful.
Introduction In older adults with type 2 diabetes (T2D), overtreatment with hypoglycaemic drugs (HDs: sulfonylureas, glinides and/or insulins) is frequent and associated with increased 1-year mortality. Deintensification of HD is thus a key issue, for which evidence is though limited. The primary objective of this study will be to estimate the effect of deintensifying HD on clinical outcomes (hospital admission or death) within 3 months in older adults (≥75 years) with T2D. Methods We will emulate with real-world data a target trial, within The Health Improvement Network cohort, a large-scale database of data collected from electronic medical records of 2000 general practitioners in France. From 1 January 2010 to 28 February 2019, we will include eligible patients ≥75 years who will have T2D, a stable dose of HDs, glycated haemoglobin A1c (HbA1c) value <75 mmol/mol (9.
0%) and no deintensification in the past year. The target trial will be sequentially emulated (ie, eligibility assessed) every month in the database. Patients will be classified at baseline of each sequential trial in the intervention arm (deintensification of HDs: decrease of ≥50% in the total dose of HDs, including complete cessation) or control arm (no deintensification of HDs). The pooled dataset for all sequential emulated trials will be analysed. The primary outcome will be time to first occurrence of hospital admission or death, within 3 months. Secondary outcomes will be hospitalisation, death, appropriateness of glycaemic control and occurrence of HbA1c >75 mmol/mol within 1 year. Participants will be followed from baseline to 12 months after randomisation, administrative censoring, or death, whichever occurs first.
⇒ The database will not include information on patients' willingness or motivation regarding the deintensification of HDs, nor any data about the occurrence of hypoglycaemic events or the patients' functional status. Open access worsens the well-being of patients and generates excess costs for healthcare systems. [6] [7] [8] [9]  In particular, in some older patients, the risks of morbidity and mortality due to short-term and medium-term hypoglycaemia outweigh the benefits of glucose-lowering drugs. 10  Therefore, tailoring glycaemic management in older adults with T2D has become the gold-standard practice in recent years, as stated by recommendations provided by clinical practice guidelines developed by major scientific societies. 10  As suggested by a systematic review of recent international recommendations, scientific societies recommend deintensifying glucose-lowering drugs (ie, reducing the total dose or the number of drugs, including total discontinuation of the treatment  11  ), in particular limiting the use of HDs, for various profiles of older patients with T2D (eg, those with high risk of hypoglycaemia, those with uncertain benefit of glucose-lowering drugs, and those unable to provide self-care  [12] [13] [14] [15] [16] [17]  ).
Data sources Data will be extracted from The Health Improvement Network (THIN) database from GERS DATA (Groupement pour l'Elaboration et la Réalisation de Statistiques, Cegedim SA). 26  THIN is a large-scale database, collecting fully anonymised real-life longitudinal patient data in several European countries; we will use data from France. THIN has been used in more than 1900 publications since 2012, including pharmacoepidemiological studies. 27  Data from France have been collected since 1994 from electronic health records of about 3000 physicians in primary care (2000 general practitioners (GPs)) using the CrossWay software (electronic medical record device from CEGEDIM SA). The French population included in THIN is representative of the whole French population, in terms of age, sex and geographical area.
The data encoded by the GP at each visit of a patient include sociodemographic characteristics (age, sex, place of residency), diagnoses (codes of the International Classification of Diseases, 10th Revision), medications prescribed by the GP (codes of the Anatomical Therapeutic Chemical classification system), acts performed by the GP during the patient visit, and laboratory test results (including glycated haemoglobin A1c (HbA1c) values). For each patient, reimbursement data are obtained from the Historique des Remboursements (from the French Health Insurance system) and include reimbursements for medical acts, treatment from the pharmacy and hospitalisations. Patient's vital status was collected from the French National Register. 28  Data (from medical records, reimbursement and vital status) are linked for each patient.
Study timeframe The inclusion period will be from 1 January 2010 to 28 February 2019. We will limit inclusions up to 28 February 2019 in order to have 1 year of potential follow-up and avoid measurement of outcomes during the COVID-19 pandemic period, which could have affected their incidence. Data will be extracted to span the period from 1 January 2009 to 28 February 2020, to have a 1-year lookback to assess medical history before inclusion and a potential 1 year of follow-up. Figure  1  presents the timeframe defining the baseline, which is each month in which all eligibility criteria are met.
Open access HDs (ie, sulfonylureas, glinides and/or insulins), and HbA1c level < 75 mmol/mol (9. 0%) during the inclusion timeframe (figure  1 ). T2D will be defined by a diagnosis of T2D (according to the International Classification of Diseases, 10th Revision code for T2D: E11), and/or a prescription of any glucose-lowering treatment (according to Anatomical Therapeutic Chemical codes). A specific search algorithm will be used to capture all patients with T2D in the database according to this definition (online supplemental file A). Among the glucose-lowering treatment available, we will distinguish between HDs (that is, sulfonylureas, glinides and/or insulins) and non-HDs (ie, all other glucose-lowering treatment; eg, metformin, glucoselike peptide-1 receptor agonist, dipeptidyl-peptidase 4 inhibitor, etc).
The stability of HDs is defined by the prescription of the same medication(s) and dose(s) of HD over 6 months (without any change) (eg, at least two consecutive identical prescriptions 6 months apart, with no change between them). All doses of Same as for the target trial and additionally: ► At least 1 year in the database (to assess previous de-intensification) ► At least 1 year of potential follow-up in the database (ie, patients must be eligible up to the date of 28 February 2019) Baseline will be defined each month that all eligibility criteria are met.
Causal contrast Intention-to-treat effect Per-protocol effect Observational analogue of intention-to-treat effect Observational analogue of per-protocol effect Analysis Intention-to-treat analysis Per-protocol analysis: follow-up of patients will be censored if they deviate from their assigned treatment arm at randomisation, and a strategy of inverse probability weighting will be implemented to adjust for prerandomisation and postrandomisation prognostic factors associated with treatment strategy adherence. Same as for the target trial. The target trial will be sequentially emulated every month (at the first day of each month) in the database, and the analyses will be additionally adjusted for covariates measured at baseline for each sequential emulated trial. DDD, defined daily dose; HDs, hypoglycaemic drugs; T2D, type 2 diabetes mellitus.
5% when no HD is prescribed, or an HbA1c between 7. 0% and 8. 5% when HD is prescribed (at the time of the HbA1c measurement). The assessment of glycaemic control appropriateness will be performed on the first HbA1c value available between 3 months and 12 months after baseline. This definition is adapted (according to the data available in THIN database) from the HbA1c targets suggested by the clinical practice guidelines of the Endocrine Society  14  and used in previous studies. 30 31  Follow-up Participants will be followed from baseline to the occurrence of the outcome of interest, death, exit from the THIN database (lost to follow-up), or up to 12 months, whichever occurs first. Baseline for a given patient is the month in which all eligibility criteria are met (figure  1 ). Patients will not be followed up beyond 28 February 2020 (cut-off date of the database).
Covariates We will consider a large panel of covariates, selected after a review of the literature on this subject. 3 11 20 31-34  The covariates of interest will be age, sex, physician practice region, patient's place of residence (home vs nursing home), HbA1c value, duration of diabetes (time from diabetes diagnosis), age at diabetes diagnosis, glucoselowering treatment (drugs and doses in DDD), complications of diabetes (ie, microangiopathy, including diabetic polyneuropathy and diabetic retinopathy), hypertension, other comorbidities (including those of the Charlson Comorbidity Index  35  : myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular accident or transient ischaemic attack, severe neurocognitive troubles, chronic obstructive pulmonary disease, connective tissue disease, peptic ulcer disease, liver disease, hemiplegia, moderate to severe chronic kidney disease, solid tumour, leukaemia, lymphoma and AIDS), claim-based frailty index developed by Segal et al  36 37  (computed on the basis of the comorbidities collected in the database), number of hospitalisation in the past year, number of GP contacts in the past year and total number of drugs per day.
Emulation of the target trial The treatment strategies of each cloned copy will be assessed based on prescription data (or the last prescription available) at the beginning of that given month as well as the status for potential confounders (or the last available value for time-varying potential confounders). Then, if the target trial can be emulated (ie, if at least one cloned copy classified in the intervention arm can be compared with at least one cloned copy classified in the control arm), the cloned copies will be retained. To avoid immortal time and selection biases,  22  we will create coinciding eligibility, treatment assignment and time zero (or baseline of the sequential nested trial) by restarting the beginning of follow-up at this particular month in the database (first day of the month).
These three stepsassessment of eligibility of cloned copy, assignment of treatment strategy and reinitialisation of the follow-upwill be repeated every month in the database. Therefore, a patient can contribute to several sequential nested trials but with potential various treatment assignments and baseline confounders among the different cloned copies (figure  2 ). All the cloned copies of sequential nested trials will be then stacked in a pooled dataset for analysis. In this study, the target trial will be emulated several times sequentially. Each calendar month in the database, cloned copies of individuals eligible for the target trial will be created. 38  The treatment strategies of each cloned copy will be assessed based on prescription data (or the Open access Figure  2  Theoretical example of the sequential emulations of the target trial.
(A) In this theoretical example, five patients (P1-P5) were included from the database. Available data for these patients are represented on the timeline. (B) The target trial is sequentially emulated each month, and examples of these sequential emulations are provided for January (emulation #1), February (emulation #2), March (emulation #3), and June (emulation #x). For emulation #1, one cloned copy of patient (P5) started deintensification, which can be compared with three cloned copies of patients (P1, P2 and P3). If the cloned copies meet the eligible criteria of the target trial, they are kept for analysis. Treatment assignment is based on the prescription data compatible with the treatment strategy, and confounders at baseline of the sequential trial (ie, calendar month in the database) are also measured for each cloned copy (eg, comedication).
To ensure coinciding of eligibility, treatment assignment and start of follow-up, thus avoiding immortal-time and selection biases,  22  the follow-up of each cloned copy is restarted at the beginning of the sequential trial. For emulation #2, one cloned copy of patient P2 that started deintensification can be compared with two cloned copies of patients P1 and P3 that did not. Cloned copies of patients (P5″) are no longer eligible for the sequential emulated trial, according to eligibility criteria of the target trial: the patient already began deintensification in the past year (or in other words, a new-user design is applied). Assignment to treatment strategy, confounders measurement and follow-up reinitialisation are the same as for emulation #1. For emulation #3, no participant started deintensification in the database, so the comparison between deintensification and no deintensification is not feasible, and the cloned copies are not kept for analysis.
These steps are repeated as many times as the target trial can be emulated, that is, each month when at least one eligible cloned copy starts the deintensification strategy and can be compared with a cloned copy belonging to the no-intensification arm. (C) Finally, the cloned copies of all sequential emulated trials are stacked in the final dataset for pooled analysis. HD, hypoglycaemic drug; M, months. Open access last prescription available) at the beginning of that given month, as well as the status for potential confounders (or the last available value for time-varying potential confounders). Then, if the target trial can be emulated (ie, if at least one cloned copy classified in the intervention arm can be compared with at least one cloned copy classified in the control arm), the cloned copies will be retained.
To avoid immortal time and selection biases,  22  we will make coincide eligibility, treatment assignment and time zero (ie, baseline of the sequential nested trial) by restarting the beginning of follow-up at this particular month in the database (first day of the month). These three steps (assessment of eligibility of cloned copy, assignment of treatment strategy and reinitialisation of the follow-up) will be repeated every month in the database. Therefore, a patient can contribute to several sequential nested trials but with potential various treatment assignment and baseline confounders among the different cloned copies (figure  2 ). All the cloned copies of sequential nested trials will be then stacked in a pooled dataset for analysis.
40  Then, to assess residual confounding related to frailty (a condition commonly associated with deintensification and pneumopathy), that may have been mismeasured in this database, we will assess the effect of deintensifying HDs on the risk of pneumopathy diagnosis at 1 year of follow-up, which is expected to be null (negative control). In addition, the analyses described above will be repeated in different relevant subgroups. Among these, we will include the different profiles of patients for whom deintensification is suggested by recent clinical practice guidelines  12  (ie, multiple comorbidities (≥ 5 comorbidities), tight glycaemic control (HbA1c<48 mmol/mol (6. 5%)), polypharmacy (≥ 5 drugs/day), impaired renal function (end-stage renal disease), advanced age (≥ 80 years), long duration of diabetes (≥ 20 years), severe neurocognitive troubles, nursing home resident).
ETHICS AND DISSEMINATION The study will be a retrospective analysis of fully anonymised secondary data. Therefore, individual consent is waived, and no ethical approval is needed. The data collection by THIN was approved by the French National Data Protection Authority (CNIL) in 2002, and the database complies with the European general data protection regulations. We will submit the results to a peer-reviewed scientific journal, and will present them during scientific conferences. The reporting of the study will follow the REporting of studies Conducted using Observational Routinely-collected health Data for pharmacoepidemiology (RECORD-PE) reporting guidelines. 41.