dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  P. Turnbaugh  for providing genome sequences for 26 Eggerthella lenta strains,  J. Krumsiek  for communicating mouse-associated microbial species,  C. Thinnes  for valuable discussions, and  L. Moussu  and  S. Smajic  for their help with the comparative genomic effort. This study was funded by grants from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 757922 ) to I. T. , from the  National Institute on Aging  (grants no. 1RF1AG058942  and no. 1U19AG063744 ), from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Actions  (grant agreement no. 859890 ), from the  Marine Institute  ( Grant-Aid  Agreement No. PBA/BIO/20/03 ) and  Science Foundation Ireland  funded by the  Irish Government  under the  BlueBio ERA-NET Co-fund  ( H2020  Project number  817992 ) and from the  Science Foundation Ireland  under grant no.
f Comparison of qualitative prediction accuracies per model of the various resources on the three experimental datasets (NJC19, BacDive, Madin) with AGORA2 Accuracies in qualitative prediction of metabolite uptake and secretion of the various resources on the three experimental datasets (NJC19, BacDive,Madin) 7,302 7,302 72 7,279 8,075 1,333 Average no. of reactions 1,723. 12 1,305. 10 2,348. 47 1,834. 30 1,895. 03 1,461. 73 Average no. of metabolites 1,538. 67 1,238. 41 1,716. 21 1,231. 92 1,693. 58 1,450. 44 Average no. of genes 906. 71 944. 79 1,188. 50 955. 83 817. 16 0. 00 Average no. of stoichiometrically consistent reactions 1. 00 1. 00 0. 96 0. 98 1. 00 1. 00 Average no. of flux consistent reactions 0. 68 0. 60 0. 82 0. 98 0. 56 0. 43 ATP yield on aerobic CM 75. 74 871. 83 778. 74 709.
27 85. 60 984. 67 ATP yield on anaerobic CM 48. 81 862. 67 740. 36 684. 66 49. 94 984. 64 d Overview on reconstructions and predictions tested using three experimental datasets (NJC19, BacDive, Madin) NJC19 BacDive Madin No. of models No. of models No. of models No. of models No. of models No. of models (no. of (no. of (no. of (no. of (no. of (no. of predictions predictions predictions predictions predictions predictions uptake) secretion) uptake) secretion) enzymes) uptake) AGORA2 5,130 (62,996) 5,009 (51,312) 675 (9,999) 535 (789) 541 (6,211) 326 (2,787) KBase 5,130 (62,996) 5,009 (51,312) 675 (9,999) 535 (789) 541 (6,211) 326 (2,787) CarveMe 5,120 (62,906) 4,998 (51,237) 674 (9,986) 534 (788) 540 (6,202) 326 (2,787) BiGG 59 (1,026) 59 (1,177) 12 (139) 12 (33) 11 (81) 0 (0) MAGMA 103 (1,211) 117 (715) 91 (890) 81 (97) 72 (1,097) 46 (333) gapseq 169 (1,765) 183 (951) 265 (3,598) 212 (288) 199 (2,634) 95 (770) ** NA *** *** *** *** NA *** *** *** *** *** *** *** *** ** *** NA *** *** *** *** *** *** AGORA2 versus KBase AGORA2 versus CarveMe AGORA2 versus BiGG AGORA2 vs MAGMA AGORA2 versus gapseq No.
Comparison of AGORA2-refined reconstructions, draft reconstructions and three other reconstructions resources 18Compared were the 7,302 AGORA2 and KBase draft reconstructions, 72 manually curated reconstructions from the BiGG database28, 5,587 reconstructions built through CarveMe 15 , 8,075 reconstructions built through gapseq18and 1,333 MAGMA reconstructions.
X-ray scattering measurements Small-and near wide-angle X-ray scattering (SAXS/ NWAXS) measurements were performed using a pointfocusing SAXS instrument (originally Molecular Metrology, recently considerably upgraded by SAXSLAB, now Xenocs) at room temperature. Rigaku Micromax-003, a low-power micro source equipped with X-ray optics working at U = 50 kV and I = 0. 6 mA generated a CuKα beam with a wavelength of λ = 0. 154 nm. Scattering was detected by the 2D detector Pilatus3 300K (Dectris) at 0. 449 m and 0. 0573 m from the sample. Merging data from these two distances allowed reaching reliable q interval from 0. 13 to 35 nm -1 , where q is the magnitude of the scattering vector q = 4π sin(θ)/λ and θ is half of the scattering angle. Samples were sealed in 1. 5 mmdiameter quartz capillaries (the scattering of an empty capillary was not subtracted from the scattering curves of samples).
Sample to detector distance was calibrated using silver behenate or Si standard. Data were azimuthally averaged and adjusted to an absolute scale using a glassy carbon standard. solenoid coil was used. We applied a spectral width of ±250 kHz using quadrature phase detection using the standard phase cycled quadrupolar echo pulse sequence  (28)  (the length of the 90 • pulses was 2. 5-3. 5 μs 90 • , pulses were separated by a 30 μs delay). The recycle delay was 50 s for samples that showed very broad components from orthorhombic phases to ensure full relaxation to thermal equilibrium to enable quantification of the proportion of the individual phases  (15). Samples were investigated at temperatures of 25, 32, 50, and 65 • C and processed using a program written in Mathcad (MathSoft, Cambridge, MA)  (29).
Relaxation measurement data analysis 13 C NMR relaxation data were processed in Bruker Topspin and relaxation rate constants were extracted from the resulting spectra using INFOS  (31). The resulting relaxation rate constants (2 T 1 , 2 NOE, 2 T 1ρ ) were processed with the detector analysis (32) using the pyDIFRATE software (https:// github. com/alsinmr/pyDR), to yield 6 detector windows (analysis archived at Github (https://doi. org/10. 5281/ zenodo. 7347151). Additional details on processing and the relaxation rate constants and resulting fits found in the Supporting Information.
Neutron diffraction The samples for the ND experiment were prepared from ≈ 17 mg or 10 mg of the protonated or deuterated lipid mixture, respectively. The dried lipid mixtures were redissolved in hexane/ethanol (96%) = 2:1 (v/v) at the saturated concentration at 50 • C. The lipid solution was spread in few layers on a silicon wafer (Crystal GmbH, Berlin, Germany) preheated to 50 • C. The traces of the solvents were removed in a vacuum overnight and the sample was annealed at 70 • C in the presence of water vapor for 30 min with subsequent slow cooling to the room temperature. ND data (10. 5291/ Institut Laue-Langevin (ILL)-DATA. 9-13-825) were collected at the smallangle diffraction instrument D16 located at the ILL (Grenoble, France). The neutron radiation (λ = 0. 449 nm, Δλ/λ = 0. 01) was selected by a graphite monochromator consisting of nine Highly Oriented Pyrolytic Graphite crystals (mosaic spread 0.
S6 ), and the phase angles were determined according to the method of Franks and Lieb  (61). The phase angles of the protonated Cer [EOS]/Cer[NS]/FFA/Chol sample, measured only at one contrast condition, were determined according to the previously published data: ND of a similar LPP model with d = 12. 9 nm, albeit having more complex Cer composition, was measured at 50% 2 H 2 O (62). The absolute values of form factors F (1), F (2), F (3), F (4), and F (6) had a similar distribution of amplitudes relative to those determined for our sample Cer[EOS]/Cer[NS]/ FFA/Chol. The reconstructed relative NSLD profiles of the protonated and the deuterated sample (supplemental Fig. S7 ) agreed with the previously published data  (62). The protonated sample showed NSLD maxima at d/2 and at the distance z = 2.
Funding and additional information The study was supported by the  Deutsche Forschungsgemeinschaft  (project numbers: 490761708 (D. H. ) and  450148812 (A. A. S. ) ) and the  Czech Science Foundation  (project number:  22-20839K (T. H. , L. O. , P. P. , V. O. , K. V. )) and by the project  EFSA-CDN (CZ. 02. 1. 01/0. 0/0. 0/16_019/0000841 ; K. V. , L. N. ) cofunded by the  European Union , and by  Institut Laue-Langevin  for allocation of beamtime and support during the experiment. T. H. and V. O. acknowledge support by the project " Grant Schemes at CU " (reg. no. CZ. 02. 2. 69/0. 0/0. 0/19_073/0016935 ). O. E. acknowledges financial support by the  Sigrid Juselius Foundation , the  Ruth and Nils-Erik Stenb ¨ack's Foundation , and the  Magnus Ehrnrooth Foundation.
Molecular Docking Subsequently to the conformational analysis, the preferential conformatio DFMBA with a torsion angle value of -27° was retrieved and a rigid ligand dock performed on Staphylococcus aureus SaFtsZ (PDB: 6YD1) with Arguslab  [37]  with a box of 15 Å × 15 Å × 15 Å centered on the ligand using the genetic algorithm with parameters. The docking result was superimposed to the structure of the co-cry DFMBA (PDB: 6YD1). 3D-Visualization was performed with PyMOL which was create Figure  1   ues against three S. aureus strains: a reference strain (ATCC29213), a methicillinstrain (SF8300)  [34]  and a clinically isolated daptomycin-resistant strain (ST20171 (Table  1 ). 3-HBA and DFHBA, used as references, exert MIC values of 256 µ g/m µ g/mL consistent with the literature  [14] [15] [16].
Molecular Docking Subsequently to the conformational analysis, the preferential conformatio DFMBA with a torsion angle value of -27° was retrieved and a rigid ligand dock performed on Staphylococcus aureus SaFtsZ (PDB: 6YD1) with Arguslab  [37]  with a box of 15 Å × 15 Å × 15 Å centered on the ligand using the genetic algorithm with parameters. The docking result was superimposed to the structure of the co-cry DFMBA (PDB: 6YD1). 3D-Visualization was performed with PyMOL which was create Figure  1.
Molecular Docking Subsequently to the conformational analysis, the preferential conformation of the DFMBA with a torsion angle value of -27 • was retrieved and a rigid ligand docking was performed on Staphylococcus aureus SaFtsZ (PDB: 6YD1) with Arguslab  [37]  with a docking box of 15 Å × 15 Å × 15 Å centered on the ligand using the genetic algorithm with default parameters. The docking result was superimposed to the structure of the co-crystallized DFMBA (PDB: 6YD1). 3D-Visualization was performed with PyMOL which was used to create Figures  1  and 4. 2D-representation was created with LigPlot+  [38]  for Figure  1.
This behaviour, often referred to as 'doctorshopping', results in a lack of medical management because the physician does not know that others are also prescribing the same drug. Doctor-shopping is a critical mechanism that contributes to the non-medical use of prescription drugs. On one hand, it increases the risks for harmful outcomes, such as high-risk use, hospital admission and fatal overdose  [12] [13] [14]. On the other hand, it plays an important role in supplying the black market  [15, 16]. In response to the increasing prevalence of non-medical use of prescription drugs, many methods were set up to assess doctorshopping using claims databases  [17, 18]. Nevertheless, most studies focused upon a single drug or a single pharmacological class, mainly opioids. To our knowledge, there has been no systematic assessment of doctor-shopping for all psychoactive prescription drugs.
Data source We used data from the French National Health Data System (Système national des données de santé, SNDS). Because coverage by the French Health Insurance is mandatory, the SNDS covers at least 99% of the French population permanently, regardless of socio-economic and employment status. The database prospectively records claims for reimbursed health-care, including drugs dispensed in all community pharmacies throughout the country. Each healthcare consumer and health professional is identified by a pseudonym that is stable over time and across geography. Drugs are coded according to the Anatomical Therapeutic Chemical (ATC) classification. Each drug packaging is identified by a code to link additional information, such as active ingredient, number of units, dose per unit and route of administration.
Data extraction Dispensings of psychoactive prescription drugs were extracted from the SNDS in 2010, 2015 and 2019. Psychoactive prescription drugs were defined as drugs acting on the central nervous system, including anaesthetics (ATC code N01), analgesics (N02), antiepileptics (N03), anti-Parkinson drugs (N04), psycholeptics (N05), psychoanaleptics (N06), other nervous system drugs (N07) and antihistamines for systemic use (R06). These amount to 214 drugs. Only drugs dispensed to more than 5000 patients were included in the analyses because the indicators may be distorted in small populations.
Use levels of drugs Use levels of drugs were estimated based on the quantity dispensed, expressed in defined daily doses (DDD). The DDD is defined by the World Health Organization as the assumed average maintenance dose per day for a drug used for its main indication in adults  [22]. Data presented in DDD/1000 inhabitants/day provide a rough estimate of the proportion of the study population treated daily with a given drug  [23]. The ATC/DDD system provides standardization throughout drugs, allowing the comparison of use levels and assessment of trends over time.
Detection and quantification of doctor-shopping The detection and quantification of doctor-shopping relied upon an algorithm that detects overlapping prescriptions from repeated visits to different physicians. Since its first publication in 2004  [24] , it was used for many pharmacological classes, and has shown its external validity as a proxy for non-medical use of prescription drugs in France  [25]. The algorithm is published as an open-source R package at: https://gitlab. com/soeiro/shopping/. First, the algorithm detects treatment periods based on dates of dispensing, while taking treatment interruptions into account (Figure  1a ). A treatment period is defined as the period lasting from the first to the last dispensing of a given drug prescribed by the same physician. Because scheduled drugs are dispensed for a maximum of 4 weeks, if a new dispensing of the drug occurs later than 35 days after the previous one (i.
ACKNOWLEDGEMENTS This work is part of the  MEGADOSE  project, which was funded by the  French Medicines Agency (Agence Nationale de Sécurité du Médicament et des Produits de Santé ). The primary research question and analysis plan were pre-registered on the  ENCePP EU PAS Register , no. EUPAS24364. We thank  Jean-François Laurent  from Thésorimed for providing reference data (e. g. number of units and dose per unit for each drug packaging) needed for data preparation. We also thank  Véronique Channaut Soeiro  for her careful proofreading of the manuscript.
In all experiments, after addition of an initial aliquot of 0. 5 μL, 10 aliquots of 3. 5 μL of the syringe solution were delivered over 7 s for each injection. The time interval between two consecutive injections was defined in order to obtain a systematic and complete return to baseline (100-150 s for TM-β-CD and 1500 s for γ-CD). The agitation speed was 1000 rpm. The resulting heat flow was recorded as a function of time. All measurements were performed at 298. 15 K. Prior to data analysis, blank titrations were carried out under the same experimental conditions by injecting buffer into buffer in order to determine the heat induced by injections. These values were subtracted from the measured heats in the presence of DCV and CDs. The peak area following each injection was obtained by integration of the resulting signal and was expressed as the heat effect per injection.
Molecular modeling The 3D structures of γ-CD and TM-β-CD were released from Cambridge Structural Database (CSD)  (Thomas et al. , 2010) , entries MUXBIT  (Catenacci et al. , 2020)  and GELKEN10  (Harata et al. , 1992) , respectively. All structures were prepared using the build function and model kits and tools provided by Spartan'10 Version 1. 1. 0 (Wavefunction Inc. , Irvine, CA, USA) program  (Shao et al. , 2006)  for building and editing organic molecules. The 3D structures of DCV, RRRR-DCV RSSR-DCV, the isoleucine analog Ile-DCV, the analog lacking a MOC-Val side chain (Mono-DCV) as well as the synthetic precursors BOC-bis-pyrrolidinylimidazolyl biphenyl (BOC-BPIB) and bis-pyrrolidinyl-imidazolyl biphenyl (BPIB) (for the structures of DCV stereoisomers and analogs see Fig. S1 , Supplementary material), were treated as fully protonated in accord with the experimental pH = 2.
Running the AMBER pmemd. cuda  (Salomon-Ferrer et al. , 2013)  code in parallel on 4 GeForce Nvidia RTX2080 Ti graphics processing units (GPUs), each simulation contained the following stages. Periodic boundary conditions were applied to simulate a continuous system. Upon reorientation of all solute hydrogens and water molecules, first, a 1000-steps solvent minimization was performed using the steepest descent method with macrocycle restrained via a force of 40 kcal⋅mol -1. Then, the system was gradually heated over 0. 5 ns to a target temperature with the macrocycle restrained via a force of 40 kcal⋅mol -1. The solvent underwent a 1000steps minimization with a restriction of 40 kcal⋅mol -1. A short 0. 2 ns NPT ensemble MD run was performed with a 30 kcal⋅mol -1 restrain, followed by another 10,000-steps energy minimization process.
The application of SHAKE to all bonds allowed an integration time step of 2 fs to be used. A cut-off distance of 8 Å was used for the nonbonded interactions and the list of nonbonded pairs was updated every 25 steps. Periodic boundary conditions were applied, and electrostatic interactions were represented using the smooth particle mesh Ewald method  (Darden et al. , 1993)  with a grid spacing of 1 Å. The coupling constant for the temperature and pressure baths was 0. 2 ps. The resulting set of 2500 structures for each complex was analyzed with the cpptraj module in AMBER  (Roe & Cheatham, 2013)  to measure relevant distances and angles, along with R scripts (Core Team, 2017) developed in house. For each complex, the total interaction energy was calculated. The reported energies are mean values that were calculated from 2500 × complexes extracted by snapshots taken every 40 ps from the trajectories of three 100 ns MD replicates, if not indicated otherwise.
By Jeroen Hendrikx In silico modeling, like physiologically-based pharmacokinetics (PBPK) modeling, are routinely used in drug development to predict pharmacokinetic processes of drugs and to evaluate effects of age, diseases and drug-drug interactions  (Rowland et al. 2011 ). In PBPK models, a database of physiological population parameters (like body composition, (organ) blood flow and receptor expression) is combined with drug data (like administered dose, renal clearance rate and receptor binding affinity) to simulate blood and organ concentrations of the drug over time. These models can also be used to optimize clinical study designs and select dosing strategies. For radioligand-based therapies, the use of PBPK models can help to select the best radioligand(s) for clinical efficacy in an early stage by estimation of tumor and organ uptake based on (radio)chemical characteristics and receptor affinity.
Western blotting In a lysis buffer containing proteinase inhibitors, the samples were homogenized. A nuclear extraction kit (ab1133474) was used for nuclear protein extraction. The Bradford method was used to determine the total protein content. SDS-PAGE was used to separate 40 µg of total proteins, which were then electro-transferred onto PVDF membranes and blocked in TBST containing 5% skimmed milk. The blocked membranes were examined using anti-Nrf2 (dilution 1:500), and anti-β-actin (dilution 1:3000) antibodies. The membranes were rinsed three times with TBST before an hour-long incubation with an ALP-conjugated secondary antibody at room temperature. The BCIP/NPT kit was used to detect the protein expression after several washings. With ImageJ® software, protein bands were semi-quantified.
Statistical analysis The data were analyzed using SPSS version 26. 0. The analysis involved presenting descriptive statistics in terms of frequencies and percentages for categorical variables, and means (± standard deviation, SD) for continuous variables. To validate the structure of the comprehension of prescription orders index, factor analysis was conducted using principal component analysis (PCA) with Promax rotated component matrix. The model adequacy was confirmed by measuring the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and the Bartlett's test for sphericity. The internal consistency and reproducibility of the index was confirmed by measuring Pearson correlation coefficients between each item of the index with its corresponding subscale, as well as with the overall index.
This was unlike our study where we compared the comprehension of written orders without pictograms versus with pictograms. No assessment of verbal counseling or reinforcement was done in this study as the data collection was done via web-based platforms and not face-toface interviews. Several demographic and socioeconomic factors that affected the comprehension of pictograms among the Lebanese population were identified in this study. A multivariable linear regression demonstrated a significant negative association between age and the difference of comprehension scores, with older participants having significantly lower score difference compared to younger ones. This means that older age was associated with lower comprehension of pictograms. Low health literacy, high medication burden and poly-pharmacy, visual disorders, and cognitive aging in the older adult population are contributing factors to the misunderstanding of medication instructions  [30] [31] [32].
Strengths and Limitations Several limitations to our study should be highlighted. First, the survey administered was self-reported by the participants. So, respondents might tend to avoid extreme responses or might exhibit acquiescence. Also, this was an observational study; hence, it does not prove cause and effect relationships. Among the limitations of the current study, as well, is the fact that we did not assess participants' visual acuity. It is possible that some participants did not understand some of the pictograms because of vision problems. In addition, we did not assess health literacy, especially that pictograms are often implemented to help people with low levels of health literacy. Moreover, we were not able to assess the impact of verbal counseling or reinforcement since the data collection was done via web-based platforms and not faceto-face interviews.
Acknowledgements We thank  Dr. Georges Massiot  ( Université de Reims and CNRS, France ) for providing NMR spectra of natural normavacurine. Dr. Georges Massiot  and  Prof. Luc Angenot  ( University of Liège, Belgium ) are also acknowledged for insightful discussions concerning the isolation of the mavacuran alkaloids and their NMR analysis. We also thank  Dr. Laurent Evanno  and  Prof. Erwan Poupon  ( BioCIS, Université Paris-Saclay, France ) for helpful suggestions. The  MESRI  ( ED 2MIB ,  Université Paris-Saclay)  is gratefully acknowledged for the PhD fellowship of A. M. Financial support for this work was provided by the  ANR  ( ANR-15-CE29-0001 , " Mount Indole "),  Université Paris-Saclay  and the  CNRS. This work was granted access to the HPC resources of CINES under the allocation 2020-A0070810977 made by  GENCI.
Emerging Research and Industrial Interest for Chitosan The first interest in commercializing chitin was held back in the 1930s because of the strong competition with synthesized polymers at that time. Large-scale production of chitin regained attention in the mid-1970s, when regulations aiming to reduce the dumping of shellfish waste were introduced. Regarding research interest, 108,025 references concerning "chitin/chitosan" were found using SciFinder (years between 1970 and 2022). A remarkable increase in the number of publications was evidenced at the beginning of the 1990s, indicating a real emerging research interest in the academic world (Figure  2 ). On the other hand, according to statistics from WIPO's database, PATENTSCOPE, the potential commercial applications of CS have grown steadily since the late twentieth century, whereas the number of patent applications concerning CS has exploded in the last three decades.